vectorDB.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "First method: FAISS embeddings"
      ],
      "metadata": {
        "id": "WUTBu_jOofXh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "possibly provide a summary with llama as metadata in vectordb\n",
        "\n",
        "https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/\n"
      ],
      "metadata": {
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      "source": [
        "!pip install langchain_community"
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    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain"
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      "metadata": {
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      },
      "execution_count": null,
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        {
          "output_type": "stream",
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          "text": [
            "Collecting langchain\n",
            "  Downloading langchain-0.1.16-py3-none-any.whl (817 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m817.7/817.7 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain) (6.0.1)\n",
            "Requirement already satisfied: SQLAlchemy<3,>=1.4 in /usr/local/lib/python3.10/dist-packages (from langchain) (2.0.29)\n",
            "Requirement already satisfied: aiohttp<4.0.0,>=3.8.3 in /usr/local/lib/python3.10/dist-packages (from langchain) (3.9.5)\n",
            "Requirement already satisfied: async-timeout<5.0.0,>=4.0.0 in /usr/local/lib/python3.10/dist-packages (from langchain) (4.0.3)\n",
            "Requirement already satisfied: dataclasses-json<0.7,>=0.5.7 in /usr/local/lib/python3.10/dist-packages (from langchain) (0.6.4)\n",
            "Requirement already satisfied: jsonpatch<2.0,>=1.33 in /usr/local/lib/python3.10/dist-packages (from langchain) (1.33)\n",
            "Requirement already satisfied: langchain-community<0.1,>=0.0.32 in /usr/local/lib/python3.10/dist-packages (from langchain) (0.0.34)\n",
            "Requirement already satisfied: langchain-core<0.2.0,>=0.1.42 in /usr/local/lib/python3.10/dist-packages (from langchain) (0.1.45)\n",
            "Collecting langchain-text-splitters<0.1,>=0.0.1 (from langchain)\n",
            "  Downloading langchain_text_splitters-0.0.1-py3-none-any.whl (21 kB)\n",
            "Requirement already satisfied: langsmith<0.2.0,>=0.1.17 in /usr/local/lib/python3.10/dist-packages (from langchain) (0.1.49)\n",
            "Requirement already satisfied: numpy<2,>=1 in /usr/local/lib/python3.10/dist-packages (from langchain) (1.25.2)\n",
            "Requirement already satisfied: pydantic<3,>=1 in /usr/local/lib/python3.10/dist-packages (from langchain) (2.7.0)\n",
            "Requirement already satisfied: requests<3,>=2 in /usr/local/lib/python3.10/dist-packages (from langchain) (2.31.0)\n",
            "Requirement already satisfied: tenacity<9.0.0,>=8.1.0 in /usr/local/lib/python3.10/dist-packages (from langchain) (8.2.3)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (23.2.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.9.4)\n",
            "Requirement already satisfied: marshmallow<4.0.0,>=3.18.0 in /usr/local/lib/python3.10/dist-packages (from dataclasses-json<0.7,>=0.5.7->langchain) (3.21.1)\n",
            "Requirement already satisfied: typing-inspect<1,>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from dataclasses-json<0.7,>=0.5.7->langchain) (0.9.0)\n",
            "Requirement already satisfied: jsonpointer>=1.9 in /usr/local/lib/python3.10/dist-packages (from jsonpatch<2.0,>=1.33->langchain) (2.4)\n",
            "Requirement already satisfied: packaging<24.0,>=23.2 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.2.0,>=0.1.42->langchain) (23.2)\n",
            "Requirement already satisfied: orjson<4.0.0,>=3.9.14 in /usr/local/lib/python3.10/dist-packages (from langsmith<0.2.0,>=0.1.17->langchain) (3.10.1)\n",
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            "Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1->langchain) (4.11.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain) (2024.2.2)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from SQLAlchemy<3,>=1.4->langchain) (3.0.3)\n",
            "Requirement already satisfied: mypy-extensions>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain) (1.0.0)\n",
            "Installing collected packages: langchain-text-splitters, langchain\n",
            "Successfully installed langchain-0.1.16 langchain-text-splitters-0.0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_community.document_loaders import DirectoryLoader\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain_community.vectorstores import Chroma\n",
        "from langchain_community.embeddings import HuggingFaceEmbeddings"
      ],
      "metadata": {
        "id": "Rrnv4_-znoPK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install unstructured"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KzpJb_blr1Ed",
        "outputId": "9caea6c3-a859-4c16-8bbd-54ec5af1c9da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting unstructured\n",
            "  Downloading unstructured-0.13.2-py3-none-any.whl (1.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: chardet in /usr/local/lib/python3.10/dist-packages (from unstructured) (5.2.0)\n",
            "Collecting filetype (from unstructured)\n",
            "  Downloading filetype-1.2.0-py2.py3-none-any.whl (19 kB)\n",
            "Collecting python-magic (from unstructured)\n",
            "  Downloading python_magic-0.4.27-py2.py3-none-any.whl (13 kB)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.10/dist-packages (from unstructured) (4.9.4)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (from unstructured) (3.8.1)\n",
            "Requirement already satisfied: tabulate in /usr/local/lib/python3.10/dist-packages (from unstructured) (0.9.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from unstructured) (2.31.0)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from unstructured) (4.12.3)\n",
            "Collecting emoji (from unstructured)\n",
            "  Downloading emoji-2.11.0-py2.py3-none-any.whl (433 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m433.8/433.8 kB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: dataclasses-json in /usr/local/lib/python3.10/dist-packages (from unstructured) (0.6.4)\n",
            "Collecting python-iso639 (from unstructured)\n",
            "  Downloading python_iso639-2024.2.7-py3-none-any.whl (274 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m274.7/274.7 kB\u001b[0m \u001b[31m16.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting langdetect (from unstructured)\n",
            "  Downloading langdetect-1.0.9.tar.gz (981 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m981.5/981.5 kB\u001b[0m \u001b[31m21.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from unstructured) (1.25.2)\n",
            "Collecting rapidfuzz (from unstructured)\n",
            "  Downloading rapidfuzz-3.8.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m29.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: backoff in /usr/local/lib/python3.10/dist-packages (from unstructured) (2.2.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from unstructured) (4.11.0)\n",
            "Collecting unstructured-client<=0.18.0 (from unstructured)\n",
            "  Downloading unstructured_client-0.18.0-py3-none-any.whl (21 kB)\n",
            "Requirement already satisfied: wrapt in /usr/local/lib/python3.10/dist-packages (from unstructured) (1.14.1)\n",
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            "Requirement already satisfied: charset-normalizer>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from unstructured-client<=0.18.0->unstructured) (3.3.2)\n",
            "Collecting dataclasses-json-speakeasy>=0.5.11 (from unstructured-client<=0.18.0->unstructured)\n",
            "  Downloading dataclasses_json_speakeasy-0.5.11-py3-none-any.whl (28 kB)\n",
            "Requirement already satisfied: idna>=3.4 in /usr/local/lib/python3.10/dist-packages (from unstructured-client<=0.18.0->unstructured) (3.7)\n",
            "Collecting jsonpath-python>=1.0.6 (from unstructured-client<=0.18.0->unstructured)\n",
            "  Downloading jsonpath_python-1.0.6-py3-none-any.whl (7.6 kB)\n",
            "Requirement already satisfied: marshmallow>=3.19.0 in /usr/local/lib/python3.10/dist-packages (from unstructured-client<=0.18.0->unstructured) (3.21.1)\n",
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            "Requirement already satisfied: packaging>=23.1 in /usr/local/lib/python3.10/dist-packages (from unstructured-client<=0.18.0->unstructured) (23.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from unstructured-client<=0.18.0->unstructured) (2.8.2)\n",
            "Requirement already satisfied: six>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from unstructured-client<=0.18.0->unstructured) (1.16.0)\n",
            "Requirement already satisfied: typing-inspect>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from unstructured-client<=0.18.0->unstructured) (0.9.0)\n",
            "Requirement already satisfied: urllib3>=1.26.18 in /usr/local/lib/python3.10/dist-packages (from unstructured-client<=0.18.0->unstructured) (2.0.7)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->unstructured) (2.5)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk->unstructured) (8.1.7)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk->unstructured) (1.4.0)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk->unstructured) (2023.12.25)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk->unstructured) (4.66.2)\n",
            "Building wheels for collected packages: langdetect\n",
            "  Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for langdetect: filename=langdetect-1.0.9-py3-none-any.whl size=993227 sha256=b6b8d4dc2e1210deb574c7b249a0d92711b30ac272f454ebbc738e12615bfca7\n",
            "  Stored in directory: /root/.cache/pip/wheels/95/03/7d/59ea870c70ce4e5a370638b5462a7711ab78fba2f655d05106\n",
            "Successfully built langdetect\n",
            "Installing collected packages: filetype, rapidfuzz, python-magic, python-iso639, langdetect, jsonpath-python, emoji, dataclasses-json-speakeasy, unstructured-client, unstructured\n",
            "Successfully installed dataclasses-json-speakeasy-0.5.11 emoji-2.11.0 filetype-1.2.0 jsonpath-python-1.0.6 langdetect-1.0.9 python-iso639-2024.2.7 python-magic-0.4.27 rapidfuzz-3.8.1 unstructured-0.13.2 unstructured-client-0.18.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loader = DirectoryLoader(\"/content/drive/MyDrive/Paper Abstracts\", glob=\"**/*.txt\", show_progress=True, use_multithreading=True)\n",
        "documents  = loader.load()\n",
        "print(f\"num of documents loaded: {len(documents)}\")\n",
        "\n",
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n",
        "docs = text_splitter.split_documents(documents)\n",
        "\n",
        "#vectordb = Chroma.from_documents(documents=docs, embedding=HuggingFaceEmbeddings(), persist_directory=\"C:/Users/silviachen/Downloads/Paper Abstracts/chromadb\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QEv-ohabmpoS",
        "outputId": "ed0321bf-c712-4788-c34e-4b265904fcf4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 751/751 [00:25<00:00, 29.24it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "num of documents loaded: 751\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install nltk"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iMzV2aHOs_5R",
        "outputId": "1d2a24c8-f828-4018-fb67-a677eac55e19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (3.8.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk) (8.1.7)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk) (1.4.0)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk) (2023.12.25)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk) (4.66.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pypdf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d0dJIqye2OOT",
        "outputId": "e87b4d7c-93b5-4d35-80bc-54696a45548f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pypdf\n",
            "  Using cached pypdf-4.2.0-py3-none-any.whl (290 kB)\n",
            "Requirement already satisfied: typing_extensions>=4.0 in /usr/local/lib/python3.10/dist-packages (from pypdf) (4.11.0)\n",
            "Installing collected packages: pypdf\n",
            "Successfully installed pypdf-4.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U langchain-text-splitters"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z1VWTf6K4ot1",
        "outputId": "bf24f140-19a1-4ef9-b2a6-7f09cf24292b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting langchain-text-splitters\n",
            "  Downloading langchain_text_splitters-0.0.1-py3-none-any.whl (21 kB)\n",
            "Collecting langchain-core<0.2.0,>=0.1.28 (from langchain-text-splitters)\n",
            "  Downloading langchain_core-0.1.42-py3-none-any.whl (287 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m287.5/287.5 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.2.0,>=0.1.28->langchain-text-splitters) (6.0.1)\n",
            "Collecting jsonpatch<2.0,>=1.33 (from langchain-core<0.2.0,>=0.1.28->langchain-text-splitters)\n",
            "  Downloading jsonpatch-1.33-py2.py3-none-any.whl (12 kB)\n",
            "Collecting langsmith<0.2.0,>=0.1.0 (from langchain-core<0.2.0,>=0.1.28->langchain-text-splitters)\n",
            "  Downloading langsmith-0.1.47-py3-none-any.whl (113 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m113.0/113.0 kB\u001b[0m \u001b[31m14.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting packaging<24.0,>=23.2 (from langchain-core<0.2.0,>=0.1.28->langchain-text-splitters)\n",
            "  Downloading packaging-23.2-py3-none-any.whl (53 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.0/53.0 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pydantic<3,>=1 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.2.0,>=0.1.28->langchain-text-splitters) (2.6.4)\n",
            "Requirement already satisfied: tenacity<9.0.0,>=8.1.0 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.2.0,>=0.1.28->langchain-text-splitters) (8.2.3)\n",
            "Collecting jsonpointer>=1.9 (from jsonpatch<2.0,>=1.33->langchain-core<0.2.0,>=0.1.28->langchain-text-splitters)\n",
            "  Downloading jsonpointer-2.4-py2.py3-none-any.whl (7.8 kB)\n",
            "Collecting orjson<4.0.0,>=3.9.14 (from langsmith<0.2.0,>=0.1.0->langchain-core<0.2.0,>=0.1.28->langchain-text-splitters)\n",
            "  Downloading orjson-3.10.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (144 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m144.8/144.8 kB\u001b[0m \u001b[31m11.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests<3,>=2 in /usr/local/lib/python3.10/dist-packages (from langsmith<0.2.0,>=0.1.0->langchain-core<0.2.0,>=0.1.28->langchain-text-splitters) (2.31.0)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1->langchain-core<0.2.0,>=0.1.28->langchain-text-splitters) (0.6.0)\n",
            "Requirement already satisfied: pydantic-core==2.16.3 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1->langchain-core<0.2.0,>=0.1.28->langchain-text-splitters) (2.16.3)\n",
            "Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1->langchain-core<0.2.0,>=0.1.28->langchain-text-splitters) (4.11.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langsmith<0.2.0,>=0.1.0->langchain-core<0.2.0,>=0.1.28->langchain-text-splitters) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langsmith<0.2.0,>=0.1.0->langchain-core<0.2.0,>=0.1.28->langchain-text-splitters) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langsmith<0.2.0,>=0.1.0->langchain-core<0.2.0,>=0.1.28->langchain-text-splitters) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langsmith<0.2.0,>=0.1.0->langchain-core<0.2.0,>=0.1.28->langchain-text-splitters) (2024.2.2)\n",
            "Installing collected packages: packaging, orjson, jsonpointer, jsonpatch, langsmith, langchain-core, langchain-text-splitters\n",
            "  Attempting uninstall: packaging\n",
            "    Found existing installation: packaging 24.0\n",
            "    Uninstalling packaging-24.0:\n",
            "      Successfully uninstalled packaging-24.0\n",
            "Successfully installed jsonpatch-1.33 jsonpointer-2.4 langchain-core-0.1.42 langchain-text-splitters-0.0.1 langsmith-0.1.47 orjson-3.10.0 packaging-23.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.document_loaders import TextLoader\n",
        "\n",
        "loader = TextLoader(\"/content/phil-textbook.txt\")\n",
        "documents = loader.load()\n",
        "text_splitter = CharacterTextSplitter(chunk_size=4000, chunk_overlap=0)\n",
        "docs = text_splitter.split_documents(documents)"
      ],
      "metadata": {
        "id": "jDRS9CTQ8dZg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_community.document_loaders import PyPDFLoader\n",
        "\n",
        "loader = PyPDFLoader(\"/content/2304.08485.pdf\")\n",
        "pages = loader.load_and_split()"
      ],
      "metadata": {
        "id": "QIsyVXZX35nC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pages[0].page_content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "id": "lDEL8y3b59He",
        "outputId": "faa893c5-8153-47ae-ff49-ac9b48165cd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Visual Instruction Tuning\\nHaotian Liu1∗, Chunyuan Li2∗, Qingyang Wu3, Yong Jae Lee1\\n1University of Wisconsin–Madison2Microsoft Research3Columbia University\\nhttps://llava-vl.github.io\\nAbstract\\nInstruction tuning large language models (LLMs) using machine-generated\\ninstruction-following data has been shown to improve zero-shot capabilities on\\nnew tasks, but the idea is less explored in the multimodal field. We present the\\nfirst attempt to use language-only GPT-4 to generate multimodal language-image\\ninstruction-following data. By instruction tuning on such generated data, we in-\\ntroduce LLaV A: Large Language andVision Assistant, an end-to-end trained\\nlarge multimodal model that connects a vision encoder and an LLM for general-\\npurpose visual and language understanding. To facilitate future research on visual\\ninstruction following, we construct two evaluation benchmarks with diverse and\\nchallenging application-oriented tasks. Our experiments show that LLaV A demon-\\nstrates impressive multimodal chat abilities, sometimes exhibiting the behaviors\\nof multimodal GPT-4 on unseen images/instructions, and yields a 85.1% rela-\\ntive score compared with GPT-4 on a synthetic multimodal instruction-following\\ndataset. When fine-tuned on Science QA, the synergy of LLaV A and GPT-4\\nachieves a new state-of-the-art accuracy of 92.53%. We make GPT-4 generated\\nvisual instruction tuning data, our model, and code publicly available.\\n1 Introduction\\nHumans interact with the world through many channels such as vision and language, as each\\nindividual channel has a unique advantage in representing and communicating certain concepts, and\\nthus facilitates a better understanding of the world. One of the core aspirations in artificial intelligence\\nis to develop a general-purpose assistant that can effectively follow multi-modal vision-and-language\\ninstructions, aligned with human intent to complete various real-world tasks in the wild [4, 27, 26].\\nTo this end, the community has witnessed an emergent interest in developing language-augmented\\nfoundation vision models [ 27,16], with strong capabilities in open-world visual understanding\\nsuch as classification [ 40,21,57,54,39], detection [ 29,62,33], segmentation [ 25,63,58] and\\ncaptioning [ 50,28], as well as visual generation and editing [ 42,43,56,15,44,30]. We refer readers\\nto the Computer Vision in the Wild reading list for a more up-to-date literature compilation [ 12]. In\\nthis line of work, each task is solved independently by one single large vision model, with the task\\ninstruction implicitly considered in the model design. Further, language is only utilized to describe\\nthe image content. While this allows language to play an important role in mapping visual signals to\\nlanguage semantics—a common channel for human communication, it leads to models that usually\\nhave a fixed interface with limited interactivity and adaptability to the user’s instructions.\\nLarge language models (LLM), on the other hand, have shown that language can play a wider\\nrole: a universal interface for a general-purpose assistant, where various task instructions can be\\nexplicitly represented in language and guide the end-to-end trained neural assistant to switch to the\\ntask of interest to solve it. For example, the recent success of ChatGPT [ 35] and GPT-4 [ 36] have\\ndemonstrated the power of aligned LLMs in following human instructions, and have stimulated\\ntremendous interest in developing open-source LLMs. Among them, LLaMA [ 49] is an open-\\nsource LLM that matches the performance of GPT-3. Alpaca [ 48], Vicuna [ 9], GPT-4-LLM [ 38]\\n37th Conference on Neural Information Processing Systems (NeurIPS 2023).arXiv:2304.08485v2  [cs.CV]  11 Dec 2023'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pages[1].page_content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "id": "NoGVe63A6rdv",
        "outputId": "ede34863-de1a-47da-916a-15a0a9f02e88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'utilize various machine-generated high-quality instruction-following samples to improve the LLM’s\\nalignment ability, reporting impressive performance compared with proprietary LLMs. Importantly,\\nthis line of work is text-only .\\nIn this paper, we present visual instruction-tuning , the first attempt to extend instruction-tuning to\\nthe language-image multimodal space, to pave the way towards building a general-purpose visual\\nassistant. In particular, our paper makes the following contributions:\\n•Multimodal instruction-following data . One key challenge is the lack of vision-language\\ninstruction-following data. We present a data reformation perspective and pipeline to convert\\nimage-text pairs into an appropriate instruction-following format, using ChatGPT/GPT-4.\\n•Large multimodal models . We develop a large multimodal model (LMM), by connecting the\\nopen-set visual encoder of CLIP [ 40] with the language decoder Vicuna [ 9], and fine-tuning\\nend-to-end on our generated instructional vision-language data. Our empirical study validates\\nthe effectiveness of using generated data for LMM instruction-tuning, and suggests practical\\ntips for building a general-purpose instruction-following visual agent. When ensembled with\\nGPT-4, our approach achieves SoTA on the Science QA [34] multimodal reasoning dataset.\\n•Multimodal instruction-following benchmark . We present LLaV A-Bench with two challenging\\nbenchmarks, with a diverse selection of paired images, instructions and detailed annotations.\\n•Open-source . We release the following assets to the public: the generated multimodal instruction\\ndata, the codebase, the model checkpoints, and a visual chat demo.\\n2 Related Work\\nMultimodal Instruction-following Agents. In computer vision, existing works that build instruction-\\nfollowing agents can be broadly categorized into two classes: (i)End-to-end trained models, which\\nare separately explored for each specific research topic. For example, the vision-language navigation\\ntask [ 3,19] and Habitat [ 47] require the embodied AI agent to follow natural language instructions\\nand take a sequence of actions to complete goals in visual environments. In the image editing domain,\\ngiven an input image and a written instruction that tells the agent what to do, InstructPix2Pix [ 6]\\nedits images by following the human instructions. (ii)A system that coordinates various models\\nvia LangChain [ 1] / LLMs [ 35], such as Visual ChatGPT [ 53], X-GPT [ 63], MM-REACT [ 55],\\nVisProg [ 18], and ViperGPT [ 46]. While sharing the same goal in building instruction-following\\nagents, we focus on developing an end-to-end trained language-vision multimodal model for multiple\\ntasks.\\nInstruction Tuning. In the natural language processing (NLP) community, to enable LLMs such\\nas GPT-3 [ 7], T5 [ 41], PaLM [ 10], and OPT [ 60] to follow natural language instructions and\\ncomplete real-world tasks, researchers have explored methods for LLM instruction-tuning [ 37,52,51],\\nleading to instruction-tuned counterparts such as InstructGPT [ 37]/ChatGPT [ 35], FLAN-T5 [ 11],\\nFLAN-PaLM [ 11], and OPT-IML [ 22], respectively. It turns out that this simple approach can\\neffectively improve the zero- and few-shot generalization abilities of LLMs. It is thus natural\\nto borrow the idea from NLP to computer vision. More broadly, the teacher-student distillation\\nideas with foundation models have been studied in other topics such as image classification [ 14].\\nFlamingo [ 2] can be viewed as the GPT-3 moment in the multimodal domain, due to its strong\\nperformance on zero-shot task transfer and in-context-learning. Other LMMs trained on image-\\ntext pairs include BLIP-2 [ 28], FROMAGe [ 24], and KOSMOS-1 [ 20]. PaLM-E [ 13] is an LMM\\nfor embodied AI. Based on the recent “best” open-source LLM LLaMA, OpenFlamingo [ 5] and\\nLLaMA-Adapter [ 59] are open-source efforts that enable LLaMA to use image inputs, paving the\\nway to build open-source multimodal LLMs. While these models present promising task transfer'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cot_string = pages[1].page_content.replace(\"\\n\", \" \")\n",
        "cot_string"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "id": "vMbGOWK8SYyt",
        "outputId": "14cfca2d-5d6f-4a22-b0a5-8d267acf3162"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'utilize various machine-generated high-quality instruction-following samples to improve the LLM’s alignment ability, reporting impressive performance compared with proprietary LLMs. Importantly, this line of work is text-only . In this paper, we present visual instruction-tuning , the first attempt to extend instruction-tuning to the language-image multimodal space, to pave the way towards building a general-purpose visual assistant. In particular, our paper makes the following contributions: •Multimodal instruction-following data . One key challenge is the lack of vision-language instruction-following data. We present a data reformation perspective and pipeline to convert image-text pairs into an appropriate instruction-following format, using ChatGPT/GPT-4. •Large multimodal models . We develop a large multimodal model (LMM), by connecting the open-set visual encoder of CLIP [ 40] with the language decoder Vicuna [ 9], and fine-tuning end-to-end on our generated instructional vision-language data. Our empirical study validates the effectiveness of using generated data for LMM instruction-tuning, and suggests practical tips for building a general-purpose instruction-following visual agent. When ensembled with GPT-4, our approach achieves SoTA on the Science QA [34] multimodal reasoning dataset. •Multimodal instruction-following benchmark . We present LLaV A-Bench with two challenging benchmarks, with a diverse selection of paired images, instructions and detailed annotations. •Open-source . We release the following assets to the public: the generated multimodal instruction data, the codebase, the model checkpoints, and a visual chat demo. 2 Related Work Multimodal Instruction-following Agents. In computer vision, existing works that build instruction- following agents can be broadly categorized into two classes: (i)End-to-end trained models, which are separately explored for each specific research topic. For example, the vision-language navigation task [ 3,19] and Habitat [ 47] require the embodied AI agent to follow natural language instructions and take a sequence of actions to complete goals in visual environments. In the image editing domain, given an input image and a written instruction that tells the agent what to do, InstructPix2Pix [ 6] edits images by following the human instructions. (ii)A system that coordinates various models via LangChain [ 1] / LLMs [ 35], such as Visual ChatGPT [ 53], X-GPT [ 63], MM-REACT [ 55], VisProg [ 18], and ViperGPT [ 46]. While sharing the same goal in building instruction-following agents, we focus on developing an end-to-end trained language-vision multimodal model for multiple tasks. Instruction Tuning. In the natural language processing (NLP) community, to enable LLMs such as GPT-3 [ 7], T5 [ 41], PaLM [ 10], and OPT [ 60] to follow natural language instructions and complete real-world tasks, researchers have explored methods for LLM instruction-tuning [ 37,52,51], leading to instruction-tuned counterparts such as InstructGPT [ 37]/ChatGPT [ 35], FLAN-T5 [ 11], FLAN-PaLM [ 11], and OPT-IML [ 22], respectively. It turns out that this simple approach can effectively improve the zero- and few-shot generalization abilities of LLMs. It is thus natural to borrow the idea from NLP to computer vision. More broadly, the teacher-student distillation ideas with foundation models have been studied in other topics such as image classification [ 14]. Flamingo [ 2] can be viewed as the GPT-3 moment in the multimodal domain, due to its strong performance on zero-shot task transfer and in-context-learning. Other LMMs trained on image- text pairs include BLIP-2 [ 28], FROMAGe [ 24], and KOSMOS-1 [ 20]. PaLM-E [ 13] is an LMM for embodied AI. Based on the recent “best” open-source LLM LLaMA, OpenFlamingo [ 5] and LLaMA-Adapter [ 59] are open-source efforts that enable LLaMA to use image inputs, paving the way to build open-source multimodal LLMs. While these models present promising task transfer'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.vectorstores import FAISS\n",
        "from langchain.embeddings import HuggingFaceBgeEmbeddings"
      ],
      "metadata": {
        "id": "Tm8FRpPj1qBb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"BAAI/bge-large-en-v1.5\"\n",
        "model_kwargs = {'device': 'cuda'}\n",
        "encode_kwargs = {'normalize_embeddings': True}"
      ],
      "metadata": {
        "id": "xCumHfCypE2i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = HuggingFaceBgeEmbeddings(\n",
        "    model_name=model_name,\n",
        "    model_kwargs=model_kwargs,\n",
        "    encode_kwargs=encode_kwargs,\n",
        "    query_instruction=\"Represent this sentence for searching relevant passages:\"\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493,
          "referenced_widgets": [
            "a5d281e14aab42faaca83bfb0eaca565",
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            "a37eef343391473e92682a6b4d9f0c3c",
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          ]
        },
        "id": "4vJpskcUpOMD",
        "outputId": "49bfb2bb-622f-45d6-cbff-5cd4ddbb8f96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/94.6k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
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              "model_id": "33a0336a21d843539f462159d9f9f399"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "db = FAISS.from_documents(docs, model) # alternate:embeddings\n",
        "db.save_local(\"faiss_index\")"
      ],
      "metadata": {
        "id": "Qv9SXHnTuNWP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "db = FAISS.load_local(\"/faiss_index\", query_embedding)\n",
        "retriever=db.as_retriever(search_type=\"mmr\", search_kwargs = {\n",
        "      'k': 2\n",
        "    })\n",
        "context = retriever.get_relevant_documents(questions)"
      ],
      "metadata": {
        "id": "7weAADNJ1Q-k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "second: ChromaDB with sentence-transformers"
      ],
      "metadata": {
        "id": "vqkMxD0Wp2Vh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "performances details: https://www.sbert.net/docs/pretrained_models.html"
      ],
      "metadata": {
        "id": "ObHasTrXq6Wk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "other models to be evaluated with different criteria"
      ],
      "metadata": {
        "id": "4tqjilbCrRxT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install chromadb"
      ],
      "metadata": {
        "id": "PF1tQCjkiQAD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d988733a-696d-46be-c90c-aa1f84595ac9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting humanfriendly>=9.1 (from coloredlogs->onnxruntime>=1.14.1->chromadb)\n",
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            "Building wheels for collected packages: pypika\n",
            "  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pypika: filename=PyPika-0.48.9-py2.py3-none-any.whl size=53724 sha256=1aec6b174ee48ea9d3eab2a4c4cea9afd4511925c381a9fd4b75d70cb7a9647e\n",
            "  Stored in directory: /root/.cache/pip/wheels/e1/26/51/d0bffb3d2fd82256676d7ad3003faea3bd6dddc9577af665f4\n",
            "Successfully built pypika\n",
            "Installing collected packages: pypika, monotonic, mmh3, websockets, uvloop, python-dotenv, pulsar-client, overrides, opentelemetry-util-http, opentelemetry-semantic-conventions, opentelemetry-proto, importlib-metadata, humanfriendly, httptools, h11, deprecated, chroma-hnswlib, bcrypt, backoff, asgiref, watchfiles, uvicorn, starlette, posthog, opentelemetry-exporter-otlp-proto-common, opentelemetry-api, coloredlogs, opentelemetry-sdk, opentelemetry-instrumentation, onnxruntime, kubernetes, fastapi, opentelemetry-instrumentation-asgi, opentelemetry-exporter-otlp-proto-grpc, opentelemetry-instrumentation-fastapi, chromadb\n",
            "  Attempting uninstall: importlib-metadata\n",
            "    Found existing installation: importlib_metadata 7.1.0\n",
            "    Uninstalling importlib_metadata-7.1.0:\n",
            "      Successfully uninstalled importlib_metadata-7.1.0\n",
            "Successfully installed asgiref-3.8.1 backoff-2.2.1 bcrypt-4.1.2 chroma-hnswlib-0.7.3 chromadb-0.4.24 coloredlogs-15.0.1 deprecated-1.2.14 fastapi-0.110.2 h11-0.14.0 httptools-0.6.1 humanfriendly-10.0 importlib-metadata-7.0.0 kubernetes-29.0.0 mmh3-4.1.0 monotonic-1.6 onnxruntime-1.17.3 opentelemetry-api-1.24.0 opentelemetry-exporter-otlp-proto-common-1.24.0 opentelemetry-exporter-otlp-proto-grpc-1.24.0 opentelemetry-instrumentation-0.45b0 opentelemetry-instrumentation-asgi-0.45b0 opentelemetry-instrumentation-fastapi-0.45b0 opentelemetry-proto-1.24.0 opentelemetry-sdk-1.24.0 opentelemetry-semantic-conventions-0.45b0 opentelemetry-util-http-0.45b0 overrides-7.7.0 posthog-3.5.0 pulsar-client-3.5.0 pypika-0.48.9 python-dotenv-1.0.1 starlette-0.37.2 uvicorn-0.29.0 uvloop-0.19.0 watchfiles-0.21.0 websockets-12.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re, nltk\n",
        "nltk.download('punkt')"
      ],
      "metadata": {
        "id": "CvnKBM_4iZ8c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install chromadb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s18hI7w4zQJl",
        "outputId": "dfec0c95-6f1f-4fd8-c262-6b131abe4086"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting chromadb\n",
            "  Downloading chromadb-0.5.0-py3-none-any.whl (526 kB)\n",
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            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
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            "Building wheels for collected packages: pypika\n",
            "  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pypika: filename=PyPika-0.48.9-py2.py3-none-any.whl size=53724 sha256=efd25afd94f75aa6d3d59ab785de8fc54884feea7d16ad73e68b362c54da481d\n",
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            "Successfully built pypika\n",
            "Installing collected packages: pypika, monotonic, mmh3, websockets, uvloop, python-dotenv, overrides, orjson, opentelemetry-util-http, opentelemetry-semantic-conventions, opentelemetry-proto, importlib-metadata, humanfriendly, httptools, h11, deprecated, chroma-hnswlib, bcrypt, backoff, asgiref, watchfiles, uvicorn, starlette, posthog, opentelemetry-exporter-otlp-proto-common, opentelemetry-api, coloredlogs, opentelemetry-sdk, opentelemetry-instrumentation, onnxruntime, kubernetes, fastapi, opentelemetry-instrumentation-asgi, opentelemetry-exporter-otlp-proto-grpc, opentelemetry-instrumentation-fastapi, chromadb\n",
            "  Attempting uninstall: importlib-metadata\n",
            "    Found existing installation: importlib_metadata 7.1.0\n",
            "    Uninstalling importlib_metadata-7.1.0:\n",
            "      Successfully uninstalled importlib_metadata-7.1.0\n",
            "Successfully installed asgiref-3.8.1 backoff-2.2.1 bcrypt-4.1.2 chroma-hnswlib-0.7.3 chromadb-0.5.0 coloredlogs-15.0.1 deprecated-1.2.14 fastapi-0.110.3 h11-0.14.0 httptools-0.6.1 humanfriendly-10.0 importlib-metadata-7.0.0 kubernetes-29.0.0 mmh3-4.1.0 monotonic-1.6 onnxruntime-1.17.3 opentelemetry-api-1.24.0 opentelemetry-exporter-otlp-proto-common-1.24.0 opentelemetry-exporter-otlp-proto-grpc-1.24.0 opentelemetry-instrumentation-0.45b0 opentelemetry-instrumentation-asgi-0.45b0 opentelemetry-instrumentation-fastapi-0.45b0 opentelemetry-proto-1.24.0 opentelemetry-sdk-1.24.0 opentelemetry-semantic-conventions-0.45b0 opentelemetry-util-http-0.45b0 orjson-3.10.1 overrides-7.7.0 posthog-3.5.0 pypika-0.48.9 python-dotenv-1.0.1 starlette-0.37.2 uvicorn-0.29.0 uvloop-0.19.0 watchfiles-0.21.0 websockets-12.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WS6FDGouh8mQ"
      },
      "outputs": [],
      "source": [
        "from chromadb.utils import embedding_functions\n",
        "import chromadb"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sentence-transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_mO5c4kaiNlj",
        "outputId": "4b17a626-d363-4bc2-a425-1d11b4795cc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting sentence-transformers\n",
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            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, sentence-transformers\n",
            "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 sentence-transformers-2.7.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=\"all-mpnet-base-v2\")"
      ],
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        },
        "outputId": "8ea2136b-2abc-42d8-d3ba-8d4c1bd7d964"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
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          "output_type": "display_data",
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          "metadata": {}
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        {
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          "metadata": {}
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
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          "metadata": {}
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          "metadata": {}
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        {
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          "metadata": {}
        },
        {
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"csv\", data_files=\"13k-recipes.csv\",keep_default_na=False )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
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        "outputId": "910dd96c-a632-4f91-80ef-bfca0c3ee9f4"
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      "execution_count": null,
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          "data": {
            "text/plain": [
              "{'Unnamed: 0': 1,\n",
              " 'Title': 'Crispy Salt and Pepper Potatoes',\n",
              " 'Ingredients': \"['2 large egg whites', '1 pound new potatoes (about 1 inch in diameter)', '2 teaspoons kosher salt', '¾ teaspoon finely ground black pepper', '1 teaspoon finely chopped rosemary', '1 teaspoon finely chopped thyme', '1 teaspoon finely chopped parsley']\",\n",
              " 'Instructions': 'Preheat oven to 400°F and line a rimmed baking sheet with parchment. In a large bowl, whisk the egg whites until foamy (there shouldn’t be any liquid whites in the bowl). Add the potatoes and toss until they’re well coated with the egg whites, then transfer to a strainer or colander and let the excess whites drain. Season the potatoes with the salt, pepper, and herbs. Scatter the potatoes on the baking sheet (make sure they’re not touching) and roast until the potatoes are very crispy and tender when poked with a knife, 15 to 20 minutes (depending on the size of the potatoes).\\nTransfer to a bowl and serve.',\n",
              " 'Image_Name': 'crispy-salt-and-pepper-potatoes-dan-kluger',\n",
              " 'Cleaned_Ingredients': \"['2 large egg whites', '1 pound new potatoes (about 1 inch in diameter)', '2 teaspoons kosher salt', '¾ teaspoon finely ground black pepper', '1 teaspoon finely chopped rosemary', '1 teaspoon finely chopped thyme', '1 teaspoon finely chopped parsley']\"}"
            ]
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          "metadata": {},
          "execution_count": 23
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 58782,\n  \"fields\": [\n    {\n      \"column\": \",Title,Ingredients,Instructions,Image_Name,Cleaned_Ingredients\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 58231,\n        \"samples\": [\n          \"Add the drained macaroni to the pot with the cheese sauce and stir well to coat. Transfer macaroni mixture to the buttered baking dish and place on a rimmed baking sheet. Sprinkle topping evenly over macaroni and bake until golden and bubbling, 18\\u00e2\\u0080\\u009322 minutes. Let cool 15 minutes before serving.\\\",our-favorite-macaroni-and-cheese-51255890,\\\"['2 tablespoons unsalted butter, plus more for baking dish', '2 tablespoons extra-virgin olive oil', '2 cups panko (Japanese breadcrumbs)', '2 large garlic cloves, minced', '2 ounces finely grated Parmesan (about 1/2 cup)', '1/2 teaspoon kosher salt', '2 teaspoons kosher salt, plus more', '1 pound dried elbow macaroni', '1/2 cup (1 stick) unsalted butter', '6 tablespoons all-purpose flour', '4 cups whole milk', '3/4 cup heavy cream', '1/2 teaspoon ground white pepper', '1 1/2 tablespoons mustard powder', '16 ounces coarsely grated extra-sharp cheddar (about 6 cups)', '2 ounces grated Parmesan (about 1/2 cup)', '9- by 13-inch baking dish']\\\"\",\n          \"Pulse pecans, sugar, and cinnamon in a food processor until finely chopped.\",\n          \"5. Meanwhile, wet-chop the vegetables: Fill the Vitamix container with water up to the 5-cup (1.2-L) mark, add the onion, and secure the lid. Select Variable 8. Use the On/Off switch to quickly pulse 3 times. Drain well and repeat the process with the celery (3 pulses) and carrot (4 pulses), draining thoroughly in a colander.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def perform_search(query,count, k):\n",
        "\n",
        "    #load_csv here\n",
        "\n",
        "    chroma_client = chromadb.PersistentClient(path=\"/content/drive/MyDrive/persistent_db\") #see if the chromaDB has already been created\n",
        "\n",
        "    if count == 1:\n",
        "        context = chroma_client.create_collection(name=\"context11\", embedding_function=sentence_transformer_ef)\n",
        "        context.add(\n",
        "            ids=[str(i) for i in range(0, 100)],  # IDs are just strings\n",
        "            documents=[dataset['train'][i]['Cleaned_Ingredients'] for i in range(0, 100)\n",
        "    ],\n",
        "            metadatas=[{'Instructions':dataset['train'][i]['Instructions']} for i in range(0, 100)\n",
        "    ],\n",
        ")\n",
        "    else:\n",
        "        context = chroma_client.get_collection(name=\"context11\", embedding_function=sentence_transformer_ef) #if the vectorstore already exist\n",
        "\n",
        "    results = context.query(\n",
        "    query_texts=query,\n",
        "    n_results=k\n",
        "    )\n",
        "    return results['metadatas']"
      ],
      "metadata": {
        "id": "aPvhKvFn5UA1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "context = perform_search(\"potato\", 2, 1)# change from 6000 to 1000\n",
        "context[0][0][\"Instructions\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "id": "RxAhdHHD391X",
        "outputId": "5e3a2997-30ff-4164-c9ad-7cdd23591284"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Cover potatoes, garlic, and 1 Tbsp. salt with cold water in a large pot. Bring to a low boil, then reduce heat and simmer (do not boil) until potatoes are very tender when pierced with the tip of a paring knife but not falling apart, 20–25 minutes.\\nMeanwhile, heat milk, thyme, and 3/4 cup butter in a small pot over medium, stirring, until butter is melted. Remove from heat; set aside.\\nDrain potatoes and garlic; return to pot. Toss over low heat until moisture evaporates, 1–2 minutes. Using potato ricer or food mill, immediately press potatoes and garlic into a large bowl (do not let cool).\\nDiscard thyme from milk mixture and gradually stir into potatoes, reserving about 1/2 cup if you plan to make in advance (see Do Ahead). Season with 2 tsp. salt and 3/4 tsp. pepper. Fold in sour cream and stir with a spoon until incorporated and very smooth (do not overmix or potatoes will become gummy). Taste and adjust seasonings. Serve topped with remaining 2 Tbsp. butter and more pepper.\\nMashed potatoes can be made 2 hours ahead; cover and store at room temperature. Or chill, covered, up to 8 hours; reheat over medium with reserved 1/2 cup milk mixture.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def perform_search(query,count, k):\n",
        "\n",
        "    #load_csv here\n",
        "\n",
        "    chroma_client = chromadb.PersistentClient(path=\"/content/drive/MyDrive/persistent_db\") #see if the chromaDB has already been created\n",
        "\n",
        "    if count == 1:\n",
        "        context = chroma_client.create_collection(name=\"context5\", embedding_function=sentence_transformer_ef)\n",
        "        context.add(\n",
        "            ids=[str(i) for i in range(0, 1868)],  # IDs are just strings\n",
        "            documents=[docs[i].page_content for i in range(0, 1868)\n",
        "    ],\n",
        "            metadatas=[{\"source\": docs[i].metadata['source']} for i in range(0, 1868)\n",
        "    ],\n",
        ")\n",
        "    else:\n",
        "        context = chroma_client.get_collection(name=\"context5\", embedding_function=sentence_transformer_ef) #if the vectorstore already exist\n",
        "\n",
        "    results = context.query(\n",
        "    query_texts=query,\n",
        "    n_results=k\n",
        "    )\n",
        "    return results\n"
      ],
      "metadata": {
        "id": "2SH2iYgFiojc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "p = perform_search(\"machine learning\", 2, 200)\n",
        "threshold = 2\n",
        "p[\"distances\"]\n",
        "index = len([score for score in p[\"distances\"][0] if score < threshold])\n",
        "print(p[\"documents\"][0][:index]) # we can see how many documents are retrieved for a specific threshold"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p1la0ai7tOmb",
        "outputId": "9cd2c7d5-8a76-4b48-a0d4-b59e7bab3b15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['algorithm.', 'classification accuracy up to 77%.', 'self-supervised learning models.', \"The rapid development in data collecting devices and computation platforms produces an emerging number of agents, each equipped with a unique data modality over a particular population of subjects. While an agent's predictive performance may be enhanced by transmitting others' data to it, this is often unrealistic due to intractable transmission costs and security concerns. In this paper, we propose a method named ASCII for an agent to improve its classification performance through assistance\", 'proposed algorithm.', 'classifier, using supervised learning. Only 20% of the data needs to be manually annotated with the proposed method, to achieve the accuracy with all the data annotated. In addition, the proposed method clearly outperforms other active learning algorithms proposed for sound event classification through all the experiments, simulating varying fraction of data that is manually labeled.', 'advantageous the proposed algorithm is.', 'tree, and neural network, and they may be heterogeneous among agents. We demonstrate the proposed method with extensive experimental studies.', 'dataset, and it achieved superior performance compared to latest approaches.', 'framework that allows the algorithm to request the label for a bag of samples at a time. Although this label will come in the form of proportions of class labels in the bags and therefore encode less information, we demonstrate that we can still learn effectively.', 'Active Learning (AL) refers to the setting where the learner has the ability to perform queries to an oracle to acquire the true label of an instance or, sometimes, a set of instances. Even though Active Learning has been studied extensively, the setting is usually restricted to assume that the oracle is trustworthy and will provide the actual label. We argue that, while common, this approach can be made more flexible to account for different forms of supervision. In this paper, we propose a new', 'classification is also proposed to alleviate the class imbalance problem. Experiment results on three public datasets show that the proposed system outperforms four state-of-the-art algorithms in most cases.', 'elaborated in the second step by a neural network providing a real output whose magnitude was associated to the surgeon ability. Experimental tests correctly classify all operators in a group.', 'feature learning, which can effectively reduce the proportion of noisy features. Besides, a regularization term is introduced into our model to make the sample-to-clusters memberships of each sample have suitable sparsity. Specifically, we design an effective strategy to determine the value of the regularization parameter. The experimental results on both synthetic and real-world datasets demonstrate that our model has better performance than other classical algorithms.', 'operation on smooth low-rank matrices. This technique is able to predict iteratively wh ich entries are the most informative. Our numerical examples illustrate that AdSSA performs significantly better than the Uniform Random Sampling (URS) and the Query by Committee (QbC). In addition, AdSSA algorithm can be straightforwardly implemented in an onIine and efficient manner, which constitutes another advantage.', 'results showing the validity of the proposed approach. For a complete reproducibility of the work, the source code is available at: http://iebil.di.unimi.it/cnnALL/index.htm.', 'provide comparisons for each of the steps and share the implementation of the most relevant modules of our system. Our system scored second in the challenge in terms of the primary metric (diarization error rate) and first according to the secondary metric (Jaccard error rate).', 'dictionary size. Numerical examples show the efficacy of the proposed algorithm.', 'significantly better than the others, and that combinations of top systems are capable of reaching close to perfect performance on the given data.', 'dataset.', 'gradients. Experimental results show robust detection in various scenarios.', 'Clustering task has been greatly developed in recent years like partition-based and graph-based methods. However, in terms of improving robustness, most existing algorithms only focus on noise and outliers between data, while ignoring the noise in feature space. To deal with this situation, we propose a novel weight learning mechanism to adaptively reweight each feature in the data. Combining with the clustering task, we further propose a robust fuzzy K-Means model based on the auto-weighted', 'large-scale image classification benchmark datasets (MS-COCO and NUS-WIDE) show that the discriminator improves generalization ability for different kinds of models.', 'from other agents, without sharing proprietary data and model information. The main idea is to iteratively interchange an ignorance value between 0 and 1 for each collated sample among agents, where the value represents the urgency of further assistance needed. The method is naturally suitable for privacy-aware, transmission-economical, and decentralized learning scenarios. The method is also general as it allows the agents to use arbitrary classifiers such as logistic regression, ensemble', 'Anomaly detection involves the recognition of patterns outside of what is considered normal, given a certain set of input data. This presents a unique set of challenges for machine learning, particularly if we assume a semi-supervised scenario in which anomalous patterns are unavailable at training time meaning algorithms must rely on non-anomalous data alone. Anomaly detection in time series adds an additional level of complexity given the contextual nature of anomalies. For time series', 'observed matrices with a random set of revealed entries close to the informationtheoretical limits, thereby significantly improving on prior methods.', 'techniques to compute eigenvectors of the relevant matrices. Simulations over synthetic data show that the regression coefficients from the proposed online PLS algorithm converge to those of its block-based counterparts.', 'Our method adaptively selects the most critical features that mainly contribute towards a prediction to probe the model by finding the activated features. Experimental results show that the proposed method can reduce the execution time up to 30% while enhancing competitive interpretability without compromising the quality of explanation generated.', 'accuracy of 6.7% with samples generated by a random model.', 'implementing feature selection on mobile devices, and we evaluate popular feature selection methods against constantly alternating activity labels. We perform a qualitative analysis to determine the dominant sensing modality that dictates the activities of a certain time duration. The results indicate that online feature selection performance changes among consecutive data partitions, leading to the conclusion that the type of available activity influences significantly the feature selection', 'framework, where the expected number of observed variables is small compared to the dimension of the vector of interest, and propose a design of optimal sub-sampling probabilities and an active covariance matrix estimation algorithm.', 'annotation continues to the unexamined sounds with mismatched prediction results from two classifiers, a nearestneighbor classifier and a model-based classifier, both trained with annotated data. The annotation on the segments with mismatched predictions are ordered by the distance to the nearest annotated sample, farthest first. The evaluation is made on a public environmental sound dataset. The labels obtained through a labeling process controlled by the proposed method are used to train a', 'machine learning algorithms, specifically support vector machine (SVM) and artificial neural network (ANN), for solving combinatorial antenna selection problems. Numerical examples are presented to validate the effectiveness and efficiency of machine learning algorithms for sparse array design. Moreover, the SVM based antenna selection is robust against DOA estimate uncertainties.', 'task. One of the challenges with SLA is the wide range of concepts that can introduce bias in training data, for example L1, age, acoustic conditions, and particular human graders, or the grading instructions. Simply generating large quantities of expert marked data to check for all forms of bias is impractical. This paper uses CAVs applied to the training data to identify concepts that might be of concern, allowing a more targeted dataset to be collected to assess bias. The ability of CAVs to', 'data.', 'systems.', 'with other existing methods.', 'This paper compares several linear and non-linear multivariate models for the detection of abnormal patterns in neuroimaging data, when comparing a single subject to a normal control group. The proposed methods learn the manifold spanned by the normal controls using non-linear dimension reduction techniques. The image of a subject is projected on the control group manifold either via a standard projection or through an embedding/reconstruction scheme. A comparison of the reconstruction with the', 'performance of the proposed models using the degradation in classification accuracy and imperceptibility measures in comparison to perturbations generated to fool one-stage classifiers.', 'labeled data is low, and it is competitive with other methods in the fully labeled case.', 'indicate that our method can be successfully used to refine NMF-based dictionaries.', 'estimation errors. A classification-aided module is also included to further suppress prediction outliers. Our model achieves the state-of-the-art performance in simulated and real-world data environments, where the results are strongly correlated with the corresponding reference-based objective scores.', 'We consider the problem of designing a robust classifier in the presence of an adversary who aims to degrade classification performance by elaborately falsifying the test instance. We propose a modelagnostic defense approach wherein the true class label of the falsified instance is inferred by analyzing its proximity to each class as measured based on class-conditional data distributions. We present a knearest neighbors type approach to perform a sample-based approximation of the aforementioned', 'proposed method reduces the minimum detection cost relative to the baseline system, which uses linear discriminant analysis transformed i-vectors as features.', 'subspace, the final decision rule being obtained by majority voting rule. The experimental results on multispectral and PolyU palmprint datasets show very encouraging performances compared to state-of-the-art techniques.', 'This paper proposes an active learning method to control a labeling process for efficient annotation of acoustic training material, which is used for training sound event classifiers. The proposed method performs K-medoids clustering over an initially unlabeled dataset, and medoids as local representatives, are presented to an annotator for manual annotation. The annotated label on a medoid propagates to other samples in its cluster for label prediction. After annotating the medoids, the', 'matrices. We showcase the performance of the proposed method on simulated observations of a mechanical system and on two real-world applications: sleep stage identification and pre-epileptic seizure prediction.', 'recognition performance is still an open problem to address. Considering the presence of synchronized aggregate activity in the basal ganglia, this paper presents an FFT-based synchronization approach to automatically select a relevant pair of LFPs and use the pair together with an SVM-based MKL classifier for behavior recognition purposes. Our experiments on five subjects show the superiority of the proposed approach compared to other methods used for behavior classification.', \"Explainable AI (XAI) is an active research area to interpret a neural network's decision by ensuring transparency and trust in the task-specified learned models. Recently, perturbation-based model analysis has shown better interpretation, but backpropagation techniques are still prevailing because of their computational efficiency. In this work, we combine both approaches as a hybrid visual explanation algorithm and propose an efficient interpretation method for convolutional neural networks.\", 'training data. A statistical test is then deployed to perform a systematic detection of anomalies. We demonstrate the performance of the proposed approach on real data from an experimental dike setup.', 'This paper proposes a novel active learning method to save annotation effort when preparing material to train sound event classifiers. K-medoids clustering is performed on unlabeled sound segments, and medoids of clusters are presented to annotators for labeling. The annotated label for a medoid is used to derive predicted labels for other cluster members. The obtained labels are used to build a classifier using supervised training. The accuracy of the resulted classifier is used to evaluate the', 'association. To model the relationship, inspired by the elasticity, an adaptive elastic loss is proposed to reduce the deviation between difficult samples and their true categories. Moreover, five evaluation metrics are designed to measure the segmentation performance, and the Window Precision is particularly valuable for the evaluation of semi-supervised algorithms due to its robustness to the mislabeling. Finally, on a cervical histology dataset, benchmark experiments based on deep networks', 'embedding-based features, their computationally intensive pre-processing makes their use difficult in low resource systems. For such systems, optical flow or raw pixels-based features might be better suited.', 'We investigate random projections in the context of randomly projected linear discriminant analysis (LDA). We consider the case in which the data of dimension p is randomly projected onto a lower dimensional space before being fed to the classifier. Using fundamental results from random matrix theory and relying on some mild assumptions, we show that the asymptotic performance in terms of probability of misclassification approaches a deterministic quantity that only depends on the data', 'dimensionality and model performance.', 'A novel gesture binary classification procedure is presented to determine surgical ability. To this aim a sensory glove was employed to track surgical hand movements and sensors data were recorded to be processed by a specific algorithm. The classification task was able to discriminate a gesture made by an expert surgeon with respect to a novice one, thanks to a two steps classification strategy. The first one produced a binary tree of parameters associated to a sensor time function; they were', 'supervised learning procedures-is shown to possess a richer structure consisting of various skip connections and shortcuts than other competing architectures. Moreover, our proposed network also delivers state-of-the-art unmixing performance compared to competing methods.', 'propose the use of 11 statistics to describe the distributions and test with several classifiers. In our experiments with 249 normal and 297 abnormal acoustic signals from 40 knees, a Support Vector Machine with linear kernel gave the best results with an error rate of 13.9%.', 'Structures such as seawalls, levees and dikes prevent low lying land from flooding. The structural health of these constructions is critical and needs to be maintained. In this paper, we present a data-driven approach that uses the information of different in-situ measurements to detect structural anomalies at an early stage. Our approach is based on system identification, in which the dike is modeled as a single-input, multipleoutput, linear system whose parameters can be learned based on', 'using any isolated training data.', 'We study covariance matrix estimation for the case of partially observed random vectors, where different samples contain different subsets of vector coordinates. Each observation is the product of the variable of interest with a 0-1 Bernoulli random variable. We analyze an unbiased covariance estimator under this model, and derive an error bound that reveals relations between the sub-sampling probabilities and the entries of the covariance matrix. We apply our analysis in an active learning', 'Machine learning models are vulnerable to adversarial attacks that can often cause misclassification by introducing small but well designed perturbations. In this paper, we explore, in the setting of classical composite hypothesis testing, a defense strategy based on the generalized likelihood ratio test (GLRT), which jointly estimates the class of interest and the adversarial perturbation. We evaluate the GLRT approach for the special case of binary hypothesis testing in white Gaussian noise', 'active learning to automatically select simulated training examples. With both user-simulated and interactive human evaluations, we show that the ASU approach successfully interprets user utterances in a dialogue system, including those with referring expressions.', 'technique can effectively reduce the error probability.', 'modeling power even when given very limited in-context training samples.', 'Adversarial attacks have exposed the vulnerability of onestage classifiers to carefully crafted perturbations which were shown to drastically alter their predictions while remaining imperceptible. In this paper, we examine the susceptibility of coarse-to-fine hierarchical classifiers to such types of attacks. We formulate convex programs to generate perturbations attacking these models and propose a generic solution based on the Alternating Direction Method of Multipliers (ADMM). We evaluate the', 'robustness in noise.', 'The problem of anomaly detection among multiple processes is considered within the framework of sequential design of experiments. The objective is an active inference strategy consisting of a selection rule governing which process to probe at each time, a stopping rule on when to terminate the detection, and a decision rule on the final detection outcome. The performance measure is the Bayes risk that takes into account not only sample complexity and detection errors, but also costs associated', 'In this paper, we use algorithm unrolling approaches in order to design a new neural network structure applicable to hyperspectral unmixing challenges. In particular, building upon a constrained sparse regression formulation of the underlying unmixing problem, we unroll an ADMM solver onto a neural network architecture that can be used to deliver the abundances of different (known) endmembers given a reflectance spectrum. Our proposed network-which can be readily trained using standard', 'the model-agnostic meta-learning tasks.', 'models for reverberant speech, exploiting a partial knowledge of the actual target environment. Given a set of pre-trained models, a GMM-based scheme is used to select the best model for a given (unknown) reverberant condition. A well-known recognition task of connected digits represents a comprehensive experimental setup that validates the proposed strategy.', 'method is evaluated on both simulated and real-world data. Results show that the proposed method has smaller estimation bias on the random effects compared to traditional inference methods including MCMC; it also achieves a state-of-the-art predictive performance, meanwhile offering a richer estimation of the variance function.', 'learning to reduce the annotation cost of a sound classification dataset unique to a particular problem. We evaluate three certainty-based active learning query strategies and propose a new strategy: alternating confidence sampling. Using this strategy, we demonstrate reduced annotation costs when actively training models with both experts and non-experts, and we perform a qualitative analysis on 20k unlabeled recordings to show our approach results in a model that generalizes well to unseen', 'confirm that our proposed framework outperforms stateof-the-art algorithms in both quantitative assessment and visual interpretation.', 'divided into two alternating stages: unknown parameters are first estimated by Expectation Maximization (EM), with which the next node to sample is selected based on historical observations according to predictive uncertainty. Validated by simulations compared with related approaches, the proposed algorithm can reduce the sample size to reach a certain estimation accuracy.', 'Classification of moving objects for video surveillance applications still remains a challenging problem due to the video inherently changing conditions such as lighting or resolution. This paper proposes a new approach for vehicle/pedestrian object classification based on the learning of a static kNN classifier, a dynamic Hidden Markov Model (HMM)-based classifier, and the definition of a fusion rule that combines the two outputs. The main novelty consists in the study of the dynamic aspects of', 'to reduce the amount of necessary labeled training data. We employ two different active learning query strategies based on uncertainty sampling. Since the classification task is performed on 2D image slices, but the human observer labels complete 3D image volumes, we present a method to select representative 3D images instead of independant 2D image slices. The performance is evaluated on in-vivo MR image data.', 'The result is then recognized including user dependent knowledge on a client, e.g., a smart phone. We achieved a word error rate reduction of 17% on the Wall Street Journal Corpus.', 'shape approach.', 'Interactive voice technologies can leverage biosignals, such as heart rate (HR), to infer the psychophysiological state of the user. Voicebased detection of HR is attractive because it does not require additional sensors. We predict HR from speech using the SRI BioFrustration Corpus. In contrast to previous studies we use continuous spontaneous speech as input. Results using random forests show modest but significant effects on HR prediction. We further explore the effects on HR of speaking', 'which is significantly higher than that of a state of the art Convolutional Neural Network (CNN)-based model (73%) and the linear model (69%).', 'annotating time-frequency regions where a source can be labeled as either active, inactive, or well-separated within the displayed spectrogram. This user feedback information, which is partially new with respect to the state-of-the-art annotations, is then taken into account in a proposed uncertainty-based learning algorithm to constraint the source estimates in next separation step. The considered framework is based on non-negative matrix factorization and is shown to be effective even without', 'proposed in the literature to solve this type of graph learning formulations, it lacks a rigorous convergence analysis and appears to have a slow empirical performance. In this paper, we cast the graph learning formulation as a nonsmooth, strictly convex optimization problem and develop an efficient alternating direction method of multipliers to solve it. We show that our algorithm converges to the global minimum with arbitrary initialization. We conduct extensive experiments on various', 'Human Activity Recognition (HAR) must currently face up to the challenge of rethinking analytics from the perspective of real-time operation, wherein biophysical sensing streams are efficiently intertwined at close vicinity to the point of sensing. As such, feature selection techniques, traditionally employed for off-line data processing, should be evaluated with respect to their ability to filter out redundant information in real-time. In this work, we propose an online architecture for', 'Configurations (RC) with Gaussian Mixture Model (GMM) as classifier. The experimental results gave lower Equal Error Rate (EER) of 11.73 % with TECC features and further reduced to 10.30 % with score-level fusion of LFCC and TECC features on evaluation dataset of ASVspoof 2017 challenge version 2.0 database.', 'using a series of field seismic datasets. The classification results show that our framework can not only remove signal noise effectively but also improve the classification accuracy.', 'show that our proposed AED consistently outperforms the existing state-of-the-art OKD methods on various datasets.', 'higher dimensional space. Such high dimensional spaces are frequently encountered in functional magnetic resonance imaging (fMRI) data for the analysis of brain activation detection. We propose a methodology that works based on the latent variables in a lower dimensional space. A low-rank decomposition of the sample covariance matrix is derived based on the singular value decomposition (SVD) and an adaptive basis selection method is used to decide which eigen-vectors are useful in data', 'also propose objective criteria to evaluate the quality and diversity of the generated samples based on classification accuracy. The experiments on the DCASE 2016 Task 1 acoustic scene data show that with the generated audio samples, a classification accuracy of 65.5% can be achieved compared to samples generated by a random model of 6.7% and samples from real recording of 83.1%. The performance of a classifier trained only on generated samples achieves an accuracy of 51.3%, as opposed to an', 'simulation and experiment.', 's-separability and show that for the special case of (c-1)-separable training data with c classes, a neural network with (d + 2c) parameters can achieve 100% training classification accuracy, where d is the dimension of data. It is also shown that if the number of free parameters is at least (d+2p), where p is the size of the training set, the neural network can memorize each training example. Finally, a framework is introduced for finding a neural network achieving a given training error,', 'An active inference problem of detecting an anomalous process among M heterogeneous processes is considered. At each time, a subset of processes can be probed. The objective is to design a sequential probing strategy that dynamically determines which processes to observe at each time and when to terminate the search so that the expected detection time is minimized under a constraint on the probability of misclassifying any process. This problem falls into the general setting of sequential design', 'Artificial Neural NetCANN3. The data. potential features of the ANN motivated to appreciable pursue research in the field with much signal accelerated vigour with a -hope of achieving simulation human like performance in the fields of yari speech and image recognition 12.3', 'personal information, i.e., the true hypotheses, albeit with different statistics. The user manages data release in an online fashion such that maximum amount of information is revealed about the latent useful variable, while the confidence for the sensitive variable is kept below a predefined level. For the utility, we consider both the probability of correct detection of the useful variable and the mutual information (MI) between the useful variable and released data. We formulate both', 'investigated: weak supervision and domainadversarial training. Our experiments show that the weak supervision with the knowledge of the number of sources can significantly improve the performance of an unadapted model. However, the domain-adversarial training does not yield significant improvement for this particular problem.', 'multi-temporal data (VHR satellite images and geographic information data). First, the illegal urban areas are extracted using an original SVM based technique integrating expert knowledge and auxiliary data by means of ontology construction. This leads to the formalization of the expert semantic information and the urban construction rules (often in sentences form) and their confrontation with the classification results. Secondly, the SITS classified images are modeled using Spatial-Object', 'existing methods, instruments, and software tools.', 'performance of the proposed method. The evaluation made on a public environmental sound dataset shows that the proposed method outperforms reference methods (random sampling, certainty-based active learning and semi-supervised learning) with all simulated labeling budgets, the number of available labeling responses. Through all the experiments, the proposed method saves 50%-60% labeling budget to achieve the same accuracy, with respect to the best reference method.', 'reducing computational requirements. For ACC, we propose an adaptive probabilistic approach to optimize the measurement and basis matrices. Based on real data sets, we show that Compressed Classification yields high classification accuracy close to results obtained for the complete data. Using the proposed adaptive approach, even higher accuracies are achieved in all tested cases.', 'in the field, one can efficiently sample the field to locate the target using O(m log m log n) samples on an n × n grid where m n is a parameter specifying the group size.', 'In particular, the worst-case complexity of our proposed algorithm is polynomial, which makes it suitable for solving large-scale problems. Numerical results demonstrate the effectiveness and efficiency of our proposed algorithm.', 'using dictionary learning. We demonstrate the LST method on densely, but irregularly sampled synthetic slowness maps.', 'studies.', 'The recently proposed deep clustering framework represents a significant step towards solving the cocktail party problem. This study proposes and compares a variety of alternative objective functions for training deep clustering networks. In addition, whereas the original deep clustering work relied on k-means clustering for test-time inference, here we investigate inference methods that are matched to the training objective. Furthermore, we explore the use of an improved chimera network', 'We present a comparative study of the performance of reported neural net algorithms for the detection of first breaks in seismic reflection data with regard to accuracy, learning rate and generalisability. In addition we suggest a new approach that produces improved results.', 'recognizing it. However, our framework uses only samples of the desired activities in the training phase and learns to detect the NULL samples based on a modified variational autoencoder model that outputs reconstruction probability. Experimental results show that in detecting six ADL with accelerometer data, our system achieves 14% higher F1-score compared to the models that use training samples of NULL activities.', 'techniques. This suggests that knowledge of the underlying noise type can meaningfully inform the choice of adaptation technique.', 'multi-media (text, speech, video) which are heterogeneous digital phenotyping data, to build a database. The features of each heterogeneous digital phenotyping data are extracted independently. Lasso Regression and ElasticNet Regression methods are employed to predict the score of Hamilton Depression Rating Scale (HAM-D) and Young Mania Rating Scale (YMRS), as a reference for the evaluation of BD. As incomplete and missing data are very common in medical research, the ensemble method is adopted', 'outperform the baselines with a statistically significant margin.', \"The following paper presents the methodology for the acoustic analysis of wood boring insects' activity. Their detection in consumer wood has a significant economic impact. The most important is the measurement of energy generated by the larvae feeding on the piece of wood. To detect the candidate sounds, a moving narrow time window was implemented. The analysis was performed in a noisy environment, therefore a Support Vector Machine (SVM) classifier was used to distinguish between the\", 'This paper presents a novel problem of detection and localization of anomalous events due to a certain class of objects in video data with applications to smart surveillance. A baseline system is proposed that uses a convolutional neural network (CNN) to generate pixel level masks corresponding to objects of a class of interest. A Restricted Boltzmann Machine (RBM) is then trained on the mask to learn patterns of normal behavior. The free energy of the RBM is used to detect the presence of an', 'State of the art acoustic scene classification techniques often employ features of large dimensionality, which are then used to train and perform inferences with kernel machines such as Support Vector Machines. However, the complexity of computing the non-linear kernel matrix for these methods increases with the dimensionality of the features and the size of the dataset. In this work, we introduce a new scheme that hashes features, which combined with a linear function approximates a non-linear', 'In this paper, we develop new multiclass classification algorithms for detecting people and vehicles by fusing data from a multimodal, unattended ground sensor node. The specific types of sensors that we apply in this work are acoustic and seismic sensors. We investigate two alternative approaches to multiclass classification in this context-the first is based on applying Dempster-Shafer Theory to perform score-level fusion, and the second involves the accumulation of local similarity evidences', 'Temporal Adjacency Graphs (SOTAG) constructed for each region of the first image. These graphs are then classified using a Marginalized Graph Kernel (MGK) SVM based classification in order to extract regions with similar temporal evolution. We are focusing mainly on monitoring legal and illegal urban expansions. The resulted spatio-temporal map describes urban areas types and their changes over time.', 'various real-world background noises showed that the proposed method performs better than some state-of-the-art methods.', 'shift in this paper. The experimental study presented in this paper leverages the architecture of Domain Adversarial Neural Networks (DANNs) [1] which uses data from two different domains. The DANN is a Y-shaped network that consists of a multi-layer CNN feature extractor module that is common to a label (senone) classifier and a so-called domain classifier. The utility of DANNs is evaluated on multiple datasets with domain shifts caused due to differences in gender and speaker accents.', 'average (EMA) of the student model, which generates final predictions for SED during inference. Experiments on the DCASE2018 task4 dataset demonstrate the ability of the proposed method. Specifically, an F1-score of 42.1% is achieved, significantly outperforming the 32.4% achieved by the winning system, or the 39.3% by the previous perturbation based method.', 'to achieve sub-linear regret. It is shown that if every user employs the proposed algorithm, the system wide regret is of the order O(T 3 4) over a horizon of time T. The algorithm is then extended to the dynamic case where the number of users in the system evolves over time, and it is shown to lead to sub-linear regret.', 'development set of DCASE challenge 2019 task 1B with only ∼70K parameters. It achieves 70.1% average classification accuracy for device B and device C. It performs on par with the best single model system of the DCASE 2019 challenge and outperforms the baseline system by 28.7% (absolute).', 'iterative algorithm that may be considered a generalization of the traditional total-variation regularization method on the gradient domain, and subsequently unroll the half-quadratic splitting algorithm to construct a neural network. Our proposed deep network achieves significant practical performance gains while enjoying interpretability at the same time. Experimental results show that our approach outperforms many state-of-the-art methods.', 'effectiveness of the proposed method is evaluated through extensive experiments using the Touchalytics database which comprises of touch data over time. The results show that the performance of the proposed approach is better than the state-of-the-art method.', 'higher weight to contextual information. The Experimental results on common benchmarks such as VQA-v2 have demonstrated the superiority of our method compared with state of the art.', 'In medical imaging, the acquired images are usually analyzed by a human observer and rated with respect to a diagnostic question. However, this procedure is time-demanding and expensive. Furthermore, the lack of a reference image makes this task challenging. In order to support the human observer in assessing image quality and to ensure an objective evaluation, we extend in this paper our previous no-reference magnetic resonance (MR) image quality assessment system with an active learning loop', \"human audio perception than features such as Mel-frequency spectral coefficients (MFSC). We use features extracted by the A-DCTNet as input for classifiers. Experimental results show that the A-DCTNet and Recurrent Neural Networks (RNN) achieve state-of-the-art performance in bird song classification rate, and improve artist identification accuracy in music data. They demonstrate A-DCTNet's applicability to signal processing problems.\", 'Identifying network anomaly detection is important since they may carry critical information in circumstances such as a burst of intrusions, privacy theft, system damage and fraudulent activities. In recent years, there are many detection methods for network anomalies are proposed, however, a single model always faces the problems of over or under-fitting, high bias and variance. An improved method is to comprehensively use the results of multiple models and then reform the final predictions.', 'discriminate legitimate users from bots and to identify the individual bot clusters.', 'Weak convergence methods are used to analyse generalized learning automata algorithms. The REINFORCE algorithm has been analysed. It is shown by an example that this algorithm can exhibit unbounded behaviour. A modification based on constrained optimization principles is proposed to overcome this problem. The relationship between the asymptotic behaviour of the modified algorithm and the Kuhn-Tucker points of the related constrained optimisation problem is brought out.', 'analysis shows that the overall complexity of the proposed algorithm is low, making it suitable for real-time application.', 'compatibility to the old databases used for training and validation.', 'methods in terms of robustness to noise and detection rate.', 'There are many sound classification problems that have target classes which are rare or unique to the context of the problem. For these problems, existing data sets are not sufficient and we must create new problem-specific datasets to train classification models. However, annotating a new dataset for every new problem is costly. Active learning could potentially reduce this annotation cost, but it has been understudied in the context of audio annotation. In this work, we investigate active', 'The use of non-convex sparse regularization has attracted much interest when estimating a very sparse model on high dimensional data. In this work we express the optimality conditions of the optimization problem for a large class of non-convex regularizers. From those conditions, we derive an efficient active set strategy that avoids the computing of unnecessary gradients. Numerical experiments on both generated and real life datasets show a clear gain in computational cost w.r.t. the state of', 'each iteration, the weights in the aggregation function are adjusted according to the discriminabilities of features in the current working scenario. This way, the algorithm not only possesses improved robustness but also relieves the burden of setting the parameters manually. Experimental verification shows that the algorithm outperforms existing peer algorithms in terms of conunonly used evaluation metrics, while using a low computational cost.', 'Partial Least Squares (PLS) has been gaining popularity as a multivariate data analysis tool due to its ability to cater for noisy, collinear and incomplete data-sets. However, most PLS solutions are designed as block-based algorithms, rendering them unsuitable for environments with streaming data and non-stationary statistics. To this end, we propose an online version of the nonlinear iterative PLS (NI-PALS) algorithm, based on a recursive computation of covariance matrices and gradient-based', 'the parameters of the model. The proposed method leads to significant performance improvements over several competitive normalization approaches, as demonstrated using a large-scale limit order book dataset.', \"Probabilistic inference is a versatile tool to solve a large variety of pixel-labeling problems in computer vision such as stereo matching and image denoising. Belief Propagation (BP) is an effective method for such inference tasks, and has also shown attractive errorresilience properties-the ability to converge to usable solutions in the presence of low-level hardware errors. This is of increasing interest, as the looming end of Moore's Law scaling brings with it a vast increase in the\", 'In this paper we propose novel randomized subspace methods to detect anomalies in Internet Protocol networks. Given a data matrix containing information about network traffic, the proposed approaches perform a normal-plusanomalous matrix decomposition aided by the randomized sampling scheme and subsequently detect traffic anomalies in the anomalous subspace using a statistical test. Simulation results demonstrate improvement over the traditional principal component analysis-based subspace', 'two stage algorithm which aims to separate the joint and sub-specific information from the temporally concatenated multi-subject datasets. The proposed method is based on the singular value decomposition (SVD) and penalized rank-one matrix approximation. Simulation experiments are used to demonstrate this ability of the proposed algorithm followed by validation on real experimental task-fMRI datasets.', 'The performance of iterative algorithms aimed at solving a regularized least squares problem typically depends on the value of some regularization parameter. Tuning the regularization parameter value is a fundamental step necessary to control the strength of the regularization and hence ensure a good performance. We address the problem of finding the optimal regularization parameter in such iterative algorithms. We propose to adaptively adjust the regularization parameter throughout the', 'Extracting information from functional magnetic resonance images (fMRI) has been a major area of research for more than two decades. The goal of this work is to present a new method for the analysis of fMRI data sets, that is capable to incorporate a priori available information, via an efficient optimization framework. Tests on synthetic data sets demonstrate significant performance gains over existing methods of this kind.', 'enclosures are unknown. Our FCN is trained in a supervised fashion to predict the likelihood of reflective surfaces at specific distances and directions-of-arrival (DOA). When a single reflective surface is present, up to 80% of real and virtual sources are detected, while this figure approaches 50% in rectangular rooms. Experiments on real-world recordings report similar accuracy as with artificially reverberated speech signals, validating the generalization capabilities of the framework.', 'superior to those of a state-of-the-art neural retrieval system, outperforming it by 2% and 5% alignment F1 scores respectively.', 'performance of the MUSIC estimators can also be predicted using our algorithm.', 'that lead to high utilities. To efficiently learn these preferred actions, we follow a deep Q-learning approach, where the Q-value function is parametrized using a multi-layer perceptron. In particular, we implement a rainbow agent, which incorporates several empirical improvements over the basic deep Q-network. Numerical experiments based on the NS3 simulator reveal that the proposed RL agent performs close to optimal and markedly improves upon existing learning and non-learning based', 'sequences in the active set. The active set is carefully selected at each iteration based on the current detection result and the first-order optimality condition of the MLE problem. Simulation results show that the proposed active set algorithm enjoys significantly better computational efficiency (in terms of the CPU time) than the state-of-the-art algorithms.', 'the absence/presence of P-waves. Outputs of these classifiers are then combined for final detection of the P-waves. The proposed algorithm was trained and validated on a database which consists of more than 111000 annotated heart beats and the results show consistently high classification accuracy and sensitivity of around 98.48% and 97.22%, respectively.', 'characterize the image structures and refine the interclass structure representations. The proposed DCADL jointly learns a convolutional analysis dictionary and a universal classifier, while greatly reducing the time complexity in both training and testing phases, and achieving a competitive accuracy, thus demonstrating great performance in many experiments with standard databases.', 'Crowd counting, i.e., estimation number of the pedestrian in crowd images, is emerging as an important research problem with the public security applications. A key component for the crowd counting systems is the construction of counting models which are robust to various scenarios under facts such as camera perspective and physical barriers. In this paper, we present an adaptive scenario discovery framework for crowd counting. The system is structured with two parallel pathways that are trained', 'Wireless body area network (WBAN) is emerging in the mobile healthcare area to replace the traditional wire-connected monitoring devices. As wireless data transmission dominates power cost of sensor nodes, it is beneficial to reduce the data size without much information loss. Compressive sensing (CS) is a perfect candidate to achieve this goal compared to existing compression techniques. In this paper, we proposed a general framework that utilize CS and online dictionary learning (ODL)', 'Automatic speaker verification (ASV) is one of the core technologies in biometric identification. With the ubiquitous usage of ASV systems in safety-critical applications, more and more malicious attackers attempt to launch adversarial attacks at ASV systems. In the midst of the arms race between attack and defense in ASV, how to effectively improve the robustness of ASV against adversarial attacks remains an open question. We note that the self-supervised learning models possess the ability to', 'Recent advances in neural network based acoustic modelling have shown significant improvements in automatic speech recognition (ASR) performance. In order for acoustic models to be able to handle large acoustic variability, large amounts of labeled data is necessary, which are often expensive to obtain. This paper explores the application of adversarial training to learn features from raw speech that are invariant to acoustic variability. This acoustic variability is referred to as a domain', \"In this paper we address the detection of Alzheimer's disease based solely on EEG recordings. We assume that the state of Alzheimer's disease can be described by a latent manifold, captured by the EEG sensors and apply alternating diffusion to reveal this common underlying manifold from multiple EEG sensors. We show that based on a small number of EEG electrodes, a new representation can be obtained, which allows a clear distinction between healthy subjects and Alzheimer patients in different\", 'significant improvement over an unadapted model.', 'Electrocardiogram (ECG) delineation, which provides clinically useful information for the diagnosis of cardiovascular disease, is an essential task in automated ECG analysis. The discrepancies among ECG signals from different datasets, namely domain shifts, may bring severe challenges to the cross-dataset performance of ECG delineation algorithms. The domain shifts are generally caused by the differences of conditions, collecting devices, and individual characteristics, and are inherent and', 'Many state-of-the-art automatic speech recognition (ASR) systems adopt system combination techniques to improve recognition performance. In this paper, we investigate the possibility of transferring knowledge between models for different noisy speech domains and integrating these models via system combination. The first contribution of our work is the use of progressive neural networks for modeling the acoustic features of noisy speech. We train progressive neural networks on subdivided noisy', 'statistics and the dimensions involved. Such results permits to reliably predict the performance of projected LDA as a function of the reduced dimension d < p and thus helps to determine the minimum d to achieve a certain desired performance. Finally, we validate our results with finite-sample settings drawn from both synthetic data and the popular MNIST dataset.', 'a coherent merging strategy; (3) the number of groups is decided automatically based on the similarity graph of individuals. Experimental results show that the proposed framework is competitive against the stateof-the-art methods.', 'Classification of environmental sounds is a fundamental procedure for a wide range of real-world applications. In this paper, we propose a novel acoustic feature extraction method for classifying the environmental sounds. The proposed method is motivated from the image processing technique, local binary pattern (LBP), and works on a spectrogram which forms two-dimensional (time-frequency) data like an image. Since the spectrogram contains noisy pixel values, for improving classification', 'Active learning aims to reduce the time and cost of developing speech recognition systems by selecting for transcription highly informative subsets from large pools of audio data. Previous evaluations at OpenKWS and IARPA BABEL have investigated data selection for low-resource languages in very constrained scenarios with 2-hour data selections given a I-hour seed set. We expand on this to investigate what happens with larger selections and fewer constraints on language modeling data. Our', 'to combine the results from different models trained with different combinations of missing data. The collected heterogeneous digital phenotyping data from 84 BD patients were used for training and evaluation of the proposed approach based on five-fold cross validation method. Experimental results show that the performance of the assessment system using the proposed method are encouraging.', 'dataset, with excellent generalization to unknown devices. In particular, our approach achieved second place in 2019 DCASE Task 1b (0.4% behind the winning entry), and the best Task 1B evaluation results (by a large margin of over 5%) on test data from a device not used to record any training data.', \"There is growing interest in understanding the impact of architectural parameters such as depth, width, and the type of activation function on the performance of a neural network. We provide an upper-bound on the number of free parameters a ReLU-type neural network needs to exactly fit the training data. Whether a net of this size generalizes to test data will be governed by the fidelity of the training data and the applicability of the principle of Occam's Razor. We introduce the concept of\", 'recognition experiments on the Librispeech dataset, and compare the performance with state-of-the-art projected gradient descent (PGD)-based adversarial training which employs only CE objective. The proposed HAT improves adversarial accuracy by absolute 3.29% and 3.18% for PGD and Carlini-Wagner (CW) attacks respectively, while retaining high accuracy on benign examples.', 'available Comma.ai and Udacity benchmark dataset and GTA-V dataset. Our experiments demonstrate the superior performance of the proposed approach compared to the current state-of-the-art methods.', 'Motivated by methods used in language modeling and grammar induction, we propose the use of pragmatic constraints and perplexity as criteria to filter the unlabeled data used to generate the semantic similarity model. We investigate unsupervised adaptation algorithms of the semantic-affective models proposed in [1, 2]. Affective ratings at the utterance level are generated based on an emotional lexicon, which in turn is created using a semantic (similarity) model estimated over raw, unlabeled', 'framework which can automatically learn the hyper-parameter in the denoising algorithm so that we do not need to manually set the hyper-parameter. Specifically, our network structure consists of two modules, an adaptive filtering module for signal denoising, and a classification module for signal classification. We further develop two mechanisms of the adaptive filtering module, namely, sample-specific mechanism and dataset-specific mechanism. We validate the performance of our detection method', 'A significant concern with deep learning based approaches is that they are difficult to interpret, which means detecting bias in network predictions can be challenging. Concept Activation Vectors (CAVs) have been proposed to address this problem. These use representations-perturbations of activation function outputs-of interpretable concepts to analyse how the network is influenced by the concept. This work applies CAVs to assess bias in a spoken language assessment (SLA) system, a regression', 'connected-tube MPP model first. Then a new tube-based segmentation method is applied to the extracted tubes. We test this method on STARE and DRIVE databases and the results show that not only do we extract the retina vessel network accurately, but we also achieve high Gmeans score for vessel segmentation, without using labeled training data.', 'nature. In this paper we propose a data-driven adaptive normalization layer which is capable of learning the most appropriate normalization scheme that should be applied on the data. To this end, the proposed method first identifies the distribution from which the data were generated and then it dynamically shifts and scales them in order to facilitate the task at hand. The proposed normalization scheme is fully differentiable and it is trained in an end-to-end fashion along with the rest of', 'results, on four languages from the final BABEL OP3 period, show that active learning is helpful at larger selections with consistent gains up to 14 hours. We also find that the impact of additional language model data is orthogonal to the impact of the active learning selection criteria.', 'dataset that contains 105 sound categories, the proposed method produces the superior performance (98.62%) compared to the other methods, exhibiting significant improvements over the standard LBP method as well as robustness to noise and low computation time.', 'the mean opinion score.', 'A strategy for active target detection suitable for the use of mobile agents in a field is presented. In particular, there is an interest in autonomous underwater vehicles. By exploiting notions from group testing, the proposed algorithm decides when to collect new samples depending on whether the mobile agent perceives the sensor measurements correspond to noise or a target pattern. Under suitable assumptions about the field emanated by the target, i.e. the target signature is locally low rank', \"In mental health disorder, Bipolar Disorder (BD) is one of the most common mental illness. Using rating scales for assessment is one of the approaches for diagnosing and tracking BD patients. However, the requirement for manpower and time is heavy in the process of evaluation. In order to reduce the cost of social and medical resources, this study collects the user's data by the App on smartphones, consisting of location data (GPS), self-report scales, daily mood, sleeping time and records of\", \"subject's original neuroimaging data allows for the detection of abnormal patterns by way of statistical tests on the residuals. The different abnormality detection methods are assessed on synthetic data and real (MRI) neuroimaging data. The importance of non-linear modeling of the manifold in the reduced-dimension subspace is highlighted, as well as robustness to large abnormalities.\", 'demonstrate that the proposed technique is more accurate than the existing methods by a factor of 10 7 or more for 2 ≤ K ≤ 20.', 'features between segments of speech before and after the change instant. Further, a comparison with a machine system designed for the same task of TCD using speaker diarization principles showed a poor performance relative to the humans.', 'predictors of energy usage, although the number of multiplies is more predictive than the number of model parameters. We also confirm that models with the highest accuracies are, unsurprisingly, the most power hungry.', 'our approach is to eliminate the shuffled structure via symmetric polynomials, which leads to a system of polynomial equations. Considering one of the solutions of the resulting polynomial system as an initialization to the Expectation Maximization algorithm, we propose the Algebraically-Initialized Expectation Maximization algorithm. Computational experiments with synthetic data show that our proposed algorithm is extensively efficient, and it performs well even with noise.', \"the problem of discriminating positively and negatively excited emotions. The evaluation is conducted using the ENTERFACE'06 dataset allowing us to study the behaviour of the tested features across different subjects. Our results prove the usefulness of various new spectral features even in single-channel setups. We also observe that the optimal selection of features is highly subject-dependent. Finally combining different groups of features we find the valence recognition accuracy to be\", 'features from audio spectrograms such as Mel Coefficients and other filter responses. Instead of using pre-designed features, we propose to allow a deep-learning algorithm to find the features that are most important for classification. In work reported in this paper, we used two deep-learning methods that apply convolutional neural networks (CNN) to anuran classification. Transfer learning was also used. The CNN methods was tested on our dataset of 15 frog species, and produced a', 'incrementally increasing the number of clusters, estimating the quality of identified clusters, and providing a series of clustering reliability tests. Consequently, AMOS outputs clusters of minimal model order with statistical clustering reliability guarantees. Comparing to three other automated graph clustering methods on real-world datasets, AMOS shows superior performance in terms of multiple external and internal clustering metrics.', 'Recognizing activities of daily living (ADL) in-the-wild, while users follow their daily routine, is challenging due to the presence of various activities that do not belong to the set of desired activities in which the system is interested (i.e., NULL class). In this paper, we propose a framework for ADL recognition via wearable motion sensors with the ability to detect NULL class. Existing ADL recognition systems either ignore the NULL class or use some training data to train a model for', 'problems as a Markov decision process (MDP), and numerically solve them by advantage actor-critic (A2C) deep reinforcement learning (RL).', 'using the autoregressive (AR) model. In this framework, the training area is automatically optimized. A consistency criterion also enables to assess and regularize the model. Moreover, a post-processing step enables to remove the remaining seam artefacts. A comparison with the state-of-the-art is provided for both subjective quality and complexity which remains a major constraint for texture completion.', 'This paper presents an approach based on acoustic analysis to describe gender equality in French audiovisual streams, through the estimation of male and female speaking time. Gender detection systems based on Gaussian Mixture Models, i-vectors and Convolutional Neural Networks (CNN) were trained using an internal database of 2,284 French speakers and evaluated using REPERE challenge corpus. The CNN system obtained the best performance with a framelevel gender detection F-measure of 96.52 and a', 'Radio frequency (RF) signal classification has significantly been used for detecting and identifying the features of unknown unmanned aerial vehicles (UAVs). This paper proposes a method using empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) on extracting the communication channel characteristics of intruding UAVs. The decomposed intrinsic mode functions (IMFs) except noise components are selected for RF signal pattern recognition based on machine learning', 'covariance matrices. Compared to its implementation on a CPU, the FPGA implementation is 2 to 4 times faster using fixed-point or floating-point designs.', 'propagation algorithm is performed for the optimal solution of the HMM model. Simulations show promising results in detecting important types of structural variation. We have applied the algorithm on the maize B73 and MO17 genome data and compared the results with those obtained from arrayCGH method based micro-array data. Inconsistency between the two sets of data is discussed.', 'Palmprint recognition is an important and widely used biometric modality with high reliability, stability and user acceptability. In this paper we propose a simple and effective ensemble learning method for palmprint identification based on Random Subspace Sampling (RSS). To achieve it, we rely on 2D-PCA to build the random subspaces. As 2D-PCA is an unsurpevised technique, features are extracted in each subspace using 2D-LDA. A simple 1-Nearest Neighbor classifier is associated to each', 'Recent work has shown that exploiting relations between labels improves the performance of multi-label classification. We propose a novel framework based on generative adversarial networks (GANs) to model label dependency. The discriminator learns to model label dependency by discriminating real and generated label sets. To fool the discriminator, the classifier, or generator, learns to generate label sets with dependencies close to real data. Extensive experiments and comparisons on two', 'We examine the relation between the sounds emitted by the knee joint during walking and its condition, with particular focus on osteoarthritis, and investigate their potential for noninvasive detection of knee pathology. We present a comparative analysis of several features and evaluate their discriminant power for the task of normal-abnormal signal classification. We statistically evaluate the feature distributions using the two-sample Kolmogorov-Smirnov test and the Bhattacharyya distance. We', 'large. Furthermore, the proposed test offers considerable reduction in implementation complexity.', \"work, we address the regularization issue from a different perspective. We manipulate the expression of the GMVP weights to convert it to an inner product of two vectors; then, we focus on obtaining accurate estimations of these vectors. We show that this approach results in a formula similar to those of the RSCM based methods, yet with a different interpretation of the regularization parameter's role. In the proposed approach, the regularization parameter is adjusted adaptively based on the\", 'There has been a growing recent interest of applying Kolmogorov complexity and its related normalized information distance (NID) measures in real-world problems, but their application in the field of medical image processing remains limited. In this work we attempt to incorporate NID in the design of windowing operators for optimal visualization of high dynamic range (HDR) medical images, where predefined intensity interval of interest needs to be mapped to match the low dynamic range (LDR) of', 'Previous work has shown that it is possible to improve speech recognition by learning acoustic features from paired acoustic-articulatory data, for example by using canonical correlation analysis (CCA) or its deep extensions. One limitation of this prior work is that the learned feature models are difficult to port to new datasets or domains, and articulatory data is not available for most speech corpora. In this work we study the problem of acoustic feature learning in the setting where we have', 'The paper considers the problem of estimating a covariate parameter shared by multiple statistical models. Under the objective of estimating the parameter with target reliability with the fewest number of samples from these models, a fundamental question is how to glean samples from the statistical models. This question is especially important when the models are not equally descriptive or informative about the parameter, each being the most informative only for a specific regime of the', 'intent recognition tasks. We also perform multilingual training of our intent classifier and show improved cross-lingual transfer and zero-shot performance on an unknown language within the same language family.', 'Video representation is an important and challenging task in the computer vision community. In this paper, we assume that image frames of a moving scene can be modeled as a Markov random process. We propose a sparse coding framework, named adaptive video dictionary learning (AVDL), to model a video adaptively. The developed framework is able to capture the dynamics of a moving scene by exploring both sparse properties and the temporal correlations of consecutive video frames. The proposed method', 'baseline scheme. The proposed SegNet based approach has an average pixel classification accuracy of 99.3% across all the subjects with only 2 rtMRI videos (∼180 frames) per subject for training.']\n"
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        "1. some texts are cut into a single word by chunk textSplitter, would it be better to read in a whole abstract as a doc to perform vector similarity search? We are reading from the full source but not querying with full source abstract vectorized 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)\n",
        "\n",
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n",
        "docs = text_splitter.split_documents(documents)\n",
        "\n",
        "2. determine the best distance as searching threshold\n",
        "\n",
        "3. generate tags for further search? how are we going to get the head title for the paper abstract? by source name?\n",
        "\n",
        "4. would it be better to use open source models to save some cost?"
      ],
      "metadata": {
        "id": "Q1F_ov8uZyss"
      }
    }
  ]
}