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Constructing the ChatGPT for PDF Files with Langchain - AI 7

Constructing the ChatGPT for PDF Files with Langchain - AI

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Prem Jacob, T. Bizotto, Beatriz Lucia Salvador Sathiyanarayanan, mithileysh Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai India University Center Unifacvest Department of Social And Applied Sciences Lages Brazil Research & Innovation MIT Square London United Kingdom

ISBN: (纸本)9798350359299

Queries in PDFs can be time-consuming and labor-intensive because of the unstructured nature of the PDF document type and the need for accurate and relevant search results. By applying cutting-edge algorithms for natural language processing to examine PDF documents and extract relevant data, LangChain solves these difficulties. It makes use of an easy search interface, adjustable filters, and efficient indexing and retrieval mechanisms to enhance the search experience. To efficiently retrieve relevant information from PDF documents, users can annotate critical portions, store queries, and create bookmarks with LangChain. The characteristics of LangChain improve overall productivity and greatly simplify PDF querying. Semantic search, driven by the latest Transformer language models, represents a significant evolution in information retrieval systems. This research work explores the capabilities of semantic search to efficiently retrieve documents from large collections in response to natural language queries. Unlike traditional keyword-based approaches, semantic search connects the power of Transformer models to discern meaning, providing users with more contextually relevant and accurate results within seconds. This technology not only enhances the user experience by delivering superior matches from document collections but also lays the foundation for tackling more intricate tasks, like text summarization and question-answering. The research investigates the impact of semantic search on information retrieval efficiency and accuracy, comparing its performance with conventional methods. The findings presented herein not only showcase the immediate benefits of semantic search but also open paths for future research and development in natural language processing and its applications. © 2024 IEEE.

关键词： Information retrieval systems

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Smaller ACC0 Circuits for Symmetric Functions 13

Smaller ACC0 Circuits for Symmetric Functions

引用

13th Innovations in Theoretical computer science Conference, ITCS 2022

作者： Chapman, Brynmor Williams, R. Ryan Department of Electrical Engineering and Computer Science MIT CambridgeMA United States

ISBN: (纸本)9783959772174

What is the power of constant-depth circuits with MODm gates, that can count modulo m? Can they efficiently compute MAJORITY and other symmetric functions? When m is a constant prime power, the answer is well understood. In this regime, Razborov and Smolensky proved in the 1980s that MAJORITY and MODm require super-polynomial-size MODq circuits, where q is any prime power not dividing m. However, relatively little is known about the power of MODm gates when m is not a prime power. For example, it is still open whether every problem decidable in exponential time can be computed by depth-3 circuits of polynomial-size and only MOD6 gates. In this paper, we shed some light on the difficulty of proving lower bounds for MODm circuits, by giving new upper bounds. We show how to construct MODm circuits computing symmetric functions with non-prime power m, with size-depth tradeoffs that beat the longstanding lower bounds for AC0[m] circuits when m is a prime power. Furthermore, we observe that our size-depth tradeoff circuits have essentially optimal dependence on m and d in the exponent, under a natural circuit complexity hypothesis. For example, we show that for every Ε > 0, every symmetric function can be computed using MODm circuits of depth 3 and 2nΕ size, for a constant m depending only on Ε > 0. In other words, depth-3 CC0 circuits can compute any symmetric function in subexponential size. This demonstrates a significant difference in the power of depth-3 CC0 circuits, compared to other models: for certain symmetric functions, depth-3 AC0 circuits require 2Ω(√n) size [Håstad 1986], and depth-3 AC0[pk] circuits (for fixed prime power pk) require 2Ω(n1/6) size [Smolensky 1987]. Even for depth-2 MODp ◦ MODm circuits, 2Ω(n) lower bounds were known [Barrington Straubing Thérien 1990]. © Brynmor Chapman and R. Ryan Williams;licensed under Creative Commons License CC-BY 4.0

关键词： Timing circuits

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Enhancing Security and Privacy in Health Care Using Cyber-physical Systems Through Machine Learning 8th

Enhancing Security and Privacy in Health Care Using Cyber-ph...

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Takale, Dattatray G. Gawali, Piyush P. Deshmukh, Gopal B. Mahalle, Parikshit N. Mehta, Pradnya S. Kashid, Shraddha S. Sule, Bipin Derle, Deepak R. Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology SPPU Pune India Department of Computer Engineering MIT ADT University Pune India and Humanities Vishwakarma Institute of Technology Pune India Department of Computer Science and Application KTHM College Nashik India

ISBN: (纸本)9789819766772

The importance of securing cyber-physical systems (CPS) is paramount to safeguarding the interconnected networks and devices, especially in sectors such as blockchain, health care, and the "Internet of Things" (IoT). The importance of authentication for users and devices in CPS security is highlighted in this paper. As a result of the intricate interconnectivity of networks, the integration of cyber-physical systems (CPS) in health care has revolutionized the delivery of medical services in an era of rapidly advancing technology. There are, however, serious concerns surrounding the privacy and security of sensitive healthcare data as a result of this integration. In this paper, a revolutionary approach to address these concerns is presented by utilizing machine learning techniques to address these problems. To enhance security measures within healthcare CPSs and protect privacy, Random Forest, a powerful ensemble learning algorithm, is employed as an integral part of the security measures. The proposed system utilizes a combination of anomaly detection, adaptive learning, and multi-attribute feature selection in order to identify potential security breaches while simultaneously ensuring patient confidentiality. As an additional security precaution, encryption protocols are implemented to ensure that the transmission of data is secure, preventing unauthorized access to the data as well. "As a comprehensive evaluation of the system's performance, a number of evaluation metrics are used, including Accuracy, Precision, Recall, F1 Score, and AUC-ROC". Based on these results, it is evident that this approach is effective in providing robust security and privacy protections in healthcare CPS, thereby establishing a new paradigm for delivering safe and confidential health care in the digital age. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cybersecurity

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Photonic Indistinguishability of the Tin-Vacancy Center in Diamond

Photonic Indistinguishability of the Tin-Vacancy Center in D...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Arjona, Jesús Parker, Ryan Chen, Kevin Purser, Carola Li, Linsen Michaels, Cathryn Stramma, Alexander Debroux, Remain Harris, Isaac Hayhurst, Martin Nichols, Eleanor Trusheim, Matthew Gangloff, Dorian Englund, Dirk Atature, Mete Cavendish Laboratory University of Cambridge Cambridge United Kingdom Department of Electrical Engineering and Computer Science MIT Cambridge United States

Tin-vacancy centres in diamond are spin-photon interfaces with intrinsic environmental noise insensitivity. We reveal their high optical coherence in a nanostructured environment and generate single photons with a 99.... 详细信息

ISBN: (纸本)9781957171258

关键词： Photons

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RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow Matching 41

RNAFlow: RNA Structure & Sequence Design via Inverse Folding...

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41st International Conference on Machine Learning, ICML 2024

作者： Nori, Divya Jin, Wengong Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Broad Institute of MIT and Harvard CambridgeMA United States

The growing significance of RNA engineering in diverse biological applications has spurred interest in developing AI methods for structure-based RNA design. While diffusion models have excelled in protein design, adapting them for RNA presents new challenges due to RNA's conformational flexibility and the computational cost of fine-tuning large structure prediction models. To this end, we propose RNAFlow, a flow matching model for protein-conditioned RNA sequence-structure design. Its denoising network integrates an RNA inverse folding model and a pre-trained RosettaFold2NA network for generation of RNA sequences and structures. The integration of inverse folding in the structure denoising process allows us to simplify training by fixing the structure prediction network. We further enhance the inverse folding model by conditioning it on inferred conformational ensembles to model dynamic RNA conformations. Evaluation on protein-conditioned RNA structure and sequence generation tasks demonstrates RNAFlow's advantage over existing RNA design methods. Copyright 2024 by the author(s)

关键词： Inverse problems

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Image-based Addiction Detection: An Automated Approach 5

Image-based Addiction Detection: An Automated Approach

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Zutshi, Tejasvi Yeola, Rutuja Kantroo, Diya Apte, Rashmi Bedekar, Mangesh Mandke, Sakshi MKSSS Cummins College of Engineering Department of Computer Engineering Pune India MKSSS Cummins College of Engineering Department of Information Technology Pune India Koushiki Innovision Pune India Dr. Vishwanath Karad MIT World Peace University School of Computer Science and Engineering Kothrud Pune India

ISBN: (纸本)9798350367171

The emergence of digital imagery and advancements in computer vision techniques have opened new opportunities for addressing public health concerns, including addiction detection. This paper focuses on automated recognition of cigarette smoking behavior in images, leveraging the YOLOv8 architecture. This study compiles a comprehensive dataset of annotated cigarette images and fine-tune the proposed model through iterative training. Experimental results demonstrate high accuracy and recall in cigarette detection, highlighting the efficacy of our approach. This research study offers a robust solution with broad implications for public health surveillance, tobacco control, movie censorship, and regulatory compliance monitoring. © 2024 IEEE.

关键词： Image annotation

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Enhancing Intrusion Prevention with Explainable Convolutional Neural Networks: Recent Developments and Promising Applications 8

Enhancing Intrusion Prevention with Explainable Convolutiona...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Chitnis, Ajay Chandre, Pankaj Pathan, Shafi DevOps Architect Hcl Technologies Uk Limited Pune India Mit Art Design & Technology University Mit School of Computing Department of Computer Science & Engineering Pune India

ISBN: (纸本)9798350396638

The proliferation of cyberattacks in recent years has led to an increased demand for effective intrusion prevention systems (IPS). Convolutional Neural Networks (CNNs) have shown promise in improving the accuracy of IPS, but their lack of interpretability hinders their wider adoption. Explainable AI (XAI) techniques have emerged to address this issue, allowing users to understand the reasoning behind a model's predictions. This survey paper provides an overview of recent developments in enhancing IPS with XAI techniques in CNNs. This study reviews state-of-the-art research on integrating interpretability into CNN models, including methods such as attention mechanisms, saliency maps, and LIME. Also, this study discusses about the promising applications of CNN-based IPS with XAI, including network traffic analysis, malware detection, and anomaly detection. Finally, we highlight some of the challenges and opportunities for future research in this area, including the need for standardized evaluation metrics and the potential for combining XAI techniques with other AI methods such as reinforcement learning. Overall, this survey paper aims to provide a comprehensive understanding of the recent advances in using CNNs with XAI for enhancing IPS, and to identify promising directions for future research in this field. © 2023 IEEE.

关键词： Convolutional neural networks

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Position: A Roadmap to Pluralistic Alignment 41

Position: A Roadmap to Pluralistic Alignment

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41st International Conference on Machine Learning, ICML 2024

作者： Sorensen, Taylor Moore, Jared Fisher, Jillian Gordon, mitchell Mireshghallah, Niloofar Rytting, Christopher Michael Ye, Andre Jiang, Liwei Lu, Ximing Dziri, Nouha Althoff, Tim Choi, Yejin Department of Computer Science University of Washington SeattleWA United States Department of Computer Science Stanford University StanfordCA United States Department of Statistics University of Washington SeattleWA United States Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Allen Institute for Artificial Intelligence SeattleWA United States

With increased power and prevalence of AI systems, it is ever more critical that AI systems are designed to serve all, i.e., people with diverse values and perspectives. However, aligning models to serve pluralistic human values remains an open research question. In this piece, we propose a roadmap to pluralistic alignment, specifically using large language models as a test bed. We identify and formalize three possible ways to define and operationalize pluralism in AI systems: 1) Overton pluralistic models that present a spectrum of reasonable responses;2) Steerably pluralistic models that can steer to reflect certain perspectives;and 3) Distributionally pluralistic models that are well-calibrated to a given population in distribution. We also formalize and discuss three possible classes of pluralistic benchmarks: 1) Multi-objective benchmarks, 2) Tradeoff steerable benchmarks that incentivize models to steer to arbitrary trade-offs, and 3) Jury-pluralistic benchmarks that explicitly model diverse human ratings. We use this framework to argue that current alignment techniques may be fundamentally limited for pluralistic AI;indeed, we highlight empirical evidence, both from our own experiments and from other work, that standard alignment procedures might reduce distributional pluralism in models, motivating the need for further research on pluralistic alignment. Copyright 2024 by the author(s)

关键词： Artificial intelligence

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Plant Species Recognition Using Custom-Developed Neural Network with Optimized Hyperparameters 4th

Plant Species Recognition Using Custom-Developed Neural Netw...

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4th International Conference on Machine Intelligence and Signal Processing, MISP 2022

作者： Anubha Pearline, S. Sathiesh Kumar, V. Department of Computer Science Engineering SRM Institute of Science and Technology Vadapalani Campus Tamilnadu Chennai600026 India Department of Electronics Engineering MIT Campus Anna University Tamilnadu Chennai600044 India

ISBN: (纸本)9789819900848

computer vision-based plant species recognition in unconstrained environment is challenging due to the enormous diversity in plant species, illumination changes, variation in background and orientation or viewpoint changes. In this paper, a Parametric Rectified Linear Unit (PReLU)-based Neural Network (PNN) with back-propagation algorithm is proposed to perform an effective recognition of plant species. The optimization of hyperparameters (Optimizers, Learning Rate, Activation Function, Epoch) is carried out. Three datasets (Flavia, Swedish Leaf and Custom-developed Leaf 12) are considered for evaluating the proposed architecture. From the experimental analysis, it is observed that the accuracy in plant species recognition is greatly improved by utilizing the optimized hyperparameters (Adam optimizer, Learning rate of 0.001, PReLU-based Activation function with α value of 0.1). Higher accuracies for the Flavia (Top-1 (85.83%) and Top-5 (98.75%)) and Swedish Leaf datasets (Top-1 (94.97%) and Top-5 (100%)) are obtained at 200 Epoch, while custom-developed Leaf 12 (Top-1 (87.50%) and Top-5 (99.41%)) dataset exhibited better accuracy at 50 Epochs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Chemical activation

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Identification of Diabetic Retinopathy by Transfer Learning Based Retinal Images 9

Identification of Diabetic Retinopathy by Transfer Learning ...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Sagili, Sreenivas Reddy Goswami, Chandrashekhar Bharathi, V.C. Ananthi, S. Rani, K. Sathya, R. Rochester Institute Of Technology Department of computer science Lomb Memorial Drive RochesterNY14623 United States Mit School of Computing Mit Art Design and Technology University Department of Computer Science & Engineering Maharashtra Pune India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Advanced Research Centre for Future Networks and Digital Twin Sri Eshwar College of Engineering Tamil Nadu Coimbatore India M. Kumarasamy College of Engineering Department of Information Technology Tamilnadu Karur India Kongunadu College of Engineering and Technology Department of It Tamil Nadu Trichy India

ISBN: (纸本)9798350377972

The human eye is frequently considered to deliver a space into a person's strength because it is only with diabetic retinopathy (DR), a devastating eye illness caused by diabetes mellitus, that one may understand the subject's unprotected surface without invasive operations. There are certain disorders, notably vascular illness, that authority telltale indicators in the eye retina. Microaneurysms (MAs) are primary indicators of diabetic retinopathy (DR), making their detection crucial for an effective screening program that adheres to clinical procedures. Retinal examinations can reveal pathological deviations caused by limited ocular diseases such as arteriosclerosis, diabetes, cardiovascular disease hypertension and stroke. The proposed work introduces image processing algorithms, such as shady object identification, to predict the disorder or improve the contribution frame, thereby improving the perceptibility of vessels in color fundus frames. Following this enhancement, an automated classification algorithm, specifically a Convolutional Neural Network (CNN), is executed. The CNN architecture is designed to efficiently extract features from retinal frames, capturing complex patterns connected with diabetic retinopathy. The trained CNN is then assessed on a separate test condition, with its performance system of measurement-accuracy, specificity, recall, and Fl measure-being reported. © 2024 IEEE.

关键词： Convolutional neural networks

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