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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Yu, Xuemin Dalvi, Fahim Durrani, Nadir Nouri, Marzia Sajjad, Hassan Faculty of Computer Science Dalhousie University Canada Qatar Computing Research Institute Hamad Bin Khalifa University Qatar

ISBN: (纸本)9798891761643

Interpreting and understanding the predictions made by deep learning models poses a formidable challenge due to their inherently opaque nature. Many previous efforts to explain these predictions rely on input features, specifically, the words within NLP models. However, such explanations are often less informative due to the discrete nature of the words and their lack of contextual verbosity. To address this limitation, we introduce Latent Concept Attribution (LACOAT), which generates explanations for predictions based on latent concepts. Our intuition is that a word can exhibit multiple facets depending on the context in which it is used. Therefore, given a word in context, the latent space derived from our training process reflects a specific facet of that word. LACOAT functions by mapping the representations of salient input words into the training latent space, enabling it to provide latent context-based explanations of the prediction. © 2024 Association for Computational Linguistics.

关键词： Prediction models

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An effective object detection via BS2ResNet and LTK-Bi-LSTM

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Multimedia Tools and Applications 2025年 1-20页

作者： V, Arulalan V, Premanand Kumar, Dhananjay Department of Computing Technologies SRM Institute of Science and Technology Chengalpattu District Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering Vellore Institute Technology Chennai Campus Tamil Nadu Chennai India Department of Information Technology Anna University MIT Campus Tamil Nadu Chennai India

Video analytics faces complex challenges in object detection and classification. Deep learning based approaches have achieved remarkable success in past decade. However, existing object identification models that utilize backbone’s core features still present challenges due to their lack of semantic information. To address these issues, a novel object detection and classification framework utilizing Batch normalization and Softswish activation adapted ResNet (BS2ResNet) and Logistic Tanh Kaiming Bi-directional Long Short Term Memory (LTK-Bi-LSTM) techniques was proposed. The framework employs frame conversion, noise removal, and contrast elevation during frame pre-processing, followed by background subtraction using the Supreme Distance-centered Fuzzy C-Means (SD-FCM) clustering algorithm, and edge detection using the Hyperbolic Tangent Kernel Canny Edge Detector (HTKCED). BS2ResNet is then used for object detection, and features are extracted and passed to the LTK-Bi-LSTM neural network for object classification. The proposed system was found to improve object detection and classification accuracy, outperforming existing techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Long short-term memory

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An All-Inclusive Daily Task Tracker App for Enhancing College Student Productivity 15

An All-Inclusive Daily Task Tracker App for Enhancing Colleg...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Singh, Nongmeikapam Thoiba Preeti Kumar, Rohit Choubey, Anish Suresh Singh, Jaspreet Chandigarh University Department of Computer Science and Engineering Punjab India University Institute of Computing Chandigarh University Punjab India

ISBN: (纸本)9798350370249

This research proposes a comprehensive daily task tracker app designed to alleviate the challenges faced by college students in effectively managing their busy schedules. The app offers a user-friendly interface with intuitive features aimed at enhancing productivity and organization. Key components include task creation, allowing users to input and categorize assignments, deadlines, and activities. Customizable priority levels and reminders assist users in staying on track with their tasks. Flexibility is integrated to accommodate the dynamic nature of college schedules, enabling users to adjust and update tasks as needed. Seamless integration across multiple devices facilitates access from smartphones, tablets, and computers. Emphasizing the user experience, the app employs a visually appealing design and intuitive navigation for accessibility across all technical backgrounds. Continuous improvement and updates are prioritized through feedback collection and iterative refinement. Ultimately, the daily task tracker app aims to empower college students to efficiently manage their time, reduce stress, and achieve academic and personal goals effortlessly. © 2024 IEEE.

关键词： Students

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Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

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Intelligent Automation & Soft computing 2023年第1期35卷 1151-1163页

作者： M.Mohan P.C.Kishore Raja P.Velmurugan A.Kulothungan Department of Computer Science&Engineering SRM UniversitySonepatHaryanaIndia Department of Electronics&Communication Engineering SRM UniversitySonepatHaryanaIndia Department of Computing Technologies SRM Institute of Science and TechnologyChennaiIndia Department of Computer Science&Engineering SRM Institute of Science and TechnologyModinagarGhaziabadIndia

Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of *** proposed model uses a real time dataset offifteen Stocks as input into the system and based on the data,predicts or forecast future stock prices of different companies belonging to different *** dataset includes approximatelyfifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not;the forecasting is done for the next *** model uses 3 main concepts for forecasting *** one is for stocks that show periodic change throughout the season,the‘Holt-Winters Triple Exponential Smoothing’.3 basic things taken into conclusion by this algorithm are Base Level,Trend Level and Seasoning *** value of all these are calculated by us and then decomposition of all these factors is done by the Holt-Winters *** second concept is‘Recurrent Neural Network’.The specific model of recurrent neural network that is being used is Long-Short Term Memory and it’s the same as the Normal Neural Network,the only difference is that each intermediate cell is a memory cell and retails its value till the next feedback *** third concept is Recommendation System whichfilters and predict the rating based on the different factors.

关键词： Stock market stock market prediction time series forecasting efficient market hypothesis National stock exchange India smoothing observation trend level seasonal factor

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CampusConnect: A Collaborative College Hub for Reselling and Sharing Knowledge 2

CampusConnect: A Collaborative College Hub for Reselling and...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Singh, Nongmeikapam Thoiba Rani, Jyoti Preeti Kaur, Pardeep Grover, Richa Himanshu Chandigarh University Department of Computer Science And Engineering Punjab India University Institute of Computing Chandigarh University Punjab India

ISBN: (纸本)9798350368413

CampusConnect is an innovative online platform designed to enhance the college experience by providing a unified space for social networking, knowledge sharing, and financial transactions. This study aims to evaluate the impact of CampusConnect on student engagement, knowledge sharing, and community building. A mixedmethods approach, combining quantitative and qualitative research, was employed to assess the effectiveness of the platform. Survey results indicate high levels of student satisfaction with CampusConnect's functionality, with active participation in buying and selling, discussions, and resource sharing. The study highlights the positive impact of CampusConnect on academic engagement, financial literacy, and fostering a sense of belonging within the university community. Additionally, it identifies areas for future improvement, such as enhancing usability, customization options, and integration with existing educational systems. CampusConnect has emerged as a valuable tool that supports student learning, promotes community engagement, and prepares students for lifelong learning in the digital age. © 2024 IEEE.

关键词： case studies countermeasures cybersecurity data integrity ethical implications keyloggers

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Healthcare Monitoring Revolution through Face Detection Using BP and SpO2 5

Healthcare Monitoring Revolution through Face Detection Usin...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Singh, Nongmeikapam Thoiba Kaur, Harkamal Dhiman, Richa Kumar, Manish Hans, Aarti Sharma, Preety Chandigarh University Department of Computer Science and Engineering Punjab India University Institute of Computing Chandigarh University Punjab India

ISBN: (纸本)9798350383522

The integration of facial recognition technologies with the monitoring of vital signs, especially blood pressure (BP) and oxygen saturation (SpO2) measurements, has emerged as a promising approach in healthcare quality management. Facial recognition and SpO2 pressure detection represent a major advance in health and wellness monitoring, providing a seamless and comprehensive approach to monitoring individual well-being. As technology continues to evolve, this system has the potential to improve how we manage and manage healthcare, delivering positive outcomes and improving the lives of individuals around the world. © 2024 IEEE.

关键词： computer vision face detection healthcare monitoring machine learning SpO2 detection vital sign monitoring

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OpenCV-enabled Gesture Control for Smart Devices: A User-Centric Approach 4

OpenCV-enabled Gesture Control for Smart Devices: A User-Cen...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Devi, Priyanka Chadha, Shivani Bhardwaz, Saumyamani Chandigarh University Department of Computer Science and Engineering Mohali India University Institute of Computing Chandigarh University Mohali India

ISBN: (纸本)9798350349900

This research delves into the realm of human-computer-interaction, presenting an innovative user-centric approach togesture control for smart devices. Integrating the powerful OpenCV (Open-Source computer Vision Library), our system demonstrates enhanced accuracy, responsiveness, and user satisfaction in real-world scenarios. The study includes a comprehensive evaluation of the system's performance, addressing identified limitations, proposing improvement strategies, and exploring potential applications in emerging technologies. By analyzing a diverse dataset and employing a Convolutional Neural Network (CNN), our OpenCV-enabled gesture control system showcases its adaptability to varied gestures and environmental conditions. Future directions include addressing limitations, expanding application scopes, and integrating with emerging technologies, positioning this research at the forefront of advancements in gesture recognition technology. © 2024 IEEE.

关键词： Gesture recognition

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Multi-level Fuzzing for Document File Formats with Intermediate Representations 35

Multi-level Fuzzing for Document File Formats with Intermedi...

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35th IEEE International Symposium on Software Reliability Engineering, ISSRE 2024

作者： Wang, Yifan Xu, Jun Stevens Institute of Technology Computer Science Department United States The University of Utah Kahlert School of Computing United States

ISBN: (纸本)9798350353884

This paper focuses on fuzzing document software or precisely, software that processes document files (e.g., HTML, PDF, and DOCX). Document software typically requires highly-structured inputs, which general-purpose fuzzing cannot handle well. We propose two techniques to facilitate fuzzing on document software. First, we design an intermediate document representation (DIR) for document files. DIR describes a document file in an abstract way that is independent of the underlying format. Reusing common SDKs, a DIR document can be lowered into a desired format without a deep understanding of the format. Second, we propose multi-level mutations to operate directly on a DIR document, which can more thoroughly explore the searching space than existing single-level mutations. Combining these two techniques, we can reuse the same DIR-based generations and mutations to fuzz any document format, without separately handling the target format and re-engineering the generation/mutation *** assess utility of our DIR-based fuzzing, we applied it to 6 PDF and 6 HTML applications (48-hour) demonstrated superior performance, outpacing general mutation-based fuzzing (AFL++), ML-based PDF fuzzing (Learn Fuzz), and structure-aware mutation-based fuzzing ((NAUTILUS) by 33.87%, 127.74%, and 25.17% in code coverage, respectively. For HTML, it exceeded AFL++ and generation-based methods (FreeDom and Domato) by 28.8% and 14.02%. © 2024 IEEE.

关键词： HTML

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Near-Optimal Cell-Free Beamforming for Physical Layer Multigroup Multicasting

Near-Optimal Cell-Free Beamforming for Physical Layer Multig...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zaher, Mahmoud Björnson, Emil Petrova, Marina Kth Royal Institute of Technology Department of Computer Science Sweden Rwth Aachen University Mobile Communications and Computing Germany

ISBN: (纸本)9798350351255

Physical layer multicasting is an efficient transmission technique that exploits the beamforming potential at the transmitting nodes and the broadcast nature of the wireless channel, together with the demand for the same content from several UEs. This paper addresses the max-min fair multigroup multicast beamforming optimization, which is an NP-hard problem. We propose a novel iterative elimination procedure coupled with semidefinite relaxation (SDR) to find the near-global optimum rank-1 beamforming vectors in a cell-free massive MIMO (multiple-input multiple-output) network setup. The proposed optimization procedure shows significant improvements in computational complexity and spectral efficiency performance compared to the SDR followed by the commonly used randomization procedure and the state-of-the-art difference-of-convex approximation algorithm. The significance of the proposed procedure is that it can be utilized as a rank reduction method for any problem in conjunction with SDR. © 2024 IEEE.

关键词： Approximation algorithms

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Design and Development of Algorithmic Trading in Stock Market Using Artificial Intelligence 4

Design and Development of Algorithmic Trading in Stock Marke...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Verma, Vaibhav Jangra, Kushagra Chawla, Jasneet Singh, Harmanjeet Chandigarh University Department of Computer Science & Engineering Punjab India University Institute of Computing Chandigarh University Punjab India

ISBN: (纸本)9798350387490

Algorithmic trading has become an increasingly popular method of investing in stock requests due to its potential to offer progressive returns and lower risk in comparison to traditional styles. The current status of algorithmic trading in stock requests is examined in this review paper, along with the creative tactics used by dealers. We analyze the advantages and disadvantages of algorithmic trading, along with the variables that may affect its performance. We also discuss the difficulties dealers have enforcing algorithmic trading and the possibility that algorithmic trading will increase request insecurity. Finally, we conclude by making suggestions for future research in the area of algorithmic trading in stock requests. Overall, the objective of this review is to give a comprehensive overview of algorithmic trading in the stock request and the implicit effects it has on financial assiduity. © 2024 IEEE.

关键词： Decentralized finance

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