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arXiv 2024年

作者： Deroy, Aniket Ghosh, Kripabandhu Ghosh, Saptarshi Computer Science and Engineering IIT Kharagpur West Bengal Kharagpur721302 India Computational and Data Sciences IISER Kolkata West Bengal Kolkata741246 India

Automatic summarization of legal case judgements, which are known to be long and complex, has traditionally been tried via extractive summarization models. In recent years, generative models including abstractive summarization models and Large language models (LLMs) have gained huge popularity. In this paper, we explore the applicability of such models for legal case judgement summarization. We applied various domain-specific abstractive summarization models and general-domain LLMs as well as extractive summarization models over two sets of legal case judgements - from the United Kingdom (UK) Supreme Court and the Indian (IN) Supreme Court - and evaluated the quality of the generated summaries. We also perform experiments on a third dataset of legal documents of a different type - Government reports from the United States (US). Results show that abstractive summarization models and LLMs generally perform better than the extractive methods as per traditional metrics for evaluating summary quality. However, detailed investigation shows the presence of inconsistencies and hallucinations in the outputs of the generative models, and we explore ways to reduce the hallucinations and inconsistencies in the summaries. Overall, the investigation suggests that further improvements are needed to enhance the reliability of abstractive models and LLMs for legal case judgement summarization. At present, a human-in-the-loop technique is more suitable for performing manual checks to identify inconsistencies in the generated summaries. Copyright © 2024, The Authors. All rights reserved.

关键词： Law enforcement

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Augmented Reality and Artificial Intelligence in Sign Language Expression

Augmented Reality and Artificial Intelligence in Sign Langua...

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IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS)

作者： Kaviyaraj R P. Sathya P. Nandhini Vinoth Kumar P Sureshkumar Chelliah Ezhilmathi S Department of computational intelligence SRM institute of science and technology Kattankulathur India Department of Computer Science and Engineering Sri Shanmuga Collage of Engineering and Technology Sankari India Department of Artificial Intelligence and Data Science St. Joseph's Institute of Technology OMR Chennai

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Sign language, the vibrant tapestry of hand gestures and facial expressions, is the lifeblood of Deaf and hardof-hearing communities. For millions of signers, American Sign Language (ASL) runs deeper than communication, fundamental to identity, expression, and belonging. And yet an unshakeable communication gap leaves users of ASL frequently marooned away from the hearing world, kept from education, healthcare, or employment, or from basic, everyday transactions. By posing this new unsolved challenge to the power and promise of Artificial Intelligence (AI), this work leads the way towards closing that chasm by real-time recognition and translation of full ASL. Our approach employs a novel variant of Random Forest and utilizes cutting-edge video processing techniques to identify and understand the nuanced, often exquisitely delicate detail of ASL signing, at unprecedented levels of accuracy, and at speed. Another layer of innovation that characterizes our work is our integration of augmented reality (AR). By embedding AR along with our translator of artificial intelligence tech, we intend to completely change the way American Sign Language (ASL) is conveyed by directly engraining our already robust Random Forest model and advanced video processing techniques to project the ASL translation directly into your visual field in real time. The goal to demystify this complex and vivid language and, in doing so, to remove the communication barriers that persist between the Deaf community and the rest of the world, thus, fostering inclusion.

关键词： Sign language computational modeling Signal processing algorithms Assistive technologies Predictive models Streaming media Feature extraction

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Dual-neighbourhood information aggregation and feature fusion for prediction of miRNA–disease association

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computers in Biology and Medicine 2024年 181卷 109068-109068页

作者： Liu, Wei Lan, Zixin Li, Zejun Sun, Xingen Lu, Xu School of Computer Science Xiangtan University Xiangtan411105 China School of Computer Science and Engineering Hunan Institute of Technology Hengyang421002 China School of Mathematics and Computational Science Xiangtan University Xiangtan411105 China School of Computer Science Guangdong Polytechnic Normal University Guangdong Provincial Key Laboratory of Intellectual Property Big Data Guangzhou510665 China

Studying the intricate relationship between miRNAs and diseases is crucial to prevent and treat miRNA-related disorders. Existing computational methods often overlook the importance of features of different nodes and the propagation of features among heterogeneous nodes. Many prediction models focus only on the feature coding of miRNA and diseases and ignore the importance of feature aggregation. We propose a prediction method via dual-neighbourhood feature aggregation and feature fusion, which uses multiple sources of information, aggregates information on homogeneous and heterogeneous nodes and fuses learned features to predict multiple representations of disease nodes. We constructed similarity networks of multiple homogeneous nodes based on different similarity computation methods respectively, and fused the attention mechanism by using graph convolutional networks to obtain information of different levels of importance. To alleviate the problem of sparse connectivity in the dataset, we built a two-neighbourhood heterogeneous graph neural network model to integrate the homogeneous similarity network into a miRNA-disease heterogeneous network by using known miRNA–disease association information. We used the neighbourhood information associated with the nodes in the network to perform feature aggregation. In addition, we used a feature fusion module to learn the importance of different types of nodes to predict miRNA–disease associations. Our experimental results on the Human microRNA Disease database (HMDD v3.2) show that the model demonstrates superior performance. This work demonstrates the capability of our model to identify potential miRNAs associated with diseases through a case study of two common cancers. © 2024

关键词： Prediction models

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Video Object Forgery Detection using Image Processing Techniques

Video Object Forgery Detection using Image Processing Techni...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： P. Lavanya Kumari M. Siva N. Muthukumaran D. Dharani K. Kavitha Sahithi V. Ashwin Kumar Department of Computer Science and Engineering (Data Science) Vignan Institute of Technology and Science Hyderabad India Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

Video surveillance has been crucial for security in recent years, thus it’s critical to guarantee the accuracy of these recordings. Unfortunately, it is easy to fake surveillance films by erasing an object from a scene and leaving no visual evidence. In this study, an approach for video intra-frame forgery forensics based on the SSIM (Structural Similarity Index Measure) is proposed. This system can recognize forged video frames automatically. In order to extract the steganography features, the method first decompresses the input video into a sequence of frames. It next calculates each frame’s motion residual map. The ability to tell whether or not an object is deleted from a video is a key problem in video security.

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Reliable Vertical Ground Reaction Force Estimation with Smart Insole During Walking

arXiv

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arXiv 2025年

作者： Olugbon, Femi Ghoreishi, Nozhan Huang, Ming-Chun Xu, Wenyao Chen, Diliang Department of Electrical and Computer Engineering University of New Hampshire DurhamNH03824 United States Department of Data and Computational Science Duke Kunshan University Suzhou China Department of Computer Science & Engineering University at Buffalo The State University of New York BuffaloNY14260 United States

The vertical ground reaction force (vGRF) and its characteristic weight acceptance and push-off peaks measured during walking are important for gait and biomechanical analysis. Current wearable vGRF estimation methods suffer from drifting errors or low generalization performances, limiting their practical application. This paper proposes a novel method for reliably estimating vGRF and its characteristic peaks using data collected from the smart insole, including inertial measurement unit data and the newly introduced center of the pressed sensor data. These data were fused with machine learning algorithms including artificial neural networks, random forest regression, and bi-directional long-short-term memory. The proposed method outperformed the state-of-the-art methods with the root mean squared error, normalized root mean squared error, and correlation coefficient of 0.024 body weight (BW), 1.79% BW, and 0.997 in intra-participant testing, and 0.044 BW, 3.22% BW, and 0.991 in inter-participant testing, respectively. The difference between the reference and estimated weight acceptance and push-off peak values are 0.022 BW and 0.017 BW with a delay of 1.4% and 1.8% of the gait cycle for the intra-participant testing and 0.044 BW and 0.025 BW with a delay of 1.5% and 2.3% of the gait cycle for the inter-participant testing. The results indicate that the proposed vGRF estimation method has the potential to achieve accurate vGRF measurement during walking in free-living environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Mean square error

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A Comparison of Global Sensitivity Methods for Power Systems

A Comparison of Global Sensitivity Methods for Power Systems

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International Conference on System Reliability and science (ICSRS)

作者： Ruilong Yue Hui Duan Giray Ökten Bahri Uzunoğlu Department of Mathematics Florida State University USA Department of Electrical Engineering Computational and Data-Enabled Science and Engineering in Energy Systems Uppsala University Sweden

The sensitivity of power system operations uncertainties that originate from distributed renewable generation, natural disasters like hurricanes, and changing loads such as vehicle charging needs careful engineering for reliable, resilient and robust power systems. The power system is a global sensitivity problem since each input uncertainty varies the output and all the variables simultaneously that have higher order interactions between inputs, unlike local sensitivity. The article assesses and compares the global sensitivity methods of Sobol’ sensitivity indices, the activity scores of the active subspace method, and Shapley values, to assess the importance of input parameters in the IEEE 14-bus modified test system. The limitations and advantages of each approach are illustrated to reduce the complexity of the model by performing uncertainty quantification for the output by global sensitivity indices in the IEEE 14-bus modified test system.

关键词： Renewable energy sources Sensitivity Uncertainty Wind power generation Reliability engineering Hurricanes Safety

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Guardian of the Ensembles: Introducing Pairwise Adversarially Robust Loss for Resisting Adversarial Attacks in DNN Ensembles

Guardian of the Ensembles: Introducing Pairwise Adversariall...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Shubhi Shukla Subhadeep Dalui Manaar Alam Shubhajit Datta Arijit Mondal Debdeep Mukhopadhyay Partha Pratim Chakrabarti Centre for Computational and Data Sciences IIT Kharagpur India Computer Science and Engineering Department IIT Kharagpur India Center for Cyber Security New York University Abu Dhabi UAE Department of Artificial Intelligence IIT Kharagpur India Computer Science and Engineering Department IIT Patna India

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Adversarial attacks rely on transferability, where an adversarial example (AE) crafted on a surrogate classifier tends to mislead a target classifier. Recent ensemble methods demonstrate that AEs are less likely to mislead multiple classifiers in an ensemble. This paper proposes a new ensemble training using a Pairwise Adversarially Robust Loss (PARL) that by construction produces an ensemble of classifiers with diverse decision boundaries. PARL utilizes outputs and gradients of each layer with respect to network parameters in every classifier within the ensemble simultaneously. PARL is demonstrated to achieve higher robustness against black-box transfer attacks than previous ensemble methods as well as adversarial training without adversely affecting clean example accuracy. Extensive experiments using standard Resnet20, WideResnet28-10 classifiers demonstrate the robustness of PARL against state-of-the-art adversarial attacks. While maintaining similar clean accuracy and lesser training time, the proposed architecture has a 24.8% increase in robust accuracy (∊= 0.07) from the state-of-the art method. Code is available at: https://***/shubhishukla10/PARL

关键词： Training computer vision Accuracy Perturbation methods Neural networks Closed box computer architecture Robustness Ensemble learning Standards

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Optimized Transfer Learning Model for Lung Cancer Stage Classification Using Computed Tomography Images

Optimized Transfer Learning Model for Lung Cancer Stage Clas...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： R Deepika P. Shanmugam K. Moorthi P. K. Manoj Kumar S. L. Swarna Ramnath. K Department of Artificial Intelligence and Data Science B V Raju Institute of Technology Narsapur Telangana India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Electronics and Communication Engineering Sri Sairam Engineering College Chennai India School of Computational Intelligence Joy University Tirunelveli India Department of Computer Science and Engineering Sona college of Technology Salem Tamilnadu India Department of Computer Science and Engineering K.L.N College of Information Technology Madurai Tamilnadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Lung cancer has become one of the primary causes of death worldwide due to cancer, which indicates the necessity of early diagnosis. In this regard, the present work proposes an optimized model of transfer learning based on characterizing different stages of lung cancer by using computed tomography images acquired from the IQ-OTH/NCCD lung cancer database downloadable from Kaggle. It makes use of a two-step process: first, a U-Net model is used on the patient's CT images to reconstruct anatomical features and reduce noise; second, a ShuffleNet architecture is used to predict the patient's CT stage using the reconstructed image. In addition, to enhance classification, PSO was incorporated into the model so that it can optimize the hyperparameters. The accuracy of the model was at 97.85 percent, and it suggests that the model can become a very useful tool for clinicians to bring about earliest identification and staging for lung cancer.

关键词： Accuracy databases computational modeling Computed tomography Transfer learning Noise Lung cancer Predictive models Image reconstruction Optimization

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Automated Assignment Management System: Integrating Email Inbox Parsing, database Updates, and Notification

Automated Assignment Management System: Integrating Email In...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： P Varun R.S. Rashika S Kayalvizhi D Preetha P Naveen T A Mohanaprakash Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Department of CSE Panimalar Engineering College Chennai Department of Computer Science and Engineering Velammal Engineering College Department of Information Technology Alpha College of Engineering Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chennai Department of CSE School of Engineering & Technology CMR University Bengalunu Karnataka

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

This paper presents the design and implementation of an automated assignment management system utilizing a bot interface. The system’ s primary objective is to streamline the process of collecting and tracking student assignments, ensuring timely updates to a central database, and facilitating communication between students and teachers. The bot employs email inbox parsing techniques to identify incoming assignment submissions, extracting relevant data such as student names, assignment details, and submission timestamps. Upon receiving a submission, the bot updates an Excel database with the pertinent information, maintaining an organized record of completed ***, the system incorporates a notification feature to promptly alert students who have not yet submitted their assignments by the deadline. Leveraging email integration, the bot sends personalized notifications reminding students of impending deadlines and urging them to submit their work promptly. Additionally, the bot generates a list of incomplete assignments and forwards it to the respective teacher, providing a consolidated overview of students who require *** automating the assignment management process, this system reduces manual intervention, minimizes administrative burden, and enhances efficiency in monitoring student progress. The integration of email parsing, database updates, and notification functionalities ensures seamless communication and facilitates timely submission of assignments, contributing to improved academic outcomes and instructor-student engagement

关键词： Automation databases Manuals Chatbots Feature extraction data mining Informatics

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Version Innovation Age and Age of Incorrect Version for Monitoring Markovian Sources

Version Innovation Age and Age of Incorrect Version for Moni...

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International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

作者： Mehrdad Salimnejad Marios Kountouris Anthony Ephremides Nikolaos Pappas Department of Computer and Information Science Linköping University Linköping Sweden Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Spain Electrical and Computer Engineering University of Maryland College Park MD USA

ISBN: (数字)9783903176652

ISBN: (纸本)9798331508722

In this paper, we study the problem of real-time remote tracking of a two-state Markov process. We introduce a modified randomized stationary sampling and transmission policy where the decision to perform sampling occurs probabilistically depending on the current state of the source and whether the system was in a sync state during the previous time slot or not. We then propose two new performance metrics, coined the Version Innovation Age (VIA) and the Age of Incorrect Version (AoIV), and analyze their performance under the modified randomized stationary and other state-of-the-art sampling and transmission policies. Specifically, we derive closed-form expressions for the distribution and the average of VIA and AoIV under these policies. Furthermore, we formulate and solve two constrained optimization problems. The first optimization problem aims to minimize the average VIA subject to constraints on the time-averaged sampling cost and time-averaged reconstruction error. In the second problem, the objective is to minimize the average AoIV while considering a constraint on the time-averaged sampling cost. Finally, we compare the performance of various sampling and transmission policies and identify the conditions under which each policy outperforms the others in optimizing the proposed metrics.

关键词： Measurement Technological innovation Costs Transmitters Wireless networks System performance Receivers Synchronization Optimization Monitoring

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