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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Suman Chahar Kawaljit Kaur Kuldeep Singh Kaswan Jagjit Singh Dhatterwal University Institute of Computing Chandigarh University Mohali India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Decentralized governance is going through radical changes due to the innovative use of blockchain which allows decisions to be made in a transparent, secure and more im- portantly, immutable way, without the authority of a single individual or party. Thus, the introduction of artificial intelligence (AI) in blockchain governing systems complicates such thing as transparency and trust, especially in the case of deep AI systems. But more on this later – this is where Explainable AI or XAI comes into the picture. XAI methods enable understanding of the reasons behind the choices made by AI models and solves the "black box" problem that is predominant in machine learn- ing. When applied to decentralised governance, XAI provides clarifications of AI’s decision making in such areas as voting, resource distribution and the enactment of smart contracts. This way, the stakeholders are confident that the decision-making is fair and impartial and this is made possible by the use of XAI. This paper aims to find out more about the applicability of XAI concept in blockchain –based governance systems in terms of bias and fairness in automated decisions and explanation of the decisions made. The reasons why interpretability plays the crucial role in decentralized systems are explained with the help of the comparison between the classical artificial intelligence and the Xin Intelligent. The study also looks at various use cases such as, Decentralized Autonomous Organizations (DAOs), and voting systems implemented on blockchain platforms and how XAI can enhance the performance of the governance systems. As this study shows, XAI is vital in building trust, improving responsibility, and guaranteeing balanced majorities in decentralized blockchain governance structures.

关键词： Explainable AI Decentralized autonomous organization Computational modeling Smart contracts Decision making Blockchains Resource management Stakeholders Security Decision trees

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Cost Sensitive Ensemble Learning Framework for EEG- Based Emotion Recognition 2nd

Cost Sensitive Ensemble Learning Framework for EEG- Based Em...

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2nd International Conference on data science and Artificial Intelligence, DSAI 2024

作者： Ganepola, Dasuni Maduranga, M.W.P. Wanniarachchi, W.A.A.M. Wijayaratne, W.M.S.R.B. Faculty of Computing General Sir John Kotelawala Defence University Rathmalana Sri Lanka Department of Data Science Sri Lanka Technology Campus Padukka Sri Lanka

ISBN: (纸本)9789819797929

Cost-sensitive learning (CSL) emerges as a promising approach for machine learning (ML) model performance evaluation. CSL weighs the severity of the prediction errors/ misclassifications made in the form of costs. Varying consequences of misclassification costs are often overlooked in existing research that uses standard metrics such as prediction accuracy leading to suboptimal performances in real-world settings. This paper emphasizes the need of CSL for emotion recognition models that use Electroencephalography (EEG) data. It proposes a novel cost sensitive framework that addresses the challenge of reducing the overall misclassification cost of existing EEG based emotion recognition models and thereby, creating a tradeoff with prediction accuracy. This proposed cost-sensitive framework is based on ensemble ML. Random Forest, Support Vector Machine, and k-Nearest Neighbor algorithms were heterogeneously ensembled to create this framework. It was compared with individual ML models built from same algorithms, which are considered cost insensitive as they neglect the varying misclassification costs. EEG data that classifies students as confused and non-confused during learning were used as the dataset. Results indicated that the designed ensemble model achieved a misclassification cost of 1811.5, which is a reduction of 38.20% compared to the highest misclassification cost incurred by a cost-insensitive individual model. Moreover, the ensemble model reached a prediction accuracy of 70.01%, whereas the individual models did not exceed 70% accuracy. This demonstrates the effectiveness of the proposed cost-sensitive model, as the cost-insensitive models could not achieve this level of tradeoff between misclassification cost and prediction accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Descriptor: Egocentric Action Recognition for Bengali Sign Language Detection dataset (Ego-SLD)

IEEE Data Descriptions

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IEEE data Descriptions 2025年 2卷 102-112页

作者： Asfak Ali Bibek Das Ahmad Sami Al-Shamayleh Sujatro Sarkar Chinmoyee Ray Sayoni Mandal Saifuddin Sk Adnan Akhunzada Department of Electronics and Telecommunication Engineering Jadavpur University Kolkata India Department of Data Science and Artificial Intelligence Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan Department of Electrical Engineering Jadavpur University Kolkata India College of Computing and Information Technology University of Doha for Science and Technology Doha Qatar

Sign language recognition integrates computer vision and natural language processing to automatically interpret hand gestures and translate them into spoken or written Bengali. The primary goal is to bridge the communication gap between sign language users and non-users by recognizing gestures, movements, postures, and facial expressions that correspond to spoken language elements. Since hand gestures are the cornerstone of sign language communication, they play a pivotal role in improving the accuracy of sign language recognition systems. This article introduces Ego-SLD, a video dataset specifically designed for egocentric action recognition of Bangla sign language used in daily life situations. The dataset contains videos of 16 commonly used words, collected from 12 individuals (ten males and two females) aged between 16 and 42 years. Each participant provided two samples of each word in an indoor environment with standard lighting conditions. Ego-SLD is a crucial resource for the automatic recognition of Bangla sign language and holds significant potential to benefit the deaf community. Moreover, it serves as a valuable dataset for researchers focusing on vision-based sign language detection, hand gesture recognition, and egocentric action recognition. IEEE SOCIETY/COUNCIL Computer Society data TYPE/LOCATION Video; Jadavpur University, Kolkata, West Bengal, India data DOI/PID https://***/10.21227/kbeg-fa06

关键词： Sign language Hands Recording Annotations Machine learning Accuracy Lighting Face recognition Cleaning Training

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The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption 30

The Power of Bamboo: On the Post-Compromise Security for Sea...

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30th Annual Network and Distributed System Security Symposium, NDSS 2023

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

ISBN: (纸本)1891562835

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2023 30th Annual Network and Distributed System Security Symposium, NDSS 2023. All Rights Reserved.

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Machine Learning With data Assimilation and Uncertainty Quantification for Dynamical Systems:A Review

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IEEE/CAA Journal of Automatica Sinica 2023年第6期10卷 1361-1387页

作者： Sibo Cheng César Quilodrán-Casas Said Ouala Alban Farchi Che Liu Pierre Tandeo Ronan Fablet Didier Lucor Bertrand Iooss Julien Brajard Dunhui Xiao Tijana Janjic Weiping Ding Yike Guo Alberto Carrassi Marc Bocquet Rossella Arcucci Data Science Institute Department of ComputingImperial College LondonSW72AZ London Department of Earth Science and Engineering Imperial College LondonSW72AZ London Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China the IMT Atlantique Lab-STICCUMR CNRS 6285France and OdysseyInria/IMTFrance.P.Tandeo is also with RIKEN Center for Computational ScienceKobeJapan the CEREA École des Ponts and EDF R&Dîle-de-FranceFrance the Laboratoire Interdisciplinaire des Sciences du Numérique CNRSParis-Saclay UniversityF-91403OrsayFrance the Electricitéde France(EDF) 78401 ChatouFranceInstitut de Mathématiques de Toulouse31062 ToulouseFrance and SINCLAIR AI LabSaclayFrance the Sorbonne University ParisFranceand also with Nansen Environmental and Remote Sensing Center(NERSC)BergenNorway the School of Mathematical Sciences Tongji UniversityShanghai 200092China the Mathematical Institute for Machine Learning and Data Science KU Eichstaett-IngolstadtBavariaGermany the School of Information Science and Technology Nantong UniversityNantong 226019China the Department of Physics and Astronomy“Augusto Righi” University of Bologna40124 BolognaItaly

data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal *** applications span from computational fluid dynamics(CFD)to geoscience and climate ***,much effort has been given in combining DA,UQ and machine learning(ML)*** research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive *** paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of *** review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their ***,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice *** exciting perspectives of this rapidly developing research field are also *** Terms-data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).

关键词： assimilation overcome apply

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MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts

arXiv

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arXiv 2024年

作者： Zhou, Jianan Cao, Zhiguang Wu, Yaoxin Song, Wen Ma, Yining Zhang, Jie Xu, Chi College of Computing and Data Science Nanyang Technological University Singapore School of Computing and Information Systems Singapore Management University Singapore Department of Information Systems Eindhoven University of Technology Netherlands Institute of Marine Science and Technology Shandong University China Singapore

Learning to solve vehicle routing problems (VRPs) has garnered much attention. However, most neural solvers are only structured and trained independently on a specific problem, making them less generic and practical. In this paper, we aim to develop a unified neural solver that can cope with a range of VRP variants simultaneously. Specifically, we propose a multi-task vehicle routing solver with mixture-of-experts (MVMoE), which greatly enhances the model capacity without a proportional increase in computation. We further develop a hierarchical gating mechanism for the MVMoE, delivering a good trade-off between empirical performance and computational complexity. Experimentally, our method significantly promotes zero-shot generalization performance on 10 unseen VRP variants, and showcases decent results on the few-shot setting and real-world benchmark instances. We further conduct extensive studies on the effect of MoE configurations in solving VRPs, and observe the superiority of hierarchical gating when facing out-of-distribution data. The source code is available at: https://***/RoyalSkye/Routing-MVMoE. Copyright © 2024, The Authors. All rights reserved.

关键词： Vehicle routing

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Classified Recognition Method for Analyzing User Sentiments Using Social Media Posts 9

Classified Recognition Method for Analyzing User Sentiments ...

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9th International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Ramesh, S. Vishnupriya, K. Anandaraj, A. Satheesh, N.P. Richard, Titus Bohra, Dixit Dutt College of Engineering & Technology Srm Institute of Science And Technology Department of Computing Technologies Tamilnadu Kattankulathur India Karpagam College of Engineering Department of Artificial Intelligence And Data Science Tamilnadu Coimbatore India Sri Eshwar College of Engineering Department of Cse Tamilnadu Coimbatore India Bannari Amman Institute of Technology Department of Ai&ds Tamilnadu Erode India Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Saveetha University Department of Biosciences Chennai India Iit Jodhpur Department of Cse Rajasthan 34200 India

ISBN: (纸本)9798350365092

Social media texts and posts are analyzed through modern computing and artificial intelligence systems to detect user sentiments. The severity of the words and their related activities are analyzed to detect the user sentiments. In this paper, a Classified Sentiment Recognition Method (CSRM) is introduced. The proposed method utilizes deep learning to identify and classify sentiments using their associated words. Using the associated labels, deep learning trains the context and occurrence of the texts/ posts to detect precise sentiments. Thus the learning steps are induced with label, context, and occurrence-based training to improve the sentiment detection and type classification. This proposed method is verified using recognition accuracy, classification rate, and recognition error. © 2024 IEEE.

关键词： Social networking (online)

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Physics-based fuid simulation in computer graphics: Survey, research trends, and challenges

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Computational Visual Media 2024年第5期10卷 803-858页

作者： Xiaokun Wang Yanrui Xu Sinuo Liu Bo Ren Jiri Kosinka Alexandru C.Telea Jiamin Wang Chongming Song Jian Chang Chenfeng Li Jian Jun Zhang Xiaojuan Ban Beijing Advanced Innovation Center for Materials Genome Engineering and School of Intelligence Science and Technologyand Shunde Innovation School University of Science and Technology BeijingBeijing 100083China National Center for Computer Animation Faculty of Media and CommunicationBournemouth UniversityPoole BH125BBUK. Bernoulli Institute University of GroningenGroningen 9700 AKthe Netherlands School of Computer Science Peking UniversityBeijing 100871China. TMCC College of Computer ScienceNankai UniversityTianjin 300350China Department of Information and Computing Sciences Utrecht UniversityUtrecht 3584 CCthe Netherlands. Zienkiewicz Institute for Modelling Data and AISwansea UniversitySwansea SA18ENUK. Institute of Materials Intelligent Technology Liaoning Academy of MaterialsShenyang 110004China.

Physics-based fluid simulation has played an increasingly important role in the computer graphics *** methods in this area have greatly improved the generation of complex visual effects and its computational *** techniques have emerged to deal with complex boundaries,multiphase fluids,gas-liquid interfaces,and fine *** parallel use of machine learning,image processing,and fluid control technologies has brought many interesting and novel research *** this survey,we provide an introduction to theoretical concepts underpinning physics-based fuid simulation and their practical implementation,with the aim for it to serve as a guide for both newcomers and seasoned researchers to explore the field of physics-based fuid simulation,with a focus on developments in the last *** by the distribution of recent publications in the field,we structure our survey to cover physical background;discretization approaches;computational methods that address scalability;fuid interactions with other materials and interfaces;and methods for expressive aspects of surface detail and *** a practical perspective,we give an overview of existing implementations available for the above methods.

关键词： computer graphics physical simulation fluid simulation fluid coupling

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ClimaEmpact: Domain-Aligned Small Language Models and datasets for Extreme Weather Analytics

arXiv

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arXiv 2025年

作者： Varshney, Deeksha Ong, Keane Mao, Rui Cambria, Erik Mengaldo, Gianmarco College of Design and Engineering National University of Singapore Singapore Department of Mathematics National University of Singapore Singapore College of Computing and Data Science Nanyang Technological University Singapore

Accurate assessments of extreme weather events are vital for research and policy, yet localized and granular data remain scarce in many parts of the world. This data gap limits our ability to analyze potential outcomes and implications of extreme weather events, hindering effective decision-making. Large Language Models (LLMs) can process vast amounts of unstructured text data, extract meaningful insights, and generate detailed assessments by synthesizing information from multiple sources. Furthermore, LLMs can seamlessly transfer their general language understanding to smaller models, enabling these models to retain key knowledge while being fine-tuned for specific tasks. In this paper, we propose Extreme Weather Reasoning-Aware Alignment (EWRA), a method that enhances small language models (SLMs) by incorporating structured reasoning paths derived from LLMs, and ExtremeWeatherNews, a large dataset of extreme weather event-related news articles. EWRA and ExtremeWeatherNews together form the overall framework, ClimaEmpact, that focuses on addressing three critical extreme-weather tasks: categorization of tangible vulnerabilities/impacts, topic labeling, and emotion analysis. By aligning SLMs with advanced reasoning strategies on ExtremeWeatherNews (and its derived dataset ExtremeAlign used specifically for SLM alignment), EWRA improves the SLMs’ ability to generate well-grounded and domain-specific responses for extreme weather analytics. Our results show that the approach proposed guides SLMs to output domain-aligned responses, surpassing the performance of task-specific models and offering enhanced real-world applicability for extreme weather analytics. © 2025, CC BY.

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Deep Learning for Disease Identification in Cassava Plants Using RGB Imaging 6th

Deep Learning for Disease Identification in Cassava Plants U...

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6th International Conference on Recent Innovations in computing, ICRIC 2023

作者： Sutar, Shivam Mohite, Snehal Kamble, Tushar Kolhar, Shrikrishna Jagtap, Jayant Shastri, Rajveer Joshi, Shubham Vidya Pratishthan’s Kamalnayan Bajaj Institute of Engineering and Technology Maharashtra Baramati India Maharashtra Pune India Marik Institute of Computing Artificial Intelligence Robotics and Cybernetics NIMS University Rajasthan Jaipur303121 India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Maharashtra Pune411037 India

ISBN: (纸本)9789819778614

Agriculture is the most critical sector in the world economy. Food security and consistent food supply are the key challenges due to rapid growth in the world population and crop diseases. Crop yield gets significantly hampered due to diseases attacking large crop fields. The precise classification of plant diseases is essential to provide appropriate pesticide supplements. This study applies deep learning (DL) models to identify diseases in cassava plants using red–green–blue (RGB) images. Three models, namely Xception-net, MobileNetV2, and EfficientNetB0, are trained and evaluated on the cassava disease dataset publicly available on Kaggle. Since the dataset is imbalanced, five-fold cross-validation is used to check the consistency of the results. Transfer learning and random weight initialization were used for model training. The Xception-net is the clear winner among the three models, with an accuracy of 87.8% using transfer learning, 81.33% using random weight initialization, and 85% average accuracy using five-fold cross-validation. Further, this work is extended to develop web and mobile applications with DL models deployed at the backend to predict plant diseases from input images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Plant diseases

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