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Advances in Computing research on science engineering and technology (ACROSET), International Conference on

作者： Yuvraj Panchal Rashid Sheikh Kamal Kumar Sethi Praveen Kumar Bhanodia Shiv Shankar Rajput Narendra Pal Singh Rathore Department of Computer Science And Engineering Acropolis Institute Of Technology And Research Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

In the field of data science, privacy-preserving methods are essential to guaranteeing the moral and secure use of data. This extensive overview study offers an in-depth examination of privacy-preserving approaches, including federated learning, homomorphic encryption, secure multi-party computation (SMPC), anonymization strategies, and differential privacy. This paper explains the theoretical underpinnings, real-world applications, difficulties, and future possibilities of privacy-preserving strategies in data science. The study explores anonymization strategies such generalization, suppression, k-anonymity, l-diversity, and t-closeness after providing an outline of privacy risks in data science. The concepts and uses of federated learning, homomorphic encryption, SMPC, and differential privacy are then examined, emphasizing how these techniques protect sensitive data and promote teamwork in analysis. Examples from the fields of healthcare, banking, telecommunications, social media, and government are shown to show how useful privacy-preserving techniques are in the real world. We hope to give scholars, practitioners, and policymakers a thorough knowledge of privacy-preserving strategies and their implications for ethical and long-term data-driven decision-making through this extensive review.

关键词： Surveys Ethics Technological innovation Privacy Reviews Decision making Data science Information filtering Homomorphic encryption Information integrity

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Which is more faithful,seeing or saying? Multimodal sarcasm detection exploiting contrasting sentiment knowledge

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CAAI Transactions on Intelligence technology 2025年第2期10卷 375-386页

作者： Yutao Chen Shumin Shi Heyan Huang School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications BeijingChina

Using sarcasm on social media platforms to express negative opinions towards a person or object has become increasingly ***,detecting sarcasm in various forms of communication can be difficult due to conflicting *** this paper,we introduce a contrasting sentiment-based model for multimodal sarcasm detection(CS4MSD),which identifies inconsistent emotions by leveraging the CLIP knowledge module to produce sentiment features in both text and ***,five external sentiments are introduced to prompt the model learning sentimental preferences among ***,we highlight the importance of verbal descriptions embedded in illustrations and incorporate additional knowledge-sharing modules to fuse such imagelike *** results demonstrate that our model achieves state-of-the-art performance on the public multimodal sarcasm dataset.

关键词： CLIP image-text classification knowledge fusion multi-modal sarcasm detection

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Enhancing Data Forwarding Efficiency in SIoT with Multidimensional Social Relations

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computers, Materials & Continua 2024年第1期78卷 1095-1113页

作者： Fang Xu Songhao Jiang Yi Ma Manzoor Ahmed Zenggang Xiong Yuanlin Lyu School of Computer and Information Science Hubei Engineering UniversityXiaogan432000China School of Computer Science and Information Engineering Hubei UniversityWuhan430062China Artificial Intelligence Industrial Technology Research Institute Hubei Engineering UniversityXiaogan432000China

Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research *** paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this *** proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among *** new metrics are defined:the intensity of node social relationships,node activity,and community *** the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node *** a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between *** proposed algorithm was compared to three existing routing algorithms in simulation *** indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.

关键词： SIoT data forwarding social attributes social relations community

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Transfer Learning for Multiclass Classification of Wheat Leaf Diseases

Transfer Learning for Multiclass Classification of Wheat Lea...

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Advances in Computing research on science engineering and technology (ACROSET), International Conference on

作者： Riya Verma Narendra Pal Singh Rathore Kamal Kumar Sethi Artificial Intelligence and Data Science Acropolis Institute of Technology and Research Indore India Computer Science and Engineering Acropolis Institute of Technology and Research Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

In the following paper, we explore a study focusing on the detection of wheat leaf diseases using transfer learning techniques combined with cutting-edge convolutional neural network (CNN) architectures, namely VGG16, ResNet152V2, and Inception. The aim is to enhance the accuracy of disease detection while minimizing computational resources and training time. We conducted experiments on a dataset consisting of wheat leaf images affected with several diseases. Through transfer learning, we fine-tuned pre-trained models on our dataset to leverage their learned features. Our results demonstrate that VGG16 consistently outperforms ResNet152V2 and Inception, achieving the highest accuracy of 98.66% in disease detection. This indicates the efficacy of VGG16 for wheat leaf disease classification tasks, highlighting its potential for agricultural applications requiring robust and accurate detection of plant diseases.

关键词： Training Plant diseases Accuracy Computational modeling Transfer learning Focusing computer architecture Vectors Convolutional neural networks Diseases

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UAV Localization with Unreliable Observations in Hostile Underground Environments

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Journal of computer science & technology 2024年第6期39卷 1401-1418页

作者： Peng-Peng Chen Kui-Yuan Zhang Shou-Wan Gao Si-Yi Ren School of Computer Science and Technology China University of Mining and TechnologyXuzhou 221116China Mine Digitization Engineering Research Center Ministry of EducationXuzhou 221116China Shenzhen Research Institute China University of Mining and TechnologyShenzhen 518057China

The accurate and robust unmanned aerial vehicle(UAV)localization is significant due to the requirements of safety-critical monitoring and emergency wireless communication in hostile underground *** range-based localization approaches fundamentally rely on the assumption that the environment is relatively ideal,which enables a precise range for ***,radio propagation in the underground environments may be dramatically influenced by various equipments,obstacles,and ambient *** this case,inaccurate range measurements and intermittent ranging failures inevitably occur,which leads to severe localization performance *** address the challenges,a novel UAV localization scheme is proposed in this paper,which can effectively handle unreliable observations in hostile underground *** first propose an adaptive extended Kalman filter(EKF)based on the fusion of ultra-wideband(UWB)and inertial measurement unit(IMU)to detect and adjust the inaccurate range *** to deal with intermittent ranging failures,we further design the constraint condition by limiting the system ***,the auto-regressive model is proposed to implement the localization in the ranging blind areas by reconstructing the lost ***,extensive simulations have been conducted to verify the *** carry out field experiments in an underground garage and a coal mine based on P440 UWB *** show that the localization accuracy is improved by 16.9%compared with the recent methods in the hostile underground environments.

关键词： unmanned aerial vehicle(UAV)localization Kalman filter ultra-wideband(UWB)observation hostile underground environment

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Contrastive Domain Adaptation for Time-Series Via Temporal Mixup

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1185-1194页

作者： Eldele, Emadeldeen Ragab, Mohamed Chen, Zhenghua Wu, Min Kwoh, Chee-Keong Li, Xiaoli School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Centre for Frontier Ai Research Agency for Science Technology and Research Singapore138632 Singapore Institute for Infocomm Research Agency for Science Technology and Research Singapore138632 Singapore

Unsupervised domain adaptation (UDA) has emerged as a powerful solution for the domain shift problem via transferring the knowledge from a labeled source domain to a shifted unlabeled target domain. Despite the prevalence of UDA for visual applications, it remains relatively less explored for time-series applications. In this work, we propose a novel lightweight contrastive domain adaptation framework called CoTMix for time-series data. Unlike existing approaches that either use statistical distances or adversarial techniques, we leverage contrastive learning solely to mitigate the distribution shift across the different domains. Specifically, we propose a novel temporal mixup strategy to generate two intermediate augmented views for the source and target domains. Subsequently, we leverage contrastive learning to maximize the similarity between each domain and its corresponding augmented view. The generated views consider the temporal dynamics of time-series data during the adaptation process while inheriting the semantics among the two domains. Hence, we gradually push both domains toward a common intermediate space, mitigating the distribution shift across them. Extensive experiments conducted on five real-world time-series datasets show that our approach can significantly outperform all state-of-the-art UDA methods. Impact Statement-Unsupervised domain adaptation (UDA) aims to reduce the gap between two related but shifted domains. CurrentUDAmethods for time-series data are based on adversarial or discrepancy approaches. Thesemethods are complex in training and cannot efficiently address the large domain shift. Therefore, in this work, we propose a time-series UDA framework based purely on contrastive learning, which is simpler in implementation and training. To leverage contrastive learning to mitigate domain shift, we propose a temporal mixup strategy to generate augmentations that are robust to the domain shift and can move both domains towards an intermediate

关键词： Semantics

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A Facial Expression Recognition Method Integrating Uncertainty Estimation and Active Learning

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computers, Materials & Continua 2024年第10期81卷 533-548页

作者： Yujian Wang Jianxun Zhang Renhao Sun School of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China Data Space Research Institute of Hefei Comprehensive National Science Center Hefei230088China

The effectiveness of facial expression recognition(FER)algorithms hinges on the model’s quality and the availability of a substantial amount of labeled expression ***,labeling large datasets demands significant human,time,and financial *** active learning methods have mitigated the dependency on extensive labeled data,a cold-start problem persists in small to medium-sized expression recognition *** issue arises because the initial labeled data often fails to represent the full spectrum of facial expression *** paper introduces an active learning approach that integrates uncertainty estimation,aiming to improve the precision of facial expression recognition regardless of dataset scale *** method is divided into two primary ***,the model undergoes self-supervised pre-training using contrastive learning and uncertainty estimation to bolster its feature extraction ***,the model is fine-tuned using the prior knowledge obtained from the pre-training phase to significantly improve recognition *** the pretraining phase,the model employs contrastive learning to extract fundamental feature representations from the complete unlabeled *** features are then weighted through a self-attention mechanism with rank ***,data from the low-weighted set is relabeled to further refine the model’s feature extraction *** pre-trained model is then utilized in active learning to select and label information-rich samples more *** results demonstrate that the proposed method significantly outperforms existing approaches,achieving an improvement in recognition accuracy of 5.09%and 3.82%over the best existing active learning methods,Margin,and Least Confidence methods,respectively,and a 1.61%improvement compared to the conventional segmented active learning method.

关键词： Expression recognition active learning self-supervised learning uncertainty estimation

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An Intelligent System to Predict Diabetic Retinal Diseases Based on Diabetic Attributes

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SN computer science 2024年第6期5卷 755页

作者： Eswari, M. Shanmuga Balamurali, S. Ramkumar, K. Department of Computer Applications Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Rajalakshmi Institute of Technology

The most common illness among individuals and the general population in the medical field is diabetes. This is coupled with a careful diabetic retinal that has no signal. The historical record offers unrecoverable insight. To avoid a hallucinatory dispute at the time, early detection of retinal illness is necessary. Positively, the results of diabetic patients have been able to foretell the threat of diabetic-related eyeailments such as Diabetic Retinopathy, Glaucoma, and Diabetic Macular Edema.A diabetic retinal disease prediction exemplary have been created by via system learning rules, such as narrow neural network, bilayered neural network, wide neural network and trilayer neural network, in close proximity to the onset of the disease. The trilayer neural network with various activation functions accuracy based results servedas the foundation for the final model that evaluates real time data to predict the likelihood of *** learner representation accuracy of 0.95and the evaluation phase accuracy of 0.94 are *** ideal diabetic features-based diabetic retinal system outcome occupies a crucial place in the medical decision support system to make prompt assessments in the field of E-healthcare system to connect ophthalmology field to eradicate insulin based eye problems at the time of regular assessment. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Boosting computer-aided Design Diabetic Mellitus Ensemble Machine Learner Retinal eye Disease

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Detection of Perfect Stealthy Attacks on Cyber-Physical Systems Subject to Measurement Quantizations: A Watermark-Based Strategy

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 114-125页

作者： Yu-Ang Wang Zidong Wang Lei Zou Bo Shen Hongli Dong the College of Information Science and Technology Donghua University the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education IEEE the College of Electrical Engineering and Automation Shandong University of Science and Technology the Department of Computer Science Brunel University London Uxbridge the Sanya Offshore Oil and Gas Research Institute Northeast Petroleum University the Artificial Intelligence Energy Research Institute Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University

In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.

关键词： Attack detection cyber-physical systems (CPSs) perfect stealthy attacks watermark-based strategy

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Automated Subject Allotment to Faculty in an Academic institute Using Maxflow Problem

Automated Subject Allotment to Faculty in an Academic Instit...

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Advances in Computing research on science engineering and technology (ACROSET), International Conference on

作者： Neha Sehta Naman Shah Department of Computer Science and Engineering Acropolis Institute of Technology and Research Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

presently, educational organizations are utilizing manual procedures to assign subjects to faculties depending on their own preferences, which gives rise to prejudice and therefore, discontentment. The best approach would be to allocate the most qualified individual to instruct the appropriate course, taking into account their knowledge and experience. Many instructional units rely on manual trial-and-error methods, which are time-consuming, inefficient, and prone to biased judgment. Moreover, the distribution is likely disadvantageous for several academic personnel. This study introduces an automated allocation of subjects to the faculties, taking into account the preferences in an effective way. The subject allocation problem is mapped into a maxflow problem and solved using Edmonds-Karp algorithm. A satisfaction metric can be computed to evaluate the effectiveness of the solution proposed which is average calculated on the distribution of subject as per the choice of faculty.

关键词： Productivity Measurement Heuristic algorithms Scalability Manuals Organizations Resource management Personnel research and development Testing

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