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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Santosh Kumar Kapil Joshi Sumeshwar Singh Harishchander Anandaram Kawerinder Singh Sindhu Satish Kumar Mahariya Vishal Kaushik Department of Mathematics Sharda School of Basic Sciences and Research Sharda University Greater Noida UP India Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Centre for Computational Engineering and Networking Amrita School of Artificial Intelligence Coimbatore India Uttaranchal University Dehradun India GSSS Mahawa Neemkathana Sikar Rajasthan India School of Computer Science UPES India

The given study focuses on the influence of artificial intelligence (AI) on adjudicatory procedures mostly on observance of such right to an impartial trial in several law-related fields with respect to existing decisions. The consideration of such substance of the right to an impartial trial serves as the beginning stage for the debate, with a special focus on the autonomy and impartiality of the judiciary. Following that, the article analyses the prospect of minimizing the individual role in court processes in charge of making decisions using information technologies. This paper analyses the admissibility of using AI tools in the judiciary and contains considerations on ethical aspects of AI application in judicial proceedings and whether an AI system can take over the role of a decision-maker in judicial proceedings, thereby replacing or supporting the judge. The paper with a view of its legal compliance, non-discrimination, transparency, and efficiency of legal proceedings. The study employs the method of analyzing the law in force and investigating theories of law, taking into account the critical analysis of the literature at the decision level.

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Few-Shot Medical Image Segmentation with High-Fidelity Prototypes

arXiv

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arXiv 2024年

作者： Tang, Song Yan, Shaxu Qi, Xiaozhi Gao, Jianxin Ye, Mao Zhang, Jianwei Zhu, Xiatian IMI Group School of Health Sciences and Engineering University of Shanghai for Science and Technology Shanghai China TAMS Group Department of Informatics Universität Hamburg Hamburg Germany School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Surrey Institute for People-Centred Artificial Intelligence Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China

Few-shot Semantic Segmentation (FSS) aims to adapt a pretrained model to new classes with as few as a single labelled training sample per class. Despite the prototype based approaches have achieved substantial success, existing models are limited to the imaging scenarios with considerably distinct objects and not highly complex background, e.g., natural images. This makes such models suboptimal for medical imaging with both conditions invalid. To address this problem, we propose a novel Detail Self-refined Prototype Network (DSPNet) to constructing high-fidelity prototypes representing the object foreground and the background more comprehensively. Specifically, to construct global semantics while maintaining the captured detail semantics, we learn the foreground prototypes by modelling the multi-modal structures with clustering and then fusing each in a channel-wise manner. Considering that the background often has no apparent semantic relation in the spatial dimensions, we integrate channel-specific structural information under sparse channel-aware regulation. Extensive experiments on three challenging medical image benchmarks show the superiority of DSPNet over previous state-of-the-art methods. The code and data are available at https://***/tntek/DSPNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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A transformer based multi task learning approach to multimodal hate speech detection

Natural Language Processing Journal

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Natural Language Processing Journal 2025年 11卷

作者： Prashant Kapil Asif Ekbal Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar India School of Artificial Intelligence and Data Science Indian Institute of Technology Jodhpur Rajasthan India

Online hate speech has become a major social issue in recent years, affecting both individuals and society as a whole. Memes are a multimodal kind of internet hate speech that is growing more common. Online memes are often entertaining and harmless. The seemingly innocent meme, on the other hand, transforms into a multimodal form of hate speech—a hateful meme—when specific types of text, graphics, or combinations of both are used. The spread of these harmful or undesirable memes has the potential to disrupt societal peace. Therefore, it is vital to limit inappropriate memes on social media. Multimodal hate speech identification is an inherently difficult and open question. It necessitates collaborative language, visual perception, and multimodal reasoning. This line of research has been progressed in this work by building a multi-task learning-based multimodal system for detecting hateful memes by training four hateful meme data sets concurrently. This MTL framework, which consists of Contrastive Language Image Pretraining (CLIP), UNiversal Image-TExt Representation Learning (UNITER), and BERT, was trained collaboratively to transfer common knowledge while simultaneously training four meme datasets. The results show that the recommended strategy outperforms unimodal and multimodal approaches on four multilingual benchmark datasets, with considerable AUC-ROC, accuracy, and F1-score. The ablation studies are undertaken to emphasise the impact of the sub-component in the MTL model. The confusion matrix is shown as quantitative analysis.

关键词： Multi-task learning UNITER CLIP AUC-ROC F1 score

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Predictive Maintenance in IoT Networks Using a Temporal Attention Mechanism Based Federated Convolutional Neural Network

Predictive Maintenance in IoT Networks Using a Temporal Atte...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Naga Venkata Ramakrishna Guduri R.M. Dilip Charaan Kalaiyarasi R Samuthira Pandi V D.Laxma Reddy Selvaprabhu. T Faculty of Engineering University of Technology and Applied Sciences-Al Musannah Sultanate of Oman Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Chennai Tamil Nadu India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of ECE MLR Institute of Technology Hyderabad India Department of CSE Dhanalakshmi College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

In the era of Industry 4.0, edge devices in industrial settings generate vast amounts of data crucial for process optimization and Predictive Maintenance (PM). Traditional cloud-based approaches for handling this data are becoming untenable due to cost, latency, and privacy issues. Federated learning emerges as a viable solution, offering localized and efficient data processing while mitigating these challenges. This paper presents a Federated Convolutional Neural Network with Temporal Attention Mechanism (FedCNN-TAM) for PM tasks in Internet of Things (IoT) networks. This model leverages temporal features in sensor data to enhance predictive accuracy. Evaluated on the CMAPSS dataset, FedCNN-TAM outperforms representative models with a significant performance gain. This model exhibits the potential for broader applications in industrial IoT, setting the stage for future research on its scalability and adaptability to multi-modal data types.

关键词： Adaptation models Temperature distribution Technological innovation Attention mechanisms Accuracy Predictive models data models Real-time systems Convolutional neural networks Predictive maintenance

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Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?

arXiv

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arXiv 2024年

作者： Bassi, Pedro R.A.S. Li, Wenxuan Tang, Yucheng Isensee, Fabian Wang, Zifu Chen, Jieneng Chou, Yu-Cheng Roy, Saikat Kirchhoff, Yannick Rokuss, Maximilian Huang, Ziyan Ye, Jin He, Junjun Wald, Tassilo Ulrich, Constantin Baumgartner, Michael Maier-Hein, Klaus H. Jaeger, Paul Ye, Yiwen Xie, Yutong Zhang, Jianpeng Chen, Ziyang Xia, Yong Xing, Zhaohu Zhu, Lei Sadegheih, Yousef Bozorgpour, Afshin Kumari, Pratibha Azad, Reza Merhof, Dorit Shi, Pengcheng Ma, Ting Du, Yuxin Bai, Fan Huang, Tiejun Zhao, Bo Wang, Haonan Li, Xiaomeng Gu, Hanxue Dong, Haoyu Yang, Jichen Mazurowski, Maciej A. Gupta, Saumya Wu, Linshan Zhuang, Jiaxin Chen, Hao Roth, Holger Xu, Daguang Blaschko, Matthew B. Decherchi, Sergio Cavalli, Andrea Yuille, Alan L. Zhou, Zongwei Department of Computer Science Johns Hopkins University United States Department of Pharmacy and Biotechnology University of Bologna Italy Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Italy NVIDIA United States Germany Germany ESAT-PSI KU Leuven Belgium Faculty of Mathematics and Computer Science Heidelberg University Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Germany Shanghai Jiao Tong University China Shanghai Artificial Intelligence Laboratory China Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany DKFZ Germany School of Computer Science and Engineering Northwestern Polytechnical University China Australian Institute for Machine Learning The University of Adelaide Australia College of Computer Science and Technology Zhejiang University China Hong Kong University of Science and Technology Guangzhou China Hong Kong University of Science and Technology Hong Kong Faculty of Informatics and Data Science University of Regensburg Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Fraunhofer Institute for Digital Medicine MEVIS Germany Electronic & Information Engineering School Harbin Institute of Technology Shenzhen China China The Chinese University of Hong Kong Hong Kong Peking University China Department of Electrical and Computer Engineering Duke University United States Stony Brook University United States Department of Computer Science and Engineering Department of Chemical and Biological Engineering Division of Life Science Hong Kong University of Science and Technology Hong Kong Data Science and Computation Facility Fondazione Istituto Italiano di Tecnologia Italy Ecole Polytechnique Fédérale de Lausanne Switzerland

How can we test AI performance? This question seems trivial, but it isn’t. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain. Copyright © 2024, The Authors. All rights reserved.

关键词： Open systems

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Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection

arXiv

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arXiv 2024年

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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A data Sharing Scheme in NDN Based on MOR Primitive

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International Journal of Network Security 2022年第6期24卷 994-1001页

作者： Shi, Jiaoli Deng, Anyuan Luo, Chao Yao, Shimao He, Kai School of Computer and Big Data Science Jiujiang University Jiangxi Jiujiang China Institute of Information Security Jiujiang University Jiangxi Jiujiang China School of Computer Science and Artificial Intelligence Wuhan Textile University Hubei Wuhan China Hubei Clothing Information Engineering Technology Research Center Hubei Wuhan China

This work is aimed at a data sharing scheme over NDN (Named data Networking) with multiple demands: loosecouple, fine-grained control, and dynamic authority management. Firstly, a data access control scheme is proposed. Publisher generates data, encrypts the data to ciphertext, binds the ciphertext with an access policy, and publishes the ciphertext on an NDN router. Subscriber can access the part of data authorized when his private key meets the policy bound to the ciphertext, which realizes the loose-couple and fine-grained access control. The scheme can be constructed through Matrix computation, based on the MOR primitive instantiated by LWE. Secondly, a dynamic authority management method is presented. A subscriber’s latest identity public key is generated and stored on edge routers. When the subscriber applies to ciphertext, the edge routers attach the latest identity public key of the subscribers to the ciphertext and send it to the subscriber. This design can drive a subscriber to use his latest private key. © 2022, International Journal of Network Security. All Rights Reserved.

关键词： Access control

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Direct Growth of Graphene on V2O5 for Optoelectronic Devices

Direct Growth of Graphene on V2O5 for Optoelectronic Devices

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Conference on Lasers and Electro-Optics (CLEO)

作者： Liangchen Hu Yibo Dong Fengsong Qian Yiyang Xie Chen Xu Key Laboratory of Optoelectronics Technology Ministry of Education Beijing University of Technology Beijing China Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

We report a large area uniform graphene was graphically grown directly on a mental-oxide-semiconductor substrate at low temperature. The graphene was grown and used for the fabrication of graphene-V 2 O 5 -Si Schottky... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Performance evaluation Fabrication Semiconductor lasers Graphene Metals Photodetectors Junctions

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MuTCELM: An optimal multi-TextCNN-based ensemble learning for text classification

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Heliyon 2024年第19期10卷 e38515页

作者： Agbesi, Victor Kwaku Chen, Wenyu Yussif, Sophyani Banaamwini Ukwuoma, Chiagoziem C. Gu, Yeong Hyeon Al-antari, Mugahed A. School of Computer Science and Engineering University of Electronic Science and Technology of China No. 2006 Xiyuan Ave West Hi-Tech Zone Sichuan Chengdu China Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application Chengdu University of Technology No. 610059 Sichuan Chengdu China Department of Artificial Intelligence and Data Science College of AI Convergence Daeyang AI Center Sejong University Seoul 05006 South Korea

Feature extraction plays a critical role in text classification, as it converts textual data into numerical representations suitable for machine learning models. A key challenge lies in effectively capturing both semantic and contextual information from text at various levels of granularity while avoiding overfitting. Prior methods have often demonstrated suboptimal performance, largely due to the limitations of the feature extraction techniques employed. To address these challenges, this study introduces Multi-TextCNN, an advanced feature extractor designed to capture essential textual information across multiple levels of granularity. Multi-TextCNN is integrated into a proposed classification model named MuTCELM, which aims to enhance text classification performance. The proposed MuTCELM leverages five distinct sub-classifiers, each designed to capture different linguistic features from the text data. These sub-classifiers are integrated into an ensemble framework, enhancing the overall model performance by combining their complementary strengths. Empirical results indicate that MuTCELM achieves average improvements across all datasets in accuracy, precision, recall, and F1-macro scores by 0.2584, 0.2546, 0.2668, and 0.2612, respectively, demonstrating significant performance gains over baseline models. These findings underscore the effectiveness of Multi-TextCNN in improving model performance relative to other ensemble methods. Further analysis reveals that the non-overlapping confidence intervals between MuTCELM and baseline models indicate statistically significant differences, suggesting that the observed performance improvements of MuTCELM are not attributable to random chance but are indeed statistically meaningful. This evidence indicates the robustness and superiority of MuTCELM across various languages and text classification tasks. © 2024 The Author(s)

关键词： Deep learning Ensemble learning Feature extraction Multi-TextCNN Text classification

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Quantifying the Impact of AI and Machine Learning on data Access Optimization

Quantifying the Impact of AI and Machine Learning on Data Ac...

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Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Harsimrat Khandari Girija Shankar Sahoo Mahesh TR M. Rajeswari Prranjali Jadhav Mansingh Meena Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Karnataka India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Electrical Engineering Vivekananda Global University Jaipur India

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

Quantifying the impact of synthetic intelligence (AI) and machine getting to know (ML) on information access Optimization (DAO) and its ability to enhance overall performance and performance inside organizations has been a topic of research for decades. This newsletter investigates how such technology is essential in optimizing records access and its effect on the rate and accuracy of operations. We speak about how cutting-edge advances in AI and ML can be utilized to detect patterns in facts, reduce latency, and improve the general quality of data. Furthermore, we discuss how that technology can reduce manual techniques and get rid of errors due to human mistakes, hence enhancing performance and productiveness. Sooner or later, we talk about the main challenges associated with enforcing AI and ML within a company and identify capacity areas of development.

关键词： Performance evaluation Machine learning algorithms Machine learning Companies Manuals Reliability Object recognition

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