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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Yu, Ye Wu, Yao Liang, Xi Ma, Cheng Lu, Qiang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (纸本)9781665405270

Academic collaboration has become one of the topics of large-scale social network analysis. The information extracted from academic collaboration also shows the contextual value in combination with other data. For academic collaborative social networks with intricate collaborative relationships, previous research methods mainly focused on network structure and community discovery, lacking in mining the characteristics of relationships in collaborative networks. There were some limitations in the research perspective. In this work, we propose a collaborative relationship representation method that is more suitable for academic collaborative social networks. It marks the attribute characteristics of nodes in collaborative relationships and highlights collaborative relationships in visualization. By adopting this new representation of collaborative relationships, we designed a visual analysis framework NcoVis, to visualize academic collaborative social networks. We further visually analyze the link relationship groups guided by nodes, and explore the potential representation of the structure and attribute characteristics of link relationships, so that analysts can explore the theme and structural change characteristics of link relationships, communities and the whole network in collaborative social networks. To prove the proposed framework, we conducted a case study on the academic collaboration literature data set, and analyzed the questionnaire with Likert scale through the user evaluation experiment, which verified the effectiveness of our method and the practicability of NcoVis. © 2022 IEEE.

关键词： Visualization

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GAEM: Graph-driven Attention-based Entropy Model for LiDAR Point Cloud Compression

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Cui, Mingyue Zhong, Yuyang Feng, Mingjian Long, Junhua Ling, Yehua Xu, Jiahao Huang, Kai Sun Yat-sen University School of Computer Science and Engineering Guangzhou400100 China Sun Yat-sen University key Laboratory of Machine Intelligence and Advanced Computing Guangzhou400100 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Guangxi Transportation Science and Technology Group Co. Ltd Nanning530029 China

High-quality LiDAR point cloud (LPC) coding is essential for efficiently transmitting and storing the vast amounts of data required for accurate 3D environmental representation. The Octree-based entropy coding framework has emerged as the predominant method, however, previous study usually overly relies on large-scale attention-based context prediction to encode Octree nodes, overlooking the inherent correlational properties of this structure. In this paper, we propose a novel Graph-driven Attention-based Entropy Model (GAEM), which adopts partitioned graph attention mechanisms to uncover contextual dependencies among neighboring nodes. Different from the Cartesian coordinate-based coding mode with higher redundancy, GAEM uses the multi-level spherical Octree to organize point clouds, improving the quality of LPC reconstruction. GAEM combines graph convolution for node feature embedding and grouped-graph attention for exploiting dependency among contexts, which preserves performance in low-computation using localized nodes. Besides, to further increase the receptive field, we design a high-resolution cross-attention module introducing sibling nodes. Experimental results show that our method achieves state-of-the-art performance on the LiDAR benchmark SemanticKITTI and MPEG-specified dataset Ford, compared to all baselines. Compared to the benchmark GPCC, our method achieves gains of up to 53.9% and 53.6% on SemanticKITTI and Ford while compared to the sibling-introduced methods, we achieve up to 42.3% and 44.7% savings in encoding/decoding time. In particular, our GAEM allows for extension to downstream tasks (i.e., vehicle detection and semantic segmentation), further demonstrating the practicality of the method. (Figure presented) © 1991-2012 IEEE.

关键词： Semantic Segmentation

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Machine Reading Comprehension Based on Hybrid Attention and Controlled Generation 19th

Machine Reading Comprehension Based on Hybrid Attention and ...

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19th International Conference on Web Information Systems and Applications, WISA 2022

作者： Gao, Feng Yang, Zihang Gu, Jinguang Cheng, Junjun School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan430065 China Laboratory of Content Organization and Knowledge Service for Rich Media Digital Publishing Wuhan University of Science and Technology Wuhan430065 China China Information Security Evaluation Center Beijing100083 China

ISBN: (纸本)9783031203084

With the development of natural language processing technology, machine reading comprehension has been widely used in various fields such as QA systems and Intelligence engineering. However, despite numerous models are proposed in the general domain, there is still a lack of appropriate dataset and models for specific domains like anti-terrorism or homeland security to address this problem. Therefore, a Chinese reading comprehension data set in the field of anti-terrorism (ATCMRC) is constructed, and a generative machine reading comprehension model (AT-MT5) is proposed. ATCMRC was constructed in a semi-automated manner, and a domain-specific vocabulary is created based on the dataset to assist the AT-MT5. The model uses a hybrid attention layer is and a controlled answer generation layer to enhance text perception. Finally, the ATCMRC dataset and the AT-MT5 model is evaluated against existing approaches. The experimental results show that ATCMRC covers key issues in the domain and presents challenging MRC tasks for existent models, while AT-MT5 achieves better results in the domain specific dataset than the existing methods. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Terrorism

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MISA: UNVEILING THE VULNERABILITIES IN SPLIT FEDERATED LEARNING

arXiv

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arXiv 2023年

作者： Wan, Wei Ning, Yuxuan Hu, Shengshan Xue, Lulu Li, Minghui Zhang, Leo Yu Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab China

Federated learning (FL) and split learning (SL) are prevailing distributed paradigms in recent years. They both enable shared global model training while keeping data localized on users' devices. The former excels in parallel execution capabilities, while the latter enjoys low dependence on edge computing resources and strong privacy protection. Split federated learning (SFL) combines the strengths of both FL and SL, making it one of the most popular distributed architectures. Furthermore, a recent study has claimed that SFL exhibits robustness against poisoning attacks, with a fivefold improvement compared to FL in terms of robustness. In this paper, we present a novel poisoning attack known as MISA. It poisons both the top and bottom models, causing a misalignment in the global model, ultimately leading to a drastic accuracy collapse. This attack unveils the vulnerabilities in SFL, challenging the conventional belief that SFL is robust against poisoning attacks. Extensive experiments demonstrate that our proposed MISA poses a significant threat to the availability of SFL, underscoring the imperative for academia and industry to accord this matter due attention. Copyright © 2023, The Authors. All rights reserved.

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Wheat Disease Recognition Based on a Fusion Model with Improved Spatial and Channel Reconstruction Convolution Combined with Contextual Transformer

Wheat Disease Recognition Based on a Fusion Model with Impro...

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computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Zicong Yang Aitong Jin Junxi Wang Haifan Xie Xin Zhan Qiaolin Li Department of Computer Zhuhai College of Science and Technology Zhuhai China Department of Statistics and Data Science Zhuhai College of Science and Technology Zhuhai China Department of Mechanical Engineering Zhuhai College of Science and Technology Zhuhai China School of Traditional Chinese Medicine Fujian University of Traditional Chinese Medicine Fuzhou China

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

Crop diseases cause loss of agricultural products and have a critical impact on society, the economy, and the environment, so it is crucial for early defending the wheat disease. However, seeking an expert diagnosis can contribute to delays and higher costs. Therefore, this paper proposes a deep learning neural network with improved spatial and channel reconstruction convolution combined with CoT Attention module, named SCCoT-WheatNet. This innovated neural network can recognize wheat disease. The experimental dataset was obtained from the Internet and some publicly available datasets using web crawler was technology. This modification is based on the VGG13 model. In the final two sections of the VGG13 model, feature extraction replaces conventional convolutional layers with Contextual Transformer module and spatial and channel reconstruction convolution block. The experiment adopts a cross-fold approach. In the first fold test, the results show that the average recognition accuracy of the SCCoT-WheatNet network is $\mathbf{9 6. 9 4 \%}$, which is higher than the classical VGG network and the Vision Transformer models of the last two years.

关键词： Deep learning computer vision Accuracy Convolution Neural networks Transformers Task analysis

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KANsformer for Scalable Beamforming

arXiv

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arXiv 2024年

作者： Xie, Xinke Lu, Yang Chi, Chong-Yung Chen, Wei Ai, Bo Niyato, Dusit The School of Computer and Technology Beijing Jiaotong University Beijing100044 China The Institute of Communications Engineering Department of Electrical Engineering National Tsing Hua University Hsinchu30013 Taiwan The School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore

This paper proposes an unsupervised deep-learning (DL) approach by integrating transformer and Kolmogorov–Arnold networks (KAN) termed KANsformer to realize scalable beamforming for mobile communication systems. Specifically, we consider a classic multi-input-single-output energy efficiency maximization problem subject to the total power budget. The proposed KANsformer first extracts hidden features via a multi-head self-attention mechanism and then reads out the desired beamforming design via KAN. Numerical results are provided to evaluate the KANsformer in terms of generalization performance, transfer learning and ablation experiment. Overall, the KANsformer outperforms existing benchmark DL approaches, and is adaptable to the change in the number of mobile users with real-time and near-optimal inference. © 2024, CC BY.

关键词： Budget control

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Identification of Cardiovascular Disease using ECG Images based on Deep Learning Procedure

Identification of Cardiovascular Disease using ECG Images ba...

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

作者： Hannah Rose Esther T P. N. Periyasamy Rajesh Kumar. K P. GururamaSenthilvel N. Duraimutharasan Department of Artificial Intelligence and Data Science Velammal Engineering College Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore Tamil Nadu Department of Electronics and Communication Engineering Chendu College of Engineering Madurantakam Tamil Nadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Department of Information Technology AMET University Kanathur Tamil Nadu

When it comes to finding out what's wrong with your heart, cardiac imaging is crucial (CVD). Its previous purpose was restricted to qualitative and quantitative evaluations of the heart. The advent of big data and machine learning, however, has opened up new possibilities for developing AI tools that can aid the physician in the diagnosis of CVDs. Our study offers a unique approach to ECG illness prediction by combining cardiology, signal processing techniques, and a deep learning model. Then, using wavelet transformation and cardiology, we convert the ECG data into a time-frequency representation. Next, a deep convolutional network is used to categories the results of the time-frequency analysis. We find that by combining the benefits of signal processing with those of deep learning, we can significantly enhance the precision of categorization using our approach. The suggested methodology has the potential to enhance performance and reduces staffing costs for major general hospitals, all while lowering the rates at which primary care facilities make diagnostic errors.

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OpenKG Chain:A Blockchain Infrastructure for Open Knowledge Graphs

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data Intelligence 2021年第2期3卷 205-227页

作者： Huajun Chen Ning Hu Guilin Qi Haofen Wang Zhen Bi Jie Li Fan Yang College of Computer Science&AZFT Knowledge Engine Lab Zhejiang UniversityHangzhou 310058China Ontology Foundation Venture Drive#11-31Vision ExchangeSingapore 608526Singapore School of Computer Science and Engineering Southeast UniversityNanjing 211189China Intelligent Big Data Visualization Lab Tongji UniversityShanghai 200092China

The early concept of knowledge graph originates from the idea of the semantic Web,which aims at using structured graphs to model the knowledge of the world and record the relationships that exist between *** publishing knowledge bases as open data on the Web has gained significant *** China,Chinese Information Processing Society of China(CIPS)launched the OpenKG in 2015 to foster the development of Chinese Open Knowledge *** existing open knowledge-based programs,OpenKG chain is envisioned as a blockchain-based open knowledge *** article introduces the first attempt at the implementation of sharing knowledge graphs on OpenKG chain,a blockchain-based trust *** have completed the test of the underlying blockchain platform,and the on-chain test of OpenKG’s data set and tool set sharing as well as fine-grained knowledge crowdsourcing at the triple *** have also proposed novel definitions:K-Point and OpenKG Token,which can be considered to be a measurement of knowledge value and user value.1,033 knowledge contributors have been involved in two months of testing on the blockchain,and the cumulative number of on-chain recordings triggered by real knowledge consumers has reached 550,000 with an average daily peak value of more than 10,*** the first time,we have tested and realized on-chain sharing of knowledge at entity/triple granularity *** present,all operations on the data sets and tool sets at ***,as well as the triplets at OpenBase,are recorded on the chain,and corresponding value will also be generated and assigned in a trusted *** this effort,OpenKG chain looks forward to providing a more credible and traceable knowledge-sharing platform for the knowledge graph community.

关键词： Open knowledge graph Blockchain Decentralized distributed network Decentralized knowledge graph

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Negation Blindness in Large Language Models: Unveiling the ‘NO Syndrome’ in Image Generation

arXiv

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arXiv 2024年

作者： Nadeem, Mohammad Sohail, Shahab Saquib Cambria, Erik Schuller, Björn W. Hussain, Amir Department of Computer Science Aligarh Muslim University Aligarh India School of Computing Science and Engineering VIT Bhopal University MP Sehore466114 India College of Computing and Data Science Nanyang Technological University Singapore Technical University of Munich Germany Imperial College London United Kingdom School of Computing Engineering and the Built Environment Edinburgh Napier University Scotland United Kingdom

Foundational Large Language Models (LLMs) have changed the way we perceive technology. They have been shown to excel in tasks ranging from poem writing and coding to essay generation and puzzle solving. With the incorporation of image generation capability, they have become more comprehensive and versatile AI tools. At the same time, researchers are striving to identify the limitations of these tools to improve them further. Currently identified flaws include hallucination, biases, and bypassing restricted commands to generate harmful content. In the present work, we have identified a fundamental limitation related to the image generation ability of LLMs, and termed it "The NO Syndrome". This negation blindness refers to LLMs inability to correctly comprehend ‘NO’ related natural language prompts to generate the desired images. Interestingly, all tested LLMs including GPT-4, Gemini, and Copilot were found to be suffering from this syndrome. To demonstrate the generalization of this limitation, we carried out simulation experiments and conducted entropy-based and benchmark statistical analysis tests on various LLMs in multiple languages, including English, Hindi, and French. We conclude that the NO syndrome is a significant flaw in current LLMs that needs to be addressed. A related finding of this study showed a consistent discrepancy between image and textual responses as a result of this NO syndrome. We posit that the introduction of a ‘negation context-aware’ reinforcement learning based feedback loop between the LLM’s textual response and generated image could help ensure the generated text is based on both the LLM’s correct contextual understanding of the negation query and the generated visual output. © 2024, CC BY-NC-SA.

关键词： Reinforcement learning

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Fraud Detection and Prevention in E-Commerce: Machine Learning Approaches to Secure Transactions

Fraud Detection and Prevention in E-Commerce: Machine Learni...

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Madhira Srinivas Mrudula Kilaru Divya Jain Kawerinder Singh Sidhu Department of Computer Science & Engineering - Data Scienc Geethanjali College of Engineering & Technology (GCET) Cheeryala Hyderabad India Siddhartha Academy of Higher Education Velagapudi Ramakrishna Siddhartha School of Engineering Department of Mathematics Jaypee University of Engineering and Technology Guna Madhya Pradesh Uttaranchal Institute of Management l Uttaranchal University Dehradun Uttarakhand India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

E-commerce has grown as a leading marketplace for world trade, but this market has been contaminated by new risks like fraud. Some of the past techniques of detecting fraud do not do well in handling new methods of fraud in the market. The purpose of this research is to understand the offers of the ML approaches for increasing the efficiency of the fraud detection and prevention in e-Commerce processer. This study shows the utility of using different types of the ML solution alone and in comparison, to one another for real-time cyber fraud identification by demonstrating how and why their false positives and overall effectiveness are lower than other methods, as well as how the models adapt to new tactics. Issues like quality of data used, bias in either selection or process of the algorithm are also covered.

关键词： Machine learning algorithms Prevention and mitigation Machine learning Real-time systems Fraud Pattern recognition Electronic commerce Stakeholders Security Unsupervised learning

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