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5th International Conference on Intelligent computing and Human-computer Interaction, ICHCI 2024

作者： Zhang, Jiali Qiao, Xiaoyan School of Computer Science and Technology Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China School of Mathematics and Information Science Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China

ISBN: (纸本)9798350368284

Methods based on dynamically expanding architectures can effectively mitigate catastrophic forgetting in class incremental learning (CIL), but they often overlook information sharing and integration between subnetworks, leading to performance degradation and increased parameter count and memory consumption as the network expands. To address these complex issues, we introduce a new method called Task Separation and Knowledge Sharing (TSKS), which transfers knowledge at both the logical and feature levels through Hybrid Knowledge Distillation (HKD). In logical distillation, we combine Global Knowledge Distillation (GKD) and Task Knowledge Distillation (TKD) to establish connections between tasks. To prevent the model from favoring new classes, we design an Attention- Based Feature Fusion (ABFF) mechanism to capture important features. Additionally, we introduce a Soft-Margin Auxiliary Loss (SMAL) to help the model learn new concepts. Extensive experiments show that TSKS outperforms existing models on CIFAR100 and ImageNet100, regardless of whether memory samples are used. Notably, TSKS demonstrates superior performance in memory-free settings, indicating a reduced reliance on rehearsal memory. © 2024 IEEE.

关键词： Adversarial machine learning

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Multi-Granularity History and Entity Similarity Learning for Temporal Knowledge Graph Reasoning

Multi-Granularity History and Entity Similarity Learning for...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Mingcong, Shi Zhu, Chunjiang Zhang, Detian Wen, Shiting Li, Qing School of Computer Science and Technology Soochow University Suzhou China Department of Computer Science University of North Carolina Greensboro United States School of Computer and Data Engineering NingboTech University Ningbo China Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9798891761643

Temporal Knowledge Graph (TKG) reasoning, aiming to predict future unknown facts based on historical information, has attracted considerable attention due to its great practical value. Insight into history is the key to predict the future. However, most existing TKG reasoning models singly capture repetitive history, ignoring the entity's multi-hop neighbour history which can provide valuable background knowledge for TKG reasoning. In this paper, we propose Multi-Granularity History and Entity Similarity Learning (MGESL) model for Temporal Knowledge Graph Reasoning, which models historical information from both coarse-grained and fine-grained history. Since similar entities tend to exhibit similar behavioural patterns, we also design a hypergraph convolution aggregator to capture the similarity between entities. Furthermore, we introduce a more realistic setting for the TKG reasoning, where candidate entities are already known at the timestamp to be predicted. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our proposed model. © 2024 Association for Computational Linguistics.

关键词： Knowledge graph

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Sentiment classification of MOOC courses by merging local context focus and Bi-directional gated recurrent unit 4

Sentiment classification of MOOC courses by merging local co...

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4th International Conference on computer Vision, Application, and Algorithm, CVAA 2024

作者： Jin, Jiayong Dong, Yongquan He, Bugui Zhou, Nan Yan, Linke School of Computer Science and Technology Jiangsu Normal University Jiangsu Xuzhou221116 China Xuzhou Cloud Computing Engineering Technology Research Center Jiangsu Xuzhou221116 China Jiangsu Engineering Technology Research Center of ICT in Education Jiangsu Xuzhou221116 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Existing MOOC review sentiment classification methods do not fully utilize the local context information associated with aspect, and they ignore the connection between local and global contexts, while resulting in modeled features that lack the information connection between aspect and contexts. In this paper, we propose a model that incorporates Local Context Focus (LCF) and Bi-Directional Gated Recurrent Unit (Bi-GRU). First, the BERT model is used to dynamically encode course reviews. Then, global semantic features are extracted using the Bi-GRU model to strengthen the connection between the preceding and following texts. Then, the LCF model based on multi-head self-attention is used to obtain local contextual features and splice them with global semantic features. Finally, the Softmax function is utilized to output the classification results. The experimental accuracies of the proposed model on the three MOOC course review datasets reach 97.96%, 96.76%, and 94.16%, respectively, which are improved by 0.70%, 0.42%, and 0.03% over the suboptimal baseline model. The proposed model significantly improves the effectiveness of MOOC course review sentiment classification, and provides a useful reference for the optimization and improvement of MOOC courses. © 2025 SPIE.

关键词： Semantics

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HSToR: Enhancing Scalability for Optical Circuit Switched Networks with Recirculation 10

HSToR: Enhancing Scalability for Optical Circuit Switched Ne...

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10th International Conference on computer and Communications, ICCC 2024

作者： Zheng, Bowen Yang, Fan Wang, Cenman Tian, Ye Anhui Key Laboratory on High Performance Computing School of Computer Science and Technology Hefei China University of Science and Technology of China Hefei China

Combining optical circuit switches (OCSes) and electrical packet switches (EPSes) has emerged as an appealing architecture for data centers. However, in this hybrid data center network architecture with widespread application, challenges persist in data packet scheduling and buffer management for OCS-based matching patterns due to spatial and temporal traffic pattern constraints. Given the demand-agnostic way for OCS configurations, where each switch can only provide matching between input and output ports at any given time. In other words, the availability of optical circuits is intermittent, resulting in potential blockage of flows between them when optical circuits are unavailable. Traditionally, in the event of flow blockage, multiple queue buffers are maintained at Top-of- Rack (ToR) switches, with the number of queues corresponding to the number of destinations. Unfortunately, the 'per-destination queue' approach suffers from poor scalability. To address this issue, we propose HSToR, a novel design for ToR switches, aimed at enhancing scalability and data transfer efficiency in optical circuit switched networks. Experimental results demonstrate that HSToR achieves high scalability, maintains lower latency as the network scales, better addresses packet loss during buffering, and sustains high throughput and link utilization. © 2024 IEEE.

关键词： Iterated switching networks

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Hate Speech Detection And Classification Using NLP 2

Hate Speech Detection And Classification Using NLP

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Ilhan, Syed Shahidh Sivakumar, Soubraylu Nagaraj, J. Ramesh, S. Sreeram, N. Rajalakshmi, Ratnavel School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Madanapalle Institute of Technology& Science Andhra Pradesh Madanapalle India Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350383867

Detected hate speech instances are appropriately classified, rebuilding the intricate landscape of disparaging language in an increasingly digitized society. We present an exhaustive analysis of the techniques, gravitating towards their efficiency, precision, and capability to adapt to the evolving dynamics of language manifestation online. The study uncovers fascinating insights into the relative strengths, weaknesses, limitations, and potential areas of improvement of Bi-LSTM and the Bi-LSTM with GRU in the realm of hate speech detection and classification. The results of Bi-LSTM with GRU has obtained an accuracy of 95% with an improvement of 1% over Bi-LSTM model. Serving as a cornerstone in the burgeoning NLP research, our findings provide promising direction and robust groundwork for future improvements and attempts in the continuous drive towards safer, more respectful digital communication platforms. © 2024 IEEE.

关键词： Tweets

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Automated Detection of Multi-type Small Lesions in CT Images Based on Deep Learning 9

Automated Detection of Multi-type Small Lesions in CT Images...

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9th International Conference on Image, Vision and computing, ICIVC 2024

作者： Li, Shuhua Samonte, Mary Jane C. Yan, Fenglong School of Information Technology Mapua University Manila Philippines School of Computer Science and Technology Guangdong University of Science and Technology Dongguan China School of Computer & Software Dalian Neusoft University of Information Dalian China

ISBN: (纸本)9798350385991

With the rapid development of deep learning, the medical image analysis method has become one of the most concerned issues in the field of medical imaging diagnosis. Medical image analysis and processing are essential for effective treatment and excellent prognosis of medical lesions. In this paper, High-definition computed tomography (CT) images of multi-type lesions are analyzed. To solve the problem of insufficient medical images, Mosaic is used within DeepLesion database for data combination and enhancement. In addition, images from COVID-CT are incorporated into the dataset. Concerning data noise, different kinds of filtering methods are tested and noise removal is performed using non-local mean filtering. Based on deep separable convolution (DSC), the accuracy of the training network is improved by using the enhanced dataset. The experimental results show that the average accuracy of the model is 80.5% at 29.23 frames per second, which satisfies the real-time detection. It provides a feasibility study for the practical application of computer-aided diagnosis. © 2024 IEEE.

关键词： computerized tomography

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Leveraging Deep Learning for Precise Chronic Bronchitis Identification in X-Ray Modalities

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computers, Materials & Continua 2025年第4期83卷 381-405页

作者： Fahad Ahmad Saad Awadh Alanazi Kashaf Junaid Maryam Shabbir Asim Ali School of Computing Faculty of TechnologyUniversity of PortsmouthWinston Churchill Ave SouthseaPortsmouthPO12UPUK Portsmouth Artificial Intelligence and Data Science Center University of PortsmouthWinston Churchill AveSouthseaPortsmouthPO12UPUK Department of Computer Science College of Computer and Information SciencesJouf UniversitySakakaAljouf72341Saudi Arabia School of Biological and Behavioural Sciences Queen Mary University of LondonLondonE14NSUK Department of Computer Sciences Bahria UniversityLahorePunjab54700Pakistan

Image processing plays a vital role in various fields such as autonomous systems,healthcare,and cataloging,especially when integrated with deep learning(DL).It is crucial in medical diagnostics,including the early detection of diseases like chronic obstructive pulmonary disease(COPD),which claimed 3.2 million lives in ***,a life-threatening condition often caused by prolonged exposure to lung irritants and smoking,progresses through *** diagnosis through image processing can significantly improve survival *** encompasses chronic bronchitis(CB)and emphysema;CB particularly increases in smokers and generally affects individuals between 50 and 70 years *** damages the lungs’air sacs,reducing oxygen transport and causing symptoms like coughing and shortness of *** such as beta-agonists and inhaled steroids are used to manage symptoms and prolong lung ***,COVID-19 poses an additional risk to individuals with CB due to its impact on the respiratory *** proposed system utilizes convolutional neural networks(CNN)to diagnose *** this system,CNN extracts essential and significant features from X-ray modalities,which are then fed into the neural *** network undergoes training to recognize patterns and make accurate predictions based on the learned *** leveraging DL techniques,the system aims to enhance the precision and reliability of CB *** research specifically focuses on a subset of 189 lung disease images,carefully selected for model *** further refine the training process,various data augmentation and noise removal techniques are *** techniques significantly enhance the quality of the training data,improving the model’s robustness and *** a result,the diagnostic accuracy has improved from 98.6%to 99.2%.This advancement not only validates the efficacy of our proposed model but also represents a significant improvement over existing *** h

关键词： Deep learning chronic obstructive pulmonary disease chronic bronchitis convolutional neural network X-ray images

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Aggregation of Antagonistic Contingent Preferences: When Is It Possible?

arXiv

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arXiv 2024年

作者： Deng, Xiaotie Tao, Biaoshuai Wang, Ying Center on Frontiers of Computing Studies School of Computer Science Peking University Beijing China John Hopcroft Center for Computer Science School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

We study a two-alternative voting game where voters’ preferences depend on an unobservable world state and each voter receives a private signal correlated to the true world state. We consider the collective decision when voters can collaborate in a group and have antagonistic preferences—given the revealed world state, voters will support different alternatives. We identify sharp thresholds for the fraction of the majority-type voters necessary for preference aggregation. We specifically examine the majority vote mechanism (where each voter has one vote, and the alternative with more votes wins) and pinpoint a critical threshold, denoted as θmaj, for the majority-type proportion. When the fraction of majority-type voters surpasses θmaj, there is a symmetric strategy for the majority-type that leads to strategic equilibria favoring informed majority decisions. Conversely, when the majority-type proportion falls below θmaj, equilibrium does not exist, rendering the aggregation of informed majority decisions impossible. Additionally, we propose an easy-to-implement mechanism that establishes a lower threshold θ∗ (with θ∗ ≤ θmaj) for both equilibria and informed majority decision aggregation. We demonstrate that θ∗ is optimal by proving a general impossibility result: if the majority-type proportion is below θ∗, with mild assumptions, no mechanism can aggregate the preferences, meaning that no equilibrium leads to the informed majority decision for any mechanism. Copyright © 2024, The Authors. All rights reserved.

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Securing Consumer IoT Swarms Using Graph Transformers and SRAM for Firmware Attestation 22

Securing Consumer IoT Swarms Using Graph Transformers and SR...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Kohli, Varun Kohli, Bhavya Aman, Muhammad Naveed Sikdar, Biplab National University of Singapore Department of Electrical and Computer Engineering Singapore Center for Machine Intelligence and Data Science Indian Institute of Technology Bombay India School of Computing University of Nebraska-Lincoln United States

ISBN: (纸本)9798331508050

Consumer Internet of Things (IoT) networks have gained widespread popularity due to their convenience, automation, and security provisions in personal and home environments. Ubiquitous resource-constrained devices, however, are plagued with security issues that often arise from firmware-related issues and their propagated effects. While various studies on firmware attestation are available, they require firmware copies, specific hardware, and complex computation on the IoT device. This paper presents a study on the application of Graph Transformer Networks (GTN) in verifying the firmware integrity of consumer IoT swarms using SRAM as an attestation feature. The proposed method achieves an overall 0.99 accuracy on authentic samples from development and physical twin networks, 0.99 on malware, and 0.97 on propagated misbehavior at a ∼ 10-4 second inference latency on a laptop CPU. © 2025 IEEE.

关键词： computer viruses

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UAV-NIDD: A Dynamic Dataset for Cybersecurity and Intrusion Detection in UAV Networks

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IEEE Transactions on Network science and Engineering 2025年第4期12卷 2739-2757页

作者： Hadi, Hassan Jalil Cao, Yue Khan, Muhammad Khurram Ahmad, Naveed Hu, Yulin Fu, Chao Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430073 China King Saud University Center of Excellence in Information Assurance Riyadh Saudi Arabia Prince Sultan University College of Computer and Information Science Riyadh Saudi Arabia Wuhan University School of Electronic Information China Information and Communication Technology Co. LtdChina China

Unmanned Aerial Vehicles (UAVs) are necessary for numerous tasks but are vulnerable to cyber threats due to their widespread use and connectivity. The lack of a comprehensive dataset necessitates the development of effective detection and mitigation solutions. Our work introduces UAV-NIDD, a new dataset that addresses the gaps in understanding and countering both cyber and physical threats in UAV networks. It includes three distinct attack scenarios: compromised UAV initiating a network-wide attack, access point compromised network-wide intrusion, and compromised Ground Control Station (GCS) establishing a network-wide attack. We develop a real-time testbed for creating UAV-NIDD (Unmanned Aerial Vehicles-Network Intrusion Detection Dataset), incorporating UAV devices, data collection tools, and controllers. Our testbed facilitates cyberattack execution and data gathering under normal and attack conditions. Our dataset covers various cyber-attacks like Scanning, Reconnaissance, DoS, DDoS, GPS Jamming & Spoofing, MITM, Replay, Evil Twin, Brute-Force, and Fake Landing packet attacks. Additionally, UAV-NIDD presents a valuable resource for AI and ML solutions, strengthening UAV networks against evolving cyber threats. Moreover, we offer open access and cooperative innovation in terms of long-term updating of dataset. © 2013 IEEE.

关键词： Network intrusion

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