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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Du, Boyu Zhou, Jingya Liu, Ling She, Xiaolong School of Computer Science and Technology Soochow University China Engineering Lab.of Big Data and Intelligence of Jiangsu Province Soochow University China State Key Lab.for Novel Software Technology Nanjing University China School of Computer Science Georgia Institute of Technology United States

ISBN: (纸本)9781643685489

Graph Neural Networks (GNNs) have become an important graph feature learning paradigm that is extensively applied to graph inference ***, GNNs still have limitations in some aspects such as representation capability, interpretability, *** this end, this paper introduces a novel hybrid Fuzzy-Logic Graph Neural Network (FL-GNN) that synergistically combines Fuzzy Neural Network (FNN) and GNN to effectively capture and aggregate local information flows within the ***-GNN exhibits three distinctive ***, we incorporate a specialized structure fuzzy rule to enhance FL-GNN's graph inference capability, surpassing representative GNN ***, we augment the interpretability of FL-GNN by integrating analytical exploration methods from two perspectives: Fuzzy Inference System and Message Passing Algorithm (MPA).Lastly, we refine the structure of FL-GNN based on MPA to optimize its calculation complexity and consequently improve learning *** results show that FL-GNN outperforms existing representative graph neural networks for graph inference tasks. © 2024 The Authors.

关键词： Graph neural networks

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An effective fault localization approach for Verilog based on enhanced contexts

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Frontiers of computer Science 2024年第5期18卷 223-225页

作者： Zhuo ZHANG Ya LI Lei XIA Jianxin XUE Jiang WU Xiaoguang MAO School of Computer Science and Engineering Xi'an University of TechnologyXi'an 710048China Ningbo Artificial Intelligence Institute Shanghai Jiao Tong UniversityNingbo 315000China No.83 Army Joint and Truma Disease Treatment Centre of PLA Xinxiang 453000China Department of Software Engineering Shanghai Second Polytechnic UniversityShanghai 201209China College of Computer National University of Defense TechnologyChangsha 410073China

New computer architecture innovationswith diverse functionalities and comprehensive features continue to emerge incessantly,resulting in a rising trend of incorporating a larger number of circuit devices into these products[1].In the case of a sophisticated and expansive integrated circuit chip,the presence of defective or malfunctioning components can significantly impact the overall performance of the *** situation may even result in costly repercussions.

关键词： Verilog computer circuit.

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Research on NetPad Teaching Mode Based on ARCS Motivational Model 17th

Research on NetPad Teaching Mode Based on ARCS Motivational ...

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17th International Conference on computer Science and Education, ICCSE 2022

作者： Zhang, Jing Gao, Jinghui Lei, Siran Chen, Ruxian Li, Ya Rao, Yongsheng Institute of Computing Science and Technology Guangzhou University Guangzhou China Software Engineering Institute of Guangzhou Guangzhou China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China

ISBN: (纸本)9789819924455

Stimulating and maintaining students’ learning motivation is the key to fully mobilizing the enthusiasm of students’ active participation and realizing the unity of students’ learning and teachers’ teaching. The ARCS motivational model is an instructional design model proposed by Professor Keller that focuses on motivating and sustaining students’ learning motivation. Additionally, NetPad is a web-based dynamic mathematics software with powerful functions and user-friendly interface, it can be organically integrated with teaching, enrich the teaching style, and strengthen students’ learning motivation. In this paper, we propose the NetPad motivational teaching mode based on the ARCS model and NetPad. This mode consists of four procedures: interesting cases introduction, detailed cases explanation, group inquiry, and mutual evaluation and sharing. Each procedure is progressive and complementary, reflecting the hierarchy of motivational teaching from shallow to deep and the synergy of motivation stimulating and maintaining. The teaching practice under the guidance of this mode has verified its effectiveness. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Motivation

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Afienet: Feature Interaction Enhancement Network for Adaptive Text

SSRN

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SSRN 2024年

作者： Su, Rui Gao, Shangbing Zhao, Kefan Zhang, Junqiang School of Computer and Software Engineering Huaiyin Institute of Technology Huai'an Jiangsu223003 China Jiangsu Internet of Things Mobile Internet Technology Engineering Laboratory Huai'an Jiangsu223001 China

To address the difficulties existing text categorization models face in capturing global text semantics and local details, we propose an Adaptive Feature Interactive Enhancement Network (AFIENet). This network uses two branches to model the text globally and locally. The adaptive segmentation module in the local network can dynamically split the text and capture key phrases. The global network grasps the overall central semantics. After obtaining the results from the two branches, an interaction valve is designed to evaluate the confidence of the global features and selectively fuse them with the local features effectively. Finally, the interactively enhanced features are re-input into the classifier to improve text classification performance. Experiments verify that AFIENet can effectively improve the performance of basic networks like TextCNN, RNN, and Transformer with the introduction of fewer parameters. The accuracy reaches up to 97.55% on the THUCNews dataset, 6.53 percentage points higher than the original model. The average accuracy improvement is 2.11 percentage points compared to the original model when tested on multiple datasets. Comparable results to MacBERT are obtained when using static word vectors, reflecting the wide applicability of the model. © 2024, The Authors. All rights reserved.

关键词： Semantics

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Privacy Protection Based on Packet Filtering for Home Internet-of-Things 26

Privacy Protection Based on Packet Filtering for Home Intern...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Cheng, Beibei Zhu, Yiming Chen, Yuxuan Gu, Xiaodan Dong, Kai Southeast University School of Computer Science and Engineering China Southeast University School of Cyber Science and Engineering China Southeast University School of Software Engineering China Nanjing Yizhi Cyberspace Technology Innovation Institute China

ISBN: (纸本)9798350331684

The development of home internet of things (H-IoT) devices brings convenience but poses significant privacy and security risks. Existing research minimizes data uploaded to the cloud but fails to process data locally, resulting in a trade-off between privacy and functionality. In this paper, we propose a privacy-preserving method that identifies and processes sensitive data sent from H-IoT devices at the edge side, ensuring functionality while preserving privacy. Our method applies different identification strategies to packets with different features, making it applicable to most H-IoT devices and scenarios. We validate our approach through experiments on a prototype system that monitors multiple cameras, demonstrating its effectiveness in preserving privacy while maintaining functionality. © 2023 IEEE.

关键词： Internet of things

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Multi-View Incongruity Learning for Multimodal Sarcasm Detection 31

Multi-View Incongruity Learning for Multimodal Sarcasm Detec...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Guo, Diandian Cao, Cong Yuan, Fangfang Liu, Yanbing Zeng, Guangjie Yu, Xiaoyan Peng, Hao Yu, Philip S. Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science University of Illinois Chicago United States

ISBN: (纸本)9798891761964

Multimodal sarcasm detection (MSD) is essential for various downstream tasks. Existing MSD methods tend to rely on spurious correlations. These methods often mistakenly prioritize non-essential features yet still make correct predictions, demonstrating poor generalizability beyond training environments. Regarding this phenomenon, this paper undertakes several initiatives. Firstly, we identify two primary causes that lead to the reliance of spurious correlations. Secondly, we address these challenges by proposing a novel method that integrate Multimodal Incongruities via Contrastive Learning (MICL) for multimodal sarcasm detection. Specifically, we first leverage incongruity to drive multi-view learning from three views: token-patch, entity-object, and sentiment. Then, we introduce extensive data augmentation to mitigate the biased learning of the textual modality. Additionally, we construct a test set, SPMSD, which consists potential spurious correlations to evaluate the the model's generalizability. Experimental results demonstrate the superiority of MICL on benchmark datasets, along with the analyses showcasing MICL's advancement in mitigating the effect of spurious correlation. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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Traffic signal image detection technology based on YOLO

Traffic signal image detection technology based on YOLO

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2021 International Conference on computer engineering and Innovative Application of VR, ICCEIA VR 2021

作者： Gao, Hui Wang, Wenhao Yang, Chengjin Jiao, Weile Chen, Ziwei Zhang, Tong Faculty of Computer and Software Engineering Huaiyin Institute of Technology Jiangsu Huai'an223003 China

The detection and recognition of traffic signal image is an important content in intelligent transportation system. It can be applied to driver assistance system to effectively recognize traffic signal signs on the road, so as to reduce the occurrence of traffic accidents. At the same time, it also provides strong technical support for the future unmanned driving system. The main content of this paper is based on the deep learning method, using YOLOv3 and YOLOv4 algorithm to detect and recognize the traffic signal image on the road. The experimental results show that the recognition result of YOLOv4 network is better than that of YOLOv3 network. © Published under licence by IOP Publishing Ltd.

关键词： Traffic signals

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Fingerprint Agreement Using Enhanced Kerberos Authentication Protocol on M-Health

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computer Systems Science & engineering 2022年第11期43卷 833-847页

作者： A.S.Anakath S.Ambika S.Rajakumar R.Kannadasan K.S.Sendhil Kumar School of Computing E.G.S.Pillay Engineering CollegeNagapattinam611002India Department of Computer Science and Engineering University College of Engineering AriyalurAriyalur621705India Department of Mathematics University College of Engineering AriyalurAriyalur621705India Department of Software Systems School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014India Department of IoT School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014India

Cloud computing becomes an important application development platform for processing user data with high *** providers are accustomed to providing storage centers outside the trusted location preferred by the data ***,ensuring the security and confidentiality of the data while processing in the centralized network is very *** secured key transmission between the sender and the receiver in the network is a huge challenge in managing most of the sensitive data transmission among the cloud *** are very active over the network like real authenticated user to hack the personal sensitive data,such as bank balance,health data,personal data,and confidential documents over the cloud *** this research,a secured key agreement between the sender and the receiver using Kerberos authentication protocol with fingerprint is proposed to ensure security in *** of patients are monitored using wireless sensor devices and are then transferred to the *** protocol helps in avoiding unnecessary communication of authenticated data over the cloud *** security process is a procedure with the best security in most of the authentication *** node is responsible in carrying data packets from the sender to the receiver in the cloud *** Kerberos protocol is used in trust node to ensure *** communication between the local health center and the healthcare server is ensured by using a fingerprint feature called minutiae form,which refers to the fingerprint image of both sender and *** computational and communicational cost of the proposed system is lesser when compared with other existing authentication methods.

关键词： Protocol security m-health cloud computing biometric fingerprint kerberos protocol

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Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus 25

Transformer-based Text Classification on Unified Bangla Mult...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Sourav, Md Sakib Ullah Mahmud, Mohammad Sultan Zheng, Hua Aljaidi, Mohammad Talukder, Md. Simul Hasan Bin Sulaiman, Rejwan Nur, Abdullah Hafez Al-Qerem, Ahmad Shandong University of Finance and Economics School of Management Science and Engineering Jinan China Shenzhen University College of Computer Science and Software Engineering Senzhen China Software Engineering Institute of Guangzhou Department of Software Engineering Guangzhou510990 China Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Dhaka University of Engineering and Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh Northumbria University Newcastle Tayne School of Computer Science & Technology Northumbria United Kingdom International Islamic University Chittagong Department of Electronic and Telecommunication Engineering Chittagong Bangladesh Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan

ISBN: (纸本)9798331540012

Due to its importance in studying people's thoughts on various Web 2.0 services, emotion classification is a critical undertaking. Most existing research is focused on the English language, with little work on low-resource languages. Though sentiment analysis, particularly emotion classification in English, has received increasing attention in recent years, little study has been done in the context of Bangla, one of the world's most widely spoken languages. In this research, we propose a complete set of approaches for identifying and extracting emotions from Bangla texts. We provide a Bangla emotion classifier for six classes, i.e., anger, disgust, fear, joy, sadness, and surprise, from Bangla words using transformer-based models, which exhibit phenomenal results in recent days, especially for high-resource languages. The Unified Bangla Multi-class Emotion Corpus (UBMEC) is used to assess the performance of our models. UBMEC is created by combining two previously released manually labelled datasets of Bangla comments on six emotion classes with fresh manually labelled Bangla comments created by us. The corpus dataset and code we used in this work are publicly available. © 2024 IEEE.

关键词： Classification (of information)

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An Intelligent Machine Learning Approach for Prediction of Road Accident Severity 3

An Intelligent Machine Learning Approach for Prediction of R...

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3rd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Ragam, Prashanth Rahul, Namana Sri Balaji, Pandi Jyothi, Sankati Danaveni, Pavithra School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India VIT-AP University Software Development Cell Andhra Pradesh Amaravati522237 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad500090 India

ISBN: (纸本)9798331533205

Road accidents are a primary global concern for public safety, with India having a very high death toll. This study presents an intelligent machine learning approach to predict the severity of road accidents, contributing to the development of more effective and preventive measures. We utilized a comprehensive dataset of road accidents in India, employing advanced machine learning algorithms, including Histogram-based Gradient Boosting (HGB), Random Forest (RF), Support Vector Machines (SVM), and K-Nearest Neighbors (KNN). Our methodology involved extensive data pre-processing, feature engineering, and model optimization. The Random Forest algorithm demonstrated superior performance, achieving an accuracy of 94.7% and an F1-score of 0.947. This research provides a robust predictive model and information on the key factors influencing the accident's severity. The novelty of our approach lies in the comprehensive feature engineering tailored to the Indian context and the application of ensemble learning techniques to capture complex, nonlinear relationships in accident data. The findings of this study have significant implications for policymakers, urban planners, and law enforcement agencies in their efforts to improve road safety. © 2025 IEEE.

关键词： Highway accidents

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