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4th International Conference on computer Graphics, Image, and Virtualization, ICCGIV 2024

作者： Peng, Yusha Huang, Bin School of Computer Science and Network Engineering Guangzhou University Guangdong Guangzhou China

ISBN: (纸本)9781510683242

In recent years, the rapid advancement of automotive and computer vision technologies has made self-driving cars a focal point of interest. A critical factor in the safe and efficient operation of self-driving cars is their ability to accurately recognize traffic signs. Consequently, traffic sign recognition has become essential for autonomous driving systems. This study introduces an optimized YOLOv8 method to enhance traffic sign recognition (TSR) performance. Recognizing that most targets are small and pose a challenge for model accuracy, we employ three different data augmentation techniques on the input images. Additionally, we improve the YOLOv8 loss function by incorporating the Wasserstein distance, which enhances the model's efficiency in detecting small targets. To validate the proposed method's effectiveness, we conducted comparative and ablation experiments. Experimental evaluations on the TT100K dataset indicate that the mAP and precision improved by 1.3% and 0.90%, compared to the standard YOLOv8. These results confirm the proposed method's superior performance in traffic sign detection. © 2024 SPIE.

关键词： Traffic signals

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SEED: Accelerating Reasoning Tree Construction via Scheduled Speculative Decoding 31

SEED: Accelerating Reasoning Tree Construction via Scheduled...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Zhenglin Wu, Jialong Lai, Yilong Zhang, Congzhi Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

ISBN: (纸本)9798891761964

Large Language Models (LLMs) demonstrate remarkable emergent abilities across various tasks, yet fall short of complex reasoning and planning tasks. The tree-search-based reasoning methods address this by encouraging the exploration of intermediate steps, surpassing the capabilities of chain-of-thought prompting. However, significant inference latency is introduced due to the systematic exploration and evaluation of multiple thought paths. This paper introduces SEED, a novel and efficient inference framework to improve both runtime speed and GPU memory management concurrently. Based on a scheduled speculative execution, SEED efficiently handles multiple iterations for thought generation and state evaluation, leveraging a rounds-scheduled strategy to manage draft model dispatching. Extensive experimental evaluations on three reasoning datasets demonstrate the superior speedup performance of SEED. © 2025 Association for Computational Linguistics.

关键词： Memory management

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A Literature Review on Model Conversion, Inference, and Learning Strategies in EdgeML with TinyML Deployment

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computers, Materials & Continua 2025年第4期83卷 13-64页

作者： Muhammad Arif Muhammad Rashid Department of Computer Science and Artificial Intelligence Umm Al-Qura UniversityMakkah Al-Mukarama21955Saudi Arabia Department of Computer and Network Engineering Umm Al-Qura UniversityMakkah Al-Mukarama21955Saudi Arabia

Edge Machine Learning(EdgeML)and Tiny Machine Learning(TinyML)are fast-growing fields that bring machine learning to resource-constrained devices,allowing real-time data processing and decision-making at the network’s ***,the complexity of model conversion techniques,diverse inference mechanisms,and varied learning strategies make designing and deploying these models ***,deploying TinyML models on resource-constrained hardware with specific software frameworks has broadened EdgeML’s applications across various *** factors underscore the necessity for a comprehensive literature review,as current reviews do not systematically encompass the most recent findings on these ***,it provides a comprehensive overview of state-of-the-art techniques in model conversion,inference mechanisms,learning strategies within EdgeML,and deploying these models on resource-constrained edge devices using *** identifies 90 research articles published between 2018 and 2025,categorizing them into two main areas:(1)model conversion,inference,and learning strategies in EdgeML and(2)deploying TinyML models on resource-constrained hardware using specific software *** the first category,the synthesis of selected research articles compares and critically reviews various model conversion techniques,inference mechanisms,and learning *** the second category,the synthesis identifies and elaborates on major development boards,software frameworks,sensors,and algorithms used in various applications across six major *** a result,this article provides valuable insights for researchers,practitioners,and *** assists them in choosing suitable model conversion techniques,inference mechanisms,learning strategies,hardware development boards,software frameworks,sensors,and algorithms tailored to their specific needs and applications across various sectors.

关键词： Edge machine learning tiny machine learning model compression inference learning algorithms

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FHGraph:A Novel Framework for Fake News Detection Using Graph Contrastive Learning and LLM

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computers, Materials & Continua 2025年第4期83卷 309-333页

作者： Yuanqing Li Mengyao Dai Sanfeng Zhang School of Cyber Science and Engineering Southeast UniversityNanjing210096China Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of EducationNanjing210096China

Social media has significantly accelerated the rapid dissemination of information,but it also boosts propagation of fake news,posing serious challenges to public awareness and social *** real-world contexts,the volume of trustable information far exceeds that of rumors,resulting in a class imbalance that leads models to prioritize the majority class during *** focus diminishes the model’s ability to recognize minority class ***,models may experience overfitting when encountering these minority samples,further compromising their generalization *** node-level classification tasks,fake news detection in social networks operates on graph-level samples,where traditional interpolation and oversampling methods struggle to effectively generate high-quality graph-level *** challenge complicates the identification of new instances of false *** address this issue,this paper introduces the FHGraph(Fake News Hunting Graph)framework,which employs a generative data augmentation approach and a latent diffusion model to create graph structures that align with news communication *** the few-sample learning capabilities of large language models(LLMs),the framework generates diverse texts for minority class *** comprises a hierarchical multiview graph contrastive learning module,in which two horizontal views and three vertical levels are utilized for self-supervised learning,resulting in more optimized *** results show that FHGraph significantly outperforms state-of-the-art(SOTA)graph-level class imbalance methods and SOTA graph-level contrastive learning ***,FHGraph has achieved a 2%increase in F1 Micro and a 2.5%increase in F1 Macro in the PHEME dataset,as well as a 3.5%improvement in F1 Micro and a 4.3%improvement in F1 Macro on RumorEval dataset.

关键词： Graph contrastive learning fake news detection data augmentation class imbalance LLM

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AlFloos: a balanced dataset for the sixth issue of the Saudi Arabian currency banknote*

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Journal of Umm Al-Qura University for engineering and Architecture 2024年第4期15卷 446-454页

作者： Bati, Ghassan F. Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia

Artificial Intelligence, especially machine learning, is considered by many researchers the new electricity and an important building block of the fourth industrial revolution. Saudi Arabia is amongst the first nations worldwide to realize this importance and apply it in its school systems at various levels. This interest motivates researchers to create Arabic datasets for research and educational purposes, particularly with the popularity of English sources and the absence of Arabic sources. This study attempts to lessen this gap by creating for the first time ever a dataset for the sixth issue of the Saudi Arabian currency and analyzing it using the famous machine learning tools: Orange Data Mining, Google Teachable Machine, and ***, which require no coding. The importance of the study to the community is as follows: 1—It provides the first work ever to establish a balanced dataset for the sixth issue of the Saudi Arabian currency banknote, which contains images for the banknote denominations and the tabular data generated using deep learning. 2—It is the first scientific work that uses shallow machine learning and deep learning models to create good-performing models for classifying the sixth issue of the Saudi Arabian currency without coding. This work paves the way for researchers and those interested from various fields and backgrounds to develop machine learning applications to classify the sixth issue of the Saudi Arabian currency, especially in mobile phones or in microcontrollers, to inspire multiple IoT and Tiny machine learning applications like currency recognition and classification for special needs people, automatic counting and sorting of banknotes in banks and stores, and fake currency detection. © The Author(s) 2024.

关键词： Deep learning Image classification Image embedding Saudi Arabian Currency Classification Supervised machine learning

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Adaptive VDI Session Placement via User Logoff Prediction

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Machine Intelligence Research 2025年第1期22卷 189-200页

作者： Wenping Fan Puhui Meng Yu Tian Min-Ling Zhang Yao Zhang School of Computer Science and Engineering Southeast UniversityNanjing210096China VMware Information Technology(China)Limited Beijing100190China Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of EducationNanjing210096China

After the global pandemic,DaaS(desktop as a service)has become the first choice of many companies’remote working *** the desktops are usually deployed in the public cloud when using DaaS,customers are more cost-sensitive which boosts the requirement of proactive power *** researches in this area focus on virtual desktop infrastructure(VDI)session logon behavior modeling,but for the remote desktop service host(RDSH)-shared desktop pools,logoff optimization is also *** systems place sessions by round-robin or in a pre-defined order without considering their logoff ***,these approaches usually suffer from the situation that few left sessions prevent RDSH servers from being powered-off which introduces cost *** this paper,we propose session placement via adaptive user logoff prediction(SODA),an innovative compound model towards proactive RDSH session ***,an ensemble machine learning model that can predict session logoff time is combined with a statistical session placement bucket model to place RDSH sessions with similar logoff time in a more centralized manner on RDSH ***,the infrastructure cost-saving can be improved by reducing the resource waste introduced by those RDSH hosts with very few hanging sessions left for a long *** on real RDSH pool data demonstrate the effectiveness of the proposed proactive session placement approach against existing static placement techniques.

关键词： Virtual desktop infrastructure(VDI)resource management remote desktop service logoff prediction adaptive modeling session placement

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Resonant Beam Communications:A New Design Paradigm and Challenges

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Journal of Communications and Information networks 2024年第1期9卷 80-87页

作者： Yuanming Tian Dongxu Li Chuan Huang Qingwen Liu Shengli Zhou Future Network of Intelligence Institute and School of Science and Engineering the Chinese University of Hong KongShenzhen 518172China School of Science and Engineering and the Future Network of Intelligence Institute the Chinese University of Hong KongShenzhen 518172China College of Electronics and Information Engineering Tongji UniversityShanghai 201804China Department of Electrical and Computer Engineering University of ConnecticutStorrsCT 06250USA

Resonant beam communications (RBCom), which adopts oscillating photons between two separate retroreflectors for information transmission, exhibits potential advantages over other types of wireless optical communications (WOC). However, echo interference generated by the modulated beam reflected from the receiver affects the transmission of the desired information. To tackle this challenge, a synchronization-based point-to-point RBCom system is proposed to eliminate the echo interference, and the design for the transmitter and receiver is discussed. Subsequently,the performance of the proposed RBCom is evaluated and compared with that of visible light communications(VLC)and free space optical communications (FOC). Finally, future research directions are outlined and several implementation challenges of RBCom systems are highlighted.

关键词： resonant beam communications(RBCom) wireless optical communications(WOC) echo interference elimination mobility multiple access

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Using dummy data for RFID tag and reader authentication

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Digital Communications and networks 2022年第5期8卷 804-813页

作者： Baha’A.Alsaify Dale R.Thompson Abdallah Alma'aitah Jia Di Department of Network Engineering and Security Jordan University of Science and TechnologyIrbid21163Jordan Computer Science and Computer Engineering Department University of ArkansasFayetteville72701ARUSA

Radio-Frequency IDentification(RFID)technology is an essential enabler of a multitude of intelligent *** robust authentication of RFID system components is critical in providing trustworthy data delivery from/to *** this paper,we propose an authentication protocol based on monitoring the transmissions between readers and tags in the *** proposed authentication scheme is based on injecting decoys within the exchanged communications(between RFID readers and tags)and is used in the authentication ***,the proposed authentication scheme is mathematically modeled and validated using extensive *** simulations results show that the proposed scheme provides a 100%confidence level in the authentication of tags and detection of compromised readers.

关键词： RFID Authentication Dummy data Security Decoys

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ENHANCING HEALTH CARE THROUGH REMOTE SERVICES AND INTERACTIVE TECHNOLOGIES 9

ENHANCING HEALTH CARE THROUGH REMOTE SERVICES AND INTERACTIV...

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9th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2024, the 10th International Conference on Connected Smart Cities, CSC 2024 and the 16th International Conference on e-Health, EH 2024, Part of the 18th Multi Conference on computer Science and Information Systems 2024, MCCSIS 2024

作者： Lu, Ssu-Hsuan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan Taiwan

ISBN: (纸本)9789898704597

With the aging population, health issues have become a significant challenge for society. The increasing demand for remote medical services stems from changing interests in reducing healthcare costs and the sudden outbreak of COVID-19 globally. As the Internet of Things (IoT) and cloud technology advance rapidly worldwide, this trend has driven the digital transformation of biotechnology and pharmaceutical-related industries. Health care is shifting from a focus on medical institutions to a decentralized approach, emphasizing care facilities and home-based services. However, remote medical care brings about certain challenges, such as limited appointment availability due to network constraints, concerns about internet security, data encryption, privacy issues, and lack of awareness among people in remote areas. With the continuous development and widespread adoption of technology, smart home systems have become an integral part of modern life. In this project, the system emphasizes interaction with the elderly and utilizes the multifunctionality of touch screens. It provides various features through a simple and user-friendly interface, including health monitoring, outpatient services, life assistance, and social interaction functions. In the future, the system can incorporate more interactive features for the elderly, such as mini-games to enhance cognitive abilities, hand-eye coordination, or rehabilitation training. © 2024 Big Data Analytics, Data Mining and Computational Intelligence. All Rights Reserved.

关键词： Touch screens

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Learning label-specific features for decomposition-based multi-class classification

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Frontiers of computer Science 2023年第6期17卷 101-110页

作者： Bin-Bin JIA Jun-Ying LIU Jun-Yi HANG Min-Ling ZHANG College of Electrical and Information Engineering Lanzhou University of TechnologyLanzhou 730050China School of Computer Science and Engineering Southeast UniversityNanjing 210096China Key Laboratory of Computer Network and Information Integration(Ministry of Education) Southeast UniversityNanjing 210096China

Multi-class classification can be solved by decomposing it into a set of binary classification problems according to some encoding rules,e.g.,one-vs-one,one-vs-rest,error-correcting output *** works solve these binary classification problems in the original feature space,while it might be suboptimal as different binary classification problems correspond to different positive and negative *** this paper,we propose to learn label-specific features for each decomposed binary classification problem to consider the specific characteristics containing in its positive and negative ***,to generate the label-specific features,clustering analysis is respectively conducted on the positive and negative examples in each decomposed binary data set to discover their inherent information and then label-specific features for one example are obtained by measuring the similarity between it and all cluster *** clearly validate the effectiveness of learning label-specific features for decomposition-based multi-class classification.

关键词： machine learning multi-class classification error-correcting output codes label-specific features

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