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14th International Conference on Frontier Computing, FC 2024

作者： Chiu, Chuan-Feng Huang, Chun-Hong Tseng, Hsiang-Chieh Wu, Pei-Jung Lin, Wei-Sheng Peng, Hsiang-Min Department of Information Management Minghsin University of Science and Technology Hsinchu Taiwan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan City Taiwan

ISBN: (纸本)9789819623945

Because of the population of photography camera, human being could have photography equipment to take a picture became an easy task. However, to have a good photography is not an easy task. The basic of a good photo is the composition of the photography. A better photography composition could lead better visual effect. So, in this paper we intent to design a photography guiding system framework for users to have good photos especially for portrait photography. In our work, we design a system framework using VGG16 model based on Rule of Thirds and luminance distribution to guide user to select better photography composition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Photographic equipment

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Blockchain-based knowledge-aware semantic communications for remote driving image transmission

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Digital Communications and networks 2025年第2期11卷 317-325页

作者： Lin, Yangfei Murase, Tutomu Ji, Yusheng Bao, Wugedele Zhong, Lei Li, Jie Department of Computer and Network Engineering University of Electro-Communications Tokyo Chofu182-8585 Japan Information Technology Center Nagoya University Nagoya464-8601 Japan Tokyo101-8430 Japan Hohhot Minzu College Hohhot010051 China Toyota Motor Corporation Tokyo112-8701 Japan Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai200240 China

Remote driving, an emergent technology enabling remote operations of vehicles, presents a significant challenge in transmitting large volumes of image data to a central server. This requirement outpaces the capacity of traditional communication methods. To tackle this, we propose a novel framework using semantic communications, through a region of interest semantic segmentation method, to reduce the communication costs by transmitting meaningful semantic information rather than bit-wise data. To solve the knowledge base inconsistencies inherent in semantic communications, we introduce a blockchain-based edge-assisted system for managing diverse and geographically varied semantic segmentation knowledge bases. This system not only ensures the security of data through the tamper-resistant nature of blockchain but also leverages edge computing for efficient management. Additionally, the implementation of blockchain sharding handles differentiated knowledge bases for various tasks, thus boosting overall blockchain efficiency. Experimental results show a great reduction in latency by sharding and an increase in model accuracy, confirming our framework's effectiveness. © 2024 Chongqing University of Posts and Telecommunications

关键词： Latent semantic analysis

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TinyML Model for Solar Cell Defect Classification Based on Electroluminescence Images 4th

TinyML Model for Solar Cell Defect Classification Based on E...

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4th International Conference on Electronic engineering and Renewable Energy Systems, ICEERE 2024

作者： Boubaker, S. Moussaoui, C. Mellit, Adel Benghanem, M. Department of Computer and Network Engineering College of Computer Science and Engineering University of Jeddah Jeddah21959 Saudi Arabia Faculty of Sciences and Technology University of Jijel Jijel18000 Algeria Department of Physics Faculty of Science Islamic University of Madinah Madinah42351 Saudi Arabia

ISBN: (纸本)9789819606436

Being largely utilized as alternative sources of energy, photovoltaic (PV) panels are being deployed in many locations. However, those panels are extensively being prone to faults. A careful detection and diagnosis of those faults becomes a must for solar energy engineers, practitioners and decision-makers in order to extract the maximum of energy from them. The integration of artificial intelligence (AI) in the domain of solar energy management has become a trend in the few previous years because of the high potential provided by machine learning (ML) and deep learning (DL) algorithms in handling issues related to PV systems such as control and fault detection and isolation (FDI). In this paper, TinyML models for defect classification of photovoltaic modules based on electroluminescence (EL) images were developed. An open-source platform, Edge Impulse, was used to design and deploy the models. Two convolutional neural network models (mobileNetV1 and mobileNetV2) have been used and compared. A dataset of EL with three anomalies was used to develop the models. The validation test shows an accuracy of 88.44 and 76.38% for respectively MobileNet-V1 and MobileNet-V2. The quantized (Int8) model has been integrated into a microcontroller (Arduino Portenta H7) for real-time application. The obtained results can be considered as promising and can be extended to other case studies with more defects and larger datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision making

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Proactive Robust Hardening of Resilient Power Distribution network: Decision-Dependent Uncertainty Modeling and Fast Solution Strategy

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IEEE Transactions on Automation Science and engineering 2025年 22卷 14953-14966页

作者： Ma, Donglai Cao, Xiaoyu Zeng, Bo Jia, Qing-Shan Chen, Chen Zhai, Qiaozhu Guan, Xiaohong Xi’an Jiaotong University School of Automation Science and Engineering Ministry of Education Key Laboratory for Intelligent Networks and Network Security Shaanxi Xi’an710049 China University of Pittsburgh Department of Industrial Engineering Department of Electrical and Computer Engineering PittsburghPA15106 United States Tsinghua University Center for Intelligent and Networked Systems Department of Automation Beijing100084 China Xi’an Jiaotong University School of Electrical Engineering Shaanxi Xi’an710049 China

To address the power system hardening problem, traditional approaches often adopt robust optimization (RO) that considers a fixed set of concerned contingencies, regardless of the fact that hardening some components actually renders relevant contingencies impractical. In this paper, we directly adopt a dynamic uncertainty set that explicitly incorporates the impact of hardening decisions on the worst-case contingencies, which leads to a decision-dependent uncertainty (DDU) set. Then, a DDU-based robust-stochastic optimization (DDU-RSO) model is proposed to support the hardening decisions on distribution lines and distributed generators (DGs). Also, the randomness of load variations and available storage levels is considered through stochastic programming (SP) in the innermost level problem. Various corrective measures (e.g., the joint scheduling of DGs and energy storage) are included, coupling with a finite support of stochastic scenarios, for resilience enhancement. To relieve the computation burden of this new hardening formulation, an enhanced customization of parametric column-and-constraint generation (P-C&CG) algorithm is developed. By leveraging the network structural information, the enhancement strategies based on resilience importance indices are designed to improve the convergence performance. Numerical results on 33-bus and 118-bus test distribution networks have demonstrated the effectiveness of DDU-RSO aided hardening scheme. Furthermore, in comparison to existing solution methods, the enhanced P-C&CG has achieved a superior performance by reducing the solution time by a few orders of magnitude. © 2004-2012 IEEE.

关键词： Stochastic programming

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Baby Suffocation Prediction System

Baby Suffocation Prediction System

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Rasha Jamal Atwah Mashael Alharthi Lamees Alharbi Jana Bashnini Nujoud Al-Sharif Department of Computer and Network Engineering College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

The number of suffocation cases among babies during their sleeping is increased due to the presence of blankets and sheets. Therefore, it is crucial to have a reliable system that can monitor babies during bedtime. In order to ensure the safety of children, we have developed an AI-based comprehensive smart system that provides real-time monitoring to predict suffocation incidents as soon as possible. The proposed system offers babies protection by promoting suffocation prediction within an appropriate time frame. The system is built based on the BlazeFace detection model to monitor the baby and send notifications and sound alarms if the baby's face is covered or missing for a certain amount of time. The notification is sent through the mobile application to alert the baby's parents or caregivers if there is a suffocation case that may occur. Additionally, an audible alarm is activated to grab the attention of the child's parents or caregivers and ensure quick intervention. We conducted an experiment to assess the system's effectiveness, and the prototype shows adequate accuracy and quick response. There is still space for further development, which we suggest as future work.

关键词： Pediatrics Technological innovation Accuracy Smart systems Real-time systems Sensor systems Internet of Things Biomedical monitoring Monitoring Intelligent sensors

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Smart Visual Pollution Detection System

Smart Visual Pollution Detection System

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Rothana Alsolami Rasha Atwah Ranad Alajlani Sara Alhajaili Department of Computer and Network Engineering College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

As Saudi Arabia moves forward with its Vision 2030 initiative to improve the quality of life and services for its people, visual pollution continues to have a negative impact on the environment. However, assessing and addressing visual pollution in cities remains a challenging task due to its subjective nature and complexity. One of the main challenges is the lack of powerful and reliable intelligent systems for detecting visual pollution. To tackle this issue, we have invested our efforts in developing a cost-effective intelligent system using advanced technologies like the Internet of Things (IoT) and computer vision. This system will help detect and monitor visual pollution, including items such as water cans, paper, and plastic cups. Our smart system uses sensors like cameras to collect data and then analyzes it to display the information on a user-friendly control panel for administrators to monitor the area under study. The system has been successfully tested under various scenarios and has shown promising accuracy rates, reaching up to 87% in detecting recyclable materials. We believe that there are numerous opportunities to further enhance the performance of future designs.

关键词： Visualization Pollution Accuracy Smart cities Cameras Real-time systems Water pollution Internet of Things Intelligent systems Monitoring

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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Science China(Information Sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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LASS: A Novel and Economical Data Augmentation Framework Based on Language Models for Debiasing Opinion Summarization 31

LASS: A Novel and Economical Data Augmentation Framework Bas...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhang, Yanyue Li, Pengfei Lai, Yilong He, Yulan Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King's College London United Kingdom The Alan Turing Institute United Kingdom

ISBN: (纸本)9798891761964

As more than 70% of reviews in the existing opinion summary data set are positive, current opinion summarization approaches are hesitant to generate negative summaries given the input of negative texts. To address such sentiment bias, a direct approach without the reliance on a specific structure is to generate additional data based on large language models to balance the emotional distribution of the dataset. However, large-scale data augmentation based on large language models faces an apparent disadvantage, the expensive costs. Therefore, in this paper, we propose LASS, a novel data augmentation framework based on both LArge and Small language models for debiaSing opinion summarization. Specifically, a small number of synthesized negative reviews is obtained by rewriting the positive text via a large language model. Then, a disentangle reconstruction model is trained based on the generated data. After training, a large amount of synthetic data can be obtained by decoding the new representation obtained from the combination of different sample representations and filtering based on perplexity degree and sentiment classification. Experiments have proved that LASS can effectively alleviate emotional bias, similar to using only large models, but in a more economical way. © 2025 Association for Computational Linguistics.

关键词： Data assimilation

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Unveiling the Superiority of Unsupervised Learning on GPU Cryptojacking Detection: Practice on Magnetic Side Channel-based Mechanism

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4874-4889页

作者： Cao, Hangcheng Xu, Guowen He, Ziyang Shi, Shaoqing Xu, Shengmin Wu, Cong Ning, Jianting Hunan University College of Computer Science and Electronic Engineering China City University of Hong Kong Department of Computer Science Hong Kong University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China Zhengzhou University School of Cyber Science and Engineering Zhengzhou China Zhengzhou University International College Zhengzhou China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou China Nanyang Technological University College of Computing and Data Science Singapore Wuhan University School of Cyber Science and Engineering Wuhan China City University of Macau Faculty of Data Science China

Ample profits of GPU cryptojacking attract hackers to recklessly invade victims’ devices, for completing specific cryptocurrency mining tasks. Such malicious invasion undoubtedly obstructs normal device usage and wastes computation resources. To resist the threat of GPU cryptojacking, existing works aim to timely detect and clear away it, by distinguishing the dissimilitude between it and legitimate applications. However, these detection mechanisms inappropriately rely on two conflict cornerstones, manifested in leveraging mutable samples of illegitimate cryptojacking to design supervision-based detection models requiring samples with stable patterns. This limitation compromises the practicability of existing detection mechanisms in the face of mutable cryptojacking samples. To fill the gap, we explore the superiority of unsupervised learning in handling this issue and further propose an unsupervised manner-enabled detection mechanism named MagInspector, only using legitimate applications’ magnetic signatures from GPU side channels for model construction. MagInspector innovates in training an unsupervised autoencoder network by an adversarial mode that well learns the stable signature patterns of legitimate applications, while incompatible with mutable cryptojacking ones. In the process of model training, we elaborately extract mutual energy cumulation distribution features to represent legitimate applications to overcome the impact of their inter-type differences. Meanwhile, a locality sensitive hashing-driven outlier removal algorithm is designed to enhance MagInspector’s robustness to the noise samples. Finally, extensive experiments are conducted on GPUs covering four generations of common NVIDIA architectures and two generations of AMD architectures;the results show that applying MagInspector to mutable cryptojacking signature detection achieves a significant average accuracy improvement of 25.5% and 17.8%, respectively. © 2005-2012 IEEE.

关键词： Unsupervised learning

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Secrecy Rate Maximization for STAR-RIS-Assisted MIMO Cognitive Radio networks

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IEEE Wireless Communications Letters 2025年第5期14卷 1281-1285页

作者： Huang, Zihao Lei, Xianfu Shen, Meng Wu, Mingjiang Li, Xingwang Karagiannidis, George K. Southwest Jiaotong University School of Information Science and Technology Chengdu610031 China Chengdu Technological University School of Network and Communication Engineering Chengdu611730 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454000 China Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki541 24 Greece

In this paper, we study secure transmission in a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted multiple-input multiple-output cognitive radio network, where a secondary transmitter (ST) sends confidential information to a secondary user while sharing the same frequency band with a primary user in the presence of an eavesdropper. We aim to maximize the secrecy rate of the secondary system by jointly optimizing the transmit precoding matrices at the ST, along with the transmit and reflect coefficients of the STAR-RIS. An efficient alternating optimization algorithm based on the penalty method and the generalized S-procedure is proposed to solve the formulated non-convex problem. Simulation results show that the proposed STAR-RIS-based cognitive radio scheme significantly outperforms the conventional RIS in improving the secrecy rate. © 2012 IEEE.

关键词： MIMO Optimization Interference Reflection Eavesdropping Cognitive radio Vectors Security Radio transmitters Precoding

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