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An infrastructure software perspective toward computation offloading between executable specifications and foundation models

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science China(Information sciences) 2025年第4期68卷 380-382页

作者： Dezhi RAN Mengzhou WU Yuan CAO Assaf MARRON David HAREL Tao XIE Key Laboratory of High Confidence Software Technologies (PKU) Ministry of Education School of Computer SciencePeking University School of Electronics Engineering and Computer Science Peking University Department of Computer Science and Applied Mathematics Weizmann Institute of Science

Foundation models(FMs) [1] have revolutionized software development and become the core components of large software systems. This paradigm shift, however, demands fundamental re-imagining of software engineering theories and methodologies [2]. Instead of replacing existing software modules implemented by symbolic logic, incorporating FMs' capabilities to build software systems requires entirely new modules that leverage the unique capabilities of ***, while FMs excel at handling uncertainty, recognizing patterns, and processing unstructured data, we need new engineering theories that support the paradigm shift from explicitly programming and maintaining user-defined symbolic logic to creating rich, expressive requirements that FMs can accurately perceive and implement.

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GPS: graph contrastive learning via multi-scale augmented views from adversarial pooling

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science China(Information sciences) 2025年第1期68卷 145-158页

作者： Wei JU Yiyang GU Zhengyang MAO Ziyue QIAO Yifang QIN Xiao LUO Hui XIONG Ming ZHANG School of Computer Science National Key Laboratory for Multimedia Information ProcessingPeking University Artificial Intelligence Thrust The Hong Kong University of Science and Technology Department of Computer Science University of California

Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks. A large number of graph contrastive learning approaches have shown promising performance for representation learning on graphs, which train models by maximizing agreement between original graphs and their augmented views(i.e., positive views). Unfortunately, these methods usually involve pre-defined augmentation strategies based on the knowledge of human experts. Moreover, these strategies may fail to generate challenging positive views to provide sufficient supervision signals. In this paper, we present a novel approach named graph pooling contrast(GPS) to address these *** by the fact that graph pooling can adaptively coarsen the graph with the removal of redundancy, we rethink graph pooling and leverage it to automatically generate multi-scale positive views with varying emphasis on providing challenging positives and preserving semantics, i.e., strongly-augmented view and weakly-augmented view. Then, we incorporate both views into a joint contrastive learning framework with similarity learning and consistency learning, where our pooling module is adversarially trained with respect to the encoder for adversarial robustness. Experiments on twelve datasets on both graph classification and transfer learning tasks verify the superiority of the proposed method over its counterparts.

关键词： graph representation learning graph neural networks graph contrastive learning graph augmentations graph pooling

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AmplitudeArrow: On-the-Go AR Menu Selection Using Consecutive Simple Head Gestures and Amplitude Visualization

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IEEE Transactions on Visualization and computer Graphics 2025年第05期31卷 3408-3417页

作者： Tian, Yang Zhang, Youpeng Yan, Yukang Zhao, Shengdong Ma, Xiaojuan Shi, Yuanchun Guangxi University Department of Computer Science Guangxi Key Laboratory of Multimedia Communications and Network Technology China University of Rochester Department of Computer Science United States City University of Hong Kong School of Creative Media and the Department of Computer Science Hong Kong Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Tsinghua University Department of Computer Science and Technology China

Heads-up computing aims to provide synergistic digital assistance that minimally interferes with users' on-the-go daily activities. Currently, the input modalities of heads-up computing are mainly voice and finger gestures. In this work, we propose and evaluate the AmplitudeArrow (AA) technique designed for on-the-go AR menu selection to demonstrate that consecutive simple head gestures can also be an effective input modality for heads-up computing. Specifically, AA arranges menu icons into one/two row(s). To select a target icon, the user first makes their head yaw to pre-select the target icon or the column containing it and then makes their head pitch to make the arrow in the target icon expand until the arrow covers the target icon completely, i.e., the pitch amplitude surpasses the selection confirmation threshold. User studies indicated that AA demonstrated robust resistance to walking-caused head perturbation and external factors such as other people/obstacles, delivering high accuracy (error rate © 1995-2012 IEEE.

关键词： Augmented reality

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Static video summarization based on genetic algorithm and deep learning approach

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Multimedia Tools and Applications 2025年第13期84卷 12487-12512页

作者： Benoughidene, Abdelhalim Titouna, Faiza Boughida, Adil Computer Science Department LaSTIC Laboratory University Batna 2 Batna05000 Algeria Computer Science Department LabSTIC Laboratory University of 08 may 1945 Guelma Guelma24000 Algeria

The development of information technology has led to the rise of big data. A large portion of this big data comes in the form of video information. The automatic analysis of this exponential growth in video content has become a popular research area. This research focuses on finding a video’s keyframes through a proposed static video summarization method. The method uses a deep learning-based shot boundary detection approach as a pre-processing step and exploits DBSCAN clustering to extract keyframes. A genetic algorithm is used to optimize the hyper-parameters of DBSCAN rather than having the user pre-tune them because the number of keyframes in a video can vary depending on the content of the video. The experimental results on standard databases Open Video Project (OVP) and YouTube (YT) show that the proposed method produces better results than existing methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Genetic algorithms

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A systematic and comprehensive review on low power wide area network: characteristics, architecture, applications and research challenges

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Discover Internet of Things 2025年第1期5卷 1-26页

作者： Diane, Ass Diallo, Ousmane Ndoye, El Hadji Malick Laboratory Department of Computer Science Assane SECK University of Ziguinchor Ziguinchor Senegal

The Internet of Things (IoT) has become a rapidly growing research field. This is due to the advancement of digital technologies, miniaturization, and the reduction of the cost of IoT devices and wireless connectivity, among others. Despite the plethora of technologies used for the Internet of Things, the trade-off between long data transmission range and low power consumption was not found until the advent of Low Power Wide Area Network (LPWAN) technologies. This paper reviews the main aspects of LPWANs and their technologies based on an exhaustive search in several online scientific databases, such as Springer, IEEE Xplore, the ACM digital library, and Google Scholar. This research methodology enabled us to gather recent work on LPWANs, which forms the basis of this article. It is informative and knowledge-updating support in the LPWANs’ environment that broadly covers LPWANs. This research work has developed the characteristics of LPWANs and the techniques used to achieve long-range energy efficiency, high scalability, and low cost. In addition, it presents the application areas of LPWAN technologies with use-case network architectures for each area, addresses spectrum and energy optimization, and discusses open research challenges that need to be focused to provide guidelines for further contributions. © The Author(s) 2025.

关键词： Applications Architecture IoT LPWAN Research challenges Standardization

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Real-Time Multi-object Tracking Using YOLOv8 and SORT on a SoC FPGA 21st

Real-Time Multi-object Tracking Using YOLOv8 and SORT on a...

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21st International Symposium on Applied Reconfigurable Computing, ARC 2025

作者： Danilowicz, Michal Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031879944

Multi-object tracking (MOT) is one of the most important problems in computer vision and a key component of any vision-based perception system used in advanced autonomous mobile robotics. Therefore, its implementation on low-power and real-time embedded platforms is highly desirable. Modern MOT algorithms should be able to track objects of a given class (e.g. people or vehicles). In addition, the number of objects to be tracked is not known in advance, and they may appear and disappear at any time, as well as be obscured. For these reasons, the most popular and successful approaches have recently been based on the tracking paradigm. Therefore, the presence of a high quality object detector is essential, which in practice accounts for the vast majority of the computational and memory complexity of the whole MOT system. In this paper, we propose an FPGA (Field-Programmable Gate Array) implementation of an embedded MOT system based on a quantized YOLOv8 detector and the SORT (Simple Online Realtime Tracker) tracker. We use a modified version of the FINN framework to utilize external memory for model parameters and to support operations necessary required by YOLOv8. We discuss the evaluation of detection and tracking performance using the COCO and MOT15 datasets, where we achieve 0.21 mAP and 38.9 MOTA respectively. As the computational platform, we use an MPSoC system (Zynq UltraScale+ device from AMD/Xilinx) where the detector is deployed in reprogrammable logic and the tracking algorithm is implemented in the processor system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： System-on-chip

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Edge-aware Feature Aggregation Network for Polyp Segmentation

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Machine Intelligence Research 2025年第1期22卷 101-116页

作者： Tao Zhou Yizhe Zhang Geng Chen Yi Zhou Ye Wu Deng-Ping Fan PCA Lab Key Laboratory of Intelligent Perception and Systems for High-dimensional Information of Ministry of EducationSchool of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing210094China School of Computer Science and Engineering Northwestern Polytechnical University(NPU)Xi’an710129China School of Computer Science and Engineering Southeast UniversityNanjing211189China Computer Vision Lab ETH ZürichZürich8092Switzerland

Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer(CRC)in clinical ***,due to scale variation and blurry polyp boundaries,it is still a challenging task to achieve satisfactory segmentation performance with different scales and *** this study,we present a novel edge-aware feature aggregation network(EFA-Net)for polyp segmentation,which can fully make use of cross-level and multi-scale features to enhance the performance of polyp ***,we first present an edge-aware guidance module(EGM)to combine the low-level features with the high-level features to learn an edge-enhanced feature,which is incorporated into each decoder unit using a layer-by-layer ***,a scale-aware convolution module(SCM)is proposed to learn scale-aware features by using dilated convolutions with different ratios,in order to effectively deal with scale ***,a cross-level fusion module(CFM)is proposed to effectively integrate the cross-level features,which can exploit the local and global contextual ***,the outputs of CFMs are adaptively weighted by using the learned edge-aware feature,which are then used to produce multiple side-out segmentation *** results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and *** implementation code and segmentation maps will be publicly at https://***/taozh2017/EFANet.

关键词： Colorectal cancer polyp segmentation edge-aware guidance module scale-aware convolution module cross-level fusion module

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A survey on cross-user federated recommendation

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science China(Information sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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Deep learning based on hand pose estimation methods: a systematic literature review

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Multimedia Tools and Applications 2025年 1-38页

作者： Roumaissa, Bekiri Mohamed Chaouki, Babahenini Computer Science Department LESIA Laboratory Mohamed Khider University Biskra07000 Algeria

Estimating hand pose is a challenge that has significantly benefited from using deep learning-based algorithms. This study area holds critical significance across various computer vision and robotics domains, including applications in sign language interpretation, computer-Aided Design (CAD), and 3D humanoid reconstruction systems. These technologies flourish in augmented reality systems, facilitating immersive interactions within virtual reality contexts. The complexity of human hand anatomy and its intricate range of motions intensifies the difficulty of accurate pose estimation, posing significant academic and technical hurdles. Recent advancements have seen the emergence of rapid and comprehensive methods for hand pose estimation, driven by advancements in-depth camera technology and Deep Neural Networks (DNNs). The previous surveys studied most of these hand pose issues, including hand parsing, data labeling technologies, hand motion, fingertip detection, hand localization, and self-occlusion. This paper addresses the aforementioned challenges in hand pose estimation. Further, we propose a novel taxonomy based on deep-learning-based approaches that aim to gather the previous research advances systems that tackled these actual challenges. We provide an overview of existing research, discussing their strengths and limitations. Additionally, we identify various benchmark datasets, their characteristics and prevalent evaluation metrics used to assess these approaches. Finally, we explore potential research directions focusing on speed, accuracy, and type of deep learning architecture in this rapidly evolving field. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Virtual reality

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Steganography in IoT: A Comprehensive Survey on Approaches, Challenges, and Future Directions

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IEEE Access 2025年 13卷 74840-74871页

作者： Driss, Maha Berriche, Lamia Atitallah, Safa Ben Rekik, Siwar College of Computer and Information Sciences Prince Sultan University Robotics and Internet-of-Things Laboratory Computer Science Department Riyadh11586 Saudi Arabia University of Manouba RIADI Laboratory Manouba2010 Tunisia Prince Sultan University College of Computer and Information Sciences Computer Science Department Riyadh11586 Saudi Arabia

The Internet of Things (IoT) has raised significant security concerns, especially with regard to secure data transfer among resource-constrained devices. While effective, traditional encryption techniques are often computationally expensive and easily identifiable, making them unsuitable for many IoT applications. Steganography is an intriguing approach that allows hiding sensitive information within seemingly ordinary data, preventing unauthorized parties from detecting and accessing it. Existing studies on IoT security lack a comprehensive analysis of steganographic techniques tailored for IoT-specific constraints. This study bridges this gap by providing a comprehensive review of steganographic methods for IoT, exploring approaches across many domains such as spatial, frequency, and hybrid methods used in images, videos, audio, text, network traffic, hybrid approaches, and quantum-based methods. We evaluated and validated the techniques employed using key metrics such as imperceptibility, robustness, and embedding capacity while emphasizing the computational restrictions, real-time processing requirements of IoT devices, and the vital requirement for energy-efficient algorithms. This survey also investigates the integration of steganography and cryptographic approaches, as well as advances in Machine Learning (ML), Deep Learning (DL), and quantum techniques that could revolutionize the field. The study ends with future research directions that underscore the importance of innovative steganographic techniques that strike a compromise between security, efficiency, and scalability in IoT applications. © 2013 IEEE.

关键词： Steganography

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