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IEEE International Conference on Communications (ICC)

作者： Yuhui Zhu Zhenxiang Chen Qiben Yan Shanshan Wang Alberto Giaretta Enlong Li Lizhi Peng Chuan Zhao Mauro Conti Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan China School of Information Science and Engineering University of Jinan China Department of Computer Science and Engineering Michigan State University USA AASS MPI Lab Örebro University Sweden Department of Mathematics University of Padua Italy

One of the innovations brought by Mirai and its derived malware is the adoption of self-contained loaders for infecting IoT devices and recruiting them in botnets. Functionally decoupled from other botnet components and not embedded in the payload, loaders cannot be analysed using conventional approaches that rely on honeypots for capturing samples. Different approaches are necessary for studying the loaders evolution and defining a genealogy. To address the insufficient knowledge about loaders' lineage in existing studies, in this paper, we propose a semantic-aware method to measure, categorize, and compare different loader servers, with the goal of highlighting their evolution, independent from the payload evolution. Leveraging behavior-based metrics, we cluster the discovered loaders and define eight families to determine the genealogy and draw a homology map. Our study shows that the source code of Mirai is evolving and spawning new botnets with new capabilities, both on the client side and the server side. In turn, shedding light on the infection loaders can help the cybersecurity community to improve detection and prevention tools.

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Report Based Face Detection and Recognition 7

Report Based Face Detection and Recognition

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7th Annual International Conference on network and Information Systems for computers, ICNISC 2021

作者： Ahmed, Hameer Li, Hengjian Dharejo, Fayaz Hussain Laghari, Asif Ali Karim, Shahid University of Jinan Department of Information Science and Engineering Jinan China University of Chinese Academy of Sciences Computer Network Information Center Chinese Academy of Sciences Beijing China Sindh Madressautal Islam University Department of Computer Science Pakistan Barani Institute of Management Science Department of Computer Science Rawalpindi Pakistan

ISBN: (纸本)9781665402323

Face recognition is a computer dynamic that determines the position and size of a human face in a digital image. This study suggests a system that will help you recognize students' faces, know entrance times, and create timing reports with 'smart surveillance and instant reporting. An instant report that displays the admittances results in the form of a graphical report as well as a textual report. © 2021 IEEE.

关键词： Face recognition

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The Terms Estimation of S Robotics using MTRS as well as FEM

The Terms Estimation of S Robotics using MTRS as well as FEM

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： R. Gopal Mustafa Mudhafar Amjad Hashem Venkadeshan Ramalingam Dibyahash Bordoloi Muhammad Numan Ali Khan Myasar Mundher Adnan Zahraa N. Abdulhussain Assistant Professor Information and communication Engineering College of Engineering University of Buraimi Sultanate of Oman Department of Medical Physics Faculty of Medical Applied Sciences University of Kerbala Karbala Iraq Computer Engineer Department Al-kunooze University College Basra Iraq Network Computing College of Computing and Information Sciences University of Technology and Applied Sciences– Shinas Sultanate of Oman Dept of CSE Graphic Era Hill University Adjunct professor Graphic Era Deemed to be University Dehradun Uttarakhand Faculty – Network Computing College of Computing and Information Sciences University of Technology and Applied Sciences – Shinas Sultanate of Oman Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq College of Technical Engineering National University of Science and TechnologyDhi Qar Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

This look at addresses the critical project of reaching dependable and precise proprioception in soft robots, it really is essential for his or her effective deployment in complicated environments. By integrating multi-tap resistive sensors in the robot’s form, our technique lets in for the distinct sensing of nearby deformations at various factors throughout the robotics’ frame, moving beyond traditional techniques that anticipate steady curvature. We beautify the decision of these deformations and couple the sensory facts with a strong finite detail technique (FEM) model. This included framework allows the correct estimation of the smooth robot’s form and the significance of outside forces applied to it, attaining a mean relative inaccuracy of about 4%. Additionally, our technique proves its software program in tender manipulation structures, in which it achieves thirteen% relative mistakes in external force estimation inside diverse zero to five N. The approach gives a promising direction for enhancing robotic interactions in sensitive and dynamic responsibilities, providing massive enhancements in robotic proprioception without reliance on outdoor monitoring generation.

关键词： Deformable models Deformation Force Estimation Soft robotics Robot sensing systems Software

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An Overview on Economic Analysis of Internet of Everything

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IEEE Communications Surveys and Tutorials 2025年

作者： Ding, Ningning Ouyang, Xiaomin Gao, Lin Huang, Jianwei Xing, Guoliang Guangzhou511453 China Hong Kong University of Science and Technology Department of Computer Science and Engineering 999077 Hong Kong Harbin Institute of Technology School of Electronics and Information Engineering Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen China Chinese University of Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network CSIJRI Joint Research Centre on Smart Energy Storage Guangdong Shenzhen518172 China The Chinese University of Hong Kong Department of Information Engineering 999077 Hong Kong

The Internet of Everything (IoE) integrates people, processes, data, and things into a unified network, enabling highly interconnected systems with enhanced performance in terms of delay, reliability, and connection density. With the advent of next-generation networks, IoE has garnered significant attention for its transformative potential across various domains. However, while most existing studies focus on developing enabling technologies and deployment frameworks, the economic dimensions of IoE systems remain critically underexplored, despite being essential for ensuring their sustainability and scalability. This paper addresses this gap by providing a comprehensive review of the economic analysis of IoE systems. Specifically, we categorize existing research into four key aspects: information collection, information transmission, information fusion, and service application. We identify major economic challenges and propose modeling approaches, including game-theoretic and learning-based methods. Furthermore, we explore diverse IoE application scenarios, such as metaverse solutions and large-scale industrial systems, and summarize prevailing business patterns in the industry. By highlighting the economic dimension as a core component of IoE systems, this survey offers valuable insights into future research directions and outlines critical open challenges. © 1998-2012 IEEE.

关键词： Internet of Everything

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DSD-MatchingNet:Deformable sparse-to-dense feature matching for learning accurate correspondences

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Virtual Reality & Intelligent Hardware 2022年第5期4卷 432-443页

作者： Yicheng ZHAO Han ZHANG Ping LU Ping LI Enhua WU Bin SHENG Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China ZTE Corporation Shenzhen 518057China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen 518057China Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China School of Design The Hong Kong Polytechnic UniversityHong Kong 999077China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China Faculty of Science and Technology University of MacaoMacao 999078China

Background Exploring correspondences across multiview images is the basis of various computer vision ***,most existing methods have limited accuracy under challenging *** To learn more robust and accurate correspondences,we propose DSD-MatchingNet for local feature matching in this ***,we develop a deformable feature extraction module to obtain multilevel feature maps,which harvest contextual information from dynamic receptive *** dynamic receptive fields provided by the deformable convolution network ensure that our method obtains dense and robust ***,we utilize sparse-to-dense matching with symmetry of correspondence to implement accurate pixel-level matching,which enables our method to produce more accurate *** Experiments show that our proposed DSD-MatchingNet achieves a better performance on the image matching benchmark,as well as on the visual localization ***,our method achieved 91.3%mean matching accuracy on the HPatches dataset and 99.3%visual localization recalls on the Aachen Day-Night dataset.

关键词： Image matching Deformable convolution network Sparse-to-dense matching

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On the Effectiveness of Distillation in Mitigating Backdoors in Pre-trained Encoder

arXiv

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arXiv 2024年

作者： Han, Tingxu Huang, Shenghan Ding, Ziqi Sun, Weisong Feng, Yebo Fang, Chunrong Li, Jun Qian, Hanwei Wu, Cong Zhang, Quanjun Liu, Yang Chen, Zhenyu The State Key Laboratory for Novel Software Technology Nanjing University Jiangsu210093 China The School of Computer Science University of New South Wales NSW2052 Australia The School of Computer Science and Engineering Nanyang Technological University Nanyang639798 Singapore The Department of Computer Science The Director of the Network & Security Research Laboratory The University of Oregon United States

With the development of deep learning, self-supervised learning (SSL) has become particularly popular. SSL pre-trains an encoder (e.g., an image encoder) to learn generic features from a large amount of unlabeled data, releases it on a third-party platform, and allows any downstream task (e.g., an image classification task) to further fine-tune the encoder with task-specific data to derive the model for the task. However, despite the tremendous performance improvements it brings, SSL faces serious security risks. An adversary can poison pre-training datasets used to pre-train an encoder or publish a poisoned encoder directly to third-party platforms, which will also poison the model for any downstream task that uses these encoders. In this paper, we study a defense against poisoned encoders in SSL called distillation, which is a defense used in supervised learning originally. Distillation aims to distill knowledge from a given model (a.k.a the teacher net) and transfer it to another (a.k.a the student net). Now, we use it to distill benign knowledge from poisoned pre-trained encoders and transfer it to a new encoder, resulting in a clean pre-trained encoder. In particular, we conduct an empirical study on the effectiveness and performance of distillation against poisoned encoders. Using two state-of-the-art backdoor attacks against pre-trained image encoders and four commonly used image classification datasets, our experimental results show that distillation can reduce attack success rate from 80.87% to 27.51% while suffering a 6.35% loss in accuracy. Moreover, we investigate the impact of three core components of distillation on performance: Teacher net, student net, and distillation loss. By comparing 4 different teacher nets, 3 student nets, and 6 distillation losses, we find that fine-tuned teacher nets, warm-up-training-based student nets, and attention-based distillation loss perform best, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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Maximizing Energy Efficiency of HSRC: A Cache-Aided Time-Domain Power Allocation

Maximizing Energy Efficiency of HSRC: A Cache-Aided Time-Dom...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Yeshen Li Ke Xiong Han Li Bo Ai Pingyi Fan Khaled Ben Letaief Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing China State Key Laboratory of Electronic and Information Engineering School Beijing Jiaotong University Beijing China Department of Electronic Engineering Tsinghua University Beijing China Department of Electrical and Computer Engineering Hong Kong University of Science and Technology (HKUST) Hong Kong

This paper presents a cache-aided time-domain power allocation (CaTDPA) for 5G high-speed railway communications (HSRC), where two kinds of users, online interactive application users (OIUs) and online non-interactive application users (ONIUs), are jointly taken into account. For OIUs, their minimum ground-train data rate demand is guaranteed. For ONIUs, their minimum cache-releasing data rate demand is guaranteed. To achieve the green HSRC design, an optimization problem is formulated to maximize the system energy efficiency (EE) by jointly optimizing the power allocation (PA) and cache releasing start time (CRST) subject to the constraints of total available energy, maximum transmit power, ONIUs’ ground-train data rate threshold, ONIUs’ cache-releasing data rate threshold, and causality of CRST. To address such a non-convex and fractional problem, a two-step solution is designed. First, with a given CRST, a Dinkelbach-based method is applied to obtain the correspondingly optimal PA. Second, the one-dimensional traversal method is used to find the optimal CRST. Experimental results show that our proposed CaTDPA achieves the maximum EE among multiple compared methods while meeting the requirements of OIUs and ONIUs. Moreover, EE will decrease as the OIUs’ ground-train data rate threshold and the train’s moving speed increase, while it is observed that the ONIUs’ cache-releasing data rate threshold has a relatively small effect on the EE of HSRC.

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WiOpen: A Robust Wi-Fi-based Open-set Gesture Recognition Framework

arXiv

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arXiv 2024年

作者： Zhang, Xiang Huang, Jingyang Yan, Huan Zhao, Peng Zhuang, Guohang Liu, Zhi Liu, Bin CAS Key Laboratory of Electromagnetic Space Information University of Science and Technology of China Hefei230026 China School of Computer and Information Hefei University of Technology Hefei230601 China School of Big Data and Computer Science Guizhou Normal University Guiyang550025 China Department of Computer and Network Engineering The University of Electro-Communications Tokyo1828585 Japan

Recent years have witnessed a growing interest in Wi-Fi-based gesture recognition. However, existing works have predominantly focused on closed-set paradigms, where all testing gestures are predefined during training. This poses a significant challenge in real-world applications, as unseen gestures might be misclassified as known classes during testing. To address this issue, we propose WiOpen, a robust Wi-Fi-based Open-Set Gesture Recognition (OSGR) framework. Implementing OSGR requires addressing challenges caused by the unique uncertainty in Wi-Fi sensing. This uncertainty, resulting from noise and domains, leads to widely scattered and irregular data distributions in collected Wi-Fi sensing data. Consequently, data ambiguity between classes and challenges in defining appropriate decision boundaries to identify unknowns arise. To tackle these challenges, WiOpen adopts a two-fold approach to eliminate uncertainty and define precise decision boundaries. Initially, it addresses uncertainty induced by noise during data preprocessing by utilizing the CSI ratio. Next, it designs the OSGR network based on an uncertainty quantification method. Throughout the learning process, this network effectively mitigates uncertainty stemming from domains. Ultimately, the network leverages relationships among samples’ neighbors to dynamically define open-set decision boundaries, successfully realizing OSGR. Comprehensive experiments on publicly accessible datasets confirm WiOpen’s effectiveness. Notably, WiOpen also demonstrates superiority in cross-domain tasks when compared to state-of-the-art approaches. © 2024, CC BY-SA.

关键词： Learning systems

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Deep Reinforcement Learning based Personalized Locomotion Planning for Lower-Limb Exoskeletons

Deep Reinforcement Learning based Personalized Locomotion Pl...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Javad K. Mehr Eddie Guo Mojtaba Akbari Vivian K. Mushahwar Mahdi Tavakoli Department of Electrical and Computer Engineering and the Department of Medicine University of Alberta Edmonton Alberta Canada Sensory Motor Adaptive Rehabilitation Technology (SMART) Network University of Alberta Edmonton Alberta Canada Cumming School of Medicine University of Calgary Calgary Alberta Canada Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada Department of Medicine Division of Physical Medicine and Rehabilitation University of Alberta Edmonton Alberta Canada

This paper introduces intelligent central pattern generators (iCPGs) that can plan personalized walking trajectories for lower-limb exoskeletons. This can make walking more comfortable for the users by resolving one of the significant shortcomings of most commercially available exoskeletons, which is the use of pre-defined fixed trajectories for all users. The proposed method combines reinforcement learning (RL) with previously introduced adaptable central pattern generators (ACPGs) to learn a user's physical interaction behaviour and refine the exoskeleton's walking trajectories. The ACPG method embeds physical human-robot interaction (pHRI) in CPGs to make changing gait trajectories in real-time, possible. However, to effectively refine gait trajectories based on pHRIs, the parameters must be precisely identified and updated as a user interacts with the exoskeleton. Our proposed method uses RL to modify (amplify/attenuate) the pHRI energy based on a user's interaction behaviour, and form an effective energy value which can facilitate reaching desired gait pattern for users via iCPG dynamics. The proposed method can resolve the aforementioned challenges with ACPGs and personalized trajectory generation. The simulation and experimental results provide evidence that the proposed method can effectively adapt to the user's behaviour in different walking scenarios with the Indego lower-limb exoskeleton.

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Glnet: A network with Different Cascading Methods for Global and Local Features

SSRN

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SSRN 2025年

作者： Du, Jiao Su, Chengxin Qun, Zhao Yu, Xiaoyu Department of Computer Science and Network Engineering Guangzhou University Guangzhou510006 China

Segmenting the liver in medical images is crucial for a variety of clinical applications, providing important information for early diagnosis and surgical planning of liver-related diseases. While existing models commonly rely on convolutional neural networks for hierarchical modeling of local features for a long time, Transformer-based architectures provide a new and enhanced perspective through the ability of long-range dependency capture of global context, and the complementarity between the two in terms of local accuracy and global consistency is becoming a research focus. However, Transformer often struggles to preserve detailed features, which may limit its segmentation performance. To address this question, we propose a novel segmentation model that achieves two-way dynamic fusion of local details and global context through a synergistic and complementary mechanism of hierarchical local feature enhancement and global semantic association modeling. At the core of our approach is a Gaussian weighting mechanism that enhances texture and edge information by assigning higher weights to pixels closer to the center of the feature while suppressing interference from surrounding regions. This mechanism enables the model to preserve key details while effectively capturing global information. In addition, we introduce the Fourier transform to extract high-frequency components from low-resolution feature maps, further enhancing the model's ability to capture features, which are critical for accurate segmentation. Experimental results performed on four publicly available datasets show that our proposed model achieves state-of-the-art in both segmentation accuracy and generalization ability, highlighting its effectiveness in medical image segmentation tasks. The code will be available at https://***/chengxin-su/***. © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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