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35th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2024

作者： Zhang, Shenglin Zhu, Pengtian Ma, Minghua Wang, Jiagang Sun, Yongqian Li, Dongwen Wang, Jingyu Guo, Qianying Hua, Xiaolei Zhu, Lin Pei, Dan Nankai University China Haihe Laboratory of Information Technology Application Innovation China Microsoft Tsinghua University China Tianjin Key Laboratory of Software Experience and Human Computer Interaction China China Mobile Research Institute China Beijing National Research Center for Information Science and Technology China

ISBN: (纸本)9798350367041

Large language models (LLMs) excel at general question-answering (Q&A) but often fall short in specialized domains due to a lack of domain-specific knowledge. Commercial companies face the dual challenges of privacy protection and resource constraints when involving LLMs for fine-tuning. This paper propose a novel framework, Self-Evolution, designed to address these issues by leveraging lightweight open-source LLMs through multiple iterative fine-tuning rounds. To enhance the efficiency of iterative fine-tuning, Self-Evolution employ a strategy that filters and reinforces the knowledge with higher value during the iterative process. We employed Self-Evolution on Qwen1.5-7B-Chat using 4,000 documents containing rich domain knowledge from China Mobile, achieving a performance score 174% higher on domain-specific question-answering evaluations than Qwen1.5-7B-Chat and even 22% higher than Qwen1.5-72B-Chat. Self-Evolution has been deployed in China Mobile's daily operation and maintenance for 117 days, and it improves the efficiency of locating alarms, fixing problems, and finding related reports, with an average efficiency improvement of over 18.6%. In addition, we release Self-Evolution framework code in https://***/Zero-Pointer/Self-Evolution. © 2024 IEEE.

关键词： Data mining

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Exploring Digital Misinformation as a Sociotechnical Phenomenon: Insights from a Small-scale Study 23

Exploring Digital Misinformation as a Sociotechnical Phenome...

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Proceedings of the XXII Brazilian Symposium on human Factors in Computing Systems

作者： Alisson Andery Puska Roberto Pereira Human-Computer Interaction Laboratory Universidade Federal do Paraná Brasil Department of Computer Science Universidade Federal do Paraná Brasil

ISBN: (纸本)9798400717154

This article addresses the challenge of comprehending digital misinformation as a sociotechnical phenomenon within the field of human-computer interaction, particularly in the context of COVID-19-related misinformation in Brazil. The study employed an exploratory approach to analyze misinformation cases using various artifacts and conducted a focus group with eight experts in the field. The findings revealed 137 sociotechnical aspects and 29 stakeholders, shedding light on the complex interplay of factors influencing digital misinformation’s creation, dissemination, and consumption. The study emphasizes the necessity of adopting a sociotechnical approach and highlights the challenges in comprehensively analyzing stakeholders, communication channels, and the stages of the misinformation lifecycle. Overall, the research contributes to a deeper understanding of digital misinformation in sociotechnical contexts and its implications for human-computer interaction.

关键词： misinformation misinformation life cycle organizational semiotics sociotechnical analysis

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Exploring Multiscale Navigation of Homogeneous and Dense Objects with Progressive Refinement in Virtual Reality

Exploring Multiscale Navigation of Homogeneous and Dense Obj...

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IEEE Annual International Symposium Virtual Reality

作者： Leonardo Pavanatto Alexander Giovannelli Brian Giera Timo Bremer Haichao Miao Doug A. Bowman Department of Computer Science Center for Human-Computer Interaction Virginia Tech USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory USA

ISBN: (数字)9798331536459

ISBN: (纸本)9798331536466

Locating small features in a large, dense object in virtual reality (VR) poses a significant interaction challenge. While existing multiscale techniques support transitions between various levels of scale, they are not focused on handling dense, homogeneous objects with hidden features. We propose a novel approach that applies the concept of progressive refinement to VR navigation, enabling focused inspections. We conducted a user study where we varied two independent variables in our design, navigation style (STRUCTURED vs. UNSTRUCTURED) and display mode (SELECTION vs. EVERYTHING), to better understand their effects on efficiency and awareness during multiscale navigation. Our results showed that unstructured navigation can be faster than structured and that displaying only the selection can be faster than displaying the entire object. However, using an everything display mode can support better location awareness and object understanding.

关键词： human computer interaction Location awareness Three-dimensional displays Navigation Virtual reality Inspection

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VRoxy: Wide-Area Collaboration From an Office Using a VR-Driven Robotic Proxy 23

VRoxy: Wide-Area Collaboration From an Office Using a VR-Dri...

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Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology

作者： Mose Sakashita Hyunju Kim Brandon Woodard Ruidong Zhang François Guimbretière Information Science Cornell University United States Computer Science/Brown University/Human-Computer Interaction Laboratory Brown University United States

ISBN: (纸本)9798400701320

Recent research in robotic proxies has demonstrated that one can automatically reproduce many non-verbal cues important in co-located collaboration. However, they often require a symmetrical hardware setup in each location. We present the VRoxy system, designed to enable access to remote spaces through a robotic embodiment, using a VR headset in a much smaller space, such as a personal office. VRoxy maps small movements in VR space to larger movements in the physical space of the robot, allowing the user to navigate large physical spaces easily. Using VRoxy, the VR user can quickly explore and navigate in a low-fidelity rendering of the remote space. Upon the robot’s arrival, the system uses the feed of a 360 camera to support real-time interactions. The system also facilitates various interaction modalities by rendering the micro-mobility around shared spaces, head and facial animations, and pointing gestures on the proxy. We demonstrate how our system can accommodate mapping multiple physical locations onto a unified virtual space. In a formative study, users could complete a design decision task where they navigated and collaborated in a complex 7.5m x 5m layout using a 3m x 2m VR space.

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Decentralized Federated Graph Learning with Lightweight Zero Trust Architecture for Next-Generation Networking Security

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1908-1922页

作者： Zhou, Xiaokang Liang, Wei Wang, Kevin I-Kai Yada, Katsutoshi Yang, Laurence T. Ma, Jianhua Jin, Qun Kansai University Faculty of Business Data Science Osaka565-0823 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Hunan University of Technology and Business School of Computer Science Changsha410205 China Xiangjiang Laboratory Changsha410205 China The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Hosei University Faculty of Computer and Information Sciences Chiyoda-ku102-8160 Japan Waseda University Faculty of Human Sciences Tokorozawa359-1192 Japan

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users' efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa

关键词： Federated learning

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Tonic and Event-Related Phasic Transcutaneous Auricular Vagus Nerve Stimulation Alters Pupil Responses in the Change-Detection Task

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Neuromodulation 2025年第4期28卷 652-662页

作者： Chen, Jingxin Ke, Yufeng Ni, Guangjian Liu, Shuang Ming, Dong Academy of Medical Engineering and Translational Medicine Tianjin International Joint Research Centre for Neural Engineering Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin University Tianjin China Department of Biomedical Engineering College of Precision Instrument and Optoelectronics Engineering Tianjin University Tianjin China Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin China

Background: Transcutaneous auricular vagus nerve stimulation (taVNS) has emerged as a potential modulator of cognitive behavior that activates the locus coeruleus-noradrenaline (LC-NA) system. Previous studies explored both phasic and tonic taVNS by investigating their impact on LC-NA markers such as pupil dilation and heart rate variability (HRV). Objective: Inconsistencies persist in the identification of reliable markers for assessing the effects of taVNS on noradrenergic activity. Furthermore, it remains unclear whether the effects of taVNS extend beyond pure vagal nerve responses, particularly in specific cognitive domains such as working memory. In the present study, we investigated the effects of taVNS on working memory capacity and LC-NA markers using a change-detection task. Materials and Methods: Twenty-two healthy, right-handed university students participated in a sham-controlled, randomized cross-over experiment with four sessions. We applied two types of phasic and event-related stimulation (Pre-event and Event-synchronous), tonic stimulation (Pre-task), and sham stimulation across different sessions. Pupil size and electrocardiogram data were recorded during the tasks. Results: taVNS did not significantly modulate behavioral performance on the change-detection task, specifically working memory capacity. However, both tonic and event-related phasic taVNS significantly influenced the pupillary response during the task. In addition, the Pre-task condition of the taVNS affected the low-frequency parameter of HRV. Conclusions: Our findings suggest that tonic and event-related phasic taVNS may modulate noradrenergic activity, as evidenced by pupil responses and HRV changes during the change-detection task. This study provides new evidence regarding the impact of taVNS on cognitive tasks, thus supporting the development of noninvasive neuromodulation interventions. © 2025 International Neuromodulation Society

关键词： Heart rate variability (HRV) LC-NA pupil response pupillometry taVNS

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A 2D Geometry Based Grasping Pose Generation Algorithm for a Two-Finger Robot Hand

A 2D Geometry Based Grasping Pose Generation Algorithm for a...

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Iranian Conference on Electrical Engineering (ICEE)

作者： Arash Akbari Arman Akbari Mehdi Tale Masouleh Department of Computer Science University of Tehran Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran

Robot grasping is of paramount importance in industrial and service robotics. In recent years, various data-driven algorithms have been proposed to solve the problem of grasp detection and a part of them are based on reinforcement learning (RL) approaches. In a variety of proposed algorithms, random key points are being employed which will make the learning process inefficient and time-consuming. In this paper, a geometry-based algorithm is presented which can find grasp poses based on the geometry of the unknown object and propose the ones which may lead to successful grasping. For the grasp contacts computation part, the presented algorithm produces a finite number of key points based on the 2D shape of the object from a specific point of view. Afterward, it will narrow down the candidate points and output a finite number of successful grasp poses based on three grasp quality metrics for various unknown objects. Three approaches are proposed in order to achieve center points which can describe different parts of a 2D shape. Then, the obtained points are used as the center of circles which are tangent to the 2D shape contour. Also, a new grasp quality metric is proposed. The time of the grasp and the amount of object disorientation after grasping are considered as a metric to evaluate the successfulness of the grasp. Simulation results demonstrate that the proposed algorithm for unknown object grasping can find a finite number of successful grasp poses for different seen or unseen objects without using any random point,

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An Enhanced Authentication Protocol Suitable for Constrained RFID Systems

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IEEE Access 2024年 12卷 61610-61628页

作者： Hosseinzadeh, Mehdi Servati, Mohammad Reza Rahmani, Amir Masoud Safkhani, Masoumeh Lansky, Jan Janoscova, Renata Ahmed, Omed Hassan Tanveer, Jawad Lee, Sang-Woong Duy Tan University Institute of Research and Development Da Nang550000 Viet Nam Duy Tan University School of Medicine and Pharmacy Da Nang550000 Viet Nam Shahid Rajaee Teacher Training University Department of Computer Engineering Tehran16788-15811 Iran National Yunlin University of Science and Technology Future Technology Research Center Douliou Yunlin64002 Taiwan School of Computer Science Tehran19395-5746 Iran University of Finance and Administration Faculty of Economic Studies Department of Computer Science and Mathematics Prague101 00 Czech Republic University of Human Development Department of Information Technology Sulaymaniyah0778-6 Iraq Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of Gachon University Pattern Recognition and Machine Learning Laboratory Department of Ai Software Seongnam-si13557 Korea Republic of

RFID technology offers an affordable and user-friendly solution for contactless identification of objects and individuals. However, the widespread adoption of RFID systems raises concerns regarding security and privacy. Vulnerabilities such as message tampering, interception, and eavesdropping pose significant risks to the integrity of the system. This study examines the effectiveness of two recently proposed ultra-lightweight RFID authentication protocols, URASP and KUAJB, in addressing these security challenges. Surprisingly, our findings reveal that both URASP and KUAJB protocols are susceptible to secret disclosure attacks, despite their claims of providing robust security. Consequently, the development of a truly ultra-lightweight protocol that ensures adequate security becomes a formidable task. As a potential solution, the enhancement of the URASP protocol through the integration of a simple cryptographic primitive is suggested to bolster its security measures. © 2013 IEEE.

关键词： Hash functions

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LabelEase: A Semi-Automatic Tool for Efficient and Accurate Trace Labeling in Microservices

LabelEase: A Semi-Automatic Tool for Efficient and Accurate ...

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International Symposium on Software Reliability Engineering (ISSRE)

作者： Shenglin Zhang Zeyu Che Zhongjie Pan Xiaohui Nie Yongqian Sun Lemeng Pan Dan Pei Nankai University Haihe Laboratory of Information Technology Application Innovation Computer Network Information Center Chinese Academy of Sciences Tianjin Key Laboratory of Software Experience and Human Computer Interaction Huawei Beijing National Research Center for Information Science and Technology Tsinghua University

ISBN: (数字)9798350353884

ISBN: (纸本)9798350353891

Trace data is crucial for system observability and maintainability within microservices architectures, and many operation algorithms depend heavily on trace data, including anomaly detection, root cause analysis, etc. However, the actual performance of these algorithms might be unsatisfactory due to the absence of high-quality labeled datasets for effective training and evaluation. Since billions of traces could be generated daily for large-scale microservices, labeling overhead is the main hurdle to obtaining high-quality trace *** this paper, we propose LabelEase, a novel semi-automatic trace labeling tool, which uses active learning techniques to achieve efficient and accurate trace labeling. For anomaly trace labeling, LabelEase clusters similar traces with a graph-based trace representation technique and selects a few representative traces for human labeling, avoiding labeling most of the traces. For root cause labeling, LabelEase aggregates the labeled anomalous traces and identifies the service’s failures for operators to label. Our systematic experiments on two large-scale datasets show that LabelEase achieves over 0.98 F 1 -score in anomaly trace labeling and 0.89 precision of failure detection in root cause labeling, LabelEase can reduce operators’ labeling overhead by more than 99.9%. To the best of our knowledge, we are the first to propose a semi-automatic trace labeling tool capable of achieving efficient and accurate trace labeling.

关键词： Training Location awareness Accuracy Costs Transfer learning Microservice architectures Manuals Data models Labeling Anomaly detection

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In Vivo Bioelectronic Nose Based on a Bioengineered Rat for the Explosive Odors with Benzene Ring Detection and Identification 24

In Vivo Bioelectronic Nose Based on a Bioengineered Rat for ...

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11th International Conference on Biomedical and Bioinformatics Engineering, ICBBE 2024

作者： Ma, Jing Wang, Yimeng Yang, Jun Sun, Biao Yang, Qing Zheng, Chenguang Academy of Medical Engineering and Translational Medicine Medical College Tianjin University Tianjin China College of Precision Instruments & Optoelectronics Engineering Tianjin University Tianjin China School of Electrical and Information Engineering Tianjin University Tianjin China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin China

ISBN: (纸本)9798400718274

Accidents caused by explosives occur frequently, which seriously threaten people's lives and health, so it is urgent to develop new technologies for sensitively and rapidly detecting trace explosives. In this study, a bioengineered rat with the olfactory receptor (OR3) overexpression in the olfactory epithelium (OE) was successfully developed, and the unique features of three representative explosives (NT, DNT, and TNT) were conducted by recording electrophysiological-responsive signals from the olfactory bulb (OB) with implanted multichannel microelectrodes during awake-state-independent sniffing behavior induced by explosives and non-explosives. These three explosives were regarded as goal odors, while citral and air were no-goal odors. The analysis of local field potential (LFP) power changes revealed that, goal, and no-goals could be distinguished in both the vector group and the OR3 group at concentrations as low as 10-12 mol/L, and the classification performance of the OR3 group was superior to that of the vector group overall. Our findings suggest that OR3 bioengineered rats can reliably and accurately identify trace explosives, which provides a novel approach for detecting and identifying explosives with promising applications in odor discrimination in complex environments. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Electronic nose

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