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49th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2023

作者： Di Dario, Dario Bilott, Umberto Sibilio, Maurizio Gravino, Carmine Palomba, Fabio University of Salerno SeSa Lab Department of Computer Science Salerno Italy University of Salerno Lab H Department of Human Science Salerno Italy

ISBN: (纸本)9798350342352

In the era of social distancing, distance learning represents a crucial educational challenge. Several 2D information technologies have been provided, yet these share multiple limitations and have negative social, educational, and psychological implications for learners. Metaverse promises to revolutionize education as we know it: this is a persistent, virtual, three-dimensional environment that is supposed to address most of the limitations of 2D information technologies. Nonetheless, there are still software engineering challenges to face to enable such a metaverse, especially when turning to software security and privacy. In this paper, we aim at performing the first steps toward an improved understanding of the security perspective of educational metaverse, by analyzing how blockchain can be employed within educational environments and how applications may be designed. Our ultimate goal is to provide insights into how blockchain can be further tailored in the context of educational metaverse. We conduct a systematic literature review, which targets 20 primary studies. The key findings of the study showcase the use of blockchain in 3 educational tasks, other than describing the blockchain design approaches, which protocol they commonly use and the associated limitations. We conclude by developing a conceptualization of a blockchain-based educational metaverse. © 2023 IEEE.

关键词： Blockchain

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Astrocytic TRPA1 as a target in reversing social avoidance by low-intensity focused ultrasound stimulation in mice 46

Astrocytic TRPA1 as a target in reversing social avoidance b...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Wei, Ganjiang Wang, Ling Zhu, Jingnan Wang, Xiao Zang, Yuran Yang, Jiajia Zheng, Chenguang Tianjin University Academy of Medical Engineering and Translational Medicine Medical College 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)9798350371499

Social avoidance is a prominent clinical feature of multiple neuropsychiatric disorders. Increasing evidence showed that the medial prefrontal cortex (mPFC) regulates social avoidance with suppression of excitatory neurons. Our previous study found that the low-intensity focused ultrasound (LIFUS), as a novel noninvasive neuromodulation technique, could alleviate the social avoidance of social defeat stress (SDS) model mice by increasing the mPFC excitation. However, the underlying target of LIFUS on mPFC neurons in social defeated mice remains unclear. Here we injected AAV-shRNA to knock down astrocytic TRPA1 in the mPFC, and investigated whether astrocytic TRPA1 played a crucial role in LIFUS modulation. We found that the social avoidance of SDS mice with TRPA1 knocked down was not affected by LIFUS. Furthermore, LIFUS didn't induce enhanced expression of membrane NMDAR2A and NMDAR2B in SDS mice with TRPA1 knocked down, compared with that in mice infected with the control virus. It is believed that LIFUS can act on astrocytic TRPA1 to improve social avoidance and hence synaptic function. © 2024 IEEE.

关键词： Ultrasonics

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Study on the Temporal Patterns of CA1 Pyramidal Neuron Activity in Mice Hippocampus under Simulated Spaceflight Composite Environment 46

Study on the Temporal Patterns of CA1 Pyramidal Neuron Activ...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Sun, Shufan Ren, Jing Zhao, Meiling Wang, Ling Zheng, Chenguang Yang, Jiajia Ming, Dong Academy of Medical Engineering and Translational Medicine Tianjin University Brain-Computer Interaction and Human-Machine Fusion Haihe Laboratory Tianjin University Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin University Tianjin 300072 China

ISBN: (纸本)9798350371499

With the gradual realization of manned spaceflight goals, the physical and mental health of astronauts has become a core concern. Numerous studies in recent years have indicated that the aerospace special environment (ASE) can lead to alterations in astronaut cognitive function. However, the adaptive changes in neuronal activity underlying these alterations remain unclear. This study utilized animal models and two-photon imaging technology to investigate the adaptive changes in the activity characteristics and synchronicity of pyramidal neurons in the CA1 region of the mice hippocampus under simulated spaceflight composite environment (SSCE). The results indicate that, with prolonged modeling time, the number of active pyramidal neurons in the CA1 region gradually decreases. Significant reductions in both the frequency and amplitude of neuronal calcium transients occur, stabilizing at lower levels. The calcium transient decay constant extends, and significant decorrelation in neuronal calcium activity is observed. Thus, we posit that these changes may constitute crucial factors leading to the alterations in individual cognitive function induced by the aerospace special environment. © 2024 IEEE.

关键词： Mammals

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Tactile Comfort: Lowering Heart Rate Through interactions with a Pocket Robot

Tactile Comfort: Lowering Heart Rate Through Interactions wi...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Morten Roed Frederiksen Kasper Stoy Maja Matarić Interaction Lab at the University of Southern California Computer Science Department The IT-University of Copenhagen

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Children diagnosed with anxiety disorders are taught a range of strategies to navigate situations of heightened anxiety. Techniques such as deep breathing and repetition of mantras are commonly employed, as they are known to be calming and reduce elevated heart rates. Although these strategies are often effective, their successful application relies on prior training of the children for successful use when faced with challenging situations. This paper investigates a pocket-sized companion robot designed to offer a relaxation technique requiring no prior training, with a focus on immediate impact on the user’s heart rate. The robot utilizes a tactile game to divert the user’s attention, thereby promoting relaxation. We conducted two studies with children who were not diagnosed with anxiety: a 14-day pilot study with two children (age 8) and a main study with 18 children (ages 7-8). Both studies employed a within-subjects design and focused on measuring heart rate during tactile interaction with the robot and during non-use. Interacting with the robot was found to significantly lower the study participants’ heart rate (p<0.01) compared to the nonuse condition, indicating a consistent calming effect across all participants. These results suggest that tactile companion robots have the potential to enhance the therapeutic value of relaxation techniques.

关键词： Heart rate Training Pediatrics Atmospheric measurements Navigation Anxiety disorders Games Particle measurements Rhythm Robots

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LabelEase: A Semi-Automatic Tool for Efficient and Accurate Trace Labeling in Microservices 35

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

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35th IEEE International Symposium on Software Reliability Engineering, ISSRE 2024

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

ISBN: (纸本)9798350353884

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 F1-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. © 2024 IEEE.

关键词： Labeled data

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Quantifying Spatial Domain Explanations in BCI using Earth Mover’s Distance

Quantifying Spatial Domain Explanations in BCI using Earth M...

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International Joint Conference on Neural Networks (IJCNN)

作者： Param Rajpura Hubert Cecotti Yogesh Kumar Meena Human-AI Interaction (HAIx) Lab IIT Gandhinagar India Department of Computer Science California State University Fresno USA

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Brain-computer interface (BCI) systems facilitate unique communication between humans and computers, benefiting severely disabled individuals. Despite decades of research, BCIs are not fully integrated into clinical and commercial settings. It’s crucial to assess and explain BCI performance, offering clear explanations for potential users to avoid frustration when it doesn’t work as expected. This work investigates the efficacy of different deep learning and Riemannian geometry-based classification models in the context of motor imagery (MI) based BCI using electroencephalography (EEG). We then propose an optimal transport theory-based approach using earth mover’s distance (EMD) to quantify the comparison of the feature relevance map with the domain knowledge of neuroscience. For this, we utilized explainable AI (XAI) techniques for generating feature relevance in the spatial domain to identify important channels for model outcomes. Three state-of-the-art models are implemented - 1) Riemannian geometry-based classifier, 2) EEGNet, and 3) EEG Conformer, and the observed trend in the model’s accuracy across different architectures on the dataset correlates with the proposed feature relevance metrics. The models with diverse architectures perform significantly better when trained on channels relevant to motor imagery than data-driven channel selection. This work focuses attention on the necessity for interpretability and incorporating metrics beyond accuracy, underscores the value of combining domain knowledge and quantifying model interpretations with data-driven approaches in creating reliable and robust Brain-computer Interfaces (BCIs).

关键词： Measurement Earth Accuracy computer architecture Brain modeling Motors Feature extraction

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Shift Aliasing Signal Data Augmentation Method for SSVEP-based BCIs

Shift Aliasing Signal Data Augmentation Method for SSVEP-bas...

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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Jiayang Huang Hao Wen Pengfei Yang Keyi Yang Bang Xiong School of Computer Science and Technology The Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province Xidian University Xi’an China

ISBN: (数字)9798350391916

ISBN: (纸本)9798350391923

The performance of training-based spatial filtering methods for steady-state visual evoked potential (SSVEP) decoding heavily relies on the availability of sufficient and high-quality calibration data. Limited calibration data can lead to suboptimal spatial filters and low signal-to-noise ratios in individual SSVEP templates, ultimately degrading the decoding accuracy and efficiency. To address this challenge, we propose a novel data augmentation method called Shift Aliasing Signal (SAS), which generates synthetic SSVEP epochs by estimating a mixing matrix from the available training data and applying time-shift operations while maintaining the essential features of SSVEP signals, such as their phase-locked nature and frequency-specific patterns. The SAS method introduces meaningful variations to enhance the training dataset. We integrate the SAS method with state-of-the-art training-based spatial filtering methods to create a powerful decoding framework for SSVEP-based brain-computer interface (BCI) systems. Extensive experiments on benchmark datasets demonstrate that the proposed SAS method significantly improves classification accuracy and information transfer rate, particularly in scenarios with limited calibration data, outperforming state-of-the-art training-based spatial filtering methods and other data augmentation techniques.

关键词： Training Accuracy Filtering Data augmentation Spatial filters Feature extraction Electroencephalography Decoding Calibration Synthetic aperture sonar

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MixSSC: Forward-Backward Mixture for Vision-based 3D Semantic Scene Completion

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Meng Ding, Yan Liu, Yumeng Qin, Yunchuan Li, Ruihui Tang, Zhuo The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing100190 China

Vision-based semantic scene completion task aims to predict dense geometric and semantic 3D scene representations from 2D images. However, 3D modeling from a single view is an ill-posed problem, limited by the field of view and occlusion problems caused by image input. Moreover, existing methods tend to produce erroneous scene hallucinations and overly smooth boundary segmentation due to a lack of information. To address this problem, we propose MixSSC, which mixes the sparsity of forward projection with the denseness of depth-prior backward projection. The aim is to use sparse features to fill information-poor regions and dense features to enhance visible regions. Specifically, we develop the forward-backward mixture module, which enables the generation of scene mixture voxel representation by leveraging the benefits of both forward and backward projection. Subsequently, we design the semantic-spatial fusion module, which utilizes a coarse-to-fine approach to process mixture voxel features at the semantic-spatial level. Extensive experimental results on the SemanticKITTI, SSCBench-KITTI-360 and nuScenes datasets demonstrate the superiority of MixSSC. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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Resilient strategies for socially compliant autonomous assistive dressing robots 23

Resilient strategies for socially compliant autonomous assis...

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1st International Symposium on Trustworthy Autonomous Systems, TAS 2023

作者： Parnell, Katie J. Merriman, Siobhan E. Getir Yaman, Sinem Plant, Katherine L. Calinescu, Radu Human Factors Engineering University of Southampton United Kingdom Department of Computer Science University of York United Kingdom

ISBN: (纸本)9798400707346

Developing resilient autonomous systems requires an interdisciplinary approach that can understand performance variability and respond to critical events when they occur. Resilience within autonomous systems must also account for social norms as well as broader ethical and legal considerations. Within this paper we outline the importance of embedding Social, Legal, Ethical, Empathetic and Cultural (SLEEC) constraints within the development of future autonomous systems. A novel methodological approach is presented that combines human Factors methods with computer science techniques to generate the environmental and situational requirements in combination with a computer rule-based requirements language. This approach also provides a possible structure for capturing contextual and situational information from key stakeholders in the development of autonomous systems. This structure will enable engagement with the stakeholders with respect to key elements identified from this interdisciplinary approach in a responsible way to ensure that future autonomous systems are user centred. The approach is domain independent, but it is applied here to the case of an autonomous assistive dressing robot that aids a user in a dressing task, with a specific critical event that requires a SLEEC resilient response © 2023 ACM.

关键词： Robots

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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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