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Toward a Secure Educational Metaverse: A Tale of Blockchain Design for Educational Environments 49

Toward a Secure Educational Metaverse: A Tale of Blockchain ...

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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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"I am a Technology Creator": Black Girls as Technosocial Change Agents in a Culturally-Responsive Robotics Camp 25

"I am a Technology Creator": Black Girls as Technosocial Cha...

引用

2025 CHI Conference on human Factors in Computing Systems, CHI 2025

作者： Li, Chun Solyst, Jaemarie Scott, Safiyyah Howse, Gabriella Nkrumah, Tara Walker, Erin Ogan, Amy Stewart, Angela E.B. Department of Informatics and Networked System University of Pittsburgh PittsburghPA United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States School of Computer Science Carnegie Mellon University PittsburghPA United States Center for Gender Equity in Science and Technology Arizona State University TempeAZ United States Department of Computer Science University of Pittsburgh PittsburghPA United States

ISBN: (纸本)9798400713941

Black girls and women have long been creators in computing spaces. However, much computing education positions Black girls as workers who execute tasks for others' purposes. Our work takes a different approach by positioning Black girls as technosocial change agents who challenge dominant narratives and construct more liberating identities and social relations as they create new technologies. We draw on data from seven Black girls, ages 9-12, who participated in a 20-hour culturally responsive computing (CRC) camp focused on robotics. Using a thematic analysis approach, we explore how these Black girls demonstrate and enhance their technosocial change agency (TSCA) throughout the camp. We identify themes related to how creating technology helps Black girls refine and fulfill their definitions of technical creators and develop agency through technology creation. We discuss computing education and technology design recommendations within the TSCA framework to support learners' emerging TSCA in future CRC programs. © 2025 Copyright held by the owner/author(s).

关键词： Educational robots

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Movits: a Minimalist Toolkit for Embodied Sketching

Movits: a Minimalist Toolkit for Embodied Sketching

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2024 ACM Designing Interactive Systems Conference, DIS 2024

作者： Vega-Cebrián, José Manuel Vidal, Laia Turmo Tajadura-Jiménez, Ana Covas, Tomás Bonino Segura, Elena Márquez Department of Computer Science and Engineering Universidad Carlos III de Madrid Madrid Spain Media Technology and Interaction Design KTH Royal Institute of Technology Stockholm Sweden UCL Interaction Centre University College London London United Kingdom ESITEF Madrid Spain

ISBN: (纸本)9798400705830

We present the design and evaluation of the Movits, a minimalist toolkit for embodied sketching design explorations. The toolkit includes technology probes featuring minimalist wearable digital units that support the hands-on exploration and design of movement-driven interactions using multisensory feedback. The Movits are self-contained and generate audiovisual or vibrotactile patterns in response to movement-based inputs. We present the theoretical and empirical grounding driving our design process. We discuss the fndings of using the Movits during four co-design workshops with design students, technologists, dancers and physiotherapists, where they resulted in being generative and adaptable to a range of embodied design approaches. We contend that the Movits can be favourable for those interested in a holistic design approach to wearables in general and specifcally for those targeting movement-based application domains. © 2024 Copyright held by the owner/author(s).

关键词： Biofeedback

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Post-Training Quantization in Brain-computer Interfaces Based on Event-Related Potential Detection

Post-Training Quantization in Brain-Computer Interfaces Base...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Hubert Cecotti Dalvir Dhaliwal Hardip Singh Yohesh Kumar Meena Department of Computer Science California State University Fresno USA Human-AI Interaction (HAIx) Lab IIT Gandhinagar India

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Post-training quantization (PTQ) is a technique used to optimize and reduce the memory footprint and computational requirements of machine learning models. It has been used primarily for neural networks. For Brain-computer Interfaces (BCI) that are fully portable and usable in various situations, it is necessary to provide approaches that are lightweight for storage and computation. In this paper, we propose the evaluation of post-training quantization on state-of-the-art approaches in brain-computer interfaces and assess their impact on accuracy. We evaluate the performance of the single-trial detection of event-related potentials representing one major BCI paradigm. The area under the receiver operating characteristic curve drops from 0.861 to 0.825 with PTQ when applied on both spatial filters and the classifier, while reducing the size of the model by about x 15. The results support the conclusion that PTQ can substantially reduce the memory footprint of the models while keeping roughly the same level of accuracy.

关键词： Quantization (signal) Accuracy Memory management Deep architecture Receivers Machine learning Brain modeling Spatial filters Brain-computer interfaces Hardware

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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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The Pattern of CA1 c-Fos Expressing Place Cells Correlates with Learning Progression 46

The Pattern of CA1 c-Fos Expressing Place Cells Correlates w...

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

作者： Wang, Ning Zhang, Anqi Yang, Jiajia Wang, Ling 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

Spatial memory supported by the hippocampus is a critical cognitive function in daily life. It helps us to navigate to salient goal locations. Hippocampal place cells stably represent the spatial location, while c-Fos-expressing cells (engram cells) contain rich information about the environment, contributing to the encoding and retrieving of memories. However, the role of cFos as a gateway to a more profound comprehension of the mechanisms underlying the process of goal-oriented spatial memory remained poorly understood. In this study, we labeled rats' c-Fos+ neurons by Channelrhodopsin-2 during memory encoding of a specific goal location. A subset of place cells whether they expressed c-Fos was identified optogenetically. As the rats learned the new goal locations, the spatial correlation between task periods remained stable for the c-Fos- place cell, while increased for the c-Fos+ place cell. This finding shed light on the processing of goal-oriented spatial memories by the hippocampal network involved in c-Fos. C-Fos- place cells consistently maintained a stable representation of the locations, while c-Fos+ place cells exhibited significant flexibility of the goal. © 2024 IEEE.

关键词： Signal encoding

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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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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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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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Diversity, Equity, and Inclusion in Computing science: Culture is the Key, Curriculum Contributes 2024

Diversity, Equity, and Inclusion in Computing Science: Cultu...

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29th Working Group Reports on Innovation and Technology in computer science Education, ITiCSE WGR 2024

作者： Toti, Giulia Lindner, Peggy Gao, Alice Baghban Karimi, Ouldooz Engineer, Rutwa Hur, Jinyoung Mcneill, Fiona Reckinger, Shanon Robinson, Rebecca Sollazzo, Anna Wicentowski, Richard Department of Computer Science University of British Columbia VancouverBC Canada Department of Information Science Technology University of Houston HoustonTX United States Department of Computer Science University of Toronto TorontoON Canada School of Computing Science Simon Fraser University SurreyBC Canada Department of Mathematical and Computational Sciences University of Toronto Mississauga MississaugaON Canada Department of Computer Science University of Illinois Urbana-Champaign Urbana-ChampaignIL United States School of Informatics University of Edinburgh Edinburgh United Kingdom Department of Computer Science University of Illinois Chicago ChicagoIL United States Department of Human Centred Computing Monash University ClaytonVIC Australia School of Computing Science University of Glasgow United Kingdom Computer Science Department Swarthmore College SwarthmorePA United States

ISBN: (纸本)9798400712081

Undergraduate computer science programs worldwide struggle to attract and retain underrepresented students for many reasons. Culture, stereotype threats, uneven gender and racial representations, lack of role models, and uncertain career prospects for minority groups are among the many reasons behind this situation. Many computer science programs are trying to change course through strategies to foster equity, diversity, and inclusion (EDI), aimed at improving outreach, recruitment, admissions, and retention of underrepresented students. EDI approaches may also include modifications to the undergraduate computer science curriculum. However, if not properly planned, these modifications risk amplifying existing stereotypes rather than producing positive change [38]. In this study, through an extensive literature review, a rigorous curriculum analysis of 49 computer science programs across the globe, and qualitative and quantitative analysis of surveys and interviews bringing in the voices of 613 students and 30 educators participating from around the world, we explore equity, diversity, and inclusion in the computer science curriculum. We highlight the role of inclusive content and course design, discuss program flexibility, and the impact of inclusive courses and program design in attracting and retaining historically marginalized students. Finally, we provide concrete steps to make computing science undergraduate curricula more appealing to a diverse audience. © 2024 Copyright held by the owner/author(s).

关键词： Students

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