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

作者： Yarmand, Matin Li, Haowei Weibel, Nadir Computer Science and Engineering The Design Lab UC San Diego San Diego CA United States Graduate School of Education University of Pennsylvania Philadelphia PA United States

ISBN: (纸本)9798400713941

Online education - given the enhanced access for diverse populations and flexible participation - has been a topic of interest for many computer science and learning science researchers. The sudden shift to online settings during the COVID-19 Emergency Remote Teaching (ERT) provided a valuable opportunity to examine the use of educational technologies on a global scale with various digital readiness skills, beyond many past works that relied on small lab studies. Following a PRISMA-inspired methodology grounded on Moore's three types of classroom interaction, this descriptive review investigates 22 empirical research papers published during the COVID-19 ERT era focused on higher-education online classrooms. We explore the empirical evidence reported in the collected corpus, and given how ERT remains a likely future occurrence, we suggest key directions for future research, including a new learning paradigm that centralizes and augments Learner-Content interaction to balance between flexibility and structure of online learning. © 2025 Copyright held by the owner/author(s).

关键词： Emergency Remote Education Higher Education Learning Interactions Online Classroom

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User-Centric Crowdsourcing Approach to Improve Urban Accessibility Data Collection

User-Centric Crowdsourcing Approach to Improve Urban Accessi...

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2024 IEEE MIT Undergraduate Research Technology Conference, URTC 2024

作者： Ortiz, Tyler Tang, Vicky Sutton, Karla The City College of New York Cuny New York Grove School of Engineering Computer Science New York United States School of Constructed Environments Parsons School of Design New York New York United States Lab School for Collaborative Studies New York Department of Computer Science New York United States

ISBN: (纸本)9798331531003

While crowdsourcing is an effective method for collecting large-scale datasets, it often faces challenges related to data quality and quantity. This study employs crowdsourcing to collect comprehensive urban accessibility data, focusing on storefront accessibility information in a large city. This task presents significant challenges in terms of data completeness and quality. To address these issues, we propose a user-centric, request-driven crowdsourcing approach that gathers accessibility data based on requests from individuals who are blind or have low vision (BLV). This approach strengthens the user-volunteer feedback loop, increasing volunteer engagement and enhancing user experience. It allows BLV individuals to effectively utilize the collected storefront accessibility data for independent travel. A preliminary user study confirms the effectiveness of the proposed approach. © 2024 IEEE.

关键词： Crowdsourcing

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Explore the Future Earth with Wander 2.0: AI Chatbot Driven By Knowledge-base Story Generation and Text-to-image Model

Explore the Future Earth with Wander 2.0: AI Chatbot Driven ...

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Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023

作者： Sun, Yuqian Xu, Ying Cheng, Chenhang Li, Yihua Lee, Chang Hee Asadipour, Ali Computer Science Research Centre Royal College of Art London United Kingdom Department of Industrial Design School of Art and Design Wuhan University of Technology Wuhan China Shanghai China Department of Product Design College of Fashion and Design Donghua University Shanghai China Affective Systems and Cognition Lab Industrial Design Department College of Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of

ISBN: (纸本)9781450394222

People always envision the future of earth through science fiction (Sci-fi), so can we create a unique experience of "visiting the future earth"through the lens of artificial intelligence (AI)? We introduce Wander 2.0, an AI chatbot that co-creates sci-fi stories through knowledge-based story generation on daily communication platforms like WeChat and Discord. Using location information from Google Maps, Wander generates narrative travelogues about specific locations (e.g. Paris) through a large-scale language model (LLM). Additionally, using the large-scale text-to-image model (LTGM) Stable Diffusion, Wander transfers future scenes that match both the text description and location photo, facilitating future imagination. The project also includes a real-time visualization of the human-AI collaborations on a future map. Through journeys with visitors from all over the world, Wander demonstrates how AI can serve as a subjective interface linking fiction and reality. Our research shows that multi-modal AI systems have the potential to extend the artistic experience and creative world-building through adaptive and unique content generation for different people. Wander 2.0 is available at http://***/ © 2023 Owner/Author.

关键词： Knowledge based systems

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Roopkotha: A Companion Robot for Enhancing Interactive Storytelling with Natural Interaction 3

Roopkotha: A Companion Robot for Enhancing Interactive Story...

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3rd International Conference on Image Processing and Robotics, ICIPRoB 2024

作者： Mehzabin, Kazi Mayesha Islam, Md. Zahidul Nur, Md. Ashaduzzaman Shidujaman, Mohammad Samani, Hooman Mi, Haipeng Independent University Bangladesh RIoT Center and Department of Computer Science and Engineering Dhaka Bangladesh Creative Computing Institute University of the Arts London London United Kingdom Tsinghua University Media and Interaction Lab Academy of Arts and Design Beijing China

ISBN: (纸本)9798350374766

Roopkotha is a storytelling robot that seamlessly combines traditional storytelling methods and technology, creating a captivating robot storyteller. We are creating a special prototype in the world of robots that tells stories in a way that is easy to understand and enjoy. In this era of technological advancement, Roopkotha combines voice recognition with Bangla Language processing, emotion recognition, human behavior detection. Roopkotha aims to revolutionize the way stories are told and engage with users on a deep emotional level. Furthermore, Roopkotha is equipped with advanced facial expressions and emotion recognition technology. The emotion recognition feature helps the robot to have a profound connection with the users. © 2024 IEEE.

关键词： Emotion Recognition

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Data Quality Assessment of Substation Data in Bangladesh: Insights from Handwritten Data Digitization 10

Data Quality Assessment of Substation Data in Bangladesh: In...

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10th IEEE International Conference on Power Systems, ICPS 2023

作者： Al Amin, Murshed Jawad-Al-Mursalin Hoque, M.D. Nazzum, Zakia Sayed, Md Abu Tanveer Ahmed Rumee, Sarker Zaber, Moinul Islam Military Institute of Science and Technology Bangladesh Independent University Bangladesh Data and Design Lab Dhaka1000 Bangladesh University of Dhaka Department of Computer Science & Engineering Dhaka1000 Bangladesh United Nations University Guimarães Portugal

ISBN: (纸本)9798350318739

The assessment of power quality is vital for evaluating the existing state of the electricity supply, pinpointing its deficiencies, and guiding enhancements to guarantee a stable and consistent power supply, particularly in the face of rising demands from both industrial and residential sectors. In the context of Bangladesh, however, most substation data are still recorded manually, despite ongoing digitization efforts. This paper focuses on the quality of digitized transcription of handwritten substation data to determine its suitability for further analytical use. Our analysis revealed that the paper-based data from the substations exhibited significant gaps, undermining its reliability and accuracy. Through comprehensive evaluation, we concluded that the data, in its current state, is inadequate for in-depth analysis without the application of robust imputation methods. By tackling these challenges, our research aims to contribute to more informed decision-making and the efficient management of Bangladesh's electrical infrastructure. This study not only diagnoses the current shortcomings in data quality but also lays the groundwork for future improvements in the country's approach to energy data management. © 2023 IEEE.

关键词： Power quality

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Wireless Federated Learning With Hybrid Local and Centralized Training: A Latency Minimization design

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IEEE Journal on Selected Topics in Signal Processing 2023年第1期17卷 248-263页

作者： Huang, Ning Dai, Minghui Wu, Yuan Quek, Tony Q. S. Shen, Xuemin State Key Lab of Internet of Things for Smart City China University of Macau Department of Computer and Information Science China University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science China Zhuhai Um Science and Technology Research Institute Zhuhai519031 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore National Cheng Kung University Taiwan University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Wireless federated learning (FL) is a collaborative machine learning (ML) framework in which wireless client-devices independently train their ML models and send the locally trained models to the FL server for aggregation. In this paper, we consider the coexistence of privacy-sensitive client-devices and privacy-insensitive yet computing-resource constrained client-devices, and propose an FL framework with a hybrid centralized training and local training. Specifically, the privacy-sensitive client-devices perform local ML model training and send their local models to the FL server. Each privacy-insensitive client-device can have two options, i.e., (i) conducting a local training and then sending its local model to the FL server, and (ii) directly sending its local data to the FL server for the centralized training. The FL server, after collecting the data from the privacy-insensitive client-devices (which choose to upload the local data), conducts a centralized training with the received datasets. The global model is then generated by aggregating (i) the local models uploaded by the client-devices and (ii) the model trained by the FL server centrally. Focusing on this hybrid FL framework, we firstly analyze its convergence feature with respect to the client-devices' selections of local training or centralized training. We then formulate a joint optimization of client-devices' selections of the local training or centralized training, the FL training configuration (i.e., the number of the local iterations and the number of the global iterations), and the bandwidth allocations to the client-devices, with the objective of minimizing the overall latency for reaching the FL convergence. Despite the non-convexity of the joint optimization problem, we identify its layered structure and propose an efficient algorithm to solve it. Numerical results demonstrate the advantage of our proposed FL framework with the hybrid local and centralized training as well as our proposed alg

关键词： Information management

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Energy consumption forecasting for laser manufacturing of large artifacts based on fusionable transfer learning

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 19-32页

作者： Linxuan Wang Jinghua Xu Shuyou Zhang Jianrong Tan Shaomei Fei Xuezhi Shi Jihong Pang Sheng Luo State Key Lab of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhouZhejiang 310058China Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province Zhejiang UniversityHangzhouZhejiang 310058China State Key Lab of Materials Processing and Die&Mould Technology Huazhong University of Science and TechnologyWuhanHubei 430074China School of Marine Engineering Equipment Zhejiang Ocean UniversityZhoushanZhejiang 316022China College of Business Shaoxing UniversityShaoxingZhejiang 312000China College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhouZhejiang 325000China

This study presents an energy consumption(EC)forecasting method for laser melting manufacturing of metal artifacts based on fusionable transfer learning(FTL).To predict the EC of manufacturing products,particularly from scale-down to scale-up,a general paradigm was first developed by categorizing the overall process into three main *** operating electrical power was further formulated as a combinatorial function,based on which an operator learning network was adopted to fit the nonlinear relations between the fabricating arguments and ***-arranged networks were constructed to investigate the impacts of fabrication variables and devices on *** the interconnections among these factors,the outputs of the neural networks were blended and fused to jointly predict the electrical *** innovatively,large artifacts can be decomposed into timedependent laser-scanning trajectories,which can be further transformed into fusionable information via neural networks,inspired by large language ***,transfer learning can deal with either scale-down or scale-up forecasting,namely,FTL with scalability within artifact *** effectiveness of the proposed FTL was verified through physical fabrication experiments via laser powder bed *** relative error of the average and overall EC predictions based on FTL was maintained below 0.83%.The melting fusion quality was examined using metallographic *** proposed FTL framework can forecast the EC of scaled structures,which is particularly helpful in price estimation and quotation of large metal products towards carbon peaking and carbon neutrality.

关键词： Energy consumption forecasting Large metal artifacts Carbon peaking and carbon neutrality Laser powder bed fusion Fusionable transfer learning

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Assessment of Serum Creatinine and Serum Sodium Prognostic Potential in Heart Failure Patients Using Machine Learning 46

Assessment of Serum Creatinine and Serum Sodium Prognostic P...

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

作者： Alyounis, Sona Khandoker, Ahsan Stefanini, Cesare Hadjileontiadis, Leontios Scuola Superiore Sant'Anna Creative Engineering Design Lab The BioRobotics Institute Pontedera Italy Khalifa University of Science and Technology Healthcare Engineering Innovation Center Department of Biomedical Engineering P.O. Box 127788 Abu Dhabi United Arab Emirates Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki Greece

ISBN: (纸本)9798350371499

Heart failure (HF) is the leading etiology for hospital admissions and ranks among the foremost contributors to mortality. This complex clinical syndrome with various phenotypes is categorized by left ventricle ejection fraction levels (LVEF), namely preserved (HFpEF), mid-range (HFmEF), and reduced (HFrEF). This study investigates the prognostic impact of serum creatinine and serum sodium levels in HF patients across these three classes using machine learning tools. A comprehensive dataset of HF patients' medical records including serum sodium and serum creatinine was utilized. Machine learning regression models were employed to predict the LVEF levels. Additionally, classification models were implemented to categorize patients into HFpEF, HFmEF, and HFrEF classes. Regression analyses revealed the predictive capabilities of serum sodium and serum creatinine in estimating the progression of HF severity. Furthermore, classification models successfully differentiated between the three EF classes, providing valuable insights into the classification patterns of HF patients based on these biomarkers. The results demonstrated the significance of serum sodium serum creatinine as prognostic markers in HF, and this contributes to a more nuanced approach to HF management, paving the way for targeted interventions and improved patient outcomes. Moreover, this study highlights the potential of machine learning techniques to enhance risk stratification and classification in HF patients, enabling personalized prognostication and treatment strategies. © 2024 IEEE.

关键词： Creatinine

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Attention-Enhanced Lightweight Hourglass Network for Human Pose Estimation

Attention-Enhanced Lightweight Hourglass Network for Human P...

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2024 Australasian Conference on Robotics and Automation, ACRA 2024

作者： Kappan, Marsha Mariya Sandoval, Eduardo Benitez Meijering, Erik Cruz, Francisco School of Computer Science and Engineering University of New South Wales Sydney Australia School of Art and Design Creative Robotics Lab. University of New South Wales Sydney Australia Escuela de Ingeniería Universidad Central de Chile Santiago Chile

Pose estimation is a critical task in computer vision with a wide range of applications from activity monitoring to human-robot interaction. However, most of the existing methods are computationally expensive or have complex architecture. Here we propose a lightweight attention based pose estimation network that utilizes depthwise separable convolution and Convolutional Block Attention Module on an hourglass backbone. The network significantly reduces the computational complexity (floating point operations) and the model size (number of parameters) containing only about 10% of parameters of original eight stack Hourglass network. Experiments were conducted on COCO and MPII datasets using a two stack hourglass backbone. The results showed that our model performs well in comparison to six other lightweight pose estimation models with an average precision of 72.07. The model achieves this performance with only 2.3M parameters and 3.7G FLOPs. © 2024 Australasian Robotics and Automation Association. All rights reserved.

关键词： Convolution

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Investigating the Transferability of Evasion Attacks in Network Intrusion Detection Systems Considering Domain-Specific Constraints 1

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25th International Conference on Information Security Applications, WISA 2024

作者： Mbow, Mariama Roman, Rodrigo Seal, Ayan Wang, Kevin I-Kai Mohanty, Sraban Kumar Sakurai, Kouichi Department of Information Science and Technology Kyushu University Fukuoka Japan Lab University of Malaga Málaga Spain PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur482005 India Department of Electrical Computer and Software Engineering The University of Auckland Auckland1010 New Zealand

ISBN: (数字)9789819616244

ISBN: (纸本)9789819616237

Recently, adversarial attacks against machine learning-based network intrusion detection systems (NIDS) have gained significant attention in cybersecurity. This study investigates the transferability of these adversarial attacks in the context of NIDS. In addition, most existing studies in adversarial learning against NIDS adopted attack strategies designed originally for image classification without considering network traffic characteristics. However, this is impractical and will fail in the real world as network traffic features are constrained, and ignoring the functional behavior of the network traffic features leads to invalid network traffic flow or produces adversarial samples that do not retain their original functionality (malicious or benign). To address these issues, we propose a constrained momentum iterative fast gradient sign method (C-MIFGSM) to generate adversarial network flows that can successfully evade an ML-based IDS through transfer-based attacks while preserving the functional behavior of the network traffic. Our approach was validated using several target NIDS models built with the NSLKDD benchmark dataset. Experimental results demonstrate that even without knowledge of the target model and under feature constraints, it is possible to generate adversarial network traffic flows that achieve a high evasion attack success rate against NIDS built with deep learning and classical ML models. For example, the attack degraded the detection rate of the DoS traffic drops from 92.35% to 20.27% for the MLP model with an evasion increase rate of 78.04%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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