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International Conference on Data engineering

作者： David Alexander Tedjopurnomo Zhifeng Bao Baihua Zheng Farhana Murtaza Choudhury A. K. Qin School of Computing Technologies RMIT University Australia School of Computing and Information Systems Singapore Management University Singapore School of Computing and Information Systems The University of Melbourne Australia Department of Computer Science and Software Engineering Swinburne University of Technology Australia

In this modern era, traffic congestion has become a major source of negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate this issue is through traffic prediction. This research field has evolved greatly ever since its inception in the late 70s. Recently, deep neural network models have gained popularity thanks to its predictive power, but despite this, literature surveys of such methods are rare; making it difficult to ascertain the progress of this research field. In this work, we address this issue by presenting an up-to-date survey of deep neural network for traffic prediction. We provide detailed explanations of popular deep neural network architectures used in the traffic flow prediction literatures, categorize and describe the literatures themselves, present an overview of the commonalities and differences among different works, and finally provide a discussion regarding the challenges and future directions for this field.

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Deep Learning-Based E-Learning Solution for Identifying and Bridging the Knowledge Gap in Primary Education

Deep Learning-Based E-Learning Solution for Identifying and ...

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International Research Conference on Smart computing and Systems engineering (SCSE)

作者： D.P.H Arunoda S.R Walpola S.M.I Piumira A.D.M.P Athukorala Thusithanjana Thilakarathna Sanjeevi Chandrasiri Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

Educational teaching apps are primarily available in app stores to educate students in various contexts. Lack of educational resources, physical and mental health conditions, and poverty cause some students to skip school and move on to the next school grade without completing the course content of the previous grade. Most of the available apps focus on specific content to cover. The Smart Primary Education Tutor (SPET) teaching app specifically focuses on the missed content by analyzing their knowledge gap and providing lessons to cover the missed content. The main objective of SPET is to develop a methodology to identify the gap in student knowledge and fill the knowledge gap by teaching using smart techniques. SPET is determined to identify students’ interactions (attention, emotions) with the system to identify students’ ability to use the learning tool, identifying gaps in students’ knowledge levels compared to their actual grades using activities and voice-based technologies, teaching to cover the knowledge gap by providing engaging activities and lessons and evaluating students by conducting a final assessment and analyze students’ knowledge and performance obtained through the system. Students between the ages of 5 and 8 are targeted in the community to apply. The solution embeds deep learning-based models including attention classification models using head posture estimation, facial expression recognition, and eye gaze estimation, speech recognition models to identify provided verbal answers, handwriting recognition models to evaluate student performance, and smart teaching. The child emotion recognition model achieved 93% accuracy. The Attention span evaluation model achieved 85% accuracy. The handwritten numerical and English character data recognition model which detects answers for the final assessment paper achieved 85% percent of accuracy.

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How Far Have Edge Clouds Gone? A Spatial-Temporal Analysis of Edge Network Latency In the Wild 31

How Far Have Edge Clouds Gone? A Spatial-Temporal Analysis o...

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Zhang, Heng Huang, Shaoyuan Xu, Mengwei Guo, Deke Wang, Xiaofei Leung, Victor C.M. Wang, Wenyu College of Intelligence and Computing Tianjin University Tianjin China Beijing University of Posts and Telecommunications Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering VancouverV6T 1Z4 Canada Co. Ltd Shanghai China

ISBN: (纸本)9798350399738

The emergence of next-generation latency-critical applications places strict requirements on network latency and stability. Edge cloud, an instantiated paradigm for edge computing, is gaining more and more attention due to its benefits of low latency. In this work, we make an in-depth investigation into the network QoS, especially end-to-end latency, at both spatial and temporal dimensions on a nationwide edge computing platform. Through the measurements, we collect a multi-variable large-scale real-world dataset on latency. We then quantify how the spatial-temporal factors affect the end-to-end latency, and verified the predictability of end-to-end latency. The results reveal the limitation of centralized clouds and illustrate how could edge clouds provide low and stable latency. Our results also point out that existing edge clouds merely increase the density of servers and ignore spatial-temporal factors, so they still suffer from high latency and fluctuations. Based on the observations, we propose a robust prototype edge cloud model based on lessons we learn from the measurement and evaluate its performance in the production environment. The further evaluation result shows that edge clouds achieve 84.1% latency reduction with 0.5ms latency fluctuation and 73.3% QoS improvement compared with the centralized clouds. © 2023 IEEE.

关键词： Edge computing

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Co-Production of a Cognitive Behavioral Therapy Digital Platform for Families of Individuals Impacted by Psychosis

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Journal of Technology in Behavioral Science 2024年第1期9卷 140-148页

作者： Cheng, Sunny Chieh Kopelovich, Sarah Si, Dong Divina, Myra Gao, Ningjun Serene Wang, Mia Yunqi Kim, Jamie Jaesook Li, Ziyi Blank, Jennifer Brian, Rachel Turkington, Douglas School of Nursing and Healthcare Leadership University of Washington Tacoma WA United States Department of Psychiatry and Behavioral Sciences University of Washington School of Medicine Seattle WA United States Division of Computing and Software Systems University of Washington Bothell WA United States Department of Human Centered Design & Engineering University of Washington Seattle WA United States Department of Economics University of Washington Seattle WA United States Department of Statistics University of Washington Seattle WA United States Newcastle University Newcastle Upon Tyne United Kingdom

Education and skills training have consistently been recognized as an unmet need for caregivers of individuals with psychosis, as traditional models of clinician-delivered family interventions remain inaccessible to most caregivers across low-, middle-, and high-income countries. We endeavored to expand access to psychosis education and skills training by co-developing a digital training platform with experts and families. We engaged in collaborative co-design sessions with caregivers who had received training in Psychosis REACH—an evidence-based psychosocial family intervention for psychosis—to elicit design principles, develop a functional prototype, and engage in usability testing of an online interactive training program. Seven participants reported six design principles for a prototype. These included simplicity, accessibility, skill acquisition, usability, sense of community, and hope. Guided by these principles, participants’ preferred features were (1) online modules with didactic content, (2) a training “coach” conversational agent to provide instruction and feedback to users, and (3) a conversational agent simulation of an individual experiencing psychosis with whom users can practice skills. Participants completed the usability testing. The levels of ease of use ranged from 4.2 to 4.8 on a 5-point scale with 5 being easiest to use and the level of usefulness ranged from 4 to 4.75 on a 5-point scale, with 5 being most useful. This study is the first to develop a digital platform for family caregivers to support interactive skills training for psychosis caregiving at home. Iterative user-centered design resulted in a functional alpha prototype ripe for testing. Future directions include testing the design requirements on more diverse users and evaluating the effects of the online platform on the caregiving experience. © 2024, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Caregiver Co-production Cognitive behavior therapy Digital platform Mental health Psychosis Usability User-centered design

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Mahalanobis Distance-based Multi-view Optimal Transport for Multi-view Crowd Localization

arXiv

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arXiv 2024年

作者： Zhang, Qi Zhang, Kaiyi Chan, Antoni B. Huang, Hui Visual Computing Research Center College of Computer Science and Software Engineering Shenzhen University Shenzhen China China Department of Computer Science City University of Hong Kong Hong Kong

Multi-view crowd localization predicts the ground locations of all people in the scene. Typical methods usually estimate the crowd density maps on the ground plane first, and then obtain the crowd locations. However, the performance of existing methods is limited by the ambiguity of the density maps in crowded areas, where local peaks can be smoothed away. To mitigate the weakness of density map supervision, optimal transport-based point supervision methods have been proposed in the single-image crowd localization tasks, but have not been explored for multi-view crowd localization yet. Thus, in this paper, we propose a novel Mahalanobis distance-based multi-view optimal transport (M-MVOT) loss specifically designed for multi-view crowd localization. First, we replace the Euclidean-based transport cost with the Mahalanobis distance, which defines elliptical iso-contours in the cost function whose long-axis and short-axis directions are guided by the view ray direction. Second, the object-to-camera distance in each view is used to adjust the optimal transport cost of each location further, where the wrong predictions far away from the camera are more heavily penalized. Finally, we propose a strategy to consider all the input camera views in the model loss (M-MVOT) by computing the optimal transport cost for each ground-truth point based on its closest camera. Experiments demonstrate the advantage of the proposed method over density map-based or common Euclidean distance-based optimal transport loss on several multi-view crowd localization datasets. Project page: MVOT Project. © 2024, CC BY.

关键词： Cost functions

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VIDEO ABNORMAL EVENT DETECTION VIA CONTEXT CUEING GENERATIVE ADVERSARIAL NETWORK

VIDEO ABNORMAL EVENT DETECTION VIA CONTEXT CUEING GENERATIVE...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Zhang, Zhi Zhong, Sheng-Hua Liu, Yan College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781665438643

Video abnormal event detection is a significant research subject in intelligent surveillance, referring to identifying events that do not conform to expected behaviors. It is a challenging task due to the diversity of video content and the various definition of anomaly depending on contexts. How to grasp the nuances of anomalies from diverse video content under associated contexts? In this paper, we propose a context cueing generative adversarial network (CC-GAN) to scop attention to specific regions of interest (ROIs). Different from existing work, the designed framework takes advantage of the context cues to preserve the associations between ROIs and contexts. By intrinsic learning of static spatial layouts and apparent velocities, context cues can help to understand ROIs. Optimized in a crop-inpainting fashion, CC-GAN learns to generate ROIs matching given spatio-temporal context and discriminate the distorted or incongruous ones, corresponding to abnormal scenes. Compared with the state-of-the-art method and other abnormal event detection approaches, the proposed framework demonstrates its effectiveness, reliability, and good generalization ability across various scenes and events. © 2021 IEEE Computer Society. All rights reserved.

关键词： Generative adversarial networks

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Reliable Service Recommendation: A Multi-modal Adversarial Method for Personalized Recommendation under Uncertain Missing Modalities

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IEEE Transactions on Services computing 2025年第3期18卷 1724-1738页

作者： Chen, Junyang Yang, Ruohan Guo, Jingcai Wang, Huan Wu, Kaishun Zhang, Liangjie Shenzhen University College of Computer Science and Software Engineering China Huazhong Agricultural University College of Informatics Wuhan China The Hong Kong Polytechnic University Department of Computing Hong Kong DSA Thrust and IoT Thrust Center China

Personalized recommendation is of paramount importance in online content platforms like Kuai and Tencent. To ensure accurate recommendations, it is crucial to consider multi-modal information in both items and user-user/item interactions. While existing works on multimedia recommendation have made strides in leveraging multi-modal contents to enrich item representations, many of them overlook the practical scenario of multiple modality missing. As a result, the performance of recommendation systems can be significantly compromised in such cases. In this paper, we introduce a novel multi-modal adversarial method called MMAM, which aims to provide reliable personalized recommendation services even in the presence of uncertain missing modalities. The core idea behind MMAM is to design a generator that can effectively encode both user-user/item interactions and multi-modal contents, taking into account various missing cases. The generator is trained to learn transferable features from different combinations of missing modalities in order to deceive a discriminative classifier. Additionally, we propose a modal discriminator that can classify the missing cases of multi-modalities, further enhancing the capability of the model. Moreover, a well-equipped predictor utilizes the transferable features to predict potential user interests. To improve the prediction accuracy, we design a type discriminator that enhances the classification of link types. By employing a mini-max game between the generator and the discriminators, MMAM successfully obtains transferable features that encompass multi-modal contents, even when facing uncertain missing modalities. We conduct extensive experiments on industrial datasets, including Kuai and Tencent. Comparing with state-of-the-art approaches, MMAM achieves improvements in personalized recommendation tasks under uncertain missing modalities. MMAM holds promise for enhancing multi-modal personalized recommendations in real-world applications

关键词： Discriminators

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A Holistic QoS View of Crowdsourced Edge Cloud Platform 31

A Holistic QoS View of Crowdsourced Edge Cloud Platform

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Shen, Shihao Feng, Yicheng Xu, Mengwei Zhang, Cheng Wang, Xiaofei Wang, Wenyu Leung, Victor C.M. College of Intelligence and Computing Tianjin University Tianjin China Beijing University of Posts and Telecommunications Beijing China Institute of Technology Tianjin University of Finance and Economics Tianjin China Co. Ltd. Shanghai China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350399738

Edge clouds have become a de-facto paradigm to deliver low and stable networks to delay-critical applications such as web services and AR/VR. A unique form of edge clouds is those crowdsourced from third parties, e.g., idle PCs or workstations. Such crowdsourced edge platforms can better sink computations closer to users, reduce the purchase cost, and eliminates the carbon generated during manufacturing. Yet, they also face the challenge of out-of-control hardware, e.g., a server dropping in/out anytime. In this paper, we perform the first-of-its-kind measurement of Quality of Service (QoS) for a large-scale crowdsourced edge platform, which covers over 10,000 edge servers, 100,000 users and 10,000,000 user requests. The measurement takes a holistic QoS view: (1) First, we look at how much hardware resources are provided by edge servers, how much time they are available for service deployment, and what are the major abnormal behaviors. (2) Second, we analyze the factors affecting service stability and quantify the resource utilization pattern of containerized services hosted on those edge servers. (3) Third, we investigate the spatial and temporal features of user requests handled by the platform. Many useful and somehow surprising findings are obtained through the above measurements. We also derive insightful implications that could help edge platforms and edge applications to better deliver their services to users. © 2023 IEEE.

关键词： Edge computing

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“Do you have Time for a Quick Call?”: Exploring Remote and Hybrid Requirements engineering Practices and Challenges in Industry

“Do you have Time for a Quick Call?”: Exploring Remote and...

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IEEE International Conference on Requirements engineering

作者： Ze Shi Li Delina Ly Lukas Nagel Nowshin Nawar Arony Daniela Damian Department of Computer Science University of Victoria Canada Application Lifecycle Management VX Company The Netherlands Department of Information and Computing Sciences Utrecht University The Netherlands Software Engineering Group Leibniz University Hannover Germany

ISBN: (数字)9798350395112

ISBN: (纸本)9798350395129

With the onset of the COVID-19 pandemic and the ensuing shift away from co-located work arrangements towards working from home, practitioners encountered many collabora-tive and coordination challenges. While companies are slowly moving back towards in-person arrangements, many employers have permanently adopted fully remote and hybrid work modes. However, the work modes are more blurred, as hybrid work makes coordinating the requirements engineering practices that rely on rich interactions more challenging. Therefore, gaining more understanding of RE challenges and practices from prac-titioners transitioning to the new modes of work is imperative to identify insights that can be useful for organizations shifting to hybrid and remote work. In this paper, we use a mixed-methods approach to gain insights into remote and hybrid requirements engineering practices and challenges in the industry. Through interviews with 12 industry practitioners and a survey with 49 practitioners, we report on 7 adopted practices and 7 challenges encountered in these work arrangements. We found challenges such as organizing co-located tasks, lack of interpersonal con-nections, keeping everyone in the loop, and engagement barriers, which fall under coordination, communication and collaboration. To offset such challenges, we provide 20 recommendations based on our findings, such as proactive planning and using newer tools that support comprehensive tracking of important knowl-edge for requirements documentation. Our findings suggest that practitioners are facing challenges in remote and hybrid work arrangements, which they are mitigating with various strategies. Nonetheless, there remains a need for further research, as not all challenges are equally addressed across different work contexts.

关键词： Industries Surveys Prevention and mitigation Remote working Teamwork Planning Requirements engineering

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A Novel AI Approach for Privacy Preserving In Biomedical Using Federated Learning

A Novel AI Approach for Privacy Preserving In Biomedical Usi...

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Australasian Telecommunication Networks and Applications Conference, ATNAC

作者： Hong Nhung Nguyen Hong-Duc Phan Department of AI and Software Engineering School of Computing Gachon University Gyeonggi-do Republic of Korea Knowledge Bridge Research Institute Hochiminh city Vietnam RMIT University Melbourne Australia

ISBN: (数字)9798350379112

ISBN: (纸本)9798350379129

One of the pivotal applications of artificial intel-ligence (AI) in the healthcare sector is to reduce dependency on human physicians, or at least diminish the frequency of their necessity. While AI cannot yet entirely replace doctors, it functions as an indispensable tool that significantly enhances their capabilities. This research summarizes various technologies that facilitate the integration of AI into healthcare applications. In particular, the development of effective AI systems necessitates the accumulation of vast datasets. Centralizing this data is imperative, which requires establishing a robust platform capable of storing and providing seamless access to this data for future use. Federated learning, a type of distributed and decentralized machine learning, is particularly useful when users cannot or do not wish to share their data with a central server due t o privacy and security concerns. This paper also summarizes general solutions to the statistical, system, and privacy challenges in federated learning, highlighting some results of machine learning techniques applied to healthcare. Ultimately, this paper can serve as a foundation for guiding future research in AI based on federated learning for healthcare challenges.

关键词： Deep learning Privacy Federated learning Reviews Hospitals Distributed databases Medical services Demand response Servers Security

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