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Examining the Use of Non-fungible Tokens (NFTs) as a Trading Mechanism for the Metaverse 1

29th European Systems, software and Services Process Improvement, EuroSPI 2022

作者： Yilmaz, Murat Hacaloğlu, Tuna Clarke, Paul Department of Computer Engineering Engineering Faculty Gazi University Ankara Turkey Metaverse Research and Development Laboratory Gazi University Ankara Turkey Department of Information Systems Engineering Atilim University Ankara Turkey School of Computing Dublin City University Dublin Ireland Lero the Science Foundation Ireland Research Centre for Software Limerick Ireland

ISBN: (数字)9783031155598

ISBN: (纸本)9783031155581

The notion of a metaverse seems hard to define but encourages the impression that it can be considered as a new virtual metaphysical landscape that somehow goes beyond our geographical locations and understanding (i.e., independent of time and space). Based on virtual reality, augmented reality, and blockchain, it is envisioned as an independent but extended world that is planned to be a digital virtuality entrenched not only in our old habits such as gaming and entertainment but also in virtual asset trade. In particular, trading is a pillar of the virtual economy, and auction houses will be crucial for Metaverse trading. This exploratory study examines the possibility of using an auction environment to improve the trading capabilities in a virtual universe. We investigate the cases of creating a virtual auction house with the potential of social trading of virtual assets with crypto coins and bartering. To this end, we built a virtual auction house and tested it initially using a set of scenarios. Our preliminary findings suggest that creating a virtual trading environment would be beneficial as an environment for buying and selling virtual assets and exploring their consequences. © 2022, Springer Nature Switzerland AG.

关键词： Augmented reality

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Deep boundary-aware semantic image segmentation

Deep boundary-aware semantic image segmentation

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作者： Wu, Huisi Li, Yifan Chen, Le Liu, Xueting Li, Ping College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong

While extensive research efforts have been made in semantic image segmentation, the state-of-the-art methods still suffer from blurry boundaries and mismatched objects due to the insufficient multiscale adaptability. In this paper, we propose a two-branch convolutional neural network (CNN) approach to capture the multiscale context and the boundary information with the two branches, respectively. To capture the multiscale context, we propose to embed self-attention mechanism to the atrous spatial pyramid pooling network. To capture the boundary information, we propose to fuse the low-level features in boundary feature extraction for refining the extracted boundaries via a feature fusion layer (FFL). With FFL, our method can improve the segmentation result with clearer boundaries. A new loss function is proposed which contains a segmentation loss and a boundary loss. Experiments show that our method can predict the boundaries of objects more clearly and have better performance for small-scale objects. © 2021 John Wiley & Sons, Ltd.

关键词： Semantic Segmentation

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Analysis of Web Monitoring Servers and Tools for Cloud computing Services 1st

Analysis of Web Monitoring Servers and Tools for Cloud Compu...

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1st International Conference on engineering software for Modern Challenges, ESMoC 2021

作者： Sarwar, Nadeem Qureshi, Junaid Nasir Iqbal, Tahir Waheed, Haroon Abdul Yasin, Muhammad Hammad Irshad, Asma Computer Science Department Bahria University Lahore Campus Lahore Pakistan Department of Software Engineering University of Central Punjab Lahore Pakistan Faculty of Computing Riphah International University Islamabad Pakistan School of Biochemistry & Biotechnology University of the Punjab Lahore Pakistan

ISBN: (纸本)9783031199677

This research aims to find best cloud service monitoring tools and techniques according to trending parameters. The services of cloud computing are most popular services now a days. Many organizations are migrating to cloud technology in order to reduce the management’s complications of resources. The effective monitoring methodologies should be implemented, to certify the Quality of Experience (QoE) and Quality of Services (QoS) of cloud computing services for end users. For this purpose, cloud services should be monitor and control using different tools and techniques. There are so many server-based or web-based monitoring tools are available. In this paper, analysis of different tools is based on their pricing, working efficiency, key features, platforms and environments in which they could be used. This paper also analyzes the cloud computing structure and investigates that which type of monitoring services are required at which level. This research paper shows that no monitoring tool is mature enough to fulfil all requirements and desires of clients. This research also interprets that which tool is recommended for which kind of platform and environment. © 2022, Springer Nature Switzerland AG.

关键词： Quality of service

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CLDG: Contrastive Learning on Dynamic Graphs 39

CLDG: Contrastive Learning on Dynamic Graphs

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Xi'an Jiaotong University Department of Computer Science and Technology China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Xi'an Jiaotong University Department of Distance Education China Beihang University School of Software China Beihang University Advanced Innovation Center for Big Data and Brain Computing China

ISBN: (纸本)9798350322279

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph's augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2023 IEEE.

关键词： Graph neural networks

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Enhancing Early Childhood Learning using Image Processing for Sinhala Medium Education

Enhancing Early Childhood Learning using Image Processing fo...

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Advancements in computing (ICAC), International Conference on

作者： R.G.V Saranga A.H.N. Jayasinghe U.A.S. Divyantha S.W.L.P de Silva N.H.P. Ravi Supunya Swarnakantha P. K. Suriya Kumari 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

The study aims to develop a mobile application for young children to learn Sinhala letters, shapes, colors, and storytelling incorporating machine learning models to evaluate and enhance educational activities. With the rise of online education during the COVID-19 pandemic, the familiarity of children with mobile devices provides an opportunity to create an engaging and educational experience. The application will teach Sinhala letters using object images, allowing children to upload their own images for feedback. It also includes a feature for children to practice writing letters and analyze their progress. Also, the application introduces colors and shapes in Sinhala, encouraging children to draw and track their improvement. Additionally, the application aims to generate stories in Sinhala to improve children's creativity and thinking knowledge. This research addresses a critical gap in existing Sinhala learning applications by integrating machine learning for activity assessment, promising to significantly impact and improve early language education for children.

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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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A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (Extended Abstract)

A Survey on Modern Deep Neural Network for Traffic Predictio...

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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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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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