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Metaverse: A Vision, Architectural Elements, and Future Directions for Scalable and Realtime Virtual Worlds

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arXiv 2023年

作者： Ismail, Leila Buyya, Rajkumar Lab School of Computing and Information Systems The University of Melbourne Australia Lab Department of Computer Science and Software Engineering National Water and Energy Center United Arab Emirates University United Arab Emirates

With the emergence of Cloud computing, Internet of Things-enabled Human-Computer Interfaces, Generative Artificial Intelligence, and high-accurate Machine and Deep-learning recognition and predictive models, along with the Post Covid-19 proliferation of social networking, and remote communications, the Metaverse gained a lot of popularity. Metaverse has the prospective to extend the physical world using virtual and augmented reality so the users can interact seamlessly with the real and virtual worlds using avatars and holograms. It has the potential to impact people in the way they interact on social media, collaborate in their work, perform marketing and business, teach, learn, and even access personalized healthcare. Several works in the literature examine Metaverse in terms of hardware wearable devices, and virtual reality gaming applications. However, the requirements of realizing the Metaverse in realtime and at a large-scale need yet to be examined for the technology to be usable. To address this limitation, this paper presents the temporal evolution of Metaverse definitions and captures its evolving requirements. Consequently, we provide insights into Metaverse requirements. In addition to enabling technologies, we lay out architectural elements for scalable, reliable, and efficient Metaverse systems, and a classification of existing Metaverse applications along with proposing required future research *** Codes ***, *** © 2023, CC BY.

关键词： Energy efficiency

On Understanding Sports-HAR: Hierarchical, Mobile, Multi-Sensor based Classification of Table-Tennis Strokes 1

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On Understanding Sports-HAR: Hierarchical, Mobile, Multi-Sen...

1st International Conference of Intelligent Methods, systems and Applications, IMSA 2023

作者： Ezzeldin, Mustafa Ghoneim, Amr S. Abdelhamid, Laila Atia, Ayman Helwan University Faculty of Computers AI Software Engineering Cairo Egypt Helwan University Faculty of Computers and AI Computer Science Cairo Egypt Helwan University Faculty of Computers and AI Information Systems Cairo Egypt Helwan Univeristy HCI-LAB Faculty of Computers and AI Computer Science Cairo Egypt Faculty of Computer Science Giza Egypt

ISBN: (纸本)9798350335569

Table Tennis is a renowned competitive and recreational sport. An Olympic sport since 1988, table tennis in-cludes several movements, shots (i.e., strokes), and positions. Consequently, many factors can affect the strokes' accuracy and strength. This paper aims twofold: to classify the type of shot and to enhance the performance of the classification by improving the recognition accuracy and reducing the computational time. To track the movement of the body parts and extract table tennis stroke information, sensors (both Accelerometers and Gyroscopes) were utilized. The extracted data are processed and passed to a P-Dollar classifier to recognize six different table tennis strokes. This work utilizes two classification approaches to evaluate the performance, both linear and hierarchical. The presented results demonstrate that the hierarchical classification yields a better accuracy of 88%, achieved in 4 seconds, while traditional linear classification achieved 83% within 6 seconds. © 2023 IEEE.

关键词： Gyroscopes

Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation

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Procedia computer science 2023年 220卷 339-347页

作者： Leila Ismail Muhammad Danish Waseem Clouds and Distributed Computing and Systems (CLOUDS) Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia. Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE. National Water and Energy Center United Arab Emirates University UAE.

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately.

关键词： Computer Vision Deep Learning Image Processing Machine Learning Pain Detection Patient-Centric Smart healthcare eHealth

SPLC 2023 Industry Variability Challenges Workshop 23

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SPLC 2023 Industry Variability Challenges Workshop

Proceedings of the 27th ACM International systems and software Product Line Conference - Volume A

作者： Rick Rabiser Martin Becker Mitsuhiro Kitani CDL VaSiCS LIT CPS Lab Johannes Kepler University Linz Linz Austria Fraunhofer Institute for Experimental Software Engineering Kaiserslautern Germany Center for Technology Innovation - Systems Engineering Hitachi Yokohama Japan

ISBN: (纸本)9798400700910

The idea of the SPLC 2023 Industry Variability Challenges Workshop is to bring together practitioners and researchers to discuss the challenges regarding variability companies are currently facing. Companies can tell the researchers what they could/should work on regarding highly variable/configurable systems, identify areas where they would like to get some help from the SPLC community, network with other companies in other domains potentially having related issues, and identify possibilities to collaborate with researchers. Researchers can learn about current challenges of companies, relate industry challenges with ongoing research, and identify possibilities to collaborate with companies. After a motivating introduction by the organizers, each company pitches their setting, especially drivers, challenges, and lessons learned with regard to dealing with variability/highly variable/configurable systems in their context. Workshop organizers document challenges live on a shared board. This is followed by open discussion guided by the workshop organizers to further extend the challenges collected on the shared board and discuss them together with all participants.

关键词： Industry Challenges

Multi-Granularity Detector for Vulnerability Fixes

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arXiv 2023年

作者： Nguyen, Truong Giang Le-Cong, Thanh Kang, Hong Jin Widyasari, Ratnadira Yang, Chengran Zhao, Zhipeng Xu, Bowen Zhou, Jiayuan Xia, Xin Hassan, Ahmed E. Le, Xuan-Bach D. Lo, David The School of Computing and Information Systems Singapore Management University Singapore The Software Engineering Application Technology Lab Huawei China School of Computing Queen’s University Canada School of Computing and Information Systems The University of Melbourne Australia

With the increasing reliance on Open Source software, users are exposed to third-party library vulnerabilities. software Composition Analysis (SCA) tools have been created to alert users of such vulnerabilities. SCA requires the identification of vulnerability-fixing commits. Prior works have proposed methods that can automatically identify such vulnerability-fixing commits. However, identifying such commits is highly challenging, as only a very small minority of commits are vulnerability fixing. Moreover, code changes can be noisy and difficult to analyze. We observe that noise can occur at different levels of detail, making it challenging to detect vulnerability fixes accurately. To address these challenges and boost the effectiveness of prior works, we propose MiDas (Multi-Granularity Detector for Vulnerability Fixes). Unique from prior works, MiDas constructs different neural networks for each level of code change granularity, corresponding to commit-level, file-level, hunk-level, and line-level, following their natural organization. It then utilizes an ensemble model that combines all base models to generate the final prediction. This design allows MiDas to better handle the noisy and highly imbalanced nature of vulnerability-fixing commit data. Additionally, to reduce the human effort required to inspect code changes, we have designed an effort-aware adjustment for MiDas’s outputs based on commit length. The evaluation results demonstrate that MiDas outperforms the current state-of-the-art baseline in terms of AUC by 4.9% and 13.7% on Java and Python-based datasets, respectively. Furthermore, in terms of two effort-aware metrics, EffortCost@L and Popt@L, MiDas also outperforms the state-of-the-art baseline, achieving improvements of up to 28.2% and 15.9% on Java, and 60% and 51.4% on Python, respectively. © 2023, CC BY.

关键词： Python

A Distributed MAPE-K Framework for Self-Protective IoT Devices

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A Distributed MAPE-K Framework for Self-Protective IoT Devic...

SEAMS International Workshop on software engineering for Adaptive and Self-Managing systems, ICSE

作者： Michael Riegler Johannes Sametinger Michael Vierhauser LIT Secure and Correct Systems Lab Johannes Kepler University Linz Linz Austria Department of Business Informatics - Software Engineering Johannes Kepler University Linz Linz Austria

Internet of Things (IoT) devices have become ubiquitous in our everyday life, with security becoming an ever-growing issue as more and more cyber-attack incidents being reported, primarily due to deficiencies in existing security mechanisms. However, while, for example, cloud-based applications, or industrial automation systems of systems possess significant resources for monitoring health, and determining their status and correct behavior at runtime, IoT devices operate with limited hardware capabilities and under tight resource constraints, making monitoring, analysis, and response activities a challenging endeavor. Following the NIST Cybersecurity Framework, IoT devices need to identify, protect, detect, respond, and recover from cyber-attacks, unauthorized access, and other security threats. A common way to provide self-adaptation to changing conditions is the MAPE-K loop with four pivotal phases: Monitor, Analyze, Plan, and Execute. This paper presents DSec4IoT, a “Distributed MAPE-K Framework for Self-Protective IoT Devices”. Our framework leverages the idea of distributed MAPE-K patterns and establishes a model for managing and controlling Self-Protective IoT Devices. We evaluate our approach by simulating port scans and performing adaptation activities. Results have confirmed that DSec4IoT can be easily applied to detect and mitigate them.

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Mitigating Catastrophic Forgetting in Multi-domain Chinese Spelling Correction by Multi-stage Knowledge Transfer Framework

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arXiv 2024年

作者： Xing, Peng Li, Yinghui Ma, Shirong Liang, Xinnian Huang, Haojing Li, Yangning Zheng, Hai-Tao Jiang, Wenhao Shen, Ying Tsinghua Shenzhen International Graduate School Tsinghua University China State Key Lab of Software Development Environment Beihang University China China School of Intelligent Systems Engineering Sun-Yat Sen University China

Chinese Spelling Correction (CSC) aims to detect and correct spelling errors in given sentences. Recently, multi-domain CSC has gradually attracted the attention of researchers because it is more practicable. In this paper, we focus on the key flaw of the CSC model when adapting to multi-domain scenarios: the tendency to forget previously acquired knowledge upon learning new domain-specific knowledge (i.e., catastrophic forgetting). To address this, we propose a novel model-agnostic Multi-stage Knowledge Transfer (MKT) framework, which utilizes a continuously evolving teacher model for knowledge transfer in each domain, rather than focusing solely on new domain knowledge. It deserves to be mentioned that we are the first to apply continual learning methods to the multi-domain CSC task. Experiments 1 prove the effectiveness of our proposed method, and further analyses demonstrate the importance of overcoming catastrophic forgetting for improving the model performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Domain Knowledge

GustosonicSense: Towards understanding the design of playful gustosonic eating experiences

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arXiv 2024年

作者： Wang, Yan Obie, Humphrey O. Li, Zhuying Salim, Flora D. Grundy, John Mueller, Florian Floyd Exertion Game Lab Department of Human-Centred Computing Monash University MelbourneVIC Australia Department of Computing Technologies Swinburne University of Technology HumaniSE Lab Department of Software Systems and Cybersecurity Monash University MelbourneVIC Australia School of Computer Science and Engineering Southeast University Jiangsu Nanjing China Sydney SydneyNSW Australia HumaniSE Lab Department of Software Systems and Cybersecurity Monash University MelbourneVIC Australia Exertion Games Lab Department of Human-Centred Computing Monash University MelbourneVIC Australia

The pleasure that often comes with eating can be further enhanced with intelligent technology, as the field of human-food interaction suggests. However, knowledge on how to design such pleasure-supporting eating systems is limited. To begin filling this knowledge gap, we designed "GustosonicSense", a novel gustosonic eating system that utilizes wireless earbuds for sensing different eating and drinking actions with a machine learning algorithm and trigger playful sounds as a way to facilitate pleasurable eating experiences. We present the findings from our design and a study that revealed how we can support the "stimulation", "hedonism", and "reflexivity" for playful human-food interactions. Ultimately, with our work, we aim to support interaction designers in facilitating playful experiences with food. © 2024, CC BY.

关键词： Learning algorithms

Quantum Reinforcement Learning with Quantum World Model

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Quantum Reinforcement Learning with Quantum World Model

IEEE Conference on Global Communications (GLOBECOM)

作者： Peigen Zeng Ying He F. Richard Yu Victor C.M. Leung Shenzhen Key Lab of Digital & Intelligent Technologies & Systems College of Computer Science and Software Engineering Shenzhen University China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China

Quantum reinforcement learning (QRL) can outperform classical reinforcement learning (RL) by utilizing quantum parallel theory and quantum phenomena such as superposition and entanglement. Although some excellent work has been done on QRL, most existing works either fail to show the exponential advantage of quantum computation over classical computation in terms of performance or are too demanding on quantum devices. In this paper, we provide a novel perspective on combining quantum computing and RL with faster convergence speed and relatively relaxed demands on quantum devices. Specifically, we propose a method to construct a world model with quantum circuit that allows it to interact in a quantum way. In addition, we use Grover's algorithm to efficiently extract high-value information from the quantum world model. Extensive simulation results show that the proposed method can have superior performance compared to classical RL algorithms.

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