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Component-Based Software Engineering 1

丛书名： Lecture Notes in computer science

1000年

作者： Ian Gorton George T. Heineman Ivica Crnković Heinz W. Schmidt Judith A. Stafford Clemens Szyperski Kurt Wallnau

ISBN: (数字)9783540356295

ISBN: (纸本)9783540356288

On behalf of the Organizing Committee I am pleased to present the proceedings of the 2006 Symposium on Component-Based Software Engineering (CBSE). CBSE is concerned with the development of software-intensive systems from reusable parts (components), the development of reusable parts, and system maintenance and improvement by means of component replacement and customization. CBSE 2006 was the ninth in a series of events that promote a science and technology foundation for achieving predictable quality in software systems through the use of software component technology and its associated software engineering practices. We were fortunate to have a dedicated Program Committee comprising 27 internationally recognized researchers and industrial practitioners. We received 77 submissions and each paper was reviewed by at least three Program Committee members (four for papers with an author on the Program Committee). The entire reviewing process was supported by Microsoft’s CMT technology. In total, 22 submissions were accepted as full papers and 9 submissions were accepted as short papers. This was the first time CBSE was not held as a co-located event at ICSE. Hence special thanks are due to Ivica Crnkovic for hosting the event. We also wish to thank the ACM Special Interest Group on Software Engineering (SIGSOFT) for their sponsorship of CBSE 2005. The proceedings you now hold were published by Springer and we are grateful for their support. Finally, we must thank the many authors who contributed the high-quality papers contained within these proceedings.

关键词： Software Engineering/Programming and Operating systems Software Engineering Programming Techniques Programming Languages, Compilers, Interpreters Logics and Meanings of Programs computer Communication Networks

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Blockchain-Empowered Lifecycle Management for AI-Generated Content (AIGC) Products in Edge Networks

arXiv

引用

arXiv 2023年

作者： Liu, Yinqiu Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Miao, Chunyan Shen, Xuemin Jamalipour, Abbas The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering University of Waterloo Canada The School of Electrical and Information Engineering University of Sydney Australia

The rapid development of Artificial Intelligence-Generated Content (AIGC) has brought daunting challenges regarding service latency, security, and trustworthiness. Recently, researchers presented the edge AIGC paradigm, effectively optimize the service latency by distributing AIGC services to edge devices. However, AIGC products are still unprotected and vulnerable to tampering and plagiarization. Moreover, as a kind of online non-fungible digital property, the free circulation of AIGC products is hindered by the lack of trustworthiness in open networks. In this article, for the first time, we present a blockchain-empowered framework to manage the lifecycle of edge AIGC products. Specifically, leveraging fraud proof, we first propose a protocol to protect the ownership and copyright of AIGC, called Proof-of-AIGC. Then, we design an incentive mechanism to guarantee the legitimate and timely executions of the funds-AIGC ownership exchanges among anonymous users. Furthermore, we build a multi-weight subjective logic-based reputation scheme, with which AIGC producers can determine which edge service provider is trustworthy and reliable to handle their services. Through numerical results, the superiority of the proposed approach is demonstrated. Last but not least, we discuss important open directions for further research. © 2023, CC BY.

关键词： Blockchain

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ProSecutor: Protecting Mobile AIGC Services on Two-Layer Blockchain via Reputation and Contract Theoretic Approaches

arXiv

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arXiv 2024年

作者： Liu, Yinqiu Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Shen, Xuemin The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Electrical and Information Engineering University of Sydney Australia The Department of Electrical and Computer Engineering University of Waterloo Canada

Mobile AI-Generated Content (AIGC) has achieved great attention in unleashing the power of generative AI and scaling the AIGC services. By employing numerous Mobile AIGC Service Providers (MASPs), ubiquitous and low-latency AIGC services for clients can be realized. Nonetheless, the interactions between clients and MASPs in public mobile networks, pertaining to three key mechanisms, namely MASP selection, payment scheme, and fee-ownership transfer, are unprotected. In this paper, we design the above mechanisms in a systematic approach and present the first blockchain to protect mobile AIGC, called ProSecutor. Specifically, by roll-up and layer-2 channels, ProSecutor forms a two-layer architecture, realizing tamper-proof data recording and atomic fee-ownership transfer with high resource efficiency. Then, we present the Objective-Subjective Service Assessment (OS2A) framework, which effectively evaluates the AIGC services by fusing the objective service quality with the reputation-based subjective experience of the service outcome (i.e., AIGC outputs). Deploying OS2A on ProSecutor, firstly, the MASP selection can be realized by sorting the reputation. Afterward, the contract theory is adopted to optimize the payment scheme and help clients avoid moral hazards in mobile networks. We implement the prototype of ProSecutor on BlockEmulator. Extensive experiments demonstrate that ProSecutor achieves 12.5× throughput and saves 67.5% storage resources compared with BlockEmulator. Moreover, the effectiveness and efficiency of the proposed mechanisms are validated. © 2024, CC BY.

关键词： Blockchain

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Self-learning Scheduling Approach for Wireless Sensor Network

Self-learning Scheduling Approach for Wireless Sensor Networ...

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The 2010 2nd International Conference on Future computer and Communication(第二届未来计算机与通信国际会议 ICFCC 2010)

作者： Jianjun Niu State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China Beijing Special Engineering Design and Research Institute Beijing China

ISBN: (纸本)9781424458219;9781424458240

Energy efficiency is one of key issues of wireless sensor network (VVSN). In this paper, we propose a self-learning scheduling approach (SSA) to reduce energy consumption for wireless sensor network (WSN). This approach integrates sleep scheduling together with packet transmission scheduling to reduce energy consumption. It enables nodes to learn continuous transmission parameters and sleep parameter through interacting with the WSN. The continuous value of transmission parameter is achieved by our extension of Q-learning method, and the value of sleep parameter can be calculated from the transmission parameter. We valid this approach in a MAC protocol and compare some network performances between the SSA and SMAC protocol. The simulation results show that our SSA performs much better than SMAC protocol in these QoS metrics.

关键词： Wireless sensor network scheduling reinforcement learning

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Connection Performance Modeling and Analysis of a Radiosonde Network in a Typhoon

引用

IEEE Transactions on Communications 2024年

作者： Liu, Hanyi Cao, Xianbin Yang, Peng Xiong, Zehui Quek, Tony Q. S. Wu, Dapeng Oliver Beihang University School of Electronic and Information Engineering Beijing China Pengcheng Laboratory China State Key Laboratory of CNS/ATM Beijing China Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore Singapore City University of Hong Kong Kowloon Department of Computer Science Hong Kong Hong Kong

This paper is concerned with the theoretical modeling and analysis of uplink connection performance of a radiosonde network deployed in a typhoon. Similar to existing works, the stochastic geometry theory is leveraged to derive the expression of the uplink connection probability (CP) of a radiosonde. Nevertheless, existing works assume that network nodes are spherically or uniformly distributed. Different from the existing works, this paper investigates two particular motion patterns of radiosondes in a typhoon, which significantly challenges the theoretical analysis. According to their particular motion patterns, this paper first separately models the distributions of horizontal and vertical distances from a radiosonde to its receiver. Secondly, this paper derives the closed-form expressions of cumulative distribution function (CDF) and probability density function (PDF) of a radiosonde's three-dimensional (3D) propagation distance to its receiver. Thirdly, this paper derives the analytical expression of the uplink CP for any radiosonde in the network. Finally, extensive numerical simulations are conducted to validate the theoretical analysis, and the influence of various network design parameters is comprehensively discussed. Simulation results show that when the signal-to-interference-noise ratio (SINR) threshold is below -35 dB, and the density of radiosondes remains under 0.01/km³, the uplink CP approaches 26%, 39%, and 50% in three patterns. © 1972-2012 IEEE.

关键词： Distribution functions

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Blockchain-Empowered Lifecycle Management for AI-Generated Content (AIGC) Products in Edge Networks

TechRxiv

引用

TechRxiv 2023年

关键词： Blockchain

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Lightweight Wireless Sensing Through RIS and Inverse Semantic Communications

Lightweight Wireless Sensing Through RIS and Inverse Semanti...

引用

IEEE Conference on Wireless Communications and Networking

作者： Hongyang Du Jiacheng Wang Dusit Niyato Jiawen Kang Zehui Xiong Junshan Zhang Xuemin Sherman Shen The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering University of California Davis USA The Department of Electrical and Computer Engineering University of Waterloo Canada

Thanks to the ubiquitous and easily accessible nature of wireless signals, wireless sensing is regarded as one of the promising techniques in the next-generation Internet of Things. In this paper, we propose the inverse semantic communications as a new paradigm to achieve lightweight wireless sensing using the reconfigurable intelligent surface (RIS). Instead of extracting semantic information from messages, we aim to encode the task-related source messages into a hyper-source message. Specifically, we first develop a novel RIS hardware for encoding several signal spectrums into one MetaSpectrum. We then propose a self-supervised learning method for decoding the MetaSpectrums to obtain the original signal spectrums. Using the sensing data collected from the real world, we show that our framework can reduce the data volume by 90% compared to that before encoding, without affecting the execution of various sensing tasks. Experiment results also demonstrate that the amplitude response matrix of the RIS enables the encryption of the sensing data.

关键词： Wireless communication Wireless sensor networks Semantics Self-supervised learning Encoding Sensors Decoding

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Defining Problem from Solutions: Inverse Reinforcement Learning (IRL) and Its Applications for Next-Generation Networking

arXiv

引用

arXiv 2024年

作者： Liu, Yinqiu Zhang, Ruichen Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Performance optimization is a critical concern in networking, on which Deep Reinforcement Learning (DRL) has achieved great success. Nonetheless, DRL training relies on precisely defined reward functions, which formulate the optimization objective and indicate the positive/negative progress towards the optimal. With the ever-increasing environmental complexity and human participation in Next-Generation Networking (NGN), defining appropriate reward functions become challenging. In this article, we explore the applications of Inverse Reinforcement Learning (IRL) in NGN. Particularly, if DRL aims to find optimal solutions to the problem, IRL finds a problem from the optimal solutions, where the optimal solutions are collected from experts, and the problem is defined by reward inference. Specifically, we first formally introduce the IRL technique, including its fundamentals, workflow, and difference from DRL. Afterward, we present the motivations of IRL applications in NGN and survey existing studies. Furthermore, to demonstrate the process of applying IRL in NGN, we perform a case study about human-centric prompt engineering in Generative AI-enabled networks. We demonstrate the effectiveness of using both DRL and IRL techniques and prove the superiority of IRL. © 2024, CC BY.

关键词： Reinforcement learning

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Exploring Attention-Aware Network Resource Allocation for Customized Metaverse Services

arXiv

引用

arXiv 2022年

作者： Du, Hongyang Wang, Jiacheng Niyato, Dusit Kang, Jiawen Xiong, Zehui Shen, Xuemin Kim, Dong In The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Emerging with the support of computing and communications technologies, Metaverse is expected to bring users unprecedented service experiences. However, the increase in the number of Metaverse users places a heavy demand on network resources, especially for Metaverse services that are based on graphical extended reality and require rendering a plethora of virtual objects. To make efficient use of network resources and improve the Quality-of-Experience (QoE), we design an attention-aware network resource allocation scheme to achieve customized Metaverse services. The aim is to allocate more network resources to virtual objects in which users are more interested. We first discuss several key techniques related to Metaverse services, including QoE analysis, eye-tracking, and remote rendering. We then review existing datasets and propose the user-object-attention level (UOAL) dataset that contains the ground truth attention of 30 users to 96 objects in 1, 000 images. A tutorial on how to use UOAL is presented. With the help of UOAL, we propose an attention-aware network resource allocation algorithm that has two steps, i.e., attention prediction and QoE maximization. Specially, we provide an overview of the designs of two types of attention prediction methods, i.e., interest-aware and time-aware prediction. By using the predicted user-object-attention values, network resources such as the rendering capacity of edge devices can be allocated optimally to maximize the QoE. Finally, we propose promising research directions related to Metaverse services. © 2022, CC BY.

关键词： Eye tracking

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Seeing the Arrow of Time

Seeing the Arrow of Time

引用

IEEE Conference on computer Vision and Pattern Recognition

作者： Lyndsey C. Pickup Zheng Pan Donglai Wei YiChang Shih Changshui Zhang Andrew Zisserman Bernhard Scholkopf William T. Freeman Visual Geometry Group Department of Engineering Science University of Oxford Department of Automation Tsinghua University State Key Lab of Intelligent Technologies and Systems Tsinghua National Laboratory for Information Science and Technology (TNList) Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Max Planck Institute for Intelligent Systems

ISBN: (纸本)9781479951192

We explore whether we can observe Time's Arrow in a temporal sequence-is it possible to tell whether a video is running forwards or backwards? We investigate this some-what philosophical question using computer vision and machine learning techniques. We explore three methods by which we might detect Time's Arrow in video sequences, based on distinct ways in which motion in video sequences might be asymmetric in time. We demonstrate good video forwards/backwards classification results on a selection of YouTube video clips, and on natively-captured sequences (with no temporally-dependent video compression), and examine what motions the models have learned that help discriminate forwards from backwards time.

关键词： YouTube Training Support vector machines Physics Testing Histograms Visualization

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