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SSRN 2022年

作者： Mampage, Anupama Karunasekera, Shanika Buyya, Rajkumar Laboratory School of Computing and Information Systems The University of Melbourne Australia

Serverless computing has sparked a massive interest in both the cloud service providers and their clientele in recent years. This model entails the shift of the entire matter of resource management of user applications to the service provider. In serverless systems, the provider is highly motivated to attain cost efficient usage of their infrastructure, given the granular billing modules involved. However, due to the dynamic and multi-tenant nature of the serverless workloads and systems, achieving efficient resource management while maintaining function performance is a challenging task. Rapid changes in demand levels for applications cause variations in actual resource usage patterns of function instances. This leads to performance variations in co-located functions which compete for similar resources, due to resource contentions. Most existing serverless scheduling works offer heuristic techniques for function scheduling, which are unable to capture the true dynamism in these systems caused by multi-tenancy and varying user request patterns. Further, they rarely consider the often contradicting dual objectives of achieving provider resource efficiency along with application performance. In this article, we propose a novel technique incorporating Deep Reinforcement Learning (DRL) to overcome the aforementioned challenges for function scheduling in a highly dynamic serverless computing environment with heterogeneous computing resources. We train and evaluate our model in a practical setting incorporating Kubeless, an open-source serverless framework, deployed on a 23-node Kubernetes cluster setup. Extensive experiments done on this testbed environment show promising results with improvements of up to 24% and 34% in terms of application response time and resource usage cost respectively, compared to baseline techniques. © 2022, The Authors. All rights reserved.

关键词： Scheduling

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Placement of Microservices-based IoT Applications in Fog computing: A Taxonomy and Future Directions

arXiv

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arXiv 2022年

作者： Pallewatta, Samodha Kostakos, Vassilis Buyya, Rajkumar Laboratory School of Computing and Information Systems University of Melbourne Australia

The Fog computing paradigm utilises distributed, heterogeneous and resource-constrained devices at the edge of the network for efficient deployment of latency-critical and bandwidth-hungry IoT application services. Moreover, MicroService Architecture (MSA) is increasingly adopted to keep up with the rapid development and deployment needs of fast-evolving IoT applications. Due to the fine-grained modularity of the microservices and their independently deployable and scalable nature, MSA exhibits great potential in harnessing Fog and cloud resources, thus giving rise to novel paradigms like Osmotic computing. The loosely coupled nature of the microservices, aided by the container orchestrators and service mesh technologies, enables the dynamic composition of distributed and scalable microservices to achieve diverse performance requirements of the IoT applications using distributed Fog resources. To this end, efficient placement of microservice plays a vital role, and scalable placement algorithms are required to utilise the said characteristics of the MSA while overcoming novel challenges introduced by the architecture. Thus, we present a comprehensive taxonomy of recent literature on microservices-based IoT applications placement within Fog computing environments. Furthermore, we organise multiple taxonomies to capture the main aspects of the placement problem, analyse and classify related works, identify research gaps within each category, and discuss future research directions. Copyright © 2022, The Authors. All rights reserved.

关键词： Fog computing

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Container Orchestration in Edge and Fog computing Environments for Real-Time IoT Applications

arXiv

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arXiv 2022年

作者： Wang, Zhiyu Goudarzi, Mohammad Aryal, Jagannath Buyya, Rajkumar Laboratory School of Computing and Information Systems The University of Melbourne Australia

Resource management is the principal factor to fully utilize the potential of Edge/Fog computing to execute real-time and critical IoT applications. Although some resource management frameworks exist, the majority are not designed based on distributed containerized components. Hence, they are not suitable for highly distributed and heterogeneous computing environments. Containerized resource management frameworks such as FogBus2 enable efficient distribution of framework’s components alongside IoT applications’ components. However, the management, deployment, health-check, and scalability of a large number of containers are challenging issues. To orchestrate a multitude of containers, several orchestration tools are developed. But, many of these orchestration tools are heavy-weight and have a high overhead, especially for resource-limited Edge/Fog nodes. Thus, for hybrid computing environments, consisting of heterogeneous Edge/Fog and/or cloud nodes, lightweight container orchestration tools are required to support both resource-limited resources at the Edge/Fog and resource-rich resources at the cloud. Thus, in this paper, we propose a feasible approach to build a hybrid and lightweight cluster based on K3s, for the FogBus2 framework that offers containerized resource management framework. This work addresses the challenge of creating lightweight computing clusters in hybrid computing environments. It also proposes three design patterns for the deployment of the FogBus2 framework in hybrid environments, including 1) Host Network, 2) Proxy Server, and 3) Environment Variable. The performance evaluation shows that the proposed approach improves the response time of real-time IoT applications up to 29% with acceptable and low overhead. Copyright © 2022, The Authors. All rights reserved.

关键词： Fog computing

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HighlightRemover: Spatially Valid Pixel Learning for Image Specular Highlight Removal 24

HighlightRemover: Spatially Valid Pixel Learning for Image S...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Ling Ma, Yidong Jiang, Zhi He, Weilei Bao, Zhongyun Fu, Gang Xu, Wenju Xiao, Chunxia School of Computer Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Realtime Industrial Systems Wuhan University of Science and Technology Wuhan China School of Computer Science Wuhan University Wuhan China Department of Computing Hong Kong Polytechnic University Hong Kong Amazon Palo Alto United States

ISBN: (纸本)9798400706868

Recently, learning-based methods have made significant progress for image specular highlight removal. However, many of these approaches treat all the image pixels uniformly, overlooking the negative impact of invalid pixels on feature reconstruction. This oversight often leads to undesirable outcomes, such as color distortion or residual highlights. In this paper, we propose a novel image specular highlight removal network called HighlightRNet, which utilizes valid pixels as references to reconstruct the highlight-free image. To achieve this, we introduce a context-aware fusion block (CFBlock) that aggregates information in four directions, effectively capturing global contextual information. Additionally, we introduce a location-aware feature transformation module (LFTModule) to adaptively learn the valid pixels for feature reconstruction, thereby avoiding information errors caused by invalid pixels. With these modules, our method can produce high-quality highlight-free results without color distortion and highlight residual. Furthermore, we develop a multiple light image-capturing system to construct a large-scale highlight dataset called NSH, which exhibits minimal misalignment in image pairs and minimal brightness variation in non-highlight regions. Experimental results on various datasets demonstrate the superiority of our method over state-of-the-art methods, both qualitatively and quantitatively. © 2024 ACM.

关键词： Pixels

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Securing the Future after Pager Bombs:Lifecycle Protection of Smart Devices via Blockchain Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2355-2358页

作者： Fei Lin Qinghua Ni Jing Yang Juanjuan Li Nan Zheng Levente Kovács Radu Prodan Mariagrazia Dotoli Qing-Long Han Fei-Yue Wang the Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBei-jing 100049China IEEE Obuda University John von Neumann Faculty of InformaticsBecsi Str.96/bH-1034 BudapestHungary the Institute of Information Technology University of Klagenfurt9020 Klagenfurt am WortherseeAustria the Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190

THE Lebanese wireless device explosion incident has drawn widespread attention,involving devices such as pagers,walkie-talkies,and other common devices[1].This event has revealed and highlighted the security vulnerabilities in global supply chains from raw material manufacturing and distribution to the usage of devices and equipment,signaling the onset of a new wave of"supply chain warfare"[2].

关键词： Smart cycle chains

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ChatGPT, Let Us Chat Sign Language: Experiments, Architectural Elements, Challenges and Research Directions

ChatGPT, Let Us Chat Sign Language: Experiments, Architectur...

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International Symposium on Networks, Computers and Communications (ISNCC)

作者： Nada Shahin Leila Ismail Department of CS and Software Engineering Intelligent Distributed Computing and Systems (INDUCE) Lab College of IT National Water and Energy Center UAE University Al-Ain UAE CLOUDS lab School of Computing and Information Systems The University of Melbourne Melbourne Australia

ChatGPT is a language model based on Generative AI. Existing research work on ChatGPT focused on its use in various domains. However, its potential for Sign Language Translation (SLT) is yet to be explored. This paper addresses this void. Therefore, we present GPT's evolution aiming a retrospective analysis of the improvements to its architecture for SLT. We explore ChatGPT's capabilities in translating different sign languages in paving the way to better accessibility for deaf and hard-of-hearing community. Our experimental results indicate that ChatGPT can accurately translate from English to American (ASL), Australian (AUSLAN), and British (BSL) sign languages and from Arabic Sign Language (ArSL) to English with only one prompt iteration. However, the model failed to translate from Arabic to ArSL and ASL, AUSLAN, and BSL to Arabic. Consequently, we present challenges and derive insights for future research directions.

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Strong Co-Location Pattern Mining Incorporating Multi-Path and Distance Decay Effects

Strong Co-Location Pattern Mining Incorporating Multi-Path a...

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Algorithms, computing and Artificial Intelligence (ACAI), International Conference on

作者： Yonggui He Peizhong Yang Lizhen Wang Hongmei Chen School of Information Science and Engineering Yunnan University Kunming China School of Information Science and Engineering Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming China

ISBN: (数字)9798331529314

ISBN: (纸本)9798331529321

Spatial co-location pattern mining (SCPM) is a sub-field of data mining, which aims to discover the subset of spatial features whose instances are frequently located in proximate areas. SCPM has broad prospects in many applications, such as ecology, public health, smart cities, etc. In recent years, improving and applying SCPM technology with the constraints of road networks has emerged as a prominent research focus. However, existing studies solely focus on the shortest path between instances when assessing the proximity relationships, neglecting other proximity paths, and thus may overlook co-location patterns with strong associations. To address this issue, this paper introduces a novel metric called Strong Proximity Score, which integrates both multi-path proximity and distance decay effects to measure the strength of proximity relationships. Additionally, the Minimum First Search (MFS) algorithm is presented, which utilizes the strategy of minimum instance pair search to accelerate the calculation of Strong Proximity Score. Extensive experiments conducted on real datasets of points of interest demonstrate the superiority of the method based on Strong Proximity Score over traditional SCPM methods and confirm the efficiency of MFS algorithm.

关键词： Measurement Smart cities Roads Search problems Ecology Data mining Public healthcare Artificial intelligence

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Temperature Control Method for Batch Reactors Based on Adaptive Delay Compensation

Temperature Control Method for Batch Reactors Based on Adapt...

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Chinese Automation Congress (CAC)

作者： Dehui Mao Peng Li Mingxi Ai Jiande Wu Chuangyan Yang Hancheng Wang School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

To address the instability issue caused by time-delay and nonlinear characteristics of intermittent reactions in batch reactors, a temperature control method for batch reactors based on adaptive delay compensation was proposed. By integrating the chemical reaction mechanism inside the reactor with graphical analysis and actual production data, a time-varying second-order delayed transfer function model for the reactor is established. The adaptive rate for controlling the time-varying delayed system is designed based on the control error between the actual controlled object and the nominal model. Furthermore, standard Smith predictor is employed to compensate for the delay in the error term. When applied to temperature control in batch reactors, experimental results demonstrate that this method effectively reduces the temperature control errors caused by time-varying delays, significantly accelerates the convergence speed and diminishes the oscillation amplitude of the temperature curve.

关键词： Adaptation models Adaptive systems Transfer functions Production Temperature control Delays Inductors Time-varying systems Standards Convergence

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Graph Neural Network with Heterogeneous Attributes for Node Classification

Graph Neural Network with Heterogeneous Attributes for Node ...

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Neural Networks, information and Communication Engineering (NNICE), International Conference on

作者： Bohou Jiang Jiahui Wang Kun Yue School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

Node classification on heterogeneous graphs with multiple node types and relationships has been widely used in commodity classification, friend recommendation, and so on. Most of the existing models need to define the meta paths manually with incomplete attribute information, leading to the low accuracy of classification. To improve the accuracy of node classification task, we propose a novel graph neural network with heterogeneous attributes (GNNHA) by introducing the heterogeneous attributes of nodes and edges without defining meta paths. First, we design a spatial mapping for heterogeneous edges by mapping and fusing the source and target nodes to a hidden layer space. Then, we make a unique linear projection for each node to obtain the embeddings containing the attributes of nodes and edges by aggregating the information of neighbor nodes. Finally, we provide an improved loss function by leveraging the information loss of mapping and node class imbalance. Our model can capture more complete heterogeneous graph attributes and obtain more objective and accurate classification results. Experimental results show that our proposed model outperforms state-of-the-art ones.

关键词：

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Comparative Analysis of Hate Speech Detection: Traditional vs. Deep Learning Approaches

Comparative Analysis of Hate Speech Detection: Traditional v...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Haibo Pen Nicole Teo Zhaoxia Wang Key Laboratory of Smart Grid of Ministry of Education Tianjin University Tianjin China School of Computing and Information Systems Singapore Management University Singapore

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Detecting hate speech on social media poses a significant challenge, especially in distinguishing it from offensive language, as learning-based models often struggle due to nuanced differences between them, which leads to frequent misclassifications of hate speech instances, with most research focusing on refining hate speech detection methods. Thus, this paper seeks to know if traditional learning-based methods should still be used, considering the perceived advantages of deep learning in this domain. This is done by investigating advancements in hate speech detection. It involves the utilization of deep learning-based models for detailed hate speech detection tasks and compares the results with those obtained from traditional learning-based baseline models through multidimensional aspect analysis. By considering various aspects to gain a comprehensive understanding, we can discern the strengths and weaknesses in current state-of-the-art techniques. Our research findings reveal the performance of traditional learning-based hate speech detection outperforms that of deep learning-based methods. While acknowledging the potential demonstrated by deep learning methodologies, this study emphasizes the significance of traditional machine learning approaches in effectively addressing hate speech detection tasks. It advocates for a balanced perspective, highlighting that dismissing the capabilities of traditional methods in favor of emerging deep learning-based techniques may not consistently yield the most effective results.

关键词： Deep learning Learning systems Measurement Support vector machines Analytical models Logistic regression Social networking (online)

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