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Microservice Auto-Scaling Algorithm Based on Workload Prediction in Cloud-Edge Collaboration Environment

Microservice Auto-Scaling Algorithm Based on Workload Predic...

IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Zijun Peng Bing Tang Wei Xu Qing Yang Ehsanullah Hussaini Yuqiang Xiao Haiyan Li School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China Center of Network and Education Technology Guangzhou Maritime University Guangzhou China

Cloud computing centrally consolidates hardware and computing resources, offering efficient and cost-effective services. However, as cloud computing centers are predominantly built and operated in a fully centralized fashion, the increased distance between these centers and users can lead to a decline in service quality. Real-time interaction and high business continuity are crucial in scenarios like traffic monitoring, AR/VR applications, and the Internet of Things (IoT). Edge computing is better suited to meet the demands of such latency-sensitive business needs. By analyzing and processing massive data directly at edge computing nodes, which focus on network edge devices, reliance on transmission resources is reduced, consequently improving the overall quality and performance of services. Nevertheless, resource-constrained edge nodes require efficient utilization of available infrastructure capacity to ensure specific service level objectives (SLO) for applications. Therefore, this paper introduces XScale, a cloud-edge collaborative system that enables microservices to adaptively scale elastically. XScale applies a Bi-LSTM with an attention mechanism to forecast the workload of microservices. When combined with mechanisms designed to handle burst traffic and a cloud-edge collaborative load forwarding strategy, it achieves both adaptive elastic scaling and proactive load forwarding. Experimental results, obtained using real-world microservice workloads, indicate that the XScale system can significantly reduce SLO violations by 88%, increase resource utilization by 15%, and decrease average response time by 21% when compared to existing advanced reactive scaling methods.

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A Blockchain-based PHR Sharing Scheme with Attribute Privacy Protection

A Blockchain-based PHR Sharing Scheme with Attribute Privacy...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Chaohe Lu Zhongyuan Yu Guijuan Wang Anming Dong Xiang Tian School of Computer Science Qufu Normal University Rizhao China School of Information Science and Engineering Lanzhou University Lanzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

With the rapid advancement and application of the Internet of Medical Things (IoMT), personal health records (PHRs) are now increasingly comprised of data collected by Internet of Things (IoT) devices and medical records documented by healthcare professionals. Personal health record (PHR) sharing demonstrates great potential in improving the accuracy of disease diagnosis. However, PHR sharing also brings risks such as illegal access and personal information leakage. Some works explored using blockchain or attribute-based encryption (ABE) to solve these privacy leakage problems, but those solutions did not pay attention to the user’s attribute privacy. In this work, we combine a linear secret sharing scheme (LSSS) and zero-knowledge succinct non-interactive argument of knowledge (zkSNARK) scheme to design an efficient zero-knowledge proof protocol called zk-AHSNARK. It can verify the user’s attribute permissions while also hiding attribute information. Based on zk-AHSNARK, we propose a novel PHR sharing scheme that protects attribute privacy. Data security is ensured by storing encrypted data in the interplanetary file system (IPFS). In addition, we introduce keyword ciphertext search to achieve fast data retrieval, and we implement the search and verification algorithms via a smart contract, ensuring the trustworthiness and integrity of the execution. Finally, through a large number of simulations, we demonstrated the suggested scheme’s viability and security.

关键词： Access control Privacy Smart contracts Blockchains Encryption InterPlanetary File System Medical diagnosis Protection Secure storage Medical diagnostic imaging

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A Robust Semantic Communication System for Image Transmission

A Robust Semantic Communication System for Image Transmissio...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xiang Peng Zhijin Qin Xiaoming Tao Jianhua Lu Khaled B. Letaief Department of Electronic Engineering Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology (BNRist) Beijing China State Key Laboratory of Space Network and Communications Beijing China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Semantic communications have gained significant attention as a promising approach to address the transmission bottleneck, especially with the continuous development of 6G techniques. Distinct from the well investigated physical channel impairments, this paper focuses on semantic impairments in images, particularly those arising from adversarial perturbations. Specifically, we propose a novel metric for quantifying the intensity of semantic impairment and develop a semantic impairment dataset. Furthermore, we introduce a deep learning enabled semantic communication system, termed as DeepSC-RI, to enhance the robustness of image transmission, which incorporates a multi-scale semantic extractor with a dual-branch architecture for extracting semantics with varying granularity, thereby improving the robustness of the system. The fine-grained branch incorporates a semantic importance evaluation module to identify and prioritize crucial semantics, while the coarse-grained branch adopts a hierarchical approach for capturing the robust semantics. These two streams of semantics are seamlessly integrated via an advanced cross-attention-based semantic fusion module. Experimental results demonstrate the superior performance of DeepSC-RI under various levels of semantic impairment intensity.

关键词： Measurement Image communication Perturbation methods Semantics Rician channels Semantic communication Transformers Robustness Streams Signal to noise ratio

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HPCNet: A Hybrid Progressive Coupled network for Image Deraining

HPCNet: A Hybrid Progressive Coupled Network for Image Derai...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Qiong Wang Kui Jiang Jinyi Lai Zheng Wang Jianhui Zhang National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Huawei Technologies Cloud BU College of Computer Science and Technology Hangzhou Dianzi University

Convolutional neural networks (CNNs) and Transformers have significantly succeeded in low-level vision tasks. Although prominent complementary characteristics exist regarding the larger receptive field and better convergence, only some efforts have compacted them efficiently due to their individual and nonnegligible weakness. In this paper, we propose a hybrid progressive coupled network (HPCNet) for rain perturbation removal, which integrates the advantages of these two architectures while maintaining both effectiveness and efficiency. In particular, we achieve the progressive decomposition and association of rain-free and rain features, designed as an asymmetrical dual-path mutual representation network to alleviate the computational cost. Meanwhile, we equip the network with high-efficiency convolutions and resolution rescaling strategy to trade off the computational complexity. Extensive experiments show that our method outperforms MPRNet on average while saving 87.2% and 61.1% of the computational cost and parameters.

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An Ensemble Convolutional Neural network for Recognizing and Counting of Mango Pests using Superpixel Segmentation 10

An Ensemble Convolutional Neural Network for Recognizing and...

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10th International Conference on Information Systems and Computing technology, ISCTech 2022

作者： Yang, Fuqiang Choi, Yeonsung Yang, Fuping Xin, Ke Zhao, Xuechen School of Data and Computer Science Shandong Women's University Jinan China School of Computer Information and Communication Engineering Kunsan National University Kunsan Korea Republic of Shandong Labor Vocational and Technical College Network Center Jinan China College of Information Science and Engineering Binzhou University Binzhou China School of Computing National University of Defense Technology Changsha China

ISBN: (纸本)9798350332933

In the agricultural field., the problem of pests attacking crops has long plagued farmers. In order to detect and monitor the development of pests in time, compared with traditional methods, deep learning techniques that have gradually matured and become effective in recent years can be used. The success of deep learning technology in the field of computer vision in recent years can provide support for the solution of insect recognizes and counts in images under the complex background of agriculture. In recent studies, convolutional neural networks have improved the efficiency for the reorganization of insects in specific categories of crops. This paper suggested a method for recognizing and counting mango insects. In order to improve the accuracy and practicability of the model, this paper has done the following work: First of all, optimized and upgraded the A Large-Scale Benchmark Dataset for Insect Pest Recognition IP102 dataset version. Some of the pictures were cleaned, and unclear pictures that were difficult to extract in the early insects were deleted. Secondly, this paper suggested the EnRDNet algorithm to ensemble the ResNet and DenseNet models that have outstanding performance in the field of insect recognition. Third, this paper proposes to add a layer of confidence to the trained model when used in actual scene prediction which named Smart-EnRDNet algorithm to determine whether there are insect objects in the picture, and then output the insect category if there is. Experiment shows the efficiency of the proposed algorithms. © 2022 IEEE.

关键词： Convolutional neural networks

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CHECKWHY: Causal Fact Verification via Argument Structure

arXiv

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arXiv 2024年

作者： Si, Jiasheng Zhao, Yibo Zhu, Yingjie Zhu, Haiyang Lu, Wenpeng Zhou, Deyu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences China School of Computer Science and Engineering Southeast University China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science China

With the growing complexity of fact verification tasks, the concern with "thoughtful" reasoning capabilities is increasing. However, recent fact verification benchmarks mainly focus on checking a narrow scope of semantic factoids within claims and lack an explicit logical reasoning process. In this paper, we introduce CHECKWHY, a challenging dataset tailored to a novel causal fact verification task: checking the truthfulness of the causal relation within claims through rigorous reasoning steps. CHECKWHY consists of over 19K "why" claim-evidence-argument structure triplets with supports, refutes, and not enough info labels. Each argument structure is composed of connected evidence, representing the reasoning process that begins with foundational evidence and progresses toward claim establishment. Through extensive experiments on state-of-the-art models, we validate the importance of incorporating the argument structure for causal fact verification. Moreover, the automated and human evaluation of argument structure generation reveals the difficulty in producing satisfying argument structure by fine-tuned models or Chain-of-Thought prompted LLMs, leaving considerable room for future improvements1 Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Bi-layered coded metasurface for multi-functional hologram with broadband transmission and efficient reflection

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Chinese Optics Letters 2024年第5期22卷 8-16页

作者：朱磊李绪胜董亮谢文静尚关玉 Shah Nawaz Burokur 丁旭旻 School of Computer and Control Engineering Qiqihar UniversityQiqihar 161006China Communication and Electronics Engineering Institute Qiqihar UniversityQiqihar 161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar UniversityQiqihar 161000China Department of Microwave Engineering Harbin Institute of TechnologyHarbin 150001China LEME UPLUniversitéParis NanterreF92410 Ville d’AvrayFrance Advanced Microscopy and Instrumentation Research Center Harbin Institute of TechnologyHarbin 150080China Key Laboratory of Millimeter Waves Southeast UniversityNanjing 210096China

A multi-functional full-space metasurface based on frequency and polarization multiplexing is *** metasurface unit consists of metallic patterns printed on the two faces of a single-layered dielectric *** unit cell can control electromagnetic wavefronts to achieve a broadband transmission with amplitudes greater than 0.4 from 4.4 to 10.4 ***,at 11.7 GHz and 15.4 GHz,four high-efficiency reflection channels with a reflection amplitude greater than 0.8 are also *** illuminated by linearly polarized waves,five different functions can be realized at five different frequencies,which are demonstrated by theoretical calculations,full-wave simulations,and experimental measurements.

关键词： multi-functional metasurface broadband transmission efficient reflection full space

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IRL-PM: An Inverse Reinforcement Learning-based Power Minimization in Multi-User MISO networks

IRL-PM: An Inverse Reinforcement Learning-based Power Minimi...

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International Conference on computers, Communications, and Systems (ICCCS)

作者： Xingcong Tian Bo Gao Ming Liu Ke Xiong Pingyi Fan Khaled Ben Letaief Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing China National Engineering Research Center of Advanced Network Technologies Beijing Jiaotong University Beijing China Department of Electronic Engineering Tsinghua University Beijing China The Hong Kong University of Science and Technology Hong Kong

This paper aims to efficiently save energy consumption of multiple-input-single-output (MISO) networks. To this end, an optimization problem is formulated to minimize the transmit power of the base station (BS) while ensuring the minimal required information rate of each user. Reinforcement learning (RL) is a significant solution to tackle this non-convex problems. Nevertheless, designing efficient reward functions that accurately describe the involved optimization problems for RL is a challenging problem, which may result in low learning efficiency and even inaccurate learning. To cope with such an issue, this paper proposes an inverse reinforcement learning (IRL)-based approach to minimize the transmit power of MISO networks, in which the generative adversarial imitation learning (GAIL) is employed as the IRL framework and the reward function is represented by an artificial neural network (ANN) model, whose parameters are learned automatically in terms of an expert policy based on the semidefinite relaxation (SDR)-based approach. Since SDR is able to achieve the near optimal solution, the learned reward function is more accurate than traditional artificially designed one. Simulations show that, the proposed IRL-based approach reaches similar energy consumption to the SDR-based approach, and reduces about 50% transmit power compared to traditional RL-based one.

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XoT (Xducation of Things): Harnessing AI and edge computing to educate all things

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IEEE Access 2024年

作者： Nurtantyana, Rio Hwang, Wu-Yuin Hariyanti, Uun Research Center for Data and Information Sciences Bandung Indonesia Telkom University School of Computing Indonesia Telkom University Center of Excellence Artificial Intelligence for Learning and Optimization Bandung Indonesia National Dong Hwa University College of Science and Engineering Department of Computer Science and Information Engineering Taiwan National Central University Graduate Institute of Network Learning Technology Taiwan Universitas Brawijaya Faculty of Computer Science Department of Information System Indonesia

Most English foreign language (EFL) studies focus solely on human beings. This research explores how edge computing can facilitate learning for all things. The XoT (Xducation of Things) framework was proposed to educate both human and all things. All things encompass two terms: AI-Agent and smartthings (covering physical and digital smart objects). At the core of this framework is Smart Question Answer Forwarding Mechanism (SQA-Forwarding), specifically designed to assist all things in building knowledge. To demonstrate this, the smartXoT environment was developed based on XoT framework, and its impacts on EFL learners was assessed. A quasi-experimental study involving 26 EFL learners, divided into an experimental group (EG) and a control group (CG), examined the differences in learning achievement of smartthings and EFL learners when using the smartXoT environment with/without SQA-Forwarding. Findings, on one hand, indicated that smartthings in the EG developed knowledge bases greater than those in the CG. On the other hand, the interaction between EFL learners and smartthings with SQA-Forwarding significantly improved learners' writing skills, with revisions playing a crucial role in enhancing writing quality. Thus, the XoT framework offers a novel and promising approach to educating both humans and all things. © 2013 IEEE.

关键词： Contrastive Learning

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Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking

arXiv

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arXiv 2024年

作者： Chen, Xi Qin, Chuan Fang, Chuyu Wang, Chao Zhu, Chen Zhuang, Fuzhen Zhu, Hengshu Xiong, Hui University of Science and Technology of China China Computer Network Information Center Chinese Academy of Sciences China Baidu Inc China Institute of Artificial Intelligence Beihang University China SKLSDE School of Computer Science Beihang University China AI Thrust The Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The HongKong University of Science and Technology Hong Kong

In a rapidly evolving job market, skill demand forecasting is crucial as it enables policymakers and businesses to anticipate and adapt to changes, ensuring that workforce skills align with market needs, thereby enhancing productivity and competitiveness. Additionally, by identifying emerging skill requirements, it directs individuals towards relevant training and education opportunities, promoting continuous self-learning and development. However, the absence of comprehensive datasets presents a significant challenge, impeding research and the advancement of this field. To bridge this gap, we present Job-SDF, a dataset designed to train and benchmark job-skill demand forecasting models. Based on millions of public job advertisements collected from online recruitment platforms, this dataset encompasses monthly recruitment demand. Our dataset uniquely enables evaluating skill demand forecasting models at various granularities, including occupation, company, and regional levels. We benchmark a range of models on this dataset, evaluating their performance in standard scenarios, in predictions focused on lower value ranges, and in the presence of structural breaks, providing new insights for further research. Our code and dataset are publicly accessible via the https://***/Job-SDF/benchmark. © 2024, CC BY-NC-SA.

关键词： Prediction models

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