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Correction to: A comprehensive performance evaluation of container migration strategies

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computing 2025年第4期107卷 1-2页

作者： Feitosa, Leonel Barbosa, Vandirleya Sabino, Arthur Lima, Luiz Nelson Fé, Iure Silva, Luis Guilherme Callou, Gustavo Carvalho, Juliana Leão, Erico Nguyen, Tuan Anh Rego, Paulo Silva, Francisco Airton Laboratory of Applied Research to Distributed Systems (PASID) Federal University of Piauí (UFPI) Picos Brazil Department of Computing Federal Rural University of Pernambuco (UFRPE) Recife Brazil Department of Software Engineering Faculty of Information Technology University of Industry and Trade (HUIT) Ho Chi Minh City Vietnam Department of Computer Science Federal University of Ceará (UFC) Fortaleza Brazil

Quantum cloud computing: A Review, Open Problems, and Future Directions

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arXiv 2024年

作者： Nguyen, Hoa T. Krishnan, Prabhakar Krishnaswamy, Dilip Usman, Muhammad Buyya, Rajkumar Laboratory School of Computing and Information Systems The University of Melbourne ParkvilleVIC Australia Center for Cybersecurity Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri Campus Kerala India New Jersey NJ United States School of Physics The University of Melbourne ParkvilleVIC Australia Data61 CSIRO ClaytonVIC Australia

Quantum cloud computing is an emerging paradigm of computing that empowers quantum applications and their deployment on quantum computing resources without the need for a specialized environment to host and operate physical quantum computers. This paper reviews recent advances, identifies open problems, and proposes future directions in quantum cloud computing. It discusses the state-of-the-art quantum cloud advances, including the various cloud-based models, platforms, and recently developed technologies and software use cases. Furthermore, it discusses different aspects of the quantum cloud, including resource management, quantum serverless, security, and privacy problems. Finally, the paper examines open problems and proposes the future directions of quantum cloud computing, including potential opportunities and ongoing research in this emerging field. © 2024, CC BY.

关键词： Resource allocation

Design of Malicious Code Detection System Based on Convolutional Neural Network 6th

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Design of Malicious Code Detection System Based on Convoluti...

6th International Conference on Smart Grid and Internet of Things, SGIoT 2022

作者： Wu, Yumeng Zeng, Jianjun Zhang, Zhenjiang Li, Wei Zhang, Zhiyuan Zhang, Yang School of Electronic and Information Engineering Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing100044 China College of Intelligence and Computing Tianjin University Tianjin China Beijing InchTek Technology Co. Ltd. Beijing100012 China The Classified Information Carrier Safety Management Engineering Technology Research Center of Beijing Beijing China Beijing Jinghang Computation and Communication Research Institute Beijing China

ISBN: (纸本)9783031312748

With the rapid development of Internet of things, cloud computing, edge computing and other technologies, malicious code attacks users and even enterprises more and more frequently with the help of software and system security vulnerabilities, which poses a serious threat to network security. The traditional static or dynamic malicious code detection technology is difficult to solve the problem of high-speed iteration and camouflage of malicious code. The detection method based on machine learning algorithm and data mining idea depends on manual feature extraction, and can not automatically and effectively extract the deeper features of malicious code. In view of the traditional malicious code detection methods and the related technologies of deep learning, this paper integrates deep learning into the dynamic malicious code detection system, and proposes a malicious code detection system based on convolutional neural network. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Network security

Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization

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arXiv 2023年

作者： Chen, Jinbiao Wang, Jiahai Zhang, Zizhen Cao, Zhiguang Ye, Te Chen, Siyuan School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Computing and Information Systems Singapore Management University Singapore

Recently, neural heuristics based on deep reinforcement learning have exhibited promise in solving multi-objective combinatorial optimization problems (MOCOPs). However, they are still struggling to achieve high learning efficiency and solution quality. To tackle this issue, we propose an efficient meta neural heuristic (EMNH), in which a meta-model is first trained and then fine-tuned with a few steps to solve corresponding single-objective subproblems. Specifically, for the training process, a (partial) architecture-shared multi-task model is leveraged to achieve parallel learning for the meta-model, so as to speed up the training;meanwhile, a scaled symmetric sampling method with respect to the weight vectors is designed to stabilize the training. For the fine-tuning process, an efficient hierarchical method is proposed to systematically tackle all the subproblems. Experimental results on the multi-objective traveling salesman problem (MOTSP), multi-objective capacitated vehicle routing problem (MOCVRP), and multi-objective knapsack problem (MOKP) show that, EMNH is able to outperform the state-of-the-art neural heuristics in terms of solution quality and learning efficiency, and yield competitive solutions to the strong traditional heuristics while consuming much shorter time. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

Out-of-Distribution Detection in Long-Tailed Recognition with Calibrated Outlier Class Learning

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arXiv 2023年

作者： Miao, Wenjun Pang, Guansong Li, Tianqi Bai, Xiao Zheng, Jin School of Computer Science and Engineering Beihang University China School of Computing and Information Systems Singapore Management University Singapore State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University China State Key Laboratory of Virtual Reality Technology and Systems Beihang University China

Existing out-of-distribution (OOD) methods have shown great success on balanced datasets but become ineffective in long-tailed recognition (LTR) scenarios where 1) OOD samples are often wrongly classified into head classes and/or 2) tail-class samples are treated as OOD samples. To address these issues, current studies fit a prior distribution of auxiliary/pseudo OOD data to the long-tailed in-distribution (ID) data. However, it is difficult to obtain such an accurate prior distribution given the unknowingness of real OOD samples and heavy class imbalance in LTR. A straightforward solution to avoid the requirement of this prior is to learn an outlier class to encapsulate the OOD samples. The main challenge is then to tackle the aforementioned confusion between OOD samples and head/tail-class samples when learning the outlier class. To this end, we introduce a novel calibrated outlier class learning (COCL) approach, in which 1) a debiased large margin learning method is introduced in the outlier class learning to distinguish OOD samples from both head and tail classes in the representation space and 2) an outlier-class-aware logit calibration method is defined to enhance the long-tailed classification confidence. Extensive empirical results on three popular benchmarks CIFAR10-LT, CIFAR100-LT, and ImageNet-LT demonstrate that COCL substantially outperforms state-of-the-art OOD detection methods in LTR while being able to improve the classification accuracy on ID data. Code is available at https://***/mala-lab/COCL. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

A Two-Stage Enhancement Method for Object Detection on Low-Resolution Images

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A Two-Stage Enhancement Method for Object Detection on Low-R...

International Joint Conference on Neural Networks (IJCNN)

作者： Zijian Yuan Mingyang Ling Kan Chang Mengyu Yin Minghong Li Boning Chen School of Computer and Electronic Information Guangxi University Nanning China School of Electrical Engineering Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China School of Automation Central South University Changsha China School of Computing and Information Systems The University of Melbourne Melbourne Australia

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Detecting objects on low-resolution (LR) images is a challenging task, as small structures and details contained in LR images are insufficient. To improve the detection accuracy on LR images, a two-stage enhancement framework is proposed in this paper. In the first stage, a lightweight image super-resolution (SR) sub-network is built by incorporating the external guidance obtained by the detection backbone, so that the potential discriminant features can be well preserved. Moreover, to alleviate the hardship of directly learning a nonlinear mapping from the LR space to the high-resolution (HR) space, an auxiliary module which maps the SR results back to the degraded LR images provides additional constraint. In the second stage, the multi-scale features produced by the detection heads are further enhanced by the lightweight feature adaptation modules, which aim to further bridge the gap between the SR result and the corresponding HR image. During training, the detector is kept frozen in our framework, leading to a flexible plug-and-play mechanism. Experiments demonstrate that our approach achieves better performance on LR images than many state-of-the-art methods. To facilitate further study, our source code will be available at: https://***/Zijian-Yuan/TSE-YOLO.

关键词： Training Upper bound Head Accuracy Source coding Superresolution Detectors

Security Enhancing for IRS-Aided NOMA Networks via Constructive Interference Precoding 10

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Security Enhancing for IRS-Aided NOMA Networks via Construct...

10th IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, MAPE 2024

作者： Bao, Jingying Cao, Yang Qin, Xiaoqi Xu, Lexi Zhao, Nan Niyato, Dusit School of Information and Communication Engineering Dalian University of Technology Dalian116024 China Singapore University of Technology and Design Engineering Systems and Design Pillar Singapore487372 Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Research Institute China United Network Communications Corporation Beijing100048 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

ISBN: (纸本)9798331533922

In this paper, we propose a constructive interference precoding (CIP) enabled secure intelligent reflecting surface (IRS)-non-orthogonal multiple access (NOMA) scheme, lever-aging both the inter-user interference and artificial noise to boost the legitimate transmission of far user, while inducing the eavesdropper to decode the deceptive information. Based on this CIP-NOMA scheme, we minimize the transmit power under the perfect channel state information (CSI), subject to the CIP constraint for the far user and eavesdropper, while guaranteeing the quality of service and successive interference cancellation for the near user and the IRS unit modulus constraint. To handle this non-convex problem, an alternating optimization algorithm is proposed. Specifically, by alternately optimizing the precoding vectors at the base station and the IRS reflecting matrix via the successive convex approximation and the penalty-based algorithm, respectively, a reliable solution can be obtained. Simulation results demonstrate that the proposed scheme can achieve better security performance with less energy consumption compared to the conventional NOMA. © 2024 IEEE.

关键词： Multiple access interference

Automatic Noisy Label Correction for Fine-Grained Entity Typing

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arXiv 2022年

作者： Pan, Weiran Wei, Wei Zhu, Feida Laboratory School of Computer Science and Technology Huazhong University of Science and Technology China China School of Computing and Information Systems Singapore Management University Singapore

Fine-grained entity typing (FET) aims to assign proper semantic types to entity mentions according to their context, which is a fundamental task in various entity-leveraging applications. Current FET systems usually establish on large-scale weakly-supervised/distantly annotation data, which may contain abundant noise and thus severely hinder the performance of the FET task. Although previous studies have made great success in automatically identifying the noisy labels in FET, they usually rely on some auxiliary resources which may be unavailable in real-world applications (e.g., pre-defined hierarchical type structures, human-annotated subsets). In this paper, we propose a novel approach to automatically correct noisy labels for FET without external resources. Specifically, it first identifies the potentially noisy labels by estimating the posterior probability of a label being positive or negative according to the logits output by the model, and then relabel candidate noisy labels by training a robust model over the remaining clean labels. Experiments on two popular benchmarks prove the effectiveness of our method. Our source code can be obtained from https://***/CCIIPLab/DenoiseFET. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

Simple Image-level Classification Improves Open-vocabulary Object Detection

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arXiv 2023年

作者： Fang, Ruohuan Pang, Guansong Bai, Xiao School of Computer Science and Engineering Beihang University China School of Computing and Information Systems Singapore Management University Singapore State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University China

Open-Vocabulary Object Detection (OVOD) aims to detect novel objects beyond a given set of base categories on which the detection model is trained. Recent OVOD methods focus on adapting the image-level pre-trained vision-language models (VLMs), such as CLIP, to a region-level object detection task via, e.g., region-level knowledge distillation, regional prompt learning, or region-text pre-training, to expand the detection vocabulary. These methods have demonstrated remarkable performance in recognizing regional visual concepts, but they are weak in exploiting the VLMs' powerful global scene understanding ability learned from the billion-scale image-level text descriptions. This limits their capability in detecting hard objects of small, blurred, or occluded appearance from novel/base categories, whose detection heavily relies on contextual information. To address this, we propose a novel approach, namely Simple Image-level Classification for Context-Aware Detection Scoring (SIC-CADS), to leverage the superior global knowledge yielded from CLIP for complementing the current OVOD models from a global perspective. The core of SIC-CADS is a multi-modal multi-label recognition (MLR) module that learns the object co-occurrence-based contextual information from CLIP to recognize all possible object categories in the scene. These image-level MLR scores can then be utilized to refine the instance-level detection scores of the current OVOD models in detecting those hard objects. This is verified by extensive empirical results on two popular benchmarks, OV-LVIS and OV-COCO, which show that SIC-CADS achieves significant and consistent improvement when combined with different types of OVOD models. Further, SIC-CADS also improves the cross-dataset generalization ability on Objects365 and OpenImages. Code is available at https://***/mala-lab/SIC-CADS. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection