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Generating software test data by particle swarm optimization

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Lecture Notes in Computer Science (including subseries Lectu...

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8886卷 37-47页

作者： Jia, Ya-Hui Chen, Wei-Neng Zhang, Jun Li, Jing-Jing School of Information Science and Technology Sun Yat-sen University Guangzhou China School of Advanced Computing Sun Yat-sen University Guangzhou China Key Lab. Machine Intelligence and Advanced Computing Ministry of Education China Engineering Research Center of Supercomputing Engineering Software MOE China School of Computer Science South China Normal University Guangzhou China

Search-based method using meta-heuristic algorithms is a hot topic in automatic test data generation. In this paper, we develop an automatic test data generating tool named particle swarm optimization data generation tool (PSODGT). The PSODGT is characterized by the following two features. First, the PSODGT adopts the condition-decision coverage (C/DC) as the criterion of software testing, aiming to build an efficient test data set that covers all conditions. Second, the PSODGT uses a particle swarm optimization (PSO) approach to generate test data set. In addition, a new position initialization technique is developed for PSO. Instead of initializing the test data randomly, the proposed technique uses the previously-found test data that can reach the target condition as the initial positions so that the search speed of PSODGT can be further accelerated. The PSODGT is tested on four practical programs. Experimental results show that the proposed PSO approach is promising. © Springer International Publishing Switzerland 2014.

关键词： Software testing

Structure of campus network based on reconfigurable architecture

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Tongxin Xuebao/Journal on Communications 2013年第SUPPL.2期34卷 107-110页

作者： Yuan, Hu-Sheng Feng, Hua Li, Hui Zhong, Zhi-Bin Pan, Kai Network and Information Center of Shenzhen University Town Shenzhen 518055 China Peking University Shenzhen Graduate School Shenzhen 518055 China Shenzhen Key Lab. of Cloud Computing Technology and Application Shenzhen 518055 China Shenzhen Engineering Lab. of Converged Network Technology Shenzhen 518055 China Huaqiang Vocational Technical School Shenzhen 518000 China

Because of the petrifaction of the existing network, it is difficult to achieve the controllab.lity, adaptability and expansibility in current structure. Simultaneously, in order to have better campus network services in teaching, researching and studying, a new structure based on reconfigurable architecture for campus network was designed based on service set handling capacity of various kinds of business in reconfigurable network as the central idea. After the evolution from tradition to flat structure, a more condensed structure was proposed by adopting virtualization technology on switches and routers. It has the advantages of high performance, high reliability, manageability and augmentability.

关键词： Virtualization

Contextual Embedding Learning to Enhance 2D Networks for Volumetric Image Segmentation

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arXiv 2024年

作者： Wang, Zhuoyuan Sun, Dong Zeng, Xiangyun Wu, Ruodai Wang, Yi National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Lab Shenzhen China Lab Shenzhen China Huawei Cloud Computing Technologies Company Limited China Department of Radiology Shenzhen University General Hospital Shenzhen University Shenzhen China

The segmentation of organs in volumetric medical images plays an important role in computer-aided diagnosis and treatment/surgery planning. Conventional 2D convolutional neural networks (CNNs) can hardly exploit the spatial correlation of volumetric data. Current 3D CNNs have the advantage to extract more powerful volumetric representations but they usually suffer from occupying excessive memory and computation nevertheless. In this study we aim to enhance the 2D networks with contextual information for better volumetric image segmentation. Accordingly, we propose a contextual embedding learning approach to facilitate 2D CNNs capturing spatial information properly. Our approach leverages the learned embedding and the slice-wisely neighboring matching as a soft cue to guide the network. In such a way, the contextual information can be transfered slice-by-slice thus boosting the volumetric representation of the network. Experiments on challenging prostate MRI dataset (PROMISE12) and abdominal CT dataset (CHAOS) show that our contextual embedding learning can effectively leverage the inter-slice context and improve segmentation performance. The proposed approach is a plug-and-play, and memory-efficient solution to enhance the 2D networks for volumetric segmentation. Our code is publicly availab.e at https://***/JuliusWang-7/CE_Block. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

Hyperbolic Contrastive Learning with Second Order Sampling for Collab.rative Filtering

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Hyperbolic Contrastive Learning with Second Order Sampling f...

IEEE International Conference on Web Services (ICWS)

作者： Longchuan Tu Shunmei Meng Lianyong Qi Xiaolong Xu Xuyun Zhang School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Lab. for Novel Software Technology Nanjing University China College of Computer Science and Technology China University of Petroleum(East China) China School of Computer and Software Nanjing University of Information Science and Technology China School of Computing Macquarie University Australia

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

Recently, hyperbolic spaces have proven beneficial for service recommendation due to their exponentially growing spatial properties conforming to power-law distributed user-item networks. Among them, the combination of hyperbolic space with graph convolution has achieved great success. However, hyperbolic convolutional models still perform multi-layer convolution in tangent space (Euclidean space), leading to the still inevitable problem of over-smoothing arising from multi-layer convolution. In addition, most of these models randomly draw negative samples from items that users have not interacted with, so that some of the samples obtained may not be well suited for model optimization. To tackle the above challenges, we propose a new Hyperbolic GCN model based on Contrastive Learning and Second-order Reachable sampling for collab.rative filtering (HG-CLSR), which improve high quality of representations by exploring the distribution of users and items in hyperbolic space. Specifically, We first introduce a root alignment approach to encourage embeddings to align with the tangent space, thereby reducing distortions during the embedding mapping process in space. Then, we perform contrastive learning in hyperbolic space to motivate the spatial distribution of nodes to better fit the hyperbolic space. Moreover, we sample in the user’s second-order reachable item set, which ensures that the negative sample is more similar to the positive sample, so that the negative node can provide better information for guiding model optimization. Extensive experiments on three real-world datasets demonstrate that the HG-CLSR is significantly superior compared to existing hyperbolic models.

关键词： Degradation Graphical models Convolution Web services Collab.rative filtering Design methodology Contrastive learning Distortion Optimization Distribution functions

TileSR: Accelerate On-Device Super-Resolution with Parallel Offloading in Tile Granularity

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TileSR: Accelerate On-Device Super-Resolution with Parallel ...

IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Ning Chen Sheng Zhang Yu Liang Jie Wu Yu Chen Yuting Yan Zhuzhong Qian Sanglu Lu State Key Lab. for Novel Software Technology Nanjing University P.R. China School of Computer and Electronic Information and the School of AI Nanjing Normal University P.R. China Center for Networked Computing Temple University USA

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Recent years have witnessed the unprecedented performance of convolutional networks in image super-resolution (SR). SR involves upscaling a single low-resolution image to meet application-specific image quality demands, making it vital for mobile devices. However, the excessive computational and memory requirements of SR tasks pose a challenge in mapping SR networks on a single resource-constrained mobile device, especially for an ultra-high target resolution. This work presents TileSR, a novel framework for efficient image SR through tile-granular parallel offloading upon multiple collab.rative mobile devices. In particular, for an incoming image, TileSR first uniformly divides it into multiple tiles and selects the top-K tiles with the highest upscaling difficulty (quantified by mPV). Then, we propose a tile scheduling algorithm based on multi-agent multiarmed bandit, which attains the accurate offload reward through the exploration phase, derives the tile packing decision based on the reward estimates, and exploits this decision to schedule the selected tiles. We have implemented TileSR fully based on COTS hardware, and the experimental results demonstrate that TileSR reduces the response latency by 17.77-82.2% while improving the image quality by 2.38-10.57% compared to other alternatives.

关键词： Image quality Performance evaluation Schedules Accuracy Scheduling algorithms Superresolution Memory management

Over-the-Air Federated Learning via Second-Order Optimization

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arXiv 2022年

作者： Yang, Peng Jiang, Yuning Wang, Ting Zhou, Yong Shi, Yuanming Jones, Colin N. The Shanghai Key Lab. of Trustworthy Computing Software Engineering Institute East China Normal University Shanghai200062 China The Automatic Control Laboratory EPFL Lausanne1015 Switzerland The School of Information Science and Technology ShanghaiTech University Shanghai201210 China

Federated learning (FL) is a promising learning paradigm that can tackle the increasingly prominent isolated data islands problem while keeping users' data locally with privacy and security guarantees. However, FL could result in task-oriented data traffic flows over wireless networks with limited radio resources. To design communication-efficient FL, most of the existing studies employ the first-order federated optimization approach that has a slow convergence rate. This however results in excessive communication rounds for local model updates between the edge devices and edge server. To address this issue, in this paper, we instead propose a novel over-the-air second-order federated optimization algorithm to simultaneously reduce the communication rounds and enable low-latency global model aggregation. This is achieved by exploiting the waveform superposition property of a multi-access channel to implement the distributed second-order optimization algorithm over wireless networks. The convergence behavior of the proposed algorithm is further characterized, which reveals a linear-quadratic convergence rate with an accumulative error term in each iteration. We thus propose a system optimization approach to minimize the accumulated error gap by joint device selection and beamforming design. Numerical results demonstrate the system and communication efficiency compared with the state-of-the-art approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： Wireless networks

Machine learning-based orchestration of containers: A taxonomy and future directions

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arXiv 2021年

作者： Zhong, Zhiheng Xu, Minxian Rodriguez, Maria Alejandra Xu, Chengzhong Buyya, Rajkumar Cloud Computing and Distributed Systems Laboratory School of Computing and Information Systems University of Melbourne Grattan Street Parkville MelbourneVICVIC 3010 Australia Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen University Town 1068 Xueyuan Avenue Guangdong Shenzhen518055 China State Key Lab of IOTSC University of Macau Avenida da Universidade Taipa 999078 China

Containerization is a lightweight application virtualization technology, providing high environmental consistency, operating system distribution portability, and resource isolation. Existing mainstream cloud service providers have prevalently adopted container technologies in their distributed system infrastructures for automated application management. To handle the automation of deployment, maintenance, autoscaling, and networking of containerized applications, container orchestration is proposed as an essential research problem. However, the highly dynamic and diverse feature of cloud workloads and environments considerably raises the complexity of orchestration mechanisms. Machine learning algorithms are accordingly employed by container orchestration systems for behavior modelling and prediction of multi-dimensional performance metrics. Such insights could further improve the quality of resource provisioning decisions in response to the changing workloads under complex environments. In this paper, we present a comprehensive literature review of existing machine learning-based container orchestration approaches. Detailed taxonomies are proposed to classify the current researches by their common features. Moreover, the evolution of machine learning-based container orchestration technologies from the year 2016 to 2021 has been designed based on objectives and metrics. A comparative analysis of the reviewed techniques is conducted according to the proposed taxonomies, with emphasis on their key characteristics. Finally, various open research challenges and potential future directions are highlighted. © 2021, CC0.

关键词： Containers

Design and implementation of E-government oriented rapid BPM OA platform

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Design and implementation of E-government oriented rapid BPM...

International Conference on Systems and Informatics (ICSAI)

作者： Yukun Wang Kai Lei Jincheng Wu Xiaohui Huang Yuming Lu Institute of Big Data Technologies Peking University Shenzhen P.R. China Shenzhen Cloud Computing Center National Supercomputing Center in Shenzhen Shenzhen P.R. China Shenzhen Key Lab for Cognitive Services Engineering Shenzhen Institute of Information Technology Shenzhen P.R. China

The e-government system plays a prominent part in government's office work, however, in the development process, there exist some problems currently, such as repeated development of common functions but hard to satisfy various personalized needs, independence of different systems which makes information difficult to share. These problems have seriously hindered development progress, increased development cost, and may result in information isolated islands. To solve these problems, this paper proposes an e-government oriented BPM OA platform, and introduces its design and implementation process. To achieve rapid development, by analysis and abstraction, we extract main features of common e-government systems and make them into configurable functions. The powerful form builder, the explicit authority management method and the BPMN 2.0 based workflow engine can significantly simplify the development and deployment process, promote the inter-system information exchange, and improve development efficiency.

关键词： Engines Electronic government Process control Tools Monitoring

Byzantine-resilient federated machine learning via over-the-air computation

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arXiv 2021年

作者： Huang, Shaoming Zhou, Yong Wang, Ting Shi, Yuanming School of Information Science and Technology ShanghaiTech University Shanghai201210 China Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences Beijing100049 China Shanghai Key Lab. of Trustworthy Computing School of Software Engineering East China Normal University

Federated learning (FL) is recognized as a key enabling technology to provide intelligent services for future wireless networks and industrial systems with delay and privacy guarantees. However, the performance of wireless FL can be significantly degraded by Byzantine attack, such as data poisoning attack, model poisoning attack and free-riding attack. To design the Byzantine-resilient FL paradigm in wireless networks with limited radio resources, we propose a novel communication-efficient robust model aggregation scheme via over-the-air computation (AirComp). This is achieved by applying the Weiszfeld algorithm to obtain the smoothed geometric median aggregation against Byzantine attack. The additive structure of the Weiszfeld algorithm is further leveraged to match the signal superposition property of multiple-access channels via AirComp, thereby expediting the communication-efficient secure aggregation process of FL. Numerical results demonstrate the robustness against Byzantine devices and good learning performance of the proposed approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Wireless networks