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On Multiobjective Knapsack Problems with Multiple Decision Makers

On Multiobjective Knapsack Problems with Multiple Decision M...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Zhen Song Wenjian Luo Xin Lin Zeneng She Qingfu Zhang Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Shenzhen Guangdong China Peng Cheng Laboratory Shenzhen Guangdong China School of Computer Science and Technology University of Science and Technology of China Hefei Anhui China Department of Computer Science City University of Hong Kong Hong Kong

ISBN: (纸本)9781665487696

Many real-world optimization problems require optimizing multiple conflicting objectives simultaneously, and such problems are called multiobjective optimization problems (MOPs). As a variant of the classical knapsack problems, multi-objective knapsack problems (MOKPs), exist widely in the real-world applications, e.g., cargo loading, project and investment selection. There is a special class of MOKPs called multiparty multiobjective knapsack problems (MPMOKPs), which involve multiple decision makers (DMs) and each DM only cares about some of all the objectives. To the best of our knowledge, little work has been conducted to address MPMOKPs. In this paper, a set of benchmarks which have common Pareto optimal solutions for MPMOKPs is proposed. Besides, we design a SPEA2-based algorithm, called SPEA2-MP to solve MPMOKPs, which aims at finding the common Pareto optimal solutions to satisfy multiple decision makers as far as possible. Experimental results on the benchmarks have demonstrated the effectiveness of the proposed algorithm.

关键词： Loading Pareto optimization Benchmark testing Optimization Computational intelligence Investment

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DeepBoundary: A Coverage Testing Method of Deep Learning software based on Decision Boundary Representation

DeepBoundary: A Coverage Testing Method of Deep Learning Sof...

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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Yue Liu Lichao Feng Xingya Wang Shiyu Zhang College of Computer Science and Technology Nanjing Tech University Nanjing Jiangsu China Institute of Command and Control Engineering Army Engineering University of PLA Nanjing Jiangsu China State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu China

With the increasing application of Deep Learning (DL) software in safety-critical fields such as autonomous driving, we need adequate testing to ensure software quality. Observing the decision-making behavior of a Deep Neural Network (DNN) is an essential step in DL software testing. Taking Guiding Deep Learning System Testing Using Surprise Adequacy (SADL) as an example, it uses the independent neuron activation values in the DNN to represent the decision-making behavior. However, the behavior of the DNN needs to be jointly determined by the continuous outputs of all neurons. As a result, the coverage value of SADL constant volatility and lack of stability. To mitigate this problem, we propose a coverage testing method based on the decision boundary representation, DeepBoundary, for the decision-making behavior of DL software. Unlike SADL, DeepBoundary generates decision boundary data to represent the decision behavior of the DNN, which makes the testing results more stable. On this basis, we calculate the kernel density between the testing data and the decision boundary data. It measures the position of the testing data in the decision space and the distance from the decision boundary. Finally, as an adequacy indicator, we calculate the decision boundary density coverage (DBC) of the entire testing set. The experiment on the dataset MNIST and two DL software shows that DeepBoundary can generate actual decision boundary data. The average confidence error in the DNNs output layer is only 4.20E-05. Compared with SADL, DeepBoundary has a stronger correlation with the defect detection ratio, which can more accurately represent testing adequacy.

关键词： Deep learning software testing System testing Correlation Decision making Neurons software quality

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Hero: On the Chaos When PATH Meets Modules

Hero: On the Chaos When PATH Meets Modules

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International Conference on software Engineering (ICSE)

作者： Ying Wang Liang Qiao Chang Xu Yepang Liu Shing-Chi Cheung Na Meng Hai Yu Zhiliang Zhu Software College Northeastern University China State Key Laboratory for Novel Software Technology and Department of Computer Science and Technology Nanjing University China Southern University of Science and Technology China The Hong Kong University of Science and Technology China Virginia Tech USA

Ever since its first release in 2009, the Go programming language (Golang) has been well received by software communities. A major reason for its success is the powerful support of library-based development, where a Golang project can be conveniently built on top of other projects by referencing them as libraries. As Golang evolves, it recommends the use of a new library-referencing mode to overcome the limitations of the original one. While these two library modes are incompatible, both are supported by the Golang ecosystem. The heterogeneous use of library-referencing modes across Golang projects has caused numerous dependency management (DM) issues, incurring reference inconsistencies and even build failures. Motivated by the problem, we conducted an empirical study to characterize the DM issues, understand their root causes, and examine their fixing solutions. Based on our findings, we developed Hero, an automated technique to detect DM issues and suggest proper fixing solutions. We applied Hero to 19,000 popular Golang projects. The results showed that Hero achieved a high detection rate of 98.5% on a DM issue benchmark and found 2,422 new DM issues in 2,356 popular Golang projects. We reported 280 issues, among which 181 (64.6%) issues have been confirmed, and 160 of them (88.4%) have been fixed or are under fixing. Almost all the fixes have adopted our fixing suggestions.

关键词： Chaos computer languages Ecosystems Benchmark testing Libraries software software engineering

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Research on key Word Information Retrieval Based on Inverted Index 8th

Research on Key Word Information Retrieval Based on Inverted...

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8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Qi, Meihan Fang, Wei Zhao, Yongming Sha, Yu Sheng, Victor S. School of Computer and Software Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences Beijing100081 China China Meteorological Administration Training Center Beijing China Department of Computer Texas Tech University LubbockTX79409 United States

ISBN: (纸本)9783031067600

With the advent of the era of big data, data has penetrated into every aspect of social life and become an important production factor in various industries. However, while providing convenience to our life, massive information also brings difficulties to information retrieval, which has problems of low retrieval efficiency and poor retrieval accuracy. Therefore, how to improve the efficiency and accuracy of information retrieval has become a key technical problem to be solved. In this paper, we propose a key word information retrieval scheme based on inverted index, which uses the inverted index segmentation algorithm based on keywords to realize the block retrieval of information, improves the efficiency of information retrieval, and improves the accuracy of retrieval content through reasonable error correction of keywords. The results of evaluation indicate that compared with the mainstream information retrieval methods, the keyword information retrieval scheme based on inverted index can carry out faster and more accurate information retrieval. Experimental results show that the keyword information retrieval scheme with inverted index can further improve the efficiency and accuracy of information retrieval. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Information retrieval

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Vehicle Check-In Data-driven POI Recommendation Based on Improved SVD and Graph Convolutional Network

Vehicle Check-In Data-driven POI Recommendation Based on Imp...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Yuwen Liu Jie Zhang Ruihan Dou Xiaokang Zhou Xiaolong Xu Shoujin Wang Lianyong Qi College of Computer Science and Technology China University of Petroleum (East China) Qingdao China Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University China Shanghai Meteorological Information and Technology Support Center China Faculty of Mathematics University of Waterloo Waterloo Canada Faculty of Data Science Shiga University Japan RIKEN Center for Advanced Intelligence Project RIKEN Japan School of Computer and Software Nanjing University of Information Science and Technology China School of Computing Macquarie University Australia School of Computer Science Qufu Normal University China

The application of automobile tool software is becoming increasingly widespread, and its functions are becoming increasingly diverse. Using vehicle traffic data to give users with recommendations for Points-of-interest (POI) is becoming an intriguing research challenge. However, the current POI recommendation is either a sequence-based method, ignoring the interaction information between users and POIs. Or it needs a lot of model training time to use graph correlation method to mine the interaction between users or vehicles and POIs. In order to address these issues, we propose a method for vehicle check-in data-driven POI recommendation based on improved Singular Value Decomposition (SVD) and Graph Convolutional Network (GCN). First, we apply SVD to replace the neighborhood aggregation update method in traditional GCN, update the node embedding with fewer parameters and faster speed. Second, we propose an information enhancement strategy to make up for the information loss caused by the SVD. Experiments on large vehicle check-in datasets demonstrate that our method is both faster and more effective when making POI recommendations.

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Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection 39

Look Inside for More: Internal Spatial Modality Perception f...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)157735897X

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Feature extraction

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A Self-adaptive PSO-Based Dynamic Scheduling Method on Hierarchical Cloud Computing 1

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9th EAI International Conference on Cloud Computing, CloudComp 2019 and the 4th EAI International Conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2019

作者： Meng, Shunmei Huang, Weijia Xu, Xiaolong Li, Qianmu Dou, Wanchun Liu, Bowen Department of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9783030485139

ISBN: (纸本)9783030485122

Edge computing has been envisioned as an emerging and prospective computing paradigm for its advantage of low latency, which uses local resources. However, the edge resources are usually limited and could not meet end-users’ diversified requirement. Cloud computing paradigm could provide scalable and centralized resources with high computational capabilities, but it has latency issues. Thus it is suggested to combine both computing paradigms together to improve the performance of mobile applications. In this paper, we propose a self-adaptive dynamic scheduling approach based on hierarchical heterogeneous clouds. Our scheduling mechanism considers not only schedule planning but also dynamic scheduling on heterogeneous clouds. Firstly, a self-adaptive scheduling mechanism based on a meta-heuristic optimization algorithm, PSO (Particle Swarm Optimization), is presented for schedule planning. Then a dynamic scheduling mechanism on dynamic partial workflow model is proposed for dynamic optimization during the execution. Finally, external experiments compared with other methods are conducted to demonstrate the effectiveness of our proposal. © 2020, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Mobile edge computing

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Controllable Quantum Computing Privacy via Inherent Noises and Quantum Error Mitigation 23

Controllable Quantum Computing Privacy via Inherent Noises a...

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23rd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2024

作者： Ju, keyi Zhong, Hui Zhang, Xinyue Qin, Xiaoqi Pan, Miao Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States Kennesaw State University Department of Computer Science MariettaGA30060 United States

Quantum computing has revolutionized the approach to solving complex problems and handling vast datasets. However, data leakage in quantum computing may present privacy risks. While differential privacy (DP) has been a classical solution to protect privacy by injecting artificial noises, the implementation of DP within the quantum domain remains an under-explored area. We observe that there is a potential to leverage the inherent noises generated by Noisy Intermediate-Scale Quantum (NISQ) devices during quantum operations for achieving DP in quantum computing. Given that these inherent noises are uncontrollable, we take the lead to utilize quantum error mitigation (QEM) techniques to manage quantum differential privacy (QDP) protection levels. In our approach, we offer a tangible description of the "closeness"between neighboring quantum datasets and introduce a novel QDP definition based on observables of interest. Our simulations reveal that factors like the distance between neighboring quantum states and the number of circuit executions influence the privacy budget. Notably, by controlling the QEM samples while keeping the number of circuit executions fixed, QEM allows for substantial noise control to meet a desired privacy budget. Furthermore, we extend analysis from single-qubit to multiple qubits and discuss the impact of observables of interest on the privacy budget. © 2024 IEEE.

关键词： Qubits

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Effective Entry-Wise Flow for Molecule Generation

Effective Entry-Wise Flow for Molecule Generation

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International Conference on Data Engineering

作者： Qifan Zhang Junjie Yao Yuquan Yang Yizhou Shi Wei Gao Xiaoling Wang School of Computer Science and Technology East China Normal University Software Engineering Institute East China Normal University Shanghai Key Laboratory of Trustworthy Computing Department of Chemistry University of Massachusetts Boston University of Michigan Ann Arbor.

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Molecule generation is a critical process in the fields of drug discovery and materials science. Recently, generative models based on normalizing flows have demonstrated significant potential in this domain. These models are particularly suited for handling the symmetrical and complex chemical structures often encountered in molecular datasets. Despite their promising nature, normalizing flow-based models for molecule generation face considerable challenges. The complexity of molecule representation, the rigorous demands of optimization, and the scarcity of training labels in molecular datasets contribute to these difficulties. Additionally, adequately and comprehensively learning the distribution of molecular datasets remains a formidable task. In this paper, we delve into the intricate entry-wise modules in vanilla flows, introducing an effective variation of flow-based models. Our proposed approach innovatively encapsulates affine coupling transformations within normalizing flows. Furthermore, we deconstruct existing invertible flow models, integrating them with newly developed entry-wise transformations. Our experimental studies demonstrate that these proposed entry-wise modules, when incorporated into standard flow-based models, surpass other generative models in performance on various representative datasets and generation tasks. Notably, in the context of low-resourced molecular graph generation, our model achieves remarkable performance compared to its counterparts.

关键词： Training Couplings Materials science and technology Interpolation Data engineering Drug discovery Task analysis

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Channel-Wise and Spatial Feature Recalibration Network for Nuclear Cataract Classification

Channel-Wise and Spatial Feature Recalibration Network for N...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Zhang, Xiaoqing Xu, Gelei Shen, Junyong Xiao, Zunjie Yan, Qiuyang Yuan, Jin Higashita, Risa Liu, Jiang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China Sun Yat-sen University State Key Laboratory of Ophthalmology Guangzhou China Tomey Corporation Japan Cixi Institute of Biomedical Engineering Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Nuclear cataract (NC) is a prior age-related disease for blindness and vision impairment globally. Anterior segment optical coherence tomography (AS-OCT) image is a new ophthalmology image, which can capture the lens nucleus region clearly compared with other ophthalmic images, e.g., slit lamp images. Clinical research has suggested that features e.g., mean from AS-OCT images have varying correlations with NC severity levels. However, existing convolutional neural network (CNN) based NC classification works have not incorporated the clinical features into the network design to improve the performance. To this end, we propose a novel channel-wise and spatial feature recalibration network (CSFR-Net) to predict NC severity levels automatically, which is built on a stack of channel-wise and spatial feature recalibration (CSFR) modules. In each CSFR module, we construct a channel-wise feature recalibration block and a spatial feature recalibration block to recalibrate intermediate feature maps dynamically. This feature recalibration strategy enables CSFR-Net to highlight feature representations and suppress unnecessary ones in a global-and-local manner. We conduct extensive experiments on a clinical AS-OCT image dataset and CIFAR benchmarks. The results show that our CSFR-Net achieves better performance than state-of-the-art methods with less model complexity. © 2022 IEEE.

关键词： Clinical research

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