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Towards Exploring Large Molecular Space:An Efficient Chemical Genetic Algorithm

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Journal of computer science & Technology 2022年第6期37卷 1464-1477页

作者：朱健甫郝中楷刘淇阴钰陆承镪黄振亚陈恩红 Anhui Province Key Laboratory of big data Analysis and Application School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefei 230026China

Generating molecules with desired properties is an important task in chemistry and pharmacy.An efficient method may have a positive impact on finding drugs to treat diseases like COVID-19.data mining and artificial intelligence may be good ways to find an efficient method.Recently,both the generative models based on deep learning and the work based on genetic algorithms have made some progress in generating molecules and optimizing the molecule’s properties.However,existing methods have defects in the experimental evaluation standards.These methods also need to be improved in efficiency and performance.To solve these problems,we propose a method named the Chemical Genetic Algorithm for Large Molecular Space(CALM).Specifically,CALM employs a scalable and efficient molecular representation called molecular matrix.And we design corresponding crossover,mutation,and mask operators inspired by domain knowledge and previous studies.We apply our genetic algorithm to several tasks related to molecular property optimization and constraint molecular optimization.The results of these tasks show that our approach outperforms the other state-of-the-art deep learning and genetic algorithm methods,where the z tests performed on the results of several experiments show that our method is more than 99%likely to be significant.At the same time,based on the experimental results,we point out the defects in the experimental evaluation standard which affects the fair evaluation of all previous work.Avoiding these defects helps to objectively evaluate the performance of all work.

关键词： data mining molecular generation genetic algorithm drug discovery artificial intelligence

Neighbor Library-Aware Graph Neural Network for Third Party Library Recommendation

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清华大学学报:自然科学版(英文版) 2023年第4期28卷 769-785页

作者： Ying Jin Yi Zhang Yiwen Zhang school of Artificial Intelligence and big data Hefei UniversityHefei 230601China school of computer science and Technology Anhui UniversityHefei 230601China

Modern software development has moved toward agile growth and rapid delivery,where developers must meet the changing needs of users instantaneously.In such a situation,plug-and-play Third-Party Libraries(TPLs)introduce a considerable amount of convenience to developers.However,selecting the exact candidate that meets the project requirements from the countless TPLs is challenging for developers.Previous works have considered setting up a personalized recommender system to suggest TPLs for developers.Unfortunately,these approaches rarely consider the complex relationships between applications and TPLs,and are unsatisfactory in accuracy,training speed,and convergence speed.In this paper,we propose a new end-to-end recommendation model called Neighbor Library-Aware Graph Neural Network(NLA-GNN).Unlike previous works,we only initialize one type of node embedding,and construct and update all types of node representations using Graph Neural Networks(GNN).We use a simplified graph convolution operation to alternate the information propagation process to increase the training efficiency and eliminate the heterogeneity of the app-library bipartite graph,thus efficiently modeling the complex high-order relationships between the app and the library.Extensive experiments on large-scale real-world datasets demonstrate that NLA-GNN achieves consistent and remarkable improvements over state-of-the-art baselines for TPL recommendation tasks.

关键词： Third-Party Library(TPL) TPL recommendation Graph Neural Network(GNN) bipartite graph

An Improved Honey Badger Algorithm through Fusing Multi-Strategies

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computers, Materials & Continua 2023年第8期76卷 1479-1495页

作者： Zhiwei Ye Tao Zhao Chun Liu Daode Zhang Wanfang Bai school of computer science Hubei University of TechnologyWuhan430068China Xining big data Service Administration Xining810000China

TheHoney Badger Algorithm(HBA)is a novelmeta-heuristic algorithm proposed recently inspired by the foraging behavior of honey badgers.The dynamic search behavior of honey badgers with sniffing and wandering is divided into exploration and exploitation in HBA,which has been applied in photovoltaic systems and optimization problems effectively.However,HBA tends to suffer from the local optimum and low convergence.To alleviate these challenges,an improved HBA(IHBA)through fusing multi-strategies is presented in the paper.It introduces Tent chaotic mapping and composite mutation factors to HBA,meanwhile,the random control parameter is improved,moreover,a diversified updating strategy of position is put forward to enhance the advantage between exploration and exploitation.IHBA is compared with 7 meta-heuristic algorithms in 10 benchmark functions and 5 engineering problems.The Wilcoxon Rank-sum Test,Friedman Test and Mann-WhitneyU Test are conducted after emulation.The results indicate the competitiveness and merits of the IHBA,which has better solution quality and convergence traits.The source code is currently available from:https://***/zhaotao789/IHBA.

关键词： Honey Badger Algorithm multi-strategies fusion tent chaotic mapping compound random factors

Enhancing wireless sensing via a target-mounted intelligent reflecting surface

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National science Review 2023年第8期10卷 13-14页

作者： Xiaodan Shao Rui Zhang school of science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and computer Engineering National University of Singapore

An intelligent reflecting surface(IRS),or its various equivalents such as an reconfigurable intelligent surface(RIS), is an emerging technology to control radio signal propagation in wireless systems. An IRS is a digitally controlled metasurface consisting of a large number of passive reflecting elements, which are connected to a smart controller to enable dynamic adjustments of the amplitude and/or phase of the incident signal on each element independently [1].

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Resource investment for DDoS attack resistant SDN: a practical assessment

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science China(Information sciences) 2023年第7期66卷 112-129页

作者： Bin YUAN Fan ZHANG Jun WAN Huan ZHAO Shui YU Deqing ZOU Qiangsheng HUA Hai JIN school of Cyber science and Engineering Huazhong University of Science and Technology National Engineering Research Center for big data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on big data Security Shenzhen Huazhong University of science and Technology Research Institute school of computer science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab school of computer science University of Technology Sydney

Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of service(DDoS) attack, which aims to drain the resources of SDN switches and controllers,is one of the most common. Once the switch or controller is damaged, the network services can be affected.Many defense schemes against DDoS attacks have been proposed from the perspective of attack detection;however, such defense schemes are known to suffer from a time consuming and unpromising accuracy, which could result in an unavailable network service before specific countermeasures are taken. To address this issue through a systematic investigation, we propose an elaborate resource-management mechanism against DDoS attacks in an SDN. Specifically, by considering the SDN topology, we leverage the M/M/c queuing model to measure the resistance of an SDN to DDoS attacks. Network administrators can therefore invest a reasonable number of resources into SDN switches and SDN controllers to defend against DDoS attacks while guaranteeing the quality of service(QoS). Comprehensive analyses and empirical data-based experiments demonstrate the effectiveness of the proposed approach.

关键词： SDN security DDoS attacks resource management queueing theory QoS

SAND: semi-automated adaptive network defense via programmable rule generation and deployment

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science China(Information sciences) 2022年第7期65卷 121-138页

作者： Haoyu CHEN Deqing ZOU Hai JIN Shouhuai XU Bin YUAN National Engineering Research Center for big data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology National Engineering Research Center for big data Technology and System Services Computing Technology and System LabHubei Engineering Research Center on Big Data Security School of Cyber Science and EngineeringHuazhong University of Science and Technology Department of computer science University of Colorado at Colorado Springs

Cyber security is dynamic as defenders often need to adapt their defense postures. The state-ofthe-art is that the adaptation of network defense is done manually(i.e., tedious and error-prone). The ideal solution is to automate adaptive network defense, which is however a difficult problem. As a first step towards automation, we propose investigating how to attain semi-automated adaptive network defense(SAND). We propose an approach extending the architecture of software-defined networking, which is centered on providing defenders with the capability to program the generation and deployment of dynamic defense rules enforced by network defense tools. We present the design and implementation of SAND, as well as the evaluation of the prototype implementation. Experimental results show that SAND can achieve agile and effective dynamic adaptations of defense rules(less than 15 ms on average for each operation), while only incurring a small performance overhead.

关键词： network defense Network defense adaptive defense automated defense programmable defense security services software-defined networking security management

Reveal training performance mystery between Tensor Flow and PyTorch in the single GPU environment

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science China(Information sciences) 2022年第1期65卷 147-163页

作者： Hulin DAI Xuan PENG Xuanhua SHI Ligang HE Qian XIONG Hai JIN National Engineering Research Center for big data Technology and System Service Computing Technology and System LabSchool of Computer Science and Technology Huazhong University of Science and Technology Department of computer science University of Warwick

Deep learning has gained tremendous success in various fields while training deep neural networks(DNNs) is very compute-intensive, which results in numerous deep learning frameworks that aim to offer better usability and higher performance to deep learning practitioners. Tensor Flow and Py Torch are the two most popular frameworks. Tensor Flow is more promising within the industry context, while Py Torch is more appealing in academia. However, these two frameworks differ much owing to the opposite design philosophy:static vs dynamic computation graph. Tensor Flow is regarded as being more performance-friendly as it has more opportunities to perform optimizations with the full view of the computation graph. However, there are also claims that Py Torch is faster than Tensor Flow sometimes, which confuses the end-users on the choice between them. In this paper, we carry out the analytical and experimental analysis to unravel the mystery of comparison in training speed on single-GPU between Tensor Flow and Py Torch. To ensure that our investigation is as comprehensive as possible, we carefully select seven popular neural networks, which cover computer vision, speech recognition, and natural language processing(NLP). The contributions of this work are two-fold. First, we conduct the detailed benchmarking experiments on Tensor Flow and Py Torch and analyze the reasons for their performance difference. This work provides the guidance for the end-users to choose between these two frameworks. Second, we identify some key factors that affect the performance,which can direct the end-users to write their models more efficiently.

关键词： deep learning performance comparison TensorFlow PyTorch

Expression Recognition Method Based on Convolutional Neural Network and Capsule Neural Network

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computers, Materials & Continua 2024年第4期79卷 1659-1677页

作者： Zhanfeng Wang Lisha Yao school of computer science and Artificial Intelligence Chaohu UniversityHefei238000China school of big data and Artificial Intelligence Anhui Xinhua UniversityHefei230088China

Convolutional neural networks struggle to accurately handle changes in angles and twists in the direction of images,which affects their ability to recognize patterns based on internal feature levels. In contrast, CapsNet overcomesthese limitations by vectorizing information through increased directionality and magnitude, ensuring that spatialinformation is not overlooked. Therefore, this study proposes a novel expression recognition technique calledCAPSULE-VGG, which combines the strengths of CapsNet and convolutional neural networks. By refining andintegrating features extracted by a convolutional neural network before introducing theminto CapsNet, ourmodelenhances facial recognition capabilities. Compared to traditional neural network models, our approach offersfaster training pace, improved convergence speed, and higher accuracy rates approaching stability. Experimentalresults demonstrate that our method achieves recognition rates of 74.14% for the FER2013 expression dataset and99.85% for the CK+ expression dataset. By contrasting these findings with those obtained using conventionalexpression recognition techniques and incorporating CapsNet’s advantages, we effectively address issues associatedwith convolutional neural networks while increasing expression identification accuracy.

关键词： Expression recognition capsule neural network convolutional neural network

Effort-aware cross-project just-in-time defect prediction framework for mobile apps

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Frontiers of computer science 2022年第6期16卷 15-29页

作者： Tian CHENG Kunsong ZHAO Song SUN Muhammad MATEEN Junhao WEN school of big data and Software Engineering Chongqing UniversityChongqing401331China school of computer science Wuhan UniversityWuhan430072China Department of computer science Air University Multan CampusMultan60000Pakistan

As the boom of mobile devices,Android mobile apps play an irreplaceable roles in people’s daily life,which have the characteristics of frequent updates involving in many code commits to meet new requirements.Just-in-Time(JIT)defect prediction aims to identify whether the commit instances will bring defects into the new release of apps and provides immediate feedback to developers,which is more suitable to mobile apps.As the within-app defect prediction needs sufficient historical data to label the commit instances,which is inadequate in practice,one alternative method is to use the cross-project model.In this work,we propose a novel method,called KAL,for cross-project JIT defect prediction task in the context of Android mobile apps.More specifically,KAL first transforms the commit instances into a high-dimensional feature space using kernel-based principal component analysis technique to obtain the representative features.Then,the adversarial learning technique is used to extract the common feature embedding for the model building.We conduct experiments on 14 Android mobile apps and employ four effort-aware indicators for performance evaluation.The results on 182 cross-project pairs demonstrate that our proposed KAL method obtains better performance than 20 comparative methods.

关键词： kernel-based principal component analysis adversarial learning just-in-time defect prediction cross-project model