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Computational Experiments for Complex Social Systems:Experiment Design and Generative Explanation

IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 1022-1038页

作者： Xiao Xue Deyu Zhou Xiangning Yu Gang Wang Juanjuan Li Xia Xie Lizhen Cui Fei-Yue Wang the School of Computer software College of Intelligence and ComputingTianjin UniversityTianjin 300350China the School of software and the Joint SDUNTU Centre for Artificial intelligence Research(C-FAIR) Shandong UniversityJinan 250101China the Information School Xi’an University of Financial and EconomicsXi’an 710100China the State key laboratory of Multimodal Artificial intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Hainan University Haikou 570228China the State key laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 1001901China

Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system emergence.To address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating laws.Finally,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.

关键词： Agent-based modeling computational experiments cyber-physical-social systems(CPSS) generative deduction generative experiments meta model

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XRL-SHAP-Cache:an explainable reinforcement learning approach for intelligent edge service caching in content delivery networks

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Science China(Information Sciences) 2024年第7期67卷 46-71页

作者： Xiaolong XU Fan WU Muhammad BILAL Xiaoyu XIA Wanchun DOU Lina YAO Weiyi ZHONG School of software Nanjing University of Information Science and Technology School of computing and Communications Lancaster University School of computing Technologies Royal Melbourne Institute of Technology State key laboratory for novel software technology Nanjing University School of Computer Science and Engineering University of New South Wales Data 61 Commonwealth Scientific and Industrial Research Organization School of Computer Science Qufu Normal University

Content delivery networks(CDNs) play a pivotal role in the modern internet infrastructure by enabling efficient content delivery across diverse geographical regions. As an essential component of CDNs, the edge caching scheme directly influences the user experience by determining the caching and eviction of content on edge servers. With the emergence of 5G technology, traditional caching schemes have faced challenges in adapting to increasingly complex and dynamic network environments. Consequently, deep reinforcement learning(DRL) offers a promising solution for intelligent zero-touch network governance. However, the blackbox nature of DRL models poses challenges in understanding and making trusting decisions. In this paper,we propose an explainable reinforcement learning(XRL)-based intelligent edge service caching approach,namely XRL-SHAP-Cache, which combines DRL with an explainable artificial intelligence(XAI) technique for cache management in CDNs. Instead of focusing solely on achieving performance gains, this study introduces a novel paradigm for providing interpretable caching strategies, thereby establishing a foundation for future transparent and trustworthy edge caching solutions. Specifically, a multi-level cache scheduling framework for CDNs was formulated theoretically, with the D3QN-based caching scheme serving as the targeted interpretable model. Subsequently, by integrating Deep-SHAP into our framework, the contribution of each state input feature to the agent's Q-value output was calculated, thereby providing valuable insights into the decision-making process. The proposed XRL-SHAP-Cache approach was evaluated through extensive experiments to demonstrate the behavior of the scheduling agent in the face of different environmental inputs.The results demonstrate its strong explainability under various real-life scenarios while maintaining superior performance compared to traditional caching schemes in terms of cache hit ratio, quality of service(QoS),and space utilization.

关键词： deep reinforcement learning (DRL) explainable artificial intelligence (XAI) multi-level cache content delivery network (CDN) D3QN algorithm Deep-SHAP

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Towards High-Performance Graph Processing: From a Hardware/software Co-Design Perspective

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Journal of Computer Science & technology 2024年第2期39卷 245-266页

作者：廖小飞赵文举金海姚鹏程黄禹王庆刚赵进郑龙张宇邵志远 National Engineering Research Center for Big Data technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services computing technology and System laboratory School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid computing laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang lab Hangzhou 311121China

Graph processing has been widely used in many scenarios,from scientific computing to artificial intelligence.Graph processing exhibits irregular computational parallelism and random memory accesses,unlike traditional workloads.Therefore,running graph processing workloads on conventional architectures(e.g.,CPUs and GPUs)often shows a significantly low compute-memory ratio with few performance benefits,which can be,in many cases,even slower than a specialized single-thread graph algorithm.While domain-specific hardware designs are essential for graph processing,it is still challenging to transform the hardware capability to performance boost without coupled software codesigns.This article presents a graph processing ecosystem from hardware to software.We start by introducing a series of hardware accelerators as the foundation of this ecosystem.Subsequently,the codesigned parallel graph systems and their distributed techniques are presented to support graph applications.Finally,we introduce our efforts on novel graph applications and hardware architectures.Extensive results show that various graph applications can be efficiently accelerated in this graph processing ecosystem.

关键词： graph processing hardware accelerator software system high performance ecosystem

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Adversarial image detection based on the maximum channel of saliency maps

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Optoelectronics Letters 2022年第5期18卷 307-312页

作者： FU Haoran WANG Chundong LIN Hao HAO Qingbo key laboratory of Computer Vision and System Tianjin Key Laboratory of Intelligence Computing and Novel Software TechnologyTianjin University of TechnologyTianjin 300384China

Studies have shown that deep neural networks(DNNs) are vulnerable to adversarial examples(AEs) that induce incorrect behaviors. To defend these AEs, various detection techniques have been developed. However, most of them only appear to be effective against specific AEs and cannot generalize well to different AEs. We propose a new detection method against AEs based on the maximum channel of saliency maps(MCSM). The proposed method can alter the structure of adversarial perturbations and preserve the statistical properties of images at the same time. We conduct a complete evaluation on AEs generated by 6 prominent adversarial attacks on the Image Net large scale visual recognition challenge(ILSVRC) 2012 validation sets. The experimental results show that our method performs well on detecting various AEs.

关键词： networks neural maps

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Unsupervised image-to-image translation by semantics consistency and self-attention

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Optoelectronics Letters 2022年第3期18卷 175-180页

作者： ZHANG Zhibin XUE Wanli FU Guokai The key laboratory of Computer Vision and System of Ministry of Education Tianjin Key Laboratory of Intelligence Computing and Novel Software TechnologyTianjin University of TechnologyTianjin 300384China

Unsupervised image-to-image translation is a challenging task for computer vision. The goal of image translation is to learn a mapping between two domains, without corresponding image pairs. Many previous works only focused on image-level translation but ignored image features processing, which led to a certain semantics loss, such as the changes of the background of the generated image, partial transformation, and so on. In this work, we propose a method of image-to-image translation based on generative adversarial nets(GANs). We use autoencoder structure to extract image features in the generator and add semantic consistency loss on extracted features to maintain the semantic consistency of the generated image. Self-attention mechanism at the end of generator is used to obtain long-distance dependency in image. At the same time, as expanding the convolution receptive field, the quality of the generated image is enhanced. Quantitative experiment shows that our method significantly outperforms previous works. Especially on images with obvious foreground, our model shows an impressive improvement.

关键词： translation features semantic

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ThyroidNet:A Deep Learning Network for Localization and Classification of Thyroid Nodules

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 361-382页

作者： Lu Chen Huaqiang Chen Zhikai Pan Sheng Xu Guangsheng Lai Shuwen Chen Shuihua Wang Xiaodong Gu Yudong Zhang Ultrasonic Department Zhongda Hospital Affiliated to Southeast UniversityNanjing210009China School of Physics and Information Engineering Jiangsu Second Normal UniversityNanjing211200China School of computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia State key laboratory of MillimeterWaves Southeast UniversityNanjing210096China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Jiangsu Second Normal UniversityNanjing211200China School of software Engineering Quanzhou Normal UniversityQuanzhou362000China Department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou215123China

Aim:This study aims to establish an artificial intelligence model,ThyroidNet,to diagnose thyroid nodules using deep learning techniques accurately.Methods:A novel method,ThyroidNet,is introduced and evaluated based on deep learning for the localization and classification of thyroid nodules.First,we propose the multitask TransUnet,which combines the TransUnet encoder and decoder with multitask learning.Second,we propose the DualLoss function,tailored to the thyroid nodule localization and classification tasks.It balances the learning of the localization and classification tasks to help improve the model’s generalization ability.Third,we introduce strategies for augmenting the data.Finally,we submit a novel deep learning model,ThyroidNet,to accurately detect thyroid nodules.Results:ThyroidNet was evaluated on private datasets and was comparable to other existing methods,including U-Net and TransUnet.Experimental results show that ThyroidNet outperformed these methods in localizing and classifying thyroid nodules.It achieved improved accuracy of 3.9%and 1.5%,respectively.Conclusion:ThyroidNet significantly improves the clinical diagnosis of thyroid nodules and supports medical image analysis tasks.Future research directions include optimization of the model structure,expansion of the dataset size,reduction of computational complexity and memory requirements,and exploration of additional applications of ThyroidNet in medical image analysis.

关键词： ThyroidNet deep learning TransUnet multitask learning medical image analysis

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Light field imaging based on a parallel SVM method for recognizing 2D fake pedestrians

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Optoelectronics Letters 2022年第1期18卷 48-53页

作者： JIA Chen ZHANG Yao SHI Fan ZHAO Meng key laboratory of Computer Vision and System of Ministry of Education Tianjin Key Laboratory of Intelligence Computing and Novel Software TechnologyTianjin University of TechnologyTianjin 300384China The MOE key laboratory of Weak-Light Nonlinear Photonics Nankai UniversityTianjin 300457China

It is novel to apply three-dimensional(3D)light field imaging technology to recognize two-dimensional(2D)fake pedestrians.In this paper,we propose a parallel support vector machine(SVM)method based on 3D light field imaging(light field camera)and machine learning techniques.A light field(LF)camera with robust sensors,which is able to record rich 3D information,is used as hardware equipment.Histogram of oriented gradient(HOG)feature extraction algorithm and SVM classification method are used to recognize the real and 2D fake pedestrians efficiently.Besides,we carry out an experiment on our improved LF pedestrian dataset.The experimental results of parameter optimization study show that in the case of 400 training samples(200 positive samples and 200 negative samples),120 to 420 testing samples,and an HOG cellsize as 8×8,the best recognition accuracy with polynomial kernel function is improved by more than 2%compared with the previous method.The best accuracy is 99.17%.Otherwise,the recognition accuracy of more than 98.00%will be obtained even under other experimental conditions.

关键词： hardware field method

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Realize ultralow-energy-consumption photo-synaptic device based on a single(Al,Ga)N nanowire for neuromorphic computing

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Nano Research 2024年第3期17卷 1933-1941页

作者： Xiushuo Gu Min Zhou Yukun Zhao Qianyi Zhang Jianya Zhang Yonglin Huang Shulong Lu College of Electronic and Optical Engineering&College of Flexible Electronics(Future technology) Nanjing University of Posts and TelecommunicationsNanjing 210023China key lab of Nanodevices and Applications Suzhou Institute of Nano-Tech and Nano-Bionics(SINANO)Chinese Academy of Sciences(CAS)Suzhou 215123China School of Nano-Tech and Nano-Bionics University of Science and Technology of ChinaHefei 230026China Jiangsu key laboratory of Micro and Nano Heat Fluid Flow technology and Energy Application School of Physical Science and TechnologySuzhou University of Science and TechnologySuzhou 215009China

The rapid development of artificial intelligence poses an urgent need for low-energy-consumption and small-sized artificial photonic synapses.Here,it is pretty novel to demonstrate a light-stimulated synaptic device based on a single(Al,Ga)N nanowire successfully.Thanks to the presence of vacancy defects in the single nanowire,the artificial synaptic device can simulate multiple functions of biological synapses under stimulation of both 310 and 365 nm light photons,including paired-pulse facilitation,spike timing dependent plasticity,and memory learning capabilities.The energy consumption of artificial synaptic device can be reduced as little as 5.58×10^(-13) J,which is close to that of the biological synapse in human brain.Furthermore,the synaptic device is demonstrated to have the high stability for both long-time stimulation and long-time storage.Based on the experimental conductance of long-term potentiation and long-term depression,the simulated three-layer neural network can achieve a high recognition rate of 92%after only 10 training epochs.With a brain-like behavior,the single-nanowire-based synaptic devices can promote the development of visual neuromorphic computing technology and artificial intelligence systems requiring ultralow energy consumption.

关键词： single(Al Ga)N nanowire light-stimulated synaptic device low-energy-consumption neural network

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A novel policy iteration algorithm for solving the optimal consensus control problem of a discrete-time multiagent system with unknown dynamics

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Science China(Information Sciences) 2023年第8期66卷 269-270页

作者： Wenkai XU Li WANG Shiwen SUN Chengyi XIA Zengqiang CHEN tianjin key laboratory of intelligence computing and novel software technology Tianjin University of Technology The Engineering Research Center of Learning-Based Intelligent System Ministry of Education College of Artificial intelligence Nankai University

At present, an increasing number of researchers have noticed the importance of optimal consensus control(OCC) of multiagent systems(MASs) because of their rich practical applications in various areas [1–4]. To accomp...

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Microsnoop: A generalist tool for microscopy image representation

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The Innovation 2024年第1期5卷 109-117页

作者： Dejin Xun Rui Wang Xingcai Zhang Yi Wang Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China State key lab of Computer-Aided Design&Computer Graphics Zhejiang UniversityHangzhou 310058China John A.Paulson School of Engineering and Applied Sciences Harvard UniversityCambridgeMA 02138USA Innovation Institute for Artificial intelligence in Medicine of Zhejiang University Hangzhou 310018China National key laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314100China

Accurate profiling of microscopy images from small scale to high throughput is an essential procedure in basic and applied biological research.Here,we present Microsnoop,a novel deep learning–based representation tool trained on large-scale microscopy images using masked self-supervised learning.Microsnoop can process various complex and heterogeneous images,and we classified images into three categories:single-cell,full-field,and batch-experiment images.Our benchmark study on 10 high-quality evaluation datasets,containing over 2,230,000 images,demonstrated Microsnoop’s robust and state-ofthe-art microscopy image representation ability,surpassing existing generalist and even several custom algorithms.Microsnoop can be integrated with other pipelines to perform tasks such as superresolution histopathology image and multimodal analysis.Furthermore,Microsnoop can be adapted to various hardware and can be easily deployed on local or cloud computing platforms.We will regularly retrain and reevaluate the model using communitycontributed data to consistently improve Microsnoop.

关键词： hardware image representation

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