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14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Gu, Xue Meng, Ziyao Liang, Yanchun Xu, Dong Huang, Han Han, Xiaosong Wu, Chunguo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Zhuhai College of Jilin University Zhuhai519041 China Department of Electrical Engineering and Computer Science Informatics Institute and Christopher S. Bond Life Sciences Center University of Missouri ColumbiaMO65211 United States School of Software Engineering South China University of Technology GuangZhou510006 China

ISBN: (纸本)9789811534249

Although deep neural networks (DNNs) play important roles in many fields, the architecture design of DNNs can be challenging due to the difficulty of input data representation, the huge number of parameters and the complex layer relationships. To overcome the obstacles of architecture design, we developed a new method to generate the optimal structure of DNNs, named Evolutionary Strategy-based Architecture Evolution (ESAE), consisting of a bi-level representation and a probability distribution learning approach. The bi-level representation encodes architectures in the gene and parameter levels. The probability distribution learning approach ensures the efficient convergence of the architecture searching process. By using Fashion-MNIST and CIFAR-10, the effectiveness of the proposed ESAS is verified. The evolved DNNs, starting from a trivial initial architecture with one single convolutional layer, achieved the accuracies of 94.48% and 93.49% on Fashion-MNIST and CIFAR-10, respectively, and require remarkably less hardware costs in terms of GPUs and running time, compared with the existing state-of-the-art manual screwed architectures. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Application of ontology and multivariate decision diagram in cloud monitor systems

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International Journal of Performability Engineering 2019年第12期15卷 3227-3236页

作者： Xu, Han Chung Chu, William Cheng Luo, Jie School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Department of Computer Science Tunghai University Taiwan40704 Taiwan School of Computer Science and Engineering Beihang University Beijing100191 China State Key Laboratory of Software Development Environmen Beihang University Beijing100191 China

Cloud computing is different from distributed computing and grid computing and has its own characteristics. The existing cloud system is not sufficient in the unified identification of cloud resources and the dynamic joining management of new resources. According to the characteristics of cloud computing, this paper introduces the idea of ontology on cloud monitor system (CMS) based on bionic autonomic nervous system (BANS) and uses ontology web language (OWL) language to describe the resources of the system. It also establishes a reusable extended resource expression model. At the same time, the use of the third-party tool Jena for OWL semantic query also gives the monitoring system the characteristics of a rapid semantic query, which further enhances the convenience of cloud resource management. In addition, based on the application of ontology, we also introduce multivariate decision diagram (MDD) multi-valued decision graph technology, which allows B-CMS to self-diagnose complex system faults. The combination of ontology and MDD greatly simplifies the monitoring and management of large-scale systems, providing a fast and standardized means for the intelligent diagnosis of systems. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Ontology

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An attention-based unsupervised adversarial model for movie review spam detection

arXiv

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arXiv 2021年

作者： Gao, Yuan Gong, Maoguo Xie, Yu Qin, A.K. The School of Electronic Engineering Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University Xi'anShanxi Province710071 China The department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia

With the prevalence of the Internet, online reviews have become a valuable information resource for people. However, the authenticity of online reviews remains a concern, and deceptive reviews have become one of the most urgent network security problems to be solved. Review spams will mislead users into making suboptimal choices and inflict their trust in online reviews. Most existing research manually extracted features and labeled training samples, which are usually complicated and time-consuming. This paper focuses primarily on a neglected emerging domain - movie review, and develops a novel unsupervised spam detection model with an attention mechanism. By extracting the statistical features of reviews, it is revealed that users will express their sentiments on different aspects of movies in reviews. An attention mechanism is introduced in the review embedding, and the conditional generative adversarial network is exploited to learn users' review style for different genres of movies. The proposed model is evaluated on movie reviews crawled from Douban, a Chinese online community where people could express their feelings about movies. The experimental results demonstrate the superior performance of the proposed approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Research of Insect Recognition Based on Improved YOLOv5

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Journal on Artificial Intelligence 2021年第4期3卷 145-152页

作者： Zhong Yuan Wei Fang Yongming Zhao Victor S.Sheng School of Computer and Software Engineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesBeijing100081China China Meteorological Administration Training Center Beijing100081China Department of Computer Texas Tech UniversityLubbockTX 79409USA

Insects play an important role in the natural ecology,it is of great significance for ecology to research on ***,the invasion of alien species has brought serious troubles and a lot of losses to local ***,there is still much room for improvement in the accuracy of insect recognition to effectively prevent the invasion of alien *** the latest target detection algorithm,YOLOv5 has been used in various scene detection tasks,because of its powerful recognition capabilities and extremely high *** the problem of imbalance of feature maps at different scales will affect the accuracy of recognition,we propose that adding an attention mechanism based on *** channel attention module and the spatial attention module are added to highlight the important information in the feature map and weaken the secondary information,enhancing the recognition ability of the *** training on self-made insect data sets,experimental results show that the mAP@0.5 value reaches 92.5%and the F1 score reaches *** with YOLOv5,the map has increased by 1.7%,and the F1 score has increased by 0.02,proving the effectiveness of insect recognition based on improved *** conclusion,we provide effective technical support for insect identification,especially for pest identification.

关键词： YOLOv5 attention mechanism insect identification

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Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning

arXiv

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arXiv 2020年

作者： Ye, Han-Jia Chen, Hong-You Zhan, De-Chuan Chao, Wei-Lun State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Computer Science and Engineering The Ohio State University United States

Classifiers trained with class-imbalanced data are known to perform poorly on test data of the "minor" classes, of which we have insufficient training data. In this paper, we investigate learning a ConvNet classifier under such a scenario. We found that a ConvNet significantly over-fits the minor classes, which is quite opposite to traditional machine learning algorithms that often under-fit minor classes. We conducted a series of analysis and discovered the feature deviation phenomenon — the learned ConvNet generates deviated features between the training and test data of minor classes — which explains how over-fitting happens. To compensate for the effect of feature deviation which pushes test data toward low decision value regions, we propose to incorporate class-dependent temperatures (CDT) in training a ConvNet. CDT simulates feature deviation in the training phase, forcing the ConvNet to enlarge the decision values for minor-class data so that it can overcome real feature deviation in the test phase. We validate our approach on benchmark datasets and achieve promising performance. We hope that our insights can inspire new ways of thinking in resolving class-imbalanced deep learning. © 2020, CC BY-NC-ND.

关键词： Benchmarking

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Faster and simpler siamese network for single object tracking

arXiv

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arXiv 2021年

作者： Jiang, Shaokui Xu, Baile Zhao, Jian Shen, Furao State Key Laboratory for Novel Software Technology Nanjing University 163 Xianlin Avenue Qixia District Nanjing210023 China Department of Computer Science and Technology Nanjing University 163 Xianlin Avenue Qixia District Nanjing210023 China School of Electronic Science and Engineering Nanjing University 163 Xianlin Avenue Qixia District Nanjing210023 China

Single object tracking (SOT) is currently one of the most important tasks in computer vision. With the development of the deep network and the release for a series of large scale datasets for single object tracking, siamese networks have been proposed and perform better than most of the traditional methods. However, recent siamese networks get deeper and slower to obtain better performance. Most of these methods could only meet the needs of real-time object tracking in ideal environments. In order to achieve a better balance between efficiency and accuracy, we propose a simpler siamese network for single object tracking, which runs fast in poor hardware configurations while remaining an excellent accuracy. We use a more efficient regression method to compute the location of the tracked object in a shorter time without losing much precision. For improving the accuracy and speeding up the training progress, we introduce the Squeeze-and-excitation (SE) network into the feature extractor. In this paper, we compare the proposed method with some state-of-the-art trackers and analysis their performances. Using our method, a siamese network could be trained with shorter time and less data. The fast processing speed enables combining object tracking with object detection or other tasks in real time. © 2021, CC0.

关键词： Large dataset

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Towards the hierarchical state machine oriented proteus systems programming language

Towards the hierarchical state machine oriented proteus syst...

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Accelerating Space Commerce, Exploration, and New Discovery Conference, ASCEND 2020

作者： Tellier, Daniel Millman, Meyer McClelland, Brian Go, Kate Beatrix Balayan, Alice Munje, Michael J. Dewey, Kyle Ho, Nhut Havelund, Klaus Ingham, Michel Department of Computer Science California State University Northridge NorthridgeCA91330 United States Department of Electrical and Computer Engineering California State University Northridge NorthridgeCA91330 United States Department of Mechanical Engineering California State University Northridge NorthridgeCA91330 United States Laboratory for Reliable Software Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Systems Engineering Division Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States

ISBN: (纸本)9781624106088

In space applications, hierarchical state machines (HSMs) are often used for writing simulation and control software, including that of the Curiosity rover. At the Jet Propulsion Lab (JPL), multiple programming languages have been developed specifically for writing HSM-based software, and these have been used in practice. However, we observe that the existing languages developed have significant issues with one or more of usability, performance, and safety, making them problematic for HSM-based development. To address these concerns, we are taking lessons learned from these languages and developing a new programming language named Proteus. Proteus builds HSM support directly into the language, and permits complex HSMs to be defined which communicate with each other. Proteus is designed with a look and feel similar to C/C++, making it usable and overall approachable for use by JPL systems engineers. Proteus itself compiles to C++, allowing it to fit easily into existing development toolchains, and making it amenable to embedded real-time systems. To ensure that Proteus will be of use to its target audience, it is being developed iteratively through a series of prototypes which are regularly evaluated by key stakeholders at JPL, allowing us to refine its design if we go off course. While Proteus is still very young in its development, we demonstrate its basic viability on an example utilizing multiple independent HSMs communicating with each other, and relevant execution traces. In the future, we plan to apply Proteus to larger HSMs taken from real space applications, and many additional relevant features are planned. © 2020 The MITRE Corporation. All Rights Reserved.

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An iterative sinogram metal artifact reducdion based on UNet

An iterative sinogram metal artifact reducdion based on UNet

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IEEE International Conference on Big Data

作者： Zichong An Xuemei Zhu Xiangling Fu Junqi Ma Chenyi Guo Zheng Zhang School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China The University Hospital of Beijing University of Posts and Telecommunications Beijing China Yofo Medical Technology Co. Ltd Beijing China Department of Electronic Enginerring Tsinghua University Beijing China School of Modern Post(School of Automation) Beijing University of Posts and Telecommunications Beijing China

In the practice of dentistry, oral dental CT images are frequently used to assist doctors in diagnosis. Filtered back projection (FBP) technique is widely employed in practice for the reconstruction of CT images obtained from X-ray calculations. However, when metal objects occur in a patient’s oral cavity, the CT images would show density discontinuities due to the metals’ “X-ray absorption coefficient is much larger than human tissues. When the FBP algorithm is applied to CT images with metals, severe metal artifacts would be obtained, which significantly reconstructed images. Therefore, metal artifact reduction (MAR) work is becoming an important problem in dentistry image processing. In this paper, we propose a novel iterative sinogram metal artifact reduction model (IS-MARM) to solve the problem. Inspired by the Diffusion model, we propose a new method to reduce metal artifacts and interpolate new data in sinogram of dentistry images iteratively. This approach reduces the difficulty of model learning and achieves good results. Secondly, we proposed a new simple method of iterative data generating to simulate real-world metals in CT sinogram images. Finally, we have demonstrated the effectiveness of our method through experiments on dental CT MAR work.

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An HHL-based algorithm for computing hitting probabilities of quantum walks

arXiv

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arXiv 2020年

作者： Guan, Ji Wang, Qisheng Ying, Mingsheng State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100190 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Centre for Quantum Software and Information University of Technology SydneyNSW2007 Australia

We present a novel application of the HHL (Harrow-Hassidim-Lloyd) algorithm - a quantum algorithm solving systems of linear equations - in solving an open problem about quantum walks, namely computing hitting (or absorption) probabilities of a general (not only Hadamard) one-dimensional quantum walks with two absorbing boundaries. This is achieved by a simple observation that the problem of computing hitting probabilities of quantum walks can be reduced to inverting a matrix. Then a quantum algorithm with the HHL algorithm as a subroutine is developed for solving the problem, which is faster than the known classical algorithms by numerical experiments. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum theory

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Incorporating Surprisingly Popular Algorithm and Euclidean Distance-based Adaptive Topology into PSO

arXiv

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arXiv 2021年

作者： Wu, Xuan Han, Jizong Wang, Di Gao, Pengyue Cui, Quanlong Chen, Liang Liang, Yanchun Huang, Han Lee, Heow Pueh Miao, Chunyan Zhou, You Wu, Chunguo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University 639798 Singapore State Key Laboratory of Superhard Materials International Center for Computational Method and Software College of Physics Jilin University Changchun130012 China Commercial Quality and Efficiency Department Baidu Inc Beijing100085 China Department of Computer Science Shantou University Shantou515063 China School of Computer Science Zhuhai College of Science and Technology Zhuhai519041 China School of Software Engineering The South China University of Technology Guangzhou510641 China Department of Mechanical Engineering National University of Singapore 117575 Singapore School of Computer Science and Engineering Nanyang Technological University 639798 Singapore College of Software Jilin University Changchun130012 China

While many Particle Swarm Optimization (PSO) algorithms only use fitness to assess the performance of particles, in this work, we adopt Surprisingly Popular Algorithm (SPA) as a complementary metric in addition to fitness. Consequently, particles that are not widely known also have the opportunity to be selected as the learning exemplars. In addition, we propose a Euclidean distance-based adaptive topology to cooperate with SPA, where each particle only connects to k number of particles with the shortest Euclidean distance during each iteration. We also introduce the adaptive topology into heterogeneous populations to better solve large-scale problems. Specifically, the exploration sub-population better preserves the diversity of the population while the exploitation sub-population achieves fast convergence. Therefore, large-scale problems can be solved in a collaborative manner to elevate the overall performance. To evaluate the performance of our method, we conduct extensive experiments on various optimization problems, including three benchmark suites and two real-world optimization problems. The results demonstrate that our Euclidean distance-based adaptive topology outperforms the other widely adopted topologies and further suggest that our method performs significantly better than state-of-the-art PSO variants on small, medium, and large-scale problems. Copyright © 2021, The Authors. All rights reserved.

关键词： Small-world networks

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