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ASBSO: An improved brain storm optimization with flexible search length and memory-based selection

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arXiv 2021年

作者： Yu, Yang Gao, Shangce Wang, Yirui Cheng, Jiujun Todo, Yuki The Faculty of Engineering University of Toyama Toyama930-8555 Japan Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science and Technology Tongji University Shanghai200092 China The School of Electrical and Computer Engineering Kanazawa University Kanazawa-shi920-1192 Japan

Brain storm optimization (BSO) is a newly proposed population-based optimization algorithm which uses a logarithmic sigmoid transfer function to adjust its search range during the convergent process. However, this adjustment only varies with the current iteration number, and lacks of flexibility and variety which makes a poor search efficiency and robustness of BSO. To alleviate this problem, an adaptive step length structure together with a success memory selection strategy are proposed to be incorporated into BSO. This proposed method, adaptive step length based on memory selection BSO, namely ASBSO, applies multiple step lengths to modify the generation process of new solutions, thus supplying a flexible search according to corresponding problems and convergent periods. The novel memory mechanism which is capable of evaluating and storing the degree of improvements of solutions is used to determine the selection possibility of step lengths. A set of 57 benchmark functions are used to test ASBSO's search ability, and four real-world problems are adopted to show its application value. All these test results indicate the remarkable improvement in solution quality, scalability and robustness of ASBSO. Copyright © 2021, The Authors. All rights reserved.

关键词： Swarm intelligence

Finite time convergence of pinning synchronization with a single nonlinear controller

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arXiv 2021年

作者： Chen, Tianping Lu, Wenlian Liu, Xiwei The School of Computer Sciences/Mathematics Fudan University Shanghai200433 China The Centre for Computational Systems Biology School of Mathematical Sciences Fudan University China Department of Computer Science and Technology Tongji University The Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai200092 China

DenseNet+inception and its application for electronic transaction fraud detection 21

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DenseNet+inception and its application for electronic transa...

21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and systems, HPCC/SmartCity/DSS 2019

作者： Chen, Zhuo Liu, Guanjun Department of Computer Science Key Laboratory of the Ministry of Education for Embedded System and Service Computing Shanghai Electronic Transactions and Information Service Collaborative Innovation Center Tongji University Shanghai China

ISBN: (纸本)9781728120584

With the development of online payment, electronic transaction fraud events also take place often and result in huge financial losses. Recently, DenseNet as one of the most prominent Convolutional Neural Networks (CNNs) has achieved a remarkable success in many applications such as image recognition. However, when we apply DenseNet to the electronic transaction fraud detection in which the original data is directly as the input of DenseNet, a perfect performance cannot be obtained since the transaction data is different from the image data and the features of the original transaction data are not rich. The Inception module used in GoogLeNet (a kind of CNN) has different convolution kernels of different sizes and thus can enhance the feature mining ability. In this paper, we improve DenseNet (iDenseNet for short) in order to suit electronic transaction data and then propose two integration patterns of iDenseNet and Inception module. Experiments are done on two large datasets with millions of transaction records and compare our methods with CNN, DenseNet and Random Forest that are the state-of-the-art methods used in electronic transaction fraud detection. Results illustrate better performances of our methods. © 2019 IEEE.

关键词： Large dataset

A Blockchain Based Automatic Access Scheme Design and Implement for Small Cell Base Station

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A Blockchain Based Automatic Access Scheme Design and Implem...

International Conference on Communication Technology (ICCT)

作者： Jingchao Liang Jun Wu Bin Tan Haoqi Ren Zhifeng Zhang Key Laboratory of Embedded System and Service Computing Ministry of Education School of Computer Science and Technology Tongji University Shanghai China School of Computer Science Fudan University Shanghai China Electronics and Information Engineering Jinggangshan University Ji'an China Electronics and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9781665432078

5G technology is constrained by its higher frequency band and smaller coverage area, which leads to the need for operators to use technologies such as small cell base stations to increase the density of base station deployment to ensure signal coverage quality, which leads to more enormous construction costs. Therefore, it is urgent to find a safe and reliable solution that can use the existing public network to realize small cell base stations with automatic access. Based on this demand, we summarize various existing small cell base station automatic access technology solutions and their advantages and disadvantages while combing the characteristics of blockchain technology and its application solutions in similar scenarios. And then, we propose a new blockchain-based base station automatic access solution and implements the system in a practical scenario. In our solution, we innovatively introduce blockchain as an intermediate manager for small base stations and core networks, which solves traditional solutions requiring customized equipment while ensuring safety and reliability. It reduces costs and improves efficiency, but some problems are brought by the “decentralization” of blockchain waiting to be solved.

关键词： Base stations Costs 5G mobile communication Conferences Communications technology Blockchains Safety

DSC: Deep scan context descriptor for large-scale place recognition

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arXiv 2021年

作者： Cui, Jiafeng Huang, Tengfei Cai, Yingfeng Zhao, Junqiao Xiong, Lu Yu, Zhuoping School of Automobile Tongji University Shanghai China Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China

LiDAR-based place recognition is an essential and challenging task both in loop closure detection and global relocalization. We propose Deep Scan Context (DSC), a general and discriminative global descriptor that captures the relationship among segments of a point cloud. Unlike previous methods that utilize either semantics or a sequence of adjacent point clouds for better place recognition, we only use raw point clouds to get competitive results. Concretely, we first segment the point cloud egocentrically to acquire centroids and eigenvalues of the segments. Then, we introduce a graph neural network to aggregate these features into an embedding representation. Extensive experiments conducted on the KITTI dataset show that DSC is robust to scene variants and outperforms existing methods. © 2021, CC BY.

关键词： Eigenvalues and eigenfunctions

Unsupervised joint learning of depth, optical flow, ego-motion from video

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arXiv 2021年

作者： Li, Jianfeng Zhao, Junqiao Song, Shuangfu Feng, Tiantian The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China School of Surveying and Geoinfomatics Tongji University Shanghai China Insititute of Intelligent Vehicle Tongji University Shanghai China

Estimating geometric elements such as depth, camera motion, and optical flow from images is an important part of the robot's visual perception. We use a joint selfsupervised method to estimate the three geometric elements. Depth network, optical flow network and camera motion network are independent of each other but are jointly optimized during training phase. Compared with independent training, joint training can make full use of the geometric relationship between geometric elements and provide dynamic and static information of the scene. In this paper, we improve the joint self-supervision method from three aspects: network structure, dynamic object segmentation, and geometric constraints. In terms of network structure, we apply the attention mechanism to the camera motion network, which helps to take advantage of the similarity of camera movement between frames. And according to attention mechanism in Transformer, we propose a plug-and-play convolutional attention module. In terms of dynamic object, according to the different influences of dynamic objects in the optical flow self-supervised framework and the depth-pose self-supervised framework, we propose a threshold algorithm to detect dynamic regions, and mask that in the loss function respectively. In terms of geometric constraints, we use traditional methods to estimate the fundamental matrix from the corresponding points to constrain the camera motion network. We demonstrate the effectiveness of our method on the KITTI dataset. Compared with other joint self-supervised methods, our method achieves state-of-the-art performance in the estimation of pose and optical flow, and the depth estimation has also achieved competitive *** Codes 65Dxx © 2021, CC BY-NC-ND.

关键词： Geometry

Behavior Consistency Computation for Workflow Nets With Unknown Correspondence

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 281-291页

作者： Mimi Wang Guanjun Liu Peihai Zhao Chungang Yan Changjun Jiang Department of Computer Science and Technology the Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China IEEE

Consistency degree calculation is established on the basis of known correspondence, but in real life, the correspondence is generally unknown, so how to calculate consistency of two models under unknown correspondence has become a problem. For this condition, we should analyze unknown correspondence due to the influence of different *** this paper we obtain the relations of transitions based on event relations using branching processes, and build a behavioral matrix of relations. Based on the permutation of behavioral matrix, we express different correspondences, and define a new formula to compute the maximal consistency degree of two workflow nets. Additionally, this paper utilizes an example to show these definitions, computation as well as the advantages.

关键词： Behavioral matrix branching process consistency degree unknown correspondence workflow net(WF-nets)

An Automated Feature Engineering Method for Online Payment Fraud Detection

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Jisuanji Xuebao/Chinese Journal of Computers 2020年第10期43卷 1983-2001页

作者： Wang, Cheng Wang, Chang-Qi Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai201804 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

Internet finance fraud is an increasingly serious social and economic problem. Online payment services (OPSs) are the typical models of Internet finance, and the fraudulent transaction in OPSs is also a typical fraud pattern. The method of identifying fraudulent transactions by constructing a fraud detection model based on machine learning has become a promising idea for online payment anti-fraud. In the process of constructing fraud detection models, the feature engineering is the most critical step. It is also one of the most time-consuming and specialized steps in the relevant area. In the study of feature engineering, the existing online payment fraud detection models are mainly carried out by experts in the form of manual construction based on business knowledge. However, there are many fraud scenarios in OPSs where the process of feature construction is so different. Artificial feature construction methods can no longer meet the increasing demand of anti-fraud. An important way to solve this problem is to automate feature engineering. In the field of Internet financial anti-fraud, the expressibility and interpretability of features play a pivotal role. It is helpful to understand the original source fields and their construction process of important features. This is useful for mining and analyzing the characteristics of fraud methods and follow-up improvement rules engines. These are of great significance for fraud detection models. Therefore, the interpretability of the model method is particularly important. Usually, the optimization of detection accuracy is carried out under the premise of ensuring interpretability. This paper proposed a lightweight, tree-structure, high efficiency and scalable automatic feature engineering method for fraud detection of online payment. The method is as follows: (1) The method has low requirements on the calculation conditions and little dependence on the dataset samples. To realize this advantage, it used the tree structur

关键词： Feature extraction

Modeling and analysis of three properties of mobile interactive systems based on variable Petri nets

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arXiv 2021年

作者： Yang, Ru Ding, Zhijun Jiang, Changjun Zhou, MengChu The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University The Department of Computer Science and Technology Tongji University Shanghai201804 China The Institute of Systems Engineering Macau University of Science and Technology 999078 China The Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Due to the mobility and frequent disconnections, the correctness of mobile interaction systems, such as mobile robot systems and mobile payment systems, are often difficult to analyze. This paper introduces three critical properties of systems, called system connectivity, interaction soundness and data validity, and presents a related modeling and analysis method, based on a kind of Petri nets called VPN. For a given system, a model including component nets and interaction structure nets is constructed by using VPNs. The component net describes the internal process of each component, while the interaction structure net reflects the dynamic interaction between components. Based on this model, three properties are defined and analyzed. The case study of a practical mobile payment system shows the effectiveness of the proposed method. Copyright © 2021, The Authors. All rights reserved.

关键词： Petri nets