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Visual Comparison of Customer Stickiness in Retail Stores

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Chinese Journal of Electronics 2018年第5期27卷 951-958页

作者： JIANG Tao SHI Lei ZHAO Ye ZHANG Xiatian LU Yao HUANG Congcong SKLCS Institute of SoftwareChinese Academy of Sciences department of computer science Kent State University

Understanding market trends and forming competitive promotion strategies has always been a major task of retail store managers. One big challenge is the lack of effective tools for in-depth customer behavior analysis. In this paper, we apply visual analytics techniques to address the challenge, which is built up on the emerging and mobile location big data. We present a system that focuses on the analysis of customer stickiness which represents customers’ affinity to retail stores. The system integrates mobile data pre-processing, customer stickiness analysis, multi-view visualization, and a set of *** visual analytics techniques are mainly designed for two types of user tasks: 1) understanding the spatio-temporal distribution of customer traces related to retail stores; 2)evaluating the performance and trend of multiple retail stores through visual comparison. We have demonstrated the effectiveness of the system through two case studies including advertisement placement and business branch reconfiguration.

关键词： Spatio-Temporal Visual comparison Retail store analysis Location based marketing

Constructing Rough Set Based Unbalanced Binary Tree for Feature Selection

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Chinese Journal of Electronics 2014年第3期23卷 474-479页

作者： LU Zhengcai QIN Zheng JIN Qiao LI Shengnan department of computer science and Technology Tsinghua University School of software Tsinghua University

Feature selection is one of the challenging problems facing data analysis in areas such as pattern recognition, data mining, and decision support. Many rough set algorithms for feature selection have been developed, most of which are essentially dependent on the definite information contained within the lower approximation. This paper proposes a novel approach, called Unbalanced binary tree based feature selection(UBT-FS), which utilizes the indefinite information contained within rough set boundary region for reduction. UBT-FS designs the underlying mechanism for obtaining the boundary region from the unbalanced binary tree and adopts the boundary region based significance for determining the optimal search path as well as the boundary region based evaluation criterion for identifying feature subsets. These allow UBT-FS to have considerable ability in finding an optimal or suboptimal reduct whilst simultaneously achieving obviously better computational efficiency than other available algorithms, which is also supported by the experimental results.

关键词： Feature selection Rough set theory Boundary region Unbalanced binary tree

Categorizing methods for integrating machine learning with executable specifications

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science China(Information sciences) 2024年第1期67卷 5-19页

作者： David HAREL Raz YERUSHALMI Assaf MARRON Achiya ELYASAF department of computer science and Applied Mathematics Weizmann Institute of Science department of software and Information Systems Engineering Ben-Gurion University of the Negev

Deep learning(DL),which includes deep reinforcement learning(DRL),holds great promise for carrying out real-world tasks that human minds seem to cope with quite *** promise is already delivering extremely impressive results in a variety of ***,while DL-enabled systems achieve excellent performance,they are far from *** has been demonstrated,in several domains,that DL systems can err when they encounter cases they had not hitherto ***,the opacity of the produced agents makes it difficult to explain their behavior and ensure that they adhere to various requirements posed by human *** the other end of the software development spectrum of methods,behavioral programming(BP) facilitates orderly system development using self-standing executable modules aligned with how humans intuitively describe desired system *** this paper,we elaborate on different approaches for combining DRL with BP and,more generally,machine learning(ML) with executable specifications(ES).We begin by defining a framework for studying the various approaches,which can also be used to study new emerging approaches not covered *** then briefly review state-of-the-art approaches to integrating ML with ES,continue with a focus on DRL,and then present the merits of integrating ML with *** conclude with guidelines on how this categorization can be used in decision making in system development,and outline future research challenges.

关键词： machine learning artificial intelligence grey box learning domain knowledge rules behavioral programming deep reinforcement learning survey

Reinforcement learning based energy efficient robot relay for unmanned aerial vehicles against smart jamming

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science China(Information sciences) 2022年第1期65卷 234-246页

作者： Xiaozhen LU Jingfang JIE Zihan LIN Liang XIAO Jin LI Yanyong ZHANG department of Information and Communication Engineering Xiamen University School of computer science and Technology University of Science and Technology of China School of computer science and Educational software Guangzhou University

Unmanned aerial vehicles(UAVs) with limited energy resources, severe path loss, and shadowing to the ground base stations are vulnerable to smart jammers that aim to degrade the UAV communication performance and exhaust the UAV energy. The UAV anti-jamming communication performance, such as the outage probability, degrades if the robot relay is not aware of the jamming policies and the UAV network topology. In this paper, we propose a robot relay scheme for UAVs against smart jamming, which combines reinforcement learning with a function approximation approach named tile coding, to jointly optimize the robot moving distance and relay power with the unknown jamming channel states and locations. The robot mobility and relay policy are chosen based on the received jamming power, the robot received signal quality,location and energy consumption, and the bit error rate of the UAV messages. We also present a deep reinforcement learning version for the robot with sufficient computing resources. It uses three deep neural networks to choose the robot mobility and relay policy with reduced sample complexity, so as to avoid exploring dangerous policies that lead to the high outage probability of the UAV messages. The network architecture of the three networks is designed with fully connected layers instead of convolutional layers to reduce the computational complexity, which is analyzed by theoretical analyses. We provide the performance bound of the proposed schemes in terms of the bit error rate, robot energy consumption and utility based on a game-theoretic study. Simulation results show that the performance of our proposed relay schemes,including the bit error rate, the outage probability, and the robot energy consumption outperforms the existing schemes.

关键词： unmanned aerial vehicles relay jamming game theory reinforcement learning

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A constraint-weaving approach to points-to analysis for AspectJ

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Frontiers of computer science 2014年第1期8卷 52-68页

作者： Qiang SUN Yuting CHEN Jianjun ZHAO department of computer science and Engineering and School of software Shanghai Jiao Tong University Shanghai 200240 China State Key Laboratory of computer science Institute of Software Chinese Academy of Sciences Beijing 100190 China

Points-to analysis is a static code analysis tech- nique that establishes the relationships between variables of references and allocated objects. A number of points-to analysis algorithms have been proposed for procedural and object-oriented languages like C and Java, while few of them can be used for AspectJ as we know so far. One main rea- son is that Aspect/is an aspect-oriented language which im- plements the separation of crosscutting concerns by advices, pointcuts, and inter-type declarations, while a points-to anal- ysis of AspectJ programs may be imprecise because any as- pect woven into the base code may change the points-to rela- tions in the program and thus a conservative analysis has to be taken in order to handle the aspects. In this paper, we pro- pose a context-sensitive points-to analysis technique called AJPoints for AspectJ. Similar to the weaving mechanism for AspectJ, AJPoints obtains the constraints and templates on the points-to relations for the base code and the aspects, re- spectively, but weaves and solves them in an iterative manner in order to cross the boundary between the base code and the aspects. We have implemented AJPoints on abc AspectJ compiler and evaluated it by using twelve AspectJ benchmark programs. The experimental can achieve a high precision pectJ programs. results show that our technique about points-to relations in As-

关键词： points-to analysis context-sensitive analysis,AspectJ

A probabilistic approach for predictive congestion control in wireless sensor networks

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Journal of Zhejiang University-science C(computers and Electronics) 2014年第3期15卷 187-199页

作者： R.ANNIE UTHRA S.V.KASMIR RAJA A.JEYASEKAR Anthony J.LATTANZE department of computer science and Engineering SRM University department of software Engineering Carnegie Mellon University

Any node in a wireless sensor network is a resource constrained device in terms of memory, bandwidth, and energy, which leads to a large number of packet drops, low throughput, and significant waste of energy due to retransmission. This paper presents a new approach for predicting congestion using a probabilistic method and controlling congestion using new rate control methods. The probabilistic approach used for prediction of the occurrence of congestion in a node is developed using data traffic and buffer occupancy. The rate control method uses a back-off selection scheme and also rate allocation schemes, namely rate regulation(RRG) and split protocol(SP), to improve throughput and reduce packet drop. A back-off interval selection scheme is introduced in combination with rate reduction(RR) and RRG. The back-off interval selection scheme considers channel state and collision-free transmission to prevent congestion. Simulations were conducted and the results were compared with those of decentralized predictive congestion control(DPCC) and adaptive duty-cycle based congestion control(ADCC). The results showed that the proposed method reduces congestion and improves performance.

关键词： Congestion Rate allocation Congestion control Packet loss Back-off interval Rate control

Snapshot boosting: a fast ensemble framework for deep neural networks

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science China(Information sciences) 2020年第1期63卷 77-88页

作者： Wentao ZHANG Jiawei JIANG Yingxia SHAO Bin CUI Center for Data science Peking University National Engineering Laboratory for Big Data Analysis and Applications department of computer science Beijing Key Lab of Intelligent Telecommunications software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications Key Lab of High Confidence software Technologies Department of Computer SciencePeking University

Boosting has been proven to be effective in improving the generalization of machine learning models in many fields. It is capable of getting high-diversity base learners and getting an accurate ensemble model by combining a sufficient number of weak learners. However, it is rarely used in deep learning due to the high training budget of the neural network. Another method named snapshot ensemble can significantly reduce the training budget, but it is hard to balance the tradeoff between training costs and diversity. Inspired by the ideas of snapshot ensemble and boosting, we propose a method named snapshot boosting. A series of operations are performed to get many base models with high diversity and accuracy, such as the use of the validation set, the boosting-based training framework, and the effective ensemble strategy. Last, we evaluate our method on the computer vision(CV) and the natural language processing(NLP) tasks, and the results show that snapshot boosting can get a more balanced trade-off between training expenses and ensemble accuracy than other well-known ensemble methods.

关键词： ensemble learning deep learning boosting neural network snapshot ensemble

Real-time Compliance Control of an Assistive Joint Using QNX Operating System

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International Journal of Automation and computing 2013年第6期10卷 506-514页

作者： Shuang Gu Cheng-Dong Wu Yong Yue Carsten Maple Da-You Li Bei-Sheng Liu department of computer science and Technology University of Bedfordshire School of Information science and Engineering Northeastern University department of computer science and software Engineering Xi'an Jiaotong-Liverpool University

An assistive robot is a novel service robot, playing an important role in the society. For instance, it can amplify human power not only for the elderly and disabled to recover/rehabilitate their lost/impaired musculoskeletal functions but also for healthy people to perform tasks requiring large forces. Consequently, it is required to consider both accurate position control and human safety, which is the compliance. This paper deals with the robot control compliance problem based on the QNX real-time operating system. Firstly, the mechanical structure of a compliant joint on the assistive robot is designed using Solidworks. Then the parameters of the assistive robot system are identified. The software of robot control includes data acquisition and processing, and control to meet the compliance requirement of the joint control. Finally, a Hogan impedance control experiment is carried out. The experimental results prove the effectiveness of the method proposed.

关键词： Assistive robot QNX system compliance control impedance control parameters identification.

A program logic for obstruction-freedom

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Frontiers of computer science 2024年第6期18卷 1-16页

作者： LI Zhao-Hui FENG Xin-Yu School of computer science and Technology University of Science and Technology of China department of computer science and Technology Nanjing University State Key Laboratory for Novel software Technology Nanjing University

Though obstruction-free progress property is weaker than other non-blocking properties including lock-freedom and wait-freedom, it has advantages that have led to the use of obstruction-free implementations for software transactional memory (STM) and in anonymous and fault-tolerant distributed computing. However, existing work can only verify obstruction-freedom of specific data structures (e.g., STM and list-based algorithms).In this paper, to fill this gap, we propose a program logic that can formally verify obstruction-freedom of practical implementations, as well as verify linearizability, a safety property, at the same time. We also propose informal principles to extend a logic for verifying linearizability to verifying obstruction-freedom. With this approach, the existing proof for linearizability can be reused directly to construct the proof for both linearizability and ***, we have successfully applied our logic to verifying a practical obstruction-free double-ended queue implementation in the first classic paper that has proposed the definition of obstruction-freedom.

关键词： verification program logic progress properties obstruction-freedom concurrent objects