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The Effect of Online Channel Addition on Store Performance:Empirical Evidence from Chinese Chain Retailers

Journal of Systems Science and Information 2021年第4期9卷 356-377页

作者： Yixin WANG Qiguo GONG Tingyan WANG Xin TIAN School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Shanghai HEADING Information Engineering Co. LtdShanghai 201112China Key Labomtory of Big Data Mining and Knowledge Managements Chinese Academy of SciencesBeijing 100190China Nuffield Department of Medicine University of OxfordSouth Parks RoadOxford OXl 3SYUnited Kingdom NIHR Oxford Biomedical Research Centre Big Data InstituteUniversity of OxfordOld Road CampusOxford OX37LFUnited Kingdom Research Center on Fictitious Economy&Data Science Chinese Academy of SciencesBeijing 100190China

With the rapid development of e-Commerce and takeaway platforms, retailers have gradually developed multi-channel operations. However, limited empirical studies explored the effects of an online channel offered by takeaway platforms on the store performance. Does an online channel addition have a synergy effect or a cannibalization effect on store performance? We empirically investigate these effects by analyzing a large dataset including diverse samples collected from multiple retailers across various regions. The dataset includes 2115 stores across 25 retailers for 10 months that includes two types of retail formats and covers 16 provinces and 21 cities of China. We study the impacts of the newly introduced online channel on the incumbent offline channel and the overall store *** empirical results reveal that the online channel addition mainly has a synergy effect. Specifically,for the overall store, it has a positive effect on the sales and product variety, whereas it has a negative effect on the basket size. Surprisingly, an online channel addition also has a positive effect on the offline sales. Our study adds novel values to multi-channel retailing literature by empirically researching the cannibalization and synergy effect of a new type of online channel, offered by takeaway platforms. It can provide insights for retail enterprises who are interested in introducing O2 O model.

关键词： online channel addition cannibalization effect synergy effect propensity score matching(PSM) different-in-different(DID)method

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Improving Generalization of Neural Vehicle Routing Problem Solvers Through the Lens of Model Architecture

arXiv

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arXiv 2024年

作者： Xiao, Yubin Wang, Di Wu, Xuan Wu, Yuesong Li, Boyang Du, Wei Wang, Liupu Zhou, You 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 639956 Singapore WeBank-NTU Joint Research Institute on Fintech Nanyang Technological University 639956 Singapore College of Computing and Data Science Nanyang Technological University 639956 Singapore

Neural models produce promising results when solving Vehicle Routing Problems (VRPs), but may often fall short in generalization. Recent attempts to enhance model generalization often incur unnecessarily large training cost or cannot be directly applied to other models solving different VRP variants. To address these issues, we take a novel perspective on model architecture in this study. Specifically, we propose a plug-and-play Entropy-based Scaling Factor (ESF) and a Distribution-Specific (DS) decoder to enhance the size and distribution generalization, respectively. ESF adjusts the attention weight pattern of the model towards familiar ones discovered during training when solving VRPs of varying sizes. The DS decoder explicitly models VRPs of multiple training distribution patterns through multiple auxiliary light decoders, expanding the model representation space to encompass a broader range of distributional scenarios. We conduct extensive experiments on both synthetic and widely recognized real-world benchmarking datasets and compare the performance with seven baseline models. The results demonstrate the effectiveness of using ESF and DS decoder to obtain a more generalizable model and showcase their applicability to solve different VRP variants, i.e., travelling salesman problem and capacitated VRP. Notably, our proposed generic components require minimal computational resources, and can be effortlessly integrated into conventional generalization strategies to further elevate model generalization. Copyright © 2024, The Authors. All rights reserved.

关键词： Combinatorial optimization

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data Stream Classification Based on Extreme Learning Machine: A Review

引用

Big data Research 2022年 30卷

作者： Zheng, Xiulin Li, Peipei Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology Hefei 230601 China Mininglamp Academy of Sciences Mininglamp Technology Beijing 100084 China

Many daily applications are generating massive amount of data in the form of stream at an ever higher speed, such as medical data, clicking stream, internet record and banking transaction, etc. In contrast to the traditional static data, data streams are of some inherent properties, to name a few, infinite length, concept drift, multiple labels and concept evolution. Among all the data mining tasks, classification is one of the basic topics in data stream mining and has gained more and more attentions among different research communities. Extreme Learning Machine (ELM) has drawn much interests in data classification due to its high efficiency, universal approximation capability, generalization ability, and simplicity, which have greatly inspired the development of many ELM-based algorithms and their applications during the past decades. In this paper, we mainly provide a comprehensive review on ELM theoretical research and its variants in data stream classification, and categorize these algorithms from different perspectives. Firstly, we briefly introduce the basic principles of ELM and its characteristics. Secondly, we give an overview of different ELM variants to address the particular issues of data stream classification. Thirdly, we present an overview of different strategies to optimize the ELM, which have further improved the stability, accuracy and generalization ability of ELM, and briefly introduce some practical applications of ELM in data stream classification. Finally, we conduct several groups of experiments to compare the performance of ELM based models addressing the focused issues. Also, the open issues and prospects of ELM models used for stream classification are discussed, which are worthwhile to be further studied in the future. © 2022

关键词： Classification data stream Extreme learning machine

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Spatiotemporal Effects of Built Environment on Varying Rail Transit Station Ridership Patterns

SSRN

引用

SSRN 2022年

作者： Wang, Jing Wan, Feng Dong, Chunjiao Yin, Chaoying Chen, Xiaoyu Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University Beijing100044 China Hikvision Research Institute Hangzhou310012 China College of Automobile and Traffic Engineering Nanjing Forestry University Nanjing210037 China School of Computer and Information Technology Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China

Although many studies have explored the impacts of the built environment on rail transit station ridership, spatiotemporal heterogeneity of the effects and different ridership patterns are rarely considered. Using multi-source data from China, three direct models (DRMs) based on the geographically and temporally weighted regression (GTWR) model are proposed to reveal the spatiotemporal effects of daily, weekday hourly and weekend hourly ridership patterns. The results show that employment density, entertainment places, housing prices, and entrances/exits are the dominant variables affecting ridership in the three models;however, their effects vary in spatiotemporal ranges. For example, the positive effect of employment density decreases from Monday to Sunday, and its coefficients during the peak hours are highest in the northwest center, where is the largest technology park situated. The results also highlight the importance of bus stations and parking lots in attracting commuter ridership. The findings provide insights into urban rail transit planning. © 2022, The Authors. All rights reserved.

关键词： Housing

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Document-level Relation Extraction with Relation Correlations

arXiv

引用

arXiv 2022年

作者： Han, Ridong Peng, Tao Wang, Benyou Liu, Lu Wan, Xiang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China College of Software Jilin University China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China

Document-level relation extraction faces two overlooked challenges: long-tail problem and multi-label problem. Previous work focuses mainly on obtaining better contextual representations for entity pairs, hardly address the above challenges. In this paper, we analyze the co-occurrence correlation of relations, and introduce it into DocRE task for the first time. We argue that the correlations can not only transfer knowledge between data-rich relations and data-scarce ones to assist in the training of tailed relations, but also reflect semantic distance guiding the classifier to identify semantically close relations for multi-label entity pairs. Specifically, we use relation embedding as a medium, and propose two co-occurrence prediction sub-tasks from both coarse- and fine-grained perspectives to capture relation correlations. Finally, the learned correlation-aware embeddings are used to guide the extraction of relational facts. Substantial experiments on two popular DocRE datasets are conducted, and our method achieves superior results compared to baselines. Insightful analysis also demonstrates the potential of relation correlations to address the above challenges. Copyright © 2022, The Authors. All rights reserved.

关键词： Extraction

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Big data Technology in Intelligent Distribution Network: Demand and Applications 6th

Big Data Technology in Intelligent Distribution Network: De...

引用

6th International Conference on Advanced Intelligent Systems and Informatics, AISI 2020

作者： Ye, Zhi-Peng Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou China Fuzhou University No. 33 Xuefu South Road New District FuzhouFujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9783030586683

Nowadays, with the development and maturity of big data technology, big data analysis technology is more and more widely used in practice, and more and more data are gradually applied to the smart grid of our country. Since the data in the smart grid meets the 4V characteristics of big data (large quantity, fast speed, many types, low value density), the use of big data technology can provide more accurate and cheaper data for power information generation Economic value and significance. Through the analysis of the development process of Chinas power industry, the development of distribution network in China obviously lags behind the development of power generation and transmission network. At present, more than 95% of the blackouts are caused by the distribution network, and half of the power loss occurs in the distribution network, so the automation of the distribution network system urgently needs the support of new technologies. This paper first enumerates several key points of big data technology, including big data collection, storage and analysis, and then expounds several methods of big data analysis. On this basis, big data technology is applied to the field of intelligent distribution network. Especially in the application of distribution forecasting, it can provide more powerful technical support for the operation of smart distribution network, continuously improve the technical level of Chinas smart distribution network, and promote the optimization and upgrading of smart grid system. Finally, an optimized prediction model is proposed, and the application of the new technology (5G technology) developed at the present stage is prospected, and its contribution to the data acquisition and application of big data technology is analyzed. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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UAV Swarm-enabled Collaborative Post-disaster Communications in Low Altitude Economy via a Two-stage Optimization Approach

arXiv

引用

arXiv 2025年

作者： Zheng, Xiaoya Sun, Geng Li, Jiahui Wang, Jiacheng Wu, Qingqing Niyato, Dusit Jamalipour, Abbas College of Computer Science d Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University 639798 Singapore College of Computing and ta Science Nanyang Technological University 639798 Singapore Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Electrical and Computer Engiering The University of Sydney SydneyNSW2006 Australia

The low-altitude economy (LAE), as a new economic paradigm, plays an indispensable role in cargo transportation, healthcare, infrastructure inspection, and especially post-disaster communication. Specifically, unmanned aerial vehicles (UAVs), as one of the core technologies of the LAE, can be deployed to provide communication coverage, facilitate data collection, and relay data for trapped users, thereby significantly enhancing the efficiency of post-disaster response efforts. However, conventional UAV self-organizing networks exhibit low reliability in long-range cases due to their limited onboard energy and transmit ability. Therefore, in this paper, we design an efficient and robust UAV-swarm enabled collaborative self-organizing network to facilitate post-disaster communications. Specifically, a ground device transmits data to UAV swarms, which then use collaborative beamforming (CB) technique to form virtual antenna arrays and relay the data to a remote access point (AP) efficiently. Then, we formulate a rescue-oriented post-disaster transmission rate maximization optimization problem (RPTRMOP), aimed at maximizing the transmission rate of the whole network. Given the challenges of solving the formulated RPTRMOP by using traditional algorithms, we propose a two-stage optimization approach to address it. In the first stage, the optimal traffic routing and the theoretical upper bound on the transmission rate of the network are derived. In the second stage, we transform the formulated RPTRMOP into a variant named V-RPTRMOP based on the obtained optimal traffic routing, aimed at rendering the actual transmission rate closely approaches its theoretical upper bound by optimizing the excitation current weight and the placement of each participating UAV via a diffusion model-enabled particle swarm optimization (DM-PSO) algorithm. Simulation results show the effectiveness of the proposed two-stage optimization approach in improving the transmission rate of the construct

关键词： Unmanned aerial vehicles (UAV)

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Article Reranking by Memory-Enhanced key Sentence Matching for Detecting Previously Fact-Checked Claims

arXiv

引用

arXiv 2021年

作者： Sheng, Qiang Cao, Juan Zhang, Xueyao Li, Xirong Zhong, Lei Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China China

False claims that have been previously fact-checked can still spread on social media. To mitigate their continual spread, detecting previously fact-checked claims is indispensable. Given a claim, existing works retrieve fact-checking articles (FC-articles) for detection and focus on reranking candidate articles in the typical two-stage retrieval framework. However, their performance may be limited as they ignore the following characteristics of FC-articles: (1) claims are often quoted to describe the checked events, providing lexical information besides semantics;and (2) sentence templates to introduce or debunk claims are common across articles, providing pattern information. In this paper, we propose a novel reranker, MTM (Memoryenhanced Transformers for Matching), to rank FC-articles using key sentences selected using event (lexical and semantic) and pattern information. For event information, we propose to finetune the Transformer with regression of ROUGE. For pattern information, we generate pattern vectors as a memory bank to match with the parts containing patterns. By fusing event and pattern information, we select key sentences to represent an article and then predict if the article fact-checks the given claim using the claim, key sentences, and patterns. Experiments on two real-world datasets show that MTM outperforms existing methods. Human evaluation proves that MTM can capture key sentences for explanations. The code and the dataset are at https://***/ICTMCG/MTM. © 2021, CC BY-SA.

关键词： Semantics

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Power Grid Critical State Search Based on Improved Particle Swarm Optimization 6th

Power Grid Critical State Search Based on Improved Particle ...

引用

6th International Conference on Advanced Intelligent Systems and Informatics, AISI 2020

作者： Luo, Jie Deng, Hui-Qiong Li, Qin-Bin Zheng, Rong-Jin Li, Pei-Qiang Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou350108 China Fujian Provincial University Engineering Research Center of Smart Grid Simulation Analysis and Integrated Control Fuzhou350108 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9783030586683

This article proposes a method to find the closest critical initial running state of power grid, aiming at the possible chain reaction failure caused by branch fault in power system. First of all, assuming the initial failure of power grid, considering the specific performance of relay protection, analyze the critical state of the branch in cascading trip in detail, the electrical distance between the current running state and the interlocking fault expressed in the form of nodal injection power is given. Secondly, on the basic of the relationship between the nodal injections power and the system safety, a safety level model that the system will not trigger cascading failures is established. Finally, the improved simplified mean particle swarm optimization algorithm is used to solve the model, and an example is analyzed on IEEE-14 node system. An example shows the effectiveness and feasibility of the proposed method, which provides a reference for further prevention of cascading trip accidents. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Outages

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Attention-based feature enhanced dehazing network

Attention-based feature enhanced dehazing network

引用

2021 International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2021

作者： Cheng, Yuanshun Tan, Anhui Yang, Chuansheng Yang, Yueting Wang, Chao Tianfu College of SWUFE Sichuan Mianyang621000 China School of Information Engineering Zhejiang Ocean University Zhejiang Zhoushan316022 China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhejiang Zhoushan316022 China School of Mathematics and Statistics Beihua University Jilin Jilin132013 China

Presence of haze in images obscures underlying information, which is undesirable in applications requiring accurate environment information. To recover such an image, a dehazing algorithm should enhance the feature information of the background while weakening the feature information of haze. In this paper, we propose an end-to-end attention-based feature enhanced dehazing network (AEDNet), which integrates enhancement strategy and attention mechanism, to achieve haze removal. The network is based on U-Net, which has the advantages of retaining information, obtaining multi-scale features and so on. In the training of the network, pixel loss and perceptual loss are used to preserve feature information and improve the overall quality of results. The extensive evaluation shows that the proposed model performs significantly better than previous dehazing methods on various benchmarks. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Image enhancement

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