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Stochastic joint homecare service and capacity planning with nested decomposition approaches

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第1期295卷 203-222页

作者： Zheng, Chenyang Wang, Shuming Li, Ningxin Wu, Yuanhao Univ Chinese Acad Sci Sch Econ & Management Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Columbia Univ Fu Fdn Sch Engn & Appl Sci New York NY 10027 USA Beijing Normal Univ Sch Management Zhuhai Peoples R China

In this paper, we study a joint service authorization and capacity planning problem for the home health care (HHC) system with demand uncertainty. We formulate the problem as a two-stage stochastic programming with recourse which maximizes the expected total revenue. The service authorization and capacity planning are the mixed binary-and-continuous first stage decisions, and the service hour (resource) allocation is modeled as the second-stage decision which adapts to the demand realizations. The resulting sample average approximation problem (of the stochastic program) could be a large scaled mixed integer linear program. To solve the model in a more scalable fashion, we propose a supergradient-based nested decomposition algorithm that exploits the nice decomposable structure of the problem to cope with the binary and continuous variables separately. The proposed nested decomposition scheme consists of two-layer of decomposition, where the outer decomposition solves for the authorization decision (binaries) with the supergradient cuts and each supergradient can be computed iteratively in the inner decomposition scheme. The proposed nested decomposition algorithm is feasibility-cut-free and is guaranteed to reach the exact optimality in finite steps. Furthermore, we extend our stochastic HHC planning model to a more general framework of Conditional Value-at-Risk (CV@R), and by model transformation with variable change we show that the CV@R model can actually be reformulated in a structure that the proposed nested decomposition scheme can be applied almost directly. Finally, a comprehensive computational study is performed which demonstrates the effectiveness of our model and the performance of the algorithm. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Decomposition algorithm Linear programming Joint service authorization and capacity planning

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Impact of IT Capability on Inventory management: An Empirical Study

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Procedia Computer Science 2022年 199卷 142-148页

作者： Xin Tian Haoqing Wang Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China

Despite the widespread adoption of technology in inventory management, empirical research that examines the effect of these technologies is scarce. Does IT capability really have a positive effect on the inventory management? This paper empirically studies the usage of information technology by the definition of IT capability. Our unique panel data set allows us to explore the relationship between technology usage and inventory management. Our results show that IT capability has a positive effect on the inventory strategy and inventory operation process, and has a negative effect on the out-of-stock level. Our research demonstrates the superiority of the usage of technology.

关键词： intelligent warehousing business intelligence inventory management logistics

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Value of High-Quality Distribution in Front Warehouse Mode Retailing

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Procedia Computer Science 2022年 199卷 110-117页

作者： Jiayi Zhu Xin Tian Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China

Front warehouse mode retailing originates from the e-commerce logistics industry, pre-positioning the warehouse and placing customer orders close to the community service station, so that goods can be delivered quickly. Consumers have put forward higher requirements for the timeliness of logistics distribution and the convenience of online purchases in the front warehouse mode. Using panel data of 37,362 transactions from 159 front warehouses in the online market, we study how consumers’ satisfaction on products and logistics, as well as the punctuality of sorting and delivery affect sales performance. Our results show that negative review of products and logistics will significantly reduce retail sales performance. Interestingly, we find that punctual sorting and delivery increase the number of daily orders while reduce the average sales amount per order. When the punctuality of delivery service is guaranteed, consumers are willing to place orders more frequently even for some low-priced daily goods. This paper emphasizes the importance of the quality of delivery service in the front warehouse mode, and inspires retailers to give full play to the role of the front warehouse in meeting the daily needs of consumers for high-frequency and fast-moving consumer goods.

关键词： front warehouse delivery service customer satisfaction sales performance retailing

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Identifying compromised hosts under APT using DNS request sequences

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2021年 152卷 67-78页

作者： Li, Ming Li, Qiang Xuan, Guangzhe Guo, Dong Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Ctr Big Data & Network Management Changchun 130012 Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Peoples R China

Advanced persistent threats (APTs) have become a major cyber threat to large organizations. To steal confidential data from specific organizations, attackers adopt highly targeted intrusion schemes. Prior to stealing critical data, APT activities hide themselves in legitimate activities and consistently elevate their privileges, making them very difficult to detect. The detection of malicious domains during domain name service (DNS) analysis accounts for the majority of existing detection methods. However, a limited number of available samples and rapidly changing sets of malicious domain names reduce the efficacy of such approaches. By investigating numerous APT reports, we determined that the activities of DNS requests in APT attacks exhibit clear temporal patterns that are ignored by most existing schemes. Therefore, we can analyze the DNS sequences requested by each host and their time-related features to identify compromised hosts. This paper summarizes the patterns of host DNS requests and proposes several assumptions. We take advantage of machine learning to identify compromised hosts by quantifying these assumptions in the form of feature vectors. We deployed the proposed approach into large-scale network environments and experimental evaluations demonstrated that our method is able to detect hosts compromised by APTs efficiently with a precision of 97.3% and detection rate of 96.2%. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Advanced persistent threat (APT) Temporal patterns Unsupervised learning Compromised hosts Domain name service (DNS) request sequence

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Review of graph construction and graph learning in stock price prediction

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Procedia Computer Science 2022年 214卷 771-778页

作者： Yunong Wang Yi Qu Zhensong Chen Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China School of Management and Engineering Capital University of Economics and Business Beijing 100070 China

Precise prediction of stock prices leads to more profits and more effective risk prevention, which is of great significance to both investors and regulators. Recent years, various kinds of information not directly-relevant with stock prices have received more attention, such as texts, images or connections. These external information has the potential of reflecting or influencing fluctuations, and thus, given the utilization of advanced analyzing techniques, the forecasting performance of stock prices could be promoted substantially. For instance, graph neural network models have expanded into many other disciplines including stock price prediction, and exhibited impressive representation learning ability. However, in stock markets, well-defined graphs are rarely seen and how to formulate the graph structures needed remains a challenging problem. Towards this end, this article presents a comprehensive overview of graph construction and graph learning in stock price prediction, by reviewing the existing studies, summarizing its general paradigm, special cases and proposing possible prospects. Our research not only systematically reveals the feasible ways of constructing graphs in financial markets, but also provides insights for further implementations of graph learning models into stock prediction tasks.

关键词： Stock price prediction Graph construction Graph learning data structure

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The Further Development of Intelligent knowledge for Wisdom 9th

The Further Development of Intelligent Knowledge for Wisdom

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9th International Conference on Information Technology and Quantitative management, ITQM 2022

作者： Gu, Jifa Xiao, Xiao Zhang, Lingling Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Moe Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at Ucas Beijing100190 China

This paper introduces some modes from D, I, K to W, M. From different angles we investigate the various process. Especially we utilize the intelligent knowledge to develop the whole process. Finally we design a new medical hall for workshop of meta-synthetic engineering for helping patient to get the appropriate treatment and useful knowledge for health. © 2022 The Authors. Published by Elsevier B.V.

关键词： Patient treatment

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Semi-Supervised Concept Learning by Concept-Cognitive Learning and Concept Space

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IEEE TRANSACTIONS ON knowledge AND data ENGINEERING 2022年第5期34卷 2429-2442页

作者： Mi, YunLong Liu, Wenqi Shi, Yong Li, Jinhai Cent South Univ Sch Business Changsha 410083 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Kunming Univ Sci & Technol Data Sci Res Ctr Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Fac Sci Kunming 650500 Yunnan Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Univ Nebraska Omaha Coll Informat Sci & Technol Omaha NE 68182 USA

In human concept learning, people can naturally combine a handful of labeled data with abundant unlabeled data when they make classification decisions, which is also known as semi-supervised learning (SSL) in machine learning. Especially, human concept learning not only is a static process in human cognition but also can vary gradually with dynamic environments. Nevertheless, the classical SSL algorithms must be redesigned to accommodate newly input data. In this sense, concept-cognitive learning may be a good choice, as it can implement dynamic processes by imitating human cognitive processes. Meanwhile, numerous SSL methods were designed based on the feature vector information of instances, while ignoring concept structural information that is a very important process in human knowledge organization. Based on this idea, a novel SSL method, named semi-supervised concept learning method (S2CL), is proposed for dynamic SSL by employing concept spaces, in which knowledge is represented by hierarchical concept structures. Moreover, to make full use of the global and local conceptual information, we further propose an extended version of S2CL (namely, S2CL(alpha)) for concept learning. More specifically, to effectively exploit the unlabeled data, this paper first shows some new related theories for S2CL (or S2CL(alpha)) based on a regular formal decision context;then a novel SSL framework is designed, and its corresponding algorithm is developed. Finally, we conduct some experiments on various datasets to demonstrate the effectiveness of our methods, which include concept classification and incremental learning under a large quantity of unlabeled data.

关键词： Task analysis Heuristic algorithms Machine learning big data Semisupervised learning Clustering algorithms Concept-cognitive learning conceptual clustering top-K set similarity incremental learning semi-supervised learning

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The asset allocation utility of digital cryptocurrency

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Procedia Computer Science 2022年 214卷 1174-1181页

作者： Wen Long Maohua Wang Man Guo School of Economics and Management University of Chinese Academy of Sciences Beijing 100090 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing 100190 China

In recent years, the digital cryptocurrency market has witnessed rapid development, but its asset allocation function has yet to be verified. In this paper, DCC-GARCH model is used to estimate the dynamic correlation between digital cryptocurrency and current mainstream assets, and the corresponding hedging effect of digital cryptocurrency is studied. Then, three mainstream asset allocation strategies are selected to analyze the risk and return of asset allocation portfolio after adding digital cryptocurrency, which verifies the asset allocation utility of digital cryptocurrency. This study can provide investment basis for investors and has certain theoretical and practical significance.

关键词： Digital Currency Hedging Effect Asset Allocation DCC-GARCH Model

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CDSM: Cascaded Deep Semantic Matching on Textual Graphs Leveraging Ad-hoc Neighbor Selection

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ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2023年第2期14卷 32-32页

作者： Yao, Jing Liu, Zheng Yang, Junhan Dou, Zhicheng Xie, Xing Wen, Ji-Rong Microsoft Res Asia Beijing 100080 Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China Renmin Univ China Gaoling Sch Artificial Intelligence Beijing 100872 Peoples R China Beijing Key Lab Big Data Management & Anal Method Key Lab Data Engn & Knowledge Engn MOE Beijing 100872 Peoples R China

Deep semantic matching aims at discriminating the relationship between documents based on deep neural networks. In recent years, it becomes increasingly popular to organize documents with a graph structure, then leverage both the intrinsic document features and the extrinsic neighbor features to derive discrimination. Most of the existing works mainly care about how to utilize the presented neighbors, whereas limited effort is made to filter appropriate neighbors. We argue that the neighbor features could be highly noisy and partially useful. Thus, a lack of effective neighbor selection will not only incur a great deal of unnecessary computation cost but also restrict the matching accuracy severely. In this work, we propose a novel framework, Cascaded Deep Semantic Matching (CDSM), for accurate and efficient semantic matching on textual graphs. CDSM is highlighted for its two-stage workflow. In the first stage, a lightweight CNN-based ad-hod neighbor selector is deployed to filter useful neighbors for the matching task with a small computation cost. We design both one-step and multi-step selection methods. In the second stage, a high-capacity graph-based matching network is employed to compute fine-grained relevance scores based on the well-selected neighbors. It is worth noting that CDSM is a generic framework which accommodates most of the mainstream graph-based semantic matching networks. The major challenge is how the selector can learn to discriminate the neighbors' usefulness which has no explicit labels. To cope with this problem, we design a weak-supervision strategy for optimization, where we train the graph-based matching network at first and then the ad-hoc neighbor selector is learned on top of the annotations from the matching network. We conduct extensive experiments with three large-scale datasets, showing that CDSM notably improves the semantic matching accuracy and efficiency thanks to the selection of high-quality neighbors. The source code is rele

关键词： Semantic matching textual graph neighbor selection

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Assessing the extent and persistence of major crisis events in the crude oil market and economy: evidence from the past 30 years

HUMANITIES & SOCIAL SCIENCES COMMUNICATIONS

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HUMANITIES & SOCIAL SCIENCES COMMUNICATIONS 2024年第1期11卷 903页

作者： Zhang, Qi Hu, Yi Jiao, Jianbin Wang, Shouyang Beijing Technol & Business Univ Business Sch Beijing 100048 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China UCAS MOE Social Sci Lab Digital Econ Forecasts & Policy Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Emergency Management Sci & Engn Beijing 100190 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China ShanghaiTech Univ Sch Entrepreneurship & Management Shanghai 201210 Peoples R China

This study investigates the extent and persistence of major crisis events in the crude oil market and economy and searches for general rules of event impact. Although the short-term effects of such crises may quickly become evident, their long-term implications can be challenging to uncover. To this end, we analyzed 50 major crisis events across four categories using a Proxy structural vector autoregressive (Proxy-SVAR) model. The results show that these events substantially impacted crude oil production, world crude oil prices, and inflation in China and the United States. Generally speaking, major crisis events have a more severe impact on the crude oil market, but their impact on the economy lasts longer. Notably, geopolitical conflicts have led to a sharp decline in S&P500, resulting in a simultaneous reduction in the industrial production indices of the world, China, and the US. Among the different types of crises, the financial crisis had the most prolonged impact, persisting for approximately four years. The public health emergency represented by COVID-19 resulted in a decline in actual oil prices, with its impact on the oil market continuing for over three years. Considering our findings, we suggest four policy measures to enhance economic resilience.

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