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A semi-supervised concept-cognitive computing system for dynamic classification decision making with limited feedback information

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第3期315卷 1123-1138页

作者： Mi, Yunlong Wang, Zongrun Quan, Pei Shi, Yong Cent South Univ Sch Business Changsha 410083 Peoples R China Beijing Univ Technol Coll Econ & Management Beijing 100124 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

In dynamic environments, making classification decisions based on classical intelligent decision support systems is a challenge, as the classification performance of decision -making and the time -cost of learning need to be considered simultaneously. Moreover, many tasks of classification decisions lack label information because annotating data is time-consuming, labor-intensive and expensive process. This means that some standard intelligent decision support systems will perform inferior performance if they cannot dynamically make full use of the information behind abundant unlabeled data. Therefore, by incorporating knowledge representation and dynamic updating mechanisms into concept learning processes, we introduce a novel dynamic concept learning approach, namely semi -supervised concept -cognitive computing system (s2C3S), for making classification decisions by jointly utilizing some labeled data and abundant unlabeled data under dynamic environments. A theoretical analysis has shown that the proposed s2C3S can achieve significantly lower computational costs and higher classification accuracies than the existing incremental K Nearest Neighbor method (IKNN) and concept -cognitive computing system (C3S). The experimental results on various datasets further demonstrated that our system is effective for dynamic classification decision -making with limited labeled data under dynamic learning processes. Additionally, s2C3S can also be applied to computer -assisted intelligent diagnosis from the given medical images (such as chest X-ray images) dynamically and accurately.

关键词： Decision support systems Dynamic decision-making data streams Semi-supervised learning Concept-cognitive computing

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Joint Double Auction-Based Channel Selection in Wireless Monitoring Networks

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IEEE Transactions on Network and Service management 2025年第3期22卷 2412-2426页

作者： Xia, Na Chen, Lei Li, Meng Yin, Yutao Zhang, Ke Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data China State Grid Electric Power Research Institute Co. Ltd Hefei230088 China

In wireless networks, utilizing sniffers for fault analysis, traffic traceback, and resource optimization is a crucial task. However, existing centralized algorithms cannot be applied to high-density wireless networks. Therefore, distributed optimization of channel selection to maximize the monitoring rate of sensors in Wireless Monitoring Networks (WMNs) is a challenge. This paper proposes a joint double auction-based distributed channel selection algorithm (J2A-CS) to maximize overall quality of monitoring (QoM). First, sniffers are redundantly deployed in WMNs, and an initial channel allocation strategy is formulated. Subsequently, sniffers collectively act as buyers and sellers at different stages. Finally, buyers bid asynchronously, and sellers settle synchronously to maximize the seller's marginal revenue and update the channel selection scheme. As a distributed channel selection algorithm, J2A-CS addresses the highest overall QoM issue in WMNs, demonstrating high scalability and fault tolerance. Simulation results show that J2A-CS significantly improves QoM compared to existing distributed algorithms and outperforms centralized algorithms in high-density scenarios. © 2004-2012 IEEE.

关键词： Sales

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Aperture shape optimization in intensity-modulated radiation therapy planning

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Nuclear Science and Techniques 2023年第9期34卷 106-117页

作者： Li-Yuan Zhang Zhi-Guo Gui Peng-Cheng Zhang Jie Yang Shanxi Provincial Key Laboratory for Biomedical Imaging and Big Data North University of ChinaTaiyuan 030051China School of Medicine Management Shanxi University of Chinese MedicineTaiyuan 030619China

The gradient element of the aperture gradient map is utilized directly to generate the aperture shape without *** process can be likened to choosing the direction of negative gradient descent for the generic aperture shape *** negative gradient descent direction is more suitable under local conditions and has a slow convergence *** overcome these limitations,this study introduced conjugate gradients into aperture shape optimization based on gradient ***,the aperture gradient map of the current beam was obtained for the proposed aperture shape optimiza-tion method,and the gradients of the aperture gradient map were modulated using conjugate gradients to form a modulated gradient *** aperture shape was generated based on the modulated gradient *** proposed optimization method does not change the optimal solution of the original optimization problem,but changes the iterative search direction when generating the aperture *** performance of the proposed method was verified using cases of head and neck cancer,and prostate *** optimization results indicate that the proposed optimization method better protects the organs at risk and rapidly reduces the objective function value by ensuring a similar dose distribution to the planning target *** to the contrasting methods,the normal tissue complication probability obtained by the proposed optimization method decreased by up to 4.61%,and the optimization time of the proposed method decreased by 5.26%on average for ten cancer *** effectiveness and acceleration of the proposed method were verified through comparative *** to the comparative experiments,the results indicate that the proposed optimization method is more suitable for clinical *** is feasible for the aperture shape optimization involving the proposed method.

关键词： Aperture shape Column generation Conjugate gradient Gradient modulation Direct aperture optimization

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Trade credit or credit insurance? A green supply chain finance design scheme with multi-objective programming

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Journal of Intelligent and Fuzzy Systems 2023年第2期45卷 2707-2721页

作者： Zhang, Linzi Shi, Yong School of Economics and Management University of Chinese Academy of Sciences Beijing China University of Birmingham Edgbaston Birmingham United Kingdom Research Center on Fictitious Economy Data Science Chinese Academy of Science Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

Classical supply chain finance (SCF) primarily focuses on the financial service among all upstream and downstream supply chain participants. Due to the continuously deteriorating of the ecological environment, an environmental-friendly SCF system is urgently needed. In this paper, we propose a novel SCF design scheme with environmental concerns, i.e., green supply chain finance (GSCF), consider the financing channels both from banks and from consumers, and design a bi-objective optimization model that depicts the trade-off between the benefit and the emission. Further, an improved normalized normal constraint (INNC) Pareto method is developed to address the optimal financing strategy of the bi-objective model. We then conduct a numerical case of a Taiwanese steel firm to verify the effectiveness and efficiency of our method. Results show that our model provides a portfolio of optimal solutions on Pareto frontier which can be applied as an effective decision support system when designing a GSCF. Furthermore, the sensitivity analysis also presents the impact of environmental investment cost, technological ratio of companies and the interest rate of trade credit on the optimal configuration of the GSCF. © 2023 - IOS Press. All rights reserved.

关键词： Multiobjective optimization

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Self-Adaptive Imbalanced Domain Adaptation With Deep Sparse Autoencoder

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1293-1304页

作者： Zhu, Yi Wu, Xindong Li, Yun Qiang, Jipeng Yuan, Yunhao Yangzhou University School of Information Engineering Yangzhou225012 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education of China Hefei230002 China

Domain adaptation aims to transfer knowledge between different domains to develop an effective hypothesis in the target domain with scarce labeled data, which is an effective method for remedying the problem of labeled data requirement in deep learning. In reality, it is unavoidable that the dataset has a large gap in the number of positive and negative instances across different categories in source and target domains, which is the imbalanced domain adaptation problem. However, since the imbalanced degree always varies greatly in different source- and target-domain datasets, most of the existing imbalanced domain adaptation models fix the imbalanced parameters, which cannot adapt to the change of the proportion between positive and negative instances in different domains. To address this problem, in this article, we propose a self-adaptive imbalanced domain adaptation method via a deep sparse autoencoder, which can adjust the model automatically according to the imbalanced extent for bridging the chasm of domains. More specifically, the self-adaptive imbalanced cross-entropy loss is designed for emphasizing more on minority categories and compensating the bias of training loss automatically. In addition, to alleviate the deficient problem of labeled data, we further propose the unlabeled information incorporating method by minimizing the distribution discrepancy of high-level representation space between the source and target domains. Experiments on several real-world datasets demonstrate the effectiveness of our method compared to other state-of-the-art methods. © 2020 IEEE.

关键词： Noise abatement

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Analysis on the Effect of Government Investment on Private Investment in Western China——Take Province A as an Example

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Journal of Systems Science and Information 2023年第3期11卷 297-313页

作者： Ming CHEN Yingjie TIAN School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy and Data Science Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China

Since China began reforming and opening up its economy,and especially since the launch of development projects in western China,province A has attracted an increasing amount of investment,which is the main driving force for provincial economic ***,this study uses a state space model to examine how government investment has affected economic growth in province A in western China,and explains whether there is a crowding-in effect or a crowding-out effect of local government investment on private *** findings indicate that both government and private investments have a positive,stimulating influence on economic growth in province A,with the latter being more impactful than the *** and non-productive investments have different effects on province A’s economic *** the perspective of the trajectory of government investment elasticity,the elasticity of government and private investments in province A presents a very large spatio-temporal *** is,from 1994 to 2009,government investment in province A had a crowding-in effect on private investment,but from 2010 to 2017,a crowding-out effect was observed.

关键词： province A in western China government investment private investment state space model

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A Bibliometric Overview and Visualization of the International Journal of Information Technology and Decision Making Between 2012 and 2022

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2024年第1期23卷 171-195页

作者： Liao, Huchang Jin, Xiaowan Shi, Yong Kou, Gang Sichuan Univ Business Sch Chengdu 610064 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100080 Peoples R China Chengdu Univ Sch Business Chengdu 610106 Peoples R China

The International Journal of Information Technology and Decision Making (IJITDM) was launched in 2002 to study how the information technology and decision-making technique affect each other. To summarize the progress of the journal in the latest 11 years, a bibliometric overview of the IJITDM publications from 2012 to 2022 is provided to identify the conceptual evolution and development situation of the journal during this time slot. To do this, first, the publication and citation structures of the publications in this journal is presented. Then, the scientific network map analysis, including keywords analysis, co-authorship analysis and co-citation analysis, is performed in details. Furthermore, the development trends of information technologies and decision-making methods are discussed to dig out factors that influence the evolution of topics in IJITDM publications. Finally, conclusions of this study are offered. It is found that the IJITDM grew significantly from 2012 to 2022. The work presented in this paper will help scholars to obtain systematic knowledge and structured understanding of the research hotspots and development trends of the journal.

关键词： Bibliometrics citations VOSviewer keywords analysis information technology intelligence decision-making

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High Dynamic Collaborative Team Query via Multi-Fuzzy-Constrained Graph Pattern Matching 9

High Dynamic Collaborative Team Query via Multi-Fuzzy-Constr...

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9th International Conference on Cloud Computing and big data Analytics, ICCCBDA 2024

作者： Hu, Tao Zhang, Zan Bu, Chenyang Li, Lei Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798350373554

Graph pattern matching is a technique widely used in various fields such as protein structure analysis, social group querying, and expert localization. This technique involves finding matching subgraphs in large social networks that align with the patterns specified in the pattern graph. In this paper, we focus on a specific sub-problem in social group querying, known as the cooperative team query, which arises from practical applications, where the nodes in the pattern graph and the data graph represent team member entities, while the edges represent their social relationships. We note that the requirements of many teams in the real world are dynamic, necessitating iterative computation for graph pattern matching using traditional methods. To address this challenge in highly dynamic systems, we propose a graph pattern matching method based on core pattern graph matching cache. This approach involves extracting the core pattern graph, and comprising core team members based on the characteristics of cooperative teams. The core graph-based matching cache enables the second half of the algorithm to operate on an order-of-magnitude smaller graph, significantly improving efficiency. Additionally, the multi-threaded approach fully leverages hardware resources, synchronizing multiple matching result of the core pattern graph to reduce matching time. Experimental results on three real social network datasets demonstrate that our proposed algorithm, Core Pattern Graph Matching Cache-based Multi-threaded Exploration (CCMTE), significantly outperforms existing methods in terms of efficiency. © 2024 IEEE.

关键词： Efficiency

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Math Word Problem Generation via Disentangled Memory Retrieval

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ACM TRANSACTIONS ON knowledge DISCOVERY FROM data 2024年第5期18卷 1-21页

作者： Qin, Wei Wang, Xiaowei Hu, Zhenzhen Wang, Lei Lan, Yunshi Hong, Richang Hefei Univ Technol Minist Educ Key Lab Knowledge Engn Big Data Tuxin Rd 193 Hefei 230009 Peoples R China Singapore Management Univ Sch Comp & Informat Syst Singapore Singapore East China Normal Univ Sch Data Sci & Engn Shanghai Peoples R China

The task of math word problem (MWP) generation, which generates an MWP given an equation and relevant topic words, has increasingly attracted researchers' attention. In this work, we introduce a simple memory retrieval module to search related training MWPs, which are used to augment the generation. To retrieve more relevant training data, we also propose a disentangled memory retrieval module based on the simple memory retrieval module. To this end, we first disentangle the training MWPs into logical description and scenario description and then record them in respective memory modules. Later, we use the given equation and topic words as queries to retrieve relevant logical descriptions and scenario descriptions from the corresponding memory modules, respectively. The retrieved results are then used to complement the process of the MWP generation. Extensive experiments and ablation studies verify the superior performance of our method and the effectiveness of each proposed module. The code is available at https://***/mwp-g/MWPG-DMR.

关键词： Memory retrieval math word problem text generation

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An Evolutionary Multitasking Algorithm for Efficient Multiobjective Recommendations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

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