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SSRN 2023年

作者： Yang, Ying Zhang, Ling-Feng Ren, Gang Wang, Gang School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei230009 China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Hefei230009 China

Online product reviews have been increasingly adopted by consumers to express their evaluation for products on e-commercial platforms. Consumers are encouraged to evaluate the review quality by helpful votes according to their human intelligence, and considerable machine learning approaches have been developed to help detect helpful reviews automatically. However, previous studies largely depend on machine intelligence and neglect the complementary strength between machine algorithms and human intelligence. This study proposes an evidence-based multimodal fusion approach for predicting the helpfulness of reviews by aggregating the human intelligence from user votes and machine intelligence from texts and images. Review helpfulness is measured by a belief distribution and represented by the evidence obtained from each modality data. An evidential reasoning rule-based fusion strategy is proposed to combine the hybrid intelligence considering their complementarity. A new dataset is also developed for the task of multimodal review helpfulness prediction by crawling online product reviews from *** and annotated by human scoring. Experimental results on the dataset show that the proposed approach for predicting review helpfulness has superior predictive power and outperforms existing methods. This study also contributes to e-commerce by providing an enhanced method for predicting review helpfulness from both human and machine intelligence. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Hierarchically-Trusted Evidential Fusion Method with Consistency Learning for Multimodal Language Understanding

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SSRN 2024年

作者： Yang, Ying Yan-Qiu, Yang Ren, Gang School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei230009 China Ministry of Education Engineering Research Centre for Intelligent Decision-Making & Information System Technologies Hefei230009 China

Human communication in the real world is based on multimodal data. Multimodal language understanding tasks such as multimodal intention recognition and multimodal emotion recognition are key steps in many applications, such as dialogue systems and social media. Although many studies have explored multimodal fusion methods and applied these methods to multimodal language understanding tasks including intent recognition and sentiment analysis, it is still a knotty problem whether the classification results are trusted. Therefore, this paper proposes a hierarchically-trusted evidential fusion method (HTEF) for multimodal language understanding. We construct multiple views from multi-modal data in a hierarchical manner, and extract multiple pieces of evidence from them. Then, we introduce Dempster-Shafer evidence theory(DST) to fuse evidence obtained from multimodal data to ensure the reduction of overall uncertainty. In addition, we explore the feature-level consistency and decision-level consistency of multi-modal data to avoid the conflict across information captured from different modalities. The method is evaluated on the public multimodal intent recognition dataset MIntRec and the emotion recognition dataset MELD. The experimental results show that our model outperforms both existing single-modal and multimodal baseline models across multiple evaluation metrics, which verifies the effectiveness of the proposed method. © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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Approximation algorithms for two-stage supply chain scheduling of production and distribution

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International Journal of Systems Science: Operations and Logistics 2015年第2期2卷 78-89页

作者： Cheng, Bayi Li, Kai Hu, Xiaoxuan School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei China

In this paper, we investigate a class of two-stage optimisation problems for manufacturers in supply chains. The objective is to optimise production and outbound distribution. In the production part, the manufacturer has identical batch-processing machines with limited capacity in size. The processing times and sizes of jobs are determined by the customer. As long as the total size of jobs in a batch does not exceed the machine capacity, the batch can be processed on any machine. The processing of a batch cannot be interrupted until all the jobs in the batch have been completed. In the distribution part, the manufacturer assigns the completed jobs in groups and delivers them to the customer with its own vehicles. We propose the model of minimising service span, which lasts from the processing of the first job to the delivery of the last job. We investigate two types of machine configuration, including single machine and parallel machines. In the vehicle configuration, we consider two cases including infinite vehicles and a fixed number of vehicles. We show all the problems under investigation are NP-hard. For each problem, we design an effective polynomial time algorithm. We analyse the time complexity and show the performance guarantees of the algorithms. © 2014, © 2014 Taylor & Francis.

关键词： approximation algorithm arbitrary-size jobs batch-processing machines supply chain scheduling

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Pricing decisions under manufacturer's component open-supply strategy

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arXiv 2021年

作者： Zhu, Peiya Qian, Xiaofei Liu, Xinbao Lu, Shaojun School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei230009 China Ministry of Education Engineering Research Center for Intelligent Decision-Making and Information System Technologies Hefei230009 China

Faced with huge market potential and increasing competition in emerging industries, product manufacturers with key technologies tend to consider whether to implement a component open-supply strategy. This study focuses on a pricing game induced by the component open-supply strategy between a vertically integrated manufacturer (who produces key components and end products) and an exterior product manufacturer (who produces end products using purchased key components) with different customer perceived value and different cost structure. This study first establishes a three-stage pricing game model and proposes demand functions by incorporating relative customer perceived value. Based on the demand functions, we obtain feasible regions of the exterior manufacturer's sourcing decision and the optimal price decision in each region. Then the effects of relative customer perceived value, cost structure, and market structure on price decisions and optimal profits of the vertically integrated manufacturer are demonstrated. Finally, as for the optimal component supply strategy, we present a generalized closed-supply Pareto zone and establish supply strategy Pareto zones under several specific configurations. Copyright © 2021, The Authors. All rights reserved.

关键词： Game theory

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Predicting Helpfulness of Multimodal Reviews with Customer Confirmation Bias: A Hierarchically-Trusted Multi-View Deep Learning Method

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SSRN 2024年

作者： Yang, Ying Tang, Si Ren, Gang School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei230009 China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Hefei230009 China

Accurately identifying high quality online-reviews is beneficial to both consumers and e-commerce platforms. Consumers usually exhibit confirmation bias phenomenon when evaluating the helpfulness of product reviews. However, there has been limited research on simulating and incorporating this information processing bias into predicting multimodal review helpfulness. To address this challenge, we combine confirmation bias theory with deep learning techniques, and propose a hierarchically-trusted multi-view deep learning method for predicting the helpfulness of multimodal reviews. Drawing upon the elaborate likelihood model and confirmation bias theory, multiple views are designed to represent consumers’ initial beliefs, central cues and peripheral cues of multimodal reviews. A hierarchically-trusted multi-view fusion strategy is designed to dynamically aggregate the different views considering their interactions. Experimental results on the *** review dataset demonstrate that the proposed method effectively improves the accuracy of evaluating the values of online reviews. This research contributes to the improvement of platform service operations and enhances consumer experience. © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Generative Adversarial Network-Based Optimal Energy Storage Capacity Configuration for Newly–Build Microgrids

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SSRN 2024年

作者： Zhou, Kunshu Zhou, Kaile Yang, Shanlin School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education Hefei University of Technology Hefei230009 China

In the initial stage of microgrid construction, historical data is limited. It is challenging to address the issues of the small amount of data and high uncertainty of renewable energy when proceeding with capacity configuration. This study performs Wasserstein distance-based generative adversarial network with gradient penalty (WGAN-GP) to enhance the original 30 days PV power generation scenario data. Furthermore, the improved data is used to build a distributionally robust optimization model based on various discrete scenarios to determine the optimal capacity configuration of energy storage in newly–built microgrids. The results obtained from the solution are compared with those of traditional stochastic optimization and robust optimization, and the robustness of the results is verified by sensitivity analysis. The comparison results demonstrate that the strategy employed in this study can better address the issues of limited data and uncertainty in renewable energy in the capacity configuration process of newly-built microgrids. © 2024, The Authors. All rights reserved.

关键词： Sensitivity analysis

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Analysis on Medicine Compounding for Stroke Prevention Treated by Xin'an Physicians Based on Association Rules

Analysis on Medicine Compounding for Stroke Prevention Treat...

引用

International Conference on intelligent Computation Technology and Automation, ICICTA

作者： Luan Gao Feng-gang Li Jian Wang Yu Liang Yu Li Anhui Chinese Medical College of Chinese Medicine Clinic Institute Hefei China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei University of Technology Hefei China

The purpose of this paper is to probe into the rules of medicine compounding for stroke prevention treated by Xin'an physicians by data mining. The method is in two steps. First step is to build the database of the Xin'an physical stroke prevention. Second step is to use the method of association rules contained in professional data mining software and probe into the medicine rules for stroke prevention treated by Xin'an physicians. The result is that Chenpi, Fuling, Gancao, Luxiancao, Xiqiancao, Baijili, and Shinanye which are of the seven common Chinese medical compounding prescriptions can be used for Xin'an physicians' stroke prevention. The conclusion is that we should utilize the eliminating phlegm, calming wind and clearing the channels when Xin'an physicians treat stroke problem.

关键词： Medical diagnostic imaging Association rules Itemsets Diseases Drugs

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Disagreement-based semi-supervised learning method for enterprise credit risk assessment

引用

ICIC Express Letters 2015年第6期9卷 1671-1676页

作者： Wang, Gang Li, Ningning School of Management Hefei University of Technology No. 193 Tunxi Road Hefei China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei University of Technology No. 193 Tunxi Road Hefei China

The enterprise credit risk assessment problem has long been regarded as an important and widely studied issue in both academia and industry. However, unla-beled data problem is paid less attention to in the credit risk assessment community. To fill in this research gap, one of popular disagreement semi-supervised learning methods, co-training, is introduced to exploit the unlabeled data to improve the enterprise credit risk assessment performance. Co-training allows one to start with just a few labeled instances to produce an initial weak classifier and then use unlabeled data to improve the performance. Experiments based on two credit datasets reveal that co-training SVM&DT (Support Vector Machine & decision Tree) gets the better performance than the compared methods. Based on these results, we can conclude that co-training SVM&DT can be used as an alternative method for enterprise credit risk assessment. © 2015 ICIC International.

关键词： decision trees

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CO-RS: A new method for opinion mining based on semi-supervised learning and ensemble learning

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ICIC Express Letters 2015年第7期9卷 1803-1808页

In recent years many machine learning methods have been proposed for opinion mining and the effectiveness of applying them to opinion mining has also been approved theoretically. However, machine learning methods encounter many problems when they are used in practice. One of the problems is to construct learning systems with strong generalization ability. In this study, we propose, Co-RS, a semi-supervised method for opinion mining based on co-training and Random Subspace. Co-RS tries to get the strong generalization ability by integrating the strength of two machine learning paradigms, i.e., semi-supervised learning and ensemble learning. Moreover, four public opinion datasets were investigated to verify the effectiveness of proposed method. Empirical results reveal that Co-RS gets the best result among the compared methods. These results illustrate that Co-RS can be used as a viable method for opinion mining. © 2015, ISSN 1881-803X.

关键词： Sentiment analysis

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Evaluation on regional innovation performance of China's patent data based on two-stage model

Evaluation on regional innovation performance of China's pat...

引用

IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Su Yang Changyong Liang School of Management Anhui University of Architecture Hefei China School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei China

ISBN: (纸本)9781467384810

Being paid great attention to its operating performance, patent is the key element for a country or area to gain long-term advantage in various competitions. This paper, based on Chinese patent data, proposes a two-stage model with factor analysis and clustering analysis. It also proposes a patent innovation performance evaluation system in view of the input-output index of twenty patents, giving factor analysis to principal components, combining the various indexes to create new factors through calculating, extracting and aggregating, and then with this new factor score, this article evaluates the patent innovation performance systematically using the clustering analysis. The results show that, obviously, patent modification can promote innovation patent performance, especially the second modification. And at last, this article provides suggestion and development strategies to the improvement of patent system.

关键词： decision support systems Conferences Logistics Informatics

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