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SSRN 2024年

作者： Fang, Lang Pan, Zhendong Tang, Jiafu School of Management Science & Engineering Liaoning Key Laboratory of Big Data Management and Optimization Decision Dongbei University of Finance and Economics Dalian China

We consider how to make dynamic pricing decision for Chinese Online (COL) at T time-points, an online publisher that allow authors to sell their ongoing book projects. Instead of paying for a book, readers pay for each chapter (pay-per-chapter mode) of the ongoing book project. This mode allows readers to pay for as many chapters as they want without taking the risk that the releasing of new chapters might be delayed or stopped. Despite of the dynamics of chapter-by-chapter released of COL products, the fixed pricing strategy (FPS) does not make fully use of the reading data generated by releasing chapters of the ongoing book. We propose a learning-based dynamic pricing strategy (LDPS) that exploits the newly information to maximize cumulative revenue for the publisher. The LDPS captures the ever changing features of readers. It employs the Thompson sampling method to balance the exploration of the readers’ features with the exploitation of publisher’s knowledge to determine optimal price. Taking COL as a case study and implementing our policies in the context of the aforementioned real-life data set, we show that LDPS outperform several classical strategies such as Greedy, Prior-Free TS and Prior-Given TS, and average revenue of LDPS is increased by 0.5% average per time-point compared to the publisher’s historical decisions. We also provide some management implications for the COL publisher by analyzing the pricing range of different genres of books and choice of the exploration threshold parameter. © 2024, The Authors. All rights reserved.

关键词： Costs

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Multi-objective Reinforcement Learning - Concept, Approaches and Applications 10

Multi-objective Reinforcement Learning - Concept, Approaches...

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10th International Conference on Information Technology and Quantitative Management, ITQM 2023

作者： Zhang, Linzi Qi, Zhiquan Shi, Yong School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

Real-world decision-making tasks are generally complicated and require trade-offs between multiple, even conflicting, objectives. As the advent and great development of advanced information technology, it has evolved into using reinforcement learning (RL) algorithms to tackle the multi-objective decision making (MODM) problems. In this paper, we will first identify the basic concepts and factors when modelling the MODM tasks with reinforcement learning, and then review the traditional RL, such as Sarsa, Q-Learning, Policy Gradients, Actor-Critic, Monte-Carlo learning, and modern deep RL algorithms applied in this process. Furthermore, the specific practical scenarios described in MODM problems will be summarized through analyzing some typical articles. Finally, the future trends of multi-objective reinforcement learning will be discussed. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information Technology and Quantitative Management.

关键词： Reinforcement learning

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A Prompt-Based Representation Individual Enhancement Method for Chinese Idiom Reading Comprehension 28th

A Prompt-Based Representation Individual Enhancement Method ...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Sha, Ying Wu, Mingmin Zeng, Zhi Ge, Xing Huang, Zhongqiang Wang, Huan College of Informatics Huazhong Agricultural University Wuhan China Key Laboratory of Smart Farming for Agricultural Animals Wuhan China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan China

ISBN: (纸本)9783031306747

Chinese idiom is a distinctive language phenomenon, which usually consists of four Chinese characters and expresses a non-compositional and metaphorical meaning. Therefore, Chinese idioms pose unique challenges for Chinese machine reading comprehension. To address this issue, researchers proposed a Chinese idiom cloze task and a large-scale Chinese idioms dataset ChID. Existing methods have proposed a number of models and achieved reasonable performance on ChID. However, they fall short of fully exploring the precise representations and distinctions of the meanings of idioms, especially idioms with similar meanings. In this paper, we propose a prompt-based representation individual enhancement method (PRIEM). This method fuses the context-specific representation and the generic definition representation of the idioms, and uses the prompt method to guide the model in learning the metaphorical meanings of idioms purposefully. To further improve the distinction representations of idioms with similar meanings, PRIEM adopts a method of idiom representation mapping and decomposing based on orthogonal projection to obtain the common and individual representations of idioms respectively. Experimental results on ChID show that our model outperforms state-of-the-art models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Large dataset

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A Fault Diagnostic Method for Rolling Bearings Based on CAR-SNN Model

A Fault Diagnostic Method for Rolling Bearings Based on CAR-...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Li, Qiuting Wang, Xiuqing Du, Wenxia Hebei Normal University College of Computer and Cyber Security Shijiazhuang050024 China Hebei Provincial Key Laboratory of Network & Information Security Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang China Hebei Normal University College of China Combustion Engineering Shijiazhuang050024 China

ISBN: (纸本)9798350337754

In the era of big data, high-performance rolling bearing fault diagnostic methods are of great significance to ensure manufacturing smoothly and avoid mass economic losses. A rolling bearing fault diagnostic CAR-SNN model based on Convolutional Block Attention Module (CBAM), Residual Network (ResNet), and Spiking Neural Networks (SNNs) is proposed based on deep learning (DL). The CAR-SNN model combines CBAM, ResNet, and SNNs to extract critical features while avoiding gradient vanishing or explosion phenomena. The ability of the CAR-SNN model to process spatio-temporal information is improved by using SNNs that incorporate spatio-temporal information simultaneously. The proposed model is used for the rolling bearing fault diagnosis, which datasets are from the experimental platforms of CWRU and Jiangnan University, with the highest diagnostic accuracy 99.995% and 99.18%, respectively. The experimental results validate the effectiveness and stability of the proposed model. The results of comparison experiments show that the CAR-SNN model has better fault diagnostic accuracy and stability than that of the CAR model and other comparison methods. The proposed data-driven rolling bearing fault diagnostic method based on the CAR-SNN model is easy to be implemented, and meeting the needs of high fault diagnostic accuracy in practical applications, and provides valuable input for fault diagnosis of rolling bearings. © 2023 IEEE.

关键词： Failure analysis

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Privacy Protection for Image Sensors Using Convolutional Neural Network and Generative Adversarial Nets 10

Privacy Protection for Image Sensors Using Convolutional Neu...

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10th International Conference on Computer and Communications, ICCC 2024

作者： Tang, Shihao Wang, Yaoqi Meng, Tao Qin, Ji Lin, Liu School of Mechanical Engineering Guizhou University Guiyang550025 China Guizhou Provincial Key Laboratory of Public Big Data Guiyang550025 China

An image privacy information protection method that does not damage the original image quality is proposed considering the risk of privacy information leakage due to the widespread use of image sensors and image sharing in social platforms. A convolutional neural network is used to detect the privacy information of the image. A generative adversarial network is then employed for the detected privacy content to protect image privacy by transforming the privacy information into non-privacy information without damaging the original image quality. Numerous studies have been conducted on real-world images., and results prove the efficiency and effectiveness of the proposed method. © 2024 IEEE.

关键词： Convolutional neural networks

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Design and implementation of algorithm execution time predictor for remote sensing product workflow 3

Design and implementation of algorithm execution time predic...

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3rd International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2023

作者： Zhou, Bing Wu, Qiong Li, Jiaguo Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475001 China School of Computer and Information Engineering Henan University Kaifeng475001 China Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing100101 China

ISBN: (纸本)9781510668522

Remote sensing product production is an important task in the field of remote sensing engineering. Compared with the production process of remote sensing products, the implementation process of remote sensing product production is a multi-Task serial and highly concurrent process that integrates a large number of remote sensing digital image processing algorithms and various processes. Remote sensing product workflows transform complex computing tasks into processes that can be described, managed, and automated. In the workflow, algorithms are the smallest execution unit of tasks, and their execution time has a significant impact on the performance and efficiency of the entire workflow. Therefore, accurate prediction of algorithm execution time is crucial for optimizing workflow performance. This paper proposes and designs an algorithm execution time predictor for remote sensing product workflows. Firstly, feature extraction algorithms are used to extract features from algorithm execution logs, and the prediction model suitable for this scenario is determined by analyzing the characteristics of operators in remote sensing product workflows. Then, the model is trained using historical data sets, and the test set results are compared with the actual execution time to verify the accuracy and practicality of the model. Experimental results show that the proposed algorithm execution time predictor outperforms the baseline model in terms of prediction accuracy and time efficiency © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Remote sensing

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Federated Causally Invariant Feature Learning 39

Federated Causally Invariant Feature Learning

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Guo, Xianjie Yu, Kui Cui, Lizhen Yu, Han Li, Xiaoxiao School of Computer Science and Information Engineering Hefei University of Technology China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education China School of Software Shandong University China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia Canada Vector Institute Canada

ISBN: (纸本)157735897X

Federated feature selection (FFS) is a promising field for selecting informative features while preserving data privacy in federated learning (FL) settings. Existing FFS methods focus on capturing the correlations between features and labels. They struggle to achieve satisfactory performance in the face of data distribution heterogeneity among FL clients, and cannot address the out-of-distribution (OOD) problem that arises when a significant portion of clients do not actively participate in FL training. To address these limitations, we propose Federated Causally Invariant Feature Learning (FedCIFL), a novel approach for learning causally invariant features in a privacy-preserving manner. We design a sample reweighting strategy to eliminate spurious correlations introduced by selection bias and iteratively estimate the federated causal effect between each feature and the labels (with the remaining features initially treated as confounders). By iteratively refining the confounding feature set to identify the true confounders, FedCIFL mitigates the impact of limited local data on the accuracy of federated causal effect estimation. Theoretical analysis proves the correctness of FedCIFL under reasonable assumptions. Extensive experiments on synthetic and real-world datasets demonstrate the superiority of FedCIFL against eight state-of-the-art baselines, beating the best-performing approach by 3.19%, 9.07% and 2.65% in terms of average test Accuracy, RMSE and F1 score, respectively. It is a first-of-its-kind FFS approach capable of handling Non-IID and OOD data simultaneously. The source code is available at https://***/Xianjie-Guo/FedCIFL. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Disentangling Heterogeneous knowledge Concept Embedding for Cognitive Diagnosis on Untested knowledge

arXiv

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arXiv 2024年

作者： Zhang, Miao Wang, Ziming Xing, Runtian Xiao, Kui Li, Zhifei Zhang, Yan Tang, Chang School of Computer Science and Information Engineering Hubei University China Key Laboratory of Intelligent Sensing System and Security China Hubei Key Laboratory of Big Data Intelligent Analysis and Application China

Cognitive diagnosis is a fundamental and critical task in learning assessment, which aims to infer students’ proficiency on knowledge concepts from their response logs. Current works assume each knowledge concept will certainly be tested and covered by multiple exercises. However, whether online or offline courses, it’s hardly feasible to completely cover all knowledge concepts in several exercises. Restricted tests lead to undiscovered knowledge deficits, especially untested knowledge concepts(UKCs). In this paper, we propose a novel framework for Cognitive Diagnosis called Disentangling Heterogeneous knowledge Cognitive Diagnosis(DisKCD) on untested knowledge. Specifically, we leverage course grades, exercise questions, and learning resources to learn the potential representations of students, exercises, and knowledge concepts. In particular, knowledge concepts are disentangled into tested and untested based on the limiting actual exercises. We construct a heterogeneous relation graph network via students, exercises, tested knowledge concepts(TKCs), and UKCs. Then, through a hierarchical heterogeneous message-passing mechanism, the fine-grained relations are incorporated into the embeddings of the entities. Finally, the embeddings will be applied to multiple existing cognitive diagnosis models to infer students’ proficiency on UKCs. Experimental results on real-world datasets show that the proposed model can effectively improve the performance of the task of diagnosing students’ proficiency on UKCs. Our code is available at https://***/Hubuers/DisKCD. © 2024, CC BY.

关键词： Students

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Learning Concept Prerequisite Relation via Global knowledge Relation Optimization 39

Learning Concept Prerequisite Relation via Global Knowledge ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Miao Wang, Jiawei Xiao, Kui Wang, Shihui Zhang, Yan Chen, Hao Li, Zhifei School of Computer Science and Information Engineering Hubei University China Hubei Key Laboratory of Big Data Intelligent Analysis and Application China Key Laboratory of Intelligent Sensing System and Security Ministry of Education China

ISBN: (纸本)157735897X

Learning concept prerequisite relations helps better master and build a logically coherent knowledge structure. Many studies use graph neural networks to create heterogeneous knowledge networks that enhance concept representations. However, different types of relations in these networks can influence each other. Existing research often focuses solely on concept relations, neglecting other types of knowledge connections. To address this issue, this paper proposes a novel concept prerequisite relation learning model, named the Global knowledge Relation Optimization Model(GKROM). Specifically, we capture the impact of different knowledge relation types on document and concept semantic representations separately, integrating the document and concept semantic representations. Then, we introduce multi-objective learning to optimize the knowledge relation network from a global perspective. Through the above optimization, GKROM learns richer semantic representations for concepts and documents, improving the accuracy of concept prerequisite relation learning. Extensive experiments on public datasets demonstrate the effectiveness of our GKROM, achieving state-of-the-art performance in concept prerequisite relation learning. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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The asset allocation utility of digital cryptocurrency 9th

The asset allocation utility of digital cryptocurrency

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

作者： Long, Wen Wang, Maohua Guo, Man School of Economics and Management University of Chinese Academy of Sciences Beijing100090 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 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. © 2022 The Authors. Published by Elsevier B.V.

关键词： Electronic money

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