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arXiv 2023年

作者： Yin, Mingjia Wang, Hao Xu, Xiang Wu, Likang Zhao, Sirui Guo, Wei Liu, Yong Tang, Ruiming Lian, Defu Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Huawei Singapore Research Center Singapore Huawei Noah's Ark Lab Shenzhen China

The sequential recommendation system has been widely studied for its promising effectiveness in capturing dynamic preferences buried in users' sequential behaviors. Despite the considerable achievements, existing methods usually focus on intra-sequence modeling while overlooking exploiting global collaborative information by inter-sequence modeling, resulting in inferior recommendation performance. Therefore, previous works attempt to tackle this problem with a global collaborative item graph constructed by pre-defined rules. However, these methods neglect two crucial properties when capturing global collaborative information, i.e., adaptiveness and personalization, yielding sub-optimal user representations. To this end, we propose a graph-driven framework, named Adaptive and Personalized Graph Learning for Sequential Recommendation (APGL4SR), that incorporates adaptive and personalized global collaborative information into sequential recommendation systems. Specifically, we first learn an adaptive global graph among all items and capture global collaborative information with it in a self-supervised fashion, whose computational burden can be further alleviated by the proposed SVD-based accelerator. Furthermore, based on the graph, we propose to extract and utilize personalized item correlations in the form of relative positional encoding, which is a highly compatible manner of personalizing the utilization of global collaborative information. Finally, the entire framework is optimized in a multi-task learning paradigm, thus each part of APGL4SR can be mutually reinforced. As a generic framework, APGL4SR can not only outperform other baselines with significant margins, but also exhibit promising versatility, the ability to learn a meaningful global collaborative graph, and the ability to alleviate the dimensional collapse issue of item embeddings. The code is available at https://***/Graph-Team/APGL4SR. © 2023, CC BY.

关键词： Recommender systems

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A Novel Approach for Auto-Formulation of Optimization Problems

arXiv

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arXiv 2023年

作者： Ning, Yuting Liu, Jiayu Qin, Longhu Xiao, Tong Xue, Shangzi Huang, Zhenya Liu, Qi Chen, Enhong Wu, Jinze Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China iFLYTEK AI Research iFLYTEK Co. Ltd China

In the Natural Language for Optimization (NL4Opt) NeurIPS 2022 competition1, competitors focus on improving the accessibility and usability of optimization solvers, with the aim of subtask 1: recognizing the semantic entities that correspond to the components of the optimization problem;subtask 2: generating formulations for the optimization problem. In this paper, we present the solution of our team. First, we treat subtask 1 as a named entity recognition (NER) problem with the solution pipeline including pre-processing methods, adversarial training, post-processing methods and ensemble learning. Besides, we treat subtask 2 as a generation problem with the solution pipeline including specially designed prompts, adversarial training, post-processing methods and ensemble learning. Our proposed methods have achieved the F1-score of 0.931 in subtask 1 and the accuracy of 0.867 in subtask 2, which won the fourth and third places respectively in this competition. Our code is available at https://***/bigdata-ustc/nl4opt. Copyright © 2023, The Authors. All rights reserved.

关键词： Pipelines

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Zero-1-to-3: Domain-Level Zero-Shot Cognitive Diagnosis via One Batch of Early-Bird Students towards Three Diagnostic Objectives

arXiv

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arXiv 2023年

作者： Gao, Weibo Liu, Qi Wang, Hao Yue, Linan Bi, Haoyang Gu, Yin Yao, Fangzhou Zhang, Zheng Li, Xin He, Yuanjing University of Science and Technology of China China Anhui Province Key Laboratory of Big Data Analysis and Application State Key Laboratory of Cognitive Intelligence China Artificial Intelligence Research Institute iFLYTEK Co. Ltd China The Open University of China China

Cognitive diagnosis seeks to estimate the cognitive states of students by exploring their logged practice quiz data. It plays a pivotal role in personalized learning guidance within intelligent education systems. In this paper, we focus on an important, practical, yet often underexplored task: domain-level zero-shot cognitive diagnosis (DZCD), which arises due to the absence of student practice logs in newly launched domains. Recent cross-domain diagnostic models have been demonstrated to be a promising strategy for DZCD. These methods primarily focus on how to transfer student states across domains. However, they might inadvertently incorporate non-transferable information into student representations, thereby limiting the efficacy of knowledge transfer. To tackle this, we propose Zero-1-to-3, a domain-level zero-shot cognitive diagnosis framework via one batch of early-bird students towards three diagnostic objectives. Our approach initiates with pre-training a diagnosis model with dual regularizers, which decouples student states into domain-shared and domain-specific parts. The shared cognitive signals can be transferred to the target domain, enriching the cognitive priors for the new domain, which ensures the cognitive state propagation objective. Subsequently, we devise a strategy to generate simulated practice logs for cold-start students through analyzing the behavioral patterns from early-bird students, fulfilling the domain-adaption goal. Consequently, we refine the cognitive states of cold-start students as diagnostic outcomes via virtual data, aligning with the diagnosis-oriented goal. Finally, extensive experiments on six real-world datasets highlight the efficacy of our model for DZCD and its practical application in question recommendation. The code is publicly available at https://***/bigdataustc/Zero-1-to-3. Copyright © 2023, The Authors. All rights reserved.

关键词： Students

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Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction

arXiv

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arXiv 2022年

作者： Wu, Likang Wang, Hao Chen, Enhong Li, Zhi Zhao, Hongke Ma, Jianhui Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Anhui Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Anhui Hefei China College of Management and Economics Tianjin University Tianjin China

Network-aware cascade size prediction aims to predict the final reposted number of user-generated information via modeling the propagation process in social networks. Estimating the user's reposting probability by social influence, namely state activation plays an important role in the information diffusion process. Therefore, Graph Neural Networks (GNN), which can simulate the information interaction between nodes, has been proved as an effective scheme to handle this prediction task. However, existing studies including GNN-based models usually neglect a vital factor of user's preference which influences the state activation deeply. To that end, we propose a novel framework to promote cascade size prediction by enhancing the user preference modeling according to three stages, i.e., preference topics generation, preference shift modeling, and social influence activation. Our end-to-end method makes the user activating process of information diffusion more adaptive and accurate. Extensive experiments on two large-scale real-world datasets have clearly demonstrated the effectiveness of our proposed model compared to state-of-the-art baselines. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Simulation Study on the Development of Chinese Bank Industry Based on System Dynamics 12th

Simulation Study on the Development of Chinese Bank Industry...

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12th EAI International Conference on Simulation Tools and Techniques, SIMUtools 2020

作者： Li, Cheng Gang Zuo, Xin Tong Guizhou Key Laboratory of Big Data Statistical Analysis Guizhou University of Finance and Economics Guiyang Guizhou550025 China School of Big Data Application and Economics Guizhou University of Finance and Economics Guiyang Guizhou550025 China

ISBN: (纸本)9783030727918

There are many factors influencing the development of the bank industry, and scholars have paid much attention to the influence of these factors on the development of the bank industry. In order to better promoting the healthy and steady development of China’s bank industry, studying the influence of the factors on the development of the bank industry, this paper uses Vensim PLE software to build a simulation model for the development of the bank industry. This paper simulates and analyzes the impact of the changes in money supply and supervision level on the profit level of the bank industry. The simulation results show that the profit level of the bank industry will increase when the money supply is reduced. Higher levels of regulation make banks less profitable. The simulation results also show that the change of money supply has no obvious effect on the nonperforming loan balance. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2021.

关键词： Profitability

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Innovative remote sensing methodologies and applications in coastal and marine environments

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地球空间信息科学学报（英文版） 2024年第3期27卷 836-853页

作者： Qing Zhao Antonio Pepe Virginia Zamparelli Pietro Mastro Francesco Falabella Saygin Abdikan Caglar Bayik Fusun Balik Sanli Mustafa Ustuner Nevin Betul Avşar Jingjing Wang Peng Chen Zhengjie Li Adam T.Devlin Fabiana Calò Key Laboratory of Geographical Information Science Ministry of EducationEast China Normal UniversityShanghaiChina School of Geographic Sciences East China Normal UniversityShanghaiChina Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural ResourcesShanghaiChina Institute for Electromagnetic Sensing of the Environment(IREA) Italian National Research CouncilNapoliItaly School of Engineering University of BasilicataPotenzaItaly Institute for Electromagnetic Sensing of the Environment(IREA) Italian National Research CouncilNapoliItaly Institute for Electromagnetic Sensing of the Environment(IREA) Italian National Research CouncilNapoliItaly Institute of Methodologies for Environmental Analysis(IMAA) Italian National Research CouncilTito ScaloItaly Department of Geomatics Engineering Hacettepe UniversityAnkaraTurkey Department of Geomatics Engineering Zonguldak Bulent Ecevit UniversityZonguldakTurkey Department of Geomatic Engineering Yildiz Technical UniversityIstanbulTurkey Department of Geomatic Engineering Artvin Çoruh UniversityArtvinTurkey Department of Geomatics Engineering Izmir Katip Celebi UniversityİzmirTurkey School of Ocean and Earth Science and Technology University of Hawaii at MānoaHonoluluHIUSA

Remote sensing(RS)technologies are extensively exploited by scientists and a vast audience of local authorities,urban managers,and city *** regions,geohazard-prone areas,and highly populated cities represent natural laboratories to apply RS technologies and test new *** the last decades,many efforts have been spent on improving Earth's surface monitoring,including intensifying Earth Observation(EO)operations by the major national space *** oversee to plan and make operational constellations of satellite sensors providing the scientific community with extensive research and development oppor-tunities in the geoscience *** instance,within this framework,the European Space Agency(ESA)and the Ministry of Science and Technology of China(MOST)have sponsored,since the early 2000s,the DRAGON initiative jointly carried out by the European and Chinese RS scientific *** manuscript aims to provide a synthetic overview of some research activities and new methods recently designed and applied and trace the route for further *** main findings are related to ⅰ)the analysis of flood risk in China,ⅱ)the potential of new methods for the estimation and removal of ground displacement biases in small-baseline oriented interferometric Synthetic Aperture Radar(SAR)methods,ⅲ)the analy-sis of the inundation risk in low-lying regions using coherent and incoherent SAR methods;andⅳ)the use of SAR-based technologies for marine applications.

关键词： Disaster risk management Remote Sensing(RS) Earth Observation(EO) Synthetic Aperture Radar(SAR) flooding subsidence coastal/marine environments

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Unified Uncertainty Estimation for Cognitive Diagnosis Models

arXiv

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arXiv 2024年

作者： Wang, Fei Liu, Qi Chen, Enhong Liu, Chuanren Huang, Zhenya Wu, Jinze Wang, Shijin School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China The University of Tennessee Business Analytics and Statistics Knoxville United States iFLYTEK Research State Key Laboratory of Cognitive Intelligence Hefei China State Key Laboratory of Cognitive Intelligence Hefei China

Cognitive diagnosis models have been widely used in different areas, especially intelligent education, to measure users’ proficiency levels on knowledge concepts, based on which users can get personalized instructions. As the measurement is not always reliable due to the weak links of the models and data, the uncertainty of measurement also offers important information for decisions. However, the research on the uncertainty estimation lags behind that on advanced model structures for cognitive diagnosis. Existing approaches have limited efficiency and leave an academic blank for sophisticated models which have interaction function parameters (e.g., deep learning-based models). To address these problems, we propose a unified uncertainty estimation approach for a wide range of cognitive diagnosis models. Specifically, based on the idea of estimating the posterior distributions of cognitive diagnosis model parameters, we first provide a unified objective function for mini-batch based optimization that can be more efficiently applied to a wide range of models and large datasets. Then, we modify the reparameterization approach in order to adapt to parameters defined on different domains. Furthermore, we decompose the uncertainty of diagnostic parameters into data aspect and model aspect, which better explains the source of uncertainty. Extensive experiments demonstrate that our method is effective and can provide useful insights into the uncertainty of cognitive diagnosis. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense

arXiv

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arXiv 2022年

作者： Yu, Yang Liu, Qi Wu, Likang Yu, Runlong Yu, Sanshi Lei Zhang, Zaixi Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China

Federated recommendation (FedRec) can train personalized recommenders without collecting user data, but the decentralized nature makes it susceptible to poisoning attacks. Most previous studies focus on the targeted attack to promote certain items, while the untargeted attack that aims to degrade the overall performance of the FedRec system remains less explored. In fact, untargeted attacks can disrupt the user experience and bring severe financial loss to the service provider. However, existing untargeted attack methods are either inapplicable or ineffective against FedRec systems. In this paper, we delve into the untargeted attack and its defense for FedRec systems. (i) We propose ClusterAttack, a novel untargeted attack method. It uploads poisonous gradients that converge the item embeddings into several dense clusters, which make the recommender generate similar scores for these items in the same cluster and perturb the ranking order. (ii) We propose a uniformity-based defense mechanism (UNION) to protect FedRec systems from such attacks. We design a contrastive learning task that regularizes the item embeddings toward a uniform distribution. Then the server filters out these malicious gradients by estimating the uniformity of updated item embeddings. Experiments on two public datasets show that ClusterAttack can effectively degrade the performance of FedRec systems while circumventing many defense methods, and UNION can improve the resistance of the system against various untargeted attacks, including our ClusterAttack. Copyright © 2022, The Authors. All rights reserved.

关键词： Embeddings

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Consistency-aware Multi-modal Network for Hierarchical Multi-label Classification in Online Education System 12

Consistency-aware Multi-modal Network for Hierarchical Multi...

引用

12th IEEE International Conference on Big Knowledge, ICBK 2021

作者： Lei, Siqi Huang, Wei Tong, Shiwei Liu, Qi Huang, Zhenya Chen, Enhong Su, Yu University of Science and Technology of China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology China IFLYTEK Research IFLYTEK Co. Ltd China

ISBN: (纸本)9781665438582

In the online education system, predicting the knowledge of exercises is a fundamental task of many applications, such as cognitive diagnosis. Usually, experts consider this problem as Hierarchical Multi-label Classification (HMC), since the knowledge concepts exhibit a multi-level structure. However, existing methods either sacrificed knowledge consistency for classification accuracy or sacrificed classification accuracy for knowledge consistency. Maintaining the balance is difficult. To forgo this dilemma, in this paper, we develop a novel frame-work called Consistency-Aware Multi-modal Network (Cam-Net). Specifically, we develop a multi-modal embedding module to learn the representation of the multi-modal exercise. Then, we adopt a hybrid prediction method consisting of the flat prediction module and the local prediction module. The local prediction module deals with the relation between the knowledge hierarchy and the input exercise. The flat prediction module focuses on maintaining knowledge consistency. Finally, to balance classification accuracy and knowledge consistency, we combine the outputs of two modules to make a final prediction. Extensive experimental results on two real-world datasets demonstrate the high performance and the ability to reduce knowledge inconsistency of CamNet. © 2021 IEEE.

关键词： Forecasting

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Semantic-Preserving Abstractive Text Summarization with Siamese Generative Adversarial Net

Semantic-Preserving Abstractive Text Summarization with Siam...

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2022 Findings of the Association for Computational Linguistics: NAACL 2022

作者： Sheng, Xin Xu, Linli Xu, Yinlong Jiang, Deqiang Ren, Bo School of Computer Science and Technology University of Science and Technology of China. China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China IFLYTEK Co. Ltd. China Tencent Youtu Lab China

ISBN: (纸本)9781955917766

We propose a novel siamese generative adversarial net for abstractive text summarization (SSPGAN), which can preserve the main semantics of the source text. Different from previous generative adversarial net based methods, SSPGAN is equipped with a siamese semanticpreserving discriminator, which can not only be trained to discriminate the machine-generated summaries from the human-summarized ones, but also ensure the semantic consistency between the source text and target summary. As a consequence of the min-max game between the generator and the siamese semantic-preserving discriminator, the generator can generate a summary that conveys the key content of the source text more accurately. Extensive experiments on several text summarization benchmarks in different languages demonstrate the effectiveness of the proposed method. © Findings of the Association for Computational Linguistics: NAACL 2022 - Findings.

关键词： Semantics

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