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User Rating Prediction Based on Trust-Driven Probabilistic Matrix Factorization

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Ruan Jian Xue Bao/Journal of Software 2018年第12期29卷 3747-3763页

作者： Du, Dong-Fang Xu, Tong Lu, Ya-Nan Guan, Chu Liu, Qi Chen, En-Hong Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei230027 China School of Computer Science and Technology University of Science and Technology of China Hefei230027 China

The development of Internet has brought convenience to the public, but also troubles users in making choices among enormous data. Thus, recommender systems based on user understanding are urgently in need. Different from the traditional techniques that usually focus on individual users, the social-based recommender systems perform better with integrating social influence modeling to achieve more accurate user profiling. However, current works usually generalize influence in simple mode, while deep discussions on intrinsic mechanism have been largely ignored. To solve this problem, this paper studies the social influence within users who affects both rating and user attributes, and then proposes a novel trust-driven PMF (TPMF) algorithm to merge these two mechanisms. Furthermore, to deal with the task that different user should have personalized parameters, the study clusters users according to rating correlation and then maps them to corresponding weights, thereby achieving the personalized selection of users' model parameters. Comprehensive experiments on open data sets validate that TPMF and its derivation algorithm can effectively predict users' rating compared with several state of the art baselines, which demonstrates the capability of the presented influence mechanism and technical framework. © Copyright 2018, Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Recommender systems

Versatile hydrogel based on polyvinyl alcohol/chitosan/regenerated silk fibroin

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Journal of Physics: Conference Series 2021年第1期2011卷

作者： Qinyue Sun Jizhu Fu Heng Zhang Huihui Yang Yumin Wang Yi Xiong Wei Zeng Faniu Wang Chao Xu National Engineering Research Center for Agro-Ecological Big Data Analysis & Application School of Electronics and Information Engineering Anhui University Hefei 230601 Anhui People's Republic of China School of Physics and Technology MOE Key Laboratory of Artificial Micro- and Nano-Structures and Center for Electron Microscopy Wuhan University Wuhan 430072 Hubei People's Republic of China Science and Technology Institute Key Laboratory of Textile Fiber and Products Ministry of Education Laboratory for Electron Microscopy Wuhan Textile University Wuhan 430073 Hubei People's Republic of China

Versatile sensors have broad application prospects in human motion detection, health monitoring, wearable electronic devices and flexible electronic skin and other emerging fields. In this work, the enhanced hydrogel was prepared by freezing and thawing process with calcium ion crosslinking. This composite material shows excellent flexibility and elasticity, and after being cut in half, it can automatically heal well in a short time without external force. It shows great potential in flexible and wearable devices.

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A generalization theory based on independent and task-identically distributed assumption

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arXiv 2019年

作者： Zheng, Guanhua Sang, Jitao Li, Houqiang Yu, Jian Xu, Changsheng University of Science and Technology of China School of Computer and Information Technology Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Hefei230026 China National Lab of Pattern Recognition Institute of Automation CAS Beijing100190 China University of Chinese Academy of Sciences

—Existing generalization theories analyze the generalization performance mainly based on the model complexity and training process. The ignorance of the task properties, which results from the widely used IID assumption, makes these theories fail to interpret many generalization phenomena or guide practical learning tasks. In this paper, we propose a new Independent and Task-Identically Distributed (ITID) assumption, to consider the task properties into the data generating process. The derived generalization bound based on the ITID assumption identifies the significance of hypothesis invariance in guaranteeing generalization performance. Based on the new bound, we introduce a practical invariance enhancement algorithm from the perspective of modifying data distributions. Finally, we verify the algorithm and theorems in the context of image classification task on both toy and real-world datasets. The experimental results demonstrate the reasonableness of the ITID assumption and the effectiveness of new generalization theory in improving practical generalization performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Classification (of information)

EKT: Exercise-aware knowledge tracing for student performance prediction

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arXiv 2019年

作者： Liu, Qi Huang, Zhenya Yin, Yu Chen, Enhong Xiong, Hui Su, Yu Hu, Guoping Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Techonology University of Science and Technology of China Management Science and Information Systems Department Rutgers Business School State University of New Jersey Rutgers United States Iflytek Research Iflytek Co. Ltd Hefei Anhui000000 China

For offering proactive services (e.g., personalized exercise recommendation) to the students in computer supported intelligent education, one of the fundamental tasks is predicting student performance (e.g., scores) on future exercises, where it is necessary to track the change of each student's knowledge acquisition during her exercising activities. Unfortunately, to the best of our knowledge, existing approaches can only exploit the exercising records of students, and the problem of extracting rich information existed in the materials (e.g., knowledge concepts, exercise content) of exercises to achieve both more precise prediction of student performance and more interpretable analysis of knowledge acquisition remains underexplored. To this end, in this paper, we present a holistic study of student performance prediction. To directly achieve the primary goal of performance prediction, we first propose a general Exercise-Enhanced Recurrent Neural Network (EERNN) framework by exploring both student's exercising records and the text content of corresponding exercises. In EERNN, we simply summarize each student's state into an integrated vector and trace it with a recurrent neural network, where we design a bidirectional LSTM to learn the encoding of each exercise from its content. For making final predictions, we design two implementations on the basis of EERNN with different prediction strategies, i.e., EERNNM with Markov property and EERNNA with Attention mechanism. Then, to explicitly track student's knowledge acquisition on multiple knowledge concepts, we extend EERNN to an explainable Exercise-aware Knowledge Tracing (EKT) framework by incorporating the knowledge concept information, where the student's integrated state vector is now extended to a knowledge state matrix. In EKT, we further develop a memory network for quantifying how much each exercise can affect the mastery of students on multiple knowledge concepts during the exercising process. Finally, we con

关键词： Forecasting

application and development of elastic fibers focusing on composition and structure

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Journal of Physics: Conference Series 2021年第1期2021卷

作者： Jin Zhang Qinyue Sun Yumin Wang Xiongwei Dong Yi Xiong Wei Zeng Department of Pharmacy Zhongnan Hospital Wuhan University Wuhan 430071 People's Republic of China National Engineering Research Center for Agro-Ecological Big Data Analysis & Application School of Electronics and Information Engineering Anhui University Hefei 230601 Anhui People's Republic of China School of Physics and Technology MOE Key Laboratory of Artificial Micro-and Nano-Structures Wuhan University Wuhan 430072 People's Republic of China Science and Technology Institute Key Laboratory of Textile Fiber and Products Ministry of Education Laboratory for Electron Microscopy Wuhan Textile University Wuhan 430073 Hubei People's Republic of China

Elastic fabric is comfortable to wear, and has been a consumer textile due to its good shape retention and wrinkle resistance after washing. This paper briefly introduces the types and performance characteristics of several common elastic fibers, and analyzes the relationship between the structure and elasticity of different elastic fibers. Finally, the development trend of elastic fibers is proposed.

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Skeptical deep learning with distribution correction

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arXiv 2018年

作者： An, Mingxiao Chen, Yongzhou Liu, Qi Liu, Chuanren Lv, Guangyi Wu, Fangzhao 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 Drexel University Microsoft Research Asia China

Recently deep neural networks have been successfully used for various classification tasks, especially for problems with massive perfectly labeled training data. However, it is often costly to have large-scale credible labels in real-world applications. One solution is to make supervised learning robust with imperfectly labeled input. In this paper, we develop a distribution correction approach that allows deep neural networks to avoid overfitting imperfect training data. Specifically, we treat the noisy input as samples from an incorrect distribution, which will be automatically corrected during our training process. We test our approach on several classification datasets with elaborately generated noisy labels. The results show significantly higher prediction and recovery accuracy with our approach compared to alternative methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

How images inspire poems: Generating classical Chinese poetry from images with memory networks

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arXiv 2018年

作者： Xu, Linli Jiang, Liang Qin, Chuan Wang, Zhe Du, Dongfang Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China AI Department Ant Financial Services Group

With the recent advances of neural models and natural language processing, automatic generation of classical Chinese poetry has drawn significant attention due to its artistic and cultural value. Previous works mainly focus on generating poetry given keywords or other text information, while visual inspirations for poetry have been rarely explored. Generating poetry from images is much more challenging than generating poetry from text, since images contain very rich visual information which cannot be described completely using several keywords, and a good poem should convey the image accurately. In this paper, we propose a memory based neural model which exploits images to generate poems. Specifically, an Encoder-Decoder model with a topic memory network is proposed to generate classical Chinese poetry from images. To the best of our knowledge, this is the first work attempting to generate classical Chinese poetry from images with neural networks. A comprehensive experimental investigation with both human evaluation and quantitative analysis demonstrates that the proposed model can generate poems which convey images accurately. Copyright © 2018, The Authors. All rights reserved.

关键词： Natural language processing systems

H2O2-Responsive NIR-II AIE Nanobomb for Carbon Monoxide Boosting Low-Temperature Photothermal Therapy

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Angewandte Chemie 2022年第36期134卷

作者： Gongcheng Ma Zhongke Liu Chunguang Zhu Prof. Huajie Chen Prof. Ryan T. K. Kwok Prof. Pengfei Zhang Ben Zhong Tang Prof. Lintao Cai Prof. Ping Gong Guangdong Key Laboratory of Nanomedicine Shenzhen Engineering Laboratory of Nanomedicine and Nanoformulations CAS-HK Joint Lab for Biomaterials Research Laboratory for Biomedical Optics and Molecular Imaging Shenzhen Key Laboratory for Molecular Imaging CAS Key Lab for Health Informatics Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China University of Chinese Academy of Sciences Beijing 100049 China Contribution: Data curation (lead) Validation (lead) Writing - original draft (lead) Nano Science and Technology Institute University of Science & Technology of China Suzhou 215123 China Contribution: Data curation (equal) Investigation (equal) Key Laboratory of Environmentally Friendly Chemistry and Application of the Ministry of Education College of Chemistry Xiangtan University Xiangtan 411105 China Contribution: Data curation (supporting) Methodology (supporting) Contribution: Project administration (supporting) Resources (supporting) Supervision (supporting) Writing - review & editing (equal) Department of Chemistry Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction and Institute for Advanced Study The Hong Kong University of Science and Technology (HKUST) Clear Water Bay Kowloon Hong Kong China Contribution: Funding acquisition (equal) Supervision (equal) Writing - review & editing (equal) Contribution: Conceptualization (lead) Formal analysis (lead) Methodology (equal) Project administration (lead) Resources (equal) Supervision (equal) Writing - original draft (equal) Writing - review & editing (lead) School of Science and Engineering Shenzhen Institute of Aggregate Science and Technology The Chinese University of Hong Kong Shenzhen Guangdong 518172 China Contribution: Formal analysis (supporting) Funding acquisition (equal) Writing - review & editing (supporting) Contribution: Funding acquisition (lead) Project administration (lead) Supervision (lead) Writing - revi

Low-temperature photothermal therapy (PTT), which circumvents the limitations of conventional PTT (e.g., thermotolerance and adverse effects), is an emerging therapeutic strategy which shows great potential for future clinical applications. The expression of heat shock proteins (HSPs) can dramatically impair the therapeutic efficacy of PTT. Thus, inhibition of HSPs repair and reducing the damage of nearby normal cells is crucial for improving the efficiency of low-temperature PTT. Herein, we developed a nanobomb based on the self-assembly of NIRII AIE polymer PBPTV and carbon monoxide (CO) carrier polymer mPEG(CO). This smart nanobomb can be exploded in a tumor microenvironment in which hydrogen peroxide is overexpressed and release CO into cancer cells to significantly inhibit the expression of HSPs and hence improve the antitumor efficiency of the low-temperature PTT.

关键词： Aggregation Induced Emission Gas Therapy Heat Shock Protein NIR-II Photothermal Therapy

Non-autoregressive neural machine translation with enhanced decoder input

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arXiv 2018年

作者： Guo, Junliang Tan, Xu He, Di Qin, Tao Xu, Linli Liu, Tie-Yan Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Microsoft Research School of EECS Peking University

Non-autoregressive translation (NAT) models, which remove the dependence on previous target tokens from the inputs of the decoder, achieve significantly inference speedup but at the cost of inferior accuracy compared to autoregressive translation (AT) models. Previous work shows that the quality of the inputs of the decoder is important and largely impacts the model accuracy. In this paper, we propose two methods to enhance the decoder inputs so as to improve NAT models. The first one directly leverages a phrase table generated by conventional SMT approaches to translate source tokens to target tokens, which are then fed into the decoder as inputs. The second one transforms source-side word embeddings to target-side word embeddings through sentence-level alignment and word-level adversary learning, and then feeds the transformed word embeddings into the decoder as inputs. Experimental results show our method largely outperforms the NAT baseline (Gu et al. 2017) by 5.11 BLEU scores on WMT14 English-German task and 4.72 BLEU scores on WMT16 English-Romanian task. Copyright © 2018, The Authors. All rights reserved.

关键词： Embeddings