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25th International Conference on Neural Information processing, ICONIP 2018

作者： Wang, Yingyao Dai, Jianhua Shi, Hong School of Computer Science and Technology Tianjin University Tianjin300350 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha410081 China

ISBN: (纸本)9783030041816

Rule induction method based on rough set theory (RST) which can generate a minimal set of decision rules by using attribute reduction and approximations has received much attention recently. In real-life, the variation of objects, attributes and attributes’ values affects reducts and approximations, e.g., the coarsening and refining of attributes’ values. The goal of this paper is dynamic maintenance of decision rules for decision attribute values’ coarsening and refining. Two incremental rough-set based methods are proposed to deal with this issue by updating assignment discernibility matrix dynamically without recomputing the reducts from the beginning, which increases the efficiency. © Springer Nature Switzerland AG 2018.

关键词： Rough set theory

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Food recommendation: Framework, existing solutions and challenges

arXiv

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arXiv 2019年

作者： Min, Weiqing Jiang, Shuqiang Jain, Ramesh Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 University of Chinese Academy of Sciences Beijing100049 China Department of Computer Science University of California IrvineCA United States

A growing proportion of the global population is becoming overweight or obese, leading to various diseases (e.g., diabetes, ischemic heart disease and even cancer) due to unhealthy eating patterns, such as increased intake of food with high energy and high fat. Food recommendation is of paramount importance to alleviate this problem. Unfortunately, modern multimedia research has enhanced the performance and experience of multimedia recommendation in many fields such as movies and POI, yet largely lags in the food domain. This article proposes a unified framework for food recommendation, and identifies main issues affecting food recommendation including building the personal model, analyzing unique food characteristics, incorporating various context and domain knowledge. We then review existing solutions for these issues, and finally elaborate research challenges and future directions in this field. To our knowledge, this is the first survey that targets the study of food recommendation in the multimedia field and offers a collection of research studies and technologies to benefit researchers in this field. Copyright © 2019, The Authors. All rights reserved.

关键词： Domain Knowledge

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Label distribution learning based on ensemble neural networks 25th

Label distribution learning based on ensemble neural network...

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25th International Conference on Neural Information processing, ICONIP 2018

作者： Zhai, Yansheng Dai, Jianhua Shi, Hong School of Computer Science and Technology Tianjin University Tianjin300350 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha410081 China

ISBN: (纸本)9783030041816

Label distribution learning (LDL), as an extension of multi-label learning, is a new arising machine learning technique to deal with label ambiguity problems. The maximum entropy model is commonly used in label distribution learning. However, it does not consider the correlation between the labels and is not suitable for nonlinear relationships, and the prediction performance is also limited. In this paper, we propose a label distribution learning algorithm based on ensemble neural networks. The algorithm trains neural networks with preferences using training sets with different label sets to construct base learners, and combines the base learners with the weights, which is learned by the combined learner to obtain the final learning results. Experimental results show that the proposed algorithm is effective for label distribution data. © Springer Nature Switzerland AG 2018.

关键词： Learning algorithms

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In Vitro Fertilization (IVF) cumulative pregnancy rate prediction from basic patient characteristics

arXiv

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arXiv 2019年

作者： Zhang, Bo Cui, Yuqi Wang, Meng Li, Jingjing Jin, Lei Wu, Dongrui Reproductive Medicine Center Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei430030 China Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan Hubei430074 China

Tens of millions of women suffer from infertility worldwide each year. In vitro fertilization (IVF) is the best choice for many such patients. However, IVF is expensive, time-consuming, and both physically and emotionally demanding. The first question that a patient usually asks before the IVF is how likely she will conceive, given her basic medical examination information. This paper proposes three approaches to predict the cumulative pregnancy rate after multiple oocyte pickup cycles. Experiments on 11,190 patients showed that first clustering the patients into different groups and then building a support vector machine model for each group can achieve the best overall performance. Our model could be a quick and economic approach for reliably estimating the cumulative pregnancy rate for a patient, given only her basic medical examination information, well before starting the actual IVF procedure. The predictions can help the patient make optimal decisions on whether to use her own oocyte or donor oocyte, how many oocyte pickup cycles she may need, whether to use embryo frozen, etc. They will also reduce the patient's cost and time to pregnancy, and improve her quality of life. Copyright © 2019, The Authors. All rights reserved.

关键词： Obstetrics

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Object Detection Algorithm for Improving Non-Maximum Suppression Using GIoU

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IOP Conference Series: Materials Science and Engineering 2020年第1期790卷

作者： Zhiqiang Hou Xiaoyi Liu Lilin Chen College of Computer Science and Technology Xian University of Posts and Telecommunications Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China

To address the problem of object miss detection and object false detection in single threshold-Non-Maximum Suppression algorithm, this paper proposed a GDT-NMS (Generalized Intersection over Union Dual Threshold NMS, GDT-NMS)) algorithm which using GIoU(Generalized Intersection over Union). Using the GIoU indicator computing the similarity between objects, can better describe the relative position and overlap between the objects. And we proposed the dual-threshold NMS algorithm, which can balance the relationship between the object missed detection problem and the object false detection problem, reduce 'false positive example' problem. By nonlinearly processing the weight function, the object is better distinguished. The algorithm uses Faster R-CNN as the detector. The experimental results show that the improved algorithm has outstanding performance.

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iSplit LBI: Individualized partial ranking with ties via split LBI

arXiv

引用

arXiv 2019年

作者： Xu, Qianqian Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Microsoft Research Asia State Key Laboratory of Information Security Institute of Information Engineering CAS School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management CAS Peng Cheng Laboratory Department of Mathematics Hong Kong University of Science and Technology Hong Kong

Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different individuals often hold distinct preferences. It might be misleading to merely look at a global partial ranking while ignoring personal diversity. In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective. Particularly, we formulate a unified framework which not only can be used for individualized partial ranking prediction, but also be helpful for abnormal user selection. This is realized by a variable splitting-based algorithm called iSplitLBI. Specifically, our algorithm generates a sequence of estimations with a regularization path, where both the hyperparameters and model parameters are updated. At each step of the path, the parameters can be decomposed into three orthogonal parts, namely, abnormal signals, personalized signals and random noise. The abnormal signals can serve the purpose of abnormal user selection, while the abnormal signals and personalized signals together are mainly responsible for individual partial ranking prediction. Extensive experiments on simulated and real-world datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives. The code is now availiable at https://***/qianqianxu010/ NeurIPS2019-iSplitLBI. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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Adaptive Teacher-and-Student Model for Heterogeneous Domain Adaptation

Adaptive Teacher-and-Student Model for Heterogeneous Domain ...

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IEEE International Conference on Data Mining (ICDM)

作者： Yunyun Wang Xuzhang Chen Songcan Chen Hui Xue College of Computer Science Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts & Telecommunications Nanjing China College of Computer Science & Technology/AI MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics & Astronautics Nanjing China College of Computer Science and Engineering Southeast University Nanjing China

In heterogeneous domain adaptation (HDA), since the feature spaces of the source and target domains are different, knowledge transfer from the source to the target domain is really challenging. How to align the different feature spaces and then adaptively transfer the related knowledge is critical for HDA. In this paper, we develop an adaptive teacher-and-student model for heterogeneous domain adaptation (AtsHDA). In AtsHDA, the source domain as a teacher and the target domain as a student are aligned or co-adapted to each other first, so that their correlation can be maximized. Then the target domain adaptively learns from the source domain. Specifically, there is a balance between the learning by the target domain itself and the instruction from the source domain. That is, when the guidance from the source domain is helpful for learning, the learning of target classifier emphasizes the instruction of source knowledge, and considers its own knowledge more, otherwise. Further, an ensemble method is designed to decide such a balance. Finally, empirical results show that AtsHDA can achieve competitive results compared with the state-of-arts.

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Bidirectional LSTM-CRF for biomedical named entity recognition 14

Bidirectional LSTM-CRF for biomedical named entity recogniti...

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14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2018

作者： Yang, Xuemin Gao, Zhihong Li, Yongmin Pan, Chuandi Yang, Ronggen Gong, Lejun Yang, Geng Jiangsu Key Laboratory of Big Data Security Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210003 China Zhejiang Engineering Research Center of Intelligence Medicine Wenzhou325035 China Faculty Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing211169 China

ISBN: (纸本)9781538680971

Bio-medical entity recognition extracts significant entities, for instance cells, proteins and genes, which is an arduous task in an automatic system that mine knowledge in bioinformatics texts. In this thesis, we utilized a bidirectional long short-term memory (Bi-LSTM) combined with conditional random fields (CRFs) approach to automatically obtain word representation, obliterated the need for a marvelous number of feature engineering tasks. The consequences of this experiment represent the word representation method can effectually acquire potential semantic information. Without relying on any artificial features, the result on the test dataset obtained 76.81 % F-score. Therefore, the proposed method is expected to advance biomedical text mining in bioinformatics entity recognition. © 2018 IEEE.

关键词： Bioinformatics

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The propositional normal default logic and the finite/infinite injury priority method

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Science China(Information Sciences) 2017年第9期60卷 99-108页

作者： Wei LI Yuefei SUI Yuhui WANG State Key Laboratory of Software Development Environment Beihang University Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences

In propositional normal default logic, given a default theory(?, D) and a well-defined ordering of D, there is a method to construct an extension of(?, D) without any injury. To construct a strong extension of(?, D) given a well-defined ordering of D, there may be finite injuries for a default δ∈ D. With approximation deduction ?s in propositional logic, we will show that to construct an extension of(?, D) under a given welldefined ordering of D, there may be infinite injuries for some default δ∈ D.

关键词： default extension strong extension finite/infinite injury priority method recursively enumerable sets

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Hybrid roadside devices placement for advertisement disseminations in vehicular CPS 11th

Hybrid roadside devices placement for advertisement dissemin...

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11th EAI International Conference on Communications and Networking in China, ChinaCom 2016

作者： Cui, Junshan Li, Peng Yue, Dongdong Jin, Yu Liu, Yu Liu, Qin College of Computer Science and Technology Wuhan University of Science and Technology Hubei430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei430065 China School of Computer Wuhan University Hubei430074 China

ISBN: (纸本)9783319666273

There are two types of roadside devices for advertisement dissemination in the Vehicular Cyber-Physical Systems (VCPS), one is roadside units (RSUs) and the other is roadside access points (RAPs). The placement cost of RSUs is lower than RAPs. However, the coverage of RSUs is limited. In this paper, we investigate the hybrid roadside device placement problem in the Vehicular Cyber-Physical Systems (VCPS). Given the budget constraint and the distribution of traffic conditions, our goal is to optimize the deployment of the hybrid roadside device for the merchants to maximize their benefits from advertisement dissemination. With the purpose of all advertisement can be effectively served, we propose a corresponding hybrid greedy placement algorithm. Our algorithm not only obtains the more benefits, but also consider the placement cost. Finally, we evaluate the performance of our proposed algorithm. Extensive simulations show that the performance of our proposed algorithm is superior to the other algorithms. © 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Cyber Physical System

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