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24th ACM International Conference on Information and knowledge Management, CIKM 2015

作者： Zhao, Tong McAuley, Julian King, Irwin Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications Shenzhen Research Institute Chinese University of Hong Kong Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong Department of Computer Science and Engineering UC San Diego San DiegoCA United States

ISBN: (纸本)9781450337946

Latent Factor models, which transform both users and items into the same latent feature space, are one of the most successful and ubiquitous models in recommender systems. Most existing models in this paradigm define both users' and items' latent factors to be of the same size and use an inner product to represent a user's 'compatibility' with an item. Intuitively, users' factors encode 'preferences' while item factors encode 'properties', so that the inner product encodes how well an item matches a user's preferences. However, a user's opinion of an item may be more complex, for example each dimension of each user's opinion may depend on a combination of multiple item factors simultaneously. Thus it may be better to view each dimension of a user's preference as a personalized projection of an item's properties so that the preference model can capture complex relationships between items' properties and users' preferences. Therefore, in this paper we propose a novel personalized feature projection method to model users' preferences over items. Specifically, for each user, we define a personalized projection matrix, which takes the place of user-specific factors from existing models. This matrix describes a mapping between items' factors and users' preferences in order to build personalized preference models for each user and item. The proposed personalized feature projection method is quite general and existing latent factor models, for example, can be cast as a special case. We present three objective functions to optimize predictions in the form of ranked lists of users' preferences over items, and demonstrate how each can be used to improve one-class recommendation performance. Experiments are conducted on four real-world datasets and our results show that our personalized feature projection method outperforms several state-of-the-art methods on various evaluation metrics. © 2015 ACM.

关键词： Collaborative filtering

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Multiscale fast correlation filtering tracking algorithm based on a feature fusion model

Multiscale fast correlation filtering tracking algorithm bas...

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作者： null null null null null null null School of Computer and Communication Engineering and Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha China School of Information Science and Engineering Fujian University of Technology Fujian China Hunan Valin Lianyuan Iron & Steel Corporation Loudi China Electronics & Information School Yangtze University Jingzhou China Hunan Institute of Scientific and Technical Information Changsha China Technical Quality Department Hunan ZOOMLION Heavy Industry Intelligent Technology Corporation Limited Changsha China

关键词： Object tracking

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The Fingerprinting Analysis of Power Amplifier Predistortion Error from Wideband Signals

The Fingerprinting Analysis of Power Amplifier Predistortion...

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IEEE Information Technology and Mechatronics engineering Conference (ITOEC)

作者： Jingzhi Zhang Xiang Chen Nae Zheng Long Zhang School of Data and Target Engineering Information Engineering University Zhengzhou China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE) Luoyang China

ISBN: (纸本)9781538653746

Radio frequency fingerprinting is caused by the hardware non-ideality, especially the analog components' non-ideality. With the rapidly increasing demands for higher data transmission rate, the requirements for efficient modulation schemes and wider channel bandwidth are continuously increasing, which needs digital predistortion (DPD) to eliminate the nonlinear and memory effect of the power amplifier. Aiming at the power amplifier predistortion error, this paper analyzes and identifies typical DPD architectures. Firstly, a power amplifier behavior model which coefficients extracted from a real PA is used. Then, three typical DPD architectures are employed to compensate the PA's nonlinear and memory effect respectively. The DPD and PA architecture is stimulated by a 5 MHz long term evolution (LTE) signal. Finally, the three DPD and PA architectures are identified above a neural network classifier using overall AM-AM and AM-PM performance. The analysis and simulation results demonstrate the effectiveness of the proposed method.

关键词： Predistortion Fingerprint recognition Nonlinear distortion Finite impulse response filters Modulation

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Understanding collective behaviors in reinforcement learning evolutionary games via a belief-based formalization

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Physical Review E 2020年第4期101卷 042402-042402页

作者： Ji-Qiang Zhang Si-Ping Zhang Li Chen Xu-Dong Liu Beijing Advanced Innovation Center for Big Data and Brain Computing School of Comuter Science and Engineering Beihang University Beijing 100191 China The Key Laboratory of Biomedical Information Engineering of Ministry of Education The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs and Institute of Health and Rehabilitation Science School of Life Science and Technology Xi'an Jiaotong University Xi'an 710049 China School of Physics and Information Technology Shaanxi Normal University Xi'an 710062 China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing 100191 China

Collective behaviors by self-organization are ubiquitous in nature and human society and extensive efforts have been made to explore the mechanisms behind them. Artificial intelligence (AI) as a rapidly developing field is of great potential for these tasks. By combining reinforcement learning with evolutionary game (RLEG), we numerically discover a rich spectrum of collective behaviors—explosive events, oscillation, and stable states, etc., that are also often observed in the human society. In this work, we aim to provide a theoretical framework to investigate the RLEGs systematically. Specifically, we formalize AI-agents' learning processes in terms of belief switches and behavior modes defined as a series of actions following beliefs. Based on the preliminary results in the time-independent environment, we investigate the stability at the mixed equilibrium points in RLEGs generally, in which agents reside in one of the optimal behavior modes. Moreover, we adopt the maximum entropy principle to infer the composition of agents residing in each mode at a strictly stable point. When the theoretical analysis is applied to the 2×2 game setting, we can explain the uncovered collective behaviors and are able to construct equivalent systems intuitively. Also, the inferred composition of different modes is consistent with simulations. Our work may be helpful to understand the related collective emergence in human society as well as behavioral patterns at the individual level and potentially facilitate human-computer interactions in the future.

关键词： Collective dynamics Complex systems Evolutionary dynamics

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Leveraging social connections to improve personalized ranking for collaborative filtering 14

Leveraging social connections to improve personalized rankin...

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23rd ACM International Conference on Information and knowledge Management, CIKM 2014

ISBN: (纸本)9781450325981

Recommending products to users means estimating their preferences for certain items over others. This can be cast either as a problem of estimating the rating that each user will give to each item, or as a problem of estimating users' relative preferences in the form of a ranking. Although collaborative-filtering approaches can be used to identify users who rate and rank products similarly, another source of data that informs us about users' preferences is their set of social connections. Both rating- and ranking-based paradigms are important in real-world recommendation settings, though rankings are especially important in settings where explicit feedback in the form of a numerical rating may not be available. Although many existing works have studied how social connections can be used to build better models for rating prediction, few have used social connections as a means to derive more accurate ranking-based models. Using social connections to better estimate users' rankings of products is the task we consider in this paper. We develop a model, SBPR (Social Bayesian Personalized Ranking), based on the simple observation that users tend to assign higher ranks to items that their friends prefer. We perform experiments on four real-world recommendation data sets, and show that SBPR outperforms alternatives in ranking prediction both in warm- and cold-start settings. Copyright 2014 ACM.

关键词： Collaborative filtering

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Another Two-Timescale Duplex Neurodynamic Approach to Portfolio Selection 11

Another Two-Timescale Duplex Neurodynamic Approach to Portfo...

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11th International Conference on Intelligent Control and Information Processing, ICICIP 2021

作者： Leung, Man-Fai Wang, Jun Che, Hangjun Hong Kong Metropolitan University School of Science and Technology Hong Kong City University of Hong Kong Department of Computer Science School of Data Science Hong Kong Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing China

ISBN: (纸本)9781665425155

This paper is concerned with portfolio selection based on the Markowitz mean-variance framework using neurodynamic optimization. The portfolio optimization problem is formulated as a biconvex optimization problem. A two-timescale duplex neurodynamic approach is then applied for solving the profolio selection problem. The approach makes use of two recurrent neural networks (RNNs) which operate at different timescales for local search. A particle swarm optimization algorithm is employed to update the neuronal states of the two RNNs for global optima. Experimental results on four stock market datasets show the superior performance of the neurodynamic approach in terms of long-term expected returns. © 2021 IEEE.

关键词： Recurrent neural networks

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Stability Improvement Without External Communication for DC Microgrid Equipped With Positive Feedback Islanding Detection

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IEEE Transactions on Consumer Electronics 2024年

作者： Shi, Tianling Zhang, Xiaokang Xiang, Xin Liu, Boxin Zhu, Yuxin Wu, Songmei Li, Wuhua Shanghai Key Laboratory of Power Station Automation Technology Department of Electrical Engineering School of Mechatronics Engineering and Automation Shanghai200444 China University of Technology Sydney School of Electrical and Data Engineering SydneyNSW2007 Australia Zhejiang University College of Electrical Engineering Hangzhou310027 China

Unplanned islanding events in dc microgrids bring severe safety hazards to distributed generators (DG) and consumers. The positive feedback islanding detection method (IDM) provides guaranteed protection for consumers due to its small non-detection zone and high detection speed. However, the positive feedback loop continuously injects disturbances into DG and thus will result in a reduction in the stability margin of the grid-connected dc microgrid. Conventional solutions usually provide stability compensation based on grid current in the dc/dc converter to avoid conflicts with IDM, but it requires external communication and thus reduces system reliability. To address this issue, the cascaded impedance model of the grid-connected dc system is developed first in this paper, which reveals the instability mechanism caused by the IDM. On top of that, the phase stability region of the voltage source converter (VSC) is explored and described to guide the damper design. Furthermore, an active damping method is proposed to realize phase compensation for the VSC impedance. In this manner, the negative effect of the islanding detection loop on the stability margin is effectively mitigated without external communication links. Finally, the proposed method is validated in hardware-in-loop experiments. © 1975-2011 IEEE.

关键词： Microgrids

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Adaptive Loose Optimization for Robust Question Answering

arXiv

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arXiv 2023年

作者： Ma, Jie Wang, Pinghui Wang, Zewei Kong, Dechen Hu, Min Han, Ting Liu, Jun The Ministry of Education of Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The Ministry of Education of Key Laboratory for Intelligent Networks and Network Security School of Automation Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The China Mobile Research Institute China The Shannxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China

Question answering methods are well-known for leveraging data bias, such as the language prior in visual question answering and the position bias in machine reading comprehension (extractive question answering). Current debiasing methods often come at the cost of significant in-distribution performance to achieve favorable out-of-distribution generalization ability, while non-debiasing methods sacrifice a considerable amount of out-of-distribution performance in order to obtain high in-distribution performance. Therefore, it is challenging for them to deal with the complicated changing real-world situations. In this paper, we propose a simple yet effective novel loss function with adaptive loose optimization, which seeks to make the best of both worlds for question answering. Our main technical contribution is to reduce the loss adaptively according to the ratio between the previous and the current optimization state on mini-batch training data. This loose optimization can be used to prevent non-debiasing methods from overlearning data bias while enabling debiasing methods to maintain slight bias learning. Experiments on the visual question answering datasets, including VQA v2, VQA-CP v1, VQA-CP v2, GQA-OOD, and the extractive question answering dataset SQuAD demonstrate that our approach enables QA methods to obtain state-of-the-art in- and out-of-distribution performance in most cases. The source code has been released publicly in https://***/reml-group/ALO. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Detection of Venous Thromboembolism Using Recurrent Neural Networks with Time-Series data 24

Detection of Venous Thromboembolism Using Recurrent Neural N...

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3rd Asia Conference on Algorithms, Computing and Machine Learning, CACML 2024

作者： Xu, Can Huang, Yaqin Xiang, Xinni Lei, Haike Yang, Jie Shanghai Jiao Tong University Shanghai China Chongqing University Cancer Hospital Chongqing China West China Hospital Sichuan University Chengdu China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition China Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment China West China School of Medicine China Chongqing Cancer Multi-omics Big Data Application Engineering Research Center China

ISBN: (纸本)9798400716416

Machine Learning (ML) has been widely applied to medical science for decades. As common knowledge, the progress of many diseases is often chronic and dynamic. Longitudinal data, or time-series data, has better descriptions of certain diseases, as it naturally captures the dynamic over time. Venous thromboembolism (VTE), as a regular complication of cancer tumour treatments, has been emphasized in many studies. Recent research has shown that ML algorithms incredibly contribute to VTE risk estimation and prediction. However, existing methods rarely consider the dynamic changes during the development of VTE, and often make decisions based on time-point data, such as the latest blood test results of patients. In this paper, we propose a VTE detection model that takes advantage of capturing dynamic information using time-series data. The model consists of a Recurrent Neural Network (RNN) based framework with time embedding and Attention mechanism to encode longitudinal features of patients. We test our proposed model on a real-world dataset that contains multiple times of blood test results of patients together with other clinical features. The network is compared with several commonly used ML models in previous VTE studies. The results show that our model outperforms all baseline models, and achieved a state-of-art performance with 80.0% accuracy and 0.881 AUC. © 2024 ACM.

关键词： Risk assessment

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Joint multi-task cascade for instance segmentation

Joint multi-task cascade for instance segmentation

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作者： Wen, Yaole Hu, Fuyuan Ren, Jinchang Shang, Xinru Li, Linyan Xi, Xuefeng School of Electronic & Information Engineering Suzhou University of Science and Technology SuzhouJiangsu215009 China Centre for Signal and Image Processing University of Strathclyde Glasgow United Kingdom Suzhou Institute of Trade & Commerce SuzhouJiangsu215009 China Virtual Reality Key Laboratory of Intelligent Interaction and Application Technology of Suzhou Suzhou University of Science and Technology SuzhouJiangsu215009 China Suzhou Key Laboratory for Big Data and Information Service Suzhou University of Science and Technology SuzhouJiangsu215009 China

Instance segmentation requires both pixel-level classification accuracy and high-level semantic features at the target instance level, which is very challenging, and the cascade structure can effectively improve both of these problems. To make full use of the relationship between detection and segmentation, this paper proposes a joint multi-tasking cascade structure, which is not simply to cascade the two tasks of detection and segmentation, but to unitedly put them into multi-stage processing, and especially to integrate the information at different stages of the mask branch. The entire structure can effectively utilize the superior characteristics of each stage in the matter of detection and segmentation, thus improving the quality of mask prediction. The feature fusion process is introduced in the full convolution networks (FCN) branch, and the high-level and low-level features are effectively fused to enhance the contextual information of the picture semantic features. The experiments demonstrate the better results on the COCO dataset. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Semantics

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