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Geo-Spatial Information Science 2019年第4期22卷 251-264,I0003页

作者： Yunzhe Liu Alex Singleton Daniel Arribas-Bel Geographic data Science lab Department of Geography and PlanningUniversity of LiverpoolLiverpoolUK

A geodemographic classification aims to describe the most salient characteristics of a small area zonal ***,such representations are influenced by the methodological choices made during their *** particular debate are the choice and specification of input variables,with the objective of identifying inputs that add value but also aim for model *** this context,our paper introduces a principal component analysis(PCA)-based automated variable selection methodology that has the objective of identifying candidate inputs to a geodemographic classification from a collection of *** proposed methodology is exemplified in the context of variables from the UK 2011 Census,and its output compared to the Office for National Statistics 2011 Output Area Classification(2011 OAC).Through the implementation of the proposed methodology,the quality of the cluster assignment was improved relative to 2011 OAC,manifested by a lower total withincluster sum of square *** the UK,more than 70.2%of the Output Areas(OAs)occupied by the newly created classification(***-OAC)outperform the 2011 OAC,with particularly strong performance within Scotland and Wales.

关键词： Geodemographics variable selection UK census spatial data mining principal component analysis

A Multi-semantics Classification Method Based on Deep Learning for Incredible Messages on Social Media

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Chinese Journal of Electronics 2019年第4期28卷 754-763页

作者： WU Lianwei RAO Yuan YU Hualei WANG Yiming AMBREEN Nazir lab of Social Intelligence and Complex data Processing School of Software Engineering Xi'an Jiaotong University

How to classify incredible messages has attracted great attention from academic and industry nowadays. The recent work mainly focuses on one type of incredible messages(a.k.a rumors or fake news) and achieves some success to detect them. The existing problem is that incredible messages have different types on social media, and rumors or fake news cannot represent all incredible messages. Based on this, in the paper, we divide messages on social media into five types based on three dimensions of information evaluation metrics. And a novel method is proposed based on deep learning for classifying the five types of incredible messages on social *** specifically, we use attention mechanism to obtain deep text semantic features and strengthen emotional semantics features, meanwhile, construct universal metadata as auxiliary features, concatenating them for incredible messages classification. A series of experiments on two representative real-world datasets demonstrate that the proposed method outperforms the state-of-the-art methods.

关键词： Information credibility evaluation Rumor detection Social media Text classification

Federated learning-outcome prediction with multi-layer privacy protection

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Frontiers of Computer Science 2024年第6期18卷 186604页

作者： ZHANG Yupei LI Yuxin WANG Yifei WEI Shuangshuang XU Yunan SHANG Xuequn School of Computer Science Northwestern Polytechnical University MIIT lab of Big data Storage and Management

Learning-outcome prediction (LOP) is a long-standing and critical problem in educational routes. Many studies have contributed to developing effective models while often suffering from data shortage and low generalization to various institutions due to the privacy-protection issue. To this end, this study proposes a distributed grade prediction model, dubbed FecMap, by exploiting the federated learning (FL) framework that preserves the private data of local clients and communicates with others through a global generalized model. FecMap considers local subspace learning (LSL), which explicitly learns the local features against the global features, and multi-layer privacy protection (MPP), which hierarchically protects the private features, including model-shareable features and not-allowably shared features, to achieve client-specific classifiers of high performance on LOP per institution. FecMap is then achieved in an iteration manner with all datasets distributed on clients by training a local neural network composed of a global part, a local part, and a classification head in clients and averaging the global parts from clients on the server. To evaluate the FecMap model, we collected three higher-educational datasets of student academic records from engineering majors. Experiment results manifest that FecMap benefits from the proposed LSL and MPP and achieves steady performance on the task of LOP, compared with the state-of-the-art models. This study makes a fresh attempt at the use of federated learning in the learning-analytical task, potentially paving the way to facilitating personalized education with privacy protection.

关键词： federated learning local subspace learning hierarchical privacy protection learning outcome prediction privacy-protected representation learning

Extending attributes page:a scheme for enhancing the reliability of storage system metadata

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2009年第8期10卷 1106-1113页

作者： Juan WANG Dan FENG Fang WANG Cheng-tao LU MOE Key lab of data Storage System Huazhong University of Science and Technology Wuhan 430074 China

In an object-based storage system,a novel scheme named EAP(extending attributes page) is presented to enhance the metadata reliability of the system by adding the user object file information attributes page for each user object and storing the file-related attributes of each user object in object-based storage *** EAP scheme requires no additional hardware equipments compared to a general method which uses backup metadata servers to improve the metadata *** a Markov chain,this paper compares the metadata reliability of the system using the EAP scheme with that using only metadata servers to offer the file metadata *** results demonstrate that the EAP scheme can dramatically enhance the reliability of storage system metadata.

关键词： Metadata server Metadata reliability Markov model Extending attributes page (EAP)

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Computing infrastructure for big data processing

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Frontiers of Computer Science 2013年第2期7卷 165-170页

作者： Ling LIU Distributed data Intensive Systems lab School of Computer Science Georgia Institute of TechnologyUSA Atlanta 30332

With computing systems undergone a fundamen- tal transformation from single-processor devices at the turn of the century to the ubiquitous and networked devices and the warehouse-scale computing via the cloud, the parallelism has become ubiquitous at many levels. At micro level, par- allelisms are being explored from the underlying circuits, to pipelining and instruction level parallelism on multi-cores or many cores on a chip as well as in a machine. From macro level, parallelisms are being promoted from multiple ma- chines on a rack, many racks in a data center, to the glob- ally shared infrastructure of the Internet. With the push of big data, we are entering a new era of parallel computing driven by novel and ground breaking research innovation on elas- tic parallelism and scalability. In this paper, we will give an overview of computing infrastructure for big data processing, focusing on architectural, storage and networking challenges of supporting big data paper. We will briefly discuss emerging computing infrastructure and technologies that are promising for improving data parallelism, task parallelism and encour- aging vertical and horizontal computation parallelism.

关键词： big data cloud computing data analytics elas-tic scalability heterogeneous computing GPU PCM bigdata processing

Theory and methodology of research on cloud security

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Science China(Information Sciences) 2016年第5期59卷 15-17页

作者： Hai JIN Weiqi DAI Deqing ZOU Services Computing Technology and System lab Big Data Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology

Cloud computing is a model for enabling ubiquitous,convenient,on-demand access to a shared pool of configurable computing *** to Gartner’s survey,cloud computing has been at the head of a list of the top ten IT strategic technologies for some *** computing is currently undergoing vigorous development and promotion by many leading Chinese and foreign enterprises,and it has become the basis for a new

关键词： TPM Theory and methodology of research on cloud security

Towards dataflow based graph processing

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Science China(Information Sciences) 2017年第12期60卷 274-276页

作者： Hai JIN Pengcheng YAO Xiaofei LIAO Service Computing Technology and System lab Cluster and Grid Computing Lab Big Data Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology

Modern graph processing is widely used for solving a vast variety of real-world problems,e.g.,web sites ranking[1]and community detection[2].To better adapt and express the procedure of graph iteration,a wide spectrum... 详细信息

关键词： ILP cycle Towards dataflow based graph processing

OTB-morph:One-time Biometrics via Morphing

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Machine Intelligence Research 2023年第6期20卷 855-871页

作者： Mahdi Ghafourian Julian Fierrez Ruben Vera-Rodriguez Aythami Morales Ignacio Serna Biometrics and data Pattern Analytics laboratory(BiDA lab) Universidad Autonoma de MadridMadrid28049Spain

Cancelable biometrics are a group of techniques to transform the input biometric to an irreversible feature intentionally using a transformation function and usually a key in order to provide security and privacy in biometric recognition *** transformation is repeatable enabling subsequent biometric *** paper introduces a new idea to be exploited as a transformation function for cancelable biometrics aimed at protecting templates against iterative optimization *** proposed scheme is based on time-varying keys(random biometrics in our case)and morphing *** experimental implementation of the proposed scheme is given for face *** results confirm that the proposed approach is able to withstand leakage attacks while improving the recognition performance.

关键词： Biometrics face recognition template protection morphing security

Automatic mode-locking fiber lasers: progress and perspectives

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Science China(Information Sciences) 2020年第6期63卷 119-142页

作者： Guoqing PU Li ZHANG Weisheng HU Lilin YI State Key lab of Advanced Communication Systems and Networks Shanghai Institute for Advanced Communication and Data Science Shanghai Jiao Tong University

Polarization control in nonlinear polarization rotation based mode-locked fiber lasers is a longterm challenge. Suffering from the polarization drifts induced by environmental disturbances, nonlinear polarization rotation based mode-locked fiber lasers is difficult in continuously operating under the desired pulsation regime thereby substantially hindering their utilizations. The appearance of automatic modelocking techniques brings the light in addressing this challenge. Combining with various algorithms and electrical polarization control, automatic mode-locking techniques resolve the dilemma of nonlinear polarization rotation based mode-locked fiber lasers. We review the research progress of automatic mode-locking techniques in detail. Furthermore, we comment on the perspectives and potential applications of automatic mode-locking techniques.

关键词： nonlinear polarization rotation ultrashort pulses automatic mode-locking electrical polarization control optimization algorithms machine learning time stretch

Learning-based counterfactual explanations for recommendation

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Science China(Information Sciences) 2024年第8期67卷 115-129页

作者： Jingxuan WEN Huafeng LIU Liping JING Jian YU School of Computer and Information Technology Beijing Jiaotong University Beijing Key lab of Traffic data Analysis and Mining Beijing Jiaotong University

Counterfactual explanations provide explanations by exploring the changes in effect caused by changes in cause. They have attracted significant attention in recommender system research to explore the impact of changes in certain properties on the recommendation mechanism. Among several counterfactual recommendation methods, item-based counterfactual explanation methods have attracted considerable attention because of their flexibility. The core idea of item-based counterfactual explanation methods is to find a minimal subset of interacted items(i.e., short length) such that the recommended item would topple out of the top-K recommendation list once these items have been removed from user interactions(i.e., good quality). Usually, explanations are generated by ranking the precomputed importance of items, which fails to characterize the true importance of interacted items due to separation from the explanation generation. Additionally, the final explanations are generated according to a certain search strategy given the precomputed importance. This indicates that the quality and length of counterfactual explanations are deterministic;therefore, they cannot be balanced once the search strategy is fixed. To overcome these obstacles, this study proposes learning-based counterfactual explanations for recommendation(LCER) to provide counterfactual explanations based on personalized recommendations by jointly modeling the factual and counterfactual preference. To achieve consistency between the computation of importance and generation of counterfactual explanations, the proposed LCER endows an optimizable importance for each interacted item, which is supervised by the goal of counterfactual explanations to guarantee its credibility. Because of the model's flexibility, the trade-off between quality and length can be customized by setting different proportions. The experimental results on four real-world datasets demonstrate the effectiveness of the proposed LCER over several

关键词： recommender system explainable recommendation item-based explanation counterfactual inference counterfactual explanation

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