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2016 IEEE International Conference on Progress in Informatics and Computing

作者： Siyuan Lu Hainan Wang Xueyan Wu Shuihua Wang School of Computer Science and Technology Nanjing Normal University State Key Lab of CAD & CG Zhejiang University Key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University Key Laboratory of Statistical information technology and data mining State Statistics Bureau Department of Electrical Engineering The City College of New YorkCUNY

Magnetic resonance imaging(MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography(CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies(WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine(OS-ELM) was trained to differentiate pathological brains from the healthy *** experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%,which suggested that our method is effective.

关键词： magnetic resonance imaging wavelet entropy online sequential extreme learning machine Classification pattern recognition

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VID Join： Mapping Trajectories to Points of Interest to Support Location-Based Services

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Journal of Computer Science & Technology 2015年第4期30卷 725-744页

作者：商烁谢珂心郑凯刘家俊文继荣 Department of Computer Science China University of Petroleum Beijing 102249 China School of Information Technology and Electrical Engineering The University of Queensland Brisbane QLD 4072 Australia School of Computer Science and Technology Soochow University Suzhou 215006 China Commonwealth Scientific and Industrial Research Organisation Kenmore QLD 4069 Australia Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Beijing 100080 China

Variable influence duration （VID） join is a novel spatio-temporal join operation between a set T of trajectories and a set P of spatial points. Here, trajectories are traveling histories of moving objects （e.g., travelers）, and spatial points are points of interest （POIs, e.g., restaurants）. VID join returns all pairs of （τs, p） if τs is spatially close to p for a long period of time, where τs is a segment of trajectory τ ∈ T and p ∈ P. Each returned （τs, p） implies that the moving object associated with τs stayed at p （e.g., having dinner at a restaurant）. Such information is useful in many aspects, such as targeted advertising, social security, and social activity analysis. The concepts of influence and influence duration are introduced to measure the spatial closeness between τ and p, and the time spanned, respectively. Compared to the conventional spatio-temporal join, the VID join is more challenging since the join condition varies for different POIs, and the additional temporal requirement cannot be indexed effectively. To process the VID join e？ciently, three algorithms are developed and several optimization techniques are applied, including spatial duplication reuse and time duration based pruning. The performance of the developed algorithms is verified by extensive experiments on real spatial data.

关键词： trajectory spatial database spatial join spatio-temporal join

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Introduction

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2016年 9792 LNCS卷 1-8页

作者： González-Beltrán, Alejandra Osborne, Francesco Peroni, Silvio Oxford e-Research Centre University of Oxford Oxford United Kingdom Data Science Group Knowledge Media Institute The Open University Milton Keynes United Kingdom Digital and Semantic Publishing Laboratory Department of Computer Science and Engineering University of Bologna Bologna Italy

We provide the motivation and overview of the SAVE-SD workshop series and introduce the manuscripts that were accepted as part of these proceedings of the SAVE-SD 2016 edition. © Springer International Publishing...

ISBN: (纸本)9783319536361

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Publish me and protect me: Personalized and flexible location privacy protection in mobile social networks

Publish me and protect me: Personalized and flexible locatio...

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International Workshop on Quality of Service

作者： Yao Wu Hui Peng Xiaoying Zhang Hong Chen Cuiping Li Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing China

With the increasing proliferation of the Mobile Social Networks (MSN) and the Location Based Service (LBS), location privacy has attracted broad attention in recent years. Most researches have been done with the assumption that the server is untrusted and a trusted third party is introduced to protect the user location privacy when a user sends queries for the location service. In this paper, we reconsider this assumption and propose a Personalized and Flexible location privacy protection Model (PFM) based on user relationship strength. We conduct researches in the situation that the server is trusted while malicious users in the MSN can disguise as a friend to break location privacy. We present an entropic TF-IDF based approach to measure the bi-directional relationship strength and propose probability distribution based cloaking model to protect user location privacy. We thoroughly evaluate our methods based on Quality of Privacy (QoP) via real and synthetic data.

关键词： Legged locomotion Weight measurement

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Pathological brain detection based on online sequential extreme learning machine

Pathological brain detection based on online sequential extr...

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Siyuan Lu Hainan Wang Xueyan Wu Shuihua Wang School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China State Key Lab of CAD & CG Zhejiang University Hangzhou Zhejiang China Key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University Changchun Jilin China Key Laboratory of Statistical information technology and data mining State Statistics Bureau Chengdu Sichuan China Department of Electrical Engineering CUNY New York NY USA

ISBN: (纸本)9781509034857

Magnetic resonance imaging (MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography (CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies (WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine (OS-ELM) was trained to differentiate pathological brains from the healthy controls. The experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%, which suggested that our method is effective.

关键词： Entropy Discrete wavelet transforms Feature extraction Pathology Training Magnetic resonance imaging Diseases

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Publisher Correction: Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

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Eye (London, England) 2024年第11期38卷 2229-2231页

作者： Konrad Pesudovs Hugh R. Taylor Tabassom Sedighi Rupert R. A. Bourne Shahina Pardhan Van Charles Lansingh John H. Kempen Ian Tapply Arthur G. Fernandes Maria Vittoria Cicinelli Alessandro Arrigo Nicolas Leveziel Serge Resnikoff Seth Flaxman Mukkharram M. Bikbov Tasanee Braithwaite Alain Bron Ching-Yu Cheng Monte A. Del Monte Joshua R. Ehrlich Leon B. Ellwein David Friedman João M. Furtado Gus Gazzard Ronnie George M. Elizabeth Hartnett Jost B. Jonas Rim Kahloun Moncef Khairallah Rohit C. Khanna Janet Leasher Julie-Anne Little Vinay Nangia Michal Nowak Tunde Peto Pradeep Ramulu Fotis Topouzis Mitiadis Tsilimbaris Ya Xing Wang Ningli Wang Paul Svitil Briant Theo Vos Katrin Burkart Kaleb Coberly Xiaochen Dai Stephen S. Lim Tomislav Mestrovic Ali H. Mokdad Christopher J. L. Murray Jaimie D. Steinmetz Awoke Misganaw Yohannes Habtegiorgis Abate Mohammad Abdollahi Mozhan Abdollahi Ayele Mamo Abebe Olumide Abiodun Richard Gyan Aboagye Woldu Aberhe Abrha Hasan Abualruz Hiwa Abubaker Ali Eman Abu-Gharbieh Salahdein Aburuz Tadele Girum Girum Adal Mesafint Molla Adane Isaac Yeboah Addo Qorinah Estiningtyas Sakilah Adnani Muhammad Sohail Afzal Muhammad Umair Shahin Aghamiri Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Ali Ahmadi Siamak Sabour Ayman Ahmed Haroon Ahmed Ahmad Samir Alfaar Abid Ali Syed Shujait Shujait Ali Awais Altaf Hubert Amu Emmanuel Manu Sofia Androudi Rodrigo Anguita Abhishek Anil Rimple Jeet Kaur Muhammad Aaqib Shamim Saeid Anvari Anayochukwu Edward Anyasodor Francis Appiah Jalal Arabloo Haleh Ayatollahi Mosab Arafat Damelash Areda Reza Arefnezhad Brhane Berhe Aregawi Akeza Awealom Asgedom Tahira Ashraf Seyyed Shamsadin Athari Bantalem Tilaye Tilaye Atinafu Birhan Tsegaw Taye Maha Moh’d Wahbi Atout Alok Atreya Ahmed Y. Azzam Hassan Babamohamadi Sara Bagherieh Yogesh Bahurupi Atif Amin Baig Shumaila Nargus Biswajit Banik Mainak Bardhan Saurav Basu Kavita Batra Nebiyou Simegnew Bayileyegn Fatemeh Bazvand Addisu Shunu Beyene Awoke Masrie Asrat Derese Devidas S. Bhagat Akshaya Srikanth Bhagavathula Pankaj Bh Medicine & Health University of New South Wales Sydney NSW Australia School of Optometry and Vision Science University of New South Wales Sydney NSW Australia School of Population and Global Health University of Melbourne Carlton VIC Australia Vision and Eye Research Institute Anglia Ruskin University Cambridge UK HelpMeSee Instituto Mexicano de Oftalmologia Santiago de Querétaro Mexico University of Miami Gables FL USA University of Utah Salt Lake City UT USA Chief Medical Office HelpMeSee New York NY USA Mexican Institute of Ophthalmology Queretaro Mexico Department of Ophthalmology Massachusetts Eye and Ear/Shepens Eye Research Institute/Harvard Medical School Boston MA USA Eye Unit MyungSung Christian Medical Center (MCM) Comprehensive Specialized Hospital and MyungSun Medical College Addis Ababa Ethiopia Department of Ophthalmology Addis Ababa University Addis Ababa Ethiopia Sight for Souls Bellevue WA USA Department of Ophthalmology Harvard University Boston MA USA Eye Unit MyungSung Medical College Addis Ababa Ethiopia Addenbrooke’s Hospital Cambridge UK Department of Ophthalmology Cambridge University Hospitals Cambridge UK Federal University of Sao Paolo Sao Paolo SP Brazil University of Calgary Calgary AB Canada Department of Ophthalmology and Visual Sciences Federal University of São Paulo Sao Paulo Brazil School of Medicine Vita-Salute San Raffaele University Milan Italy Department of Ophthalmology IRCCS San Raffaele Scientific Institute Milan Italy Department of Ophthalmology San Raffaele Scientific Institute Milano Italy IRCCS San Raffaele Scientific Institute Vita-Salute University Milan Italy Scientific Institute San Raffaele Hospital Vita-Salute University Milan Italy University of Poitiers Poitiers France CHU de Poitiers Poitiers France Ophthalmology Department CHU de Poitiers (Poitiers University Hospital) Poitiers France Unité 1084 National Institute of Health and Medical Research (INSERM) Poitiers France

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Enable Privacy Preservation and Result Verification for Top-k Query in Two-Tiered Sensor Networks

Enable Privacy Preservation and Result Verification for Top-...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Hui Peng Xiaoying Zhang Hong Chen Yuncheng Wu Yao Wu Juru Zeng Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing China

ISBN: (纸本)9781467379533

Wireless sensor network is an important part of the Internet of Things. Preservation of privacy and integrity in wireless sensor networks is extremely urgent and challenging. This paper proposes an efficient mechanism, called PTK, for define and solve the practical and challenging problem of privacy-preserving and verifiable Top-k query processing in two-tier sensor network. PTK prevents adversaries from gaining sensitive information of both queries issued by users and data collected by sensor nodes while allows the ink to verify whether the results are valid. It offers confidentiality of queries and data by constructing a special code, and also achieves high defense capability against the background analysis attack through the implementation of obfuscation coding mechanism. Moreover, PTK provides integrity verification by the correlation among data. Finally, the experimental results show the high performance of proposed schemes in terms of privacy preservation and query efficiency.

关键词： data privacy Privacy Wireless sensor networks Protocols Query processing Cryptography data models

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Enable Privacy Preservation for k-NN Query in Two-tiered Wireless Sensor Networks

Enable Privacy Preservation for k-NN Query in Two-tiered Wir...

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IEEE International Conference on Communications

作者： Hui Peng Xiaoying Zhang Hong Chen Yao Wu Juru Zeng Deying Li Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education School of Information Renmin University of China

ISBN: (纸本)9781467364300

Wireless sensor network is an important part of the Internet of Things. Preservation of privacy and integrity in wireless sensor networks is extremely urgent and challenging. To address this problem, we propose PPKN, an efficient and privacy-preserving k-NN query protocol in two-tiered wireless sensor networks. Our proposal prevents adversaries from gaining sensitive information of both queries issued by users and data collected by sensor nodes while allows the sink to verify whether results are valid. It offers confidentiality of queries and data by constructing a special code, provides integrity verification by the correlation among data. Moreover, by the implementation of KNQ query framework, PPKN also achieves high efficient in query response and energy consumption. Finally, the theoretical analysis and experiment results show the high performance of PPKN in terms of privacy preservation and query efficiency.

关键词： Wireless sensor networks Energy consumption data privacy Privacy Protocols Cryptography Storage management

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Controlled and collaborative presentation via tablet PCs for classroom and meeting room uses

Controlled and collaborative presentation via tablet PCs for...

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International Joint Conference on Computer Science and Software engineering (JCSSE)

作者： Prissadang Suta Pornchai Mongkolnam Wichai Eamsinvattana Data and Knowledge Engineering Laboratory King Mongkut's University of Technology Thonburi Bangkok Thailand

We propose a simple yet effective controlled and collaborative presentation system using tablet PCs for a classroom or meeting room use. The system does not require the Internet to be functioning, but simply needs a local Wi-Fi connection via an access point. This makes our system invaluably suitable for the classroom uses, especially in some rural parts of the world where the Internet accesses are impossible. It allows a presenter to synchronously change presentation slides of viewers in the control mode where the presenter has the control of a current page. The viewers, on the other hand, can get out of the control mode and be in the edit mode where they can add annotations or choose to view other pages asynchronously different from the presenter. The viewers could choose some or all of their annotations to be shared and shown to public through the presenter. The presenter could select and allow any annotations from the viewers to be shown on a projector's screen. This would help facilitate collaboration among the viewers. Each viewer can save the presenter's annotations, allowable other viewers' annotations and his annotations altogether in one file for a further review or later use. The system had been evaluated on three key aspects, including usefulness, user interface, and performance, by 26 participants. On the usefulness, 81 percent of the users agreed or strongly agreed with the system. On the user interface, only 56 percent agreed or strongly agreed, while 64 percent agreed or strongly agreed on the system performance.

关键词： Tablet computers Collaboration Browsers Internet Servers User interfaces Portable computers

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Monitoring and visualizing the daily activities and in-house locations using smartphone

Monitoring and visualizing the daily activities and in-house...

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International Joint Conference on Computer Science and Software engineering (JCSSE)

作者： Sittichai Sukreep Pornchai Mongkolnam Chakarida Nukoolkit Data and Knowledge Engineering Laboratory King Mongkut's University of Technology Thonburi Bangkok Thailand

Fall is a leading cause of accidental injury deaths and a key cause of significant health problems, especially for elderly people who live alone. To assist those people for seeking help when falling and keeping records of key daily movements, we propose a simple yet effective system to monitor the daily activities and in-house locations using smartphone. We also test the system for the optimum arrangement of our Wi-Fi access points. First, the data mining classification is applied through the threshold model to detect the common activities like sitting, standing, lying down, walking, running, walking up/downstairs, falling, and in-house locations. Then the system gives out a warning when unhealthy activities or falls are detected, using an alarm sound and short messages sent to those who are in contact or caretakers. In addition, it provides various forms of visualization such as a health risk level summary, daily activity summary, and in-house location summary.

关键词： Monitoring Real-time systems Legged locomotion Sensors IEEE 802.11 Standard data mining Accuracy

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