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作者： Chen, Renhai Wang, Yi Hu, Jingtong Liu, Duo Shao, Zili Guan, Yong Beijing Advanced Innovation Center for Imaging Technology Beijing100048 China College of Information Engineering Capital Normal University Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74074 United States College of Computer Science Chongqing University Chongqing400044 China Embedded Systems and CPS Laboratory Department of Computing Hong Kong Polytechnic University Hong Kong

I/O is becoming one of major performance bottlenecks in NAND-flash-based mobile devices. Novel nonvolatile memories (NVMs), such as phase change memory and spin-transfer torque random access memory, can provide fast read/write operations. In this paper, we propose a unified NVM/flash architecture to improve the I/O performance. A transparent scheme, virtualized flash (vFlash), is also proposed to manage the unified architecture. Within vFlash, interapp and intra-app techniques are proposed to optimize the application performance by exploiting the historical locality and I/O access patterns of applications. Since vFlash is on the bottom of the I/O stack, the application features will be lost. Therefore, we also propose a cross-layer technique to transfer the application information from the application layer to the vFlash layer. The proposed scheme is evaluated based on an Android platform, and the experimental results show that the proposed scheme can effectively improve the I/O performance of mobile devices. © 1982-2012 IEEE.

关键词： Phase change memory

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Semi-hierarchical naive Bayes classifier

Semi-hierarchical naive Bayes classifier

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International Joint Conference on Neural Networks

作者： Hasna Njah Salma Jamoussi Walid Mahdi Multimedia InfoRmation system and Advanced Computing Laboratory (MIRACL) University of Sfax College of Computers and Information Technology Taif University

ISBN: (纸本)9781509006212

The classification of high dimensional data is an arduous task especially with the emergence of high quality data acquisition techniques. This problem is accentuated when the whole set of features is needed to learn a classifier such as the case of genomic data. The Bayesian approach is suitable for these applications because it represents graphically and statistically the dependencies between the features. Unfortunately, learning a Bayesian classifier using a high number of features does not ensure a tradeoff between the dimensions' reduction, the semantic of the model and the predictive performance. We propose a new semi-hierarchical naive Bayes that uses the latent variables for abstracting the features of a given dataset in order to reduce the dimensionality. These variables are suitable for finding graphically and semantically analyzable models. We combined them with the observed variables in a tree-augmented naive Bayes structure in order to improve the prediction accuracy. An excessive experimental study showed that our method is suitable for high dimensional data and overcomes the existing methods.

关键词： Bayesian classifier Latent variable High dimensional data Bayesian classifier dimensional data graphically Bayes tradeoffs

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Cooperative human-machine data extraction from biological collections

Cooperative human-machine data extraction from biological co...

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IEEE International Conference on e-Science and Grid computing

作者： Icaro Alzuru Andréa Matsunaga Maurício Tsugawa José A. B. Fortes Advanced Computing and Information Systems(ACIS) Laboratory University of Florida Gainesville USA

Historical data sources, like medical records or biological collections, consist of unstructured heterogeneous content: handwritten text, different sizes and types of fonts, and text overlapped with lines, images, stamps, and sketches. The information these documents can provide is important, from a historical perspective and mainly because we can learn from it. The automatic digitization of these historical documents is a complex machine learning process that usually produces poor results, requiring costly interventions by experts, who have to transcribe and interpret the content. This paper describes hybrid (Human- and Machine-Intelligent) workflows for scientific data extraction, combining machine-learning and crowdsourcing software elements. Our results demonstrate that the mix of human and machine processes has advantages in data extraction time and quality, when compared to a machine-only workflow. More specifically, we show how OCRopus and Tesseract, two widely used open source Optical Character Recognition (OCR) tools, can improve their accuracy by more than 42%, when text areas are cropped by humans prior to OCR, while the total time can increase or decrease depending on the OCR selection. The digitization of 400 images, with Entomology, Bryophyte, and Lichen specimens, is evaluated following four different approaches: processing the whole specimen image (machine-only), processing crowd cropped labels (hybrid), processing crowd cropped fields (hybrid), and cleaning the machine-only output. As a secondary result, our experiments reveal differences in speed and quality between Tesseract and OCRopus.

关键词： Optical character recognition software Data mining Crowdsourcing Biology Image resolution Character recognition Training

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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An UML class recommender system for software design

An UML class recommender system for software design

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ACS/IEEE International Conference on Computer systems and Applications

作者： Akil Elkamel Mariem Gzara Hanene Ben-Abdallah Faculty of Computing and Information Technology Northern Border University Rafha KSA Multimedia InfoRmation systems and Advanced Computing Laboratory University of Sfax Tunisia Higher School of Computer Science and Mathematics University of Monastir Tunisia Faculty of Computing and Information Technology King Abdulaziz University Jeddah KSA

Recommendation systems provide suggestions for items that are potentially interesting for a user in a given context. The provided recommendations are extracted generally from a huge amount of data collected from several sources of information. Thus a recommendation system requires firstly a pre-treatment step to prepare the data and secondly the application of some techniques such as data mining techniques to handle and extract the knowledge to be recommended to the user from the data. Our contribution consists on proposing a Recommendation System for Software Engineering (RSSE). This system recommends UML classes in the design phase of UML classes diagrams. Our RSSE is composed by two main phases: an off-line phase in which we use a clustering algorithm to partition UML classes collected from several UML classes diagrams based on the semantic relations existing between their characteristics. We have defined a metric that measures the similarity between UML classes. The second is an online phase in which we use the obtained clusters of UML classes to propose suggestions to the user based on elements added to his UML classes diagram under construction. The proposed system is then experimentally evaluated by using a UML classes corpus collected from several UML classes diagrams. The experimental evaluation shows very encouraging ratio of useful recommendations.

关键词： Unified modeling language Software Software engineering Recommender systems Computer architecture Databases Collaboration

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ETL transformation algorithm for Facebook opinion data 11

ETL transformation algorithm for Facebook opinion data

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11th International Conference on Web information systems and Technologies, WEBIST 2015

作者： Walha, Afef Ghozzi, Faiza Gargouri, Faïez Multimedia InfoRmation Systems and Advanced Computing Laboratory Sfax Tunisia Institute of Computer Science and Multimedia Sfax University Sfax Tunisia

ISBN: (纸本)9789897581069

Considered as a rich source of information, social networking sites have been created lot of buzz because people share and discuss their opinions freely. Sentiment analysis is used for knowing voice or response of crowd for products, services, organizations, individuals, events, etc. Due to their importance, people opinions are analyzed in several domains including information retrieval, semantic web, text mining. These researches define new classification techniques to assign positive or negative opinion. Decisional systems like WeBhouse, known by their data-consuming must be enriched by this kind of pertinent opinions to give better help to decision makers. Nevertheless, cleaning and transformation processes recognized by several approaches as a key of WeBhouse development, don't deal with sentiment analysis. To fulfill this gap, we propose a new analysis algorithm which determines user's sentiment score of a post shared on the social network Facebook. This algorithm analyzes user's opinion depending on opinion terms and emoticons included in his comments. This algorithm is integrated in transformation process of ETL approach.

关键词： Sentiment analysis

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Deep Kernel-SVM Network

Deep Kernel-SVM Network

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International Joint Conference on Neural Networks

作者： Ilyes Rebai Yassine BenAyed Walid Mahdi Multimedia InfoRmation system and Advanced Computing Laboratory University of Sfax Tunisia CIT College Computer Science Departement Taif University KSA Saudi Arabia

ISBN: (纸本)9781509006212

Deep learning techniques have claimed state-of-the-art results in a wide range of tasks, including classification. Despite the promising results, there are limitations for these large networks. In fact, deep neural networks have a poor generalisation performance on small data sets, such as biologic data. This paper describes a new machine learning algorithm for classification tasks. We introduce a Multi-Layer Multiple Kernel Learning (ML-MKL) framework. The input data are first transformed through a set of weighted non-linear kernel functions in a multilayer structure. Then, an SVM classifier is used to make the final decision. The proposed network is trained to minimize the error function. Indeed, we propose to optimize the network over an adaptive backpropagation algorithm. The generalization performance of the proposed method is compared over various state-of-the-art multiple kernel algorithms on several benchmark and two real world applications, including object recognition and spoken language recognition. Experimental results show that the ML-MKL generally outperforms existing kernel methods.

关键词： Kernel Support vector machines Machine learning Optimization Neural networks Nonhomogeneous media Backpropagation algorithms

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Comment on "Inherent security of phase coding quantum key distribution systems against detector blinding attacks" [Laser Phys. Lett. 15, 095203 (2018)]

arXiv

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arXiv 2018年

作者： Fedorov, Aleksey Gerhardt, Ilja Huang, Anqi Jogenfors, Jonathan Kurochkin, Yury Lamas-Linares, Antía Larsson, Jan-Åke Leuchs, Gerd Lydersen, Lars Makarov, Vadim Skaar, Johannes Russian Quantum Center Skolkovo Moscow143025 Russia QRate Skolkovo Moscow143025 Russia QApp Skolkovo Moscow143025 Russia Institute of Physics University of Stuttgart Institute for Quantum Science and Technology Pfaffenwaldring 57 StuttgartD-70569 Germany Max Planck Institute for Solid State Research Heisenbergstraße 1 StuttgartD-70569 Germany Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Department of Electrical Engineering Linköping University LinköpingSE-58183 Sweden Texas Advanced Computing Center University of Texas at Austin AustinTX United States Max Planck Institute for the Science of Light University of Erlangen-Nürnberg ErlangenD-91058 Germany Kringsjåvegen 3E TrondheimNO-7032 Norway National University of Science and Technology MISIS Moscow119049 Russia Department of Technology Systems University of Oslo Box 70 KjellerNO-2027 Norway

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VMKDO: Verifiable multi-keyword search over encrypted cloud data for dynamic data-owner

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Peer-to-Peer Networking and Applications 2016年第2期11卷 287-297页

作者： Yinbin Miao Jianfeng Ma Ximeng Liu Zhiquan Liu Limin Shen Fushan Wei State Key Laboratory of Integrated Services Networks Xidian University Xi’an China School of Information Systems Singapore Management University Singapore Singapore State Key Laboratory of Mathematical Engineering and Advanced Computing The PLA Information Engineering University Zhengzhou China

The advantages of cloud computing encourage individuals and enterprises to outsource their local data storage and computation to cloud server, however, data security and privacy concerns seriously hinder the practicability of cloud storage. Although searchable encryption (SE) technique enables cloud server to provide fundamental encrypted data retrieval services for data-owners, equipping with a result verification mechanism is still of prime importance in practice as semi-trusted cloud server may return incorrect search results. Besides, single keyword search inevitably incurs many irrelevant results which result in waste of bandwidth and computation resources. In this paper, we are among the first to tackle the problems of data-owner updating and result verification simultaneously. To this end, we devise an efficient cryptographic primitive called as verifiable multi-keyword search over encrypted cloud data for dynamic data-owner scheme to protect both data confidentiality and integrity. Rigorous security analysis proves that our scheme is secure against keyword guessing attack (KGA) in standard model. As a further contribution, the empirical experiments over real-world dataset show that our scheme is efficient and feasible in practical applications.

关键词：

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Decision tree transformation for knowledge warehousing 17

Decision tree transformation for knowledge warehousing

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17th International Conference on Enterprise information systems, ICEIS 2015

作者： Ayadi, Rim Hachaichi, Yasser Alshomrani, Saleh Feki, Jamel Multimedia InfoRmation Systems and Advanced Computing Laboratory Sakiet Ezzit Tunisia University of Sfax Sfax Tunisia Faculty of Computing and IT University of Jeddah Jeddah Saudi Arabia

ISBN: (纸本)9789897580970

Explicit knowledge extracted from data, formalized tacit knowledge from experts or even knowledge existing in business sources may be in several heterogeneous formal representations and structures: as rules, models, functions, etc. However, a knowledge warehouse should solve this structural heterogeneity before storing knowledge. This requires specific tasks of harmonizing. This paper first presents our proposed definition and architecture of a knowledge warehouse, and then presents some languages for knowledge representations as particular the MOT (Modeling with Object Types) language. In addition, we suggest a metamodel for the MOT, and a metamodel for the explicit knowledge obtained using decision trees technique. As we aim to represent knowledge having different modeling formalisms into MOT, as a unified model, then we suggest a set of transformation rules that assure the move from the decision tree source model into the MOT target model. This work is still in progress, it is currently completed with tranformations for additional.

关键词： Data mining

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