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2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

作者： Das, Polash Kumar Hu, Bin Liu, Chang Cui, Kaixin Ranjan, Prabhat Xiong, Gang School of Electronic and Information Engineering South China University of Technology Guangzhou China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Science Beijing China College of Computer Science Sichuan University Chengdu China Cloud Computing Center Chinese Academy of Science Dongguan China

ISBN: (纸本)9781728128535

In biometrics, face recognition methods are achieving momentum with recent progress in the computer vision(CV). Face recognition is widely used in the identification of an individual's identity. Unfortunately, in recent research work has revealed this face biometrics system is unprotected to spoofing attacks using by very low price instrument such as printed 2D photos attack, 3D masking attack and taking videos using smart devices (reply attack). Therefore, a Liveness Attack Detection (LAD) approach is needed to improve the high-quality security of face recognition system. Most of the earlier worked LAD methods for face anti-spoofing methods have highlight on using the handcrafted features, which are developed by expert knowledge of researcher. As example Gabor filter, Histogram of Oriented Gradients, local ternary pattern, and the Local Binary Pattern. Because of that, the extracted features consider limited factors of the problem, yielding a capture accuracy that is very low and changes with the point of presentation in attack face images. The deep learning method has developed in the computer vision research community, which is proven to be suitable for automatically training. In this article, we approach to mix or combine the handcrafted features and deep neural network features to design the discriminant face spoofing detection. The handcrafted features were based on LBP analysis. We examine the features information from the brightness and the chrominance channels using LBP descriptor. In deep features, we present an approach based on pre-trained convolutional neural network VGG-16 model using static features to recognize video and printed(2D) photo attacks. By attaching this two types of image features on our dataset and public databases, we get good results to identify real and attack images feature, called hybrid features, which has better discrimination ability to understand spoofing image feature. © 2019 IEEE.

关键词： Local binary pattern

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A QoS-Based Trust Approach for Service Selection and Composition via Bayesian Networks

A QoS-Based Trust Approach for Service Selection and Composi...

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IEEE International Conference on Web Services (ICWS)

作者： Mohamad Mehdi Nizar Bouguila Jamal Bentahar Engineering and Computer Science Concordia University Montreal Canada Concordia Institute for Information Systems Engineering Montreal Canada

Service oriented computing is being increasingly exploited in the architecture of current web applications. The interactions among the deployed web services are becoming vital to accomplish heterogeneous and compound business goals. Service selection and composition are two common tasks which are highly influenced by the quality of these interactions. Thus, assigning web services QoS-based trust scores that are incrementally updated, provides a means to assist both tasks. Then, services with higher trust scores are more likely to be selected than those with smaller ones. They are, additionally, more prone to be incorporated as part of composite services. In this paper, we propose a probabilistic approach based on Bayesian networks (BN) to learn the composition structure of composite services and compute QoS-based trust scores in an online setting. The learning of the BN structure and parameters is based on modeling the QoS, which is represented by the BN's variables, using a multinomial generalized Dirichlet distribution (MGDD). The effectiveness of our approaches is empirically assessed using real and synthetic data. Our experimental results show that MGDD provides a flexible and accurate representation of the QoS. They also prove the capability of the BN approach to learn the composition structure, and further the responsibility of the constituent services in the quality of the composite service even when their QoS is partially observed.

关键词： Web services Quality of service Bayes methods Computational modeling Probabilistic logic Vectors Measurement

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BLSTM Neural Network Based Word Retrieval for Hindi Documents

BLSTM Neural Network Based Word Retrieval for Hindi Document...

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International Conference on Document Analysis and Recognition

作者： Raman Jain Volkmar Frinken C.V. Jawahar R. Manmatha International Institute of Information Technology Hyderabad Center for Visual Information Technology Hyderabad India Institute of Computer Science and Applied Mathematics University of Bern Bern Switzerland Department of Computer Science University of Massachusetts Amherst MA USA

Retrieval from Hindi document image collections is a challenging task. This is partly due to the complexity of the script, which has more than 800 unique ligatures. In addition, segmentation and recognition of individual characters often becomes difficult due to the writing style as well as degradations in the print. For these reasons, robust OCRs are non existent for Hindi. Therefore, Hindi document repositories are not amenable to indexing and retrieval. In this paper, we propose a scheme for retrieving relevant Hindi documents in response to a query word. This approach uses BLSTM neural networks. Designed to take contextual information into account, these networks can handle word images that can not be robustly segmented into individual characters. By zoning the Hindi words, we simplify the problem and obtain high retrieval rates. Our simplification suits the retrieval problem, while it does not apply to recognition. Our scalable retrieval scheme avoids explicit recognition of characters. An experimental evaluation on a dataset of word images gathered from two complete books demonstrates good accuracy even in the presence of printing variations and degradations. The performance is compared with baseline methods.

关键词： Vectors Training Optical character recognition software Robustness Neural networks Character recognition Image segmentation

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Large memory high performance computing enables comparison across human gut microbiome of patients with autoimmune diseases and healthy subjects 13

Large memory high performance computing enables comparison a...

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Conference on Extreme science and Engineering Discovery Environment, XSEDE 2013

作者： Wu, Sitao Li, Weizhong Smarr, Larry Nelson, Karen Yooseph, Shibu Torralba, Manolito Center for Research for Biological Systems United States California Institute for Telecommunication and Information Technology United States Department of Computer Science and Engineering United States J. Craig Venter Institute Rockville MD United States J. Craig Venter Institute San Diego CA United States

ISBN: (纸本)9781450321709

Microbial communities that live on the outside and inside of the human body dramatically influence human health and diseases. In recent years, major progress has been made in understanding the human microbiome communities through projects such as the Human Microbiome Project (http://***/hmp/), using next generation sequencing technologies and metagenomic approaches. In this paper, we describe a comparative computational analysis of 183 human gut microbiome sequence datasets, drawn from healthy individuals as well as those with autoimmune diseases. About 2.4 TB of Illumina deep sequencing metagenomic data were analyzed using computational workflows we developed, which run multiple steps of data- and comput-ingintensive analyses such as mapping, sequence assembly, gene identification, clustering and functional annotations. The analyses were carried out on the Gordon supercomputer at the San Diego Supercomputer Center (SDSC), using ∼180,000 core hours and tens of TB storage space. Our analysis reveals the detailed microbial composition, dynamics, and functional profiles of the samples and provides new insight into how to correlate microbial profiles with human health and disease states. © 2013 by the Association for Computing Machinery, Inc.

关键词： Supercomputers

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Demand Response Estimation based on Aggregate Data from Heterogeneous Users

Demand Response Estimation based on Aggregate Data from Hete...

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Mediterranean Conference on Control and Automation

作者： Donatello Materassi Mardavij Roozbehani Munther A. Dahleh Department of Electrical Engineering and Computer Science University of Tennessee Laboratory for Information and Decision Systems is with Massachusetts Institute of Technology

ISBN: (纸本)9781467383479

The article presents methodologies for the pre-diction of the aggregate consumption of a large number of heterogeneous energy users with deferrable loads. It is assumed that the users respond to an identical coordinating signal (e.g. a price) broadcast by the system operator. The main contributions of the article are two: (i) the presence of heterogeneous users is explicitely addressed by considering the possibility of several "classes" of consumers;(ii) the described methodologies provide predictions and estimates that have the advantage of making use of aggregate data only. When a dynamic response mechanism is implemented, the presence of consumers with deferrable loads introduces memory in the system in the form of the backlogged demand that has to be satisfied in the near future. From the perspective of the system operator, an accurate estimate of the current backlogged demand is of paramount importance, since it allows one to better predict what the aggregate consumption will be for a given coordinating signal. Thus, mathematical models need to be derived in order to predict future aggregate consumption using the accessible information. Unfortunately, consumers tends to be highly heterogeneous, depending on several factors including individual preferences, the interrupt-ability or the criticality of the tasks and the presence of hard deadlines. These factors can not be measured directly. Indeed, both privacy issues and practical constraints on processing and communicating data are likely to prevent utility companies and system operators from collecting and using measurements at the individual user level. Thus, methodologies for predicting aggregate demand response from aggregate data, such as the ones suggested in the article, can help overcome all these challenges.

关键词： Aggregates Load management Markov processes Mathematical model Data privacy Probabilistic logic Optimization

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Iris Liveness Detection Using Fragmental Energy of Haar Transformed Iris Images Using Ensemble of Machine Learning Classifiers

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computer Modeling in Engineering & sciences 2023年第7期136卷 323-345页

作者： Smita Khade Shilpa Gite Sudeep D.Thepade Biswajeet Pradhan Abdullah Alamri Symbiosis International(Deemed University) Symbiosis Institute of TechnologyPune412115India Symbiosis Centre for Applied Artificial Intelligence Symbiosis International(Deemed University)Pune412115India Computer Engineering Pimpri Chinchwad College of EngineeringPune411044India Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) School for Civil and Environmental EngineeringFaculty of Engineering and Information TechnologyUniversity of Technology SydneySydneyNew SouthWales2007Australia Earth Observation Center Institute of Climate ChangeUniversity Kebangsaan MalaysiaUKMBangiSelangor43600Malaysia Department of Geology&Geophysics College of ScienceKing Saud UniversityP.O.Box 2455Riyadh11451Saudi Arabia

Contactless verification is possible with iris biometric identification,which helps prevent infections like COVID-19 from *** systems have grown unsteady and dangerous as a result of spoofing assaults employing contact lenses,replayed the video,and print *** work demonstrates an iris liveness detection approach by utilizing fragmental coefficients of Haar transformed Iris images as signatures to prevent spoofing attacks for the very first time in the identification of iris *** assorted feature creation ways are studied in the presented solutions,and these created features are explored for the training of eight distinct machine learning classifiers and *** predicted iris liveness identification variants are evaluated using recall,F-measure,precision,accuracy,APCER,BPCER,and *** standard datasets were used in the *** main contribution of our study is achieving a good accuracy of 99.18%with a smaller feature *** fragmental coefficients of Haar transformed iris image of size 8∗8 utilizing random forest algorithm showed superior iris liveness detection with reduced featured vector size(64 features).Random forest gave 99.18%***,conduct an extensive experiment on cross datasets for detailed *** results of our experiments showthat the iris biometric template is decreased in size tomake the proposed framework suitable for algorithmic verification in real-time environments and settings.

关键词： Iris images liveness identification Haar transform machine learning biometric feature formation ensemble model

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Space Shift Keying with Reconfigurable Intelligent Surfaces: Phase Configuration Designs and Performance Analysis

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2021年 2卷 322-333页

作者： Li, Qiang Wen, Miaowen Wang, Shuai Alexandropoulos, George C. Wu, Yik-Chung Department of Electronic Engineering College of Information Science and Technology Jinan University Guangzhou510632 China School of Electronic and Information Engineering South China University of Technology Guangzhou510640 China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Sifakis Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens15784 Greece Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong Hong Kong

Reconfigurable intelligent surface (RIS)-assisted transmission and space shift keying (SSK) appear as promising candidates for future energy-efficient wireless systems. In this article, two RIS-based SSK schemes are proposed to efficiently improve the error and throughput performance of conventional SSK systems, respectively. The first one, termed RIS-SSK with passive beamforming (RIS-SSK-PB), employs an RIS for beamforming and targets the maximization of the minimum squared Euclidean distance between any two decision points. The second one, termed RIS-SSK with Alamouti space-time block coding (RIS-SSK-ASTBC), employs an RIS for ASTBC and enables the RIS to transmit its own Alamouti-coded information while reflecting the incident SSK signals to the destination. A low-complexity beamformer and an efficient maximum-likelihood (ML) detector are designed for RIS-SSK-PB and RIS-SSK-ASTBC, respectively. Approximate expressions for the average bit error probabilities of the source and/or the RIS are derived in closed-form assuming ML detection. Extensive computer simulations are conducted to verify the performance analysis. Results show that RIS-SSK-PB significantly outperforms the existing RIS-free and RIS-based SSK schemes, and RIS-SSK-ASTBC enables highly reliable transmission with throughput improvement. © 2020 IEEE.

关键词： Beamforming

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Towards Building Knowledge by Merging Multiple Ontologies with CoMerger: A Partitioning-based Approach

arXiv

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arXiv 2020年

作者： Babalou, Samira König-Ries, Birgitta Heinz-Nixdorf Chair for Distributed Information Systems Institute for Computer Science Friedrich Schiller University Jena Germany

Ontologies are the prime way of organizing data in the Semantic Web. Often, it is necessary to combine several, independently developed ontologies to obtain a knowledge graph fully representing a domain of interest. The complementarity of existing ontologies can be leveraged by merging them. Existing approaches for ontology merging mostly implement a binary merge. However, with the growing number and size of relevant ontologies across domains, scalability becomes a central challenge. A multi-ontology merging technique offers a potential solution to this problem. We present CoMerger, a scalable multiple ontologies merging method. For efficient processing, rather than successively merging complete ontologies pairwise, we group related concepts across ontologies into partitions and merge first within and then across those partitions. The experimental results on well-known datasets confirm the feasibility of our approach and demonstrate its superiority over binary strategies. A prototypical implementation is freely accessible through a live web portal. Copyright © 2020, The Authors. All rights reserved.

关键词： Ontology

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Proactive Document Distribution with a Self-Enforcing Network

Proactive Document Distribution with a Self-Enforcing Networ...

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作者： David Bergman Gregor Fuhs Christina Klüver Institute for Computer Science and Business Information Systems(ICB) FIR e.V.an der RWTH Aachen

Nowadays, construction projects are planned and carried out by large, interdisciplinary and mostly spatially separated teams. Project members have different data sets, which are often exchanged via complex and error-prone e-mail traffic. This leads to cumbersome and often chaotic data storage. In the following, a method with SEN as core element is described that is intended to improve data exchange through pre-planning data synchronization. The aim of the project is to develop a cloud-based collaboration software for the construction industry that sustainably increases the quality and speed of construction processes and reduces errors caused by old or incorrect data.

关键词： Self-Enforcing Network Document Management Document Access Prediction

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Integrating Data Protection at Interface of RISC-V Processor Core

Integrating Data Protection at Interface of RISC-V Processor...

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Panhellenic Conference on Electronics and Telecommunications (PACET)

作者： Ján Mach Lukáš Kohútka Pavel Čičák Institute of Computer Engineering and Applied Informatics Slovak University of Technology Slovakia Institute of Informatics Information Systems and Software Engineering Slovak University of Technology Slovakia

Fulfilling increasing performance demands of space and automotive applications can be problematic as high dependability is required. Memory is one of the most radiationsensitive parts, so it is often protected with information redundancy. This paper describes an integration of error correction and detection techniques into the interface of RISC-V processor to protect the data stored in the memory. We also provide a hardware-software interface for reporting errors and software routines for correcting data in memory. Our solution has a negligible impact (-3.9%) on the overall performance of the core with a small area and power consumption overhead.

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