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International Conference on Computer and Information Technology

作者： Md. Zubair Ibne Alam Arnab Rahman Chowdhury Mehedi Hassan Hasan Mahmud Md. Kamrul Hasan Systems and Software Lab (SSL) Department of Computer Science and Engineering (CSE) Islamic University of Technology (IUT) Gazipur Bangladesh

ISBN: (纸本)9781509040919

Text communication or messaging is devoid of emotional expression, gesture and intonation. As a result text communication has become a robotic conversation between human beings. People uses various applications like, Facebook Messenger, Viber, WhatsApp etc. for the purpose of messaging, where they follow the traditional system of writing messages and sending to the receivers. This paper, presents a new idea of messaging through gestures which is one of the primary elements of interaction. The message will be produced through the gestures that the sender will make. If, the sender shows a 'thumbs up' gesture, a text or emoticon is generated and sent to the receiver. One of the best features of our system is, it can generate Bengali messages. The system can recognize 'thumbs up' gesture which is converted to, means 'Well Done' and sent to the receiver. We have evaluated our designed prototype and got satisfactory results.

关键词： Hand gestures Interactive messaging Kinect Human Computer Interactions

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GB-RC4: Effective brute force attacks on RC4 algorithm using GPU 7

GB-RC4: Effective brute force attacks on RC4 algorithm using...

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7th International Green and Sustainable Computing Conference, IGSC 2016

作者： Xue, Pei Li, Tao Dong, Han Liu, Chunbo Ma, Wenjing Pei, Songwen College of Computer and Control Engineering Nankai University Tianjin300071 China Information Security Evaluation Center Civil Aviation University of China Tianjin300300 China Laboratory of Parallel Software and Computational Science State Key Laboratory of Computing Science Institute of Software CAS Beijing China Shanghai Key Lab of Modern Optical Systems Department of Computer Science and Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9781509051175

Encryption algorithms are applied to a variety of fields and the security of encryption algorithms depends heavily on the computational infeasibility of exhaustive key-space search. RC4 algorithm has an extensive application for stream encryption, however, the disadvantages of traditional RC4 serial algorithm are large computational quantity and slow computation speed, which means a great challenge for throughput of computing system. Graphics processors recently have been widely used as accelerators for high throughput computing. In this paper, GPU is used to improve the efficiency and performance of attacking RC4 algorithm. First, we revised RC4 algorithm to make it easier to parallelize on a GPU. Then we take advantage of constant memory and shared memory in GPU to search the key space of RC4 algorithm efficiently. Besides, we solved the bank conflict problem in shared memory to further improve the performance. Our approach was evaluated in the whole key space of 40-bit, and the results show it can complete searching of the whole key space within 12.8 hours. © 2016 IEEE.

关键词： Graphics processing unit

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Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles

arXiv

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arXiv 2018年

作者： Binder, Alexander Bockmayr, Michael Hägele, Miriam Wienert, Stephan Heim, Daniel Hellweg, Katharina Stenzinger, Albrecht Parlow, Laura Budczies, Jan Goeppert, Benjamin Treue, Denise Kotani, Manato Ishii, Masaru Dietel, Manfred Hocke, Andreas Denkert, Carsten Müller, Klaus-Robert Klauschen, Frederick Machine-Learning Group Department of Software Engineering and Theoretical Computer Science Technical University of Berlin Marchstr. 23 Berlin10587 Germany Systems Pathology Lab Institute of Pathology Charité University Hospital Charitéplatz 1 Berlin10117 Germany Department of Internal Medicine Infectious Diseases and Pulmology Charitéplatz 1 Berlin10117 Germany Institute of Pathology University of Heidelberg Im Neuenheimer Feld 224 Heidelberg Germany Laboratory of Cellular Dynamics Immunology Frontier Research Center Osaka University 2-2 Yamada-oka Suita Osaka565-0871 Japan ISTD Pillar Singapore University of Technology and Design 8 Somapah Road Singapore487372 Singapore Charité Berlin Charitéplatz 1 Berlin10117 Germany Department of Brain and Cognitive Engineering Korea University 145 Anam-ro Seongbukgu Seoul136-713 Korea Republic of Max-Planck-Institute for Informatics Saarbrücken Germany Department of Pediatric Hematology and Oncology University Medical Center Hamburg-Eppendorf Hamburg Germany

Recent advances in cancer research largely rely on new developments in microscopic or molecular profiling techniques offering high level of detail with respect to either spatial or molecular features, but usually not both. Here, we present a novel machine learning-based computational approach that allows for the identification of morphological tissue features and the prediction of molecular properties from breast cancer imaging data. This integration of microanatomic information of tumors with complex molecular profiling data, including protein or gene expression, copy number variation, gene methylation and somatic mutations, provides a novel means to computationally score molecular markers with respect to their relevance to cancer and their spatial associations within the tumor microenvironment. Copyright © 2018, The Authors. All rights reserved.

关键词： Fluorescence microscopy

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Algorithms for determining semantic relations of formal concepts by cognitive machine learning based on concept algebra

Algorithms for determining semantic relations of formal conc...

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IEEE International Conference on Cognitive Informatics

作者： Mehrdad Valipour Yingxu Wang Omar A. Zatarain Marina L. Gavrilova Denotational Mathematics Software Science and Cognitive Systems Dept. of Electrical and Computer Engineering University of Calgary Calgary Alberta Canada Information Systems Lab Packard Dept. of Electrical Engineering Stanford University Stanford CA USA

ISBN: (纸本)9781509038473

It is recognized that the semantic space of knowledge is a hierarchical concept network. This paper presents theories and algorithms of hierarchical concept classification by quantitative semantic relations via machine learning based on concept algebra. The equivalence between formal concepts are analyzed by an Algorithm of Concept Equivalence Analysis (ACEA), which quantitatively determines the semantic similarity of an arbitrary pair of formal concepts. This leads to the development of the Algorithm of Relational Semantic Classification (ARSC) for hierarchically classify any given concept in the semantic space of knowledge. Experiments applying Algorithms ACEA and ARSC on 20 formal concepts are successfully conducted, which encouragingly demonstrate the deep machine understanding of semantic relations and their quantitative weights beyond human perspectives on knowledge learning and natural language processing.

关键词： Writing Instruments Decision support systems Ink Animals Handheld computers Lead

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Online Machine Learning for Big Data Analytics by Cognitive Robots

Online Machine Learning for Big Data Analytics by Cognitive ...

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2016 IEEE International Conference of Online Analysis and Computing Science(ICOACS 2016)

作者： Yingxu Wang FICIC FWIF Information Systems Lab Stanford University Laboratory for Computational Intelligence Denotational Mathematicsand Software Science Dept.of Electrical and Computer EngineeringSchulich School of Engineering and Hotchkiss Brain InstituteUniversity of Calgary

It is recognized that both sources of and solutions to the big data challenges are human collective *** are an abstract representation of the quantity of realistic entities and perceived *** data play an indispensable role not only in a wide range of engineering applications,but also in the cognitive mechanisms of both humans and cognitive robots such as sensation,quantification,qualification,estimation,memory,and *** keynote lecture presents online big data analytic theories by machine learning as well as knowledge extraction by cognitive ***,information,knowledge,and intelligence are the four hierarchical layers of cognitive objects in the brain and cognitive systems from the bottom *** is discovered by the author that,although the cognitive unit of data is bit,that of knowledge is bir,i.e.,a binary relation.A resent finding towards big data science is that big data systems in nature are a recursive ndimensional typed hyperstructure(RNDTHS).This topological property of big data reveals that the mathematical foundation of big data science is underpinned by big data algebra(BDA),which is a denotational mathematical structure for efficiently dealing with the inherited complexities and unprecedented challenges in big data *** leads to a coherent theory for big data modeling,analyses,mining,information elicitation,knowledge extraction,and intelligent generation for cognitive *** experiments demonstrate that cognitive robots may autonomously transform big data in vast linguistic databases(corpuses) into sophistic knowledge bases by cognitive machine learning.

关键词： Big data big data science denotational mathematics mathematical models recursively hierarchical hyperstructures big data algebra cogniitve machine learning formal concepts knowledge extraction knowledge generation cognitive informatics cognitive computing computational intelligence applications

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Fault-based product-line testing: Effective sample generation based on feature-diagram mutation 15

Fault-based product-line testing: Effective sample generatio...

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19th International software Product Line Conference, SPLC 2015

作者： Reuling, Dennis Bürdek, Johannes Rotärmel, Serge Lochau, Malte Kelter, Udo Software Engineering Group University of Siegen Germany Real-Time Systems Lab TU Darmstadt Germany

ISBN: (纸本)9781450336130

Testing every member of a product line individually is often impracticable due to large number of possible product configurations. Thus, feature models are frequently used to generate samples, i.e., subsets of product configurations under test. Besides the extensively studied combinatorial interaction testing (CIT) approach for coverage-driven sample generation, only few approaches exist so far adopting mutation testing to emulate faults in feature models to be detected by a sample. In this paper, we present a mutationbased sampling framework for fault-based product-line testing. We define a comprehensive catalog of atomic mutation operators on the graphical representation of feature models. This way, we are able (1) to also define complex mutation operators emulating more subtle faults, and (2) to classify operators semantically, e.g., to avoid redundant and equivalent mutants. We further introduce similarity-based mutant selection and higher order mutation strategies to reduce testing efforts. Our implementation is based on the graph transformation engine Henshin and is evaluated concerning effectiveness/efficiency trade-offs. © 2015 ACM.

关键词： software testing

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LRI: A LOW RANK APPROACH TO NON-LOCAL SPARSE REPRESENTATION FOR IMAGE INTERPOLATION

LRI: A LOW RANK APPROACH TO NON-LOCAL SPARSE REPRESENTATION ...

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International Joint Conference on Neural Networks

作者： Mingli Zhang Qiang Qu Sadegh Nobari Christian Desrosiers Software and IT Engineering école de Technologie Supérieure Montreal Canada H3C 1K3 Data Science and Information Systems (Dainfos) Lab Innopolis University Russia

ISBN: (纸本)9781509006212

The sparse representation models for image super-resolution have shown great potential in various imaging and vision tasks. However, most of them are challenged by the accuracy issue especially when images are significantly down-sampled. In this paper, we aim to improve the performance of sparse representation. We propose to incorporate a low rank approach into image non-local sparse representation model. To the best of our knowledge, this is the first work to integrate low rank approaches into non-local spare representation for image interpolation. The proposed method can obtain good estimation of sparse coefficients of original images. Experimental results show the effectiveness of our proposed method compared with the state-of-the-art.

关键词： Mathematical model Image reconstruction Image resolution Interpolation Dictionaries Artificial neural networks Estimation

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Experiments on the supervised learning algorithm for formal concept elicitation by cognitive robots

Experiments on the supervised learning algorithm for formal ...

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IEEE International Conference on Cognitive Informatics

作者： Omar A. Zatarain Yingxu Wang Laboratory for Computational Intelligence Denotational Mathematics and Software Science International Institute of Cognitive Informatics and Cognitive Computing (ICIC) Schulich School of Engineering and Hotchkiss Brain Institute University of Calgary Calgary Alberta Canada Information Systems Lab Stanford University Stanford CA USA

ISBN: (纸本)9781509038473

Concept elicitation is a fundamental methodology for knowledge extraction and representation in cognitive robot learning. Traditional machine learning technologies deal with object identification, cluster classification, functional regression, and behavior acquisition. This paper presents a supervised machine knowledge learning methodology for concept elicitation from sample dictionaries in natural languages. Formal concepts are autonomously generated based on collective intention of attributes and collective extension of objects elicited from informal definitions in dictionaries. A system of formal concept generation for a cognitive robot is implemented by the Algorithm of Machine Concept Elicitation (AMCE) in MATlab. Experiments on machine learning for creating a set of twenty formal concepts reveal that the cognitive robot is able to learn synergized concepts in human knowledge in order to build its own cognitive knowledge base. The results of machine-generated concepts demonstrate that the AMCE algorithm can over perform human knowledge expressions in dictionaries in terms of relevance, accuracy, quantitativeness, and cohesiveness.

关键词： Algebra Handheld computers Writing Instruments Animals Semantics

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Investigating bias in the search phase of software engineering secondary studies 18

Investigating bias in the search phase of Software Engineeri...

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18th Ibero-American Conference on software engineering, CIBSE 2015

作者： Santos, José Amancio Macedo Santos, Alcemir Rodrigues De Mendonça, Manoel Gomes Technology Department State University of Feira de Santana Bahia Brazil Reuse in Software Engineering Lab RiSE Labs Federal University of Bahia Bahia Brazil Fraunhofer Project Center for Software and Systems Engineering Federal University of Bahia Bahia Brazil

ISBN: (纸本)9789972825804

Context. Researchers are increasingly resorting of secondary studies (e.g. systematic literature reviews and mapping studies) in software engineering. This method is strongly dependent on the source of primary studies adopted, which is a bias. We did not find guidelines or benchmarks to evaluate the sources in a systematic way. Objective. In this paper we aim to tackle the selection of electronic data sources while conducting such kind of studies evaluating the equilibrium between the volume and number of relevant papers. Method. In this sense, we proceed towards a secondary study to analyze the overlapping of three different electronic data sources. We also compared our results with other similar studies. Results. Our results show minimum overlapping and no effortless combination of electronic data sources at all. Conclusion. We conclude that researchers shall resort of completeness to work with a feasible set of papers to review. Specially in secondary studies adopting general and no standardized terms. Copyright © 2015 by the authors.

关键词： software engineering

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Re-engineering automation systems as dynamic software product lines 11

Re-engineering automation systems as dynamic software produc...

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11th Dagstuhl-Workshop Modellbasierte Entwicklung Eingebetteter Systeme, MBEES 2015 - 11th Dagstuhl-Workshop on Model-Based Development of Embedded systems, MBEES 2015

作者： Lity, Sascha Burdek, Johannes Lochau, Malte Berens, Markus Schurr, Andy Schaefer, Ina Institute for Programming and Reactive Systems TU Braunschweig Germany Real-Time Systems Lab TU Darmstadt Germany Eckelmann AG Germany Institute of Software Engineering and Automotive Informatics TU Braunschweig Germany

software engineering is an important development task in the automation domain due to the increasing number of embedded systems applied for controlling various system functions. In general, those automation systems are highly configurable, but share common functionality and engineering artifacts. If the knowledge about commonality and variability is not taken into account, this would result in a separate development process for each new system without reusing engineering artifacts. software product line engineering is a promising approach for the development of highly configurable automation systems explicitly exploiting the knowledge about commonality and variability. In addition to the specification of the problem space, i.e., all possible system configurations derivable from a feature model, reusable engineering artifacts are defined in the solution space by incorporating variability mechanisms. In this paper, we describe the re-engineering of an existing family of automation systems, a medical device for a novel radiotherapy, i.e., as a software product line. According to the concepts of software product line engineering, we describe (1) how we created a corresponding feature model for the problem space specification, and (2) how we integrated variability modeling into existing engineering artifacts. We provide details about our real-world case study and further explain the adaptation of its re-engineered version as dynamic software product line allowing for re-configuration at runtime. © Tagungsband - Dagstuhl-Workshop MBEES 2015: Modellbasierte Entwicklung Eingebetteter Systeme XI. All rights reserved.

关键词： Specifications

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