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11th Asia Joint Conference on Information Security, AsiaJCIS 2016

作者： Kuo, Wen-Chung Li, Jyun-Jia Wang, Chun-Cheng Wuu, Lih-Chyau Huang, Yu-Chih Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Taiwan Graduate School of Engineering Science and Technology Doctoral Program National Yunlin University of Science and Technology Taiwan Department of Information Management Tainan University of Technology Taiwan

ISBN: (纸本)9781509022854

Recently, a modified data hiding scheme based on pixel value differencing and improving exploiting modification directions is proposed by Shen and Huang. There are two major contributions in this scheme. One is to enhance the embedding rate and good embedding capacity and the other is an optimization problem used to solve the overflow/ underflow problem. In fact, they propose the similar brute force method to solve overflow/underflow problem in their proposed scheme. To overcome the overflow/underflow problem exactly, we will propose the close form to solve this problem in this paper. Then, an improvement data hiding scheme based on FFEMD (Formula Fully Exploiting Modification Directions) and pixel value differencing method is proposed. According to our analysis, it is not only to keep the advantages of Shen-Huang scheme but also to use the close form to solve the overflow/ underflow problem. © 2016 IEEE.

关键词： Pixels

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Comparative analysis of RC4+ algorithm, RC4 NGG algorithm and RC4 GGHN algorithm on image file security

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IOP Conference Series: Materials Science and engineering 2018年第1期420卷

作者： F Akbar H Mawengkang S Efendi Master Program (S2) of Informatics Engineering Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia. Department of Mathematics University Sumatera Utara Medan Indonesia. Faculty of Computer Science and Information Technology University of Sumatera Utara Medan Indonesia.

File security is very important in maintaining the confidentiality of the information contained in the file. For that we need a way for information to be kept secrecy, that is with the science of cryptography. Cryptography is one method that disguises data to form encrypted data. In the symmetric RC4+ (Ron Code or Rivest's Cipher) algorithm, it provides secure message security and is an efficient stream cipher. RC4 NGG stream cipher algorithm 3-5 times faster than RC4 algorithm and but still have weakness in KSA randomization. The RC4 GGHN stream cipher algorithm is based on the RC4 algorithm and is relatively more efficient but still has weaknesses. Based on literature review from several research sources, it was stated that RC4+, RC4 NGG, RC4 GGHN are algorithms that are faster and more efficient than RC4 algorithm. Therefore, it is necessary to compare the encryption process with the aim of comparing and knowing which algorithm is better from running time and algorithm complexity. In this research we will perform two tests on each algorithm, image encryption using different key length and same image size and image encryption using the same key length and different image size. The results obtained in this research is RC4 NGG algorithm is the fastest algorithm between the three algorithms that is 13.3% faster than RC4+ and 10.2% faster than RC4 GGHN where the key lengths used in encryption do not affect the processing time and image size used in encryption very affects the processing time.

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Guest editorial: The 2016 capstone design conference

Guest editorial: The 2016 capstone design conference

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作者： Brackin, Patsy Howe, Susannah Rogers, Peter Stanfill, R. Keith Beyerlein, Steven Kanai, Junichi Vallino, Jim Palo, Scott Department of Mechanical Engineering Rose-Hulman Institute of Technology 5500 Wabash Avenue CM 165 Terre HauteIN47803 United States Picker Engineering Program Smith College 151 Ford Hall NorthamptonMA01063 United States Department of Engineering Education Ohio State University 3070 Smith Laboratory 174 W. 18th Avenue ColumbusOH43210 United States Engineering Innovation Institute University of Florida P.O. Box 116000 GainesvilleFL32611-6000 United States Department of Mechanical Engineering University of Idaho 875 Perimeter Drive MoscowID83844-0902 United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Department of Software Engineering Rochester Institute of Technology 134 Lomb Memorial Drive RochesterNY14623-5608 United States Department of Aerospace Engineering Sciences University of Colorado 429 UCB BoulderCO80309 United States

The goal of the 2016 Capstone Design Conference held in Columbus, OH was to build upon the success of previous conferences (2007 and 2010 in Boulder, CO, 2012 in Champaign, IL, and 2014 in Columbus, OH) and expand the community of educators, students, and industry members engaged in discussing, analyzing, and improving capstone design education. Sessions at the 2016 Capstone Design Conference were designed for vibrant sharing of ideas and experiences across the capstone community via interactive panel sessions, poster session socials, hands-on workshops, and other formal and informal networking activities. This editorial discusses conference planning, structure, and feedback. Technical papers that follow in this issue document scholarship surrounding noteworthy capstone course innovations. Most of these began as four page peer-reviewed papers included in the conference proceedings. © 2017 TEMPUS Publications.

关键词： Teaching

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Total variation superiorized conjugate gradient method for image reconstruction

arXiv

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arXiv 2017年

作者： Zibetti, Marcelo V. W. Lin, Chuan Herman, Gabor T. Graduate Program in Electrical Engineering and Computer Science Federal University of Technology - Paraná Curitiba80230-901 Brazil School of Electrical Engineering Southwest Jiaotong University Chengdu Sichuan610031 China Department of Computer Science Graduate Center City University of New York New YorkNY10016 United States

The conjugate gradient (CG) method is commonly used for the relatively-rapid solution of least squares problems. In image reconstruction, the problem can be ill-posed and also contaminated by noise;due to this, approaches such as regularization should be utilized. Total variation (TV) is a useful regularization penalty, frequently utilized in image reconstruction for generating images with sharp edges. When a non-quadratic norm is selected for regularization, as is the case for TV, then it is no longer possible to use CG. Nonlinear CG is an alternative, but it does not share the efficiency that CG shows with least squares and methods such as fast iterative shrinkage-thresholding algorithms (FISTA) are preferred for problems with TV norm. A different approach to including prior information is superiorization. In this paper it is shown that the conjugate gradient method can be superiorized. Five different CG variants are proposed, including preconditioned CG. The CG methods superiorized by the total variation norm are presented and their performance in image reconstruction is demonstrated. It is illustrated that some of the proposed variants of the superiorized CG method can produce reconstructions of superior quality to those produced by FISTA and in less computational time, due to the speed of the original CG for least squares problems. In the Appendix we examine the behavior of one of the superiorized CG methods (we call it S-CG);one of its input parameters is a positive number ". It is proved that, for any given " that is greater than the half-squared-residual for the least squares solution, S-CG terminates in a finite number of steps with an output for which the half-squared-residual is less than or equal to ". Importantly, it is also the case that the output will have a lower value of TV than what would be provided by unsuperiorized CG for the same value " of the half-squared residual. Copyright © 2017, The Authors. All rights reserved.

关键词： Conjugate gradient method

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Analysis of user readiness toward ICT usage at small medium enterprise in south tangerang

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Journal of Physics: Conference Series 2018年第1期1007卷

作者： D Napitupulu M Syafrullah R Rahim D. Abdullah MI Setiawan Research Center for Quality System & Testing Technology Indonesian Institute of Sciences Tangerang Selatan Banten Indonesia Program of Master of Computer Science Universitas Budi Luhur Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Malaysia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Civil Engineering Narotama University Surabaya Indonesia

Utilization of Information and Communication technology (ICT) is still relatively low in the level of SMEs due to various limitations ranging from access to capital, till the marketing network. ICT is present to provide the ability for SMEs in improving the benefits and competitive advantage of the organization. This study aims to determine the level of readiness of SMEs in utilizing technology, especially ICT. The methodology used is a survey to see the technology readiness of 107 SMEs in South Tangerang selected by purposive sampling. The approach used is TRI (technology Readiness Index) which is the individual perception of technology based on four criteria that is optimism, innovativeness, discomfort and insecurity. The results showed that the optimism and innovativeness variables significantly positively influence the technology readiness while the variables of discomfort and insecurity also significantly positively influence the readiness of ICT.

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Analysis of e-learning implementation readiness based on integrated elr model

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Journal of Physics: Conference Series 2018年第1期1007卷

作者： K Adiyarta D Napitupulu R Rahim D. Abdullah MI Setiawan Program of Master of Computer Scocience Universitas Budi Luhur Indonesia Research Center for Quality System & Testing Technology Indonesian Institute of Sciences Tangerang Selatan Banten Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Malaysia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Civil Engineering Narotama University Surabaya Indonesia

E-learning nowadays has become a requirement for institutions to support their learning activities. To adopt e-learning, an institution requires a large strategy and resources for optimal application. Unfortunately, not all institutions that have used e-learning got the desired results or expectations. This study aims to identify the extent of the level of readiness of e-learning implementation in institution X. The degree of institutional readiness will determine the success of future e-learning utilization. In addition, institutional readiness measurement are needed to evaluate the effectiveness of strategies in e-learning development. The research method used is survey with questionnaire designed based on integration of 8 best practice ELR (e-learning readiness) model. The results showed that from 13 factors of integrated ELR model being measured, there are 3 readiness factors included in the category of not ready and needs a lot of work. They are human resource (2.57), technology skill (2.38) and content factors (2.41). In general, e-learning implementation in institutions is in the category of not ready but needs some of work (3.27). Therefore, the institution should consider which factors or areas of ELR factors are considered still not ready and needs improvement in the future.

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Author Correction: RADICL-seq identifies general and cell type-specific principles of genome-wide RNA-chromatin interactions

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Nature communications 2021年第1期12卷 3128页

作者： Alessandro Bonetti Federico Agostini Ana Maria Suzuki Kosuke Hashimoto Giovanni Pascarella Juliette Gimenez Leonie Roos Alex J Nash Marco Ghilotti Christopher J F Cameron Matthew Valentine Yulia A Medvedeva Shuhei Noguchi Eneritz Agirre Kaori Kashi Joachim Luginbühl Riccardo Cazzoli Saumya Agrawal Nicholas M Luscombe Mathieu Blanchette Takeya Kasukawa Michiel de Hoon Erik Arner Boris Lenhard Charles Plessy Gonçalo Castelo-Branco Valerio Orlando Piero Carninci RIKEN Center for Integrative Medical Sciences Yokohama Kanagawa 230-0045 Japan. alessandro.bonetti@riken.jp. Laboratory of Molecular Neurobiology Department Medical Biochemistry and Biophysics Karolinska Institutet Stockholm Sweden. alessandro.bonetti@riken.jp. The Francis Crick Institute 1 Midland Road London NW1 1AT UK. RIKEN Center for Integrative Medical Sciences Yokohama Kanagawa 230-0045 Japan. Department of Medicine (H7) Karolinska Institutet Stockholm 141 86 Sweden. Epigenetics and Genome Reprogramming Laboratory IRCCS Fondazione Santa Lucia Rome Italy. Faculty of Medicine Imperial College London Institute of Clinical Sciences London W12 0NN UK. MRC London Institute of Medical Sciences London W12 0NN UK. School of Computer Science McGill University Montréal QC Canada. Department of Biochemistry and Goodman Cancer Research Centre McGill University Montréal QC Canada. Institute of Bioengineering Research Centre of Biotechnology Russian Academy of Science 117312 Moscow Russia. Department of Computational Biology Vavilov Institute of General Genetics Russian Academy of Science 119991 Moscow Russia. Department of Biological and Medical Physics Moscow Institute of Physics and Technology 141701 Dolgoprudny Moscow Region Russia. Laboratory of Molecular Neurobiology Department Medical Biochemistry and Biophysics Karolinska Institutet Stockholm Sweden. Department of Experimental Oncology IEO European Institute of Oncology IRCCS Milan Italy. UCL Genetics Institute University College London London WC1E 6BT UK. Okinawa Institute of Science and Technology Graduate University 1919-1 Tancha Onna-son Kunigami-gun Okinawa 904-0495 Japan. Sars International Centre for Marine Molecular Biology University of Bergen 5008 Bergen Norway. Epigenetics and Genome Reprogramming Laboratory IRCCS Fondazione Santa Lucia Rome Italy. valerio.orlando@kaust.edu.sa. KAUST Environmental Epigenetics Program King Abdullah University of Science and Technology (KAUS

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Publisher Correction: Brain charts for the human lifespan

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Nature 2022年第7931期610卷 E6页

作者： R A I Bethlehem J Seidlitz S R White J W Vogel K M Anderson C Adamson S Adler G S Alexopoulos E Anagnostou A Areces-Gonzalez D E Astle B Auyeung M Ayub J Bae G Ball S Baron-Cohen R Beare S A Bedford V Benegal F Beyer J Blangero M Blesa Cábez J P Boardman M Borzage J F Bosch-Bayard N Bourke V D Calhoun M M Chakravarty C Chen C Chertavian G Chetelat Y S Chong J H Cole A Corvin M Costantino E Courchesne F Crivello V L Cropley J Crosbie N Crossley M Delarue R Delorme S Desrivieres G A Devenyi M A Di Biase R Dolan K A Donald G Donohoe K Dunlop A D Edwards J T Elison C T Ellis J A Elman L Eyler D A Fair E Feczko P C Fletcher P Fonagy C E Franz L Galan-Garcia A Gholipour J Giedd J H Gilmore D C Glahn I M Goodyer P E Grant N A Groenewold F M Gunning R E Gur R C Gur C F Hammill O Hansson T Hedden A Heinz R N Henson K Heuer J Hoare B Holla A J Holmes R Holt H Huang K Im J Ipser C R Jack A P Jackowski T Jia K A Johnson P B Jones D T Jones R S Kahn H Karlsson L Karlsson R Kawashima E A Kelley S Kern K W Kim M G Kitzbichler W S Kremen F Lalonde B Landeau S Lee J Lerch J D Lewis J Li W Liao C Liston M V Lombardo J Lv C Lynch T T Mallard M Marcelis R D Markello S R Mathias B Mazoyer P McGuire M J Meaney A Mechelli N Medic B Misic S E Morgan D Mothersill J Nigg M Q W Ong C Ortinau R Ossenkoppele M Ouyang L Palaniyappan L Paly P M Pan C Pantelis M M Park T Paus Z Pausova D Paz-Linares A Pichet Binette K Pierce X Qian J Qiu A Qiu A Raznahan T Rittman A Rodrigue C K Rollins R Romero-Garcia L Ronan M D Rosenberg D H Rowitch G A Salum T D Satterthwaite H L Schaare R J Schachar A P Schultz G Schumann M Schöll D Sharp R T Shinohara I Skoog C D Smyser R A Sperling D J Stein A Stolicyn J Suckling G Sullivan Y Taki B Thyreau R Toro N Traut K A Tsvetanov N B Turk-Browne J J Tuulari C Tzourio É Vachon-Presseau M J Valdes-Sosa P A Valdes-Sosa S L Valk T van Amelsvoort S N Vandekar L Vasung L W Victoria S Villeneuve A Villringer P E Vértes K Wagstyl Y S Wang S K Warfield V Warrier E Westman M L Westwater H C Whalley A V Witte N Yang B Yeo H Y Autism Research Centre Department of Psychiatry University of Cambridge Cambridge UK. rb643@medschl.cam.ac.uk. Brain Mapping Unit Department of Psychiatry University of Cambridge Cambridge UK. rb643@medschl.cam.ac.uk. Department of Psychiatry University of Pennsylvania Philadelphia PA USA. jakob.seidlitz@pennmedicine.upenn.edu. Department of Child and Adolescent Psychiatry and Behavioral Science The Children's Hospital of Philadelphia Philadelphia PA USA. jakob.seidlitz@pennmedicine.upenn.edu. Lifespan Brain Institute The Children's Hospital of Philadelphia and Penn Medicine Philadelphia PA USA. jakob.seidlitz@pennmedicine.upenn.edu. Department of Psychiatry University of Cambridge Cambridge UK. MRC Biostatistics Unit University of Cambridge Cambridge UK. Department of Psychiatry University of Pennsylvania Philadelphia PA USA. Lifespan Informatics & Neuroimaging Center University of Pennsylvania Philadelphia PA USA. Department of Psychology Yale University New Haven CT USA. Developmental Imaging Murdoch Children's Research Institute Melbourne Victoria Australia. Department of Medicine Monash University Melbourne Victoria Australia. UCL Great Ormond Street Institute for Child Health London UK. Weill Cornell Institute of Geriatric Psychiatry Department of Psychiatry Weill Cornell Medicine New York USA. Department of Pediatrics University of Toronto Toronto Canada. Holland Bloorview Kids Rehabilitation Hospital Toronto Canada. The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for NeuroInformation University of Electronic Science and Technology of China Chengdu China. University of Pinar del Río "Hermanos Saiz Montes de Oca" Pinar del Río Cuba. MRC Cognition and Brain Sciences Unit University of Cambridge Cambridge UK. Autism Research Centre Department of Psychiatry University of Cambridge Cambridge UK. Department of Psychology School of Philosophy Psychology and Language Sciences University of Edinburgh Edinburgh UK. Queen's

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Quantum annealing versus classical machine learning applied to a simplified computational biology problem

arXiv

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arXiv 2018年

作者： Li, Richard Di Felice, Rosa Rohs, Remo Lidar, Daniel Department of Chemistry University of Southern California Los AngelesCA90089 United States Computational Biology and Bioinformatics Program Department of Biological Sciences University of Southern California Los AngelesCA90089 United States Center for Quantum Information Science & Technology University of Southern California Los AngelesCA90089 United States Department of Physics and Astronomy University of Southern California Los AngelesCA90089 United States Center S3 CNR Institute of Nanoscience Via Campi 213/A Modena41125 Italy Department of Computer Science University of Southern California Los AngelesCA90089 United States Department of Electrical Engineering University of Southern California Los AngelesCA90089 United States

Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the ability of a quantum machine learning approach to predict binding specificity. Using simplified datasets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding. The results were compared to state-of-the-art classical approaches for the same simplified datasets, including simulated annealing, simulated quantum annealing, multiple linear regression, LASSO, and extreme gradient boosting. Despite technological limitations, we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets. Thus, we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems. Copyright © 2018, The Authors. All rights reserved.

关键词： Simulated annealing

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Raman study of lattice vibrations in type II superlattice InAs/InAs1-xSbx

arXiv

引用

arXiv 2017年

作者： Liu, Henan Zhang, Yong Steenbergen, Elizabeth H. Liu, Shi Lin, Zhiyuan Zhang, Yong-Hang Kim, Jeomoh Ji, Mi-Hee Detchprohm, Theeradetch Dupuis, Russell D. Kim, Jin K. Hawkins, Samuel D. Klem, John F. Optical Science and Engineering Graduate Program Department of Electrical and Computer Engineering University of North Carolina at Charlotte Charlotte NC United States Center for Photonics Innovation and School of Electrical Computer and EnergyEngineering Arizona State University Tempe AZ United States Center for Compound Semiconductors and School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA United States Sandia National Laboratories Albuquerque NM United States

InAs/InAs1-xSbx superlattice system differ distinctly from two well-studied superlattice systems GaAs/AlAs and InAs/GaSb, in terms of electronic band alignment, common-element at the interface, and phonon spectrum overlapping of the constituents. This fact leads to unique electronic and vibrational properties of the InAs/InAs1-xSbx system when compared to the other two systems. In this work, we report a polarized Raman study on the vibrational properties of the InAs/InAs1-xSbx SLs as well as selected InAs1-xSbx alloys, all grown on GaSb substrates by either MBE or MOCVD, from both growth surface and cleaved edge. In the SL, from the (001) backscattering geometry, an InAs-like LO mode is observed as the primary feature and its intensity is found to increase with increasing Sb composition;from the (110) cleaved edge backscattering geometry, an InAs-like TO mode is observed as the main feature in two cross-polarization configurations, but an additional InAs-like "forbidden" LO mode was observed in two parallel-polarization configurations. InAs1-xSbx alloys lattice-matched to the substrate (xSb ~ 0.09) grown by MBE were also found to exhibit the "forbidden" LO mode, implying the existence of some unexpected [001] modulation, but the strained xSb ~ 0.35 samples grown by MOCVD were found to behavior like a disordered alloy. The primary conclusions are (1) the InAs-like LO or TO mode could be either a confined or quasi-confined mode in the InAs layers of the SL or extended mode of the whole structure, depending on the Sb composition;(2) InAs/InAsSb and InAs/GaSb SLs exhibit significantly different behaviors in the cleaved edge geometry, but qualitatively similar in the (001) geometry;and (3) the appearance of the "forbidden" LO-like mode is a universal signature for SLs and bulk systems resulting from mixing of phonon modes due to structural modulation or symmetry reduction. Copyright © 2017, The Authors. All rights reserved.

关键词： III-V semiconductors

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