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Hybrid cryptosystem multi-power RSA with N=PmQ and VMPC

IOP Conference Series: Materials Science and Engineering 2020年第1期725卷

作者： U Erdiansyah MKM Nasution Sawaluddin Master Program (S2) of Informatics Engineering Faculty of Computer Science and Information Technology University of Sumatera Utara Medan Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences University of Sumatera Utara Medan Indonesia

In digital world, data and information is very valuable and much targeted by unauthorized parties. One way to secure messages is cryptography. Cryptography itself is a branch of science in concealing messages. In further development a hybrid cryptosystem was formed which combines symmetrical and asymmetric cryptography, which in this paper uses VMPC and Multi-Power.. The RSA Multi-Power algorithm is an asymmetric algorithm that is excellent at message encryption but requires a long process time because it uses very complicated calculations. While VMPC has advantages in the process with fast time and relatively small size of ciphertext. Here we have analyzed the performance of various variants of RSA that have been hybridized with VMPC and given the results.

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Combination of 3des with dct algorithm in message security

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IOP Conference Series: Materials Science and Engineering 2020年第1期725卷

作者： S. Guntur MKM Nasution RW Nasution Master Program (S2) of Informatics Engineering Faculty of Computer Science and Information Technology University of Sumatera Utara Medan Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences University of Sumatera Utara Medan Indonesia

In the digital era, communication through computer networks plays an important role. Through electronic communication, a person can make transactions or communication very quickly and practically. Sending data / messages from one place to another is much constrained by the issue of confidentiality. There are many ways you can hide data / messages that will be sent. First, using cryptographic techniques, namely by encoding data / messages using certain algorithms. Another technique is to insert a message that will be sent to other media, so that the message will be hidden and what will appear is other media used to insert *** will be performed a Performance Analysis of the Combination of 3DES and DCT Algorithms in Securing Message Files into the Image.

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Author Correction: The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci

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Nature computational science 2023年第7期3卷 658页

作者： Gengjie Jia Yu Li Xue Zhong Kanix Wang Milton Pividori Rabab Alomairy Aniello Esposito Hatem Ltaief Chikashi Terao Masato Akiyama Koichi Matsuda David E Keyes Hae Kyung Im Takashi Gojobori Yoichiro Kamatani Michiaki Kubo Nancy J Cox James Evans Xin Gao Andrey Rzhetsky Shenzhen Branch Guangdong Laboratory of Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China. jiagengjie@***. Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia. Computer Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong People's Republic of China. Department of Medicine and Vanderbilt Genetics Institute Vanderbilt University Medical Center Nashville TN US. Department of Medicine Institute of Genomics and Systems Biology Committee on Genomics Genetics and Systems Biology University of Chicago Chicago IL US. Department of Operations Business Analytics and Information Systems University of Cincinnati Cincinnati OH US. Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia PA US. Extreme Computing Research Center Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia. HPE HPC/AI EMEA Research Laboratory Bristol UK. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Clinical Research Center Shizuoka General Hospital Shizuoka Japan. Department of Applied Genetics The School of Pharmaceutical Sciences University of Shizuoka Shizuoka Japan. Department of Ophthalmology Graduate School of Medical Sciences Kyushu University Fukuoka Japan. Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Tokyo Japan. Biological and Environmenta

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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 2023年第6期20卷 824-835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

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Higher Education Student’s Academic Performance Analysis through Predictive Analytics

Higher Education Student’s Academic Performance Analysis th...

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2018年第七届软件与计算机应用国际会议

作者： Ceasar Ian P.Benablo Evangeline T.Sarte Joe Marie D.Dormido Thelma Palaoag Information Technology Education Program University of the Immaculate Conception2. College of Engineering and Technology Our Lady of the Pillar College3. Institute of Information Technology Carlos Hilado Memorial State College College of Information Technology and Computer Science University of the Cordilleras

As both localization and globalization continue to demand for knowledgeable and skilled individuals, it is imperative that higher education institutions（HEIs） produce quality graduates that can cope-up with it. Since then, this has been the goal of many of these institutions in the Philippines. However, the efforts of these institutions to deliver quality education aimed to equip the students often fall short when students are found to be underperforming academically because of over-exposure and addiction to social media. The challenge now with educators is to assess the performance of these students at an early stage and do the necessary interventions. With this, the researcher saw an opportunity to create a predictive classification model that can be used in predicting the academic performance of higher education students. Through Support Vector Machine（SVM）, the academic profile of past students are categorized into performing and underperforming and this formed part of the predictive classification model. The accuracy of the model have been measured through cross-validation. The test results revealed that the model were able to appropriately predict if a student needs intervention or not with respect to academic performance.

关键词： Academic Performance Predictive Analytics Predictive Classification Model Social Media Support Vector Machine

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Numerical analysis of general-vibration-based method for structural health monitoring

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AIP Conference Proceedings 2020年第1期2227卷

作者： Fergyanto E. Gunawan Benfano Soewito Nico Surantha Tuga Mauritsius Nobumasa Sekishita 1Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480 2Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 3Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 4Department of Mechanical Engineering Toyohashi University of Technology Toyohashi Aichi 441-8580 Japan

For damage detection, this research article discusses an easy-to-compute damage index derived from the governing dynamic of the structure that has potential practical application in Structural Health Monitoring (SHM). The research uses simplified structural models to explore the sensitivity of the index to damages, to compare the index performance with a traditional but popular damage detection method, and to understand the local/global predictive capability of the index. The research uses two simple models, namely, single- and two-degree-of-freedom systems. The results suggest that the damage index is local, that can only monitor damages occurring near the points of measurements, but it is sensitive to damages, unlike the natural frequency, which is global but less sensitive.

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The accuracy of the F statistic for structural health monitoring

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AIP Conference Proceedings 2020年第1期2227卷

作者： Fergyanto E. Gunawan Benfano Soewito Nico Surantha Tuga Mauritsius Nobumasa Sekishita 1Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480 2Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 3Information Systems Management Department BINUS Graduate Program – Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 4Department of Mechanical Engineering Toyohashi University of Technology Toyohashi Aichi 441-8580 Japan

Understanding the reliability of engineering methods is crucial for its adoption and deployment. This research focuses on the reliability of the Power Spectral Density (PSD) method via the use of the F statistic for damage detection. To the author best knowledge, the method is rather classic but its realibility has not been discussed in the context of a large data size. Priory, the research anticipates that the accuracy is a function of the damage level. In this study, we evaluate 3500 cases with five levels of structural integrity, namely, healthy condition and damaged conditions with 1%, 5%, 10%, and 20% damage levels. The dataset is established via a numerical analysis of a seven degree-of-freedom system loaded with a concentrated dynamic force with random magnitude. A spring on the system is reduced in its stiffness to simulate damages. Our significant findings are the following: it is challenging for the PSD-based method to differentiate the healthy condition from the damaged conditions when the damage level is small. However, the reliability is high at 95% probability when the structural integrity has dropped by five percent.

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Analysis of Knowledge Management System in Pamulang University Library Based on SUMI (Software Usability Measurement Inventory)

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IOP Conference Series: Materials Science and Engineering 2020年第1期879卷

作者： T Thoyyibah Agung Trisetyarso Wayan Saputra Chul Ho Kang Bahtiar Saleh Abbas Computer Science Department Binus Graduate Program-Doctor of Computer Science Bina Nusantara University Jl. Kebon Jeruk Raya No.27South Tangerang Banten Faculty of Information Technology University Of Pamulang jl.Jl. Raya PuspitekSerpong No. 10 South Tangerang Banten

We would like to measured the information system of Pamulang University library through the usability evaluation. The method in this study uses the adoption of the Knowledge Management System Life Cycle (KMSLC) life cycle. Where the stages used consist of Evaluation of Existing Infrastructure Analysis, Capture Knowledge, Implementing the KM system, Evaluation. The steps taken are system analysis, knowledge mapping in the application, implementation results and evaluation using usability. The questionnaire was conducted on 20 respondents, including 10 students and 10 Pamulang University lecturers. The questionnaire was conducted in 3 categories namely effectiveness, efficiency and satisfaction with 10 questions each so that there were 60 questions and 3 linkers namely 4 if they all agreed, 2 if they did not know and 0 did not agree. The results of the SUMI questionnaire against the Library are 90; 75; 75; 87.5; 90; 75. The usability evaluation results are above average, meaning that the usability in the UNPAM Library system is already in the good category.

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Power of Pausing: Advancing Understanding of Thermalization in Experimental Quantum Annealers

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Physical Review Applied 2019年第4期11卷 044083-044083页

作者： Jeffrey Marshall Davide Venturelli Itay Hen Eleanor G. Rieffel USRA Research Institute for Advanced Computer Science Mountain View California 94035 USA USRA NAMS Quantum Academy R&D Student Program NASA Ames Research Center Mountain View California 94035 USA Department of Physics and Astronomy and Center for Quantum Information Science & Technology University of Southern California Los Angeles California 90089 USA QuAIL NASA Ames Research Center Moffett Field California 94035 USA Information Sciences Institute University of Southern California Marina del Rey California 90292 USA

We investigate alternative annealing schedules on the current generation of quantum-annealing hardware (the D-Wave 2000Q), which includes the use of forward and reverse annealing with an intermediate pause. This work provides new insights into the inner workings of these devices (and quantum devices in general), particularly into how thermal effects govern the system dynamics. We show that a pause midway through the anneal can cause a dramatic change in the output distribution, and we provide evidence suggesting thermalization is indeed occurring during such a pause. We demonstrate that upon pausing the system in a narrow region shortly after the minimum gap, the probability of successfully finding the ground state of the problem Hamiltonian can be increased over an order of magnitude. We relate this effect to relaxation (i.e., thermalization) after diabatic and thermal excitations that occur in the region near to the minimum gap. For a set of large-scale problems of up to 500 qubits, we demonstrate that the distribution returned from the annealer very closely matches a (classical) Boltzmann distribution of the problem Hamiltonian, albeit one with a temperature at least 1.5 times higher than the (effective) temperature of the annealer. Moreover, we show that larger problems are more likely to thermalize to a classical Boltzmann distribution.

关键词： Adiabatic quantum optimization Open quantum systems & decoherence Quantum information architectures & platforms Quantum information with solid state qubits Simulated annealing Statistical methods

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ThermalProGAN: A sequence-based thermally stable protein generator trained using unpaired data

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Journal of bioinformatics and computational biology 2023年第1期21卷 2350008页

作者： Hui-Ling Huang Chong-Heng Weng Torbjörn E M Nordling Yi-Fan Liou International Program of Health Informatics and Management College of Management Chang Gung University No. 259 Wenhua 1st Road Guishan District Taoyuan City 33302 Taiwan. Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Road Zhongli District Taoyuan City 320317 Taiwan. Department of Mechanical Engineering National Cheng Kung University No. 1 University Road Tainan City 701 Taiwan. Department of Applied Physics and Electronics Umeå University 90187 Umeå Sweden. Department of Virtual-Reality Interaction with Artificial Intelligence Technology Coretronic Reality Incorporation No. 5 Wenhua Rd. Hukou Township Hsinchu County 303 Taiwan.

MOTIVATION:The synthesis of proteins with novel desired properties is challenging but sought after by the industry and academia. The dominating approach is based on trial-and-error inducing point mutations, assisted by structural information or predictive models built with paired data that are difficult to collect. This study proposes a sequence-based unpaired-sample of novel protein inventor (SUNI) to build ThermalProGAN for generating thermally stable proteins based on sequence information. RESULTS:The ThermalProGAN can strongly mutate the input sequence with a median number of 32 residues. A known normal protein, 1RG0, was used to generate a thermally stable form by mutating 51 residues. After superimposing the two structures, high similarity is shown, indicating that the basic function would be conserved. Eighty four molecular dynamics simulation results of 1RG0 and the COVID-19 vaccine candidates with a total simulation time of 840[Formula: see text]ns indicate that the thermal stability increased. CONCLUSION:This proof of concept demonstrated that transfer of a desired protein property from one set of proteins is feasible. The source code of ThermalProGAN can be freely accessed at https://***/markliou/ThermalProGAN/ with an MIT license. The website is https://***:433. Supplementary data are available on Github.

关键词： CycleGAN Protein synthesis generative adversarial neural network thermal stability

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