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IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW

作者： Arilo Claudio Dias Neto Rosiane de Freitas Rodrigues Guilherme Horta Travassos Computer Science Department Federal University of Amazonas Manaus Brazil Systems Engineering and Computer Science Program-COPPE Federal University of Rio de Janeiro Rio de Janeiro Rio de Janeiro Brazil

The combination of testing techniques is considered an effective strategy to evaluate a software product. However, the selection of which techniques to combine in a software project has been an interesting challenge in the Software engineering field. This paper presents a proposal extending an approach developed to support the combined selection of model-based testing (MBT) techniques, named Porantim, applying Multiobjective Combinatorial Optimization strategy by determining the smallest dominating set in a bipartite and weighted graph. Thus, a local search strategy algorithm is proposed generating solutions aiming at maximizing the coverage of software project characteristics and skills required by the testing team to use the techniques and minimizing the eventual effort to construct models used for test cases generation. A preliminary evaluation analyzes this new approach when compared to the Porantim's original version, and the results indicate improvements in the MBT techniques selection.

关键词： Software Testing Analytical models Software engineering Optimization programming Search problems

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RJSSim: A reconfigurable job scheduling simulator for parallel processing learning 33

RJSSim: A reconfigurable job scheduling simulator for parall...

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33rd Annual Frontiers in Education, FIE 2003

作者： Góes, Luís F.W. Martins, Carlos A.P.S. Pontifical Catholic University of Minas Gerais Post-Graduation Program in Electrical Engineering Computational and Digital Systems Laboratory Belo HorizonteMG Brazil Pontifical Catholic University of Minas Gerais Computer Science Department Post-Graduation Program in Electrical Engineering Computational and Digital Systems Laboratory Belo HorizonteMG Brazil

ISBN: (纸本)0780379616

In this work, we present and analyze the use of a reconfigurable job scheduling simulator called RJSSim as an aid tool for parallel processing learning. This software is a functional and performance Java-based simulator of job scheduling policies. Our objectives are: to present RJSSim;to show the use of RJSSim and reconfigurability concepts for parallel processing learning. First, we present a prototype of RJSSim and the reconfigurability concept. Then we describe two case studies remarking the main parallel processing concepts and skills students can learn. In the first case study, we analyze two parallel algorithm models. In the second, we do some performance tests among parallel (reconfigurable) scheduling policies and architectures. Then, we analyze the performance through some metrics like response time, idle time etc. Finally we discuss and analyze the use of RJSSim for parallel processing learning. Our main contribution is: an implementation of RJSSim for parallel processing learning. © 2003 IEEE.

关键词： Parallel algorithms

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KLF2 maintains lineage fidelity and suppresses CD8 T cell exhaustion during acute LCMV infection

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science (New York, N.Y.) 2025年第6735期387卷 eadn2337页

作者： Fagerberg, Eric Attanasio, John Dien, Christine Singh, Jaiveer Kessler, Emily A. Abdullah, Leena Shen, Jian Hunt, Brian G. Connolly, Kelli A. De Brouwer, Edward He, Jiaming Iyer, Nivedita R. Buck, Jessica Borr, Emily R. Damo, Martina Foster, Gena G. Giles, Josephine R. Huang, Yina H. Tsang, John S. Krishnaswamy, Smita Cui, Weiguo Joshi, Nikhil S. Department of Immunobiology Yale University School of Medicine New Haven CT United States Program in Computational Biology & Bioinformatics Yale University New Haven CT United States Department of Microbiology and Immunology Geisel School of Medicine at Dartmouth NH United States Department of Pathology Feinberg School of Medicine at Northwestern University Chicago IL United States Department of Genetics and Computer Science Yale University School of Medicine New Haven CT United States Department of Medicine University of Chicago Chicago IL United States Section of Hematology Department of Internal Medicine Yale University School of Medicine New Haven CT United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania Philadelphia PA United States Institute for Immunology Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Parker Institute for Cancer Immunotherapy Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Yale Center for Systems and Engineering Immunology Yale University School of Medicine New Haven CT United States New Haven CT United States Department of Biomedical Engineering Yale University New Haven CT United States Computational Biology and Bioinformatics Program Yale University CT USA New Haven United States Applied Math Program Yale University New Haven CT United States

Naïve CD8 T cells have the potential to differentiate into a spectrum of functional states during an immune response. How these developmental decisions are made and what mechanisms exist to suppress differentiation toward alternative fates remains unclear. We employed in vivo CRISPR-Cas9-based perturbation sequencing to assess the role of ~40 transcription factors (TFs) and epigenetic modulators in T cell fate decisions. Unexpectedly, we found that knockout of the TF Klf2 resulted in aberrant differentiation to exhausted-like CD8 T cells during acute infection. KLF2 was required to suppress the exhaustion-promoting TF TOX and to enable the TF TBET to drive effector differentiation. KLF2 was also necessary to maintain a polyfunctional tumor-specific progenitor state. Thus, KLF2 provides effector CD8 T cell lineage fidelity and suppresses the exhaustion program.

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Phylogeographic and genetic network assessment of COVID-19 mitigation protocols on SARS-CoV-2 transmission in university campus residences

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eBioMedicine 2025年 116卷 105729-105729页

作者： Wertheim, Joel O. Vasylyeva, Tetyana I. Wood, Robert J. Cantrell, Kalen Contreras, Soraya Piña Feldheim, Aryeh Goyal, Ravi Havens, Jennifer L. Knight, Rob Laurent, Louise C. Moshiri, Niema Neuhard, Robert Sathe, Shashank Satterlund, Alysson Scioscia, Angela Song, Angela Y. Aigner, Stefan Andersen, Kristian G. Baer, Nathan A. Betty, Maryann Birmingham, Amanda Castro-Martinez, Anelizze Cheung, Willi De Hoff, Peter Fisch, Kathleen M. King, Alison J. Gangavarapu, Karthik Hakim, Abbas Henson, Benjamin Jepsen, Kristen Mac, Christina H. Ngo, Toan T. Nguyen, Kelly N. Ostrander, Tyler R. Perkins, Sarah Plascencia, Ashley Rivera, Andrea Rivera, Ariana Salido, Rodolfo A. Saucedo, Kieran C. Schwab, Madison Steedman, Allison L. Veder, Anthony Weiss, Alana Yeo, Gene W. Zeller, Mark Schooley, Robert T. Anderson, Cheryl M. Martin, Natasha K. Department of Medicine University of California San Diego La Jolla CA United States Department of Population Health and Disease Prevention University of California Irvine Irvine CA United States Herbert Wertheim School of Public Health and Human Longevity Science University of California San Diego La Jolla CA United States Department of Computer Science & Engineering University of California San Diego La Jolla CA United States Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA United States Department of Pediatrics University of California San Diego La Jolla CA United States Department of Bioengineering University of California San Diego La Jolla CA United States Department of Computer Science and Engineering University of California San Diego La Jolla CA United States Center for Microbiome Innovation University of California San Diego La Jolla CA United States Halıcıoğlu Data Science Institute University of California San Diego La Jolla CA United States Department of Obstetrics Gynecology and Reproductive Sciences University of California San Diego La Jolla CA United States Sanford Consortium of Regenerative Medicine University of California San Diego La Jolla CA United States University of California San Diego La Jolla CA United States

Background: Congregate living provides an ideal setting for SARS-CoV-2 transmission in which many outbreaks and superspreading events occurred. To avoid large outbreaks, universities turned to remote operations during the initial COVID-19 pandemic waves in 2020 and 2021. In late-2021, the University of California San Diego (UC San Diego) facilitated the return of students to campus with comprehensive testing, vaccination, masking, wastewater surveillance, and isolation policies. Methods: We performed molecular epidemiological and phylogeographic analysis of 4418 SARS-CoV-2 genomes sampled from UC San Diego students during the Omicron waves between December 2021 and September 2022, representing 58% of students with confirmed SARS-CoV-2 infection. We overlaid these analyses across on-campus residential information to assess the spread and persistence of SARS-CoV-2 within university residences. Findings: Within campus residences, SARS-CoV-2 transmission was frequent among students residing in the same room or suite. However, a quarter of pairs of suitemates with concurrent infections had distantly related viruses, suggesting separate sources of infection during periods of high incidence in the surrounding community. Students with concurrent infections residing in the same building were not at substantial increased probability of being members of the same transmission cluster. Genetic network and phylogeographic inference indicated that only between 3.1 and 12.4% of infections among students could be associated with transmission within buildings outside of individual suites. The only super-spreading event we detected was related to a large event outside campus residences. Interpretation: We found little evidence for sustained SARS-CoV-2 transmission within individual buildings, aside from students who resided in the same suite. Even in the face of heightened community transmission during the 2021–2022 Omicron waves, congregate living did not result in a heightened risk

关键词： Mitigation Phylogeography SARS-CoV-2 Transmission cluster University

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A Game-Theoretic Approach to Safe Crowd Evacuation in Emergencies

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computers, Materials & Continua 2024年第4期79卷 1631-1657页

作者： Maria Gul Imran Ali Khan Gohar Zaman Atta Rahman Jamaluddin Mir Sardar Asad Ali Biabani May IssaAldossary Mustafa Youldash Ashraf Saadeldeen Maqsood Mahmud Asiya Abdus Salam Dania Alkhulaifi Abdullah AlTurkey31Department of Computer Science COMSATS University IslamabadAbbottabad CampusAbbottabad22010Pakistan Department of Computer Science Abbottabad University of Science and TechnologyAbbottabad22010Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Science and Technology Unit Umm Al-Qura UniversityMakkah21955Saudi Arabia Deanship of Scientific Research Umm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Engineering College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Science Faculty of Computer Science and Information TechnologyOmdurman Islamic UniversityOmdurman14415Sudan Business Analytic Program Department of Management and MarketingCollege of Business AdministrationUniversity of BahrainSakhir32038Bahrain

Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, manyresearchers proposed game theoreticmodels to avoid and remove obstacles for crowd evacuation. Game theoreticalmodels aim to study and analyze the strategic behaviors of individuals within a crowd and their interactionsduring the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. Thesemodels consider a group of individuals as homogeneous objects with the same goals, involve complex mathematicalformulation, and cannot model real-world scenarios such as panic, environmental information, crowds that movedynamically, etc. The proposed work presents a game theoretic model integrating an agent-based model to removethe obstacles from exits. The proposed model considered the parameters named: (1) obstacle size, length, andwidth, (2) removal time, (3) evacuation time, (4) crowd density, (5) obstacle identification, and (6) route *** proposed work conducts various experiments considering different conditions, such as obstacle types, obstacleremoval, and several obstacles. Evaluation results show the proposed model’s effectiveness compared with existingliterature in reducing the overall evacuation time, cell selection, and obstacle removal. The study is potentially usefulfor public safety situations such as emergency evacuations during disasters and calamities.

关键词： Safe crowd evacuation public safety emergency transition probability cooperation

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Importance sampling for general hybrid Bayesian networks

Importance sampling for general hybrid Bayesian networks

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11th International Conference on Artificial Intelligence and Statistics, AISTATS 2007

作者： Yuan, Changhe Druzdzel, Marek J. Department of Computer Science and Engineering Mississippi State University Mississippi State MS 39762 United States Decision Systems Laboratory School of Information Sciences and Intelligent Systems Program University of Pittsburgh Pittsburgh PA 15260 United States

Some real problems are more naturally modeled by hybrid Bayesian networks that consist of mixtures of continuous and discrete variables with their interactions described by equations and continuous probability distributions. However, inference in such general hybrid models is hard. Therefore, existing approaches either only deal with special instances, such as Conditional Linear Gaussians (CLGs), or approximate a general model with a restricted version and then perform inference on the simpler model. However, results thus obtained highly depend on the quality of the approximations. This paper describes an importance sampling-based algorithm that directly deals with hybrid Bayesian networks constructed in the most general settings and guarantees to converge to the correct answers given enough time.

关键词： Importance sampling

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Application of factor analysis method to support the users acceptance model of ERP systems implementation 4

Application of factor analysis method to support the users a...

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4th International Conference on Combinatorics, Graph Theory, and Network Topology, ICCGANT 2020

作者： Sardjono, W. Sudirwan, J. Priatna, W. Putra, G.R. Information Systems Management Department BINUS Graduate Program Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia School of Information Systems Department BINUS Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia Informatics Engineering Engineering Bhayangkara University Jakarta Raya Indonesia Computer Science Study Program Faculty of Mathematics and Natural Sciences Pakuan University Bogor Indonesia

An e-Commerce company has been using an Enterprise Resource Planning (ERP) system for several years, but is still constrained in its implementation, this is reflected in the number of issue/change request tickets submitted by users who have not been able to reach ideal conditions. To overcome this problem, we need an appropriate reference model, the ERP system user acceptance model in companies. The study conducted factors that influence the level of acceptance of SAP ERP users in the XYZ group to then determine a model that is able to explain practically the level of ERP system user acceptance, which in turn can produce concrete proposals to increase ERP system user acceptance. Research is quantitative descriptive using factor analysis and regression analysis methods. The research method uses a questionnaire with a total of 107 valid respondents coming from 8 company names under the XYZ group in 6 Southeast Asian countries. The conclusion of the study is that there are 24 indicators and 4 factors that influence user acceptance of the SAP ERP system. These four factors are the low contribution of the ERP system, the quality of ERP systems and data, the development of ERP systems, and also the low quality of ERP system supporters. It is hoped that by optimizing the value of the 4 factors, the XYZ group can increase user acceptance of the SAP ERP system so that the system can provide benefits expected by the company to be competitive advantage. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Factor analysis

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A New Tree Structure for Local Diagnosis

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Journal of Information science and engineering 2024年第2期40卷 231-244页

作者： Chen, Meirun Li, Xiao-Yan Lin, Cheng-Kuan Pai, Kung-Jui School of Mathematics and Statistics Xiamen University of Technology Xiamen361024 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology National Defense University Taoyuan30010 Taiwan Department of Industrial Engineering and Management Ming Chi University of Technology New Taipei City243303 Taiwan

Diagnosability is an important parameter to measure the fault tolerance of a multiprocessor system. If we only care about the state of a node, instead of doing the global diagnosis, Hsu and Tan proposed the idea of local diagnosis. Chiang and Tan provided an extended star structure to diagnose a node under comparison model. In this work, we evaluate the local diagnosability better by proposing a tree structure around this node. We provide the corresponding algorithm to diagnose the node. Simulation results are presented for different failure probability of a node in the tree and different percentage of faulty nodes in the tree, showing the performance of our algorithm. © 2024 Institute of Information science. All rights reserved.

关键词： comparison model diagnosis algorithm fault diagnosis local diagnosability tree

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Early Detection and Classification of Melanoma Based on Android MobileNet V2 Convolutional Neural Network

Early Detection and Classification of Melanoma Based on Andr...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Prabandari, Jasmine Iswarini Dyah Sari, Christy Atika Rachmawanto, Eko Hari Irawan, Candra Altamer, Bilal R. Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Information Systems Semarang Indonesia Mosul University Presidency University of Mosul Mosul Iraq National University of Science and Technology Department of Computer Science DhiQar Iraq

ISBN: (纸本)9798350339215

Melanoma is a malignant form of cancer that affects the skin and has a particularly high mortality rate, so it requires early detection to increase the level of safety for users. Diagnosis and detection of skin cancer are usually done through manual screening and visual inspection. This process requires a long time, has high complexity, is subjective, and is prone to errors. CNN is one of the algorithms with advantages in accurate classification. In this research, early detection and classification of melanoma cancer were carried out based on two classes, namely benign and malignant using the Convolutional Neural Network method. Our proposed method yields an accuracy of 81.11% for the validation data. The accuracy results obtained can be improved by using more datasets and increasing the number of layers used. This study uses the CNN method using MobileNet V2 architecture to detect melanoma skin cancer. The class used is benign and malignant. © 2023 IEEE.

关键词： Convolutional neural networks

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Assembling a Network out of Ambiguous Patches

Assembling a Network out of Ambiguous Patches

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Annual Allerton Conference on Communication, Control, and Computing

作者： Lyudmila Yartseva Jefferson Elbert Sim?es Matthias Grossglauser School of Computer and Communication Sciences école Polytechnique Fédérale de Lausanne (EPFL) Switzerland Systems Engineering and Computer Science Program COPPE Federal University of Rio de Janeiro Brazil

ISBN: (纸本)9781509045518

Many graph mining and network analysis problems rely on the availability of the full network over a set of nodes. But inferring a full network is sometimes non-trivial if the raw data is in the form of many small patches or subgraphs, of the true network, and if there are ambiguities in the identities of nodes or edges in these patches. This may happen because of noise or because of the nature of data;for instance, in social networks, names are typically not unique. Graph assembly refers to the problem of reconstructing a graph from these many, possibly noisy, partial observations. Prior work suggests that graph assembly is essentially impossible in regimes of interest when the true graph is Erdos-Rényi. The purpose of the present paper is to show that a modest amount of clustering is sufficient to assemble even very large graphs. We introduce the G(n,p;q) random graph model, which is the random closure over all open triangles of a G(n,p) Erdos-Rényi, and show that this model exhibits higher clustering than an equivalent Erdos-Rényi . We focus on an extreme case of graph assembly: the patches are small (1-hop egonets) and are unlabeled. We show that in realistic regimes, graph assembly is fundamentally feasible, because we can identify, for every edge e, a subgraph induced by its neighbors that is unique and present in every patch containing e. Using this result, we build a practical algorithm that uses canonical labeling to reconstruct the original graph from noiseless patches. We also provide an achievability result for noisy patches, which are obtained by edge-sampling the original egonets.

关键词： mining network analysis

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