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19th International Conference on Human-computer Interaction, Interaccion 2018

作者： Arango-López, Jeferson Gallardo, Jesús Gutiérrez-Vela, Francisco Luis Amengual, Esperança Collazos, Cesar A. University of Cauca FIET Department of Systems Colombia University of Zaragoza Department of Informatics and Systems Engineering Spain University of Granada ETSIIT Department of Languages and Informatics Systems Spain Universitat de les Illes Balears Department of Mathematical and Computer Science Spain

ISBN: (纸本)9781450364911

Many experiences have been applied with games in different areas of knowledge to obtain beter results or to simply increase motivation or atract potential users. 1This is the case of the learning processes in the educational context, or in the rehabilitation of patients in the health context. Currently, there are new technologies that have allowed to advance in the design and development of this kind of games. The so-called Pervasive Games (PG) have not been the exception, and by mixing elements of traditional games with elements of reality, objects, characters, scenarios, etc., the PG has achieved a high point in motivation and experience of user. However, previous studies have identified the need for a methodology that directs each phase of the creation of pervasive gaming experiences due to the special characteristics that these games have. This paper presents a methodology that is integrated with the design of the pervasive narrative as the core of the geolocated game experience. This methodology presents different general components that incorporate the elements of the game, the visions of the game world and the pervasive devices together with the interaction between them. In addition, a specific gaming experience is implemented that shows positive results in the learning of basic programming concepts in the first semester students of computer engineering. © 2018 Association for Computing Machinery.

关键词： Motivation

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Upsilon-SVR Polynomial Kernel for Predicting the Defect Density in New software Projects 17

Upsilon-SVR Polynomial Kernel for Predicting the Defect Dens...

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17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018

作者： Lopez-Martin, Cuauhtemoc Azzeh, Mohammad Bou-Nassif, Ali Banitaan, Shadi Department of Information Systems Universidad de Guadalajara Mexico Department of Software Engineering Applied Science Private University Amman Jordan Department of Electrical and Computer Engineering University of Sharjah Sharjah United Arab Emirates Department of Mathematics Computer Science and Software Engineering University of Detroit Mercy DetroitMI United States

ISBN: (纸本)9781538668047

An important product measure to determine the effectiveness of software processes is the defect density (DD). In this study, we propose the application of support vector regression (SVR) to predict the DD of new software projects obtained from the International software Benchmarking Standards Group (ISBSG) Release 2018 data set. Two types of SVR (i.e., ϵ-SVR and υ-SVR) were applied to train and test these projects. Each SVR used four types of kernels. The prediction accuracy of each SVR was compared to that of a statistical regression (i.e., a simple linear regression, SLR). Statistical significance test showed that υ-SVR with polynomial kernel was better than that of SLR when new software projects were developed on mainframes and coded in programming languages of third generation. © 2018 IEEE.

关键词： Defect density

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Secure data sharing for mobile cloud computing using RSA

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IOP Conference Series: Materials Science and engineering 2021年第1期1055卷

作者： M Arumugam S Deepa G Arun P Sathishkumar K Jeevanantham Assistant Professor Department of Computer Technology - PG Kongu Engineering College Perundurai Erode India Assistant Professor Department of Information Technology MP Nachimuthu M Jaganathan Engineering College Chennimalai India Assistant Professor Department of Computer Science and Engineering Bannariamman Institute of Technology Sathyamangalam India Currently pursuing Master's degree programme in Software Systems Kongu Engineering College Perundurai Erode India

Mobile cloud computing (MCC) refers to an infrastructure in which data processing and storage can take place far from mobile devices. The convergence of compact registration and network distributed computing has created scalable distributed computing. This technology provides consumers a number of points of interest, similar to storage limits, reliability, scalability and access to real-time information. As a result, it is expanding steadily and is undoubtedly organised into a daily day-to-day life. The Cloud servers can be used for the preparation and storage of ***, in the current conditions, the secrecy of photos and information is generally important. In this paper, we concentrate mainly on the safe re-appropriation of photographs.

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Estimating adaptive cruise control model parameters from on-board radar units

arXiv

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arXiv 2019年

作者： Wang, Yanbing Gunter, George Nice, Matthew Work, Daniel B. Department of Civil and Environmental Engineering Institute for Software Integrated Systems Vanderbilt University 1025 16th Avenue South NashvilleTN37212 Department of Electrical Engineering and Computer Science Institute for Software Integrated Systems Vanderbilt University 1025 16th Avenue South NashvilleTN37212

Two new methods are presented for estimating car-following model parameters using data collected from Adaptive Cruise Control (ACC) enabled vehicles. The vehicle is assumed to follow a constant time headway relative velocity model in which the parameters are unknown and to be determined. The first technique is a batch method that uses a least-squares approach to estimate the parameters from time series data of the vehicle speed, space gap, and relative velocity of a lead vehicle. The second method is an online approach that uses a particle filter to simultaneously estimate both the state of the system and the model parameters. Numerical experiments demonstrate the accuracy and computational performance of the methods relative to a commonly used simulation-based optimization approach. The methods are also assessed on empirical data collected from a 2019 model year ACC vehicle driven in a highway environment. Speed, space gap, and relative velocity data are recorded directly from the factory-installed radar unit via the vehicle's CAN bus. All three methods return similar mean absolute error values in speed and spacing compared to the recorded data. The least-squares method has the fastest run-time performance, and is up to 3 orders of magnitude faster than other methods. The particle filter is faster than real-time, and therefore is suitable in streaming applications in which the datasets can grow arbitrarily large. Copyright © 2019, The Authors. All rights reserved.

关键词： Monte Carlo methods

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Smart City Cybersecurity Projects Financing Model in Case of Description of Investors’ Resources With Fuzzy Sets

Smart City Cybersecurity Projects Financing Model in Case of...

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IEEE International Conference on Advanced Trends in Information Theory (ATIT)

作者： Valeriy Lakhno Yurii Matus Volodymyr Malyukov Alyona Desyatko Tetyana Hnatchenko Department of Computer systems and networks National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Software Engineering and Cyber Security National University of Trade and Economics Kyiv Ukraine

ISBN: (数字)9781728161440

ISBN: (纸本)9781728161457

The paper describes the model of choosing mutual investment strategies for Smart City cybersecurity projects with incomplete information on the financial resources of the second investor. The case when the financial resources of the parties belong to fuzzy sets is considered. The model is designed for the developing decision support system in the tasks of choosing rational options for investing in Smart City cybersecurity projects. The solution was obtained through the use of tools of the theory of multi-step games with several terminal surfaces. Distinctive feature of the model is the assumption that the first investor does not have complete information about both the financial strategies of the second investor and of his financial resources state. Verifying the model was performed using computational experiment in MatLab.

关键词： Smart cities computer security Investment Computational modeling Mathematical model Fuzzy sets Games

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Correction to: Industrial-iot-hardware security-improvement using plan load optimization method in cloud

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International Journal of System Assurance engineering and Management 2021年第6期13卷 3108-3108页

作者： Basheer, Shakila Gopu, Magesh Mathew, Rincy Merlin Bivi, Maryam Aysha Prabu, M. Department of Information System College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Department of Software and Systems Engineering Vellore Institute of Technology Vellore India Computer Science Department College of Science and Arts King Khalid University Khamis Mushayt Abha Saudi Arabia Department of Computer Science College of Computer Science King Khalid University Abha Saudi Arabia Department of Computer Science Christ University Bangalore India

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Generation and application of constrained interaction test suites using base forbidden tuples with mixed neighborhood tabu search

arXiv

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arXiv 2019年

作者： Hasan, Imad H. Ahmed, Bestoun S. Potrus, Moayad Y. Zamli, Kamal Z. Erbil44002 Iraq Department of Mathematics and Computer Science Karlstad University Karlstad651 88 Sweden Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Gambang Pahang26300 Malaysia

To ensure the quality of current highly configurable software systems, intensive testing is needed to test all the configuration combinations and detect all the possible faults. This task becomes more challenging for most modern software systems when constraints are given for the configurations. Here, intensive testing is almost impossible, especially considering the additional computation required to resolve the constraints during the test generation process. In addition, this testing process is exhaustive and time-consuming. Combinatorial interaction strategies can systematically reduce the number of test cases to construct a minimal test suite without affecting the effectiveness of the tests. This paper presents a new efficient search-based strategy to generate constrained interaction test suites to cover all possible combinations. The paper also shows a new application of constrained interaction testing in software fault searches. The proposed strategy initially generates the set of all possible t − tuple combinations;then, it filters out the set by removing the forbidden t − tuples using the base forbidden tuple (BFT) approach. The strategy also utilizes a mixed neighborhood tabu search (TS) to construct optimal or near-optimal constrained test suites. The efficiency of the proposed method is evaluated through a comparison against two well-known state-of-the-art tools. The evaluation consists of three sets of experiments for 35 standard benchmarks. Additionally, the effectiveness and quality of the results are assessed using a real-world case study. Experimental results show that the proposed strategy outperforms one of the competitive strategies, ACTS, for approximately 83% of the benchmarks and achieves similar results to CASA for 65% of the benchmarks when the interaction strength is 2. For an interaction strength of 3, the proposed method outperforms other competitive strategies for approximately 60% and 42% of the benchmarks. The proposed strategy can also

关键词： software testing

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Patient comprehension of oncologic surgical procedures using 3D printed surgical planning prototypes

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Annals of 3D Printed Medicine 2022年 7卷 100068-100068页

作者： Tejo-Otero, A. Valls-Esteve, A. Fenollosa-Artés, F. Siles-Hinojosa, A. Nafria, B. Ayats, M. Buj-Corral, I. Otero, MC. Rubio-Palau, J. Munuera, J. Krauel, L. Centre CIM Universitat Politècnica de Catalunya (CIM UPC) Carrer de Llorens i Artigas 12 Barcelona 08028 Spain Innovation Department. Hospital Sant Joan de Déu Universitat de Barcelona Santa Rosa 39-57 Esplugues de Llobregat 08950 Spain 3D Unit (3D4H) Hospital Sant Joan de Déu Universitat de Barcelona Spain Department of Pediatric Surgery Hospital Sant Joan de Déu Universitat de Barcelona Spain Maxillofacial Unit Department of Pediatric Surgery Hospital Sant Joan de Déu Universitat de Barcelona Spain Department of Diagnostic Imaging Hospital Sant Joan de Déu Universitat de Barcelona Spain KIDS Barcelona Sant Joan de Déu Research Foundation Barcelona Spain Universitat Politècnica de Catalunya Department of Mechanical Engineering. School of Engineering of Barcelona (ETSEIB) Av. Diagonal 647 Barcelona 08028 Spain University of the Basque Country (UPV/EHU) Department of Computer Languages and Systems. School of Engineering of Vitoria-Gasteiz Nieves Cano 12 Vitoria-Gasteiz 01006 Spain

Purpose: Patient understanding of complex surgical procedures and post-intervention consequences is often poor. Little is known about the effectiveness of 3D printed models to improve the comprehension of the medical information provided to patients. The purpose of this study was to determine if 3D printed patient-specific anatomical models could help improve patients’ satisfaction and understanding of complex oncological surgical procedures, their risks, benefits, and alternatives. Basic procedure: A randomized, controlled crossover experiment was performed, where subjects were randomly assigned to different treatments of the study. This experiment involved teenage patients experts from Kids Barcelona, a Young Person's Advisory Group. The team (n = 14, age range 14–20, 9 females and 5 males) was divided into two groups involved in two simulated pre-surgical outpatient visits for complex oncologic surgical procedures: a high-risk stage 4 abdominal neuroblastoma, and a biliary tract rhabdomyosarcoma. Two senior oncologic surgeons participated in the study by performing the structured outpatient pre-surgical visit. Each participant received information before the study explaining the study methodology and was given a questionnaire. Main findings: Data analysis of the group using the 3D printed model for the neuroblastoma case showed better results than without the 3D model. On the other hand, conversely, on the data analysis of the rhabdomyosarcoma case with the 3D printed model no better results were observed as compared to the case of not using a 3D model. However, the results of the participants’ knowledge were still better than before the intervention. Satisfaction was significantly better with a 3D model in both cases. Conclusion: The use of 3D physical models improves the patient's knowledge and shows the effectiveness of 3D printed models to enhance the comprehension of the medical information provided to patients and improve satisfaction. © 2022 The Authors

关键词： 3d printing Additive manufacturing health Patient experience Phantoms Preoperative planning Surgical education Surgical planning prototypes

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Scalability Evaluation of Big Data Processing Services in Clouds 1st

Scalability Evaluation of Big Data Processing Services in Cl...

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1st International Symposium on Benchmarking, Measuring, and Optimization, Bench 2018

作者： Zhou, Xin Jiang, Congfeng Qiu, Yeliang Fan, Tiantian Wang, Yumei Zhang, Liangbin Wan, Jian Shi, Weisong Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou Dianzi University Hangzhou310037 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310037 China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China Department of Computer Science Wayne State University DetroitMI48202 United States

ISBN: (纸本)9783030328122

Currently, many cloud providers deploy their big data processing systems as cloud services, which helps users conveniently manage and process their data in clouds. Among different service providers’ big data processing services, how to evaluate and compare their scalability is an interesting and challenging work. Most traditional benchmark tools focus on performance evaluation of big data processing systems, such as aggregated throughput and IOPS, but fail to conduct a quantitative analysis of their scalability. In this paper, we propose a measurement methodology to quantify the scalability of big data processing services, which makes the cloud services scalability comparable. We conduct a group of comparative experiments on AliCloud E-MapReduce and Baidu MRS, and collect their respective scalability characteristics under Hadoop and Spark workloads. The scalability characteristics observed in our work could help cloud users choose the best cloud service platform to set up an optimized big data processing system to achieve their specific goals more successfully. © 2019, Springer Nature Switzerland AG.

关键词： Big data

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Explaining predictions of deep neural classifier via activation analysis

arXiv

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arXiv 2020年

作者： Stano, Martin Benesova, Wanda Marták, Lukáš Samuel Department of Informatics Informational Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava842 16 Slovakia Department of Computer Engineering and Applied Informatics Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava842 16 Slovakia Institute of Computational Perception Johannes Kepler University Linz4040 Austria LIT Artificial Intelligence Lab Johannes Kepler University Linz4040 Austria

In many practical applications, deep neural networks have been typically deployed to operate as a black box predictors. Despite high amount of work on interpretability and high demand on reliability of these systems, they typically still have to include a human actor in the loop, to validate the decisions and handle unpredictable failures and unexpected corner cases. This is true in particular for failure-critical application domains, such as medical diagnosis. We present a novel approach to explain and support an interpretation of a decision-making process to a human expert operating a deep learning system based on Convolutional Neural Network (CNN). By modeling activation statistics on selected layers of a trained CNN via Gaussian Mixture Models (GMM), we develop a novel perceptual code in binary vector space that describes how input sample is processed by the CNN. By measuring distances between pairs of samples in this perceptual encoding space, for any new input sample, we can now retrieve a set of most perceptually similar and dissimilar samples from an existing atlas of labeled samples, to support and clarify the decision made by the CNN model. Possible uses of this approach include for example computer-Aided Diagnosis (CAD) systems working with medical imaging data, such as Magnetic Resonance Imaging (MRI) or Computed Tomography (CT) scans. We demonstrate the viability of our method in the domain of medical imaging for patient condition diagnosis, as proposed decision explanation method via similar ground truth domain examples (e.g. from existing diagnosis archives) will be interpretable by the operating medical personnel. Our results indicate that our method is capable of detecting distinct prediction strategies which enables to identify most similar predictions from an Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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