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International Conference on Computer Supported Cooperative Work in Design

作者： Chessman Corrêa Lucas Nóbrega Luiz Felipe Martinez Carlos Eduardo Barbosa Matheus Argôlo Herbert Salazar Jano Moreira de Souza Geraldo Xexéo Systems Engineering and Computing Program Universidade Federal do Rio de Janeiro (UFRJ) Centro Universitário Serra dos Órgãos (UNIFESO) Centro de Análises de Sistemas Navais (CASNAV) - Marinha do Brasil

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Software systems are critical resources for our daily lives. Due to that, computer science students need to learn software engineering knowledge and skills to develop software systems with the expected quality, under budget, and on schedule. The size and complexity of software systems and the short time to market make it necessary to have various software developers working as a team. In this scenario, the success of a project depends on coordination, communication, and collaboration. The evolution of network, communication, and computer technologies made it possible for people from different countries to work on the same project. Therefore, software engineers must collaborate remotely with others in Global Software Engineering (GSE) projects. Hence, students must also learn how to collaborate with people from different countries, with specific expectations, and different cultures and languages. This study lists the main GSE challenges the students must be ready to deal with, how researchers have addressed GSE, and how GSE education has been approached in the last five years. Researchers focused on teaching approaches, what students experienced during GSE courses, and the importance of GSE in industry. Students had the opportunity to face unsolved GSE challenges, and GSE courses still need to be improved.

关键词： Industries Computer science Education Collaboration Time to market Software systems Complexity theory Software engineering Systematic literature review Software development management

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D-ViShaDeRec: Double Intensity of Video Shadow Detection, Removal, and Re-coloring in Autonomous Vehicle 9

D-ViShaDeRec: Double Intensity of Video Shadow Detection, Re...

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9th International Conference on Computer, Control, Informatics and Its Applications: Digital Transformation Towards Sustainable Society for Post Covid-19 Recovery, IC3INA 2022

作者： Risnandar Nabila, Deeva Physics Study Program State University of Jakarta Jakarta Indonesia DKI Jakarta Jakarta Indonesia The IVDA Research Group-Research Center for Information and Data Sciences-BRIN Bandung Indonesia The Intelligent Systems Research Group-School of Computing-Telkom University Bandung Indonesia

ISBN: (纸本)9781450397919

Shadows are the obstacle parts of a vision-based during the driving an autonomous vehicle (AV). The shadow also can be detected as an object. It is feared the AV's system doesn't well-work or hits another object. Many methods of the images and video shadow detection and removal in the AVs. However, to handle the various backgrounds on the video shadow and objects, we need to exploit many scenarios of single video shadow detection, removal, and semantic segmentation. We propose a new novelty method in the shadow video detection and removal which is combined with the re-coloring method in the segmentation process. It's namely the D-ViShaDeRec. We also propose a new modified Sobel algorithm which has a kernel size of 5 × 5. The D-ViShaDeRec's outcomes show and of the recall, selectivity, precision, NPV, accuracy, and F1-Score, respectively. © 2022 ACM.

关键词： Autonomous vehicles

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Opportunities and Challenges from Using Animal Videos in Reinforcement Learning for Navigation

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IFAC-PapersOnLine 2023年第2期56卷 9056-9061页

作者： Vittorio Giammarino James Queeney Lucas C. Carstensen Michael E. Hasselmo Ioannis Ch Paschalidis Division of Systems Engineering Boston University Boston MA 02215 USA Center for Systems Neuroscience Department of Psychological and Brain Sciences and Graduate Program for Neuroscience Boston University Boston MA 02215 USA College of Engineering and Hariri Institute for Computing & Computational Science & Engineering Boston University Boston MA 02215 USA

We investigate the use of animal videos (observations) to improve Reinforcement Learning (RL) efficiency and performance in navigation tasks with sparse rewards. Motivated by theoretical considerations, we make use of weighted policy optimization for off-policy RL and describe the main challenges when learning from animal videos. We propose solutions and test our ideas on a 2D navigation task. We show how the use of animal videos improves performance over RL algorithms that do not leverage such observations.

关键词： Learning for Control Reinforcement Learning Deep Learning RL with demonstrations Imitation Learning Bio-inspired Control

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Predicting User Preferences with Neural Network Learning to Rank Method 5

Predicting User Preferences with Neural Network Learning to ...

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5th IEEE International Conference on Information Technology, Information systems and Electrical Engineering, ICITISEE 2021

作者： Qomariyah, Nunung Nurul Fajar, Ahmad Nurul Bina Nusantara University Faculty Of Computing And Media Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program-Master Of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9781665401968

User preference learning has been around for many years. This is a common problem arise in e-commerce system, where the companies need to understand their customers in order to sell the correct products to their target customers. The user preference is also needed in the movie domain area, as the film companies who produce the movie also need to understand how the customers value their movie products, whether the market like it or not. This can help them to predict the best components for the future movie. In order to produce the recommendation to the customer, we can show a list of item in a correct order, sorted from the most relevant/the most liked one to the most irrelevant item. In this paper, we utilize the Learning to Rank (LTR) approach from information retrieval domain area to learn the user preference in recommender system domain. We applied LTR with pairwise approach from the two most popular algorithms in this area, i.e. RankNet and LambdaRank, on 25, 345 user ratings from 5, 000 movie dataset and evaluate both algorithms. We used a Neural Network architecture as the learning mechanism. We also measuring the speed performance of the two algorithms. © 2021 IEEE.

关键词： Sales

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Expanding PyAFAR: A Novel Privacy-Preserving Infant AU Detector

Expanding PyAFAR: A Novel Privacy-Preserving Infant AU Detec...

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International Conference on Automatic Face and Gesture Recognition

作者： Itir Onal Ertugrul Saurabh Hinduja Maneesh Bilalpur Daniel S. Messinger Jeffrey F. Cohn Department of Information and Computing Sciences Utrecht University Utrecht The Netherlands Department of Psychology University of Pittsburgh Pittsburgh PA USA Intelligent Systems Program University of Pittsburgh Pittsburgh USA Department of Psychology University of Miami Miami FL USA

ISBN: (数字)9798350394948

ISBN: (纸本)9798350394955

We enhance PyAFAR 1 1 Code will be available on: https:\\***/PyAFARI, an open source, Python-based library for facial action unit detection by introducing a privacy-protected infant AU detector. To prevent reconstruction of the training images, we train the infant AU detector by extracting histogram of gradients (HoG) features and using an efficient Light Gradient Boosting Machine (LightGBM) classifier. Models are trained with two large, well-annotated databases. The performance of our approach is comparable to previously developed deep models that have not been released due to privacy concerns. Our models are available for use and further fine-tuning, contributing to the advancement of facial action unit detection.

关键词： Training Gold Privacy Histograms Face recognition Detectors Gesture recognition

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Three-Dimensional Wound Reconstruction using Point Descriptors: A Comparative Study

Three-Dimensional Wound Reconstruction using Point Descripto...

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Annual IEEE Symposium on Computer-Based Medical systems

作者： José R. A. Souto Fellipe M. C. Barbosa Bruno M. Carvalho Graduate Program in Systems and Computing Federal University of Rio Grande do Norte Natal Brazil Department of Informatics and Applied Mathematics Federal University of Rio Grande do Norte Natal Brazil

Precise measurements of chronic wound areas are very important for measuring the efficacy of different treatments, since it provides a clear picture of the chosen treatment evolution. The most used techniques still in use for measuring the wound areas involve manual measuring, paper scales and/or acetate tracing, with each health professional possibly measuring the wounds in slightly different ways, specially when dealing with the wound's depth. Thus, it is common to observe inconsistent wound area estimations by different health workers on the same wounds. Moreover, the measuring process requires direct contact with the wound, increasing the contamination and infection risks, besides possibly causing discomfort for the patient. An alternative that has already been proved successful is the use of Structure from Motion (SfM) to recover three-dimensional representations of a wound's surface and estimate that area. In this work, we analyze the effectiveness of several widely used point descriptors (BRIEF, DRINK, FREAK, ORB, SIFT and SURF) in the SfM wound reconstruction task when applied to real images of realistic synthetic wounds made of latex placed on different areas of the body. The results showed that SIFT, SURF and DRINK produced better area estimates. Since the execution time is a factor, we selected DRINK as the standard descriptor for our system, since it is approximately 9.3 and 4.9 times faster than SIFT and SURF, respectively.

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Acceptance Analysis of IWB systems in Education using UTAUT2 Model: The Moderating Role of Perceived Technological Innovations

Acceptance Analysis of IWB Systems in Education using UTAUT2...

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IEEE International Conference on Automatic Control and Intelligent systems (I2CACIS)

作者： Noelyn M. De Jesus Eltimar T. Castro Cory Antonio Buyan College of Informatics & Computing Sciences Batangas State University Batangas Phlippines Graduate Program Department Technological Institute of the Philippines Manila Philippines Business Technology Ballard Power Systems Burnaby Canada

Interactive Whiteboard (IWB) systems are increasingly being used to replace traditional whiteboards in educational activities, making them more efficient and interesting for both teachers and students. However, no studies have been conducted to investigate how age, educational level, and employment status can impact the adoption and usage of IWB systems in the educational setting. The study aims to analyze the acceptance and the influencing factors on faculty members’ intention and usage behavior towards IWB systems in teaching, utilizing the Modified-Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. 135 faculty members from various colleges and departments across Batangas State University ARASOF-Nasugbu Campus were surveyed and data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS software for hypotheses testing. The results of the study indicate that faculty members’ behavioral intention to use interactive whiteboard systems in teaching is positively influenced by performance expectancy and social influence. Moreover, facilitating conditions and habit positively impact usage behavior. This study, therefore, concludes that there is significant roles of performance expectancy and social influence as well as the moderating effect of age on the behavioral intention to use interactive whiteboard (IWB) systems in education. Furthermore, the study confirms that effort expectancy, hedonic motivation, and status as moderating variables significantly influence faculty members’ usage behavior when using IWB systems in teaching.

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Double-SP Method to Enhance Privacy and Security of Smart City Applications 10

Double-SP Method to Enhance Privacy and Security of Smart Ci...

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17th INDIACom;2023 10th International Conference on computing for Sustainable Global Development, INDIACom 2023

作者： Bahbouh, Nour Mahmoud Hakami, Abdulelah Alhussain Hassan Aljohani, Yaser Mohammed Sen, Adnan Ahmed Abi Alkhodre, Ahmad Alsughayyir, Aeshah Granada University Department of Information and Communication Sciences Granada Spain Saudi Electronic University Al-Madinah Saudi Arabia YasRef Program Yanbu Saudi Arabia Computing and Information Technology Islamic University Al-Madinah Saudi Arabia Islamic University Faculty of Computer and Information Systems Al-Madinah Saudi Arabia College of Computer Science and Engineering Taibah University Al-Madinah Saudi Arabia

ISBN: (纸本)9789380544472

Smart cities promise a lot of well-being to their users in all areas of life through millions of applications and services. Smart services rely heavily on collecting data and the preferences of users. But on the dark side, the users' information is posed to threat and penetration during transmission or stored far in the clouds. Depending on encryption only is not sufficient if the attacker has strong resources or if the attacker is the service provider (SP) itself. In addition, changing data before sending is not a practical solution in many systems because of the adverse impact on the quality of a main service. This research presented a new idea to address the issue of protection. The proposed method enhanced the privacy and security of users' data without affecting the accuracy of service. The core of the suggested solution relies on a knowledge base of services that are managed by experts. Also, the solution depends on fog nodes to measure the level of security and privacy of users' queries without delay. Moreover, the fog nodes manage contact with SPs. Finally, the proposed method divided the SP into two, one for user queries and the other for user data. The simulation and analytical discussion on a practical case in smart cities demonstrated the superiority of the proposed approach over previous methods in the level of protection by maintaining the quality of services, and the resistance to attacks. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Fog

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Determining environmental indicators related to the propagation of contagious diseases and health issues: A Systematic Literature Review

Determining environmental indicators related to the propagat...

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Iberian Conference on Information systems and Technologies (CISTI)

作者： Janeth Domínguez Portillo Laura M. Rodríguez Peralta Paulo N. M. Sampaio Eldman de Oliveira Nunes Faculty of Information Technologies and Data Science Universidad Popular Autónoma del Estado de Puebla (UPAEP) México Graduate Program in Computing and Systems University of Salvador (UNIFACS) Salvador Bahia Brazil

The environmental indicators provide significant information in order to describe the state of a phenomenon, environment or area [1]. This paper addresses the indicators present in the air and their relationship in the accommodation and transmission of viruses, as well as how these environmental indicators can lead to health problems. Therefore, the main goal of this work is to review the scientific contributions in the literature in order to determine and select the potential environmental indicators present in indoor environments which have an impact on the health of people who work and live within these spaces. For this purpose, we carried out a Systematic Literature Review (SLR) related to environmental monitoring, COVID-19, Internet of things, air pollution and environmental indicators within indoor spaces. This SLR allowed us to identify indicators that according to their level of presence can represent a high risk for people’s health. Therefore, the process of planning, conducting and reporting the Systematic Literature Review enabled us not only to identify some environmental indicators, but also their corresponding monitoring advantages and disadvantages, and multiple perspectives and approaches of each contribution in the literature. Further on, this study will be the basis for the proposal of an automatic and intelligent platform based on Internet of Things for monitoring indoor environments in order to detect high risk levels for the contamination of contagious diseases.

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Early Detection of Retinitis Pigmentosa

Early Detection of Retinitis Pigmentosa

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IEEE International Conference on Intelligent computing and Information systems (ICICIS)

作者： Adham Mohammed El Gazoly Karma Yasser Ismail Mahmoud Ahmed El-Tohamy Ali Ahmed Hassan Omar Mohammed Hamed Mohammed Maryam Nabil Al-Berry Hasnaa Sayed Abdo Mohammed Yousra Samir Al Amir Bioinformatics Program Information Systems Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Scientific Computing Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

Retinitis pigmentosa (RP) is a debilitating inherited retinal disorder that affects a significant number of individuals worldwide. Existing diagnostic methods have limitations in detecting early-stage RP and they highlight the need for using additional imaging modalities. In this paper, a groundbreaking approach for RP diagnosis is proposed using three imaging modalities and a CNN-based classification model achieving 97.7% accuracy. The proposed model demonstrates superior performance compared to previous work as it achieves higher accuracy. A segmentation model was also proposed to segment pigment spots and provide doctors with effective monitoring of the disease. The proposed segmentation model outperforms previously used methods by achieving 99.61% accuracy and higher values in most of the performance metrics, including specificity, precision, and Area Under Curve (AUC). The Segmentation and classification models were integrated into a web application providing a user-friendly and intuitive tool for RP diagnosis and paving the way for more accessible and effective diagnostic tools. This innovative approach democratizes healthcare by making RP diagnosis affordable and accessible to more people and improving the speed of clinical decision-making.

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