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The Consistency of Gamification User Types: A Study on the Change of Preferences over Time

Proceedings of the ACM on Human-computer Interaction 2023年第CHI PLAY期7卷 1253-1281页

作者： Santos, Ana Cláudia Guimarães Oliveira, Wilk Hamari, Juho Joaquim, Sivaldo Isotani, Seiji University of São Paulo Institute of Mathematics and Computer Science São Carlos Brazil Tampere University Gamification Group Faculty of Information Technology and Communication Sciences Tampere Finland Federal University of Alagoas Education Center Maceió Brazil Harvard Graduate School of Education CambridgeMA United States

In recent decades, several studies have suggested and validated user models (e.g., Bartle, and Hexad) to represent different user profiles in games and gamified environments. However, when applying these user models in practice (e.g., to personalize gamification), several studies reported contradictory outcomes. Recently, some studies outlined that one of the possible explanations for these contradictory findings is that people can present changes in their user profiles over time. In this study (N = 118), we present an analysis of the consistency of gamification user orientations after six months of the initial identification, by analyzing the association between user orientations in the first and second data collection. Overall, our results corroborate prior research demonstrating that user orientations can not be considered stable over time and also that the strongest tendency of the users might not be sufficient to determine how users change. Furthermore, we were able to identify that some user orientations can be more stable than others and model some relationships between their profiles after six months. Based on the results, we indicate a research agenda that can further the knowledge about the topic, as well as indicate a set of suggestions on how to model user profiles based on our results. © 2023 Owner/Author.

关键词： User profile

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Trends of Digital Transformation in the Property Management Industry: A Systematic Literature Review

Trends of Digital Transformation in the Property Management ...

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2023 IEEE International Conference on Smart information Systems and Technologies, SIST 2023

作者： Saputra, Prasetya Cahya Prabowo, Harjanto Ramadhan, Arief Madyatmadja, Evaristus Didik Binus University Binus Graduate Program-Computer Science Computer Science Department Jakarta Indonesia Binus Business School Doctor of Research in Management Binus University Management Department Jakarta Indonesia Telkom University Bandung Indonesia School of Information System Binus University Jakarta Indonesia

ISBN: (纸本)9798350335040

After the Covid 19 pandemic, the property management industry is slowly starting to revive. Even though the occupancy rate has not returned to normal like the Covid19 pandemic, its growth is starting to move in a positive direction. The Covid19 pandemic has provided many lessons for all stakeholders in the property management industry. Digital transformation has become a part that has received important attention in the property management industry worldwide. With digital transformation in the property management industry, all stakeholders are expected to receive various benefits, directly or indirectly. The implementation of digital transformation will involve 6 components, namely: Strategy, Organization, Culture, Customer, People, and technology. The implementation of digital transformation in the property management industry will require support from many parties, not only internal parties but also external parties. Technological developments, IoT, and demands from residents and property owners are challenges that must be answered by property managers. This systematic literature review will examine articles that have been published in the Scopus database regarding the development of digital transformation in the property management industry from various countries. The purpose of this SLR is to be able to see how the digital transformation trend and the application of its components are in the current property management industry. © 2023 IEEE.

关键词： Apartment Property Management Systematic Literature Review Transformation Digital

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RepATT: Re-parameterization and Attention-guided Hybrid Anchor Classification for Real-time Lane Detection

RepATT: Re-parameterization and Attention-guided Hybrid Anch...

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2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023

作者： Yao, Xin-Wei Zhang, Kai-Jie Yang, Yang Pu, Gui-Yang Li, Qiang Zhejiang University of Technology College of Computer Science and Technology Hangzhou China Zhejiang University of Technology Institute for Frontier and Interdisciplinary Sciences Hangzhou China Ocean University of China Faculty of Information Science and Engineering Qingdao China Information Technology Co. Ltd Hangzhou China

ISBN: (纸本)9798350312270

Lane detection is a critical technology in autonomous driving, which requires both high detection accuracy and real-time efficiency, especially in complex scenarios like severe occlusions, and dazzling light conditions. To achieve the above requirements, based on re-parameterization and attention mechanism we propose a real-time lane detection network with a classification-based manner named RepATT. A new backbone Extended RepVGG (ExtRep) which subtly decouples the training time and inference time is proposed as the feature extractor. Specifically, the ExtRep has two additional branches of varying complexities and scales compared to the original RepVGG. This enhancement improves the performance without any additional inference cost by enhancing the representational capacity of single convolution with re-parameterization methods. In addition, by integrating the Convolutional Block Attention Module(CBAM), our model improves the performance in the no-visual evidence scenarios. CBAM generates the attention maps via channel and spatial dimensions enabling the model to concentrate more on the lanes. Through comprehensive experiments conducted on CULane and TuSimple datasets, the results demonstrate our proposed method achieves the fastest inference speed, which surpasses the baseline model by 24% to 363 FPS with lower computing cost, while the F1-measure of RepATT also outperforms the previous methods on both datasets. © 2023 IEEE.

关键词： Convolution

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Using SYCLomatic to Migrate CUDA Code to oneAPI Adapting NVIDIA GPU

Using SYCLomatic to Migrate CUDA Code to oneAPI Adapting NVI...

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IEEE International Conference on Cluster Computing Workshops and Posters (CLUSTER WORKSHOPS)

作者： Wentao Liang Norihisa Fujita Ryohei Kobayashi Taisuke Boku Master's Program in Computer Science Degree Program in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Center for Computational Sciences University of Tsukuba

ISBN: (数字)9798350383454

ISBN: (纸本)9798350383461

Nowadays, the use of accelerators in high performance computing has become more common than ever before. The most used accelerators must be the Graphics Processing Unit (GPU). It has emerged as an important component in most of the parallel computing scenarios, surpassing the capabilities of the traditional Central Processing Unit (CPU) in perspective of both performance and energy efficiency.

关键词： Codes Conferences High performance computing Graphics processing units Cluster computing Parallel processing Energy efficiency Central Processing Unit

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The information Systems and Model for Succession Planning in Higher Education Institutions

The Information Systems and Model for Succession Planning in...

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computer Applications (ICCA), IEEE Conference on

作者： Devyano Luhukay Ford Lumban Gaol Meyliana Harjanto Prabowo Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331534592

ISBN: (纸本)9798331534608

Succession planning is important in organization management. It is a strategic level management that involves not only finding the best successor but also including the development program for the successor so that they will be ready for the new prospective position. The integration and collaboration between all related stakeholders are important. Specifically, in succession planning at higher education, key positions play an important role so that institutional operations can run well. In this research, a succession planning model in higher education was built using the Systematic Literature Review(SLR), and the Software Development Lifecycle(SDLC) method was used for the application development, also, to be able to evaluate the implementation of one of the steps of the model, the simulation technique used machine learning (ML) to find the best algorithm for predicting the readiness of potential successors at higher education. Six algorithms were used: Logistic Regression, Decision Tree, K-Nearest Neighbor, Linear Discriminant Analysis, Gaussian Naïve Bayes, and Support Vector Machine. The data prepared for the simulation is based on the factors for the best successor candidate. The results obtained in this research are the succession planning model and the application design, as well as the Support Vector Machine (SVM) algorithm as the best algorithm for predicting the readiness status of successor candidates based on SVM’s accuracy score that is compared to the other algorithm in some variance amounts of data. The Succession Planning Model that is actualized in the application design provides a reference for the higher education institution to implement the Succession Model in their institution.

关键词： Support vector machines Machine learning algorithms Systematics Education Software algorithms Machine learning Predictive models Prediction algorithms Planning information systems

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2D-LLFE-NVC: 2D Laser Line Feature Extraction Using Normal Vector Clustering 30

2D-LLFE-NVC: 2D Laser Line Feature Extraction Using Normal V...

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30th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2024

作者： Ou, Jinshun Ren, Jiying Li, Wenguang Huang, Panling Zhou, Jun Zhou, Jiehan Zhang, Tao Shandong University School of Mechanical Engineering Jinan China Shandong University of Science and Technology School of Computer Science and Technology Qingdao China University of Oulu Faculty of Information Technology and Electrical Engineering Oulu Finland Qingdao Hanhe Cable Co. Ltd. Qingdao China

ISBN: (纸本)9798350391916

This paper presents a novel 2D laser data line feature extraction algorithm based on normal vector clustering(2D-LLFE-NVC). The algorithm consists of normal vector calculation, normal normalization based on multi-scale operator normal difference and median filter, and line feature extraction based on normal vector clustering. Unlike existing methods, 2D-LLFE-NVC leverages the continuity of laser point clouds and detects breakpoints using multi-scale operators based on normal differences. The extracted line features are more accurate and correct on a global scale. To validate its performance, we conducted SLAM experiments in a 5000-square-meter indoor environment using both the proposed algorithm and existing methods. By comparing the line feature points extracted by these different approaches with manually annotated line feature points, and demonstrating the superior performance of the proposed method in terms of correctness and completeness. © 2024 IEEE.

关键词： Median filters

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Behavioral Authentication Using Activity-Based Model Segregation in Convolutional Neural Networks for Mobile Sensor Data

Behavioral Authentication Using Activity-Based Model Segrega...

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International Joint Conference on computer science and Software Engineering (JCSSE)

作者： Thapanapong Rukkanchanunt Saksinkarn Petchkuliinda Hideya Ochiai Department of Computer Science Faculty of Science Chiang Mai University Chiang Mai Thailand Graduate School of Information Science and Technology The University of Tokyo Tokyo Japan

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

This study explores user authentication through Convolutional Neural Networks (CNN) on mobile sensor data, aiming to enhance security in mobile computing. We utilized the MobiAct dataset to authenticate individuals based on behav-ioral patterns captured by smartphone sensors. Our approach compares different data segmentation strategies, focusing on the impact of window size and sliding step on four performance metrics: accuracy, precision, recall, and Fl-score. Results demonstrate that larger window sizes generally lead to increased accuracy. We examine the implications of activity-based model segregation, leading to improvements in precision and overall model stability. We also confirm that walking is most suitable activity in identifying individual. This research contributes to the growing field of behavioral-based user authentication, showcasing the potential of using inherent motion sensor data in smartphones for creating more personalized and secure user experiences.

关键词： Accuracy Authentication Sensitivity and specificity Stability analysis Motion detection Security Convolutional neural networks

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Privacy-Enhanced Data Deduplication Computational Intelligence Technique for Secure Healthcare Applications

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computers, Materials & Continua 2022年第2期70卷 4169-4184页

作者： Jinsu Kim Sungwook Ryu Namje Park Department of Convergence Information Security Graduate SchoolJeju National University63294Korea Master’s Program in Future Strategy Korea Advanced Institute of Science and Technology34141Korea Department of Computer Education Teachers CollegeJeju National University63294Korea

A significant number of cloud storage environments are already implementing deduplication *** to the nature of the cloud environment,a storage server capable of accommodating large-capacity storage is *** storage capacity increases,additional storage solutions are *** leveraging deduplication,you can fundamentally solve the cost ***,deduplication poses privacy concerns due to the structure *** this paper,we point out the privacy infringement problemand propose a new deduplication technique to solve *** the proposed technique,since the user’s map structure and files are not stored on the server,the file uploader list cannot be obtained through the server’s meta-information analysis,so the user’s privacy is *** addition,the personal identification number(PIN)can be used to solve the file ownership problemand provides advantages such as safety against insider breaches and sniffing *** proposed mechanism required an additional time of approximately 100 ms to add a IDRef to distinguish user-file during typical deduplication,and for smaller file sizes,the time required for additional operations is similar to the operation time,but relatively less time as the file’s capacity grows.

关键词： Computational intelligence cloud multimedia data deduplication

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Data-efficient Large Vision Models through Sequential Autoregression 41

Data-efficient Large Vision Models through Sequential Autore...

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41st International Conference on Machine Learning, ICML 2024

作者： Hao, Zhiwei Guo, Jianyuan Wang, Chengcheng Tang, Yehui Wu, Han Hu, Han Han, Kai Xu, Chang School of Information and Electronics Beijing Institute of Technology Beijing China School of Computer Science Faculty of Engineering University of Sydney Sydney Australia Huawei Noah's Ark Lab Beijing China

Training general-purpose vision models on purely sequential visual data, eschewing linguistic inputs, has heralded a new frontier in visual understanding. These models are intended to not only comprehend but also seamlessly transit to out-of-domain tasks. However, current endeavors are hamstrung by an over-reliance on colossal models, exemplified by models with upwards of 3B parameters, and the necessity for an extensive corpus of visual data, often comprising a staggering 400B tokens (Bai et al., 2023). In this paper, we delve into the development of an efficient, autoregression-based vision model, in-novatively architected to operate on a limited dataset. We meticulously demonstrate how this model achieves proficiency in a spectrum of visual tasks spanning both high-level and low-level semantic understanding during the testing phase. Our empirical evaluations underscore the model's agility in adapting to various tasks, heralding a significant reduction in the parameter footprint, and a marked decrease in training data requirements, thereby paving the way for more sustainable and accessible advancements in the field of generalist vision models. The code is available at https://***/ggjy/DeLVM. Copyright 2024 by the author(s)

关键词： Spatio-temporal data

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Dental Disease Detection based on CNN for Panoramic Dental Radiographs 11

Dental Disease Detection based on CNN for Panoramic Dental R...

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11th IEEE International Conference on Intelligent Computing and information Systems, ICICIS 2023

作者： El Bagoury, Manar Al-Shetairy, Mirna Ahmed, Omar Mehanny, Samah S Hamdy, Tassneem Omar, Gihan Akram, Marwan Mohamed, Rana Amr, Hebatullah Gad, Walaa Ain Shams University Faculty of Computer and Information Sciences Bioinformatics Program Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Computer Science Department Cairo Egypt Faculty of Dentistry Cairo and Galala Universities Oral Biology Department Cairo Egypt Cairo University Faculty of Dentistry Oral and Maxillofacial Radiology Department Cairo Egypt Future University in Egypt Faculty of Oral and Dental Medicine Oral Radiology Department Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Information Systems Department Cairo Egypt

ISBN: (纸本)9798350322101

Panoramic dental radiography is one of the examination tools frequently performed in dental clinics and hospitals as it captures a wide range of the dental region and its surroundings, but due to the complex structure of the dental area and lack of time, dentists focus on tiny parts of the images. With the help of Machine Learning, dentists can detect dental defects and anomalies faster and more accurately. In this study, a data set of 733 panoramic radiographs of adult patients was collected from the faculty of Oral and Dental Medicine, Future University, Egypt. Using a combination of pre-trained CNN architecture and a finetuned network, the collected panoramic radiographs were used to determine the optimal architecture. The purpose of this research was to develop a computer-aided detection system based on a finetuned CNN algorithm, and to evaluate the accuracy and usefulness of this system for the detection of proximal and occlusal caries, interdental and inter radicular alveolar bone loss, periapical lesions, and impacted teeth in panoramic radiographs. Different object detection models were tested to detect selected dental diseases that were found in the panoramic radiographs, and after a few experiments, the YOLOv5 architecture was proved to be the most optimal after achieving 0.61 mAP@0.5 and 0.28 mAP@ [0.5-0.95] for the total six classes. This is the best-achieved results to our knowledge. © 2023 IEEE.

关键词： Object detection

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