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30th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, WSCG 2022

作者： Praschl, Christoph Stradner, Michael Ono, Yuta Zwettler, Gerald Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Austria Graduate School of Software and Information Science Iwate Prefectural University Japan Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Austria

ISBN: (纸本)9788086943336

Aerial photographs of buildings are often used as memorabilia sold by trading companies. Such photographs come with an issue regarding the address of the shown buildings, since the recording location of the camera may be known, but shows a spatial distance to the actual subject of the image. In addition to that, also this recording location is often not known in detail but only roughly in the form of the flight route/area. To address this problem, a methodology for reverse geocoding is proposed, allowing to identify the position of buildings that are photographed from aerial vehicles. This is done using a process for extending recording locations and a second process based on the registration of invariant features within aerial shots compared to maps. © 2022 University of West Bohemia. All rights reserved.

关键词： Aerial photography

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Implementation of Real-Time Lane Detection on Autonomous Mobile Robot

Implementation of Real-Time Lane Detection on Autonomous Mob...

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Advanced Telecommunication and Networking Technologies (ATNT), IEEE International Conference on

作者： Midriem Mirdanies Roni Permana Saputra Edwar Yazid Rozeha A. Rashid Telecommunication Software and Systems Research Group Faculty of Electrical Engineering Universiti Teknologi Malaysia (UTM) Johor Bahru Johor Malaysia Research Center for Smart Mechatronics National Research and Innovation Agency (BRIN) Bandung Indonesia

ISBN: (数字)9798350353501

ISBN: (纸本)9798350353518

This paper describes the implementation of a learning-based lane detection algorithm on an autonomous mobile robot. More specifically, it explores the implementation of the Ultra Fast Lane Detection algorithm for real-time application on the SEATER (Single-passenger Electric Autonomous Transporter) prototype using a camera, with an emphasis on optimizing its performance on a Jetson Nano platform. Multi-core processing on the CPU and GPU has been implemented in this paper to optimize the application program and avoid delays or buffering. Preliminary experiments have been conducted to evaluate the algorithm's performance in terms of data processing speed and accuracy using two datasets: a public dataset for the outdoor environment and another dataset collected from the indoor area of the Workshop Building at BRIN Bandung. The experimental results show that after conversion to TensorRT, the algorithm achieves significantly improved performance on the Jetson Nano platform compared to the ONNX model, with processing speeds of approximately 101 ms using CULane and 105 ms using TuSimple-about 22 times faster than the previous model. While the algorithm demonstrates good accuracy on the public outdoor dataset, its performance falls short on the indoor dataset. Future work should focus on transfer learning and fine-tuning to improve indoor lane detection accuracy.

关键词： Accuracy Lane detection Multicore processing Transfer learning Robot vision systems Graphics processing units Cameras Real-time systems Mobile robots Autonomous robots

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What Drives the Use of Generative Artificial Intelligence to Promote Educational Sustainability? Evidence from SEM-ANN Approach

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TechTrends 2025年 1-15页

作者： Arpaci, Ibrahim Al-Emran, Mostafa Al-Qaysi, Noor Al-Sharafi, Mohammed A. Management Information Systems College of Business Administration Gulf University for Science and Technology Mishref Kuwait Department of Software Engineering Faculty of Engineering and Natural Sciences Bandirma Onyedi Eylul University Balikesir Turkey Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea Faculty of Engineering & IT The British University in Dubai Dubai United Arab Emirates Engineering Technical College Al-Bayan University Baghdad Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq IRC for Finance and Digital Economy KFUPM Business School King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

The literature on generative “Artificial Intelligence” (AI) in education primarily focuses on its immediate benefits and applications, such as personalized learning, student engagement, and content generation. However, there is a notable absence of empirical research concerning the holistic use of generative AI within educational institutions and its long-term impact on educational sustainability. This study investigates the factors that predict the use of generative AI and its subsequent influence on educational sustainability. This study enhanced the extended “Unified Theory of Acceptance and Use of Technology” (UTAUT-2) by including personal innovativeness as an external variable and educational sustainability as an outcome. A hybrid methodology, integrating “Structural Equation Modeling” (SEM) and “Artificial Neural Network” (ANN), was used to evaluate the research model using data collected from 1,011 university students. The SEM analysis confirmed all hypotheses. Specifically, a positive relationship was found between use behavior and educational sustainability. These results show that the combined variables collectively account for 63% and 66% of the variance in use behavior and educational sustainability, respectively. The sensitivity analysis identified habit as the most important predictor of use behavior. The findings offer both theoretically important and practically valuable implications for designing user-centered AI integration in education. © Association for Educational Communications & Technology 2025.

关键词： Artificial intelligence Educational sustainability SEM-ANN UTAUT-2

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Exploiting the Potential of Self-Supervised Monocular Depth Estimation via Patch-Based Self-Distillation

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IEEE Internet of Things Journal 2025年第11期12卷 18042-18055页

作者： Yu, Ting Pan, Shan Chen, Wei Tian, Zijian Wang, Zehua Yu, F. Richard Leung, Victor C. M. Postdoctoral Research Station China RongTong Academy of Sciences Group Corporation Limited Beijing100022 China China RongTong Artificial Intelligence Research Center Beijing100022 China China University of Mining and Technology School of Artificial Intelligence Beijing100083 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China School of Artificial Intelligence Beijing100083 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

Perceiving scene depth and 3D structure is one of the key tasks for Internet of Video Things (IoVT) devices to understand and interact with the environment. Self-supervised monocular depth estimation has demonstrated significant potential in leveraging large-scale unlabeled datasets to achieve competitive performance, thereby playing an increasingly important role in depth estimation. Despite recent methods providing additional supervisory signals through self-distillation strategies to improve depth estimation, an effective method for generating pseudo-depth labels suitable for addressing occlusion issues among elements far from the camera remains unexplored. To address this limitation, we propose a Patch-based Self-Distillation Learning Framework to exploit the potential of self-supervised monocular depth estimation in recovering fine-grained scene depth. In the proposed framework, elements far from the camera within the input image are enlarged by enlarging and cropping operations in the patch-based self-distillation branch. Guided by photometric consistency, the model learns the detailed occlusion relationships among elements from the enlarged patches, producing patch depth maps with fine structures. In the main branch, which takes full-scale images as input, patch depth maps serve as pseudo-depth labels through self-distillation loss to provide additional supervisory signals for regions where photometric consistency fails to offer effective supervision. This forces the depth estimation network to recover fine structures of elements far from the camera in full-scale input images. Regarding the architecture of the depth estimation network, we introduce a bin-center prediction. In this prediction, a global aggregator based on self-attention provides additional scene structure queries for adaptive scene depth discretization. Finally, to encourage the model to explore more general cues for depth inference beyond road plane cues, we propose a PatchMix data augmentati

关键词： Image enhancement

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CRESCO Framework and Checker: Automatic generation of Reflective UML State Machine's C++ Code and Checker 31

CRESCO Framework and Checker: Automatic generation of Reflec...

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31st IEEE International Symposium on software Reliability engineering Workshops, ISSREW 2020

作者： Illarramendi, Miren Etxeberria, Leire Larrinaga, Felix Sagardui, Goiuria Software and Systems Engineering Research Group Arrasate Spain

ISBN: (纸本)9781728198705

software systems are becoming increasingly complex leading to new Validation Verification challenges. Model checking and testing techniques are used at development time while runtime verification aims to verify that a system satisfies a given property at runtime. This second technique complements the first one. This paper presents a tool that enables the developers to generate automatically reflective UML State Machine controllers and the Runtime Safety Properties Checker (RSPC) which checks a component-based software system's safety properties defined at design phase. We address embedded systems whose software components are designed by Unified Modelling Language-State Machines (UML-SM) and their internal information can be observed in terms of model elements at runtime. RESCO (REflective State Machines-based observable software COmponents) framework, generates software components that provide this runtime observability. The checker uses software components' internal status information to check system level safety properties. The checker detects when a system safety property is violated and starts a safe adaptation process to prevent the hazardous scenario. Thus, as demonstrated in the evaluated experiment but not shown in the paper due to the space limitation, the safety of the system is enhanced. © 2020 IEEE.

关键词： Embedded systems

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OptiTwin: Data-Driven Machining Process Optimization Platform for SMEs

OptiTwin: Data-Driven Machining Process Optimization Platfor...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： José Joaquín Peralta Abadía Felix Larrinaga Barrenechea Mikel Cuesta Zabaljauregui Xabier Badiola Aiestaran Aitor Duo Zubiaurre Gorka Olalde Mendia Txema Perez Lazare Software and System Engineering Research Group Mondragon Goi Eskola Politeknikoa Arrasate Spain High Performance Machining Research Group Mondragon Goi Eskola Politeknikoa Arrasate Spain Electronic Power Systems Applied to the Control Electric Energy Research Group Mondragon Goi Eskola Politeknikoa Arrasate Spain Data Analysis and Cybersecurity Research Group Mondragon Goi Eskola Politeknikoa Arrasate Spain

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

The manufacturing industry is constantly seeking innovative solutions to optimize machining processes. However, there is a lack of efficient digital platforms that fully meet the flexibility, service composition, and affordability needs of the manufacturing industry, in particular for small and mediumsized enterprises (SMEs). This paper introduces the OptiTwin platform, a novel data-driven system designed to enhance machining process optimization for SMEs. The OptiTwin platform was developed with a focus on data acquisition, management, and analysis based on data driven models. The functionalities of the platform were validated through a drilling use case at Mondragon University's high-performance machining laboratory, demonstrating its effectiveness in real-time tool condition monitoring. The results showcase the potential of OptiTwin in optimizing machining processes and empowering SMEs with data-driven insights for enhanced productivity and quality assurance.

关键词： Manufacturing industries Productivity Drilling Condition monitoring Quality assurance Data acquisition Real-time systems Data models Optimization Manufacturing automation

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muco: A MUSIC COMPUTING LEARNING APPLICATION 19

muco: A MUSIC COMPUTING LEARNING APPLICATION

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19th Sound and Music Computing Conference, SMC 2022

作者： Hu, Patricia Hödl, Oliver Reichl, Peter Kayali, Fares Eibensteiner, Iris Taufner, Bernhard Schefer-Wenzl, Sigrid Miladinovic, Igor Cooperative Systems Research Group University of Vienna Austria Centre for Teacher Education University of Vienna Austria Software Design and Engineering Department University of Applied Sciences Campus Vienna Austria Institute of Computational Perception Johannes Kepler University Linz Austria

ISBN: (纸本)9782958412609

Following a computational approach to music creation can serve as a transdisciplinary bridge between computer science, music and education by providing rich opportunities for teaching computing skills in interdisciplinary contexts. Making use of such learning contexts is one of the cornerstones of STEAM (STEM + Arts) education, a pedagogic approach focusing on integrating the arts and humanities into STEM fields. Despite numerous benefits reported in the literature, the number of STEAM programs integrated into formal learning contexts remains limited due to various integration challenges. In addressing these challenges, we developed and evaluated a gamified music computing learning application called muco, using the MVC software architecture along with React as the front-end, and Node and MariaDB as back-end technologies. The goal of the application is to make programming concepts more accessible and comprehensible to a broader public, and to simultaneously spark interest in music and music making. Making use of game mechanics, muco is conceptualised to teach computational thinking, and more specifically, introductory programming, in non-formal learning contexts. The usability of the application and impact of interdisciplinary learning is evaluated by means of both formative and summative testing. Results show that the application provides an engaging learning environment which encourages the exploration of both the computing and music domain. Copyright: © 2022 Hu P. et al.

关键词： Application programs

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Online Working Set Change Detection with Constant Complexity: The Cornerstone for Memory Management Algorithms in Scalable systems 2021

Online Working Set Change Detection with Constant Complexity...

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The International Symposium on Memory systems

作者： L J Gokul Vasan Éder F. Zulian Christian Weis Matthias Jung Norbert Wehn Nvidia Germany Microelectronic Systems Design Research Group University of Kaiserslautern Germany Fraunhofer Institute for Experimental Software Engineering (IESE) Germany Microelectronic Systems Design Research Group TU Kaiserslautern Germany

ISBN: (纸本)9781450385701

In advanced scalable multiprocessor systems with multiple applications running concurrently, the optimal utilization of memory resources plays an essential role in achieving high system performance and energy efficiency. Locality of reference is one of the basis of memory management techniques. In operating systems, the memory management benefits from temporal and spatial locality when deciding which page shall be moved down the memory hierarchy to make room for a new page. However, current algorithms are oblivious to the moments when individual tasks transition to a new working set, which leads to suboptimal decisions. Furthermore, the eviction of idle pages from main memory is postponed to the last minute, which leads to system unresponsiveness and thrashing. In this context, memory locality reasoning based on the phase transition model is a central aspect to consider. We perceive that incorporating such a model in memory management algorithms provides the operating system with ways to trigger proactive page reclamation or compaction, to collect memory related statistics, and to trigger other crucial decision-making. In this paper, we present for the first time a hardware-agnostic online working set change detection algorithm for each process in a system. The working set change is detected with incomplete knowledge of a task’s memory access behavior. Our novel online algorithm perceives the occurrence of concavity in a program’s lifetime function as an outlier and heuristically attempts to classify such from the normal observations. Furthermore, we implemented and evaluated our solution in the Linux kernel. It executes with time and space complexity. Evaluations using real-world data-intensive applications show that the algorithm detects the working set change with an average accuracy of 89% and F0.5-Score of 74%, adding negligible computation overhead of 0.506% in average.

关键词： online detection memory management locality of reference phase transition behavior working set paging

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Designing a Distributed Multi-agent System for Compiler Optimization

Designing a Distributed Multi-agent System for Compiler Opti...

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2021 Zooming Innovation in Consumer Technologies Conference, ZINC 2021

作者： Sari, Alparslan Sahin, Cagri Butun, Ismail University of Delaware Electrical and Computer Engineering Computer Systems and Networking NewarkDE United States Gazi University Computer Engineering Software Research Group Ankara Turkey Chalmers University of Technology Computer Science and Engineering Network and Systems Division Gothenburg Sweden

ISBN: (纸本)9781665404174

This paper explores the run time performance improvements using different GCC optimization flags in program compilation. As multi-core microprocessor systems replacing legacy single-core ones, tremendous effort is needed to address to optimize the associated compilers for newly designed architectures in order to suit them for running parallel programming on multiple cores. Therefore, the aim of this paper is to address this challenge by designing an optimum distributed multi-agent system to perform compiler optimization. A multi-agent framework is adopted to utilize random and genetic algorithm-based search algorithm to find the best GCC optimization flags for a given program. The framework is highly scalable and can be extended with distributed system concept to perform code compilation in parallel to find the best-optimized code sequence in a short amount of time. The initial performance results have promising indicators which clearly show that the performance improvement is achieved. © 2021 IEEE.

关键词： Multi agent systems

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Modeling of Sensor Enabled IrrigationManagement for Intelligent Agriculture Using Hybrid Deep Belief Network

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Computer systems Science & engineering 2023年第8期46卷 2319-2335页

作者： Saud Yonbawi Sultan Alahmari B.R.S.S.Raju Chukka Hari Govinda Rao Mohamad Khairi Ishak Hend Khalid Alkahtani JoséVarela-Aldás Samih M.Mostafa Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Aditya Engineering College Affiliated to JNTUK(Kakinada) SurampalemIndia Department of MBA Vignan’s Institute of Information and Technology(A)DuvvadaVisakhapatnamAP530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia SISAu Research Group Universidad Tecnológica IndoaméricaAmbato180103Ecuador Faculty of Computers and Information South Valley UniversityQena83523Egypt

Artificial intelligence(AI)technologies and sensors have recently received significant interest in intellectual *** the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart *** irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation *** learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an *** this motivation,this study develops a modified black widow optimization with a deep belief network-based smart irrigation system(MBWODBN-SIS)for intelligent *** MBWODBN-SIS algorithm primarily enables the Internet of Things(IoT)based sensors to collect data forwarded to the cloud server for examination ***,the MBWODBN-SIS technique applies the deep belief network(DBN)model for different types of irrigation classification:average,high needed,highly not needed,and not *** MBWO algorithm is used for the hyperparameter tuning process.A wideranging experiment was conducted,and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

关键词： Agriculture smart farming hyperparameter tuning artificial intelligence irrigation management sensors deep learning

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