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Examining the Software Developers’ Perception in Open-Source Software of Blockchain Project Using Association Rules Mining 9th

Examining the Software Developers’ Perception in Open-Sourc...

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9th International Conference on Computing and Informatics, ICOCI 2023

作者： Abd Wahab, Alawiyah Hj. Ibrahim, Huda SarkinTudu, Shehu M. Romo, Bilyaminu A. School of Computing University Utara Malaysia Kedah Sintok06010 Malaysia Department of Computer and Information Technology Sokoto State University P.M.B 2134 Near Airport Road Sokoto State Sokoto Nigeria School of Architecture Computing and Engineering University of East London London United Kingdom

ISBN: (纸本)9789819995882

Developers are vital in enhancing blockchain projects like Bitcoin through feature additions, bug fixes, and performance optimization. However, comprehending developers’ perception of the growing amount of information regarding new features and bug fixes becomes challenging as blockchain projects gain popularity. Data mining, a technique that extracts valuable patterns and information from extensive data sets, assists in decision-making. The Apriori algorithm, widely used in data mining, uncovers association rules among sets of items. Despite its effectiveness, the Apriori algorithm remains relatively underutilized in the field of Open Source Software (OSS) developer turnover. Previous studies have employed approaches like mining software project repositories, social network analysis, quantitative data analysis, and surveys, which shed light on turnover but fail to reveal interesting relationships and patterns related to subjective factors and collaboration. To address this gap, this paper proposes combining survey data with association rule mining. This approach aims to identify co-occurrence patterns between specific personal and project-related variables (e.g., intention to learn or system integration). By analyzing these variables and their associations, the paper intends to provide valuable insights to project leaders, aiding decision-making in developer turnover management. Ultimately, this research contributes to enhancing the quality of blockchain projects. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Association rules

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SiamCircle: Trajectory Representation Learning in Free Settings 23rd

SiamCircle: Trajectory Representation Learning in Free Sett...

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23rd International Symposium on Intelligent Data Analysis, IDA 2025

作者： Nasri, Maedeh Baratchi, Mitra Koutamanis, Alexander Rieffe, Carolien Department of Developmental Psychology Leiden University Leiden Netherlands Leiden Institute of Advanced Computer Science Leiden University Leiden Netherlands Faculty of Architecture and the Built Environment Delft University of Technology Delft Netherlands Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands Department of Psychology and Human Development Institute of Education UCL London United Kingdom

ISBN: (纸本)9783031913976

Trajectory representation learning (TRL) is an intermediate step in handling trajectory data to realize various downstream machine-learning tasks. While most previous TRL research focuses on modeling structured movements in large-scale urban spaces (e.g., cars or pedestrians on streets), this paper focuses on a more challenging scenario of modeling free movement in small-scale social spaces (e.g., children playing in a schoolyard). We present a TRL model, SiamCircle, to process raw trajectories without additional feature extraction to prevent information loss. SiamCircle adopts a Siamese network with Circle Loss to learn trajectory embeddings. Furthermore, SiamCircle employs a data augmentation process to enable self-supervised learning and enrich the input data to address the limited access to high-quality data and ground truth. We evaluate the performance of SiamCircle in downstream tasks using trajectory ranking and clustering performance via seven evaluation metrics collectively. Using an ablation study, we explored the impact of different loss functions on the model’s performance. Accordingly, we selected a 2-D convolutional design with Circle Loss as the best-performing model. In a comparative study, we compared our model against three other baselines. We observed up to 19% improvements in trajectory ranking tasks and achieved the highest average rank in supervised clustering tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Sorting

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Enhancing the Smart Readiness Indicator Scheme: Methodology Assessment through 20 Use Cases

Enhancing the Smart Readiness Indicator Scheme: Methodology ...

引用

International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Afroditi Zamanidou Despoina Kothona Paris Fokaides Georgios C. Christoforidis Electrical & Computer Engineering Department Faculty of Engineering University of Western Macedonia Greece Faculty of Civil Engineering and Architecture Kaunas University of Technology Kaunas Lithuania

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

The European Union developed the Smart Readiness Indicator (SRI) to enhance energy efficiency and encourage the adoption of smart technologies in buildings, tackling their high energy usage and carbon emissions. This study evaluates the SRI methodology through the assessment of 20 diverse use cases, including residential and non-residential buildings, using Methods A and B of the SRI assessment scheme. Key variables such as climate zone, building type, year of construction, and the integration of smart-ready services were considered to ensure a comprehensive *** engineers assessed the buildings using the SRI EU Excel Tool and rated the methodology based on criteria including accuracy, usability, comprehensiveness, flexibility, and impact on decision-making. The results revealed high ratings for accuracy (45% rated as "Very Good" and 30% as "Excellent") and usability (50% rated as "Very Good"), while comprehensiveness was rated more moderately (40% "Good"). However, flexibility and adaptability emerged as areas requiring improvement, with 45% of assessments rated as "Fair." The SRI methodology demonstrated strong potential but requires further refinement to address the needs of diverse building types and more complex smart technologies. The study concludes that the SRI framework is a valuable tool for promoting energy efficiency and smart technology adoption across the EU building sector. However, additional research is recommended to integrate real-time data and automation technologies, and to enhance the framework's scalability and adaptability for future building systems.

关键词： Accuracy Automation Scalability Buildings Decision making Europe Solids Energy efficiency Real-time systems Usability

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Adaptation of AI-Accelerated CFD Simulations to the IPU Platform 14th

Adaptation of AI-Accelerated CFD Simulations to the IPU P...

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14th International Conference on Parallel Processing and Applied Mathematics, PPAM 2022

作者： Rościszewski, Pawel Krzywaniak, Adam Iserte, Sergio Rojek, Krzysztof Gepner, Pawel Graphcore Gdańsk Poland Department of Construction and Mechanical Engineering Universitat Jaume I Castellón de la Plana Spain Barcelona Supercomputing Center Barcelona Spain Institute of Computer and Information Sciences Czȩstochowa University of Technology Czȩstochowa Poland Faculty of Mechanical and Industrial Engineering Warsaw University of Technology Warszawa Poland Department of Computer Architecture Faculty of Electronics Telecommunications and Informatics Gdańsk University of Technology Narutowicza 11/12 Gdańsk80-233 Poland

ISBN: (纸本)9783031304446

Intelligence Processing Units (IPU) have proven useful for many AI applications. In this paper, we evaluate them within the emerging field of AI for simulation, where traditional numerical simulations are supported by artificial intelligence approaches. We focus specifically on a program for training machine learning models supporting a computational fluid dynamics application. We use custom TensorFlow provided by the Poplar SDK to adapt the program for the IPU-POD16 platform and investigate its ease of use and performance scalability. Training a model on data from OpenFOAM simulations allows us to get accurate simulation state predictions in test time. We show how to utilize the popdist library to overcome a performance bottleneck in feeding training data to the IPU on the host side, achieving up to 34% speedup. Due to communication overheads, using data parallelism to utilize two IPUs instead of one does not improve the throughput. However, once the intra-IPU costs have been paid, the hardware capabilities for inter-IPU communication allow for good scalability. Increasing the number of IPUs from 2 to 16 improves the throughput from 560.8 to 2805.8 samples/s. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Risk Management for 5G-Enabled Internet of Things by Using Machine Learning: A Survey

Risk Management for 5G-Enabled Internet of Things by Using M...

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ICT Conference (AICTC), Arab

作者： Aysha Alfaw Wael Elmedany Mhd Saeed Sharif College of Information Technology University of Bahrain Sakir Bahrain Department Computer Science and Digital Technologies School of Architecture Computing and Engineering London UEL UK

ISBN: (数字)9798350342086

ISBN: (纸本)9798350342093

Nowadays, there is a noticeable increase in the development and use of the Internet of Things(loT). With this rapid increase, IoT devices may face several challenges when used in the real world through applications. Using 5G with large numbers of Internet-connected devices of IoT puts many devices at risk and requires risk management. The growing of IoT leads to the growth of cyber-attackers and allow them to expose the vulnerabilities. This paper will present the risk management of 5G-enabled IoT technology and the use of machine learning to mitigate the risk and reduce the attacks on these technologies, and finding solutions through previous researches. This research aims to identify and eliminate potential risks in the IoT based 5G by machine learning. The paper also aims to present a solution to the security problems and risks faced when integrating the fifth-generation network and the Internet of Things. With 5G-enabled IoT, the risk management helps organizations use emerging technologies effectively while mitigating potential underlying risks such as security breaches, and data loss. Authentication,encryption, access control, and communication security are essential for making security. Machine learning algorithms have the potential to remove many obstacles to implementing the security of the Internet of Things, paving the door for the use of sophisticated technology like 5G. With new 5G networks, it is expected that the current IoT will be significantly expanded, which will improve cellular operations and the security of IoT, as well as push the future of the Internet to its edges. Machine learning (ML) creates a secure and intelligent system and provides a robust security mechanism and dynamic for 5G networks. Therefore, this time will also present previous solutions with machine learning against the risks to IoT and 5G. This paper will present a set of previous studies related to insurance of risk management for the 5G-enabled IoT, Which aims to find previ

关键词： Surveys Machine learning algorithms 5G mobile communication Insurance Machine learning Organizations Internet of Things Security Risk management Intelligent systems

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Optimal Synthesis of Memristive Mixed-Mode Circuits

Optimal Synthesis of Memristive Mixed-Mode Circuits

引用

Design, Automation and Test in Europe Conference and Exhibition

作者： Ilia Polian Xianyue Zhao Li-Wei Chen Felix Bayhurst Ziang Chen Heidemarie Schmidt Nan Du University of Stuttgart Institute of Computer Architecture and Computer Engineering Stuttgart Germany Department of Quantum Detection Friedrich Schiller University Jena Institute for Solid State Physics Leibniz Institute of Photonic Technology Jena Germany

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Memristive crossbars are attractive for in-memory computing due to their integration density combined with compute and storage capabilities of their basic devices. However, yield and fidelity of emerging memristive technologies can make their reliable operation unattainable, thus raising interest in simpler topologies. In this paper, we consider synthesis of Boolean functions on 1D memristive line arrays. We propose an optimal procedure that can fully utilize the rich electrical behavior of memristive devices, mixing stateful (resistance-input) and non-stateful (voltage-input) operations as desired by the designer, leveraging their respective strengths. The synthesis method is based on Boolean satisfiability (SAT) solving and supports flexible constraints to enforce, e.g., restrictions of the available peripher-als. We experimentally validate memristive logic circuits beyond individual logic gates by demonstrating the operation of a Galois field multiplier using a 1D line array of 10 memristors in parallel, highlighting the robust performance of our proposed mixed-mode circuit and its synthesis procedure.

关键词： Boolean functions Memristors Europe Logic gates In-memory computing Topology Logic Reliability Galois fields Logic arrays

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The zero-tax data center: a use case through quantum resilient communications

The zero-tax data center: a use case through quantum resilie...

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International Conference on Transparent Optical Networks

作者： Daniel Lawo Michal Podleś Raphael Frantz Abraham Cano Aguilera Dismothenis Iliadis-Apostolidis Jeronimo Sanchez Sokol Kosta Idelfonso Tafur Monroy José Luis Imaña J.J. Vegas Olmos Department of Electrical Engineering Eindhoven University of Technology Eindhoven The Netherlands Department of Electronic Systems Aalborg University Copenhagen Denmark Department of Computer Architecture and Automation Complutense University of Madrid Spain Software Architecture NVIDIA Corporation Ofer Industrial Park Yokneam Israel

ISBN: (数字)9798350377323

ISBN: (纸本)9798350377330

Data centers, cloud and edge computing platforms are absorbing heavy AI-based workloads. This raises two concerns: how to reduce the networking overhead by host processors in order to focus only on AI-processing, and how to provide quantum resilient level protection for any connection running such workloads. In this paper, we will show case the concept of zero-tax data center, which is achieved through offloading of networking functions from the host to the network. In particular, the implementation of postquantum cryptography algorithms providing quantum resilient links and how the processing its complex processing is moved from host to network through the utilization of data-processing units.

关键词： Data centers Cloud computing Quantum computing Program processors Optical fiber networks Cryptography Network interfaces

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Human-computer Testing of Students’ Knowledge

Human-Computer Testing of Students’ Knowledge

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Smart Information Systems and Technologies (SIST), IEEE International Conference on

作者： Iryna Aznaurian Yurii Pohrebniak Yuliia Riabchun Maksym Delembovskyi Nataliia Poltorachenko Oleksiy Panko Department of Physics Kyiv National University of Construction and Architecture Kyiv Ukraine Department of Cybersecurity and Computer Engineering Kyiv National University of Construction and Architecture Kyiv Ukraine Department of Information Technology Kyiv National University of Construction and Architecture Kyiv Ukraine Department of Information Systems and Technologies Taras Shevchenko National University of Kyiv Kyiv Ukraine Department of Information Technology Design and Applied Mathematics Kyiv National University of Construction and Architecture Kyiv Ukraine Department of Architectural Structures Kyiv National University of Construction and Architecture Kyiv Ukraine

ISBN: (数字)9798350374865

ISBN: (纸本)9798350374872

The modern rapid development of means and technologies for education is aimed at reducing classroom classes and remote control of knowledge and skills of education seekers. The adaptation of the education system to the remote regime has become especially relevant throughout the world during the period of quarantine restrictions caused by COVID-19. In Ukraine, the urgency of developing and implementing info-communication systems in the education process has been intensified by the martial law since February 24, 2022. The urgent need to rebuild a large number of buildings and structures destroyed and damaged by hostilities makes special demands on graduates of construction educational institutions. However, the fundamental studying of specialists in this field in remote mode requires specialized info-communication systems, creating of which need taking part of specialists in the field. On-line testing is the main means of guaranteeing the quality of the modern educational process. Therefore, the article presents the specialized on-line testing system "Fast Knowledge Test", which was developed at the Kyiv National University of Construction and architecture with the participation of stakeholders. The system operates during 2021-2023 and while the monitoring of students’ knowledge and skills remains with the teacher, but the service provides for its automation.

关键词： COVID-19 Automation Education Mathematics Stakeholders Task analysis Physics

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Deep Learning Techniques for Overcoming Interoperability Challenges in Smart Grid Systems 2

Deep Learning Techniques for Overcoming Interoperability Cha...

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Devi, M. Kumar Rai, Anjani Reddy, Avala Raji Neelima, Gogineni Obaid, Saif Chidambaranathan, Senthilnathan New Prince Shri Bhavani College of Engineering and Technology Department of EEE Tamil Nadu Chennai600073 India GLA University Department of Computer Engineering & Applications Mathura India Department of Mechanical Engineering CMR Technical Campus Telangana Hyderabad India GRIET Bachupally Department of CSE Telangana Hyderabad India College of Technical Engineering Department of Computers Techniques Engineering The Islamic University Najaf Iraq Virtusa Corporation Department of Architecture and Design New Jersey United States

ISBN: (纸本)9798350368109

The application of deep learning techniques to design and develop smart grid systems present opportunities to counteract interoperability barriers ultimately improving the smart grid usability, reliability, stability and security. This paper highlights the fairly new robust Deep Learning techniques and how they can be used to solve various smart grid interoperation challenges. In this regard, following a step-by-step approach, we to detail on how data are collected and pre-processed, as well as, how deep learning models are selected and trained with corresponding performance metrics made on how the same models can be deployed in some prominent applications such as;predictive maintenance, energy consumption prediction, and anomaly detection, and lastly in cybersecurity. In addition, we continue the description of works in the field of grids standardization and protocol to achieve unification of communication between grid entities. The findings shown here prove that deep learning can enhance computational grid administration, security, and interaction ideals. There is still a need to address the issues related to the complexity of deep learning models as well as their response to dynamic gird systems in the literature;Future work should also consider the applicability of new architectures in modern grid challenges like integration of renewable energy systems and impact of EVs. In conclusion, the application of deep learning as part of the techniques offers a promising approach to improving smart grid systems and moving towards the progressive development of energy infrastructure with increased reliability and efficiency. © 2024 IEEE.

关键词： Smart power grids

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Anomaly Localization at High-Density Crowd Using Motion Shape Image (MSI) 1st

Anomaly Localization at High-Density Crowd Using Motion Shap...

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1st International Conference on Artificial Intelligence for Smart Community, AISC 2020

作者： Farooq, M.U. Mohamad Saad, M.N. Daud Khan, S. Saleh, Y. Universiti Teknologi PETRONAS Tronoh Malaysia Department of Computer Science National University of Technology Islamabad Pakistan Electrical Engineering Department Faculty of Engineering and Islamic Architecture Umm Al-Qura University Makkah Saudi Arabia

ISBN: (纸本)9789811621826

Anomaly localization plays a critical role if a disaster occurs in a high-density crowd to efficiently rescue the crowd from the right location. This paper enriches anomaly localization by introducing new localization features, in addition to the ‘only’ localization feature of source/start point detection existing literature offers. New features introduced include crowd density estimation in localized regions and direction/angle of a localized region. A motion shape image (MSI) based approach is introduced for localization and features detection and experimentation is performed on benchmark and our proposed high-density crowd datasets. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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