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Attributed Network Clustering through Mutual Affinity Propagated Non-negative Matrix Factorization

SN computer Science 2025年第5期6卷 1-17页

作者： Pahari, Saikat Dey, Paramita Department of Computer Science Engineering OmDayal College of Engineering and Architecture West Bengal Howrah 711316 India Department of Information Technology Government College of Engineering and Ceramic Technology West Bengal Kolkata 700010 India

Attributed Network Clustering (ANC) has garnered significant attention in research for identifying communities within a complex network like a social, biological, or information network. Generally, such networks are represented as a graph where the nodes denote entities and edges are relationships between two adjacent nodes. In real-world networks, nodes are associated with attributes to express their properties. In this context, a community is defined as a group of nodes that interact more frequently with each other than with nodes outside their group. ANC involves creating a mapping function that converts nodes and associated attributes into lower-dimensional vector representations. Once transformed into these vectors, various machine learning algorithms are used to cluster the nodes and reveal the underlying communities. However, most ANC methods confront two major challenges. First, they assimilate node attribute information from the local neighborhood, as a result, they can’t explore the mutual affinity between node and attribute. Second, they do not consider the values of the community membership of the nodes at the time of clustering. This leads to poor performance, as the embedding method and clustering methods are independent. To address this, this article proposes a method named Mutual Affinity Propagation-based Nonnegative Matrix Factorization (MAP-NMF). The proposed method works in two phases. In the first phase, an attribute-augmented transition probability matrix is constructed to capture the mutual affinity between the node and attribute and obtain node representation using singular value decomposition of the same. The second phase produces community membership from the obtained latent representation through an NMF-based joint optimization. Moreover, a novel updating rule is proposed for the joint optimization method. Empirical study on both real-world and artificial networks proves the superiority of our proposed algorithm. © The Author(s), under exc

关键词： Attribute augmented graph Attributed network embedding Joint optimization Non-negative matrix factorization

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RedTops: real-time energy-aware dynamic task offloading via federated mountain gazelle optimisation in SDN-enhanced edge computing

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Neural Computing and Applications 2025年 1-39页

作者： Al Aghbari, Zaher Khedr, Ahmed M. Ahmed, Naveed Girija, Shini Baker, Thar Department of Computer Science University of Sharjah Sharjah United Arab Emirates School of Architecture Technology and Engineering University of Brighton BrightonBN2 4GJ United Kingdom

Offloading computational tasks is vital for real-time applications on mobile devices with limited resources. Mobile edge computing (MEC) is deemed a solution that puts computational resources closer to users. Nevertheless, there are many associated concerns during the offloading procedure (i.e., privacy, delay, and high energy consumption). Federated learning (FL) has been considered a solution to address MEC’s data privacy issues;however, it comes with its own resource consumption issues. To address these issues, this paper proposes a distributed learning paradigm inspired by FL. We propose an optimisation technique for offloading computational tasks that aims to reduce both total delay and energy consumption by using the mountain gazelle optimisation algorithm, which shows it can reduce both delay and energy consumption in dynamic situations. Additionally, an improved variant known as the improved mountain gazelle optimiser is integrated into a distributed SDN controller architecture to create an offloading policy model for optimal edge node selection. We also present a new SDN-enabled edge computing architecture that achieves the best task distribution through task offloading using federated mountain gazelle optimisation (RedTops). Energy usage, delay, and bandwidth are considered by RedTops, which successfully addresses high training costs, dependability issues, and privacy concerns in MEC. Based on the outcomes of five extensive simulations, RedTops is more energy-efficient and faster at completing tasks than four state-of-the-art offloading methods (DDLO, DROO, DRL without TL and SDN, and DTRL). © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Federated learning

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Development and Investigation of Vision System for a Small-Sized Mobile Humanoid Robot in a Smart Environment

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International Journal of Crowd Science 2025年第1期9卷 29-43页

作者： Abu-Jassar, Amer Tahseen Attar, Hani Amer, Ayman Lyashenko, Vyacheslav Yevsieiev, Vladyslav Solyman, Ahmed Department of Computer Science College of Information Technology Amman Arab University Amman11937 Jordan Faculty of Engineering Zarqa University Zarqa2000 Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Department of Media Systems and Technology Kharkiv National University of Radio Electronics Kharkiv61166 Ukraine Department of Electrical and Electronics Engineering Faculty of Engineering and Architecture Nişantaşı University Istanbul34398 Turkey

The conducted research aims to develop a computer vision system for a small-sized mobile humanoid robot. The decentralization of the servomotor control and the computer vision systems is investigated based on the hardware solution point of view, moreover, the required software level to achieve an efficient matched design is obtained. A computer vision system using the upgraded tiny-You Only Look Once (YOLO) network model is developed to allow recognizing and identifying objects and making decisions on interacting with them, which is recommended for crowd environment. During the research, a concept of a computer vision system was developed, which describes the interaction between the main elements, on the basis of which hardware modules were selected to implement the task. A structure of information interaction between hardware modules is proposed, and a connection scheme is developed, on the basis of which a model of a computer vision system is assembled for research, with the required algorithmic and software for solving the problem. To ensure the high speed of the computer vision system based on the ESP32-CAM module, the neural network was improved by replacing the Visual Geometry Group 16 (VGG-16) network as the base network for extracting the functions of the Single Shot Detector (SSD) network model with the tiny-YOLO lightweight network model, which made it possible to preserve the multidimensional structure of the network model feature graph, resulting in increasing the detection accuracy, while significantly reducing the amount of calculations generated by the network operation, thereby significantly increasing the detection speed, due to a limited set of objects. Finally, a number of experiments were carried out, both in static and dynamic environments, which showed a high accuracy of identifications. © The author(s) 2025.

关键词： Anthropomorphic robots

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Machine learning model for predicting long-term energy consumption in buildings

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Discover Internet of Things 2025年第1期5卷 1-24页

作者： Hussien, Aseel Maksoud, Aref Al-Dahhan, Aisha Abdeen, Ahmed Baker, Thar Department of Architectural Engineering University of Sharjah Sharjah United Arab Emirates Department of Computer Science University of Sharjah Sharjah United Arab Emirates Civil Mining Environmental and Architectural Engineering University of Wollongong Wollongong Australia School of Architecture Technology and Engineering University of Brighton Brighton United Kingdom

The rapid growth in the construction sector has led to increased energy consumption and carbon emissions. Calculating energy usage and emissions is essential to energy security and promoting sustainable sector development. Therefore, the study objective is to investigate the utilazation of machine learning algorithm to predict long-term energy consumption in buildings sector, aiming to improve sustainable design and energy optimization, via the implementation of three machine learning models, XGBoost, Support Vector Regression, and Long-Short-Term Memory networks, to predict energy consumption. These models are adept at capturing complex interactions between building characteristics, environmental factors, and energy patterns. Although previous studies have explored various machine learning techniques for energy efficiency, limited research links these models to practical applications in building performance simulation. Furthermore, there is a lack of comparative evaluation of advanced machine learning models such as XGBoost, Support Vector Regression, and Long-Short-Term Memory to predict the energy consumption of building envelopes, particularly in hot climates such as the UAE. This research aims to fill this gap by providing a detailed comparison of these models against alternative approaches mentioned in the literature. The findings position Long-Short-Term Memory as a transformative force in predictive modeling, demonstrating exceptional precision with an R-squared value of 0.993 and a Mean Squared Error of 0.004. In contrast, Support Vector Regression and XGBoost showed limited predictive capabilities, with R-squared values of 0.462 and 0.94, respectively. This study establishes a solid data-driven foundation for architects and engineers to inform decisions on energy-efficient building designs, advocating Long-Short-Term Memory as the superior model for predicting energy performance. © The Author(s) 2025.

关键词： Green buildings

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Detecting Scam Tokens and Backdoor Functions in EVM Based Networks 6th

Detecting Scam Tokens and Backdoor Functions in EVM Based ...

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6th International Congress on Blockchain and Applications, BLOCKCHAIN 2024

作者： Lorenzo, Sergio Anguita Miguel-Alonso, Jose Gomez-Goiri, Aitor Department of Computer Architecture and Technology University of the Basque Country UPV/EHU Paseo M. Lardizabal 1 San Sebastian20018 Spain Astondo Bidea 700 Derio48160 Spain

ISBN: (纸本)9783031819278

The blockchain ecosystem is currently becoming an intricate landscape given the large number of attacks, scams, and hacks that occur. With such risk surrounding Blockchain DApps and users, we propose a method to help in the earlier detection of scam tokens. While blockchain technology continues to grow in multiple industries, the proliferation of fraudulent activities in the decentralized ecosystem has become a significant threat to the well-being and trustworthiness of the whole ecosystem. This scam tokens are deployed in plain sight in decentralized networks and listed in platforms such as CoinMarketCap and other token issuance platforms. In this paper, we identify over 1450 unreported scam tokens, 167 different backdoor functions, and 1428 malicious contract creators. By increasing the collaboration between industry stakeholders and web3 companies, our scam tokens dataset and detection tools can help in web3 threat detection while creating a more secure and resilient blockchain environment, fostering trust and innovation in the decentralized space. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Phishing

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Inspiring Creativity: A Comparative Study of Hands-On Artifact Interaction and 3D Art Viewing 10

Inspiring Creativity: A Comparative Study of Hands-On Artifa...

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10th International Conference on Digital Arts, Media and technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Sookkaew, Jirawat Saephoo, Pipatpong Changjaikla, Pheeraphon University of Phayao School of Information and Communication Technology Department of Computer Graphic and Multimedia Thailand University of Phayao School of Information and Communication Technology Department of Digital Business Thailand Kasem Bundit University School of of Architecture Department of Visual Communication Design Bangkok Thailand

ISBN: (纸本)9798331543273

The use of 3D technology in creative processes not only facilitates the creation of complex and intricate works but also provides opportunities for creators to experiment and develop unique and engaging concepts. Additionally, multimedia and 3D technology enhance the diversity and interest of the audience's experience. This study focuses on comparing the impact of using 3D models versus viewing ancient artifacts in traditional formats on enhancing creativity among students in different groups. Descriptive data analysis revealed that both groups had equal mean outputs (7.14 pieces per person), but the group using 3D models (Group 2) showed greater consistency in their work, with lower variance (5.14) and standard deviation (2.27) compared to the group using traditional visuals (Group 1), which had higher variance (27.24) and standard deviation (5.22).The findings suggest that implementing 3D technology in education goes beyond preserving historical information - it serves as a crucial tool for stimulating creativity and fostering innovation. This technology enables the development of immersive learning environments that can adapt to individual learning styles while promoting collaborative and cross-disciplinary approaches to education. © 2025 IEEE.

关键词： 3D modeling

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Assessment of changes in a technical condition of railway bridges using a novel probabilistic algorithm

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Structure and Infrastructure Engineering 2025年

作者： Fabianowski, Dariusz Jakiel, Przemyslaw Rydel, Marek Bialic, Grzegorz Stanislawski, Rafal Faculty of Civil Engineering and Architecture Opole University of Technology Opole Poland Department of Computer Science Opole University of Technology Opole Poland

Planning of rehabilitation work is important for the safety of transport infrastructure. This problem needs to be properly solved, therefore a new algorithm for planning railway bridge maintenance is proposed. The input data come from periodic inspections that assess the technical condition of a bridge. These assessments are processed using hybrid Extended Fuzzy Analytic Hierarchy Process and Dominant Analytic Hierarchy Process methods. The main part of the algorithm is the Statistic Bridge Analyser, which allows a multi-variant analysis of the impact of inaccuracy and uncertainties in sub-assessments. It also takes into account a relationship between individual bridge components and estimates missing assessments using archive data. The algorithm performance has been confirmed by analysing nine different scenarios involving ranking of 10 selected railway bridges on Maintenance, Repair & Rehabilitation (MR&R) list. It has been demonstrated that even a small error in the sub-assessment of the technical condition of one element can result in a significant change in assessment of the bridge technical condition. Considering the relationship between criteria also generates changes in the final assessment of the bridge technical condition and may result in changes in position on MR&R list. This allows to avoid sudden failures, especially with limited financial resources. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Railroad bridges

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Integrating environmental sustainability in construction Time-Cost trade-off for decision-making using hybrid NSGA-III and MOPSO approach

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Asian Journal of Civil Engineering 2025年第4期26卷 1527-1542页

作者： Nagayo, Analene Montesines Singh, Rekha Dhawan, Amit Manjunath, T.C. Qasem, Altayeb Sethi, Krushna Chandra Sharma, Kamal Department of Engineering and Technology University of Technology and Applied Sciences-Al Musanna Al Muladdah Oman Department of Civil Engineering Raj Kumar Goel Institute of Technology Ghaziabad India Department of Civil Engineering Dr. Bhim Rao Ambedkar Polytechnic College Gwalior India Computer Science Engineering Department Rajarajeswari College of Engineering Bangalore India Department of Building Engineering College of Architecture and Planning Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Department of Civil Engineering Centurion University of Technology and Management Gajapati India Department of Civil Engineering Madhav Institute of Technology and Science Gwalior India

Construction project management often involves optimizing time and cost while ensuring minimal environmental impact. This study presents an innovative hybrid approach combining non-dominated sorting genetic algorithm III (NSGA-III) and multi-objective particle swarm optimization (MOPSO) to address the time-cost-environmental sustainability trade-off (TCEST) in construction projects. The proposed model aims to minimize project completion time and cost while maximizing environmental sustainability. A case study is conducted to validate the model, incorporating diverse construction activities and their respective time, cost, and environmental sustainability metrics. The results reveal Pareto-optimal solutions demonstrating significant trade-offs among the three objectives. The hybrid approach outperforms standalone algorithms in terms of solution diversity, convergence, and hypervolume indicators. Weighted sum methods are employed to select the most suitable solution from the Pareto front based on project priorities. Correlation analysis further explores interdependencies among objectives, emphasizing the feasibility of balancing these critical factors. The study contributes a robust decision-support tool for sustainable project planning, facilitating informed decision-making in modern construction management. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Hybrid Optimization MOPSO NSGA-III Sustainable Construction Time–Cost-Environmental Trade-off

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Opposition-based multi-objective ant colony optimization framework for sustainable retrofitting: time–cost–energy–risk trade-offs

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Asian Journal of Civil Engineering 2025年第5期26卷 2223-2239页

作者： Pokkuluri, Kiran Sree Chauhan, Trupti Ravindra Macedo, Víctor Daniel Jiménez Manjunath, T.C. Bharadwaj, Manish Sethi, Krushna Chandra Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Design Manipal School of Architecture and Planning Manipal Academic of Higher Education Manipal Karnataka Udupi India Mechanical Engineering Faculty Michoacan University of Saint Nicholas of Hidalgo Morelia Mexico Department of Computer Science and Engineering Rajarajeswari College of Engineering Bengaluru India Department of Management Studies Atal Bihari Vajpayee Indian Institute of Information Technology and Management Gwalior India Civil Engineering Department Centurion University of Technology and Management Gajapati India

The increasing emphasis on sustainable retrofitting of buildings has brought attention to the need for optimizing trade-offs among critical objectives, such as time, cost, energy consumption, and risk. This study proposes an opposition-based multi-objective ant colony optimization (OB-MOACO) framework to address these challenges effectively. The framework incorporates opposition-based learning to enhance solution exploration and convergence in multi-objective optimization problems, specifically tailored for sustainable retrofitting projects. The study develops a time–cost–energy–risk trade-off (TCERT) model with four objectives: minimizing retrofitting time, cost, energy consumption, and risks. The model integrates constraints related to budget, project timelines, and energy efficiency to ensure feasibility and sustainability. A detailed case study of retrofitting a mixed-use building is presented, encompassing 11 aspects such as structural reinforcement, energy efficiency enhancement, and accessibility improvements. Results demonstrate the superiority of OB-MOACO over conventional methods like NSGA-III, MODE, and MOPSO in achieving Pareto-optimal solutions. Key performance metrics, including hypervolume (HV) and inverted generational distance (IGD), highlight the model's efficiency in balancing competing objectives. The study contributes to advancing sustainable retrofitting practices by providing an innovative, cost-effective, and energy-efficient decision-making framework. Implications for practitioners and policymakers are discussed, alongside recommendations for future research. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Multi-objective optimization Opposition-based ant colony optimization Pareto optimization Sustainable retrofitting Time–cost–energy–risk trade-off

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Exploring Children’s Strategies in Response to Robot’s Advice During a Group Task with iCub and Nao 16th

Exploring Children’s Strategies in Response to Robot’s A...

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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Pusceddu, Giulia Sangineto, Mariapia Cocchella, Francesca Bogliolo, Michela Belgiovine, Giulia Lastrico, Linda Casadio, Maura Rea, Francesco Gena, Cristina Sciutti, Alessandra Robotics Brain and Cognitive Science Department Italian Institute of Technology Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Department of Computer Science University of Turin Turin Italy Cognitive Architecture for Collaborative Technologies Unit Italian Institute of Technology Genoa Italy Scuola di Robotica Genoa Italy

ISBN: (纸本)9789819635184

This study investigates children’s trust in two humanoid robots, Nao and iCub, through a cooperative game designed to elicit spontaneous behaviors and group dynamics. We investigate whether participants change their choices after hearing advice from a robot, considering rounds where the advice is correct or incorrect, with the robot apologizing for its mistakes. Results reveal that, in most cases, participants prefer not to change their moves after the robot’s advice. Additionally, after the robots made mistakes, participants are less likely to actively follow their advice and more likely to reject it. No significant differences in strategies among the iCub and Nao samples are evidenced. Furthermore, we examine the influence of group dynamics, identifying "Proactive Players" who consistently initiated moves. A significant association between the strategies of this player and their teammates is found, suggesting potential influence. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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