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International Conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech)

作者： Daniel Anando Wangean Derwin Suhartono Master of Computer Science Binus Graduate Program Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

Low resolution image-face recognition system is one of the challenging aspects of face recognition models' development. From machine learning, deep learning, and into ensemble learning are implemented to develop face recognition models. Including mpdCNN that published by Mishra in a journal article. This mpdCNN already achieved 88.6% accuracy score on SCFace dataset while published. Based on literature review, ensemble learning model can be improved by modifying parallel layers. The purpose of this research is to improve mpdCNN's performance by implementing some modifications include adding parallel layers, alternating the fusion layers, expanding the layers, and adding residual connections. Some modifications were successfully improved mpdCNN's accuracy score. By adding parallel layers and residual connections into mpdCNN's architecture, the modified mpdCNN that proposed in this research achieved 92.33% accuracy score, measured by Rank-k evaluation metric.

关键词： Measurement Deep learning Accuracy Image resolution Image recognition Face recognition Instruments Robustness Ensemble learning Systematic literature review

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Indoor Positioning System Classification with Wi-Fi Signal Data: A Machine Learning Approach

Indoor Positioning System Classification with Wi-Fi Signal D...

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International Conference on Knowledge and Smart Technology (KST)

作者： Ivan Alexander Gede Putra Kusuma Yaya Heryadi Widodo Budiharto Computer Science Departement Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Departement School of Computer Science - Computer Science Department Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331520403

ISBN: (纸本)9798331520410

Indoor positioning systems (IPS) are critical for enabling accurate navigation, asset tracking, and emergency response in indoor environments where GPS fails. This study evaluates five machine learning algorithms-K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Decision Tree, and Random Forest-using two benchmark datasets, UJIIndoorLoc and UTSIndoorLoc. The results reveal that SVM achieves the highest accuracy, with 81.3 % on UJIIndoorLoc and 92 % on UTSIndoorLoc, followed by MLP with $\mathbf{7 8. 8 \%}$ and 90.9 %, respectively. Random Forest provides stable performance, with 77.6 % and 86.08 %, while KNN reaches 75 % and 89.7 %, performing well in structured environments. Decision Tree shows the lowest accuracy, 71.2 % and 76.54 %, highlighting its limitations with complex data. The UTSIndoorLoc dataset consistently yields higher accuracies, demonstrating its structured signal distribution. These findings underscore SVM and MLP as optimal algorithms for Wi-Fi fingerprinting IPS, offering robust and scalable solutions for indoor localization.

关键词： Support vector machines Accuracy Machine learning algorithms Nearest neighbor methods Fingerprint recognition Decision trees IP networks Random forests Wireless fidelity Indoor positioning systems

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The Effect of WebRTC Jitter Buffer on Cloud Streaming Game QoE

The Effect of WebRTC Jitter Buffer on Cloud Streaming Game Q...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Naoyasu Kaneko Toshiro Nunome Department of Engineering Computer Science Program Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

This paper considers a cloud streaming game using WebRTC. Since the cloud streaming game consists of a server and a client, the network quality between them affects the game. Among the network quality degradation factors, we focus on delay jitter. We reduce the effect of the jitter by buffering. We conduct a subjective experiment of a single-player action game and evaluate application-level QoS and QoE. We investigate the impact of buffering on the video and audio quality of the user playing the game.

关键词： Degradation Games Quality of service Jitter Streaming media Cloud gaming Delays Quality of experience Servers WebRTC

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Robust temporal knowledge inference via pathway snapshots with liquid neural network

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Methods 2025年 241卷 24-32页

作者： Han, Peifu Wang, Jianmin Liu, Dayan Liu, Lin Song, Tao College of Computer Science and Technology China University of Petroleum Qingdao 266580 China The Interdisciplinary Graduate Program in Integrative Biotechnology Yonsei University Incheon 21983 South Korea Department of Stomatology The First Medical Center Chinese PLA General Hospital Beijing 100853 China

Static graphs play a pivotal role in modeling and analyzing biological and biomedical data. However, many real-world scenarios—such as disease progression and drug pharmacokinetic processes—exhibit dynamic behaviors. Consequently, static graph methods often struggle to robustly address new environments characterized by complex and previously unseen relationship changes. Here, we propose a method for constructing temporal knowledge inference agents tailored to disease pathways, enabling effective relation reasoning beyond their training environment under complex shifts. To achieve this, we developed an imitation learning framework using liquid neural networks, a class of continuous-time neural models inspired by the brain function that are causal and adaptable to changing conditions. Our findings indicate that liquid agents can distill the essential tasks from knowledge graph inputs while accounting temporal evolution, thereby enabling the transfer of temporal skills to novel time nodes. Compared to state-of-the-art deep reinforcement learning agents, experiments demonstrate that temporal robustness in decision-making emerges uniquely in liquid networks. © 2025

关键词： Disease Pathways Liquid Neural Networks Relation Reasoning Robust Decision-Making Temporal Knowledge Inference

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Enhancing cybersecurity resilience through advanced red-teaming exercises and MITRE ATT&CK framework integration: A paradigm shift in cybersecurity assessment

Cyber Security and Applications

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Cyber Security and Applications 2025年 3卷

作者： Yulianto, Semi Soewito, Benfano Gaol, Ford Lumban Kurniawan, Aditya Computer Science Department Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

As cybersecurity threats evolve alarmingly, conventional defense strategies are becoming increasingly ineffective. In response to this urgent challenge, our study presents a transformative approach to red-teaming exercises by integrating the MITRE ATT&CK framework. This innovative integration leverages real-world attacker tactics and behaviors to create highly realistic scenarios that rigorously test defenses and uncover previously unidentified vulnerabilities. Our comprehensive evaluation demonstrates a significant enhancement in the realism and effectiveness of red-teaming, leading to improved vulnerability identification and the generation of actionable insights for proactive remediation. This study uniquely contributes by providing a structured, data-driven methodology that aligns current defenses with emerging threat tactics and promotes continuous improvement in cybersecurity resilience. Such advancements are essential for organizations seeking to stay ahead of attackers and maintain robust cyber defenses in dynamic threat environments. The significance of this study lies in offering a structured approach to strengthen cybersecurity resilience against evolving threats and providing a model for continual defense enhancement. Future research should investigate the impact of this integration on long-term cybersecurity strategies. © 2024 The Authors

关键词： Continuous defense improvement Cyber resilience Cybersecurity MITRE ATT&CK framework Red-teaming exercises

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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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Challenges and Recommendations for Improving Logistic Skills Through Simulation-Based Learning Technology

Challenges and Recommendations for Improving Logistic Skills...

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Advancement in Data science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Tamsir Hasudungan Sirait Meyliana Yulyani Arifin Budi Yulianto Binus Graduate Program Doctor of Computer Scienceept Bina Nusantara University Jakarta Indonesia Computer Science Department Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Simulation-based learning is one of the most powerful tools to support the educational process. This learning offers an opportunity to learn based on experiences that are very similar to real-world environments. In the context of logistics, which can be considered a complex system, significant efforts are required to enhance students' understanding and practical skills. For that reason, simulations are used so that benefits such as skills and knowledge in the field of logistics can be achieved. However, there are currently several challenges in developing simulation-based learning in higher education. These challenges include the need for simulations that are appropriate for the current learning context, where the students are generally from Generation Z, and how to improve their learning quality in the era of Industry 4.0. To address these challenges, more suitable simulations need to be developed, and higher education institutions must carefully examine how to develop them, including identifying the essential components in the development of simulations for logistics learning. In this research, a systematic literature review using the PRISMA method and also creating a conceptual model of simulation that better meets the current needs of logistics education will be proposed. The results of this research can be used as a basis for developing updated simulations to better prepare students for the challenges of Industry 4.0.

关键词： Analytical models Computational modeling Education Prototypes Data models Fourth Industrial Revolution Proposals Logistics Systematic literature review Investment

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Load Balancing to Optimize MPEG-DASH Video Streaming

Load Balancing to Optimize MPEG-DASH Video Streaming

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International Conference on Information Networking

作者： Edenilson Jônatas Dos Passos Adriano Fiorese Department of Computer Science (DCC) Graduate Program in Apllied Computing (PPGCAP) Santa Catarina State University (UDESC) Joinville Brazil

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

Recent advancements in on-demand multimedia streaming have revealed the potential of this business model, driven by the convenience of accessing content anytime and anywhere. However, delivering a seamless user experience remains challenging due to the demanding nature of audiovisual streams, despite significant resource allocation. A promising solution involves using load balancers to distribute workloads evenly among processing units. This study proposes optimizing the processing and transmission resources by means of Software-Defined Networks (SDN). By continuously monitoring performance metrics like CPU usage and throughput, the SDN network controller determines the best server for handling new connections, even allowing seamless server migration during playback. The results show up to 80% faster response times and more consistent video quality, with reduced latency and uninterrupted playback. The approach also confirms the feasibility of SDN in streaming services, providing a foundation for future network improvements.

关键词： Transform coding Streaming media Load management User experience Quality assessment Servers Time factors Software defined networking Video recording Monitoring

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IoT-based Efficient Waste Management on University Campus : Enhancing Monitoring and Optimization through Machine Learning

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Procedia computer science 2025年 258卷 4157-4168页

作者： Sumathi M. Hanumanthappa J. K.S. Santhosh Kumar S.P. Shiva Prakash Meghana J. Department of Studies in Computer Science Manasagangothri Campus University of Mysore Mysuru Karnataka 570006 India Department of Artificial Intelligence and Machine Learning Mysore University School of Engineering Manasagangothri Campus University of Mysore Mysuru Karnataka 570006 India Department of Information Science and Engineering & Computer Science and Business Systems JSS Science and Technology University Mysuru Karnataka 570006 India Department of Computer Science Mahajana Post Graduate Center Mysuru Karnataka 570016 India

The introduction of an IoT-based smart waste management system is proposed to optimize various aspects of university campus waste recycle management processes, such as garbage collection scheduling, waste type identification, and collection routes. This system utilizes advanced data analytics algorithms, wireless communication protocols, and sensors to measure and document variations in trash levels, enabling real-time monitoring of waste accumulation. Through this IoT-based infrastructure, university administrators and waste management staff gain fine-grained visibility and control over garbage collection processes. The implications for sustainability advocacy and environmental stewardship in academic environments are significant. By fostering a culture of environmental awareness and accountability, universities can instill ideals of conservation and resourcefulness in their student populations and staff. The proposed IoT-enabled waste bin system exemplifies the synergy between technological innovation and environmental responsibility, serving as a model for institutions worldwide. Collaboration among universities can leverage IoT technology to drive positive change, mitigate environmental degradation, and promote sustainable practices beyond campus boundaries. Efficient waste management is crucial for nurturing sustainability on college and university campuses, as evidenced by the urgent need for the collection and recycling of compost, general waste, and recyclables. Our models consistently achieve high performance, with perfect scores (1.000) across all split ratios, indicating flawless classification of waste types. Although the Artificial Neural Network (ANN) demonstrates slightly lower accuracy, ranging from 0.973 to 0.997 depending on data splits, it still performs well. As a result, Naive Bayes produces less accurate results, typically surpassing 0.93 but falling short of Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)’s flawless

关键词： Artificial Intelligence Smart Parking: Smart city Machine Learning Smart Applications

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Blockchain Developer Experience: A Multivocal Literature Review

arXiv

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arXiv 2025年

作者： Soares, Pamella Araújo, Allysson Allex Destefanis, Giuseppe Neykova, Rumyana Saraiva, Raphael Souza, Jerffeson Graduate Program in Computer Science State University of Ceará Fortaleza Brazil Science and Technology Center Federal University of Cariri Juazeiro do Norte Brazil Department of Computer Science Brunel University London United Kingdom

The rise of smart contracts has expanded blockchain’s capabilities, enabling the development of innovative decentralized applications (dApps). However, this advancement brings its own challenges, including the management of distributed architectures and immutable data. Addressing these complexities requires a specialized approach to software engineering, with blockchain-oriented practices emerging to support development in this domain. Developer Experience (DEx) is central to this effort, focusing on the usability, productivity, and overall satisfaction of tools and frameworks from the engineers’ perspective. Despite its importance, research on Blockchain Developer Experience (BcDEx) remains limited, with no systematic mapping of academic and industry efforts. To bridge this gap, we conducted a Multivocal Literature Review analyzing 62 to understand the distribution of BcDEx sources, practical implementations, and their impact. Our findings revealed that academic focus on BcDEx is limited compared to the coverage in gray literature, which primarily includes blogs (41.8%) and corporate sources (21.8%). Particularly, development efficiency, multi-network support, and usability are the most addressed aspects in tools and frameworks. In addition, we found that BcDEx is being shaped through five key perspectives: complexity abstraction, adoption facilitation, productivity enhancement, developer education, and BcDEx evaluation. © 2025, CC BY.

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