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arXiv 2025年

作者： Abdullah, Abdulhady Abas Taher Karim, Sarkhel H. Ahmed, Sara Azad Tariq, Kanar R. Rashid, Tarik A. Artificial Intelligence and Innovation Centre University of Kurdistan Hewler Erbil Iraq Computer Science Department College of Science University of Halabja Kurdistan Region Iraq Computer Engineering Dep Komar University of Science and Technology Iraq Information Technology Department Technical College of Informatics Sulaimani Polytechnic University Sulaymaniyah Iraq

Speaker diarization, a core problem in speech processing, entails partitioning a given audio stream according to the speakers. Even though progress has been made in the development of the models for high-resource languages, there is still a set of specific difficulties in going through a similar process for low-resource languages such as Kurdish: there are very few annotated datasets available;the language has dialects;speakers use code-switching a lot. These challenges are met in this study by training the Wav2Vec 2.0 SSL model on a Kurdish dataset prepared for this purpose. Thanks to transfer learning, it was possible to transfer multilingual representations learnt in other languages to the phonetic and acoustic features of Kurdish speech. The general Diarization Error Rate (DER) was reduced by 7.2%, and the cluster purity increased by 13% when compared to the baseline algorithm. They show that making improvements in any state-of-the-art model can help in enhancing the performance of under-resourced languages. Implications of this work include transcription services for Kurdish-language media programs, as well as speaker segmentation in multilingual call centers, teleconferencing, and videoconferencing systems. Therefore, this work demonstrates that self-supervised and transfer techniques can improve speaker diarization for Kurdish and other low-resource languages with diverse features. The approach provides a base for building effective diarization systems in other understudied languages, which remains essential for speech technology’s equity. © 2025, CC BY.

关键词： Speech enhancement

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Dual Approach in Autonomous Directional Drilling: Innovations with Drillbotics 1.5 Inch Automated RSS and Virtual Rig Platforms

Dual Approach in Autonomous Directional Drilling: Innovation...

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2025 SPE/IADC International Drilling Conference and Exhibition, DC 2025

作者： Soilemezidis, C. Mensah, J.S. Hollstein, W. Al Maasarani, K. Alkhawaja, A. Jamali, F. Dörfler, G. Hölzner, N. Jarbouh, K.A. Ustaoglu, D. Alukkal, A.T. Gudhane, L.A.Y. Jaeger, P. Feldmann, E. Carvajal, C.A.P. Yin, A.O. Sinkuu, L. Quayson, J. Amoah, F.B. Yahaya, H.S. Owusu, K.A. Dongoyo, C.K.N. Owusu, A. Amorin, R. Florence, F. Institute of Subsurface Energy Systems Clausthal University of Technology Lower Saxony Clausthal-Zellerfeld Germany Department of Petroleum and Natural Gas Engineering University of Mines and Technology Tarkwa Ghana Institute of Mechanical Engineering Clausthal University of Technology Lower Saxony Clausthal-Zellerfeld Germany Institute of Economics Clausthal University of Technology Lower Saxony Clausthal-Zellerfeld Germany Institute of Informatics Clausthal University of Technology Lower Saxony Clausthal-Zellerfeld Germany Department of Electrical and Electronics Engineering University of Mines and Technology Tarkwa Ghana Department of Computer Science and Engineering University of Mines and Technology Tarkwa Ghana Department of Petroleum Geoscience University of Mines and Technology Tarkwa Ghana Rig Operations AustinTX United States

ISBN: (纸本)9781959025641

SPE’s Drilling Systems Automation Technical Section (DSATS) sponsors the Drillbotics® autonomous drilling contest to promote advanced learning at participating universities. Two groups worked separately to drill a directional well hands-free and to connect to a well model to detect/close in a kick. This paper consolidates the efforts of both teams. Team A, the directional drilling team from the University of Mines and Technology (UMaT) in Ghana, developed a virtual model that replicated full-scale directional drilling operations, facilitating the development and testing of innovative control schemes and automation strategies. Leveraging state-of-the-art drilling software, real-time data analytics, and advanced control systems, the project demonstrated the potential of automated drilling technologies across both the planning and execution phases of drilling projects. Team B, from Technical University Clausthal (TUC) in Germany, designed and built a mini-rig that included a working scaled version of a rotary steerable system (RSS) tool to steer a wellbore through a block of sandstone to reach two non-planer targets. The work incorporates machine learning techniques to evaluate downhole sensor data and improve steering signal input. The modular design of the second-generation surface actuation system offers a more compact and efficient solution than its predecessor. The control architecture and network protocols are analyzed, demonstrating network delays of under 2 msec using a refined second-generation translator setup. The achievements of both teams are important to the future of drilling automation, as their approaches could be used to verify and validate third-party app’s communications prior to field release on full-scale rigs. Copyright 2025, SPE/IADC International Drilling Conference and Exhibition.

关键词： Directional drilling

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Towards an Automated Dissemination Process of Cyber Threat Intelligence Data using STIX 6

Towards an Automated Dissemination Process of Cyber Threat I...

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6th International Workshop on Big Data and Information Security, IWBIS 2021

作者： Briliyant, Obrina Candra Tirsa, Nusranto Pratama Hasditama, Maulana Andika Politeknik Siber Dan Sandi Negara Cyber Security Engineering Program Department of Cyber Security Bogor Indonesia National Cyber and Crypto Agency Depok Indonesia Faculty of Electrical Engineering and Informatics Budapest Computer Engineering Budapest Hungary

ISBN: (数字)9781665424516

ISBN: (纸本)9781665424516

Cyber Threat Intelligence (CTI) is an actionable, evidence-based knowledge management system of cyber threats data. CTI consists of 3 (three) major parts, namely information gathering, analysis, and dissemination. To carry out dissemination in CTI, Structured Threat Information Expression (STIX) and Trusted Automated Exchange of Intelligence Information (TAXII) have been developed as open and interoperable standards for the CTI dissemination. An effective CTI is a CTI that can be integrated into the organization's security operations processes. Many organizations today runs CTI with a mechanism for reporting and distributing information in the dissemination process or known as cyber threat information sharing which has significant effects on security operations performance. In this study, user requirements analysis and design of cyber threat information sharing processes were carried out. From the results of the requirement analysis, a suitable business process re-engineering were proposed to accelerate the automation of CTI, especially in the information sharing part. Lastly, the designed processes were tested by simulating the processes using STIX and TAXII-based platform. The results of this study indicate that the designed processes can be run on STIX and TAXII-based platforms, ergo a step toward to a fully automated CTI. © 2021 IEEE.

关键词： Information dissemination

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On Real-time Failure Localization via Instance Correlation in Optical Transport Networks

On Real-time Failure Localization via Instance Correlation i...

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IFIP Networking Conference

作者： Yan Jiao Pin-Han Ho Xiangzhu Lu Kairan Liang Yuren You János Tapolcai Bingbing Li Limei Peng Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Huawei Ottawa Research and Development Centre Ottawa Canada Department of Telecommunications and Media Informatics Budapest University of Technology and Economics Hungary School of Computer Science Hubei University of Technology Wuhan China School of Computer Science and Engineering Kyungpook National University Daegu South Korea

Failure localization serves as a key to an effective fault management plane in the Internet backbone. This paper investigates a novel failure localization approach, namely Instance Correlation based Fault Diagnosis (IC-FD), for achieving efficient fault management in Optical Transport Networks (OTN). The IC-FD is aimed at real-time localization of failed components in the optical layer of OTN through correlation of alarms and status changes of network devices (referred to as instances) via a learned binary classifier. The outcome of IC-FD is one or multiple instance correlation trees (ICT) where the instances corresponding to the faulty network devices are taken as the tree roots. Notably, the proposed binary classifier is characterized by an intelligent feature extraction of historical instance correlation in dimensions of time, board/alarm attribute, network topology, and traffic distribution. Extensive case studies are conducted to demonstrate the advantages gained by IC-FD in terms of its high precision and low computation complexity, as well as analysis of its performance due to various environmental turbulence such as network topology, traffic diversity and noise alarms.

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Towards Enabling Next Generation Societal Virtual Reality Applications for Virtual Human Teleportation

arXiv

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arXiv 2022年

作者： Chakareski, Jacob Khan, Mahmudur Yuksel, Murat Department of Informatics New Jersey Institute of Technology United States Department of Electrical and Computer Engineering York College United States Department of Electrical and Computer Engineering University of Central Florida United States

Virtual reality (VR) is an emerging technology of great societal potential. Some of its most exciting and promising use cases include remote scene content and untethered lifelike navigation. This article first highlights the relevance of such future societal applications and the challenges ahead towards enabling them. It then provides a broad and contextual high-level perspective of several emerging technologies and unconventional techniques and argues that only by their synergistic integration can the fundamental performance bottlenecks of hyper-intensive computation, ultra-high data rate, and ultra-low latency be overcome to enable untethered and lifelike VR-based remote scene immersion. A novel future system concept is introduced that embodies this holistic integration, unified with a rigorous analysis, to capture the fundamental synergies and interplay between communications, computation, and signal scalability that arise in this context, and advance its performance at the same time. Several representative results highlighting these trade-offs and the benefits of the envisioned system are presented at the end. Copyright © 2022, The Authors. All rights reserved.

关键词： Virtual reality

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University Department Recommendations Using Subject-Score-Based Skyline Queries

University Department Recommendations Using Subject-Score-Ba...

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Information Technology and Computing (ICITCOM), International Conference on

作者： Cahya Damarjati Galang Wicaksana Slamet Suripto Heri Wijayanto Haris Setyawan Hsing-Chung Chen Department of Information Technology Universitas Muhammadiyah Yogyakarta Yogyakarta Indonesia Department of Electrical Engineering Universitas Muhammadiyah Yogyakarta Yogyakarta Indonesia Department of Informatics Engineering Universitas Mataram Mataram Indonesia Department of Computer Science and Information Engineering Department of Medical Research Asia University China Medical University Taichung Taiwan

ISBN: (数字)9798350379839

ISBN: (纸本)9798350379846

Selecting the right university department is a critical decision-making problem for high school graduates, significantly influenced by limited guidance and personal preferences. Traditional counseling methods often do not fully utilize available academic data, resulting in less effective recommendations. This study proposed a Subject-Score-Based Skyline Queries Algorithm to enhance decision-making by recommending university departments based on candidates' high school subject scores. It used the score differences and minimum score rule in addition to the native Skyline Queries. The Algorithm identifies departments where a candidate's grades meet or exceed minimum requirements across multiple academic subjects, thereby suggesting optimal matches. This approach aims to mitigate uncertainty and enhance decision confidence among prospective students, potentially reducing dropout rates and improving academic outcomes. Experimental results demonstrate the method's effectiveness in providing personalized recommendations, underscoring its applicability beyond university admissions to various decision-making contexts. Future research could extend this approach by incorporating non-academic factors for more comprehensive guidance.

关键词： Employee welfare Economics Uncertainty Accuracy Decision making Optimal matching Information technology

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Low Complexity Deep Learning Based Coordinated Beamforming for mmWave Massive MIMO Vehicular Networks

Low Complexity Deep Learning Based Coordinated Beamforming f...

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Panhellenic Conference on Electronics and Telecommunications (PACET)

作者： Vasileios P. Rekkas Sotirios P. Sotiroudis Panagiotis Sarigiannids George K. Karagiannidis Christos G. Christodoulou Sotirios K. Goudos ELEDIA@AUTH School of Physics Aristotle University of Thessaloniki Thessaloniki Greece Department of Informatics and Telecommunications Engineering University of Western Macedonia Kozani Greece School of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Department of Engineering and Computing The University of New Mexico USA Department of Electronics and Communication Engineering Bharath University Chennai Tamilnadu India

Enabling high mobility in millimeter-wave (mmWave) systems is crucial for next-generation wireless communication systems. Challenges arise in applications such as vehicular communications and wireless virtual/augmented reality. MmWave systems grapple with concerns related to narrow beams, signal vulnerability to obstructions that affect coverage, and the necessity for frequent handovers. Additionally, identifying optimal beamforming vectors in large antenna array mmWave systems involves substantial training overhead, impacting efficiency. This paper introduces a novel solution to address challenges in enabling high mobility in mmWave systems. The approach combines deep learning (DL) and coordinated beamforming to support applications such as vehicular communications and wireless virtual/augmented reality. The solution involves multiple coordinating base stations (BSs) serving a mobile user, utilizing a single pilot training sequence. A DL model predicts beamforming vectors based on received signals, providing reliable coverage, low latency, and minimal training overhead for highly mobile mmWave applications. Simulations demonstrate its effectiveness in high-mobility scenarios, approaching the performance of an optimal solution, a genie-aided solution that knows the optimal beamforming vectors without any training overhead.

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Enhancing Accuracy in WBANs Through Optimal Sensor Positioning and Signal Processing: A Systematic Methodology

Enhancing Accuracy in WBANs Through Optimal Sensor Positioni...

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International Conference on Electrical engineering, computer Science and informatics (EECSI)

作者： Israa Ibraheem Al Barazanchi Mayasa M. Abdulrahman Reema Thabit Wahidah Hashim Amol M. Dalavi Ravi Sekhar College of Computing and Informatics Universiti Tenaga Nasional (UNITEN) Malaysia College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Computer Engineering Department College of Engineering University of Baghdad Baghdad Iraq Symbiosis Institute of Technology (SIT) Pune Campus Symbiosis International (Deemed University) (SIU) Pune India

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

Wireless Body Area Networks (WBANs) have transformed fitness monitoring by providing continuous real-time data collection from small sensors placed on or inside the human body. Even though WBANs have important benefits, their accuracy and dependability are jeopardized by issues with sensor placement and signal processing. Incorrect sensor positioning can lead to inaccurate information, while noisy surroundings make signal processing more challenging. This focuses on using a single methodical approach to improve sensor placement and enhance signal processing in WBANs. We suggest a comprehensive solution that tackles placement sensitivity and noise reduction by combining anatomical modeling, real-time feedback mechanisms, and innovative machine learning algorithms. Experimental results show a 25% increase in signal quality and a 35% improvement in data precision when compared to conventional techniques. This method not only enhances the dependability of health monitoring systems but also validates their ability to be scaled and their effectiveness in various medical uses. Our results highlight the potential of WBANs to transform health care services, providing more accurate diagnostics and customized treatments.

关键词： Wireless communication Sensor placement Accuracy Machine learning algorithms Medical services Signal processing Real-time systems Sensors Reliability Monitoring

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A two-stage direction-guided evolutionary algorithm for large-scale multiobjective optimization

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Information Sciences 2024年 674卷

作者： Zou, Juan Tang, Li Liu, Yuan Yang, Shengxiang Wang, Shiting Hunan Engineering Research Center of Intelligent System Optimization and Security Xiangtan University Hunan Province Xiangtan411105 China Faculty of School of Computer Science School of Cyberspace Science of Xiangtan University Xiangtan411105 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

Large-scale multiobjective optimization problems (LSMOPs) have exponential growth in the search space as the decision variables increase, and the vast search space poses a challenge to the performance of multiobjective evolutionary algorithms (MOEAs). Many current large-scale MOEAs need to consume a large amount of computational resources to get good performance. This paper proposes a two-stage direction-guided evolutionary algorithm for large-scale multiobjective optimization (LMOEA-S2D) to balance the performance and computational resource overhead. The algorithm exploits the Pareto-optimality property of domination and the diversity-preserving property of decomposition to optimize the performance in the two stages, respectively, and designs a corresponding direction-guided mechanism to improve search efficiency. LMOEA-S2D designs global direction search and local direction search in the domination-based stage for efficient exploitation to accelerate population convergence. To promote greater population diversity, a hybrid direction search was devised to aid diversity exploration in the decomposition-based stage, and this facilitates even distribution of candidate solutions across the Pareto optimal frontier. LMOEA-S2D is compared with five state-of-the-art large-scale MOEAs on some large-scale multiobjective test suites with 100 to 5,000 decision variables. The experimental results show that LMOEA-S2D significantly outperformed all compared algorithms under limited computational resources. © 2024 Elsevier Inc.

关键词： Multiobjective optimization

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Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact

arXiv

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arXiv 2025年

作者： Bilal, Mohsin Aadam Raza, Manahil Altherwy, Youssef Alsuhaibani, Anas Abduljabbar, Abdulrahman Almarshad, Fahdah Golding, Paul Rajpoot, Nasir Information Systems Department College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia Luddy School of Informatics Computing and Engineering Indiana University Indianapolis United States Tissue Image Analytics Centre Department of Computer Science University of Warwick Coventry United Kingdom Frontier AI San Francisco Bay Area United States

From self-supervised, vision-only models to contrastive visual-language frameworks, computational pathology has rapidly evolved in recent years. Generative AI "co-pilots" now demonstrate the ability to mine subtle, sub-visual tissue cues across the cellular-to-pathology spectrum, generate comprehensive reports, and respond to complex user queries. The scale of data has surged dramatically, growing from tens to millions of multi-gigapixel tissue images, while the number of trainable parameters in these models has risen to several billion. The critical question remains: how will this new wave of generative and multi-purpose AI transform clinical diagnostics? In this article, we explore the true potential of these innovations and their integration into clinical practice. We review the rapid progress of foundation models in pathology, clarify their applications and significance. More precisely, we examine the very definition of foundational models, identifying what makes them foundational, general, or multipurpose, and assess their impact on computational pathology. Additionally, we address the unique challenges associated with their development and evaluation. These models have demonstrated exceptional predictive and generative capabilities, but establishing global benchmarks is crucial to enhancing evaluation standards and fostering their widespread clinical adoption. In computational pathology, the broader impact of frontier AI ultimately depends on widespread adoption and societal acceptance. While direct public exposure is not strictly necessary, it remains a powerful tool for dispelling misconceptions, building trust, and securing regulatory support. © 2025, CC BY-NC-SA.

关键词： Visual languages

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