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arXiv 2025年

作者： Almodóvar, Alejandro Javaloy, Adrián Parras, Juan Zazo, Santiago Valera, Isabel Information Processing and Telecommunications Center Universidad Politécnica de Madrid Madrid Spain School of Informatics University of Edinburgh Edinburgh United Kingdom Department of Computer Science Saarland University Saarbrücken Germany Max Planck Institute for Software Systems Saarbrücken Germany

Causal generative models (CGMs) have recently emerged as capable approaches to simulate the causal mechanisms generating our observations, enabling causal inference. Unfortunately, existing approaches either are overly restrictive, assuming the absence of hidden confounders, or lack generality, being tailored to a particular query and graph. In this work, we introduce DeCaFlow, a CGM that accounts for hidden confounders in a single amortized training process using only observational data and the causal graph. Importantly, DeCaFlow can provably identify all causal queries with a valid adjustment set or sufficiently informative proxy variables. Remarkably, for the first time to our knowledge, we show that a confounded counterfactual query is identifiable, and thus solvable by DeCaFlow, as long as its interventional counterpart is as well. Our empirical results on diverse settings—including the Ecoli70 dataset, with 3 independent hidden confounders, tens of observed variables and hundreds of causal queries—show that DeCaFlow outperforms existing approaches, while demonstrating its out-of-the-box flexibility. © 2025, CC BY-NC-ND.

关键词： Generative adversarial networks

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Performance and emissions of gas turbine engines fueled with karanja oil-based biofuel blends: a machine learning approach using Lasso regression

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Aircraft Engineering and Aerospace Technology 2025年

作者： Aldehim, Ghadah Allafi, Randa Alkharashi, Abdulwhab A, Sagai Francis Britto J, Vijayalakshmi A, Sumaiya Begum M.C, Sashikkumar Velusamy, Priya Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science College of Science Northern Border University Arar Saudi Arabia Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Mechanical Engineering Rohini College of Engineering and Technology Palkulam India Department of Electronics and Communication Engineering Kongu Engineering College Perundurai India Department of Electronics and Communication Engineering R.M.D. Engineering College Chennai India Department of Civil Engineering Centre for Water Resources Anna University Chennai India Department of Civil Engineering GMR Institute of Technology Rajam India

Purpose: This study aims to investigate the performance and emission characteristics of gas turbine engines operating on biofuel blends derived from karanja oil as a potential alternative to conventional Jet-A fuel. Design/methodology/approach: The study tested three biofuel blends: JA20 (20% karanja oil, 80% Jet-A), JA30 (30% karanja oil, 70% Jet-A) and JA40 (40% karanja oil, 60% diesel). Engine performance parameters, including thrust output, thrust-specific fuel consumption (TSFC) and turbine inlet temperature (TIT), were measured at engine speeds ranging from 30,000 to 80,000 rpm. Exhaust emissions of carbon monoxide (CO), carbon dioxide (CO2) and nitrogen oxides (NOx) were analyzed using a gas analyzer. Additionally, a Lasso regression model was used to predict micro gas turbine (MGT) performance and emissions based on experimental data. Findings: Increasing the karanja oil content in the fuel blends reduced overall thrust output, increased TSFC and lowered TIT compared to Jet-A fuel. The JA40 blend exhibited the most significant performance reduction, with a 20% decrease in thrust output, a 7.5% increase in TSFC and a 4.1% decrease in TIT. However, biofuel blends consistently resulted in lower CO, CO2 and NOx emissions compared to Jet-A fuel, with reductions of up to 36%, 6.9% and 13.6%, respectively, for the JA40 blend. The Lasso regression model effectively captured the influence of engine speed and fuel blend composition on emissions, achieving an R² of 0.95 for CO2 and 0.94 for CO predictions. Originality/value: This study provides insights into the feasibility of using karanja oil-based biofuels in gas turbine engines, demonstrating their potential to reduce emissions while highlighting tradeoffs in performance. The use of a Lasso regression model for predicting emissions offers a novel approach to assessing biofuel performance in MGTs. © 2025, Emerald Publishing Limited.

关键词： Polynomial regression

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Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT Networks

arXiv

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arXiv 2025年

作者： Yang, Xiaohong Liwang, Minghui Fu, Liqun Su, Yuhan Hosseinalipour, Seyyedali Wang, Xianbin Hong, Yiguang School of Informatics Xiamen University Fujian China Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China School of Electronic Science and Engineering Xiamen University Xiamen China Department of Electrical Engineering University at Buffalo-SUNY BuffaloNY United States Department of Electrical and Computer Engineering Western University ON Canada

Hierarchical Federated Learning (HFL) introduces intermediate aggregation layers, addressing the limitations of conventional Federated Learning (FL) in geographically dispersed environments with limited communication infrastructure. An application of HFL is in smart IoT systems, such as remote monitoring, disaster response, and battlefield operations, where cellular connectivity is often unreliable or unavailable. In these scenarios, UAVs serve as mobile aggregators, providing connectivity to the terrestrial IoT devices. This paper studies an HFL architecture for energy-constrained UAVs in smart IoT systems, pioneering a solution to minimize global training cost increased caused by UAV disconnection. In light of this, we formulate a joint optimization problem involving learning configuration, bandwidth allocation, and device-to-UAV association, and perform global aggregation in time before UAV drops disconnect and redeployment of UAVs. The problem explicitly accounts for the dynamic nature of IoT devices and their interruptible communications and is unveiled to be NP-hard. To address this, we decompose it into three subproblems. First, we optimize the learning configuration and bandwidth allocation using an augmented Lagrangian function to reduce training costs. Second, we propose a device fitness score, integrating data heterogeneity (via Kullback-Leibler divergence), device-to-UAV distances, and IoT device resources, and develop a twin-delayed deep deterministic policy gradient (TD3)-based algorithm for dynamic device-to-UAV assignment. Third, We introduce a low-complexity two-stage greedy strategy for finding the location of UAVs redeployment and selecting the appropriate global aggregator UAV. Experiments on real-world datasets demonstrate significant cost reductions and robust performance under communication interruptions. © 2025, CC BY-NC-ND.

关键词： Federated learning

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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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Net scheduling in high-level synthesis

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IEEE DESIGN & TEST OF computerS 1996年第1期13卷 26-35页

作者： Prihozhy, A Computer Systems Department State University of Informatics and Radioelectronics of Belarus Belarus

A new net scheduling and allocation model generates net schedules that minimize either execution time or resources. The author tested the model within a VHDL-based high-level synthesis system called Ahiles.

关键词： Scheduling High Level Synthesis Finite State Machines VHDL systems

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Non-Orthogonal Multiple Access Based Multi-Objective Optimization in Emergency Task Offloading

Non-Orthogonal Multiple Access Based Multi-Objective Optimiz...

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IEEE Conference on Wireless Communications and Networking

作者： Caijuan Chen Chaofeng Zhang Gaolei Li Yuchen Liu The Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan Advanced Institute of Industrial Technology Tokyo Japan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science NC State University North Carolina US

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The escalating frequency of geological disasters, local conflicts, and public health crises necessitates robust emergency communication systems within Internet of Things (IoT) frameworks to ensure public safety. Employing Power-Domain NOMA (PD-NOMA) and Successive Interference Cancellation (SIC) enables effective multi-device communication that is crucial for emergency responses. However, the practical implementation of NOMA technologies in emergency scenarios is hindered by existing resource constraints and the requirement of prioritizing critical emergency information. To address these limitations, we propose a multi-objective optimization-based NOMA framework to maximize network throughput while ensuring quality of service (QoS) and energy efficiency. This framework incorporates a novel communication grouping algorithm based on queue penalty optimization and an Actor-Critic-based dynamic updating algorithm for multiple resource allocation. Our study proposes a practical approach to enhance emergency communication in disaster areas, ensuring efficient and reliable network performance under various constraints.

关键词： NOMA Interference cancellation Heuristic algorithms Disasters Quality of service Throughput Dynamic scheduling Internet of Things Resource management Optimization

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Facial Tilt Detection and Correction in Panoramic X-Rays Using Depth Maps from 2D Stereo Images: A Novel Approach to Minimize Positioning Errors in Dental Imaging 10th

Facial Tilt Detection and Correction in Panoramic X-Rays U...

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10th International Conference on Health informatics and Medical systems, HIMS 2024 and 10th International Conference on Biomedical Engineering, BIOENG 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

作者： Trader, Elizabeth A. Joshi, Aishwarya Gurupur, Varadraj P. Vu, Giang T. Department of Electrical and Computer Engineering University of Central Florida 4000 Central Florida Blvd OrlandoFL32816 United States School of Global Health Management and Informatics University of Central Florida 4000 Central Florida Blvd OrlandoFL32816 United States Center for Decision Support Systems and Informatics University of Central Florida 4000 Central Florida Blvd OrlandoFL32816 United States

ISBN: (纸本)9783031859076

Panoramic x-rays are vital tools in the diagnosis and detection of various dental conditions. However, the presence of positioning errors in panoramic X-rays can greatly diminish the quality of the radiograph and render it undiagnostic. The purpose of this study is to provide a low-cost, simple technique for decreasing positioning errors in panoramic X-rays in dental offices. This paper uses a depth map calculated using two stereo images combined with facial landmark detection to determine whether tilt is proper for patient X-ray positioning. If the patient’s head is tilted too far back or then it produces a prompt to adjust the patient’s head positioning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Stereo image processing

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Educational data mining: a 10-year review

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Discover Computing 2025年第1期28卷 1-25页

作者： Kalita, Emi Oyelere, Solomon Sunday Gaftandzhieva, Silvia Rajesh, Kandala N. V. P. S. Jagatheesaperumal, Senthil Kumar Mohamed, Asmaa Elbarawy, Yomna M. Desuky, Abeer S. Hussain, Sadiq Cifci, Mehmet Akif Theodorou, Paraskevi Hilčenko, Slavoljub Hazarika, Jiten Ali, Tazid Centre for Computer Science and Applications Dibrugarh University Dibrugarh 786004 India Department of Computer Science Electrical and Space Engineering Luleå University of Technology Skellefteå 931 87 Sweden Department of Computer Science Faculty of Environment Science and Economy University of Exeter Exeter United Kingdom Research Group On Data Artificial Intelligence and Innovations for Digital Transformation Johannesburg Business School (JBS) University of Johannesburg Johannesburg South Africa Faculty of Mathematics and Informatics University of Plovdiv “Paisii Hilendarski” Plovdiv Bulgaria School of Electronics Engineering VIT-AP University Vijayawada India Department of Electronics & Communication Engineering Mepco Schlenk Engineering College Sivakasi 626005 India Department of Mathematics Faculty of Science Al-Azhar University (Girls Branch) Cairo 11754 Egypt Department of Computer Engineering Bandirma Onyedi Eylul University Balikesir 10200 Turkey Engineering and Informatics Department Klaipėdos Valstybinė Kolegija/Higher Education Institution Klaipeda 92294 Lithuania Department of Digital Systems University of Piraeus Piraeus Greece College for Vocational Education of Preschool Teachers and Coaches Subotica Serbia Department of Statistics Dibrugarh University Dibrugarh 786004 India

This systematic review comprehensively examines the application and impacts of Educational Data Mining (EDM) over the past decade. It explores the use of various data mining tools and techniques, statistics, and machine learning algorithms in education. The review discusses how EDM helps understand and improve the learning experience, educational strategies, and institutional efficiency. It highlights the iterative process of EDM, its applications, and the benefits it offers to different stakeholders, including students, teachers, and educational institutions. The paper also discusses the challenges related to data ethics, privacy, and security in EDM. Key sections include a methodology for conducting the systematic review, exploring different data mining techniques and learning styles, and using Artificial Intelligence in EDM. The review concludes with a discussion of findings, future research directions, and a summary of the study’s contributions and limitations. © The Author(s) 2025.

关键词： Artificial intelligence in education Education data mining Explainability in education Multimodal learning analytics

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Gender Classification From Pashto Handwritten Text Images

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IEEE Access 2025年 13卷 81736-81754页

作者： Sultan, Khan Ahmad, Riaz Muhammad, Siraj Almutairi, Sulaiman Abohashrh, Mohammed Ullah, Khalil Namoun, Abdallah Hussain, Ibrar Shaheed Benazir Bhutto University Department of Computer Science Dir Upper Khyber Pakhtunkhwa Sheringal Pakistan Qassim University College of Applied Medical Sciences Department of Health Informatics Qassim1452 Saudi Arabia King Khalid University College of Applied Medical Sciences Department of Basic Medical Sciences Abha61421 Saudi Arabia University of Malakand Department of Software Engineering KP Dir Lower Pakistan Islamic University of Madinah AI Center Faculty of Computer and Information Systems Madinah42351 Saudi Arabia Shaheed Benazir Bhutto University Quality Enhancement Cell Sheringal Khyber Pakhtunkhwa Dir Upper Pakistan

computer vision (CV) is a subfield of computer science that enables machines to perceive, interpret, and understand visual data. It combines image processing, analysis, and machine learning to extract meaningful insights from images and videos. One key application of CV is the classification of gender from handwritten text images-a complex task, especially in languages such as Pashto due to the intricacy of the script and the limited resources. This research presents a novel approach to gender classification using Pashto Handwritten Text Images (PHTI). The study evaluates and compares the performance of traditional machine learning and modern deep learning models: Support Vector Machine (SVM) and You Only Look Once version 8 (YOLO8), a CNN-based object detection framework. The PHTI dataset, introduced in this study, consists of 36, 086 text line images written by 200 male and 200 female native Pashto writers. The goal is to assess how effectively SVM and YOLO8 can classify gender based on visual handwriting patterns in the Pashto language. The experimental results reveal that YOLO8 outperforms SVM in classification accuracy. Although SVM, a widely used machine learning model, achieved an accuracy of 61.29%, YOLO8 reached a significantly higher accuracy of 77% in the same data set. This performance gap highlights the advantages of using advanced deep learning models for complex pattern recognition tasks in CV. This research provides a pioneering contribution by focusing on Pashto, a low-resource language, in the context of gender classification using handwritten text images. It opens new pathways for applying CV in regional languages and supports the development of intelligent systems capable of handling various scripts. © 2013 IEEE.

关键词： Machine vision

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Dialogue-to-Video Retrieval via Multi-Grained Attention Network

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IEEE Access 2025年 13卷 82302-82311页

作者： Yu, Yi Zhang, Jiwei Tang, Suhua Li, Wei Hiroshima University Graduate School of Advanced Science and Engineering Higashihiroshima739-0046 Japan Human-Centered AI Laboratories KDDI Research Japan Wakayama University Department of Systems Engineering 930 Sakaedani Wakayama640-8441 Japan The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo Chofu182-8585 Japan Fudan University School of Computer Science Shanghai200433 China

Dialogue-to-video retrieval is an interesting while challenging task, which exploits an AI agent to retrieve the video that matches and aligns with the conversational context between users. In particular, given a history of dialogue exchanges, the agent is expected to identify the most fitting video content that complements the ongoing conversation. The computational complexity posed by the processing of videos within deep neural networks encourages us to adapt CLIP, a cutting-edge large multi-modal models (LLMs), into the dialogue-video domain. On this basis, we propose a multi-grained attention network (MGAT), integrating query-scoring, dual-softmax and query-bank normalization techniques. We design a multi-grained attention module to optimize the inadequate modeling of conversation semantics in existing pre-trained models, dynamically assign weights during conversation feature extraction, and introduce conversation context features in multimodal alignment. Most importantly, fine-grained similarity by per-round query-frame scoring and coarse-grained similarity by high-level semantics of all rounds of dialogues for each video are respectively calculated, which are further learned via multi-grained attention mechanism. This approach effectively transfers CLIP’s text-image multimodal knowledge into the dialogue-video retrieval system, alleviating the need for resource-intensive and costly fine-tuning dialogue-video procedures. Extensive experiments on multiple datasets demonstrate that our method outperforms state-of-the-art approaches. © 2013 IEEE.

关键词： Deep neural networks

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