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2nd Workshop in South East Asian Language Processing, SEALP 2025

作者： Artkaew, Phakphum Department of Electrical and Computer Engineering Tandon School of Engineering New York University United States

ISBN: (纸本)9798891762190

Commonsense reasoning is one of the important aspects of natural language understanding, with several benchmarks developed to evaluate it. However, only a few of these benchmarks are available in languages other than English. Developing parallel benchmarks facilitates cross-lingual evaluation, enabling a better understanding of different languages. This research introduces a collection of Winograd Schemas in Thai, a novel dataset designed to evaluate commonsense reasoning capabilities in the context of the Thai language. Through a methodology involving native speakers, professional translators, and thorough validation, the schemas aim to closely reflect Thai language nuances, idioms, and cultural references while maintaining ambiguity and commonsense challenges. We evaluate the performance of popular large language models on this benchmark, revealing their strengths, limitations, and providing insights into the current state-of-the-art. Results indicate that while models like GPT-4 and Claude-3-Opus achieve high accuracy in English, their performance significantly drops in Thai, highlighting the need for further advancements in multilingual commonsense reasoning. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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A Risk Identification Method for Power Operation Scenarios Using Image Caption and Semantic Text Similarity Analysis

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IEEE Transactions on Industrial informatics 2025年第6期21卷 4488-4498页

作者： Li, Wei Ma, Fuqi Jia, Rong Wang, Bo Wang, Hongxia Alharbi, Abdullah M. Xi'an University of Technology School of Electrical Engineering Xi'an710048 China Wuhan University School of Electrical Engineering and Automation Wuhan430072 China University of Denver Department of Electrical and Computer Engineering DenverCO80208 United States Prince Sattam bin Abdulaziz University Department of Electrical Engineering College of Engineering Wadi ad-Dawasir11942 Saudi Arabia

Power production is a complex process that involves multiple interactions, which require rich semantic knowledge to categorize and evaluate. Utilizing high-level image understanding to accurately identify risks is significant in ensuring staff safety and grid stability. In this work, we propose image captioning and semantic text similarity analysis (IC-STSA), a risk identification method for complex power operation scenarios the employs image captioning and semantic text similarity analysis. First, image caption is used to transform an image at the site into the text that best describes the visual data. Next, a safe power production (SPP) knowledge base is established, after which semantic text similarity analysis is introduced to transform the risk identification into a text classification problem. By comparing the image caption information and the SPP knowledge base and identifying similarities, we achieve refined descriptions of operation scenarios and thereby risk identification. In the final step, actual images are used to verify the findings of the IC-STSA. Results show the proposed method performs well, generating accurate, targeted descriptions, and risk identification. © 2025 IEEE. All rights reserved.

关键词： Semantics Decoding Visualization Safety Production Feature extraction Electronic mail Vectors Long short term memory Knowledge based systems

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Multi-objective scheduling of heterogeneous parallel systems using the VITS algorithm

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Journal of Supercomputing 2025年第7期81卷 1-39页

作者： Bakhoda, Saeedeh Abdollahi Azgomi, Mohammad Ebrahimi Dishabi, Mohammad Reza Department of Computer Engineering Miyaneh Branch Islamic Azad University Miyaneh Iran School of Computer Engineering Iran University of Science and Technology Tehran Iran

Heterogeneous systems are widely used to implement a wide range of critical services. Resource management is a key challenge in parallel systems and becomes more complicated when system resources are heterogeneous. The issue of resource heterogeneity from different aspects simultaneously has not received much attention in the literature. This study addresses this issue and investigates multi-objective scheduling in heterogeneous parallel environments regarding processing speed and cost. The main objectives are increasing the system's throughput by completing more tasks, improving the system's profitability, and reducing the total completion time and runtime. Proper task allocation and scheduling on heterogeneous resources are effective in achieving goals. This paper introduces a vector allocation and scheduling approach improved by an extended tabu search-based strategy. In the proposed methodology, abbreviated as the VITS, a vector approach is first used to allocate and schedule tasks on heterogeneous resources. Then, the vector is improved using an extended tabu search-based strategy to obtain better results for the objectives. The proposed methodology utilizes several efficient genetic algorithm mutation methods to produce better and high-quality solutions. The proposed algorithm was simulated and evaluated on several benchmark files of different sizes, containing a minimum of 100 tasks and 10 heterogeneous resources and a maximum of 500 tasks and 80 heterogeneous resources. The simulation results are compared with other selected algorithms, including a vector allocation method improved by genetic (VIGA) and simulated annealing (VISA) algorithms. The evaluation of the results verifies the superiority of the proposed algorithm. Comparing the results on the various test files demonstrates that the proposed VITS compared to VIGA and VISA have respectively an average percentage improvement of 6.9 and 7.7 in task completion rate, 3.8 and 5.3 in profitability, 4 and 2.

关键词： Genetic algorithms

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Measuring the Discrepancy between 3D Geometric Models using Directional Distance Fields

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 1-16页

作者： Ren, Siyu Hou, Junhui Chen, Xiaodong Xiong, Hongkai Wang, Wenping City University of Hong Kong Department of Computer Science Hong Kong Tianjin University School of Precision Instruments and OptoElectronic Engineering Tianjin300072 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China Texas A& M University Department of Computer Science & Engineering United States

Qualifying the discrepancy between 3D geometric models, which could be represented with either point clouds or triangle meshes, is a pivotal issue with board applications. Existing methods mainly focus on directly establishing the correspondence between two models and then aggregating point-wise distance between corresponding points, resulting in them being either inefficient or ineffective. In this paper, we propose DirDist, an efficient, effective, robust, and differentiable distance metric for 3D geometry data. Specifically, we construct DirDist based on the proposed implicit representation of 3D models, namely directional distance field (DDF), which defines the directional distances of 3D points to a model to capture its local surface geometry. We then transfer the discrepancy between two 3D geometric models as the discrepancy between their DDFs defined on an identical domain, naturally establishing model correspondence. To demonstrate the advantage of our DirDist, we explore various distance metric-driven 3D geometric modeling tasks, including template surface fitting, rigid registration, non-rigid registration, scene flow estimation and human pose optimization. Extensive experiments show that our DirDist achieves significantly higher accuracy under all tasks. As a generic distance metric, DirDist has the potential to advance the field of 3D geometric modeling. © 1979-2012 IEEE.

关键词： Mesh generation

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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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AN 8-WAY RF FEED NETWORK FOR 5.3 GHZ C-BAND SAR: DESIGN, SIMULATION, AND ANALYSIS USING STANDARD METHODS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 21-43页

作者： Kothapudi, Venkata Kishore Thokala, Niharika Vallabhaneni, Ramyasri Sanaka, Poojitha Kumar, Vijay Pappula, Lakshman Gottapu, Srinivasa Kiran Bhattacharyya, Arka Guntur District Andhra Pradesh Vadlamudi522213 India Department of Communication Engineering School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Electrical Electronics and Communication Engineering GITAM School of Technology GITAM University Andhra Pradesh Visakhapatnam530045 India School of Science and Engineering Engineering and Computer Science Department Benedict College 1600 Harden St ColumbiaSC29204 United States

This study proposes the design and analysis of an eight-way power divider for unequal division at 5.3 GHz for C-band frequency. Many transmission line pieces make up the current power divider. These transmission lines were printed on a substrate material (RT/duroid 5880, 1.6 mm thick, permittivity 2.2, and loss tangent 0.0002). The eight-way power divider’s unequal amplitudes are constructed using Taylor’s 1-parameter method to achieve the side projection levels –17 dB down as from the main beam. The proposed power divider consists of using two transmission lines with a 3/4 frequency connected to branched microstriplines, providing excellent isolation and matching between the power divider’s two output ports. This power divider is composed of three stages. A 1:2 power divider is used in the first stage, a 1:4 power divider is used in the second stage, and a 1:8 power divider is used in the third stage. According to the simulation results, 1:2 delivers half of the input power, 1:4 produces one-fourth of the power, and 1:8 delivers one-eighth of the input power. All of these assessments were done with the advanced design system (ADS) simulation program. According to both simulated and measured data, the return loss exceeds –35 dB, and the isolation across the output terminals exceeds –40 dB. The proposed circuit operates effectively at C-band frequencies in terms of return loss and isolation (4 GHz and 8 GHz). © 2025 by Begell House, Inc.

关键词： Integrated circuit design

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Vehicle to Vehicle Communication and Smart Traffic Management Using LoRa 12th

Vehicle to Vehicle Communication and Smart Traffic Managemen...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Thangam, S. Thodathara, Harshitha Reddy Aryagopal Sai Jagadeesh, Dumpa Venkata Maddi, Anish Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9789819789450

The twenty-first century presents a number of urgent challenges, including global warming, and vehicle-to-vehicle (V2V) communication is one of the key components of an energy-efficient and sustainable transportation system. By adding distance sensors, our research elevates vehicle-to-vehicle (V2V) communication to a new level. This enables vehicles to share not only speed and distance information but also real-time instructions based on their proximity to other vehicles. This system of dynamic control minimizes emissions while optimizing fuel consumption. Beyond the efficiency of a single vehicle, our system uses LoRa technology to integrate with traffic light management. Our method, which treats every car as a node, gathers and analyzes data in the cloud to inform judgments about the length of red lights at intersections. This data-driven traffic analysis streamlines traffic, reduces needless stops, gives commuters time back, and improves overall fuel economy. The application of LoRa technology enhances our traffic analysis's accuracy and effectiveness. Because of its long range, LoRa enables wide-ranging and dependable communication between cars and cloud infrastructure. Because of this improved connectivity, traffic signal timings can be promptly and intelligently adjusted to reflect the dynamically changing patterns of vehicular flow. The end result is a precisely calibrated traffic management system that strategically reduces needless stops while simultaneously optimizing traffic flow and overall fuel efficiency, saving commuters valuable time. Our incorporation of LoRa technology is evidence of the revolutionary potential of sophisticated communication protocols in generating thoughtful and flexible responses to modern transportation problems. To put it briefly, our study offers a novel and comprehensive use of vehicle-to-vehicle (V2V) communication, utilizing LoRa and distance sensors to solve energy-saving issues and transform traffic control for a more env

关键词： Traffic signals

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An Initial Risk Assessment for Multimodal with LSTM-Based Trust Evaluation Framework for Autonomous Vehicle Security

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SN computer Science 2025年第2期6卷 1-15页

作者： Renjith, P.N. Balasubramani, S. Ramesh, K. Patnala, Eswar School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

This research aims to develop a new approach to increase the safety and reliability of Autonomous Vehicle (AV) through the proposed risk assessment framework, supported by the trust evaluation approach derived from a machine learning algorithm. This work proposes a comprehensive risk assessment model with real-time sensor data, Vehicle-to-Vehicle (V2V), key communication reliability, environmental cognition, and Improved Long Short-Term Memory (I-LSTM). This framework is multidimensional compared to most conventional models, focusing primarily on assessing individual threat factors. Here, the machine learning portion is designed to evaluate and predict trust by quickly processing vast amounts of information from sensors, signals, and prior operation history. Such an approach to trust assessment enables the system to continuously evaluate the dependability of automated vehicle operation and identify threats to security with improved accuracy. The effectiveness of the performance of this method is substantiated by a number of stand-alone and live scenarios, where the model's performance is higher in perceiving possible risks, avoiding threats, and enhancing safety than the conventional approaches. The findings suggest that the safety regulations of the AVs will be improved by using a multi-dimensional risk assessment model and integrating the I-LSTM trust rating with 95.5% accuracy, 96.7% risk identification, and 180 ms response time. This will cause AV and its surrounding environment to be accepted by the public and used on the roads. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Autonomous vehicles LSTM model Machine learning Risk assessment Trust evaluation

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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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computer Modeling in engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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Deep Learning-Based Production Forecasting for Liquid-Rich Gas in the Duvernay Shale Play

Deep Learning-Based Production Forecasting for Liquid-Rich G...

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2025 SPE Canadian Energy Technology Conference and Exhibition, CET 2025

作者： Xu, Ziming Liu, Hongxuan Leung, Juliana Y. School of Mining & Petroleum Engineering Department of Civil & Environmental Engineering University of Alberta Canada Department of Computer Engineering University of Alberta Canada

ISBN: (纸本)9781959025672

The Duvernay Formation is one of the most significant unconventional hydrocarbon formations in the Western Canada Sedimentary Basin (WCSB), known for its high liquid hydrocarbon content. Due to hydraulic fracturing being widely applied, the significant reservoir heterogeneity makes forecasting the newly developed well extremely challenging compared to traditional methods. Our previous work successfully applied a deep learning-based production forecasting model to the Montney shale gas play. However, Duvernay shale play exhibits significant variability in gas and liquid production proportions across different regions. This variation introduces challenges in accurately predicting multi-phase flow production behaviour. This study enhances our previously developed Masked Encoding and Decoding (MED) architecture for forecasting multi-phase hydrocarbon production from the Duvernay Formation. To mitigate the accumulation of errors typically encountered in recursive generation methods for the three production phases (oil, gas, and water), the model adopts a Non-Autoregressive Generation (NAG) approach, which predicts future production in a single step. The model integrates geostatic properties and continuously updates as new production data becomes available. Experiments were conducted using a dataset of 2, 700 wells from the Duvernay Formation, with oil, gas, and water production rates preprocessed using a novel Arp's decline denoising method to enhance model stability during training. Results demonstrate the enhanced MED model's superior accuracy compared to other well-known sequence-to-sequence models, effectively capturing complex gas-liquid ratio variability and dynamically updating predictions with new data. Copyright 2025, Society of Petroleum Engineers

关键词： Hydraulic fracturing

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