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1st Workshop on Language Models for Low-Resource Languages, LoResLM 2025 - co-located at the 31st International Conference on Computational Linguistics, COLING 2025

作者： Mokhtarabadi, Hojjat Zamani, Ziba Maazallahi, Abbas Manshaei, Mohammad Hossein Department of Electrical and Computer Engineering Isfahan University of Technology Iran Department of Computer Engineering Shahid Bahonar University of Kerman Iran Department of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Computer Science Hunter College City University of New York NY United States

ISBN: (纸本)9798891762152

Instruction-tuned large language models have demonstrated remarkable capabilities in following human instructions across various domains. However, their proficiency remains notably deficient in many low-resource languages. To address this challenge, we begin by introducing FarsInstruct: a comprehensive instruction dataset designed to enhance the instruction-following ability of large language models specifically for the Persian language—a significant yet underrepresented language globally. FarsInstruct encompasses a wide range of task types and datasets, each containing a mix of straightforward to complex manual written instructions, as well as translations from the Public Pool of Prompts, ensuring a rich linguistic and cultural representation. Furthermore, we introduce Co-CoLA, a framework designed to enhance the multi-task adaptability of LoRA-tuned models. Through extensive experimental analyses, our study showcases the effectiveness of the FarsInstruct dataset coupled with training by the Co-CoLA framework, in improving the performance of large language models within the Persian context. As of the current writing, FarsInstruct comprises 197 templates across 21 distinct datasets, and we intend to update it consistently, thus augmenting its applicability. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Investigation of unsupervised and supervised hyperspectral anomaly detection

Investigation of unsupervised and supervised hyperspectral a...

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Applications of Machine Learning 2024

作者： Hossain, Mazharul Robinson, Aaron Wang, Lan Preza, Chrysanthe Computer Science Department United States Electrical and Computer Engineering Department The University of Memphis MemphisTN United States

ISBN: (纸本)9781510679368

Hyperspectral sensing is a valuable tool for detecting anomalies and distinguishing between materials in a scene. Hyperspectral anomaly detection (HS-AD) helps characterize the captured scenes and separates them into anomaly and background classes. It is vital in agriculture, environment, and military applications such as RSTA (reconnaissance, surveillance, and target acquisition) missions. We previously designed an equal voting ensemble of hyperspectral unmixing and three unsupervised HS-AD algorithms. We later utilized a supervised classifier to determine the weights of a voting ensemble, creating a hybrid of heterogeneous unsupervised HS-AD algorithms with a supervised classifier in a model stacking, which improved detection accuracy. However, supervised classification methods usually fail to detect novel or unknown patterns that substantially deviate from those seen previously. In this work, we evaluate our technique and other supervised and unsupervised methods using general hyperspectral data to provide new insights. © 2024 SPIE.

关键词： Unmanned aerial vehicles (UAV)

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Breaking the Shield: Systematic Security Analysis on Pulse Fingerprinting LiDAR Systems for Autonomous Driving

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IEEE Sensors Journal 2025年第11期25卷 20523-20537页

作者： Hayakawa, Yuki Sato, Takami Suzuki, Ryo Ikeda, Kazuma Sako, Ozora Nagata, Rokuto Yoshida, Ryo Chen, Qi Alfred Yoshioka, Kentaro Keio University Department of Electronics and Electrical Engineering Tokyo108-0073 Japan University of California Department of Computer Science IrvineCA92697 United States

LiDAR sensors play a crucial role in autonomous driving (AD) systems because of their high-definition 3D sensing capabilities. While LiDAR spoofing attacks that project fake ranging signals pose a critical security threat, new-generation (new-gen) LiDARs have introduced pulse fingerprinting (PF), an authentication mechanism that distinguishes reflected signals from external interference. Although PF offers a promising defense against spoofing attacks, its security implications remain unexplored. This paper presents the first comprehensive security evaluation of PF features. While current PF-equipped LiDARs use the time difference of pulse pairs as authentication to counter conventional spoofing attacks, we demonstrate their vulnerability to periodic malicious laser pulses through two novel attacks: High-Frequency Removal (HFR) and Adaptive HFR (A-HFR). The basic HFR attack employs high-frequency laser pulses, while A-HFR incorporates weak synchronization and selective range limiting to prevent laser overheating, achieving spoofing frequencies 25 times higher than HFR. Our evaluation of three commercial LiDARs with PF reveals their vulnerability to these attacks, with over 96% of the point cloud data vanishing within the attack target area. Through attack simulations against Autoware using real attack data, we show that successful attacks in over 90% of frames invariably lead to accidents, highlighting significant limitations in current PF implementations. We conclude by proposing enhanced security measures for both AD systems and PF mechanisms. © 2001-2012 IEEE.

关键词： Authentication

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A Smart Vehicle Alert System with Intelligent Vehicle Safety

A Smart Vehicle Alert System with Intelligent Vehicle Safety

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Faisal, Iftear Farooq, Omar Malik, Sumaiya Alam, Mohammad Tawhidul Mondal, Shuvra Anowar, Tanbir Ibne Department of Electrical and Electronics Engineering Dhaka Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331540685

Road safety is a critical concern worldwide, with millions of lives lost and countless injuries sustained in traffic accidents annually. To address this pressing issue, a costeffective and reliable solution is proposed for enhancing road safety. The system leverages affordable sensors, wireless communication, and data analytics to combine accident detection, driver drowsiness monitoring, and real-time vehicle tracking. An accelerometer is utilized to detect collisions, an eye blink sensor to monitor driver drowsiness, and GPS for precise vehicle tracking. Upon accident detection or driver drowsiness, the system sends alerts via SMS and a mobile app, providing real-time location information. The microcontroller processes sensor data and controls communication modules, prioritizing critical events. Extensive testing demonstrates high accuracy in accident detection (95% in cities, 70% in rural areas), driver drowsiness detection (96%), and unauthorized access detection (97%). The system also excels in detecting accidents in parking mode (95%) and offers a battery backup of 1-2 days. Comparative analysis highlights the system's superiority in terms of accuracy, comprehensive features, and integration of multiple safety aspects. The proposed system's impact extends beyond individual vehicles, offering opportunities for innovation, economic growth, and sustainable transportation. With ongoing improvements and consideration of ethical and societal factors, this system has the potential to revolutionize vehicle safety and contribute to a safer, smarter transportation ecosystem. © 2024 IEEE.

关键词： Global positioning system

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Anomaly-Focused Augmentation Method for Industrial Visual Inspection

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IEEE Access 2025年 13卷 89118-89139页

作者： Melvinlara, Melvin A. Hameed, Ibrahim A. Koziorek, Jiri Faculty of Electrical Engineering and Computer Science Department of Cybernetics and Biomedical Engineering Ostrava70800 Czech Republic Department of Mechanical Engineering and Technology Management Ås1433 Norway

In industrial inspection, the detection of surface defects - such as scratches, dents, or other defects - is crucial for ensuring product quality. However, the limited availability of annotated images of such defects poses challenges for developing reliable detection models. This paper proposes a novel image augmentation algorithm to address this limitation by generating synthetic imperfections from a small sample set. Unlike traditional approaches that augment entire images, the proposed approach isolates defects and applies spatial transformations (e.g., rotation, shear) before blending them into background images. This allows for the creation of diverse training datasets. The algorithm employs a 2D binary map to encode the spatial relationship between defect and background regions of images and then applies an AI-driven model to generate new realistic blended images. By leveraging a generator-discriminator framework, the augmentation process is iteratively refined during training to maintain the style and texture of the original imperfections. Experimental results show that the proposed method significantly enhances the performance of defect detection models compared to existing augmentation techniques while preserving the realism of the generated images. © 2013 IEEE.

关键词： Photointerpretation

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Deeper: A shared liquidity decentralized exchange design for low trading volume tokens to enhance average liquidity

Deeper: A shared liquidity decentralized exchange design fo...

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作者： Singh, Srisht Fateh Michalopoulos, Panagiotis Veneris, Andreas Department of Electrical and Computer Engineering University of Toronto TorontoON Canada Department of Computer Science University of Toronto TorontoON Canada

This paper presents Deeper, a design for a decentralized exchange that enhances liquidity via reserve sharing. By doing this, it addresses the problem of shallow liquidity in low trading volume token pairs. Shallow liquidity impairs the functioning of on-chain markets by creating room for unwanted phenomena such as high slippage and sandwich attacks. Deeper solves this by allowing liquidity providers of multiple trading pairs against a common token to share liquidity. This is achieved by creating a common reserve pool for the shared token that is accessible by each trading pair. Independent from the shared liquidity, providers are free to add liquidity to individual token pairs without any restriction. The trading between one token pair does not affect the price of other token pairs even though the reserve of the shared token changes. The proposed design is an extension of concentrated liquidity automated market maker DEXs that is simple enough to be implemented on smart contracts. This is demonstrated by providing a template for a hook-based smart contract that adds our custom functionality to Uniswap V4. Experiments on historical prices show that for a batch consisting of eight trading pairs, Deeper enhances liquidity by over 2.6– (Formula presented.). The enhancement in liquidity can be increased further by increasing the participating tokens in the shared pool. While providing shared liquidity, liquidity providers should be cautious of certain risks and pitfalls, which are described. Overall, Deeper enables the creation of fair markets for low trading volume token pairs. © 2024 The Authors. International Journal of Network Management published by John Wiley & Sons Ltd.

关键词： Smart contract

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Enhancing wireless sensing via a target-mounted intelligent reflecting surface

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National science Review 2023年第8期10卷 13-14页

作者： Xiaodan Shao Rui Zhang School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

An intelligent reflecting surface(IRS),or its various equivalents such as an reconfigurable intelligent surface(RIS), is an emerging technology to control radio signal propagation in wireless systems. An IRS is a digitally controlled metasurface consisting of a large number of passive reflecting elements, which are connected to a smart controller to enable dynamic adjustments of the amplitude and/or phase of the incident signal on each element independently [1].

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Effect of pulsed current cathodic protection on pipeline steel API 5L X65 corrosion mitigation: An investigation and machine learning-assisted modeling

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Corrosion Communications 2023年第4期12卷 29-45页

作者： Hosein Eslamian Mehdi Javidi Mohammad Reza Zamani Mohammad Mahdi Dana Eghbal Mansoori Department of Materials Science and Engineering School of Engineering Shiraz University School of Metallurgy and Materials Engineering College of Engineering University of Tehran Computer Science Eng.and IT Dept. School of Electrical and Computer Engineering Shiraz University

Pulsed current cathodic protection(PCCP) could be more effective than direct current cathodic protection(DCCP)for mitigating corrosion in buried structures in the oil and gas industries if appropriate pulsed parameters are chosen. The purpose of this research is to present the corrosion prevention mechanism of the PCCP technique by taking into account the effects of duty cycle as well as frequency, modeling the relationships between pulse parameters(frequency and duty cycle) and system outputs(corrosion rate, protective current and pipe-to-soil potential) and finally identifying the most effective protection conditions over a wide range of frequency(2–10 kHz) and duty cycle(25%-75%). For this, pipe-to-soil potential, pH, current and power consumption, corrosion rate, surface deposits and investigation of pitting corrosion were taken into account. To model the input-output relationship in the PCCP method, a data-driven machine learning approach was used by training an artificial neural network(ANN). The results revealed that the PCCP system could yield the best protection conditions at 10 kHz frequency and 50% duty cycle, resulting in the longest protection length with the lowest corrosion rate at a consumption current 0.3 time that of the DCCP method. In the frequency range of 6–10 kHz and duty cycles of 50%-75%, SEM images indicated a uniform distribution of calcite deposits and no pits on cathode surface.

关键词： Pulsed current cathodic protection Duty cycle Frequency Artificial neural network API X65 pipeline steel

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IoT Threat Mitigation: Machine Learning-Assisted Heavy Hitter Detection in P4-Enabled Networks 5

IoT Threat Mitigation: Machine Learning-Assisted Heavy Hitte...

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5th CPSSI International Symposium on Cyber-Physical Systems (Applications and Theory), CPSAT 2024

作者： Hooshangi-Naghani, Mehrdad Rezaei, Negar Dolati, Mahdi Khonsari, Ahmad Rastegar, Seyed Hamed Sharif University of Technology Department of Computer Engineering Tehran Iran University of Tehran School of Electrical and Computer Engineering Iran School of Computer Science Tehran Iran Shiraz University School of Electrical and Computer Engineering Shiraz Iran

ISBN: (纸本)9798331529284

The Internet of Things (IoT) is crucial in various sectors, making IoT networks prime targets for denial of service attacks. Detecting heavy hitters-primary sources of such attacks-is essential for network security. We address this in IoT gateway networks, where direct traffic measurement faces privacy and scalability issues. Existing methods like sampling and sketch-based approaches suffer from information loss and inflexibility. We propose a two-step scheme: a Convolutional Neural Network (CNN) selects relevant gateways, followed by a tomography-based approach to identify heavy hitters. Our data collection framework uses SNMP and P4 technologies for enhanced flexibility and accuracy. Extensive numerical evaluations demonstrate the proposed algorithm's effectiveness in accurately identifying heavy hitters, despite potential estimation errors in the tomography method. © 2024 IEEE.

关键词： Convolutional neural networks

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Modelling and simulation of energy management of power-split hybrid electric vehicles using the discrete EVent system specification

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International Journal of Vehicle Systems Modelling and Testing 2024年第1期18卷 33-61页

作者： Maatoug, Abdelfettah Kharoubi, Sahraoui Maaskri, Moustafa Belalem, Ghalem Mahmoudi, Said Faculty of Applied Sciences Science and Technology Department University of Tiaret Tiaret14000 Algeria Faculty of Mathematics and Computer Science Computer Science Department University of Tiaret Tiaret14000 Algeria Faculty of Applied Sciences Electrical Engineering Department University of Tiaret Tiaret14000 Algeria Faculty of Exact and Applied Sciences Computer Science Department University of Oran 1 Ahmed Ben Bella Oran31000 Algeria Faculty of Engineering Computer Science Department University of Mons Mons7000 Belgium

This work provides a basis for studying energy management optimisation in power-split hybrid electric vehicles (PSHEVs) to reduce fuel consumption and increase powertrain efficiency by enforcing a strategy related to battery level and vehicle speed. Three modes were used to operate the vehicle to meet different situations related to available energy and desired speed. These are particularly abundance mode, ascetic mode and optimal mode. For this, a system was designed based on several sub-models written in the discrete EVent system (DEVS) specification. The vehicle's power train and control strategy models were created in the DEVSimPy environment, and a joint simulation was carried out from the composite sub models until the overall model is reached. Simulation results under several driving cycles showed that the proposed system improves the energy consumption of the vehicle in different operating modes of the engines and gives more opportunities to adopt electric motors in vehicle transmission. © 2024 Inderscience Enterprises Ltd.

关键词： Energy management

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