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33rd ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, FPGA 2025

作者： Cheng, Jianyi Wang, Lianghui Jiang, Zijian Bao, Yungang Shi, Kan School of Informatics The University of Edinburgh Edinburgh United Kingdom Institute of Computing Technology Chinese Academy of Sciences University of Science and Technology of China Beijing China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400713965

Dataflow high-level synthesis (HLS) tools automatically map a high-level software program to a dataflow hardware design. When testing the design, the HLS tool takes a testing function written in the same software language and translates it into the corresponding hardware testbench for cycle-accurate simulation. However, the generated testbench only follows a fixed schedule to feed inputs and collect outputs, without considering external dynamic behaviors of the test data (also known as latency insensitivity testing). Overlooking these test cases could miss bugs introduced by the designer that occur in certain events, such as late input arrival and output backpressure, leading to higher debugging costs. In this paper, we present an HLS testing toolflow named PEDIA that automatically generates testbenches to detect latency insensitivity bugs. We expand the test space from varying input values to varying input delays. PEDIA automatically determines an efficient test space of input delays based on the HLS schedule for better scalability;and tests the HLS design on a parallel hardware testing platform for high performance. Over two representative sets of existing benchmarks, we show that PEDIA can help identify potential bugs missed by existing approaches and achieves an average speedup of 4.53× on running 1500 test cases. © 2025 Copyright held by the owner/author(s).

关键词： Software testing

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FlexiSecure - AI-Powered Fraud Detection with Real-Time Alerts and Customizable Security

FlexiSecure - AI-Powered Fraud Detection with Real-Time Aler...

引用

Advanced Research in computing (ICARC), International Conference on

作者： Nooha Mumthas School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

The rapid expansion of online financial transactions has escalated the risk of fraud, posing significant challenges for banks and financial institutions, particularly in Sri Lanka. Traditional rule-based fraud detection systems often find it challenging to balance speed and accuracy, rendering them less effective against advanced and evolving fraud schemes. This study proposes a hybrid machine learning model combining decision trees and neural networks to enhance fraud detection performance. The model incorporates advanced preprocessing techniques, including feature scaling and outlier detection, to improve data quality. It employs supervised learning to achieve high accuracy and adapt to emerging fraud patterns. Additionally, explainable AI (XAI) is incorporated to ensure transparency in detection outcomes, where the user would be entrusted to make the final decision on the transaction, thereby fostering user trust and compliance with regulations in the financial sector. The model is designed to process large volumes of transactions efficiently, which demonstrates its scalability and suitability for high-volume banking environments.

关键词： Adaptation models Accuracy Explainable AI Scalability Neural networks Real-time systems Regulation Fraud Security Decision trees

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A Hybrid Architecture with Efficient Fine Tuning for Abstractive Patent Document Summarization

arXiv

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arXiv 2025年

作者： Jayatilleke, Nevidu Weerasinghe, Ruvan School of Computing Informatics Institute of Technology Colombo Sri Lanka

Automatic patent summarization approaches that help in the patent analysis and comprehension procedure are in high demand due to the colossal growth of innovations. The development of natural language processing (NLP), text mining, and deep learning has notably amplified the efficacy of text summarization models for abundant types of documents. Summarizing patent text remains a pertinent challenge due to the labyrinthine writing style of these documents, which includes technical and legal intricacies. Additionally, these patent document contents are considerably lengthier than archetypal documents, which intricates the process of extracting pertinent information for summarization. Embodying extractive and abstractive text summarization methodologies into a hybrid framework, this study proposes a system for efficiently creating abstractive summaries of patent records. The procedure involves leveraging the LexRank graph-based algorithm to retrieve the important sentences from input parent texts, then utilizing a Bidirectional Auto-Regressive Transformer (BART) model that has been fine-tuned using Low-Ranking Adaptation (LoRA) for producing text summaries. This is accompanied by methodical testing and evaluation strategies. Furthermore, the author employed certain meta-learning techniques to achieve Domain Generalization (DG) of the abstractive component across multiple patent fields. Copyright © 2025, The Authors. All rights reserved.

关键词： Patents and inventions

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Transforming Music: A Review of Artificial Intelligence in Jazz Reharmonization

Transforming Music: A Review of Artificial Intelligence in J...

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Advanced Research in computing (ICARC), International Conference on

作者： Sachindu Jayasinghe Kushan Bhareti School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Jazz reharmonization is a complex musical technique that improves harmonic frameworks by inventive chord progressions, augmenting the depth and emotional resonance of pieces. This paper examines the use of artificial intelligence, namely transformer networks, in the automation of jazz reharmonization, ensuring melodic coherence and artistic fidelity. This review compiles current research to highlight essential approaches, including early rule-based systems, neural networks, and advanced transformer models while emphasizing their benefits and limits. Particular emphasis is placed on the difficulties associated with dataset preparation, assessment measures, and the adaptation of AI systems to the intricacies of jazz harmony. The evaluation, via a critical examination of previous studies, identifies areas for enhancement, including real-time applications, multi-modal data integration, and cross-genre adaptation. The insights provided seek to direct future research and position transformers as a revolutionary instrument in computational musicology. This document is an extensive reference for scholars and professionals investigating the convergence of artificial intelligence and jazz music.

关键词： Reviews Computational modeling Instruments Current measurement Music Transformers Harmonic analysis Real-time systems Resonance Artificial intelligence

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SSL400 - A Comprehensive Word Level Dataset for Sinhala Sign Language Recognition

SSL400 - A Comprehensive Word Level Dataset for Sinhala Sign...

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Advanced Research in computing (ICARC), International Conference on

作者： Yohan Abhishek T. G. D. K. Sumanathilaka School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

In the context of increasing digital accessibility and the need for inclusive communication technologies, the demand for automated sign language recognition has grown significantly. However, Sinhala Sign Language (SSL), used by the Sri Lankan deaf community, remains underexplored due to the lack of labelled datasets and the challenges of developing recognition models for low-resource languages. This study addresses this gap by introducing an SSL dataset, specifically curated to support real-time recognition of 384 commonly used words in daily and work-related contexts. The methodology for dataset creation involved expert consultations, a structured data collection process, and multi-step labelling. These steps ensured the accurate labeling of video data, which can be used for machine learning tasks like gesture recognition, sentence formation, and speech translation. The proposed dataset lays the foundation for SSL recognition models that are fast, lightweight, and optimized for low-resource environments. Additionally, we explore the performance of initial recognition models using this dataset, demonstrating its applicability in real-world scenarios. The dataset and research outcomes provide valuable resources for the development of inclusive AI-driven communication tools for the Sinhala-speaking deaf community. This contribution addresses the pressing need for better technological support for underrepresented languages in the field of sign language recognition and processing.

关键词： Sign language Translation Speech recognition Pressing Machine learning Streaming media Data collection Real-time systems Communications technology Labeling

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Systematic Review of Fine-tuning Approaches for Large Language Models in the Context of Sinhala

Systematic Review of Fine-tuning Approaches for Large Langua...

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Advanced Research in computing (ICARC), International Conference on

作者： Sachin Hansaka Deshan Sumanathilaka School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

The rapid development of large language models (LLMs) such as GPT-3, GPT-4, LlaMA, and mBERT has significantly advanced the natural language processing (NLP) field across many widely spoken languages. However, the effectiveness of these LLMs on low-resource languages such as Sinhala remained unexplored. With the scarcity of high-quality data and linguistic resources, fine-tuning LLMs in Sinhala has gained significant challenges. With a focus on Sinhala specifically, this systematic review addresses the methodologies and outcomes of adapting large language models for underrepresented languages. The purpose of this review is to synthesize existing research, identify effective strategies, contributions, and improvements, and highlight the challenges and opportunities in this domain. By analyzing various approaches, including data augmentation, transfer learning, cross-lingual transfer, parameter-efficient fine-tuning, adapter tuning, and model adaptation likewise, this review provides a comprehensive and precise idea of the current state of LLM fine-tuning on low-resource languages like Sinhala. The results highlight the importance of using creative methods and collaboration to improve LLM performance and usefulness in linguistic environments with limited resources.

关键词： Translation Large language models Transfer learning Collaboration Linguistics Data augmentation Natural language processing Tuning Systematic literature review Synthetic data

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Advancements in Natural Language Processing for Automatic Text Summarization

arXiv

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arXiv 2025年

作者： Jayatilleke, Nevidu Weerasinghe, Ruvan Senanayake, Nipuna School of Computing Informatics Institute of Technology Colombo Sri Lanka

The substantial growth of textual content in diverse domains and platforms has led to a considerable need for Automatic Text Summarization (ATS) techniques that aid in the process of text analysis. The effectiveness of text summarization models has been significantly enhanced in a variety of technical domains because of advancements in Natural Language Processing (NLP) and Deep Learning (DL). Despite this, the process of summarizing textual information continues to be significantly constrained by the intricate writing styles of a variety of texts, which involve a range of technical complexities. Text summarization techniques can be broadly categorized into two main types: abstractive summarization and extractive summarization. Extractive summarization involves directly extracting sentences, phrases, or segments of text from the content without making any changes. On the other hand, abstractive summarization is achieved by reconstructing the sentences, phrases, or segments from the original text using linguistic analysis. Through this study, a linguistically diverse categorizations of text summarization approaches have been addressed in a constructive manner. In this paper, the author explored existing hybrid techniques that have employed both extractive and abstractive methodologies. In addition, the pros and cons of various approaches discussed in the literature are also investigated. Furthermore, a comparative analysis conducted to evaluate the generated summaries utilizing language generation models, employing different techniques and matrices. This survey endeavors to provide a comprehensive overview of ATS by presenting the progression of language processing regarding this task through a breakdown of diverse systems and architectures accompanied by technical and mathematical explanations of their operations. Finally, following the conclusion, a discussion is held regarding the limitations, challenges, and potential future work that can be conducted within this

关键词： Deep learning

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An Examination of Harmonizing Multi-Vendor Cardiac MRI Data for Vendor-Neutral Image Segmentation

An Examination of Harmonizing Multi-Vendor Cardiac MRI Data ...

引用

Advanced Research in computing (ICARC), International Conference on

作者： Hamdaan Mohideen Mohamed Ayoob Oshan Nettasinghe Vithushan Sylvester Helmini Bowala School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Vendor-specific scanner hardware, software, and image acquisition protocols can introduce systematic biases into MRI scans. This introduces non-biological artifacts which introduce variations across MRI images from different vendors. Further, we ratified such non-biological variances by conducting a discrepancy study on the MnMs-2 Cardiac MRI dataset. Data harmonization is the process of combining datasets from different sources while removing unwanted variations that are not caused by biological differences. This is important because it allows researchers to increase the understanding of biological differences in data, while mitigating vendor-specific biases. We used a domain adaptation methodology (which is a data harmonization method) on the dataset to unlearn domain (or vendor) specific biases by introducing a confusion loss to confuse the encoder. Furthermore, we introduced a 3D segmentation model to this method to capture spatial information. This method demonstrates the performance on the main task (segmentation) and compares the domain predictor, before and after unlearning. This shows the magnitude of mitigating vendor-specific biases of the model. In addition to the model architecture performance, we documented the discrepancy study between inter-vendor model performance to demonstrate the magnitude of impact in which vendor-specific, and non-biological variations of MRI scans can affect the model.

关键词： Training Image segmentation Analytical models Adaptation models Three-dimensional displays Accuracy Magnetic resonance imaging Computational modeling Data models Biological information theory

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Advancing general robotic manipulation with multimodal foundation models: An embodied AI paradigm

引用

Science China Technological Sciences 2025年第5期68卷 294-296页

作者： Shifeng HUANG He WANG Xing ZHOU Wenkai CHEN Haibin YANG Jianwei ZHANG Department of Mechanical and Aerospace Engineering Hong Kong University of Science and Technology Foshan Institute of Intelligent Equipment Technology Foshan Huashu Robot Co. Ltd. Center on Frontiers of Computing Studies (CFCS) School of Computer SciencePeking University Beijing Academy of Artificial Intelligence Department of Informatics Universit?t Hamburg

Can current robotic technologies truly replicate the full scope and intricacies of human labour? In practice, the adoption of robots remains limited, especially in open, unstructured environments commonly encountered in everyday scenarios such as services, healthcare, agriculture,construction, and numerous other fields. From the perspective of general robotic manipulation, the challenges arise from three factors.

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Cutting-edge approaches in intrusion detection systems: a systematic review of deep learning, reinforcement learning, and ensemble techniques

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Iran Journal of Computer Science 2025年 1-31页

作者： Kalpani, Nethma Rodrigo, Nureka Seneviratne, Dilmi Ariyadasa, Subhash Senanayake, Janaka Department of Computer Science and Informatics Uva Wellassa University Badulla Sri Lanka School of Computing Engineering and Technology Robert Gordon University Aberdeen AB10 7GJ United Kingdom

The growing number of networked devices and complex network infrastructures necessitates robust network security measures. Network intrusion detection systems are crucial for identifying and mitigating malicious activities within network environments. Traditional intrusion detection systems (IDS) often struggle to adapt to new and evolving threats. To address these limitations, researchers are increasingly turning to advanced methodologies such as deep learning (DL), reinforcement learning (RL), and ensemble learning (EL), which offer enhanced capabilities in detecting sophisticated and previously unseen attacks. This systematic literature review (SLR) evaluates 33 technical studies from 2020 to 2024 on the application of DL, RL, and EL in IDS. The study reviews the DL and RL methods used in IDS, along with detection methods that employ EL techniques, to combine the strengths of these approaches. It highlights the advantages, disadvantages, and applicability of the proposed techniques, while also suggesting potential improvements. The aim of this SLR is to provide researchers and practitioners with a comprehensive understanding of current IDS methodologies leveraging DL, RL, and EL, enabling the development of more effective and resilient network security solutions. In addition, this review identifies gaps in the existing literature and outlines potential future research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Deep learning Ensemble learning Intrusion detection systems Network security Reinforcement learning

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