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37th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2024

作者： Sahil, Sahil Kumar, P. Sreenivasa Department of Computer Science and Engineering Indian Institute of Technology Madras India

BERT-based models possess impressive language understanding capabilities but often lack domain-specific knowledge, limiting their performance on specialised tasks such as medical multiple-choice question answering (MCQA). In this paper, we study how biomedical ontologies, rich repositories of medical knowledge, can be harnessed to enhance BERT-based models for medical MCQA task. Our contributions include OntoMCQA-Gen, a system which leverages different biomedical ontologies to construct BioOntoMCQA, a large synthetic MCQA dataset. OntoMCQA-Gen exploits the subclass-class relationships, definitions of concepts, and also synonym relationships from the ontologies to create this dataset of MCQs automatically. We then use this synthetic dataset to fine-tune various BERT-based models to answer medical MCQs. We evaluated these fine-tuned BERT models on the challenging MedMCQA and MedQA datasets of questions from admission examinations for medical degrees in India and USA, respectively. Our evaluation study on these datasets shows that fine-tuning the BERT-based models on BioOntoMCQA results in significantly improved accuracy scores. BioBERT and PubMedBERT, pretrained on the large medical corpus, have also shown significant improvements with our technique of fine-tuning ontology-generated synthetic data. This finding highlights the effectiveness of incorporating biomedical ontologies to enhance the BERT-based model in the medical domain. Moreover, our results underscore the importance of using ontology-generated data along with model adaptation for specialised domains, contributing to a novel advancement in natural language processing. Copyright © 2024 by the authors.

关键词： Ontology

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Cross-lingual Editing in Multilingual Language Models 18

Cross-lingual Editing in Multilingual Language Models

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18th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2024 - Findings of EACL 2024

作者： Beniwal, Himanshu Kowsik Nandagopan, D. Singh, Mayank Department of Computer Science and Engineering Indian Institute of Technology Gandhinagar India

ISBN: (纸本)9798891760936

The training of large language models (LLMs) necessitates substantial data and computational resources, and updating outdated LLMs entails significant efforts and resources. While numerous model editing techniques (METs) have emerged to efficiently update model outputs without retraining, their effectiveness in multilingual LLMs, where knowledge is stored in diverse languages, remains an underexplored research area. This research paper introduces the cross-lingual model editing (XME) paradigm, wherein a fact is edited in one language, and the subsequent update propagation is observed across other languages. To investigate the XME paradigm, we conducted experiments using BLOOM, mBERT, and XLM-RoBERTa using the two writing scripts: Latin (English, French, and Spanish) and Indic (Hindi, Gujarati, and Bengali). The results reveal notable performance limitations of state-of-the-art METs under the XME setting, mainly when the languages involved belong to two distinct script families. These findings highlight the need for further research and development of XME techniques to address these challenges. For more comprehensive information, the dataset used in this research and the associated code are publicly available at the following URL. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Efficient Parallel Algorithms For Exact SimRank Computations 31

Efficient Parallel Algorithms For Exact SimRank Computations

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31st IEEE International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Mundhara, Aditya Nijhara, Prajjwal Banerjee, Dip Sankar Department of Computer Science and Engineering Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798331509118

SimRank is a popular measure for evaluating node similarities in graphs, but its high computational cost limits scalability for large graphs. The ExactSim [1] algorithm achieves precise single-source SimRank similarities but suffers from significant computation time. In this work, we parallelize key components of ExactSim - Personalized PageRank (PPR) computations and SimRank calculations on a CPU-based shared memory multi-core system. By optimizing these modules, we significantly reduce runtime, making ExactSim more practical for large-scale applications. Our results (on graph sizes ranging from 0.1 million edges to 100 million edges) demonstrate the effectiveness of parallelization in improving both efficiency and scalability. © 2024 IEEE

关键词： Graph algorithms

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A Machine Learning and Nature Inspired Algorithm Based Hybrid Approach for Detection of IoT Attacks 4

A Machine Learning and Nature Inspired Algorithm Based Hybri...

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4th International Conference on computer, Communication, Control and Information technology, C3IT 2024

作者： Sikdar, Subinoy Kule, Malay Indian Institute of Engineering Science and Technology Department of Computer Science and Technology Howrah Shibpur India

ISBN: (纸本)9798350389128

Internet of Things (IoT) is revolutionizing our daily life, shifting manual systems to computerized ones. With the proliferation of IoT devices connected via the internet, ensuring security against malicious attacks is critical. So, the security of information is the greatest concern in modern days as the IoT devices are attacked by various malicious services. In this paper, we have proposed a hybrid attack detection method using machine learning and nature inspired algorithm. The main objective of this paper is to find out the best feature(s) as well as the best algorithms, which detect the attack accurately. We have used CICIoT2023 dataset to classify the malicious attacks and benign traffic. Decision Tree and One Rule algorithm has been applied on the dataset and both the algorithms reached the accuracy up to 98.22% and 98.99% respectively. An ensemble learning method was also developed using Random Forest algorithm. Furthermore, a hybrid attack detection approach has been implemented using Decision Tree and Cuckoo Search Algorithm, which can perform better than Random Forest model. The hybrid classifier has obtained the accuracy up to 99.40%, whereas the Random Forest model reached the accuracy up to 99.28%. © 2024 IEEE.

关键词： Adversarial machine learning

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Phrase-level emotion intensity detection of text using lexicon-based unit circle and pipelined neural networks approaches

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Neural Computing and Applications 2025年第5期37卷 3151-3166页

作者： Patil, Bhuvaneshwari Ajit Vadlakunta, Vishnu Vardhan Guruvu, Poojaa Pranathi Mathew, Meenu Prakash, Jay Computer Science and Engineering National Institute of Technology Calicut Kozhikode India

Emotions have a significant impact on how people make decisions. Due to its potential applications in various fields, emotion intensity detection has attracted a lot of attention recently. Several methods have been proposed in the past using natural language processing to recognize emotions from the text. These methods include the keyword-based approach, the lexicon-based approach and the machine learning approaches. Nevertheless, there were a few limitations with the lexicon-based unit circle approach, as it focuses on detecting emotion intensity at the word-level. This research proposes an integrated model by considering the advantages of the lexicon-based unit circle and pipelined neural networks approaches. The model is trained on a large corpus of text data. The proposed approach aims to determine emotion intensity, analyzing text at the phrase-level, thereby achieving higher performance in terms of accuracy and F1-score compared to other models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Neural networks

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A Systematic Study for Understanding the Security Risks in 5G Core Network 16

A Systematic Study for Understanding the Security Risks in 5...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Dixit, Unnati Vittal, Shwetha Antony Franklin, A. Indian Institute of Technology Hyderabad Department of Computer Science and Engineering India

ISBN: (纸本)9798350383119

The widespread adoption of 5G networks has revolutionized communication, providing great connectivity and services across various verticals. Specifically, on the 5G Core (5GC) this has been made possible by leveraging network slicing and Network Function Virtualization (NFV) where multiple Network Functions (NFs) are involved to serve different user requests on the control plane and a variety of service traffic on the data plane. However, as 5G networks embrace zero trust and multi-tenant architectures, this rapid technological advancement has also exposed the 5GC to various security risks such as Distributed Denial of Service (DDoS), Man in The Middle (MITM), cross-slice disruptions, and side-channel attacks. In this regard, this paper presents a comprehensive approach to enhance the security of 5GC through a systematic study of dependency graphs, Betweenness Centrality (BC), and attack graphs. Potential attack scenarios are carefully analyzed by modeling the 3GPP-defined 5GC, performing vulnerability assessments, building attack graphs, analyzing them, and presenting the key inferences from the study. © 2024 IEEE.

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Fastlane: A framework for building fast path network applications

Fastlane: A framework for building fast path network applica...

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2024 IEEE Conference on Network Function Virtualization and Software Defined Networks, NFV-SDN 2024

作者： Das, Debojeet Lakshya Vutukuru, Mythili Department of Computer Science and Engineering Indian Institute of Technology Bombay India

ISBN: (纸本)9798350380538

To leverage the full potential of high-speed network interface cards (NICs), it is essential for network applications to use high-performance packet I/O frameworks. However, porting applications to use these specialized frameworks is a tedious task. While prior work has proposed frameworks to ease the development of network functions over fast packet I/O mechanisms, there has not been much work done to ease the porting of applications that are built to run on the kernel network stack. This paper presents Fastlane, a framework to easily port the "fast path" of traditional network applications to use fast packet I/O mechanisms while retaining the "slow path" to run on the traditional network stack. Fastlane provides APIs by which application developers can configure, use, and switch between different fast packet I/O mechanisms like DPDK and CNDP in the application’s fast path. Additionally, Fastlane provides APIs for the fast path to communicate with the slow path running on the kernel network stack. Fastlane also allows applications to be deployed in an unprivileged Kubernetes pod, easing application development for cloud deployments. We evaluate the effectiveness of Fastlane by porting a production-grade 5G user plane function to use our framework. Our evaluation shows that Fastlane enables developers to build high-performance network applications easily and imposes minimal performance overheads. ©2024 IEEE.

关键词： HIgh speed networks

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PCLive: Pipelined Restoration of Application Containers for Reduced Service Downtime 15

PCLive: Pipelined Restoration of Application Containers for ...

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15th Annual ACM Symposium on Cloud Computing, SoCC 2024

作者： Tripathi, Shiv Bhushan Mishra, Debadatta Department of Computer Science and Engineering Indian Institute of Technology Kanpur India

ISBN: (纸本)9798400712869

Application containers are widely used in contemporary cloud computing environments. Migration of containers across hosts provides cost-effective cloud management by enabling improved server consolidation, load balancing and enhanced fault tolerance. One of the primary objectives of container migration is to reduce the service downtime of applications hosted in containers. The service downtime depends on performing the migration activities efficiently, specifically from the time the container is stopped on the source host till it is restored and fully functional at the destination host. In this paper, we show that, the state-of-the-art pre-copy migration strategy for containers using checkpoint and restore techniques (e.g., CRIU) inflates the downtime due to its inherent limitations in the restoration procedures, particularly for containers with large memory working set size. We propose PCLive to address this bottleneck using a pipelined restore mechanism. Compared to the baseline CRIU pre-copy migration, PCLive results in up to ∼38.8x reduction in restoration time which leads to a reduction of service downtime by up to ∼2.7x for migration of a container hosting the Redis key-value store over an one Gbps network. We also present comprehensive comparative analysis of the resource cost for the proposed solution along with additional optimizations to demonstrate that PCLive can reduce the application downtime in a resource efficient manner leveraging its flexible and efficient design choices. © 2024 Owner/Author.

关键词： Restoration

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Impact of Constraint Modifications on the CRYSTALS-Kyber Hardware Design 31

Impact of Constraint Modifications on the CRYSTALS-Kyber Har...

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31st IEEE International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Bohra, Dixit Dutt Banerjee, Dip Sankar Sanadhya, Somitra Department of Computer Science and Engineering Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798331509118

The lattice-based post-quantum cryptography scheme is widely regarded as one of the most secure solutions to address the emerging threats posed by advancements in quantum computing. Various groups have analyzed and studied the hardware implementation of the CRYSTALS-KYBER design, and they continue to work on optimizing the design. Researchers have applied various techniques throughout the design to enhance power consumption, area utilization, and output speed. The main goal of this work is to present the impact of constraints on design performance, analyzing factors such as Worst Negative Slack (WNS) and total power. After introducing input and output delay constraints, we found that the actual operating frequency was 91.29 MHz, deviating significantly from the initially claimed 169.92 MHz. © 2024 IEEE

关键词： Quantum computers

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MTUNet + + : explainable few-shot medical image classification with generative adversarial network

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Multimedia Tools and Applications 2025年第11期84卷 9219-9239页

作者： Titoriya, Ankit Kumar Singh, Maheshwari Prasad Singh, Amit Kumar Department of Computer Science and Engineering National Institute of Technology Patna India

Medical imaging, a cornerstone of disease diagnosis and treatment planning, faces the hurdles of subjective interpretation and reliance on specialized expertise. Deep learning algorithms show improvements in automating medical image analysis, reducing radiologists' burden, and potentially enhancing patient outcomes. However, these algorithms require substantial quantities of high-quality labelled data for effective training and refinement. This paper proposes an innovative approach that harnesses few-shot learning (FSL) and generative adversarial networks (GANs) to overcome conventional methods' limitations in medical image classification. FSL, capable of learning from limited labelled examples, holds promise for scenarios where labelled data is scarce. However, the lack of interpretability in existing FSL models impedes their clinical adoption. To tackle this, this paper proposes a explainable FSL network, "MTUNet + +," which integrates an attention mechanism to emphasize relevant regions in medical images. Furthermore, integrating a generative adversarial network, enhances the performance of MTUNet + + by generating synthetic medical images. Systematically eliminating misleading synthetic images improves the reliability and accuracy of medical image classification. Empirical evaluation on benchmark datasets underscores the effectiveness of the approach, achieving 85.19% and 69.28% accuracy for the HAM10000 and Kvasir datasets, respectively. This paper contributes to advancing AI-driven solutions in clinical practice, facilitating enhanced patient care and streamlined workflows within real-world healthcare settings. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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