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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Sinhababu, Nilanjan Parry, Andrew Ganguly, Debasis Samanta, Debasis Mitra, Pabitra Centre for Computational and Data Sciences IIT Kharagpur India School of Computing Science University of Glasgow United Kingdom Department of Computer Science and Engineering IIT Kharagpur India

ISBN: (纸本)9798891761681

A supervised ranking model, despite its effectiveness over traditional approaches, usually involves complex processing - typically multiple stages of task-specific pre-training and fine-tuning. This has motivated researchers to explore simpler pipelines leveraging large language models (LLMs) that can work in a zero-shot manner. However, since zero-shot inference does not make use of a training set of pairs of queries and their relevant documents, its performance is mostly worse than that of supervised models, which are trained on such example pairs. Motivated by the existing findings that training examples generally improve zero-shot performance, in our work, we explore if this also applies to ranking models. More specifically, given a query and a pair of documents, the preference prediction task is improved by augmenting examples of preferences for similar queries from a training set. Our proposed pairwise few-shot ranker demonstrates consistent improvements over the zero-shot baseline on both in-domain (TREC DL) and out-domain (BEIR subset) retrieval benchmarks. Our method also achieves a close performance to that of a supervised model without requiring any complex training pipeline. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Probabilistic Reliability via Subsystem Structures of Arrangement Graph Networks

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IEEE Transactions on Reliability 2024年第1期73卷 279-289页

作者： Huang, Yanze Lin, Limei Xu, Li Hsieh, Sun-Yuan Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fuzhou350118 China Fujian Normal University College of Computer and Cyber Security Key Laboratory of Network Security and Cryptology Fuzhou350117 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

With the rapid growth of the number of processors in a multiprocessor system, faulty processors occur in it with a probability that rises quickly. The probability of a subsystem with an appropriate size being fault-free in a definite time interval is a significant and practical measure of the reliability for a multiprocessor system, which characterizes the functionality of a multiprocessor system well. Motivated by the study of subgraph reliability, as well as the attractive structure and fault tolerance properties of (n, k)-arrangement graph A n, k, we focus on the subgraph reliability for A n, k under the probabilistic fault model in this article. First, we investigate intersections of no more than four subgraphs in A n, k, and classify all the intersecting modes. Second, we focus on the probability P(q, A n, k n-1, k-1) with which at least one (n-1, k-1)-subarrangement graph is fault-free in A n, k, when given a uniform probability q with which a single vertex is fault-free, and we establish the P(q, A n, kn-1, k-1) by adopting the principle of inclusion-exclusion under the probabilistic fault model. Finally, we study the probabilistic fault model involving a nonuniform probability with which a single vertex is fault-free, and we prove that the P(q, A n, kn-1, k-1) under both models is very close to the asymptotic value by both theoretical arguments and experimental results. © 1963-2012 IEEE.

关键词： Fault tolerance

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Comparative Analysis of CNN and SVM Machine Learning Techniques for Plant Disease Detection 4th

Comparative Analysis of CNN and SVM Machine Learning Techniq...

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4th International Conference on data, Engineering, and Applications, IDEA 2022

作者： Adeniyi, Abidemi Emmanuel Madamidola, Olugbenga Ayomide Awotunde, Joseph Bamidele Misra, Sanjay Agrawal, Akshat Department of Computer Science College of Computing and Communication Studies Bowen University Iwo Nigeria Department of Information Technology Federal University of Technology Akure Nigeria Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Department of Applied Data Science Institute for Energy Technology Halden Norway Department of Computer Science & Engineering Amity University Haryana Gurugram122412 India

ISBN: (纸本)9789819700363

The growing advancement of different jobs, skills and businesses has stimulated the spread of diseases. Most of the time, these diseases can cross countries’ borders and spread at a very rapid rate. Avoiding and detecting these infections have proved to be a major problem for our public health and one that must be treated with great importance. Crop disease has haunted, farmers have been victimized for several decades, and a remedy must be offered to halt this damage. As a result, a system should be developed to detect plant disease. Untreated plant diseases ultimately result in a loss to the farmer. Traditional methods of detecting plant disease have become less efficient and inaccurate;thus there is need to improve on the techniques of plant disease detection, as a reliable detection of plant diseases will greatly improve the quality of food crops produced and decrease the threat plant diseases pose to food security. Therefore, this study is aimed at analyzing comparatively, two machine learning language techniques convolutional neural network (CNN) and support vector machine (SVM) in terms of their respective accuracy in detecting plant diseases through their leaves in which both algorithms are used for the automatic diagnosis of plant disease. From the experimental test conducted on various plants using CNN and SVM algorithms, CNN had an accuracy of 98%, while SVM had an accuracy of 62% on various plant leaves used. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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A Deep Learning and Machine Learning Approach to Crop Recommendation for Sustainable Farming in Bangladesh

A Deep Learning and Machine Learning Approach to Crop Recomm...

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2024 International Conference on Innovations in science, Engineering and Technology, ICISET 2024

作者： Hossain, Meharaz Pias, Shakil Ahmed Hossain, Md. Mashrur Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Jahangirnagar University Department of Statistics and Data Science Dhaka Bangladesh Military Institute of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350355499

The agricultural sector in Bangladesh contributes approximately 11 to 12% of the country's GDP, with the total B2C market size for agriculture-related products estimated at 47.54 billion. This study focuses on the use of machine learning algorithms to forecast accurate crop recommendations based on environmental and soil characteristics, such as nitrogen, phosphorus, potassium, temperature, humidity, pH value, and rainfall. By utilizing standard machine learning tools, particularly deep learning, we have achieved an impressive 99.55 % accuracy in determining the most suitable crops to grow under specific conditions. This high accuracy significantly aids farmers in making informed decisions regarding the optimal crops to cultivate, both under current conditions and in the anticipated conditions of the near future. Moreover, this technique enhances crop yield and production, contributing to improved farming practices tailored to weather patterns and soil types. © 2024 IEEE.

关键词： Adversarial machine learning

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Machine Learning-Enhanced Self-Management for Energy-Effective and Secure Statistics Assortment in Unattended WSNs

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SN computer science 2025年第2期6卷 1-10页

作者： Biswas, Preesat Mishra, Aishwarya Dixit, Rinku Sharma Dwivedi, Abhishek Choudhary, Shailee Lohmor Tiwari, Shweta Chakravarthi, M. Ayyappa Electronics & Telecommunications Government Engineering College C. G Jagdalpur India Department of Computer Science & Engineering IES College of Technology M.P Bhopal India Department of Artificial Intelligence and Machine Learning New Delhi Institute of Management Delhi India Department of Data Science and Engineering School of AIML IoT & IS CCE DS and Computer Applications Manipal University Jaipur Jaipur Rajasthan India Department of Computer Science and Engineering Gyan Ganga Institute of Technology and Sciences M.P Jabalpur India Department of CSE-Data Science KKR and KSR Institute of Technology and Sciences Guntur India

Unattended Wireless Sensor Networks (UWSNs) operate without human supervision and have limited resources. In a conventional WSN with a fixed sink for data collection, nodes one hop away from the sink consume more energy, causing a hotspot issue. This leads to network fragmentation and reduced sensing efficiency. To address this, a mobile sink is proposed. This study details the development and implementation of Secure MRL, focusing on legitimate message communication to the mobile sink via convex nodes. By using these convex nodes as data collection points, the mobile sink avoids traversing the entire network. We employ an enhanced system to generate keys and authenticate messages, assessing its resilience against various attacks, including sleep deprivation, snooze, network substitution, and modification. Findings indicate that Secure MRL maintains consistent performance across different node counts and significantly reduces energy consumption by 84%, improving the speed of attack detection. This improvement is achieved by monitoring the digital signature and residual energy of nodes. The proposed SVM-based network training establishes a residual energy cut-off, while convex nodes mitigate synchronization issues among other sensor nodes in Secure MRL. Additionally, convex nodes manage the availability checks for the mobile sink. The proposed approach demonstrates a 92% improvement in packet delivery ratio (PDR) and an 89% reduction in end-to-end delay, highlighting its efficacy in enhancing network performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Duty-cycled UWSN MRL Sec-MRL SEC-TMP SVM UWSN

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Guardians of Truth: Exploring Techniques for Veracity Identification in social media during an Infodemic 3

Guardians of Truth: Exploring Techniques for Veracity Identi...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Suthanthiradevi, P. Revathy, G. Sathish Kumar, P. Karthikeyan, B. Muthu, Lakshmi.V SRM institute of Science and Technology Data Science and Business Systems School of Computing India Vels Institute of science Technology and Advanced studies Department of Computer science and engineering India Rajalakshmi institute of technology Computer and Communication Engineering India SASTRA Deemed University School of Computing India St. Joseph's Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350372120

With the recent advent of technology, social networks are accessible 24/7 using mobile devices. During covid-19 pandemic the propagation of misinformation are mostly related to the disease, its cures and prevention. With Twitter as the medium this information was spreaded to millions of people who lack domain specific knowledge. This can lead to bloster fear and direct damage to the people. This research focuses on identifying the veracity of the Twitter posts behind pandemic situation. The authors have proposed a Transformer Model using Bat Algorithm for identifying the fake news. This model is trained and evaluated using infodemic metadata. Our experimental results shows by which proposed model accurately detected the fake news and achieved 96.5% of f1-score. © 2024 IEEE.

关键词： Social networking (online)

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Public Awareness of Phishing in Indonesia: System Literature Review

Public Awareness of Phishing in Indonesia: System Literature...

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2023 International Conference on data science and Its Applications, ICoDSA 2023

作者： Chandra, Anthony William Chandiarta, Zefanya Setiyawan, Lutfi Gunawan, Alexander Agung Santoso Edbert, Ivan Sebastian Bina Nusantara University Cyber Security Program School of Computer Science Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350305197

In an era where technology is advancing at an unprecedented rate, the internet has become an indispensable tool for everyday life, offering both convenience and efficiency. While the internet has simplified tasks ranging from communication to information retrieval, it also poses significant challenges in the realm of cyber security. Users are not just potential victims but can also become unwitting perpetrators of cybercrimes. This highlights the urgent need for cyber security awareness among the general populace. Focusing on Indonesia, a nation where cyber security education is still in its infancy, this study aims to assess the level of public awareness regarding the specific threat of phishing. Employing the Systematic Literature Review (SLR) methodology, this research analyzes a comprehensive range of literature encompassing subjects such as phishing, data leakage, broader cyber security threats, and the current state of public awareness about phishing. The objective is to provide a detailed overview of existing knowledge and identify areas where educational efforts should be concentrated. The results indicate a considerable gap in the understanding of phishing among Indonesians. Many studies reviewed did not offer a clear definition or explanation of phishing, creating an environment where individuals are more susceptible to these cyber-attacks. This research underscores the urgent need for targeted educational initiatives aimed at increasing public awareness and understanding of phishing and other cyber threats. © 2023 IEEE.

关键词： Cybersecurity

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MMTraP: Multi-Sensor Multi-Agent Trajectory Prediction in BEV

IEEE Open Journal of Vehicular Technology

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IEEE Open Journal of Vehicular Technology 2025年 6卷 1551-1567页

作者： Sharma, Sushil Das, Arindam Sistu, Ganesh Halton, Mark Eising, Ciaran University of Limerick Department Electronic and Computer Engineering LimerickV94 T9PX Ireland University of Limerick SFI CRT Foundations in Data Science Ireland Research Centre Ireland

Accurate detection and trajectory prediction of moving vehicles are essential for motion planning in autonomous driving systems. While traffic regulations provide clear boundaries, real-world scenarios remain unpredictable due to the complex interactions between vehicles. This challenge has driven significant interest in learning-based approaches for trajectory prediction. We present MMTraP: Multi-Sensor and Multi-Agent Trajectory Prediction in BEV. This method integrates camera, LiDAR, and radar data to create detailed Bird's-Eye-View representations of driving scenes. Our approach employs a hierarchical vector transformer architecture that first detects and classifies vehicle motion patterns before predicting future trajectories through spatiotemporal relationship modeling. This work specifically focuses on vehicle interactions and environmental constraints. Despite its significance, multi-agent trajectory prediction and moving object segmentation are still underexplored in the literature, especially in real-time applications. Our method leverages multisensor fusion to obtain precise BEV representations and predict vehicle trajectories. Our multi-sensor fusion approach achieves the highest vehicle Intersection over Union (IoU) of 63.23% and an overall mean IoU (mIoU) of 64.63%, demonstrating its effectiveness in utilizing all available sensor modalities. Additionally, we demonstrate vehicle segmentation and trajectory prediction capabilities across various lighting and weather conditions. The proposed approach has been rigorously evaluated using the nuScenes dataset. Results show that our method improves the accuracy of trajectory predictions and outperforms state-of-the-art techniques, particularly in challenging environments such as congested urban areas. For instance, in complex traffic scenarios, our approach achieves a relative improvement of 5% in trajectory prediction accuracy compared to baseline methods.. This work advances vehicle-focused prediction syst

关键词： Semantic Segmentation

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Document-level Clinical Entity and Relation Extraction via Knowledge Base-Guided Generation 23

Document-level Clinical Entity and Relation Extraction via K...

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23rd Meeting of the ACL Special Interest Group on Biomedical Natural Language Processing, BioNLP 2024

作者： Bhattarai, Kriti Oh, Inez Y. Abrams, Zachary B. Lai, Albert M. Institute for Informatics Data Science & Biostatistics Washington University School of Medicine United States Department of Computer Science Washington University in St. Louis United States

ISBN: (纸本)9798891761308

Generative pre-trained transformer (GPT) models have shown promise in clinical entity and relation extraction tasks because of their precise extraction and contextual understanding capability. In this work, we further leverage the Unified Medical Language System (UMLS) knowledge base to accurately identify medical concepts and improve clinical entity and relation extraction at the document level. Our framework selects UMLS concepts relevant to the text and combines them with prompts to guide language models in extracting entities. Our experiments demonstrate that this initial concept mapping and the inclusion of these mapped concepts in the prompts improves extraction results compared to few-shot extraction tasks on generic language models that do not leverage UMLS. Further, our results show that this approach is more effective than the standard Retrieval Augmented Generation (RAG) technique, where retrieved data is compared with prompt embeddings to generate results. Overall, we find that integrating UMLS concepts with GPT models significantly improves entity and relation identification, outperforming the baseline and RAG models. By combining the precise concept mapping capability of knowledge-based approaches like UMLS with the contextual understanding capability of GPT, our method highlights the potential of these approaches in specialized domains like healthcare.. ©2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Transfer Learning Based Models for Food Detection Using ResNet-50

Transfer Learning Based Models for Food Detection Using ResN...

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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Senapati, Biswaranjan Talburt, John R. Naeem, Awad Bin Batthula, Venkata Jaipal Reddy Parker Hannifin Corp Department of Computer Science Ohio United States University of Arkansas Ualr Department of Computer Science United States Department of Computer Science United States Data Scientist Skj Healthcare Llc CamdenAR United States

ISBN: (纸本)9781665493765

Being overweight may be caused by eating too many calories. It is a curable medical condition defined by abnormal fat accumulation in the body. Diabetes, excessive cholesterol, and heart attacks are the most common, although high blood pressure, colon cancer, and prostate cancer are also common. computer techniques are often utilized to address such difficulties. In this work, we develop a system that detects and identifies food allergies using food photographs. To summaries, powerful computer algorithms such as transfer learning (ResNet50) have been taught to detect food type and validate the identified label in dataset food 101, as well as supply nutrients. The fundamental purpose of this study was to create a single framework capable of managing the difficult process of detecting, localizing, and classifying food allergies. Furthermore, larger weight parameter optimization using Adam and RMS Prop optimizers was attempted to increase their performance on healthy and allergic food image datasets. The Resnet-50 was trained to obtain the greatest mean average accuracy when compared to the other transfer learning meta-architectures. It achieved the best-identifying results by utilizing an Adam optimizer and obtaining 95% accuracy. The suggested technique was discovered to be novel since it detects all food types and then provides the nutrients of that meal from another dataset. In reality, employing the transfer learning technique to successfully diagnose food allergies would assist to prevent the adverse application of issues in diet management. © 2023 IEEE.

关键词： Learning systems

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