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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Bohra, Bhavesh Kumar Rajesh, Bulla Javed, Mohammed Doermann, David Iiit Computer Vision and Biometrics Lab Department of It Allahabad India Iiit Sricity Department of Computer Science and Engineering India University at Buffalo Department of Computer Science and Engineering BuffaloNY United States

ISBN: (纸本)9798350376876

Due in part to global warming, the frequency of wildfires has increased in recent years, significantly impacting humans, wildlife, and forest ecosystems. To address this challenge, using technology for early fire detection through smoke can help firefighters and other stakeholders limit their spread and reduce the impact. In this research, we adopt the latest object detection model - YOLOv8 and use a fine-tuned version for detecting smoke and fire using the latest datasets. YOLOv8 backbone begins with two convolutional (Conv) layers, followed by a series of alternating Conv layers and C2F modules, and ends with a Spatial Pyramid Pooling - Fast (SPPF) module. Our improvement involves adding two extra Conv layers at the beginning of the backbone, between the alternating sequence of Conv and C2F layers. As the smoke and fire detection problem involves searching for smaller objects in the image, increasing the feature extraction layers improves the performance of smoke and fire detection. The fine-tuned model is tested with the FASDD v4 dataset, which depicts fire, smoke, non-fire, and non-smoke scenarios. © 2024 IEEE.

关键词： Premixed flames

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Substructure Discovery in Heterogeneous Multilayer Networks 15

Substructure Discovery in Heterogeneous Multilayer Networks

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15th IEEE International Conference on Knowledge Graph, ICKG 2024

作者： Bolaj, Kiran Santra, Abhishek Chakravarthy, Sharma The University of Texas at Arlington It Lab and Department of Computer Science & Engineering United States

ISBN: (纸本)9798331508821

Graph mining analyzes graphs to find core substructures (connected subgraphs) in applications that are modeled using graphs. These identified substructures, based on frequency or some other metric, are important as they reveal an inherent feature or property in the given graph/forest. Interesting substructures that are frequent or compress the graph well offer insights into hidden regularities. The process of finding these interesting patterns using unsupervised learning is termed substructure discovery. SUBDUE is an early main-memory algorithm developed for substructure discovery. Since then, this algorithm has been extended using disk-based, database-oriented approaches, and more recently using the Map/Reduce paradigm to exploit distributed and parallel processing. Graph sizes have increased steadily, thanks to the advent of the Internet and social network applications. To model complex datasets - with multiple types of entities and relationships - multilayer networks (or MLNs) have been shown to be effective. MLNs have also been shown to be superior as compared to simple and attributed graphs for modeling complex data. MLNs are also useful for modeling different data types using distinct layers. This paper focuses on substructure discovery in heterogeneous multilayer networks (one type of MLN) using the novel decoupling-based approach. In this approach, each layer is processed independently and then the results from two or more layers are composed to identify substructures in the entire MLN. The algorithm is designed and implemented, including the composition part, using the Map/Reduce paradigm for understanding speedup. After validating accuracy of results with ground truth, we analyze speedup and response time of the proposed algorithm and approach through extensive experimental analysis on large synthetic datasets with diverse graph characteristics. © 2024 IEEE.

关键词： Unsupervised learning

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Determining Legal Relevance with LLMs using Relevance Chain Prompting 37

Determining Legal Relevance with LLMs using Relevance Chain ...

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

作者： Bilgin, Onur Licato, John Lab Department of Computer Science and Engineering University of South Florida United States

In legal reasoning, part of determining whether evidence should be admissible in court requires assessing its relevance to the case, often formalized as its probative value—the degree to which its being true or false proves a fact in issue. However, determining probative value is an imprecise process and must often rely on consideration of arguments for and against the probative value of a fact. Can generative language models be of use in generating or assessing such arguments? In this work, we introduce relevance chain prompting, a new prompting method that enables large language models to reason about the relevance of evidence to a given fact and uses measures of chain strength. We explore different methods for scoring a relevance chain grounded in the idea of probative value. Additionally, we evaluate the outputs of large language models with ROSCOE metrics and compare the results to chain-of-thought prompting. We test the prompting methods on a dataset created from the Legal Evidence Retrieval dataset. After postprocessing with the ROSCOE metrics, our method outperforms chain-of-thought prompting. Copyright © 2024 by the authors.

关键词： Statistical tests

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Real-Time On-Site Ground-Motion Prediction Using ResNet

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Seismological Research Letters 2025年第3期96卷 1834-1845页

作者： Yang, Tzu-Yi Chen, Da-Yi Chin, Tai-Lin Mobile Computing and Networking Lab Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Seismological Center of Central Weather Administration Taipei Taiwan

On-site warnings can decrease the range of late alert zone during earthquakes. This study develops a deep learning model to predict whether the maximum peak ground acceleration at a station exceeds 25 Gal based on the waveform of the initial P-wave. A ResNet architecture model is developed to address the degradation problem in multilayered models, thereby enhancing performance. The model exhibits high performance for a test dataset, achieving F1-scores higher than 90% over various time windows, and outperforms the traditional progressive displacement value (Pdv) method and newer convolutional neural network (CNN) methods. Ground motion of two actual earthquakes are applied to test the model and validate its prediction capabilities, and the results indicate that the proposed model has higher accuracy and speed than Pdv and CNN methods. In addition, the model is integrated with the Earthworm earthquake early warning system for real-time waveform analysis and prediction, extending the model’s applicability beyond experimental stages to online systems. The proposed method is also tested using data from earthquakes that occurred in March 2023. © Seismological Society of America.

关键词： Convolutional neural networks

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MILP Acceleration:A Survey from Perspectives of Simplex Initialization and Learning-Based Branch and Bound

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Journal of the Operations Research Society of China 2025年第1期13卷 1-55页

作者： Meng-Yu Huang Ling-Ying Huang Yu-Xing Zhong Hui-Wen Yang Xiao-Meng Chen Wei Huo Jia-Zheng Wang Fan Zhang Bo Bai Ling Shi Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong KongChina Theory Lab Huawei Hong Kong Research CentreHong KongChina

Mixed integer linear programming(MILP)is an NP-hard problem,which can be solved by the branch and bound algorithm by dividing the original problem into several subproblems and forming a search *** each subproblem,linear programming(LP)relaxation can be solved to find the bound for making the following ***,with the increasing dimension of MILPs in different applications,how to accelerate the solution process becomes a huge *** this survey,we summarize techniques and trends to speed up MILP solving from two ***,we present different approaches in simplex initialization,which can help to accelerate the solution of LP relaxation for each ***,we introduce the learning-based technologies in branch and bound algorithms to improve decision making in tree *** also propose several potential directions and extensions to further enhance the efficiency of solving different MILP problems.

关键词： MILP acceleration Simplex initialization Linear programming Mixed integer linear programming Machine learning

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Advancements in Credit Scoring, Profit Scoring, and Portfolio Optimization for P2P Lending

Advancements in Credit Scoring, Profit Scoring, and Portfoli...

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Communication, Control and Intelligent Systems (CCIS)

作者： Premkumar Nayaka Anusha Hegde Biswajit Bhowmik Department of Computer Science and Engineering Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory National Institute of Technology Karnataka Surathkal Bharat Department of Computer Science and Engineering BRICS Laboratory National Institute of Technology Karnataka Surathkal Bharat

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

The Peer-to-peer (P2P) lending platform allows borrowers to connect directly with lenders outside traditional banking systems. Therefore, for the sustainability of these platforms, they must accurately assess the credit risk and profitability of the loans. Various credit scoring techniques, including Logistic Regression, neural networks, and ensemble methods, can be used to estimate the likelihood of borrower default. It is imperative to analyze the profit the lenders generated and enhance the credit scoring so that the investors face minimum loss. Once the profit analysis is done, then it is crucial to advise the investors about the portfolio of loans. This paper presents recent credit scoring, profit scoring, and portfolio optimization trends for P2P lending. We highlight the significant issues in incorporating machine learning models into credit scoring systems. The analysis emphasizes the need for a data-driven approach to perfecting lending practices, thus benefiting both borrowers and investors in the rapidly changing P2P landscape.

关键词： Logistic regression Profitability Fintech Neural networks Market research Peer-to-peer computing Sustainable development Intelligent systems Portfolios Optimization

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Partial Sum Attack on Round-Reduced AES Utilizing Parallel Computation Approaches

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SN computer science 2025年第3期6卷 1-21页

作者： Pal, Debranjan Gupta, Ankit Das, Abhijit Chowdhury, Dipanwita Roy Crypto Research Lab Department of Computer Science and Engineering IIT Kharagpur West Bengal Kharagpur 721301 India

The Advanced Encryption Standard (AES) is a widely recognized, extensively utilized, and thoroughly studied industry-standard symmetric key block cipher. Consequently, cryptanalysis of AES is a highly relevant and significant task for cryptography researchers. In the field of cryptanalysis, the Square attack is a powerful key recovery technique used against symmetric ciphers. This study aims to provide two efficient implementations of the partial sum attack (PSA), a variant of the square attack, on reduced rounds of AES. The goal is to demonstrate the effectiveness of these implementations in terms of reduced time complexity for full key recovery. We demonstrate two efficient implementations of PSA on reduced rounds of AES. The first implementation is a GPU-based attack applied to five and six rounds of AES, achieving full key recovery in 0.8s and 7.58 days, respectively. The second implementation is cluster-based, applied to six rounds of AES, with an elapsed time of 2.5 days for full key recovery. Both implementations leverage parallelization to enhance performance, showing a significant reduction in attack time proportional to the number of working threads. The findings confirm that PSA effectively reduces the time complexity for full key recovery in reduced rounds of AES, demonstrating the practicality and efficiency of these implementations. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： AES Chosen plaintext Cluster GPU Parallel computing Partial sum attack Square attack

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Application of the Deep Convolutional Neural Network for the Classification of Auto Immune Diseases

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computers, Materials & Continua 2023年第10期77卷 647-664页

作者： Fayaz Muhammad Jahangir Khan Asad Ullah Fasee Ullah Razaullah Khan Inayat Khan Mohammed ElAffendi Gauhar Ali Department of Computer Science and Information Technology Sarhad University of Science and Information TechnologyPeshawar25000Pakistan Department of Computer Science University of Engineering and TechnologyMardan23200Pakistan EIAS Data Science and Blockchain Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

IIF(Indirect Immune Florescence)has gained much attention recently due to its importance in medical *** primary purpose of this work is to highlight a step-by-step methodology for detecting autoimmune *** use of IIF for detecting autoimmune diseases is widespread in different medical *** 80 different types of autoimmune diseases have existed in various body *** IIF has been used for image classification in both ways,manually and by using the computer-Aided Detection(CAD)*** data scientists conducted various research works using an automatic CAD system with low *** diseases in the human body can be detected with the help of Transfer Learning(TL),an advanced Convolutional Neural Network(CNN)*** baseline paper applied the manual classification to the MIVIA dataset of Human Epithelial cells(HEP)type II cells and the Sub Class Discriminant(SDA)analysis technique used to detect autoimmune *** technique yielded an accuracy of up to 90.03%,which was not reliable for detecting autoimmune disease in the mitotic cells of the *** the current research,the work has been performed on the MIVIA data set of HEP type II cells by using four well-known models of *** augmentation and normalization have been applied to the dataset to overcome the problem of overfitting and are also used to improve the performance of TL *** models are named Inception V3,Dens Net 121,VGG-16,and Mobile Net,and their performance can be calculated through parameters of the confusion matrix(accuracy,precision,recall,and F1 measures).The results show that the accuracy value of VGG-16 is 78.00%,Inception V3 is 92.00%,Dense Net 121 is 95.00%,and Mobile Net shows 88.00%accuracy,***,DenseNet-121 shows the highest performance with suitable analysis of autoimmune *** overall performance highlighted that TL is a suitable and enhanced technique compared to its ***,the proposed technique is used

关键词： Indirect immune fluorescence computer-aided diagnosis transfer learning confusion matrix

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Accurate threat hunting in industrial internet of things edge devices

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Digital Communications and Networks 2023年第5期9卷 1123-1130页

作者： Abbas Yazdinejad Behrouz Zolfaghari Ali Dehghantanha Hadis Karimipour Gautam Srivastava Reza M.Parizi Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical and Software Engineering University of CalgaryAlbertaCanada Department of Mathematics and Computer Science Brandon UniversityBrandonCanada College of Computing and Software Engineering Kennesaw State UniversityGAUSA Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Mathematics Lebanese American UniversityBeirut1102Lebanon

Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire ***,they can allow malicious software installed on end nodes to penetrate the *** paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge *** proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority *** evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.

关键词： IIoT Threat hunting Edge devices Multi-class anomalies Ensemble methods

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Advancements in Image Caption Bots: A Deep Learning Approach 6

Advancements in Image Caption Bots: A Deep Learning Approach

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Dabral, Adarsh Gusain, Raj Shekhar, Sushant Vidyarthi, Anurag Prakash, Rishi Gowri, R. Propagation Research Lab Department of Computer Science Engineering Dehradun India Propagation Research Lab Department of Electronics and Communication Engineering Dehradun India Kanpur India Graphic Era Hill University Department of Computer Science Engineering Dehradun India

ISBN: (纸本)9798350306613

In recent years, artificial intelligence (AI) has changed the area of computer vision, and image captioning is one of the many applications that have benefited from these advancements. An image caption bot is an intelligent system that generates a textual description of an image. This research paper will explore the development of image caption bots, the underlying technologies, and the various techniques used to build these systems. Image caption bots are developed with the help of deep learning models (DLM) such as convolutional neural networks (CNNs) the most popular nowadays and recurrent neural networks (RNN s) which is also popular in researchers. Featureextraction has been done from the images with CNN sand RNN s are used to generate the captions. These deep-learning models have significantly improved the accuracy and performance of image captioning systems. During training, the CNN extracts the relevant features from the images, and the RNN generates the corresponding captions. In order to decrease the discrepancy between generated and ground truth captions, the model is trained. The accuracy and efficiency of image caption procedures could be greatly increased by using the image caption bots developed in this research. These systems have several applications and can greatly aid visually impaired individuals in understanding images, assist search engines to better index and search images, and improve the accessibility of social media platforms for users with disabilities. Further research in this field could lead to more accurate and comprehensive Image captioning systems. © 2024 IEEE.

关键词： Caption-Bots CNN Deep Learning Image caption bot RNN

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