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DivLog: Log Parsing with Prompt Enhanced In-Context Learning

DivLog: Log Parsing with Prompt Enhanced In-Context Learning

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International Conference on Software engineering (ICSE)

作者： Junjielong Xu Ruichun Yang Yintong Huo Chengyu Zhang Pinjia He School of Data Science The Chinese University of Hong Kong Shenzhen China Dept. of Computer Science & Engineering The Chinese University of Hong Kong China ETH Zurich Switzerland Shenzhen Research Institute of Big Data China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Log parsing, which involves log template extraction from semi-structured logs to produce structured logs, is the first and the most critical step in automated log analysis. However, current log parsers suffer from limited effectiveness for two reasons. First, traditional data-driven log parsers solely rely on heuristics or handcrafted features designed by domain experts, which may not consistently perform well on logs from diverse systems. Second, existing super-vised log parsers require model tuning, which is often limited to fixed training samples and causes sub-optimal performance across the entire log source. To address this limitation, we propose Di-vLog, an effective log parsing framework based on the in-context learning (ICL) ability of large language models (LLMs). Specifically, before log parsing, DivLog samples a small amount of offline logs as candidates by maximizing their diversity. Then, during log parsing, DivLog selects five appropriate labeled candidates as examples for each target log and constructs them into a prompt. By mining the semantics of examples in the prompt, DivLog generates a target log template in a training-free manner. In addition, we design a straightforward yet effective prompt format to extract the output and enhance the quality of the generated log templates. We conducted experiments on 16 widely-used public datasets. The results show that DivLog achieves (1) 98.1% Parsing Accuracy, (2) 92.1% Precision Template Accuracy, and (3) 92.9% Recall Template Accuracy on average, exhibiting state-of-the-art performance.

关键词： Training Measurement Analytical models Semantics Feature extraction Tuning Software engineering

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IABCP: an Integrated Approach for Breast Cancer Prediction 2

IABCP: an Integrated Approach for Breast Cancer Prediction

引用

2nd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2022

作者： Pati, Abhilash Parhi, Manoranjan Pattanayak, Binod Kumar Siksha '0' Anusandhan University Dept. of Computer Science and Engg. Odisha Bhubaneswar India Siksha '0' Anusandhan University Centre for Data Science Odisha Bhubaneswar India

ISBN: (纸本)9781665478397

Breast cancer detection and prognosis may result in better medication being provided early, lowering the number of deaths caused by the illness each year and presenting a potentially significant danger to human health. Even though many academics have developed Additional research is also being undertaken in this field by other researchers employing Machine Learning (ML) approaches for the prognostication of chronic illnesses such as diabetes, heart disease, and cancer using both manually generated datasets and publicly available datasets. Based on the support vector machine (SVM) classifier, principal component analysis (PCA), and recursive feature elimination (RFE), this study suggested an Integrated Approach for Breast Cancer Prediction (IABCP) for predicting this chronic disease. Several experiments were carried out using the UCI-ML warehouse-based dataset WBCD, i.e., the Wisconsin breast cancer dataset (WBCD), and Python coding in a Jupyter notebook. Various experiments are taken place and results are obtained as accuracy, precision, recall, and f-measures of 97.87%, 98.55%, 98.77%, and 97.14% respectively. The results revealed that the recommended technique outperforms other typical approaches and is effective in the context of this research. © 2022 IEEE.

关键词： Support vector machines

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A Characterization of Google Stadia Traffic for Modeling Real-Time Streaming Service 13

A Characterization of Google Stadia Traffic for Modeling Rea...

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Kim, Youngboo Oh, Seungmin Jeong, Junho Kim, Gayoung Kongju National University Dept. of Computer Science and Engineering Cheonan Korea Republic of Dongguk University Dept. of Computer Engineering Seoul Korea Republic of Kangnam University Dept. of Faculty of General Education Yongin Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Google stadia is a cloud service that provides real-time gaming to remote users, and it pursues to deliver a high volume of multimedia data within low latency for a high level of user experience. It is known well that the google stadia service work without any considerable disturbances on the wired network. However, the wireless network has difficulty serving the high-volume and low-latency services such as the google stadia, and the various type of next-generation wireless communication systems also orients to solve this limitation. Meanwhile, in the wireless network, to design any proper scheme that could deal with the high-volume and low-latency traffic sophisticatedly, the elaborated traffic modeling method is also required. For this purpose, in this paper, we provide our findings for the implementing traffic generator based on the previous work which is related to the analysis of google stadia traffic. © 2022 IEEE.

关键词： Wireless networks

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Ship data Clustering for Improved Fuel Efficiency Optimization Decision Support Systems

Ship Data Clustering for Improved Fuel Efficiency Optimizati...

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OCEANS 2024 - Halifax, OCEANS 2024

作者： Piercey, Caitlin Hamilton, Matthew Veitch, Brian Barnes, Joshua Jiang, Xianta Memorial University of Newfoundland Dept. of Computer Science St. John's Canada Memorial University of Newfoundland Dept. of Engineering and Applied Sciences St. John's Canada Ocean Coastal and River Engineering National Research Council Canada St. John's Canada

ISBN: (纸本)9798331540081

In this paper, we propose a hybrid clustering algorithm based on hierarchical clustering in combination with string clustering, similar to that used in clustering genetic sequences, to extract clusters of operational activities from ship data. After extracting clusters (n=9), the distribution and summary statistics of each cluster are compared to Canadian Coast Guard provided operational definitions to obtain context from the clusters. The clustered data is used as the input to a fuel flow prediction model, which will in turn form the basis of an on-board decision support system for marine vessels with the goal of reducing fuel consumption. Finally, we compare the results of fuel flow predictions with models trained on a) different clusters and b) the full dataset, to determine the usefulness of these clusters in improving the performance of a fuel-optimization decision support system for on-board marine vessel use. © 2024 IEEE.

关键词： Ships

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Evaluating datagram Congestion Control Protocol (DCCP) for Real-Time Applications: A Comparative Study with TCP in Multi-Node Networks 10

Evaluating Datagram Congestion Control Protocol (DCCP) for R...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Hassan, Aqib Memon, Vengas Shaikh, Farhan Bashir Alahmari, Saad Iqbal, Mansoor Azam, Farooq School of Computing and Mathematical Sciences University of Greenwich London United Kingdom Universiti Tunku Faculty of Information and Communication Tech. Abdul Rahman Perak Malaysia Northern Border University Department of Computer Science Arar Saudi Arabia University of Wah Department of Computer Science Wah Cantt Pakistan Nust Dept. of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9798331532895

The rise in demand for real-time applications, such as live streaming, online gaming, and Internet telephony, has highlighted the necessity for transport protocols that offer low latency and network stability. Traditional protocols, namely Transmission Control Protocol (TCP) and User datagram Protocol (UDP), have inherent limitations. TCP ensures reliable data delivery but suffers from latency, making it unsuitable for real-time needs, while UDP provides fast data transmission but lacks congestion control, which can lead to network instability. This gap underscores the need for a protocol that combines high-speed data transfer with effective congestion management. To address this issue, the Internet engineering Task Force (IETF) introduced the datagram Congestion Control Protocol (DCCP), designed to optimize data delivery speed while managing congestion. This study evaluates DCCP's performance against TCP in network scenarios of 8 and 16 nodes using the NS2 simulation environment. By comparing throughput and delay across various bandwidth and latency settings, the study finds that DCCP offers up to 35% higher throughput in 8-node setups and reduces packet loss by 20% in high-bandwidth scenarios. These results suggest that DCCP is well-suited for real-time applications, achieving a crucial balance between fast data transmission and network stability. © 2024 IEEE.

关键词： Congestion control (communication)

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Development of QR Code-Guided Autonomous Navigation System for Grape Cultivation Robot in Overhead Trellis Vineyard 26

Development of QR Code-Guided Autonomous Navigation System f...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Fujimoto, Ren Siang Leow, Chee Yajima, Hideaki Makino, Koji Mao, Xiaoyang Nishiizaki, Hiromitsu University of Yamanashi Dept. of Mechatoronics Kofu Japan University of Yamanashi Dept. of Computer Science and Engineering Kofu Japan

ISBN: (纸本)9798331521950

This paper presents a novel autonomous navigation system for grapevine cultivation robots operating in overhead trellis vineyards, a challenging environment characterized by irregular grape cluster distribution and complex 3D structures. The system integrates two complementary navigation approaches: high-precision positioning under target grape bunches using YOLOv8-based object detection of QR codes, and large-scale vineyard navigation using global navigation satellite system (GNSS). Each QR code uniquely identifies a grape bunch and provides access to its associated cultivation data. Experimental evaluations in a simulated vineyard environment demonstrated an 83.3% success rate for object detection-based positioning beneath grape bunches and 90% accuracy for GNSS-based navigation within a 1.0 m radius of the target locations. These results validate the system's effectiveness in automating navigation tasks within overhead trellis vineyards, offering a promising solution to address labor shortages and improve the efficiency of grape cultivation practices. © 2025 IEEE.

关键词： Precision agriculture

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A Deep Learning Approach for Classification and Segmentation of Leafy Vegetables and Diseases

A Deep Learning Approach for Classification and Segmentation...

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Next-Generation Computing, IoT and Machine Learning (NCIM), International Conference on

作者： Ashraful Islam Syeda Rafiatus Sama Raisa Nahiyan Habib Khan Ariful Islam Rifat Dept. of Computer Science and Engineering Sylhet Engineering College Sylhet Bangladesh Dept. of Computer Science and Engineering American International University-Bangladesh Dhaka Bangladesh

Bangladesh's economy is heavily dependent on agriculture, making the region an exceedingly crucial sector that requires great vigilance. In comparison to other vegetables, leafy greens have yet to garner as much attention as other crops that this study's large-scale analysis comes up with, particularly when categorizing readily available leafy vegetables and identifying the affected area and whether they are affected or not. Plant diseases can cause substantial losses in agricultural productivity, and many researchers are working to address the difficulties farmers encounter in identifying and treating these conditions while diagnosing their diseases. The purpose of this study is to develop a dependable and effective approach for classifying seven varieties of leafy vegetables and detecting seven types of plant diseases precisely in Bangladesh using machine learning and deep learning CNN models. To this end, this paper analyzed and provided solutions using a total of eight models: five classification models (VGG16, VGG19, ResNet50, YOLOv5, and YOLOv8) and three instance segmentation models (YOLOv5, YOLOv7, and YOLOv8). Two distinct datasets, “LeafyVclassify7BD” with 3306 images and “LeafyVdisease7BD” with 4493 images, were developed to classify leaves, segment the diseased area, and classify and detect damaged leaves. The research outputs will help strengthen the region's ability to control plant diseases more effectively while also cutting diagnostic costs and enhancing disease detection accuracy in the country's agricultural surroundings.

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Exploring the Impact of Univariate Feature Selection Method on Machine Learning Algorithms for Heart Disease Prediction

Exploring the Impact of Univariate Feature Selection Method ...

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Next-Generation Computing, IoT and Machine Learning (NCIM), International Conference on

作者： Mainul Islam Rafiqul Islam Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Computer Science and Engineering Dhaka University of Engineering & Technology Gazipur Bangladesh

This study compares various machine learning techniques such as logistic regression, support vector machine (SVM), random forest, Naïve Bayes, and decision tree. Later, a feature selection approach that considers the Univariate Feature Selection (UFS) method was applied to select the best features based on statistical testing. In this research, we employed the ANOVA F-test to pick the most significant features, and we used a cutting-edge dataset for experimental analysis. The ANOVA F-test was used to exploit the target variable's dependency on the continuous variables. The results show that employing the UFS approach for picking features improves the performance of the algorithms. According to the experimental results, using UFS with the logistic regression technique yields 93.3% accuracy, whereas the standalone Naïve Bayes algorithm achieved the best accuracy.

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Remote Patient Monitoring Innovations Using IoT for Improved Healthcare Outcomes 4

Remote Patient Monitoring Innovations Using IoT for Improved...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Veeraiah, Vivek Shetty, Aishwarya D. Thejaswini, K.O. Srinivas, P. Tripathi, Sudhanshu Mohammad, Faiz Sri Siddhartha Academy of Higher Education Dept. of Computer Science Karnataka India Nmamit Dept. of Cse Nitte India Sri Siddhartha Academy of Higher Education Dept. of Physiology Karnataka India Sri Siddhartha Academy of Higher Education Dept. of Pediatrics Karnataka India Amity University Tashkent Uzbekistan Rungta College of Engineering and Technology Dept. of Cse Chhattisgarh Bhilai India

ISBN: (纸本)9798350387490

RPM has now become a revolutionary concept in the field of healthcare through IoT technology improvement to patients' care. New RPM systems on IoT have been covered by the study which permits constant health indicators monitoring of the patient even under non-conventional clinical settings by means of real-time capacity. Such systems have RT-PCR using IoT sensors, wearable devices, and data analytics coupled with immediate access to intervention, chronic health checking, and personalized therapies. This article includes a debate of the state-of-the-art in the field of IoT. Among them the enhanced methods of data manipulation, wireless link technologies, and intelligent measurement of critical signs by sensors. Some of the concerns addressed to with regard to RPM based on Internet of Things include patient engagement, privacy of data, and integration. In this paper, RPM is analyzed in context with chronic diseases, with an evaluation of its impact on medication compliance and scenarios of its potential for lessening hospitalization in existing scholarly articles. The findings presented in the paper reveal how advancements in the context of IoT can modify the ways of the healthcare distribution through the promotion of enhanced health result from the patient-centered therapy implemented continuously. © 2024 IEEE.

关键词： Electronic health record

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Pattern Recognition in Lung Disease Classification: Leveraging Convolutional Neural Networks that Employs Local Phase Quantization Features

Pattern Recognition in Lung Disease Classification: Leveragi...

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International Conference on Computing and Networking Technology (ICCNT)

作者： M Ramya Sree Mettu Siddhartha Poli Vamsi Vardhan Reddy Tripty Singh Rekha R Nair Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bangalore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Lung diseases are wide group of diseases related to the respiratory system, and is ranging from acute infections to chronic disorders with multifactorial causes. Recognition of the lung disease is very crucial in the proper diagnosis, forecasting through prognosis, and remedies. Hence proposed lung disease classification with feature extraction technique. The proposed work carried out feature extraction with Gray-Level Co-occurrence Matrix (GLCM) and Local Phase Quantization (LPQ) combined with Convolutional Neural Network(CNN). The goal is to create a model that reduces diagnostic errors, ensuring more precise early detection and classification of pulmonary disorders. This initiative facilitates the advancement of pulmonary medicine by promoting accurate radiologic imaging, while future directions emphasize integrating medical workflows to enable personalized and timely diagnosis and treatment. Additionally, the ResNet model demonstrates superior performance in hernia detection, achieving a test accuracy of 89.43% for GLCM based feature extraction and Pneumonia detection with 79.20% test accuracy for LPQ based feature extraction.

关键词： Visualization Accuracy Quantization (signal) Pneumonia Lungs Computational modeling Feature extraction Convolutional neural networks X-ray imaging Medical diagnostic imaging

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