The popularity of Web Service-based systems is due to the advantage of flexibility, reusability and support for rapid system development. Web Service selection is a very important step for these web service-based syst...
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The proceedings contain 15 papers. The special focus in this conference is on softwareengineering and Data engineering. The topics include: Implementation of bert based machine learning model to extract cancer–mirna...
The proceedings contain 15 papers. The special focus in this conference is on softwareengineering and Data engineering. The topics include: Implementation of bert based machine learning model to extract cancer–mirna relationship from research literature;separated feature learning for music composition using memory-based neural networks;estimating attention level from blinks and head movement;model checking mutual inclusion and mutual exclusion algorithms;vs-tap: Veteran services tracking and analytics program;automated generation of stand-alone source codes for software libraries;dbknot a transparent and seamless, pluggable, tamper evident database;comparing blockchain platforms for smart contracts: A preliminary framework;automated light system in smart home for elderly and disabled people using mosquitto server;using virtual reality and machine learning techniques to visualize the human spine;studying the covid-19 impact on the antarctic glacier melting rate;optimized bootstrap sampling for σ-aqp error estimation: A pilot study.
This systematic literature review examines the state of LGBTQIA+ inclusion in softwareengineering education. The review synthesizes findings from various studies t o highlight the experiences and perceptions of LGBTQ...
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Railway web page traffic manipulate is a complex and dynamic method that includes making real-time picks based mostly on various uncertain and difficult to understand factors, collectively with fluctuating passenger d...
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Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remain...
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Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various
Rust is an emerging, strongly-typed programming language focusing on efficiency and memory safety. With increasing projects adopting Rust, knowing how to use Unsafe Rust is crucial for Rust security. We observed that ...
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The publication of scientific results together with the underlying experiments is an important source of further research. In 2016, the "FAIR Guiding Principles for scientific data management and stewardship"...
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As an important industrial control component, the safe operation and real-time monitoring of electric ball valves are crucial. This article proposes a state monitoring system for electric ball valves based on embedded...
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According to recent research of sports in computer vision techniques and technologies of AI/ML, it achieved improvements in digital sports. Sports and games are part of human lives;they focus on health, fitness, passi...
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Business processes underpin enterprise execution, coordination, and management. However, differing levels of familiarity with modeling languages among users can create an understanding gap, potentially disrupting the ...
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ISBN:
(纸本)9789819608041;9789819608058
Business processes underpin enterprise execution, coordination, and management. However, differing levels of familiarity with modeling languages among users can create an understanding gap, potentially disrupting the process flow. Business process documentation bridges this gap. Current methods, such as manual writing and rule-based generation, face inefficiency, errors, and limitations. We innovate by harnessing large language models for documentation generation. Our approach involves defining a Refined Process Structure Tree (RPST) meta-model and mapping rules, then constructing fine-grained RPSTs and crafting sentences using a hierarchical construction method. Finally, global optimization enhances the documentation. Tested on 100 diverse process models, our method outperforms benchmarks in robustness, and it achieves 6% and 1% higher semantic similarity scores by n-gram and semantics metrics.
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