Quantum Cellular Automata, a proposed nanotechnology for circuit implementation, stands out due to its high computational power and high operational frequency, along with the simplicity of designing logical circuits. ...
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Modeling software systems as transition systems can improve system comprehension for novice engineers and graduating students. However, this requires them to learn the vocabulary of transition systems and its use. We ...
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The advancement of Industrial IoT (IIoT) calls for devices that can provide fast communication, for real time operations. Ensuring Quality of Service (QoS) for devices in a network requires effective management of ban...
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Log-based anomaly detection is an essential aspect of maintaining software reliability, particularly in the context of microservice systems. However, existing log-based anomaly detection approaches rely on historical ...
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In this paper, we present ML-Quadrat, an open-source research prototype that is based on the Eclipse Modeling Framework (EMF) and the state of the art in the literature of Model-Driven softwareengineering (MDSE) for ...
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ISBN:
(纸本)9781665495981
In this paper, we present ML-Quadrat, an open-source research prototype that is based on the Eclipse Modeling Framework (EMF) and the state of the art in the literature of Model-Driven softwareengineering (MDSE) for smart Cyber-Physical Systems (CPS) and the Internet of Things (IoT). Its envisioned users are mostly software developers who might not have deep knowledge and skills in the heterogeneous IoT platforms and the diverse Artificial Intelligence (AI) technologies, specifically regarding Machine Learning (ML). ML-Quadrat is released under the terms of the Apache 2.0 license on Github(l). Additionally, we demonstrate an early tool prototype of DriotData, a web-based Low-Code platform targeting citizen data scientists and citizen/end-user software developers. DriotData exploits and adopts ML-Quadrat in the industry by offering an extended version of it as a subscription-based service to companies, mainly Small- and Medium-Sized Enterprises (SME). The current preliminary version of DriotData has three web-based model editors: text-based, tree-/form-based and diagram-based. The latter is designed for domain experts in the problem or use case domains (namely the IoT vertical domains) who might not have knowledge and skills in the field of IT. Finally, a short video demonstrating the tools is available on YouTube: https://***/VAuz25w0a5k.
Massive Open Online Courses (MOOCs) allow their participants to acquire knowledge in various fields, such as STEM, at low cost or for free, provided they have access to the Internet and a web-enabled device. This stud...
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ISBN:
(纸本)9783031856518;9783031856525
Massive Open Online Courses (MOOCs) allow their participants to acquire knowledge in various fields, such as STEM, at low cost or for free, provided they have access to the Internet and a web-enabled device. This study investigates a critical phenomenon in STEM MOOCs: dropout rates from a gender perspective. Our objective is to identify retention factors of women in engineering and STEM MOOCs through a PRISMA-based Systematic Literature Review of conference papers and journal articles retrieved from IEEE Xplore and Scopus. Seventy-two papers were retrieved, of which 15 were relevant, with the majority of the information pertaining to the strategies and best practices enhancing women's retention and engagement in STEM MOOCs. Gender socialization, lower self-efficacy and time constraints were the main dropout factors, although female students have been found to adapt more easily to digital learning environments. This comprehensive analysis aims to establish solid foundations for future MOOCs and academic studies on this topic.
The exponential growth of open-source package ecosystems, particularly NPM and PyPI, has led to an alarming increase in software supply chain poisoning attacks. Existing static analysis methods struggle with high fals...
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ISBN:
(数字)9798400712487
ISBN:
(纸本)9798400712487
The exponential growth of open-source package ecosystems, particularly NPM and PyPI, has led to an alarming increase in software supply chain poisoning attacks. Existing static analysis methods struggle with high false positive rates and are easily thwarted by obfuscation and dynamic code execution techniques. While dynamic analysis approaches offer improvements, they often suffer from capturing non-package behaviors and employing simplistic testing strategies that fail to trigger sophisticated malicious behaviors. To address these challenges, we present OSCAR, a robust dynamic code poisoning detection pipeline for NPM and PyPI ecosystems. OSCAR fully executes packages in a sandbox environment, employs fuzz testing on exported functions and classes, and implements aspect-based behavior monitoring with tailored API hook points. We evaluate OSCAR against six existing tools using a comprehensive benchmark dataset of real-world malicious and benign packages. OSCAR achieves an F1 score of 0.95 in NPM and 0.91 in PyPI, confirming that OSCAR is as effective as the current state-of-the-art technologies. Furthermore, for benign packages exhibiting characteristics typical of malicious packages, OSCAR reduces the false positive rate by an average of 32.06% in NPM (from 34.63% to 2.57%) and 39.87% in PyPI (from 41.10% to 1.23%), compared to other tools, significantly reducing the workload of manual reviews in real-world deployments. In cooperation with Ant Group, a leading financial technology company, we have deployed OSCAR on its NPM and PyPI mirrors since January 2023, identifying 10,404 malicious NPM packages and 1,235 malicious PyPI packages over 18 months. This work not only bridges the gap between academic research and industrial application in code poisoning detection but also provides a robust and practical solution that has been thoroughly tested in a real-world industrial setting.
With the development of machine learning, fair machine learning has started to receive gradual attention. How to mitigate or eliminate the possible unfair decision results of machine learning has become a popular rese...
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In edge-cloud systems, the quality of infrastructure deployment is crucial for delivering high-quality services, especially when using popular Infrastructure as Code (IaC) tools like Ansible. Ensuring the reliability ...
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ISBN:
(纸本)9783031751097;9783031751103
In edge-cloud systems, the quality of infrastructure deployment is crucial for delivering high-quality services, especially when using popular Infrastructure as Code (IaC) tools like Ansible. Ensuring the reliability of such large-scale code systems poses a significant challenge due to the limited testing resources. software defect prediction (SDP) addresses this limitation by identifying defectprone software modules, allowing developers to prioritize testing resources effectively. This paper introduces a Large Language Model (LLM)-based approach for SDP in Ansible scripts with Code-Smell-guided Prompting (CSP). CSP leverages code smell indicators extracted from Ansible scripts to refine prompts given to LLMs, enhancing their understanding of code structure concerning defects. Our experimental results demonstrate that CSP variants, particularly the Chain of Thought CSP (CoT-CSP), outperform traditional prompting strategies, as evidenced by improved F1-scores and Recall. To the best of our knowledge, this is the first attempt to employ LLMs for SDP in Ansible scripts. By employing a code smell-guided prompting strategy tailored for Ansible, we anticipate that the proposed method will enhance software quality assurance and reliability, thereby increasing the overall reliability of edge-cloud systems.
Massive Open Online Courses (MOOC) is a fruitful attempt for online education to target the whole society and achieve comprehensive educational equity, serving as a powerful supplement and enrichment to offline educat...
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