In softwareengineering domain, SPC is currently utilized only by organizations which have high maturity levels according to the process improvement models like ISO/IEC 15504 and CMMI. In this paper, we present a soft...
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ISBN:
(纸本)9788976416094
In softwareengineering domain, SPC is currently utilized only by organizations which have high maturity levels according to the process improvement models like ISO/IEC 15504 and CMMI. In this paper, we present a software tool that we developed to ease and enhance application of SPC especially for emergent organizations. Our tool has facilities to assess the suitability of software processes and metrics for SPC as well as to analyze a software process with respect to its qualifying metrics using SPC techniques like control charts, histograms, bar charts, and pareto charts. In this paper we explain the tool by means of a bug-fixing process of a system and software development organization.
The ATT&CK MITRE framework serves as an expansive repository of adversary tactics, techniques, and procedures. Given the sheer volume of these intricate attack patterns, the conventional manual navigation method p...
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ISBN:
(数字)9798350349719
ISBN:
(纸本)9798350349726
The ATT&CK MITRE framework serves as an expansive repository of adversary tactics, techniques, and procedures. Given the sheer volume of these intricate attack patterns, the conventional manual navigation method proves to be time-intensive and less efficient for security analysts. As a remedy, there arises a compelling necessity to present the rich content encapsulated within the ATT&CK MITRE repository through interactive visualizations. In this paper ATT&CKViz an interactive web-based cyber-attack patterns visualization tool is presented that aims to empower security analysts with the ability to effortlessly explore the repository from diverse angles, which facilitates deep insights into cyber threats. The aim is to understand cyber threat efficiently with a timely response. The empirical evaluations depict that effectiveness and user-friendliness of ATT&CKViz.
This paper proposes a new multi-agent system to solve very short-term solar forecasting problems. The system organizes the training data into clusters using Part and Select Algorithm. These clusters are used to genera...
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This paper proposes a new multi-agent system to solve very short-term solar forecasting problems. The system organizes the training data into clusters using Part and Select Algorithm. These clusters are used to generate different forecasting models, where each one is performed by a different agent. Finally, another agent is responsible for deciding which model will be applied at each forecasting situation. Results present improvements in forecasting accuracy and training performance if compared to other forecasting methods. A discussion of how to use this architecture for the implementation of a more comprehensive model is also addressed.
The development of Internet of Things (IoT) makes the application of smart homes grow rapidly. It is very popular to install smart appliances in the house. However, building a smart control system at home not only cos...
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The development of Internet of Things (IoT) makes the application of smart homes grow rapidly. It is very popular to install smart appliances in the house. However, building a smart control system at home not only costs a lot but also has many limitations. For this reason, this study proposes a smart homes control system to easily integrate IoT, WSN, smart robot and single-board computer to implement smart home applications. We use wireless technology and automatic equipment to avoid excessive communication cable and to make the house more intelligent to keep in-house movement unimpeded and indoor space tidy and to let appliance adjust appropriate environment settings automatically. This system brings intelligence and convenience to home, making living environment more comfortable.
Data aggregation is an efficient method to save energy and prolong the service life of wireless sensor networks (WSNs). In light of this, a data aggregation algorithm was constructed based on self-organizing feature m...
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Cloud Platforms are heterogeneous, and users may face interoperability issues migrating applications or exchanging data among distinct clouds due, for instance, to the lack of standards solutions. Several solutions ha...
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A multi-scale approach to topology optimization has recently emerged due to its lightweight, robust, and multi-functional characteristics. Considering material diversity, an increasing number of materials leads to a c...
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Stress detection is a growing topic in the field of natural language processing. The study of stress detection for mental health prediction has been proven to benefit the development of recommender systems and automat...
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Stress detection is a growing topic in the field of natural language processing. The study of stress detection for mental health prediction has been proven to benefit the development of recommender systems and automated mental health assessments in previous studies. Additionally, the widespread usage of social media has served as a potential data source for developing such models. Our research tried to detect whether the users of social media were under stress or not. We used a dataset from Dreaddit consisting of posts from one of the popular social media platforms, Reddit. We propose a machine learning model consisting of Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Random Forest, Bag of Words, and Term Frequency – inverse document frequency (TF-IDF) for stress detection. The final evaluation of the model achieved an 80.00% F-1 Score and 75.00% accuracy, and both were scored by SVM.
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