End-user computing empowers non-developers to manage data and applications, enhancing collaboration and efficiency. Spreadsheets, a prime example of end-user programming environments widely used in business for data a...
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End-user computing empowers non-developers to manage data and applications, enhancing collaboration and efficiency. Spreadsheets, a prime example of end-user programming environments widely used in business for data analysis. However, Excel functionalities have limits compared to dedicated programming languages. This paper addresses this gap by proposing a prototype for integrating Python’s capabilities into Excel through on-premises desktop to build custom spreadsheet functions with Python. This approach overcomes potential latency issues associated with cloud-based solutions. This prototype utilizes Excel-DNA and IronPython. Excel-DNA allows creating custom Python functions that seamlessly integrate with Excel’s calculation engine. IronPython enables the execution of these Python (CSFs) directly within Excel. C# and VSTO add-ins form the core components, facilitating communication between Python and Excel. This approach empowers users with a potentially open-ended set of Python (CSFs) for tasks like mathematical calculations, statistical analysis, and even predictive modeling, all within the familiar Excel interface. This prototype demonstrates smooth integration, allowing users to call Python (CSFs) just like standard Excel functions. This research contributes to enhancing spreadsheet capabilities for end-user programmers by leveraging Python’s power within Excel. Future research could explore expanding data analysis capabilities by expanding the (CSFs) functions for complex calculations, statistical analysis, data manipulation, and even external library integration. The possibility of integrating machine learning models through the (CSFs) functions within the familiar Excel environment.
This research presents an advanced traffic filtering technique using a Support Vector Machine (SVM) model to enhance IoT network security, achieving 85% accuracy, with high precision (87%) and recall (96%) in identify...
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Long-term time series forecasting plays a vital role in applications like financial market prediction, energy usage forecasting, and traffic flow analysis. Deep learning models, though widely used in this task, often ...
Long-term time series forecasting plays a vital role in applications like financial market prediction, energy usage forecasting, and traffic flow analysis. Deep learning models, though widely used in this task, often struggle to capture long-term dependencies and periodic patterns due to the overlapping of variables with different periods. To this end, we propose a multi-scale temporal fusion model named MSTF, which extracts sequential features and periodic patterns across multiple scales using a Time Reverse and Transform block and a Dynamic Combination Reconstruction block. Unlike traditional Transformer-based models, MSTF emphasizes overall temporal continuity rather than individual time point values. Experiments on seven datasets against ten advanced models demonstrate MSTF’s superior performance, faster training time, and reduced model complexity, particularly excelling on high-dimensional datasets.
In an era where digital transformation intersects with environmental sustainability, the software development industry must integrate practices that minimize ecological impacts. This paper proposes a comprehensive fra...
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ISBN:
(数字)9798331538156
ISBN:
(纸本)9798331538163
In an era where digital transformation intersects with environmental sustainability, the software development industry must integrate practices that minimize ecological impacts. This paper proposes a comprehensive framework for specification completion through specification mining, aimed at embedding sustainability into software development processes. Utilizing an Extended Abstraction Refinement Model (EARM), we enhance the DevOps lifecycle by mapping observed behavior to development artifacts, ensuring sustainability metrics are accessible and actionable for stakeholders such as requirements engineers, software architects, and developers. Our approach leverages emergent behavior analysis to identify sustainability impacts that manifest during runtime, enabling targeted, energy-efficient interventions without incurring the rebound effect of exhaustive evaluations. By incorporating natural language processing (NLP) techniques for automated specification mapping, the framework refines software models iteratively, integrating real-world sustainability insights. This methodology supports the reduction of the carbon footprint in software products while preserving performance and quality, contributing to the alignment of softwareengineering with global sustainability objectives.
The increasing volume of log data produced by software-intensive systems makes it impractical to analyze them manually. Many deep learning-based methods have been proposed for log-based anomaly detection. These method...
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Requirement elicitation (RE) is a cognitively challenging and time-consuming task in software development due to the numerous challenges associated with it including conflicting requirements, unspoken, or assumed requ...
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Robot Operating System (ROS) is a widely used software architecture for robotic systems, which provides hardware abstractions and common functions in robotics. Launch files, as one of the configuration files in the ac...
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Fingerprint matching,spoof mitigation and liveness detection are the trendiest biometric techniques,mostly because of their stability through life,uniqueness and their least risk of *** recent decade,several technique...
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Fingerprint matching,spoof mitigation and liveness detection are the trendiest biometric techniques,mostly because of their stability through life,uniqueness and their least risk of *** recent decade,several techniques are presented to address these challenges over well-known *** study provides a comprehensive review on the fingerprint algorithms and techniques which have been published in the last few *** divides the research on fingerprint into nine different approaches including feature based,fuzzy logic,holistic,image enhancement,latent,conventional machine learning,deep learning,template matching and miscellaneous *** these,deep learning approach has outperformed other approaches and gained significant attention for future *** reviewing fingerprint literature,it is historically divided into four eras based on 106 referred papers and their cumulative citations.
Adopting distance learning with a high level of quality in universities and educational institutions is a real challenge. Therefore, several studies and organizations have provided different standards to evaluate the ...
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This paper presents an innovative approach to conducting a Model-Based systemsengineering (MBSE) course, engaging over 80 participants annually. The course is structured around collaborative group assignments, where ...
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