Human–Computer Interaction and Participation in Software Crowdsourcing

Improvements in communication and networking technologies have transformed people’s lives and organizations’ activities. Web 2.0 innovation has provided a variety of hybridized applications and tools that have changed enterprises’ functional and communication processes. People use numerous platforms to broaden their social contacts, select items, execute duties, and learn new things. Context: Crowdsourcing is an internet-enabled problem-solving strategy that utilizes human–computer interaction to leverage the expertise of people to achieve business goals. In crowdsourcing approaches, three main entities work in collaboration to solve various problems. These entities are requestors (job providers), platforms, and online users. Tasks are announced by requestors on crowdsourcing platforms, and online users, after passing initial screening, are allowed to work on these tasks. Crowds participate to achieve various rewards. Motivation: Crowdsourcing is gaining importance as an alternate outsourcing approach in the software engineering industry. Crowdsourcing application development involves complicated tasks that vary considerably from the micro-tasks available on platforms such as Amazon Mechanical Turk. To obtain the tangible opportunities of crowdsourcing in the realm of software development, corporations should first grasp how this technique works, what problems occur, and what factors might influence community involvement and co-creation. Online communities have become more popular recently with the rise in crowdsourcing platforms. These communities concentrate on specific problems and help people with solving and managing these problems. Objectives: We set three main goals to research crowd interaction: (1) find the appropriate characteristics of social crowd utilized for effective software crowdsourcing, (2) highlight the motivation of a crowd for virtual tasks, and (3) evaluate primary participation reasons by assessing various crowds using Fuzzy AHP and TOPSIS method. Conclusion: We developed a decision support system to examine the appropriate reasons of crowd participation in crowdsourcing. Rewards and employments were evaluated as the primary motives of crowds for accomplishing tasks on crowdsourcing platforms, knowledge sharing was evaluated as the third reason, ranking was the fourth, competency was the fifth, socialization was sixth, and source of inspiration was the seventh.Princess Nourah bint Abdulrahman University Researchers Supporting - Riyadh, Saudi Arabia. Project number (PNURSP2023TR140)

An Effective Surface Defect Classification Method Based on RepVGG with CBAM Attention Mechanism (RepVGG-CBAM) for Aluminum Profiles

The automatic classification of aluminum profile surface defects is of great significance in improving the surface quality of aluminum profiles in practical production. This classification is influenced by the small and unbalanced number of samples and lack of uniformity in the size and spatial distribution of aluminum profile surface defects. It is difficult to achieve high classification accuracy by directly using the current advanced classification algorithms. In this paper, digital image processing methods such as rotation, flipping, contrast, and luminance transformation were used to augment the number of samples and imitate the complex imaging environment in actual practice. A RepVGG with CBAM attention mechanism (RepVGG-CBAM) model was proposed and applied to classify ten types of aluminum profile surface defects. The classification accuracy reached 99.41%, in particular, the proposed method can perfectly classify six types of defects: concave line (cl), exposed bottom (eb), exposed corner bottom (ecb), mixed color (mc), non-conductivity (nc) and orange peel (op), with 100% precision, recall, and F1. Compared with the existing advanced classification algorithms VGG16, VGG19, ResNet34, ResNet50, ShuffleNet_v2, and basic RepVGG, our model is the best in terms of accuracy, macro precision, macro recall and macro F1, and the accuracy was improved by 4.85% over basic RepVGG. Finally, an ablation experiment proved that the classification ability was strongest when the CBAM attention mechanism was added following Stage 1 to Stage 4 of RepVGG. Overall, the method we proposed in this paper has a significant reference value for classifying aluminum profile surface defects

Application Of Formal Specification Technique To Microgrid Representation

This thesis uses formal specification techniques to analyze and model a microgrid. A microgrid is a small, local electrical grid, often supplied by a single generator, that can connect to the larger electrical grid, but can also disconnect from it, going into “island mode.” Thanks to the growth in renewable energy, microgrids represent a growing segment of the electrical power generation domain. And like any member of the domain they are safety-critical systems, meaning that even a small mistake in their implementation risks damage to life and property.Formal specification is a way to abrogate the risks of safety critical systems by ensuring that the system under consideration is fully described, modeled, and analyzed prior to implementation, and the description and model are robust and error-free. However, at present there is no established approach to the use of formal specification techniques of microgrid systems. This thesis proposes a specification that can serve as a foundation for future work in the microgrid domain as well as an aid to communication about microgrids. The work uses Unified Modeling Language (UML) graphical notation and an accompanying Object Constraint Language (OCL) formal specification. The model transformation accomplished through the use of Iterative Development techniques is outlined in detail to serve as a guide to future researchers

Recent advances and applications of machine learning in metal forming processes

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Recent Advances and Applications of Machine Learning in Metal Forming Processes

Machine learning (ML) technologies are emerging in Mechanical Engineering, driven by the increasing availability of datasets, coupled with the exponential growth in computer performance. In fact, there has been a growing interest in evaluating the capabilities of ML algorithms to approach topics related to metal forming processes, such as: Classification, detection and prediction of forming defects; Material parameters identification; Material modelling; Process classification and selection; Process design and optimization. The purpose of this Special Issue is to disseminate state-of-the-art ML applications in metal forming processes, covering 10 papers about the abovementioned and related topics