This research explores into the utilization of synthetic data within image classification tasks and evaluates its efficiency in comparison to the utilization of real data. To facilitate this investigation, we employ the CIFAKE dataset, comprising the well-established CIFAR10 dataset and an equivalent number of images synthetically generated using the Latent Diffusion Model (LDM). The increasing demand for diverse and abundant labeled datasets has prompted the emergence of synthetic data as a potential solution to address data scarcity. Within this study, we scrutinize the performance of image classification models trained on both real and synthetic datasets. To ensure a comprehensive evaluation, we alternately apply test data across different models. Our analysis encompasses diverse factors, including classification accuracy, generalization capabilities, and robustness in various scenarios. The findings provide valuable insights into the efficacy of synthetic data as a viable alternative or complement to real data in the realm of image classification.

Noise removal in image processing and computer vision is a crucial preprocessing step employing a spectrum of techniques. In recent years, autoencoders exhibit remarkable efficacy in mapping noisy images to clean counterparts, capturing intricate relationships for effective noise removal. Motivated by the challenges posed by noise in real-world images, this research focuses on the denoising preprocessing step, crucial for tasks like object detection and segmentation. The study explores the application of autoencoders in removing artificially added noise from images within the MNIST dataset. The MNIST dataset’s simplicity and historical significance facilitate focused examinations on specific aspects, such as the impact of different types and levels of noise. The efficacy of autoencoders for noise removal is assessed through the evaluation of results using various metrics, including SSIM, PSNR, MSE, and RMSE. In one remarkable instance, the reconstruction process achieved an impressive peak SSIM score of 99.06%, showcasing the efficacy of the method in preserving image fidelity despite the challenging presence of noise. This comprehensive analysis provides valuable insights into the performance and effectiveness of autoencoders in the context of noise reduction in various domains.

Abstract The energy demand for Internet of Things (IoT) applications is increasing with a rise in IoT devices. Rising costs and energy demands can cause serious problems. Fog computing (FC) has recently emerged as a model for location‐aware tasks, data processing, fast computing, and energy consumption reduction. The Fog computing model assists cloud computing in fast processing at the network's edge, which also exerts a vital role in cloud computing. Due to the fast computing in fog servers, different quality of service (QoS) approaches have been proposed in various sections of the fog system, and several quality factors have been considered in this regard. Despite the significance of QoS in Fog computing, no extensive study has focused on QoS and energy consumption methods in this area. Therefore, this article investigates previous research on the use and guarantee of Fog computing. This article reviews six general approaches that discuss the published articles between 2015 and late May 2023. The focal point of this paper is evaluating Fog computing and the energy consumption strategy. This article further shows the advantages, disadvantages, tools, types of evaluation, and quality factors according to the selected approaches. Based on the reviewed studies, some open issues and challenges in Fog computing energy consumption management are suggested for further study.

Summary Internet of Things (IoT) is a new phenomenon that proposes novel business opportunities. IoT allows the world to be programmable and might provide several benefits for organizations. Based on the IoT survey, cyber‐security issues are among the most extensive and complicated challenges faced by IoT devices. Threat detection is considered a preventive measure against malware threats, ransomware, and attacks, which become more serious each year because of the dramatic rise in malware attacks. This article investigates threat detection techniques that fall into three categories: malware detection, attack detection, and ransomware detection, published from 2017 to August 2021. We examine solutions, techniques, features, classifiers, and tools proposed by IoT researchers. Some questions are proposed, and answering the questions may help the researchers suggest a more efficient solution in future works. Furthermore, the achievement and disadvantages of each study are discussed. Finally, based on the reviewed studies, some open challenges and practical measures to future directions are suggested, worth further studying and researching threat detection techniques in the IoT.

To become a standard part of our daily lives, autonomous vehicles must ensure human safety. This safety comes from knowing what will happen in the future. The most common approach in state-of-the-art methods for sensorimotor driving is behavior cloning. These models struggle to anticipate what will happen in the near future to better plan their actions. Humans do so by first observing what objects are present in the environment, and by studying their type and history, they can predict how they may evolve in the near future. Based on this observation, we first demonstrate the limitation of behavior cloning in making safe and reliable decisions. Then, we propose a hierarchical approach to teach an agent how to make safer decisions based on the plausible future. The key idea is instead of hand-picking future features we integrate a high-dimensional prediction module such as predicting future RGB/semantically segmented frames into our model to allow the model to learn the required features by itself. In the end, we demonstrate qualitatively and quantitatively that this approach yields safer decisions by the agent.

Language recognition has been significantly advanced in recent years by means of modern machine learning methods such as deep learning and benchmarks with rich annotations. However, research is still limited in low-resource formal languages. This consists of a significant gap in describing the colloquial language especially for low-resourced ones such as Persian. In order to target this gap for low resource languages, we propose a “Large Scale Colloquial Persian Dataset” (LSCP). LSCP is hierarchically organized in a semantic taxonomy that focuses on multi-task informal Persian language understanding as a comprehensive problem. This encompasses the recognition of multiple semantic aspects in the human-level sentences, which naturally captures from the real-world sentences. We believe that further investigations and processing, as well as the application of novel algorithms and methods, can strengthen enriching computerized understanding and processing of low resource languages. The proposed corpus consists of 120M sentences resulted from 27M tweets annotated with parsing tree, part-of-speech tags, sentiment polarity and translation in five different languages.

Managers of enterprises concern with a major challenge for optimal management of human resources based on availability of domain experts and highly qualified personnel. The process of allocating right people to the right positions in a right time is a key to success. To achieve this goal, managers need to deploy evaluation tools integrated with the gap analysis method. This paper presents the concept and implementation details of an in-house developed software tool for competence evaluation of domain specific competencies and selection of professionals. A generic mathematical representation of competences in this project makes the software tool applied in a wide variety of organizations. A standard competence model has been first defined in this project with 5 main competence categories and related sub-categories including over 70 competence questionnaires in different managerial and employee levels. Test and evaluation of the software have been carried out by initializing the lab data of over 50 candidates with student groups involved in the project at the institute of Knowledge Based Systems and Knowledge Management, University of Siegen. The paper reflects the conception and the outcomes of the implementation of the software tool. The ultimate objective of this interdisciplinary project is to fill the gap in the selection process by means of an efficient and practical competency evaluation tool. The generic software tool is aimed to be used as a component in research and industrial projects of the institute.

Enterprises and industrial companies strive to improve their functional performance by identification of core competencies in order to utilize human resources and optimise the knowledge integration processes of the company. In addition, maintenance operations are one of the most important sections in industrial companies which consist of key personnel and also explicit and implicit knowledge resources that have direct effects on product quality and return on investment. In the context of implicit knowledge resources, the principal objective is firstly to identify knowledge holders who are mainly domain experts (e.g. Chief Maintenance Officer-CMO) and maintenance practitioners (e.g. engineers, technicians, etc.), and secondly to measure their domain expertise. This paper presents the concept and implementation results of an interdisciplinary research which aims at improving the knowledge measuring of maintenance practitioners. In this way, the companies are enabled to deduce rate of human failures in maintenance operations by allocating the right professionals in the right positions using competency profiling of employees. The implementation results in developing a competence profiling tool as an add-on for Computerized Maintenance Management Information Systems (CMMIS), which is previously developed in the Institute of Knowledge Based Systems and Knowledge Management (KBS&KM).