Recently, the necessity of video testing at the point of reception has become a challenge for video distributors. This paper presents a new system framework for managing the quality of video degradation detection. The system is based on objective video quality assessment metrics and unsupervised machine learning techniques that use the dimensionality reduction of time series. It was demonstrated that it is possible to detect anomalies in the video during video streaming in soft real time. In addition, the model discovers degradations based on the visible correlation between adjacent images in the video sequence regardless the quick or slow change of a scene in the sequence. With additional hardware manipulations on the equipment on the user side, the proposed solution can be used in practical implementations where the need for monitoring possible degradations during video streaming exists.

Software processes consist of a complex set of activities required to deliver software products within predicted quality, costs, and deadlines. To accomplish such goals, a software organization needs a quality and mature software process as a prerequisite for success. Adopting Software Capability Maturity Model Integration (CMMI) represents a well-known path in the pursuit of mature software processes. However, its implementation is a subject of a permanent effort that implies different approaches and methods, and often leads to unsuccessful or limited success, though. This is especially emphasized in small software companies given the dynamic environment influenced by different factors, including insufficient resources, changes in technology, and staff turnover. In this paper, a case study of a small software company implementing software process improvement is presented. In a tailored approach to process improvement, a specific method using the balanced scorecard as input into the IDEAL model has been designed, enabling a narrow link between business goals and specific improvement goals. The results show that the software process and selected performance indicators were improved, and suggest the potential of the proposed approach in small organizations.

The Internet of Things (IoT) is considered a new paradigm that aims to connect a large number of devices. IoT is increasingly present in domains such as healthcare, transport, agriculture, and other industrial branches. An increasing number of IoT devices, as well as the amount of data, leads to increased energy consumption and a negative impact on the environment. Therefore, researchers are focusing on the concept of Green IoT that aims to increase energy efficiency and create a safe environment. The focus of this paper is on energy-efficient techniques within green data centers. Also, the performance evaluation of data centers was performed in the GreenCloud simulator for the optimal load of data centers in terms of energy efficiency and sustainability.

Urban mobility is one of the most significant factors in the successful development and sustainable future of large cities. The increasing demand for fast, safe, and eco-friendly transportation services is a trend in modern society. These requirements pose the challenge of finding corresponding solutions for efficient mobility of people in urban areas. However, many problems are caused by the increased traffic in cities, leading to high congestion, negative impacts on the environment, rising security challenges, etc. Therefore, the research community and other stakeholders have increased their focus on finding solutions for these issues. The Internet of Things (IoT) has enabled the development of efficient and cost-effective solutions to enhance urban mobility. Enabling IoT technologies has become a significant driver for smart mobility concept development. The continuous development of IoT has led to various applications focused on urban mobility improvement. This paper presents some IoT possibilities and potentials for developing solutions for smart urban mobility.

Software process improvement implies a set of complex and systematic activities of software engineering. It requires theory and models established in management, technical and social sciences. The improvement is based on the assumption that the organization if it owns mature and capable processes, would be able to deliver quality software on time and in line with predicted costs. The maturity models are initially aimed for implementation in enterprise software organizations, government organizations and within the military industry. Their complexity and the size make them difficult to use in small software organizations and companies. In such organizations the interest for use and the efforts to make an efficient and effective organization is always presented, though. In this paper, the basic and derived capability maturity models are described and cases from their implementation are analyzed, along with assessment of results of such projects in business practices. The problem of the software process improvement in small organizations is described, extracting the risks and recommendations for its enhancement. These recommendations are provided in order to set up a foundation for implementation of these models in a specific managerial and organizational environment characterized by small organizations.

Introduction: Machine learning (ML) plays a significant role in the fight against the COVID-19 (officially known as SARS-CoV-2) pandemic. ML techniques enable the rapid detection of patterns and trends in large datasets. Therefore, ML provides efficient methods to generate knowledge from structured and unstructured data. This potential is particularly significant when the pandemic affects all aspects of human life. It is necessary to collect a large amount of data to identify methods to prevent the spread of infection, early detection, reduction of consequences, and finding appropriate medicine. Modern information and communication technologies (ICT) such as the Internet of Things (IoT) allow the collection of large amounts of data from various sources. Thus, we can create predictive ML-based models for assessments, predictions, and decisions. Methods: This is a review article based on previous studies and scientifically proven knowledge. In this paper, bibliometric data from authoritative databases of research publications (Web of Science, Scopus, PubMed) are combined for bibliometric analyses in the context of ML applications for COVID-19. Aim: This paper reviews some ML-based applications used for mitigating COVID-19. We aimed to identify and review ML potentials and solutions for mitigating the COVID-19 pandemic as well as to present some of the most commonly used ML techniques, algorithms, and datasets applied in the context of COVID-19. Also, we provided some insights into specific emerging ideas and open issues to facilitate future research. Conclusion: ML is an effective tool for diagnosing and early detection of symptoms, predicting the spread of a pandemic, developing medicines and vaccines, etc.

Routing in multidomain and multilayer networks is the subject of constant theoretical research, with special emphasis on routing optimization algorithms based on several criteria. Such research results in new proposals. The basic task of the algorithm is to perform the given task in a finite and reasonable period of time and with reasonable resource requirements. When new solutions are compared with previous solutions, it is necessary to consider as much information as possible about the characteristics and differences between these algorithms, which ultimately determines the degree of success of the algorithm. Routing algorithms depend on the goals to be achieved and most often solve a certain group of problems with certain simplifications of the overall problem and to the detriment of performance that are not crucial for a given routing optimization problem. Therefore, it is necessary to have acceptable methods for efficiency-complexity evaluation methods of routing algorithms with certain, universally applicable, metrics. Several theoretical approaches, including graph theory, optimization theory, complexity theory, allow approaches to compare the algorithms and the results achieved with the help of these algorithms.

New technologies primarily affect the lives of all people, their habits, needs, desires, but also significantly affect the demands placed on various business sectors. Discussions on the increasingly rapid development of technical-technological solutions that can be applied in the postal sector and logistics have a long history. New technologies in all areas bring a constant change in the relationship between companies and their customers, which significantly affect the quality of work and activities. In the years to come, it will be an increasing challenge for postal operators around the world, as well as for other companies, to achieve substantive communication and understanding of their customers through the application of innovative technologies. Understanding and learning about customer issues is key to offering them services that, with their precise targeting of stakeholders, quality, visibility, efficiency, and, perhaps most importantly, flexibility, will be able to meet needs that change so quickly over time. This will be possible with new technologies and innovative solutions. The paper presents a market research on the potential use of autonomous vehicles and drones in the postal sector in Bosnia and Herzegovina. The research is based on a survey questionnaire on the use of drones and autonomous vehicles in the postal sector in the segment of shipment delivery.