The prospect of joining the EU and civil service reform in the Republic of North Macedonia

The Republic of North Macedonia, a candidate country in the EU, is continuously subject to conditionality in relation to establishing a professional and effective public administration from the EU institutions and from the civil society. This paper employs the qualitative methodology of process tracing to find out whether the EU conditionality has managed to establish a merit-based civil service. The data are gathered and analyzed for a period of ten years while analyzing the legal and institutional structure of the civil service. The findings identify the factors that hampered or prolonged the implementation of reforms and they offer insights on the conditions necessary for the civil service reforms to take place

Energy Efficiency Optimization by Spectral Efficiency Maximization in 5G Networks

Energy and spectral efficiency are the mainchallenges in 5th generation of mobile cellular networks.In this paper, we propose an optimization algorithmto optimize the energy efficiency by maximizing thespectral efficiency. Our simulation results show a significantincrease in terms of spectral efficiency as well asenergy efficiency whenever the mobile user is connectedto a low power indoor base station. By applying theproposed algorithm, we show the network performanceimprovements up to 9 bit/s/Hz in spectral efficiency and20 Gbit/Joule increase in energy efficiency for the mobileuser served by the indoor base station rather than by theoutdoor base station

Guarding the Cloud: An Effective Detection of Cloud-Based Cyber Attacks using Machine Learning Algorithms

Cloud computing has gained significant popularity due to its reliability and scalability, making it a compelling area of research. However, this technology is not without its challenges, including network connectivity dependencies, downtime, vendor lock-in, limited control, and most importantly, its vulnerability to attacks. Therefore, guarding the cloud is the objective of this paper, which focuses, in a novel approach, on two prevalent cloud attacks: Distributed Denial-of-service (DDoS) attacks and Man-in-the-Cloud (MitC) computing attacks. To tackle the detection of these malicious activities, machine learning algorithms, namely Decision Trees, Support Vector Machine (SVM), Naive Bayes, and K-Nearest Neighbors (KNN), are utilized. Experimental simulations of DDoS and MitC attacks are conducted within a cloud environment, and the resultant data is compiled into a dataset for training and evaluating the machine learning algorithms. The study reveals the effectiveness of these algorithms in accurately identifying and classifying malicious activities, effectively distinguishing them from legitimate network traffic. The finding highlights Decision Trees algorithm with most promising potential of guarding the cloud and mitigating the impact of various cyber threats

Impact of Electronic Competence Based Teaching in Higher Education

In the beginning of Bologna’s process and creation of European Higher Education Area (EHEA), great importance has been to the transparency, increase of quality and concurrency between institutions of higher education. Nowadays, hot topic in higher education institutions in Europe are real-time learning outcomes, they are analyzed, projected and are evaluated today in all Europe and abroad. Traditional models and methods of success expression in learning and the degree of qualification is substituted with modern online systems. This paper proposes best practices for competence based teaching in higher education by using eCompetence software. The way these competences are organized, activities which are related to these competences and course contents which will help us to continuously evaluate students and prepare them for the labor market. Our results suggest that by implementing competence based teaching system in university evaluation and competence gaining would be more productive and would better prepare students for labor market. Consequently, this paper draws attention on provision of implementation of such a system in higher education by providing competence matrix, a competence software, and evaluation process

State of the art in privacy preservation in video data

Active and Assisted Living (AAL) technologies and services are a possible solution to address the crucial challenges regarding health and social care resulting from demographic changes and current economic conditions. AAL systems aim to improve quality of life and support independent and healthy living of older and frail people. AAL monitoring systems are composed of networks of sensors (worn by the users or embedded in their environment) processing elements and actuators that analyse the environment and its occupants to extract knowledge and to detect events, such as anomalous behaviours, launch alarms to tele-care centres, or support activities of daily living, among others. Therefore, innovation in AAL can address healthcare and social demands while generating economic opportunities. Recently, there has been far-reaching advancements in the development of video-based devices with improved processing capabilities, heightened quality, wireless data transfer, and increased interoperability with Internet of Things (IoT) devices. Computer vision gives the possibility to monitor an environment and report on visual information, which is commonly the most straightforward and human-like way of describing an event, a person, an object, interactions and actions. Therefore, cameras can offer more intelligent solutions for AAL but they may be considered intrusive by some end users. The General Data Protection Regulation (GDPR) establishes the obligation for technologies to meet the principles of data protection by design and by default. More specifically, Article 25 of the GDPR requires that organizations must "implement appropriate technical and organizational measures [...] which are designed to implement data protection principles [...] , in an effective manner and to integrate the necessary safeguards into [data] processing.” Thus, AAL solutions must consider privacy-by-design methodologies in order to protect the fundamental rights of those being monitored. Different methods have been proposed in the latest years to preserve visual privacy for identity protection. However, in many AAL applications, where mostly only one person would be present (e.g. an older person living alone), user identification might not be an issue; concerns are more related to the disclosure of appearance (e.g. if the person is dressed/naked) and behaviour, what we called bodily privacy. Visual obfuscation techniques, such as image filters, facial de-identification, body abstraction, and gait anonymization, can be employed to protect privacy and agreed upon by the users ensuring they feel comfortable. Moreover, it is difficult to ensure a high level of security and privacy during the transmission of video data. If data is transmitted over several network domains using different transmission technologies and protocols, and finally processed at a remote location and stored on a server in a data center, it becomes demanding to implement and guarantee the highest level of protection over the entire transmission and storage system and for the whole lifetime of the data. The development of video technologies, increase in data rates and processing speeds, wide use of the Internet and cloud computing as well as highly efficient video compression methods have made video encryption even more challenging. Consequently, efficient and robust encryption of multimedia data together with using efficient compression methods are important prerequisites in achieving secure and efficient video transmission and storage.This publication is based upon work from COST Action GoodBrother - Network on Privacy-Aware Audio- and Video-Based Applications for Active and Assisted Living (CA19121), supported by COST (European Cooperation in Science and Technology). COST (European Cooperation in Science and Technology) is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. www.cost.e

Increasing Trustworthiness of Face Authentication in Mobile Devices by Modeling Gesture Behavior and Location Using Neural Networks

Personal mobile devices currently have access to a significant portion of their user’s private sensitive data and are increasingly used for processing mobile payments. Consequently, securing access to these mobile devices is a requirement for securing access to the sensitive data and potentially costly services. Face authentication is one of the promising biometrics-based user authentication mechanisms that has been widely available in this era of mobile computing. With a built-in camera capability on smartphones, tablets, and laptops, face authentication provides an attractive alternative of legacy passwords for its memory-less authentication process, which is so sophisticated that it can unlock the device faster than a fingerprint. Nevertheless, face authentication in the context of smartphones has proven to be vulnerable to attacks. In most current implementations, a sufficiently high-resolution face image displayed on another mobile device will be enough to circumvent security measures and bypass the authentication process. In order to prevent such bypass attacks, gesture recognition together with location is proposed to be additionally modeled. Gestures provide a faster and more convenient method of authentication compared to a complex password. The focus of this paper is to build a secure authentication system with face, location and gesture recognition as components. User gestures and location data are a sequence of time series; therefore, in this paper we propose to use unsupervised learning in the long short-term memory recurrent neural network to actively learn to recognize, group and discriminate user gestures and location. Moreover, a clustering-based technique is also implemented for recognizing gestures and location

Securing Web services in a user-to-application model based on certificate private extensions and smartcard technology

Zsfassung in dt. u. alban. Sprache17

Blockchain invoicing for government services

Blockchain technology is known primarily through the cryptocurrency bitcoin, but has begun to find application in other areas for both public and private services, including, but not limited to, payments, electronic voting, health, government services etc. Blockchain technology potential relay on its capability to store all transactions records and makes them available to all parties with permission to view, but no one can make unauthorized changes to them. Many government service providers have to bill the government for the services they provide, but non-standardized and unverified manual or electronic invoicing often leads to double invoicing or payment. As a result of these problems extra care and controls are needed to avoid generating double invoicing or payments. With all the added care and control, human beings can make mistakes, so the purpose of this paper is to analyze the implementation of blockchain and smart contracts for invoicing efficiently government services. The paper will also analyze several government services and authorities and determine the type of blockchain to be used. Implementing blockchain and smart contracts eliminates not only the double invoicing and payments issue, but it also can transform the process, i.e. increase the transparency of invoicing and payment of services, thus offering better audit opportunities

Increasing Trustworthiness of Face Authentication in Mobile Devices by Modeling Gesture Behavior and Location Using Neural Networks

Personal mobile devices currently have access to a significant portion of their user’s private sensitive data and are increasingly used for processing mobile payments. Consequently, securing access to these mobile devices is a requirement for securing access to the sensitive data and potentially costly services. Face authentication is one of the promising biometrics-based user authentication mechanisms that has been widely available in this era of mobile computing. With a built-in camera capability on smartphones, tablets, and laptops, face authentication provides an attractive alternative of legacy passwords for its memory-less authentication process, which is so sophisticated that it can unlock the device faster than a fingerprint. Nevertheless, face authentication in the context of smartphones has proven to be vulnerable to attacks. In most current implementations, a sufficiently high-resolution face image displayed on another mobile device will be enough to circumvent security measures and bypass the authentication process. In order to prevent such bypass attacks, gesture recognition together with location is proposed to be additionally modeled. Gestures provide a faster and more convenient method of authentication compared to a complex password. The focus of this paper is to build a secure authentication system with face, location and gesture recognition as components. User gestures and location data are a sequence of time series; therefore, in this paper we propose to use unsupervised learning in the long short-term memory recurrent neural network to actively learn to recognize, group and discriminate user gestures and location. Moreover, a clustering-based technique is also implemented for recognizing gestures and location

Attack Analysis of Face Recognition Authentication Systems Using Fast Gradient Sign Method

Biometric authentication methods, representing the ”something you are” scheme, are considered the most secure approach for gaining access to protected resources. Recent attacks using Machine Learning techniques demand a serious systematic reevaluation of biometric authentication. This paper analyzes and presents the Fast Gradient Sign Method (FGSM) attack using face recognition for biometric authentication. Machine Learning techniques have been used to train and test the model, which can classify and identify different people’s faces and which will be used as a target for carrying out the attack. Furthermore, the case study will analyze the implementation of the FGSM and the level of performance reduction that the model will have by applying this method in attacking. The test results were performed with the change of parameters both in terms of training and attacking the model, thus showing the efficiency of applying the FGSM