درخت حوزه‌های تخصصی

The purpose of this paper is to investigate the role of political instability in the context of the association between board of directors (BOD) and audit committee (AC) quality as indicators of corporate governance effectiveness and earnings management (EM). The non-financial listed companies on the Palestine Exchange (PEX) between the period from 2011 to 2018 were the samples of the study. Based on the OLS regression results by using a panel data of 232 observations, the findings show that the BOD quality is positively related to EM, while AC quality is negatively associated with EM. The results also reveal that political instability plays a moderating role by weakening the relationship between the AC quality and EM during the severe unstable political situation, while the BOD quality is negatively associated with EM under the same situation. This finding implies that the BODs might substitute ACs under severe political instabilities to protect the stockholders and stakeholders’ interests. Unlike most previous studies, this article addresses political instability as a moderating variable for the relationship between the BOD and AC quality as a composite measure of EM in the most emerging and smallest stock market in the region. The results of this work may assist the regulatory bodies, policymakers, and practitioners in revising the corporate governance code to promote the efficiency of the BOD and AC monitoring functions for the purpose to improve the faithfulness and reliability of financial reports and also to deter the management from engaging in critical levels of EM.

This article discusses a device focused on images that enables users to recognise and detect many face-related features using the webcam. In this article, we are performing comprehensive and systemic studies to check the efficacy of these classic representation learning structures on class-imbalanced outcomes. We also show that deeper discrimination can be learned by creating a deep network that retains inter-cluster differences both and within groups. MobileNet, which provides both offline and real-time precision and speed to provide fast and consistent stable results, is the recently suggested Convolutional Neural network (CNN) model. The recently proposed Convolutional Neural Network (CNN) model is MobileNet, which has both offline and real-time accuracy and speed to provide fast and predictable real-time results. This also solved a problem related to the face that occurs in the identification and recognition of the face. This paper presents the different methods and models used by numerous researchers in literature to solve the issue of faces. They get a better result in using the highest number of layers. It is also noted that the combination of a machine learning approach with multiple image-based dataset increases the efficiency of the classifier to predict knowledge related to face detection and recognition

The article considers the main aspects of the use of digital platforms as a tool for the transformation of strategic consulting in public administration. The study substantiates the feasibility of using the results of consulting activities in the implementation of strategic objectives by the executive authorities at any level of government. The ways and methods of organizing consulting activities on the basis of digital platforms for the purpose of informational and analytical support of management technologies of public administration and civil service reform analyzed. Based on the results of the study, a model of using consulting services in the public administration system was developed. The criteria of efficiency of realization project management consulting in the public sector, based on digital platforms highlighted.

Recently, significant technological changes, greater customer demand and the rise of new business models have triggered a rapid increase in electronic service (e-service) innovations. Now, innovation in the provision of e-services has become one of the priorities of managers in order to gain a competitive advantage. However, few studies so far have explored the determinant factors needed in the organization in order to innovate and implement e-services. The purpose of this study is to provide a comprehensive framework that integrates the multiple factors of e-service innovation. Using the qualitative meta-synthesis research method and after a systematic review of the literature and examination of 61 articles, all factors needed for innovation in e-services have been identified and classified in 4 capabilities, 9 concepts, and 30 codes. The results show that e-service innovation depends on networking, informational, operational and supporting, and strategic capabilities. These capabilities create the required platform for innovation in e-services in the organization. This study contributes to current e-service researches by offering theoretical advances related to innovation in e-services. Furthermore, the capabilities, concepts, and codes identified in this study would be useful as a comprehensive conceptual framework for developers of e-service innovation to plan and evaluate their related initiatives.

Corona Virus (COVID-19) is a major problem among people, and it causes suffering worldwide. Yet, the traditional prediction models are not yet suitably efficient in catching the fundamental expertise as they cannot visualize the difficulty in the health's representation problem areas. This paper states prediction mechanism that uses a model of deep learning called Long Short-Term Memory (LSTM). We have carried this model out on corona virus dataset that obtained from the records of infections, deaths, and recovery cases across the world. Furthermore, producing a dataset which includes features of geographic regions (temperature and humidity) that have experienced severe virus outbreaks, risk factors, spatio-temporal analysis, and social behavior of people, a predictive model can be developed for areas where the virus is likely to spread. However, the outcomes of this study are justifiable to alert the authorities and the people to take precautions.

This paper describes an alternative view of the internationalization process in higher education by comparing the traditional and non-traditional indicators on how the level of internationalization is measured; additionally, barriers that exist on both sides are reviewed and discussed. As higher education institutions are very accustomed to and focused on the number of international students, international cooperation agreements, visiting guest professors and international projects, another dimension could be added if we start to account and measure things that are happening in the digital communication, online data exchange, usage of mobile devices and other technologies. Authors argue that it would be necessary to include this perspective in the development of internationalization strategies, institutional development plans as well as external outreach tactics. The paper is based upon empirical knowledge coming from Erasmus+ KA2 project and a brief institutional self-assessment performed by Riga Technical University International Cooperation and Foreign Students Department.

The article is devoted to a comprehensive analysis of peculiarities of the content types, which can be placed in social media and Ukrainian Internet media, its differences, types of manipulation, and methods of prevention, perception, and distribution of unreliable information, determination of difference between fact and judgment. The article also analyzes the statistics of social media usage in Ukraine and around the world, using Facebook as an example. Various kinds and types of media existing on the Internet and traditional, conducted a survey of the most popular content using the Starch method, it was possible to identify how a person perceives one content that is placed on different media. The relationship between content type and media type is established. The phenomenon of information restriction, arising as a result of modern algorithms embedded in the Internet companies and social media, their positive and negative aspects and why, therefore, we are deprived of a choice among a wide range of other topics, and learning some other points of view was considered. Examining the origin and meaning of the "Information Bubble" term, the authors propose to use a different term, "Membrane of Relevance", which, in their opinion, is more relevant to the essence of the said concept. The authors propose to use a taxonomy method to determine the impact of content on a particular type of media, which will greatly simplify the content analysis process.

Day by day demand for object tracing is increasing because of the huge scope in real-time applications. Object tracing is one of the difficult issues in the computer vision and video processing field. Nowadays, object tracing is a common problem in many applications specifically video footage, traffic management, video indexing, machine learning, artificial intelligence, and many other related fields. In this paper, the Enhanced Method of Object Tracing Using Extended Kalman Filter via Binary Search Algorithm is proposed. Initially, the background subtraction method was used for merge sort and binary search algorithm to identify moving objects from the video. Merge sort is to divide the regions and conquer the algorithm that arranges the region in ascending order. After sorting, the binary search algorithm detects the position of noise in sorted frames and then the next step extended the Kalman Filter algorithm used to predict the moving object. The proposed methodology is linear about the valuation of mean and covariance parameters. Finally, the proposed work considered less time as compared to the state of art methods while tacking the moving objects. Its shows less absolute error and less object tracing error while evaluating the proposed work.