درخت حوزه‌های تخصصی

Logistics has long been recognized as the main effective tool for generating competitive advantages in trading enterprises, and therefore there is an acute problem in finding strategic guidelines for improving logistics activities through the lens of organizational and economic support of logistic activity of a trading enterprise. The article compares the main reference models for the analysis of logistics activities and found the most appropriate for use - SCOR model, which identified the main indicators for evaluating the performance of logistics activities. An algorithm for determining the level of efficiency and performance of logistic activity have been constructed. According to the results of the study, the level of organizational and economic support, perormance and efficiency of logistic activity of trade enterprises of Ukraine of the sample population have been determined.

Learning effective segment depictions and resemblance measures are fundamental to the recuperation execution of a substance based picture recuperation (CBIR) structure. Regardless of wide research tries for a significant long time, it stays one of the most testing open gives that broadly impedes the achievements of real-world CBIR structures. The key test has been credited to the extraordinary "semantic hole" subject that happens between low-level photo pixels got by technologies and raised close semantic thoughts saw by a human. Among various techniques, AI has been successfully analyzed as a possible course to interface the semantic gap in the whole deal. Impelled by late triumphs of significant learning techniques for PC vision and various applications, in this paper, we try to address an open issue: if significant learning is a longing for spreading over the semantic gap in CBIR and how much updates in CBIR endeavors can be cultivated by exploring the front line significant learning methodology for learning feature depictions and likeness measures. Specifically, we explore a structure of significant learning with application to CBIR assignments with a wide game plan of definite examinations by investigating front line significant learning methodologies for CBIR endeavors under moved settings. From our exploratory examinations, we find some encouraging results and compress some huge bits of information for upcoming research.

Enterprise Social Media (ESM) has been normally embraced by companies to boost team performance and agility for workers. The goal of this study is to investigate the mediating effect of workplace isolation on the relationships among usage ESM, employee agility and Team Performance along with exploration of the moderating influence of workplace Integrational study model. This research adds to corporate awareness in illustrating the impact of ESM use on team performance and employee agility based on theory of social exchange, theory of information processing and the theory of transactive memory. Cross-sectional survey was employed to collect data from telecommunication firms (public & private). 400 respondents were selected through purposive sampling technique and given close ended questionnaires, 348 questionnaires were selected for further analysis; through a number of statistical tools, after screening out the filled responses. It resulted in Suggesting ESM has a favorable effect on Employee Agility and Team Performance, but the negative relationships were observed among workplace isolation and employee agility and team performance. Further, Workplace Isolation mediated the relationship among ESM, Employee Agility and Team performance. Additionally, Workplace Integration strongly moderated the relationship between ESM and Workplace Isolation.  Experts and/or administrators should pay consideration to the impact of using ESM on agility and team performance of the workers. In addition, when implementing ESM to increase team performance they should concentrate on the extent of team diversity. By presenting the relationship between use of ESM, isolation in the workplace, workplace integration, agility of workers and performance of teams; it enriches the ESM use works by exploring the moderating functions of workplace integration while describing the beneficial consequences of workplace integration.

The paper focuses on the urgency of the implementation of cloud technologies, which are a necessary condition for the development of enterprise management systems, give rise to a complex of insufficiently studied phenomena and processes and determine the need to find new tools in making and implementing reasonable management decisions. In the process of research, the sequence of construction and the overall structure of the enterprise management system, based on the use of cloud technologies, are determined, which allowed to build a mathematical model for calculating the probability of making an error-free decision, evaluating the efficiency of decision-making, a model of making a management decision for a certain time with the parallel method of operation of elements of the enterprise management system.

Numerous face recognition systems employ deep learning techniques to identify individuals in public areas such as shopping malls, airports, and other high-security zones. However, adversarial attacks are susceptible to deep learning-based systems. The adversarial attacks are intentionally generated by the attacker to mislead the systems. These attacks are imperceptible to the human eye. In this paper, we proposed a feature-based masking iterative method (F-MIM) to generate the adversarial images. In this method, we utilize the features of the face to misclassify the models. The proposed approach is based on a black-box attack technique where the attacker does not have the information related to target models. In this black box attack strategy, the face landmark points are modified using the binary masking technique. In the proposed method, we have used the momentum iterative method to increase the transferability of existing attacks. The proposed method is generated using the ArcFace face recognition model that is trained on the Labeled Face in the Wild (LFW) dataset and evaluated the performance of different face recognition models namely ArcFace, MobileFace, MobileNet, CosFace and SphereFace under the dodging and impersonate attack. The F-MIM attack is outperformed in comparison to the existing attacks based on Attack Success Rate evaluation metrics and further improves the transferability.

The introduction of information technology in the structure of procedural legal relations contributes to the full implementation of the principles of procedural law and optimization of civil proceedings. The purpose of this study is to develop scientifically sound proposals and recommendations for the latest concept of optimization of civil justice by means of information technology. The study of the impact of information technology in civil proceedings was conducted on the basis of integrated use of general and special methods of scientific knowledge. The optimization of civil proceedings as a system of theoretical ideas and views on civil procedural activity from the standpoint of means, methods and techniques of its improvement is considered. The basic elements of the latest concept are the provisions of the general methodological basis for studying the problem of optimization, the concept and structural characteristics of optimization, civil jurisdiction in terms of optimization and ensuring the right to due process, optimization of procedures for preparation and trial of civil cases, cassation in the system of instance review of court decisions. It has proved that the search for a harmonious combination of protective and conciliatory functions of civil proceedings, as a characteristic concept of modern procedural systems, determines the main directions of optimizing the preparation of civil cases for trial. It has established that in the reform of the judiciary of Ukraine it is important not only to digitize existing processes. The most important thing is to change the approach to justice itself, making it simpler and more accessible to all with the help of information technology. In this sense, the introduction of affordable online court involves not only the introduction of innovative technologies, but also the revision and significant simplification of procedural rules.

The Internet of things has become an interesting area of research in the last few years due to its ability to make human life simple and easier. Quality of Services (QoS) has gained a lot of importance due to the increasing popularity of the technology. QoS metrics help the IoT users to understand and express their requirements for the selection of services provided by IoT. Researchers in this field have come up with different types of architectures to provide a better view and define all the functions of the technology. In this paper, we have defined a few of the architectures and QoS metrics related to these architectures.

Most real-time speech signals are frequently disrupted by noise such as traffic, babbling, and background noises, among other things. The goal of speech denoising is to extract the clean speech signal from as many distorted components as possible. For speech denoising, many researchers worked on sparse representation and dictionary learning algorithms. These algorithms, however, have many disadvantages, including being overcomplete, computationally expensive, and susceptible to orthogonality restrictions, as well as a lack of arithmetic precision due to the usage of double-precision. We propose a greedy technique for dictionary learning with sparse representation to overcome these concerns. In this technique, the input signal's singular value decomposition is used to exploit orthogonality, and here the ℓ1-ℓ2 norm is employed to obtain sparsity to learn the dictionary. It improves dictionary learning by overcoming the orthogonality constraint, the three-sigma rule-based number of iterations, and the overcomplete nature. And this technique has resulted in improved performance as well as reduced computing complexity. With a bit-precision of Q7 fixed-point arithmetic, this approach is also used in resource-constrained embedded systems, and the performance is considerably better than other algorithms. The greedy approach outperforms the other two in terms of SNR, Short-Time Objective Intelligibility, and computing time.

Purpose: This research aimed to identify and evaluate the risks of content production in the governmental digital libraries of Tehran.Method: In terms of essence, this research is synthetic (library studies, qualitative and quantitative), and regarding purpose, it is an applied one.  In the first part, using the studies of the research literature, a set of indicators related to the risks of content production in the governmental libraries of Tehran was obtained. The second part of this research involved a fuzzy Delphi approach that was conducted at two stages among 20 experts. In this stage, a researcher-made questionnaire based on the indicators obtained from the first stage was used. The third part of the research was a survey-analysis conducted with a quantitative method using a questionnaire made out based on the results of qualitative stage with three criteria: the probability of occurrence, effect intensity, and the inability of organization to respond and was distributed among 100 managers and experts of governmental digital libraries in Tehran.Findings: In this research, the risks of content production were identified and fell into nine major categories (human force, environmental factors, infrastructure, protection and maintenance,  creators`  technical rights, integration, content evaluation of sources and authors and information security).Conclusion: The results of this evaluation show that digital libraries are not exactly at their best level when it comes to their responses to the risk of human resources, authors' rights, and integration risks.