Reviewerly/exHarmony huggingface dataset:
FEATURES OF FORMATION OF DIGITAL COMPETENCE AT STUDENTS OF SECONDARY PROFESSIONAL EDUCATION

The article discusses the features of the formation of digital competence of the modern generation of students in educational and professional activities. Modern students are the product of informatization and digitalization of modern society and they contain a certain gene or imprint of information technologies that have a great influence on the thinking and behavior of adolescents, both in everyday life and in professional activity.To date, a lot of works on the features of the digital generation have been published, but they generally consider the peculiarities of thinking and the higher mental functions of children and schoolchildren and do not address the problems of teaching and educating students. Of course, students also have features. At the same time, some of them are especially significant, as they are associated not only with the age characteristics of this phalanx of youth, but also with the characteristics of the leading type of activity - educational and professional.To identify the features of the formation of digital competence of modern students associated with the use of digital technologies in the implementation of the main types of educational and professional activities, a survey of first-fourth year students of all areas of full-time study in the Cheboksary Electromechanical College was conducted. Thus, 347 respondents were interviewed.The data obtained showed that students easily use digital technologies, both in everyday life and in educational and professional activities. They are not confused by the remoteness from the traditional carrier of educational information - the teacher and the textbook. They are used to virtual communication and do not experience discomfort in communicating with artificial intelligence.Teachers note that a new generation of students is becoming increasingly multitasking.But it should also be noted that some researchers observe the formation of mental distortions in children and adolescents with excessive and uncontrolled use of digital technologies, deformation of their value-motivational base, deterioration of communication skills and basic communication needs, exposure to threats of online communication, Internet manipulation and a number of others.Of course, individual pedagogical actions and activities will not have a systemic effect on the personality of a future specialist. It is necessary to correctly combine traditions and innovations, take into account the specific conditions of a particular educational institution, and much more.It is hard to say what kind of specialists today's students will become; they have a different life story, different habits, different lifestyle. One thing is clear - the safe, critical, confident use of digital technologies in various spheres of life and professions is today the most important competence of the digital age, which requires the same formation as knowledge, skills, motivation, responsibility.
Quantitative Estimation of Grammatical Ambiguity: Case of European Languages

The grammatical ambiguity (multiple sets of grammatical features for one word form or coinciding surface forms of different words) can be of different types. We distinguish six classes of grammatical ambiguity: unambiguous, ambiguous by grammatical features, by part of speech, by lemma, by lemma and part of speech, and out-of-vocabulary words. These classes are found in all languages, but word distribution may vary significantly. We calculated and analysed the statistics of these six ambiguity classes for a number of European languages. We found that the distribution of ambiguous words among these classes depends primarily on basic linguistic features of a language determining its typology class. Although it is influenced by text style and the considered vocabulary, the distinctive shape of the distribution is preserved under different conditions and differs significantly from distributions for other languages. The fact that the shape is primarily defined by linguistic properties is corroborated by the fact that closely related languages demonstrated in our research similar properties as far as their ambiguous words are concerned. We established that Slavic languages feature a low rate of part-of-speech ambiguous words and a high rate of words which are ambiguous by grammatical features. The former is also true for French and Italian, while the latter holds for German and Swedish, whereas the combination of these traits is characteristic of Slavic languages alone. The experiments showed that reduction of the grammatical feature set does not change the shape of distribution and therefore does not reflect similarity among languages. On the other hand, we found that the top 1000 most frequent words in all the languages considered have different distribution in ambiguity classes unlike in the rest of the words. At the same time, for the majority of considered languages, less frequent words are less unambiguous by part of speech. In Romance and Germanic languages, the ambiguity is reduced for less frequent words. We also investigated the differences in statistics for texts of different genres in the Russian language. We found out that fiction texts are more ambiguous by part of speech than newswire, which are in turn more ambiguous by grammatical features. Our results suggest that the quality of multilingual morphological taggers should be measured relying only on ambiguous words as opposed to all words of the processed text. Such an approach can help get a more objective linguistic picture and enhance the performance of linguistic tools.
Discovering Digital Technology Training Challenges for Future-Ready Educator: A Preliminary Study from Trainer Perspective

Currently, education globally is very challenging and experiencing rapid development that requires the educator to transform the way of teaching.Few empirical studies show that educator have yet to optimize the use of digital technologies in their educational practice, although a lot of training initiatives had been done.Therefore, the aim of this study is to explore whether there are any issues with digital technology training specifically in a post-secondary education institution of Malaysia.A qualitative approach using comparable case selection under general sampling strategies was implemented to scrutinize challenges of digital technology training based on trainer perceptions.Five interviews data were collected, four of them, right after digital technology training took place and the other one was a representative from the ministry, which data is used to support the information.A thematic analysis using thematic map technique was used to produce the related theme.The results from open coding showed 48 codes were analyzed to form ten categories.Ten categories were classified based on Social Cognitive Theory (SCT) lens then organized into personal, behavior and environmental factor that work reciprocally.Saturated themes were found from frequency table help to developed a conceptual framework of digital technology training.The finding of this study gives meaningful insights for future investigation developing a digital technology training framework for future-ready educators.
Reverse Intervention for Dealing with Malicious Information in Online Social Networks

Malicious information is often hidden in the massive data flow of online social networks. In "We Media'' era, if the system is closed without intervention, malicious information may spread to the entire network quickly, which would cause severe economic and political losses. This paper adopts a reverse intervention strategy from the perspective of topology control, so that the spread of malicious information could be suppressed at a minimum cost. Noting that as the information spreads, social networks often present a community structure and multiple malicious information promoters may appear. Therefore, this paper adopts a divide and conquer strategy and proposes an intervention algorithm based on subgraph partitioning, in which we search for some influential nodes to block or release clarification. The main algorithm consists of two main phases. Firstly, a subgraph partitioning method based on community structure is given to quickly extract the community structure of the information dissemination network. Secondly, a node blocking and clarification publishing algorithm based on the Jordan Center is proposed in the obtained subgraphs. Experiments show that the proposed algorithm can effectively suppress the spread of malicious information with a low time complexity compared with the benchmark algorithms.
CONCEPTUAL MODEL OF TECHNOLOGICAL ENVIRONMENT OF PROGRAMMING

Background. The content of the considered conceptual model is explained through the concepts of “concept”, “monad”, and concept-monadic complementation that is fundamental for the technological programming environment. The concept-monadic environment is a platform that really implements the understanding of programming as a subject-object productive activity – the complementarity of the programming process and its consequence in their cause-effect relation with the dominance of the actual process. It sets the stage for the technologization of programming industry and the creation of adaptive and flexible subject-oriented programming systems. The capabilities of the concept-monadic platform are exemplified by one of its possible implementations – reduction programming technology.Objective. The purpose of the paper is further development of the concept-monadic foundations of the subject-object technological environment, aimed at creating adaptive and flexible programming systems.Methods. Methods of introduction, exclusion of abstraction and pragmatic-driven typing, concept-monadic relativization method, reduction method, program algebra method.Results. Based on the developed conceptual system of concept-monadic and technological environment productive model of reduction environment of programming is created. The concept-monadic model of open-closed programming environment is proposed. For the first time, reduction models of programs and reduction methods of programming are considered as pragmatically conditioned concretization of this environment. It is shown that within the framework of the received concept-monadic system, the tasks and problems that are fundamental for modern programming are correctly set and solved.Conclusions. Any modern information-technology system should be oriented not so much to the notation of the decisions received, but to support (provide) the productive activity of the subject to obtain them. Effective development of such systems is possible within the framework of a concept-monadic environment that has advanced adaptation tools oriented to take into account the active role of the subject of programming.
Learning Software Constraints via Installation Attempts

Modern software systems are expected to be secure and contain all the latest features, even when new versions are released multiple times an hour. Each system may include many interacting packages. The problem of installing multiple dependent packages has been extensively studied in the past, yielding some promising solutions that work well in practice. However, these assume that the developers declare all the dependencies and conflicts between the packages. Often, the entire repository structure may not be known up front, for example, when packages are developed by different vendors. In this paper, we present algorithms for learning dependencies, conflicts, and defective packages from installation attempts. Our algorithms use combinatorial data structures to generate queries that test installations and discover the entire dependency structure. A query that the algorithms make corresponds to trying to install a subset of packages and getting Boolean feedback on whether all constraints were satisfied in this subset. Our goal is to minimize the query complexity of the algorithms. We prove lower and upper bounds on the number of queries that these algorithms require to make for different settings of the problem.
An Explorative Study on Access to Print and Electronic Information Resources among Journalists in Mysore and Mandya District of Karnataka

This explorative study focuses on access to print and electronic information resources among journalists in Mysore and Mandya district of Karnataka, India. The researchers tried to analyze frequency of library visits, average time spent, use of print and electronic information resources, criteria for evaluation of information resources, usage of social networks for accessing and sharing information, rating of online resources based on its features, preferred format of information resources, factors influencing for use of e-resources and to know the importance of e-resources for journalistic tasks. For this purpose the researchers prepared a well structured questionnaire as a tool for data collection and the collected questionnaire has been analyzed and presented with useful percentage analysis, Anova, Mean, Standard Deviation and suitable tables for presentation of data. It has been found form the study that the journalists prefer both print and electronic format of information resources. Hence, the libraries should maintain both formats of information resources till the users inculcate the habit of using information resources available in electronic format.
Figure-ground Image Segmentation via Semantic Information

Image segmentation is a technique to segment the image into particular regions which are consistent in some specific characteristics. It is still a non-trivial task to segment the foreground from the background completely, as these regions are seldom uniform in color or texture which are defined as commonly-used low-level features adopted in the traditional techniques. In this work we present a new segmentation framework by employing the semantic information, which is classified as the high-level visual features and holds important clue to indicate the foreground from the background. Firstly, the image is segmented hierarchically by using the semantic edge constraint, and the result is represented as an Ultrametric Contour Map (UCM). Then the UCM is employed in the segmentation energy function as the data term and smoothness term. Finally, the optimal segmentation labels are obtained via the Graph-cut. Experiments achieve good performance testing on the MSRC 21 dataset.
Learning Category-Specific Sharable and Exemplary Visual Elements for Image Classification

This paper presents a novel method to determine the discriminative image representation via visual dictionary learning framework for image classification task. Visual dictionary learning has the capacity to represent input image using an over-complete element set. Sparsity restrains distractors and prevents over-fitting. The two main characteristics benefit the classification solution. However, one shortcoming of existing dictionary learning is that it neglects to exploit the potential correlations across visual elements, especially from the category-specific feature space. To address this problem, we first propose to learn multiple discriminative category-specific dictionaries (DCSD) from all categories. The DCSD can explore the visual elements from each category in terms of sharable property. For this reason, these learned category-specific visual elements encourage image features from the same class to have the similar feature representations. In addition, exemplary data reflect the main characteristic of whole dataset and can improve the performance of algorithm that employs them. Therefore, we further propose a representative pattern dictionary (RPD) model to discover the exemplary visual elements for promoting the discriminative capability of feature representation. These exemplary visual elements are essentially a subset of over-complete visual elements and can represent the whole sample data effectively. Finally, we design a novel strategy that integrates the merits of object proposals and deep features jointly to strengthen the semantic information of image-level feature. Experimental results on benchmark datasets demonstrate the effectiveness of our method, which is shown to be superior to the recently competing dictionary learning and deep learning based image classification approaches.
Kalman filter observation error model applied to vehicle tracking dynamic obstacle correction

To increase accuracy for dynamic detection obstacles, this paper presents a Kalman ilter observation error model based on 77 GHz middle range radar (MRR).In recently, the vehicle equips advanced driver assistance system (ADAS), which becomes more popular.Among these, the accuracy of the obstacle information, such as distance, relative speed, and position, is the most important purpose.However, radar is one of the main detection sensors, but its data transmission delay, including millimeter-wave relection, the analogy to digital signal conversion, and data process, would inluence radar tracking correction.Therefore, algorithm of the proposed model adopts detection time and a dynamic estimated model of an obstacle to compensate detection delay, such that the dynamic correction means error can be reduced.Next, the testing condition sets RTK-GPS as the real-world reference frame, the experimental would be realized with the dynamic scene.According to the above results, when the target distance closer 100 meter, our model's dynamic correction mean error was improved 59%, 74%, 78% during in 20kph, 40kph, and 60kph relatively speed, respectively.
CONCEPTUAL REVIEW OF LEADERSHIP ON ORGANIZATIONAL PERFORMANCE

In the last few years, due to the surge in the attention towards leadership and the lack of a review mapping the effects of leadership on organizational performance, we believe its coherent to provide a clear review on leadership and how it affects organizational performance. This paper aims to review empirical studies on leadership and organizational performance with the aim of constructing a comprehensive model to conceptualize existing literature. The authors reviewed main journals with impact factor of over 2 and all Leadership titled SSCI journals. As a result, 687 studies published between 1957 and 2017 were analyzed, out of which 486 met the criteria of being empirical studies on leadership and performance. Out of the 486, 20 articles used Organizational Performance as their dependent variable, thus were included in our model. In doing so, the authors aim to extend the field in three ways: First, based on their review, the authors mapped a comprehensive model of the effects of leadership style and leadership characteristics through moderators and organizational mediators on organizational performance. Second, the authors, through vigorous examination, display and evaluate existing variables and measures on leadership and organizational performance within the literature. Finally, the authors aim to contribute to the field by presenting a detailed future research agenda and practical considerations for managerial implications.
Application of Model Compression and Acceleration Algorithms in Complex Task Model

Nowadays, the rapid development of artificial intelligence has brought great progress to computer vision (CV) technology. As the number of layers in the neural network model continues to deepen, the accuracy of the model is constantly improving, but the computational complexity of the model becomes larger and larger, and the model needs to be trained on GPU or even TPU. The huge amount of com- putation limits the many techniques of deep learning that can only be moved to the cloud and cannot be applied on mobile devices. In order to alleviate this problem, many scholars have proposed a variety of model compression and acceleration algorithms, mod- el pruning is one of the classic algorithms. As a common model compression and acceleration algorithms, model pruning can re- duce network complexity and computation effectively. However, the pruning algorithm generally has a long training time, and the accuracy of the model decreases too fast when the complex task is pruned. In view of this, this paper proposes two improved schemes, which can effectively alleviate the problem that the accuracy of model degradation is too fast, and greatly shorten the training time. When experimenting on a complex task such as the image caption model, the accuracy of the model can remain unchanged basically when the computational complexity of the model is reduced by half.
An Impulse Noise Removal Model Algorithm based on Logarithmic Image Prior

Logarithmic image prior constraint is a particularly effective and popular prior model in field-based image processing technology. This paper which focuses on the removal of impulse noise with logarithmic prior in image reconstruction. Impulse noise is often caused during image-data acquisition and transmission, there are many reasons for it. Therefore, removing the kind of noise is a very important and complex task in image reconstruction. Consequently, this paper proposes a new patch-based sparse optimization method with data-fidelity of L1. Theoretically, under reasonable assumptions, we give partial convergence analysis of the algorithm. Computationally, we use the split Bregman iterative method under the guarantee of convergence analysis and the weight of SVD decomposition, a complex problem is transformed into several simple sub-problems to solving; wherein, u-subproblem can be solved by (FFT); h, d-subproblems can be solved use shrinkage operator, respectively. In the intervening time, we also discuss how to choose the parameters. In the experimental aspects, we have done a lot of experiments and compared with other state-of-the-art methods. The experimental results show that the method is superior to other methods in terms of effectiveness.
Discriminant analysis of regularized multidimensional scaling

<p style='text-indent:20px;'>Regularized Multidimensional Scaling with Radial basis function (RMDS) is a nonlinear variant of classical Multi-Dimensional Scaling (cMDS). A key issue that has been addressed in RMDS is the effective selection of centers of the radial basis functions that plays a very important role in reducing the dimension preserving the structure of the data in higher dimensional space. RMDS uses data in unsupervised settings that means RMDS does not use any prior information of the dataset. This article is concerned on the supervised setting. Here we have incorporated the class information of some members of data to the RMDS model. The class separability term improved the method RMDS significantly and also outperforms other discriminant analysis methods such as Linear discriminant analysis (LDA) which is documented through numerical experiments.