Reviewerly/exHarmony huggingface dataset:
Psychological components of professional image of inspectors of the road patrol service of the State road safety Inspectorate and directions for its improvement

The article deals with psychological components of the phenomenon of the professional image of the inspectors of the Road Patrol Service of the State Road Safety Inspectorate are the subject of the study.&#x0D; The purposes of the scientific article are to present psychological characteristics of leading components of the professional image of the inspectors of the Road Patrol Service of the State Road Safety Inspectorate and to determine the trends of its improvement.&#x0D; It’s established that the most significant elements of the discussed professional image are appearance, knowledge of normative legal acts, the culture of dialogue, competent speech and the ability to handle stress and require urgent attention and development – a normative legal acts and speech skills. &#x0D; A synthesis of the literature and an empirical study carried out using methods of a written survey, observation, ranking, and analysis of situations has allowed not only to reveal the psychological meaning of each component image, but also to identify the likely directions of its improvement. A special place among these directions is given to improving the quality of legal and psychological training, and training in practical linguistics in the field of business communication with traffic participants.&#x0D; The scientific novelty of research consists in the reflection of the modern view on the problem of professional image of the employee through the eyes of the subjects of its formation and in the formulation of specific proposals based on the experience of the respondents and sought after in the course of further modernization of the image of the inspectors of the Road Patrol Service of the State Road Safety Inspectorate.
Formation of Pedagogical University Students’ Readness for Undergraduate and Graduate Research

There are many research opportunities for prospective teachers in Ukrainian pedagogical universities to gain their first experience of organization and conducting research work with students of various age groups and educational levels, in scholarly and scientific societies of various forms.Within the framework of this approach, the pre-service teachers' readiness for research work is an up-to-date issue that is in the focus of this paper.The study has been conducted in teacher training institutions of Central Ukraine with the experimental group consisting of 186 students and 22 teacher educators, and the control group numbering 192 students and 25 teacher educators who took part in scientific and scholarly societies and groups or were in charge of undergraduate or graduate research.A model of students' engagement into active undergraduate and graduate research has been developed, incorporating the following integral parts: the objectives component (goals, tasks, approaches, and principles), the content-and-process component (stages, content, forms, methods, technologies, means), the process component (pedagogical conditions, forms, methods and means of teaching), and the results-and-evaluation component (parameters, levels, criteria, and indicators).A combination of empirical and theoretical research methods have been used to make the study conclusions more competent and authentic.
Leveraging Comprehensive Data Analysis to Inform Parallel HPC Workloads

Alongside advancements in computer related technologies, High Performance Computing (HPC) systems continue to grow in both complexity and scale. As the complexity of hardware components increases so too does the complexity of the software solutions that leverage those resources. It is often difficult or impossible to know whether a distributed application is performing as intended without applying a specific profiling application. Furthermore, errors and adverse performance can go unnoticed until the application is executed at scale. To provide a general mechanism that addresses these types of errors we implement a data analysis pipeline that ingests, stores, and indexes data. This data can then be analyzed and displayed for users to perform analysis at varying levels of granularity. To this end, we have developed multiple hierarchical views that enable workload analysis at varying levels of granularity. The overall approach applies existing tools and leverages monitoring, alerting, ad-hoc analysis, and exploratory analysis to inform parallel HPC workloads.
End-to-end response selection based on multi-level context response matching

This paper presents our work on the Dialog System Technology Challenges 7 (DSTC7). We took part in Track 1 on sentence selection which evaluates response retrieving in dialog systems on more realistic test scenarios compared to the state-of-the-art evaluations. Our proposed dialog system matches the context with the best response by computing their semantic similarity on word and sequence levels. Evaluation results on the datasets provided show the effectiveness of our system by achieving higher performance compared to the provided baseline system. Our system enjoys the advantages of its simple and end-to-end architecture making its training and adaptation to other domains easier.
Contextual predictions and syntactic analysis: the case of ambiguity resolution

We test the hypothesis that syntactic analysis is based on contextual predictions and is guided by discourse salience of the referents.The head of the complex noun phrase tends to be more prominent in discourse as native speakers expect the continuation of the story to refer to N1 more often than to N2.It corresponds to the data on adjunct attachment interpretation.
Tensor Networks for Language Modeling.

Tensor networks are a powerful modeling framework developed for computational many-body physics, which have only recently been applied within machine learning. In this work we utilize a uniform matrix product state (u-MPS) model for probabilistic modeling of sequence data. We first show that u-MPS enable sequence-level parallelism, with length-n sequences able to be evaluated in depth O(log n). We then introduce a novel generative algorithm giving trained u-MPS the ability to efficiently sample from a wide variety of conditional distributions, each one defined by a regular expression. Special cases of this algorithm correspond to autoregressive and fill-in-the-blank sampling, but more complex regular expressions permit the generation of richly structured text in a manner that has no direct analogue in current generative models. Experiments on synthetic text data find u-MPS outperforming LSTM baselines in several sampling tasks, and demonstrate strong generalization in the presence of limited data.
Locality Parameters for Privacy Preserving Protocol and Detection of Malicious Third-Party Auditors in Cloud Computing

Cloud computing is an anticipated and inevitable solution for large storage capacities, the virtual presence of ownership and universal accessibility which extends the boundaries of a user today. These benefits also open up a challenge for being too exposed to welcoming attackers to steal potential information from those cloud environments. Irrespective of types of data, information stored on these cloud environments varies from databases to coding of software and raw and processed information of various organizations including governments. To ensure confidentiality and integrity of data input from various users, cloud service providers employ third-party auditors to verify the safety measures are intact and maintained. As the name predicts, an auditor may not possess a responsibility to protect the data after their intended verification is over. Third-Party Auditors have the function of confirming that the saved data has the same integrity as of original data, does not seek any information in near future to disturb the integrity and finally does not introduce any loopholes for new attacking attempts from external sources. This proposal intends to derive a mechanism to limit the resources provided to third-party auditors in the architecture. The location of data and users is protected in this approach along with locking of data segments to enhance the security of the architecture. Locality information of data storage space and the data information is protected from third-party service providers to enhance the privacy level. The simulation results compare the technique and prove that this method outperforms the previous techniques when addressing the sturdiness, communication complexity and time consumption concerns.
Chebyshev norm adjustment: what to expect? case study in a leveling network

This work drew attention to the Chebyshev norm minimization, a method of adjustment of observations still little explored in the geodetic literature. Chebyshev norm minimization refers to the minimization of the maximum weighted absolute residual of adjusted observations. In addition to contributions to the formulation of Chebyshev norm adjustment by linear programming, numerical examples of its application in a leveling network were presented and compared with the respective Least Squares adjustments in this work. We verified that residuals analysis of Chebyshev norm adjustment is even less effective than of Least Squares for outlier identification. We also highlighted other characteristics of the method that had never been explored in geodetic literature before. In special, Chebyshev norm adjustment presented lower maximum absolute residual, and more homogeneous absolute residuals than LS when applied with the usual distance-dependent stochastic model. More experiments should be conducted in future work to confirm these tendencies. We also analyzed the adjustment by Chebyshev norm minimization with unit weights, which generates the minimum maximum absolute residual for a network. As some characteristics of Chebyshev norm adjustment seen promising, other suggestions for future work were also made.
Reducing Shadow Effects on the Co-Registration of Aerial Image Pairs

Image registration is an important preprocessing step prior to detecting changes using multi-temporal image data, which is increasingly accomplished using automated methods. In high spatial resolution imagery, shadows represent a major source of illumination variation, which can reduce the performance of automated registration routines. This study evaluates the statistical relationship between shadow presence and image registration accuracy, and whether masking and normalizing shadows leads to improved automatic registration results. Eighty-eight bitemporal aerial image pairs were co-registered using software called Scale Invariant Features Transform (&lt;small&gt;SIFT&lt;/small&gt;) and Random Sample Consensus (&lt;small&gt;RANSAC&lt;/small&gt;) Alignment (&lt;small&gt;SARA&lt;/small&gt;). Co-registration accuracy was assessed at different levels of shadow coverage and shadow movement within the images. The primary outcomes of this study are (1) the amount of shadow in a multi-temporal image pair is correlated with the accuracy/success of automatic co-registration; (2) masking out shadows prior to match point select does not improve the success of image-to-image co-registration; and (3) normalizing or brightening shadows can help match point routines find more match points and therefore improve performance of automatic co-registration. Normalizing shadows via a standard linear correction provided the most reliable co-registration results in image pairs containing substantial amounts of relative shadow movement, but had minimal effect for pairs with stationary shadows.
Competition Numbers and Phylogeny Numbers of Connected Graphs and Hypergraphs

Let D be a digraph. The competition graph of D is the graph having the same vertex set with D and having an edge joining two different vertices if and only if they have at least one common out-neighbor in D. The phylogeny graph of D is the competition graph of the digraph constructed from D by adding loops at all vertices. The competition/phylogeny number of a graph is the least number of vertices to be added to make the graph a competition/phylogeny graph of an acyclic digraph. In this paper, we show that for any integer k there is a connected graph such that its phylogeny number minus its competition number is greater than k. We get similar results for hypergraphs.
Studi Performansi Algoritma Perencanaan Jalur diantara PRM, RRT, RRT* dan Informed-RRT

This paper will discuss a comparative performance review of several path planning algorithms. This study compares five well-known path planning algorithms, namely the Probabilistic Roadmap (PRM), Rapidly-exploring Random Tree (RRT), RRT* and Informed-RRT* algorithm. Testing is done through simulation based experiments using python. The test was conducted using several existing benchmark cases, namely narrow, maze, trap and clutter environment. The optimality criteria compared are path costs, computational time and the total number of nodes in the tree needed. The results of this study will provide information to readers about which algorithm is most suitable for use in user applications where there are several working parameters to be optimized. The findings have been summarized in the conclusion section.&#x0D; Keywords ­: Motion planning, PRM, RRT, RRT*, Informed-RRT*
Non-Asymptotic Bounds for Zeroth-Order Stochastic Optimization.

We consider the problem of optimizing an objective function with and without convexity in a simulation-optimization context, where only stochastic zeroth-order information is available. We consider two techniques for estimating gradient/Hessian, namely simultaneous perturbation (SP) and Gaussian smoothing (GS). We introduce an optimization oracle to capture a setting where the function measurements have an estimation error that can be controlled. Our oracle is appealing in several practical contexts where the objective has to be estimated from i.i.d. samples, and increasing the number of samples reduces the estimation error. In the stochastic non-convex optimization context, we analyze the zeroth-order variant of the randomized stochastic gradient (RSG) and quasi-Newton (RSQN) algorithms with a biased gradient/Hessian oracle, and with its variant involving an estimation error component. In particular, we provide non-asymptotic bounds on the performance of both algorithms, and our results provide a guideline for choosing the batch size for estimation, so that the overall error bound matches with the one obtained when there is no estimation error. Next, in the stochastic convex optimization setting, we provide non-asymptotic bounds that hold in expectation for the last iterate of a stochastic gradient descent (SGD) algorithm, and our bound for the GS variant of SGD matches the bound for SGD with unbiased gradient information. We perform simulation experiments on synthetic as well as real-world datasets, and the empirical results validate the theoretical findings.
Parallax-Tolerant Video Stitching with Moving Foregrounds

The parallax artifacts introduced due to movement of objects across different views in the overlapping area drastically degrade the video stitching quality. To alleviate such visual artifacts, this paper extend our earlier video stitching framework [1] by replacing a deep learning based object detection algorithm for parallax detection, and an optical flow estimation algorithm for parallax correction. Given a set of multi-view overlapping videos, the geometric look-up tables (G-LUT) are generated by stitching a reference frame from the multi-view input videos, which map the input video frames to the panorama domain. We propose to use a deep learning based approach to detect the moving objects in the overlapping area to identify the G-LUT control points which get affected by parallax. To compute the optimal locations of these parallax affected G-LUT control points we propose to use patch-match based optical flow (CPM-flow). The adjustment of G-LUT control points in the overlapping area may cause some unwanted geometric distortions in the non-overlapping area. Therefore, the G-LUT control points in close proximity of moving objects are also updated to ensure the smooth geometric transition between the overlapping and the non-overlapping area. Experimental results on challenging video sequences with very narrow overlapping areas ($$\mathord \sim $$3% to $$\mathord \sim $$10%) demonstrate that video stitching framework with the proposed parallax minimization scheme can significantly suppress the parallax artifacts occurring due to the moving objects. In comparison to our previous work, the computational time is reduce by $$\mathord \sim $$26% with the proposed scheme, while the stitching quality is also marginally improved.
Transformation of the academic life in the 21st century

The article is devoted to the analyses of transformation of academic life in the 21st century. Dramatic changes of everyday life during last decades, expanding informational and communicational technologies, devaluation of the role of humanities and social sciences, arising new, “digital” young generation, i.e. these and numerous factors and challenges enforced such transformation. However, the continuation of academic tradition with the relevant role of humanities and social sciences continue to be essential for human culture and the processes of socialization of person. In this regards the role of the National Academy of Educational Sciences of Ukraine as the key national institution, which provides theoretical and methodological support of teaching and learning at all levels of education is crucial, but demand a high level of flexibility in responding these challenges.
Multimedia Learning Model: Adapting an Image in a Screen Work

In the context of media space digitalization, issues of using multimedia in audiovisual works and in screen arts become increasingly important. Evolutionary processes in audiovisual production form a new system of expressive means, determine the scope of modern multimedia, transforming them into a new communicative space. The article notes that the gap between the intensity of the introduction of this type of creativity and lack of theoretical justification of the process is due to the rapid formation of a new artistic environment, its adaptation and relevance.&#x0D; The purpose of the article is to analyze the exploration of the new type of communicative space, change and adapt the art form when introducing multimedia into the movie plot.&#x0D; The author gives a generalized description of the interaction of the viewer with a work of art, equipped with elements of interactivity, which is represented in the creative dialogue of the artist and the consumer. But the design format involves fundamental knowledge in the field of cinema and practical skills when using technologies in screen arts.&#x0D; In the section on practical techniques for implementing multimedia, the author focuses on the short stories developed by the teachers of the Department of Animation and Computer Graphics for training multimedia directors at VGIK.&#x0D; Taking the work of VGIK students as an example, the article substantiates the integration of theoretical knowledge and practical skills in teaching students whose training is based on common and fine culture in combination with the skills of using digital technology.&#x0D; The article touches upon the education of a multimedia director an auteur and professional, capable of realizing their plan with audiovisual means involving the transformation of screen time and space, which is a difficult but important educational task in digital time.
A Data Mining Approach For Product Promotion and Inventory Solution using FP-Growth Algorithm

This study aims to find interesting patterns on the database transaction so that it can be used as a recommendation sales promotion and inventory product.Companies have difficulty finding interesting transaction patterns in large databases, so it will be difficult to determine the right product promotions and inventory.To resolve these problems are to use data mining techniques with association rule.In previous studies, most studies adopt Apriori algorithm to analyze the association rules.In this study, the data mining technique used is the association rules algorithm FP-Growth.In the FP-Growth algorithm did generate candidates as in Apriori algorithm and using a development concept Tree in frequent itemset search so that it requires faster than Apriori.Some of the analyzes produced in this study are higher minimum support values and minimum trust used will result in fewer items and association rules.Association rule in this study has a lift ratio value of more than 1.00, meaning that item K and L are actually bought together.The higher the lift ratio produced shows the stronger the association rules are formed.The results of this study are the minimum confidence of 97.63%, the maximum trust is 99.37% and the lift ratio is 1,00013798.These results can be used as recommendations for optimizing product promotions and inventory.
Attention-based Multi-layer Chinese Word Embedding

Word embedding is a basic task in natural language processing area. Unlike English, Chinese subword units, such as characters, radicals, and components, contain rich semantic information which can be used to enhance word embeddings. However, existing methods neglect the semantic contribution of corresponding subword units to the word. In this work, we employ attention mechanism to capture the semantic structure of Chinese words and propose a novel framework, named Attention-based multi-Layer Word Embedding model(ALWE). We also design an asynchronous strategy for updating embedding and attention efficiently. Our model learns to share subword information between distinct words selectively and adaptively. Experimental results on the word similarity, word analogy, and text classification show that the proposed model outperforms all baselines, especially when words don’t appear frequently. Qualitative analysis further demonstrates the superiority of ALWE.
Image recognition based on multi-scale dilated lightweight network model

Lightweight model is mainly applied to maintain performance and reduce the amount of parameters, simplifying the complex laboratory model to the mobile embedded device. We present a multi-scale dilated lightweight network model for image recognition. ShuffleNet is an classical lightweight neural network that proposes channel shuffle to help exchange information between groups during group convolution. However, ShuffleNet does not make full use of each group of information after channel shuffle. Since channel shuffle guarantees that each group contains the information of other groups, in this paper, we propose to process the grouping data with different dilated convolution, and obtain the multi-scale information of different receptive fields without increasing parameters. At the same time, we make an improvement on the network model to reduce the gridding artifacts caused by dilated convolution. Experiments on CIFAR-10 and EMNIST show that the improved algorithm performs better than traditional method.