Reviewerly/exHarmony huggingface dataset:
THEORETICAL AND APPLIED ASPECTS OF MASTERING THE DISCIPLINES OF THE HUMANITIES AND SOCIAL SCIENCES WHILE FORMING A POLICE OFFICER’S PERSONALITY: SOCIAL AND PHILOSOPHICAL ASPECT

The authors emphasize the importance of the disciplines of the humanities and social sciences while forming a police officer’s personality under the conditions of the educational paradigm transformation. The findings indicate that mastering this set of disciplines should combine theoretical and applied aspects. The latter are fully realized when using integrated educational technologies, which allows considering meaningful heterogeneity of disciplines, their methodological multivariance and satisfies the purpose of forming a thoroughly educated, socially adaptive and professionally mobile personality of a future specialist.
Investigation of Factors Producing a Sense of Virtual Reality Using Substitutional Reality

Substitutional Reality (SR) is a technology that can render the boundary between the real and the virtual unclear by switching between live and recorded footage. In this research, we examined factors not yet investigated by SR and conducted experiments to investigate their effectiveness in an SR context.As existing research has, in order to standardize conditions, used recorded footage only, we conducted a comparative experiment in order to investigate the effectiveness of existing research, and to investigate whether there was any discrepancy between live and recorded footage. Since the existence of inconsistencies makes it trivial to identify footage as live, and since the human element makes the introduction of inconsistencies inevitable, we also conducted an experiment to investigate the extent of the combined effect communication (which has already been recognized as effective by prior research), inconsistencies, and other factors would have. Finally, as a non-human factor, we conducted an experiment investigating the effect of being made aware of the passage of time.We found that there was not a major discrepancy between live and recorded footage, but the existence of inconsistencies did enable users to recognize live footage. We also found that communication had a comparative larger impact on the user’s sense of reality than did inconsistencies or posture. We found that the sensation of the passage of time, conveyed to the subject by “resuming” the footage, has the potential to make the distinctions between live and recorded footage less clear.
A Camera Tracking System Based on Closed-loop Kernelized Correlation Filters

Abstract Camera tracking is an important application in the computer vision. With the progress of the tracking-by-detection algorithm, industrial cameras can track targets autonomously. However, during the long-time tracking, it’s prone to miss target owing to heavy object occlusion, environment changing and objects appearance variations. Our camera tracking system is based on the kernelized correlation filter to track object, and we add a self-verification module to judge if the current tracking results are reliable. This can be useful in solving target missing when object meets occlusion or variations, and avoid model drift during long-time detection. Last but not the least, we keep the targets in the corner of the image by controlling our camera, avoiding object’s moving out of view. The kernel correlation filter bounded with self-verification and re-detection modules boost the performance. Extensive experiments on the OTB-2013 benchmark show that it performs better than state-of-the-art methods.
ON THE GENERALISATION OF SIDEL’NIKOV’S THEOREM TO -ARY LINEAR CODES

We generalise Sidel’nikov’s theorem from binary codes to $q$ -ary codes for $q&gt;2$ . Denoting by $A(z)$ the cumulative distribution function attached to the weight distribution of the code and by $\unicode[STIX]{x1D6F7}(z)$ the standard normal distribution function, we show that $|A(z)-\unicode[STIX]{x1D6F7}(z)|$ is bounded above by a term which tends to $0$ when the code length tends to infinity.
Otowik: Mining Text from Wikis to Mind Maps

The impact of the Internet on education has been recognized for decades in which learners or students are able to access educational resources cross borders and languages. However, exposure to or provision of too much information or data will lead to information overload, which dampens understanding and effective decision making. In this paper, we share a software named Otowik that is able to extract keywords and keyphrases from wiki pages and automatically construct a mind map based on the keywords and keyphrases extracted. Otowik was evaluated via a User Acceptance Test and was highly recommended by the participating respondents.
Building a Weighted Graph to Avoid Obstacles from an Image of the Environment

In robot pathfinding problems, the accuracy and efficiency of the solution path is dependent on the representation of the environment. While many local navigation systems describe pathfinding with limited information, there has been a lack of research on developing a global representation of an environment with image processing techniques. The described algorithm constructs a bidirectional weighted graph representation from an aerial input image designed to not intersect with obstacles in that image. The algorithm is structured as a four-stage pipeline, where each stage uses existing or modified algorithms. To the best of our knowledge, this algorithm is unique in the construction of a well understood representation of an environment from an aerial input image. Future work could improve algorithm runtime and overall accuracy of the output graph.
Design of an API recommendation system in android programming

Android has been the most popular operation system in the smartphone field, and Android programming is an import branch in programming work. When dealing with programming tasks, there are many APIs that are often used by programmers, and it costs much time for them to choose and check the usages of APIs repeatedly. This paper raises a design of API recommendation system in android programming, which uses AST tools in Java to extract key features of codes, and uses tf-idf, LDA and API2Vec models on the data to generate recommendation results. +The experiment results—— at maximum 80%+overall recall rate and nearly 50% top-10 recall rate prove the efficiency of the system.
Deep Mixture of MRFs for Human Pose Estimation

In this paper, we propose a new geometric model based on mixture of Markov Random Fields (MRFs) for human pose estimation. We build on previous work that expresses the global constraints on the relative locations of the body joints using an auto-encoder ConvNet which performs dimensionality reduction on the heat maps, and recovers in this manner a low dimensional manifold on which the global pose of the human body lies. To address the shortcomings of this architecture, and obtain more meaningful vectors that span the low dimensional pose space, we propose to replace the auto-encoder network layer with a layer that implements a Gaussian mixture model (GMM) that provides a soft clustering of the human pose predictions in an online fashion. We show that: (a) a large number of meaningful global poses is feasible, this way preserving the underlying structure of informative body poses; (b) the clustering helps to properly initialize the MRF filters of each different global pose, (c) a body joint masking data augmentation procedure can be better exploited and that (d) the system stability is significantly improved. To the best of our knowledge, this is the first time that a clustering algorithm like GMM is used in an online fashion for the problem of 2D human pose estimation. The efficacy of our framework has been demonstrated through extensive experiments on widely used public benchmarks.
PSYCHOLOGICAL AND PEDAGOGICAL SUPPORT OF FUTURE TRAINERS AT PRACTICAL CLASSES OF THE SUBJECT “PEDAGOGICAL SKILL”

The problem of psychological and pedagogical support for future trainers can be solved in classes on "Pedagogical Skill", where students develop the pedagogical skills that are needed during the work of the trainer. One of the most important skills of the trainer is organizational, which contributes to the development of the ability to organize training and education in trainings and competitions, to develop group interaction. These skills and abilities are also relevant in other areas of social practice of the individual, harmonize life, adjust the balance of spiritual forces, introduce guidelines in the views on the surrounding world, save from the limited consciousness and stereotypes of thinking. Successful learning and psychological development of the individual are the basis of interaction in the education and training of students in solving problems associated with psychological and pedagogical support.
Sampling and Analyzing Statistical Data to Predict the Performance of MOOC

MOOC platforms allow to accumulate a large amount of statistical data on various activities of course participants. This provides an opportunity for predicting the performance of an online course even at a stage when the course is not completed, and its students have time to correct their situation. To make a forecast, the first priority task is to create the correct sample for supervised learning models. The hypothesis that practical exercises that are performed in the first half of the course have a significant impact on the performance of the course, and among them, the most laborious in time and effort is the most influenced, received experimental confirmation. In the online course “Methods and Algorithms of Graph Theory”, Spearman’s correlation for Problem 6 using the Magu-Weismann algorithm is the highest (0.58 and 0.59).
Huffman decoding performance analysis of lossy compressed LAPAN-A3/IPB multispectral images

Abstract LAPAN-A3/IPB multispectral imager has the ability to produce realtime acquisition images anywhere in the world as long as there is X-band receiver in ground station to receive the image data. The received image data is the compressed version of the original image data, compressed by lossy algorithm of Fast-Fourier Transform and Huffman encoding. Processing of the received image data can be considered complicated in terms of decompressed image data validity and processing time needed. This research aims to develop robust decoding algorithm for LAPAN-A3/IPB multispectral image received in realtime acquisition mode from Bogor ground station. The research focuses on developing efficient method on Huffman decoding for relatively big image data by using look-up-table approach. The developed decoding algorithm has successfully produced daily LAPAN-A3/IPB realtime images with good image quality, where the results have been validated by using original images, recorded on satellite memory. However, the time processing needed for processing any single image is still poor, where 1 GB of compressed image data needs around 1 hour of processing time. Therefore, although the algorithm has been used to regularly produce daily LAPAN-A3/IPB realtime image data, the faster algorithm is needed to achieve better overall processing performance.
Connections for Path Planning in Mobile Robotics

This work presents the theoretical solutions that are possible for connecting the linear segments of the global trajectories developed for mobile robots in their operating scenes. Generated most often in a purely polygonal form by the global planner of trajectories, they cannot be executed directly than by mobile robots that have the pivotal capability around their axis of vertical symmetry. For all the other constructive structures of the mobile robots and whatever type of their control, e.g. with the trajectory stored in memory or with the wired trajectory mapped on the ground, these techniques are needed. This paper presents three techniques for connecting the polygonal paths using only pure circular arcs, circular arcs and splines curves, respectively arcs of clothoid. Theoretical support of each method, examples and the assessment of specific influences on the behavior of a mobile robot at changing sections on trajectory are included.
Evaluation of Design Pattern Utilization and Software Metrics in C# Programs

Utilization of design patterns is supposed to have a considerable impact on software quality and to correlate with different software metrics. Much experimental research has considered these issues but almost none related to C# programs. This paper examines utilization of Gang-of-Four design patterns combined with results of software metrics calculated on a set of C# programs. The design patterns have been automatically detected in source code. Analyzed applications with design patterns evaluated to be more complex but in the same time better maintainable than applications without design patterns. Usage of design patterns contributed to a growth in class encapsulation. Classes that implemented design patterns were more complex that other classes used in both types of applications with and without design patterns. The outcomes could be of importance in software maintenance, reverse engineering and program refactoring.
Heterogeneous Network Representation Learning Method Based on Meta-path

Mining the rich structure and semantic information hidden in heterogeneous information networks is one of the important tasks of network representation learning. At present, there are relatively few studies on network representation for heterogeneous information network which contains different types of nodes and link relationships. Most studies on network science are based on the homogeneous networks where nodes are objects of the same entity type and the links between nodes are also of the same type. In this paper, we propose a new network representation learning method for heterogeneous network. The core of this method contains tow parts: First, according to semantic of different meta-paths, we get weights between nodes of the same type in heterogeneous information network based on attention mechanism. And then, through biased random walk on nodes of the same type, we get node sequences which can be processed by the skip-gram model to generate representation vectors of nodes. To verify our method, we do experiments on DBLP and Aminer dataset. The experimental results demonstrate that the embeddings learned from the proposed model achieve better performance than state-of-the-art methods in tasks including node classification and similarity research.
InLinkUK

We present an initial study of an emerging digital street infrastructure in London, UK - InLinkUK kiosk network, which consist of connected screens and communication services. Early findings suggest that 1) different stakeholders behaved and reacted differently towards the kiosks and 2) tensions exist between the perceived of value and demands of the kiosks different stakeholders.
Proposal for Improvement of Professional Engineer System from the Analysis of the Cause of Accident at Fukushima Nuclear Power Plant

Nowadays, even if individual engineers are aware of technical risk in their specialty fields, it is difficult to exhaustively and accurately grasp and recognize the technical risk of the huge, synthesized and complicated science and technology as a whole. “Fukushima Nuclear Power Plant” was taken up as a representative example of science and technology of hugeness, synthesis and complication, under uncertainty. From the analysis of the cause of the accident, ability required of future engineers was organized and improvement on engineers ability required by JABEE and engineering education was proposed. Therefore, Professional Engineer system in Japan is taken up and compared with the US PE system and the UK Chartered Engineer system. Engineers at the stage of becoming professional engineers are focused on and improvement on Professional Engineer system including engineer education in Japan is proposed.
A New Concept of Nonparametric Kernel Approach for Edge Detection

An important thing is to have a piece of information if the collected data are genuine, reliable the originals, i.e. the information sources did not change their characteristics. However, all of data types can be changed in many ways. If this occurs the next question is: what is the essence of this change and where or when they have arise. This is a challenge to detect the change and qualify it accordingly. In this paper, there is proposed a new solution for abrupt changes detection, based on the Parzen kernel method of estimation of the derivatives of the regression functions in the presence of probabilistic noise. The extension to the multidimensional case is also formulated.
SecuriCast

Simple username/password logins are widely used on the web, but are susceptible to multiple security issues, such as database leaks, phishing, and password re-use. Two-factor authentication is one way to mitigate these issues, but suffers from low user acceptance due to (perceived) additional effort.
Image Encryption Using New Chaotic Map Algorithm

In this digital era, all the information takes the form of various media during communication. This leads to the need for more concern on the security measures so that the data being transmitted is resilient to various malicious attacks. In this paper, a modified line map algorithm is proposed and it is considered as a chaotic map to scramble the image. The resultant chaotic Line map has been employed to permute the image pixels, whereas diffusion is performed by XOR operation to completely encrypt the image. The robustness of the image against attacks has been increased by repeated permutation-diffusion round for ‘n’ number of times. Histogram analysis, Number of pixels change rate and correlation analysis has been performed to analyze the robustness of the proposed technique. Simulation results proves that this methodology can be implemented in real time applications.
Sparse kernel subspace method for classifying and representing patterns from data with complex structure

The subspace method based on principal component analysis (PCA) is a useful tool for classifying and representing patterns. In order to learn the dynamical nonlinear structure of data, there is currently much discussion about kernel subspace methods based on kernel PCA. By using a nonlinear transformation, we can efficiently capture the nonlinear structure of data, which leads to effective subspace construction for data having a dynamical nonlinear structure. However, the performance of a kernel subspace method becomes dramatically worse in the presence of noisy features, because the method is based on a fully dense loading matrix in the constructed subspace. This implies that the subspace for each class is constructed by using not only crucial features but also noisy features, which significantly disturb the PCA procedures. To resolve this issue, we propose a sparse kernel subspace method based on sparse PCA.In order to construct subspaces based only on crucial features, we incorporate sparsity into the eigenvector estimation procedures in the nonlinear feature space. We then construct the subspaces of nonlinear features based on the sparse eigenvectors, and classify the unknown vector by projecting it into the constructed subspace. The proposed method can effectively not only capture nonlinear structures of data but also estimate eigenvectors even in the presence of noisy features. The effectiveness of the proposed method leads to the high-precision of the classification. Monte Carlo simulations and a real data analysis show that the proposed sparse kernel subspace method provides effective results for classification.
Postural instability according to virtual reality program

Background: The popularity of virtual reality (VR) grew rapidly. Short guidelines with a lack of emphasis on safe use appears prior to usage. It is necessary for the user to realize how much potentially dangerous VR is. Aims/objectives: The aim of this study is to investigate the effect of VR on balance in normal people. Materials and methods: Mean equilibrium score (MES) of 15 adults who have normal sense of balance were obtained by using the sensory organization test (SOT). Conditions 1 and 2 were performed. Multiple VR programs were classified as three levels (Easy, Average, and Challenging) by the visual analog scale. Further SOT tests were performed during watching VR programs. MES of each test was used for statistical analysis. Results: MES of condition 1 was significantly higher than condition 2. Although there was no statistical difference between Eye open and Easy program (p = .097), MES of average and Challenging programs showed significantly decreased scores compared to Eye open. In addition, MES of Average and Challenging programs were significantly decreased than that of Easy program. Conclusions and significance: VR can cause postural imbalance to users. It is necessary to establish quantifiable and objective methods to measure imbalance caused by VR use.
A Solution to Scientific Analysis Service in Data Cloud

A design of scientific analysis service based on container technology in data cloud is presented: resource nodes are scaled up by means of automatic registration; resource nodes can be physical hosts or virtual hosts. When the utilization rate of computing resources reaches the threshold, the management node starts resource registration, subsequently, a resource host is to be registered and the available container instances are into the pool. The health of the container instance pool is maintained inside of the host. The instance lifecycle management is performed according to the maximum usage time and maximum silent time from each instance. A user can not only do analysis jobs through the interactive component, the interactive programming component or the interactive application component, which is in a separate container instance for efficient isolation of the resources, but also use data storage clusters and the high-performance computing clusters in data cloud.
P‐41: Border Fade Pattern for a Segmented Active Polarizer Dimmable Lens

Display units that utilize a dimmable smart lens may be utilized to reduce the required luminance compared to a fixed neutral density lens to provide a hidden display appearance. A lens configuration that utilizes an active polarizer in conjunction with a linear polarizer has previously been developed and reported. The use of a segmented active polarizer dimmable lens with a unique border fade pattern is explored.
Improving Security for SCADA and Administrative Networks With Limited Financial Resources

The threat of malicious cyberactivity can motivate utilities to strengthen their network security, which is often lax.
An Evidential Collaborative Filtering Dealing with Sparsity Problem and Data Imperfections

One of the most promising approaches commonly used in Recommender Systems (RSs) is Collaborative Filtering (CF). It relies on a matrix of user-item ratings and makes use of past users’ ratings to generate predictions. Nonetheless, a large amount of ratings in the typical user-item matrix may be unavailable. The insufficiency of available rating data is referred to as the sparsity problem, one of the major issues that limit the quality of recommendations and the applicability of CF. Generally, the final predictions are represented as a certain rating score. This does not reflect the reality which is related to uncertainty and imprecision by nature. Dealing with data imperfections is another fundamental challenge in RSs allowing more reliable and intelligible predictions. Thereupon, we propose in this paper a Collaborative Filtering system that not only tackles the sparsity problem but also deals with data imperfections using the belief function theory.