Reviewerly/exHarmony huggingface dataset:
Strategic narratives in the system of mechanisms for countering informational influence

The study addresses the issue of strategic communications in Ukraine’s information space. It defines strategic communications as a system of informational multi-dimensional interaction with the public on socially relevant topics through diverse mass media outlets directed at promoting national goals. Strategic communications aim at exerting influence on the individual by appealing to the freedom of speech principle and providing strong encouragement towards independent decision-making, as well as by drawing the public’s attention to socially relevant issues and the ways to approach them. Strategic communications entail the emergence of distinct strategic narratives. Therefore, gaining a better insight into strategic narratives allows determining the causes of the socially significant situation and the prospects for its resolution. The paper explores the narrative about the origin of the Ukrainians, which is steadily unfolding in the information space. This narrative represents a distinct multimodal semantic plural aimed at promoting the idea of national identity and statehood. The study has found that different versions of the strategic narrative about the origin of the Ukrainians in the Internet space have similar semantic and conceptual patterns and involve multiple narrators. At the same time, they provide varying amounts of information and commenting features, target different audiences, and, for this reason, require relevant tools of influence, namely argumentation and suggestion. Тhe methods of randomizing ideas, breaking patterns, and creating cognitive dissonance are widely used in the creolized political texts. In the context of information warfare, a powerful strategic narrative, as a rule, instigates the emergence of a counter-narrative, which has an asymmetrical structure. The struggle of narratives has become the dominant feature of modern media space.
FORMATION OF BOOK HERITAGE PRESERVATION PRINCIPLES IN THE CULTURAL INSTITUTIONS OF THE RUSSIAN FEDERATION

The article considers the personal contribution of the outstanding historian of the Russian middle ages Academician Mikhail Nikolaevich Tikhomirov to the national concept of documentary sources (book rarities) preservation. The scientist expressed a generalized view of historical science about how the work on the book heritage preservation at the national level had to be organized most effectively, systemically and “eco-friendly”. From the point of the present-day view on the practice of book rarities preservation in the Russian Federation, he justified the exceptional relevance and proposed the main principles of documents preservation, which are now basic at the law and library practice level. The historical science development determined the establishment of conservation centres in libraries, since a serious obstacle to historical research was the problem of poorly organized and, sometimes, careless storage of historical documents in libraries, museums and archives. In accord to the brief history of scientific conservation in Russia, this time is viewed as the beginning of the most active development of scientific conservation methods and their introduction into library practice. In the middle of the XX century, Tikhomirov proposed and formed the concept of book rarities preservation, consisting of such important components as: scientific research and popularization of book rarities and their value for the society, organization of public and state associations aimed at implementation of these tasks; search and collection of privately owned book rarities on the national scale (archeographic inspection of the territory of Russia), scientific publication of documents, catalogues of scientific descriptions compiling and forming the Union catalogue on their basis; scientific research of the material basis, conditions of ensuring physical safety and arrangement of work on book rarities preservation; legislative and regulatory support of all activities on book rarities preservation.
A Binary Particle Swarm Optimization with the Hybrid S-Shaped and V-Shaped Transfer Function

Binary Particle Swarm Optimization has too strong global search ability and lacks local search ability in the later stage, because it has a unreasonable transfer function. According to the analysis of the transfer function, the hybrid transfer functions of S-shaped and V-shaped function has been proposed to improve the BSPO, and an adaptive mutation method is used to obtain a new binary PSO with hybrid transfer function. The new Binary PSO has global search ability in the early stage, and has local search ability in the later stage. The experiments conducted have proved that it outperforms BPSO.
Learning to Structure an Image with Few Colors

Color and structure are the two pillars that construct an image. Usually, the structure is well expressed through a rich spectrum of colors, allowing objects in an image to be recognized by neural networks. However, under extreme limitations of color space, the structure tends to vanish, and thus a neural network might fail to understand the image. Interested in exploring this interplay between color and structure, we study the scientific problem of identifying and preserving the most informative image structures while constraining the color space to just a few bits, such that the resulting image can be recognized with possibly high accuracy. To this end, we propose a color quantization network, ColorCNN, which learns to structure the images from the classification loss in an end-to-end manner. Given a color space size, ColorCNN quantizes colors in the original image by generating a color index map and an RGB color palette. Then, this color-quantized image is fed to a pre-trained task network to evaluate its performance. In our experiment, with only a 1-bit color space (i.e., two colors), the proposed network achieves 82.1% top-1 accuracy on the CIFAR10 dataset, outperforming traditional color quantization methods by a large margin. For applications, when encoded with PNG, the proposed color quantization shows superiority over other image compression methods in the extremely low bit-rate regime. The code is available at: https://github.com/hou-yz/color_distillation.
Automatic Amharic Part of Speech Tagging (AAPOST): A Comparative Approach Using Bidirectional LSTM and Conditional Random Fields (CRF) Methods

Part of speech (POS) tagging is an initial task for many natural language applications. POS tagging for Amharic is in its infancy. This study contributes towards the improvement of Amharic POS tagging by experimenting using Deep Learning and Conditional Random Fields (CRF) approaches. Word embedding is integrated into the system to enhance performance. The model was applied to an Amharic news corpus tagged into 11 major part of speeches and achieved accuracies of 91.12% and 90% for the Bidirectional LSTM and CRF methods respectively. The result shows that the Bidirectional LSTM approach performance is better than the CRF method. More enhancement is expected in the future by increasing the size and diversity of Amharic corpus.
Towards assessment of histopathology correlation with multiple imaging modalities: A pilot study using a visible mouse

Histopathology is the accepted gold standard for identifying cancerous tissues. Validation of in vivo imaging signals with precisely correlated histopathology can potentially improve the delineation of tumors in medical images for focal therapy planning, guidance, and assessment. Registration of histopathology with other imaging modalities is challenging due to soft tissue deformations that occur between imaging and histological processing of tissue. In this paper, a framework for precise registration of medical images and pathology using white-light images (photographs) is presented. A euthanized normal mouse was imaged using four imaging modalities: CBCT, PET-CT, MRI and micro CT. The mouse was then fixed in an embedding medium, optical cutting temperature (OCT) compound, with co-registration markers and sliced at 50 m intervals in a cryostatmicrotome. The device automatically photographed each slice with a mounted camera and reconstructed the 3D white-light image of the mouse through co-registering of consecutive slices. The white-light image was registered to the four imaging modalities based on the external contours of the mouse. Six organs (brain, liver, stomach, pancreas, kidneys and bladder) were contoured on the MR image while the skeletal structure and lungs were segmented on the CBCT image. The contours of these structures were propagated to the additional imaging modalities based on the registrations to the white-light image and were analyzed qualitatively by developing an anatomical atlas of normal mouse defined using three imaging modalities. This work will serve as the foundation to include histopathology through the transfer of the imaged slice onto tape for histological processing.
A Simulation Study of Gravure Register System with Active Disturbance Rejection Control

Gravure register system is a uncertain system with multi-input-output, non-linear and strong coupling, according to two-color printing register system working principle, a mathematical model of two-color register error in shaftless driving mode is established, and extended to four-color register error model further more. Aiming to this model, an active disturbance rejection control (ADRC) controller and control system for gravure register are designed, then the simulation system is build also. The simulation results show that the designed register control system is able to realize a high precision control for the multi-color register system, this provides a viable option for gravure printing register system which meets to the requirements of high precision, high speed and low reject rate.
A Word Embedding Analysis towards Ontology Enrichment

Embedding is a set of language modeling and feature learning techniques in Natural Language Processing where words or phrases are mapped to vectors of real numbers.This approach could be used in many tasks of Natural Language Processing, such as Text Classification, Part-Of-Speech Tagging, Named Entity Recognition, Sentiment Analysis, and others.In this paper we created different Word Embedding models, using TripAdvisor's hotel reviews.The corpus was pre-processed, in order to reduce noise, and then submitted to four Word Embedding algorithms: Word2Vec, FastText, Wang2Vec, and GloVe.Finally, HOntology concepts and relations are compared with the outputs of models created aiming to improve it, enriching this domain ontology.
The Adventure of BipBop: An Android App Pathfinding Adventure Game

The purpose of this research is to build an android based pathfinding adventure game where user gets information about Indonesian culture, especially the traditional houses including the folk songs and the traditional weapons in certain areas.The method used is by questionnaires, interviews, observation, and literature study.This application also implements the A* algorithm to solve the pathfinding problems.The result of this research is "The Adventure of BipBop" app that can make the user can feel the experience of playing that is not only entertaining but also as a medium to convey about the culture of Indonesia and help preserve the traditional house of Indonesia.Most of the respondents find out that the application can provide Indonesian culture well (91.7%).Furthermore, this application can realize about 84.5% of players to help preserve the traditional culture in Indonesia.
A Tool Development for Test Case Based Code Optimization in Java

Java has been a popular programming language since its first stable release in 1996 because of its platform independence. Along with its popularity Java has been a focus of performance studies since its debut. Developments in hardware has unbelievably advanced the performance of the devices that run Java and thus software performance has lost its popularity until the release of Android OS and rapid increase in mobile device ownership Java language usage has increased once again. Mobile devices having far less system resources compared to personal computers had re-brought software performance studies into the spotlight. However mobile devices have gone into a fast-paced development like all other information technologies and this brought down the need for software performance studies, again. Also, worth mentioning that development of new Java Virtual Machine (JVM) versions has made the specialized compiler studies, which may threaten the platform independency, obsolete except for specific situations. Today it is not enough to consider code optimization solely in terms of performance improvement. Much broader vision is needed like software development processes such as Maintainability, code readability, improving cooperation in multi-programmer projects, software quality assurance.In this study, white box testing approach is adopted as the software testing technique and static code analysis method is selected to ensure line coverage. A new software (JPA) has been developed based on a currently used testing tool (PMD) to improve the user experience.
Ethnicity Classification System Based on Human Face Picture

The paper checks if convolutional neural network, is capable of handling the task of ethnicity recognition. We propose the robust system to point one of four basic ethnic groups with some variants of the person presented in the picture. The system apart from neural network basis contains of image processing module. Training of the network has been done using no high resolution data with restriction to pictures containing only faces positioned in the middle of the photo. We operate using very wide scale of age of the people presented in the photos. The paper also contains description of each selected ethnicity group and how it is affected by geographical position. Finally the reader can find achieved results with discussion which features has the most impact on the network’s final decisions and how these decisions are affected by basic factors.
Image to CAD: Feature Extraction and Translation of Raster Image of CAD Drawing to DXF CAD Format

A CAD drawing has various drawing features like entity lines, dimensional lines, dimensional arrows, dimensional text, support lines, reference lines, Circles, GD&T symbols and drawing information metadata. The problem of automated or semi-automated recognition of feature entities from 2D CAD drawings in the form of raster images has multiple usages in various scenarios. The present research work explores the ways to extract this information about the entities from 2D CAD drawings raster images and to set up a workflow to do it in automated or semi-automated way. The algorithms and workflow have been tested and refined using a set of test CAD images which are fairly representative of the CAD drawings encountered in practice. The overall success rate of the proposed process is 90% in fully automated mode for the given sample of the test images. The proposed algorithms presented here are evaluated based on F1 scores. The proposed algorithm is used to generate user editable DXF CAD file from raster images of CAD drawings which could be then used to update/edit the CAD model when required using CAD packages. The research work also provides use cases of this workflow for other applications.
A Hybrid Recommender System for Steam Games

A recommender system can be considered as an information filtering system that seeks to predict the preference a user would have for a data item. It is commonly utilized in digital stores to recommend products to their users according to the users' previous purchases. This applies to Steam as well, a widely used digital distribution platform for games. The existing recommender system mainly suggests new games to a given user by calculating similarities between games they own and those that they do not. These similarities are based on predefined attributes (game genres). Additionally, the system is able to recommend games based on the game preferences of the user's friends. In this work, we target at creating an enhanced recommender system for Steam. The goal is to design a hybrid approach for producing suggestions that will utilize data, such as playing time, game price and game release date, in addition to the genres and the preferences of friends.
The development of cognitive independence of students based on information technology

In the article the authors consider the development of cognitive independence as the leading axiological motive for teaching students at the university. The authors believe that value attitude to the world allows the student to engage in cognitive activity with the whole variety of his or her inner strengths and capabilities. Based on this, the basis for the development of cognitive independence as a personality trait is to determine the personal meaning, needs and motives of the trainees, which is inextricably linked with the value orientations and axiological potential of the individual. The article also states that cognitive independence successfully develops in the process of studying the discipline “Computer studies” at the university, where technological support for information and educational interaction is carried out by means of Web technologies. The study of the authors of the article shows that the implementation of this idea is based on the creation in the university of an information and communication environment as one of the leading trends in the development of university education and is implemented through the Platonus system, where the teacher’s educational blog was created and forums were created both by the teacher and by themselves students. The authors note that the process of using new information technologies in the development of students’ cognitive independence depends directly on teachers, and therefore, in the future they pay attention to the need to form their information technology culture.
Penerapan Pembelajaran Kooperatif Melalui Model Group Investigation Untuk Meningkatkan Literasi Ekonomi Siswa

This research was motivated by the problems in the low economic literacy of students in economic subjects are supported by still lack of economic literacy of students based on the results of observations. This research aims to reveal the differences in increasing economic literacy of students before being given treatment and after treatment is given through a learning models of Group Investigation (GI) experimental class and conventional learning (lectures) control class. This research is a quantitative research using Quasi-Experimental Design. Economic literacy Data Obtained from the post-test students in the form of essays, Reviews These results indicate that there is a difference between economic literacy of students who applied to the learning model of group investigation with the economic literacy of students who applied to conventional models. Based on the research results using t-test. this matterit can be concluded that the Group Investigation learning models can improve economic literacy. DOI: https://dx.doi.org/10.17977/um014v13i12020p030
Bitcoin: Mystery, History and Background Innovative Technology Involved

Bitcoin has been a renowned and most eminent cryptocurrency since it's inception in 2008. It is a hot topic of debate and created a lot of buzz in the financial world as well as the technology sector. Consequently there are critics as well as admirers of Bitcoin. There are many intricate features like Anonymity, Decentralization, Privacy, Peer to Peer network, Security and Robustness etc which led to the worldwide fame and exponential growth in price of Bitcoin. In this paper we will throw light on the complex features and technology behind Bitcoin. I have performed a comprehensive study of the Bitcoin. I have performed an exhaustive study on the Bitcoin price speculation and technology behind it and found it very interesting and innovative. In this research I will be evaluating all the complex features of the Bitcoin and present it in a limpid and intelligible way. I will try to clear all the buzz around the hype of the Bitcoin.
Clustering Analysis Based on Coyote Search Technique

The clustering technique, which is widely employed for data analysis and data mining, represents the way of gathering similar objects or data with each other according to similar criteria in several clusters. Recently, meta-heuristic optimization techniques have become one of the most common approaches for researchers to solve clustering problems. We propose in this paper a novel algorithm for solving the data clustering problem based on a new optimization technique namely as Coyote Clustering Technique (CCT) inspired by the coyotes’ behaviors. We have studied the proposed method by applying a famous and widely set of data used in this field. The outputs of the proposed Technique proved their efficiency by recording good results in speed, accuracy, and stability.
Optimizing the Pipelined DES Cracker Implemented in Contemporary Popular-Grade FPGA Devices

The paper proposes a highly pipelined architecture of a DES cracking engine where each cipher round is split into two pipeline stages in order to maximize efficiency of operation. The engine is designed for the known plaintext scheme of attack and finds the secret cipher key through brute-force exhaustive search of the entire key space. The paper presents the idea of intra-round cipher pipelining and organization of the entire cracking engine which is especially tuned to be able to keep up with high clock frequencies of the pipelined decrypters, comparing it with the standard pipelined case where each cipher round is a single stage. The proposed solution is implemented in the two largest Spartan-7 devices and the analysis also considers how efficiently the implementation tools handle increasing size of the crackers with rising number of parallel decrypters. The results illustrate progress in the hardware cipher implementation systems which was made possible by advances in recent FPGA families, using the DES cracking task as a testbed.
DeepHuMS: Deep Human Motion Signature for 3D Skeletal Sequences

3D Human Motion Indexing and Retrieval is an interesting problem due to the rise of several data-driven applications aimed at analyzing and/or re-utilizing 3D human skeletal data, such as data-driven animation, analysis of sports bio-mechanics, human surveillance etc. Spatio-temporal articulations of humans, noisy/missing data, different speeds of the same motion etc. make it challenging and several of the existing state of the art methods use hand-craft features along with optimization based or histogram based comparison in order to perform retrieval. Further, they demonstrate it only for very small datasets and a few classes. We make a case for using a learned representation that should recognize the motion as well as enforce a discriminative ranking. To that end, we propose, a 3D human motion descriptor learned using a deep network. Our learned embedding is generalizable and applicable to real-world data - addressing the aforementioned challenges and further enables sub-motion searching in its embedding space using another network. Our model exploits the inter-class similarity using trajectory cues, and performs far superior in a self-supervised setting. State of the art results on all these fronts is shown on two large scale 3D human motion datasets - NTU RGB+D and HDM05.