Reviewerly/exHarmony huggingface dataset:
Scholarly Resources Structuring: Use Cases for Digital Libraries

With the growing generation of links between scholarly resources, information infrastructures such as Digital Libraries (DL) are compelled to explore the potential of data (re)usability. Coupled with the need for increased (research) transparency and reproducibility, linked scholarly resources offer major convenience to researchers in their daily research work. In this way, it is easier for them to get the different research artifacts – be it publication, dataset, workflow, etc. – that form the complete research picture and (re)use any/all of its parts in their work. In this paper, we explore the potential from harnessing such links for a DL environment, model them based on an emerging standard, and represent and publish them via the Semantic Web technology stack. Moreover, to highlight our unique approach for realization of scholarly link collection, we present few use cases to illustrate the potential for a DL environment. Through this study we claim that by adoption of links as new resources, DLs can extend their collection and/or services for their users.
A New Approach to Navigation of Unmanned Aerial Vehicle using Deep Transfer Learning

With the advancement of Unmanned Aerial Vehicles (UAVs) and their application in the most diverse areas, the demand for increasingly better precision in their positioning and navigation task has arisen. Based on this context, this article proposes a new approach for localization and navigation UAVs using topological maps and Convolutional Neural Networks (CNN). CNN is used as features extractor, according to the concept of Transfer Learning. The use of topological maps helps to guide the vehicle through the exploration environment. To evaluate the performance of the approach, parameters such as Accuracy, F1-Score, and processing time are considered. For the classification were used Bayesian Classifier, k-Nearest Neighbor (kNN), Multi-layer Perceptron (MLP), Optimum-Path Forest (OPF) and Support Vector Machine (SVM). The results show that CNN achieved 99.97% Accuracy and also F1-Score in combination with most of the considered classifiers, proving the effectiveness of our approach.
Few-photon transport in strongly interacting light-matter systems: A scattering approach

Engineering strong photon–photon interactions at the quantum level have been crucial in various areas of research, notably in quantum information processing and quantum simulation. It is often done by coupling matter strongly to light. A promising way to achieve this is via waveguide quantum electrodynamics (QED). Motivated by these advancements, we study few-photon transport in waveguide QED setups. First, we present a diagrammatic technique to systematically study multiphoton scattering based on the scattering formalism and Green’s function approach. We demonstrate our proposal through physically relevant examples involving scattering of few-photon states from two-level emitters as well as from arrays of correlated Kerr nonlinear resonators described by the Bose–Hubbard model. In the second part, we apply the diagrammatic technique that was developed to perform a comprehensive study on a Bose–Hubbard lattice with a quasi-periodic potential. This model exhibits many-body localisation. We compute the two-photon transmission probability and show that it carries signatures of the underlying localisation transition with close agreement to the participation ratio of the eigenstates. The systematic scattering approach provided in this paper provides a foundation for future works at the interface between quantum optics and condensed matter.
GENDER LINGUISTICS IN THE LINGUO-LIMOLOGICAL ASPECT (BY THE MATERIAL OF THE ENGLISH-LANGUAGE NETWORK DISCOURSE)

The article shows that the gender-marked English-language network discourse can be represented as the Internet memes (textual units with indefinite subjectivity and short receptive phase addressed to universal audience). It is shown that gender nature of the Internet meme can manifest itself in different ways. The gender Internet meme is characterized by diffusion of its formal and (or) ontological boundaries. Such a feature of gender Internet memes is actualized only within the analysed subset while unveiling gender discourse, which in its turn is determined by limological parameters.
Hardware Implementation of Elliptic Curve Cryptosystem Using Optimized Scalar Multiplication

This paper presents a hardware implementation of Elliptic Curve Cryptography (ECC) with optimized scalar multiplication. In elliptic curve cryptography, scalar multiplication is an important and most time-consuming operation that dominates the ECC performance. In this paper, the scalar multiplication is carried out using the Vedic multiplier for finite field multiplication operation to improvise the performance. The proposed architecture is implemented and evaluated for the performance evaluation parameters—area, delay, and power consumption. To evaluate the efficiency of the proposed design, the results are compared with Karatsuba based ECC design. The comparative results show that ECC using Vedic multiplier outperform then Karatsuba based ECC for the area, delay and power consumption. The elliptic curve cryptosystem is implemented over GF(2m) binary field for B-233 field size, which is more secured according to NIST Digital Signature Standards. The cryptosystem is designed in Verilog HDL, synthesized and simulated using Xilinx 13.2 IDE on Virtex6 FPGA device.
Preserving Authentication and Access Control by Using Strong Passwords Through Image Fusion Mechanism

In the current digital world, huge amount of data flow takes place among various communicating entities. Here to establish access control or authentication is very desirable aspect in information security. This can be achieved by generating strong password. Usually users use the passwords that are easy to remember and recollect because they no longer memorize the passwords having good strength. A strong password string contains the items such as letters in capital case, letters in small case, numbers, operators and special symbols. Therefore, in the current work a huge pool of images is chosen from which the password string can be generated. Every time when the proposed system is used to generate the passwords the user has the freedom of taking different sets of images. Later all these images will be fused using image fusion mechanism to obtain the fused image, then on this fused image visual key cryptography will be performed to generate multiple shares. Finally, one of these multiple shares will be used to generate strong password string. By using this approach the authentication for the secret information will be preserved.
COMPONENTS OF READINESS OF FUTURE TEACHERS OF NATURAL MATHEMATICAL DISCIPLINES TO THE APPLICATION OF VISUALIZATION TECHNOLOGIES IN SUBJECT-PROFESSIONAL ACTIVITY

The article highlights the components of the future teacher of natural and mathematical subjects' readiness for the use of visualization technologies in the educational process of the secondary school (motivational-value, cognitive, operational-activity, practical-activity, reflexive-evaluation). According to the components outlined, criteria are set and indicators through which the relevant criteria are reflected. Indicators of motivational value criterion include the desire to: realize the importance of using visualization technologies in the educational process; overcoming difficulties and volitional efforts in the process of introducing visualization technologies in the educational process; interest in acquiring knowledge, skills and skills in mastering new tools of visualization technologies and mastering new forms of learning using these technologies; display of creativity in solving professional problems. The cognitive criterion is represented by indicators of knowledge: psychological and age characteristics of pupils' perception of visual materials; didactic potential of imaging technologies; methods and rules of structuring, compaction and principles of distribution of educational material; the basics of design (rules and techniques of composition and color); classification of online visualization tools for use in the learning process; the system of requirements that the visualization used in the educational process must meet. The indicators of the operational-instrumental criterion include the following skills: effective presentation of educational material by creating cognitive visual anchors and visual metaphors; store received and processed visual information in an appropriate form; to transmit information for collective use in a clear visual way and in different visual formats; create your own visuals; apply a system of requirements for the creation of visual content; possess a wide range of tools. The practical-activity criterion is represented by the ability to: use visualization technologies in the process of lesson design, to adapt the ready didactic visual aids to the content and purpose of the lesson, the contingent of students; create objective tasks on ready-made visual materials, etc .; to justify the pedagogical expediency of using visualization technologies in the lesson; organize educational, research, reflexive and other activities of students with their use; to provide students with assistance appropriate to their needs. The last criterion is a reflexive evaluation, which requires a critical assessment of: the feasibility of forms and methods of teaching based on visualization technologies for their use in the educational process; the quality of their own preparation for the use of visualization technologies in the educational process and identifying ways to improve this training; monitoring of new methods and tools for creating visual materials; systematic correction of their own preparation for the use of visualization technologies in subject learning.
Efficient Learning for Distillation of DNN by Self Distillation

Knowledge distillation is a method to create a superior student by using knowledge obtained from a trained teacher neural network. Recent studies have shown that much superior students can be obtained by distilling the trained student further as a teacher. Distilling the knowledge through multiple generations, however, takes a long time for learning. In this paper, we propose a Self Distillation(SD) method which can reduce both the number of generations and learning time for knowledge distillation. In SD, the most accurate network is obtained during intra-generation learning, and it is used as a teacher of intra-generational distillation. Our experiments for image classification task demonstrate that the proposed SD acquires high accuracy with fewer generations and less learning time than the conventional method.
Fonts Style Transfer using Conditional GAN

A font is an important element in designing printed materials including texts, such as documents, posters, leaflets, pamphlets, etc. Recently, many digital fonts with different styles are available for desktop publishing, but the number of Japanese-language fonts is smaller than that of European ones. This causes a problem when designing the materials including Japanese and European letters. Creating a new font is difficult and requires specialized knowledge and experience. Our research goal is to address this problem by transferring styles of the European fonts to Japanese characters by using a neural network. In this paper, we report some experimental results using the well-known deep learning framework called "pix2pix."
Automatic blotch removal using a perceptual approach

One of important issue needs attention in the area of old film storage and restoration is blotch handling. In this work, a new proposal including an automatic detection method and inpainting scheme is introduced. First, a technique for automatically detecting blotches based on local changes of pixels on consecutive frames is applied. Specifically, a two-stage Simplified Ranked Order Difference detector is proposed to identify blotches on frames. Next, an improved inpainting was applied to restore the blotches so that it is undetectable by viewers. Our proposal is executed automatically without extenal parameters. The proposal has been tested on a serial of natural images with different sizes and resolutions. Experimental results show that our approach has successfully removed with fairly high accuracy and quite smooth restored blotches. Based on result analysis, the proposal has many potential and applications in the future. Index Terms-blotch detection, blotch removal, restoration, inpainting.
An Improved Multiple Viewshed Algorithm for Region Partitioning for Filtering Candidate Viewpoints

The site planning based on multiple viewshed analysis is to select the minimum number of viewpoints to ensure the maximum visual coverage over a given terrain. However, with the acquisition of high-resolution terrain data, the number of terrain points will become naturally larger and larger. In this article an improved algorithm is presented based on region partitioning for filtering candidate viewpoints of a multiple viewshed. In our algorithm the viewshed overlap coverage among the viewpoints within a region is considered and an evaluation model of viewshed contribution quality of a viewpoint is proposed. The experimental results show that the presented algorithm has a great increase in total viewshed coverage rate.
Validity of Android-Based Learning Media in Subject Measurement and Instrumentation

Abstract Electrical Measurement and Instrumentation is an important thing that must be mastered by electrical engineering students. But the problem that arises is the decreased interest in reading books, so that students need the other ways of learning. Then by utilizing the development of android technology, this study aims to develop the android based learning media to assist students to understanding the material. This research was adopted 4D model that have 4 development steps (define, design, develop, disseminate). Validity of the media was measured by instrument of validity that had been validated before. The average calculation result from the validity analysis of the learning media is 85% with valid category. So it can be concluded that android based learning media on Electrical Measurement and Instrumentation course was valid to be used as a learning media.
Scenario Creation with Room Labelling: Iterative and Recursive Comparison

In a lot of sciences is always necessary to determine the performance of new algorithms, profiling is a useful tool during the process of development and testing. Is very helpful to have a huge testing dataset to ensure that the proposed methodology is robust for a wide range of possible scenarios. In most of the public dataset the number of scenarios to probe new algorithms is limited and not standard, because of that, in this work is done a comparative between two ways to create scenarios with the characteristic before mentioned: The recursive and the iterative one.
Cross-Border Trade Through Blockchain

The emergence of blockchain technology has created a buzz and a mind shift for organizations and industries. Governments and international bodies are increasingly discussing the potential implications of blockchain’s distributed ledgers on business, governments and the economy. In this paper we present theoretical study that investigates whether the technology is ready to disrupt the way business is conducted or we still at an early stage. Distributed ledgers are possibly most useful in scenarios involving multiple parties where items change their state or status frequently. These scenarios involve parties who are generally distributed and using different technologies, systems and applications. The intended purpose of the technology is to connect business network members in a decentralized fashion. An essential element of this network is adherence to a common protocol for validation of blocks. By distributing data across the network, the blockchain eliminates the risks of centralization. The technology enables exchange and sharing of information without the involvement of intermediaries acting as arbitrators. Powered by its decentralized nature, the network will not have any single point of failure and will not rely on any single entity. Multiple industries and organizations are attempting to obtain a lead position by adopting the technology in their business or within the supply chain. However, due to various technical, social and political challenges little progress has been made thus far. Although the technology of blockchain holds a lot of promise it’s still not mature enough for general implementation. Once enough evidence proves the effectiveness of the technology for different industries, the adoption level is expected to increase and the benefits will be better understood.
Research on Influencing Factors of Digital Signal Modulation Recognition

In the real environment, modulation recognition has low classification recognition rate under low SNR and is affected by many factors such as symbol rate, frequency offset and adjacent ...
Design of a Full Body Tracking-Based VR Fighting Game

With the widespread distribution of Virtual Reality (VR) devices, the number of VR users and the size of the VR contents market are steadily increasing. In response to the users’ demand, full-body tracking technologies using additional peripheral devices have been developed. Games using such technologies have begun their development as well. In order to make the games more immersive, it is imperative to develop further technologies related to full-body tracking. This paper involves the design of a VR game supporting full-body tracking. Furthermore, additional systems that are implemented in the game are also developed by designing a new submotion system. The proposed submotion system recognizes the user’s motion by dividing it into small submotions. Based on this, this paper proposes advanced damage and attacking systems. In addition to making the games more fun and immersive, the proposed system will allow for the research and development of an engine applicable to full-body tracking VR contents.
Active Learning for Evolutionary Constrained Clustering

The high cost of labeling data for analysis has increased interest in semi-supervised learning. One of its most common types is constrained clustering, which is a type of learning that does not rely on class labels for a group of objects. Instead, there is only information if some pairs of objects must be in the same cluster or in different clusters. In some applications, identifying such constraints involves reduced cost since it is less information than a class label. At the same time, Active Learning (AL) aims to minimize the cost of creating labeled datasets, trying to identify which unlabeled data are more relevant for using during the learning process, considering the labels that are already available. This paper proposes three AL strategies to an evolutionary constrained clustering algorithm (FIECE-EM) based on Gaussian Mixture Models (GMM). Experiments were executed on 10 well-known datasets, as a way to measure the impacts of each strategy. We compare the results with baseline supervised algorithms as well as COBRAS, a state-of-the-art Active Learning algorithm for constrained clustering. Two of the proposed strategies obtained significantly better results than COBRAS in our empirical evaluation. Thus, the combination of FIECE-EM with these strategies can be considered viable alternatives for AL in a constrained clustering setting.
Knowledge retention through observation of instant messaging systems

Knowledge is the most valuable asset in today's organizations. Since it offers an unbeatable competitive advantage, valuable knowledge demands strict management principles to avoid being lost. Instant messengers provide an opportunity to gather knowledge passed through individuals in the organization. By modeling that knowledge using machine learning techniques, it becomes possible to retain and make it ubiquitous throughout the organization. This paper presents a solution for gathering, modeling, and retrieving knowledge associated with the technical support in organizations, using machine learning algorithms. The solution comprises the architecture, data preparation techniques and machine learning algorithms. The experimental evaluation exhibits the algorithms with better performance for this class of problems.
Comparative Study of PSO-Based Hybrid Clustering Algorithms for Wireless Sensor Networks

Clustering is a task which creates groups depending upon the presence of similarity between the data objects. Many clustering algorithms exist, which are capable of creating well-defined clusters. One of the popular algorithms is K-means, which is generally used for data clustering where performance is dependable on initial state of centroid but have limitation of trapping in local optima. Besides K-means, K-harmonic means, and Fuzzy C-means are also popular algorithms used for data clustering but again they have the same limitation of trapping in local optima. So this creates problem while handling anomaly existing dataset in wireless sensor network. In this paper, an analysis of best suitable hybrid clustering algorithm is brought for a congregation of normal and anomalous dataset by using a stochastic tool Particle Swarm Optimization (PSO) by utilizing different sensor datasets. The results are encouraging in terms of best suitable fitness function and low computational time.
Designing A Generating System Multiple Choice Questions

Abstract The purpose of this research is to build a system of generating questions that can make as many questions as possible. The Generating System of this Problem can be used starting from elementary/middle/high school/higher education level with the type of choice questions that have 3 components, namely the problem, choice and completion. In order for the system to function properly, a template is needed for the three components above that can identify the needs of variables that might appear on a problem. Several variables are obtained from the identification of the results, namely: Numerical Variables, String Variables, Image Variables. Templates needed on the system are 3 templates, namely the template, template selection and completion template. All those three contain text, variables and identification tags. Variables that are permitted in this template are Numerical Variables, String Variables and Image Variables. While the identification tags that are permitted are numerical [var] numeric [/var]; variable tag string [str]collectname[/str]; the variable tag image [img]filename[/img], the tag tocalculate [eval]equation[/eval] to be executed and generating the value and tag equation [equ]equation[/equ]that will be displayed as an equation. It was found in the result of the implementation that one template able to build hundreds/thousands of questions, combination of choices and the completion of each question.