Reviewerly/exHarmony huggingface dataset:
Development of super-professional competencies of the production personnel in the context of university supplementary education programs

Abstract In the article, the authors explore development of super-professional competencies of the production personnel of enterprises of the military-industrial complex. Here are the most popular supplementary education programs implemented by UrFU and UrGEU and ensuring formation of professional and super-professional competencies effectiveness of which ensures competitive ability of employees and ability of enterprises themselves to switch over to the neo-industrial way of their development. Authors also suggested an express-method of assessing a quality of development of super-professional competencies of personnel in the context of universities. This method has been tested at production personnel of the enterprises of JSC EVRAZ NTMK and JSC ‘Research and production corporation ‘UralVagonZavod’ who listened to supplementary education programs. Results that have been received confirm necessity of creation of centers of supplementary vocational education expanding implementation of spectrum of competence-based university programs for development of super-professional competencies of the production personnel of enterprises of the military-industrial complex.
A Brief Analysis of the Memorial Space Design of the South China Education History Pingshi Research Base

Abstract Through literature and the public, this article had in-depth understanding of the historical events of South China education and followed three design principles of memorial space: respect for history, emotional resonance and sustainable development. Then this article had overall planning and landscape nodes design for the South China Educational History Research Base, realized the historical information spatialization, inherited and carried forward the spirit of patriotism, so that more people could recognize and remember this history. This article through analyzed and concluded the 4 design methods of the Research Base project as following: narrative, symbolism and metaphor, white space and public participation, which can provide a certain reference for the design of memorial space in the later stage of the project and other similar projects.
Breaking Reciprocity in Integrated Photonic Devices Through Dynamic Modulation

Nonreciprocal components, such as isolators and circulators, are crucialcomponents for photonic systems. In this article we review theoretical andexperimental progress towards developing nonreciprocal photonic devices basedon dynamic modulation. Particularly, we focus on approaches that operate atoptical wavelengths and device architectures that have the potential forchip-scale integration. We first discuss the requirements for constructing anisolator or circulator using dynamic modulation. We review a number ofdifferent isolator and circulator architectures, including waveguide andresonant devices, and describe their underlying operating principles. We thencompare these device architectures from a system-level performance perspective,considering how their figures of merit, such as footprint, bandwidth,isolation, and insertion loss, scale with respect to device degrees of freedom.
Denial of Service (DOS) Attack Identification As A Means To Be A Capable Guardian

The presence of computer program equipment intended to ensure the security of the operation of a business or company is surely an inevitability. This tool is a form of the presence of adequate supervision in overcoming deviant behavior (crime) in the midst of changes that occur in society. Routine Activitist Theory analysis has reminded that losses that occur in society such as financial chaos in the Indonesian banking sector can be traced through the identification of attacks on Denial of Service. As well as a combination of other factors in the form of identification is carried out on the motivated offender and the appropriate target (suitable target).
Medical Image Segmentation Algorithm Based on Information Combined Level Set

Medical images are difficult to observe due to blurred organ and tissue boundaries and uneven grayscale in the area, which interferes with the diagnosis of the disease. In this paper, a level set segmentation method combining information is proposed for the redundant effect caused by the segmentation of organ tissue in a complex environment. Simulation results show that, when the improved algorithm is used to segment medical images with blurred borders and uneven grayscale, although it requires a lot of calculation, it obviously sup- presses the redundancy effect and improves the accuracy and efficiency of the segmentation algorithm.
Special Issue Developments in Language Theory 2018 — Preface

International Journal of Foundations of Computer ScienceVol. 31, No. 06, pp. 663-665 (2020) No AccessSpecial Issue Developments in Language Theory 2018 — PrefaceShinnosuke SekiShinnosuke SekiDepartment of Computer and Network Engineering, University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 1828585, Japanhttps://doi.org/10.1142/S0129054120020025Cited by:0 Next This article is part of the issue: Special Issue: Developments in Language Theory 2018 AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Remember to check out the Most Cited Articles! Check out these Handbooks in Computer Science FiguresReferencesRelatedDetails Recommended Vol. 31, No. 06 Metrics History PDF download
The Harmonic Edit Distance

This short note introduces a new distance between strings, where the cost of an insertion or deletion is inversely proportional to the string length. It improves upon previous results by admitting a simple, explicit formula involving only the length of the longest common subsequence and satisfying the triangle inequality at the same time, while not requiring any parameter tuning.
A Fast and Scalable Authentication Scheme in IoT for Smart Living

Numerous resource-limited smart objects (SOs) such as sensors and actuators have been widely deployed in smart environments, opening new attack surfaces to intruders. The severe security flaw discourages the adoption of the Internet of things in smart living. In this paper, we leverage fog computing and microservice to push certificate authority (CA) functions to the proximity of data sources. Through which, we can minimize attack surfaces and authentication latency, and result in a fast and scalable scheme in authenticating a large volume of resource-limited devices. Then, we design lightweight protocols to implement the scheme, where both a high level of security and low computation workloads on SO (no bilinear pairing requirement on the client-side) is accomplished. Evaluations demonstrate the efficiency and effectiveness of our scheme in handling authentication and registration for a large number of nodes, meanwhile protecting them against various threats to smart living. Finally, we showcase the success of computing intelligence movement towards data sources in handling complicated services.
Development of a Person Search Components Based on an Omnidirectional Camera and a 3D-LiDAR

In this paper, we propose a method to search the specified person using a spherical camera and a 3D range sensor. This is one of the items of the Tsukuba Challenge. By using the camera and the sensor, the view field becomes wider than the conventional system. We employed color information to reduce the computational load in image processing. In addition, we focused on the view size of the target person to improve the accuracy of LiDAR. The proposed component is implemented as a ROS-based application, which allows for rapid verification as well as rapid prototyping. The validity of the proposed algorithm was confirmed by preliminary experiments simulating the Tsukuba challenge environment.
Robustified Domain Adaptation

Unsupervised domain adaptation (UDA) is widely used to transfer knowledge from a labeled source domain to an unlabeled target domain with different data distribution. While extensive studies attested that deep learning models are vulnerable to adversarial attacks, the adversarial robustness of models in domain adaptation application has largely been overlooked. This paper points out that the inevitable domain distribution deviation in UDA is a critical barrier to model robustness on the target domain. To address the problem, we propose a novel Class-consistent Unsupervised Robust Domain Adaptation (CURDA) framework for training robust UDA models. With the introduced contrastive robust training and source anchored adversarial contrastive losses, our proposed CURDA framework can effectively robustify UDA models by simultaneously minimizing the data distribution deviation and the distance between target domain clean-adversarial pairs without creating classification confusion. Experiments on several public benchmarks show that CURDA can significantly improve model robustness in the target domain with only minor cost of accuracy on the clean samples.
Examining and Mitigating Kernel Saturation in Convolutional Neural Networks using Negative Images

Neural saturation in Deep Neural Networks (DNNs) has been studied extensively, but remains relatively unexplored in Convolutional Neural Networks (CNNs). Understanding and alleviating the effects of convolutional kernel saturation is critical for enhancing CNN models classification accuracies. In this paper, we analyze the effect of convolutional kernel saturation in CNNs and propose a simple data augmentation technique to mitigate saturation and increase classification accuracy, by supplementing negative images to the training dataset. We hypothesize that greater semantic feature information can be extracted using negative images since they have the same structural information as standard images but differ in their data representations. Varied data representations decrease the probability of kernel saturation and thus increase the effectiveness of kernel weight updates. The two datasets selected to evaluate our hypothesis were CIFAR- 10 and STL-10 as they have similar image classes but differ in image resolutions thus making for a better understanding of the saturation phenomenon. MNIST dataset was used to highlight the ineffectiveness of the technique for linearly separable data. The ResNet CNN architecture was chosen since the skip connections in the network ensure the most important features contributing the most to classification accuracy are retained. Our results show that CNNs are indeed susceptible to convolutional kernel saturation and that supplementing negative images to the training dataset can offer a statistically significant increase in classification accuracies when compared against models trained on the original datasets. Our results present accuracy increases of 6.98% and 3.16% on the STL-10 and CIFAR-10 datasets respectively.
Understanding Pure Character-Based Neural Machine Translation: The Case of Translating Finnish into English

Recent work has shown that deeper character-based neural machine translation (NMT) models can outperform subword-based models. However, it is still unclear what makes deeper character-based models successful. In this paper, we conduct an investigation into pure character-based models in the case of translating Finnish into English, including exploring the ability to learn word senses and morphological inflections and the attention mechanism. We demonstrate that word-level information is distributed over the entire character sequence rather than over a single character, and characters at different positions play different roles in learning linguistic knowledge. In addition, character-based models need more layers to encode word senses which explains why only deeper models outperform subword-based models. The attention distribution pattern shows that separators attract a lot of attention and we explore a sparse word-level attention to enforce character hidden states to capture the full word-level information. Experimental results show that the word-level attention with a single head results in 1.2 BLEU points drop.
Microbial Keywording

Drawing on object-oriented-ontology we aim for a hybrid language that not only processes formal symbols but interacts with the microbes in the speaker’s mouth. We argue that the metaphors historically used to frame the relationship between oral microbiota and speech cannot account for the co-creative material relationship between human speech and microbial, environmental and biotechnological needs. In our performances we harvest oral yeast microbes from the audience. Via a spectrogram, repetitively spoken phonemes drive pattern pumps, which add pheromones to the microbes, the latter which then are faded out. In the microbes, for some replication cycles an ecological adaptation to the individual phonemes persists, affirming – in our definition – some phonemes and deleting others, thereby improving the eco-sphere in the mouth by changing the alphabetical order of the input phonemes. The audience observes the adaptation by respelling words. We propose ‘microbial speech’ as a category between semantic and phonetic meaning, with a transcorporeal mattering between speech and the body as a new ecolinguistic quality. We aim at a language becoming a biological state in order to protect its own ecology. We consider these collective posthuman forms of relationality, subjectivity and biopolitical praxis as multiple ecologies of belonging and a more ethical mode of microbes existing in-common with language, affirming the transversal relations of all living and non-living matter.
Quality Assessing Methods of Equipment Samples of the National Economy of Ukraine

In the abstract of the report “Methods for assessing the quality of samples Techniques of the National Economy of Ukraine”, issues related to the search for effective methods for assessing the state of the samples of the technology of the national economy of Ukraine, determining the rational organization of their technical, including metrological, service, as well as the optimal values of the main parameters of metrological service of the samples of the technology of the national economy of Ukraine are considered.
Library Guides: ENGL 1101 - LAVENDER: Find Reliable Internet Sources

Library guide for Professor Lavender's class when to use internet sources for a research project
GOCDB - new communities, new requirements, new architecture

GOCDB is the official repository for storing and presenting EGI and WLCG topology and resource information. It is a definitive information source, with the emphasis on user communities to maintain their own data. It is intentionally designed to have no dependencies on other operational tools for information. In recent years, funding sources and user communities have evolved and GOCDB is developing to meet the resulting new requirements, such as allowing more programmatic updates to the data within GOCDB. We will explain the roadmap for developing GOCDB and the motivation for the changes on that Roadmap. As GOCDB, and the team supporting it, has evolved, we have re-examined the underpinning architecture for GOCDB. We will set out the changes we have made as a result.
Node Similarity Preserving Graph Convolutional Networks

Graph Neural Networks (GNNs) have achieved tremendous success in various real-world applications due to their strong ability in graph representation learning. GNNs explore the graph structure and node features by aggregating and transforming information within node neighborhoods. However, through theoretical and empirical analysis, we reveal that the aggregation process of GNNs tends to destroy node similarity in the original feature space. There are many scenarios where node similarity plays a crucial role. Thus, it has motivated the proposed framework SimP-GCN that can effectively and efficiently preserve node similarity while exploiting graph structure. Specifically, to balance information from graph structure and node features, we propose a feature similarity preserving aggregation which adaptively integrates graph structure and node features. Furthermore, we employ self-supervised learning to explicitly capture the complex feature similarity and dissimilarity relations between nodes. We validate the effectiveness of SimP-GCN on seven benchmark datasets including three assortative and four disassorative graphs. The results demonstrate that SimP-GCN outperforms representative baselines. Further probe shows various advantages of the proposed framework. The implementation of SimP-GCN is available at \url{https://github.com/ChandlerBang/SimP-GCN}.
Development of a database for educating athletes in university sports

[Purpose] The aim of this study was to investigate the development of a database and system for easily creating documents for educational guidance on a personal computer and to assess the efficacy of such a system. [Participants and Methods] Thirty-seven athletic trainers of students who belonged to an athletic department participated in the study. We used FileMaker Pro 17 Advanced for document creation, and data on independent exercises for athletic rehabilitation were used for database creation. The participants created educational documents for the athletic rehabilitation of ankle sprains using paper media and this system. We surveyed the following items and compared them between the groups: 1) time taken to create the documents, 2) difficulty of creating menus for educational coaching, and 3) the potential for ongoing provision of self-training menus. [Results] The preparation times for creating the rehabilitation menu were significantly lower using the database than using paper media. As a result of the questionnaire survey, we found that using the database was significantly better than using paper media in terms of ease of menu creation and the potential for ongoing provision of self-training menus. [Conclusion] Our results suggest that educational coaching for rehabilitation using a database would be valuable for student athletic trainers.
Bidirectional quantum teleportation of arbitrary single qubit state based on the channel matrix parameter

Quantum teleportation is a procedure that allows one party (Alice) to send a quantum state to her friend (Bob) without that state being transmitted in the usual sense. In this study, bidirectional quantum teleportation of an arbitrary single-qubit state is using 4 qubit channels. The special channel is chosen for the bidirectional quantum teleportation by using the Channel Matrix Parameter, then the product state between information and channel has interacted. Furthermore, measurements were made using Bell's measurement bases by Alice and Bob. The measurement results (with Pauli matrices σx, σy, σz) are communicated classically between Alice and Bob, so that information obtained by accordance with the information sent.
Tomography of quantum dots in a non-hermitian photonic chip

Quantum optical information systems offer the potential for secure communication and fast quantum computation. To fully characterise a quantum optical system one has to use quantum tomography.1 The integration of quantum optics onto photonic chips provides advantages such as miniaturisation and stability, significantly improving quantum tomography using both re-configurable, and more recently, simpler static designs. These on-chip designs have, so far, only used probabilistic single photon sources. Here we are working towards quantum tomography using a true deterministic source - an InGaAs quantum dot.
MEASURING ENGLISH PROFICIENCY OF INDONESIAN BANKS’ FRONT-LINERS

The industrial revolution and the free market policy agreement among the countries in the world brings severe impacts in many sectors, especially economics. As one of the supporting economics systems, the banking sector also faces the most significant challenge ever. The banks should provide their best services to the customers; otherwise, they will quickly leave them. This study determines how the front-liners’ (Customer Services and Tellers) can speak English to support their workplace activities. There are ten customer services and eight tellers from four banks as the respondents. The method used is quantitative, with a structured interview as the instrument to collect data. The researcher then measured the result of the talks by using a scoring rubric. The study's development reveals that those front-liners have different English proficiency levels, ranging from high to low. The banks where they work and the universities where they learned are responsible for overcoming this. It could be by restricting the new employee's entrance exam, providing training programs for the employees, and updating the universities' curriculum with the latest material to prepare their students to face the job market, which is very demanding today.
The Essence of Project Education at the English Lessons in Higher Education Institution

The article reveals the essence of project education in the classroom in the foreign (English) language of students of non-linguistic specialties of the University. Project training is considered by the author as one of the effective teaching methods that allows you to transform academic knowledge into real life experience of students. The author draws attention to the fact that the inclusion of project activities in the educational process of students of the university contributes to the formation and development of information skills that quite effectively fits into the educational process, carried out in the form of a workshop, effective if all the stages of the project activities that necessarily include the presentation. The implementation of project training was carried out on the basis of the University. A sample of the study consisted of 20 first-year students of profile "Lawyer". The study took place in three stages. At the organizational stage, primary diagnostics of the communicative competence development level was carried out through testing and a communicative case. At the second stage, the project activity was carried out within one semester within the framework of the four topics in accordance with the thematic plan of the English language program: “Trip planning”, “The development of the ecological situation”, “Which is better: city or in the countryside?”, “Young people in modern society”. At the third stage, re-diagnosis was carried out by means of testing and a communicative case.
Guarantees on Nearest-Neighbor Condensation heuristics.

Abstract The problem of nearest-neighbor condensation aims to reduce the size of a training set of a nearest-neighbor classifier while maintaining its classification accuracy. Although many condensation techniques have been proposed, few bounds have been proved on the amount of reduction achieved. In this paper, we present one of the first theoretical results for practical nearest-neighbor condensation algorithms. We propose two condensation algorithms, called RSS and VSS, along with provable upper-bounds on the size of their selected subsets. Additionally, we shed light on the selection size of two well known condensation algorithms, called MSS and FCNN, and compare them to the new algorithms.
Report on ICT in Education in the Republic of Lithuania

Lithuania is a European country located in the Baltic region. It is situated along the southeastern shore of the Baltic Sea, to the east of Sweden and Denmark. Lithuania covers an area of 65,200 km2. It has around 99 km of sandy Baltic Sea coastline. Along with it Lithuania has a major warm-water port in the city of Klaipėda.