Reviewerly/exHarmony huggingface dataset:
A study on the Management of Online Exhibition in the Era of New Normal after COVID-19

Purpose - The purpose of this study is to find out the status of online exhibition in the era of new normal after COVID-19 by case study on the online academic convention of Y college. This research is performed to examine the management of online graduation exhibition in the era of new normal after COVID-19.Design, data, and methodology -An experimental design was used by case analysis of Online academic convention at Y College.Result - The results showed that, first, this research is performed to manage online graduation exhibition instead of offline graduation exhibition. It will be available to use online forum, webina and online seminar as a class. Second, it is necessary to reorganize curriculum development to increase the production of online contents and practical use of ability. Third, it must be used to study of lecture, feedback, sharing data, interaction. Also, it is not satisfied to learning effect, communication and technical problem in online academic convention. It is necessary to active mutual communication and discussion.Conclusions - The result offer some insights into the management of online exhibition.
Narrative Plan Generation with Self-Supervised Learning

Narrative Generation has attracted significant interest as a novel application of Automated Planning techniques. However, the vast amount of narrative material available opens the way to the use of Deep Learning techniques. In this paper, we explore the feasibility of narrative generation through self-supervised learning, using sequence embedding techniques or auto-encoders to produce narrative sequences. We use datasets of well-formed plots generated by a narrative planning approach, using pre-existing, published, narrative planning domains, to train generative models. Our experiments demonstrate the ability of generative sequence models to produce narrative plots with similar structure to those obtained with planning techniques, but with significant plot novelty in comparison with the training set. Most importantly, generated plots share structural properties associated with narrative quality measures used in Planning-based methods. As plan-based structures account for a higher level of causality and narrative consistency, this suggests that our approach is able to extend a set of narratives with novel sequences that display the same high-level narrative properties. Unlike methods developed to extend sets of textual narratives, ours operates at the level of plot structure. Thus, it has the potential to be used across various media for plots of significant complexity, being initially limited to training and generation operating in the same narrative genre.
Current and Future Contributing Factors and Trends in the Usage of IT Cloud Computing in Manufacturing and Service Sectors

Cloud computing provides organisations with the ability to quickly change infrastructure, provider and service levels. The study looks at factors affecting the usage of cloud computing by organisations in the manufacturing and service sectors in the United States. Extrinsic motivation was found to be a significant factor for change in cloud computing budget allocation in manufacturing, whereas intrinsic motivation was a significant factor in the service sector. The study also shows that organisations’ future budgets include internal and external sources to fulfil their IT needs.
Few Shot Adaptive Normalization Driven Multi-Speaker Speech Synthesis

The style of the speech varies from person to person and every person exhibits his or her own style of speaking that is determined by the language, geography, culture and other factors. Style is best captured by prosody of a signal. High quality multi-speaker speech synthesis while considering prosody and in a few shot manner is an area of active research with many real-world applications. While multiple efforts have been made in this direction, it remains an interesting and challenging problem. In this paper, we present a novel few shot multi-speaker speech synthesis approach (FSM-SS) that leverages adaptive normalization architecture with a non-autoregressive multi-head attention model. Given an input text and a reference speech sample of an unseen person, FSM-SS can generate speech in that person's style in a few shot manner. Additionally, we demonstrate how the affine parameters of normalization help in capturing the prosodic features such as energy and fundamental frequency in a disentangled fashion and can be used to generate morphed speech output. We demonstrate the efficacy of our proposed architecture on multi-speaker VCTK and LibriTTS datasets, using multiple quantitative metrics that measure generated speech distortion and MoS, along with speaker embedding analysis of the generated speech vs the actual speech samples.
The teaching potential of case-technology in the formation of professional competences of university students

The article reviewed the theoretical and methodological aspect of the problem of forming professional competencies of University students in the process of using the pedagogical potential of case-technology. The authors consider the case method as a unique technology used in the process of preparing students of special (defectological) education to work with children with special educational opportunities.
Object based Image Splicing Localization using Block Artificial Grids

People share pictures freely with their loved ones and others using smartphones or social networking sites.The news industry and the court of law use the pictures as evidence for their investigation.Simultaneously, user-friendly photo editing tools alter the content of pictures and make their validity questionable.Over two decades, research work is going on in image forensics to determine the picture's trustworthiness.This paper proposes an efficient statistical method based on Block Artificial Grids in double compressed images to identify regions attacked by image manipulation.In contrast to existing approaches, the proposed approach extracts the artefacts on individual objects instead of the entire image.A localized algorithm is proposed based on the cosine dissimilarity between objects and exploit the tampered object with maximum dissimilarity among objects.The experimental results reveals that the proposed method is superior over other current methods.
Phishing Image Spam Classification Research Trends: Survey and Open Issues

A phishing email is an attack that focused completely on people to circumvent existing traditional security algorithms.The email appears to be a dependable, appropriate, and solid communication medium for internet users.At present, the email is submerged with spam content, both in text-based form or undesired text planted inside the images.This study reviews articles on phishing image spam classification published from 2006 to 2020 based on spam classification application domains, datasets, features sets, spam classification methods, and the measurement metrics adopted in the existing studies.More than 50 articles, both from Web of Science and Scopus databases were picked.Achieving the study's target, we carried out a broad survey and analysis to identify the domains where spam classification was applied.Furthermore, several public data sets, features set, classification methods, and measuring metrics are found and the popular once were pinpointed.The study revealed that Personal Collection, Dredze, and Spam Archives datasets are the most commonly used datasets in image spam classification research.Low-level and image metadata are the most widely used features set.The methods of image spam classification as identified in this study are supervised machine learning, unsupervised machine learning, semi-supervised machine learning, content-based and statistical learning.Among these methods, the most commonly utilized is the Support Vector Machine (SVM) which falls under supervised machine learning.This is followed by Naïve Bayes and K-Nearest Neighbor.The commonly adopted metrics for the performance evaluation of the existing image spam classifiers are also identified and briefly discussed.We compared the performance of the state-of-the-art image spam models.Lastly, we pointed out promising directions for future research.
To the question on classification of digital footprint

The subject of this research is the digital footprint and its place in the traditional classification trace evidence in forensic science that is divided into material and ideal. The advent of computer technology led to the need for detailed examination of the trace pattern formed as a result of human activity in computer space, and thus to supplement trace evidence analysis with the new type of trace evidence. A number of forensic experts advances the original concepts on determination of place and role of these traces in trace evidence analysis. The author analyzes different points of view expressed by forensic experts on the matter, as well as presents an original substantiated opinion. The main conclusion of this research consists in the need for rapid adaptation of the approaches of forensic science to the current conditions of information society. The established within forensic science traditional approaches lose their relevance in the current context of the development of computer technology. Commission of unlawful actions in the computer space, particularly with application information and telecommunications technology on the Internet, contributes to the emergence of a specific trace evidence pattern that cannot be attributed to either material or ideal traces. Therefore, it is necessary to supplement the existing classification with a separate section &amp;ndash; the digital footprint, which has substantially different characteristics than material or ideal trace evidence.
Temporal Graph Modeling for Skeleton-based Action Recognition

Graph Convolutional Networks (GCNs), which model skeleton data as graphs, have obtained remarkable performance for skeleton-based action recognition. Particularly, the temporal dynamic of skeleton sequence conveys significant information in the recognition task. For temporal dynamic modeling, GCN-based methods only stack multi-layer 1D local convolutions to extract temporal relations between adjacent time steps. With the repeat of a lot of local convolutions, the key temporal information with non-adjacent temporal distance may be ignored due to the information dilution. Therefore, these methods still remain unclear how to fully explore temporal dynamic of skeleton sequence. In this paper, we propose a Temporal Enhanced Graph Convolutional Network (TE-GCN) to tackle this limitation. The proposed TE-GCN constructs temporal relation graph to capture complex temporal dynamic. Specifically, the constructed temporal relation graph explicitly builds connections between semantically related temporal features to model temporal relations between both adjacent and non-adjacent time steps. Meanwhile, to further explore the sufficient temporal dynamic, multi-head mechanism is designed to investigate multi-kinds of temporal relations. Extensive experiments are performed on two widely used large-scale datasets, NTU-60 RGB+D and NTU-120 RGB+D. And experimental results show that the proposed model achieves the state-of-the-art performance by making contribution to temporal modeling for action recognition.
Russian Print in North-East Asia: To the Compilation of the Catalogue of Hamilton Library of the University of Hawaii

The article considers the Russian printing in the countries of the Asia-Pacific region (China, Japan, Korea, USA, etc.). The author offers the review of literature published in Russian in these countries, basing on the materials of the Russian North-East Asian collection of Hamilton Library of the University of Hawaii (Honolulu, USA), which is one of the best collections in the world on this subject. The article reports on the history of the Russian collection and its creators. The author discusses the terms “Russian book Diaspora” (publishing emigrant activities within the same country — China, Japan, USA, etc.) and “Russian book community” (publishing activities of people from Russia within the same city, such as Harbin, Shanghai, Tokyo; organizations or groups of individuals, etc.). Special attention is paid to the bibliographic description of this collection, which was first published in 2002 in the publishing house of the Russian State Library “Pashkov Dom” (“Russian print in China, Japan and Korea: Catalogue of the collection of Hamilton Library of the University of Hawaii”) and reprinted in the expanded version in 2016 under the title “Russian print in the Asia-Pacific region” (in 4 parts). The article also reports on other foreign collections that contain emigrant publications, including those printed in the countries of the Asia-Pacific region (the libraries of the University of California in Berkeley, the Hoover Institute for war, revolution and peace, and the Museum of Russian culture in San Francisco). The author presents the data on the work of scientific centre of the University of Hawaii for the study of the USSR / Russia “Soviet Union in Pacific Asia Rim”. The article analyses international cooperation of the University of Hawaii library with Russian libraries, in particular, its relations with the libraries of the Russian Far East. The author describes the project of the University of Hawaii Library to create the electronic library catalogue with complete bibliographic and historical information about each edition of the collection and to expand the exchange of literature and information.
Analysis of approaches to study identification in social media

The nature of the social influence of media on social processes, the production of virtual information practices, to study these processes currently actualizes the need to use modern new tools for collecting, processing and data analysis methods. The purpose of this work is to analyze the activity of university graduates in communities, their identification through the collection of data from social networks. Assessment of the activity of graduates in social networks was carried out by “downloading” messages and news from online university communities. For each message, activity labels (“likes”, reposts, comments) were collected and graduates of these universities were identified (reconciliation with the register of graduates was carried out). The focus of the analysis is on identifying the actions of graduates - loyalty in the media space and the dissemination of information about the university community. The main methodological guideline was the approach within the framework of the microsociological paradigm, in particular, the idea of symbolic interactionism. The heuristic potential of using big data to analyze the activity of university graduates in communities allows us to expand our methodological arsenal and overcome the limitations of existing traditional methods of collection and analysis. The main research methods: interface programming, social network analysis of user interaction in social media, Web-crawling using a search engine, statistical data processing. Results: the main digital strategies of university graduates are characterized by the expansion of the audience, the promotion of content caused by the interest of users depending on the focus of the group. Four types of alumni communities have been distinguished: groups that identify with social development, with charity, with scientific research, and education. The high average value of the activity index belongs to charitable foundations, followed by the community of culture and science. The lowest average value of the activity index is recorded in educational communities.
The Measurement of Capital Stock of China

Capital stock is an important indicator in economics, it is an important indicator to measure the social and economic development status. But the research of capital stock is not wildly contributed. This paper is to measure capital stock and forecast future capital stock with time series models. The author conducts empirical methods based on the measurement methods of previous scholars. The paper concludes that the capital stock is growing stably, and with established new mathematical models and time series models, and combined with the latest data, measures the capital stock of China.
Morse Code Receiver On Invisible Light Using Background Subtraction Method

Morse code is a system of representation of letters, numbers, punctuation marks and signals using a code of dots and lines that are arranged to represent certain characters in the alphabet or certain signals (signs) that are agreed upon for use throughout the world was studied by nilas[1]. This code was coined by Samuel F.B. Morse and Alfred Vail in 1835 was studied by ming[2]. This research aims at how to apply the Background Subtraction method in the design of the morse sign language receiver in the invisible light. Build an Android-based application for recognition of Morse sign language by keeping messages sent using Morse sign language secret through infrared light from the sight of those who can read the message. From the results of the tests that have been carried out, it is concluded that the level of accuracy of testing using the False Match Rate (FMR) obtained a success percentage of 65% from the 26 character data tested. From the 26 character data tested, it was obtained data with the number of characters that were successfully counted correctly, namely 17 characters, while the character data that failed to be counted was 9 characters with a detection result of 26 characters.
Technologies for the bachelors-designers’ artistic abilities development

The artistic abilities’ development is considered as the spiritual and creative development of an individual. The structure of the artistic abilities and the psychological and pedagogical conditions of their development are analyzed. The structure and content of the technology for the abilities, principles, methods and conditions development for its implementation are considered. The innovative technology content for the development of the bachelors’ artistic abilities is described, where the abilities turn into strengths and abilities of an individual.
Modern Educational Technologies In A Technical University Pedagogical Process

The quality of a pedagogical process largely depends on the technologies used by a teacher. Currently, a sufficient number of the technologies are known, and there are various classifications of them. The research questions are to view the definitions of a pedagogical technology, its main characteristics and qualities; to give examples of modern learsning and up-bringing technologies and their usage in a teaching process. The purpose of the study is to discuss different educational technologies and the necessity of their usage in a pedagogical process. We rely on a set of the following methodological approaches: humanistic, cultural, axiological, competency-based, communicative, person-centered, system-activity, professional, integrative. The main research methods are: a retrospective analysis and literature review, an observation, a questioning, an interviewing, designing, modeling, monitoring, an experiment. There are many different technologies. One of the main aspects of an educational process is teachers and students’ interaction and cooperation, personal development without suppression of the will, without coercion, specific punishments. We consider the teaching technology of the teacher-innovator V.F. Shatalov and the technology of up-bringing of I.P. Ivanov. Thus, the quality of any educational process and the work of any educational institution are largely dependent on the knowledge and ability to put into practice by teachers a variety of technology training, education and development.
Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning

The development of deep reinforcement learning (DRL) has benefited from the emergency of a variety type of game environments where new challenging problems are proposed and new algorithms can be tested safely and quickly, such as Board games, RTS, FPS, and MOBA games. However, many existing environments lack complexity and flexibility and assume the actions are synchronously executed in multi-agent settings, which become less valuable. We introduce the Fever Basketball game, a novel reinforcement learning environment where agents are trained to play basketball game. It is a complex and challenging environment that supports multiple characters, multiple positions, and both the single-agent and multi-agent player control modes. In addition, to better simulate real-world basketball games, the execution time of actions differs among players, which makes Fever Basketball a novel asynchronized environment. We evaluate commonly used multi-agent algorithms of both independent learners and joint-action learners in three game scenarios with varying difficulties, and heuristically propose two baseline methods to diminish the extra non-stationarity brought by asynchronism in Fever Basketball Benchmarks. Besides, we propose an integrated curricula training (ICT) framework to better handle Fever Basketball problems, which includes several game-rule based cascading curricula learners and a coordination curricula switcher focusing on enhancing coordination within the team. The results show that the game remains challenging and can be used as a benchmark environment for studies like long-time horizon, sparse rewards, credit assignment, and non-stationarity, etc. in multi-agent settings.
Quantum Algorithm for Lexicographically Minimal String Rotation

Lexicographically minimal string rotation (LMSR) is a problem to find the minimal one among all rotations of a string in the lexicographical order, which is widely used in equality checking of graphs, polygons, automata and chemical structures. In this paper, we propose an $O(n^{3/4})$ quantum query algorithm for LMSR. In particular, the algorithm has average-case query complexity $O(\sqrt n \log n)$, which is shown to be asymptotically optimal up to a polylogarithmic factor, compared to its $\Omega\left(\sqrt{n/\log n}\right)$ lower bound. Furthermore, we show that our quantum algorithm outperforms any (classical) randomized algorithms in both worst and average cases. As an application, it is used in benzenoid identification and disjoint-cycle automata minimization.
Non-Triangular Self-Synchronizing Stream Ciphers

In this article, we propose an instantiation, called <inline-formula><tex-math notation="LaTeX">${\sf Stanislas}$</tex-math></inline-formula> , of a dedicated Self-Synchronizing Stream Cipher (SSSC) involving an automaton with finite input memory using non-triangular state transition functions. Previous existing SSSC are based on automata with shifts or triangular functions ( <inline-formula><tex-math notation="LaTeX">$T$</tex-math></inline-formula> –functions) as state transition functions. Our algorithm <inline-formula><tex-math notation="LaTeX">${\sf Stanislas}$</tex-math></inline-formula> admits a matrix representation deduced from a general and systematic methodology called Linear Parameter Varying (LPV). This particular representation comes from the automatic theory and from a special property of dynamical systems called flatness. Hardware implementations and comparisons with some state-of-the-art stream ciphers on Xilinx FPGAs are presented. It turns out that <inline-formula><tex-math notation="LaTeX">${\sf Stanislas}$</tex-math></inline-formula> provides bigger throughput than the considered stream ciphers (synchronous and self-synchronizing) when straightforward implementations are considered. Moreover, its synchronization delay is much smaller than the SSSC Moustique (40 clock cycles instead of 105) and the standard approach CFB1-AES128 (40 clock cycles instead of 128).