Reviewerly/exHarmony huggingface dataset:
Digital Media Hotspot Mining Algorithm Implementation with Complex Systems in the Mobile Internet Environment

With the development of the Internet, the amount of information present on the network has grown rapidly, leading to increased difficulty in obtaining effective information. Especially for individuals, enterprises, and institutions with a large amount of information, it is an almost impossible task to integrate and analyze Internet information with great difficulty just by human resources. Internet hot events mining and analysis technology can effectively solve the above problems by alleviating information overload, integrating redundant information, and refining core information. In this paper, we address the above problems and research hot event topic sentence generation techniques in the field of hot event mining and design a hybrid event candidate set construction algorithm based on topic core word mapping and event triad selection. The algorithm uses the PAT-Tree technique to extract high-frequency core words in topic hotspots and maps the high-frequency words into sentences to generate a part of event core sentences. The other part of event core sentences is extracted from the topic hotspots by making event triples as candidate elements, and sentences containing event elements are extracted from the topic hotspots. The sets of event core sentences generated by the two methods are mixed and filtered and sorted to obtain the candidate set, which can be used to build a word graph-based main service channel (MSC) model. In this paper, we also propose an improved word graph-based MSC model and use it for the extraction of event topic sentences. Based on the above research, a hot event analysis system is implemented. The system analyzes the existing topic data and uses the event topic sentence generation algorithm studied in this paper to generate the titles of hot spots, that is, hot events. At the same time, the topics are displayed from different dimensions, and data visualization is completed. The visualization includes the trend change of event hotness, trend change of event sentiment polarity, and distribution of event article sources.
A FOTOGRAFIA COMO FONTE DE PESQUISA PARA HISTÓRIA E MEMÓRIA DE UM MUNICÍPIO TOCANTINENSE

Research on photography with a focus on a city in the state of Tocantins is a way to try to tell of the history of this city, since there is little information or files that reveal part of this history. This research has as its main objective to identify, through photographic records, the history and memory of the city of Tocantinopolis - TO, Brazil. Of a qualitative approach and basic nature, the data collection instruments used were visual sources (photographs) regarding Tocantinopolis - TO, in addition to bibliographic research. Among some results found, the analyzed images revealed part of the history and the memory of this city. The information generated in the analyses helped us to identify visual elements present in the photographs that allowed us to expand the reading and interpretation capacity, which was fundamental to understand the stories, memories and the context that accompany (or accompanied) these images registered in this research.
Indeks Aktivitas Literasi Membaca Peserta Didik Dan Prestasi Akademik: Studi Korelasi Pada 34 Propinsi Di Indonesia

The Indonesian government's efforts to eradicate illiteracy deserve appreciation, but this has not been accompanied by successful efforts in building a reading culture in the community. This is in accordance with various world surveys that note the low level of literacy in Indonesia. There are several studies through quantitative studies on interest and fondness for reading in various provinces in Indonesia, and this can be used as comparison material. One of the comprehensive studies conducted by the Center for Policy Research on Education and Culture, Research and Development Agency, Ministry of Education and Culture released the Reading Literacy Activity Index (Alibaca Index) in 34 provinces in Indonesia. Seeing this data, what becomes interesting to study later is whether the reading literacy level has a correlation with the student's academic achievement variable. This is important because in the end the school/madrasah literacy movement is aimed at achieving the highest level that goes beyond the level of habituation and development, namely the level of learning. This study aims to answer the questions, 1) Is there a significant positive correlation between students' reading literacy levels and academic achievement, in 34 provinces in Indonesia? and 2) What strategies should schools/madrasahs do to increase students' reading literacy levels and at the same time increase the achievement of academic achievement? This study uses a descriptive correlational approach by collecting data through the study of documentation and interviews. The results showed that the correlation between reading activity index and academic achievement (r) was 0.766. There are several innovation ideas also described qualitatively as a result of interviews.
Real Time Human Gesture Recognition: Methods, Datasets and Strategies

Gestures are universal means of communication without any language barrier. Detecting gestures and recognition of its meaning are key steps for researchers in computer vision. Majority of the work is done in sign language already. Sign language datasets are compared with respect to their usability and diversity in terms of various signs. This paper highlights the available datasets from three dimensional body scans to hand action gestures. Their usability and strategies used to achieve the desired results are also discussed. Major neural networks are evaluated in terms of varied parameters and feutures. A Methodology for effective gesture recognition in real is proposed. Lastly Results achieved through an Open CV in combination with Sci-kit learn library based technique for gesture recognition are presented and analyzed in terms of efficacy and efficiency.
Scientometric Indicators of Media Researchers in the e-library

This study extended the author's previous research in the field of scientometrics of media researchers on the topic “Mass information. Journalism. Mass media” in Russian electronic library and Russian Science Citation Index. The methodology was a census of the personalities of the first hundred authors ranked by the level of the h-index in 2020 compared to 2017, and in some aspects — from 2016. The study analyzed the changes in the h-index over the years under study, changes in the authors' geography by federal districts and cities of the Russian Federation, their academic position, the distribution of doctors and candidates of sciences in scientific majors in accordance with the awarded degree, as well as the distribution of rating personalities by actual scientific interests and taught courses in their affiliated scientific and educational organizations. The study revealed the importance of scientometric indicators for authors and scientific and educational organizations to determine their place in the academic community, the relevance of topics and authors’ research in the general academic landscape, and material incentives. At the same time, it was noted that the system of scientometric indicators is changing: perhaps the h-index the number of published works and the number of citations in the RSCI will remain as an object of research by historians of science to determine the common place of Russian (and Soviet) scientists in the science development, and in addition or to replace them there will be new indicators. One of them is the "percentile" recently introduced into the list of scientometric indicators. Studies of the values of scientometric indicators will remain relevant in the future.
COVID-19 Impact on Media Education in Technical University

The article considers raising students' media literacy as the response to the pandemic challenges highlighting the problem of deficiency of information verifiability and the importance of raising students' critical thinking as a crucial tool in decreasing the COVID-19 pandemic potential damage. The focus of the study lies in the research what sources/genres students of technical universities resort to in getting the information concerning the pandemic, what is their ability to distinguish facts from opinions, trustworthy information from misinformation as well as what are possible differences among students of technical and humanitarian specialities in terms of media literacy. The findings of the survey of 511 students majoring in different specialities at the National Technical University of Ukraine' Igor Sikorsky Kyiv Polytechnic Institute' (after this, the official abbreviated name – Igor Sikorsky KPI) revealed a need in raising students' media literacy in terms of critically assessing information concerning the coronavirus.
A Comparative Study of Informed and Uninformed Search Algorithm to Solve Eight-Puzzle Problem

Problems in artificial intelligence can be solved using intelligent tracking methods through intelligent search mechanisms.Understandably, search algorithm performances are highly dependent on the problem solved.In this study, we evaluate and compare the performance of five uninformed and informed search (breadth-first search, depth first search, optimal search and best first search using two heuristic functions, namely mismatched tile and Manhattan distance) algorithms to solve the eight-puzzle game problem.For each algorithm, the numbers of raised and explored nodes were assessed and analyzed.Our experiment demonstrates that informed search with heuristic outperforms uninformed search significantly, both in terms of memory usage efficiency and computational power efficiency.On average, the informed search using heuristic requires only 5.33% of memory used by uninformed search and only 4.45% of computational power demanded by uninformed search.Boxplot analysis also confirms that informed search using heuristic also delivers more stable performance contrasted to uninformed search.These could be a concern for researchers and game developers to consider implementing the heuristically enhanced search algorithm to utilize memory and computational power efficiently to solve similar problems.
Bringing ethics into governance: the case of the UK COVID-19 contact tracing app

Purpose In April 2020, it was announced that NHSX, a unit of the UK National Health Service (NHS) responsible for digital innovation, was developing a contact tracing app that would offer a digital solution to managing the COVID-19 pandemic. Despite the urgency with which the app was developed, a clear commitment was made to designing the technology in a way that enshrined key ethical principles, and an ethics advisory board (EAB) was established to provide timely advice, guidance and recommendations on associated ethical issues. Alongside this, there were extensive criticisms of how NHSX adhered to ethical principles in the handling of the app development-criticisms that require empirical exploration. This paper explores how ethics was incorporated into decision-making during governance processes associated with the development of app. Design/methodology/approach Interviews were conducted with those involved in the app's development/governance, those with a consulting role associated with the app, or those who sat on the EAB. Findings The EAB fulfilled an important role by introducing ethical considerations to app developers. Though at times, it was difficult to accommodate key ethics principles into governance processes, which sometimes suffered from little accountability. Originality/value While several articles have provided overviews of ethical issues, or explored public perceptions towards contact tracing apps, to the best the authors, knowledge this is the first empirical piece analysing ethics governance issues via stakeholder interviews.
Author Profiling in Informal and Formal Language Scenarios Via Transfer Learning

The interest in author profiling tasks has increased in the research community because computer applications have shown success in different sectors such as security, marketing, healthcare, and others. Recognition and identification of traits such as gender, age or location based on text data can help to improve different marketing strategies. This type of technology has been widely discussed regarding documents taken from social media. However, its methods have been poorly studied using data with a more formal structure, where there is no access to emoticons, mentions, and other linguistic phenomena that are only present in social media. This paper proposes the use of recurrent and convolutional neural networks and a transfer learning strategy to recognize two demographic traits, i.e., gender and language variety, in documents written in informal and formal language. The models were tested in two different databases consisting of tweets (informal) and call-center conversations (formal). Accuracies of up to 75 % and 68 % were achieved in the recognition of gender in documents with informal and formal language, respectively. Moreover, regarding language variety recognition, accuracies of 92 % and 72 % were obtained in informal and formal text scenarios, respectively. The results indicate that, in relation to the traits considered in this paper, it is possible to transfer the knowledge from a system trained on a specific type of expressions to another one where the structure is completely different and data are scarcer.
Making Complex Music with Simple Algorithms, is it Even Possible?

Algorithmic composition is often limited to score generation, but may also include sound production. All levels from sound synthesis to the generation of a complete composition can be integrated into one monolithic program. A strict separation of the low level of sound synthesis and higher levels otherwise reserved for algorithmic composition is not necessary, information can flow between all levels. An interesting challenge in this kind of thorough algorithmic composition is to generate as complex music as possible with as little code as possible. The challenge has been accepted, successfully or not, in a series of compositions called Kolmogorov Variations. We discuss the techniques used in a few of the pieces as well as the promises and perils of this strict approach to algorithmic composition.
Gerçekötesi Dönemde Yapay Zekanın Fotoğraf ve Resim Sanatlarına Etkisi ve Yaratıcılık ile Sahiplik Sorunları

In arts, especially visual arts, humans have perfected their skills to create distinctive experiences by combining a multifaceted interplay between the substance and design of images. Innate mechanisms of this process have remained unknown so far, and no artificial system with similar capabilities exists yet. However, the increasing prevalence of artificial intelligence in arts has drastically changed what seems to be the underlying constants, such as artistic creativity and authorship of artworks. This phenomenon becomes even more commonplace in the post-truth era as facts become fiction in competing versions. Although art has not always been searching for truths, artificial intelligence powered by generative adversarial networks puts the entire concept of art in a much larger context. In such a context, this descriptive-qualitative study based on literature review, which aims to discuss the impacts of artificial intelligence on photography and painting in the post-truth era and the issues of creativity and authorship, first explains the concept of generative adversarial networks and then examines a sample of their uses in photography and painting with concrete examples. Later, the study discusses the effects of artificial intelligence driven by generative adversarial networks on the art market and analyzes the concept of creativity and the issue of authorship because of those impacts in the context of works of art. Finally, the notion of post-truth is explained, and what the advent of artificial intelligence is likely to mean for art in the future within the framework of post-truth is questioned. Results show that the lines between art's expressive and demonstrative functions are beginning to blur, while the border between fiction and reality is becoming increasingly blurred. AI powered by GANs re-contextualizes the meaning of the truth in artistic endeavors as it happens whenever a novel medium or technology appears and is increasingly adopted by more people at various paces and rates.
Dashboard proposal implemented according to an analysis developed on the KNIME platform

The objective of the research is to develop a methodology to analyse a set of data extracted from a learning management system, in order to implement a dashboard, which can be used by teachers to make timely and relevant decisions to improve the teaching–learning processes. The methodology used consisted of analysing 9,257 records extracted through simple random sampling from a population of 100,000 records. The indicators analysed were number of accesses, course grades, time spent, number of courses enrolled and number of activities developed. The results show that the data analysis was carried out on the (o Konstanz Information Miner (KNIME) data mining analysis platform, and the model was implemented in five phases: requirements definition, model design, development, implementation and evaluation of results. The results are taken as a recommendation to design and implement a customised dashboard for teachers to identify observable behavioural patterns that allow them to make decisions to improve the teaching–learning processes of students.&#x0D; Keywords: Analytics, dashboard, KNIME Learning, personalised, teaching
Tutorial System in Learning Activities Through Machine Learning-Based Chatbot Applications in Pharmacology Education

Based on the education index issued by the United Nations Development Program and the Global Talent Competitiveness Index, education in Indonesia still needs to be improved through various innovations and infrastructure. One of them, the needs of students in the health sector (including in the field of pharmacology) to obtain learning information easily are still not widely implemented using information technology. Usually, students need to read textbooks and journals to get information that is in accordance with the lesson because there are not many materials about the health sector circulating on the internet, especially in the form of chatbots. One of the solutions that can overcome these problems is to utilize information technology through the use of chatbots in the field of pharmacology learning. In this research, a tutorial system for learning activities is built with a machine learning-based chatbot application. The general architecture of a chatbot consists of NLU components, message processor, dialogue management, action executor, and utterance generator. Based on evaluation, this research have successfully achieved 94.3% accuracy for intent classification and 97.5% accuracy for entity extraction. Majority of the users feel that the chatbot functionality is easy to use and easy to remember. Furthermore, majority of them (90%) thought that tutorial process provided by this chatbot is similar to the real tutorial process provided by human.
Effects of electrical brain stimulation on brain indices and presence experience in immersive, interactive virtual reality

Abstract The subjective presence experience in virtual reality (VR) is associated with distinct brain activation patterns. Particularly, the dorsolateral prefrontal cortex (DLPFC) seems to play a central role. We investigated the effects of electric brain stimulation (transcranial direct current, tDCS) on the presence experience as well as on brain activity and connectivity. Thirty-eight participants received either anodal ( N = 18) or cathodal ( N = 20) stimulation of the DLPFC before interacting in an immersive VR as well as sham stimulation. During VR interaction, EEG and heart rate were recorded. After VR interaction, participants rated their subjective presence experience using standardized questionnaires. Cathodal stimulation led to stronger brain connectivity than sham stimulation. Increased brain connectivity was associated with numerically lower levels of subjective presence. Anodal stimulation did not lead to changes in brain connectivity, and no differences in subjective presence ratings were found between the anodal and sham stimulation. These results indicate that cathodal tDCS over the DLPFC leads to a more synchronized brain state, which might hamper the activity in networks, which are generally associated with the evolvement of the subjective presence experience. Our results underline the importance of the DLPFC for the presence experience in VR.
Typology of career trajectories and motivation of their choice by teachers of higher education institutions

This research article presents a typology of career trajectories for higher educational institutions’ teachers and examines their motives of a potential change in a career trajectory, their willingness to move to work in another university or leave the academic field. The motives for choosing a career path are systematized and ranked. There was carried out an empirical research using the method of semi-structured interviews with 108 professors and managerial staff representatives of Russian universities. The informants were selected according to gender, age, industry characteristics, their working experience in higher education and positions held. The results of the study identified three categories of workers: loyal to their organization (not meaning to change their working place and sphere of interests); dedicated to the profession (admitting a transfer to another university, but not planning to leave the academic field); and change-oriented (considering the possibility of choosing another job – outside the academic sector). The empirical research has shown that teachers with extensive work experience are most willing to change their current jobs. The originality of the study lies in developing a typology of career trajectories for teachers and in clarifying key motivations within the subgroups included in the typology presented, as well as in using a qualitative method for studying career trajectories in relation to scientific and pedagogical workers. A qualitative analysis of career strategies made it possible to analytically assess the informants’ opinions. The article is of interest to the higher education researchers involved in the process of personnel management and in training scientific and pedagogical workers. It will also be useful for heads of HR departments and for HEI vice-rectors dealing with personnel issues.
Specialized Tool for Editing User Interface Transitions Diagrams (UITD)

The User Interface Transition Diagram (UITD) is a graphic notation designed to simplify the specification and design of the system-user interactions without losing the technical detail that is necessary to develop the system. The UITD aims to be a good communication tool between customers and software developers. We present here a specialized graphic tool for editing User Interface Transition Diagrams: the UITD editor. It provides specialized functionalities to simplify the edition of UITD properties in comparison with existing graphic editing tools.