Reviewerly/exHarmony huggingface dataset:
Question design and the construction of populist stances in political news interviews

This paper focuses on the relationship between journalism and right wing populist discourses in the context of broadcast news interviews. We analyse a specific feature of question design in which the public is invoked as a source of opinionated positions in adversarial interviewing. Analysing data from a range of socio-political contexts, we identify a shift in adversarial questioning along a scale of ‘soft’ populism, that is the attribution of views and concerns to a generic public ‘in crisis’, to ‘hard’ populism, where interviewers construct hypothetical scenarios in which populist positions are attributed to ‘some people’. We argue that the democratic role of journalists as public watchdogs, holding politicians and public figures accountable on behalf of the public, is challenged by this normalisation of populist moral order discourses in a routine journalistic practice, both drawing on and contributing to the propagation of populist agendas and anti-democratic populist rhetoric.
Ways of conducting surveys in the student environment in remote format

The purpose of this article is to analyze the experience of conducting surveys in the student environment through the use of electronic educational resources. The article presents a survey of students about the performance of one of the most popular electronic tools Mentimeter today. The dynamics of the results of project activities of students using this service is traced. As a result, it was obtained that the use of Mentimeter makes it possible to conduct effective surveys, obtain automatic results and feedback on a specific issue, allowing you to build constructive activities in the student environment and form the professional competence of students.
Accountability in Healthcare Organizations; A Survey-Based Study among Patients in Government Hospital, Makkah

Background: In recent years, accountability has become a key component of public service reform. Improving accountability has been cited as a factor in improving the health system's efficiency. In health care sector we need to identify the factors influencing accountability in health care organizations those that should be improved. Objective: The goal of this research is to identify the accountability in health care organization from patientsâ perceptions. Subjects and Methods: The type of study was a Descriptive âCross-Sectional Designâ. Taken from a special hospital, Makkah, Saudi Arabia. A sample was collected from the 440 patients who was visited in King Abdullah Medical City (KAMC) outpatient department by using online self-made survey questionnaires. Results: Considering the patientsâ perceptions on health care organization's accountability, most of the participants agree each statement in all dimensions. When proving the hypothesis, the finding shows that there is significant relationship between accountability in healthcare and dimensions such as professionals in healthcare, Government actions, legal and ethical concerns, and administration and management actions. The correlation matrix and regression analysis show that all the four dimensions have strong correlation with accountability in healthcare settings. Conclusion and Recommendations: Health care leaders will be able to implement the findings of this study to new strategic plan to improve accountability of the patients in healthcare settings in Saudi Arabia. In order to institutionalize improvement of patientâs accountability, Policies, organizations, and procedures must be aligned and integrated. The study findings have important in policy makers to maximize their accountability by focusing on quality, processes, information, involvement, and communication to patients This study strongly recommending to for further multicenter comparative study level, which can include government hospitals and private hospitals.
Natural Language Querying and Visualization System

Data is the new gold; everything is data driven. But it is impossible for everyone to possess technical skills to be able to write queries and know different python tools used for data visualizations. The process of extracting information from a database is a mammoth task for non-technical users as it requires one to have extensive knowledge of DBMS language. But these data and visualizations are required for various everyday presentations and interactions in the professional world. This application would enable the users to overcome these obstacles. Our project aims at integrating two systems, an NLP interface to fetch data from simple English queries, and a second system where the fetched data with the help of natural language processing is used to form visualizations as demanded by the users will be created. This system would essentially help the people who are not techno-savvy or are not in the field of tech to interact with data using simple English.
Corrigendum: CSL-SHARE: A Multimodal Wearable Sensor-Based Human Activity Dataset

In the original article, there was a mistake in Table 1 as published. In the version we submitted, all 5 the activity names in the first column except "V-cut"s are lowercase, but in the published version, "Sit," 6 "Stand," "Walk," and "Run" have been changed to start with uppercase. This may cause ambiguity for 7 potential users in the usage of the labels. As a correction, please change these four words to all lowercase. Because of the recent update of the department's website, this dataset has a new link. However, to ensure 27 that the dataset can always be obtained, we have made a redirect from the original link shown in the 28 published article to the new link, so currently, there is no need to worry about its accessibility. In order to 29 ensure the dataset's availability in the future and match the site name of our new pages, please help update 30 the link in the Data Availability Statement to https://www.uni-bremen.de/en/csl/research/human-activity-31 recognition. After this update, it is expected that the site's name and the dataset link will not be changed 32 again in the foreseeable future.
Deepfakes in International Arbitration: How Should Tribunals Treat Video Evidence and Allegations of Technological Tampering?

Abstract Deepfakes can be described as videos of people doing and saying things that they have not done or said. Their potential use in international arbitration leads to two competing threats. Tribunals may be conscious of the difficulties in proving that a deepfake is, in fact, fake. If the ‘clear and convincing evidence’ standard of proof is applied, it may be very difficult, if not impossible, to prove that a sophisticated deepfake is fake. However, the burgeoning awareness of deepfakes may render tribunals less inclined to believe what they see on video even in circumstances in which the video before it is real. This may encourage parties to seek to deny legitimate video evidence as a deepfake. The ‘balance of probabilities’ standard, while not perfect, would appear to address this concern. In order to properly assess deepfakes, tribunals should apply this standard while assessing both technical and circumstantial evidence holistically.
JSON Schemas with Semantic Annotations Supporting Data Translation

As service-oriented architectures are a solution for large distributed systems, interoperability between these systems, which are often heterogeneous, can be a challenge due to the different syntax and semantics of the exchanged messages or even different data interchange formats. This paper addresses the data interchange format and data interoperability issues between XML-based and JSON-based systems. It proposes novel annotation mechanisms to add semantic annotations and complement date values to JSON Schemas, enabling an interoperability approach for JSON-based systems that, until now, was only possible for XML-based systems. A set of algorithms supporting the translation from JSON Schema to XML Schema, JSON to XML, and XML to JSON is also proposed. These algorithms were implemented in an existing prototype tool, which now supports these systems’ interoperability through semantic compatibility verification and the automatic generation of translators.
A Highly Maneuverable Hybrid Energy-Efficient Rolling/Flying System

Spherical robots are typically comprised of an actuation unit enclosed by a spherical shell. Among nonholonomic systems, spherical robots offer the best maneuverability and lowest energy consumption (due to their omnidirectional movement and single contact point with the ground). This allows them to traverse rough and uneven terrains. Further, using their ability to roll on the ground, they can provide a significantly higher operating time compared to aerial-only robots. Unfortunately, these robots are under-emphasized by researchers compared to other robots (i.e., legged or wheeled robots). Additionally, despite their potential to be used in a multitude of real-world applications, spherical robots have not been successfully adopted by the industry. This is due to the lack of controllability and traversability of the developed designs. In this paper, we introduce a hybrid rolling/flying robot. This design benefits from a flywheel to reduce the effects of the terrain (shocks and vibrations) on the camera and sensors. Our design allows the application of existing control algorithms of drones (such as PX4) on a rolling system. In addition, we propose a dynamics model that can use the point cloud representation of the terrain to simulate the motion of the system with applications in real-time modeling and control.
Aspects of programming for implementation of convolutional neural networks on multisystem HPC architectures

Abstract The training of deep learning convolutional neural networks is extremely compute intensive and takes long times for completion, on all except small datasets. This is a major limitation inhibiting the widespread adoption of convolutional neural networks in real world applications despite their better image classification performance in comparison with other techniques. Multidirectional research and development efforts are therefore being pursued with the objective of boosting the computational performance of convolutional neural networks. Development of parallel and scalable deep learning convolutional neural network implementations for multisystem high performance computing architectures is important in this background. Prior analysis based on computational experiments indicates that a combination of pipeline and task parallelism results in significant convolutional neural network performance gains of up to 18 times. This paper discusses the aspects which are important from the perspective of implementation of parallel and scalable convolutional neural networks on central processing unit based multisystem high performance computing architectures including computational pipelines, convolutional neural networks, convolutional neural network pipelines, multisystem high performance computing architectures and parallel programming models.
Research on UAV Image Ship Recognition Based on Fine-grained Classification Data Set

Ship identification is of great significance in coastal area monitoring and port military reconnaissance. The characteristics of different types of ships in high-resolution images are highly similar, and the internal details of the target are clearer. In order to effectively use the target details in the high-resolution image for target recognition, based on the UAV images of Xingcheng wharf and bingjiawan in Huludao City, Liaoning Province, the UAV ship image data sets with coarse-grained and fine-grained classification modes are established respectively. Build a deep learning platform based on tensorflow, and use SSD algorithm to identify the ships in the data set. The recognition accuracy of ship image data set based on coarse-grained classification pattern is 90.87%; The accuracy of ship image data set based on fine-grained classification model is 94.63%. The results show that: SSD algorithm can realize ship recognition of UAV image and Image data set based on fine-grained classification pattern can effectively use the detailed features of high-resolution image targets, so as to improve the accuracy of ship recognition.
Model of educational personality profile for learning management and development of personal potential

The article describes the logic of the development of the model SKQIiSPEC (“Personality diamond”), including basis of its construction and approach to practical implementation. The relevance of the article is based on the problem of reveal and support of the development of educational agency, the formation of system competencies of self-determination, goal setting, productive action and reflection. The purpose of the research described in the article is to develop a model of the educational personality profile for managing educational activity and the development of personal potential, adequate for supporting educational agency. Novelty of the suggested approach is represented by developed structure of educational profile of a student SKQIiSPEC and its specially selected elements. The model is synthesized on the basis of the following key ideas: (1) model of personality competence iSPEC, (2) model of “education” via semiotic systems, (3) distinguishing of competences via types of solved tasks and (4) distinguishing of main activity type. The model integrates selected ideas with qualitative differences opposite to comparing evaluations. Therefore, “Personality dimond” provides sufficient depth and conciseness for monitoring of navigation of a student on its individual/collectively-individual educational track as well as for creation of possibility to be used in various systems for assessing educational results. Practical significance of model SKQIiSPEC is based on potential usage for mapping of educational ambitions and demands of personality, creation of educational strategies for collective-individual and individual tracks, organization of personal educational logistic. The usage of digital technologies enables automatisation of educational profile model for educational activities’ management.
Competitive Car Racing with Multiple Vehicles using a Parallelized  Optimization with Safety Guarantee

This paper presents a novel planning and control strategy for competing with multiple vehicles in a car racing scenario. The proposed racing strategy switches between two modes. When there are no surrounding vehicles, a learning-based model predictive control (MPC) trajectory planner is used to guarantee that the ego vehicle achieves better lap timing. When the ego vehicle is competing with other surrounding vehicles to overtake, an optimization-based planner generates multiple dynamically-feasible trajectories through parallel computation. Each trajectory is optimized under a MPC formulation with different homotopic Bezier-curve reference paths lying laterally between surrounding vehicles. The time-optimal trajectory among these different homotopic trajectories is selected and a low-level MPC controller with obstacle avoidance constraints is used to guarantee system safety-critical performance. The proposed algorithm has the capability to generate collision-free trajectories and track them while enhancing the lap timing performance with steady low computational complexity, outperforming existing approaches in both timing and performance for a car racing environment. To demonstrate the performance of our racing strategy, we simulate with multiple randomly generated moving vehicles on the track and test the ego vehicle's overtake maneuvers.
Pensando-fazendo Sistemas de Informação com Ética. Da pesquisa à engenharia, e vice-versa.

The Regional School of Information Systems of the state of Rio de Janeiro (ERSI-RJ) is an event that brings together professionals, teachers, and students interested in learning and discussing problems, solutions, and concepts related to information systems. ERSI-RJ mini-courses are short-term activities (four hours long) that are part of the ERSI-RJ program. They address issues related to information systems with the aim of providing the public an environment of learning and discussion of issues such as trends and challenges in the information systems area. In 2020, the ERSI-RJ was canceled because of the SAR-COV-2 pandemic. From all of the submissions for this year (2021), six mini-courses were chosen. Besides, two other submitted proposals were selected to be added as chapters of this book. A group of professionals and professors reviewed the mini-course proposals and selected the best ones. The committee members weighed in on the proposals and chose the finest ones. The selection criteria were: relevance to the event, audience expectation, timeliness and content. This book covers: the concepts and ways of detecting fake news in digital news media; an organizational modeling language that addresses the topic of competency-based human resource strategy and alignment; the use of simple language to support transparency and the development of best practices for digital transformation; the practice of data management in the field of data journalism; the introduction to game projects with educational purposes, which includes a canvas for Endogenous Educational Game Design; the concepts, definitions, applications, and research on Ethics in the information systems area; the main protocols of the Internet of Things' application layer, with a practical emphasis on the CoAP and MQTT protocols; and an example of treatment, correlation, and reducement of trends and seasonality of data by using R to analyze data about COVID-19 and public transport in Rio de Janeiro city. We believe this material will be useful for information systems classes and for supporting discussions, research, and professional practice. We congratulate and thank the authors of the chapters. We also thank the Selection Committee for their dedication and efficiency; the Editorial Committee for their effort; and the CESI (Special Committee on Information Systems) of SBC for this publication's support. Finally, the book's cover was designed by Matheus Nunes Ritton, a graduate student in information systems at the Universidade Federal Rural do Rio de Janeiro. This is his second time working on the ERSI-RJ minicourse book covers.
DEVELOPMENT OF DIGITAL COMPETENCE OF PEDAGOGICAL STAFF IN DISTANCE LEARNING BASED ON CLOUD SERVICES

The article analyzes the experience of implementing forms and methods of development of digital competence of educators in distance education using Google Workspace for Education cloud services, based on real experience of teacher training at the university. This article discusses the concepts of competence, digital competence and distance education. The experience of the Department of Open Educational Systems and Information and Communication Technologies of the Central Institute of Postgraduate Education (Kyiv, Ukraine) in the organization of distance learning in postgraduate education is revealed, the results of research on the impact of distance technologies on the quality of education are presented. Specificity of digital competence development among pedagogical staff in the process of organizing distance education on the basis of Google Workspace for Education cloud services is determined. The article also provides an overview of the opportunities provided by digital tools and cloud services in Google Workspace for Education for distance education and highlights the effective forms and methods of developing digital competence in distance learning on the example of advanced training of university teachers. The article presents a description of effective forms of teacher training in the educational environment of Google Class, analysis of learning outcomes and identifies the benefits of joint activities of participants in the learning process. The article features the results of research on the development of teachers’ digital competence measured on the basis of selected criteria for assessing the development of digital competence of research and teaching staff. The article highlights the promising trends in the development of digital competence of pedagogical staff in terms of distance education.
Tricky two-some: The interplay between radio personalities' personal online identities and online personal brands

Radio stations encourage their presenters to utilise social&nbsp;media such as Facebook, Twitter and Instagram to create&nbsp;awareness of the station’s brand, promote their shows,&nbsp;interact with listeners, and build their professional brands.&nbsp;The question is: how does this affect their engagement with&nbsp;social media when they are not on air? This study explored&nbsp;the interplay of radio personalities’ personal online identity&nbsp;and online personal brand. A qualitative strategy was chosen&nbsp;to help navigate the investigatory efforts to explore the ways&nbsp;in which radio presenters exist online. Semi-structured&nbsp;interviews were conducted with ten radio presenters at&nbsp;a South African commercial radio station. A number of&nbsp;challenges were identified that radio presenters encounter&nbsp;when they attempt to manage their online personas. A lack&nbsp;of training in social media communication and a lack of&nbsp;knowledge about the basic principles of personal branding&nbsp;are some of the challenges. Recommendations are made to&nbsp;radio presenters as well as their employers to simplify this&nbsp;complex issue of managing the interplay between personal&nbsp;identity and personal branding in creating a branded identity&nbsp;(as opposed to a brand identity).
Monitoring of Groundwater Status Based on Geoinformation Systems and Technologies

The article explores the components of a geoinformation modeling system designed to solve groundwater monitoring problems. Also, data sizes, types, and formats were analyzed. Methods of modeling geo filtration processes in the subsurface hydrosphere, obtaining and storing data on the state of the aquifer, their processing and analysis of the components of the GeoBase structure are considered.
Methods of recognizing texts in different images

In recent years, the separation of text from docu-ment images has been one of the most studied topics in the field of image analysis and optical character recognition. These parts of document images can be used for document analysis, content analysis, document retrieval, and more. To separate documents from images, we used a very simple method based on the FAST algorithm. First, we divided the image into blocks and checked their density in each block. Large blocks were marked as text, and there was less image area or noise. We then check to see if the blocks are locked so that we can separate the piece of text from the image. This method is very fast and versatile and can be used to recognize different languages, handwriting, and even images with noise and blur. Thus, this method helps to more clearly and comprehensively identify text from document images.