Reviewerly/exHarmony huggingface dataset:
The Use of an Information System to Manage Vocational Assessment in Facing the 5.0 Digital Society

Social distancing policy is applied during the Covid-19 virus pandemic including in Batik Professional Certification Institution in Indonesia. The management should provide services for the participants without meeting them directly. This study aims to reveal the management of Batik competence tests during the Covid-19 virus pandemic. The observation focused on the performance of the administrative staff, assessors, and management staff of the Batik Professional Certification Institutions (PCI). This is a qualitative study. The data were collected through questionnaires distributed to the participants, assessors, and administrators. The research concerns on the role and impact of the use of information system management in the interaction between prospective assessment, participants, assessors, and management of PCI in the professional certification competency test (PCCT). The findings showed that the use of information systems to manage vocational assessment has been effective to inform the assessment schedule to the assessor and participant, bridging the assessment reports, and reminding license expiration to management, so the management has an efficient way to manage the information with this system automation.
Detection of Malicious Spatial-Domain Steganography over Noisy Channels Using Convolutional Neural Networks

Image steganography can have legitimate uses, for example, augmenting an image with a watermark for copyright reasons, but can also be utilized for malicious purposes. We investigate the detection of malicious steganography using neural networkbased classification when images are transmitted through a noisy channel. Noise makes detection harder because the classifier must not only detect perturbations in the image but also decide whether they are due to the malicious steganographic modifications or due to natural noise. Our results show that reliable detection is possible even for state-of-the-art steganographic algorithms that insert stego bits not affecting an image’s visual quality. The detection accuracy is high (above 85%) if the payload, or the amount of the steganographic content in an image, exceeds a certain threshold. At the same time, noise critically affects the steganographic information being transmitted, both through desynchronization (destruction of information which bits of the image contain steganographic information) and by flipping these bits themselves. This will force the adversary to use a redundant encoding with a substantial number of error-correction bits for reliable transmission, making detection feasible even for small payloads.
TS-Extractor: large graph exploration via subgraph extraction based on topological and semantic information

Exploring large graphs is difficult due to their large size and semantic information such as node attributes. Extracting only a subgraph relevant to the user-specified nodes (called focus nodes) is an effective strategy for exploring a large graph. However, existing approaches following this strategy mainly focus on graph topology and do not fully consider node attributes, resulting in the lack of clear semantics in the extracted subgraphs. In this paper, we propose a novel approach called TS-Extractor that can extract a relevant subgraph around the user-selected focus nodes to help the user explore the large graph from a local perspective. By combining the graph topology and the user-selected node attributes, TS-Extractor can extract and visualize a connected subgraph that contains as many nodes sharing the same/similar attribute values with the focus nodes as possible, thereby providing the user with clear semantics. Based on TS-Extractor, we develop a Web-based graph exploration system that allows users to interactively extract, analyze and expand subgraphs. Through two case studies and a user study, we demonstrate the usability and effectiveness of TS-Extractor.
What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams

Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community. In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. We implement both rule-based and popular neural methods by sequentially combining a document retriever and a machine comprehension model. Through experiments, we find that even the current best method can only achieve 36.7\%, 42.0\%, and 70.1\% of test accuracy on the English, traditional Chinese, and simplified Chinese questions, respectively. We expect MedQA to present great challenges to existing OpenQA systems and hope that it can serve as a platform to promote much stronger OpenQA models from the NLP community in the future.
Developing a conversational agent with a globally distributed team

In this experience report, we discuss the development of a solution that enables conflict-affected youth to discover and access relevant learning content. A team of individuals from a not-for-profit, a large multi-national technology company, and an academic institution, collaborated to develop that solution as a conversational agent named Hakeem. We provide a brief motivation and product description before outlining our design and development process including forming a distributed virtual team, engaging in user-centred design with conflict-affected youth in Lebanon, and using a minimum viable product approach while adapting Scrum for distributed development. We end this report with a reflection on the lessons learned thus far.
EDUCATIONAL PARTNERSHIP IN HIGHER SCHOOL SPACE: DOMESTIC AND FOREIGN EXPERIENCE

The article analyzes the problem of educational partnerships in higher education. The educational partnership is regarded as: a social phenomenon characterized by the objectively existing linkages between pedagogical workers of heis and education providers with the world and with each other; psychological phenomenon — a process of understanding, empathy, complicity; pedagogical phenomenon — socially meaningful, purposeful, specially organized process whereby participants of the interaction undergo positive transformations. The author determines that the educational partnership in the institution of higher education is a systemic phenomenon, which reproduces the characteristics of social and educational systems, based on the experience of social, socio-psychological, interpersonal interaction and the corresponding mechanisms of socialization, integration, identification and external economic, political, rulemaking, organizational, technological, informational, developmental, regulatory, protective, protective. The concept of “educational partnership at the university” is defined as a holistic open system, which is characterized by external and internal multifunctional connections and includes subjective, meaningful, organizational, and effective components. The basic principles of educational partnership in higher education (humanization and transparency; socioculturalis; acceptability of traditions and innovations; systemic and comprehensive; competence and science; optimism and long-term; variability and mobility; tolerance and coherence; technology and efficiency).The author emphasizes that the partnership activities of universities in network interactions are conditioned by integration processes in higher education, which determine its orientation and types. The directions of educational partnership in a modern university are identified, as well as their types in the areas of: introversion direction (corporate partnership; pedagogical partnership, scientific and methodological partnership) and extraversion direction (scientific partnership; informal partnership; education and business partnership; social partnership; sociocultural partnership, information partnership) coordinated by the mechanisms of socialization, integration, identification, and aimed at professional and personal self distribution, intellectual and moral development and further professional self-realization of applicants for higher education
Efficient Image Encryption Scheme Based on 4-Dimensional Chaotic Maps

This paper proposes a new family of 4-dimensional chaotic cat maps.This family is then used in the design of a novel block-based image encryption scheme.This scheme is composed of two independent phases, a robust light shuffling phase and a masking phase which operate on imageblocks.It utilizes measures of central tendency to mix blocks of the image at hand to enhance security against a number of cryptanalytic attacks.The mixing is designed so that while encryption is highly sensitive to the secret key and the input image, decryption is robust against noise and cropping of the cipher-image.Empirical results show high performance of the suggested scheme and its robustness against well-known cryptanalytic attacks.Furthermore, comparisons with existing image encryption methods are presented which demonstrate the superiority of the proposed scheme.
The Internet and Prospective Engineers: Results Analysis for Studies Conducted During the Pandemic

The reference and experimental groups were sampled based on students’ interviews during the transition to remote access Internet-based educational process. The practical implications of the study lie in identifying the distinctive features of teachers’ and students’ educational activities at ASAU during the pandemic. These results can be adapted and implemented in the system of prospective engineers’ training in other regional universities in the north-east of Russia.
Interactive Multi-User 3D Visual Analytics in Augmented Reality

This publication reports on a research project in which we set out to explore the advantages and disadvantages augmented reality (AR) technology has for visual data analytics. We developed a prototype of an AR data analytics application, which provides users with an interactive 3D interface, hand gesture-based controls and multi-user support for a shared experience, enabling multiple people to collaboratively visualize, analyze and manipulate data with high dimensional features in 3D space. Our software prototype, called DataCube, runs on the Microsoft HoloLens - one of the first true stand-alone AR headsets, through which users can see computer-generated images overlaid onto real-world objects in the user's physical environment. Using hand gestures, the users can select menu options, control the 3D data visualization with various filtering and visualization functions, and freely arrange the various menus and virtual displays in their environment. The shared multi-user experience allows all participating users to see and interact with the virtual environment, changes one user makes will become visible to the other users instantly. As users engage together they are not restricted from observing the physical world simultaneously and therefore they can also see non-verbal cues such as gesturing or facial reactions of other users in the physical environment. The main objective of this research project was to find out if AR interfaces and collaborative analysis can provide an effective solution for data analysis tasks, and our experience with our prototype system confirms this.
Innovative Marketing and Its Impact on Customers' Value Creation in Orange Jordan Telecom

The purpose of this study was to provide an analytical presentation of the impact of innovative marketing in creating value for Orange Jordan Telecom customers and to identify the extent to which the elements of the innovative marketing mix impact the value creation of the customer. The researchers adopted two sources to collect data for this study: Secondary data, included, references, literature, and relevant periodicals to the study to clarify basic concepts, types, and areas of innovative marketing, and to identify the most important and relevant previous studies to the subject, while the second was a field study included: design and distribution of a questionnaire to collect data from Orange Jordan Telecom Customers'. The questionnaire has been distributed to a random sample of (90) customers who deal with different orange branches for more than three years. Total of (65) valid questionnaires were retrieved and analyzed, with a percentage of (72%). The reliability test ratio of both the independent and dependent variables were (82%). The populations of the study were Orange Jordan Telecom Customers. The main findings of the study were as follows: There is a statistically significant impact of innovative marketing on creating value for Orange Jordan Telecom customers. There is a statistically significant impact of each one of the innovative marketing dimensions (innovation in marketing mix elements: innovation in product, innovation in price, innovation in promotion, and innovation in distribution) on creating value for Orange Jordan Telecom customers.
Relationship search over knowledge graphs

The recent explosive growth of knowledge graphs (KGs) has enhanced a wide range of Web applications. Among others, the established task of relationship search benefits from KGs since complex relationships that were hidden and distributed in multiple webpages are now explicitly available as subgraphs of an integrated KG. However, new challenges arose as to the efficiency of search over large KGs. In this article, we introduce our recent studies on relationship search, including search algorithms based on novel index structures, methods for relationship clustering to support result exploration, and query relaxation techniques to provide alternative results for failed searches. We also present emerging applications and discuss future research directions.
What is the impact of Apps in medical education? A study of CAPSULE, a case-based learning App

Mobile applications (Apps) are popular in medical education; yet, the actual benefits for students are yet to be formally researched. Clinical And Professional Studies Unique Learning Environment (CAPSULE) is an App created by Brighton and Sussex Medical School. The App provides 650 cases offered to students in their final two years of the undergraduate programme. The App performed consistently well in student feedback, and therefore, a study into the educational benefits of the App was constructed.A cross-sectional study was performed following two years of use by students to investigate the relationship between App usage and decile ranking.The study found that the students who completed more cases tended to score higher per case (p value=0.0037). The study also found a trend between having higher case scores and being part of a stronger decile (p value=0.019).Greater App usage was linked with performing better in the App itself and this was further associated with being in a stronger decile rank. From a user perspective, the data generated from the App could help with identifying students who are underperforming or help students to recognise areas on which they need to focus.
An expandable image database for evaluation of full-reference image visual quality metrics

Traditional approach to collect mean opinion score (MOS) values for evaluation of full-reference image quality metrics has two serious drawbacks. The first drawback is a nonlinearity of MOS, only partially compensated by the use of rank order correlation coefficients in a further analysis. The second drawback are limitations on number of distortion types and distortion levels in image database imposed by a maximum allowed time to carry out an experiment. One of the largest of databases used for this purpose, TID2013, has almost reached these limitations, which makes an extension of TID2013 within the boundaries of this approach to be practically unfeasible. In this paper, a novel methodology to collect MOS values, with a possibility to infinitely increase a size of a database by adding new types of distortions, is proposed. For the proposed methodology, MOS values are collected for pairs of distortions, one of them being a signal dependent Gaussian noise. A technique of effective linearization and normalization of MOS is described. Extensive experiments for linearization of MOS values to extend TID2013 database are carried out.
Modified Haar-cascade model for face detection issues

Amid the previous three decades, the topic of image processing has gained vital name and recognition among researchers because of their frequent look in varied and widespread applications within the field of various branches of science and engineering. As an example, image processing is helpful to issues in signature recognition, digital video processing, remote sensing and finance. Image processing models are used for detecting the face. The aim of this thesis is to solve the face-detection in the first attempt using the Haar-cascade classifier from images containing simple and complex backgrounds. It is one of the preeminent detectors in terms of reliability and speed. We introduced a new method to deal with the frontal face images by using a modified Haar cascade algorithm. By using this algorithm, we can detect the image as well as the coordinates. The main attraction of this paper is to solve different types of images having one object, two objects, and three objects which can’t be solved by any of the existing methods but can be solved by our proposed method.
Lion optimization algorithm for team orienteering problem with time window

&lt;span&gt;Over the last decade, many nature-inspired algorithms have been received considerable attention among practitioners and researchers to handle several optimization problems. Lion optimization algorithm (LA) is inspired by a distinctive lifestyle of lions and their collective behavior in their social groups. LA has been presented as a powerful optimization algorithm to solve various optimization problems. In this paper, the LA is proposed to investigate its performance in solving one of the most popular and widespread real-life optimization problems called team orienteering problem with time windows (TOPTW). However, as any population-based metaheuristic, the LA is very efficient in exploring the search space, but inefficient in exploiting it. So, this paper proposes enhancing LA to tackle the TOPTW by utilizing its strong ability to explore the search space and improving its exploitation ability. This enhancement is achieved via improving a process of territorial defense to generate a trespass strong nomadic lion to prevail a pride by fighting its males. As a result of this improving process, an enhanced LA (ILA) emerged. The obtained solutions have been compared with the best known and standard results obtained in the former studies. The conducted experimental test verifies the effectiveness of the ILA in solving the TOPTW as it obtained a very competitive results compared to the LA and the state-of-the-art methods across all tested instances.&lt;/span&gt;
Research on Password Cracking Technology Based on Improved Transformer

Abstract Password plays a vital role in identity authentication. However, password security is facing great challenges. In this paper, we research the password cracking technology based on artificial intelligence, aiming to study the probability of password cracking in common password setting methods, and provide references for the setting of password. First of all, we collected a large amount of user’s personal information and passwords, and analysed the correlation between the personal information and passwords. And then, we implemented a password guessing model based on improved Transformer in which information weights were introduced into the data pre-processing and the modified beam search algorithm was used in the model to quickly search the top ranked output results. The percentage of password cracked was 68.63%, and the average guess time was 51.99 seconds. The experiment result shows that artificial intelligence brings great challenges to user password security, and this paper puts forward suggestions on user setting passwords.
Building Systems Testing Method Using Ishihara Color Blindness

Color blind test system is needed in various needs such as the management of a driver's license, prospective students in Certain institutions, prospective employees or employees in government and private agencies. The development of a computer-based test system color blind color blind tests aims at activities that produce normal eye Conclusions on sealing, partial color blindness and total color blindness. The method used to complete this research is Ishihara, which was discovered by Dr. Shinobu Ishihara. For the construction of the Ishihara color-blind method of testing using the stages of analysis, design and implementation. The developed test system aims to be used as a color blind tests for the health test requirements as a requirement for prospective employees at PT JAVA GLOBAL FUTURES Medan. To build this color blind test system used MS-Visual Studio 2010 and the results were made using Crystal Report 2010. This research has produced a Color Blind Test System with the Ishihara roomates Computer Based Method is used for the color blind tests at PT JAVA GLOBAL FUTURES Medan. With the output in the form of computer screens and paper prints when using a printer connected to a computer in the form of a Health Certificate with test results in the form of a normal eye vision, color blindness partial or total color blindness.
Different Approaches to the Development of Online Learning in Higher Education

The processes of online learning implementation, including e-learning and distance learning technologies in higher education have been revealed. The experience of the development of the first online courses at M. K. Ammosov North-Eastern Federal University is described. As a part of the study, the main approaches to the development of online learning in higher education are identified. The authors of the study focus on the work of the advanced training courses for the academic staff as the opportunity for professional competencies development in the context of online education.
Towards a Multi-Dataset for Complex Emotions Learning Based on Deep Neural Networks

In sentiment analysis, several researchers have used emoji and hashtags as specific forms of training and supervision. Some emotions, such as fear and disgust, are underrepresented in the text of social media. Others, such as anticipation, are absent. This research paper proposes a new dataset for complex emotion detection using a combination of several existing corpora in order to represent and interpret complex emotions based on the Plutchik’s theory. Our experiments and evaluations confirm that using Transfer Learning (TL) with a rich emotional corpus, facilitates the detection of complex emotions in a four-dimensional space. In addition, the incorporation of the rule on the reverse emotions in the model’s architecture brings a significant improvement in terms of precision, recall, and F-score.