Reviewerly/exHarmony huggingface dataset:
FARGO: A Fair, Context-AwaRe, Group RecOmmender System

Lots of activities, like watching a movie or going to the restaurant, are intrinsically group-based. To recommend such activities to groups, traditional single-user recommendation techniques cannot be adopted, as a consequence, over the years, a number of group recommender systems have been developed. Recommending to groups items to be enjoyed together poses many ethical challenges, in fact, a system whose unique objective is to achieve the best recommendation accuracy possible, might learn to disadvantage submissive users in favor of more aggressive ones. In this work we investigate the ethical challenges of context-aware group recommendations, in the more general case of ephemeral groups (i.e., groups where the members might be together for the first time), using a method that can recommend also items that are new in the system. We show the goodness of our method on two real-world datasets. The first one is a very large dataset containing the personal and group choices regarding TV programs of 7,921 users w.r.t. sixteen contexts of viewing. The second one, which has been collected specifically for this work and that is made publicly available as one of the contributions of this article, gathers the musical preferences (both individual and in groups) of 280 real users w.r.t. two contexts of listening. We compare the results of our approach with seven other group recommender systems specifically developed to be fair. We evaluate the goodness of our recommendations using recall, while their fairness is assessed using two measures found in the literature, namely, score disparity and recommendation disparity. Our extensive experiments show that our method always manages to obtain the highest recall while delivering ethical guarantees in line with the other fair group recommender systems tested.
Additional Professional Education as a Space for Updating the Competencies of Teachers in Developing the Functional Literacy of Students

The article discusses the possibilities of additional professional education in the perspective of updating the competencies of teachers in developing the functional literacy of students. The essence and content of the concept of “functional literacy” are clarified, its components (value, cognitive and technological) are characterized. The authors consider the system of course and intercourse training of teachers, implemented at the Faculty of Educational Technologies and Continuing Education, Ulyanovsk State University of Education, as a source of resources for maintaining and improving the competence of teachers, which makes it possible to form the functional literacy of students. Advanced training courses, scientific and practical events, the participation of teachers in innovative activities and in the work of methodological schools, clubs, centers, and associations are considered as resources.
Autonomous Grader – a New Digital Summative Assessment Tool

Assessment is an essential part of both traditional and online education.Regardless of the learning environment, assessment can be formative and summative, each with advantages and disadvantages in the digital era.In order to harness its possibilities in an online environment, educators must overcome certain challenges that technology-based assessment imposes.This paper includes an overview of the literature that covers tools that can be used as digital alternatives to traditional pen and paper tests.In order to maintain the positive learning environment in online education, assessment tools had to be adapted to the new learning environment in order to be accessible remotely to a large number of students.The paper discusses several advantages of digital assessment tools in light of their credibility, validity, and reliability, especially in case of summative digital tools, which includes the solution presented in this paper, the Autonomous grader.This tool, created with the purpose of promoting the use of technology in teaching, improves the testing system by offering effective, precise and unbiased grading of each individual assignment, reducing the time spent on grading.The proposed solution is a test environment utilizing Selenium tests.The solution includes a language for defining tasks and evaluation criteria used to generate individual Selenium tests.The result of the automatic evaluation process is a human-readable report containing the results of individual assignments.We conclude that this type of tool can be used effectively and can be a valuable asset to assessment in the digital age.
THE EXPERIENCE OF TEACHING HIGHER MATHEMATICS IN DISTANCE LEARNING CONDITIONS

The article is devoted to topical issues of the use of digital technologies in the educational sphere of higher education in the study of higher mathematics. The introduction of federal state educational standards, which orient educational organizations to the transition to more flexible, dynamic and personalized learning, requires the university to form a learning environment that motivates students to independently search for and process information, exchange it, that is, navigate in the information space. The results of the organization of the educational process in the electronic information and educational environment of the university (EIOS) using the platforms LMS Moodle and MS Teams are presented. The advantages and disadvantages of traditional and remote technologies in the modern educational space are analyzed. The experience of using new information technologies and interactive teaching tools on the example of teaching students of the Samara State University of Communications is presented. A survey was conducted among the students of the 2nd year of the Institute of Automation, Information Technology and Construction and the Faculty of Railway Operation in order to identify and analyze the attitude of students of a technical university to the distance learning format. The authors conclude that it is necessary to combine full-time and distance learning for the successful development of an educational strategy.
Perceptual Quality Dimensions of Machine-Generated Text with a Focus on Machine Translation

The quality of machine-generated text is a complex construct consisting of various aspects and dimensions.We present a study that aims to uncover relevant perceptual quality dimensions for one type of machine-generated text, that is, Machine Translation.We conducted a crowdsourcing survey in the style of a Semantic Differential to collect attribute ratings for German MT outputs.An Exploratory Factor Analysis revealed the underlying perceptual dimensions.As a result, we extracted four factors that operate as relevant dimensions for the Quality of Experience of MT outputs: precision, complexity, grammaticality, and transparency.
Parallel learning at Krasnoyarsk State Medical University: a tool for personalised training of physicians

The aim of the research. To determine the possibilities and describe the features of parallel mastering of residency programmes and additional professional programmes to develop a technology for training of a competitive graduate sought for on the labour market. Material and methods. The study was conducted at the premises of the KrasSMU. The study group was formed by students in the 1st year of residency programmes, 270 subjects in total. The survey was conducted online with open-ended, closed-ended and menu questions to determine the focus group. Processing of the survey results was performed using the quantitative method. For segmentation of the respondents according to certain characteristics depending on the answers, the «decision tree» method was used. Results. The results of the study indicate the interest of the respondents in parallel mastering of residency programmes and additional professional programmes: 54.4% of the respondents showed the willingness to learn. Based on the results of the interviews in the focus group, 45 (30.4%) respondent were included in the educational project of parallel learning. Conclusion. The technology of parallel learning is described as a tool for personalised education for obtaining qualifications in the framework of higher education, and improving the obtained qualifications in the framework of additional professional education. The measures for implementation of the educational project and the expected effects for students, employers and educational organisations have been developed.
A Privacy-Preserving Medical Image Scheme Based on Secret Sharing and Reversible Data Hiding

With the development of online medical care, the security of patients’ private medical data in the cloud has aroused widespread concern. Currently, the security issues for medical images have been researched by some scholars, but these medical images which stored in single server may be destroyed if the server is maliciously attacked. In addition, the patient privacy information which included patients’ electronic medical records and personal information are not be protected. Therefore, in order to protect patients’ privacy information and secure storage of medical images, this paper proposed a privacy-preserving medical image scheme based on secret sharing and reversible data hiding. The medical image is encrypted at firstly, and the encrypted medical image and private information are shared by (t, n) threshold secret sharing scheme respectively. Then marked encrypted medical image shares are generated by embedding the privacy information share into the encrypted medical image share reversibly. Finally, they are sent to multiple servers for distributed storage. The receiver can combine and recover original medical images and private information after collecting no less than t shares, which greatly improves the security of medical image storage and effectively protects patients’ privacy information. Experimental results show that the proposed scheme is superior to other schemes in terms of encryption performance, embedding capacity and visual quality of decrypted medical image.
Knowledge Distillation for Translating Erroneous Speech Transcriptions

Recent studies consider knowledge distillation as a promising method for speech translation (ST) using end-to-end models. However, its usefulness in cascade ST with automatic speech recognition (ASR) and machine translation (MT) models has not yet been clarified. An ASR output typically contains speech recognition errors. An MT model trained only on human transcripts performs poorly on error-containing ASR results. Thus, it should be trained considering the presence of ASR errors during inference. In this paper, we propose using knowledge distillation for training of the MT model for cascade ST to achieve robustness against ASR errors. We distilled knowledge from a teacher model based on human transcripts to a student model based on erroneous transcriptions. Our experimental results showed that the proposed method improves the translation performance on erroneous transcriptions. Further investigation by combining knowledge distillation and fine-tuning consistently improved the performance on two different datasets: MuST-C English--Italian and Fisher Spanish--English.
Book Review: Hanieh, Adam. 'Money, Markets, and Monarchies'. Cambridge University Press, 2018

The GCC region can offer a significant research reservoir for academic research about its development as a global key financial key player. Precisely, the GCC's political econmy has been witnessing significant shifts that enhanced economic growth rates in this hub. Hanieh's book Money, Markets, and Monarchies attempts to detach this block from misconceptions which reduced it to a nere source of abundant hydrocarbon resources and fuse this block to the international financing system. In order to fulfill the mission of this book, Hanieh anaylzes the economic arms, financial strategies , and devices that ehance the GCC's role in the global financial system. Hanieh's book is thought-provoking and insightful for scholars interested in the GCC region from an economic and financial facets.
Distributed Cloud Data Center for Efficient Services in Logistics Management

An issue in research is a challenging area to be improved, eliminated and the troubling question which exist in theory, practice which needs to have meaningful investigation and information distribution. Logistics information system (LIS) is needed to drive globally decrease or increase by the nature. Thus it is required, LIS should be in the form of distributed system rather than centralized system. As there is availability of anywhere and anytime the cloud provider are providing resources for sharing information by using its services in logistics operations. Logistics under the umbrella of supply chain itself is distributed over various regions so it should also adopt distributed cloud datacenter for its efficient services. Thus the basic research problem under study is to provide an efficient and scalable solution via distributed datacenter over cloud for managing logistics.
All-Russian Conference «Trajectories of Interaction in the Development of Digital Skills»

The purpose of the conference is to assist its participants in finding new opportunities, solutions and designing joint activities so that to create and develop an effective extracurricular environment in their cities and regions, and a professional community of teachers-mentors to expand opportunities for schoolchildren to acquire relevant digital skills and self-realization using information technology. Within the framework of the conference, there is a generalization of advanced pedagogical experience and innovations in the field of information technology.
Teachers’ Understanding and Mastery of the Syntax of Advanced Reading for Elementary School Students during the Covid-19 Pandemic

This research aims to analyze the understanding and mastery of elementary school (SD) teachers in Central Java in applying the syntax of advanced reading learning models during the covid-19 pandemic.Focus group discussion (FGD) was conducted by involving 30 elementary school teachers using the open inquiry method.The discussion discussed several topics related to the teacher's perspective on the application of the advanced reading learning model that was applied during the pandemic, and the advantages and disadvantages of the learning model that became the basis for analyzing the extent to which teachers understand and master the learning models they have implemented in schools so far.Interactive analysis was used to collect, reduce, present, and draw conclusions from the data obtained.The results of this study indicate that during the covid-19 pandemic, teachers implemented advanced reading learning models both online and blended learning.Blended learning is more widely applied because if it is only online learning, there are many technical obstacles and students still need face-to-face teacher assistance.Most teachers stated that they needed a learning model that could be flexibly applied to online, offline, and mixed learning to maximize advanced reading skills.Teachers also need innovative and varied learning models to learn during the pandemic, which is uncertain when it will end.
Multi-Dimensional Shared Representation Learning with Graph Fusion Network for Session-Based Recommendation

The Session-based Recommendation (SBR) system aims to forecast anonymous users' short-term decisions. Many prior research have demonstrated that using Graph Neural Networks (GNN) and Recurrent Neural Networks (RNN) to solve SBR tasks can lead to excellent results. However, the existing SBR models only use single feature extraction method to represent an item in the recommendation process. These models either use the GNN methods which ignore the sequence location features in the training process or use RNN methods which cannot obtain the weight features of every item in the sequence. All of them fail to extract features of different dimensions at the same time, which results in low-quality item representations. To tackle the aforementioned issue, this research introduces a novel method called Multi-dimensional Shared Representation Learning (MSR). (i) To get multi-dimensional features, we use the double-flow feature extraction based on transformer layers and graph attention network (GAT) layers. (ii) Through the MSR module, we train the joint representation of items in the session by using multiple GNNs, then merge the multi-dimensional features by using an attention mechanism. A variety of experiments have been carried out and the simulation results demonstrate the designed strategy largely surpasses the state-of-the-art proposal in recommendation accuracy.
Information fusion structure of VTS sensors for multi-target tracking of vessels

Information fusion using VTS sensors of AIS, radar and camera are of great significance to the waterborne traffic supervision. Firstly, this paper invites shore-based CCTV cameras into detection and location of vessel targets combining with bounding boxes generated by deep-learning based detectors. Besides, this paper compares information fusion structure of central-level and track-level in simulated waterborne traffic scenario. Finally, this paper introduces a track selection method for sensors with large false alarm rate to obtain tracks with better performance on both fusion structure when fully considering strengths and weaknesses of all kinds of VTS sensors.
The incidence of Information Anxiety in virtual education in Mexico during the SARS-CoV-2 pandemic

The SARS-CoV-2 pandemic that occurred at the beginning of 2020 caused education to migrate to digital platforms and environments, resulting in the modification of educational models which have had a direct impact on teaching work, in the modality of learning and how virtual teaching itself was administered, such a situation caused a rethinking of the use of digital tools and its impact on said practice. Mexico has been the country that has had the most stumbling blocks with respect to this new modality in what corresponds to North America, caused by the term known as Information Anxiety, which is defined by the saturation of information, causing a lack of understanding of what is presented by part of the students, learning barriers and misuse of the information itself. This study addresses an exploratory analysis of the consequences of this phenomenon in online education in Mexico and its impact on academic society.
Bayesian network variable elimination method optimal elimination order construction

Variable Elimination (VE) is the most basic one of many Bayesian network inference algorithms. The speed and complexity of reasoning mainly depend on the order of elimination. Finding the optimal elimination order is a Nondeterministic Polynomial Hard (NP-Hard) problem, which is often solved by heuristic search in practice. In order to improve the speed of reasoning of the variable elimination method, the minimum, maximum potential, minimum missing edge and minimum added complexity search methods are studied. The Asian network is taken as an example to analyze and calculate the complexity and elimination of the above search method. Meta-order, through MATLAB R2018a, the above different search methods were constructed and reasoned separately. Finally, the performance of the four search methods was compared by inference time analysis. The experimental results show that the minimum increase complexity search method is better than other search methods, and the average time consuming is at least 0.012s, which can speed up the reasoning process of Bayesian network.
Pre-trained language models evaluating themselves - A comparative study

Evaluating generated text received new attention with the introduction of model-based metrics in recent years. These new metrics have a higher correlation with human judgments and seemingly overcome many issues of previous n-gram based metrics from the symbolic age. In this work, we examine the recently introduced metrics BERTScore, BLEURT, NUBIA, MoverScore, and Mark-Evaluate (Petersen). We investigate their sensitivity to different types of semantic deterioration (part of speech drop and negation), word order perturbations, word drop, and the common problem of repetition. No metric showed appropriate behaviour for negation, and further none of them was overall sensitive to the other issues mentioned above.
Jointly Identifying and Fixing Inconsistent Readings from Information Extraction Systems

Information extraction systems analyze text to produce entities and beliefs, but their output often has errors.In this paper we analyze the reading consistency of the extracted facts with respect to the text from which they were derived and show how to detect and correct errors.We consider both the scenario when the provenance text is automatically found by an IE system and when it is curated by humans.We contrast consistency with credibility; define and explore consistency and repair tasks; and demonstrate a simple, yet effective and generalizable, model.We analyze these tasks and evaluate this approach on three datasets.Against a strong baseline model, we consistently improve both consistency and repair across three datasets using a simple MLP model with attention and lexical features.
THE CONCEPT OF “UNCANNY VALLEY” IN THE CULTURAL DIMENSION

The concept of digital culture determines a set of values, practices and expectations regarding the format of human interaction in the modern Internet society. Predictions of digital culture describe not only the specifics of the Internet environment but also the general context of social life. The range of interpretations of digital culture varies between two poles: from the recognition of digital technologies as a way of presenting libraries, museums, historical monuments, etc. to the concepts of digital culture as a new socio-anthropological reality, the content of which is not limited to ICT. Culture as a phenomenon means the semantic unity of human activity, the desire to adjust social life to ideals and values, the movement from existing to mandatory, from realistic to potential, and digital culture is an adequate response to the demands and challenges of time.
Digital Forensics Artifacts of the Microsoft Photos Application in Windows 10

Modern operating systems come equipped with software designed to improve user experience and offer a user-friendly environment while maintaining seamless integration and constant operation times, even when tackling demanding tasks.Applications generate extensive operation logs to aid the diagnostics and maintenance processes.Modern software often relies on the mechanism for cashing and storing additional data that can later be used to access requested resources faster, improving performance.These behaviors often lead to the creation of software artifacts in the form of logs, temporary files, and databases.These artifacts become crucial sources of information in forensic investigations.This paper analyses a set of artifacts created by the Microsoft Photos application that is the default photo viewer and editor in the Windows 10 operating system.Conducted research indicates that a large amount of potentially useful information is created during normal software operation.These fragments can be located and analyzed in files located in system directories.Further exploration reveals a set of valid digital forensic assets.These consist of user action logs, facial recognition identification results, optical character recognition strings acquired from images, metadata, information on devices used to capture the photo, and other information sources.