Reviewerly/exHarmony huggingface dataset:
University students’ education by means of online technologies

Introduction . During the past ten years the important changes in educational programs of higher education point out the transition from the traditional live lecture form to the distance learning form (the online training format). Training for innovative engineering activities requires to involve students in the online training format and to explore its applicability. The aim of the present article is to identify the areas of the effective use of the online training format in the educational process for high school institutions and the prospects of further development of the distance learning. Methodology and research methods . In this article, an integration of theoretical and practical training of the innovative engineering with the methods of the analysis and synthesis, a pedagogical experiment and mathematical statistics were used. Results . The areas of use of the online learning in the field of traditional and innovative formats of training have been identified. A subsystem of storage of the information on the course of educational activities is suggested. A high efficiency of the distance learning application is confirmed by the results of a pedagogical experiment within the unified programs for a wide audience of students and persons having the right to an individual work schedule. Scientific novelty . The performed research contributes to identify the framework of the effective use of the online learning at higher education institutions. The suggested form of storage of the information on the education process events in a separate subsystem allow for processing and generalising the data with the use of the BigData and DataMining technologies. Practical significance . Further development of the research presented in this article might be focused on the establishment of numerical relations when using various education formats within various disciplines providing the greatest effect when training students at higher education institutions.
A Virtual Caregiver for Assisted Daily Living of Pre-frail Users

As Europe sees its population aging dramatically, Assisted Daily Living for the elderly becomes a more and more important and relevant research topic. The Movecare Project focuses on this topic by integrating a robotic platform, an IoT system, and an activity center to provide assistance, suggestions of activities and transparent monitoring to users at home. In this paper, we describe the Virtual Caregiver, a software component of the Movecare platform, that is responsible for analyzing the data from the various modules and generating suggestions tailored to the user’s state and needs. A preliminary study has been carried on over 2 months with 15 users. This study suggests that the presence of the Virtual Caregiver encourages people to use the Movecare platform more consistently, which in turn could result in better monitoring and prevention of cognitive and physical decline.
Multivariate functional data modeling with time-varying clustering

We consider the situation where multivariate functional data has been collected over time at each of a set of sites. Our illustrative setting is bivariate, monitoring ozone and PM$_{10}$ levels as a function of time over the course of a year at a set of monitoring sites. The data we work with is from 24 monitoring sites in Mexico City which record hourly ozone and PM$_{10}$ levels. We use the data for the year 2017. Hence, we have 48 functions to work with. Our objective is to implement model-based clustering of the functions across the sites. Using our example, such clustering can be considered for ozone and PM$_{10}$ individually or jointly. It may occur differentially for the two pollutants. More importantly for us, we allow that such clustering can vary with time. We model the multivariate functions across sites using a multivariate Gaussian process. With many sites and several functions at each site, we use dimension reduction to provide a stochastic process specification for the distribution of the collection of multivariate functions over the say $n$ sites. Furthermore, to cluster the functions, either individually by component or jointly with all components, we use the Dirichlet process which enables shared labeling of the functions across the sites. Specifically, we cluster functions based on their response to exogenous variables. Though the functions arise in continuous time, clustering in continuous time is extremely computationally demanding and not of practical interest. Therefore, we employ a partitioning of the time scale to capture time-varying clustering.
Cluster-Former: Clustering-based Sparse Transformer for Long-Range Dependency Encoding

Transformer has become ubiquitous in the deep learning field. One of the key ingredients that destined its success is the self-attention mechanism, which allows fully-connected contextual encoding over input tokens. However, despite its effectiveness in modeling short sequences, self-attention suffers when handling inputs with extreme long-range dependencies, as its complexity grows quadratically with respect to the sequence length. Therefore, long sequences are often encoded by Transformer in chunks using a sliding window. In this paper, we propose Cluster-Former, a novel clustering-based sparse Transformer to perform attention across chunked sequences. The proposed framework is pivoted on two unique types of Transformer layer: Sliding-Window Layer and Cluster-Former Layer, which encode local sequence information and global context jointly and iteratively. This new design allows information integration beyond local windows, which is especially beneficial for question answering (QA) tasks that rely on long-range dependencies. Experiments show that Cluster-Former achieves state-of-the-art performance on several major QA benchmarks.
DPMC: Weighted Model Counting by Dynamic Programming on Project-Join Trees

We propose a unifying dynamic-programming framework to compute exact literal-weighted model counts of formulas in conjunctive normal form. At the center of our framework are project-join trees, which specify efficient project-join orders to apply additive projections (variable eliminations) and joins (clause multiplications). In this framework, model counting is performed in two phases. First, the planning phase constructs a project-join tree from a formula. Second, the execution phase computes the model count of the formula, employing dynamic programming as guided by the project-join tree. We empirically evaluate various methods for the planning phase and compare constraint-satisfaction heuristics with tree-decomposition tools. We also investigate the performance of different data structures for the execution phase and compare algebraic decision diagrams with tensors. We show that our dynamic-programming model-counting framework DPMC is competitive with the state-of-the-art exact weighted model counters Cachet, c2d, d4, and miniC2D.
Machine learning model-based two-dimensional matrix computation model for human motion and dance recovery

Abstract Many regions of human movement capturing are commonly used. Still, it includes a complicated capturing method, and the obtained information contains missing information invariably due to the human's body or clothing structure. Recovery of motion that aims to recover from degraded observation and the underlying complete sequence of motion is still a difficult task, because the nonlinear structure and the filming property is integrated into the movements. Machine learning model based two-dimensional matrix computation (MM-TDMC) approach demonstrates promising performance in short-term motion recovery problems. However, the theoretical guarantee for the recovery of nonlinear movement information lacks in the two-dimensional matrix computation model developed for linear information. To overcome this drawback, this study proposes MM-TDMC for human motion and dance recovery. The advantages of the machine learning-based Two-dimensional matrix computation model for human motion and dance recovery shows extensive experimental results and comparisons with auto-conditioned recurrent neural network, multimodal corpus, low-rank matrix completion, and kinect sensors methods.
Image Encryption and Decryption Based on Chaotic Algorithm

This paper analyzes the problems in image encryption and decryption based on chaos theory.This article introduces the application of the two-stage Logistic algorithm in image encryption and decryption, then by information entropy analysis it is concluded that the security of this algorithm is higher compared with the original image; And a new image encryption and decryption algorithm based on the combination of two-stage Logistic mapping and M sequence is proposed.This new algorithm is very sensitive to keys; the key space is large and its security is higher than two-stage Logistic mapping of image encryption and decryption technology.
Attention-Enhanced Graph Neural Networks for Session-Based Recommendation

Session-based recommendation, which aims to match user needs with rich resources based on anonymous sessions, nowadays plays a critical role in various online platforms (e.g., media streaming sites, search and e-commerce). Existing recommendation algorithms usually model a session as a sequence or a session graph to model transitions between items. Despite their effectiveness, we would argue that the performance of these methods is still flawed: (1) Using only fixed session item embedding without considering the diversity of users’ interests and target items. (2) For user’s long-term interest, the difficulty of capturing the different priorities for different items accurately. To tackle these defects, we propose a novel model which leverages both the target attentive network and self-attention network to improve the graph-neural-network (GNN)-based recommender. In our model, we first model user’s interaction sequences as session graphs which serves as the input of the GNN, and each node vector involved in session graph can be obtained via the GNN. Next, target attentive network can activates different user interests corresponding to varied target items (i.e., the session embedding learned varies with different target items), which can reveal the relevance between users’ interests and target items. At last, after applying the self-attention mechanism, the different priorities for different items can be captured to improve the precision of the long-term session representation. By using a hybrid of long-term and short-term session representation, we can capture users’ comprehensive interests at multiple levels. Extensive experiments demonstrate the effectiveness of our algorithm on two real-world datasets for session-based recommendation.
GDBAlive: A Temporal Graph Database Built on Top of a Columnar Data Store

Although graph databases have extensively found applications in the relationship-centered era, a time-version support is seldom provided. While current storage systems capture the most recently updated snapshot of the underlying graph, most real world graphs embed a dynamic behavior translating the fact that vertices or edges can join or leave the graph at any time instant. Regarding that, a graph database should faithfully maintain the state of every graph's element permitting the analysis and prediction of the underlying system's performance. Since physical deletions are forbidden in such a scenario, the outgrowing size of data is a crippling restriction steering the interest in this area towards the optimization of the persistent storage. However, capturing and storing the state of the graph as full snapshots adds a storage overhead traded by faster query responses. Accordingly, the choice of an appropriate storage engine should be adapted with the threshold of accepted query latencies and the available storage resources. This paper will recognize the anterior academic work in the era of temporal graph databases while highlighting the existing tradeoff between storage and computation time costs. The implementation of GDBAlive, a temporal graph database using two state-of-the-art techniques Copy+Log and Log, is provided relying on a robust column oriented data store. In order to optimize the responsiveness of temporal queries in terms of computation times, we will introduce two fetching strategies AsyncFS and Forced Fetch and prove their efficiency on a real dataset.
Data Sovereignty Governance Framework

Data has emerged as a central commodity in most modern applications. Unregulated and rampant collection of user and usage data by applications led to concerns on privacy, trust, and ethics. This has resulted in several governments and organizations across geographies to frame laws on data (e.g., the European Union's General Data Protection Regulation (GDPR)) that govern and define boundaries for the storage, processing and transitioning of data; and thereby safeguard the interests of its citizens. Data Sovereignty and Data Localization are two important aspects, which deal with the adherence to the laws and governance structures, that define where and how data is collected and processed. The applicability of different laws depends upon several attributes such as the nature, type, and purpose of data. Non-compliance to laws/regulations can lead to serious repercussions for enterprises, ranging from hefty penalties to loss of brand value. Ensuring that all of their applications are complaint to various laws and regulations is non-trivial. Enterprises have to deal with a plethora of laws (that are constantly evolving) and are often confused even in correctly identifying all the applicable laws for their context leave alone ensuring compliance to regulations. Therefore, in this paper, we propose a knowledge graph based data sovereignty governance framework that assists in classifying data and in identifying the relevant applicable laws.
VisibilityNet: Camera visibility detection and image restoration for autonomous driving

Cameras sensors are crucial for autonomous driving as they are the only sensing modality that provide measured color information of the surrounding scene. Cameras are directly exposed to external weather conditions where visibility is dramatically affected due to various reasons such as rain, ice, fog, soil, ..etc. Hence, it is crucial to detect and remove the visibility degradation caused by the harsh weather conditions. In this paper, we focus mainly on soiling degradation. We provide methods for classification of the soiled parts as well as methods for estimating the scene behind the soiled parts. A new dataset is created providing manually annotated soiled masks knows as WoodScape dataset to encourage research in that area.
Organizational Cultures of Vocational Schools and Enterprises in Russia, China and Iran as Perceived by Students and Teachers

This study examines the problems of getting vocational students prepared to work for a modern innovative enterprise and ensure their organizational socialization within the changing technological paradigm. Research methodology was based on the organizational culture framework proposed by Kim S. Cameron and Robert E. Quinn. A survey was conducted to find out how vocational students and teachers in Russia, China and Iran perceived the organizational culture of vocational schools, businesses envisaged as the most probable employers, and businesses that had been the most effective under the existing conditions. Organizational structure of vocational schools was found to be related to the socioeconomic situation in the country. The most harmonious examples were observed in Chinese vocational schools, where students were convinced that they would be working for effective companies. Students and teachers in China are united in their assessments and want everything to stay as it is. In the long run, such attitudes may cause stagnation rather than development. Russian students believe that their vocational schools have a clan culture and would like to strengthen the clan quadrant at the expense of the hierarchical one. They tend to overestimate the innovative component and disregard it largely, being convinced that they will most probably work for an ineffective organization. Teachers see the hierarchical culture as dominant in the existing situation and want to weaken it along with a market culture and strengthen a clan-type culture instead as much as possible. Such attitudes will naturally result in a lower quality of human capital. Vocational teachers in Russia have a quite clear understanding of effective organizational cultures, yet they exert no relevant socializing influence on students and even agree with them on giving priority to clan values. In Iran, vocational students assess the culture of effective businesses more adequately than teachers, while the latter seek to preserve the unquestioned dominance of hierarchical values and minimize the innovative ones, which prevents prospective organizational socialization in vocational education. Limitations of the study are discussed, and approaches to developing organizational socialization programs are worked out.
PROBLEMATIK PEMBELAJARAN DARING MATA KULIAH BAHASA INDONESIA

This study aims to describe the problematics of online learning in Indonesian language courses. This research is descriptive research. The aim of this research is problematic learning in Indonesian language courses online. The data in this study are learning problems that occur in Indonesian language learning activities. Researchers make observations and documentation to collect data. Based on observations and data collection, it is known that online learning in Indonesian courses, there are several learning problems. The problematic of online learning that occurs in the learning process of Indonesian courses occurs in the learning components. Learning problems occur in (1) learning facilities or media and infrastructure, namely e-learning and internet access; (2) presentation of material and use of learning methods; (3) discussion activities, and (4) learning assessment. During the learning process by establishing good communication with students, as well as efforts to hold effective learning, the teacher determines learning techniques that can minimize these problems.
Waterfall and Agile information system project success rates – A South African perspective

Software projects are still failing at an alarming rate and do not provide value to the organisation at large. This has been the case for the last decade. Software projects use predominantly Waterfall as a methodology. This raises the question whether new ways of working can be introduced to improve the success rate. One such new way is Agile as an approach to developing software. A survey was done to determine whether Agile projects are more successful than Waterfall projects, thus contrasting the old and the new ways of working. Some 617 software projects were evaluated to determine the success rate based on the methodology used. Success was measured on a continuum of five levels and not just the triple constraint. The results imply that Agile projects are more successful than Waterfall projects to some extent, but that there are still concerns that need to be addressed.
A Cloud Computing Security Assessment Framework for Small and Medium Enterprises

Cloud computing plays a very important role in the development of business and competitive edge for many organisations including SMEs (Small and Medium Enterprises).Every cloud user continues to expect maximum service, and a critical aspect to this is cloud security which is one among other specific challenges hindering adoption of the cloud technologies.The absence of appropriate, standardised and self-assessing security frameworks of the cloud world for SMEs becomes an endless problem in developing countries and can expose the cloud computing model to major security risks which threaten its potential success within the country.This research presents a security framework for assessing security in the cloud environment based on the Goal Question Metrics methodology.The developed framework produces a security index that describes the security level accomplished by an evaluated cloud computing environment thereby providing the first line of defence.This research has concluded with an eight-step framework that could be employed by SMEs to assess the information security in the cloud.The most important feature of the developed security framework is to devise a mechanism through which SMEs can have a path of improvement along with understanding of the current security level and defining desired state in terms of security metric value.
Quality Assessment Protocols for Omnidirectional Video Quality Evaluation

In recent years, with the introduction of powerful HMDs such as Oculus Rift, HTC Vive Pro, the QoE that can be achieved with VR/360° videos has increased substantially. Unfortunately, no standardized guidelines, methodologies and protocols exist for conducting and evaluating the quality of 360° videos in tests with human test subjects. In this paper, we present a set of test protocols for the evaluation of quality of 360° videos using HMDs. To this aim, we review the state-of-the-art with respect to the assessment of 360° videos summarizes their results. Also, we summarize the methodological approaches and results taken for different subjective experiments at our lab under different contextual conditions. In the first two experiments 1a and 1b, the performance of two different subjective test methods, Double-Stimulus Impairment Scale (DSIS) and Modified Absolute Category Rating (M-ACR) was compared under different contextual conditions. In experiment 2, the performance of three different subjective test methods, DSIS, M-ACR and Absolute Category Rating (ACR) was compared this time without varying the contextual conditions. Building on the reliability and general applicability of the procedure across different tests, a methodological framework for 360° video quality assessment is presented in this paper. Besides video or media quality judgments, the procedure comprises the assessment of presence and simulator sickness, for which different methods were compared. Further, the accompanying head-rotation data can be used to analyze both content- and quality-related behavioural viewing aspects. Based on the results, the implications of different contextual settings are discussed.
Distance Pedagogical Education in the Conditions of the Coronavirus

The relevance of the study is due to the problem of finding optimal solutions for organizing training sessions, the need to form the personality of a teacher in a difficult social and pedagogical situation. The leading research method is a pedagogical experiment on the organization of distance learning for students, the method of theoretical substantiation of the formation of the "Digital Educational Ecosystem" project, the method of practical implementation of the digital environment in work with educational organizations and schoolchildren. This article presents practical solutions for organizing training sessions, pedagogical practices, conducting state final certification through the use of interactive educational platforms, social networks, and instant messengers. An equally significant result of the study is the educational component of the formation of a future teacher in a difficult social situation.