Reviewerly/exHarmony huggingface dataset:
Learning to Selectively Learn for Weakly-supervised Paraphrase Generation

Paraphrase generation is a longstanding NLP task that has diverse applications for downstream NLP tasks.However, the effectiveness of existing efforts predominantly relies on large amounts of golden labeled data.Though unsupervised endeavors have been proposed to address this issue, they may fail to generate meaningful paraphrases due to the lack of supervision signals.In this work, we go beyond the existing paradigms and propose a novel approach to generate high-quality paraphrases with weak supervision data.Specifically, we tackle the weakly-supervised paraphrase generation problem by: (1) obtaining abundant weakly-labeled parallel sentences via retrievalbased pseudo paraphrase expansion; and (2) developing a meta-learning framework to progressively select valuable samples for finetuning a pre-trained language model, i.e., BART, on the sentential paraphrasing task.We demonstrate that our approach achieves significant improvements over existing unsupervised approaches, and is even comparable in performance with supervised state-of-the-arts.
Semantic Coding and Model Reconstruction of Multifunction Radar Pulse Train

Retrieving the working modes of multifunction radar from electronic reconnaissance data is a difficult problem, and it has attracted widespread attention in the field of electronic reconnaissance. It is also an important task when extracting benefits from big electromagnetic data and provides straightforward support to applications, such as radar type recognition, working state recognition, radar intention inferring, and precise electronic jamming. Based on the assumption of model simplicity, this study defines a complexity measurement rule for multifunction radar pulse trains and introduces the semantic coding theory to analyze the temporal structure of multifunction radar pulse trains. The model complexity minimization criterion guides the semantic coding procedure to extract radar pulse groups corresponding to different radar functions from pulse trains. Furthermore, based on the coded sequence of the pulse train, the switching matrix between different pulse groups is estimated, and the hierarchical working model of multifunction radars is ultimately reconstructed. Simulations are conducted to verify the feasibility and performance of the new method. Simulation results indicate that the coding theory is successfully used in the proposed method to automatically extract pulse groups and rebuild operating models based on multifunction radar pulse trains. Moreover, the method is robust to data noises, such as missing pulses.
Studying in Virtual University: an approach to students’ technopedagogical profiles

This article presents some findings of ongoing investigation on preferences and activities of university students in an emergency remote education context. A qualitative research was developed through the application of two controlled instruments (questionnaires), and a more productive one (recordings of testimonies). The main contribution of this work is the identification of three student profiles based on the strategies they develop to study with technologies in their personal environments. They are known as Gutenberg, Amphibian, and Maker profiles, and some of the main characteristics of their techno-pedagogical practices are described here. The Gutenberg profile defines a group of students whose study practices and preferences are fundamentally linked to analog technologies; the Amphibian profile is defined by the combination of analogical and digital strategies, however, there is an emphasis on the emulation of analog practices. Finally, Maker profiles prefer digital technologies and they are able to recreate teaching resources into new digital objects. The results indicate that students appropriate technologies in a particular and flexible way, which puts in tension the categories native digital and millennial, since preferences and activities seem to show the presence of analogical practices along with other emerging ones.
Domain Adaptation Algorithm based on Manifold Regularization

Domain adaptation for classification is often encountered in recent years. A popular approach consists in transforming the source and target data to an identical linear space. Then the Maximum Mean Discrepancy (MMD) is used to evaluate the dissimilarity of distributions. However, the MMD only makes the source and target domain distribution consistent according to the global probability distribution, and cannot effectively protect the local geometric structure of the data. To make better use of the structure of local geometry, this paper proposes a method called domain adaptation based on manifold regularization (DAMR). First, this algorithm embeds the input data into a reproducing kernel Hilbert space (RKHS). Second, subspace-based dimensionality reduction is conducted on the RKHS. Third, a manifold regularization term is added to the learning method. Furthermore, the classification experiments demonstrate that DAMR is an accurate and effective method.
Probabilistic Modeling for Human Mesh Recovery

This paper focuses on the problem of 3D human reconstruction from 2D evidence. Although this is an inherently ambiguous problem, the majority of recent works avoid the uncertainty modeling and typically regress a single estimate for a given input. In contrast to that, in this work, we propose to embrace the reconstruction ambiguity and we recast the problem as learning a mapping from the input to a distribution of plausible 3D poses. Our approach is based on the normalizing flows model and offers a series of advantages. For conventional applications, where a single 3D estimate is required, our formulation allows for efficient mode computation. Using the mode leads to performance that is comparable with the state of the art among deterministic unimodal regression models. Simultaneously, since we have access to the likelihood of each sample, we demonstrate that our model is useful in a series of downstream tasks, where we leverage the probabilistic nature of the prediction as a tool for more accurate estimation. These tasks include reconstruction from multiple uncalibrated views, as well as human model fitting, where our model acts as a powerful image-based prior for mesh recovery. Our results validate the importance of probabilistic modeling, and indicate state-of-the-art performance across a variety of settings. Code and models are available at: this https URL.
Further Improvement on Two-Way Cooperative Collaborative Filtering Approaches for the Binary Market Basket Data

Two-way cooperative collaborative filtering (CF) has been known to be crucial for binary market basket data. We propose an improved two-way logistic regression approach, a Pearson correlation-based score, a random forests (RF) R-square-based score, an RF Pearson correlation-based score, and a CF scheme based on the RF R-square-based score. The main idea is to utilize as much predictive information as possible within the two-way prediction in order to cope with the cold-start problem. All of the proposed methods work better than the existing two-way cooperative CF approach in terms of the experimental results.
Developing a Mobile Game Using Scientific Approach to Support Mathematics Learning

The widespread use of smartphones to play games and the lack of use as a medium for learning mathematics is the reason for the need for the development of smartphone-based edutainment learning media. The purpose of this research is to design and develop interactive edutainment learning applications of mobile mathematics games to help junior high school students to learn line and angles material. This mobile game-based application incorporates graphic design, background music, and animation, and a major structural element in game development to attract students. This research belongs to the type of Research and Development (R&amp;D) and the model used is the ADDIE model that is carried out in a systematic stage. The results of the study show that the developed game meets the valid aspects, where the level of expert approval of the design functionality has an average value of 4,49 out of 5, meanwhile, the assessment of the material has an average score of 4,17 out of 5 and the user states that the game meets the practical aspect with an average score of 3,75 out of 5. It is hoped that this game-based learning application called "CORNER" for mobile devices can act as an additional tool for improving students' conceptual understanding and interest to learn through the scientific approach used.
ANALYSIS OF THE CAPABILITIES OF MOBILE AGILE SOLUTIONS FOR EFFECTIVE PROJECT ACTIVITIES

Nowadays various tools for the successful implementation of projects are used. A set of Agile, Scrum, and Kanban methodologies helps us effectively plan, evaluate, work, communicate and make decisions. Modern software containing a wide range of functionality and flexibility of interface settings helps to visualize the project progress as one of the most important tasks. The aim of the work is to find the most flexible and functional solution for the project activities of small groups. Various solutions for effective teamwork are considered. The article contains the results of a comparative analysis of the Agile tools functionality presented on the market. Result of this analytical work is a set of options for collaboration optimal solution.
E-learning Challenges during the Covid-19 Pandemic Experienced by Students

The spread of the covid-19 pandemic has changed the teaching and learning process from conventional to e-learning classes. This e-learning class brings out the challenges for students, educators, parents, and institutions. This present study was aimed to see the e-learning challenges faced by students during the Covid-19 pandemic. The participants of this study were 40 students at the Islamic Faculty of Muhammadiyah Aceh University. The qualitative descriptive was applied to analyze 20 questionnaires that were distributed through a google form. The questionnaire was divided into four sessions of challenges that involve internet connectivity, facilities support, technology competence, and self-regulation. This study revealed that students faced problems with the internet, devices, and self-regulation, while technology competence did not show significant challenges.
Maintaining and deprecating documentation

Charlotte, Karthik, and their team had settled into a comfortable pattern with launching features and updating documentation. Charlotte focused on the audio translations created by Yappr, and Karthik on the video translations.
Multilingual Training Set Selection for ASR in Under-Resourced Malian Languages

We present first speech recognition systems for the two severely under-resourced Malian languages Bambara and Maasina Fulfulde. These systems will be used by the United Nations as part of a monitoring system to inform and support humanitarian programmes in rural Africa. We have compiled datasets in Bambara and Maasina Fulfulde, but since these are very small, we take advantage of six similarly under-resourced datasets in other languages for multilingual training. We focus specifically on the best composition of the multilingual pool of speech data for multilingual training. We find that, although maximising the training pool by including all six additional languages provides improved speech recognition in both target languages, substantially better performance can be achieved by a more judicious choice. Our experiments show that the addition of just one language provides best performance. For Bambara, this additional language is Maasina Fulfulde, and its introduction leads to a relative word error rate reduction of 6.7%, as opposed to a 2.4% relative reduction achieved when pooling all six additional languages. For the case of Maasina Fulfulde, best performance was achieved when adding only Luganda, leading to a relative word error rate improvement of 9.4% as opposed to a 3.9% relative improvement when pooling all six languages. We conclude that careful selection of the out-of-language data is worthwhile for multilingual training even in highly under-resourced settings, and that the general assumption that more data is better does not always hold.
From keyboard to cloud-base network revamped data lifecycle cybersecurity

This paper review seeks to identify the need for a revamped data life cycle security in the era of pervasive threat from skill cyber criminals at this time of internet of things. The motivation is to fill the knowledge gap by presenting some of the ways of data leakages and the likely protection in the organization. The aim is to present a good practice that encourages data confidentiality, acceptable use policy, knowledge of personnel and physical security policy. The building blocks of information security infrastructure across the entire organization is implemented by Enterprise Security Architecture. Rather than focus on individual functional and non-functional components in an individual application, it focuses on a strategic design for a set of security services that can be leveraged by multiple applications, systems, or business processes.
Elastic statistical analysis of interval-valued time series

We investigate the problem of statistical analysis of interval-valued time series data - two nonintersecting real-valued functions, representing lower and upper limits, over a period of time. Specifically, we pay attention to the two concepts of phase (or horizontal) variability and amplitude (or vertical) variability, and propose a phase-amplitude separation method. We view interval-valued time series as elements of a function (Hilbert) space and impose a Riemannian structure on it. We separate phase and amplitude variability in observed interval functions using a metric-based alignment solution. The key idea is to map an interval to a point in R2 , view interval-valued time series as parameterized curves in R2 , and borrow ideas from elastic shape analysis of planar curves, including PCA, to perform registration, summarization, analysis, and modeling of multiple series. The proposed phase-amplitude separation provides a new way of PCA and modeling for interval-valued time series, and enables shape clustering of interval-valued time series. We apply this framework to three different applications, including finance, meteorology and physiology, proves the effectiveness of proposed methods, and discovers some underlying patterns in the data. Experimental results on simulated data show that our method applies to the point-valued time series.
Key aspects of the development of secondary vocational education: a case study of the Rostov Region

Introduction. Today, the effectiveness of the economic development of a region is determined by social and economic indicators, including an increase in the performance of works and the growth of employment, which in turn depend on the state of the education system in the region. Secondary vocational education is an important part of the education system, since it develops the key competences required in the region and provides the region’s economy with highly qualified human resources. Purpose. The purpose of our study was to determine and classify the indicators revealing the state of the secondary vocational education in the region by analysing the approaches to its formation within the IT environment of the region. The article substantiates the estimate of institutional and reproduction indicators of the secondary vocational education system in the Rostov Region. It also suggests a series of measures for providing the region’s economy with qualified professionals. Methodology. In our study, we used systems analysis and machine learning methods, and human capital theory. The database included statistical data regarding the social and economic performance of the Rostov Region in 2007-2017 published in a statistical digest “Regiony Rossii”, and the results of the Rosstat monitoring of the development of information society in the Russian Federation. Results. The article suggests a concept of a secondary vocational education system in the region taking into account the influence of the interrelated factors of the IT environment. The main development areas, including in the Rostov Region, are considered, and a classification of the indicators of the state of the secondary vocational education system in the region is suggested. The indicators are divided into two groups: institutional and reproduction indicators. Based on the statistical analysis and trend lines, we estimated these indicators for the Rostov Region and suggested a series of measures aimed at providing the region's economy with qualified human resources. Conclusions. The suggested development of the system of secondary vocational education should facilitate sustainable social and economic development of the region since it will help to provide the hi-tech industries of the region with an adequate number of qualified professionals.
A Path Planning Algorithm Based on Improved RRT for Lunar Subsurface Autonomous Burrowing Robot

The detection with autonomous burrowing robot might be a low-cost and high-efficient solution for a future lunar subsurface exploration mission. The path planning of underground locomotive robot in a three-dimensional (3-D) domain is a very challenging task under the circumstance of lunar subsurface segregated by lunar rocks. In this work, a pruning-improved RRT algorithm was proposed to generate robotic paths in a 3-D geological model: a confined cubic zone with distributed obstacles. This digital terrain model may be constructed based on the mapping technology of Lunar Penetrating Radar (LPR). Here, a numerical simulation scheme was adapted for a simplicity. The effects of iteration scheme of path finding and distribution of geological structures were discussed. Then, Bezier parametric curve was utilized to enhanced the smoothness of robotic trajectory. After a comprehensive study, the proposed algorithm was proven to outperform the original RRT method in both effectiveness and convergence.
DEVELOPING TRILINGUAL SMART BOOK MEDIA AS A LITERACY FACILITY FOR THIRD GRADE STUDENTS OF ELEMENTARY SCHOOL

This study aimed to develop trilingual smartbook media, to determine the feasibility of the trilingual smartbook, and to determine students' responses to trilingual smartbook. This research was conducted on 10 students at grade III of elementary school in a limited trial. This research utilized the Borg and Gall model which consisted of six stages, namely problem analysis, data collection, product design, design validation, design revision, and limited product trial. The instruments were a questionnaire sheet for product validation by expert lecturers and a questionnaire sheet for students' responses. Based on the analysis, the quality of the trilingual smartbook was categorized as very feasible with a percentage of 91% by two media experts, very feasible with a percentage of 92% by two material experts, and a feasible category with a percentage of 80% by two linguists. Moreover, the students' response to the trilingual smartbook was 96% with a very good category. Based on this, it can be said that the trilingual smartbook is appropriate to be used as a media of literacy facility for third grade elementary school students.
Teaching skills, an innovation in virtual learning environments in higher education

The emergency closure of educational institutions, in the face of the health contingency due to Covid-19, has prompted the rethinking of the models of teaching competencies to adapt to virtual learning environments (VLE). The objective of this study is to design and validate a model of teaching competencies in VLE, with the support of virtual laboratories, to ensure the quality of higher education during the covid-19 pandemic. A quantitative and correlational methodology was used in a case study. The research is divided into two methodological moments: the design of the model and its validation. The instruments used were a comparative analysis matrix and a Likert-type scale. 5 650 students and 41 teachers participated in the validation cap. The results highlight the need for teacher development, mainly in digital and research skills. One weakness of the study is the lack of weighting of the quality indicators. The proposed model represents an innovation in the definition of indicators, in the form of competencies, for the assurance of educational quality in VLE. It is concluded that the proposed model is pertinent for the assurance of educational quality in VLE.
CRITICAL SUCCESS FACTORS FOR ADOPTION OF CLOUD COMPUTING IN PUBLIC UNIVERSITIES IN KENYA

&lt;p&gt;Cloud computing technology is a distributed computing approach whereby users access shared resources under various service models through the internet. It allows individual access to information technology resources through the internet upon demand. Cloud computing is an essentially growing terminology in the IT world and has become increasingly present in the life of institutions of higher learning. Institutions of higher learning consider cloud computing and construction of digital content platforms as a way of enhancing resource utilization and improvement of service delivery. The sudden and frenzied rush for cloud computing by universities has been aggravated by exponential growth in data traffic and the need for innovative learning such as e-learning and virtual classrooms amid COVID 19 pandemic (Kenya Education Network-KENET, 2021). Perhaps it is from that realization and the need to adhere to COVID 19 protocols that most public universities in Kenya have adopted cloud computing. In this study, we sought to find out the critical success factors for adoption of cloud computing in public universities in Kenya. The study set out three (3) objectives and consequently three (3) null hypotheses to guide it. Quantitative research design was adopted for this study. Similarly, International Business Machines-IBM (2011) model for cloud adoption offered theoretical guidance. At a confidence interval of 95%, an online sample size calculator was used to arrive at three hundred and sixty two (362) respondents out of six thousand two hundred (6200) target population. Proportionate stratified random sampling technique and an online list randomizer were used to select respondents in the selected universities to participate in the study. Multiple regression was used to test the hypotheses in this study based on empirical data obtained by a survey questionnaire of thirty nine (39) questions from the two (2) public universities. Multiple regression results showed significant positive effect of Management Support (β=.257), Technical Support (β=.353), and User Preparedness (β=.475) on adoption of cloud computing at p&amp;lt;.05. Regression results gave a coefficient of determination R2=.908 which means 90.8% of the variation in adoption of cloud computing can be explained by Management Support, Technical Support, and User Preparedness combined. Based on the coefficient of determination (R2), the three null hypotheses (H01, H02, &amp;amp; H03) were rejected at p&amp;lt;0.05. Regression analysis showed that Management Support, Technical Support and User Preparedness are critical success factors in cloud adoption in public universities in Kenya. This study provides new and relevant insights to literature on cloud adoption in higher education service in Kenya.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0984/a.php" alt="Hit counter" /&gt;&lt;/p&gt;