Reviewerly/exHarmony huggingface dataset:
Fostering Students’ SRL in an Online Learning Environment

Recent research suggests that emerging technologies such as mobile devices represent unprecedented opportunities for students to be self-regulated learners, to work independently, and leverage their ability of self-regulated learning (SRL) to acquire knowledge and therefore to take responsibility for their learning regardless of any circumstance concerning learning environments. Effective pedagogical approaches such as select, organize, associate, and regulate (SOAR) is mainly developed to be an efficient instructional method for fostering students’ SRL skills. This study examined and compared the effectiveness of three mobile learning strategies: Mobile SOAR (M-SOAR), mobile SOAR with paper (M-SOAR-P), and mobile without SOAR (M-W-SOAR) to enhance students’ SRL skills, promote motivation, and improve learning performance of a science topic in an online learning environment. This study was carried out in Khartoum city, Sudan. Three classes of Sudanese higher school students participated in the study. The students were assigned to the M-SOAR approach (experimental group A, n = 34), M-SOAR-P (experimental group B, n = 32), and M-W-SOAR (control group, n = 33) as three learning methods. The online self-regulated learning questionnaire (OSLQ) and elements of attention, relevance, confidence, and satisfaction (ARCS) motivation questionnaire as well as the performance test were accomplished. The findings yielded that SRL skills and motivation of experimental group A were more positive compared to the experimental group B and the control group. The performance test computed by SPSS yielded that there was a statistically significant difference between three groups, in favor of the experimental group A. The study recommends further research into the effectiveness of a pedagogical SOAR approach on fostering students’ SRL in online learning environments.
A Decidable Non-Regular Modal Fixpoint Logic

Fixpoint Logic with Chop (FLC) extends the modal μ-calculus with an operator for sequential composition between predicate transformers. This makes it an expressive modal fixpoint logic which is capable of formalising many non-regular program properties. Its satisfiability problem is highly undecidable. Here we define Visibly Pushdown Fixpoint Logic with Chop, a fragment in which fixpoint formulas are required to be of a certain form resembling visibly pushdown grammars. We give a sound and complete game-theoretic characterisation of FLC’s satisfiability problem and show that the games corresponding to formulas from this fragment are stair-parity games and therefore effectively solvable, resulting in 2EXPTIME-completeness of this fragment. The lower bound is inherited from PDL over Recursive Programs, which is structurally similar but considerably weaker in expressive power.
LadRa-Net: Locally Aware Dynamic Reread Attention Net for Sentence Semantic Matching

Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks, such as natural language inference (NLI) and paraphrase identification (PI). Much recent progress has been made in this area, especially attention-based methods and pretrained language model-based methods. However, most of these methods focus on all the important parts in sentences in a static way and only emphasize how important the words are to the query, inhibiting the ability of the attention mechanism. In order to overcome this problem and boost the performance of the attention mechanism, we propose a novel dynamic reread (DRr) attention, which can pay close attention to one small region of sentences at each step and reread the important parts for better sentence representations. Based on this attention variation, we develop a novel DRr network (DRr-Net) for sentence semantic matching. Moreover, selecting one small region in DRr attention seems insufficient for sentence semantics, and employing pretrained language models as input encoders will introduce incomplete and fragile representation problems. To this end, we extend DRr-Net to locally aware dynamic reread attention net (LadRa-Net), in which local structure of sentences is employed to alleviate the shortcoming of byte-pair encoding (BPE) in pretrained language models and boost the performance of DRr attention. Extensive experiments on two popular sentence semantic matching tasks demonstrate that DRr-Net can significantly improve the performance of sentence semantic matching. Meanwhile, LadRa-Net is able to achieve better performance by considering the local structures of sentences. In addition, it is exceedingly interesting that some discoveries in our experiments are consistent with some findings of psychological research.
Performance improvement of weakly supervised fully convolutional networks by skip connections for brain structure segmentation

Abstract Purpose For the planning and navigation of neurosurgery, we have developed a fully convolutional network (FCN)‐based method for brain structure segmentation on magnetic resonance (MR) images. The capability of an FCN depends on the quality of the training data (i.e., raw data and annotation data) and network architectures. The improvement of annotation quality is a significant concern because it requires much labor for labeling organ regions. To address this problem, we focus on skip connection architectures and reveal which skip connections are effective for training FCNs using sparsely annotated brain images. Methods We tested 2D FCN architectures with four different types of skip connections. The first was a U‐Net architecture with horizontal skip connections that transfer feature maps at the same scale from the encoder to the decoder. The second was a U‐Net++ architecture with dense convolution layers and dense horizontal skip connections. The third was a full‐resolution residual network (FRRN) architecture with vertical skip connections that pass feature maps between each downsampled scale path and the full‐resolution scale path. The last one was a hybrid architecture with a combination of horizontal and vertical skip connections. We validated the effect of skip connections on medical image segmentation from sparse annotation based on these four FCN architectures, which were trained under the same conditions. Results For multiclass segmentation of the cerebrum, cerebellum, brainstem, and blood vessels from sparsely annotated MR images, we performed a comparative evaluation of segmentation performance among the above four FCN approaches: U‐Net, U‐Net++, FRRN, and hybrid architectures. The experimental results show that the horizontal skip connections in the U‐Net architectures were effective for the segmentation of larger sized objects, whereas the vertical skip connections in the FRRN architecture improved the segmentation of smaller sized objects. The hybrid architecture with both horizontal and vertical skip connections achieved the best results of the four FCN architectures. We then performed an ablation study to explore which skip connections in the FRRN architecture contributed to the improved segmentation of blood vessels. In the ablation study, we compared the segmentation performance between architectures with a horizontal path (HP), an HP and vertical up paths (HP+VUPs), an HP and vertical down paths (HP+VDPs), and an HP and vertical up and down paths (FRRN). We found that the vertical up paths were effective in improving the segmentation of smaller sized objects. Conclusions This paper investigated which skip connection architectures were effective for multiclass brain segmentation from sparse annotation. Consequently, using vertical skip connections with horizontal skip connections allowed FCNs to improve segmentation performance.
Research on Educational Evaluation System Related to Innovation and Entrepreneurship in Sports Colleges

According to the principles of science, orientation, motivation, operation and dynamics, this paper designs and constructs an evaluation index system Related to Innovation and Entrepreneurship consisting of five aspects: school level, management level, teaching level, teacher level and student level. Innovative and entrepreneurial education in colleges and universities provides a goal-oriented basis, promotes healthy and orderly development of education related to innovation and entrepreneurship, and achieves effective evaluation and judgment of educational work, improves the quality teaching, and delivers high-quality, high-skilled entrepreneurship for social training sports talents.
Situated Visualization of Historical Timeline Data on Mobile Devices: Design Study for a Museum Application

Many museums offer mobile apps to extend the brief descriptions of physical exhibits. However, these apps often reproduce on-site content, are not location-aware, or demand several user interactions to view the content. Therefore, we propose using visitors’ mobile devices to extend static information with time-oriented, situated information visualization. We present a design study using visualizations to guide visitors through an exhibition. In total, we performed two comparative studies: 1) with clickable mockups of three visualization concepts (Timeline, Bookshelf, and Timeflower), and based on these results, 2) with three functional prototypes of timeline visualization concepts (Stack-based, Section-based, and All-in-one) both in a lab and museum setting. Our main finding is, that museum visitors prefer familiar and linear visualization techniques. We reflect on our results and the process and define guidelines for future studies on visualization for casual users in museums.
Gathering Multi-dimensional Data of Scalable Container-based Cluster for Performance Analysis

Data collection is a systematic process of gathering, monitoring, and analysing specific information to find out solutions for relevant queries and the evaluation of the results. Whether the collected data will be used to perform research on business or academic areas, it allows gaining clear insights and first-hand knowledge of the research problem. In this paper, a new framework for gathering multi-dimensional performance data of a container-based cluster has been proposed and developed. Our main goal is to provide a systematic approach for gathering data-set and make it ready-to-use for a business or research analysis.The new framework provides the following functions: i) Multi-dimensional data collection, centrally performance metrics from the container-based cluster. iii) Re-structuring the collected data to facilitate its usage, iv) Pre-processing of the structured data by applying cleaning operations, v) Systematically storing the collected data in a public repository to be available for usages. In this work, Docker Swarm is used to building the cluster and in each stage, various technologies are utilized to collect, clean, and publish the data of the running states of the cluster. By using our framework, collected data can be used to better manage and monitor the cluster’s nodes and containers.
Dynamic Multi-Domain Translation Network for Single Image Deraining

On the single image deraining task, traditional methods are too complicated while deep learning methods lack interpretability. To solve these issues, we propose a novel deep unfolding network, which has the advantages of low complexity and high interpretability. Specifically, by transforming the rain into high-dimensional features, we propose to utilize the proximal gradient descend technique to construct an algorithm. And a new symmetry constraint is introduced to reduce the algorithm complexity effectively. Furthermore, to enhance the representation of rain features, we propose a novel dynamic multi-domain translation (DMT) module. Finally, by unrolling the algorithm, a deep unfolding network named DMTNet is established. All the parameters in DMTNet are learned end-to-end. Extensive experimental results show that the proposed DMTNet outperforms SOTA methods on several benchmark datasets.
Evaluating the Architectural Debt of IoT Projects

We observed a great and increasing interest in the last few years towards the evaluation of technical debt of software projects, in particular in the direction of code and architectural debt evaluation. This kind of analysis has not yet been performed for IoT projects. Hence, in this paper we start this exploration through the analysis of four Open Source IoT projects. We focus our attention on architectural debt and we exploit a tool, called Arcan, developed for architectural smell detection and for the computation of an architectural debt index. The results show that also IoT projects are subjected to architectural debt, and in particular to the presence of Cyclic Dependency and Unstable Dependency smells. However, there is evidence that the continuous refactoring of the code helps in avoiding the increase of debt, hence also developers of IoT projects should schedule periodical clean-ups of their code.
Special topics in electronic health data

While pragmatic trials can be conducted using electronic health data, there are aspects of these data sources that must be considered while designing and conducting a study, including issues related to missing data. Missing data is a common problem in electronic health data and can occur as a result of how data are collected in these systems. Here we discuss mechanisms for missing medication data and follow-up information in electronic medical records and insurance claims databases. Missing data issues can be mitigated by using unstructured data, such as free text from electronic medical records, to supplement the information present in the structured fields. However, the use of these more complex data types in pragmatic trial research requires efficient information extraction methods. Natural language processing techniques can be used to automatically extract information from unstructured text in electronic medical records. However, the sensitive nature and intricacies of unstructured clinical text create additional practical and technical challenges for establishing scalable and high-performing natural language processing systems for clinical free text.
Domain adaptation for an automated classification of deontic modalities in software engineering contracts

Contracts are agreements between parties engaging in economic transactions. They specify deontic modalities that the signatories should be held responsible for and state the penalties or actions to be taken if the stated agreements are not met. Additionally, contracts have also been known to be source of Software Engineering (SE) requirements. Identifying the deontic modalities in contracts can therefore add value to the Requirements Engineering (RE) phase of SE. The complex and ambiguous language of contracts make it difficult and time-consuming to identify the deontic modalities (obligations, permissions, prohibitions), embedded in the text. State-of-art neural network models are effective for text classification; however, they require substantial amounts of training data. The availability of contracts data is sparse owing to the confidentiality concerns of customers. In this paper, we leverage the linguistic and taxonomical similarities between regulations (available abundantly in the public domain) and contracts to demonstrate that it is possible to use regulations as training data for classifying deontic modalities in real-life contracts. We discuss the results of a range of experiments from the use of rule-based approach to Bidirectional Encoder Representations from Transformers (BERT) for automating the classification of deontic modalities. With BERT, we obtained an average precision and recall of 90% and 89.66% respectively.
Corporate Social Responsibility in Nigeria and Multinational Corporations in the Fight Against Human Trafficking in Oil-Producing Communities

In Nigeria, human trafficking is a hidden crime, driven by the current economic situation of the country. The Nigerian government has demonstrated significant efforts to combating human trafficking in the country by creating the National Agency for the Prohibition of Trafficking in Persons (NAPTIP). The incidents of human trafficking in the Nigeria’s oil-producing communities have remained among the highest in the country and higher than the national average. The objective of this investigation was to determine the impact of a new corporate social responsibility (CSR) model of multinational oil companies (MOCs) on the fight against human trafficking in the host communities. A total of one thousand, two hundred households were sampled across the rural communities of Niger Delta. Results from the use of a combined propensity score matching and logit model indicated that MOCs hold the key to combating human trafficking by fostering effective partnership across different sectors, if highest CSR priority is assigned to reducing incidents of human trafficking in the oil-producing communities. Embracing the fight against human trafficking should form the foundation of General Memorandum of Understanding (GMoUs) practice, which in turn will provide the enabling environment for more widespread responsible business. As most of the human trafficking in the Niger Delta is exploited by relatives or friends; MOCs should involve traditional and religious leaders in the fight and set up GMoU clusters interventions specifically for anti-trafficking agencies including NAPTIP, the police and immigration to support their actions and improve efficiency.
Unipept Visualizations: an interactive visualization library for biological data

The Unipept Visualizations library is a JavaScript package to generate interactive visualizations of both hierarchical and non-hierarchical quantitative data. It provides four different visualizations: a sunburst, a treemap, a treeview and a heatmap. Every visualization is fully configurable, supports TypeScript and uses the excellent D3.js library.The Unipept Visualizations library is available for download on NPM: https://npmjs.com/unipept-visualizations. All source code is freely available from GitHub under the MIT license: https://github.com/unipept/unipept-visualizations.
From Noon to Sunset: Interactive Rendering, Relighting, and Recolouring of Landscape Photographs by Modifying Solar Position

Abstract Image editing is a commonly studied problem in computer graphics. Despite the presence of many advanced editing tools, there is no satisfactory solution to controllably update the position of the sun using a single image. This problem is made complicated by the presence of clouds, complex landscapes, and the atmospheric effects that must be accounted for. In this paper, we tackle this problem starting with only a single photograph. With the user clicking on the initial position of the sun, our algorithm performs several estimation and segmentation processes for finding the horizon, scene depth, clouds, and the sky line. After this initial process, the user can make both fine‐ and large‐scale changes on the position of the sun: it can be set beneath the mountains or moved behind the clouds practically turning a midday photograph into a sunset (or vice versa). We leverage a precomputed atmospheric scattering algorithm to make all of these changes not only realistic but also in real‐time. We demonstrate our results using both clear and cloudy skies, showing how to add, remove, and relight clouds, all the while allowing for advanced effects such as scattering, shadows, light shafts, and lens flares.
A Combined Approach for Enhancing the Stability of the Variable Selection Stage in Binary Classification Tasks

Variable selection is an essential tool for gaining knowledge on a problem or phenomenon, by identifying the factors that shows the highest influence on it. It is also fundamental for the implementation of machine learning-based approaches to modelling and classification tasks, by improving performances and reducing computational cost. Furthermore, in many real-world applications, such as the ones in the medical field, a relevant number of variables are jointly observed, but the number of available observations is quite limited. In these cases, variable selection is clearly essential, but standard variable selection approaches become “unstable”, as the high correlation among different variables or their similar relevance with respect to the considered target lead to multiple solutions leading to similar performances. In machine-learning based classification, the stability of variable selection, namely its robustness with respect variations in the classifier training dataset, is as important as the performance of the classifier itself. The paper presents an automatic procedure for variable selection in classification tasks, which ensures excellent stability of the selection and does not require any a priori information on the available data.
Space-Related Scientific and Technical Information in Current Media Discourse: Research Results

By informing mass audience about scientific, engineering, and technological achievements, modern media contribute to the growth of the prestige of scientific and innovative activities and increase society’s susceptibility to scientific and technological progress and innovations. One of the priorities for scientific and technological development in the Russian Federation is the study and exploration of outer space. Mass media can be an effective tool for popularizing fundamental and applied space research and its impact on everyday life, thereby updating the image of space in mass consciousness and society’s representations of the present and the future. The leading federal socio-political newspapers Komsomolskaya Pravda, Rossiyskaya Gazeta, and Kommersant, as well as the online media Lenta.Ru and Gazeta.Ru, were selected as the object of this study. The research period covered January–December 2020. Publications related to space were selected and analyzed using Integrum, the information retrieval system for monitoring and analyzing the media, and the word2vec algorithm based on artificial neural networks. The original methodology made it possible to determine the ratio of publications about space in the total amount of mass media reports and to identify key topical areas of the space information agenda and features of its geography.
Mobile-based Tajwid 1 Learning Media at the Gontor Entrance Guidance Institution with the Demonstration Method

Darussalam Gontor Modern Boarding School (PMDG) graduates established tutoring institutions in their respective regions. The institution is called Bimbingan Masuk Gontor (Bimago). Currently, prospective students who want to enter Gontor have difficulty learning Tajwid 1 because of the limited learning media that still use printed books and the number is limited. This requires other media, one of which is a smartphone. The purpose of this research is to facilitate prospective students who want to learn the PMDG version of Tajweed 1. This study uses a Software Development Life Cycle approach and applies a demonstration method to its content. Application testing is done in 4 ways. With the blackbox method, it shows the application is running smoothly. The use of hardware with 5 smartphones of various brands and a screen size of at least 5.20 inches indicates the application is running well. A learning media expert gave an average of 94.38%. Six Tajwid teachers gave a score of 88.40%. The results of this study can be concluded that this application can help as an effective learning tool.