Reviewerly/exHarmony huggingface dataset:
Perception of Teachers on Collaborative Tools Knowledge Level Mediated by ICT and Their Experience with Students

In 21st century, competencies mediated with ICT are key for development at the educational area. This is the reason why this research aims to assess perception on level of competences of secondary school teachers on technological tools to promote collaborative work methodologies with their students and determine if said level has influence on the implementation of these experiences. Design implies a mixed model based on the application of a questionnaire divided in three parts: demographic aspects, level of knowledge on collaborative tools and experiences on collaborative work with students. Sample (n=542) corresponds to secondary education teachers from Dominican Republic. In analysis, nonparametric tests and categorizations were used. Results suggest that teachers require of a better formation on collaborative methodologies and tools mediated by ICT and, additionally, results indicate that there is a digital gap between male and female teachers, as the first group had more advantages.
On the mathematical theory of ensemble (linear-Gaussian) Kalman–Bucy filtering

The purpose of this review is to present a comprehensive overview of the theory of ensemble Kalman-Bucy filtering for continuous-time, linear-Gaussian signal and observation models. We present a system of equations that describe the flow of individual particles and the flow of the sample covariance and the sample mean in continuous-time ensemble filtering. We consider these equations and their characteristics in a number of popular ensemble Kalman filtering variants. Given these equations, we study their asymptotic convergence to the optimal Bayesian filter. We also study in detail some non-asymptotic time-uniform fluctuation, stability, and contraction results on the sample covariance and sample mean (or sample error track). We focus on testable signal/observation model conditions, and we accommodate fully unstable (latent) signal models. We discuss the relevance and importance of these results in characterising the filter's behaviour, e.g. it's signal tracking performance, and we contrast these results with those in classical studies of stability in Kalman-Bucy filtering.We also provide a novel (and negative) result proving that the bootstrap particle filter cannot track even the most basic unstable latent signal, in contrast with the ensemble Kalman filter (and the optimal filter). We provide intuition for how the main results extend to nonlinear signal models and comment on their consequence on some typical filter behaviours seen in practice, e.g. catastrophic divergence.
Optimal Robust Learning of Discrete Distributions from Batches

Many applications, including natural language processing, sensor networks, collaborative filtering, and federated learning, call for estimating discrete distributions from data collected in batches, some of which may be untrustworthy, erroneous, faulty, or even adversarial. Previous estimators for this setting ran in exponential time, and for some regimes required a suboptimal number of batches. We provide the first polynomial-time estimator that is optimal in the number of batches and achieves essentially the best possible estimation accuracy.
An Improved Moth Flame Optimization Algorithm for Minimizing Specific Fuel Consumption of Variable Cycle Engine

The effective selection of Variable Cycle Engine (VCE) parameters plays a key role in achieving low specific fuel consumption (SFC) of fighters. However, the selection of VCE parameters is a continuous multimodal issue involving substantial local optima, so that most swarm intelligence (SI) algorithms are easily trapped into local optimal solutions, and cannot obtain satisfactory performance. To address this problem, an improved moth flame optimization algorithm with adaptive Lévy-Flight perturbations (ALFMFO) is proposed. In ALFMFO, the current population aggregation status can be accurately judged based on the difference in fitness variance between two successive moth generations. According to the population aggregation status, the Lévy-Flight disturbance strategy can adaptively adjust the perturbation probability to enhance the ability of ALFMFO to escape from local optimal solutions and realize the minimum SFC optimization of VCE. Experimental results suggest that ALFMFO is effective and superior to other compared SI algorithms in terms of accuracy and robustness.
Changing Social Learning Theory Through Reliance on the Internet of Things and Artificial Intelligence

This paper examines the trend in the concept of the Internet of Things (IoT), Artificial Intelligence (AI), and related computer-mediated teaching and learning. Implications for social learning theory are examined based on the concept of “cyborg” education through a variety of scholarly and popular media sources with implications for schools and teacher educators. Recommendations for teacher education are posited as the topic of technological mediation between people continues to change in relation to how education will likely need to adapt to provide a prosocial environment through technological mediation, though the social learning itself operates in different ways.
IMPRO: IMMERSIVE PROTOTYPING IN VIRTUAL ENVIRONMENTS FOR INDUSTRIAL DESIGNERS

Abstract Computer-Aided Design (CAD) constitutes an important tool for industrial designers. Similarly, Virtual Reality (VR) has the capability to revolutionize how designers work with its increased sense of scale and perspective. However, existing VR CAD applications are limited in terms of functionality and intuitive control. Based on a comparison of VR CAD applications, ImPro, a new application for immersive prototyping for industrial designers was developed. The user evaluations and comparisons show that ImPro offers increased usability, functionality, and suitability for industrial designers.
A Fully Rational Account of Structured Argumentation Under Resource Bounds

ASPIC+ is an established general framework for argumentation and non-monotonic reasoning. However, ASPIC+ does not satisfy the non-contamination rationality postulates, and moreover, tacitly assumes unbounded resources when demonstrating satisfaction of the consistency postulates. In this paper we present a new version of ASPIC+ – Dialectial ASPIC+ – that is fully rational under resource bounds.
Programming of English Word Review Planning Time Based on Ebinhaus Forgetting Curve

The Ebinhaus forgetting curve reveals the law of memory forgetting and provides inspiration and guidance for the persistence of English vocabulary memory. On this basis, further exploration of English vocabulary learning strategies can produce multiplier effect. By collecting, learning, analyzing, comparing and synthesizing the advantages and disadvantages of the existing word memory learning software in the market, the author uses Crailk and Lockhart to put forward the theory of cognitive processing level to guide the design of vocabulary learning function modules; and uses multimedia picture art to guide the design of vocabulary learning function modules. The artistic rules of design guide the process of software interface design, function menu navigation and the interaction of various controls. In practical application, the process of arranging learning tables by combining curves is rather tedious. In order to reduce the inconvenience of making the schedule, the schedule is realized through the program, and the algorithm is improved to make it more reasonable.
Architecture Optimization Model for the Deep Neural Network

The daunting and challenging tasks of specifying the optimal network architecture and its parameters are still a major area of research in the field of Machine Learning (ML) till date. These tasks though determine the success of building and training an effective and accurate model, are yet to be considered on a deep network having three hidden layers with varying optimized parameters to the best of our knowledge. This is due to expert’s opinion that it is practically difficult to determine a good Multilayer Perceptron (MLP) topology with more than two or three hidden layers without considering the number of samples and complexity of the classification to be learnt. In this study, a novel approach that combines an evolutionary genetic algorithm and an optimization algorithm and a supervised deep neural network (Deep-NN) using alternative activation functions with the view of modeling the prediction for the admission of prospective university students. The genetic algorithm is used to select optimal network parameters for the Deep-NN. Thus, this study presents a novel methodology that is effective, automatic and less human-dependent in finding optimal solution to diverse binary classification benchmarks. The model is trained, validated and tested using various performance metrics to measure the generalization ability and its performance.
Providing an Integrated Access to EHR Using Electronic Health Records Aggregators.

The integration of health data systems is an open problem. Most of the active initiatives are based on the use of standards, each one proposed for a concrete use, without integrating other needs or standards allowing on homogenous use. We propose an alternative to get an unified view of health related data, valid for several uses, that can integrate heterogeneous sources. The proposal set the framework to integrate the developments made so far to automatically learn the extraction and conversion of the data. All the sources are integrated under a single access point. We present the main concepts of EHRagg as a middleware between systems that can incorporate several sources giving an unified access following the FAIR principles.
Analysis of Resampling Techniques on Predictive Performance of Credit Card Classification

Credit card fraud detection has been a very demanding research area due to its huge financial implications and rampant applications in almost every area of life. Credit card fraud datasets are naturally imbalanced by having more legitimate transaction in comparison to the fraudulent transactions.&amp;nbsp; Literature represents numerous studies that are aimed to balance the skewed datasets. There are two major techniques of resampling in balancing these sets i.e. under-sampling and oversampling. However both under-sampling and oversampling techniques suffer from their own set of problems that can seriously affect the performance of classifiers that have been inducted for credit card studies in the past. Thus to accelerate detection of credit card fraud, it is very important to implement the strategy that could possibly provide better predictive performance. This paper attempts to find out what resampling technique can work best under different skewed distributions for the domain of credit card fraud detection.
Smartphone-Based Remote Monitoring Tool for e-Learning

In this paper, a smartphone-based learning monitoring system is presented. During pandemics, most of the parents are not used to simultaneously deal with their home office activities and the monitoring of the home school activities of their children. Therefore, a system allowing a parent, teacher or tutor to assign a task and its corresponding execution time to children, could be helpful in this situation. In this work, a mobile application to assign academic tasks to a child, measure execution time, and monitor the child's attention, is proposed. The children are the users of a mobile application, hosted on a smartphone or tablet device, that displays an assigned task and keeps track of the time consumed by the child to perform this task. Time measurement is performed using face recognition, so it is possible to infer the attention of the child based on the presence or absence of a face. The app also measures the time that the application was in the foreground, as well as the time that the application was sent to the background, to measure boredom. The parent or teacher assigns a task using a desktop application specifically designed for this purpose. At the end of the time set by the user, the application sends to the parent or teacher statistics about the execution time of the task and the degree of attention of the child.
Comparison of health record vitals and continuously acquired vitals data identifies key differences in clinical impression

This study aimed to investigate how intermittent ICU data documented by nursing in the electronic medical record (EMR) corresponds to that obtained from continuous data acquisition (CDA) and the differences in perception by medical providers. Both descriptive statistics and impact on decision-making were compared. We performed a secondary analysis of de-identified physiologic data collected as part of a study exploring the impact of dexmedetomidine adjunctive sedation on ICP and primary sedation dose (C3PO study). Data was in two sets, one derived from nurse charting in the health record, the other was continuous digital acquisition. We compared the number and timing of clinical events within each dataset that were outside the clinical treatment goals for that parameter. We also made graphs of concurrent 3-h periods from the two datasets and surveyed providers about their impressions and the need for intervention based on the visual review of the trends. Data consisted of 74,143 measures of ICP compiled from 40 neurocritically ill patients. We found no significant difference in overall means. The CDA dataset captured significantly more ICP elevation events defined as ICP >20 mmHg. Survey of ICU providers revealed that CDA data segments were perceived as "less controlled" and were more likely to be considered as "not meeting goals" for the vital parameter. We conclude that there are no significant differences in mean ICP measures between EMR and CDA vitals data, however, EMR data underreported the number of ICP elevation events and may not represent the overall patient exposure to out of goal vitals measures. We conclude that potentially significant differences exist between EMR and CDA data for ICU patients and that further consideration of this is needed in ICU trial design and as more machine learning and surveillance algorithms are developed for patient monitoring and predictions of deterioration.
Multi-Agent Path Finding in Configurable Environments

Multi-Agent Path Finding (MAPF) plays an important role in many real-life applications where autonomous agents must coordinate to reach their goals without collisions. MAPF problems often take place in structured environments that are usually assumed to be static and known in advance. In this paper, we introduce C-MAPF, i.e., MAPF in Configurable environments, a novel variant of the MAPF problem in which the environment is configurable, namely its structure and topology can be controlled within some given constraints. Consider, for instance, a warehouse logistics application: the environment can be changed (at least to some degree) by the managers of the warehouse, for example by re-arranging the positions of the shelves or by removing or adding temporary walls. We study the properties of the C-MAPF problem and we devise two algorithms for solving it, both based on Conflict-Based Search (CBS), a state-of-the-art MAPF algorithm. First, we present Parallel CBS (P-CBS), that searches for a solution by simultaneously considering all the possible configurations of the environment. We then present Abstract CBS (A-CBS), an extended version of the CBS algorithm that solves C-MAPF problems by introducing a new type of conflict on the allowable configurations of the environment. We prove that our solvers are both complete and optimal and we experimentally assess their performance in different settings.
On the Privacy and Utility Properties of Triple Matrix-Masking

Privacy protection is an important requirement in many statistical studies. A recently proposed data collection method, triple matrix-masking, retains exact summary statistics without exposing the raw data at any point in the process. In this paper, we provide theoretical formulation and proofs showing that a modified version of the procedure is strong collection obfuscating: no party in the data collection process is able to gain knowledge of the individual level data, even with some partially masked data information in addition to the publicly published data. This provides a theoretical foundation for the usage of such a procedure to collect masked data that allows exact statistical inference for linear models, while preserving a well-defined notion of privacy protection for each individual participant in the study. This paper fits into a line of work tackling the problem of how to create useful synthetic data without having a trustworthy data aggregator. We achieve this by splitting the trust between two parties, the "masking service provider" and the "data collector."
The Knowledge and the Use of Educational Applications from University Students in Spain and the Czech Republic

Technological evolution has led to many changes at both professional and academic levels. The inclusion of Information and Communication Technologies (ICT) has caused dizzying changes in the different contexts of life (educational, professional, social). More and more applications are being downloaded by users to their mobile devices, both for entertainment and training. For this reason, this study is based on the need to assess the knowledge and attitude of university students towards downloading existing educational applications. In this way, the use and frequency of these tools in the teaching-learning process of the students will be analyzed. In order to carry out this study, the quantitative methodology has been taken into consideration with the collection of data with a sample conformed by a total of 278 students of Higher Education between Spain and the Czech Republic. For this purpose, a survey has been prepared as an instrument of analysis, which has been validated by experts in the field. Among the most outstanding results, it can be seen that in both countries, students download educational apps to a greater or lesser degree, according to their interest and subject matter. Likewise, there is a lack of competences and skills in the use of these new technologies as a training complement. Based on this, we propose the continuous training of students and teachers in the knowledge and pedagogical use of ICT in general and educational apps in particular.
Grapheme-to-Phoneme Conversion with a Multilingual Transformer Model

In this paper, we describe our three submissions to the SIGMORPHON 2020 shared task 1 on grapheme-to-phoneme conversion for 15 languages.We experimented with a single multilingual Transformer model.We observed that the multilingual model achieves results on par with our separately trained monolingual models and is even able to avoid a few of the errors made by the monolingual models.
Visual Analysis for Hospital Infection Control using a RNN Model

Bacteria and viruses are transmitted among patients in the hospital. Infection control experts develop strategies for infection control. Currently, this is done mostly manually, which is time-consuming and error-prone. Visual analysis approaches mainly focus disease spread on population level.We learn a RNN model for detection of potential infections, transmissions and infection factors. We present a novel interactive visual interface to explore the model results. Together with infection control experts, we apply our approach to real hospital data. The experts could identify factors for infections and derive infection control measures.
Distractor Analysis and Selection for Multiple-Choice Cloze Questions for Second-Language Learners

We consider the problem of automatically suggesting distractors for multiple-choice cloze questions designed for second-language learners.We describe the creation of a dataset including collecting manual annotations for distractor selection.We assess the relationship between the choices of the annotators and features based on distractors and the correct answers, both with and without the surrounding passage context in the cloze questions.Simple features of the distractor and correct answer correlate with the annotations, though we find substantial benefit to additionally using large-scale pretrained models to measure the fit of the distractor in the context.Based on these analyses, we propose and train models to automatically select distractors, and measure the importance of model components quantitatively.
Design and Realization of Interconnection of Multifunctional Weighing Device with Sigfox Data Network

With the increasing expansion of IoT networks, people are trying to connect more and more devices to data networks. Nowadays, such devices are made possible by platforms such as Arduino and Espressif. In recent years, one of the areas of interest for IoT has been the development and maintenance of beekeeping. Although there are various commercial solutions for multifunctional hive scales (BeeSpy or Alya), they are quite expensive. In this paper, we present a hardware and software solution to this problem. From a hardware point of view, our goal was to design such a device that would be competitive, capture various data using ambient sensors, and communicate over the new SigFox data network that was introduced in Slovakia in 2018. From a software point of view, we aimed to design and implement an application for smartphones. With this application, it is possible to fully visualize all acquired sensor data. The communication between the multifunction weighing device and the user's smartphone is ensured via SMS messages.
An Iterative Approach for Identifying Complaint Based Tweets in Social Media Platforms (Student Abstract)

Twitter is a social media platform where users express opinions over a variety of issues. Posts offering grievances or complaints can be utilized by private/ public organizations to improve their service and promptly gauge a low-cost assessment. In this paper, we propose an iterative methodology which aims to identify complaint based posts pertaining to the transport domain. We perform comprehensive evaluations along with releasing a novel dataset for the research purposes1.
An integrated and secured medical data framework for effective tele health applications

The rapid progression of healthcare technologies and transmission strategies makes the system reliable to gain, dispense and manage data over medical devices and as well improves conventional hospital information systems (HIS) to deliver effective healthcare services. When the patient medical information is communicated through wireless network, there exists a high chance of modifying the information. Therefore, a futuristic tele healthcare framework has been proposed to ensure the security, data integrity, quality and minimisation of bandwidth requirements for offering complete healthcare services at reduced cost. The proposed framework encompasses the integration of three modules viz. steganography, compression and encryption. This framework also validates the integrity of ROI, furthermore it ensures better compression ratio. Finally, the whole image is encrypted with modified chaotic map encryption in order to afford complete medical data security. Experimental outcomes demonstrate that the proposed framework offers robustness in terms of security, quality and reliability which alleviate misdiagnosis at the physician end in telemedicine applications.