Reviewerly/exHarmony huggingface dataset:
Informalizing Formal Logic

This paper presents a way in which formal logic can be understood and reformulated in terms of argumentation that can help us unify formal and informal reasoning. Classical deductive reasoning will be expressed entirely in terms of notions and concepts from argumentation so that formal logical entailment is equivalently captured via the arguments that win between those supporting concluding formulae and arguments supporting contradictory formulae. This allows us to go beyond Classical Logic and smoothly connect it with human reasoning, thus providing a uniform argumentation-based view of both informal and formal logic.
Designing a Collaborative Virtual Reality Game for Teen-Robot Interactions

Virtual reality (VR) offers potential as a prototyping tool for human-robot interaction. We explored a way to utilize human-centered design (HCD) methodology to develop a collaborative VR game for understanding teens' perceptions of, and interactions with, social robots. Our paper features three stages of the design process for teen-robot interaction in VR; ideation, prototyping, and the game development. In the ideation stage, we identified three important design principles: collaboration, customization, and robot characterization. In the prototyping stage, we developed a card game, conducted gameplay, and confirmed our design principles. Finally, we developed a low-fidelity VR game and received teens' feedback. This exploratory study highlights the potential of VR, both for collaborative robot design and teen-robot interaction studies.
Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter

The extended target probability hypothesis density (ET-PHD) filter cannot work well if the density of measurements varies from target to target, which is based on the measurement set partitioning algorithms employing the Mahalanobis distance between measurements. To tackle the problem, two measurement set partitioning approaches, the shared nearest neighbors similarity partitioning (SNNSP) and SNN density partitioning (SNNDP), are proposed in this paper. In SNNSP, the shared nearest neighbors (SNN) similarity, which incorporates the neighboring measurement information, is introduced to DP instead of the Mahalanobis distance between measurements. Furthermore, the SNNDP is developed by combining the DBSCAN algorithm with the SNN similarity together to enhance the reliability of partitions. Simulation results show that the ET-PHD filters based on the two proposed partitioning algorithms can achieve better tracking performance with less computation than the compared algorithms.
From Brain Space to Distributional Space: The Perilous Journeys of fMRI Decoding

Recent work in cognitive neuroscience has introduced models for predicting distributional word meaning representations from brain imaging data. Such models have great potential, but the quality of their predictions has not yet been thoroughly evaluated from a computational linguistics point of view. Due to the limited size of available brain imaging datasets, standard quality metrics (e.g. similarity judgments and analogies) cannot be used. Instead, we investigate the use of several alternative measures for evaluating the predicted distributional space against a corpus-derived distributional space. We show that a state-of-the-art decoder, while performing impressively on metrics that are commonly used in cognitive neuroscience, performs unexpectedly poorly on our metrics. To address this, we propose strategies for improving the model’s performance. Despite returning promising results, our experiments also demonstrate that much work remains to be done before distributional representations can reliably be predicted from brain data.
Bisimulation for Secure Information Flow Analysis of Multi-Threaded Programs

Preserving the confidentiality of information is a growing concern in software development. Secure information flow is intended to maintain the confidentiality of sensitive information by preventing them from flowing to attackers. This paper discusses how to ensure confidentiality for multi-threaded programs through a property called observational determinism. Operational semantics of multi-threaded programs are modeled using Kripke structures. Observational determinism is formalized in terms of divergence weak low-bisimulation. Bisimulation is an equivalence relation associating executions that simulate each other. The new property is called bisimulation-based observational determinism. Furthermore, a model checking method is proposed to verify the new property and ensure that secure information flow holds in a multi-threaded program. The model checking method successively refines the Kripke model of the program until the quotient of the model with respect to divergence weak low-bisimulation is reached. Then, bisimulation-based observational determinism is checked on the quotient, which is a minimized model of the concrete Kripke model. The time complexity of the proposed method is polynomial in the size of the Kripke model. The proposed approach has been implemented on top of PRISM, a probabilistic model checking tool. Finally, a case study is discussed to show the applicability of the proposed approach.
The Implementation of Problem Based Learning (PBL) Model Improving Students’ Oral Communication Skill through Lesson Study

The action research was conducted in the odd semester of 2017/2018 using Lesson study as a tool to investigate collaborative and continuous learning processes, based on the principle of collegiality. Through Lesson Study students are expected to have communication skills both verbally and in writing. Communication skills can be improved through problem based learning (PBL) learning models. This study aims to determine the improvement of students' oral communication skills through application of problem-based learning. This research was conducted on February 8, 2018, March 8, 2018, April 12, 2018. The subjects were all students who attended the Action research in the field of Education. Data was collected using the same PBL model with different learning media. Learning media used are emoticon points, interactive videos, and game points. Students' communication skills are measured using an observation sheet and an assessment column for oral communication skills. The results show that the assessment of students' communication skills is measured in group in the first cycle. In the next cycle, it is done individually and shows that 22% of students have poor communication skills, 11% of students have good communication skills, and 67% have good communication skills. Then, in the third cycle, 27% of students in the excellent category, 55% in the good category, and 18% in the fairly good category. Based on these results it can be concluded that the learning model of Problem Based Learning based on Lesson Study can improve students' oral communication skills.
Internet of Things IoT: IoT in Healthcare

IoT is a modest way to elevate our production and yield with the help of internet.We can supervise and control and communicate with the help of internet.Internet-Of-Things imagines a future physical and digital entities are to be linked by proper information and communication technologies which will sanction new class of applications and services.In this survey we will ensure a survey of applications and research difficulties for Internet-Of-Things in Healthcare.
UNIVERSITY EMPLOYEE ENGAGEMENT STUDY METHODOLOGY

This research paper describes the design and approbation of the Russian university employees’ engagement study. Employees engagement is one of the most important HR-trends of the XXI century, widely established in the business environment, but little popular in the academic community. The purpose of the study was to develop a tool for university managers to help them build and adjust the personnel policy of the educational organization. The study analysed key models of employee engagement (Aon Hewitt, Gallup Inc., Hay Group etc.), defined indicators and factors of engagement. On the basis of the obtained data, there was designed a methodology to specify the typology of university positions as oriented to the process and customer-oriented management approaches. The methodology was tested, conclusions and recommendations concerning the personnel policy of the educational organization were formulated according to the results of the pilot study. The further research challenge is validating the methodology on a larger sample and on different types of universities. This article can be useful both for universities’ whole management staff within HR-structures and for those who rely upon the employees’ development, potential, loyalty towards the organization, role engagement. Original and new in the article does appear the development of focused methods to be used in higher education institutions.
Digital Literacies Learning in Contexts of Development: A Critical Review of Six IDRC-Funded Interventions 2016–2018

As global development agencies and governments seek to address the United Nations’ Sustainable Development Goal 4 for Universal Education, evidence of the real impacts of digital literacies interventions in local contexts are needed. This critical review of the designs, impacts and markers of quality of six literacies interventions offers new insights into the strengths and weaknesses of &lt;em&gt;fixed&lt;/em&gt; and &lt;em&gt;open&lt;/em&gt; approaches to literacies learning in contexts of development. Open interventions offered greater promise for learning a range of digital literacies practices than fixed interventions, even though fixed interventions, based on mobile and web-based apps were inherently digital. This raises important questions about the ways literacies have been conceptualised in development research.
Pursuing Optimal Prediction of Discharge Time in ICUs with Machine Learning Methods

In hospital intensive care units (ICU), patients are under continuous evaluation. One of the purposes of this evaluation is to determine the expected number of days to discharge. This value is important to manage ICUs. Some studies show that health care professionals are good at predicting short-term discharge times, but not as good at long-term predictions. Machine learning methods can achieve 1.79-day average prediction error. We performed a study on 3,787 patient-days in the ICU of the Hospital Joan XXIII (Spain) to obtain a data-driven model to predict the discharge time of ICU patients, in a daily basis. Our model, which is based on random forest technology, obtained an error of 1.34 days. We studied the progression of the model as more data are available and predicted that the number of instances required to reduce the error below one day is 4,745. When we trained the model with all the available data, we obtained a mean error of less than half a day with a coefficient of determination (R2) above 97% in their predictions on either ICU survivors and not survivors. Similar results were obtained differentiating by patients’ gender and age, confirming our approach as a good means to achieve optimal performance when more data will be available.
WePBAS: A Weighted Pixel-Based Adaptive Segmenter for Change Detection

The pixel-based adaptive segmenter (PBAS) is a classic background modeling algorithm for change detection. However, it is difficult for the PBAS method to detect foreground targets in dynamic background regions. To solve this problem, based on PBAS, a weighted pixel-based adaptive segmenter named WePBAS for change detection is proposed in this paper. WePBAS uses weighted background samples as a background model. In the PBAS method, the samples in the background model are not weighted. In the weighted background sample set, the low-weight background samples typically represent the wrong background pixels and need to be replaced. Conversely, high-weight background samples need to be preserved. According to this principle, a directional background model update mechanism is proposed to improve the segmentation performance of the foreground targets in the dynamic background regions. In addition, due to the “background diffusion” mechanism, the PBAS method often identifies small intermittent motion foreground targets as background. To solve this problem, an adaptive foreground counter was added to the WePBAS to limit the “background diffusion” mechanism. The adaptive foreground counter can automatically adjust its own parameters based on videos’ characteristics. The experiments showed that the proposed method is competitive with the state-of-the-art background modeling method for change detection.
Learning Temporal Regularized Correlation Filter Tracker With Spatial Reliable Constraint

Correlation filters have achieved appealing performance with high speed in recent years. The advantage of correlation filter-based tracking methods is mainly attributed to powerful features and effective online filter learning. However, the periodic assumption of the training data would introduce unwanted boundary effects, which severely degrade the discrimination power of the correlation filter. In this paper, we construct the spatial reliable map with deep features from Convolutional Neural Network, then the map is used to adjust the filter support to the part of the object suitable for tracking. In order to further improve the long-term tracking ability, we introduce temporal regularization to DCF training, which can deal with occlusion and deformation situations. The experimental results show that the proposed algorithm achieves high tracking success rate and accuracy.
Towards Machine Learning with Zero Real-World Data

Machine Learning (ML) models are widely used to infer human activities. However, collecting data to train ML models in realworld often requires significant time and effort. In this paper, we suggest a novel data collection framework to leverage pre-built VR applications and simulating tools. In particular, we applied the concept of virtual Inertial Measurement Unit (IMU) to capture activities of an avatar in simulation. Our initial results show that Random Forest (RF), Support Vector Machine (SVM), and Long Short Term Memory (LSTM) models built with the virtual sensor data can classify three activities (i.e., standing, running, walking) over a realworld dataset at the accuracy of 80.40% (87.83% precision and 80.12% recall), 67.52% accuracy (72.24% precision and 68.15% recall), and 77.67% accuracy (86.25% precision and 77.63% recall), respectively. The early results show the initial feasibility of simulation-driven machine learning without real-world data
Variable Strength Combinatorial Testing for Deep Neural Networks

In deep neural networks (DNNs), each neuron in the post-layer receives the influence of all the neurons in the pre-layer. As we known, different connections in a DNN model have different weights. It means that, different combinations of pre-layer neurons have different effects on the post-layer neurons. Therefore, the variable strength combinatorial testing can reflect the effect of combination interaction of neurons in the pre-layer on the neurons in the post-layer. In this paper, we propose to adopt variable strength combinatorial testing technique on DNNs testing. In order to modeling the effect of combinatorial interaction of pre-layer neurons on the post-layer neurons, we propose three methods to construct variable strength combinatorial interaction relationship for DNNs. The experimental results show that, 1) variable strength combinatorial coverage criteria are discriminating to measure the adequacy of DNNs testing, and 2) there is correlation between variable strength combinatorial coverage and adversarial detection.
Paradoxes of Students’ Non-formal Education

Introduction. The research topicality lies in the inference that formal education system loses its ability to respond quickly to socio-cultural and technological changes in society. The purpose of the article is to study the international and domestic experience of non-formal education, to identify the practices of non-formal education among students, and to examine the prospects and para doxes of non-formal education. Materials and Methods. The study encompassed such levels as macrosocial, mesosocial, and microsocial. The authors’ methodology comprised two stages. The first part of the research was theoretical. The second part included in-depth interviews (N = 126) and questionnaire results processing (N = 894). Results. The examples of foreign countries show institutional opportunities, regulation mechanisms, and means of recognition of non-formal education. The scale of distribution of non-formal education in the world is presented. The empirical research shows that the absolute majority of the Russian students are not involved in non-formal educational practices. The main objective of non-formal education is to obtain actual knowledge, living and vocational skills within a short time period. Only a third of respondents admit that non-formal education has had a positive ef fect on their employment. Discussion and Conclusion. The study allows to deepen knowledge about the education system transformation, to identify possible strategies to remove young people’s qualification gaps and retards and overcome barriers existing on the labor market. The study results can be used to develop theoretical concepts and conduct a comparative analysis, as well as in practical activities of educational organizations. The study will help identify the opinion of employers regarding their readiness to recognize qualifications earned through non-formal education.
Flattening NTRU for Evaluation Key Free Homomorphic Encryption

Abstract We propose a new FHE scheme F-NTRU that adopts the flattening technique proposed in GSW to derive an NTRU based scheme that (similar to GSW) does not require evaluation keys or key switching. Our scheme eliminates the decision small polynomial ratio assumption but relies only on the standard R-LWE assumption. It uses wide key distributions, and hence is immune to Subfield Lattice Attack. In practice, our scheme achieves competitive timings compared to the existing schemes. We are able to compute a homomorphic multiplication in 24.4 msec and 76.0 msec for 5 and 30 levels, respectively, without amortization. Furthermore, our scheme features small ciphertexts, e.g. 2376 KB for 30 levels. The assurance gained by using wide key distributions along with the message space flexibility of the scheme, i.e. bits, binary polynomials, and integers with a large message space, allows the use of the proposed scheme in a wide array of applications.
Exploration of Fine Motor Skills through the Application of Paint

This study aims to prove the influence of paint applications on children's fine motor skills through coloring activities.This study uses a quasiexperimental design quantitative research design with a pretest-posttest controlled group design model.Learning in the control group used a conventional model in the form of a picture book while the experimental group applied the paint application.The study was conducted at the Mentoring State Kindergarten with research subjects 30 children for the control group and 30 children for the experimental group.The results showed that the experimental class children using the paint application had a higher average compared to the control class that used the conventional model in the form of a picture book.The average number of the experimental class is 83.25, and the average number of the control class is 76.Based on the data analysis that has been done that t-test > t table, which is 2.161 > 2.07387.Thus it can be concluded that the application of paint affects the fine motor skills of early childhood.
The Impact of the Application of IT Governance According to (COBIT 5) Framework in Reduce Cloud Computing Risks

The study aimed at identifying the impact of the application of IT governance represented by (Planning and Organization, Possessiveness and Implementation, Support and Delivery, Monitoring and Evaluation, and Guidance and Control), using (COBIT 5) framework to reduce the risks associated with cloud computing (Identity and Access Management, Data protection, Virtual operating risk, IT support, and Organization) in the Jordanian industrial companies public shareholding from the perspective of Jordanian Certified Public Accountants. The study follows sequential procedures as a strategy for the mixed methods that have been applied. The researcher collects qualitative data that are quantitatively analyzed. A questionnaire was used to achieve the purpose of the study. The study population included all external accountants practicing auditing in Jordan, who number until the end of 2017 (384), a simple random sample was drawn, the sample included (192) auditors.&#x0D; &#x0D; The study concluded that all the decisions of the (COBIT5) Committee including Planning and Organization, Possessiveness and Implementation, Support and Delivery, Monitoring and Evaluation, and Guidance and Control affect the reduction of the risk of cloud computing in terms of identity and Access Management, Data protection, Virtual operating risk, IT support, and Organization the Jordanian industrial companies public shareholding from the perspective of Jordanian Certified Public Accountants. Based on the findings of the study, the researcher recommends that Jordanian industrial companies need to activate the role of security controls and increase the level of application against the environmental risks surrounding the company likely to occur as a result of the application of cloud computing. It is also necessary to update and develop information technologies, especially those related to technology.
Intelligent Computing System to Predict Vocational High School Student Learning Achievement Using NaÃ¯ve Bayes Algorithm

Vocational High School with ICT major need an intelligent computing system that could predict the student learning achievement. The system used fifteen achievement indicators and NaÃ¯ve Bayes algorithm in data processing. Testing on student achievement data produces the conclusion that is the highest intelligent accuracy values in 53% with lowest accuracy value in 48% based on NaÃ¯ve Bayes algorithm processing. The result of mining process using NaÃ¯ve Bayes algorithm can be used to classify the 3rd year student achievement to five categories. These categories are Very Good, Good, Fair, Poor, and Failed. The system testing result showed that this intelligent computing system function was fitted with Vocational High Schoolâ€™s system requirement, system design, and system implementation.
Improving Classification with CNNs using Wavelet Pooling with Nesterov-Accelerated Adam

Wavelet pooling methods can improve the classification accuracy of Convolutional Neural Networks (CNNs). Combining wavelet pooling with the Nesterov-accelerated Adam (NAdam) gradient calculation method can improve both the accuracy of the CNN. We have implemented wavelet pooling with NAdam in this work using both a Haar wavelet (WavPool-NH) and a Shannon wavelet (WavPool-NS). The WavPool-NH and WavPool- NS methods are most accurate of the methods we considered for the MNIST and LIDC- IDRI lung tumor data-sets. The WavPool-NH and WavPool-NS implementations have an accuracy of 95.92% and 95.52%, respectively, on the LIDC-IDRI data-set. This is an improvement from the 92.93% accuracy obtained on this data-set with the max pooling method. The WavPool methods also avoid overfitting which is a concern with max pool- ing. We also found WavPool performed fairly well on the CIFAR-10 data-set, however, overfitting was an issue with all the methods we considered. Wavelet pooling, especially when combined with an adaptive gradient and wavelets chosen specifically for the data, has the potential to outperform current methods.