Reviewerly/exHarmony huggingface dataset:
Emotions identification utilizing periodic handwriting on mobile surfaces

The purpose of this study is to between the learners’ emotional characteristics and styles in touch screen environments. We propose a method to identify the variety of learners' boredom in the learning process utilising handwriting data. The novelty of the method is to avoid explicit polling of learners how do they feel. We use the recurring acquiring of personal handwriting data utilising the computational power of both a mobile device and cloud-based resources. Also, we use machine learning-based sentiment detection in the research. We smoothly inject periodic handwriting tests convolving them with learning objects to study the correlation between learners’ emotions dynamic, they demonstrate, and the ability to focus and think critically. With the help of machine-learning methods and new communication protocols, we can step up the student-centric mobile-based education process by taking advantage of the latest achievements in a big data analysis and cloud computing. Also, we clarify the conceptual model for the testbed used in the experiment. The findings may likely impact the future personalized e-learning systems.
NP-Completeness, Proof Systems, and Disjoint NP-Pairs.

The article investigates the relation between three well-known hypotheses. - H_{union}: the union of disjoint ≤^p_m-complete sets for NP is ≤^p_m-complete - H_{opps}: there exist optimal propositional proof systems - H_{cpair}: there exist ≤^{pp}_m-complete disjoint NP-pairs The following results are obtained: - The hypotheses are pairwise independent under relativizable proofs, except for the known implication H_{opps} ⇒ H_{cpair}. - An answer to Pudlak’s question for an oracle relative to which ¬H_{cpair}, ¬H_{opps}, and UP has ≤^p_m-complete sets. - The converse of Kobler, Messner, and Toran’s implication NEE ∩ TALLY ⊆ coNEE ⇒ H_{opps} fails relative to an oracle, where NEE =^{df} NTIME(2^O(2ⁿ)). - New characterizations of H_{union} and two variants in terms of coNP-completeness and p-producibility of the set of hard formulas of propositional proof systems.
Suppression Mental Questionnaire App: a mobile web service-based application for automated real-time evaluation of adolescent and adult suppression

The Suppression Mental Questionnaire System (mobile App, Web-Services, Cloud...) acts as a bridge among dynamic psychology, the cognitive studies, and modern information and telecommunication technologies (ICT). The adoption of digital tools speeds clinical investigations on defense mechanisms and makes clinical trials easier by reducing needed effort for manual scoring. It also enables faster and deeper research practices, also more appropriate to the times by enabling the real-time transfer of the results to digital archives  for statistical and psychometrical purposes. Reliability of the scale was evaluated by using Cronbach's alpha; this numerical coefficient of reliability was calculated for the three factors and for the two psychodiagnostic tools. The digital tool melts together scalability and availability provided by the mobile application, power and flexibility provided by Web 2.0 portal. The statistical analysis show a good reliability of the scale and of different factors. Differences related to the factors are considered possible on the basis of the current literature.Mobile Operating Systems, Internet, Web 2.0, and the considerable computing capabilities provided by Clouds are powerful combinations of tools able to provide real-time Open Data/Results to scientists and users, and to spread up the use of the questionnaire all over the world.
Towards an Immersive Driving Simulator to Study Factors Related to Cybersickness

The commercialization of Virtual Reality (VR) devices has made it easier for everyday users to experience VR from the comfort of their living rooms. This recent uptake in VR has also increased reported incidents of cybersickness. Cybersickness refers to the discomfort experienced by an individual while experiencing virtual environments. The symptoms are similar to those of motion sickness but are more disorienting in nature resulting in dizziness, blurred vision, etc. Cybersickness is currently one of the biggest hurdles to the widespread adoption of VR, and it is therefore critical to explore the factors that influence its onset. Towards this end, we present a proof of concept simulation to study cybersickness in highly realistic immersive virtual environments.
Embedding cover-free families and cryptographical applications

Cover-free families are set systems used as solutions for a large variety of problems, and in particular, problems where we deal with $ n $ elements and want to identify $ d $ defective ones among them by performing only $ t $ tests ($ t \leq n $). We are especially interested in cryptographic problems, and we note that some of these problems need cover-free families with an increasing size $ n $. Solutions that propose the increase of $ n $, such as monotone families and nested families, have been recently considered in the literature. In this paper, we propose a generalization that we call embedding families, which allows us to increase both $ n $ and $ d $. We propose constructions of embedding families using polynomials over finite fields embedded via extension fields; we study how different parameter combinations can be used to prioritize increase of $ d $ or of the compression ratio as $ n $ grows. We also provide new constructions for monotone families with improved compression ratio. Finally, we show how to use embedded sequences of orthogonal arrays and packing arrays to build embedding families.
Investigating Political Herd Mentality: A Community Sentiment Based Approach

Anjali Bhavan, Rohan Mishra, Pradyumna Prakhar Sinha, Ramit Sawhney, Rajiv Ratn Shah. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2019.
Count time series prediction using particle filters

Abstract Non‐Gaussian dynamic models are proposed to analyse time series of counts. Three models are proposed for responses generated by a Poisson, a negative binomial, and a mixture of Poisson distributions. The parameters of these distributions are allowed to vary dynamically according to state space models. Particle filters or sequential Monte Carlo methods are used for inference and forecasting purposes. The performance of the proposed methodology is evaluated by two simulation studies for the Poisson and the negative binomial models. The methodology is illustrated by considering data consisting of medical contacts of schoolchildren suffering from asthma in England.
Bayesian analysis of immigration in Europe with generalized logistic regression

The number of immigrants moving to and settling in Europe has increased over the past decade, making migration one of the most topical and pressing issues in European politics. It is without a doubt that immigration has multiple impacts, in terms of economy, society and culture, on the European Union. It is fundamental to policy-makers to correctly evaluate people's attitudes towards immigration when designing integration policies. Of critical interest is to properly discriminate between subjects who are favourable towards immigration from those who are against it. Public opinions on migration are typically coded as binary responses in surveys. However, traditional methods, such as the standard logistic regression, may suffer from computational issues and are often not able to accurately model survey information. In this paper we propose an efficient Bayesian approach for modelling binary response data based on the generalized logistic regression. We show how the proposed approach provides an increased flexibility compared to traditional methods, due to its ability to capture heavy and light tails. The power of our methodology is tested through simulation studies and is illustrated using European Social Survey data on immigration collected in different European countries in 2016–2017.
PENGGUNAAN SUPPORT VECTOR REGRESSION DALAM PEMODELAN INDEKS SAHAM SYARIAH INDONESIA DENGAN ALGORITME GRID SEARCH

Indonesia as the largest Muslim population country in the world is a very potential market for sharia stocks. Sharia stocks performance can be seen from the Indonesia Sharia Stock Index (ISSI). Stock index modeling is conducted to determine the factors that affect the stock index or to predict the value of the stock index. Modeling using regression analysis is based on assumptions that do not always match with the characteristics of stock data that fluctuate. Support Vector Regression (SVR) method is a non-parametric approach based on machine learning. The problem often encountered in the analysis using SVR is to determine the optimal parameters to produce the best model. The determination of the optimal parameters can be solved by using the grid search algorithm. The purpose of this research is to make ISSI model using SVR with grid search algorithm with independent variable BI Rate, money supply, and exchange rate (USD / IDR). The best SVR model was obtained using weekly data with a total of 343 periods as well as a linear kernel with parameters ε = 0.03 and C = 2. The evaluation of the best model SVR is RMSE of 2.289 and correlation value of 0.873.
MEMBANGUN BUDAYA LITERASI DI SEKOLAH DASAR NEGERI TRANGSAN 02 GATAK KABUPATEN SUKOHARJO DALAM MENDUKUNG KEBERHASILAN KEGIATAN BELAJAR ABAD 21

Primary school is one of the levels of education whose task is to realize the national education goals. In order to achieve this goal the school must have good quality. One effort to improve the quality of schools is to support facilities and infrastructure and improve the reading culture. This Best Practice is designed to support the work that the author has done as the principal. This study aims to describe the initial conditions of the school, explain the efforts, constraints and ways to overcome them in building a culture of literacy to support the success of teaching learning activities in explaining the results that have been achieved. The results showed that 1) The initial conditions of literacy were still low; 2) How to build a culture of literacy began with the procurement of facilities and infrastructure followed by habituation; 3) Constraints faced included facilitators, costs, and interests of students; 4) How to overcome existing problems namely with financial donations, attending workshops and holding tutoring and extracurricular activities; 5) The results achieved through the development of a literacy culture, namely the school can win several race champions. Suggestions are given that the government through the education office should be more focused on addressing issues related to literacy in primary schools and for principals to be role models in literacy.
The Development of Audio-Based Pop-Up Book Media on Two-Dimensional Rectangular For Junior High School Students

This study aimed to develop the learning media and investigated the effectiveness of audio-based Pop-Up Book media on the two-dimensional rectangular shape. This media was tested twice in a small group of 6 students and in a large group of 25 students. Respondents were the VIIth grade students. The result of expert validation showed the average percentage of 88,16 % and was considered valid, thus the learning media was allowed to be used and tested. The analysis of effectiveness in small and large groups in a row revealed that students’ respond was very good, with the percentage of 88.1% and 88.87%. In addition, the activities of students in the class were very good, showed by the percentage of 92% and 86.8%. Finally, the classical completeness of the learning outcomes obtained the percentage of 100% from 6 students and 92% from 23 students who completed the test. Therefore, it can be concluded that the application of audio-based Pop-Up Book on the two-dimensional rectangular shape was effective for Junior High School students.
The Active-Filler Strategy in a Move-Eager Left-Corner Minimalist Grammar Parser

Recent psycholinguistic evidence suggests that human parsing of moved elements is ‘active’, and perhaps even ‘hyper-active’: it seems that a leftward-moved object is related to a verbal position rapidly, perhaps even before the transitivity information associated with the verb is available to the listener. This paper presents a formal, sound and complete parser for Minimalist Grammars whose search space contains branching points that we can identify as the locus of the decision to perform this kind of active gap-finding. This brings formal models of parsing into closer contact with recent psycholinguistic theorizing than was previously possible.
THE SIGNIFICANCE OF DIGITAL COMPETENCES OF UNIVERSITY TEACHERS: THE VIEWS OF THE TEACHERS AND STUDENTS THEMSELVES

This study brings the results of an international study of the views of academic teachers (academics) and students from the Czech Republic, Poland, the Slovak Republic, and the Ukraine on the importance of the digital competence of academics.Digital competence is usually intertwined into a broader didactic competence that can be incorporated into the educational profile of an academic.Aggregated data in the first half of 2018 was compiled using statistical procedures for a group of academics, a group of students, and between both groups in each country, as well as across a multinational set.They point to differences and the consensus in perceiving the importance of digital components that can be used for the targeted further education of academics, setting up a system for the evaluation of studies, and for innovation in the university digital environment.
Automated model checking for topologically complex code requirements – security room case study

A review of the state of the art in the field of automated code compliance checking revealed that existing applications are limited in the scope of clauses they are able to check.While some building code clauses are straightforward, requiring direct checking of parameter values, others depend on the topological relationships among objects, making automated checking of BIM models more challenging.Moreover, existing applications require the user to extensively preprocess the model in preparation for checking.We propose applying semantic enrichment for preprocessing the BIM models.The goal of a semantic enrichment process in support of automated code compliance checking is to derive the needed clause test values automatically and to represent them explicitly.A successful semantic enrichment process can therefore widen the scope of requirements that can be checked automatically.This work demonstrates such a process for checking code clauses involving topologically complex requirements.Although semantic enrichment proved to be successful for several purposes in previous research, dealing with complex topologies involves different types of semantic enrichment tasks.We explore the subject through a test case of requirements from the Israeli code for security rooms.
Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization

Our work focuses on stochastic gradient methods for optimizing a smooth non-convex loss function with a non-smooth non-convex regularizer. Research on this class of problem is quite limited, and until recently no non-asymptotic convergence results have been reported. We present two simple stochastic gradient algorithms, for finite-sum and general stochastic optimization problems, which have superior convergence complexities compared to the current state-of-the-art. We also compare our algorithms' performance in practice for empirical risk minimization.
A Feature-Based Model for Nested Named-Entity Recognition at VLSP-2018 NER Evaluation Campaign

In this report, we describe our participant named-entity recognition system at VLSP 2018 evaluation campaign. We formalized the task as a sequence labeling problem using BIO encoding scheme. We applied a feature-based model which combines word, word-shape features, Brown-cluster-based features, and word-embedding-based features. We compare several methods to deal with nested entities in the dataset. We showed that combining tags of entities at all levels for training a sequence labeling model (joint-tag model) improved the accuracy of nested named-entity recognition.
Distinct Multiple Learner-Based Ensemble SMOTEBagging (ML-ESB) Method for Classification of Binary Class Imbalance Problems

Traditional classification algorithms often fail in learning from highly imbalanced datasets because the training involves most of the samples from majority class compared to the other existing minority class.In this paper, a Multiple Learners-based Ensemble SMOTEBagging (ML-ESB) technique is proposed.The ML-ESB algorithm is a modified SMOTEBagging technique in which the ensemble of multiple instances of the single learner is replaced by multiple distinct classifiers.The proposed ML-ESB is designed for handling only the binary class imbalance problem.In ML-ESB the ensembles of multiple distinct classifiers include Naïve Bays, Support Vector Machine, Logistic Regression and Decision Tree (C4.5) is used.The performance of ML-ESB is evaluated based on six binary imbalanced benchmark datasets using evaluation measures such as specificity, sensitivity, and area under receiver operating curve.The obtained results are compared with those of SMOTEBagging, SMOTEBoost, and cost-sensitive MCS algorithms with different imbalance ratios (IR).The ML-ESB algorithm outperformed other existing methods on four datasets with high dimensions and class IR, whereas it showed moderate performance on the remaining two low dimensions and small IR value datasets.
Application of ICT and M-Learning to Improve Collaborative Learning and Interaction Using the Nearpod Platform

At present, university teaching develops in an innovative environment, influenced to a great extent by Information and Communication Technologies (ICTs) that present challenges for collaborative and interactive learning that is still developed in a traditional way in some institutions of higher education. The methodology used was based on the use of Nearpod platform through mobile devices, tablets or laptops, to improve interactivity in the face-to-face classes provided to students. The results obtained show that the students have had a favorable opinion and are satisfied with the use of this platform, because it allows to increase the interaction and participation in the activities in class. However, managing large groups presents difficulties due to the limited number of simultaneous licenses and the student's unstable connection to the institutional Wi-Fi internet network. Nearpod as a collaborative and interactive learning platform is a system that allows students to improve their concentration and participation, which is an opportunity to complement the learning provided by the teacher.
Contributions on Clinical Decision Support from the 2018 Literature

Objectives: To summarize recent research and select the best papers published in 2018 in the field of computerized clinical decision support for the Decision Support section of the International Medical Informatics Association (IMIA) yearbook. Methods: A literature review was performed by searching two bibliographic databases for papers referring to clinical decision support systems (CDSSs). The aim was to identify a list of candidate best papers from the retrieved bibliographic records, which were then peer-reviewed by external reviewers. A consensus meeting of the IMIA editorial team finally selected the best papers on the basis of all reviews and the section editors' evaluation. Results: Among 1,148 retrieved articles, 15 best paper candidates were selected, the review of which resulted in the selection of four best papers. The first paper introduces a deep learning model for estimating short-term life expectancy (&gt;3 months) of metastatic cancer patients by analyzing free-text clinical notes in electronic medical records, while maintaining the temporal visit sequence. The second paper takes note that CDSSs become routinely integrated in health information systems and compares statistical anomaly detection models to identify CDSS malfunctions which, if remain unnoticed, may have a negative impact on care delivery. The third paper fairly reports on lessons learnt from the development of an oncology CDSS using artificial intelligence techniques and from its assessment in a large US cancer center. The fourth paper implements a preference learning methodology for detecting inconsistencies in clinical practice guidelines and illustrates the applicability of the proposed methodology to antibiotherapy. Conclusions: Three of the four best papers rely on data-driven methods, and one builds on a knowledge-based approach. While there is currently a trend for data-driven decision support, the promising results of such approaches still need to be confirmed by the adoption of these systems and their routine use.
Control of Wafer Slot-Dependent Outgassing Defects during Semiconductor Manufacture Processes

In this paper, a study on a behavior of waiting-time related defect which strongly depends on a wafer slot position in FOUP (Front Opening Unified Pod) was carried out. The occurrence of the waiting-time dependent defects in the processing tools was found to be affected by the flow pattern, moisture and process outgassing distribution in equipment EFEM (Equipment Front End Module) and FOUP. In order to control this problem, which also causes to deteriorate the production yield, it is essential to establish a system that is capable of monitoring the wafer-level waiting- time of each wafer in the processing tools. Optimizing the flow pattern of air which contains humidity and process outgassing or AMC (Airborne Molecular Contaminants), and N2 purge gas in EFEM and FOUP is required as well. Various types of load-port N2 gas purging system inside EFEM and FOUP were reviewed using CFD (Computational Fluid Dynamics) simulations and a fully charged N2 circulating EFEM exhibited the best defect control performance.
Top-Level Secure Certificateless Signature Against Malicious-But-Passive KGC

Certificateless signature (CLS) has no need of public key certificates and also avoids excessive dependence to a third party like that in identity-based setting. Recently, Shim (IEEE Systems Journal, doi:10.1109/JSYST.2018.2844809) came up with a CLS scheme independent of random oracles and asserted that the construction can be immune to the public key replacement attacks and the malicious-but-passive key generation center (KGC) attacks. In this paper, we analyze the security of Shim's scheme and point out that his conclusions are incorrect by giving two concrete counter-examples. We repair the scheme and put forward a CLS scheme secure against public key replacement attacks and malicious-but-passive KGC attacks without relying on random oracles. Compared with Shim's scheme, our construction has lower execution cost for signing and verification, and achieves Girault's top-level security, which means that a victim can repudiate the forgeries based on a false secret key generated by the KGC.