Reviewerly/exHarmony huggingface dataset:
TTYL :-) … Nonverbal cues and perceptions of personality and homophily in synchronous mediated communication

Person perception is an integral aspect of human communication and is increasingly relevant in computer-mediated communication (CMC) because of the mobile and ubiquitous nature of mediated communication. Based on the hypothesis that nonverbal cues in CMC such as emoticons, acronyms, exaggerated spellings, acronyms, and altered sentence structure are similar to face-to-face nonverbal cues and have similar communicative outcomes, this study examines the effect of nonverbal cues in online synchronous interactions on perceptions of personality and homophily. Employing a between-subjects post-test experimental design, participants (N = 505) were randomized to view one of three transcripts of an instant messaging conversation in which nonverbal cues along with the valence were manipulated. Results showed that subjects in the positive nonverbal cues condition were perceived to be extraverted and agreeable; those in the negative nonverbal cues condition were perceived to be neurotic, and those in the control condition were perceived to be conscientiousness and homophilous. The findings suggest that nonverbal cues in synchronous mediated communication allow individuals to engage in impression formation.
THE EFFECT OF JOB CRAFTING ON JOB SATISFACTION: A RESEARCH ON TEACHERS

The current study aims to determine the relationship between job crafting and job satisfaction.Data were collected from 239 primary school teachers.The results showed that only the gender and age differentiate the levels of job crafting and job satisfaction.There were no differences in job crafting and job satisfaction levels of the teachers participating in the survey due to other demographic characteristics.The analyses found that, as a on its own behalf job design, job crafting effects job satisfaction positively, and has significant effect on.Results of the regression analyses it was found that job crafting has causal effect on job satisfaction.According to these results, the job satisfaction' of the teachers who are more flexible in functioning and more effective in the decision process will be high and this situation can increase the educational quality.The findings of the study emphasize the need for management to improve the initiatives of teachers on their job.Applied implications of the results are discussed.
GSI-UPM at SemEval-2019 Task 5: Semantic Similarity and Word Embeddings for Multilingual Detection of Hate Speech Against Immigrants and Women on Twitter

This paper describes the GSI-UPM system for SemEval-2019 Task 5, which tackles multilingual detection of hate speech on Twitter.The main contribution of the paper is the use of a method based on word embeddings and semantic similarity combined with traditional paradigms, such as n-grams, TF-IDF and POS.This combination of several features is finetuned through ablation tests, demonstrating the usefulness of different features.While our approach outperforms baseline classifiers on different sub-tasks, the best of our submitted runs reached the 5th position on the Spanish subtask A.
Integration Design of Sortie Scheduling for Carrier Aircrafts Based on Hybrid Flexible Flowshop

Previous studies focused on deck-disposed and trajectory planning problem separately, and there was no systematic study from the whole process. This paper studied sortie scheduling for carrier aircrafts from the perspective of the whole scheduling and dispatch process. According to the process of sortie on flight deck, the sortie task was abstracted into six virtual stages, namely, deck-disposed, taxiing to preparing spot, taxiing from preparing spot to catapult, preparation on the catapult, takeoff, and cooling and recovery for the jet blast deflectors, and, the corresponding machines were also defined, respectively. The constraints were expressed, and the sortie scheduling model was build based on hybrid flexible flowshop scheduling. In addition, the one-sided symplectic pseudospectral method and the proposed collision avoidance strategy based on priority principle were combined to realize the collaborative trajectories planning of taxiing for multiple aircrafts. To obtain the optimal or near optimal solution, the double-layer genetic algorithm was proposed. Finally, the experiments were carried out based on the Nimitz-class aircraft carrier; the sortie task of 8 aircrafts under unknown conditions of deck-disposed and 13 aircrafts with fixed deck-disposed were studied, respectively. Experimental results show that the established model can well describe the sortie of aircraft, and it meets all constraints and actual requirements, which shows that the proposed method and the model can be used for sortie task effectively.
Service Level Agreement Violations in Cloud Storage: Insurance and Compensation Sustainability

Service Level Agreements are employed to set availability commitments in cloud services. When a violation occurs as in an outage, cloud providers may be called to compensate customers for the losses incurred. Such compensation may be so large as to erode cloud providers’ profit margins. Insurance may be used to protect cloud providers against such a danger. In this paper, closed formulas are provided through the expected utility paradigm to set the insurance premium under different outage models and QoS metrics (no. of outages, no. of long outages, and unavailability). When the cloud service is paid through a fixed fee, we also provide the maximum unit compensation that a cloud provider can offer so as to meet constraints on its profit loss. The unit compensation is shown to vary approximately as the inverse square of the service fee.
Composite weighted average consensus filtering for space object tracking

In this paper, a composite weighted average consensus filtering (CWACF) algorithm is proposed for space object tracking by combining two distributed heterogeneous nonlinear filters. In light of the sensors' different sensing accuracy and computational capability, extended Kalman filter (EKF) and sparse-grid quadrature filter (SGQF) are compositely adopted on different sensors as local filters. Then, estimates from neighbours are fused based on the weighted average consensus framework to attain better estimation performance. Moreover, it is proved that the estimation error is exponentially bounded in mean square. The performances of the proposed algorithm, the distributed extended Kalman filtering (DEKF) and the distributed sparse-grid quadrature filter (DSGQF) are compared in a space object tracking problem.
Harnessing Entropy via Predictive Analytics to Optimize Outcomes in the Pedagogical System: An Artificial Intelligence-Based Bayesian Networks Approach

Educational stakeholders would be better informed if they could use their students’ formative assessments results and personal background attributes to predict the conditions for achieving favorable learning outcomes, and conversely, to gain awareness of the “at-risk” signals to prevent unfavorable or worst-case scenarios from happening. It remains, however, quite challenging to simulate predictive counterfactual scenarios and their outcomes, especially if the sample size is small, or if a baseline control group is unavailable. To overcome these constraints, the current paper proffers a Bayesian Networks approach to visualize the dynamics of the spread of “energy” within a pedagogical system, so that educational stakeholders, rather than computer scientists, can also harness entropy to work for them. The paper uses descriptive analytics to investigate “what has already happened?” in the collected data, followed by predictive analytics with controllable parameters to simulate outcomes of “what-if?” scenarios in the experimental Bayesian Network computational model to visualize how effects spread when interventions are applied. The conceptual framework and analytical procedures in this paper could be implemented using Bayesian Networks software, so that educational researchers and stakeholders would be able to use their own schools’ data and produce findings to inform and advance their practice.
Rydberg-atom-based controlled arbitrary-phase gate and its applications

Controlled-phase (CP) gates with an arbitrary phase, which play an irreplaceable role in quantum computation and quantum information processing, are constructed between two neutral-atom qubits by using the Rydberg antiblockade regime. The phases needed for the CP gates are induced by the collective excitation of the pair of atoms. Numerical simulations show that high-fidelity CP gates can be obtained. In addition, the optimization of the scheme is given to enhance the robustness against the control error. As applications, we study in detail the preparation of n-qubit Knill&#x2013;Laflamme&#x2013;Milburn (KLM) states and a cluster state based on the Rydberg CP gates. The high-fidelity KLM states and cluster state can be obtained with the current physical parameters.
UVA Wahoos at SemEval-2019 Task 6: Hate Speech Identification using Ensemble Machine Learning

With the growth in the usage of social media, it has become increasingly common for people to hide behind a mask and abuse others.We have attempted to detect such tweets and comments that are malicious in intent, which either targets an individual or a group.Our best classifier for identifying offensive tweets for SubTask A (Classifying offensive vs. nonoffensive) has an accuracy of 83.14% and a f1score of 0.7565 on the actual test data.For SubTask B, to identify if an offensive tweet is targeted (If targeted towards an individual or a group), the classifier performs with an accuracy of 89.17% and f1-score of 0.5885.The paper talks about how we generated linguistic and semantic features to build an ensemble machine learning model.By training with more extracts from different sources (Facebook, and more tweets), the paper shows how the accuracy changes with additional training data.
A Landscape of Cryptocurrencies

Although cryptocurrencies have attracted a large amount of users and investors and there has been various researches in the area of distributed ledger technology (DLT), there is little work on cryptocurrency studies. Given the rapidly increasing number and diversity of cryptocurrencies, we present a landscape with a selective set of representative cryptocurrencies to holistically portray their characteristics and provide practitioners an analysis from both business and technical perspectives. The focused observations elaborate how the typical cryptocurrency are designed and operated in industry. This study offers a breakthrough understanding of the cryptocurrencies through the generated landscape which report the state of cryptocurrencies, and can be used as a framework for cryptocurrency analysis.
Towards a Learning-Based Performance Modeling for Accelerating Deep Neural Networks

Emerging applications such as Deep Learning are often data-driven, thus traditional approaches based on auto-tuners are not performance effective across the wide range of inputs used in practice. In the present paper, we start an investigation of predictive models based on machine learning techniques in order to optimize Convolution Neural Networks (CNNs). As a use-case, we focus on the ARM Compute Library which provides three different implementations of the convolution operator at different numeric precision. Starting from a collation of benchmarks, we build and validate models learned by Decision Tree and naive Bayesian classifier. Preliminary experiments on Midgard-based ARM Mali GPU show that our predictive model outperforms all the convolution operators manually selected by the library.
Justifying diagnosis decisions by deep neural networks

An integrated approach is proposed across visual and textual data to both determine and justify a medical diagnosis by a neural network. As deep learning techniques improve, interest grows to apply them in medical applications. To enable a transition to workflows in a medical context that are aided by machine learning, the need exists for such algorithms to help justify the obtained outcome so human clinicians can judge their validity. In this work, deep learning methods are used to map a frontal X-Ray image to a continuous textual representation. This textual representation is decoded into a diagnosis and the associated textual justification that will help a clinician evaluate the outcome. Additionally, more explanatory data is provided for the diagnosis by generating a realistic X-Ray that belongs to the nearest alternative diagnosis. With a clinical expert opinion study on a subset of the X-Ray data set from the Indiana University hospital network, we demonstrate that our justification mechanism significantly outperforms existing methods that use saliency maps. While performing multi-task training with multiple loss functions, our method achieves excellent diagnosis accuracy and captioning quality when compared to current state-of-the-art single-task methods.
An Automatic Transformation Method from the E3value Model to IFML Model: An MDA Approach

Nowadays the software industry has known a significant growth, while the end-users have become very demanding.In this sense, the model transformation has become one of the essential solutions to ensure competitiveness in the field of the software industry domain.For that, the Object Management Group (OMG) proposes for the Model-Driven Architecture (MDA) approach three abstraction levels, namely Computation Independent Model (CIM), Platform Independent Model (PIM) and Platform Specific Model (PSM).Therefore, our contribution in this paper is to shed more light on the first MDA transformation, which is the transformation from CIM to PIM levels.We propose a disciplined Model-Driven approach for the e-business information system, which generates the IFML (Interaction Flow Modeling Language) model automatically in the PIM level from business value model in the CIM level, using the ATL transformation language.For this purpose, the business value model is illustrated by the E3value model, whereas, the IFML model exhibits the front-end applications content, interface composition, user interaction and control behavior for several kinds of applications, such as web applications, mobile applications and desktop applications.
A Survey for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

Because Breast Histopathology Image Analysis (BHIA) plays a very important role in breast cancer diagnosis and medical treatment processes, more and more effective Machine Learning (ML) techniques are developed and applied in this field to assist histopathologists to obtain a more rapid, stable, objective, and quantified analysis result. Among all the applied ML algorithms in the BHIA field, Artificial Neural Networks (ANNs) show a very positive and healthy development trend in recent years. Hence, in order to clarify the development history and find the future potential of ANNs in the BHIA field, we survey more than 60 related works in this paper, referring to classical ANNs, deep ANNs and methodology analysis.
WhatsApp and Learn English: a Study of the Effectiveness of WhatsApp in Developing Reading and Writing Skills in English

This study has examined the effectiveness of using WhatsApp, as one of mobile-assisted language learning applications, in enhancing learners’ reading and writing skills in English. Twenty EFL undergraduate students of Aden University participated in a WhatsApp English medium group with the researcher where they and the researcher chat, share news articles and comment on news articles for two months. During this period, the researcher was observing their progress and then, they were given a questionnaire to report their experience about the effectiveness of WhatsApp in developing their reading and writing skills. Findings from students’ pre-test and post-test, researcher’s observations of students’ progress and students’ responses to the questionnaire revealed that WhatsApp was very effective in developing their reading and writing skills. It helped them a lot in developing their vocabulary, grammar, reading comprehension and writing skills. The study concluded that WhatsApp English medium groups provide space for practising natural language, especially in written communication, keep students learn and practice English outside the classroom and motivate them to learn from each other. As per the findings, the study has recommended EFL teachers to use WhatsApp in their teaching of English and to motivate their students to create and join WhatsApp English medium groups as that will help them in developing students’ language proficiency through natural interaction and contextualized language use that can’t be provided by the classroom.
Intelligent tutoring system to improve learning outcomes

Nowadays, society is in constant evolution, which allows constant production of new knowledge. In this way, citizens are constantly pressured to obtain new qualifications through training/requalification. The need for qualified people has been growing exponentially, which means that resources for e ducation/training are limited to being used more efficiently. In this paper we will focus in the design the user model, so, we propose an innovative approach to design a user model that monitors the user’s biometric behaviour by measuring their level of attention during e-learning activities. In addition, a machine learning categorization model is presented that oversees user activity during the session. We intend to use non-invasive methods of intelligent tutoring systems, observing the interaction of users during the session. Furthermore, this article highlights the main biometric behavioural variations for each activity and bases the set of attributes relevant to the development of machine learning classifiers to predict users’ learning preference. The results show that there are still mechanisms that can be explored and improved to better understand the complex relationship between human behaviour, attention and evaluation that could be used to implement better learning strategies. These results can be decisive in improving ITS in e-learning environments and to predict user behaviour based on their interaction with technology devices.
An improved structure learning algorithm of Bayesian Network based on the hesitant fuzzy information flow

The Bayesian Network (BN) is one of the most effective theoretical models in the fields of uncertain reasoning. With the nonlinear evolution of events and the complexity of practical problems, there will be massive data with uncertainty, bringing more challenges to the application of the BN. In this paper, by combining the advantages of the hesitant fuzzy set (HFS) in depicting uncertain information and the advantages of information flow (IF) in the causal analysis of nonlinear systems, an improved Particle Swarm Optimization (PSO) algorithm for the structure learning of the BN based on the hesitant fuzzy information flow (HFIF) is proposed. First, a new physical notion called HFIF is defined to depict the causal relationship between two intensive hesitant fuzzy variable sequences. Then the global causal analysis based on HFIF is conducted. By constructing an unconstrained optimization model, the initial structure and the optimized search space with the most significant causality are obtained, based on which, the approximate optimal structure by the PSO algorithm and the directions of the arcs by HFIF are determined at the same time. A specific implementation process of the structure learning based on the improved PSO algorithm under hesitant fuzzy environment is also presented. Moreover, the proposed algorithm is applied to the structure learning of ASIA network and BOBLO network. Comparisons between the proposed algorithm and traditional algorithms are conducted to demonstrate the effectiveness and advantages of the proposed algorithm in structure learning under hesitant fuzzy environment.
Theory Combination: Beyond Equality Sharing

Satisfiability is the problem of deciding whether a formula has a model. Although it is not even semidecidable in first-order logic, it is decidable in some first-order theories or fragments thereof (e.g., the quantifier-free fragment). Satisfiability modulo a theory is the problem of determining whether a quantifier-free formula admits a model that is a model of a given theory. If the formula mixes theories, the considered theory is their union, and combination of theories is the problem of combining decision procedures for the individual theories to get one for their union. A standard solution is the equality-sharing method by Nelson and Oppen, which requires the theories to be disjoint and stably infinite. This paper surveys selected approaches to the problem of reasoning in the union of disjoint theories, that aim at going beyond equality sharing, including: asymmetric extensions of equality sharing, where some theories are unrestricted, while others must satisfy stronger requirements than stable infiniteness; superposition-based decision procedures; and current work on conflict-driven satisfiability (CDSAT).
Design and Implementation of a Vue.js-Based College Teaching System

The proliferation of Internet information technology has fundamentally changed the way of learning. It is now a research hotspot to improve teaching effect in col-leges with Internet information technology. Starting from the actual needs of col-lege teaching, this paper designs a college teaching system based on various In-ternet information technologies. Specifically, the front end was developed under the lightweight progressive Vue.js framework, which relies on the model–view–view model (MVVM); the overall structure of the system was set up based on the browser-server (B/S) architecture; the system functions were realized through HTML5, Node.js and database technology; the compatibility between mobile terminal and desktop was realized under Bootstrap. The system test shows that the Vue.js-based college teaching system operated stably, achieved the design goals and satisfied user demand. The research findings shed important new light on modernization and quality of college teaching.
Improving Prediction Accuracy for Logistic Regression on Imbalanced Datasets

An imbalanced dataset is a dataset that has a majority class which is a class has far more example distributions than other classes. It is difficult to deal with unbalanced datasets in classification problems, and many classification algorithms do not perform well in unbalanced datasets. In this paper, we present our logistic regression analysis with Python on imbalanced datasets and determine different thresholds for classification according to the data proportion of imbalanced datasets.
Anomaly Detection in Wireless Sensor Networks Based on KNN

In recent years use of a Wireless Sensor Network (WSN) has become a leading area of research in various applications. Because of WSN limitation of its own features that low ability of calculation, small volume of storage, resource constrain, bad communicational environment of wireless, which lead to WSN be besieged by imminent security problems, the abnormal detection in WSN is vary important at this time. We focus on the quality of data, and aim at the characteristics of wireless sensor network node data with time and spatial similarity, and the current research on anomaly analysis. We use the classification to detect outliers. This paper presents a method of detecting the proximity of distance based on distance, which is the main reason for the study of the anomalous value of the network. The KNN (K-Nearest Neighbor) algorithm is used to analyze and detect the data to achieve the purpose of data anomaly detection in WSN. The algorithm of outlier detection is based on the design and achieve of QualNet simulation platform. It is effective and accurate to evaluate and test. The simulation results show the effectiveness of the proposed KNN algorithm.