Reviewerly/exHarmony huggingface dataset:
Whiteboard Animations for Flipped Classrooms in a Common Core Science General Education Course

Whiteboard animation, an engaging tool for teaching and learning, consists of a series of hand-drawing illustrations with voice-over narration to explain complex and abstract ideas. Our team had produced four short whiteboard animations tailor-made for a common core science general education (GE) course. This study aims at evaluating the effectiveness of using these whiteboard animations for flipped classrooms in the common core science GE course. The pre-tutorial survey showed that students who watched the animations got significantly higher average marks in the quizzes at the beginning of the tutorials (p&amp;lt;0.001). The post-tutorial feedback survey indicated that the whiteboard animations attracted 67% of students to watch the animations. For students who watched the animations, over 86% of them reported that the animations raised their interest in the issues discussed in the tutorial classes, and learning materials in the form of whiteboard animation were more interesting than lecture videos; more than 90% of the students agreed the whiteboard animations were helpful in (i) understanding the assigned readings, (ii) clarifying the concepts of the discussed issues, and (iii) gaining the related knowledge before the tutorial. We concluded that whiteboard animation is an effective and engaging tool for flipped classrooms in the common core science GE course.
Emerging Technologies and Cyber Risk: How do we secure the Internet of Things (IoT) environment?

Cloud computing and the Internet of Things (IoT) have transformed businesses, enabling agile and costeffective IT infrastructure. The challenge is that these new opportunities create a co-mingled architecture which is difficult to secure. The complexity of this architecture is magnified with the IoT. Based on interviews with executive leadership teams and boards of directors facing these new environments, we developed the over-arching research question: How do we secure increasingly dynamic architecture in an environment while supporting and creating agile business growth? We then narrowed this down to more specific questions dealt with in this study. The research involved an in-depth exploration of this problem using a survey instrument and multiple qualitative methods involving business leaders from 59 companies between 2017 – 2018. Based on this analysis, we developed an information security framework for executives in this new environment that builds on previous work. This framework is called the Extended Risk-Based Approach and provides businesses with an approach for securing an enterprise amidst the IoT and agile architecture. Importantly, the data analyzed suggests that this approach is critically needed to address the rapidly growing complexity of enterprise architecture and the digital world we live and work.
SMART KIDS ЯК ТЕХНОЛОГІЯ НАВЧАННЯ УЧНІВ ПОЧАТКОВОЇ ШКОЛИ

The article presents an analysis of national and foreign experience of electronic textbooks use in the system of education. It is justified the use of Smart Kids technology as a system of methods, forms, and electronic educational game resources, electronic textbooks for educational process in the system of primary school. Four forms of Smart Kids technology implementation (Smart Case, Smart Teacher, Smart Class, and Smart Kids) were described considering the facilities of every school as well as the level of information and communication technology qualification of the primary school teacher. The aim of introduction of the technology for each form of teaching, the necessary equipment, and means for its implementation in primary school environment were determined. Based on the procedural approach to work of the primary school teacher, six stages of introduction of the technology were justified. Specific aspects of introduction of blended teaching using the principles of Smart Kids technology were defined. The experience of introduction of electronic textbooks to the system of primary education of Ukraine was described, the choice of electronic textbooks by primary school teachers was justified, the comments and suggestions of teachers regarding the arrangement of electronic content in E-textbooks were summarized, the main approaches of teachers to the choice of an electronic textbook and development of their information and communication competence were specified. Proposals are given for training future primary school teachers on the use of Smart Kids technology and the inclusion in the curriculum of topics such as: the concept of EER, EEGR and electronic textbook, structure and development of EER, the use of Smart Kids technology for teaching students, the latest forms of work with students, work with network programs for organizing a survey of students. The fragments of use of electronic textbook in teaching 6-year-old pupils were provided. It was identified that the forms, methods, and techniques of use of electronic textbooks in teaching primary school pupils require further justification.
UNBNLP at SemEval-2019 Task 5 and 6: Using Language Models to Detect Hate Speech and Offensive Language

In this paper we apply a range of approaches to language modeling -including wordlevel n-gram and neural language models, and character-level neural language models -to the problem of detecting hate speech and offensive language.Our findings indicate that language models are able to capture knowledge of whether text is hateful or offensive.However, our findings also indicate that moreconventional approaches to text classification often perform similarly or better.
Spatial statistics for gaze patterns in scene viewing: Effects of repeated viewing

Scene viewing is used to study attentional selection in complex but still controlled environments. One of the main observations on eye movements during scene viewing is the inhomogeneous distribution of fixation locations: While some parts of an image are fixated by almost all observers and are inspected repeatedly by the same observer, other image parts remain unfixated by observers even after long exploration intervals. Here, we apply spatial point process methods to investigate the relationship between pairs of fixations. More precisely, we use the pair correlation function, a powerful statistical tool, to evaluate dependencies between fixation locations along individual scanpaths. We demonstrate that aggregation of fixation locations within 4° is stronger than expected from chance. Furthermore, the pair correlation function reveals stronger aggregation of fixations when the same image is presented a second time. We use simulations of a dynamical model to show that a narrower spatial attentional span may explain differences in pair correlations between the first and the second inspection of the same image.
The Impact of Pair Programming on Students Logical Thinking_A Case Study on Higher Academic Institution

Pair Programming (PP) is a well-known agile software development technique that has been widely implemented in programming classes. Through PP, students are able to share knowledge and expertise that will contribute to better programming solutions. Nevertheless, how PP can help to improve students’ cognitive abilities has yet to be explored. Therefore, this study’s aim was to investigate the impacts of implementing Pair Programming (PP) on students’ logical thinking. Logical thinking is part of the cognitive ability claimed to be one of the crucial factors that determine the success or failure of novice programmers. To achieve this, 60 students who enrolled in Diploma in Computer Science programme in Universiti Teknologi MARA Perlis Branch, Malaysia, were asked to take the pre-test and post-test of Group Assessment Logical Thinking (GALT) Test in the beginning and at the end of the semester. These students were divided into two main groups; Control and Test in the Test Group, students with low logical ability will be paired with their high logical thinking friends. Meanwhile, in the Control Group, no pair programming or collaborative technique took place. Five programming tasks were assigned to both groups to solve either collaboratively or individually. The results obtained via paired sample t-tests statistical analysis shows significant improvements in students’ logical thinking with p-value &lt;0.05 in the Test Group
Sign Clustering and Topic Extraction in Proto-Elamite

We describe a first attempt at using techniques from computational linguistics to analyze the undeciphered proto-Elamite script.Using hierarchical clustering, n-gram frequencies, and LDA topic models, we both replicate results obtained by manual decipherment and reveal previously-unobserved relationships between signs.This demonstrates the utility of these techniques as an aid to manual decipherment.
Bangla Handwritten Digit Recognition and Generation

Handwritten digit or numeral recognition is one of the classical issues in the area of pattern recognition, and has seen tremendous advancement because of the recent wide availability of computing resources. Plentiful works have already done on English, Arabic, Chinese, and Japanese handwritten scripts. Some work on Bangla also have been done but there is space for development. From that angle, in this paper, an architecture has been implemented which achieved the validation accuracy of 99.44% on BHAND dataset and outperforms Alexnet and Inception V3 architecture. Beside digit recognition, digit generation is another field which has recently caught the attention of the researchers though not many works have been done in this field especially on Bangla. In this paper, a Semi-Supervised Generative Adversarial Network or SGAN has been applied to generate Bangla handwritten numerals and it successfully generated Bangla digits.
A Soft Label Strategy for Target-Level Sentiment Classification

In this paper, we propose a soft label approach to target-level sentiment classification task, in which a history-based soft labeling model is proposed to measure the possibility of a context word as an opinion word. We also apply a convolution layer to extract local active features, and introduce positional weights to take relative distance information into consideration. In addition, we obtain more informative target representation by training with context tokens together to make deeper interaction between target and context tokens. We conduct experiments on SemEval 2014 datasets and the experimental results show that our approach significantly outperforms previous models and gives state-of-the-art results on these datasets.
Effect of service quality, company image, and customer satisfaction in Word Of Mouth

The sample of this study was 155 respondents, taken using the Random Sampling technique, with the criteria that respondents were attending an English Language Business English program at EF Denpasar, and education at least graduated from high school or equivalent. The results of the study show that (1) Service quality has a positive and significant effect on customer satisfaction. (2) The company's image has a positive and significant effect on customer satisfaction. (3) Service quality has a positive and significant effect on WOM (Word of Mouth), (4) Company image has a positive and not significant effect on WOM and (5) Customer satisfaction has a positive and significant effect on WOM managerial implications and suggestions for increasing EF English First Denpasar WOM, by increasing customer satisfaction and service quality. Customer satisfaction is enhanced by improving the company's image and increasing the professionalism of EF English First Denpasar teachers.
EPITA-ADAPT at SemEval-2019 Task 3: Detecting emotions in textual conversations using deep learning models combination

Messaging platforms like WhatsApp, Facebook Messenger and Twitter have gained recently much popularity owing to their ability in connecting users in real-time. The content of these textual messages can be a useful resource for text mining to discover and unhide various aspects, including emotions. In this paper we present our submission for SemEval 2019 task ‘EmoContext’. The task consists of classifying a given textual dialogue into one of four emotion classes: Angry, Happy, Sad and Others. Our proposed system is based on the combination of different deep neural networks techniques. In particular, we use Recurrent Neural Networks (LSTM, B-LSTM, GRU, B-GRU), Convolutional Neural Network (CNN) and Transfer Learning (TL) methodes. Our final system, achieves an F1 score of 74.51% on the subtask evaluation dataset.
A Comparison of Methods for Detecting Examinee Preknowledge of Items

In an on-demand testing program, some items are repeatedly used across test administrations. This poses a risk to test security. In this study, we considered a scenario wherein a test was divided into two subsets: one consisting of secure items and the other consisting of possibly compromised items. In a simulation study of multistage adaptive testing, we used three methods to detect item preknowledge: a predictive checking method (PCM), a likelihood ratio test (LRT), and an adapted Kullback–Leibler divergence (KLD-A) test. We manipulated four factors: the proportion of compromised items, the stage of adaptive testing at which preknowledge was present, item-parameter estimation error, and the information contained in secure items. The type I error results indicated that the LRT and PCM methods are favored over the KLD-A method because the KLD-A can experience large inflated type I error in many conditions. In regard to power, the LRT and PCM methods displayed a wide range of results, generally from 0.2 to 0.8, depending on the amount of preknowledge and the stage of adaptive testing at which the preknowledge was present.
USF at SemEval-2019 Task 6: Offensive Language Detection Using LSTM With Word Embeddings

In this paper, we present a system description for the SemEval-2019 Task 6 submitted by our team.For the task, our system takes tweet as an input and determine if the tweet is offensive or non-offensive (Sub-task A).In case a tweet is offensive, our system identifies if a tweet is targeted (insult or threat) or nontargeted like swearing (Sub-task B).In targeted tweets, our system identifies the target as an individual or group (Sub-task C).We used data pre-processing techniques like splitting hashtags into words, removing special characters, stop-word removal, stemming, lemmatization, capitalization, and offensive word dictionary.Later, we used keras tokenizer and word embeddings for feature extraction.For classification, we used the LSTM (Long shortterm memory) model of keras framework.Our accuracy scores for Sub-task A, B and C are 0.8128, 0.8167 and 0.3662 respectively.Our results indicate that fine-grained classification to identify offense target was difficult for the system.Lastly, in the future scope section, we will discuss the ways to improve system performance.
Neuro-Evolution of Mobile Robot Controller

We present a neuro-evolution design for control of a mobile robot in 2D simulation environment. The mobile robot is moving in unknown environment with obstacles from the start position to the goal position. The trajectory of the robot is controlled by a neural network – based controller which inputs are information from several laser beam sensors. The learning of the neural network controller is based on an evolutionary approach, which is provided by genetic algorithm.
Scandal-weary Swedish government takes over research-fraud investigations

The Research Misconduct Board is one of the first national agencies tasked with investigating serious research misconduct. The Research Misconduct Board is one of the first national agencies tasked with investigating serious research misconduct.
Augmented reality based mobile application for real-time Arabic language translation

Augmented Reality (AR) technologies are nowadays widely and ubiquitously used to enhance our real world experience in novel and enriched ways. With the new generation of smart phones and AR technologies, we are embracing a stimulating way of Human-Computer Interaction.&nbsp; Many AR mobile applications were developed in many fields such as education, health, design and Translation... This paper deals with text translation.&nbsp; Many applications are proposed on the market but they are yet limited to a few number of languages. Arabic language is one of these unsupported ones. To overcome this shortage, an AR mobile application for real time Arabic text translation is developed. The user simply hover the device's camera on the desired text and it will be translated automatically and rapidly. This type of application consists of three main components which are: text detection, text extraction and text translation. To ensure the application is performing well, the application is tested on different datasets under different conditions. In addition, a user study test is conducted to test the usability and user satisfaction.
Development and Implementation of Computer Assisted Instruction System in Physical Education Based on ASP.NET Technology

Due to the practicalness of physical education (PE) teaching, the computer-assisted instruction (CAI) in PE can deepen students’ understanding through vis-ual teaching methods. The purpose of this paper is to study the development and implementation of CAI system in PE. To this end, based on hardware such as campus network and internet, B/S/D three-layer system was adopted in this paper as the system architecture, and ASP.NET technology was used to realize system functions. The research results show that the system is divided into four parts: the teaching resource module, the online Q&amp;A module, the coursework module and the exam module according to the system requirements analysis. Then, taking the system login, teacher module and system test module as examples, the system operation process was designed in detail to achieve its specific functions. This re-search enriches the theoretical and practical research results of CAI system in PE and promotes the reform of physical education teaching mode.
Unboxing co-creation with students

Co-creation has received increasing interest as a way of develop products and services in collaboration with customers and clients. Recently, co-creation has been introduced in higher education; however, there is no general agreement about what this entails, and while the concept of co-creation has been used in a number of differing contexts, descriptions tend to focus on potentials only and not the tensions inherent in this kind of collaboration. &#x0D; In this paper we describe a conceptual model for co-creation with students and explore the potentials as well as the tensions inherent in co-creation with students at academic libraries. Through a case study at The Royal Danish Library, Aarhus University Library we develop a conceptual model for co-creation with students that identifies the key aspects of co-creation with students and indicates its key potentials and tensions.
Network Security Situation Factor Extraction Based on Random Forest of Information Gain

Aiming at the problem of situational element extraction, a method based on random forest of information gain for network security situation factor extraction is proposed. First, the importance of the attribute is determined by the information gain. After the threshold is set, the attribute is reduced and the redundant attribute is deleted. Secondly, the processed data is classified using the random forest classifier. Finally, in order to verify the efficiency of the algorithm, the improved method is tested by the intrusion detection data set. Compared with the traditional method, the experimental results show that the algorithm effectively improves the accuracy and achieves efficient extraction of network security situation elements.
Designing TRiDS: Treatments for Risks in Design Science

Design Science Research (DSR) has many risks. Researchers inexperienced in DSR, especially early career researchers (ECRs) and research students (e.g. PhD students) risk inefficient projects (with delays, rework, etc.) at best and research project failure at worst if they do not manage and treat DSR risks in a proactive manner. The DSR literature, such as the Risk Management Framework for Design Science Research (RMF4DSR), provides advice for identifying risks, but provides few suggestions for specific treatments for the kinds of risks that potentially plague DSR. This paper describes the development of a new purposeful artefact (TRiDS: Treatments for Risks in Design Science) to address this lack of suggestions for treatment of DSR risks. The paper describes how the purposeful artefact was developed (following a DSR methodology), what literature it draws upon to inspire its various components, the functional requirements identified for TRiDS, and how TRiDS is structured and why. The paper also documents the TRiDS purposeful artefact in detail, including four main components: (1) an extended set of risk checklists (extended from RMF4DSR), (2) a set of 47 specific suggestions for treating known risks in DSR, (3) a classification of the treatments identified into 14 different categories, and (4) a look-up table for identifying candidate treatments based on a risk in the extended risk checklists. The treatment suggestions and guidance in TRiDS serve as a supplement to RMF4DSR by helping DSR researchers to identify treatments appropriate for a particular DSR project (or program) and thereby to improve DSR project efficiency and the probability of DSR project success.
Writing Caption on Instagram as Media for Student’s Motivation and Writing Skill Improvement

This study aims to describe the use of caption on Instagram to improve students' writing skill and motivation. This research is a classroom action research (CAR). The research subject was class VIII SMP N 3 Mranggen Demak with a total of 31 students. This study was conducted in two cycles. The Pre-Test was held before the first cycle was implemented. The researcher used descriptive qualitative method to describe the use of caption on Instagram for student’s motivation and writing skill improvement. The results showed that the use of caption on Instagram can improve student’s motivation and writing skill. In the first cicle of the pre-tests that have been conducted, it was known that only 2 students (6,5%) got scores between 60 - 64. While 28 students (93%) got scores below 60. The number of students who scored above 60 increased in in the post-test I, there were 19 students with an average score of 69,5. The score of students rose significantly in the post-test II, it was found that there was a satisfactory increase in scores with the highest score of 87 and the lowest score of 66 (1 student)
Can clustering improve glucose forecasting with genetic programming models?

This study investigates how to improve the predictions of glucose values obtained with genetic programming models. A set of statistical techniques are used to discover glucose profiles that identify similar situations in patients with type 1 diabetes mellitus, and incorporate this knowledge to the models. Glucose time series are divided into 4-hour non-overlapping slots and clustered using the technique based on decision trees called chi-square automatic interaction detection, to classify glucose profiles into groups using two decision variables: day of the week and time slot of the day. The objective is to customize models for different glucose profiles that appear in the patient's day-to-day. Genetic programming models created with glucose values from the original data-set are compared to those of models created with classified glucose values. Significant differences (p-value < 0.05) and associations are observed between the glucose profiles. In general, using classified glucose values in models created with genetic programming, the accuracy of the predictions improves in comparison with those of models created with the original data-set. We concluded that the classification process can be useful to correct and improve habits or clinical therapies in patients, and obtain more accurate models through automatic learning techniques and artificial intelligence.