Reviewerly/exHarmony huggingface dataset:
Civic Education Of University Students In The Context Of Information Diversity

The article discusses various models of civic education of university students, ensuring the social involvement of students in the processes of civil society and the economic development of Russia in the context of information diversity. The authors of the article note the interrelation between the informational and communicative culture of students, their role in civil society and their potential contribution to the economic development of the region. In this regard, the article discusses the most popular among young people channels and sources of information about the most significant civil initiatives in the country, the region, and the university. The authors of the article present the results of a survey among 2nd-4th-year students of Yaroslav the Wise Novgorod State University (Russia). The survey reveals the students’ attitudes toward various sources of information about significant events in the country, the region and the university, the degree of confidence in them, and the impact of information on the social involvement of students, their participation in the public life of the university and the region. The survey discloses the role position of students in civil society and their understanding of the possible contribution to the economic development of the region. Based on the survey results, the most effective models of students’ civic education are identified. The authors of the article offer some recommendations on how to develop the social involvement of students in the life of civil society that could be useful for university administration and faculty.
Generating similarity cluster of Indonesian languages with semi-supervised clustering

&lt;p&gt;Lexicostatistic and language similarity clusters are useful for computational linguistic researches that depends on language similarity or cognate recognition. Nevertheless, there are no published lexicostatistic/language similarity cluster of Indonesian ethnic languages available. We formulate an approach of creating language similarity clusters by utilizing ASJP database to generate the language similarity matrix, then generate the hierarchical clusters with complete linkage and mean linkage clustering, and further extract two stable clusters with high language similarities. We introduced an extended k-means clustering semi-supervised learning to evaluate the stability level of the hierarchical stable clusters being grouped together despite of changing the number of cluster. The higher the number of the trial, the more likely we can distinctly find the two hierarchical stable clusters in the generated k-clusters. However, for all five experiments, the stability level of the two hierarchical stable clusters is the highest on 5 clusters. Therefore, we take the 5 clusters as the best clusters of Indonesian ethnic languages. Finally, we plot the generated 5 clusters to a geographical map.&lt;/p&gt;
Technological Platform for accessing school engagement and contextual factors, and providing online result reports

This technical report describes a web based plataform for accessing school engagement in its three dimensions (emotional, cognitive, and behavioural) and the contextual variables with influences in school engagement (family, teachers and peers). The platform delivers results online through individual reports on students sent to teachers and psychosocial teams as well as the pupil themselves and their parents or guardians, it also provides aggregate reports by grades and schools for teachers, psychosocial team and school managers to assist contextualised and informed decisions.This technological platform for accessing school engagement was developed in Chile. Ir is based on specially designed and psychometrally validated instruments for meassuring school engagement and contextual factors, and on a tool for predective modelling on student behavior and contextua factors. The platform is currently being adjusted and tested for use in other Ibero american countries (Peru, Uruguay, Colombia, and Spain).
FoRCE (Forensic Recovery of Cloud Evidence): A Digital Cloud Forensics Framework

Recently, organisations have made great efforts to migrate their infrastructure to the cloud environment. This makes the cloud a high-value target for malicious actors. Analysing a compromised cloud instance requires the use of digital forensics that follows a scientific process to support or disprove the hypothesis. The purpose of the research is to look at the potential gaps in the current process and attempt to create a framework that standardises digital forensics within cloud environments.
Sell-corpus: an Open Source Multiple Accented Chinese-english Speech Corpus for L2 English Learning Assessment

We present SELL-CORPUS, a multiple accented speech corpus for L2 English learning in China, aiming at the potential research of multiple accented acoustic model, mispronunciation detection and pronunciation assessment for future nationwide oral English tests. Our corpus contains 31.6 hour speech recordings contributed by 389 volunteer speakers, including 186 males and 203 females. Our corpus covers seven major regional dialects and provides a baseline for Chinese multiple accented automatic speech recognition system. We released our speech corpus to the public for academic research. To the best of our knowledge, it is the first open-source English speech corpus that accounts for the accents of all major Chinese regional dialects.
An approach for medical event detection in Chinese clinical notes of electronic health records

Medical event detection in narrative clinical notes of electronic health records (EHRs) is a task designed for reading text and extracting information. Most of the previous work of medical event detection treats the task as extracting concepts at word granularity, which omits the overall structural information of the clinical notes. In this work, we treat each clinical note as a sequence of short sentences and propose an end-to-end deep neural network framework. We redefined the task as a sequence labelling task at short sentence granularity, and proposed a novel tag system correspondingly. The dataset were derived from a third-level grade-A hospital, consisting of 2000 annotated clinical notes according to our proposed tag system. The proposed end-to-end deep neural network framework consists of a feature extractor and a sequence labeller, and we explored different implementations respectively. We additionally proposed a smoothed Viterbi decoder as sequence labeller without additional parameter training, which can be a good alternative to conditional random field (CRF) when computing resources are limited. Our sequence labelling models were compared to four baselines which treat the task as text classification of short sentences. Experimental results showed that our approach significantly outperforms the baselines. The best result was obtained by using the convolutional neural networks (CNNs) feature extractor and the sequential CRF sequence labeller, achieving an accuracy of 92.6%. Our proposed smoothed Viterbi decoder achieved a comparable accuracy of 90.07% with reduced training parameters, and brought more balanced performance across all categories, which means better generalization ability. Evaluated on our annotated dataset, the comparison results demonstrated the effectiveness of our approach for medical event detection in Chinese clinical notes of EHRs. The best feature extractor is the CNNs feature extractor, and the best sequence labeller is the sequential CRF decoder. And it was empirically verified that our proposed smoothed Viterbi decoder could bring better generalization ability while achieving comparable performance to the sequential CRF decoder.
Readability indices for the assessment of textbooks: a feasibility study in the context of EFL

Readability indices have been widely used in order to measure textual difficulty. They can be useful for the automatic classification of texts, especially in language teaching. Among other applications, they allow for the previous determination of the difficulty level of texts without the need of reading them through. The aim of this research is twofold: first, to examine the degree of accuracy of the six most commonly used readability indices, and second, to present a new optimized measure. The main problem is that these readability indices may offer disparity, and this is precisely what has motivated our attempt to unite their potential. A discriminant analysis of all the variables under examination has enabled the creation of a much more precise model, improving the previous best results by 15%. Furthermore, errors and disparities in the difficulty level of the analyzed texts have been detected.
Digital Skill Sets for Diverse Users: A Comparison Framework for Curriculum and Competencies

More governmental entities are supporting their residents in digital skill acquisition as part of their information and communications technology (ICT) strategies to foster digital inclusion and broadband adoption. Digital skills frameworks and curricula have been developed by community-based organizations, libraries, and companies, but there lacks a robust comparison between these resources to understand what specific skills they cover. The selection of skills and competencies covered by these trainings impacts the effectiveness of public and private investments in areas such as workforce training, education, and government application development. Understanding which skills are important and are being offered in training can help guide policy, investments and assessment. For this paper, University of Washington and the City of Seattle partnered together to identify and compare digital skills and competencies recommended by fifteen popular frameworks and curricula. This research can be used to adopt shared definitions and evaluation of skills, clarify education pathways, assess training programs, guide investments and policy, and identify important skills not currently addressed by popular resources.For the review, we examined six digital skills and competency frameworks and nine digital skills curricula from the United States and Europe targeted at high school students or adults. We compared the skills covered across the frameworks and curricula to understand what digital literacy resources covered which skills for different learner needs. The resulting comparison identified a total of 74 distinct digital skills. We then placed these skills into ten separate categories: gateway or foundational skills (11 skills), communication (8), creation (8), device ownership (4), information skills (7), lifelong learning (3), mobile (6), online life (11), privacy and security (7), and workplace (9). Overall, frameworks covered more skills (median of 31) than curricula (median of 23) but individual curriculum covered certain topics more in-depth. We also discovered that only about half of the resources had accompanying assessments for individuals or training providers to measure skills progression. The result of this work is a tool for funders, governments, and training providers to help focus and improve their digital inclusion efforts. The research paper also recommends future action and collaborative efforts to clarify and align learning pathways, expand the application of the comparative tools, develop new training and competency standards, and guide investments in digital skills training and materials.
S180. Defining Clinical Sentiment in Psychosis Patient Health Records

Recently natural language processing (NLP) tools have been developed to identify important risk factors in electronic health records (EHRs). Sentiment analysis, a field of NLP defined as an individual’s attitude towards a topic, has been widely used in non-medical industries for improving decision making. In this study, we undertook a first-of-its-kind annotation project to extend sentiment analysis to psychiatry, defining it as a clinician’s prognosis of a patient at a fixed time point. The sentiment analysis models trained using the results from this project will be incorporated in a feature engineering pipeline for a machine learning model that predicts inpatient readmission risk.
A Narrative Sentence Planner and Structurer for Domain Independent, Parameterizable Storytelling

Storytelling is an integral part of daily life and a key part of how we share information and connect with others. The ability to use Natural Language Generation (NLG) to produce stories that are tailored and adapted to the individual reader could have large impact in many different applications. However, one reason that this has not become a reality to date is the NLG story gap, a disconnect between the plan-type representations that story generation engines produce, and the linguistic representations needed by NLG engines. Here we describe Fabula Tales, a storytelling system supporting both story generation and NLG. With manual annotation of texts from existing stories using an intuitive user interface, Fabula Tales automatically extracts the underlying story representation and its accompanying syntactically grounded representation. Narratological and sentence planning parameters are applied to these structures to generate different versions of the story. We show how our storytelling system can alter the story at the sentence level, as well as the discourse level. We also show that our approach can be applied to different kinds of stories by testing our approach on both Aesop’s Fables and first-person blogs posted on social media. The content and genre of such stories varies widely, supporting our claim that our approach is general and domain independent. We then conduct several user studies to evaluate the generated story variations and show that Fabula Tales’ automatically produced variations are perceived as more immediate, interesting, and correct, and are preferred to a baseline generation system that does not use narrative parameters.
Optical Character Recognition based on Template Matching

This paper presents an innovative design for Optical Character Recognition (OCR) from text images by using the Template Matching method.OCR is an important research area and one of the most successful applications of technology in the field of pattern recognition and artificial intelligence.OCR provides full alphanumeric visualization of printed and handwritten characters by scanning text images and converts it into a corresponding editable text document. The main objective of this system prototype is to develop a prototype for the OCR system and to implement The Template Matching algorithm for provoking the system prototype. In this paper, we took alphabet (A-Z and a-z), and numbers (0-1), grayscale images, bitmap image format were used and recognized the alphabet and numbers by comparing between two images. Besides, we checked accuracy for different fonts of alphabet and numbers. Here we used Matlab R 2018 a software for the proper implementation of the system.
Development, Reliability, and Validity of Open-ended Test to Measure Student’s Digital Literacy Skill

Thisstudy aims to developing open-ended test for measuring digital literacy skills of students. There are three step of this study, it was defining the construc and formatting objectives, validity by expert review and item administration. The open-ended test was develop based on five components of digital literacy skill: information, communication, content creation, safety, and problem-solving. The open-ended test was initially piloted on four group of samples: interviews and validity by physics expert (2 Professor, 1 Doctoral), professional teachers (N=2), and college students of graduate school (N=2). Modification were made afterwards and the test was administered to a group of science students from high school in Yogyakarta province, Indonesia (N=129). Result of this study are: (1) The open-ended test instrumen proved valid and worthy of use to measure digital literacy skill; (2) Based on expert validation, the teset is valid with CVR value 1.00; (3) the average value and standard deviation of INFIT MNSQ is .83-1.21 (fit with rasch model/good item); (4) The item has a good degree of difficulty with range of difficulty level between (moderate level). The student’s digital literacy skill test can also be used to measure student’s skill with very low to excellentcategories
Sequential Far Infrared Image Mosaic Using Coarse-to-Fine Scheme

Sequential far infrared (FIR) image mosaic is a challenging task due to repeated patterns, low contrast, image blurring, illumination change, and sparse structure. In this paper, a novel matching approach is proposed for the sequential FIR image registration, which is based on coarse-to-fine strategy and grid-based motion statistics (GMS). In the coarse matching step, a transitive relation is deduced to obtain the coordinate relations of the current registering image and its adjacent images to ensure enough number of feature points and guarantee the accuracy of the preregistration results. In the fine matching step, a principle for fine-tuning registration and a strategy for changing the distribution of the feature points are developed for finding most appropriate feature points. Based on the point pairs, the position of the current registering image can be accurately located. Besides, an effective post-processing, such as gray-scale non-uniformity correction and mosaic seam elimination, is also utilized to refine the fine-tuning results to achieve the promising mosaic effect. A comparative study and quantitative evaluation with commercial software or representative image mosaic algorithm demonstrate that better quality results can be obtained by using the proposed method.
Link Prediction in Knowledge Graphs with Concepts of Nearest Neighbours

The open nature of Knowledge Graphs (KG) often implies that they are incomplete. Link prediction consists in inferring new links between the entities of a KG based on existing links. Most existing approaches rely on the learning of latent feature vectors for the encoding of entities and relations. In general however, latent features cannot be easily interpreted. Rule-based approaches offer interpretability but a distinct ruleset must be learned for each relation, and computation time is difficult to control. We propose a new approach that does not need a training phase, and that can provide interpretable explanations for each inference. It relies on the computation of Concepts of Nearest Neighbours (CNN) to identify similar entities based on common graph patterns. Dempster-Shafer theory is then used to draw inferences from CNNs. We evaluate our approach on FB15k-237, a challenging benchmark for link prediction, where it gets competitive performance compared to existing approaches.
Censorious Young: Knowledge Discovery from High-throughput Movie Rating Data with LME4

Quantitative analysis of high throughput movie rating data provides supports for one general social behavior: the young are usually more censorious than senior people when rating/evaluating the same thing. Millions of movie rating data with users’ categorical age information are analyzed by the linear mixed model with the lme4 R package. When the age factor is viewed as fixed effects, the rating scores for movies are positively related to age. In general the young people are tends to give lower score than senior people. Such a social behavior phenomenon should be carefully examined in a recommendation system and in data collection.
A

This paper presents a research platform that supports spoken dialogue interaction with multiple robots. The demonstration showcases our crafted MultiBot testing scenario in which users can verbally issue search, navigate, and follow instructions to two robotic teammates: a simulated ground robot and an aerial robot. This flexible language and robotic platform takes advantage of existing tools for speech recognition and dialogue management that are compatible with new domains, and implements an inter-agent communication protocol (tactical behavior specification), where verbal instructions are encoded for tasks assigned to the appropriate robot.
Hyper 360—Towards a Unified Tool Set Supporting Next Generation VR Film and TV Productions

We describe four fundamental challenges that complex real-life Virtual Reality (VR) productions are facing today (such as multi-camera management, quality control, automatic annotation with cinematography and 360˚ depth estimation) and describe an integrated solution, called Hyper 360, to address them.We demonstrate our solution and its evaluation in the context of practical productions and present related results.
Appearance-consistent Video Object Segmentation Based on a Multinomial Event Model

In this study, we propose an effective and efficient algorithm for unconstrained video object segmentation, which is achieved in a Markov random field (MRF). In the MRF graph, each node is modeled as a superpixel and labeled as either foreground or background during the segmentation process. The unary potential is computed for each node by learning a transductive SVM classifier under supervision by a few labeled frames. The pairwise potential is used for the spatial-temporal smoothness. In addition, a high-order potential based on the multinomial event model is employed to enhance the appearance consistency throughout the frames. To minimize this intractable feature, we also introduce a more efficient technique that simply extends the original MRF structure. The proposed approach was evaluated in experiments with different measures and the results based on a benchmark demonstrated its effectiveness compared with other state-of-the-art algorithms.
The Role of M-Learning in Decreasing Speaking Anxiety for EFL Learners.

This study examines the role of m-learning in decreasing speaking anxiety for EFL learners. The researchers believe there is a relationship between Mobile Assisted Language Learning (MALL) and anxiety. In other words, using mobiles to learn speaking can decrease the amount of anxiety the learners have and motivates them to speak in public. The study included 9 participants (6 females and 3 males); they are either students or graduates of different departments at Aleppo University. First, the learners took a FLCAS pretest. Then, they joined a group on WhatsApp for four weeks, where they had to respond to three tasks a week. At the end of the course, the learners took the FLCAS posttest. Then, 4 participants were interviewed to see their opinions, feedback, and notes about the experience they underwent. The results show that m-learning has significantly decreased the learners' anxiety in speaking English as a foreign language.
Toxic Comment Classification

Building a multi-headed model that's capable of detecting different types of toxicity like threats, obscenity, insult and identity-based hate.Discussing things you care about can be difficult.The threat of abuse and harassment online means that many people stop expressing themselves and give up on seeking different opinions.Platforms struggle to efficiently facilitate conversations, leading many communities to limit or completely shut down user comments.So far we have a range of publicly available models served through the perspective APIs, including toxicity.But the current models still make errors, and they don't allow users to select which type of toxicity they're interested in finding.