Reviewerly/exHarmony huggingface dataset:
A PILOT STUDY ON VIDEO COLOR TRANSFER: A SURVEY OF USER-TYPE OPINIONS

Multimedia software products can be used to create and edit various aspects of online media.Recently, the affordances of mobile devices and high-speed mobile data networks mean that these editing capabilities are more readily available for mobile devices enabling a broader consumer-base.However, the precise role of the user in creative practice is often neglected in favor of reporting faster, more streamlined device functionality.In this paper, we seek to identify high-level human-computer interaction issues concerning video recoloring interfaces that are driven by the needs of different user-types via a methodological and explorative process.By conducting a pilot study, we have captured both quantitative and qualitative responses that formatively explore the role of the user in video recoloring tasks carried out on mobile devices.This research presents a variety of user responses to a video recoloring application, identifying areas of future investigation for explorative practices in user interface design for video recoloring visualization.These findings present important information for researchers exploring the use of state-of-art video recoloring processes and contribute to dialogues surrounding the study of mobile technology in use.
Three-dimensional convolutional neural network (3D-CNN) for satellite behavior discovery

Space superiority includes space protection and space situational awareness (SSA), which require rapid and accurate space object behavioral motion and operational intent discovery. The presence of clutter, in addition to real-time and hidden information constraints, greatly complicates the space awareness decision-making to control both ground-based and space-based surveillance assets. Space is considered as an important concern in modern frontiers because intelligence information from the space has become extremely vital for strategic decisions, which calls for persistent Space Domain Awareness (SDA). The presence of disagreeable actors in addition to real-time and hidden information constraints greatly complicates the decision-making process in satellite behavior detection as well as operational intent discovery. This paper designs and implements 3D-Convoltional Neural Networks (CNNs) for rapid discovery of evasive satellite behaviors from ground-based sensors, which measure the ranges, azimuth angles, and elevation angles in the Adaptive Markov Inference Game Optimization (AMIGO) tool. The novel 3D CNN extends the generic 2d CNN towards analysis from many perspectives. To generate the 3D CNN model, the training and validation data are simulated based on our game theoretic reasoning engine for elusive space behaviors detection, interactive adversary awareness, and intelligent probing. The performance of the 3D CNN is compared with the 2D CNN models from previous work which is shown for a 10% increase in accuracy.
Joint Coreference Resolution and Character Linking for Multiparty Conversation

Character linking, the task of linking mentioned people in conversations to the real world, is crucial for understanding the conversations.For the efficiency of communication, humans often choose to use pronouns (e.g., "she") or normal phrases (e.g., "that girl") rather than named entities (e.g., "Rachel") in the spoken language, which makes linking those mentions to real people a much more challenging than a regular entity linking task.To address this challenge, we propose to incorporate the richer context from the coreference relations among different mentions to help the linking.On the other hand, considering that finding coreference clusters itself is not a trivial task and could benefit from the global character information, we propose to jointly solve these two tasks.Specifically, we propose C 2 , the joint learning model of Coreference resolution and Character linking.The experimental results demonstrate that C 2 can significantly outperform previous works on both tasks.Further analyses are conducted to analyze the contribution of all modules in the proposed model and the effect of all hyper-parameters.
Extending Neural Keyword Extraction with TF-IDF tagset matching.

Keyword extraction is the task of identifying words (or multi-word expressions) that best describe a given document and serve in news portals to link articles of similar topics. In this work, we develop and evaluate our methods on four novel data sets covering less-represented, morphologically-rich languages in European news media industry (Croatian, Estonian, Latvian, and Russian). First, we perform evaluation of two supervised neural transformer-based methods, Transformer-based Neural Tagger for Keyword Identification (TNT-KID) and Bidirectional Encoder Representations from Transformers (BERT) with an additional Bidirectional Long Short-Term Memory Conditional Random Fields (BiLSTM CRF) classification head, and compare them to a baseline Term Frequency - Inverse Document Frequency (TF-IDF) based unsupervised approach. Next, we show that by combining the keywords retrieved by both neural transformer-based methods and extending the final set of keywords with an unsupervised TF-IDF based technique, we can drastically improve the recall of the system, making it appropriate for usage as a recommendation system in the media house environment.
Mining and Managing Big Data Refactoring for Design Improvement: Are We There Yet?

Refactoring is a set of code changes applied to improve the internal structure of a program, without altering its external behavior. With the rise of continuous integration and the awareness of the necessity of managing technical debt, refactoring has become even more popular in recent software builds. Recent studies indicate that developers often perform refactorings. If we consider all refactorings performed across all projects, this consists of the refactoring knowledge that represents a rich source of information that can be useful for both developers and practitioners to better understand how refactoring is being applied in practice. However, mining, processing, and extracting useful insights, from this plethora of refactorings, seem to be challenging. In this book chapter, we take a dive into how refactoring can be mined and preprocessed. We discuss all design concepts and structural metrics that can also be mined along with refactoring operations to understand their impact better. We further investigate the many practical challenges for such extraction. The volume, velocity, and variety of extracted data require careful planning. We outline the appropriate techniques from a large number of available technologies for such system implementation.
Empirical game-theoretic methods to minimize regret against specific opponents

In many real-world multi-domain applications, if there is an opportunity to sense the opponent's strategy from previous rounds, an agent can exploit its opponent in payoffs by playing a specific Best Response (BR) strategy. The optimal way to exploit the opponents in such contests is to learn specific BR strategies for each candidate's strategies. However, in a large strategy space, learning BR policies for all the strategies is infeasible. We need to find a reasonable point to learn some BR strategies to exploit the opponent strategies, which motivates us to propose a novel method for finding better agent strategies while facing a specific set of opponents in repeated games. Our method is called Clustered Double Oracle Empirical Game-Theoretic Analysis (CDO-EGTA), which builds upon the classic Double Oracle (DO) framework based on Deep Q-Network (DQN) and clustering methods. Empirical results show that our method outperforms the current state-of-the-art methods in terms of regret.
Video Feature Making in ESP- Based Public Speaking Class: A Student-Centred Learning in Vocational Higher Education Context

In Indonesia, English for Specific Purpose usually offers to students of non-English Department. Actually, it must also be taught to students of English Department, especially who are in vocational higher institutions. By facilitating the learning of English for Specific Purpose by the institution, they will fit in any kinds of work fields later on. Actually, the process of learning English for Specific Purpose could be integrated in various subjects in English Department if the class is using the right learning material, like Digital Video Feature Project. It is a project assignment for Public Speaking class which is developed to improve studentsâ€™ English for Specific Purpose by applying Gall and Borg Small Scale Model. The steps are need analysis, planning objectives, developing preliminary forms of product, preliminary field testing, and main field testing. This article is only discussing the last phase, which is analysing the effect of Digital Video Feature Project on the studentâ€™ English for Specific Purpose. The sample taken was a female English Department student of Politeknik Negeri Padang.Â She enrolled in Public Speaking class, and chose English for Environmental Engineering for her English for Specific Purpose. She made a video feature about Waste Bank Program which is established by Padang Environmental Services Department. From the analysisÂ on the studentâ€™ self assessment form, the results areÂ the student is more competence writing in the context of Environmental Engineering,Â is more skillfull as a public speaker in a topic related to Environmental Engineering field, herÂ knowledge related to Environmental EngineeringÂ increases, and her social life with people related to Environmental Engineering is also built. In conclusion, video feature project of Waste Bank Program does not only influence the studentâ€™s ESP and public speaking skill, but also effect their mind and consideration to support the program.
UnibucKernel: Geolocating Swiss German Jodels Using Ensemble Learning

In this work, we describe our approach addressing the Social Media Variety Geolocation task featured in the 2021 VarDial Evaluation Campaign. We focus on the second subtask, which is based on a data set formed of approximately 30 thousand Swiss German Jodels. The dialect identification task is about accurately predicting the latitude and longitude of test samples. We frame the task as a double regression problem, employing an XGBoost meta-learner with the combined power of a variety of machine learning approaches to predict both latitude and longitude. The models included in our ensemble range from simple regression techniques, such as Support Vector Regression, to deep neural models, such as a hybrid neural network and a neural transformer. To minimize the prediction error, we approach the problem from a few different perspectives and consider various types of features, from low-level character n-grams to high-level BERT embeddings. The XGBoost ensemble resulted from combining the power of the aforementioned methods achieves a median distance of 23.6 km on the test data, which places us on the third place in the ranking, at a difference of 6.05 km and 2.9 km from the submissions on the first and second places, respectively.
Standards for the artificial intelligence community

Artificial intelligence (AI) / Machine Learning (ML) applications are widely available for different domains such as commercial, industrial, and intelligence applications. In particular, the use of AI applications for the security environment requires standards to manage expectations for users to understand how the results were derived. A reliance on “black boxes” to generate predictions and inform decisions could lead to errors of analysis. This paper explores the development of potential standards designed for each stage of the development of an AI/ML system to help enable trust, transparency, and explainability. Specifically, the paper utilizes the standards outlined in Intelligence Community Directive 203 (Analytic Standards) to hold machine outputs to the same rigorous accountability standards as performed by humans. Building on the ICD203, the Multi-Source AI Scorecard Table (MAST) was developed to support the community towards test and evaluation of AI/ML techniques. The paper provides discussion towards using MAST to rate a semantic processing tool for processing noisy, unstructured, and complex microtext in the form of streaming chat for video call outs. The scoring is notional, but provides a discussion on how MAST could be used as a standard to compare AI/ML methods that complements datasheets and model cards.
Land cover analysis using satellite imagery for humanitarian mine action and ERW survey

The number of active landmines is uncertain, however, estimated 5,554 people were killed or injured by mines in 2019. Understanding the land-cover before mine clearance process provides valuable information on the scale of the problem, the resources to clear the field and ensures all hazardous areas prioritized. In this paper, we present a new framework for land clearness prioritization using land-cover analysis. We use remote sensing images from sentinel-2 to estimate the changes in the land cover. Specifically, we estimate the changes in vegetation and non-vegetation areas. Further, we use the amount and number of land changes during a period to provide recommendations on the clearance priority for different areas. A case study for different areas in the Kingdom of Cambodia is presented with several observations of satellite images for the years 2019 and 2020. Several suspected hazardous areas (or polygons) are defined by landmine surveying expert for analysis. A change matrix for each polygon is obtained from consecutive observations. Then, a series of qualitative and quantitative 2- dimensional characteristics are extracted such as class change mask from-to, percentage loss and gain per class. The 2D characteristics, together with expert-defined scores of class-change importance are used to compute the amount and number of changes in each polygon and a recommendation on the clearance priorities. Our study demonstrates that analysing the changes in land-cover is a promising direction to help in the non-technical survey process and increasing the productivity of the land release.
Technology of Forming Future Teachers’ Readiness for Pedagogical Improvisation

The article grounds the relevance of the special preparation of students of pedagogical universities for pedagogical improvisation based on the analysis of psychological and pedagogical literature and the ways in which future teachers acquire professional knowledge and skills.&#x0D; Having analyzed the content of the educational programs of psychology and pedagogy we proved that no purposeful training of future teachers for pedagogical improvisation has been organized. It caused the need for developing an appropriate technique.&#x0D; The purpose of the article is to develop a technique to form future teachers’ readiness for pedagogical improvisation in the process of studying psychological and pedagogical disciplines.&#x0D; Methodology. The research was conducted with the application of theoretical (analysis of psychological and pedagogical literature to define a conceptual-categorical apparatus; grounding the technique of forming future teachers’ readiness for pedagogical improvisation) and empirical methods (surveys, questionnaires, testing, observation, conversation, dialogue, methods of expert evaluation, self-assessment analysis of learning outcomes, content of educational programs of psychological and pedagogical disciplines).&#x0D; In the course of the research the following results were achieved: the essence of the concepts «readiness for pedagogical improvisation», «learning technique» was defined; structural components of readiness for pedagogical improvisation were singled out; surveys, interviews and questionnaires were conducted among the future teachers to identify their level of readiness for pedagogical improvisation. Moreover, the research contributed to the development of a technique to form future teachers’ readiness for pedagogical improvisation, which led to the gradual formation of students’ skills to improvise.&#x0D; The conducted research allowed us to draw the conclusion that the readiness for pedagogical improvisation is a stable individual quality that integrates the values, knowledge, skills, which should be formed in several stages. Motivational-targeted stage was aimed at stimulating the development of positive motivation for improvisation; content and procedural stage that provided for acquiring the necessary knowledge and skills of pedagogical improvisation; evaluation-reflexive stage involving evaluation and correction of personal and professional development, content, forms and methods of training.
Disfluency Correction using Unsupervised and Semi-supervised Learning

Spoken language is different from the written language in its style and structure.Disfluencies that appear in transcriptions from speech recognition systems generally hamper the performance of downstream NLP tasks.Thus, a disfluency correction system that converts disfluent to fluent text is of great value.This paper introduces a disfluency correction model that translates disfluent to fluent text by drawing inspiration from recent encoder-decoder unsupervised style-transfer models for text.We also show considerable benefits in performance when utilizing a small sample of 500 parallel disfluent-fluent sentences in a semisupervised way.Our unsupervised approach achieves a BLEU score of 79.39 on the Switchboard corpus test set, with further improvement to a BLEU score of 85.28 with semisupervision.Both are comparable to two competitive fully-supervised models.
The Impact of Measurement Scale on the Univariate Statistics of K, Th, and U in the Earth Crust

Abstract The univariate statistics of Potassium (K), thorium (Th), and uranium (U) concentrations, in the Earth’s oceanic and continental crust are examined by different techniques. The frequency distributions of the concentrations of these elements in the oceanic crust are derived from a global catalog of mid‐ocean ridge basalts. Their frequency distributions of concentrations in the continental crust are illustrated by the North Pilbara Craton, and the West Africa Craton. For these two cratons, the distributions of K, Th, and U derived from geochemical analyses of several thousand whole rock samples differ significantly from those derived from airborne radiometric surveys. The distributions from airborne surveys tends to be more symmetric with smaller standard deviations than the right‐skewed distributions inferred from whole rock geochemical analyses. Hypothetic causes of these differences include (a) bias in rock sampling or in airborne surveys, (b) the differences between the chemistry of superficial material and rocks, and (c) the differences in scales of measurements. The scale factor, viewed as consequence of the central limit theorem applied to K, Th, and U concentrations, appears to account for most of the observed differences in the distributions of K, Th, and U. It suggests that the three scales of auto‐correlation of K, Th, and U concentrations are of the same order of magnitude as the resolution of the airborne radiometric surveys (50–200 m). Concentrations of K, Th, and U are therefore generally heterogenous at smaller scales.
Optimizing a Supervised Classifier for a Difficult Language Identification Problem.

This paper describes the system developed by the Laboratoire d’analyse statistique des textes for the Dravidian Language Identification (DLI) shared task of VarDial 2021. This task is particularly difficult because the materials consists of short YouTube comments, written in Roman script, from three closely related Dravidian languages, and a fourth category consisting of several other languages in varying proportions, all mixed with English. The proposed system is made up of a logistic regression model which uses as only features n-grams of characters with a maximum length of 5. After its optimization both in terms of the feature weighting and the classifier parameters, it ranked first in the challenge. The additional analyses carried out underline the importance of optimization, especially when the measure of effectiveness is the Macro-F1.
INFORMATIZATION AND DIGITALIZATION: COMPLEXITY APPROACH TO ASSESSING THE TRANSFORMATION OF EDUCATION

The authors try to comprehend the fea­tures of promising areas of the processes of informa­tization and digital transformation of education as a complex phenomenon. Using the theoretical con­cepts of complexity created in Russian and foreign philosophical thought, the authors prove the need to develop integrative approaches to assessing the transformations that take place in the process of in­formational changes in the sphere of education. The comparative analysis of existing models of the «сom­plex vision» of education in the process of its digital transformation and a critical review of its content revealed the advantages and disadvantages of using the principles of epistemology of complexity in the study of education. Education is a sphere of cogni­tive complication, where the development of creative and adaptive competencies necessary for success­ful implementation of activities in the conditions of the high-rate variability of the social environment takes place. Systematic update of the results and content of education, methods and organizational forms of academic work requires understanding of the complexity of this complicated process, since the changes in each of the components are interlinked, mutually complement and support each other. Orien­tation to the procedural rather than the substantial nature of the phenomena of the educational sphere should be ascribed to the advantages of the com­plexity approach to studying transformations of the educational sphere. Understanding the complexity of education involves addressing the organizational specifics of educational processes, the development of educational environment and pedagogical tech­nologies. The study of the transformation of educa­tion as a complex phenomenon involves the organi­zation of interdisciplinary interaction aimed at the synthesis and interaction of diverse forms of reflec­tion and study of education, which involves the use of transdisciplinarity principles.
Belief eXtended Classifier System: A New Approach for Dealing with Uncertainty in Sleep Stages Classification

Sleep is an essential element that affects directly our daily life thus sleep analysis is a very interesting field. Sleep stages classification represents the base of all sleep analysis activities. However, the classification of sleep stages suffers from high uncertainty between its stages which could lead to degrade the performance of classification methods. To cope partially with this issue, we propose a new approach that deals with uncertainty especially with imprecision. Our method integrates the belief function theory in eXtended Classifier System (XCS). The proposed approach shows a good performance ability comparing to classical methods.
QADI: Arabic Dialect Identification in the Wild

Proper dialect identification is important for a variety of Arabic NLP applications. In this paper, we present a method for rapidly constructing a tweet dataset containing a wide range of country-level Arabic dialects —covering 18 different countries in the Middle East and North Africa region. Our method relies on applying multiple filters to identify users who belong to different countries based on their account descriptions and to eliminate tweets that either write mainly in Modern Standard Arabic or mostly use vulgar language. The resultant dataset contains 540k tweets from 2,525 users who are evenly distributed across 18 Arab countries. Using intrinsic evaluation, we show that the labels of a set of randomly selected tweets are 91.5% accurate. For extrinsic evaluation, we are able to build effective country level dialect identification on tweets with a macro-averaged F1-score of 60.6% across 18 classes.