Reviewerly/exHarmony huggingface dataset:
“Yo me quedo en casa”: Una experiencia de aprendizaje-servicio online en el Grado de Educación Infantil para ofrecer propuestas educativas a las familias

This paper discusses "Yo me quedo en casa" ('I am staying home'), an online servicelearning experience that was developed by final-year students of the Early Childhood Education degree at the University of Cadiz.The experience was mediated by technology in times of a pandemic, a situation that has forced us to reinvent ourselves and thus has offered us the opportunity for new learning.The service-learning project was designed as a response to the social needs shown by the different families in various contexts and social networks.The students who participated in the project voiced their level of satisfaction with the experience and highlighted what they had learned throughout the project.
Coordination Boundary Identification without Labeled Data for Compound Terms Disambiguation

We propose a simple method for nominal coordination boundary identification.As the main strength of our method, it can identify the coordination boundaries without training on labeled data, and can be applied even if coordination structure annotations are not available.Our system employs pre-trained word embeddings to measure the similarities of words and detects the span of coordination, assuming that conjuncts share syntactic and semantic similarities.We demonstrate that our method yields good results in identifying coordinated noun phrases in the GENIA corpus and is comparable to a recent supervised method for the case when the coordinator conjoins simple noun phrases.
Domain-Specific Sentiment Lexicons Induced from Labeled Documents

Sentiment analysis is an area of substantial relevance both in industry and in academia, including for instance in social studies.Although supervised learning algorithms have advanced considerably in recent years, in many settings it remains more practical to apply an unsupervised technique.The latter are oftentimes based on sentiment lexicons.However, existing sentiment lexicons reflect an abstract notion of polarity and do not do justice to the substantial differences of word polarities between different domains.In this work, we draw on a collection of domainspecific data to induce a set of 24 domain-specific sentiment lexicons.We rely on initial linear models to induce initial word intensity scores, and then train new deep models based on word vector representations to overcome the scarcity of the original seed data.Our analysis shows substantial differences between domains, which make domain-specific sentiment lexicons a promising form of lexical resource in downstream tasks, and the predicted lexicons indeed perform effectively on tasks such as review classification and cross-lingual word sentiment prediction.
STEMMING DOKUMEN TEKS BAHASA BALI DENGAN METODE RULE BASE APPROACH

The Balinese are one of the ethnic groups of Indonesia, the majority of which are on the island of Bali, the language used is Balinese with three levels of sor-singgih (tigang soroh) guidelines, namely Basa Kasar, Basa Madia and Basa Alus. Balinese language also has the additions of pangater, seselan and pangiring. To facilitate the search for basic words in Balinese, a stemming process is needed. Stemming is the process of mapping and decomposing the form of a word into its basic form. The stemming process is very important in the information retrieval system process. In this study, the Balinese stemming process used the Rule Base Approach method. The data used in this study are 376 basic words in Balinese. This study aims to design an appropriate stemming application for Balinese stemming. The initial stage in the Balinese stemming process is to carry out the input process, preprocessing, filtering, case folding and tokenization. Each word is subjected to a stemming process to remove the additions of pangater, seselan, and pangiring. The results of the study indicate that the Rule Base Approach method can be used to stem Balinese texts, this can be seen from the results of the accuracy reaching 77.82%. Of course, in testing there are still failures caused by overstemming errors resulting from the stemming process.
Unifying Input and Output Smoothing in Neural Machine Translation

Soft contextualized data augmentation is a recent method that replaces one-hot representation of words with soft posterior distributions of an external language model, smoothing the input of neural machine translation systems.Label smoothing is another effective method that penalizes over-confident model outputs by discounting some probability mass from the true target word, smoothing the output of neural machine translation systems.Having the benefit of updating all word vectors in each optimization step and better regularizing the models, the two smoothing methods are shown to bring significant improvements in translation performance.In this work, we study how to best combine the methods and stack the improvements.Specifically, we vary the prior distributions to smooth with, the hyperparameters that control the smoothing strength, and the token selection procedures.We conduct extensive experiments on small datasets, evaluate the recipes on larger datasets, and examine the implications when back-translation is further used.Our results confirm cumulative improvements when input and output smoothing are used in combination, giving up to +1.9 BLEU scores on standard machine translation tasks and reveal reasons why these smoothing methods should be preferred.
Using Formative and Summative Assessments in Data Mining to Predict Students‟ Final Grades

The study intends to devise an earlier and more accurate analytical means of predicting the class of degree a student will graduate with.This will help in decision making and the design of academic programs and curriculum development; also it will help in student guidance and counseling.Because the earlier we can predict the class of degree a student is likely to graduate with, the better it will be for the student to keep it up or improve on his performance and this will consequently assist in mitigating and minimizing the student dropout, attrition, or dismissal from school after wasting reasonable amount of time without acquiring the certificate. Academic data of some Communication & InformationTechnology students; such as year of admission, year of completion, individual grades obtained from the courses he/she offered at a 1 year diploma program, 1 year advance diploma, and the class of degree he obtains from a 1year top-up degree program was introduced to a Classification Data Mining algorithms to extract a pattern and a model for students' final grade prediction.The study's result shows that timely completion of the first two programs, a high score in computer architecture course, programming, network, and discrete mathematics courses are determining factors that can be used to predict students' final grades at graduation.
Ferryman at SemEval-2020 Task 12: BERT-Based Model with Advanced Improvement Methods for Multilingual Offensive Language Identification

Indiscriminately posting offensive remarks on social media may promote the occurrence of negative events such as violence, crime, and hatred.This paper examines different approaches and models for solving offensive tweet classification, which is a part of the OffensEval 2020 competition (Zampieri et al., 2020; Zampieri et al., 2019b).The dataset is Offensive Language Identification Dataset (OLID) (Zampieri et al., 2019a), which draws 14,200 annotated English Tweet comments (Rosenthal et al., 2020).The main challenge of data preprocessing is the unbalanced class distribution, abbreviation, and emoji.To overcome these issues, methods such as hashtag segmentation, abbreviation replacement, and emoji replacement have been adopted for data preprocessing approaches.The main task can be divided into three sub-tasks, and are solved by Term Frequency-Inverse Document Frequency(TF-IDF) vectorizer, Bidirectional Encoder Representation from Transformer (BERT), and Multi-dropout respectively.Meanwhile, we applied different learning rates for different languages and tasks based on BERT and non-BERTmodels in order to obtain better results.Our team Ferryman ranked the 18th, 8th, and 21st with F1-score of 0.91152 on the English Sub-task A, Sub-task B, and Sub-task C, respectively.Furthermore, our team also ranked in the top 20 on the Sub-task A of other languages(C ¸öltekin,
Experience in Developing Imaginative and Intonational Competencies in Future Music Teachers

The content analysis of students’ training shows that the traditional approach does not use the possibilities of the integrity of choral conducting courses and the gradual and eidetic organization of such training. The research aims to develop, scientifically justify and experimentally verify the effectiveness of the methodology for developing imaginative and intonational skills in students while studying choral conducting courses. Empirical methods are pedagogical observation, talks, surveys, interviews, project methods, tests; research and search methods: experimental and creative tasks, reflexive methods, interactive methods, heuristic methods for modelling imaginative and intonational skills (creative imagination, art views), methods of statistical data processing. A relevant methodology for developing imaginative and intonational competencies in students based on the organizational and methodical model has been designed, verified and implemented in practice. The main approaches, principles, functions and teaching methods have been identified. Pedagogical conditions for effective developing imaginative and intonational competencies in students employing choral conducting courses have been outlined. The obtained results have proved the originality, effectiveness and possibility of implementing the designed methodology into the practice, as well as the practical expediency and promptness of the proposed stage-by-stage methodology. Thus, 22% of control group students and 33% of experimental group students have achieved a high level, with the difference of &lt;11%. Respectively, 44% of control group students and 48% of experimental group students have achieved an average level, with the difference of &lt;4%. Finally, 34% of control group students and 19% of experimental group students are at a low level.
Social Communication In The Information Society

This article is devoted to issues related to the processes of social communication that we are trying to comprehend.The relevance of the study is due to the fact that changes in the modern information society are happening rapidly.At the present stage of social development, new features and methods of communication are appearing.As a result of social interaction, an information field, a space of signs and meanings are created.Social interaction generates network interaction.The role of communication in the development of network society is growing, forming the basis for the semantic construction of the community.In modern social sciences, the network approach is becoming increasingly important, allowing us to conclude that the communicative strategy in modern information society can reduce the degree of uncertainty that exists in the world.The information society is formed by creating and disseminating those values and meanings that depend on the content existing in multimedia communication networks.Thanks to the restructuring of meanings and values, global changes are taking place in the social structure.These changes are the result of the features of the course of network processes: the multiplicity and alternative ways of moving information, multichannel nature, and high density of information flows.
DEVELOPING THE ADAPTIVE KNOWLEDGE MANAGEMENT IN CONTEXT OF ENGINEERING COMPANY PROJECT ACTIVITIES

The concept of Adaptive Knowledge Management is an approach essentially increasing the effectiveness of project participants in their processes that are unpredictable or initialized in advance by unknown events. The research deals with developing the strategy of proactive formation of project team competencies within an industrial enterprise. The most important component of this strategy is the formation of “knowledge coordinators” who are responsible for knowledge management and the creation of training and knowledge sharing regulations. On the basis of this strategy the method of determining the adaptation potential of team members is developed, which is based on the assessment of project task implementation of different complexity, taking into account the identifiers of continuous knowledge growth, which allows predicting the possibilities of increasing the competence level and determining the sustainability of project team members’ knowledge. This method can be used to form a project team considering the accumulated experience and knowledge of its participants.
2020 Security Review: A Year That Shook IT

Abstract Reviewing IT security incidents in 2020, Patrick O'Connor MBCS CISSP CEH, finds that while society may have slowed to a crawl in the face of Covid-19 the cyber security world is more active than ever.
DeftPunk at SemEval-2020 Task 6: Using RNN-ensemble for the Sentence Classification.

This paper describes participation in DeftEval 2020 (part of SemEval sharing task competition), and is focused on the sentence classification. Our approach to the task was to create an ensemble of several RNNs combined with fasttext and ELMo embeddings. Results show that various types of models in an ensemble give a performance boost in comparison to standard models. Our model achieved F1-score of 78% for a positive class on the DeftEval dataset.
Taking a Cue From the Human

Human beings find the process of narrative sequencing in written texts and moving imagery a relatively simple task. Key to the success of this activity is establishing coherence by using critical cues to identify key characters, objects, actions and locations as they contribute to plot development.&#x0D; In the drive to make audiovisual media more widely accessible (through audio description), and media archives more searchable (through content description), computer vision experts strive to automate video captioning in order to supplement human description activities. Existing models for automating video descriptions employ deep convolutional neural networks for encoding visual material and feature extraction (Krizhevsky, Sutskever, &amp; Hinton, 2012; Szegedy et al., 2015; He, Zhang, Ren, &amp; Sun, 2016). Recurrent neural networks decode the visual encodings and supply a sentence that describes the moving images in a manner mimicking human performance. However, these descriptions are currently “blind” to narrative coherence.&#x0D; Our study examines the human approach to narrative sequencing and coherence creation using the MeMAD [Methods for Managing Audiovisual Data: Combining Automatic Efficiency with Human Accuracy] film corpus involving five-hundred extracts chosen as stand-alone narrative arcs. We examine character recognition, object detection and temporal continuity as indicators of coherence, using linguistic analysis and qualitative assessments to inform the development of more narratively sophisticated computer models in the future.
Human-Avatar Interaction in Virtual Environment to Assess and Train Sensorimotor: Application to the Slap Shot in Hockey

Here we present the conception, implementation and application of a virtual environment simulator dedicated to the assessment and training of slap shot performance in hockey. The simulator is based on human-avatar interaction, namely a real shooter and a virtual goalkeeper whose behavior is dependent on that of the shooter. The synthesis of the virtual goalkeeper relied on a high-quality model and realistic, motion-captured movements. A regression model based on Kriging was used to predict in real-time the shooter's behavior in order to trigger the blocking moves of the virtual goalkeeper at the right time.&#x0D; Our model provided accurate predicted values as well as an estimation of the reliability of these values, which allowed us to optimize the behavioral animation of the virtual goalkeeper. We then ran a validation experiment testing the effectiveness of our simulator. The simulator proved very useful both to assess the initial performance of the players and to train and improve this performance. In particular, training as little as 3 hours with our simulator gave rise to substantial and significant improvements (up to 22 percent) of the redirection threshold, i.e., the minimum time required to successfully redirect a shot during movement execution when the outcome is imperiled. Importantly, in comparison with ‘classical’ training methods, our simulator better triggers (precisely and timely) the movements of the goalkeeper based on the movements of the shooter.
A Unifying Theory of Transition-based and Sequence Labeling Parsing

We define a mapping from transition-based parsing algorithms that read sentences from left to right to sequence labeling encodings of syntactic trees.This not only establishes a theoretical relation between transition-based parsing and sequence-labeling parsing, but also provides a method to obtain new encodings for fast and simple sequence labeling parsing from the many existing transition-based parsers for different formalisms.Applying it to dependency parsing, we implement sequence labeling versions of four algorithms, showing that they are learnable and obtain comparable performance to existing encodings.
Pemanfaatan TIK Sebagai Media Pembelajaran dan Sumber Belajar oleh Guru TIK

Information and computer technology (ICT) is currently being used in all fields, including education. One of the uses of ICT in the field of education is for learning media and learning resources that can be used by teachers. This study aims to determine the extent to which the competence or ability of teachers, especially in ICT subjects, in utilizing ICT both as a medium of learning and a learning resource. The use of ICT is carried out to make it easier for teachers to implement the learning process. The research method used is descriptive with a survey form. The data collection tools used were questionnaires and interviews. The data analysis technique used is quantitative-descriptive to process questionnaire data and qualitative-descriptive to process interview data. Based on the data obtained, the use of ICT as a Learning Media and Learning Resources by Equivalent High School teachers in East Bunguran District, Natuna Regency is at a percentage above 80% with a high category. This shows that the teacher's ability to use ICT is very good. The learning media is majority often used are presentation processing applications and projectors. Meanwhile, the most widely used learning resource is the Internet.
Analisa Morfologi Dilasi untuk Perbaikan Kualitas Citra Deteksi Tepi pada Pola Batik Menggunakan Operator Prewitt dan Laplacian of Gaussian

The results of the edge detection process using several operators are not yet optimal. Therefore we need a method to improve the quality of edge detection images, the method used in this study is morphology dilation. The results of testing the improvement of image quality using 10 batik patterns, resulting in an accuracy level on Laplacian of Gaussian operators is 80% and for Prewitt operators is 60%. In the process of improving the edge detection quality, Morphology Dilation can connect broken edges using structuring elements. therefore it can improve the quality of edge images.
FEATURE BASED OPINION CLASSIFICATION (FBOC) OF CUSTOMER REVIEWS

In the era of Web 2.0 e-commerce website plays a very important role in our everyday life.If the consumer wants to purchase a product from the e-commerce website then reviews plays a very important role in decision making about a product.The reviews are unstructured reviews because there is no fixed format for customer to write the reviews.Because of these huge quantities of unstructured reviews there is a need to develop a technique which extract the features from the reviews and gives the opinion about the features.In this proposed technique i.e Features Based Opinion Classification (FBOC), identification of features is done, then after the identification, feature is classified as according to their opinion whether positive, negative and neutral using two methods namely AFINN and VADER (Valence Aware Dictionary for sEntiment Reasoning).Summary of feature is generated showing total number of positive, negative and neutral opinion of features and visualized in a form such that it can be easily understandable by end user.
Investigation of Various Optimization Algorithms in Tuning Fuzzy Logic-based Trajectory Tracking Control of Quadcopter

The merging of the abilities of controllers and optimization algorithms to have a better optimized controller has shown a great enhancement in terms of all performance aspects for controlling complex systems. The purpose of this research paper is to tune fuzzy logic controller (FLC) for quadcopter indoor trajectory tracking control by various optimization techniques to create a hybrid FLC and highlight the performance of each optimization algorithm, giving a comparison between them to find out the best hybrid controller. Three of the most spreading algorithms in optimization field are tuning FLC, and they are genetic algorithm, particle swarm optimization, and artificial bee colony. All optimization algorithms are subjected to the same goal, which is finding the optimal tuning for FLC for trajectory tracking control, and the same constraints as laptop specifications, number of population, stop criteria, and objective function. Generally, the comparison between the hybrid controllers and manually tuned FLC shows that the performance is dramatically boosted, and specifically, based on the performance measurements taken, fuzzy logic-based particle swarm optimization controller is the best hybrid controller for quadcopter trajectory tracking control.
1292: A Multivariate Machine Learning Algorithm for Occult Hemorrhage in a Porcine Model

Klein, Samuel; Rashedi, Navid; Sun, Yifei; Murphy, Ethan; Hamlin, Alexandra; Minichiello, Joseph; Anderson, Justin; Lindqwister, Alexander; Moodie, Karen; Borza, Victor; Wanken, Zachary; Vaze, Vikrant; Halter, Ryan; Elliott, Jonathan; Paradis, Norman Author Information
Exact and Local Search Methods for Solving Travelling Salesman Problem with Practical Application

This paper investigates some exact and local search methods to solve the traveling salesman problem. The Branch and Bound technique (BABT) is proposed, as an exact method, with two models. In addition, the classical Genetic Algorithm (GA) and Simulated Annealing (SA) are discussed and applied as local search methods. To improve the performance of GA we propose two kinds of improvements for GA; the first is called improved GA (IGA) and the second is Hybrid GA (HGA).&#x0D; The IGA gives best results than GA and SA, while the HGA is the best local search method for all within a reasonable time for 5 â‰¤ n â‰¤ 2000, where n is the number of visited cities. An effective method of reducing the size of the TSP matrix was proposed with the existence of successive rules. The problem of the total cost of Iraqi cities was also discussed and solved by some methods in addition to local search methods to obtain the optimal solution.
Simple Data Augmentation for Multilingual NLU in Task Oriented Dialogue Systems

Data augmentation has shown potential in alleviating data scarcity for Natural Language Understanding (e.g. slot filling and intent classification) in task-oriented dialogue systems. As prior work has been mostly experimented on English datasets, we focus on five different languages, and consider a setting where limited data are available. We investigate the effectiveness of non-gradient based augmentation methods, involving simple text span substitutions and syntactic manipulations. Our experiments show that (i) augmentation is effective in all cases, particularly for slot filling; and (ii) it is beneficial for a joint intent-slot model based on multilingual BERT, both for limited data settings and when full training data is used.