Reviewerly/exHarmony huggingface dataset:
SCRAPING NEWS SITES AND SOCIAL NETWORKS FOR PREJUDICE TERM ANALYSIS

Computer-Mediated Communication (CMC) has paved the way for new patterns of linguistic aggravation.Hidden behind the screen, anyone can comment on any other person's opinion using an offensive or injurious tone.Besides, types of prejudice such as homophobia, sexism, racism, xenophobia, anticlericalism, body/addiction shaming, among others, are easily found nowadays in social networks and other forms of interactive Web sites potentiated by Web 2.0.This increasing violence deserves further investigation from different academic perspectives, among which Sociolinguistics stands out.This paper is concerned with the design and development of a set of computer-based tools to collect articles and posts with the respective comment threads that can be used as sources to extract prejudice terms and allow different analyses to be conducted.These prejudice terms were devised using a sociolinguistic variable stratification approach.We will focus on the filters used to extract the relevant fields from the Web pages collected, and on the converters used to adapt formats to obtain a common format for information representation.We will also introduce the statistical analysis processor that explores the extracted data, in that format, to output a set of indicators.
A Comparative Corpus Analysis of PP Ordering in English and Chinese

We present a comparative analysis of PP ordering in English and (Mandarin) Chinese, two languages with distinct typological word order characteristics.Previous work on PP orderings have mainly focused on English using data of relatively small size.Here we leverage corpora of much larger scale with straightforward annotations.We use the Penn Treebank for English, which includes three corpora that cover both written and spoken domains, and the Chinese Penn Treebank for Chinese.We explore the individual effect of dependency length, the argument status of the PP (argument or adjunct) and the traditional adverbial ordering rule, Manner before Place before Time.In addition, we evaluate the predictive power of dependency length and argument status with weights estimated from logistic regression models.We show that while dependency length plays a strong role across genre for English, it only exerts a mild effect in Chinese.On the other hand, the argument status of the PP has a pronounced role in both languages, that is, there exists a strong tendency for the argument-like PP to appear closer to the head verb than the adjunct-like PP.Our work contributes empirically to the long-standing proposal in linguistic typology that crosslinguistic word ordering preference is driven by cooperating and competing principles.
The Effectiveness of Mobile Learning Based Android in Learning English Vocabularies

&lt;p class="0abstract"&gt;This study aims to determine the effectiveness of mobile learning based android in learning vocabulary at the seventh-grade students of SMP Batara Gowa. This type of research is pre-experimental. The research design used was pre-experimental design, namely one group pre-test post-test design. The populations in this study were all seventh-grade students of SMP Batara Gowa, and the sample was class VII.1 as the experimental class which was chosen by using purposive sampling. The results of the data analysis show that the mean score of post-test (70.55) was higher than the mean score of pre-test (34.65). The result of the t-test was 30.117 and t-table 2.093. It could be indicated that the null hypothesis (H0) was rejected and the alternative hypothesis (H1) was accepted. Based on the effectiveness criteria, generally using mobile learning based android was effective in learning vocabulary at the seventh-grade students of SMP Batara Gowa.&lt;/p&gt;
Bullying y cyberbullying escolar en niños y jóvenes adolescentes: un estudio de caso

The objective of the research was to identify the incidence of bullying and cyberbullying in 983 students at age 10 to 15, from the sixth to the tenth grade of EGB, belonging to five educational institutions in the city of Cuenca, for which the variables of bullying and cyberbullying were crossed with age, sex and educational level of the surveyed students, visualizing the involvement of the protagonists, the typology and its manifestations. The methodology used was quantitative, with relational descriptive scope, making use of two measurement instruments: The European Bullying Intervention Project Questionnaire (EBIPQ) and the European Cyberbullying Intervention Project Questionnaire (ECIPQ), which allowed to identify the incidence and characteristics of bullying and cyberbullying. There was evidence of 28.3% of students’ victim of bullying, of them 20.7% were victim of bullying, 4.3% cyber victim and 3.4% victim of both bullying and cyberbullying. In bullying the main typology was direct verbal, while in cyberbullying its main manifestation were electronic insults and exclusion. A higher level of involvement of males was seen as victims and victimized aggressors. In terms of age, the 12- to 15-year-old students belonging respectively to the eighth, ninth and tenth grade presented a higher level of involvement as victimized cyber attackers. In conclusion, there is a greater presence of traditional school bullying and a lower incidence of cyberbullying, however, a direct relationship between the two forms of bullying seems to be present.
Use Case Extraction through Knowledge Acquisition

Most challenges in requirements analysis and use case extraction are related to the correct comprehension of clients' core processes and activities, as well as their needs. This information is usually available in documents, such as the business vision, written in natural language. This kind of language may lead to interpretation bias and information loss, thus causing project delays and escalation of costs. In order to overcome natural language interpretation challenges in Requirements Engineering, we propose the use of Verbka, a knowledge acquisition process based on verbal semantics, as a complement to traditional requirements analysis. This process extracts the causal relationships among the actors mentioned in the business vision document. We used this process as a use case pre-modeling tool, aiming to minimize subjectivity in the identification of actors and use cases. We tested Verbka using a business vision from a classical textbook. The results show that this process is able to obtain a list containing all requirements defined by the client, all actors involved in the business vision, and how they interact with each other. This process is systematic and provides a textual and visual representation of user requirements and use cases consistently. Its application reduced interpretation bias, thus allowing a more detailed and structured requirements analysis.
Selecting Artificially-Generated Sentences for Fine-Tuning Neural Machine Translation

Neural Machine Translation (NMT) models tend to achieve best performance when larger sets of parallel sentences are provided for training.For this reason, augmenting the training set with artificially-generated sentence pairs can boost performance.Nonetheless, the performance can also be improved with a small number of sentences if they are in the same domain as the test set.Accordingly, we want to explore the use of artificially-generated sentences along with data-selection algorithms to improve Germanto-English NMT models trained solely with authentic data.In this work, we show how artificiallygenerated sentences can be more beneficial than authentic pairs, and demonstrate their advantages when used in combination with dataselection algorithms.
Mining Process in Cryptocurrency Using Blockchain Technology: Bitcoin as a Case Study

A new digital currency in the form virtual cash, known as Bitcoin was acquainted with the world by Satoshi in 2009. Bitcoin is the most notable computerized money. It is based on peer-to-peer distributed system with no central authority controlling it. It is based on the technique known as Blockchain, a kind of peer-to-peer ledger system containing the record of all transactions. All set of transactions are logged in the blocks. The new transactions generated by the users are broadcasted using signed blocks to verify over the Bitcoin network. The transaction is verified by mining process and added to the Blockchain. In order to add the transaction into the block, the miner solves the cryptographic puzzle. The miners are rewarded for their work. This paper is focused on mining strategy and reward mechanism of the Bitcoin. The challenges in mining process are identified and probable solutions are presented.
Control System Mobile Robots with Modular Architecture as a Multi-Agent System with a Hierarchical Topology

An approach to solving an urgent scientific problem is consideredthe development of principles and methods for creating control systems of mobile robots with a modular architecture based on the theory of multi-agent systems.It is proposed to use the hierarchical topology of the robot control system.Modular architecture assumes that each mechatronic module has its own control system implemented on relatively cheap microcontrollers.The modular solution allows for the rapid reconfiguration of robotic systems and significantly reduce the cost of manufacturing robots by reducing their variety.Inter-modules information and control interaction is proposed to be organized on a hierarchical basisa group of full-featured mechatronic submodules, solving the same type of tasks and working together, are controlled by a mechatronic module of a higher level.This approach allows increasing the system performance due to the distribution of computational load between computing devices of individual mechatronic modules and submodules.The proposed control system can be represented as a multi-agent system consisting of full-featured mechatronic and/or electronic modulesagents that interact with each other to achieve a specific goal.In this structure, an analogy is seen with such area of scientific research as a multi-agent systems.
Pairwise multi-layer nets for learning distributed representation of multi-field categorical data

This paper presents a method of pairwise multi-layer networks for multi-field categorical data, which widely exists with various applications such as web search, recommender systems, social link prediction, and computational advertising. The success of non-linear models, e.g., factorization machines, boosted trees, has proved the potential of exploring the interactions among inter-field discrete categories. Inspired by Word2Vec, the distributed representation for natural language, we propose a PMLN (Pairwise Multi-Layer Nets) model to learn the distributed representation for multi-field categorical data. In PMLN, a low-dimensional continuous vector is automatically learned for each category in each field. The interactions among inter-field categories are explored by different neural gates and the most informative ones are selected by pooling layers. Such combined categories can be further explored by performing more gate interactions with another category and then selected by additional pooling operations. In our experiments, with the exploration of the interactions between pairwise categories over layers, the model outperforms state-of-the-art models in a supervised learning task, i.e., ad click prediction, while capturing the most significant interactions from the data in an unsupervised fashion.
A Corpus Based Approach to Build Arabic Sentiment Lexicon

Sentiment analysis is an application of artificial intelligence that determines the sentiment associated sentiment with a piece of text.It provides an easy alternative to a brand or company to receive customers' opinions about its products through user generated contents such as social media posts.Training a machine learning model for sentiment analysis requires the availability of resources such as labeled corpora and sentiment lexicons.While such resources are easily available for English, it is hard to find them for other languages such as Arabic.The aim of this research is to build an Arabic sentiment lexicon using a corpus-based approach.Sentiment scores were propagated from a small, manually labeled, seed list to other terms in a term co-occurrence graph.To achieve this, we proposed a graph propagation algorithm and compared different similarity measures.The lexicon was evaluated using a manually annotated list of terms.The use of similarity measures depends on the fact that the words that are appearing in the same context will have similar polarity.The main contribution of the work comes from the empirical evaluation of different similarity to assign the best sentiment scores to terms in the co-occurrence graph.
Cognitive Level of Characters in the Indonesian Novel As a Source of Learning

This qualitative study aims at finding the level of cognitive of the characters in the Indonesian novel. The data in this study were collected through intensive reading based on Maryamah Karpov. The content is the characterizations belongs to the level of cognitive including memorizing, understanding, applying, analyzing, assessing, and creating. The process of data analysis is categorized into three stages, namely data reduction, data presentation, and conclusion. The result shows that the characterization of novel is identified as the level of cognitive of understanding, applying, analyzing, evaluating, and creating. In fact, the characterization also fills each indicator of cognitive level. Novel Maryamah Karpov is at a level of judging (C5) and creating (C6). The novel is classified into a high level of cognitive and is suitable as a source of learning in high schools. The drawing of recommendation based on this study is that teachers need to study the level of cognitive of any novel as a learning resource because the harmony between the level of cognitive and the level of intelligence of the students will have a significant effect.
Learning over categorical data using counting features

Input data for many machine learning applications are often categorical and contain multiple fields. A common feature representation for such categorical data is one-hot encoding, which expresses data instances as high-dimensional sparse binary vectors. Given this encoding machine learning models, such as logistic regression or boosted trees, are trained. However, the following problems occur when dealing with large-scale data sets: (i) The binary feature space is sparse yet extremely large, which can require a large amount of computational resources. (ii) Models based on such a feature representation will typically need to be re-trained in order to keep up-to-date with any changes in the data distribution. (iii) The one-hot feature representation provides little generalisation ability. In this paper, we propose counting features, a novel statistics-based feature engineering paradigm, to address the above problems. Mathematically, we show a deterministic relationship between the optimal regression parameters of counting features and one-hot binary features. Then, in the context of click-through rate estimation in online advertising, we demonstrate that counting features indeed bring better generalisation ability. Our experiments on real-world large-scale datasets demonstrate that, despite their compressed nature, the proposed counting feature engineering outperforms the one-hot binary encoded features in various cases such as cold start training and cross-campaigns training.
Influence of water spraying on an oscillating channel

The influence of surface hydration on the fluid–structure instability underlying vocal folds auto-oscillation during voiced speech sound production is an open research question. In this work the influence of homogeneous water spraying on an oscillating channel is investigated experimentally using several vocal folds replicas. Changes to glottal flow features are systematically quantified for a rigid replica with forced oscillation. Changes to auto-oscillation features are systematically quantified by analyzing the pressure measured upstream from deformable replicas. During auto-oscillation it is observed for increasing water volume that the first harmonic frequency decreases, its amplitude increases, cycle-to-cycle as well as overall fluctuations increase and the closing-opening asymmetry changes. Nevertheless, the magnitude of these effects differs between deformable replicas so that further systematic investigation is needed to quantify observations as well as to explore underlying mechanisms. Flow tendencies observed on all replicas support that water spraying affects the glottal flow rather then structural properties. This is an important finding for future modeling of the effect of water spraying on the fluid–structure interaction.
Interactive Data and Information Visualization: Unpacking its Characteristics and Influencing Aspects on Decision-making

Background: Interactive data and information visualization (IDIV) enhances information presentations by providing users with multiple visual representations, active controls, and analytics. Users have greater control over IDIV presentations than standard presentations and as such IDIV becomes a more popular and relevant means of supporting data analytics (DA), as well as augmenting human intellect. Thus, IDIV enables provision of information in a format better suited to users' decision-making.
Identification of Trainees Enrollment Behavior and Course Selection Variables in Technical and Vocational Education Training (TVET) Program Using Education Data Mining

Producing skilled workforce according to industry required skills is quite challenging.Knowledge of trainee's enrollment behavior and trainee's course selection variables can help to address this issue.Prior knowledge of both can help to plan and target right geographic locations and right audience to produce industry required skilled workforce.Globally Technical and Vocational Education Training (TVET) is used to provide skilled workforce for the industry.TVET is an educational stream which focus learning through more practicing with less theory knowledge.In this article, we have analyzed TVET actual enrollment data of 2017 -2018 session from a TVET training provider organization of Punjab, Pakistan.The purpose of this analysis is to understand trainee's enrollment behavior and course selection variables which plays an important role in TVET course selection by the trainees.This enrollment behavior and course selection variables can be used to monitor and control industry required and produced skilled TVET workforce.We developed a framework which contain series of steps to perform this analysis to extract knowledge.We used educational data mining techniques of association, clustering and classification to extract knowledge.The analysis reveals that central Punjab youth is getting more TVET education as compare to south and north Punjab, Pakistan.Similarly, trainee's 'age group', 'qualification', 'gender', 'religion' and 'marital status' are potential variables which can play important role in TVET course selection.By controlling these variables and integrating TVET training provider institutes, funding agencies and industry, we can smartly produce TVET skilled workforce required for industry nationally and internationally.
Twitter Data Classification by Applying and Comparing Multiple Machine Learning Techniques

Having an average of five hundred million tweets sent out per day, twitter has become one of the largest platforms of data analysis for the researchers. Previously, various researches have been conducted on twitter data i.e., sentimental analysis. However, not much research has been done to classify the tweets in terms of categories so that tweets can be distributed as per user preferences. In this research we started by creating four broad categories: politics, sports, crime and natural. After that, we applied different machine learning techniques (Random Forest, K-Nearest Neighbors, Naive Bayes, Logistic Regression, Decision Tree and Support Vector Machine) to classify the twitter data. Finally, we compared the results in terms of sensitivity, specificity, precision, false positive rate and accuracy. We found that Support Vector Machine (SVM) produced the best results in terms of sensitivity, specificity, precision, false positive rate and accuracy. Hence, we concluded that a machine learning approach (Support Vector Machine) can certainly be used to classify twitter data.
A Similarity-Inclusive Link Prediction Based Recommender System Approach

Despite being a challenging research field with many unresolved problems, recommender systems are getting more popular in recent years. These systems rely on the personal preferences of users on items given in the form of ratings and return the preferable items based on choices of like-minded users. In this study, a graph-based recommender system using link prediction techniques incorporating similarity metrics is proposed. A graph-based recommender system that has ratings of users on items can be represented as a bipartite graph, where vertices correspond to users and items and edges to ratings. Recommendation generation in a bipartite graph is a link prediction problem. In current literature, modified link prediction approaches are used to distinguish between fundamental relational dualities of like vs. dislike and similar vs. dissimilar. However, the similarity relationship between users/items is mostly disregarded in the complex domain. The proposed model utilizes user-user and item-item cosine similarity value with the relational dualities in order to improve coverage and hits rate of the system by carefully incorporating similarities. On the standard MovieLens Hetrec and MovieLens datasets, the proposed similarity-inclusive link prediction method performed empirically well compared to other methods operating in the complex domain. The experimental results show that the proposed recommender system can be a plausible alternative to overcome the deficiencies in recommender systems.
‘Marsquakes’ reveal red planet’s hidden geology

NASA’s Mars InSight lander has detected more than 300 quakes and traced some back to their source. NASA’s Mars InSight lander has detected more than 300 quakes and traced some back to their source.
Authenticity in Discursive Practices of the Online Market for Second-Hand Luxury Clothing

Authenticity is a particularly sensitive and salient issue in the online market for second-hand luxury clothing, and it is still little explored in the field of consumption studies. In this study we sought to analyze how authenticity is represented in discursive practices of the Brazilian online market for second-hand luxury clothing. The corpus of the work consisted of data collected through interviews in five stores of the Brazilian online market of luxury second-hand clothing. The data were analyzed using the Critical Discourse Analysis (CDA), proposed by Fairclough (1992), articulated to the sociocultural perspective of consumption. From the discursive categories Subject, Interdiscursivity, Intertextuality, Transitivity System, and Appraisal System, we perceived that the process of legitimizing the stores and the representations of authenticity are overlapped and traversed significantly by historical, social, and cultural aspects. We conclude that insofar as it becomes difficult to ensure objective authenticity, an interpretative dimension emerges, elaborated from the influence of sociocultural factors that underlie the judgment on what is authentic luxury, which in the scenario investigated are indexes of expression of high luxury. In this case, the origins and trajectories that are recognized as references of elite distinction for Brazilian consumers are important elements for the interpretation of authenticity.