Reviewerly/exHarmony huggingface dataset:
Effects of frequency and idiomaticity on second language reading comprehension in children with English as an additional language

Vocabulary plays an important role in reading comprehension in both the L1 and the L2 (Murphy, 2018). In measuring vocabulary knowledge, however, researchers typically focus on mono-lexical units where vocabulary assessments tend not to take into account multi-word expressions which include phrasal verbs, collocations, and idioms. Omitting these multi-word lexical items can lead to an over-estimation of comprehension skills, particularly in reading. Indeed, adult learners of English comprehend texts containing a larger number of multi-word expressions less well compared to texts containing fewer of these expressions, even when the same words are used in each text (Martinez &amp; Murphy, 2011). To investigate whether children learning English as an additional language (EAL) face a similar challenge, two reading comprehension tests were administered to EAL and monolingual (non-EAL) English-speaking children in primary school. Both tests contained the same common words, but whereas in one test some of the words occurred in multi-word expressions, in the other test they did not. Reading comprehension was significantly reduced for both groups of children when multi-word expressions were included. Monolingual participants generally performed better than children with EAL on both tests further suggesting that children with EAL may face a particular disadvantage in English reading comprehension. These results are discussed within the context of the importance of developing rich vocabulary knowledge in all children, and especially emergent bilingual children, within primary school and beyond.
Generalized Hamiltonian to describe imperfections in ion-light interaction

We derive a general Hamiltonian that governs the interaction between an $N$-ion chain and an externally controlled laser field, where the ion motion is quantized and the laser field is considered beyond the plane-wave approximation. This general form not only explicitly includes terms that are used to drive ion-ion entanglement, but also a series of unwanted terms that can lead to quantum gate infidelity. We demonstrate the power of our expressivity of the general Hamiltonian by singling out the effect of axial mode heating and confirm this experimentally. We discuss pathways forward in furthering the trapped-ion quantum computational quality, guiding hardware design decisions.
Aircraft Detection System Based on Regions with Convolutional Neural Networks

Object detection in remote sensing imagery is an important topic of image processing researches.Detection of objects and regions from satellite imagery takes its place in various applications such as the detection of residential areas and agricultural lands, road lines, ships, as well as airport and hangar detections.As a more specific remote sensing imagery based object-detection process, a stationary aircraft detection system could serve as a model in some military applications.Such a model could serve the detection of stationary aircraft targets in airports.In the proposed study, a deep learning-based model detects the aircraft in the airports using the satellite images from Google Earth.The deep learning model uses the state of the art Regions with Convolutional Neural Network (RCNN).Firstly a built from scratch CNN design is used for the basic learning step of the system.Then, RCNN performs region detection, which anchors the stationary aircraft object bounding boxes.A large dataset containing aircraft images is preferred for training of CNN.To validate the system, satellite images captured from airports in Turkey are used.The results of the study show that the proposed model successfully operates aircraft detection with high-performance rates.While the classifier network structure, which constitutes the first step of the study, produces 98.4% test accuracy, the proposed aircraft detection framework has successfully performed the aircraft identification process by producing matched bounding boxes in the test images.
Project Hatemeter: helping NGOs and Social Science researchers to analyze and prevent anti-Muslim hate speech on social media

‘Hatemeter’ is a European project funded under the Rights, Equality and Citizenship Programme. The project aims to document, analyse and prevent hate speech on social media. It brings together researchers from several disciplines as well as NGOs in each of the three partner countries: Italy, France and Great Britain. Initially the researchers’ efforts focused specifically on anti-Muslim hate speech on Twitter. Following the recommendations of NGOs active on the field, the Hatemeter Platform was developed. It allows the automatic collection of large amounts of data on social media. Thanks to it, a user can: identify potential hate messages in real time, visualize the data collected through different types of graphs, and receive suggestions for counter-narratives writing. Used by Social Science researchers, it has proven to be an effective tool for exploring large amounts of data, identifying trends and then supporting more qualitative analytical approaches. For NGOs, it facilitates social media monitoring, stimulates and accelerates the training of operators who produce counter-narratives to fight hate speech.
Non-Native Arabic Learnersâ€™ Social Media Usage and Motivation Influencing Learning of Arabic Language in Malaysian Public Universities

This paper aims to clarify the concepts related to the use of social media usage and motivation and their effect on the learning of a new language. They have also investigated the relationship between these variables and determined the influence of motivation and SMU on the academic performance of the students. For this purpose, quantitative survey was employed by distributed questionnaires which adopted from Monachesi et al. (2008) data language technologies for lifelong learning along with the Motivated Strategies for Learning Questionnaires (MSLQ) amongst 317 Arabic learners to six selected universities. They carried out quantitative data analysis by using SPSS and SmartPLS. The finding reported that these learners SMU displayed a significant correlation with motivation. A variation in the results was noted according to the year of study for all the learners. Furthermore, the results indicated that self-efficacy showed a varying effect on the academic performance of the students. The researchers discussed the results related to the learning of a language and the educational practices which were implemented. The results of this study could benefit the instructors, faculty and the student population
Towards Memory-Efficient Streaming Processing with Counter-Cascading Sketching on FPGA

Obtaining item frequencies in data streams with limited space is a well-recognized and challenging problem in a wide range of applications. Sketch-based solutions have been widely used to address this challenge due to their ability to accurately record the data streams at a low memory cost. However, most sketches suffer from low memory utilization due to the adoption of a fixed counter size. Accordingly, in this work, we propose a counter-cascading scheduling algorithm to maximize the memory utilization of sketches without incurring any accuracy loss. In addition, we propose an FPGA-based system design that supports sketch parameter learning, counter-cascading record and online query. We implement our designs on Xilinx VCU118, and conduct evaluations on real-world traces, thereby demonstrating that our design can achieve higher accuracy with lower storage; the performance achieved is 10× ~ 20× better than that of state-of-the-art sketches.
CAMPI: Computer-Aided Metadata Generation for Photo archives Initiative

In the summer of 2020, CAMPI (the Computer-Aided Metadata Generation for Photoarchvies Initiative) assessed the use of computer vision in assisting in the arduous task of processing digital photograph collections. The prototype built to find duplicates and tag photos depicting similar scenes in Carnegie Mellon University Archives’ General Photography Collection was successful, proving such an interface would be both feasible and useful for cultural heritage institutions with large visual collections.This whitepaper reports on the background for this project, the specific tasks, methods, and results from our software prototype, and a summary of future directions and needs for both computer vision as well as interfaces for photo archive description. We also offer a high-level technical roadmap as well as specific implementation details and reference code for our prototype.
A Bayesian approach to restricted latent class models for scientifically structured clustering of multivariate binary outcomes

Abstract This paper presents a model‐based method for clustering multivariate binary observations that incorporates constraints consistent with the scientific context. The approach is motivated by the precision medicine problem of identifying autoimmune disease patient subsets or classes who may require different treatments. We start with a family of restricted latent class models or RLCMs. However, in the motivating example and many others like it, the unknown number of classes and the definition of classes using binary states are among the targets of inference. We use a Bayesian approach to RLCMs in order to use informative prior assumptions on the number and definitions of latent classes to be consistent with scientific knowledge so that the posterior distribution tends to concentrate on smaller numbers of clusters and sparser binary patterns. The paper derives a posterior sampling algorithm based on Markov chain Monte Carlo with split‐merge updates to efficiently explore the space of clustering allocations. Through simulations under the assumed model and realistic deviations from it, we demonstrate greater interpretability of results and superior finite‐sample clustering performance for our method compared to common alternatives. The methods are illustrated with an analysis of protein data to detect clusters representing autoantibody classes among scleroderma patients.
Machine Learning to predict tuberculosis in cattle from the state of Sao Paulo, Brazil

Abstract Tuberculosis is a well-known and worldwide spread zoonosis. In Brazil 1.594.787 cases were confirmed cases since 2001, where, in Sao Paulo state, 8.226 deaths were reported. This study aims to present steps related to the use of machine learning algorithms for predictive analysis for bovine tuberculosis. For this, an application was made based on data from farms in state of São Paulo, Brazil, of an epidemiological survey, using a specific questionnaire, carried out on farms (n = 1,743). Response variable was presented by apparent prevalence of positive properties for disease, and predictors by (k = 77) predictors related to type of farm, type of lactation, number of animals on property. Application was organized according to following steps: division of data in training (75%) and testing (25%), pre-processing of predictors, learning and model evaluation. In the learning step, algorithm for adjusting gradient boosted trees models was used. The hyperparameters of algorithms were optimized by 10-fold cross-validation, to select those corresponding to best models. Models showed an accuracy of 88.07%, with an error in learning process equal to 3%. In the test / model validation procedure (n = 436), an error in 12% estimate was observed. Five important predictors were daily milk production, number of cows, type of farm, bovine breed and slaughter of adult animals. Proportion of false positives among all individuals whose response of interest was observed was 2.06%, and proportion of false negatives among those with a response of absent interest was 9.86%. It is hoped that, with increase in trained surveillance to detect the disease and availability of data, it will be possible to develop predictive models of machine learning with potential to efficiently assist professionals in disease control and assist in education program in animal health Key messages Predictive analyzes in health: application for tuberculosis in cattle from the state of Sao Paulo, Brazil. An infectious disease and zoonosis important to the world that needs support to develop means to control and consequently eradicate it.
Formation of the Primary School Teachers’ Information Competency in Postgraduate Education

The paper investigates the problem of the information competence formation of primary school teachers in the postgraduate education. It reveals the essence of the concept of "information competency of primary school teachers". The aim of the article is to substantiate and experimentally test the methodology of the information competency formation of primary school teachers in the postgraduate education. 710 advanced training course participants of various institutes of Ukrainian postgraduate pedagogical education took part in the initial stage of the experiment; 106 primary school teachers of Zaporizhzhia Regional Institute of Postgraduate Pedagogical Education participated in the formative stage of the experiment. It was determined that the main criteria for the information competency formation of primary school teachers are the formation of motives for successful studying, cognitive interest in postgraduate education, the need for self-development and self-expression; the system of information knowledge, skills and abilities, mastery of information and communication technologies, focus on further self-development. The paper indicated the positive influence of the original methodology on the dynamics of the information competency constituents of the teachers from the experimental group: the number of teachers with high and middle levels increased by 12.6% and 21.1% under the motivational and value-based component; by 22.1% and 18.0% under the procedural and conceptual component; by 15.9% and 18.9% under the regulatory and reflexive component. This indicates the superiority of the original methodology over the existing one in the formation of information competency of primary school teachers in the postgraduate education.
Hyphenation as a compounding technique in English

Hyphenated compounds have largely been neglected in the studies of compounding, which have seldom analysed compounds in context. In this study, we argue that the hyphen use in compounds is strongly motivated. Hyphenation is used when words form a unit, which reduces the possibility of parsing them into separate units or other forms. The current study adopts a new perspective on contextual factors, namely, which part of speech (PoS) the compound as a whole belongs to and how people correctly parse a compound into a unit. This process can be observed and analysed by considering examples. This study therefore holds that hyphenation might have gradually become a compounding technique that differs from general compounding principles. To better understand hyphenated compounds and the motivation for using hyphenation, we conduct a quantitative investigation into their distribution frequency to explore how English hyphenated compounds have been used in over the last 200 years. Diachronic change in the frequency of the distribution for compounds has seldom been considered. This question is explored by using frequency data obtained from the three databases that contain hyphenated compounds. Diachronic analysis shows that the frequencies of tokens and types in hyphenated compounds have been increasing, and changes in both frequencies follow the S-curve model. Historical evidence shows that hyphenation in compounds, as an orthographic form, does not seem to disappear easily. Familiarity and economy, as suggested in the cognitive studies of compounding, cannot adequately explain this phenomenon. The three databases that we used provide cross-verification that suggests that hyphenation has evolved into a compounding technique. Language users probably unconsciously take advantage of the discriminative learning model to remind themselves that these combinations should be parsed differently. Thus the hyphenation compounding technique facilitates communication efficiency. Overall, this study significantly enhances our understanding of the nature of compounding, the motivations for using hyphenation, and its cognitive processing.
Didactic Potential of Instructive Etude and its Explication in the Process of Professional Development of a Pianist

The article proposes an analytical approach to instructive etude as a musical work whose originality is determined by its functional orientation: not to demonstrate performing skills in mastering all its components or to embody author’s reflection, but to develop and improve students' practical skills. The creation of etude forming piano music in its sense in the aggregate of composing, performing, and teaching practices at their intersection allows us to see there a reflection of all the process facets stressing one of them. The instructive branch of the study, incorporating the finds of both performers and composers, projects them onto teaching materials, highlighting individual technical and textural techniques and turning them into the units of training work. The formation and improvement of the pianistic apparatus, the accuracy of touching the keyboard, free movement within its space boundaries, ease and fluency of fingers, etc., remain the most fundamental, basic task of instructional etude. However, the faultless technique and motility do not exhaust the content of the training work proposed by an etude’s author, but include other skills that also need careful work, including auditory control, the ability to withstand the taken tempo-rhythm, mastering various articulations, etc. The breadth and variety of methodological tasks posed in individual samples of instructive etudes are pointed out, which makes it possible to refer to them as a powerful aid in all stages of teaching music to students, including higher special musical institutions. Whether to use them in academic repertoire or abandon should depend only on a particular student’s professional level, and on his being ready to play compositions of increased difficulty, including artistic etudes. The conclusion is made about didactic potential steadiness inherited in this type of works and the tendency to explicate it in a pianist’s professional development process.
Hierarchical Interpretable Topical Embeddings for Exploratory Search and Real-Time Document Tracking

Real-time monitoring of scientific papers and technological news requires fast processing of complicated search demands motivated by thematically relevant information acquisition. For this case, the authors develop an exploratory search engine based on probabilistic hierarchical topic modeling. Topic model gives a low dimensional sparse interpretable vector representation (topical embedding) of a text, which is used for ranking documents by their similarity to the query. They explore several ways of comparing topical vectors including searching with thematically homogeneous text segments. Topical hierarchies are built using the regularized EM-algorithm from BigARTM project. The topic-based search achieves better precision and recall than other approaches (TF-IDF, fastText, LSTM, BERT) and even human assessors who spend up to an hour to complete the same search task. They also discover that blending hierarchical topic vectors with neural pretrained embeddings is a promising way of enriching both models that helps to get precision and recall higher than 90%.
Understanding the power of Max-SAT resolution through UP-resilience

A typical Branch and Bound algorithm for Max-SAT computes the lower bound by estimating the number of disjoint Inconsistent Subsets (IS) of the formula. The IS detection is ensured by Simulated Unit Propagation (SUP). Then, the inference rule for Max-SAT, Max-SAT resolution, is applied to ensure that the detected IS is counted only once. Learning Max-SAT resolution transformations can be detrimental to the algorithm performance, so they are usually selectively learned if they match certain patterns. In this paper, we study the impact of the transformations by Max-SAT resolution on the SUP mechanism, indispensable for IS detection. We introduce the notion of UP-resilience of a transformation which quantifies this impact and provides, from a theoretical point of view, an explanation to the empirical efficiency of the learning schemes developed in the last ten years. We also focus on recently introduced patterns called Unit Clause Subsets (UCSs). We characterize the transformations of certain UCSs using UP-resilience and we explain how our result can help extend the current patterns. Finally, we present empirical observations that support the relevance of the UP-resilience property and further consolidate our theoretical results.
Active Forms of Training Future English Language Teachers

The content of the paper was determined by the problem of active teaching forms at English classes at the Pedagogical Universities. The modern educational environment should form an holistic system of universal knowledge, skills, as well as the experience of independent activity and personal responsibility of students. The aim of the article is to determine the effectiveness of active forms of teaching Ukrainian future teachers at English classes. Modern effective forms of teaching are based on the independent educational activities of participants. Active forms of teaching provide a solution of educational problems in various aspects: the formation of positive educational motivation; increasing cognitive activity of students; active involvement of students in the educational process; development of cognitive processes; effective assimilation of educational information; development of creative abilities and thinking; development of the communicative and emotional sphere and development of independent mental work skills and universal skills. The research was conducted on the basics of the South Ukrainian National Pedagogical University named after K.D. Ushynsky (2018-2019). An analysis of these researchers and advanced pedagogical experience shows that the most characteristic direction of increasing the university education effectiveness is the creation of psychological and pedagogical conditions in which a student can take an active personal position and educational activity. The results of the implementation of the system of using active teaching forms can be considered: systematization of program material by the criterion of the possibility of using active forms and a holistic system of exercises of active forms.
An image encryption algorithm with a novel chaotic coupled mapped lattice and chaotic image scrambling technique

In this paper, we build a novel chaotic coupled lattice mapping with positive Lyapunov exponent, and introduce a novel chaotic image scrambling mechanism. Then, we propose a chaotic image encryption algorithm which uses the introduced chaotic coupled lattice mapping to apply permutation by iteratively applying the introduced chaotic image scrambling mechanism, and diffusing the pixel values. We use a sorting approach rather than quantizing the chaotic floating-point values to construct the diffusion matrix. We also study the security of the proposed algorithm concerning several security measures including brute-force attack, differential attack, key sensitivity, and statistical attacks. Moreover, the proposed algorithm is robust against data loss and noise attacks.