Reviewerly/exHarmony huggingface dataset:
Discourje: Runtime Verification of Communication Protocols in Clojure

This paper presents Discourje: a runtime verification framework for communication protocols in Clojure. Discourje guarantees safety of protocol implementations relative to specifications, based on an expressive new version of multiparty session types. The framework has a formal foundation and is itself implemented in Clojure to offer a seamless specification–implementation experience. Benchmarks show Discourje’s overhead can be less than 5% for real/existing concurrent programs.
Detecting Phishing Attacks Using Natural Language Processing And Machine Learning

Phishing is a form of communal technology operation generally exploited in order to purloin confidential individual information such as login accrediations and bank card numbers to gain account data. This transpires when the person attacking pretences as a trustworthy system and swindles the sufferer to open an electronic mail or clamant content or textual matters. The receiver at this point is duped to click a malevolent attachment that activates the induction of malicious software causing the system to cease responding to inputs by blocking the access until the demanded sum is paid. or the betraying of sensitive information.Annihilating consequences such as betraying the delicate data can occur from these attacks. To the discrete persons, unlicensed leverages and larceny of monetary resources or identity thievery is reckoned.
Research on Technology Competencies in EFL Language Instructors: Technology-Pedagogy-Content in Language Teaching

Background:Living in the digital era where the information and communication technologies (ICTs) have extensively changed the way of teaching, language instructors, in particular, should be computer literate to put their technical knowledge into practice in such a way they effectively integrate technology into language learning classrooms.Methodology:The research contextualizes teachersâ€™ beliefs and competencies of using the Technological Pedagogical Content Knowledge (TPCK) in teaching English as a foreign language. The study is an extension of previous research carried out by Tovar et at., (2019), and Tovar (2019). It applied a survey-based questionnaire, an unstructured interview, and classroom observations for data collection. The assessment instruments were administrated to EFL language instructors, who work in the Language Center at the Technical University of Cotopaxi.Findings:Results revealed that a high percentage of the EFL language instructors are not familiar with the use of the TPCK model and its integration into their classroom practices. This support the claim that teachers probably have technological knowledge, but they are not well-prepared to combine teaching resources and appropriate pedagogical methods for language teaching and learning.Conclusions:The study hopes that research outcomes arise linguistic implications and pedagogical applications for developing teachersâ€™ TPCK competencies when integrating technology in EFL classroom settings. Limitations, as well as considerations for further research, are discussed.
GEOLOGICAL-GEOPHYSICAL PROSPECTING INDICATORS OF THE ARGANATY DISTRICT PREDICTIVE BLOCKS (EASTERN BALKHASH

The quality and depth of content Web of Science offers to researchers, authors, publishers, and institutions sets it apart from other research databases. The inclusion of News of NAS RK. Series of geology and technical sciences in the Emerging Sources Citation Index demonstrates our dedication to providing the most relevant and influential content of geology and engineering sciences to our community.Қазақстан Республикасы Ұлттық ғылым академиясы "ҚР ҰҒА Хабарлары.Геология және техникалық ғылымдар сериясы" ғылыми журналының
4-uniform permutations with null nonlinearity

Abstract We consider n -bit permutations with differential uniformity of 4 and null nonlinearity. We first show that the inverses of Gold functions have the interesting property that one component can be replaced by a linear function such that it still remains a permutation. This directly yields a construction of 4-uniform permutations with trivial nonlinearity in odd dimension. We further show their existence for all n = 3 and n ≥ 5 based on a construction in Alsalami (Cryptogr. Commun. 10 (4): 611–628, 2018). In this context, we also show that 4-uniform 2-1 functions obtained from admissible sequences , as defined by Idrisova in (Cryptogr. Commun. 11 (1): 21–39, 2019), exist in every dimension n = 3 and n ≥ 5. Such functions fulfill some necessary properties for being subfunctions of APN permutations. Finally, we use the 4-uniform permutations with null nonlinearity to construct some 4-uniform 2-1 functions from $\mathbb {F}_{2}^{n}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mrow> <mml:mi>F</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> </mml:msubsup> </mml:math> to $\mathbb {F}_{2}^{n-1}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mrow> <mml:mi>F</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>−</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msubsup> </mml:math> which are not obtained from admissible sequences. This disproves a conjecture raised by Idrisova.
Augmented Reality for Big Data Visualization: A Review

Information delivery in a visual format is always a better way of communication. Even with many data visualization techniques available, visualizing enormous amounts of data has always been a challenge. With recent advancements in technology, many new visualization techniques unfold, one of which is visualizing data through Augmented reality(AR). AR and big data have always gone together as AR requires large data sets to render information virtually in a real-time environment, and big data provides the same. In this paper, we explore some of the conventional visualization techniques and discuss the scope and possibilities for AR data visualizations. We also explore the areas implementing the technique of visualizing big data with AR. The advantages and limitations are also discussed.
Monitoring of the quality of the psychological component of teachers’ activity of higher education institutions based on Google Forms

The focus of the current research is on the quality of education as a multifaceted category, and the monitoring of the quality of education – as a purposeful and specially organized system of studying, assessment, analysis of data on the state of education of students. This study aims at exploring the nowadays conditions, and exactly the development of digital technologies. The need of using the electronic resources (Google services) has become more acute, which enables the creation of text documents, presentations, spreadsheets, forms, drawings and other documents. In order to provide the monitoring of the quality of the psychological component of the teachers’ activity of higher education institutions the Google form was elaborated to obtain the information about the psychological state and satisfaction of the quality of educational services by the fellows of the educational process, their relationships, socio-psychological climate in higher education institutions and others, and respond in a timely manner to the social and educational changes.
Multi-Column Atrous Convolutional Neural Network for Counting Metro Passengers

We propose a symmetric method of accurately estimating the number of metro passengers from an individual image. To this end, we developed a network for metro-passenger counting called MPCNet, which provides a data-driven and deep learning method of understanding highly congested scenes and accurately estimating crowds, as well as presenting high-quality density maps. The proposed MPCNet is composed of two major components: A deep convolutional neural network (CNN) as the front end, for deep feature extraction; and a multi-column atrous CNN as the back-end, with atrous spatial pyramid pooling (ASPP) to deliver multi-scale reception fields. Existing crowd-counting datasets do not adequately cover all the challenging situations considered in our work. Therefore, we collected specific subway passenger video to compile and label a large new dataset that includes 346 images with 3475 annotated heads. We conducted extensive experiments with this and other datasets to verify the effectiveness of the proposed model. Our results demonstrate the excellent performance of the proposed MPCNet.
Evaluating Learners’ Vocabulary Gain and Retention in an E-Learning Context Using Vocabulary Podcasting Tasks: A Case Study

The advent of new technologies and rapid growth of online distance education have fostered interaction as well as collaboration among learners and created a true learning community. Moreover, forming synchronous and asynchronous learning networks utilizing the Internet have been simplified by new technologies. Since the importance of technology is undeniable, teachers and instructors must use it to create new opportunities and experiences for educators. The present study investigates the use of audio podcast plus still pictures and audio podcasts plus animated pictures on learners’ vocabulary gain and retention in a process-oriented approach. The results indicate that the participants who received audio plus animated pictures had higher levels of vocabulary gain as well as retention. The present study showed that podcasting is an influential tool to course resources and increases the contact between students and teachers.
Adaptive local threshold segmentation for Fourier spatial filtering in automatic analysis of digital speckle interferogram

Automated extraction of the +1 term spectrum can greatly benefit the real-time measurement of dynamic deformation in the digital speckle pattern interferometry (DSPI). However, most spatial filtering methods are not satisfactory for automatic analysis because they need manual intervention or are unable to accurately extract multiple +1 term spectrums in one frequency distribution image. We propose an adaptive local threshold segmentation method for the Fourier spatial filtering of a three-dimensional DSPI system. This method first uses global thresholding to coarsely recognize the spectral regions and then adopts local thresholding to accurately determine the spatial filtering windows for each spectrum. Experimental result shows that the proposed adaptive local threshold segmentation can accurately extract multiple desired +1 term spectrums even if the intensities of different spectral regions differ greatly. In addition, experiments of the comparison between the automatic and manual spatial filtering and automatic analysis of dynamic deformation demonstrate that the proposed method can be used for the real-time measurement.
Orthus: A Blockchain Platform for Smart Cities

Currently, blockchain has been widely used to store decentralised and secure transactions involving cryptocurrency (for instance, Bitcoin solution). On the other hand, smart city applications are concerned about how data and services can be safely stored and shared. In this regard, this article investigates the use of blockchain and pinpoints a set of essential requirements to meet the needs of blockchain for the context of smart cities. Base on that, we propose a platform, named Orthus, to support the use of blockchain in smart city initiatives. We have demonstrated how to use the proposed platform in the context of the Natal Smart City Initiative, in Brazil, to handle land registration. Finally, we compare this platform with other implementations that use blockchain in different domains.
An Analysis of the Students' Difficulties in Writing Undergraduate Thesis at English Education Study Program of Muhammadiyah University of Bengkulu

The objectives of this research are: 1) To find out the undergraduate students’ Difficulties In Writing Thesis at The English Study Program of Muhammadiyah University Bengkulu (UMB) in Academic Year 2018-2019, and 2) To find out the most difficult variable they faced in writing theses. The design of this research is descriptive quantitative research. 74 students of the Seventh Semester Students of English Education Study Program of Muhammadiyah University of Bengkulu in the Academic Year of 2018-2019 took part in this study. The instruments were questionnaires and interview. The data were collected by assigning the students to fill out the questionnaires and being interviewed afterward. The data were analyzed by checking the students’ answer in the questionnaire, classifying the students’ answers, calculating the students’ scores of difficulties in writing a thesis, describing the data and concluding the results. The results showed the students' difficulties in writing a thesis; a) Orderly, the students' difficulties in writing a thesis are: related to difficulties of students' English proficiencies; related to difficulties of students' time management; related to difficulties of students' research methodology; related to difficulties of students' attitude; related to difficulties of students' research topic; and the least related to difficulties of a co-advisor relationship, students' plagiarism, students' personalities, the big number of supervised students and advisor workload; and difficulties of students motivation. b) The most difficulties in writing thesis are related to their English proficiency which are still low and limited. Both have become the first cause that they cannot develop their writing thesis well. The implication of this study ; 1) the students should minimize their difficulties and find the solution in the way of a) Improve their English, or at least have adequate English before writing a thesis. b) Should manage their time wisely in doing their thesis. c) Read more relevant sources that can support their research. d) Give more attention to avoid plagiarism. 2) Further researchers are suggested to do similar research and add more data with adding research instruments, such as interview guides.
Cognitive Network Science: A New Frontier

A major challenge in studying the complexity of cognition relates to quantifying abstract theoretical cognitive constructs, such as language, memory, or thinking, and studing the representation of these abstract constructs.Such quantifications of these abstract constructs are based on indirect measures of cognitive systems such as behavioral measures or neural activity.In the past two decades, an increasing number of studies have used network science methods to study complex systems.Network science is based on mathematical graph theory and offers quantitative methods to investigate complex systems [1].A network is made up of nodes, which represent the basic unit of the system (e.g., concepts in semantic memory) and links, or edges, which signify the relations between them (e.g., semantic similarity).While the application of network science methodologies has become an extremely popular approach to study brain structure and function [2], it has been used to study cognitive phenomena to a much lesser extent, despite classic cognitive theory in language and memory being highly related to a network perspective [3].So far, the application of network science to cognitive science has enabled the direct examination of the theory that highly creative individuals have a more flexible semantic memory structure [4], identified mechanisms of language development through network growth modeling [5], shed novel light on statistical learning [6], examined phonological and orthographic effects [7,8], provided new insight into the structure of semantic network of second language in bilinguals [9], and studied changes in memory structure across the lifespan [10].e aim of this special issue is to demonstrate the potential and strength of applying network science methods to study cognition (broadly defined).In the article "Cognitive Network Science: A Review of Research on Cognition through the Lens of Network Representations, Processes, and Dynamics," C. S. Q. Siew et al. provide a comprehensive review on the field of Cognitive Network Science.Specifically, their article is focused on three key main theses: (1)Network science provides a quantitative approach to represent cognitive systems; (2) network science facilitates a deeper understanding of human cognition by allowing the researcher to consider how network structure and the processes operating on the network structure interact to produce behavioral phenomena; and (3) network science provides a framework to model structural changes in cognitive systems at multiple scales.It is striking that, without any prior arrangement, the collection of articles in this special issue has aligned rather well with the main theses of the comprehensive review by C.
Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models

Recent works show that pre-trained language models (PTLMs), such as BERT, possess certain commonsense and factual knowledge. They suggest that it is promising to use PTLMs as "neural knowledge bases" via predicting masked words. Surprisingly, we find that this may not work for numerical commonsense knowledge (e.g., a bird usually has two legs). In this paper, we investigate whether and to what extent we can induce numerical commonsense knowledge from PTLMs as well as the robustness of this process. To study this, we introduce a novel probing task with a diagnostic dataset, NumerSense, containing 13.6k masked-word-prediction probes (10.5k for fine-tuning and 3.1k for testing). Our analysis reveals that: (1) BERT and its stronger variant RoBERTa perform poorly on the diagnostic dataset prior to any fine-tuning; (2) fine-tuning with distant supervision brings some improvement; (3) the best supervised model still performs poorly as compared to human performance (54.06% vs 96.3% in accuracy).
Kibo: A MIDI Controller with a Tangible User Interface for Music Education

This paper presents Kibo, a MIDI controller equipped with a simplified tangible user interface.Entirely made of wood, Kibo presents eight geometric extractable solids that can be used to trigger note events and to control musical parameters.In the framework of the cooperation with the Music Informatics Lab of the University of Milan, the distinctive features of the device and their applicability to the field of music education have been investigated.Kibo aims to offer intuitive interaction with music parameters and fosters the acquisition of specific skills in a non-formal learning environment.Benefits are particularly evident for specific target categories of users, including preschool and primary school children and people with both physical and cognitive special needs.
Hybrid Settlers - Integrating Dynamic Tiles into a Physical Board Game using Electrochromic Displays

In this paper we present a novel method of hybridizing physical board games by adding dynamic and digitally controlled fields utilizing electrochromic inks. In particular we built electrochromic displays that fit the hexagon fields on the Settlers of Catan board game and thereby added the ability to change a fields resources during game play. In this paper we report on the prototypical implementation and two preliminary studies that indicate how these dynamic fields can increase the excitement and reward of playing the game.
Scheduled DropHead: A Regularization Method for Transformer Models

In this paper, we introduce DropHead, a structured dropout method specifically designed for regularizing the multi-head attention mechanism, which is a key component of transformer, a state-of-the-art model for various NLP tasks. In contrast to the conventional dropout mechanisms which randomly drop units or connections, the proposed DropHead is a structured dropout method. It drops entire attention-heads during training and It prevents the multi-head attention model from being dominated by a small portion of attention heads while also reduces the risk of overfitting the training data, thus making use of the multi-head attention mechanism more efficiently. Motivated by recent studies about the learning dynamic of the multi-head attention mechanism, we propose a specific dropout rate schedule to adaptively adjust the dropout rate of DropHead and achieve better regularization effect. Experimental results on both machine translation and text classification benchmark datasets demonstrate the effectiveness of the proposed approach.
Multi-projection of unequal dimension optimal transport theory for Generative Adversary Networks

As a major step forward in machine learning, generative adversarial networks (GANs) employ the Wasserstein distance as a metric between the generative distribution and target data distribution, and thus can be viewed as optimal transport (OT) problems to reflect the underlying geometry of the probability distribution. However, the unequal dimensions between the source random distribution and the target data, result in often instability in the training processes, and lack of diversity in the generative images. To resolve the challenges, we propose here a multiple-projection approach, to project the source and target probability measures into multiple different low-dimensional subspaces. Moreover, we show that the original problem can be transformed into a variant multi-marginal OT problem, and we provide the explicit properties of the solutions. In addition, we employ parameterized approximation for the objective, and study the corresponding differentiability and convergence properties, ensuring that the problem can indeed be computed.
Structure-Tags Improve Text Classification for Scholarly Document Quality Prediction

Training recurrent neural networks on long texts, in particular scholarly documents, causes problems for learning. While hierarchical attention networks (HANs) are effective in solving these problems, they still lose important information about the structure of the text. To tackle these problems, we propose the use of HANs combined with structure-tags which mark the role of sentences in the document. Adding tags to sentences, marking them as corresponding to title, abstract or main body text, yields improvements over the state-of-the-art for scholarly document quality prediction. The proposed system is applied to the task of accept/reject prediction on the PeerRead dataset and compared against a recent BiLSTM-based model and joint textual+visual model as well as against plain HANs. Compared to plain HANs, accuracy increases on all three domains. On the computation and language domain our new model works best overall, and increases accuracy 4.7% over the best literature result. We also obtain improvements when introducing the tags for prediction of the number of citations for 88k scientific publications that we compiled from the Allen AI S2ORC dataset. For our HAN-system with structure-tags we reach 28.5% explained variance, an improvement of 1.8% over our reimplementation of the BiLSTM-based model as well as 1.0% improvement over plain HANs.
Alarm bells ring for patient data and privacy in the covid-19 goldrush

Patient data are being used on an unprecedented scale by governments and healthcare bodies to stop the pandemic in its tracks. But what of the long term effects of accessing such sensitive information, asks <b>David Cox</b>