Reviewerly/exHarmony huggingface dataset:
Ready, steady, go? - Results of the third BIS survey on central bank digital currency

Most central banks are exploring central bank digital currencies (CBDCs), and their work continues apace amid the Covid-19 pandemic. This BIS paper updates earlier surveys that asked central banks how their plans in this area are developing. The latest responses show that as a whole, central banks are progressing from conceptual research to practical experimentation. In addition, the paper highlights that while most have no plans to issue CBDCs in the foreseeable future, central banks collectively representing a fifth of the world's population are likely to launch retail CBDCs in the next three years. The Covid19 pandemic has added new motivations to this journey.
A Data-Driven Modeling Framework of Time-Dependent Switched Dynamical Systems via Extreme Learning Machine

In this work, a data-driven modeling framework of switched dynamical systems under time-dependent switching is proposed. The learning technique utilized to model system dynamics is Extreme Learning Machine (ELM). First, a method is developed for the detection of the switching occurrence events in the training data extracted from system traces. The training data thus can be segmented by the detected switching instants. Then, ELM is used to learn the system dynamics of subsystems. The learning process includes segmented trace data merging and subsystem dynamics modeling. Due to the specific learning structure of ELM, the modeling process is formulated as an iterative Least-Squares (LS) optimization problem. Finally, the switching sequence can be reconstructed based on the switching detection and segmented trace merging results. An example of the data-driven modeling DC-DC converter is presented to show the effectiveness of the developed approach.
Vocapia-LIMSI System for 2020 Shared Task on Code-switched Spoken Language Identification

This paper describes the systems submitted by Vocapia Research and LIMSI for the shared task on Code-switched Spoken Language Identification, organized in the conjunction with the First Workshop on Speech Technologies for Code-switching in Multilingual Communities 2020. Our primary system combines an acoustic approach based on i-vector modeling of audio segments with a phonotactic approach that focuses on sequences of language-independent phone units. Both modeling approaches provided comparable performance, and a gain was obtained by a simple linear combination of their scores, showing their complementarity. One of our submissions obtained first rank for all combinations of tasks and language pairs. For the utterancelevel detection task (task A), an F-measure of 76.0% was obtained with our combined system for which the average accuracy on the development set was 83.3%. For the frame-level detection task, the average accuracy was 81.2% on the development set and 78.7% on the evaluation set. However, a detailed analysis reveals a very high rejection of the 200ms codeswitched frames, which comprise only 12% of the corpus. This shows that a more precise modeling of code-switched segments is needed for an accurate segmentation.
A Corpus-Based Analysis of Syntactic/Semantic Errors in University Level EFL Learners’ Writing

The purpose of this study is to investigate different types of errors identified from the essays written by 68 Asian EFL learners at a Korean university. The learner corpus containing these essays consisted of 2498 errors; based on the schemes suggested by previous researchers, those errors were analyzed according to error classification, i.e., sentence fragments, run-ons, comma splices, word order, verb tense, verb form, subject verb agreement, word form, determiners, prepositions, and word choice. Of all the error types committed by college-level learners, word choice had the highest error frequency, accounting for around 33%. Word choice issues in EFL students’ writing could be due to a traditional way of English learning such as rote memorization of vocabulary and purely receptive reading skills. Thus, instructor suggestions on practical learning methods concerning word choice in writing can be certainly helpful and teachers would also need to pedagogically utilize EFL written word choice strategies in a more effective way.
Peningkatan keterampilan menulis pantun melalui penggunaan model pembelajaran concept sentence pada peserta didik kelas V sekolah dasar

The purpose of this reseacrch was to improve pantun writing skills through the use of the Concept Sentence learning model. This research is a Class Action Research which was carried out for 2 cycles, with 4 stages namely planning, implementing, acting, observing and reflecting. The research subjects were grade V students of the State Elementary School in Surakarta consisting of 14 students. Data collection techniques using observation interviews, documentation and test. The data analysis technique uses an interactive analysis model from Miles-Huberman. The results showed an increase in the first cycle obtained a percentage of 52% and in the second cycle to 92,3%. Based on the results of this research it can be concluded that the pantun writing skills increase after the application of the Concept Sentence learning model.
PSYCHOLOGICAL PROBLEMS IN SPEAKING ENGLISH OF THE STUDENTS OF VOCATIONAL SENIOR HIGH SCHOOL

The purpose of the research is to know the students’ psychological problems, and to know the efforts of the students to overcome their psychological problems. The research was conducted in SMKN 1 Lamongan at OTKP. The research is qualitative research. It used an observation and a semi-structured interview as a technique to collect the data. Based on the research, the researcher found that the students felt anxious, shy, fear of making mistakes and the students also got a lack of confidence when they were speaking in front of the class. The efforts to overcome their psychological problems were as follows. The students should have the self-confidence to speak, the student should master many vocabularies, and more practices speaking in English, the teacher should give motivation to the students and encourage a habit and accustom the students to use English. Keywords: Speaking, psychological problems
I2PA, U-prove, and Idemix: An Evaluation of Memory Usage and Computing Time Efficiency in an IoT Context

The Internet of Things (IoT), in spite of its innumerable advantages, brings many challenges namely issues about users’ privacy preservation and constraints about lightweight cryptography. Lightweight cryptography is of capital importance since IoT devices are qualified to be resource-constrained. To address these challenges, several Attribute-Based Credentials (ABC) schemes have been designed including I2PA, U-prove, and Idemix. Even though these schemes have very strong cryptographic bases, their performance in resource-constrained devices is a question that deserves special attention. Therefor, this paper aims to conduct a performance evaluation of these schemes on issuance and verification protocols regarding memory usage and computing time. Recorded results show that both I2PA and U-prove present very interesting results regarding memory usage and computing time while Idemix presents very low performance with regard to computing time compared to I2PA and U-prove.
Asymmetric production metric for calculating the similarity of objects

The purpose of this paper is to propose a new metric for determining the similarity of objects. The analysis showed that many existing metrics are focused on comparing objects, the parameters of which are estimated on the same type of scales. Examples are Euclidean metric, Minkowski metric, Manhattan metric. In addition, the described metrics do not allow setting different boundaries of deviations of the parameter values “above” or “below” from the values of the parameter of the sample object. The essence of the proposed metric is to check the deviation for each of the parameters between the value of the compared object and the sample object. Also, the metric provides operations for checking various additional conditions by which objects can be recognized as similar. As a result, the metric makes it possible to evaluate the similarity of objects, according to parameters that are described on the scale of relations, rank scale and scale of names, as well as additional conditions that are provided by the assessment of the similarity of objects. In the course of the experiment, the performance of the proposed provisions for determining the similarity metric of objects was confirmed. It was also shown that the groups of parameter values of a subset of objects, formed using the proposed metric, has less variance and a smaller range of scatter of values than similar groups of parameters of subsets of objects formed using Euclidean metrics and Minkowski metrics.
A new statistical approach for the identification of outlier genes in cancer microarray data

The aim of microarrays technology is to discover genes, which are differentially expressed as outliers between two or more groups of patients are an important task in the genomics community. The regular pattern of genes may often breakdown due to the presence of outliers and it is essential to detect those genes whose behavior looks abnormal in experimental and biological conditions. Several statistical techniques - t-statistic, cancer outlier profile analysis (COPA), outlier sums (OS), outlier robust t-statistic (ORT), maximum ordered subset t-statistics (MOST) and least sum of ordered subset square t-statistics (LSOSS) were developed to address the problem of detecting outlier genes in microarray data but these methods are affected by some problems especially if there is an unusual observation in such dataset then the standard assumptions of distribution parameter may be violated and these techniques might not be suitable to detect outliers genes as well. For these consequences, I have developed a new statistical technique that is “Propose t-statistic (PT)”. The performance of the newly proposed method PT statistic compare with the other existing methods applied to the monte carlo simulation data, package data, and real cancer datasets. The result shows that the outlier genes are identified by using the proposed method PT as well and will give the best and identical results than other methods. The performance of the proposed approach significantly improves than the traditional methods and it can extensively contribute to the medical as well as the genomic community.&#x0D; Asian J. Med. Biol. Res. December 2020, 6(4): 795-801
Semantic Taxonomy and NP-coordination in Korean

This paper discusses the novel data of Korean NP-coordinations whose non-final conjunct is semantically incompatible with the verb of which the NP-coordinations are the object in a sentence. It is shown that these “peculiar” NP-coordinations cannot be accounted for by prior approaches to coordination in the literature. In particular, any theory that directly relates the verb with such a non-final conjunct faces a significant problem of semantic incompatibility. As a solution to this problem, I propose the hypothesis that the non-final conjunct of such NP-coordination must be semantically associated with the direct hypernym of the verb. I focus on conjunctive coordinations in this paper, but it is shown that the hypothesis can be further supported by some disjunctive coordinations in Korean.
PRIMARY SCHOOL TEACHERS’ COMPONENTS, CRITERIA, INDICATORS AND READINESS LEVELS TO THE PROFESSIONAL ACTIVITIES IN THE CONDITIONS OF INCLUSIVE EDUCATION

Introduction. Scientific research is dedi- cated to the relevant pedagogical problem - readiness diagnostic of primary school teachers to educational activ- ity in the conditions of inclusive education. The problem is considered as existence of contradictions between social order on preparation of competent teacher, that is able to organize coeducation of children with the special educational needs and children with normal development as well as in the noncompliance with the requirements of the level of readiness of primary school teachers and insufficient educational-methodological and diagnostic provision of this process. The goal of this article is to separate the content-structural components of primary school teachers' readiness to the work in conditions of inclusive education, criteria, indicators and stages of teacher`s readiness to work in the conditions of inclusive education, that gives the opportunity to provide objective diagnostic. Stages of teachers` readiness are considered not only by degree of components readiness, criteria and indicators of its ap- pearance in professional activity, but also with taking into account the continuity and phasing of the process, gradu- al complication, expansion of spheres of activity, increas- ing the complexity of educational tasks in the professional context. On the basis of theoretical analysis and data generalization of experimental studies of my own pedagogical experience was founded, that teacher`s readiness to work in the inclusive education conditions is the unity of four components: motivational value, cognitive activity, personal reflective and communicative components. These components are realized by the motivational, axiological, cognitive-procedural, reflective and evaluative and communicative criteria. The same amount of indicators were selected for each component to optimize the process of diagnostics. According to the criteria, such stages of teachers` training for educational activity in the inclusive education conditions were separated: height, intermediate, middle and low.
The role of critical thinking in the digital media culture and strengthening of digital immunity

The public health emergency and the proclaimed state of the global pandemic caused by the COVID-19 virus have led to the biggest world crisis since World War II. With the introduction of quarantine measures in spring 2020, the digital transformation has accelerated in many societies and the importance of media and information literacy in these new circumstances has been clearly emphasized. The pandemic has changed the information and media habits of the citizens, work from home was introduced, online school classes became mandatory, and an uprise of e-commerce reflected the new reality. The digital space has become oversaturated with informative, educational, commercial contents and many cultural institutions and international organizations have made their cultural heritage accessible online. Interaction in the public digital space has became more and more intense, and critical thinking stood out as a necessity for strengthening the digital immunity of each individual as well as the society. The pandemia was followed by infodemia. The information channels used by citizens point to a certain re-examination of the new digital media culture, the very concept of media as well as the accompanying regulations and media policy. The findings of the paper draw attention to the media and information literacy policy framework in Serbia, and review a set of activities that the Ministry of Culture and Media has been conducting over the past two years on both national and international level. It describes the correlation between the framework and the practice and depicts the trends in the international policy in this field. The paper focuses on the strategic vision and provides information throughout the two-year period on setting up of the holistic 360 approach and setting up media literacy network of stakeholders with the aim of strengthening advocacy and developing of the media literacy field.
Improving Tail Label Prediction for Extreme Multi-label Learning

Extreme multi-label learning (XML) works to annotate objects with relevant labels from an extremely large label set. Many previous methods treat labels uniformly such that the learned model tends to perform better on head labels, while the performance is severely deteriorated for tail labels. However, it is often desirable to predict more tail labels in many real-world applications. To alleviate this problem, in this work, we show theoretical and experimental evidence for the inferior performance of representative XML methods on tail labels. Our finding is that the norm of label classifier weights typically follows a long-tailed distribution similar to the label frequency, which results in the over-suppression of tail labels. Base on this new finding, we present two new modules: (1)~\algoa~learns to re-rank the predictions by optimizing a population-aware loss, which predicts tail labels with high rank; (2)~\algob~augments tail labels via a decoupled learning scheme, which can yield more balanced classification boundary. We conduct experiments on commonly used XML benchmarks with hundreds of thousands of labels, showing that the proposed methods improve the performance of many state-of-the-art XML models by a considerable margin (6\% performance gain with respect to PSP@1 on average).
Ilustrasi Komik Sebagai Alat Penyampaian Berita

Abstract.  Currently, reading interest in Indonesia is still relatively low, therefore many people have made this an opportunity to link reading interest, including journalists. One of the creative ideas is issued by adding various illustrations. The Instagram account @jurnaliskomik uses a breakthrough by making comics a tool to convey news. This study aims to examine the relationship between the use of comic illustrations in the news. The theory used is the comic theory according to Scott McCloud, which states that comics are images and other symbols that are juxtaposed (side by side) in a certain order, aiming to provide information and / or achieve aesthetic responses from readers. The elements in the comics became the reference for researchers to test the theory. The method used in this research is correlational, which looks for the relationship between variable X and variable Y. where variable X contains comic illustrations and variable Y gives reading interest. The sample of this study was 100 selected followers of the @jurnaliskomik Instagram account. This study uses the Spearman Rank correlation analysis technique (Spearman's Rho Rank-Order Correlations), this test is used to test two variables whether there is a relationship or not. Judging from the responses of respondents, the use of images is one of the factors that can attract the attention of the community to start reading. Keywords : Illustration, comic, interest, reading and Instagram Abstrak.  Saat ini minat membaca di Indonesia masih tergolong rendah, maka dari itu banyak orang yang menjadikan kendala tersebut sebagai ajang untuk mengait minat membaca tak terkecuali para jurnalis. Ide – ide kreatif dikeluarkan salah satunya dengan menambahkan berbagai ilustrasi. Akun Instagram @jurnaliskomik menggunakan suatu terobosan dengan menjadikan komik sebagai alat untuk menyampaikan pemberitaannya. Penelitian ini bertujuan untuk menguji hubungan penggunaan ilustrasi komik pada berita. Teori yang digunakan adalah teori komik menurut Scott Mccloud yang mengatakan komik adalah gambar-gambar dan lambang-lambang lain yang terjukstaposisi (saling berdampingan) dalam urutan tertentu, bertujuan untuk memberikan informasi dan atau mencapai tanggapan estetis dari pembaca. Unsur-unsur pada komik menjadi acuan peneliti untuk menguji teori tersebut. Metode yang digunakan dalam penelitian ini adalah korelasional, dimana mencari hubungan antara variabel X dengan variabel Y. dimana variabel X berisikan ilustrasi komik dan variabel Y berisikan minat membaca. Sampel penelitian ini adalah 100 orang followers akun Instagram @jurnaliskomik yang dipilih. Penelitian ini menggunakan teknik analisis korelasi Rank Spearman ( Spearman’s Rho Rank-Order Correlations) , pengujian ini digunakan untuk menguji dua variabel apakah ada hubungan atau tidak. Dilihat dari tanggapan responden, penggunaan gambar menjadi salah satu faktor yang dapat menarik perhatian masyarakan untuk mulai membaca. Kata Kunci  : Ilustrasi, Komik, Minat, Membaca dan Instagram.
Few-Shot Website Fingerprinting Attack

This work introduces a novel data augmentation method for few-shot website fingerprinting (WF) attack where only a handful of training samples per website are available for deep learning model optimization. Moving beyond earlier WF methods relying on manually-engineered feature representations, more advanced deep learning alternatives demonstrate that learning feature representations automatically from training data is superior. Nonetheless, this advantage is subject to an unrealistic assumption that there exist many training samples per website, which otherwise will disappear. To address this, we introduce a model-agnostic, efficient, and Harmonious Data Augmentation (HDA) method that can improve deep WF attacking methods significantly. HDA involves both intra-sample and inter-sample data transformations that can be used in harmonious manner to expand a tiny training dataset to an arbitrarily large collection, therefore effectively and explicitly addressing the intrinsic data scarcity problem. We conducted expensive experiments to validate our HDA for boosting state-of-the-art deep learning WF attack models in both closed-world and open-world attacking scenarios, at absence and presence of strong defense. {For instance, in the more challenging and realistic evaluation scenario with WTF-PAD based defense, our HDA method surpasses the previous state-of-the-art results by more than 4% in absolute classification accuracy in the 20-shot learning case.
An analysis of students’ difficulties in comprehending narrative text

The objective of this study was to find out students’ difficulties faced by the Eight Grade Students at SMP N3 Tanjung Raja in Comprehending English reading text. The method was used descriptive qualitative research. The participants of this study was all of the eighth grade students of SMP Negeri 3 Tanjung Raja. The technique for collecting the data was multiple choice. Based on the result, it was found that the percentage of difficulty in indicator not clearly stated information was 55.90%, the percentage of difficulty in indicator reference of the text was 59.54%, the percentage of difficulty in indicator find moral value of the text was 64.23%, the percentage of difficulty in indicator find the deducing meaning was 55.27%, the percentage of difficulty in indicator find the specific information was 56.35%, the percentage of difficulty in indicator find the detailed information was 67.05%, and the percentage of difficulty in indicator find the main idea was 54.81%
Criminal protection of young people from alcoholism in Bosnia and Herzegovina

Alcoholism or alcohol abuse and indulgence in alcohol is one of the most prevalent antisocial phenomena in young people and one of the main causes of their criminal behavior. Namely, there is an increasing tendency for young people to abuse and consume alcohol and to indulge in alcohol frequently as well as the incidence of committing criminal offenses by young people under the decisive influence of alcohol. The paper elaborates the criminal law aspects of protecting young people from alcoholism or alcohol abuse and points out problem of uneven and inadequate legislation in Bosnia and Herzegovina, and gives appropriate proposals for changes to existing solution in order to achieve better and more effective criminal law protection of young people from alcohol abuse.