Reviewerly/exHarmony huggingface dataset:
Study on the Status Quo of Smart Campus Construction in Higher Vocational Colleges: The Case of Z School of China’s Guangdong Province

The construction of smart campuses is an inevitable trend in the future development of major colleges, especially higher vocational colleges aiming at cultivating high-quality, high-skilled, and high-professional innovative talents. To achieve this goal, higher vocational colleges must develop education informatization, which is an inevitable choice in the new era and the background of "Internet +". Based on advanced technologies, such as cloud computing, big data, Internet+, and new artificial intelligence, combined with high-quality networks and various smart devices, digital education will be carried out to promote the transformation of traditional education to intelligent and innovative education. This article uses Z School as a case study, analyzes its application status, finds its limitations, and puts forward corresponding suggestions. It also provides reference for similar higher vocational colleges so as to promote the construction of educational information in higher vocational colleges and improve the effectiveness of quality education for higher vocational colleges.
Pelatihan Penggunaan Aplikasi Quizizz Sebagai Alternatif Media Evaluasi Pembelajaran Jarak Jauh

The government has taken various policies to prevent the spread of the Covid-19 virus. Among them: working from home, physical distancing, health protocols, large-scale social restrictions, and imposing restrictions on community activities. In the education sector, the Ministry of Education and Culture has made a face-to-face learning policy that has been transformed into distance learning, both online and offline. This applies from elementary to higher education. The learning process is a unity that can not be separated with the learning evaluation. Therefore the teacher must be able to determine what kind of evaluation model is suitable for use with their students in distance learning. Currently, there are quite a lot of applications that can be used for learning evaluation, one of which is quizizz. Considering that this quizizz application is interactive and internet-based, training needs to be done in its use. For that purpose, this training activity was carried out with the participants being teachers of the Harapan 3 Private Elementary School in Medan.
La glottodidattica infantile della lingua latina: testo e multi-sensorialità

This study aims to approach the world of the glottodidactics of the Latin language to children, analyzing, on the one hand, the strategies to facilitate and understand the lexicon and the text thro...
Parsing BDD Stories for Automated Verification of Software Artefacts

This position paper reports on our ongoing developments towards the automated verification of software artefacts by parsing sentences on Behaviour- Driven Development (BDD) stories. The solution we propose is based on different strategies for analysing the consistency of user requirements specified in BDD stories on task models, graphical user interfaces (GUIs), GUI prototypes, and domain models. We illustrate our solution through concrete examples and discuss its challenges and limitations.
Natural Language Inference over Tables: Enabling Explainable Data Exploration on Data Lakes

Data lakes are repositories of data with potential for analysis. Data lakes aim to liberate data from silos, thereby enabling cross-cutting analyses that were hitherto out of reach. This gives rise to significant challenges for data scientists simply discovering what data sets may be relevant to a task-in-hand. Given a data set of interest, several proposals have been made for indexing schemes that can identify related data sets. However, such schemes tend to build on similarity metrics that stop short of providing a clear explanation as to how an identified data set relates to a provided target. We address this problem by applying Natural Language Inference (NLI) to providing explanations as to how the attributes of discovered data sets relate to those of the target, in terms of a collection of semantic relations. We provide two approaches to inferring semantic relations: (a) by performing unsupervised intensional and extensional analysis of the data sources using Natural Language Processing techniques; and (b) by performing supervised learning of semantic relations by applying BERT over source schema information. The contributions of this paper are: an NLI strategy for providing explicit characterisation of semantic relations between data sets; two approaches to inferring the semantic relations; and an empirical evaluation of the approaches using open government data.
The Development of Evaluation Instruments in Online Learning using the Quizizz Application: During Covid-19 Pandemic

This study aims to produce an online learning evaluation instrument that is considered feasible by material experts, evaluation experts and through item analysis and user response questionnaires. This research was conducted with a 4D development model. The instruments used in this study were the material expert's feasibility assessment sheet, evaluation expert, item analysis and user response questionnaires, namely lecturers and students. The data analysis in this study used a rating scale and the item analysis used the product-moment, Kuder-Richardson, the discrimination power and the difficulty level of the questions. Through the item analysis of the 25 questions selected 10 questions that meet the discrimination power,difficulty level, validation and reliability. The reliability coefficient is 0.851 which is classified as high reliability. Meanwhile, the evaluation of material experts was 85.4%, expert evaluation was 88%, student response was 90.7% and lecturers were 90%. Based on the feasibility content, the overall learning evaluation instrument using the quizizz application is declared feasible.
Gender Differences in Face-to-Face Deceptive Behavior

We study the role of face-to-face interaction for gender differences in deceptive behavior and perceived honesty. In the first part, we compare women to men’s deceptive behavior using data from an incentivized income-reporting experiment with three treatments. Reporting is fully computerized in a baseline treatment but occurs face-to-face in the second and third treatment. Lies can be detected in the course of an audit, which happens with a given probability in the first and second treatment whereas it depends on perceptions by others in the third treatment. In the computerized baseline treatment, men and women’s deceptive behavior is statistically indistinguishable. However, women’s truthfulness increases when face-to-face interaction is introduced in the second treatment. In contrast, males’ deceptive behavior does not change until the audit probability depends on their perceived honesty in the third treatment. Then, men’s truthfulness rises sharply and exceeds women’s level of honesty by far. We elaborate on these gender differences in the second part. We conduct an experiment to assess the honesty of videotaped income-reporting statements from a setting identical to the third treatment. Our findings confirm that men anticipate their low perceived honesty, which is consistent with the results from the first part.
Stable High Capacity Video Steganography in Wavelet Domain

Video Steganography provides a secure way of communication by embedding the secret message inside video frames. Supported by a real-time environment, a significant growth in choosing video as a cover file has been noticed in the last decade. Large concealing capacity, more robustness to attacks, and indistinguishability of cover video and stego video are of major concern for any video steganography technique. In this paper, a novel technique has been proposed to achieve the above-mentioned objectives. Implementation of video steganography in the transform domain inherently contributes to improved robustness. We have embedded an image inside a chosen video file using the Lifting Scheme based on the Haar Wavelet Transform. To achieve increased concealing capacity, the Singular Value Decomposition technique has been applied before the actual embedding task. The proposed work is implemented using the MATLAB environment. The suggested method is then tested and analyzed for its robustness, capacity, and imperceptibility. It is concluded that proposed technique is robust against general video processing attacks, like low-pass filtering, scaling, rotation, transformation, and histogram equalization attacks. Additionally, the values for Peak Signal to Noise Ratio, Mean Square Error, Structure Similarity Index Matrix, and Correlation Coefficients were calculated and analyzed as a performance measure of the method.
Second-order total generalized variation based model for restoring images with mixed Poisson — Gaussian noise

Introduction: A common problem in image restoration is image denoising. Among many noise models, the mixed Poisson-Gaussian model has recently aroused considerable interest. Purpose: Development of a model for denoising images corrupted by mixed Poisson-Gaussian noise, along with an algorithm for solving the resulting minimization problem. Results: We proposed a new total variation model for restoring an image with mixed Poisson-Gaussian noise, based on second-order total generalized variation. In order to solve this problem, an efficient alternating minimization algorithm is used. To illustrate its comparison with related methods, experimental results are presented, demonstrating the high efficiency of the proposed approach. Practical relevance: The proposed model allows you to remove mixed Poisson-Gaussian noise in digital images, preserving the edges. The presented numerical results demonstrate the competitive features of the proposed model.
Kapitel 19 Social Bots – wie Algorithmen Meinungen beeinflussen

The Social Media Handbook provides guidance on long-term developments in the ever-changing social media sector and explains fundamental interrelationships in this field. It describes a strategy model for the development of one’s own solutions, summarises the theories, methods and models of leading authors and shows their practical application, while also highlighting current developments and dealing with the topic of data processing in social media. An examination of the platform economy with its economic functions facilitates the classification of business models in social media. The book also shows how platforms and their algorithms can influence our actions and shape our opinions. With contributions by Prof. Karin Bjerregaard Schlüter, Andrea Braun, Franziska Geue, Tobias Knopf, Markus Korbien, Prof. Dr. Daniel Michelis, Stefan Pfaff, Thanh H. Pham, Tom Reichstein, Prof. Dr. Anna Riedel, Michael Sarbacher, Prof. Dr. Dr. Thomas Schildhauer, Prof. Dr. Hendrik Send, Dr. Stefan Stumpp, Prof. Dr. Sebastian Volkmann, Jan-Benedikt Weber, Julia Weißhaupt, Norman Wiebach und Prof. Dr. Christian Wissing.
ANALISA KUALITAS SISTEM INFORMASI E-RAPORT PADA SEKOLAH SMPN 5 KOTA TANGERANG TERHADAP KEPUASAN PENGGUNA MENGGUNAKAN METODE WEBQUAL 4.0

The e-Raport system is used as an information system designed by schools to inform parents of student development by schools, which has been implemented in all state schools in various provinces in Indonesia including Banten province. SMPN 5 Tangerang is one of the public schools in Tangerang city that uses the e-Raport System as a substitute for reports of students' learning outcomes manually. The problem raised in this study is whether the quality of the e-Raport System affects user satisfaction. This research focuses on user satisfaction e-Raport system using webQual 4.0 method consisting of 3 variables namely usability, information quality, and interaction quality. The data used in the primary data, namely by distributing questionnaires using a likert scale with 20 points of statement to 400 respondents. Data obtained and processed using multiple linear regression analysis techniques using SPSS software. Based on webqual variable 4.0, the analysis results showed that the quality of usefulness, information quality, and interaction quality influenced user satisfaction.
An Energy-Efficient Context Aware Solution for Environmental Assessment

The paper focuses on presenting the advantages of context aware cyber-physical systems through an experimental platform capable of assessing its surroundings and self-performing decisions. The context aware paradigm is present in the control law implementation with various advantages such as energy efficiency as well as in the environmental measurements that trigger the robot to perform context-relevant decisions. The platform provides high versatility and the results presented throughout the study can be adapted to a manifold of multidisciplinary fields.
Shallow Discourse Parsing for German

While the last few decades have seen impressive improvements in several areas in Natural Language Processing, asking a computer to make sense of the discourse of utterances in a text remains challenging. There are several different theories that aim to describe and analyse the coherent structure that a well-written text inhibits. These theories have varying degrees of applicability and feasibility for practical use. Presumably the most data-driven of these theories is the paradigm that comes with the Penn Discourse TreeBank, a corpus annotated for discourse relations containing over 1 million words. Any language other than English however, can be considered a low-resource language when it comes to discourse processing.This dissertation is about shallow discourse parsing (discourse parsing following the paradigm of the Penn Discourse TreeBank) for German. The limited availability of annotated data for German means the potential of modern, deep-learning based methods relying on such data is also limited. This dissertation explores to what extent machine-learning and more recent deep-learning based methods can be combined with traditional, linguistic feature engineering to improve performance for the discourse parsing task. A pivotal role is played by connective lexicons that exhaustively list the discourse connectives of a particular language along with some of their core properties.To facilitate training and evaluation of the methods proposed in this dissertation, an existing corpus (the Potsdam Commentary Corpus) has been extended and additional data has been annotated from scratch. The approach to end-to-end shallow discourse parsing for German adopts a pipeline architecture and either presents the first results or improves over state-of-the-art for German for the individual sub-tasks of the discourse parsing task, which are, in processing order, connective identification, argument extraction and sense classification. The end-to-end shallow discourse parser for German that has been developed for the purpose of this dissertation is open-source and available online.In the course of writing this dissertation, work has been carried out on several connective lexicons in different languages. Due to their central role and demonstrated usefulness for the methods proposed in this dissertation, strategies are discussed for creating or further developing such lexicons for a particular language, as well as suggestions on how to further increase their usefulness for shallow discourse parsing.
A probabilistic model of dynamic allocation of resources by investment region

Abstract The article discusses the most general, probabilistic model of the dynamic distribution of investment labor resources, describes a method for the optimal distribution of labor resources by investment region and determining the optimal modes of their redistribution taking into account changing conditions. In several regions of investment, several types of labor resources are invested simultaneously.
An Overview of Regulatory Strategies on Crypto-Asset Regulation - Challenges for Financial Regulators in the Western Balkans

The rise of the crypto-asset market has opened up a number of questions on their benefits and risks. As a new form of virtual property, cryptocurrencies and tokens of investment nature are characterized by specific technological infrastructure. In order to understand the regulatory perspective of the crypto-asset market, it is necessary to briefly present the issues related to digital assets infrastructure and the forms of crypto-assets, which blur the line between financial products and virtual property. Crypto-assets represent a form of fintech innovation that could materially affect the financial landscape (notably payments, investments and capital raising), may have impact on the financial sector and therefore may create conditions for regulatory arbitrage where regulated participants operate in a highly regulated environment. The Paper aims to identify the basic issues of the crypto-assets regulatory framework and the extent of applicable regulatory approaches. Comparative regulatory practices of selected countries, as well as the global regulatory perspective of this market, serve as a guide to assess the existing regulatory framework and regulatory challenges, in order to assess urging issues which the regulators in Western Balkan countries are facing. Regulators are confronted with a dilemma: how to promote financial innovation while preserving financial stability and protecting investors. That is why many regulators have adopted a regulatory stance in regulating distributed ledger (blockchain) technologies and tend to formulate regulatory strategies which are risk based, phased and adaptive. Regulatory responses vary from a complete ban on issuing or trading crypto-assets, warnings and principles-based regulation, to extending existing rules on the capital markets and payment systems to specific intermediaries which are licensed almost as financial institutions per se. A tendency towards developing a new set of legislation specifically aiming to register and/or license providers of services connected to virtual property based on cryptography may be observed, and a proposal for an EU regulation on Markets in Crypto-Assets is a clear example.
A multifactorial approach to Korean -(u)m and -ki

This paper takes a corpus-based approach and examines the linguistic properties of two Korean nominalizers -(u)m and -ki. From the Sejong Treebank corpus, all the sentences with -(u)m and -ki are extracted. Twenty linguistic factors are manually encoded into the extracted sentences. Then, all the encoded data are statistically analyzed with (binary) logistic regression. Although we take a monofactorial analysis, we obtain a good statistical model whose C value is 0.956. Through the analysis, the followings are observed: (i) -(u)m and -ki are used with the ratio of 1:9 in Korean, (ii) among twenty linguistic factors, only ten factors are statistically significant, and (iii) not only the verbs which take -(u)m and -ki as a complement but also the verbs which merge with these two nominalizers also play important roles in the determination of nominalizers. (Chungnam National University·Kunsan National University)
Texture based Image Splicing Forgery Recognition using a Passive Approach

With the growing usage of the internet in daily life along with the usage of dominant picture editing software tools in creating forged pictures effortlessly, make us lose trust in the authenticity of the images. For more than a decade, extensive research is going on in the Image forensic area that aims at restoring trustworthiness in images by bringing various tampering detection techniques. In the proposed method, identification of image splicing technique is introduced which depends on the picture texture analysis which characterizes the picture areas by the content of the texture. In this method, an image is characterized by the regions of their texture content. The experimental outcomes describe that the proposed method is effective to identify spliced picture forgery with an accuracy of 79.5%.
Assessment of Cluster Tendency Methods for Visualizing the Data Partitions

Clustering is a technique for grouping data objects based on similarity features that is widely used. Similarity metrics such as Euclidean, cosine, and others are used to generate similarity features. Traditional clustering approaches like k-means and other graph-based strategies are commonly used to find clusters. However, in order to determine the number of clusters, these approaches necessitate user intervention. Traditional clustering algorithms divide data without considering the number of clusters or cluster tendency beforehand. By using either k-means or graph-based clustering methods with an intractable ‘k' value set by the consumer, there is a risk of bad clustering performance. As a result, for prior knowledge of the number of clusters in clustering, it is essential to concentrate on cluster tendency methods. The various visual access tendency (VAT) methods for determining the number of clusters are presented in this paper.
Bulanık Bilişsel Harita Tabanlı PID Kontrolör Tasarımı

Linear proportional-integral-derivative (PID) controllers are the most widely used process controllers in industrial applications due to their simple structures and effective performances. However, performances of these controllers reduce as the nonlinear characteristics or the system orders of the industrial processes increases. Therefore, various nonlinear PID controller models are proposed in literature to improve the control performances of linear PID controllers. In this study, a new nonlinear PID controller design approach is proposed based on the fuzzy cognitive map (FCM) method. Two different FCM based PID controller models are introduced. The first controller model is in the conventional parallel PID structure with three inputs as the error, the derivative of the error, and the integral of the error. On the other hand, the second controller model is in the conventional fuzzy PID form with two inputs as the error and the derivative of the error. In the proposed method, each input signal is firstly fuzzified by using a membership function. Then, causal relationships between inputs and the output are determined by using weight parameters. Finally, the FCM inference is performed by using an activation function. Therefore, the proposed nonlinear PID controllers have four and six tuning parameters, respectively. Simulation studies are performed on a fourth order linear system in order to evaluate the performance of the proposed FCM based PID controller models. The performances of these controller models are compared with a conventional PID controller and a fuzzy PID controller. The comparison results show that the proposed FCM based PID controller models outperform the conventional PID and fuzzy PID controllers.
Polee: RNA-Seq analysis using approximate likelihood

Abstract The analysis of mRNA transcript abundance with RNA-Seq is a central tool in molecular biology research, but often analyses fail to account for the uncertainty in these estimates, which can be significant, especially when trying to disentangle isoforms or duplicated genes. Preserving uncertainty necessitates a full probabilistic model of the all the sequencing reads, which quickly becomes intractable, as experiments can consist of billions of reads. To overcome these limitations, we propose a new method of approximating the likelihood function of a sparse mixture model, using a technique we call the Pólya tree transformation. We demonstrate that substituting this approximation for the real thing achieves most of the benefits with a fraction of the computational costs, leading to more accurate detection of differential transcript expression and transcript coexpression.