Reviewerly/exHarmony huggingface dataset:
Teamwork Competency When Graduating from University: Comparison between Participants and Non-Participants in a Skills Training to Develop Teamwork Competency

This paper compared the teamwork competency of university students when graduating. Participants and non-participants in a skills training conducted at university to develop teamwork competency (Tabata, 2016) took part in this study. They completed self-report scales assessing general social skills, teamwork competency, communication, team orientation, back-up, monitoring, and leadership, 20 months after completing the training. Results indicated that compared to non-participants, participants in the skills training possessed higher social skills scores and many sub-elements of teamwork competency when graduating.
A7׳ta: Data on a monolingual Arabic parallel corpus for grammar checking

Grammar error correction can be considered as a "translation" problem, such that an erroneous sentence is "translated" into a correct version of the sentence in the same language. This can be accomplished by employing techniques like Statistical Machine Translation (SMT) or Neural Machine Translation (NMT). Producing models for SMT or NMT for the goal of grammar correction requires monolingual parallel corpora of a certain language. This data article presents a monolingual parallel corpus of Arabic text called A7׳ta (). It contains 470 erroneous sentences and their 470 error-free counterparts. This is an Arabic parallel corpus that can be used as a linguistic resource for Arabic natural language processing (NLP) mainly to train sequence-to-sequence models for grammar checking. Sentences were manually collected from a book that has been prepared as a guide for correctly writing and using Arabic grammar and other linguistic features. Although there are a number of available Arabic corpora of errors and corrections [2] such as QALB [10] and Arabic Learner Corpus [11], the data we present in this article is an effort to increase the number of freely available Arabic corpora of errors and corrections by providing a detailed error specification and leveraging the work of language experts.
Security Framework for Distributed Database System

This research aims to study various Symmetrical Algorithms, while the main objective of this study is to find out a suitable algorithm for the encryption of any specific size of text file where the experiment of each algorithm is based on encryption of different sizes of the text files, which are in "10 KB to 5 MB", and also to calculate the time duration that each algorithm takes to encrypt or to decrypt the particular size of each text file.There are many types of encryption algorithm, which can be used to encrypt the computerized information in different Organizations, whose all algorithms can encrypt and decrypt any size of text file, but the time duration of each Algorithm during the encryption or decryption process of specific file size is not fixed.Some of the algorithms are suitable for encryption of specific ranges of the file size, or some of algorithms are functional while encryption small size of files, and others algorithms are functional for encryption of big size of text files, based on the time duration disparity among symmetric algorithms during encryption of text files.In this study five symmetrical algorithms are merged in one program using classes and concept of inheritance in the form that if encryption is needed, the program will select the file and it checks the size of the text file.After this process the program automatically will select the suitable encryption algorithm to encrypt the specific text file according to the range of the file size.Knowing that the file size before or after encryption will not change or is stable, in this case of the decryption algorithm will apply the same process of encryption while decrypting files, the program of encryption and decryption code will write using visual Studio 2013.The result will be analyzed with R program (R software), the cipher text will appear in the format of UTF8 which means Unicode Transformation Format, "8" Means "8" bits to represent a character, the size format that will apply in the program will be in format of KB (kilo Byte).
Differentiated Distribution Recovery for Neural Text Generation

Neural language models based on recurrent neural networks (RNNLM) have significantly improved the performance for text generation, yet the quality of generated text represented by Turing Test pass rate is still far from satisfying. Some researchers propose to use adversarial training or reinforcement learning to promote the quality, however, such methods usually introduce great challenges in the training and parameter tuning processes. Through our analysis, we find the problem of RNNLM comes from the usage of maximum likelihood estimation (MLE) as the objective function, which requires the generated distribution to precisely recover the true distribution. Such requirement favors high generation diversity which restricted the generation quality. This is not suitable when the overall quality is low, since high generation diversity usually indicates lot of errors rather than diverse good samples. In this paper, we propose to achieve differentiated distribution recovery, DDR for short. The key idea is to make the optimal generation probability proportional to the β-th power of the true probability, where β &gt; 1. In this way, the generation quality can be greatly improved by sacrificing diversity from noises and rare patterns. Experiments on synthetic data and two public text datasets show that our DDR method achieves more flexible quality-diversity trade-off and higher Turing Test pass rate, as compared with baseline methods including RNNLM, SeqGAN and LeakGAN.
Further improvement of factoring &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ N = p^r q^s$\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; with partial known bits

We revisit the factoring with known bits problem on RSA moduli. In 1996, Coppersmith showed that the RSA modulus $N = pq$ with balanced $p,q$ can be efficiently factored, if the high order $\frac{1}{4} \log_2 N$ bits of one prime factor is given. Later, this important result is also generalized to the factorization of RSA variants moduli such as $N = p^r q$ or $N = p_1 p_2 ··· p_n$. In 2000, Lim et al. proposed a new RSA variant with the modulus of the form $N = p^r q^s$, which is much faster in the decryption process than the standard RSA. Then from 2015 to 2018, in order to investigate the security property of this RSA variant, Lu et al. and Coron et al. have presented three works studying the polynomial-time factorization of $N = p^r q^s$ with partial known bits of $p^u q^v$ (or one of the prime factors $p,q$) for different choices of $u, v$. In this paper, we present a new lattice construction used for Coppersmith's method, and thus improve previous results. Namely, our result requires fewer known bits to recover the prime factors $p,q$. We also generalize our result to the factorization of $N = p_1^{r_1}p_2^{r_2}··· p_n^{r_n}$.
Development of a 3D Conceptual Design Environment Using a Head Mounted Display Virtual Reality System

Engineering design is a resource intensive process with over half of a product's total cost being attributed to design stage decisions.Developing product designs often involves creating complex 3D models in a 2D environment, a non-trivial task.This paper details a proof of concept VR environment displayed in a commodity HMD, specifically the HTC Vive.The environment allows a user to create full-scale, 3D, product geometry at the conceptual phase of the design process.The conceptual design environment contains a World-in-Miniature (WIM) model for enhanced usability.This method allows a user to efficiently manipulate full-scale geometry by adjusting the corresponding parts on a WIM model.Free-form mesh deformation was also implemented to provide designers with flexibility and efficiency not found in traditional design packages.Vital design metrics (e.g., cost, weight, and center of mass) were incorporated to allow a user to perform preliminary design analysis to assess product feasibility.Throughout the development process, the unique challenges and affordances associated with commodity HMDs were identified, explored, and discussed in this paper.Ultimately, the VR application discussed in this paper allows the user to easily create, assess, and view conceptual 3D designs without the constraints of traditional CAD software.
Shelf life: Identifying the abandonment of online digital humanities projects

Abstract A large portion of the research carried out in the digital humanities has an online digital object (usually referred as a project) as one of its components. In turn, these online digital objects can be catalogued as distributed resources, which implies that the administrative control of information related to a topic may be spread across online resources and/or collections maintained by multiple scholars in different institutions. This administrative decentralization can lead to changes in content that are often unexpected by a researcher, which can be caused by different factors or circumstances. This reasoning led us to formulate the following question: When can online digital humanities projects be considered abandoned? In this article, we carry out a study on the persistence and average life span of online projects in the digital humanities. More specifically, we will elaborate on their reliance on distributed resources and methods for measuring their shelf life: the average length of time that a digital project can endure without updates until it can ultimately be considered abandoned by its researcher.
Voice Presentation Attack Detection Using Convolutional Neural Networks

Current state-of-the-art automatic speaker verification (ASV) systems are prone to spoofing. The security and reliability of ASV systems can be threatened by different types of spoofing attacks using voice conversion, synthetic speech, or recorded passphrase. It is therefore essential to develop countermeasure techniques which can detect such spoofed speech. Inspired by the success of deep learning approaches in various classification tasks, this work presents an in-depth study of convolutional neural networks (CNNs) for spoofing detection in automatic speaker verification (ASV) systems. Specifically, we have compared the use of three different CNNs architectures: AlexNet, CNNs with max-feature-map activation, and an ensemble of standard CNNs for developing spoofing countermeasures, and discussed their potential to avoid overfitting due to small amounts of training data that is usually available in this task. We used popular deep learning toolkits for the system implementation and have released the implementation code of our methods publicly. We have evaluated the proposed countermeasure systems for detecting replay attacks on recently released spoofing corpora ASVspoof 2017, and also provided in-depth visual analyses of CNNs to aid for future research in this area.
Selecting Orientation-Insensitive Features for Activity Recognition from Accelerometers

Activity recognition from sensors is a classification problem over time-series data. Some research in the area utilize time and frequency domain handcrafted features that differ between datasets. Another categorically different approach is to use deep learning methods for feature learning. This paper explores a middle ground in which an off-the-shelf feature extractor is used to generate a large number of candidate time-domain features followed by a feature selector that was designed to reduce the bias toward specific classification techniques. Moreover, this paper advocates the use of features that are mostly insensitive to sensor orientation and show their applicability to the activity recognition problem. The proposed approach is evaluated using six different publicly available datasets collected under various conditions using different experimental protocols and shows comparable or higher accuracy than state-of-the-art methods on most datasets but usually using an order of magnitude fewer features.
Using Cloud Computing in Financial Institutions in Russia

The modern economy is developing under the influence of information and communication technologies. Cloud computing, Big Data, cyberphysical systems, here is an incomplete list of new directions, which with huge success is seen in every sphere of management and economy. This chapter deals with innovative ways of development connected with the use of cloud computing in the financial sphere. The history of occurrence and the basic preconditions in the development of cloud technology are analyzed. The major stages of the adoption of cloud computing in world development are considered. The main characteristics of cloud computing are explored: the types of clouds and methods of their provision are presented. The analysis of the Russian cloud technology market is carried out, the basic features and tendencies are revealed, and the further ways of development are defined. The evaluation of the possibility of using cloud computing in the financial institutions of the Russian Federation is given.
Noise Characterization: Keeping Reduction Based Per-turbed Quantum Walk Search Optimal

In a recent work by Novo et al. (Sci. Rep. 5, 13304, 2015), the invariant subspace method was applied to the study of continuous-time quantum walk (CTQW). In this work, we adopt the aforementioned method to investigate the optimality of a perturbed quantum walk search of a marked element in a noisy environment on various graphs. We formulate the necessary condition of the noise distribution in the system such that the invariant subspace method remains effective and eﬃcient. Based on the noise, we further formulate how to set the appropriate coupling factor to preserve the optimality of the quantum walker.
THE ROLE OF FIRST-BEST GOVERNANCE INSTITUTIONS ON GROWTH IN LEAST DEVELOPED COUNTRIES

The study addresses the issue of the low level of GDP per capita growth in Least Developed Countries (LDCs), with special emphasis on the role of first-best governance institutions as a main determinant of growth.LDCs can be characterized by a clear deficit in first-best governance institutions compared to high-income OECD countries supporting the argument that economies that are different for a variety of reasons will differ both in their institutions and in their income per capita.The contribution of the study is to fill the gap in the new political economy of growth by establishing the mechanism of how first-best governance institutions affect growth through the political, economical, administrative and democratic channels.Although the adoption of first-best governance institutions of advanced countries to LDCs is not a panacea to sustainable economic growth, improvement of first-best governance institutions in LDCs significantly induce growth, since investments in governance institutions are subject to diminishing returns, so that the benefits are most pronounced for smaller and developing economies.
Distant Technologies in Teaching Legal Disciplines to Students of Pedagogical Training Programs

The relevance of the studied problem is caused by the need to improve the methodology of legal disciplines teaching for students of pedagogical training programs and the need to develop the methods and forms of application of distant technologies of teaching in this process. The article is aimed at studying the opportunities of distant technologies for improvement of assimilation quality of training material for legal disciplines by students of pedagogical training programs, in particular when studying a number of key questions of interobject character. Topical issues of teaching legal disciplines for students of higher education institutions of pedagogical professions, first of all, for future teachers of social science and law are considered. The leading method of research of this problem is the pedagogical analysis of methods and forms of the use of distant technologies of teaching legal disciplines. The results of the research are the following: the carried-out analysis of the opportunities to use distant technologies of tutoring in teaching legal disciplines for students of pedagogical training program; identification of key topical issues which arise when studying legal disciplines by future teachers; the developed recommendations about improvement of training of students of pedagogical training programs. The materials of the article can be useful for: the faculty of higher education institutions which are engaged in teaching the students of the pedagogical training programs; students and graduate students of the pedagogical training programs; for teachers, in particular young specialists; for the heads of higher education institutions that have pedagogical directions of students training; to methodologists who are engaged in curriculum development for students of pedagogical training program.
Expanding the Research Data Management Service Portfolio at Bielefeld University According to the Three-pillar Principle Towards Data FAIRness

Research Data Management at Bielefeld University is considered as a cross-cutting task among central facilities and research groups at the faculties. While initially started as project “Bielefeld Data Informium” lasting over seven years (2010–2015), it is now being expanded by setting up a Competence Center for Research Data. The evolution of the institutional RDM is based on the three-pillar principle: 1. Policies, 2. Technical infrastructure and 3. Support structures. The problem of data quality and the issues with reproducibility of research data is addressed in the project Conquaire. It is creating an infrastructure for the processing and versioning of research data which will finally allow publishing of research data in the institutional repository. Conquaire extends the existing RDM infrastructure in three ways: with a Collaborative Platform, Data Quality Checking, and Reproducible Research.
Enterprise Cloud Computing Project for Connecting Higher Education Institutions: A Case Study of the UAE

The new cloud computing technology is very much in the business and economic horizon, extending its horizons to non-business sectors like education.This study is a study of the need and applicability of the fast spreading business enterprises' external cloud computing technology, to the higher educational institution in the United Arab Emirates (UAE), the educational hub of the MENA region, in terms of three premier higher educational institutions-Zayed University (ZU), Higher College of Technology (HCT), and the UAE University (UAEU) for their collaboration services for better educational services.Employing the interview method to a sample of administrators, it was found that the institutions were lacking in cloud computing (CC) resulting in delays in student services, such as inter-university transfers of students.The administrators, however, were convinced of CC technology for inter-institutional collaboration.For this, it is suggested to establish a platform of cloud computing services, with required staff training and motivation for efficient and collaboration functioning of the HEIs in the UAE, which is evolving into a global educational center.As this is not the end of the problem, there is a need for further research in the problem, such as staff exchange, and research cooperation as it is a modest but worthwhile contribution to the educational dimension of cloud computing for educational efficiency.
Modern random number generator design – Case study on a secured PLL-based TRNG

Abstract Random number generators (RNGs) are basic cryptographic primitives. They are used to generate cryptographic keys, initialization vectors, challenges and nonces in cryptographic protocols, and random masks in countermeasures against side channel attacks. RNGs designed for cryptography must generate unpredictable random numbers. According to recent security standards, the unpredictability of generated random numbers must be thoroughly evaluated. In this paper, we provide a concrete example – a phase-locked loop based RNG protected by novel dedicated embedded tests, on which we show how stringent security requirements including unpredictability of generated numbers can be met, while respecting the standards.
Understanding mobile money grievances from tweets

Access to financial services through a mobile phone, known as Mobile Financial Services (MFS), creates an opportunity to expand the reach of financial services to the 1.7 billion unbanked adults worldwide. Nevertheless, MFS adoption has been inconsistent, which motivates a need to identify the challenges that MFS users confront in different countries. In this work, we explore the Twitter as a potential data source to understand such challenges. More broadly, we assess whether (and how) publicly available Twitter data can augment the findings of expensive, large-scale research studies on MFS barriers. Our Qualitative Content Analysis of 9,000 mobile money grievance tweets that were extracted from 54 MFS customer care twitter feeds across six countries reveals service and access issues, incorrect transactions, and fraud as three main challenges MFS users report on Twitter. We discuss the nuances around these challenges and the substantial differences between the common issues reported in different countries. Ultimately, we conclude that Twitter data can elucidate the challenges of MFS adoption and also that it can augment the results of other types of MFS studies.
Privacy Risk for Individual Basket Patterns

Retail data are of fundamental importance for businesses and enterprises that want to understand the purchasing behaviour of their customers. Such data is also useful to develop analytical services and for marketing purposes, often based on individual purchasing patterns. However, retail data and extracted models may also provide very sensitive information to possible malicious third parties. Therefore, in this paper we propose a methodology for empirically assessing privacy risk in the releasing of individual purchasing data. The experiments on real-world retail data show that although individual patterns describe a summary of the customer activity, they may be successful used for the customer re-identifiation.
The Hazards of Misusing the Smart Contract: An AHP Approach to Its Risk

This article explores four critical groups of systematic risk embedded in smart contract employment using the analytic hierarchy process (AHP).The four principal risk analysis groups include: 1) transparency in the light of corporate governance 2) IT security 3) contract management automation and 4) legality.The AHP assists both decision-makers and stakeholders alike in the evaluation process essential for identifying potential technological constraints posed within a permissioned blockchain environment using peer-to-peer format in the absence of digital currency.Based upon critical assessment, the AHP methodology enables pairwise comparisons among different features and consequently increases the knowledge regarding these attributes in light of the software's risk assessment.