Reviewerly/exHarmony huggingface dataset:
Differentially Private Ordinary Least Squares

Linear regression is one of the most prevalent techniques in machine learning; however, it is also common to use linear regression for its explanatory capabilities rather than label prediction. Ordinary Least Squares (OLS) is often used in statistics to establish a correlation between an attribute (e.g. gender) and a label (e.g. income) in the presence of other (potentially correlated) features. OLS assumes a particular model that randomly generates the data, and derives t-values - representing the likelihood of each real value to be the true correlation. Using t-values, OLS can release a confidence interval, which is an interval on the reals that is likely to contain the true correlation; and when this interval does not intersect the origin, we can reject the null hypothesis as it is likely that the true correlation is non-zero.Our work aims at achieving similar guarantees on data under differentially private estimators. First, we show that for well-spread data, the Gaussian Johnson-Lindenstrauss Transform (JLT) gives a very good approximation of t-values; secondly, when JLT approximates Ridge regression (linear regression with l2-regularization) we derive, under certain conditions, confidence intervals using the projected data; lastly, we derive, under different conditions, confidence intervals for the "Analyze Gauss" algorithm of Dwork et al (STOC 2014).
The importance of supervised practice teaching in university study of university students – future teachers of Slovak and foreign languages

The submitted article has a theoretical-empirical character.The main aim of this article is to examine the opinions of university studentsfuture teachers of Slovak and foreign languages about supervised practice teaching by the SWOT analysis.The theoretical part of the article focuses on the elucidation of the realization of the supervised practice teaching´s essence by university students of Slovak and foreign languages´ teaching in the university educational process.There is very important to characterize the objectives, functions, tasks, and the environment of the supervised practice teaching realized by university studentsfuture teachers of Slovak and foreign languages.The empirical part of the submitted contribution is based on the discovery of the surveyed university studentsfuture teachers of Slovak and foreign languages views about the realization of the pedagogical practice by SWOT analysis.The pedagogical practice of higher education teachers of languages is the part of their pre-gradual grounding.Therefore, it is necessary to analyze its importance, its positives, its significance, and also its possible weakness by the view of university students, who are preparing for the languages´ teaching profession.
An Optimized Classification of Apps Reviews for Improving Requirement Engineering

Background: The App Stores, for example, Google Play and Apple Play Store provide a platform that allows users to provide feedback on the apps in the form of reviews. An app review typically includes star rating followed by a comment. Recent studies have shown that these reviews possess a vital source of information that can be used by the app developers and the vendors for improving the future versions of an app. However, in most of the cases, these reviews are present in unstructured form and extracting useful information from them requires a great effort. Objective: This article provides an optimized classification approach that automatically classifies the reviews into a bug report, feature request, and shortcoming &amp; improvement request relevant to Requirement Engineering. Method: Our methodology merges three techniques, namely (1) Text Analysis, (2) Natural Language Processing, and (3) Sentiment Analysis to extract features set, which is then used to automatically classify app reviews into their relevant categories. Results: Result shows that we achieved best results with precision of 67.8 % and recall of 41.5 % with Logistic Regression Machine Learning technique, which we further optimized with PSO nature-inspired algorithm, i.e., with Logistic Regression + PSO, thus, resulting in a precision of 74.4 % and recall of 45.0 %. Conclusion: This optimized automatic classification improves the Requirement Engineering where developer straightforwardly knows what to improve further in the concerned app.
Explaining the Development of Information Security Climate and an Information Security Support Network: A Longitudinal Social Network Analysis

Behavioural information security (InfoSec) research has studied InfoSec at workplaces through the employees’ perceptions of InfoSec climate, which is determined by observable InfoSec practices performed by their colleagues and direct supervisors. Prior studies have identified the antecedents of a positive InfoSec climate, in particular socialisation through the employees’ discussions of InfoSec-related matters to explain the formation of InfoSec climate based on the employees’ individual cognition. We conceptualise six forms of socialisation as six networks, which comprise employees’ provisions of (1) work advice, (2) organisational updates, (3) personal advice, (4) trust for expertise, (5) InfoSec advice, and (6) InfoSec troubleshooting support. The adoption of a longitudinal social network analysis (SNA), called stochastic actor-oriented modelling (SAOM), enabled us to analyse the changes in the socialising patterns and the InfoSec climate perceptions over time. Consequently, this analysis explains the forming mechanisms of the employees’ InfoSec climate perceptions as well as their socialising process in greater detail. Our findings in relation to the forming mechanisms of InfoSec-related socialisation and InfoSec climate, provide practical recommendations to improve organisational InfoSec. This includes identifying influential employees to diffuse InfoSec knowledge within a workplace. Additionally, this research proposes a novel approach for InfoSec behavioural research through the adoption of SNA methods to study InfoSec-related phenomena.
Constructing Novel Block Layouts for Webpage Analysis

Webpage segmentation is the basic building block for a wide range of webpage analysis methods. The rapid development of Web technologies results in more dynamic and complex webpages, which bring new challenges to this area. To improve the performance of webpage segmentation, we propose a two-stage segmentation method that can combine visual, logic, and semantic features of the contents on a webpage. Specifically, we devise a new model to measure the similarities of the elements on webpages based on both visual layout and logic organization in the first stage, and we propose a novel block regrouping method using semantic statistics and visual positions in the second stage. This two-stage method can effectively conduct webpage segmentation on complicated and dynamic webpages. The performance and accuracy of the method are verified by comparing with two existing webpage segmentation methods. The experiment results show that the proposed method significantly outperforms the existing state of the art in terms of higher precision, recall, and accuracy.
Research on Application of Blockchain and Identity-Based Cryptography

The signing of electronic contracts and the certification of important documents are realized by utilizing blockchain, identity-based cryptography and electronic signature technology. Blockchain technology can realize the data distributed ledgers through the entire system nodes, the whole processes, and the entire transactions. Identity-based cryptography and electronic signature technology can achieve data integrity, privacy, and nonrepudiation. All data is shared by all participating nodes, the information in the cyberspace is more transparent, and the behavior is more traceable. A complete and compliant contract signing process and data certification is achieved.
Robust Encrypted Watermarking for Medical Images Based on DWT-DCT and Tent Mapping in Encrypted Domain

Medical images are a special kind of image, since the information contained within must be kept strictly confidential. With a view to protecting personal information, a plethora of research has been carried out in the field of digital watermarking. However, in previous studies, watermarkings were encrypted and the processing of original medical images was unencrypted. Original medical images also contain a large amount of patient information that needs to be kept confidential; therefore, the present paper proposes a robust watermarking algorithm for medical images based on DWT-DCT and a tent map in an encrypted domain. Discrete Cosine Transform (DCT) is a form of Fourier transform, and only uses the actual numbers. Discrete Wavelet Transform (DWT) is a type of signal processing tool that is used to discretize the scale and displacement of basic wavele. In the present paper, DWT and DCT are combined to increase robustness. Firstly, the original medical image and the watermarking were encrypted by chaotic tent mapping. The feature vector of the encrypted medical image was extracted by DWT-DCT, and the watermarking was subsequently embedded and extracted. Finally, the normalized correlation coefficient (NC value) between the embedded and extracted watermarking was calculated to observe its robustness. The experimental results show that this algorithm is robust against conventional and geometric attacks.
DTC-SVM based on Interval Type-2 Fuzzy Logic Controller of Double Stator Induction Machine fed by Six-Phase Inverter

The main disadvantage of the classical direct torque control is high torque and flux ripples.This is due to hysteresis comparators suffer from a variable switching frequency and a high torque ripple.Besides, a hybrid strategy; Direct Torque Control with Space Vector Modulation (DTC -SVM) is established using Interval Type-2 Fuzzy Logic Controller (IT2FLC) for enhancing control performance parameters to reducing torque and flux ripple.In this work, a IT2FLC is applied to the DTC-SVM of Double Stator Induction Machine (DSIM).Simulation results are shown to present the robustness and efficiency of the recommended control strategy.
APPLICATION OF AFFECTIVE LEXICONS IN SPORTS TEXT MINING: A CASE STUDY OF FIFA WORLD CUP 2018

World Cup is a major football event that is globally popular and has its very best influence on human emotions. As such, it affects how people verbally discuss football topics on the Internet. In addition, it shows great significance when viewers who usually do not watch other football competitions start paying close attention when their nation plays a World Cup football match. In this paper, fans’ online behaviour during World Cup 2018 was analysed using text mining methods. With the use of emotion analysis, it is noticed that there are different emotional states through which people go while sharing their thoughts with other people about football. Reddit, a discussion Internet website, was used as a generator of user data. Five supervised machine learning algorithms were used to test and revise an existing model. It is affirmed that the model successfully predicts the emotions within the text with an average accuracy of 78%.
Repackaging library workshops into disciplinary bootcamps: Creating graduate student success

Academic libraries typically offer short, stand-alone workshops focused on specific topics. These might be 60- to 120-minute sessions on topics such as an introduction to citation management software, text analysis, 3-D printing, or systematic reviews, and are predominantly offered throughout the fall and spring semesters.
Restricted Boltzmann Machine Vectors for Speaker Clustering and Tracking Tasks in TV Broadcast Shows

Restricted Boltzmann Machines (RBMs) have shown success in both the front-end and backend of speaker verification systems. In this paper, we propose applying RBMs to the front-end for the tasks of speaker clustering and speaker tracking in TV broadcast shows. RBMs are trained to transform utterances into a vector based representation. Because of the lack of data for a test speaker, we propose RBM adaptation to a global model. First, the global model—which is referred to as universal RBM—is trained with all the available background data. Then an adapted RBM model is trained with the data of each test speaker. The visible to hidden weight matrices of the adapted models are concatenated along with the bias vectors and are whitened to generate the vector representation of speakers. These vectors, referred to as RBM vectors, were shown to preserve speaker-specific information and are used in the tasks of speaker clustering and speaker tracking. The evaluation was performed on the audio recordings of Catalan TV Broadcast shows. The experimental results show that our proposed speaker clustering system gained up to 12% relative improvement, in terms of Equal Impurity (EI), over the baseline system. On the other hand, in the task of speaker tracking, our system has a relative improvement of 11% and 7% compared to the baseline system using cosine and Probabilistic Linear Discriminant Analysis (PLDA) scoring, respectively.
MIPS: Instance Placement for Stream Processing Systems Based on Monte Carlo Tree Search

For up-to-date data stream processing systems, e.g., Apache Heron, the distribution of processing units, a.k.a. instance placement, is determined in two stages, i.e., first mapping instances to containers and then mapping containers to servers. The placement, if improperly decided, can induce considerable traffic across servers and inefficient resource allocation. However, it is an open problem to decide the placement effectively, due to the complex interaction among instances, dependency between the decision making in two stages, and the trade-off between traffic reduction and resource utilization improvement. In this paper, we formulate such a problem as two sequential decision making problems. By adopting Monte Carlo Tree Search (MCTS) methods, we propose MIPS, i.e., a MCTS-based Instance Placement Scheme that decides the two-stage placement in a unified manner, achieving a well balance between computational efficiency and optimality. Results from simulations show that, with mild-value of samples, MIPS surpasses baseline schemes with significant improvement in both traffic reduction and utilization. To our best knowledge, this paper is the first to study and solve the two-staged mapping problem in such systems based on Heron.
Go-clone: graph-embedding based clone detector for Golang

Golang (short for Go programming language) is a fast and compiled language, which has been increasingly used in industry due to its excellent performance on concurrent programming. Golang redefines concurrent programming grammar, making it a challenge for traditional clone detection tools and techniques. However, there exist few tools for detecting duplicates or copy-paste related bugs in Golang. Therefore, an effective and efficient code clone detector on Golang is especially needed.
Vocabulary Learning Support System Based on Automatic Image Captioning Technology

Learning context has evident to be an essential part in vocabulary development, however describing learning context for each vocabulary is considered to be difficult. In the human brain, it is relatively easy to describe learning contexts using pictures because pictures describe an immense amount of details at a quick glance that text annotations cannot do. Therefore, in an informal language learning system, pictures can be used to overcome the problems that language learners face in describing learning contexts. The present study aimed to develop a support system that generates and represents learning contexts automatically by analyzing the visual contents of the pictures captured by language learners. Automatic image captioning, a technology of artificial intelligence that connects computer vision and natural language processing is used for analyzing the visual contents of the learners’ captured images. A neural image caption generator model called Show and Tell is trained for image-to-word generation and to describe the context of an image. The three-fold objectives of this research are: First, an intelligent technology that can understand the contents of the picture and capable to generate learning contexts automatically; Second, a leaner can learn multiple vocabularies by using one picture without relying on a representative picture for each vocabulary, and Third, a learner’s prior vocabulary knowledge can be mapped with new learning vocabulary so that previously acquired vocabulary be reviewed and recalled while learning new vocabulary.
CP-SSD: Context Information Scene Perception Object Detection Based on SSD

Single Shot MultiBox Detector (SSD) has achieved good results in object detection but there are problems such as insufficient understanding of context information and loss of features in deep layers. In order to alleviate these problems, we propose a single-shot object detection network Context Perception-SSD (CP-SSD). CP-SSD promotes the network’s understanding of context information by using context information scene perception modules, so as to capture context information for objects of different scales. Deep layer feature map used semantic activation module, through self-supervised learning to adjust the context feature information and channel interdependence, and enhance useful semantic information. CP-SSD was validated on benchmark dataset PASCAL VOC 2007. The experimental results show that, compared with SSD, the mean Average Precision (mAP) of the CP-SSD detection method reaches 77.8%, which is 0.6% higher than that of SSD, and the detection effect was significantly improved in images with difficult to distinguish the object from the background.
Development of a Decision Support System with the use of OLAP-Technologies in the National Terminological Information Environment

In order to improve analytical activities and providing support to decision makers in the field of terminology, the article proposes a decision support system developed based on the data warehouse technologies and online analytical processing OLAP.The architectural and technological model of the system, its integration with the terminological information system is presented.The role of this system in the National Terminology Information System is shown.The article also presents the structure of the data warehouse and the OLAP-cube developed on its basis.
Combining Lean and Agile Project Management in a Multi-Project Environment: Case Study in a Retail Company

Both the Last Planner System (LPS) and Scrum have been suggested as suitable planning and control methods for dealing with complex project environments.However, most previous studies have investigated the use of those methods for planning and control in single projects, in general managed separately from other projects.This paper reports the results of an investigation which aims to propose a planning and control model for managing construction projects in a multi-project environment.Using Design Science Research (DSR) as a methodological approach, an empirical study has been carried out in a fashion retailer company from Brazil.The model has been built by using a research strategy similar to Action Research.These are the main findings so far: (a) the nature of the project management activities demand a different planning and control approach, compared to what is normally found in relation to planning and control design or construction; and (b) there are challenges on the systematic use of performance measures to support learning and decision-making.These initial conclusions will serve as a basis for incorporating improvements in the model.
Linear Complexity of r-ary Sequences Derived from Euler Quotients Modulo 2p

Based on the Euler quotient modulo 2p (p is an odd prime), we extend the binary sequence with period $$2p^2$$ to r-ary sequence where r is an odd prime divisor of $$(p-1)$$ . We determine exact values of the linear complexity of the new sequences under the assumption $$r^{p-1}\not \equiv 1 \pmod {p^2}$$ , which are larger than half of the period. For cryptographic purpose, the linear complexities of the sequences in this paper are of desired values.
Context-Aware Policy Reuse

Transfer learning can greatly speed up reinforcement learning for a new task by leveraging policies of relevant tasks. Existing works of policy reuse either focus on selecting a single best source policy for reuse without considering contexts, or fail to guarantee learning an optimal policy for a target task. To improve transfer efficiency and guarantee optimality, we develop a novel policy reuse method, called Context-Aware Policy reuSe (CAPS), that enables multi-policy reuse. Our method learns when and which source policy is best for reuse, as well as when to terminate its reuse. CAPS provides theoretical guarantees in convergence and optimality for both source policy selection and target task learning. Empirical results on a grid-based navigation domain and the Pygame Learning Environment demonstrate that CAPS significantly outperforms other state-of-the-art policy reuse methods.
Variation between Different Discourse Types: Literate vs. Oral

This paper deals with the automatic identification of literate and oral discourse in German texts. A range of linguistic features is selected and their role in distinguishing between literate- and oral-oriented registers is investigated, using a decision-tree classifier. It turns out that all of the investigated features are related in some way to oral conceptuality. Especially simple measures of complexity (average sentence and word length) are prominent indicators of oral and literate discourse. In addition, features of reference and deixis (realized by different types of pronouns) also prove to be very useful in determining the degree of orality of different registers.