Reviewerly/exHarmony huggingface dataset:
Information and Communication (Computer) Technologies in the Musical Art of Pop: Pedagogical Aspect

Theincentive to writethis article was the author’s belief that one of the promising directions of further development of the musical art of pop is the introduction of information and communication (computer) technologies in the process of training students in the class of pop vocal. Special attention in the article is paid to the characterization of indicators of professional training of future musicians for working with information (electronic) resources. In the context of the use of electronic educational resources in an information educational environment, the functional actions are listed that are performed by students in mastering such resources. A review of the approaches of researchers to the interpretation of concepts is presented: “pedagogical technology”, “teaching computer technologies”, “music­computer technologies”. The author’s classification of electronic educational resources is given, which is based on the classification developed by S. P. Polozov. The modification proposed in the article is made taking into account the use of such resources in the process of teaching musicians to pop vocals.
Fast paraphrase extraction in Ancient Greek literature

Abstract In this paper, A shorter version of the paper appeared in German in the final report of the Digital Plato project which was funded by the Volkswagen Foundation from 2016 to 2019. [35], [28]. we present a method for paraphrase extraction in Ancient Greek that can be applied to huge text corpora in interactive humanities applications. Since lexical databases and POS tagging are either unavailable or do not achieve sufficient accuracy for ancient languages, our approach is based on pure word embeddings and the word mover’s distance (WMD) [20]. We show how to adapt the WMD approach to paraphrase searching such that the expensive WMD computation has to be computed for a small fraction of the text segments contained in the corpus, only. Formally, the time complexity will be reduced from <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>O</m:mi> <m:mo>(</m:mo> <m:mi>N</m:mi> <m:mo>·</m:mo> <m:msup> <m:mrow> <m:mi>K</m:mi> </m:mrow> <m:mrow> <m:mn>3</m:mn> </m:mrow> </m:msup> <m:mo>·</m:mo> <m:mo>log</m:mo> <m:mi>K</m:mi> <m:mo>)</m:mo> </m:math> \mathcal{O}(N\cdot {K^{3}}\cdot \log K) to <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>O</m:mi> <m:mo>(</m:mo> <m:mi>N</m:mi> <m:mo>+</m:mo> <m:msup> <m:mrow> <m:mi>K</m:mi> </m:mrow> <m:mrow> <m:mn>3</m:mn> </m:mrow> </m:msup> <m:mo>·</m:mo> <m:mo>log</m:mo> <m:mi>K</m:mi> <m:mo>)</m:mo> </m:math> \mathcal{O}(N+{K^{3}}\cdot \log K) , compared to the brute-force approach which computes the WMD between each text segment of the corpus and the search query. N is the length of the corpus and K the size of its vocabulary. The method, which searches not only for paraphrases of the same length as the search query but also for paraphrases of varying lengths, was evaluated on the Thesaurus Linguae Graecae ® (TLG ® ) [25]. The TLG consists of about <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mn>75</m:mn> <m:mo>·</m:mo> <m:msup> <m:mrow> <m:mn>10</m:mn> </m:mrow> <m:mrow> <m:mn>6</m:mn> </m:mrow> </m:msup> </m:math> 75\cdot {10^{6}} Greek words. We searched the whole TLG for paraphrases for given passages of Plato. The experimental results show that our method and the brute-force approach, with only very few exceptions, propose the same text passages in the TLG as possible paraphrases. The computation times of our method are in a range that allows its application in interactive systems and let the humanities scholars work productively and smoothly.
Gender Differences in Information Security Management

A wide and increasing range of different technologies, devices, platforms, applications and services are being used every day by home users.In parallel, home users are also experiencing a range of different online threats and attacks.Indeed, home users are increasingly being targeted as they lack the knowledge and awareness about potential threats and how to protect themselves.The increase in technologies and platforms also increases the burden upon a user to understand how to apply and manage security across the differing technologies, operating systems and applications.Different factors such as age, education, age and gender can have an impact on information security management and awareness.This research tries to investigate and examine the effect of gender differences on information security management and online safety for internet users.An online questionnaire has been conducted and collected 434 participants (311 males and 132 females).The results show that there is a significant difference between males and females in four of the eight identified security practices and aspects.The findings show that males are likely to have better information security behaviour and being protected online more than females.
A Requirement Engineering Approach to User-Centered Design

Abstract This paper introduces a user-centered design method in software engineering practice. This method can develop software more effectively based on the design results of UCD, and make these results be tracked and tested in the future. Another feature of this method is to make UCD as schedulable and manageable as other software development activities. This paper introduces a requirement analysis framework based on three types of usability requirements, which reflect the results of UCD activities in software development. These types of requirements come from the description of UCD in ISO 13407 model.
Sentiment Analysis with Contextual Embeddings and Self-attention

In natural language the intended meaning of a word or phrase is often implicit and depends on the context. In this work, we propose a simple yet effective method for sentiment analysis using contextual embeddings and a self-attention mechanism. The experimental results for three languages, including morphologically rich Polish and German, show that our model is comparable to or even outperforms state-of-the-art models. In all cases the superiority of models leveraging contextual embeddings is demonstrated. Finally, this work is intended as a step towards introducing a universal, multilingual sentiment classifier.
Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering

Unsupervised domain adaptation (UDA) is to make predictions for unlabeled data on a target domain, given labeled data on a source domain whose distribution shifts from the target one. Mainstream UDA methods learn aligned features between the two domains, such that a classifier trained on the source features can be readily applied to the target ones. However, such a transferring strategy has a potential risk of damaging the intrinsic discrimination of target data. To alleviate this risk, we are motivated by the assumption of structural domain similarity, and propose to directly uncover the intrinsic target discrimination via discriminative clustering of target data. We constrain the clustering solutions using structural source regularization that hinges on our assumed structural domain similarity. Technically, we use a flexible framework of deep network based discriminative clustering that minimizes the KL divergence between predictive label distribution of the network and an introduced auxiliary one; replacing the auxiliary distribution with that formed by ground-truth labels of source data implements the structural source regularization via a simple strategy of joint network training. We term our proposed method as Structurally Regularized Deep Clustering (SRDC), where we also enhance target discrimination with clustering of intermediate network features, and enhance structural regularization with soft selection of less divergent source examples. Careful ablation studies show the efficacy of our proposed SRDC. Notably, with no explicit domain alignment, SRDC outperforms all existing methods on three UDA benchmarks.
Structure Preserving Encoding of Non-euclidean Similarity Data

Domain-specific proximity measures, like divergence measures in signal processing or alignment scores in bioinformatics, often lead to non-metric, indefinite similarities or dissimilarities.However, many classical learning algorithms like kernel machines assume metric properties and struggle with such metric violations.For example, the classical support vector machine is no longer able to converge to an optimum.One possible direction to solve the indefiniteness problem is to transform the non-metric (dis-)similarity data into positive (semi-)definite matrices.For this purpose, many approaches have been proposed that adapt the eigenspectrum of the given data such that positive definiteness is ensured.Unfortunately, most of these approaches modify the eigenspectrum in such a strong manner that valuable information is removed or noise is added to the data.In particular, the shift operation has attracted a lot of interest in the past few years despite its frequently reoccurring disadvantages.In this work, we propose a modified advanced shift correction method that enables the preservation of the eigenspectrum structure of the data by means of a low-rank approximated nullspace correction.We compare our advanced shift to classical eigenvalue corrections like eigenvalue clipping, flipping, squaring, and shifting on several benchmark data.The impact of a low-rank approximation on the data's eigenspectrum is analyzed.
A Space-Variant Nonlinear Algorithm for Denoising Omnidirectional Images Corrupted by Poisson Noise

An omnidirectional camera uses a conventional camera in conjunction with a quadratic mirror for capturing a 360° field of view in real-time. The resolution of an omnidirectional image, however, is non-uniform due to the mirror curvature. It can lead to the degradation of performance of classical methods when applied directly to omnidirectional images. In this paper, we investigate an adaptive algorithm for denoising omnidirectional images corrupted by Poisson noise. We propose a space-variant regularization based on total variation, which is adaptive with the non-uniform resolution of omnidirectional images. The weighted functions based on the mean curvature of an image surface are formulated to adapt the fidelity and the regularization terms to fit the data and preserve fine details. Experimental results demonstrate that the proposed method can better remove noise while maintaining features in comparison with the state-of-the-art methods.
Side-Channel Attacks on Blinded Scalar Multiplications Revisited

In a series of recent articles (from 2011 to 2017), Schindler et al. show that exponent/scalar blinding is not as effective a countermeasure as expected against side-channel attacks targeting RSA modular exponentiation and ECC scalar multiplication. Precisely, these works demonstrate that if an attacker is able to retrieve many randomizations of the same secret, this secret can be fully recovered even when a significative proportion of the blinded secret bits are erroneous. With a focus on ECC, this paper improves the best results of Schindler et al. in the specific case of structured-order elliptic curves. Our results show that larger blinding material and higher error rates can be successfully handled by an attacker in practice. This study also opens new directions in this line of work by the proposal of a three-steps attack process that isolates the attack critical path (in terms of complexity and success rate) and hence eases the development of future solutions.
On the Importance of Word Order Information in Cross-lingual Sequence Labeling

Cross-lingual models trained on source language tasks possess the capability to directly transfer to target languages. However, since word order variances generally exist in different languages, cross-lingual models that overfit into the word order of the source language could have sub-optimal performance in target languages. In this paper, we hypothesize that reducing the word order information fitted into the models can improve the adaptation performance in target languages. To verify this hypothesis, we introduce several methods to make models encode less word order information of the source language and test them based on cross-lingual word embeddings and the pre-trained multilingual model. Experimental results on three sequence labeling tasks (i.e., part-of-speech tagging, named entity recognition and slot filling tasks) show that reducing word order information injected into the model can achieve better zero-shot cross-lingual performance. Further analysis illustrates that fitting excessive or insufficient word order information into the model results in inferior cross-lingual performance. Moreover, our proposed methods can also be applied to strong cross-lingual models and further improve their performance.
Pengembangan Modul Elektronik Dengan Feedback Berbasis Android Materi Suhu Dan Kalor Untuk Siswa SMA/MA

The integration of ICT in the learning process has a positive effect on students' concepts understanding and learning interests. Android is a learning media tool that can store lots of interesting learning materials, such as electronic modules. This study was conducted to determine the feasibility of an Android-based electronic module with temperature and heat material feedback for high school / MA students. This research method is used in the ADDIE model (analysis, design, development, implementation, and evaluation). The electronic module was tested for validation by the validator with an average value of 3.49 and the module readability test with respondents of 85 students who obtained an average value of 79.3%. Based on the data obtained, it can be conclude that the electronic module has decent criteria.
Complete Moving Object Detection in the Context of Robust Subspace Learning

Complete moving object detection plays a vital role in many applications of computer vision. For instance, depth estimation, scene understanding, object interaction, semantic segmentation, accident detection and avoidance in case of moving vehicles on a highway. However, it becomes challenging in the presence of dynamic backgrounds, camouflage, bootstrapping, varying illumination conditions, and noise. Over the past decade, robust subspace learning based methods addressed the moving objects detection problem with excellent performance. However, the moving objects detected by these methods are incomplete, unable to generate the occluded parts. Indeed, complete or occlusion-free moving object detection is still challenging for these methods. In the current work, we address this challenge by proposing a conditional Generative Adversarial Network (cGAN) conditioned on non-occluded moving object pixels during training. It therefore learns the subspace spanned by the moving objects covering all the dynamic variations and semantic information. While testing, our proposed Complete cGAN (CcGAN) is able to generate complete occlusion free moving objects in challenging conditions. The experimental evaluations of our proposed method are performed on SABS benchmark dataset and compared with 14 state-of-the-art methods, including both robust subspace and deep learning based methods. Our experiments demonstrate the superiority of our proposed model over both types of existing methods.
The Ideology Behind Sesandaran Dance Show in Bali

Street children are the most physically visible and vulnerable groups present in the society who live under extreme conditions of the street without care or guidance of parents or any adult.They are portrayed as socially unacceptable with least concerns from the policymakers.This paper explores the concept of street children in Indian context and also evaluates various policies and planning developed in India after its Independence.Further, the paper will explore the importance of childhood concept and the role of anthropologist in it.After evaluating the policies and the concept of street children and childhood, the paper also suggests several models/ approaches that will improve the lives of street children and allows the integration of these children into the mainstream society.
Assessment of road traffic performance of the Tsunami evacuation road in Padang Municipality area based on the traffic volume simulation approach

There are many cities on the west coast of the Sumatra, which are at high risk of the Tsunami disaster. Regional Regulations on Regional Spatial Planning for each City/ Regency have compiled disaster mitigation by constructing several evacuation roads. This study wants to illustrate: what are the volume of traffic generation and road performance, if there is a Tsunami disaster. The simulation is developed by predicting traffic volume based on parameters, population density, vehicle ownership, land use, and activities in the area around the road. The assessment was carried out on two tsunami evacuation roads in the city of Padang, West Sumatra Province. The results show that the highest traffic volume occurred in the period from 06.30 a.m until 3:00 p.m., during school activities. One of the roads will not be able to accommodate the volume of traffic during a disaster, due to significant traffic congestion. This study shows that: (1) the period of activity and land use are two main parameters, which must be considered in designing tsunami evacuation roads, (2) The degree of saturation ratio and the ratio between the capacity of sections of Tsunami evacuation routes can be proposed as a parameter for assessing the performance of Tsunami evacuation roads in urban areas.
Analytical Systems in the Assessment of Library Web Sites

The article considers web representations of libraries as important modern information channels through which the libraries operate in the virtual space. The author provides conceptual rationale for improving the operational efficiency of the current level of library web sites based on the data analysis of modern web analytics systems. The article studies the analytical potential of tools for optimizing web resources in the information environment, substantiates scientific and applied interest in the methods of assessment of library web site using the capabilities of analytical systems, analysed from three angles: evaluation of web resources by means of key indicators, assessment of goal achievement and evaluation by means of visual analytical maps. To solve the set tasks there were used analytical tools: Google Analytics, Yandex.Metrics. The author carried out the study to determine the goal achievement and relevance of the web site sections using the example of the State Public Scientific Technological Library of the Siberian Branch of the Russian Academy of Sciences (GPNTB SB RAS) web site. To assess the efficiency of library web resources, it is important not only to obtain statistical data from analytical tools, but also to interpret them correctly. The article presents an attempt of qualitative assessment of some key performance indicators. Collection and analysis of webometric indicators of analytical tools are of practical value, being an impetus to action — reorganization of library web resources. Based on the data analysis of Yandex.Metrics visual heat maps, the corresponding changes have been made to the web site menu. The author conducted comparative analysis of the web site traffic data before and after the changes made. The study concluded that the use of modern analytical tools in the web site management has positive dynamics for its development on the web environment. The results of the use of modern analytical tools for evaluation of library sites demonstrate the importance of their use in analysis of the efficiency of library in the virtual space, which, in turn, will have a positive impact on improving the image and competitiveness in the information and library sphere as a whole.
New Color Image Encryption for Medical Images Based on Three Dimensional Generalized Chaotic Cat Map and Combined Cellular Automata

Medical images are transmitted via the Internet or the hospital intranet which include many important information about the patient's personal information.Medical image encryption is a technology that can effectively protect the information contained in these medical images.In this paper, we give a secure and trusty Combined Cellular Automata (CoCA) based medical image encryption algorithm.CoCA is made up of a nonlinear CA and a linear 90/150 maximum length CA, and three dimensional generalized chaotic map.The proposed algorithm consists of two phases.The first phase encryption process that changes pixel values of a given image using two CoCAs.The second phase is to change the position of each pixel in the image encrypted using the three dimensional generalized chaotic cat map that can change with effect the position of pixels not only in two dimensions horizontally and vertically but also in color plane three colored channels simultaneously.We show the stability and high reliability of the given color medical image cryptosystem through detailed analysis and various statistical experimental tests.
Annotation Sniffer: A tool to Extract Code Annotations Metrics

Enterprise Java frameworks and APIs such as JPA (Java Persistence API), Spring, EJB (Enterprise Java Bean), and JUnit make extensive use of code annotations as means to allow applications to configure custom metadata and execute specific behavior.Observing the top 30 ranked Java projects on GitHub, they have, on average, 76% of classes with at least one annotation.Some projects may have more than 90% of its classes annotated.To measure code annotations usage and analyze their distribution, our work in (P.Lima et al., 2018) proposed a novel suite of software metrics dedicated to code annotations.We used a Percentile Rank Analysis approach (Meirelles, 2013) to obtain threshold values.
Guidance and Visualization of Optimized Packing Solutions

Packing optimization is a challenging and time-consuming task for a number of industry and logistics applications. Efficient packing can reduce the cost of storage and shipping and also guarantee that damage will not occur during shipping. To help address this problem, we propose a spatial augmented reality-based support system for assisting workers with packing optimization. Our packing support system first uses an RGB-D camera to acquire color and depth information of the items to be packed and the destination container. Then, object segmentation and dimension estimation are simultaneously carried out, and the position and orientation of packing items inside the container are calculated using a bin-packing algorithm. Finally, the optimized packing instructions are projected onto the user's work area. We then developed and tested two user interfaces (UI) for visualizing instructions called Rotation and Object Movement. Experimental results showed that both methods help reduce packing time up to 57.89% in Rotation and 55.63% in Object Movement, compared to a non-UI method.