Reviewerly/exHarmony huggingface dataset:
O aliciamento sexual de menores na internet: contributos para o seu conhecimento e prevenção

The growing use of the Internet and of social media by children and young people has spurred the conditions for the development of sexual grooming, a process that facilitates the perpetration of sexual crimes, as it allows an easier access to a high number of potential victims, increases the opportunities to perpetrate both online and personal contact with minors, and facilitates the identification of vulnerabilities in the victims. This is a growing phenomenon and it is urgent to know it, in order to develop effective strategies to promote the safety of children and young people on the Internet and to prevent the harm resulting from this type of abuse. In order to do it we developed a qualitative study, using semi-structured interviews and document analysis, submitted to a content analysis (a categorical semantic analysis). The sample consisted of 4 male offenders, 2 face-to-face interviewees and 2 involved in online sexualized dialogues with minors, and 1 female victim, who was also interviewed.The results allowed to distinguish two distinct approaches, one very direct, with the almost immediate introduction of sexual themes, and another approach in which the subject adopt a set of strategies that are based on the creation and maintenance of a relational bond, being consistent with what is considered the core strategies of the grooming process. While in the most direct approach the offender seems to take advantage of the typical sexual curiosity of the developmental period, in the second approach the subject uses a relational bond to get the involvement of the minor and avoid disclosure. Although grooming appears in the literature as a major component of online sexual interactions with minors, we found in our research that grooming is not necessary for achieving sexual contacts or child sexual abuse.
Interlayer Selective Attention Network for Robust Personalized Wake-Up Word Detection

Previous research methods on wake-up word detection (WWD) have been proposed with focus on finding a decent word representation that can well express the characteristics of a word. However, there are various obstacles such as noise and reverberation which make it difficult in real-world environments where WWD works. To tackle this, we propose a novel architecture called interlayer selective attention network (ISAN) which generates more robust word representation by introducing the concept of selective attention. Experiments in real-world scenarios demonstrated that the proposed ISAN outperformed several baseline methods as well as other attention methods. In addition, the effectiveness of ISAN was analyzed with visualizations.
APPLICATION OF VIRTUAL REALITY FOR EDUCATION AT TECHNICAL UNIVERSITY

Appears in: ICERI2019 Proceedings Publication year: 2019Pages: 7437-7444ISBN: 978-84-09-14755-7ISSN: 2340-1095doi: 10.21125/iceri.2019.1775Conference name: 12th annual International Conference of Education, Research and InnovationDates: 11-13 November, 2019Location: Seville, Spain
The Role and Application of Picture Books in Pre-school Practice

Picture books occupy an important place in the life of a child both in family and in school. As a unique combination of illustration and text in a pedagogical mode, picture books teach, incite creative imagination and enrich the cognitive and socio-emotional world of a child. The goal of our research is increase understanding the place and the role of genre diverse picture books in Montenegrin pre-schools in the current context. Pre-school teachers noted key challenges in the use of picture books in pre-school practice in general and singled out proposals for more efficient application. In this paper, we deal with parents’ participation in mediating picture books` content and values to pre-schoolers. We took our research sample from the population of teachers in pre-schools of central, northern and southern region of Montenegro. Our research included 209 pre-school teachers from five kindergartens and 93 respondents from the three institutions, which we interviewed during the seminars. We combined elements of both quantitative and qualitative surveying and interviewing.&#x0D; Our research indicates a significant and continued use of genre-diverse picture books, but also an absence of new titles, technical support in the selection of high-quality books, domestic authors and vague criteria in the selection of books, as well as a lack of parental involvement in the selection of books.
Constructing the Wavelet Tree and Wavelet Matrix in Distributed Memory

The wavelet tree (Grossi et al. [SODA, 2003]) is a compact index for texts that provides rank, select, and access operations. This leads to many applications in text indexing, computational geometry, and compression. We present the first distributed memory wavelet tree construction algorithms, which allow us to process inputs that are orders of magnitude larger than what current shared memory construction algorithms can work with. In addition, our algorithms can easily be adapted to compute the wavelet matrix (Claude et al. [Inf. Syst., 47:15–32,2015]), an alternative representation of the wavelet tree. In practice, one of our distributed memory wavelet matrix construction algorithms is the first parallel algorithm that can compute the wavelet matrix for alphabets of arbitrary size.
Lifelong Learning for Text Retrieval and Recognition in Historical Handwritten Document Collections

This chapter provides an overview of the problems that need to be dealt with when constructing a lifelong-learning retrieval, recognition and indexing engine for large historical document collections in multiple scripts and languages, the Monk system. This application is highly variable over time, since the continuous labeling by end users changes the concept of what a 'ground truth' constitutes. Although current advances in deep learning provide a huge potential in this application domain, the scale of the problem, i.e., more than 520 hugely diverse books, documents and manuscripts precludes the current meticulous and painstaking human effort which is required in designing and developing successful deep-learning systems. The ball-park principle is introduced, which describes the evolution from the sparsely-labeled stage that can only be addressed by traditional methods or nearest-neighbor methods on embedded vectors of pre-trained neural networks, up to the other end of the spectrum where massive labeling allows reliable training of deep-learning methods. Contents: Introduction, Expectation management, Deep learning, The ball-park principle, Technical realization, Work flow, Quality and quantity of material, Industrialization and scalability, Human effort, Algorithms, Object of recognition, Processing pipeline, Performance,Compositionality, Conclusion.
Image processing of concentrated and scattered objects

In modern control systems and information processing, the recognition of objects in the image is complicated by the fact that the impact of negative factors introduces uncertainty into this process, leading to blurring of images.In this regard, it is necessary to develop models and algorithms that would reduce the degree of uncertainty in image processing.These models are necessary, for example, when monitoring environmentally hazardous objects, for search and detection of unauthorized burial of household waste, in the field of information security, in the analysis of x-rays and thermograms, in the actions of unmanned aerial vehicles of law enforcement agencies in autonomous mode.This article presents a description of information technology for recognition in the automated mode of objects in images.The basis of this technology is the algorithm of contour analysis of images.The main distinguishing feature of the algorithm is the use of convolution of the image in four directions, as well as the tracing procedure.The aim of the study was to develop algorithms for high-speed automated visualization of external objects.We present the results of the study of the algorithm of contour analysis in the processing of various images in the visible and infrared wavelengths.Recommendations are formulated for the choice of parameters of the contour analysis algorithm, such as the mean square deviation in image blur, minimum and maximum thresholds for filtering.The results of the study can be used in production management systems, life support of the city, technical vision, environmental conditions, monitoring of business processes, as well as in the creation of simulators for training operators of complex systems, etc.In addition, we show the expediency of applying the algorithm we developed in decision support systems.
Methodical bases of formation of stage-figurative culture of the future teacher of musical art in the process of vocal preparation

The article describes the methodological bases of formation of stage-figurative culture of future teachers of musical art in the process of vocal preparation. The state of the problem under study in pedagogical theory and practice is analyzed, the purpose, tasks, the essence of the basic forms and methods in the educational process are revealed, and their influence on the professional growth of future teachers of music art is determined.The purpose of the article is to define and substantiate the methodological bases (content, forms and methods) of forming the stage-culture of future music teachers in the process of vocal training.The research showed that the introduction of certain forms and methods in the educational process contributes to the deepening of methodological knowledge, optimization of the educational process, the development of interdisciplinary relationships and the qualitative preparation of future teachers. Сertain forms and methods allow to systematically, consistently improve knowledge, skills (interpretive, acting, vocal), to carry out a reflective evaluation of their own stage-and-culture.Possession of stage-figurative culture, which is a component of the professional culture of a teacher of music art, allows to carry out the worldview, aesthetic, ethical, creative development of the child on the basis of interpretation of artistic images, their vocal design and stage expression. That is why the content, forms and methods of preparation of the teacher of music art must be transformed, taking into account the methodological bases of professional, in particular, vocal preparation of the future teacher-musician, who is intended to relay the cultural and cultural values in the educational and cultural environment
Development of a triple input musical instrument tuner using Yin algorithm

In music, it is important that the instruments sound harmonic and reproduce sound in a repeatable and universal manner.Humans have difficulty in distinguishing very similar sounds, therefore, a device capable of doing so is very useful.The tool used for this is called an instrument tuner.There is a huge variety of such devices in the market, usually capable of detecting sound and frequency from specific instruments, due to the way sound is produced.Musicians that play different instruments must own multiple tuners to suffice their needs.This article details the development of an optimized pitch detection device, running on an Android smartphone, capable of detecting and processing sound signals emitted by different kinds of instruments, with frequencies ranging from 27.5 Hz to 4186 Hz in a non-expensive manner.
Abstracting models of strong normalization for classical calculi

Modern programming languages have effects and mix multiple calling conventions, and their core calculi should too. We characterize calling conventions by their “substitution discipline” that says what variables stand for, and design calculi for mixing disciplines in a single program. Building on variations of the reducibility candidates method, including biorthogonality and symmetric candidates which are both specialized for one discipline, we develop a single uniform framework for strong normalization encompassing call-by-name, call-by-value, call-by-need, call-by-push-value, non-deterministic disciplines, and any others satisfying some simple criteria. We explicate commonalities of previous methods and show they are special cases of the uniform framework and they extend to multi-discipline programs.
A Comparative Study on Network Traffic Clustering

Accurate network traffic classification is vital to the areas of network security. There are many applications using dynamic ports and encryption to avoid detection, so previous methods such as port number and payload-based classification exist some shortfalls. An alternative approach is to use Machine learning (ML) techniques. Here we will present three clustering algorithms, K-Means, FarthestFirst and Canopy, based on flow statistic features of applications. The performance impact of the data set processed by the PCA dimension reduction algorithm on the above three algorithms will be an important topic for our discussion. Our results show that the classification accuracy and computational performance all have been significantly improved after dimension reduction.
Translation Quality Assessment Tools and Processes in Relation to CAT Tools

Modern translation QA tools are the latest attempt to overcome the inevitable subjective component of human revisers.This paper analyzes the current situation in the translation industry in respect to those tools and their relationship with CAT tools.The adoption of international standards has set the basic frame that defines "quality".Because of the clear impossibility to develop a universal QA tool, all of the existing ones have in common a wide variety of settings for the user to choose from.A brief comparison is made between most popular standalone QA tools.In order to verify their results in practice, QA outputs from two of those tools have been compared.Polls that cover a period of 12 years have been collected.Their participants explained what practices they adopted in order to guarantee quality. 89
To Balance or Not to Balance: An Embarrassingly Simple Approach for Learning with Long-Tailed Distributions.

Real-world visual data often exhibits a long-tailed distribution, where some ''head'' classes have a large number of samples, yet only a few samples are available for ''tail'' classes. Such imbalanced distribution causes a great challenge for learning a deep neural network, which can be boiled down into a dilemma: on the one hand, we prefer to increase the exposure of tail class samples to avoid the excessive dominance of head classes in the classifier training. On the other hand, oversampling tail classes makes the network prone to over-fitting, since head class samples are often consequently under-represented. To resolve this dilemma, in this paper, we propose a simple-yet-effective auxiliary learning approach. The key idea is to split a network into a classifier part and a feature extractor part, and then employ different training strategies for each part. Specifically, to promote the awareness of tail-classes, a class-balanced sampling scheme is utilised for training both the classifier and the feature extractor. For the feature extractor, we also introduce an auxiliary training task, which is to train a classifier under the regular random sampling scheme. In this way, the feature extractor is jointly trained from both sampling strategies and thus can take advantage of all training data and avoid the over-fitting issue. Apart from this basic auxiliary task, we further explore the benefit of using self-supervised learning as the auxiliary task. Without using any bells and whistles, our model achieves superior performance over the state-of-the-art solutions.
An Optimized Model for Breast Cancer Prediction Using Frequent Itemsets Mining

This presented research paper mainly studies the frequent itemsets mining approach for finding the most important attribute to overcome the existing problems in the extraction of relevant information by using data mining approaches from a huge amount of dataset.Firstly a state of art diagram for prediction is designed and data mining classifier like naive bayes, support vector machine, decision tree, k-nearest neighbour are compared and then proposed methodology with new techniques are proposed.Moreover, a new attribute filtering association frequent itemsets mining algorithm is presented.Then, by analyzing the feasibility of the proposed algorithm, the data mining classification classifier is compared.As a result, SVM produces the best result among all the classifier with attribute filtrating and without attribute filtrating.With attribute filtrating algorithm enhances the accuracy of all the other classifier.
Authorship Attribution in Bengali Literature using Convolutional Neural Networks with fastText’s word embedding model

Authorship attribution (AA) is the process of attempting to identify the likely authorship of a given document by analyzing previous works of the authors in question. This paper proposes deep neural network-based continuous skip-gram models for authorship attribution in Bengali Literature. We present a data set of 2400 Bengali blog posts from 6 authors of current time and compare the performances of traditional lexical n-gram based models to our proposed approaches. We achieve a best accuracy of more than 92% on the held-out dataset with a deep convolutional neural network with skipgram word embeddings by fastText as the feature, which outperforms the other traditional models examined in this paper on Bengali Language. The results provide a clear indication that extracting features with the use of hidden layers in deep neural networks from continuous word embeddings work better as a feature set for authorship attribution systems on Bengali Literature than sparse lexical n-gram based features and shallow classifiers.
Natural Language Processing with some Abbreviation to SQL

This paper presents an approach to convert the Natural language processing (NLP) abbreviation to SQL. SQL is domain specific language which is used for storing, manipulating and retrieving data stored in relational database, but any ordinary person who don't have any knowledge about SQL are unable to retrieve the data from database. To overcome this problem we proposed a model conversion of NLP to SQL but in this system user can also use an abbreviations to retrieve the data from the database. Those who are expert in SQL language can access information from database but non-technical usercannot retrieve data from database.This system can be used everywhere because database is everywhere.If we want some data from database this system is very much useful.In this system user can also enter the query using speech.
An enhanced hybrid genetic algorithm for solving traveling salesman problem

&lt;span&gt;Robust known the exceedingly famed NP-hard problem in combinatorial optimization is the Traveling Salesman Problem (TSP), promoting the skillful algorithms to get the solution of TSP have been the burden for several scholars. For inquiring global optimal solution, the presented algorithm hybridizes genetic and local search algorithm to take out the uplifted quality results. The genetic algorithm gives the best individual of population by enhancing both cross over and mutation operators while local search gives the best local solutions by testing all neighbor solution. By comparing with the conventional genetic algorithm, the numerical outcomes acts that the presented algorithm is more adequate to attain optimal or very near to it. Problems arrested from the TSP library strongly trial the algorithm and shows that the proposed algorithm can reap outcomes within reach optimal. For more details, please download TEMPLATE HELP FILE from the website.&lt;/span&gt;
Transaction Document Security Protection In The Form Of Image File, Jpg or Tif Interbank Transfer Using Steganography And Cryptography

Information security process can be done by hiding the information on other media or by certain methods, so that other people do not realize there is some information in the media. Technique known as Steganography and Cryptography. Steganography is a technique to hide or disguise the existence of secret messages in the media reservoirs. While Cryptography disguise the meaning of a message, but do not hide that there is a message. In a bank cannot be separated in the presence of inter-bank transactions which transfer if the transfer interbank transactions done in branch offices and nominal transaction exceeds the transaction limit branch offices will require official approval in the upper branch. How do I prove the authenticity of the document transfer transaction? Therefore, a company in the banking sector needs to be a medium to prove the validity of the document sent by the branch transaction. In this case the author has designed an application with a blend of steganography and cryptography techniques that can be used in testing the validity of the transaction document. By using steganographic techniques undercover in a media that has brought and cryptographic key assignment as random, then the document that is sent in the branch office can be proved.
GAMIFICATION AND QUESTION-DRIVEN LEARNING AIDED WITH IMMEDIATE RESPONSE SYSTEMS. SOME EXPERIENCES FROM CIVIL ENGINEERING STUDENTS

GAMIFICATION AND QUESTION-DRIVEN LEARNING AIDED WITH IMMEDIATE RESPONSE SYSTEMS. SOME EXPERIENCES FROM CIVIL ENGINEERING STUDENTS
Long Distance IM/DD Transmission with OFDM-QAM based Quantum Noise Stream Cipher

We propose a QAM-based quantum noise stream cipher (QNSC) scheme for IM/DD transmission employing orthogonal frequency division multiplexing (OFDM) modulation. Secure transmission of 2.5 Gbit/s IMDD-OFDM signal is demonstrated over 1000 km distance. The experimental results show that Q penalty using IMDD-OFDM based QNSC is only 1.6 dB compared to QPSK without encryption in the cases of back-to-back condition and 1000 km transmission.
Indexing and Search of Order-Preserving Submatrix for Gene Expression Data

Bicluster pattern discovery plays a key role in analysis of gene expression data. One vital model of bicluster mining is Order-Preserving SubMatrix (OPSM), which finds similar tendency of some genes on some conditions. Most of the OPSM discovery methods are batch mining techniques and not suitable for low latency data query. To make data analysis efficient and effective, in this paper, we first propose a prefix-tree based indexing method <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pfTree</i> , then give an optimization technique <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pIndex</i> that employs row and column header tables to search the positive, negative and time-delayed OPSMs. Meanwhile, we present an online sharing query technique to accelerate the frequent searches. Finally, we conduct extensive experiments and compare our methods with the existing approaches. Experimental results demonstrate the efficiency and effectiveness of the proposed methods.