Reviewerly/exHarmony huggingface dataset:
Design and development of a hate speech detector in social networks based on Deep Learning technologies

Recently, during the last few years, activity over Internet and social network connectivity has been increased. Social networks are platforms that ease communication between users by means of different interactions. Unfortunately, social networks have also become places for hate speech proliferation. Hate Speech has become a popular topic in recent years. This is reflected not only by the increased media coverage of this problem but also by the growing political attention it is receiving. Given the constant progression of this phenomenon, institutions, international minorities associations, researchers and social networks are trying to react as quickly as possible. Because of the massive scale of the social networks, methods that automatically detect hate speech are required. Natural Language Processing (NLP) focusing specifically on this phenomenon is required since basic word filters do not provide a sufficient remedy: a hate speech utterance might be influenced by aspects such us the domain, context, co-occurrence media objects (images, video, audio), etc. This thesis is the result of a project whose main aim has been to obtain a hate speech detector with a multilingual perspective, in order to remove all shape of hate speech that can occur in social networks, independently the origin language. During the development phase, there have been used supervised machine learning tools, NLP techniques, and Python as programming language. The proposed system is evaluated against two study cases, a participation in a internationally recognized competition, such as SemEval and facing the system against a Transfer Learning challenge across languages and hate speech traits. The extensive experimentation carried out has resulted in a very honorable position in the SemEval competition and in a demonstration of the benefits that can be brought by the appliance of Transfer Learning to the hate speech detection problem.
Codemakers find algorithms immune to quantum hacks

NIST will select cryptographic standards for the internet that could blunt future hacks by a quantum computer.
Sales Prediction using Matrix and Tensor Factorization Models

Analysis and inference of market sales data are important. Future sales of customers can be predicted by considering previous sales of customers. In this study, we tried to estimate the sales of customers by using matrix and tensor factorization models. By considering different scenarios, we experimented on real-world sales dataset and compared the performances of models
Strategies for digital literacy professional development

A mixed-methods approach was used to understand educators’ beliefs about using technology intheir classrooms, digital literacy knowledge, and professional development communicationmethods. This study reports results from an online survey, individual interviews, and smallgroupactivities. Within schools, around one-third of teachers were not offered the resources toteach digital literacy and three out of the eight digital literacy skills were mainly taught. Mosteducators felt comfortable teaching with technology and that it was effective, however, manywere concerned about overuse and staying up to date. More than half of teachers would attenddigital literacy professional classes, and some may change their mind if the promotions containedaccurate and clearly listed topics and information learned. The qualitative results showed six keythemes that would benefit teaching digital literacy in schools: 1) digital literacy school plan, 2)required technology time, 3) required digital literacy professional development, 4) digital literacyprofessional development promotion, 5) digital literacy professional development timing, and 6)digital literacy professional development communication methods. Additionally, this studystrongly shows the need for additional digital literacy professional development for educators toincrease their comfort levels with teaching with technology and using technology, understand how digital literacy can help their students in the 21st century, teach with digital toolseffectively, and be able to balance the use of technology in their classrooms.Keywords: technology, digital literacy, professional development, literacy, barriers to teaching
Classification of Customer Demands by Using Doc2Vec Feaure Extraction Method

Companies provide after-service communication with their customers through help desk systems they provide in call centers and websites. In companies with large networks, the amount of data collected by using these communication tools is increasing day by day.The separation of these collected requests and feedbacks to be transferred to the relevant units has become a very time consuming process. The prolongation of this process in customer-oriented companies can lead to customer loss.Therefore, it has importance to transfer, evaluate and return requests from such firms. In this study, firstly, two different models were used to obtain the features from the demands of customers. The features obtained were classified by multi-layer artificial neural network and it was provided the related demand was transferred to the related unit. Thus, the usability of the Doc2Vec model, which can be used as an alternative to the classic word bag, is examined in Turkish text classification studies.
Session-Based User Authentication via Mouse Dynamics

Applying mouse dynamics to address security issues like security breaches and malicious attacks is an emerging method. The Balabit dataset is one of very few datasets shared publicly and the presence of very short test sessions makes this dataset difficult. This article presents an authentication algorithm for imposer detection using this dataset via mouse dynamics. The raw data were divided into mouse movements within each session, whereas these mouse movements were classified according to their size. Separate signatures were created for each user by calculating the individual attributes extracted from short and long size mouse movements. Then a two-class classification is employed to authentication. Session-based authentication performance reached 0.93 AUC and 0.13 EER. This study shows that session-based authentication can be performed using only mouse movement curves. Unlike other studies, the effect of a curve-length-based separation on performance is stated
Human Action Recognition in First Person Videos using Verb-Object Pairs

Human action recognition problem is important for distinguishing the rich variety of human activities in first-person videos. While there has been an improvement in egocentric action recognition, the space of action categories is large and it looks impractical to label training data for all categories. In this work, we decompose action models into verb and noun model pairs and propose a method to combine them with a simple fusion strategy. Particularly, we use 3 Dimensional Convolutional Neural Network model, C3D, for verb stream to model video-based features, and we use object detection model, YOLO, for noun stream to model objects interacting with human. We present experiments on the recently introduced large-scale EGTEA Gaze+ dataset with 106 action classes, and show that our model is comparable to the state-of-the-art action recognition models.
Macroconnectomic networks of the human brain

Abstract Part of why we often cause deficits we don’t understand is that we are working with outdated models of the brain and its organization, sometimes ones created by people dead for around 130 years. The idea that higher brain functions can be assigned to individual areas of cortex has long been debunked and we should stop teaching it. We should also stop teaching trainees that there are unimportant parts of the brain. Bad models lead to bad results.
A Novel Approach for Subgraph Isomorphism Problem on Bipartite Graphs

Subgraph isomorphism problem on specialized graphs is an open research area. Most of the algorithms that had been developed for subgraph isomorphism problem are designed for general graphs. Although, they can be utilized for any graph type, this generic design prevents to take advantages of characteristics of the specialized graphs to burst performance of the subgraph isomorphism search. In this study, we will introduce a novel branch and bound algorithm for subgraph isomorphism problem on bipartite graphs. Bipartite feature of these graphs is utilized to improve search performance by limiting the size of possibility set and by getting rid of backtracking phase of branch and bound technique. Experimental results show that the algorithm provides between 10 and 120 fold better performance results on bipartite graphs when it is compared with the state-ofart algorithms.
Meta-Heuristic Algorithms Based Multi-Level Thresholding

Thresholding is a very important stage in computer vision applications. An ideal single thresholding algorithm is not available for all environments. Multi-level thresholding in environments with multiple objects is constantly being developed for interpretation of images. Kapur entropy and Otsu approaches are among the most successful algorithms in the literature. In this study, it is tried to increase the performance of Otsu and Kapur algorithms by using meta-heuristic optimization approaches. The results of the Firefly Algorithm (FF) and Real Coded Genetic Algorithm (RGA) were evaluated with PSNR, SSIM and CPU processing time criteria.
Exploring Computer Information Security Technology and Protection Methods

Social development has driven the rapid development of computer technology and networks and computers and networks have become universal from rare. But the security instability that followed has also become a major problem in testing individuals and society. Therefore, in the fast and stable establishment of the computer system, how to improve the computer security defense capability has become a difficult problem that we urgently need to study and explore. This paper first analyzes the basic concepts of information security, refines related issues and puts forward suggestions on how to prevent and measure methods based on actual conditions, hoping to help the governance business.
21. Crime and law enforcement in the information society

This chapter examines cybercrimes, cyberattacks such as denial-of-service attacks, and law enforcement in the information society. It looks at advance fee fraud, internationally known as ‘419 Fraud’ with reference to Nigeria; as well as the ‘Russian Scam’ that targets the users of online dating sites. It also examines other criminal activities common on the internet, such as privacy attacks, including phishing which illegally appropriates personal data; harassment; cyberstalking; and grooming, and also considers identity theft and identity fraud, as well as cyberterrorism. The chapter presents case studies dealing with cybercrimes, and, finally, it discusses the efforts of the Council of Europe Convention on Cybercrime to harmonize international cybercrime laws.
ANALYSIS OF SYSTEM QUALITY, INFORMATION QUALITY AND PERCEIVED USEFULNESS TO USER SATISFACTION

This study aims to test and analyze the quality of the system against perceived usefulness, the quality of information against perceived usefulness, the quality of the system to user satisfaction, Information Quality to user satisfaction and perceived usefulness to user satisfaction. &#x0D; The population in this research is employees teller and customer service department of Bank BJB in subang city. Sampling technique is nonprobability sampling by using purposive sampling method. The type of data used is primary data with questionnaire data collection method. Hypothesis testing used is Structural Equation Modeling (SEM) approach with variance Structural equation test or better known as Partial Least Square (PLS).&#x0D; The result of the research shows that Quality System has a significant positive effect on Perceived Usefulness, Quality of Information has a significant positive effect on Perceived Usefulness, Quality of System has a significant positive effect on user satisfaction, Information Quality has positive significant effect on user satisfaction, and Perceived Usefulness has no effect to User Satisfaction
Item Prediction with RNN Using Different Types of User-Item Interactions

This paper deals with the session-based recommendations of different types of user-item interactions. Every user session includes sequences of item interactions such as item viewing, putting into basket and purchasing. Sequences that are constituted short events make the recommendation problems more challenging. Therefore, we applied a powerful state of the art Recurrent Neural Networks (RNN) with Gated Recurrent Unit (GRU) to train data and to predict the next item to be purchased. In the proposed method, Node2Vec representations of items are obtained using the probabilities of different user-item interactions. These representations are used as the initial weights of the GRU inputs. Experimental results are obtained on nearly one million sessions that are constituted by view, basket and purchase interactions which were collected from a Turkish e-commerce website. Experiments that are evaluated by using Mean Reciprocal Rank (MRR) and Recall metrics show that the proposed method performs 63% Recall and 41% MRR results.
Regulation of Blockchain Token Sales in the United States

This chapter provides an overview of how US securities regulation applies to the sale of cryptographic tokens using a distributed ledger, so-called initial coin offerings. Token sale transactions that meet the definition of ‘investment contract’ qualify as regulated securities transactions following the seminal 1946 court decision in the Securities Exchange Commission’s lawsuit against the W. J. Howey company. Currently, there exists substantial legal uncertainty regarding the regulatory classification of token sales involving utility tokens that provide their holders with non-financial, software-based functionality. As implied in a June 2018 speech by a high-ranking SEC official, sales of tokens may <italic>initially</italic> qualify as regulated securities transactions, yet <italic>later</italic> fail to qualify as regulated investment contracts if the tokens’ underlying network becomes sufficiently decentralized. Distributed ledger technology is disrupting the nature and operation of early-stage fundraising and access to software services and enabling the sale of digital tokens that operate as a cryptocurrency or provide access to a software service through the use of a blockchain or distributed ledger. The sale of such tokens, so-called initial coin offerings (‘ICOs’), is often in exchange for cryptocurrencies, such as Ethereum or Bitcoin (however, tokens could be sold in exchange for fiat currency). From January to May 2018, globally US$13.7 billion in tokens were sold by 537 companies or projects, an amount greater than all previous time periods combined. This chapter discusses under what circumstances US securities law applies to the sale of such tokens.
Short Paper: Initial Recommendations for the Design of Privacy Management Tools for Smartphones

The continuing rise in the popularity of smartphones has led to an accompanying rise in the exposure of users to privacy threats as in the case of unintended leakage of personal information from apps. To improve transparency and the ability of users to control data leakage, the design of privacy-enhancing tools aimed at reducing the burden of informed privacy-decisions should be grounded upon users’ tacit needs and preferences. To this end, the present study explores users’ personal perception and concerns toward privacy and their expectations. Initial recommendations include: (1) consideration of the preferences of users for preserving functionalities of their apps, informing users about both (2) the real benefits and actual possibility of using privacy management tools and (3) suspected applications’ data collection behaviours in a way that matches their real concerns and values.
DATA MINING UNTUK MENENTUKAN PEMILIHAN CELULAR CARD DI KOTA BATAM

Batam City is an industrial city that is on a strategic track, with locations bordering other countries, namely Malaysia and Singapore. Batam City also has a function as an international world shipping port, so that information technology, especially communication is very necessary and must be applied to the industrial world in Batam City. One of the factors of communication problems in Batam City, there are still many cellular cards that are used are not affordable for all locations because they are still using signals that have not been reached so that communication often has problems. The level of card usage by cellular customers is highly dominated by prepaid card customers. Prepaid card customers can easily move to another operator by re-registering the card. Prepaid SIM cards are now easily available everywhere at very affordable and low-cost prices. Offers from all cellular operators are as good as quality, price or service. Another challenge faced is that customers are more loyal to use cellular cards. The purpose of this study is to select celular cards in Batam City. Completion of this problem using Data mining with the Association method with a priori algorithm. With the implementation of the software using Tanagra 4.1 sorfware.
Identifying Image Related Sentences in News Articles

With the increasing availability of images on the web, identifying image related sentences has become an important problem. This research area is also important for the news publishing community for automatic captioning of news images and summarization. Although a large body of research has been devoted to image captioning, it is still a challenging problem. Previous works on image captioning mostly focus on generating new captions for the images. The problem of identifying image related sentences in news articles is discussed in this study for the first time and is novel because we do not try to generate a caption from scratch, but we try to select the most appropriate set of sentences for the image from the news text itself. We have used the CNN news dataset which only contains the text parts of news as basis and we have augmented the dataset by collecting the images of the news articles. We generated two class ground truth for the image and sentences of news by using Tf-Idf and Word2Vec vectors cosine and SEMILAR sentence-tosentence similarity methods, respectively. The experiment results show that Naive Bayes classifier with HOG feature selection gives better results.
An XML Parser for Turkish Wikipedia

Nowadays, visual and written data that can be easily accessed over the internet has enabled the development of research in many different fields. However, the availability of data is not sufficient by itself. It is of great importance that these data can be effectively utilized and interpreted in accordance with the requirements. Access to written content in the Wikipedia encyclopedia, which is becoming increasingly common in Turkish natural language processing, can be done via XML dumps. In this study, our aim is to develop and demonstrate the applicability of an XML parser for the processing of Turkish Wikipedia dumps. The use of the open-source parser, which allows information extraction at different levels of granularity, is reported on pages containing biography infoboxes and textual contents.
Scene Classification: A Comprehensive Study Combining Local and Global Descriptors

In this paper, local region characteristics and overall structure of scene images are used for scene classification by combining different local and global descriptors. For this purpose, GIST, Histogram of Oriented Gradients (HOG), dense Scale-Invariant Feature Transform (SIFT), dense Speed-Up Robust Features (SURF), Daisy and Local Binary Patterns (LBP) features are classified individually and jointly with Support Vector Machine (SVM) by using different sizes of training sets. Evaluation tests were conducted on Places15, MIT indoor, SUN397 and Places365 datasets. Most used machine learning algorithms in scene classification literature -SVM with RBF and linear kernels, K-Nearest Neighbors and Random Forest- were evaluated on Places15 dataset for comparison. Besides accuracy, recall and precision, processing time for testing with SVM was measured individually and jointly for a deeper evaluation of the features.
Analytical Modelling and Performance Evaluation of Security Issues for Cloud Computing

Nowadays, Information technologies have an indisputable role in human life. In recent years, cloud computing technologies provide many opportunities for the users and is one of the technologies that keep popularity at the highest level. Briefly, cloud computing is a system that provides common data storage and usage for users. However, there are disadvantages as much as the advantages of cloud computing technology. The main disadvantage is security vulnerabilities. In this study, the behaviour of cloud computing system was analysed mathematically after the attack occurred and until the normal working order of the system. In this study, QoS analysis was carried by analysing response time and throughput by examine the effects of attacks on system performance. As a result of the propose analyses, the system's efficiency rate has decreased rapidly when the attacks are more frequent. For this reason, it has been determined that we need to develop preventive and fault tolerance systems.
Noise Reduction Filter Optimization For Prostate Cancer Localization

Multispectral Magnetic Resonance Imaging (MRI) images are commonly used in prostate cancer diagnosis. However noise in raw data makes it difficult to process images. Therefore MR images must be filtered as a pre-processing step prior to automated localization. In the literature, filters and their parameters are generally selected depending on experiences in the field. In this research, a method, not found in the literature, is proposed such that the system can choose optimal filter parameters to maximize cancer localization. In order to use on KEL, KEP, and IAUC 30, 60, 90 parameters, obtained from multispectral MR images, 3 different filters (wiener filter, total variance filter and wavelet thresholding) and a parameter for each filtering is chosen to maximize localization performance. Linear discriminant analysis is used for localization and observed that optimally selecting the filtering method and its parameter improves prostate cancer localization performance.
A Noise-Robust Fast Sparse Bayesian Learning Model.

This paper utilizes the hierarchical model structure from the Bayesian Lasso in the Sparse Bayesian Learning process to develop a new type of probabilistic supervised learning approach. The hierarchical model structure in this Bayesian framework is designed such that the priors do not only penalize the unnecessary complexity of the model but will also be conditioned on the variance of the random noise in the data. The hyperparameters in the model are estimated by the Fast Marginal Likelihood Maximization algorithm which can achieve sparsity, low computational cost and faster learning process. We compare our methodology with two other popular learning models; the Relevance Vector Machine and the Bayesian Lasso. We test our model on examples involving both simulated and empirical data, and the results show that this approach has several performance advantages, such as being fast, sparse and also robust to the variance in random noise. In addition, our method can give out a more stable estimation of variance of random error, compared with the other methods in the study.