Reviewerly/exHarmony huggingface dataset:
Characteristics of Local Intrinsic Dimensionality (LID) in Subspaces: Local Neighbourhood Analysis

The local intrinsic dimensionality (LID) model enables assessment of the complexity of the local neighbourhood around a specific query object of interest. In this paper, we study variations in the LID of a query, with respect to different subspaces and local neighbourhoods. We illustrate the surprising phenomenon of how the LID of a query can substantially decrease as further features are included in a dataset. We identify the role of two key feature properties in influencing the LID for feature combinations: correlation and dominance. Our investigation provides new insights into the impact of different feature combinations on local regions of the data.
Increasing the Economic Value from Digitalisation through Eye-tracking

The objective of this position paper is to demonstrate that eye-tracking is a cross-sectional technology for digitalisation because it provides a bridge between “machines” on the one hand, i.e. computers, networks, robots, and other man-made results of digitalisation like web pages, and humans on the other hand, who interact with them. Eye-tracking is utilised at the interface between the human and the technology side of digitalisation and it is exactly at that interface, where technological innovations translate into economic values through productivity gains, increased sales, higher realised prices, improved customer satisfaction, fewer litigation cases, longer lasting customer relationships and thereby increased lifetime customer value, more profitable management decisions and improved shareholder trust. Unfortunately, numerous frictions between “machines” and humans exist at these interfaces. Over the last decades, scientists have refined methods and invented tools to detect those frictions, reflected in visual perception: eye-tracking. While the causes of these frictions are countless, eye-tracking is a single scientific method for detecting very many of them. Eye-tracking – so to speak – is “digitalisation’s best friend”. Because of the numerous applications eye-tracking provides for reducing these frictions at the human-machine interface, users pursuing a digitalisation strategy should become aware of the financial benefits by using this scientific method in their applied research and development.
DEVELOPMENT OF SMART CITIES IN TAIWAN FROM THE PERSPECTIVE OF CLOUD COMPUTING SECURITY

Abstract. Cloud computing is an important part of the development of smart cities and also the focus of the information and communication technology (ICT) industry. From the concept of cloud computing, people and objects in cities are organized based on their application needs and computed in real time. It is a comprehensive utilization of the new generation of information and communication technology. This paper first introduces the concept of cloud computing, smart city construction in Taiwan, and the information security management standard of cloud computing, then describes the cloud computing security framework from three areas,and summarizes the implementation status of cloud computing in Taiwan, government policies, and measures, reaching the conclusion that information transmission must be fast and reliable and ensure personal privacy and security. Any type of information security problem will bring catastrophic consequences. Therefore, cloud computing also brings severe challenges to the traditional information security system.
Electronic Textbook as a Component of Smart Kids Technology of Education of Elementary School Pupils.

The article sets out to analyze national and foreign experience of use of elec-tronic textbooks in the system of education; to justify the use of Smart Kids technology as a system of methods, forms, and electronic educational game resources, electronic textbooks for educational process in the system of ele-mentary school. Four forms of implementation of Smart Kids technology (Smart Case, Smart Teacher, Smart Class, and Smart Kids) were described considering the facilities of every school as well as the level of information and communication technology qualification of the elementary school teach-er. The aim of introduction of the technology for each form of teaching, the necessary equipment, and means for its implementation in elementary school environment were determined. Based on the procedural approach to work of an elementary school teacher, six stages of introduction of the technology were justified. Specific aspects of introduction of blended teaching using the principles of Smart Kids technology were defined. The experience of intro-duction of electronic textbooks to the system of elementary education of Ukraine was described, the choice of electronic textbooks by elementary school teachers was justified, the comments and suggestions of teachers re-garding the arrangement of electronic content in E-textbooks were summa-rized, the main approaches of teachers to the choice of an electronic text-book and development of their information and communication competence were specified. It was identified that the forms, methods, and techniques of use of electronic textbooks in teaching elementary school pupils require fur-ther justification.
Software component for finding solutions to a system of partial differential equations in GTU by the method of group accounting of arguments

In geometric control theory (GTU), models of control objects described by systems of nonlinear ordinary differential equations are converted into equivalent linear models in the Brunovsky form. Then, using linear models, they determine the optimal laws of control of linear objects, and then using special transformations transfer these control laws to models of the original nonlinear objects. To determine the transformation functions (FPs) connecting the variables of linear and nonlinear models, it is necessary to solve systems of differential equations in partial derivatives. Since there are no universal methods for solving such systems of equations, a method for searching for a phase transition based on the multi-row GMDH algorithm is proposed. Verification of the proposed method in solving a number of problems with the help of gas turbines confirmed its efficiency. Figs.: 1. Refs.: 14 titles.Keywords: geometric control theory; pseudostratified GMDH algorithm; conversion function; system of partial differential equations
A Comparative Study of Evolutionary Methods for Feature Selection in Sentiment Analysis

With the recent surge of social media and other forums, availability of a large volume of data has rendered sentiment analysis an important area of research. Though current state-of-the-art systems have been demonstrated impressive performance, there is still no consensus on the optimum feature selection algorithm for the task of sentiment analysis. Feature selection is an indispensable part of the pipeline in natural language models as the data in this domain has extremely high dimensionality. In this work, we investigate the performance of two meta-heuristic feature selection algorithms namely Binary Bat and Binary Grey Wolf. We compare the results obtained to employing Genetic Algorithm for the same task. We report the results of our experiments on publicly available datasets drawn from two different domains, viz. tweets and movie reviews. We have used SVM, k-NN and Random Forest as the classification algorithms.
A Latency-Sensitive Elastic Adaptive Scheduling in Distributed Stream Computing Systems

With the massive growth of big data applications, the requirement for data processing speed is getting higher and higher in stream computing systems. The Storm, as one of the most popular distributed stream computing systems, has received more attention. However, the Storm's traditional scheduling strategy is not ideal for processing a large volume of streaming data. The resource scheduling in a distributed stream computing system should consider not only node allocation status but also fluctuating input rates of data stream. To address this problem, this paper has completed the following work: (1) A performance model La-Stream (latency-sensitive elastic adaptive scheduling) is proposed and built by adopting a quantitative method for calculating the amount of computation required between task map nodes and node communication. (2) A La-Stream based algorithm is proposed. The algorithm dynamically plans a resource allocation scheme with minimal data processing latency among available resources to achieve optimal allocation. (3) Three functional modules of La-steam are proposed and implemented: module Monitor, module Optimizer and module Scheduler. The three modules are integrated into the Storm platform with minimal overhead. Several sets of experiments are conducted, verifying the feasibility and effectiveness of La-Stream.
Collaboration Of High School And School In Ensuring Socialization Of Students

The article discusses the characteristics of the socio-cultural situation in conditions of instability of the development of society and digitalization. The contradictions and complexities of this process for a teenager are determined, including changes in the psychological characteristics of schoolchildren, a decrease in the significance and influence of the family. Emphasis is placed on the consideration of the virtual space as a significant factor of secondary socialization. The vocational guidance was assigned to the leading factors of socialization, and its effectiveness was assessed. In the cited results of monitoring the views of parents of schoolchildren, it is concluded that, despite the development of digital technologies and their influence on the formation of the value system of schoolchildren, parents are diagnosed with a very high level of their trust in school as the leading factor in the socialization of students. As a result, a system of pedagogical education and vocational guidance of schoolchildren in the format of project pedagogical shifts during the year, implemented within the university cluster, is presented. The multifunctionality of the activity of pedagogical shifts is determined, the main of which is the function of creating an emotionally saturated communicative space that provides meaningful life orientation of schoolchildren.
Learning Validation for Lung CT Images by Multivariable Class Imbalance

Anatomical detection of objects of interest or nodules in lung is a not easy task. We present a three-stage method which enables localization and identification of small nodules in medical 3D image space: primitive detector is supervised learned from a small set of annotated abdomen CT slides; the second stage implements the detection to perform multifeatured classification of image space; the last stage regulates the clusters’ allocation with spatial-sensitive analysis of undersampling to achieve better classifier performance. Our main novel contribution is to implement appropriate multivariable imbalancing for improvement of small nodules prediction in CT lung images. The imbalancing task is proposed to apply the outcomes of second learning stage, namely early lung segmentation and initial nodules allocation. In the long term, such method can also be used for object detection with class imbalance in other medical voxel images.
Recommender Systems Fairness Evaluation via Generalized Cross Entropy

This work was supported in part by the Center for Intelligent InformationRetrieval and in part by project TIN2016-80630-P (MINECO)
Fast and Efficient Text Classification with Class-based Embeddings

Current state-of-the-art approaches for Natural Language Processing tasks such as text classification are either based on Recurrent or Convolutional Neural Networks. Notwithstanding, those approaches often require a long time to train, or large amounts of memory to store the entire trained models. In this paper, we introduce a novel neural network architecture for ultra-fast, memory-efficient text classification. The proposed architecture is based on word embeddings trained directly over the class space, which allows for fast, efficient, and effective text classification. We divide the proposed architecture into four main variations that present distinct capabilities for learning temporal relations. We perform several experiments across four widely-used datasets, in which we achieve results comparable to the state-of-the-art while being much faster and lighter in terms of memory usage. We also present a thorough ablation study to demonstrate the importance of each component within each proposed model. Finally, we show that our model predictions can be visualized and thus easily explained.
Detecting the opinions of the secondary school administrators regarding the use of mobile technologies for educational purposes

The goal of this research is to detect the opinions of secondary school administrators regarding the use of mobile technologies for educational purposes. 15 secondary school administrators form the participants of this research. The data of the research are collected in the fall semester of the 2017-2018 educational year. The work is designed through the qualitative research model. The personal information form and the semi-structured interview form are prepared by the researcher while collecting the data. The data obtained have been resolved by using the content analysis method. According to the obtained findings from this research; secondary school administrators have expressed that they use mobile learning in their teaching practices “in need of momentary information”, “momentary communication”, and “in the transfer of the audiovisual data used in the courses to the smart board”. The administrators have expressed that while choosing the mobile technologies they take into account the content of the course and the features of the action which is going to take place during the course. It has been stated that with the use of mobile tools for educational purposes, there has been seen positive changes in the teaching activities and that the courses has started continuing more effectively and efficiently, and that they benefit from the audiovisual materials more during the course, and that by moving away from the traditional teaching method the student starts learning more active and enjoys.
Hardness of Bichromatic Closest Pair with Jaccard Similarity

Consider collections A and B of red and blue sets, respectively. Bichromatic Closest Pair is the problem of finding a pair from A x B that has similarity higher than a given threshold according to some similarity measure. Our focus here is the classic Jaccard similarity |a cap b|/|a cup b| for (a,b) in A x B.We consider the approximate version of the problem where we are given thresholds j_1 > j_2 and wish to return a pair from A x B that has Jaccard similarity higher than j_2 if there exists a pair in A x B with Jaccard similarity at least j_1. The classic locality sensitive hashing (LSH) algorithm of Indyk and Motwani (STOC '98), instantiated with the MinHash LSH function of Broder et al., solves this problem in O(n^(2-delta)) time if j_1 >= j_2^(1-delta). In particular, for delta=Omega(1), the approximation ratio j_1/j_2 = 1/j_2^delta increases polynomially in 1/j_2.In this paper we give a corresponding hardness result. Assuming the Orthogonal Vectors Conjecture (OVC), we show that there cannot be a general solution that solves the Bichromatic Closest Pair problem in O(n^(2-Omega(1))) time for j_1/j_2 = 1/j_2^o(1). Specifically, assuming OVC, we prove that for any delta>0 there exists an epsilon>0 such that Bichromatic Closest Pair with Jaccard similarity requires time Omega(n^(2-delta)) for any choice of thresholds j_2 < j_1 < 1-delta, that satisfy j_1 <= j_2^(1-epsilon).
Relabeling with Mask-S for Imbalanced Class Distribution

The article explores how class imbalance representation in the probabilistic models is evolved to achieve robust and competent method for balancing distributions of learning data. We tackle the challenging question of imbalance in data level by combining neighborhood relation of samples and the condition of class imbalance in an adaptive model of the k-Nearest Neighbors algorithm. Neighborhood relation with samples in opposite imbalance classes is studied giving expected distribution for minority class samples. It is used to regulate labels of neighbor samples of majority class realizing the classification performance, persistent with the given imbalanced benchmark datasets. Experiments show that our method is capable to accommodate the variation of data types. We thus conclude that machine learning techniques that support aspects of generic classification for data continue to enforce specific profound aspects like class imbalance.
A Framework for Context-dependent User Interface Adaptation

Mobile information systems are operated in a large variety of different contexts – especially during intermodal journeys. Every context has a distinct set of properties, so the suitability of user interfaces differs in various contexts. But currently, the representation of information on user interfaces is hard-coded. Therefore, we propose a dynamic adaptation of user interfaces to the context of use to increase the value of an information system to the user. The proposed system focuses on travel information systems but is designed in a way that it is generalizable to other application domains. The adaptation system works as an independent service that acts as a broker in the communication between an application and the user. This service transforms messages between a user- and a system-oriented representation. The context of use, the device configuration, and user preferences affect the calculated user-oriented representation.
SQLIADP: A Novel Framework to Detect and Prevent SQL Injection Attacks

Nowadays, Web-Based systems face a lot of threats. Web-Based systems completely rely on databases. SQL injection attacks are the most common and complex threat to the databases of Web-Based systems. Several approaches that protect web applications from SQL Injection attacks are available. Most of the techniques apply defense mechanics that perform on the SQL Injection attacks; much of them produce huge false positives and maximum response time. In this paper, we are proposing a novel framework, SQL Injection Attack Detection and Prevention (SQLIADP), combination of Java, web technologies, and Special Text Strings to protect the Web-Based systems against the SQL Injection attacks with zero false reduction and minimal response time. The implemented framework is very efficient and works effectively against SQL Injection.
Digital Signature Algorithms Based on Hidden Discrete Logarithm Problem

The discrete logarithm problem in a hidden group, which is defined over finite non-commutative associative algebras, represents interest for constructing post-quantum public-key cryptoschemes. The currently known form of the hidden logarithm problem suits well for designing the public-key agreement protocols and public encryption algorithms, but not suits for designing the digital signature algorithms. In the present paper, there are introduced novel forms of defining the hidden discrete logarithm problem, on the base of which two digital signature algorithms are proposed. Two different four-dimensional finite non-commutative associative algebras have been used in the proposed signature algorithms. In one of the proposed algorithms, there are used globally non-invertible vectors that are invertible locally. A large set of the left-side and a large set of the right-side local units relates to some fixed globally non-invertible vector. Several different local units are used to define one of the proposed forms of the hidden logarithm problem.
INTERCULTURALITY AND GAMIFICATION IN TEACHER TRAINING

The present work refers to gamification in the context of Intercultural Education, as a playful technology that motivates and stimulates students' effort and acuity and, in addition to teamwork, enables students to learn to work in cooperation with others, to achieve the goal with a specific, neuroeducational style, methodology and resources, a science that develops brain functions with certain educational strategies and technologies. Intercultural Education is based on the teaching of values such as communication, solidarity, respect, tolerance and inclusion of diverse students in school and the cultural and social integration of people in a common project. With this look, we present a training process through a learning workshop through gamification techniques such as less escape space and brain, along with inclusive dynamics such as “Visual Intercultural”, “Comparing Interculturality with” and “Unboxing Intercultural ”, Foster reflection on the conceptual and experiential framework regarding interculturality, migration and its challenges, impacts and processes with factors considered 'for' and 'against', close to education and their relationship with interculturality.
Evaluating Ontology Matchers on Real-World Financial Services Data Models.

Financial data in enterprises is often stored using different data models, yet, it needs to be integrated in order to foster comprehensive evaluations. Conceptually, each of those data models can be understood as an ontology, and automated ontology matching can be applied as a first step towards data integration. In this paper, we analyze the performance of existing ontology matching tools for matching financial data models. The data has been provided by SAP SE and consists of real data schemas that are used in the financial services area and mappings between them. We have created five data sets by translating enterprise data schemas to ontologies and expert mappings to ontology alignmentgold standards. We evaluate state of the art ontology matchers on our newly created data set. Our experiments show that current matching systems struggle to handle enterprise data sets and achieve significantly lower scores compared to data sets of other evaluation initiatives.
Improving Robustness in Real-World Neural Machine Translation Engines

As a commercial provider of machine translation, we are constantly training engines for a variety of uses, languages, and content types. In each case, there can be many variables, such as the amount of training data available, and the quality requirements of the end user. These variables can have an impact on the robustness of Neural MT engines. On the whole, Neural MT cures many ills of other MT paradigms, but at the same time, it has introduced a new set of challenges to address. In this paper, we describe some of the specific issues with practical NMT and the approaches we take to improve model robustness in real-world scenarios.