Reviewerly/exHarmony huggingface dataset:
Single-Component Privacy Guarantees in Helper Data Systems and Sparse Coding with Ambiguation

We investigate the privacy of two approaches to (biometric) template protection: Helper Data Systems and Sparse Ternary Coding with Ambiguation. In particular, we focus on a privacy property that is often overlooked, namely how much leakage exists about one specific binary property of one component of the feature vector. This property is e.g. the sign or an indicator that a threshold is exceeded.We provide evidence that both approaches are able to protect such sensitive binary variables, and discuss how system parameters need to be set.
Analysis of LEGO Mindstorm Activity Levels Based on the Van Hiele Levels of Development in Geometry

Technology-based convergence education is being emphasized for students in the era of the fourth industrial revolution. In math education, students need to increase their capabilities in the future by having them experience mathematical problems using robots and sensors, a key technology in the era of the fourth industrial revolution. To this end, it is necessary to present educational uses for educational robots in relation to math and curriculum from a 'mathematics education perspective' and analyze its educational use in relation to the mathematics and curriculum, considering the role of mathematics at the base of the process of exploring real-world phenomena and solving problems. Based on the analysis of Van Hiele levels of development in geometry and the LOGO activity level of Olson et al.(1987), this study analyzed and presented the level of LEGO Mindstorm activity, a representative educational Robot capable of collecting and analyzing data and programming in the form of block language, in the first to fourth level.
Impact of Music on Person-Agent Interaction

In the previous chapter I established, in fulfillment of Contribution 3 of this work, that music indeed alters people’s decision-making process in a nontrivial way, and that this effect can be modeled computationally. In this chapter I take this research thread two steps further: first to provide evidence that the impact of music on human decision-making goes beyond binary decisions; and second to show results suggesting that this effect can be modeled in real-time by a learning agent in order to induce better human-agent interaction. One of the core contributions of this book, described in Sect. 1.1, is the development of approaches and techniques for multiagent collaboration in musical environments, and specifically human-agent collaboration, constituting Contribution 4. Providing evidence that an agent can represent music as part of its world state and utilize this knowledge for better interaction is a substantial component of fulfilling Contribution 4. The other aspect of this contribution, which is the study of how multiple agents with varying tastes (which are not directly known to the other agents) can collaborate jointly, is studied in Chap. 7.
Fully Homomorphic Encryption Scheme Based On Complex Numbers

In this paper, we present a new Somewhat Homomorphic Encryption (SHE) scheme using computation over complex numbers.We then use Bootstrapping technique to make the scheme Fully Homomorphic (FH) and supports unbounded number of circuit depth.In addition to its homomorphic properties and security level, a main characteristic of the proposed new scheme is its simplicity as it is merely based on addition and multiplication operations over complex numbers.The new scheme is implemented under Python using SAGEMath library and evaluated.Then a crypt-analysis based on Approximate GCD problem is done.A comparison with the BGV, a well known Fully Homomorphic Encryption (FHE) scheme, shows that this new scheme is an efficient homomorphic encryption scheme.
The library axiology within the framework of bibliosophy

Library axiology is interpreted as a section of the philosophy of the bibliosphere. The subject of library axiology is the value relationship and values of library activities. The monograph by G. M. Vikhreva "Value foundations of Russian libraries" [in Russian] is reviewed. Vikhreva examines the following problems: the axiological model of the library, libraries in the context of postmodernist society and globalization, the levels of the value foundations of library activities. The traditions of enlightenment are pursued in the value-orientated activities of Russian libraries. The reviewer argues that in the axiological hierarchy, the vertex value is the national idea expressing elements that must be unconditionally protected and preserved. Awareness of the national idea is just as important for society as it is important for an individual to understand the meaning of his/her life. False national ideas are dangerous because they disorient the state, society and individuals. To comprehend interrelationships between library axiology and axiology of culture, one has to go beyond the library when comprehending the value foundations of library activities and refer to the theoretical baggage of bibliosophy. The monograph by G. M. Vikhreva deserves the attention of library community, since it offers scientific novelty and relevance as a new vector of the library science.
A Literature Review of Quality Evaluation of Large-Scale Recommendation Systems Techniques

The sudden increase of online or internet based cooperates have led to the migration to RS. These systems shall provide accurate predictions and recommendations of services and products to the users of the same interest. The recommendation and prediction performance should strictly audited and evaluated to maintain the optimum quality of service that is being served and to ensure the continuity of such technologies through various considered factors. However, due to the exponential growth of number of the services available online, new challenges have erupted leading to many defects that affect drastically the quality of accurate prediction and recommendation of these systems such as data sparsity, the problem of scalability and cold start. These challenges have attracted many researchers and data scientists to investigate and further exploration of the main source of these raising issues especially in large scale and distributed systems.
Adaptive Edge-based Image Contrast Enhancement using Multi Sub-Histogram Analysis

Edge-based image contrast enhancement is a contrast enhancement method that focuses on the thickness of edge. Splitting histogram of an image into two sub-histograms, which is called bi-histogram, can be used to enhance the image contrast based on edge. However, image histogram has various modality, which causes bi-histogram separation may be incorrect. This paper proposed an adaptive edge-based contrast enhancement method using multi sub-histogram analysis. Hierarchical cluster analysis (HCA) splits the histogram iteratively to build multi sub-histogram. Then, the edge-based contrast enhancement process is performed with adaptive plateau limit for generating probability and cumulative density function for each sub-histogram. Finally, the enhanced image is achieved by the transformation function with guided filter. Assessment of the proposed method for evaluation is using absolute mean brightness error (AMBE), standard deviation (STD), contrast improvement index (CII), discrete entropy (DE), and perceptual image sharpness index (PSI). The evaluation shows that the proposed method has the best AMBE, CII, and DE of 6.53, 9.49, and 6.21, respectively. It means that the proposed method can maintain the brightness of the image, change the contrast significantly, and provide better edge information extraction, respectively. Therefore, the assessment proves effectiveness of the proposed method to enhance the contrast by separating regions of histogram correspond to the number of the modal contained in the histogram.
Ensemble Learning for Network Embeddings

This paper investigates network representation learning which involves network structure and nodes' labels. Most methods proposed so far try to utilize different kinds of network data available in just one perfect model to learn the set of perfect embeddings, and then evaluate its performance against other methods via network analysis tasks, such as node classification. But in this paper, we do not follow this pattern. Inspired by the idea of ensemble learning, we subtly design two separate neural models to learn from network structure and nodes' labels respectively. Then we integrate embeddings of our two models and find that when the precondition of ensemble learning is satisfied, set of integrated embeddings can perform better than any of its components. Experimental results show that our method works very well and the integrated embeddings generally achieve better performance for node classification than many other excellent baselines under almost all our experimental settings.
TO THE QUESTION OF THE SYSTEMATIC NATURE OF ELECTRONIC EDUCATIONAL MATERIALS FOR HIGHER EDUCATION

The relevance of the issue under consideration in the article is connected with the confusion in scientific publications of the concepts of “electronic educational materials” and “electronic educational resources”. The article discusses the concept of “electronic educational materials” from the perspective of general systems theory. And their system character is proved. This allows them to be represented as a single complex of structured information of a specific subject area and didactic materials. These didactic materials support the learning process at all stages of its didactic cycle in accordance with the chosen learning technology based on the didactic capabilities of information technologies. It is concluded that the system of high school electronic materials allows to expand the boundaries of the design activity of the teacher, provide management of the student’s thinking activity, to implement a competence approach to the learning process at university
Incentivizing Services Sharing in IoT with OSGi and HashGraph

Service Oriented Architecture is a viable option for developing applications in an Internet of Things (IoT) environment. One important consideration in developing services for an IoT environment is how to incentivize service providers and consumers so that a healthy IoT marketplace can come into practice with a balanced supply and demand for services. We argue that service providers should be specifically incentivized in some form to offer quality services in an IoT environment. In this paper, we present an IoT ecosystem, where each exchange of a service between a service provider and service consumer is logged as a transaction in a distributed ledger. For service sharing, we used OSGi Remote Services implementation of the Eclipse Communication Framework. For the distributed ledger, we used Swirlds Hashgraph. Each OSGi remote service is requested by digitally signing a commitment to use the service and upon service exchange, the signature is logged as a Hashgraph transaction. A proof-of-concept prototype has been implemented with positive results.
Online Grooming: An Exploration into the Genetic-Social Variables Which Enable Victimisation

Within contemporary society, it is evident that cybercrime has progressively become an unavoidable danger. This risk is particularly alarming in relation to the vulnerable members of society where social networking sites have become tools to target, allure, and infiltrate a child’s domain. My research analyses three distinct zones which enable and facilitate online grooming: paedophilic pathways, self-endorsed victimisation, and societal failures. By focusing on these three distinct areas, I will be able to identify the specific facilitators of online grooming. Within ‘paedophilic pathways’, my objective is to commence a discussion as to why paedophiles are empowered through the online world. This will initially consider the difference between online and offline grooming, and will reflect on their merging. Subsequently, within the ‘self-endorsed victimisation’ dimension, the researcher would like to analyse target stages of how and why adolescents capacitate victimisation and will consist of two strands. Firstly, the unconscious authorisation, where the adolescent is mostly unaware of their predator prompting vulnerability and secondly, self-endorsed victimisation where the researcher will attempt to understand why teenagers purposely evoke vulnerability online. Within the ‘societal failures’ dimension, the research will attempt to investigate what preventative measures are currently in place concerning family life, the education system and law enforcement, to suggest why they are ultimately failing. The overall aim of my research will be to deliberate a contingency plan in order to advocate an individual and societal cut-off by categorising elements of risk detection, grooming obstruction and possible further paedophilic prohibition.
106-Point Facial Landmark Localization with Mobile Networks Based on Regression

Sparse facial landmark localization has lower precision for face reconstruction, while more point landmarks are competent to depict the structure of facial components. In this paper, the pipeline of detecting 106-point facial landmarks with regression is proposed. Based on the convergence and practical application of multi-points regression, we design MobileNetV2-FL and VGG16-FL. Besides, an effective data preprocessing strategy and some training tricks, such as the Online Hard Example Mining algorithm and Wing loss are applied to the issue. Experimental results show that the proposed method has lower failure rate, and is an effective and robust facial landmark localization method.
Digit Classification Based on Mechanisms Used by Human Brain

One of the obvious differences between present machine learning and performance of human brain lies in the number labelled data that needs to be provided to the model for classification. The ultimate target of machine learning algorithms should be to learn from very few examples as demonstrated by human brain. The work presents an instance of reducing sample complexity in handwritten digit classification using the concept followed in object recognition with reduced training set by humans. The characteristics of selectivity and invariance to transformations in visual cortex lead to a feature representation which can learn from few examples. The same has been implemented for digit classification, and promising results are obtained which can be extended to variety of object classification/recognition tasks involving images, strings, documents, etc.
Data Warehouse Design for Security Applications Using Distributed Ontology-Based Knowledge Representation

The primary goal of this paper is to develop a distributed ontology-based knowledge representation approach useful for data warehouses design in the security applications area. The paper proposes a novel database design for registering security incidents in critical infrastructure on railways. We propose an approach based on the data warehouse architecture that consists of distributed smart database micro services patterns, which are represented by the distributed ontology. This representation is using novel distributed dynamic description logic for knowledge representation. We give the base of distributed dynamic description logic and forming the queries to the designed distributed knowledge bases.
Multiple-Valued Artificial Neural Networks

The generalizations of common single-valued artificial neural networks are proposed. The main proposition is to consider multiple-valued networks. Such neural networks can have multiple values of elements at given time moment. Neural networks with strong anticipation property are considered as the examples. Also some new research problems are proposed.
Distributed Algorithms for Finding Meta-Paths of a Large Heterogeneous Information Network on Cloud

Meta-path is an important concept of heterogeneous information networks (HINs). Meta-paths were used in many tasks such as information retrieval, decision making, and product recommendation. Normally meta-paths were proposed by human experts. Recently, works on meta-path discovery have proposed in-memory solutions that fit in one computer. With large HINs, the whole HIN cannot be loaded in the memory. In this chapter, the authors proposed distributed algorithms to discover meta-paths of large HINs on cloud. They develop the distributed algorithms to discover the significant meta-path, maximal significant meta-path, and top-k meta-paths between two vertices of HIN. Calculation of the support of meta-paths or performing breadth first search can be computational costly in very large HINs. Conveniently, the distributed algorithms utilize the GraphFrames library of Apache Spark on cloud computing environment to efficiently query large HINs. The authors conduct the experiments on large DBLP dataset to prove the performance of our algorithms on cloud.
Mending is Better than Ending: Adapting Immutable Classifiers to Nonstationary Environments using Ensembles of Patches

In times of abundant streams of data, the only constant we can rely on is change itself. Given an existing classification model, it might be impractical or impossible to retrain when concept drift impairs the performance. In this paper, we propose an ensemble learning approach that fixes errors of a given model via patches: smaller, local models that are applied when the base model is likely to fail. These patches are added to the ensemble when the base learner shows decreased performance for one or multiple classes, which we discover by applying explicit drift detection. Each patch covers the drift of a subset of the classes. Via the drift detection method, the misclassification behavior of the base learner can be matched to an existing patch. This reduces the total amount of patches to the number of seen concept drifts, which is an advantage in the long run. Patches can be added to the ensemble and refined incrementally. The ensemble decision is conducted by a sequential classifier, which allows a high number of ensemble members that can be added incrementally. Experimental results show that ensemble patching performs well w.r.t. accuracy and adaptation speed.
Hashed Watermarking Algorithm for 3D Authentication Model

A blind and robust hybrid hashing/watermarking method for inserting watermarks into 3D models is presented in this paper. This method makes the watermarks unnoticeable to the human visual system and extremely difficult to detach. To enhance the performance and the security of the suggested watermarking methodology, the watermark is constructed using hashing function based on Arnold transform and singular value decomposition (SVD). The embedding process hides the watermark by modifying the vertex position depending on measuring the valance or degree of faces around the vertices. The vertices are classified based on their mean curvature values to insert the watermark in optimal regions. Moreover, the watermark not making noticeable changes in the original 3D model and resistant watermark to numerous types of attacks such as translation, adding noise, subdividing, scaling, and rotation. The hybridization between hashing and 3D model watermark enables a secure and robust watermarking method that achieves high levels of imperceptibility and robustness.
Collaborative Management System Driven by Task Flow in Supercomputing Environment

In order to improve the integration of super computer and advanced manufacturing, and establish a multi-disciplinary collaborative research and development mechanis-m based on supercomputer, this paper proposes a collaborative flow management solution driven by task flow. Then, Co-llaborative management platform driven by task flow is designed and developed. The platform, which can provide good scalability and flexible configuration strategy in network communication, task flow processing, access control, concur-ency control, etc. Finally, based on this platform, Collaborative management system driven by task flow is built and imple-mented. The system has been integrated with the super-computer job management.
A Preliminary Study on Mobile Learning

With the deep integration of information technology and education, especially the rise and deep using of new generation information technologies such as cloud computing, Internet of Things, big data, artificial intelligence, mobile learning are gradually becoming the mainstream of online education. Moreover, it is an indispensable and innovative learning method and also a new model for future learning. By analyzing the concept, characteristics, and composition of mobile learning, we give a feasibility analysis of mobile learning in its own advantages, network environment, professional construction, and requirement for industrial education integration. In addition, we also analyzed the current problems in the development of mobile learning and gave some corresponding solutions. This provides a complete basis for mobile learning in practice of distance education, online education, professional technology improvement, professional construction, etc. and also lays a solid theoretical foundation for the implementation of mobile learning.
Prediction of Breast Cancer Diagnosis by Blood Biomarkers Using Artificial Neural Networks

The use of artificial neural networks (ANNs) has been very helpful in carrying out prediction, classification and data optimization tasks. In this work, ANNs were used to predict the diagnosis for breast cancer in a women population with overweight or obese and possible diabetes, coupled with a pre or postmenopausal stage and compared against other machine learning techniques reported in literature. The algorithms used to train the ANNs models were Scaled Conjugate Gradient, Resilient Backpropagation and Conjugate Gradient Backpropagation with Powell Beale Restarts. The algorithms results were compared with the original dataset creator’s work, as well as other authors using the same dataset for classification task, a better classification was accomplished using this work ANNs. With four predictors the next values were obtained, AUC = 0.96, sensitivity = 0.96, specificity = 0.96 and Youden index = 0.92. With nine predictors the next values were obtained, AUC = 0.96, sensitivity = 0.95, specificity = 0.97 and Youden index = 0.92. Different strategies are suggested to improve the results, exploring more hidden layers and a different validation method.