Reviewerly/exHarmony huggingface dataset:
Perceptual Computing with Comparative Linguistic Expressions

A perceptual computer generates appropriate word recommendations, rankings and/or classifications for any given application context. During the preparation of the codebook, perceptions of individuals are obtained in the form of end-points of intervals, followed by the selection of words from the codebook at a later stage. Keeping in mind the fact that humans are hesitant while providing responses, the perceptual computing paradigm does not handle such hesitancy at any stage. To conquer this deficiency within the framework of perceptual computing, we propose the idea of perceptual computing with comparative linguistic expressions. Through this proposal, we engage the hesitancy faced by people at different stages of the perceptual computing framework. We also introduce two different types of hesitancies that can occur, depending on the types of elements.
Digital Image De-noising and Restoration method using Differential Filters for Improving the Image Quality

Digital photography is been enhanced in excess in past years, and it makes a leading trend for novel and innovative approach towards the forge images. The work is associated with extracting the tampered image in any form and processing it to obtain the required result. Commonly, in an image, it has some noise. The main aim is to de-noise the tampered image and reveal the original image without losing the image features (edges, corners and other sharp structures) by the restoration process. The de-noising is done by the median filter, the noise is been removed and then the restoration is done by the inverse filter.
A Survey of KYC/AML for Cryptocurrencies Transactions

KYC (know your customer) and AML (anti-money laundering) practices have been designed and implemented in traditional financial transactions for some years now. However, it has been complicated to find a balance between business efficiency, innovations, financial inclusion, and compliance, both in the specification of what should be done and in the implementation of a risk-based approach that satisfies the required specification during real business operation. This chapter presents a survey of traditional practices to KYC/AML, highlighting a subset of existing challenges in these practices, taking into consideration the innovation of cryptocurrencies transactions and related innovations, such as digital identity, and the financial inclusion of unbanked people without identity papers. The authors finish this chapter by discussing existing solutions to these challenges both by adopting new KYC/AML practices and by using innovative technological solutions.
PRINCESS: Prediction of Individual Breast Cancer Evolution to Surgical Size

Modelling surgical size is not inherently meant to replicate the tumor's exact form and proportions, but instead to elucidate the degree of the tissue volume that may be surgically removed in terms of improving patient survival and minimize the risk that subsequent operations will be needed to eliminate all malignant cells entirely. Given the broad range of models of tumor growth, there is no specific rule of thumb about how to select the most suitable model for a particular breast cancer type and whether that would influence its subsequent application in surgery planning. Typically, these models require tumor biology-dependent parametrization, which hardly generalizes to cope with tumor heterogeneity. In addition, the datasets are limited in size, owing to the restricted or expensive measurement methods. We address the shortcomings that incomplete biological specifications, the variety of tumor types, and the limited size of the data bring to existing mechanistic tumor growth models and introduce a Machine Learning model for the PRediction of INdividual breast Cancer Evolution to Surgical Size (PRINCESS). This is a data-driven model based on neural networks capable of unsupervised learning of cancer growth curves. PRINCESS learns the temporal evolution of the tumor along with the underlying distribution of the measurement space. We demonstrate the superior accuracy of PRINCESS, against four typically used tumor growth models, in learning tumor growth curves from a set of four clinical breast cancer datasets. Our experiments show that, without any modification, PRINCESS can accurately predict tumor sizes while being versatile between breast cancer types.
Dual-discriminator GAN: A GAN way of profile face recognition

A wealth of angle problems occur when facial recognition is performed. At present, the feature extraction network presents eigenvectors with large differences between the frontal face and profile face recognition of the same person in many cases. For this reason, the state-of-the-art facial recognition network have a relatively poor performance on profile faces. In this paper, we proposed a method of image-to-image generating frontal faces from profile faces based on Generative Adversarial Network (GAN). By adopting a new dual discriminator Gan structure and an artificial programed training process, the authority and identity of the generated frontal faces have been greatly improved.
Sequential bargaining based incentive mechanism for collaborative Internet access

Rapid growth in mobile data traffic and technological improvements in mobile devices revealed the concept of collaborative Internet access via User Provided Networks (UPN). According to this approach, users with strong cellular connection share their own connections with other users that have limited or no cellular connection. However, in such services, one of the key issues is the users' willingness to contribute. In this study, we propose a novel incentive mechanism based on Rubinstein's sequential bargaining model. In the proposed scheme, incentives are given by the mobile network operator as virtual currencies, according to the bargaining of users and experienced quality of service. Decisions of users for requesting or offering UPN services depend on energy cost and utility estimations according to the actual communication signal parameters. For this purpose, we introduce realistic energy consumption and data rate models after performing extensive set of experiments and applying various regression methods. We implement the proposed system in a real environment and show its effectiveness in various scenarios.
The integrity of machine learning algorithms against software defect prediction

The increased computerization in recent years has resulted in the production of a variety of different software; however, measures need to be taken to ensure that the produced software is not defective. Many researchers have worked in this area and have developed different machine learning-based approaches that predict whether the software is defective or not. This issue cannot be resolved simply by using different conventional classifiers because the dataset is highly imbalanced, i.e., the number of defective samples detected is extremely less as compared to the number of nondefective samples. Therefore, to address this issue, certain sophisticated methods are required. The different methods developed by the researchers can be broadly classified into resampling, cost-sensitive learning, and ensemble learning-based methods. Among these methods, this chapter analyzes the performance of the online sequential extreme learning machine (OS-ELM) proposed by Liang et al. against several classifiers such as logistic regression, support vector machine, random forest, and Naïve Bayes after oversampling the data. OS-ELM trains faster than conventional deep neural networks and always converges to the globally optimal solution. A comparison is performed on the original dataset as well as the oversampled dataset. The oversampling technique used is cluster-based oversampling with noise filtering. This technique is better than several state-of-the-art techniques for oversampling. The analysis is carried out on three projects KC1, PC4, and PC3 by the NASA group. The metrics used for measurement are recall and balanced accuracy . The results are higher for OS-ELM as compared to other classifiers in both scenarios.
Prerequisites for the digital transformation of the Russian education system

Abstract: The authors note the tendency of digital transformation of different spheres of human activity. Many scientific studies describe the results of the digital transformation of education, characterize ways of adapting teachers and students to the conditions of the new digital environment and consider the formation of digital competencies of teachers and digital literacy of students. At the same time, scientists do not consider the reasons that actualize digital transformation. The purpose of the study is to identify external and internal factors that determine the digital transformation of the Russian education system at the present stage of development of society. The authors are building a causal relationship model of the digital transformation of the education system. The authors choose the principles of a systematic approach as the methodological basis of the study. They consider the digital transformation of education as a system of transformations that have a complex transformative effect. This impact is based on taking into account the diversity of interconnections between the components of the education system. The authors substantiate the nature of digital transformation as a large-scale transformation, as a process of implementing digital technologies, as an improvement in the quality of the results of the educational process. The authors consider the factors and results of digital transformation in various projections. In accordance with the ideas of a systematic approach, the authors describe the essence of digital transformation in the form of a causal relationship model of the digital transformation of the education system. The causal relationship model of the digital transformation of education allows us to systematically see the development trends of Russian education and in the future to develop the right strategies to improve its quality and competitiveness. The use of digital technologies in the educational process provides a rational assessment of their need for solving specific educational problems. The main role of digital technologies consists in technological improvement of the educational process, expansion of the didactic capabilities of the teacher, activation and motivation of personal interaction between the teacher and students.
Mobile robot for running exercises

Abstract Progress does not stand still, and machines are involved in the process of training professional athletes, where the greatest restriction is economic in nature - simulators are not available to a wide range of users. The field of running sports is currently practically not provided with robotic support in such a way that with the help of machines it was possible to implement the process of training an athlete. This article is devoted to the construction of the concept and principles of the implementation of a mobile robot for performing running exercises.
Bridge Ontology Architecture for Knowledge Management in Bridge Maintenance

The construction sector depends on a broad variety of information from very different sources. Especially when constructing bridges, huge experience, and extensive knowledge is important. Knowledge Management (KM) is the key to share explicit and tacit knowledge in the bridge construction. As an initiation for the KM, an ontology for bridge maintenance was developed. As the first step bridge maintenance related data were collected. Then taxonomy was developed. As a next step, the bridge maintenance ontology was developed using Protégé OWL Ontology Editor. Finally, the modeled ontology was evaluated using tools and ontology experts. We strongly believe that our novel approach to bridge maintenance ontology can support all the actors on the construction site and as well as students and researchers.
Ontological Relation Classification Using WordNet, Word Embeddings and Deep Neural Networks

Learning ontological relations is an important step on the way to automatically developing ontologies. This paper introduces a novel way to exploit WordNet [16], the combination of pre-trained word embeddings and deep neural networks for the task of ontological relation classification. The data from WordNet and the knowledge encapsulated in the pre-trained word vectors are combined into an enriched dataset. In this dataset a pair of terms that are linked in WordNet through some ontological relation are represented by their word embeddings. A Deep Neural Network uses this dataset to learn the classification of ontological relations based on the word embeddings. The implementation of this approach has yielded encouraging results, which should help the ontology learning research community develop tools for ontological relation extraction.
3D Full-Field Deformation Measuring Technique Using Digital Image Correlation

Full-field deformation measurements are crucial as it offers detailed information to better understand both micro and macroscopic nature of material behavior. The practice of employing Digital Image Correlation (DIC) based measuring techniques in experiments has increased due to its ability to generate full-field deformation information with minimal effort. Even though DIC systems are commercially available, the affordability of those systems is questionable in local context due to high capital costs. Most of the past studies related to DIC were focused on testing concrete, masonry and metallic alloy specimens, and little effort has been made on materials with recoverable large elongations. This paper presents a 3D full-field deformation measuring system that has been developed with a special focus on hyperelastic materials. The proposed system requires two common digital cameras for image acquisition, as the depth information is of interest. Images are then processed using the MATLAB-based algorithm developed to produce the full-field deformation map. Hyperelastic specimens of two different thicknesses were tested over 70% strain and the accuracy of the strain measurement using the proposed system is validated against physical measurements. The results have shown that the strains can be captured to an accuracy greater than 90% using the proposed technique.
Semi‐supervised logistic learning based on exponential tilt mixture models

Consider semi‐supervised learning for classification, where both labelled and unlabelled data are available for training. The goal is to exploit both datasets to achieve higher prediction accuracy than just using labelled data alone. We develop a semi‐supervised logistic learning method based on exponential tilt mixture models by extending a statistical equivalence between logistic regression and exponential tilt modelling. We study maximum nonparametric likelihood estimation and derive novel objective functions that are shown to be Fisher probability consistent. We also propose regularized estimation and construct simple and highly interpretable expectation–maximization (EM) algorithms. Finally, we present numerical results that demonstrate the advantage of the proposed methods compared with existing methods.
Meta-learning to Select the Best Metaheuristic for the MaxSAT Problem

Several metaheuristics can be considered for solving a given optimization problem. Unfortunately none of them is better on all instances. Selecting a priori the best metaheuristic for a given instance is a difficult task which can be addressed using meta-learning. In this work, we propose a method to recommend, for a MaxSAT instance, the best metaheuristic among three: Genetic Algorithm (GA), Bee Swarm Optimization (BSO) and Greedy Randomized Adaptive Search Procedure (GRASP). Basically, a learning model is trained to induce associations between MaxSAT instances’ characteristics and metaheuristics’ performances. The built model is able to select the best metaheuristic for a new MaxSAT instance. We experiment different learning algorithms on different instances from several benchmarks. Experimental results show that the best metaheuristic is selected with a prediction rate exceeding 80% regardless the learning algorithm. They also prove the effect of instances used in training on the model performance.
A-LINK: Recognizing Disguised Faces via Active Learning based Inter-Domain Knowledge

Recent advancements in deep learning have significantly increased the capabilities of face recognition. However, face recognition in an unconstrained environment is still an active research challenge. Covariates such as pose and low resolution have received significant attention, but “disguise” is considered an onerous covariate of face recognition. One primary reason for this is the unavailability of large and representative databases. To address the problem of recognizing disguised faces, we propose an active learning framework A-LINK <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> , that intelligently selects training samples from the target domain data, such that the decision boundary does not overfit to a particular set of variations, and better generalizes to encode variability. The framework further applies domain adaptation with the actively selected training samples to fine-tune the network. We demonstrate the effectiveness of the proposed framework on DFW and Multi-PIE datasets with state-of-the-art models such as LCSSE and DenseNet.
Hybrid Approach for Accurate and Interpretable Representation Learning of Knowledge Graph

Representation learning of knowledge graph aims to embed both entities and relations into a low-dimensional space. However, there are still some gaps in the knowledge graph embedding methods in providing interpretation of knowledge graph while encoding the semantic meaning of the concepts and structured information of knowledge graphs. To address this issue, we propose a hybrid approach for Accurate and Interpretable Representation Learning (AIRL) method for embedding entities and relations of knowledge graphs by utilizing the rich information located in entity descriptions and hierarchical types of entities. Here we use hybrid approach to learn interpretable knowledge representations by capturing the semantics and structure of entities using this rich information. We adopt FB15K dataset generated from a large knowledge graph freebase, to evaluate the performance of the proposed model. The results of experiments demonstrate AIRL significantly outperforms translation embeddings and other state-of-the-art methods.
A Collective Intelligence Strategy for Enhancing Population-based optimization Algorithms

Population-based algorithms are a well-established category of metaheuristic optimization algorithms in which individuals collaborate with each other to find the optimal solution in a search space. During the search process, each individual provides a partial intelligence which can assist the population movement toward promising regions. In this paper, a dimension-wise strategy is proposed to collect the intelligence of whole population to generate a new trial candidate solution. For new individual, the value of each variable is calculated using the votes of a more-crowded cluster of individuals obtained on each dimension (one-dimensional clustering). Accordingly, a group of candidate solutions in the population collaborate to determine a variable value of new individual. Utilizing this strategy, collective intelligence (CI) aims the algorithm to find better candidate solutions. Since the proposed method keeps untouched all other parts of the algorithm, it can be used with any population-based algorithm. This paper presents the modification of two well-known population-based algorithms based on the proposed strategy in utilizing Collective Intelligence (CI), Differential Evolution (CIDE) and Particle Swarm optimization (CIPSO). In conducted experiments, two proposed algorithms are compared with classical version of DE and PSO on 30 functions of CEC2017 benchmark. The results indicate that the proposed method generates an individual with better objective function value than many of the individuals in the population which leads totally better results in overall.
Improving the Iranian Industrial Property System (IPS): A comparative case study of Iran and South Korea

This study analyzes the Industrial Property System (IPS) of Iran and proposes solutions for improving the system. Documentary research and a comparative study of the South Korea IPS are the main sources of information. The results indicate that although Iran has taken important steps towards improving its IPS, there are lingering problems. The organizational set-up of the IPS needs restructuring. Moreover, intellectual property (IP) management professionals and policy makers will benefit from IP training programs. Finally, IP policy making and IPS reform should be based on the needs of the industry.
The Role of Modeling in Blockchain Process Design

Blockchain could introduce a new approach by which organizations view and govern their information and technology projects. Processes involving blockchain are oftentimes transaction-based, decentralized, and require coordinated communization before implementation. This approach may introduce new challenges to conventional modeling techniques. In this study, the role of modeling during the design phase of blockchain processes is explored. This role is described through a theory derived from 30 semi-structured interviews, two case studies, and two focus groups. The results are applied to inter-organizational business process modeling. The role of modeling, the effects on this role caused by the introduction of blockchain, and the shortcomings of current modeling techniques are described. Additionally, the study provides several opportunities for future research.
Complex Approach in Cryptanalysis of Internet of Things (IoT) Using Blockchain Technology and Lattice-Based Cryptosystem

In the given paper, we offer complex approach to secure The Internet of Things (IoT). IoT turns each physical object into a smart object sensing the environment surrounding us and brings new security risks in business sector, healthcare and in all the aspects of human life. The exponentially growing numbers of devices connected to IoT networks makes very hard to authenticate and secure all these devices within IoT network. It’s very difficult to maintain and manage a centralised IoT security model which is prone to failure and DDoS attacks. Wi-Fi technology is one of the leading technology. Wi-Fi uses radio waves prone to eavesdropping. WPA2 protocol security issues discovered recently by Computer Scientists and WPA3 has been developed to improve Wi-Fi security aspects but in a new WPA3-Personal protocol have been found the vulnerabilities allowing intruders to crack Wi-Fi passwords and intercept encrypted traffic sent between the Wi-Fi users. Blockchain mechanisms (BCMs) play a role in securing many IoT-oriented applications by becoming part of a security mosaic, in the context of a defenses-in-depth/Castle Approach. A blockchain is a database that stores all processed transactions – or data – in chronological order, in a set of computer memories that are tamperproof to adversaries. These transactions are then shared by all participating users. Information is stored and/or published as a public ledger that is infeasible to modify; every user or node in the system retains the same ledger as all other users or nodes in the network. This paper highlights some IoT environments where BCMs play an important role, while at the same time pointing out that BCMs are only part of the IoT Security (IoTSec) solution. We highlight blockchain’s important role for securing IoT. Cryptography technologies are the best solution to withstand communication attacks. We analyse NTRU cryptosystem capable to implement the NTRUEncrypt public key encryption algorithm withstanding the attacks from the quantum computers.