Reviewerly/exHarmony huggingface dataset:
Prototype to Optimize the Management of Information Security Used by Internal Users in a Public Organization of Ecuador

The security with respect to the information systems causes a constant discussion problem due to the vulnerabilities that the processes of the public organizations face, for this reason, the prototypes that offer a certain degree of security are taken into account: loss of information, attacks cybernetics, damage to high performance equipment, among others. The objective is to analyze and determine the conclusions of each one of the security prototypes that evaluate their characteristics, functionalities, usability, etc. To analyze the information of the articles mentioned in this document, the deductive method was used. It turned out that prototypes oriented to information security for users in organizations, social networks, networks. The stages of the processes of the information systems, the power to determine the type of service that is presented and, in this way, take the appropriate measures to counteract it. It was concluded that security prototypes are an alternative that provide in one way or another security when running information systems processes, but nothing guarantees that they are 100% reliable, which would cause the loss of information or computer attacks in public organizations in Ecuador. We can also conclude that the prototype PMI (interactive maturity prototype) offers optimal security in terms of information systems for internal users in public organizations.
A Deep Learning Application System Based on Blockchain Technology for Clicks-And-Mortar Businesses

Nowadays, e-commerce has made its official way into the clicks-and-mortar era. The advantage of the clicks-and-mortar strategy is that it enjoys logistics support from the “mortar” counterpart, which complements any operational inadequacy on the side of the virtual website - the “clicks” counterpart. In the future, the clicks-and-mortar model can facilitate the merging of different businesses and strategic partnerships that will diversify sales channels as well as client sources. This study proposes developing an e-commerce clicks-and-mortar application system based on blockchain technology. The research shall focus on the following topics: (1) developing a privacy-ensuring blockchain technology by using the dark web to ensure the blockchain's privacy and safety features; (2) developing smart contracts based on blockchains to speed up transactions and reduce fees of intermediate certificate authorities; (3) creating a physical store recommendation system through deep learning so that e-commerce businesses can apply their desired criteria and search for the optimal location area for their physical store. The first year of the project will be dedicated towards developing a blockchain mechanism, creating smart contracts to expedite and smooth the way for transactions, and eliminating third-party transaction certification fees through blockchain's decentralization attribute; meanwhile, the physical store recommendation system is capable of performing analysis of multi-aspect data, such as client distribution, and select the optimal location for the store so as to provide clients with more physical experiences and services.
Face Recognition Using Depth Images Base Convolutional Neural Network

Face recognition based on depth images is widely studied due to its advantages of 3 dimensional information and environment illumination insensitivity. The traditional recognition methods in this field mainly focus on hand-crafted feature design, which cannot achieve satisfactory result. In addition, there is no fixed face feature extraction method. To achieve a better face recognition performance on depth images, this paper proposes a method based on Convolutional Neural Networks(CNN). The experiment performed on database IIITD Kinect suggests that the proposed CNN architecture has better recognition performance than some traditional manual feature extraction methods, such as HOG and LBP.
Comparative study of user experience evaluation techniques based on mouse and gaze tracking

Monitoring techniques have been used in the most diverse systems to evaluate user experience. Specifically in Web environments, techniques such as mouse tracking and eye-tracking generate interaction data that can be analyzed and used for layout and usability improvements. In this context, the present article proposes to analyze and confront the data obtained with the tools Artificial Intelligence and Mouse Tracking-based User eXperience Tool (AIMT-UXT), to collect the data of mouse-tracking, and SearchGaze.js, which is used during the collection of eye-tracking data and eye gaze fixed points. In addition, heat maps are used to verify whether eye tracking follows mouse tracking.
Private Data Aggregation over Selected Subsets of Users

Aggregator-oblivious ( $$\mathsf {AO}$$ ) encryption allows the computation of aggregate statistics over sensitive data by an untrusted party, called aggregator. In this paper, we focus on exact aggregation, wherein the aggregator obtains the exact sum over the participants. We identify three major drawbacks for existing exact $$\mathsf {AO}$$ encryption schemes—no support for dynamic groups of users, the requirement of additional trusted third parties, and the need of additional communication channels among users. We present privacy-preserving aggregation schemes that do not require any third-party or communication channels among users and are exact and dynamic. The performance of our schemes is evaluated by presenting running times.
Event-Triggered State Estimation of High Dimensional Nonlinear Systems With Highly Nonlinear State Space Model Using Cubature Kalman Filter

In this paper we design a state estimator which is proper for high dimensional nonlinear system with highly nonlinear state space model with noisy measurements over a wireless network using Cubature Kalman Filter (CKF). We show that by using the event-triggered cubature Kalman filter, the number of transmission through the communication channels between the measuring sensors and the remote state estimator will be reduced while the estimation quality can be guaranteed. An example shows the effectiveness of the proposed algorithm.
Use of Machine Learning Methods for Solving Problem of User Identifying by Keyboard Handwriting

This article deals with the problem of ensuring protection against unauthorized access to data by identifying users according to biometric characteristics. The authors consider keyboard typing, which is a set of characteristics of the user on the keyboard. During the experiments were obtained handwriting samples of several users who printed phrases consisting of all letters of the alphabet. As a result, a statistical sample was obtained for each participant of the experiment, and it allowed concluding that the handwriting samples of each person are stable and sufficient for identification. Keyboard handwriting is a dynamic (changing) characteristic that can be described by many parameters, so the choice of data processing is a difficult task. To analyze the data and identify the author of the handwriting sample, the article provides an overview of existing methods and discusses statistical methods such as k-means and regression analyses, which are the most commonly, used statistical classification methods. Evaluation of the effectiveness of the results is evaluated using the coefficients of false access (FAR) and false denial of access (FRR). The results suggest that these methods do not effectively solve the problem, so it is necessary to use other methods. Thus, the authors proposed the use of artificial intelligence methods that allow us to find hidden patterns and dependencies in the data stream.
VisualJFML: A Visual Environment for Designing Fuzzy Systems according to IEEE Std 1855-2016

Sponsored by the IEEE Computational Intelligence Society, IEEE Std 1855 is aimed at defining a standard language, named Fuzzy Markup Language, capable of modeling fuzzy systems without considering hardware/software constraints and enabling, in this way, fuzzy systems sharing. In order to provide runnable fuzzy systems in accordance with IEEE Std 1855, recently, a Java library, named JFML, has been developed. Unfortunately, in spite of its benefits, JFML enables the modeling of fuzzy systems only for Java programmers. In order to overcome this drawback, this paper introduces a new open source fuzzy system software capable of enabling the modeling of fuzzy systems in accordance to IEEE Std 1855 enhancing JFML through a visual environment based on graphical user interfaces. Hence, VisualJFML represents a remarkable contribution to the literature since it allows modelling sharable fuzzy systems also to designers without programming skills. The user-friendly graphical interface provided by VisualJFML is shown by means of a case study dealing with the Iris classification problem.
Extracting Benefits from Standardization of Digital Forensic Practices

Abstract Standardization offers consistencies for interoperability between jurisdictions and organizational entities. In some instances, accreditation and certification services are available to assure the reputational transfer of conformance and the compliance with best practice requirements. The impact of these benefits is a more predictable environment in which to deliver sensitive and high-risk services, leading to the reduction in failures and the enhancement of inter-party trust. In this article, the case of international standardization for digital forensic and related services is articulated as an example of structured planning for robust processes and practices that have user trust. Political and economic expediency has always reduced the implementation of standardization to functional attributes and short-term returns. The advocacy is for the long-term investment in standardization and the creation of trusted practices for predictable and robust scientific procedures in the transfer of operational knowledge and the preservation of digital evidences.
Differential Cryptanalysis on Chaotic Based Image Encryption Scheme

Image encryption schemes based on chaotic maps and systems have been investigated for more than one decade. However, many proposed schemes that obtained good score in the statistical tests, i.e. number of pixel changing rate and unified average changed intensity were proven to be insecure against differential cryptanalysis. Differential cryptanalysis is a chosen plaintext attack that utilizes the high probabilities of occurrence of a specific plaintext-ciphertext difference. In this paper, we present the weaknesses of these image encryption schemes against differential cryptanalysis and discuss how a cryptanalyst can exploit them to demonstrate the security deficiency of the schemes.
Heritage Building Era Detection using CNN

Abstract The Indian subcontinent is a southern area of Asia continent which includes India, Bangladesh, Pakistan, Nepal, Bhutan, Maldives, and Sri Lanka. In the different periods, different rulers had ruled in these territories such as the Sultanate period (1206–1526) and Mughal period (1526–1540, 1555–1857). In addition, various ancient and heritage structure patterns show the different historical and religious characteristics of the old civilizations. This research presents a computational method for identifying the construction period or era of old heritage building by using feature detection. We have done this experiment for the application aspect, where the tour guide is not available this application will help the tourists for knowing the construction period or era by detecting the features of old spectacular architecture. In this study, we have focused on the constructional characteristics of old architectural sites by using Canny Edge Detector method. After using the Canny Edge Detector method, we have categorized different types of features among the buildings such as Dome, Minaret or Tower, Ratna, and Front. The different eras old structure contains different characteristics of the Dome, Minaret or Tower, Ratna, and Front. Finally, we have developed a neural network based model which can identify the building’s construction era of the Sultanate period and Mughal period.
The fixed point problem of a simple reversible language

SRL is a total programming language with distinctive features: (i) every program that mentions n registers defines a bijection Zn→Zn, and (ii) the generation of the SRL-program that computes the inverse of that bijection can be automatic. Containing SRL a very essential set of commands, it is suitable for studying strengths and weaknesses of reversible computations. We deal with the fixed points of SRL-programs. Given any SRL-program P, we are interested in the problem of deciding if a tuple of initial register values of P exists which remains unaltered after its execution. We show that the existence of fixed points in SRL is undecidable and complete in Σ10. We show that such problem remains undecidable even when the number of registers mentioned by P is limited to 12. Moreover, if we restrict to the linear programs of SRL, i.e. to those programs where different registers control nested loops, then the problem is already undecidable for the class of SRL-programs that mention no more than 3712 registers. Last, we show that, except for trivial cases, finding if the number of fixed points has a given cardinality is also undecidable.
Role of Mathematics in Image Processing

The various Image Processing technologies have revolutionized the way in which a particular image is captured, stored, or retrieved from a certain source. Most commonly used Image Processing tasks include; Image Restoration, Image Segmentation, Image Enhancement, De-Blurring, De-noising, etc. Such Images are well utilized in various ways by several of the Imaging techniques, like; Angiography, MRI (Magnetic Resonance Imaging), Arterial Spin Labeling (ASL), Computer Assisted Tomography (CT), Deep Brain Stimulation (DBS), Electroencephalography (EEG), etc. But, it is the, Mathematics that has played a crucial role in various Image Processing tasks mentioned above. Despite the innovation and rapid advances in the various Imaging Technology, one thing that has remained important throughout, is, the use of Mathematics.It has been observed that image processing has got strong connection with Mathematics. Many of the image processing methods rely on the basic Mathematical Techniques of Histogram Equalization, Probability and Statistics, Discrete Cosine Transforms, Fourier Transforms, Differential Equations, Integration, Matrix and Algebra. For computational purposes, Matlab is one of the most commonly used tool by the researchers in the area of Image Processing. Other Tools which are commonly used include SciLab, GNU Octave, SageMath, etc.This paper critically reviews the role of Mathematics in various Image Processing Applications. This paper would also present the basic Mathematical tools and techniques, used in Image processing, along with the literature review of the same.
Alternative Assets and Cryptocurrencies

Alternative assets, defined by their low correlation with classical financial assets, have become an important investment vehicle in times of negative interest rates and in the aftermath of the global economic and financial crisis. Hedge funds increasingly invest in physical assets such as fine art, wine, or diamonds. Although digital and not physical, cryptocurrencies share many features of alternative assets, but are hampered by high volatility, sluggish commercial acceptance, and regulatory uncertainties. This special issue covers a broad variety of topics in financial technology, and provides a state-of-the-art overview of cryptocurrencies from economic, financial, statistical and technical points of view.
OECM: A Cross-Lingual Approach for Ontology Enrichment

Due to the rapid expansion of multilingual data on the web, development of approaches to enrich ontologies has become an interesting and active subject of research. In this paper, we propose a cross-lingual matching approach for ontology enrichment (OECM) in order to enrich an ontology using another one in a different natural language. A prototype for the proposed approach has been implemented and evaluated using the MultiFarm benchmark. Evaluation results show higher precision and recall in comparison to other four state-of-the-art approaches.
Feature Aggregation Decoder for Segmenting Laparoscopic Scenes

Laparoscopic scene segmentation is one of the key building blocks required for developing advanced computer assisted interventions and robotic automation. Scene segmentation approaches often rely on encoder-decoder architectures that encode a representation of the input to be decoded to semantic pixel labels. In this paper, we propose to use the deep Xception model for the encoder and a simple yet effective decoder that relies on a feature aggregation module. Our feature aggregation module constructs a mapping function that reuses and transfers encoder features and combines information across all feature scales to build a richer representation that keeps both high-level context and low-level boundary information. We argue that this aggregation module enables us to simplify the decoder and reduce the number of parameters in the decoder. We have evaluated our approach on two datasets and our experimental results show that our model outperforms state-of-the-art models on the same experimental setup and significantly improves the previous results, $$98.44\%$$ vs $$89.00\%$$ , on the EndoVis'15 dataset.
Precursory study on varieties of DDoS attacks and its implications in Cloud Systems

Cloud Computing has emerged into an inevitable platform for computing services by effectively implementing Service Oriented Architecture (SOA) and Virtualization. However it is still vulnerable to traditional security threats and offers scope for innovative security attacks such as EDoS [1]. While it offers platform to generate innumerable Virtual components from a single physical component, it inadvertently provides wide spectrum of possibilities for distributed attacks. Moreover such attacks have adapted to cloud platform and have exploited various inherent vulnerabilities. In an unprecedented manner, they became unpredictable, evasive and challenging to Cloud Security measures. Therefore various versions of DDoS attack that targets the cloud platform have been extensively researched and narrated. The Cloud Security faces unprecedented challenges such as the Single-point-of-Failure occurs when a Cloud Supervisory Component or hypervisor fails due to a security breach. Moreover Cloud requirements often require being liberal to meet the Clients needs. This does not help the CSP to adapt traditional stringent security measures in Cloud System the reasons have been discussed in details.
VoIDext: Vocabulary and Patterns for Enhancing Interoperable Datasets with Virtual Links

Semantic heterogeneity remains a problem when interoperating with data from sources of different scopes and knowledge domains. Causes for this challenge are context-specific requirements (i.e. no "one model fits all"), different data modelling decisions, domain-specific purposes, and technical constraints. Moreover, even if the problem of semantic heterogeneity among different RDF publishers and knowledge domains is solved, querying and accessing the data of distributed RDF datasets on the Web is not straightforward. This is because of the complex and fastidious process needed to understand how these datasets can be related or linked, and consequently, queried. To address this issue, we propose to extend the existing Vocabulary of Interlinked Datasets (VoID) by introducing new terms such as the Virtual Link Set concept and data model patterns. A virtual link is a connection between resources such as literals and IRIs (Internationalized Resource Identifier) with some commonality where each of these resources is from a different RDF dataset. The links are required in order to understand how to semantically relate datasets. In addition, we describe several benefits of using virtual links to improve interoperability between heterogenous and independent datasets. Finally, we exemplify and apply our approach to multiple world-wide used RDF datasets.
Improving band to band registration accuracy of SEVIRI level 1.5 products

The rectification quality of EUMETSAT´s Meteosat Second Generation (MSG) level 1.5 imagery is routinely monitored for geometrical performance, summarily referred to as geometric quality assessment (GQA). The monitoring includes the absolute navigation error, image-to-image relative geometric accuracy and the inter-channel relative geometric accuracy. Recently, an inquiry from the user community indicated a structured residual error for certain channel pairs, when measuring the inter-channel relative accuracy using direct correlation of image channels. The residual relative interchannel geometrical accuracy shows a clear spatial banding structure in North -South direction. Within each band (i.e. in East-West direction), the measured value of the relative inter-channel geometric accuracy is not changing much, but when going from one band to the next the residual errors change more significantly. The effect is only measurable when comparing a channel located on the one focal plane with a channel on the other focal plane of MSG SEVIRI instrument. Channel - pairs located on the same focal plane do not show the effect. It has therefore been hypothesized that this effect could be associated with a problem in the ground-characterised pointing table for the SEVIRI instrument that associates a scan step with a look angle. Any error in the look angle characterisation would have the same effect on channels of one focal plane, but have a different effect on channels of different located focal planes. While the overall quality of the image is still within specification, an improvement to the processing could be achieved by updating the pointing table with in-flight measurements.
PSO for Fuzzy Clustering of Multi-view Relational Data

Particle Swarm Optimization (PSO) is a population-based meta-heuristic known for its simplicity, being successfully used in clustering task with interesting performance. Clustering of multi-view data sets has received increasing attention since it explores multiple sources or views of data sets aiming at improving clustering accuracy. Previous studies mainly focused on PSO-based clustering of single-view vector data, neither single- nor multi-view PSO-based clustering of relational received proper attention. This paper introduces a PSO-based approach to the fuzzy clustering of multi-view relational data, which can cluster data sets described by several dissimilarity matrices, each of them representing a particular view. In this work, ten fitness functions were considered, in which eight of them were adapted to deal with multi-view relational data and to consider the relevance weights of views. These fitness functions were compared to evaluate which best fit to cluster multi-view relational data. The performance and usefulness of the proposed approach, in comparison with previous single- and multi-view relational fuzzy clustering algorithms, are illustrated with several multi-view data sets. The Adjusted Rand Index (ARI) and F-measure were used to assess the quality of fuzzy partitions provided by clustering algorithms. The results have shown that the proposed methods significantly outperformed the compared algorithms in the majority of cases.