Reviewerly/exHarmony huggingface dataset:
Open Information Systems Protection by Its Continuous Change

The number of distributed systems with open code increased rapidly in recent years worldwide. Most common are distributed registry systems (blockchains). It contains cryptographic algorithms in the base and logics, which can be used to discredit them in the long-term perspective. Method of open distributed systems continuous change with the aim of its protection against discredit was suggested in frames of this paper. Unpredictable system's algorithms change does not allow to use knowledge, gained by an attacker during system's examination, or to perform long-term preparation or resources redistribution with the aim of its discredit. Methodology that allows proving an unpredictable character of information system modification, despite accessibility of its program code for all participants and procedure of new algorithms generation in untrusted program environment, was suggested in this observation. The process can be built on the base of recursive program modification by itself. Another method is to use an open procedure of modification by system's participants with confirmation of changes correctness according to Proof of Necessity of Change - PNC model. A prototype of distributed registry with the protection against examination (BlurChain) was suggested as an example of methodology implementation. A system to confirm transaction history integrity that is similar to classic block chain functions can be built based on the prototype considered. Prototype tests showed the ability of its practical use in tasks of distributed registry construction.
Dynamic Agile Distributed Development Method

“Agile” is an effective software engineering model with a high trust and acceptance rate among its users. The term agility comes from the concept of rapid development and working in a team for better results and a faster competition rate when compared with any other software engineering model. In this study, an assessment of the different patterns, frameworks, and application program interfaces available for distributed development in an agile model is given. After analyzing the state-of-the-art distributed models, a novel framework of a dynamic agile distributed development method (DADDM) is introduced in this paper. Many researchers have worked on global software development using the agile approach; however, we are presenting the idea of incorporating the agile benefits with dynamic distributed software development. The applicability of the proposed model is checked via two selected parameters: a feasibility study and a business study. The complete DADDM development life cycle is presented in the methodology section. The techniques used in DADDM and team members’ roles and responsibilities in DADDM are defined in this study. This study reflects all pillars of planning, controlling, organizing, and management of leadership. The use of DADDM in distributed agile development encourages future researchers to use this proposed framework for comparison and testing of their models and to check the effectiveness through a comparison with DADDM.
Multiscale saliency detection method for ship targets in synthetic aperture radar images

Ship detection is an important direction of synthetic aperture radar (SAR) image application in maritime surveillance. A multi-scale optimisation threshold saliency detection is developed in this letter, which is used to detect ships targets of SAR images. The SAR image is first decomposed into a pyramid image sequence. Then the saliency detection is performed using the spectral residual method for each layer in the sequence, and the salient sub-graph that contains ship targets is obtained. Finally, each sub-graph is fused and the optimisation threshold segmentation method that applies to the saliency map gives the final detection result. Experimental results show that the proposed approach has low complexity and high detection accuracy and greatly reduces the dependence on prior knowledge.
Common usage as warrant in bibliographic description

Purpose Within standards for bibliographic description, common usage has served as a prominent design principle, guiding the choice and form of certain names and titles. In practice, however, the determination of common usage is difficult and lends itself to varying interpretations. The purpose of this paper is to explore the presence and role of common usage in bibliographic description through an examination of previously unexplored connections between common usage and the concept of warrant. Design/methodology/approach A brief historical review of the concept of common usage was conducted, followed by a case study of the current bibliographic standard Resource Description and Access (RDA) employing qualitative content analysis to examine the appearances, delineations and functions of common usage. Findings were then compared to the existing literature on warrant in knowledge organization. Findings Multiple interpretations of common usage coexist within RDA and its predecessors, and the current prioritization of these interpretations tends to render user perspectives secondary to those of creators, scholars and publishers. These varying common usages and their overall reliance on concrete sources of evidence reveal a mixture of underlying warrants, with literary warrant playing a more prominent role in comparison to the also present scientific/philosophical, use and autonomous warrants. Originality/value This paper offers new understanding of the concept of common usage, and adds to the body of work examining warrant in knowledge organization practices beyond classification. It sheds light on the design of the influential standard RDA while revealing the implications of naming and labeling in widely shared bibliographic data.
Transfer Learning for the Choquet Integral

The Choquet integral (ChI) is a proven tool for information aggregation. In prior work, we showed that learning a ChI from data results in missing variables. Herein, we explore two ways to transfer a known ChI from a source domain to a new under sampled target domain. The first method is based on regularization and it listens to the full source domain ChI. The second method optimizes what we can observe (target domain supported variables) and missing variables are the only thing migrated from the source domain. Synthetic experiments, aka we know the truth, are used to show the behavior of these methods with respect to transfering between ChIs.
A Framework with Randomized Encoding for a Fast Privacy Preserving Calculation of Non-linear Kernels for Machine Learning Applications in Precision Medicine

For many diseases it is necessary to gather large cohorts of patients with the disease in order to have enough power to discover the important factors. In this setting, it is very important to preserve the privacy of each patient and ideally remove the necessity to gather all data in one place. Examples include genomic research of cancer, infectious diseases or Alzheimer’s. This problem leads us to develop privacy preserving machine learning algorithms. So far in the literature there are studies addressing the calculation of a specific function privately with lack of generality or utilizing computationally expensive encryption to preserve the privacy, which slows down the computation significantly. In this study, we propose a framework utilizing randomized encoding in which four basic arithmetic operations (addition, subtraction, multiplication and division) can be performed, in order to allow the calculation of machine learning algorithms involving one type of these operations privately. Among the suitable machine learning algorithms, we apply the oligo kernel and the radial basis function kernel to the coreceptor usage prediction problem of HIV by employing the framework to calculate the kernel functions. The results show that we do not sacrifice the performance of the algorithms for privacy in terms of F1-score and AUROC. Furthermore, the execution time of the framework in the experiments of the oligo kernel is comparable with the non-private version of the computation. Our framework in the experiments of radial basis function kernel is also way faster than the existing approaches utilizing integer vector homomorphic encryption and consequently homomorphic encryption based solutions, which indicates that our approach has a potential for application to many other diseases and data types.
An Analysis of Numerical Grid-Based Authentication

Passwords have been used as an authentication method for a long time. Many variations have also been proposed and applied. Grid-based passwords are also ones that have caught up with others in popularity. This paper analyses the security of such system. How the way that users select cell positions in different grid sizes affect overall security of the system is investigated. How they think of the ease of use of each of the grid sizes is also studied. As a result, an attempt is then made to find a balance between security and usability.
Detecting Road Irregularities by Image Enhancements and Thresholding

Significant steps have been made towards driverless cars in the last couple of years. Nowadays car's often posses cameras. They are able to alert the driver in the event of a not signalized lane change, follow lane markers, detect weather phenomena like fog, snow, rain, sun glaring, automatic video cruise control, etc. Al such behaviors enhance the driving pleasure. Detecting potholes and/or debris on the road and assessing their position relative to the car wheels in order to change the suspension properties seems right around the corner. This paper introduces a mechanism used to process the pavement images to detect potholes and or road defects. Such detection done by private cars may reduce the Road Service Department's expense if submitted to them in an organized manner. Detecting a pothole in a succession of images allows tracking and wheel-hitting estimation. Such way, the control system is able to instruct the car's suspension regarding this matter.
Event-based parsing

Event-based parsing is a largely unexplored problem. Despite several hugely popular event-based parsers like SAX, there is very little research on the ways grammar engineers can be given explicit control over handling input tokens, and the consequences of exposing this control. Tool support is also underwhelming, with no language workbenches and very few libraries to help a parser developer to get started quickly and efficiently. To explore this paradigm, we have designed a language for event-based parsing and developed a prototype that translates specifications written in that language, to parsers in C#. We also report on the comparative performance of one of the parsers we generated, and a previously used PEG parser extracted from a real compiler.
An analysis of MoLIC's consolidation

MoLIC is a language for modeling human-computer interaction as a conversation that is grounded on Semiotic Engineering theory. It was proposed as an epistemic tool aimed at supporting designers in thinking about and making decisions regarding the designer-to-user communication being conveyed through the system-user communication. In this paper, our goal is to investigate how consolidated MoLIC is since it was first published in 2003. We have carried out a Systematic Literature Review and analyzed how MoLIC has been used by the HCI community, considering how it has been used over the years, the number of authors that reference or use it, forums in which works have been published, as well as the main contributions of the research about it. Our results indicate that MoLIC is well consolidated, as it has been broadly used and studied by national and international authors. The existing version is stable and researchers are interested in applying it to system modeling or to investigate extensions and other aspects related to its use.
Poster

Screen-camera communication has attracted considerable attention as a form of visible light communication (VLC) using commercial-off-the-shelf devices. Nonetheless, the security of screen-camera communications in mobile applications has largely been disregarded, despite the fact that information displayed on an open screen is easily intercepted. One-way communication systems also make it difficult to add security features. In this paper, we propose a secure screen-camera communication system in which a visual encryption algorithm is designed to camouflage transmitted images between the screen and camera. When the targeted receiver holds a camera in a designated viewing position (i.e., directly in front of the screen at a distance of 50cm), the camouflaged image is revealed due to aliasing effect. From any other position, only the camouflaged image can be seen.
Image denoising using patch ordering and 3D transformation of patches

A novel image denoising algorithm which is based on the ordering of noisy image patches into a 3D array and the application of 3D transformations on this image dependent patch cube is proposed. For a given noisy image, the authors extract all the patches with overlaps. Then, they order these patches according to a predefined similarity measure. After reordering, a possibly separable 3D transformation is applied to the reordered 3D patch cube. The transform domain coefficients are thresholded using a suitably calculated thresholding parameter. Afterwards, the proper 3D inverse transformation is applied to these coefficients. The final denoised image is generated by repositioning the processed patches to their original locations on the image canvas. The developed algorithm presents a novel and efficient combination of patch ordering and 3D transformations. The forward analysis transform as defined by this complex procedure can get restated as the application of a single tight frame. This tight frame depends on the noisy image under consideration. This novel, image dependent forward operator which employs 3D transforms results in improved denoising performance. The experimental results indicate that the proposed algorithm achieves state-of-the-art denoising results with complexity comparable to competing methods.
Improving Feature Map Quality of SOM Based on Adjusting the Neighborhood Function

This chapter presents a study on improving the quality of the self-organizing map (SOM). We have synthesized the relevant research on assessing and improving the quality of SOM in recent years, and then proposed a solution to improve the quality of the feature map by adjusting parameters of the Gaussian neighborhood function. We have used quantization error and topographical error to evaluate the quality of the obtained feature map. The experiment was conducted on 12 published datasets and compared the obtained results with some other improving neighborhood function methods. The proposed method received the feature map with better quality than other solutions.
Geospatial distribution of heavy metal contamination in Ewekoro Limestone, SW Nigeria

Abstract Limestone exploration has numerous advantage to the nation’s economy. However, major anthropogenic activities such as mining of such mineral deposits causes environmental contamination due to heavy metals pollution. The presence of heavy metals such as lead, cadmium, mercury and arsenic can interfere with the biochemical pathways by interaction with enzymes and proteins. They equally have the capacity to alter the DNA repair mechanism and act as the inducers of carcinogenesis. The focus of the research is therefore to detect the extent of heavy metals contaminations within the sampled Ewekoro limestone, southwestern Nigeria and to ascertain the risks they posed to the quarry workers, miners and the people residing in the study area.
A CAD System for Brain Haemorrhage Detection in Head CT Scans

Medical imaging is an important medical diagnostic tool that provides visual information of the interior of the human body. In recent years computer-aided detection/diagnosis (CAD) systems became a key component of routine clinical practice in several medical areas, such as mammography and colonoscopy. However, research on brain CAD systems is still limited compared to other areas. This paper presents a solution that uses image processing techniques on brain computed tomography (CT) scans to create a CAD system that detects fresh bleeds. The system also features a basic classification method that distinguishes between an intra-axial and an extra-axial haemorrhage with the only limitation being subarachnoid haemorrhage (SAH), which is not always properly classified due to its complex structure. The techniques implemented include noise reduction methods, morphological operations and segmentation algorithms. The developed CAD system was tested on 36 brain CT sets obtained from the general hospital in Malta. Results show that the system achieves a sensitivity of 94.4%, a specificity of 94.4%, a precision of 91.259% and a classification accuracy of 88.89%.
Design and Proposal of a Database for Firearms Detection

Closed circuit television (CCTV) surveillance systems that implement monitoring operators have multiple human limitations, these systems usually don’t provide an immediate response in different situations of danger like an armed robbery. To address this security gap, a firearms detection system has been developed through convolutional neural networks (CNNs). For its development a large database of images is necessary. This article presents the creation and characteristics of this database, which is made up of 247,576 images obtained from the web. This article addresses the application of different techniques for the creation of new images from the initial ones to increase the database, obtaining up to 22.7% relative improvement in the accuracy of the network after increasing the database. The database is structured into two classes. The first class is made up of people that have a gun and the second class of people not carrying a gun. The use of this database in the development of the detection system obtained up to 90% in “Precision” and “Recall” metrics in a convolutional neural network configuration based on “VGG net”, through the use of grayscale images.
A Method of Radar Signal Feature Extraction Based on Fractional Fourier Transform

Aiming at the main problems in feature extraction of new radar signals, a method of feature extraction based on Fractional Fourier Transform is proposed in this paper. By using the difference of waveforms of different signals in fractional domain, four groups of improved resemblance coefficients are extracted to form joint eigenvectors to realize automatic sorting and recognition of radar signals. The simulation results show that the extracted improved resemblance coefficient features have better separation of different classes and better anti-noise performance. When the signal-to-noise ratio is 4 dB, the overall average recognition rate of six radar signals reaches 96.3%.
An Ensemble Angle-Based Outlier Detection for Big Data

Outlier detection is an important problem in machine learning and it has many applications such as fraud detection, network anomaly detection, medical diagnosis and data cleaning. However, traditional outlier detection techniques tend to fail in dealing with high-dimensional data especially when they encounter with big data. By the continues generation of data, we are facing massive datasets rather than just high dimensional data, in such conditions, the methods that depend on distance fail to provide correct results due to the vast distance between points which lead to similar distance between all points. Angle based outlier detection is a method proposed for outlier detection in high dimensional spaces. However, it is very time consuming and cannot be used for big data. In this paper, we introduce an angle based outlier detection ensemble method for big data. A dimensionality reduction technique called locality sensitive hash function (LSH) is used to reduce the complexity of big data. Moreover, the method utilizes an ensemble technique to improve angle based outlier detection and then a supervised combination method such as SVM is used to aggregate the ensemble subsets. The method is investigated with several real-world datasets and the results show that the proposed method can efficiently find the outliers.
Мathematical models and algorithms for modeling the location signals reflected from the underlying surfaces of the earth, sea, and coastal waters

In developing the on-board equipment of aircraft, used radar maps for navigation, there is a need for mathematical model's signals, reflected from the Earth's surface, sea surface and coastal edge. Traditionally, using a theoretical construct, stochastic signals were used as such models and its fluctuations were described by Rayleigh and Rayleigh- Rice. These models are used both for simulation signals, reflected from the Earth's surface, and for signals, reflected from the surface of the sea. At low resolution capability of on-board radars, the similar models describe quite well the statistical characteristics of fluctuating signals. Modern on-board locator has high resolution capability and the Rayleigh and Rice’s models can no longer be used in the synthesis and simulation of modern on-board navigation systems. In this paper, we propose an approach to the construction of models of radar signals using both theoretical constructs and experimental data that allows you to take into account the features of reflection of radar signals from small plots of the Underlying Surface of earth and sea. Along with it the correlations between the individual sections and anisotropic reflections are taken into account when observing sites with different angles. The reflections from the sea surface approximated by a log-normal law, reflections from the earth's surface at the sight of the manifold types of surface by the Beckmann and Weibull laws, special cases of which are the laws of Rice, Rayleigh and Hoyt. As the reflection model of the edge uses the distribution law of the vector sum of the signals, reflected from the elementary areas of earth and sea, getting in the resolution cell. In this case, the law of distribution of the total vector is subject to a law similar to Huber's law, in which the above-mentioned laws of distribution of reflections from the earth and the sea are used as the basic distributions. Previously used reflection models turned out as special cases of the proposed models. These models and the modeling algorithms developed for them can be used in the development and research of high- precision methods of radar monitoring for the purposes of environmental reconnaissance, forecasting and prompt prevention of natural and man-made emergency situations. In addition, for testing the operating modes of the equipment of unmanned aerial vehicles, including for multi-position radar systems. Algorithms for modeling location signals based on mathematical models using experimental data of reflections from various types of underlying land and sea surfaces, as well as coastal edges (coastal waters), allow us to bring the results of computer experiments to the results of actual tests of radio-electronic equipment. This reduces the time and reduces the cost of design by reducing the semi-natural and full-scale tests.