Reviewerly/exHarmony huggingface dataset:
Information Security Risk Management in the European Union

Currently the organization's risk management covers a wider range of risks, especially operational risks, reputation risks to the organization, and more recently, strategic risks. Moreover, within a growing number of organizations, responsibilities associated with risk management are assumed by the top management, which generally coordinates the teams of specialists directly responsible for monitoring the risks and the risk handling measures. This chapter focuses on how to implement an approach to reduce the identified risks to the information conveyed through computer systems and communications. In additional, it presents EU regulations relevant to the analysis and risk management information security.
Implementation of an algorithm to compute the strong apparent distance of bivariate codes

The BCH bound is the oldest lower bound for the minimum distance of a cyclic code. The study of this bound and its generalizations are classical topics, which includes the study of the very well-known family of BCH codes. In 1970, P. Camion extended the notion of BCH bound to the family of abelian codes by introducing the apparent distance of polynomials. Camion showed that the minimum value of the apparent distance of certain polynomials associated to codewords is less than or equal to the minimum distance of the code. The mentioned minimum value is known as the apparent distance of an abelian code. In 2016, Bernal-Bueno-Simón introduced the notion of strong apparent distance of polynomials and hypermatrices and developed an algorithm to compute the minimum strong apparent distance of a hypermatrix based on g-orbits manipulations. In this work, we will present the implementation of an algorithm to compute the strong apparent distance of bivariate codes.
Cheating-Immune Secret Sharing Schemes from Maiorana-McFarland Boolean Functions

We consider cheating-immune secret sharing schemes proposed by Pieprzyk and Zhang. This type of secret sharing scheme keeps dishonest participants from having a better chance (over the honest ones) of knowing the secret using their incorrect shares. We show that the class of Maiorana-McFarland Boolean functions can be used to construct such schemes. Consequently, new cheating-immune secret sharing schemes are presented.
Internet Behind Bars

This chapter focuses on the relevance of information and communication technologies (hereinafter, ICTs) as an essential part of the day-to-day life of all societies nowadays. Nevertheless, a means that continue to be behind this reality is the penitentiary area regarding inmates' rights. Introducing ICTs within prison could improve the social reinsertion of persons serving a prison sentence. Deprivation of liberty entails normal contact with the prison subculture and the harmful effects of it, causing in cases of long-term sentences the so-called phenomenon of “prisonization.” This negative effect of imprisonment could be reduced if ICTs were used inside prisons in the different areas where they can have an impact, and which are treated in this research, as (1) access to information and culture, (2) basic and advanced training, (3) employment, (4) communication with the outside world, (5) treatment, or (6) leisure and entertainment. The value that new technologies would add to these areas in prison constitutes a way of humanization of prisons in the twenty-first century.
Lifelong learning among older professionals: How competence strategies and perceptions of professional learning affect pastors’ participation

Abstract One of the challenges of keeping older employees up to date and ensuring their continued involvement in the workplace is to provide them with relevant learning opportunities. This article aims to understand the usefulness of the concept of LLL for a group of older employees (50+). This is done using interviews and a document study on the Church of Norway (CofN) to understand how the concept of LLL is interpreted and acted upon by employers and older pastors. We assume that changes occurring outside the Church provoke the need for new knowledge and new ways of working. We ask how these older professionals perceive their learning opportunities and try to ascertain how the concept of LLL is helping them to tackle the ongoing changes. The article adds to the research on the interplay between competence strategies of employers and attitudes of older employees in a well‐established profession. Results of our study show that new strategies open up for greater participation. However, older pastors’ greatest motivation for learning seems to be their ordination vows, which include a commitment to continued learning through personal studies. This influences the kind of activities in which they participate, their expectations concerning the recognition of their learning and their views on the Church as a learning organisation. We conclude that older pastors do benefit from LLL initiatives in the workplace, but they also benefit from belonging to a profession with a conscious commitment to learning.
Acceleration of Homomorphic Arithmetic Processing Based on the ElGamal Cryptosystem

In recent years, opportunities for using cloud services as computing resources have increased and there is a concern that private information may be leaked when processes data.The data processing while maintaining confidentiality is called secret computation.Cryptosystems can add and multiply plaintext through the manipulation of ciphertexts of homomorphic cryptosystems, but most of them have restrictions on the number of multiplications that can be performed.Among the different types of cryptosystems, fully homomorphic encryption can perform arbitrary homomorphic addition and multiplication, but it takes a long time to eliminate the limitation on the number of homomorphic operations and to carry out homomorphic multiplication.Therefore, in this paper, we propose an arithmetic processing method that can perform an arbitrary number of homomorphic addition and multiplication operations based on ElGamal cryptosystem.The results of experiments comparing with the proposed method with HElib in which the BGV scheme of fully homomorphic encryption is implemented showed that, although the processing time for homomorphic addition per ciphertext increased by about 35%, the processing time for homomorphic multiplication was reduced to about 1.8%, and the processing time to calculate the statistic (variance) had approximately a 15% reduction.
Another Implementation of Efficient Recoding Circuit for Signed Digit to Residue Canonical Signed Digit on Modulo 2&lt;sup&gt;n&lt;/sup&gt;-1

In this paper, we propose an efficient SD-to-RCSD recoding circuit structure on modulo. This structure is based on the parallel prefix addition circuit, that is, a carry for each digits is precalculated in parallel.
Amazon.com vs. EBSCO’s GOBI Library Solutions: Evaluating New and Used Book Vendors While Building a Diverse Collection

This article will share a small college’s comparison of the benefits and challenges that occurred when ordering a curated list of new and used print books from EBSCO’s GOBI Library Solutions, a traditional book jobber, instead of Amazon.com, a book retailer. The researchers analyzed the acquisition process, final purchase cost, and reconciliation workflow between the two vendors. Results from this study revealed a 3.1% final cost difference between the two vendors. Additionally, this case study addressed the workflow undertaken to build a curated list of new and used LGBTQ and Title IX print books. This research contributed to the literature as scholarship that compared titles in academic libraries between one subject area, gender studies, in combination with an assessment of the collection development, acquisition, and ordering software tools provided to academic libraries from a traditional book jobber and a book retailer was scant.
A Decision Support System (DSS) for Breast Cancer Detection Based on Invariant Feature Extraction, Classification, and Retrieval of Masses of Mammographic Images

This paper presents an integrated system for the breast cancer detection from mammograms based on automated mass detection, classification, and retrieval with a goal to support decision-making by retrieving and displaying the relevant past cases as well as predicting the images as benign or malignant. It is hypothesized that the proposed diagnostic aid would refresh the radiologist’s mental memory to guide them to a precise diagnosis with concrete visualizations instead of only suggesting a second diagnosis like many other CAD systems. Towards achieving this goal, a Graph-Based Visual Saliency (GBVS) method is used for automatic mass detection, invariant features are extracted based on using Non-Subsampled Contourlet transform (NSCT) and eigenvalues of the Hessian matrix in a histogram of oriented gradients (HOG), and finally classification and retrieval are performed based on using Support Vector Machines (SVM) and Extreme Learning Machines (ELM), and a linear combination-based similarity fusion approach. The image retrieval and classification performances are evaluated and compared in the benchmark Digital Database for Screening Mammography (DDSM) of 2604 cases by using both the precision-recall and classification accuracies. Experimental results demonstrate the effectiveness of the proposed system and show the viability of a real-time clinical application.
Analysis and Development Forecast of O2C Model

O2C English can name office to the customer can be interpreted as enterprise customers on hand for customized services. At the same time O2C English can also be full name online configuration, translation online configuration mode of electronic business affairs, its meaning is the customized products from the store moved to the Internet, by app or web online platform to implement the product sales configuration, such as cabinets, wardrobe and other furniture products. This paper mainly introduces the burning van Persie, good cook this two big Chinese custom platform, and this platform is a typical analysis of the A class of O2C platforms is currently in the environment and new direction.
AESTHETICIZATION OF MODERN EDUCATIONAL PROCESS: CHALLENGES OF MODERNITY

The article reflects critically on the modern educational paradigm, in the centre of which capitalist relations are hidden, changing the very purpose of education as a form of a personality’s reproduction. Modern technologies, the general crisis of the educational system, coupled with the processes of the world globalization and capitalization of relations between people, form a new type of a personality, including through filling the aesthetic beginning of human life with market ideals and images. The task is to understand the process of aestheticization in the educational sphere as a system-forming factor responsible for the final result - a personality’s formation. On the basis of the concepts of the aesthetic by W. Benjamin and G. Schulze the author shows the immanent content of the aesthetic and aestheticization at the present stage of the society development. Particular attention is paid to the figure of a lecturer in modern higher education, as well as the goals and objectives of the modern educational system.
Language mediated mentalization: A proposed model

Abstract Mentalization describes the process through which we understand the mental states of oneself and others. In this paper, I present a computational semiotic model of mentalization and illustrate it through a worked-out example. The model draws on classical semiotic ideas, such as abductive inference and hypostatic abstraction, but pours them into new ideas and tools from natural language processing, machine learning, and neural networks, to form a novel model of language-mediated-mentalization.
CGAN for simulation and digital image correction of aero transmission effect and aero heat radiation effect

The main influencing factor of imaging under high speed conditions is the aero-optical effect. Aero-optical effect is a kind of noise, which can be regarded as a superposition of three kinds of noises, system noise ，aero transmission effect and aero heat radiation effect. In this paper, we only consider aero transmission effect and aero heat radiation effect. The simulation and correction of aero-optical effects are important for terminal guidance. The aero-optical effect simulation method described in this paper uses a deep neural network (Conditional Generative Adversarial Networks). The use of big data allows the conditional generative adversarial networks to learn the mapping between clear pictures and pictures with aero-optical effects in training. Aero-optical effect correction is usually divided into two parts, the correction of the aero transmission effect and the correction of the aero heat radiation effect. In this paper, we use the conditional generative adversarial networks to train a large amount of data and learn the mapping relationship between the pictures with the aero-optical effects and the clear pictures in the training. And this mapping relationship is preserved in the form of bias and weights. It is not necessary to consider the aero-optical effect and the aero heat radiation effect separately. In this paper, the structural similarity (SSIM) between the real image and the simulated image generated by the conditional generation adversarial the network is 97.63%. The structural similarity (SSIM) between the clear image and the aero-optical effect image corrected with the conditional generation adversarial the network is 76.59%. The structural similarity (SSIM) between the original aero-optical effect image and the clear image is 55.73%.
Steady-state entanglement and heat current of two coupled qubits in two heat baths

We study the steady-state entanglement and heat current of two coupled qubits, in which one qubit is connected with either two baths with the different temperature or a bath. We construct the master equation in the eigenstate representation of two coupled qubits to describe the dynamics and derive the solutions in the steady-state with strong coupling regime between them. We have demonstrated the variations of the steady-state entanglement with respect to various parameters of the system in both equilibrium and nonequilibrium cases. We find that the coupling strengths and the energy detuning as well as the temperature gradient are beneficial to the enhancement of the entanglement in equilibrium case. In the nonequilibrium case, the temperature gradient has different effects on entanglement due to the temperature of the bath(s) which the qubits touch. We also study the heat current and their variations with the energy detuning, coupling strengths and diverse low temperature. The energy detuning has a positive (negative) effect on the heat current in the low (high) temperature; heat current also have different trends with coupling strengths increases for a given temperature. The temperature of cool bath is lower, the heat current is larger.
Exposing Australia’s drug cryptomarket

Australia is one of the leading countries partaking in the Darknet drugs trade.1 The Darknet, also known as the dark web, describes the emerging cryptomarket where sites are only accessible through browsers such as Tor. Communication is routed through numerous layers of encryption so that the identity of those using the site is protected.2 Sellers and customers can further conceal their identities by careful packaging, labelling and using false delivery addresses.
Algorithm Research on Distributed Pattern Recognition

The methodology of tradition pattern recognition is a that of from macroscopic to microcosmos, the source of a pattern is refused or mistake recognition lie in impropriety abstraction and choiceing the character. A framework of distributed pattern recognition be presented in this paper, it is a methodology of from microcosmos to macroscopic. The main innovation are: (1) avoid the difficulty of abstraction and choiceing the character, provide a new technology for complex object recognition, (2) spread pattern recognition of static sate and concentration into dynamic state and distributed.
A Simple and Effective Fusion Approach for Multi-frame Optical Flow Estimation

To date, top-performing optical flow estimation methods only take pairs of consecutive frames into account. While elegant and appealing, the idea of using more than two frames has not yet produced state-of-the-art results. We present a simple, yet effective fusion approach for multi-frame optical flow that benefits from longer-term temporal cues. Our method first warps the optical flow from previous frames to the current, thereby yielding multiple plausible estimates. It then fuses the complementary information carried by these estimates into a new optical flow field. At the time of writing, our method ranks first among published results in the MPI Sintel and KITTI 2015 benchmarks.
Adverse Effects of Image Tiling on Convolutional Neural Networks

Convolutional neural network models perform state of the art accuracy on image classification, localization, and segmentation tasks. A fully convolutional topology, such as U-Net, may be trained on images of one size and perform inference on images of another size. This feature allows researchers to work with images too large to fit into memory by simply dividing the image into small tiles, making predictions on these tiles, and stitching these tiles back together as the prediction of the whole image. We compare how a tiled prediction of a U-Net model compares to a prediction that is based on the whole image. Our results show that using tiling to perform inference results in a significant increase in both false positive and false negative predictions when compared to using the whole image for inference. We are able to modestly improve the predictions by increasing both tile size and amount of tile overlap, but this comes at a greater computational cost and still produces inferior results to using the whole image. Although tiling has been used to produce acceptable segmentation results in the past, we recommend performing inference on the whole image to achieve the best results and increase the state of the art accuracy for CNNs.
Hypergraph Approach Towards Ontology Design in Association-Oriented Metamodel

The paper presents the relationship module (RM) being a part of the semantic knowledge base used for the efficient semantic modeling of relations between concepts. The main contribution of presented solutions represents an approach based on the hypergraph-based data model expressed upon the association-oriented metamodel. The RM has been designed in accordance with a number of assumptions related to the representation of relationships, such as defining types of relations in terms of their characteristics, arity and hierarchy, transferring attribute among related concepts, instancing specific relationships, and determining potentially permitted or prohibited connections between concepts.
A Strategy of Distinguishing Texture Feature for Reversible Data Hiding Based on Histogram Shifting

Reversible data hiding has received growing attention, which not only protects the secret information but also can recover the cover image accurately. Many algorithms have aimed at embedding capacity and rarely consider the texture features of spatial images. In this paper, to better improve the image quality, a novel strategy of distinguishing texture feature for reversible data hiding based on histogram shifting is proposed. Firstly, the cover image is separated into blocks of the equal size, and the texture feature value of blocks is calculated. Then, the relatively smooth blocks are selected for information embedding. Experimental results show that our method can improve image quality effectively.
Introduction to the TPLP Special Issue on User-oriented Logic Programming and Reasoning Paradigms

With the rise of machine learning, and more recently the overwhelming interest in deep learning, knowledge representation and reasoning (KRR) approaches struggle to maintain their position within the wider Artificial Intelligence (AI) community. Often considered as part of the good old-fashioned AI (Haugeland 1985) – like a memory of glorious old days that have come to an end – many consider KRR as no longer applicable (on its own) to the problems faced by AI today (Blackwell 2015; Garnelo et al. 2016). What they see are logical languages with symbols incomprehensible by most, inference mechanisms that even experts have difficulties tracing and debugging, and the incapability to process unstructured data like text.
Max-C and Min-D Projection Auto-Associative Fuzzy Morphological Memories: Theory and an Application for Face Recognition

Max-C and min-D projection auto-associative fuzzy morphological memories (max-C and min-D PAFMMs) are two-layer feedforward fuzzy morphological neural networks designed to store and retrieve finite fuzzy sets. This paper addresses the main features of these auto-associative memories: unlimited absolute storage capacity, fast retrieval of stored items, few spurious memories, and excellent tolerance to either dilative or erosive noise. Particular attention is given to the so-called Zadeh’ PAFMM, which exhibits the most significant noise tolerance among the max-C and min-D PAFMMs besides performing no floating-point arithmetic operations. Computational experiments reveal that Zadeh’s max-C PFAMM, combined with a noise masking strategy, yields a fast and robust classifier with a strong potential for face recognition tasks.
Methodology of selecting the algorithms for optimization of arc spectral analysis

The goal of the study is the choice of algorithms for optimization of arc atomic emission spectral analysis with MAÉS to elaborate the techniques of quantitative spectral analysis of different objects. The choice of the algorithms is performed on an example of geologic objects with alumosilicate matrix. Determination of the elemental composition of such geological objects is important for obtaining markers, studying geochronological processes, and searching for mineral deposits.
Evolution of Ideas on the Nature and Structure of Ladoga Anomaly of Electrical Conductivity

Several stages of the Ladoga conductivity anomaly study are discussed. The Ladoga anomaly was discovered in the late 70s by means of magnetovariational observations by research team of the Institute of geophysics from Kyyiv (Ukraine). At the next step, in 80s the anomaly was studied by magnetotelluric method. The third stage of the deep studies was realized in 2013–2015 by means of integrated magnetotelluric and magnetovariational profiling. Each of three stages were completed by construction of specific geoelectrical models of the anomalous area up to the depth of 30–40 km. In the fourth stage, in 2015 and 2017 the studies were performed by means of DC electrical profiling with the use of multielectrode installations in complex with AMT soundings. According to results of the fourth stage, an unambiguous conclusion was drawn on the connection of the upper part of the Ladoga anomaly (till the depth of 1–2 km) with electronically-conducting sulfide-carbonaceous rocks. The structure and nature of the deeper part of the Ladoga anomaly requires further investigations on the base of integration of geological and complex geophysical methods.
Cost-Performance Comparison of Various Accelerator Implementation Platforms for Deep Convolutional Neural Network

Due to the high accuracy, DCNN is a popular deep learning approach for object recognition and classification. But, the computing complexity of DCNN is too high for real-time application. Therefore, many acceleration methods like GPU and FPGAs are developed and competing with each other. The purpose of this paper is to assess the pros and the cons of many acceleration methods including GPGPU and FPGA-based approaches like Xilinx SDSoC, and Xilinx SDAccel. We will consider the installation cost (board price) as well as the operation cost (the energy consumption) and the speed of each acceleration method in the analysis.