Reviewerly/exHarmony huggingface dataset:
Leveraging Protection and Efficiency of Query Answering in Heterogenous RDF Data Using Blockchain

In recent years, the digital world has experienced a massive amount of data being captured in various domains due to improvements in technology. Accordingly, big data management has emerged for storing, managing, and extracting valuable knowledge from collected data. Due to the explosion of the amount of data, developing tools for accurate and timely integration of inherently heterogeneous data has become a critical need. In the first part of this study, we focus on a semantic data integration approach with a case study of a plant ontology to provide a uniform query interface between users and different data sources. In the second part of this study, we propose a distributed Hyperledger-based architecture to ensure data security and privacy preservation in a semantic data integration framework. Data privacy and security can potentially be violated by unauthorized users, or malicious entities. The proposed view layer architecture between heterogeneous data sources and user interface layer using distributed Hyperledger can ensure only authorized users have access to the data sources in order to protect the system against unauthorized violation and determine the degree of users’ permission for read and write access.
A Topos View of the Type-2 Fuzzy Truth Value Algebra

It is known that fuzzy set theory can be viewed as taking place within a topos. There are several equivalent ways to construct this topos, one is as the topos of étalé spaces over the topological space $$Y=[0,1)$$ with lower topology. In this topos, the fuzzy subsets of a set X are the subobjects of the constant étalé $$X\times Y$$ where X has the discrete topology. Here we show that the type-2 fuzzy truth value algebra is isomorphic to the complex algebra formed from the subobjects of the constant relational étalé given by the type-1 fuzzy truth value algebra $$\mathfrak {I}=([0,1],\wedge ,\vee ,\lnot ,0,1)$$. More generally, we show that if L is the lattice of open sets of a topological space Y and $$\mathfrak {X}$$ is a relational structure, then the convolution algebra $$L^\mathfrak {X}$$ Harding et al. (Algebra Universalis 17:33, 2018 [5]) is isomorphic to the complex algebra formed from the subobjects of the constant relational étalé given by $$\mathfrak {X}$$ in the topos of étalé spaces over Y.
Zoom based image super-resolution using DCT with LBP as characteristic model

The prime intention of super-resolution (SR) technique is to restore the high-resolution images from one or more low-resolution (LR) images. These images are captured from the same scene with different acquisition systems with different resolution. Because these acquisition systems, images are suffered for an ill-posed problem with low visualization and picture information. Therefore, in this paper, the zoom-based super-resolution approach is proposed for super-resolution of low resolute images which are acquired from different camera zoom-lens. In this approach, three LR images of the same static scene which are acquired using three distinct zoom factors are used. Learning-based SR technique is used to enhance the spatial resolution of these LR images. The training dataset comprises three sets of captured images which are LR images, an enhanced version of LR images-HR1 and enhanced version of HR1 images-HR2. High-frequency details of the super-resolute image are learned in form of the discrete cosine transform (DCT) coefficients of HR training images. Finally, the super-resolved versions of LR observations, captured at different zoom-factors, are combined. The experimental results show that this proposed approach can be applied to various types of natural images in grayscale as well as color. The experimental results also show that this proposed approach performs better than existing approaches.
blogdown: Creating Websites With R Markdown.

blogdown: Creating Websites with R Markdown provides a practical guide for creating websites using the blogdown package in R. In this book, we show you how to use dynamic R Markdown documents to build static websites featuring R code (or other programming languages) with automatically rendered output such as graphics, tables, analysis results, and HTML widgets. The blogdown package is also suitable for technical writing with elements such as citations, footnotes, and LaTeX math. This makes blogdown an ideal platform for any website designed to communicate information about data science, data analysis, data visualization, or R programming.Note that blogdown is not just for blogging or sites about R; it can also be used to create general-purpose websites. By default, blogdown uses Hugo, a popular open-source static website generator, which provides a fast and flexible way to build your site content to be shared online. Other website generators like Jekyll and Hexo are also supported.In this book, you will learn how to:Build a website using the blogdown package;Create blog posts and other website content as dynamic documents that can be easily edited and updated;Customize Hugo templates to suit your site’s needs;Publish your website online;Migrate your existing websites to blogdown and Hugo.
Perennial secure multi-party computation of universal Turing machine

Consider a user who needs to perform computation over an initially unbounded stream of input. The user would like to compute functions of the input that change dynamically due to external events. The focus of this work is outsourcing such computation to a set of agents. The outsourcing must meet several constraints. Any large enough subset of agents must correctly emulate the user's computation on the unbounded stream of input. Any small subset of agents must obtain as little information as possible on the user's data, including the computed functions and any initial input. This privacy assurance must be maintained in an information-theoretic sense. Finally, the set of agents is dynamic with agents joining and leaving the set and different sets of agents being merged, cloned or split. In this work, we show how to securely outsource such perennial computation. The user's required computation is modeled as programs for a universal Turing machine. The only information that the agents obtain on the user's secrets is an upper bound on the space complexity required to perform the computation. Each state transition of the user's Turing machine requires computation and communication that are linear in the Turing machine's space complexity and polynomial in the number of agents performing the computation for every round of computation. The communication and computational complexity for an agent joining or leaving the set of computing agents in a transition round are linear in the space complexity of the Turing machine and polynomial in the number of agents. Some of the tools we develop may be of independent interest. We construct a strongly oblivious Turing machine, in which the tape head moves only as a function of its current location. We also show how to securely share the description of a Turing machine among several agents and how to securely compute each Turing machine's transition in a constant number of communication rounds.
Diverse mobile users: the development of library experts

Purpose The purpose of this paper is to undertake a formative evaluation of growth over time that would demonstrate diverse library users’ development as they interact with mobile digital library services. Design/methodology/approach This paper incorporated a server log analysis to evaluate first, the location of users. To study the nature of diverse user development, users from unique locations were identified and tracked over several years. The type of growth that this paper analyzes is the development of a library user from the beginning stages of use into one who is more experienced. For the purposes of this paper, the authors define library experts as experienced library users. These are users who have come back to the library over multiple sessions of learning and branched out into multiple areas of library functionality and services. Findings The findings of modular mobile use over time suggest that, while over half of users only utilized one module, 39 per cent of all users accessed more than one module. This formative approach to assessing student library engagement suggests alternative metrics for assessing outreach and distance learning. Originality/value The underlying departure point for this study is that formative models may introduce descriptive data valuable to the learning analytics toolkit. The library research literature on learning analytics, and perhaps library service offerings that support learning, may gain additional value by attending to students’ formative development as they interact with library resources. Describing the way in which mobile app users develop can yield insights about learning over time, both on campus and at a distance.
A Preventive Approach for Consistent OWL 2 DL Ontology Versions

Knowledge is continually changing over time. As such, semantic modelling knowledge formalisms, such as ontologies, must follow this evolution and change accordingly. However, ontology changes should never affect consistency. An ontology needs to remain in a consistent state along the whole ontology engineering process. In the literature, most of the approaches check/repair ontology inconsistencies in an a posteriori manner. This costs time and resources. In this article, an inconsistency prevention approach is proposed. It relies on OWL 2 DL change kits, which anticipate inconsistencies upon each change request. The proposed approach predicts potential inconsistencies, provides an a priori repair action, and applies the required changes. Consistency rules are defined and used to check logical inconsistencies, but also syntactical invalidities or style issues. A protégé extension is implemented to validate the proposal.
A Binary Optimization Approach for Constrained K-Means Clustering

K-Means clustering still plays an important role in many computer vision problems. While the conventional Lloyd method, which alternates between centroid update and cluster assignment, is primarily used in practice, it may converge to solutions with empty clusters. Furthermore, some applications may require the clusters to satisfy a specific set of constraints, e.g., cluster sizes, must-link/cannot-link. Several methods have been introduced to solve constrained K-Means clustering. Due to the non-convex nature of K-Means, however, existing approaches may result in sub-optimal solutions that poorly approximate the true clusters. In this work, we provide a new perspective to tackle this problem by considering constrained K-Means as a special instance of Binary Optimization. We then propose a novel optimization scheme to search for feasible solutions in the binary domain. This approach allows us to solve constrained K-Means clustering in such a way that multiple types of constraints can be simultaneously enforced. Experimental results on synthetic and real datasets show that our method provides better clustering accuracy with faster run time compared to several existing techniques.
A Hybrid Feature Selection Method for Effective Data Classification in Data Mining Applications

In data mining, people require feature selection to select relevant features and to remove unimportant irrelevant features from a original data set based on some evolution criteria. Filter and wrapper are the two methods used but here the authors have proposed a hybrid feature selection method to take advantage of both methods. The proposed method uses symmetrical uncertainty and genetic algorithms for selecting the optimal feature subset. This has been done so as to improve processing time by reducing the dimension of the data set without compromising the classification accuracy. This proposed hybrid algorithm is much faster and scales well to the data set in terms of selected features, classification accuracy and running time than most existing algorithms.
Full sets of pictures to encode pictures

A picture, or two-dimensional (2D) string, is a rectangular array of symbols over a finite alphabet. In this paper, we introduce the notion of fullness for sets of strings and sets of pictures. Fullness is a local counterpart of completeness. While in 1D fullness coincides with completeness, in 2D complete sets of pictures are a subset of full ones. This new notion allows introducing the encoding of a picture. The definition of encoding is based on the one of cutting decomposition. If a set of pictures X is full then any picture has an encoding over X; furthermore, the encoding is unique if X is a univocally full set. Univocally full sets coincide with the maximal strong prefix codes of pictures that were recently introduced. At last, we show an encoding algorithm for pictures, which relies on a new tree data structure to represent univocally full sets.
Proving properties of the Chord protocol using the ASM formalism

This paper describes the overlay protocol Chord using the formalism of Abstract State Machines. The formalization concerns Chord actions that maintain ring topology and manipulate distributed keys. We define a minimal set of deterministic constraints and prove that they guarantee the stability of the topological structure of the network and the consistency of data handling.
Robust Deep Softmax Regression Against Label Noise for Unsupervised Domain Adaptation

Domain adaptation aims to generalize the classification model from a source domain to a different but related target domain. Recent studies have revealed the benefit of deep convolutional features trained on a large dataset (e.g. ImageNet) in alleviating domain discrepancy. However, literatures show that the transferability of features decreases as (i) the difference between the source and target domains increases, or (ii) the layers are toward the top layers. Therefore, even with deep features, domain adaptation remains necessary. In this paper, we propose a novel unsupervised domain adaptation (UDA) model for deep neural networks, which is learned with the labeled source samples and the unlabeled target ones simultaneously. For target samples without labels, pseudo labels are assigned to them according to their maximum classification scores during training of the UDA model. However, due to the domain discrepancy, label noise generally is inevitable, which degrades the performance of the domain adaptation model. Thus, to effectively utilize the target samples, three specific robust deep softmax regression (RDSR) functions are performed for them with high, medium and low classification confidence respectively. Extensive experiments show that our method yields the state-of-the-art results, demonstrating the effectiveness of the robust deep softmax regression classifier in UDA.
Exploring demographic information in online social networks for improving content classification

The daily interaction between users within online social networks (OSNs) is an effective way to analyze and interpret its context in real time in order to capture the interests, preferences, and concerns of the OSNs users. These offer a unique information source for several applications in several fields such as trendsetting, future prediction, recommendation systems, community detection, and marketing. Most of the existing studies on text classification in OSNs rely on content based approach, in order to capture users interests through exploiting and categorizing the unstructured textual content shared by those users according to their topics. Moreover, users public profiles available on OSNs often reveal their demographic attributes such as age, gender, education, marital status, etc., which can play an essential role in identifying users interests and preferences. User demographic attributes can provide some preferences for some topics of interests. People with different demographic attributes may be interested in different topics, while people with similar demographic attributes may have the same interests. Usually, young people are more interested in technology than old people, who are more interested in the political news than young people. In this paper, we propose a demographic-content-based approach which uses both users demographic attributes and the textual content to classify OSNs posts using six classifiers ANN, k-NN, Naïve Bayes, Decision Tree, Decision rules and SVM. The experiments are done on a large Facebook dataset in order to analyze the effect of these demographic attributes on the performance of the categorization of the shared textual content in OSNs.
Research on the influences of location selection and permeability of wind power generation on cascading failure

The main cause for cascading failure is large-scale overload caused by localised abnormal flows. With the increase of wind power penetration, the complexity of flow distribution is strengthened. Therefore, it is necessary to study influences of site selection and permeability of wind power generation on cascading failure. An analysis method based on electric betweenness of nodes was put forward, and influences of permeability change on cascading failure on different location selection for wind power generation were researched. According to the study, it was found that when the electric betweenness was low, the overload of wind power led to declining uniformity of power flow distribution, resulting in transfer of flows to other lines. As a result, the heavy load of lines was further aggravated to promote the development of cascading failure. Simulation results of the IEEE30 system showed that with the increase of wind power penetration, the cascading failure was aggravated; and under the same permeability, the lower the electric betweenness of location selection was, the severer cascading failure would be.
Core Competencies and Innovation for Library and Information Science Professional in 21st Century Digital-Based Environments

Academic libraries and information technology centres are under serious threat due to the increasing pressure to achieve higher level of performance in a competitive global environment. Most of these challenges are as result of information communication technology (ICT) and digital revolution. This chapter discusses the impact of digital revolution on academic libraries and the need to acquire core competencies and new skills to effectively the 21st century academic libraries and information technology centres. Further, the author examines the sets of skills required by Library and Information Science (LIS) professionals to survive in the digital-based environments.
Image deformation based on contour using moving integral least squares

Many effective image deformation methods have been proposed in recent years, but few of them use the contours of objects as the reference of deformation. However, the contour is an important factor, and contour-based deformation can guarantee that the edge of the object after deformation is smoother compared to point-based image deformation. This article presents an image deformation method based on contours using Moving Integral Least Squares (MILS) optimisation. First, the authors set the key points in the image to create control contours as required and then adjust the positions of these points to generate the desired contours. In order to warp these contours to their new positions, the image is deformed using MILS. They derive the affine, similarity, and rigid transformations in a general framework, and users can choose different curves according to their needs. The proposed method possesses two characteristics: (i) it is able to create detail-preserving and intuitive deformations; and (ii) the solution of the deformation function has a simple closed form. They compare their method to the state-of-the-art algorithm, which is modelled by rigid transformation. Experimental results show that their deformation is more vivid.
A DPA Attack on IOA Data-Dependent Delay Countermeasure Based on an Inherent Tempo-Spatial Data Dependency

A recent attack countermeasure on an substitution box (S-box) called data dependency delay countermeasure (D3C) against a differential power analysis (DPA) has been introduced by Itamar–Osnat–Alexander (IOA). The inserted delay elements are temporally invariant however they are spatially variant. In this brief, we introduce a basic analysis of the shortcoming of flattening the power in D3C and investigate it by utilizing counterexample. In the proposed approach, we have utilized a non-true temporally random power as a shortcoming of IOA countermeasure to perform a successful DPA attack. The attack method is a chosen-plaintext attack. In this attack, the input data pattern is set to be constant to fix the delay of the D3C countermeasure. Hence, the countermeasure is bypassed and consequently the DPA attack on the S-box, which is introduced (the S-box) by IOA, is successful. By this attack, all the bits of the S-box, which was claimed as attack-proof by the D3C method, are recovered bitwise. In the first place, the S-box is modeled in an SPICE simulator to perform the proposed idea. Then, the proposed attack on IOA countermeasure is proved by HDL experimental results. The experiment is carried out via a Sakura-GW evaluation board and a Xilinx FPGA Spartan-6LX75 test-bed.
A Message Franking Channel

We pursue to formalize and instantiate a secure bidirectional channel with message franking properties. Under this model a sender may send an abusive message to the receiver and the latter wish to open it in a verifiable way to a third party. Potential malicious behavior of a sender requires message franking protocols resistant to sending messages that cannot be opened later by the receiver. An adversary impersonated by the receiver may also try to open messages that have not been sent by the sender. Wrapping a message franking protocol in a secure channel requires a more delicate treatment in order to avoid drops or replay of messages and out-of-order delivery. To the best of our knowledge we are the first to model the security of a message franking channel, which apart from integrity, confidentiality, resistance to drops, relays and out-of-order delivery is sender and receiver binding: a sender cannot send a message which cannot be opened in a verifiable way later by the receiver, and the receiver cannot claim a message that had not been truly sent by the receiver. Finally, we instantiate a bidirectional message franking channel from symmetric primitives and analyze its security.
Robust Fusion of c-VEP and Gaze

Brain computer interfaces (BCIs) are one of the developing technologies, serving as a communication interface for people with neuromuscular disorders. Electroencephalography (EEG) and gaze signals are among the commonly used inputs for the user intent classification problem arising in BCIs. Fusing different types of input modalities, i.e. EEG and gaze, is an obvious but effective solution for achieving high performance on this problem. Even though there are some simplistic approaches for fusing these two evidences, a more effective method is required for classification performances and speeds suitable for real-life scenarios. One of the main problems that is left unrecognized is highly noisy real-life data. In the context of the BCI framework utilized in this work, noisy data stem from user error in the form of tracking a nontarget stimuli, which in turn results in misleading EEG and gaze signals. We propose a method for fusing aforementioned evidences in a probabilistic manner that is highly robust against noisy data. We show the performance of the proposed method on real EEG and gaze data for different configurations of noise control variables. Compared to the regular fusion method, robust method achieves up to 15% higher classification accuracy.