Reviewerly/exHarmony huggingface dataset:
Web Portals: The Future of Information Access and Distribution

The enterprise Web portals that are appearing on campuses and in corporations are changing the way people use the Web and the way in which Web pages are built. Enterprise Web portals allow users of the Web to gain quick access to just the Web pages and services they commonly use as well as other electronic information, such as word processors and services on local area networks that they use. Enterprise Web portals are user-centric. Creating the structure and culture to build enterprise Web portals is a task much more formidable than any of the many very challenging technical hurdles that must be passed. Perhaps the best portal definition is that a portal is a user-centric customized, personalized, adaptive desktop (CPAD). The very best enterprise portals will exhibit all CPAD features.
Recognition of plastic surgery faces and the surgery types: An approach with entropy based scale invariant features

In recent years, the automatic recognition of face comprises many challenging problems which have experienced much consideration due to several applications in different fields. To solve all the situations like the pose, appearance, and lighting changes, and/or ageing the face recognition does not have many methods. The additional challenges which arise recently are Facial expression because of the plastic surgery. This paper deals with a new approach called Entropy-based Volume SIFT (EV-SIFT) for face recognition in an accurate manner after the plastic surgery. The analogous feature extracts the key points and volume of the scale-space structure for which the information rate is determined. Since the entropy is the higher order statistical feature this provides the least effect on uncertain variations in the face. For classification, the corresponding EV-SIFT features are applied to the Support vector machine. The normal SIFT feature extracts the key points on the basis of contrast of the image whereas the V- SIFT feature extracts the key points on the basis of the volume of the structure. Nevertheless, the EV-SIFT technique provides both the volume and contrast information. Finally, the experimental results demonstrate that the EV-SIFT are found to be better on recognizing the plastic surgery faces. Moreover, the methods are experimentally proven for recognizing the type of plastic surgeries such as Blepharoplasty achieves 98%, Brow lift achieves 97%, Liposhaving achieves 96%, Malar augmentation achieves 85%, Mentoplasty achieves 94%, Otoplasty achieves 99%, Rhinoplasty achieves 99% and Skin peeling achieves 91%.
Relative Pairwise Relationship Constrained Non-Negative Matrix Factorisation

Non-negative Matrix Factorisation (NMF) has been extensively used in machine learning and data analytics applications. Most existing variations of NMF only consider how each row/column vector of factorised matrices should be shaped, and ignore the relationship among pairwise rows or columns. In many cases, such pairwise relationship enables better factorisation, for example, image clustering and recommender systems. In this paper, we propose an algorithm named, Relative Pairwise Relationship constrained Non-negative Matrix Factorisation (RPR-NMF), which places constraints over relative pairwise distances amongst features by imposing penalties in a triplet form. Two distance measures, squared Euclidean distance and Symmetric divergence, are used, and exponential and hinge loss penalties are adopted for the two measures, respectively. It is well known that the so-called “multiplicative update rules” result in a much faster convergence than gradient descend for matrix factorisation. However, applying such update rules to RPR-NMF and also proving its convergence is not straightforward. Thus, we use reasonable approximations to relax the complexity brought by the penalties, which are practically verified. Experiments on both synthetic datasets and real datasets demonstrate that our algorithms have advantages on gaining close approximation, satisfying a high proportion of expected constraints, and achieving superior performance compared with other algorithms.
Using a new set of heuristics in evaluating Arabic interfaces

The Information Technology Sector has been growing rapidly in the Arab world. As a result, Arabic interfaces are now being widely used and developed in the region. However, little existing work has been done that focused on evaluating said systems. In this paper, we propose a new set of heuristics for evaluating the usability of systems that use Arabic interfaces. We focused on formal heuristic evaluation as the usability inspection method for identifying issues in these systems. In order to test the proposed set, we compared the findings of heuristics set by Nielsen with ones reported using the proposed heuristics on three Arabic interfaces. Experimental results show that using our heuristics, evaluators were able to find more severe problems than those who used Nielsen’s. Therefore, we believe that heuristic evaluation, when conducted using our proposed heuristics, can be more effective in finding usability issues in Arabic interfaces.
High Relief from Brush Painting

Relief is an art form part way between 3D sculpture and 2D painting. We present a novel approach for generating a texture-mapped high-relief model from a single brush painting. Our aim is to extract the brushstrokes from a painting and generate the individual corresponding relief proxies rather than recovering the exact depth map from the painting, which is a tricky computer vision problem, requiring assumptions that are rarely satisfied. The relief proxies of brushstrokes are then combined together to form a 2.5D high-relief model. To extract brushstrokes from 2D paintings, we apply layer decomposition and stroke segmentation by imposing boundary constraints. The segmented brushstrokes preserve the style of the input painting. By inflation and a displacement map of each brushstroke, the features of brushstrokes are preserved by the resultant high-relief model of the painting. We demonstrate that our approach is able to produce convincing high-reliefs from a variety of paintings(with humans, animals, flowers, etc.). As a secondary application, we show how our brushstroke extraction algorithm could be used for image editing. As a result, our brushstroke extraction algorithm is specifically geared towards paintings with each brushstroke drawn very purposefully, such as Chinese paintings, Rosemailing paintings, etc.
Augmenting word2vec with latent

Akshay Budhkar, Frank Rudzicz. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Computationally Efficient Off-Line Joint Change Point Detection in Multiple Time Series

In this paper, a computationally efficient algorithm for Bayesian joint change point detection (CPD) in multiple time series is presented. The data generation model includes a number of change configurations (CC), each affecting a unique subset of the time series, which introduces correlation between the positions of change points (CPs) in the monitored time series. The inference objective is to identify joint changes and the associated CC. The algorithm consists of two stages: First, a univariate CPD algorithm is applied separately to each of the involved time series. The outcomes of this step are maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> (MAP) detected CPs and posterior distributions of CPs conditioned on the MAP CPs. These outcomes are used in combination to approximate the posterior for the CCs. In the second algorithm stage, dynamic programming is used to find the maxima of this approximate CC posterior. The algorithm is applied to synthetic data, and it is shown to be both significantly faster and more accurate compared to a previously proposed algorithm designed to solve similar problems. Also, the initial algorithm is extended with steps from the maximization–maximization algorithm, which allows the hyperparameters of the data generation model to be estimated jointly with the CCs, and we show that these estimates coincide with estimates obtained from a Markov chain Monte Carlo algorithm.
Bayesian cylindrical data modeling using Abe–Ley mixtures

• A Bayesian method to estimate parameters of a cylindrical mixture distribution. • Application of cylindrical distributions to a new dataset with time-of-day as circular variable. • Estimation uncertainty is directly inferred in the proposed method without extra calculations. This paper proposes a Metropolis–Hastings algorithm based on Markov chain Monte Carlo sampling, to estimate the parameters of the Abe–Ley distribution, which is a recently proposed Weibull-Sine-Skewed-von Mises mixture model, for bivariate circular-linear data. Current literature estimates the parameters of these mixture models using the expectation-maximization method, but we will show that this exhibits a few shortcomings for the considered mixture model. First, standard expectation-maximization does not guarantee convergence to a global optimum, because the likelihood is multi-modal, which results from the high dimensionality of the mixture’s likelihood. Second, given that expectation-maximization provides point estimates of the parameters only, the uncertainties of the estimates (e.g., confidence intervals) are not directly available in these methods. Hence, extra calculations are needed to quantify such uncertainty. We propose a Metropolis–Hastings based algorithm that avoids both shortcomings of expectation-maximization. Indeed, Metropolis–Hastings provides an approximation to the complete (posterior) distribution, given that it samples from the joint posterior of the mixture parameters. This facilitates direct inference (e.g., about uncertainty, multi-modality) from the estimation. In developing the algorithm, we tackle various challenges including convergence speed, label switching and selecting the optimum number of mixture components. We then (i) verify the effectiveness of the proposed algorithm on sample datasets with known true parameters, and further (ii) validate our methodology on an environmental dataset (a traditional application domain of Abe–Ley mixtures where measurements are function of direction). Finally, we (iii) demonstrate the usefulness of our approach in an application domain where the circular measurement is periodic in time.
Multidimensional classifier design using wavelet fuzzy brain emotional learning neural networks

This paper aims to propose a more efficient algorithm for the multi-dimensional classifier design. A novel model of wavelet fuzzy brain emotional learning neural network (WFBELNN) is proposed. This model comprises a wavelet function, a fuzzy inference system and a brain emotional learning neural n etwork. As a result, the learning speed and the classifying accuracy can be effectively improved by the proposed model. The structure of WFBELNN is constructed first, and then the gradient-descent method is used to online tune the parameters of WFBELNN. Finally a medical pattern recognition system is studied to verify that the accurate multi-dimensional pattern recognition can be achieved by using the proposed model. A comparison between the proposed WFBELNN and other models shows that the proposed model can achieve the most accurate classification of the medical pattern recognition and it is also more suitable to deal with the influence of the uncertainties.
Learning Unions of k-Testable Languages

A classical problem in grammatical inference is to identify a language from a set of examples. In this paper, we address the problem of identifying a union of languages from examples that belong to several different unknown languages. Indeed, decomposing a language into smaller pieces that are easier to represent should make learning easier than aiming for a too generalized language. In particular, we consider k-testable languages in the strict sense (k-TSS). These are defined by a set of allowed prefixes, infixes (sub-strings) and suffixes that words in the language may contain. We establish a Galois connection between the lattice of all languages over alphabet $$\varSigma $$ , and the lattice of k-TSS languages over $$\varSigma $$ . We also define a simple metric on k-TSS languages. The Galois connection and the metric allow us to derive an efficient algorithm to learn the union of k-TSS languages. We evaluate our algorithm on an industrial dataset and thus demonstrate the relevance of our approach.
Multi-class Classification without Multi-class Labels.

This work presents a new strategy for multi-class classification that requires no class-specific labels, but instead leverages pairwise similarity between examples, which is a weaker form of annotation. The proposed method, meta classification learning, optimizes a binary classifier for pairwise similarity prediction and through this process learns a multi-class classifier as a submodule. We formulate this approach, present a probabilistic graphical model for it, and derive a surprisingly simple loss function that can be used to learn neural network-based models. We then demonstrate that this same framework generalizes to the supervised, unsupervised cross-task, and semi-supervised settings. Our method is evaluated against state of the art in all three learning paradigms and shows a superior or comparable accuracy, providing evidence that learning multi-class classification without multi-class labels is a viable learning option.
BAYESIAN SPECTRUM DECONVOLUTION INCLUDING UNCERTAINTIES AND MODEL SELECTION: APPLICATION TO X-RAY EMISSION DATA USING WINBUGS

Spectrum deconvolution is an important task in ionizing radiation measurements, as the pulse height spectra, or, in general, the measured data from spectrometers or other measuring instruments are usually determined by the convolution of the response function with the fluence spectra. The method presented here for obtaining fluence spectra from the measurements is an application of Bayesian parameter estimation to the deconvolution of X-ray emission data. The problem of choosing the optimal model among several possible models is also considered, as well as an approach to include contributions from various sources of uncertainty, both correlated and uncorrelated. The application is carried out using the Bayesian software WinBUGS.
The K-12 Educational Technology Handbook

This textbook provides a set of high-quality resources to university educational technology courses. All chapters are written by professionals in the field, including university researchers, teacher educators, and classroom teachers.&#x0D; The book in its entirety and each chapter can be freely accessed, downloaded, printed, and remixed. Professors of educational technology courses can select the chapters that will work best for them when creating course packets, and preservice and inservice teachers can use relevant chapters for trainings and professional development purposes.
SteganoGAN: Pushing the Limits of Image Steganography

Image steganography is a procedure for hiding messages inside pictures. While other techniques such as cryptography aim to prevent adversaries from reading the secret message, steganography aims to hide the presence of the message itself. In this paper, we propose a novel technique for hiding arbitrary binary data in images using generative adversarial networks which allow us to optimize the perceptual quality of the images produced by our model. We show that our approach achieves state-of-the-art payloads of 4.4 bits per pixel, evades detection by steganalysis tools, and is effective on images from multiple datasets. To enable fair comparisons, we have released an open source library that is available online at this https URL.
Natural deduction system in the TIL-script language

In this paper we deal with the extension of the functionalitiesof the TIL-Script language, namely the proof system based onnatural deduction. The system processes a subset of the set ofTIL-Script constructions that are typed to v-construct atruth-value. Since TIL-Script is a functional programminglanguage based on a hyperintensional lambda calculus withprocedural semantics, we also describe the way how to validlyapply beta conversion and how to operate in a hyperintensionalcontext where the very procedure is an object of predication.
Multi-Modal Super-Resolution with Deep Guided Filtering

Despite the visually appealing results, most Deep Learning-based super-resolution approaches lack the comprehensibility that is required for medical applications. We propose a modified version of the locally linear guided filter for the application of super-resolution in medical imaging. The guidance map itself is learned end-to-end from multimodal inputs, while the actual data is only processed with known operators. This ensures comprehensibility of the results and simplifies the implementation of guarantees. We demonstrate the possibilities of our approach based on multi-modal MR and cross-modal CT and MR data. For both datasets, our approach performs clearly better than bicubic upsampling. For projection images, we achieve SSIMs of up to 0.99, while slice image data results in SSIMs of up to 0.98 for four-fold upsampling given an image of the respective other modality at full resolution. In addition, end-to-end learning of the guidance map considerably improves the quality of the results.
Similar Prototype Methods for Class Imbalanced Data Classification

In this paper, new methods for solving imbalanced classification problems based on prototypes are proposed. Using similarity relations for the granulation of the universe, similarity classes are generated and a prototype is selected for each similarity class. Experimental results show that the performance of our methods is statistically superior to other imbalanced methods.
Blockchain Theory of Programmable Risk: Black Swan Smart Contracts

This chapter discusses risk in the context of blockchain technology and proposes a theory of programmable risk that can be implemented with Black Swan Smart Contracts. The theory’s foundations are developed from two perspectives: risk theorizing in philosophy, social science, and finance; and black swan financial theory (risk distributions are fat-tailed (Mandelbrotian), not normal (Gaussian)). Event distributions can be formulated as s-curves with convex, linear, and concave segments, such that a desired level of risk may be chosen (convexity equates to lower risk). A blockchain theory of user-selected programmable risk is proposed. Black Swan Smart Contracts instantiate s-curve event distributions such that risk might be more efficiently managed by selecting low-medium-high risk as a standard smart contract parameter. Potential applications of Black Swan Smart Contracts include Insurance as a Digital Service, eBay for Money, Information Markets, and Autonomous Risk Management as a Smart Network property.
A Serious Game to Learn Basic English for People with Hearing Impairments

In recent years, higher education has faced the need to apply educational changes due to the increase of students with special needs in the classroom. Day after day, we see changes in technology that can be exploited and mobile devices are an example of this trend. Mobile devices can be a tool used to improve the education of students with special needs, and people with hearing disabilities are not a exception. Thus, this research focuses on the development of accessible mobile serious games for education. Serious games allow people with hearing disabilities to learn by combining a tool such as a video game with a learning methodology. In this paper, we present My First English Game (MFEG), a video game whose main objective is to teach basic topics of the English language in an interactive way. Additionally, the video game includes accessibility features that make it inclusive for people with hearing disabilities. The final goal of this work is to contribute to the improvement of English learning for people with disabilities.
Reassessment of the geology of the southeastern Tapajós Gold Province, Amazonian Craton, Brazil, based on field, petrographic, and airborne geophysical data

The Tapajos Gold Province, central portion of the Amazonian Craton, is one of the main metallogenic provinces in Brazil, but lack geological studies at an appropriate scale in its southeastern portion. Fieldwork, petrography, and high-resolution airborne geophysics (magnetometry and gamma spectrometry) allowed us to produce a new map at the 1:100,000 scale. In this study, we identified two new geological units: (1) the volcanic and pyroclastic rocks of the Vila Riozinho Formation, previously attributed to the Iriri Group, including a newly described facies of this formation, which comprises a group of rocks with the highest magnetic contents of the study area, and (2) the Serra Alkali Feldspar Granite, which intruded into the Vila Riozinho Formation. Based on available geochronological information these units can be associated to a volcano-plutonic event that occurred in the Orosirian period, at about 1.98 Ga. In addition, our work allowed us to define better the areas of occurrence of other units. The Parauari Intrusive Suite and Iriri Group areas were strongly reduced, whereas the areas of the Creporizao Intrusive Suite and of the Novo Progresso Formation were increased.
A multi-layered policy generation and management engine for semantic policy mapping in clouds

The long awaited cloud computing concept is a reality now due to the transformation of computer generations. However, security challenges have become the biggest obstacles for the advancement of this emerging technology. A well-established policy framework is defined in this paper to generate security policies which are compliant to requirements and capabilities. Moreover, a federated policy management schema is introduced based on the policy definition framework and a multi-level policy application to create and manage virtual clusters with identical or common security levels. The proposed model consists in the design of a well-established ontology according to security mechanisms, a procedure which classifies nodes with common policies into virtual clusters, a policy engine to enhance the process of mapping requests to a specific node as well as an associated cluster and matchmaker engine to eliminate inessential mapping processes. The suggested model has been evaluated according to performance and security parameters to prove the efficiency and reliability of this multi-layered engine in cloud computing environments during policy definition, application and mapping procedures.