Reviewerly/exHarmony huggingface dataset:
Scientometrics Analysis in Google Trends

The extraction of the periodical non-stationarity feature of time series is obtained via the google trend data using keywords from modern sciences. This study aims to investigate when a keyword time series gives non-stationarity pics because this satisfies that the analysis of non-stationary categorical time series yields goodness of fit practice in the prediction issue. This method is implemented via an algorithm which is based on the extraction of the non- stationary distance as well as the formulation of the polynomial regression. The non-stationary algorithm is applied and the statistical evaluation is obtained using the non-parametric Cochran’s Q Test. The Q test leads to the conclusion that the Medicine and Biochemistry sciences are ranking in the top of the user’s preference followed by Physics, Mathematics and Social Sciences, while the emerging sciences such as Material Science are in the last rank positions.
Expressive cardinality constraints on <i>ALCSCC</i> concepts

In two previous publications we have, on the one hand, extended the description logic (DL) ALCQ by more expressive number restrictions using numerical and set constraints expressed in the quantifier-free fragment of Boolean Algebra with Presburger Arithmetic (QFBAPA). The resulting DL was called ALCSCC. On the other hand, we have extended the terminological formalism of the well-known description logic ALC from concept inclusions (CIs) to more general cardinality constraints expressed in QFBAPA, which we called extended cardinality constraints. Here, we combine the two extensions, i.e., we consider extended cardinality constraints on ALCSCC concepts. We show that this does not increase the complexity of reasoning, which is NExpTime-complete both for extended cardinality constraints in ALC and ALCSCC. The same is true for a restricted version of such cardinality constraints, where the complexity of reasoning decreases to ExpTime, not just for ALC, but also for ALCSCC.
Epistemic Answer Set Programming

This paper introduces a new epistemic extension of answer set programming ( $$\mathsf {ASP}$$ ) called epistemic ASP ( $$\mathsf {E\text {-}\mathsf {ASP}}$$ ). Then, it compares $$\mathsf {E\text {-}\mathsf {ASP}}$$ with existing approaches, showing the advantages and the novelties of the new semantics and discusses which formalisms provide more intuitive results: compared to Gelfond’s epistemic specifications ( $$\mathsf {ES}$$ ), $$\mathsf {E\text {-}\mathsf {ASP}}$$ defines a simpler, but sufficiently strong language. Its epistemic view semantics is a natural and more standard generalisation of $$\mathsf {ASP}$$ ’s original answer set semantics, so it allows for $$\mathsf {ASP}$$ ’s previous language extensions. Moreover, compared to all semantics proposals in the literature, epistemic view semantics facilitates understanding of the intuitive meaning of epistemic logic programs and solves unintended results discussed in the literature, especially for epistemic logic programs including constraints.
The Impact of Information and Communication Technologies on the Human Development in the Gulf Cooperation Council Countries : An Empirical Study

The main goal of this study is to investigate the impact of information and communication technology (ICT) on human development in the six Gulf Cooperation Council countries (GCC), particularly, the effect of ICT development index on the three-key component of human development; income, health, and education. The study focused on the years 2005-2014. Based on the theoretical background explained in the literature, the main study’s hypothesis tested using the Panel regression analysis to account for the existence of both cross sectional and time series relationship. The finding of the study shows different results ranging between positive, negative and insignificant relationship, which reflect the complexity that could associated with ICT implementation in the context of development. GCC countries spent a vast amount of financial resources to develop their ICT infrastructure, however, they failed to achieve the effective utilization of these technologies in socioeconomic development, due to mismatch between the ICT infrastructure and the availability of skilled human capital, adequate educational system, and public awareness about the importance of the ICT role in socioeconomic development. Therefore, ICTs as tool alone cannot improve human development; unless it occurs within broader context and multidimensional strategy aim to enhance the people's abilities to make the most use of these tools and techniques. Finally, the study recommends for the GCC policy makers to shift their paradigm of thinking about ICTs by changing their focus from the deployment of the ICT infrastructure to focus on analyzing and understanding the dynamic complementarities that could shape the potential role of these technologies.
Degraded image enhancement by image dehazing and Directional Filter Banks using Depth Image based Rendering for future free-view 3D-TV

DIBR-3D technology has evolved over the past few years with the demands of consumers increasing in recent times for future free-view 3D videos on their home televisions. The main issue in 3D technology is the lack of 3D content available to watch using the traditional TV systems. Although, some sophisticated devices like stereoscopic cameras have been used to fill the gap between the 3D content demand and 3D content supply. But the content generated through these sophisticated devices can not be displayed on the traditional TV systems, so there needs to be some mechanism which is inline with the traditional TV. Furthermore, the huge collection of existing 2D content should be converted to 3D using depth image-based rendering techniques. This conversion technique can highly contribute in overcoming the shortage problem of the 3D content. This paper presents a novel approach for converting 2D degraded image for DIBR 3D-TV view. This degraded or noisy/blur image is enhanced through image dehazing and Directional Filter Bank (DFB). This enhancement is necessary because of the occlusion effect or hole filling problem that occurs due to imperfect depth map. The enhanced image is then segmented into the foreground image and the background image. After the segmentation, the depth map is generated using image profiles. Moreover, Stereoscopic images are finally produced using the DIBR procedure which is based on the 2D input image and the corresponding depth map. We have verified the results of the proposed approach by comparing the results with the existing state-of-the-art techniques.
Delayed Replication Algorithm with Dynamic Threshold for Cloud Datacenters

Cloud computing offers a large amount of services as well as storage space over the Internet to a large number of customers. One of the services offered by the cloud is data as a service. Often, cloud computing is dealt with the challenge of managing data and also improving the availability of data. To ensure data availability and manage storage space, the concept of data replication has been used in this work. With the help of data replication technique, multiple copies of a data are created and distributed over geographically distributed sites. In this work, a delayed replica creation scheme based on dynamic threshold is designed and employed for creating replicas. Replication is done for the data based on its relative importance with other data. With the help of this work, the authors are able to improve the response time of accessing the data from a particular site and also reduce the cost involved in data access.
Boosting scene character recognition by learning canonical forms of glyphs

As one of the fundamental problems in document analysis, scene character recognition has attracted considerable interests in recent years. But the problem is still considered to be extremely challenging due to many uncontrollable factors including glyph transformation, blur, noisy background, uneven illumination, etc. In this paper, we propose a novel methodology for boosting scene character recognition by learning canonical forms of glyphs, based on the fact that characters appearing in scene images are all derived from their corresponding canonical forms. Our key observation is that more discriminative features can be learned by solving specially designed generative tasks compared to traditional classification-based feature learning frameworks. Specifically, we design a GAN-based model to make the learned deep feature of a given scene character capable of reconstructing corresponding glyphs in a number of standard font styles. In this manner, we obtain deep features for scene characters that are more discriminative in recognition and less sensitive against the above-mentioned factors. Our experiments conducted on several publicly available databases demonstrate the superiority of our method compared to the state of the art.
PageRank: An alarming index of probable earthquake occurrence

Here, we introduce PageRank (PR) in a seismic network as an appropriate alarming clue before the occurrence of the event to be worthwhile in hazard probabilistic evaluation of earthquakes. Studying PR changes of two main shocks in Iran and Italy by means of temporal and spatial windows reveals that their PR values increase drastically before the event, while there is no trend for other considered locations and/or other time intervals. Therefore, the PR value seems to be an appropriate index of a place induction by previous events and its susceptibility for having a new earthquake. Moreover, summing over the PRs of areas close to the Italy event location and tracking this newly defined PR behavior show an increasing trend before the main shock implying that the close regions are influenced and become highly connected before the event as well as the earthquake location itself. It is also indicated that PR behavior is not necessarily correlated to the number of occurring earthquakes and is inherently the result of points connectivity and interactions.
No-reference Sharpness Index for Scanning Electron Microscopy Images Based on Dark Channel Prior

Scanning electron microscopy (SEM) image can link with the microscopic world through reflecting interaction between electrons and materials.The SEM images are easily subject to blurring distortions during the imaging process.Inspired by the fact that dark channel prior captures the changes to blurred SEM images caused by the blur process, we propose a method to evaluate the SEM images sharpness based on the dark channel prior.A SEM image database is first established with mean opinion score collected as ground truth.For the quality assessment of the SEM image, the dark channel map is generated.Since blurring is typically characterized by the spread of edge, edge of dark channel map is extracted.Then noise is removed by an edge-preserving filter.Finally, the maximum gradient and the average gradient of image are combined to generate the final sharpness score.The experimental results on the SEM blurred image database show that the proposed algorithm outperforms both the existing state-of-the-art image sharpness metrics and the general-purpose no-reference quality metrics.
Improved Density Peak Clustering Based on Information Entropy for Ancient Character Images

A large number of IoT applications require the use of supervised machine learning, a type of machine learning algorithm that requires data to be labeled before the model can be trained. Because manually labeling large datasets is a time-consuming, error-prone, and expensive task, automated machine learning methods can be used. To tackle the challenge in which an ancient character image needs to be manually labeled, this paper explores the classification method of ancient Chinese character images based on density peak clustering. We design a metric function of density peak clustering and propose an improved density peak clustering method based on information entropy for ancient book image classification. The method enumerates the distance threshold of clustering, then calculates the information entropy of the clustering result, and determines the class distance threshold by analyzing the attenuation of the information entropy, thereby completing the image clustering process. The improved metric function is used to calculate the similarity between images. A greedy strategy is used as the basis of the merging operation of the class members to achieve the purpose of increasing the degree of information entropy attenuation. The experimental results on the dataset of the Yi character images prove that the method can accurately classify unknown character images of ancient books.
Calculation of high numerical aperture lightfield microscope point spread functions

3D deconvolution of lightfield images enables high resolution reconstruction of sample volumes. Previous point spread function calculations assume low to moderate NA objectives. Here we present a simple vectorial calculation valid for high NA objectives.
Component-wise testing of laser-written integrated coupled-mode beam splitters

Photonic integrated circuits (PICs) are important enabling technologies for the developments of areas such as quantum information processing (QIP). Coupled-mode integrated beam splitters (IBS) are widely used in many PICs, so direct and accurate testing of individual IBSs inside a PIC is increasingly desirable, as the development of PICs for QIP is scaled up. Here we demonstrate a solution for component-wise testing of coupled-mode IBSs without limitations on component location and PIC architectures. The method is based on the imaging of an individual IBS with a custom-built multifunctional adaptive optical microscope, combined with the calculation of its beam-splitting ratio through numerical modelling.
Infinets: The Parallel Syntax for Non-wellfounded Proof-Theory

Logics based on the $$\mu $$ -calculus are used to model inductive and coinductive reasoning and to verify reactive systems. A well-structured proof-theory is needed in order to apply such logics to the study of programming languages with (co)inductive data types and automated (co)inductive theorem proving. The traditional proof system suffers some defects, non-wellfounded (or infinitary) and circular proofs have been recognized as a valuable alternative, and significant progress have been made in this direction in recent years. Such proofs are non-wellfounded sequent derivations together with a global validity condition expressed in terms of progressing threads. The present paper investigates a discrepancy found in such proof systems, between the sequential nature of sequent proofs and the parallel structure of threads: various proof attempts may have the exact threading structure while differing in the order of inference rules applications. The paper introduces infinets, that are proof-nets for non-wellfounded proofs in the setting of multiplicative linear logic with least and greatest fixed-points ( $$\mu \mathsf {MLL}^{\infty }$$ ) and study their correctness and sequentialization.
ScratchThat

Speech interfaces have become an increasingly popular input method for smartphone-based virtual assistants, smart speakers, and Internet of Things (IoT) devices. While they facilitate rapid and natural interaction in the form of voice commands, current speech interfaces lack natural methods for command correction. We present ScratchThat, a method for supporting command-agnostic speech repair in voice-driven assistants, suitable for enabling corrective functionality within third-party commands. Unlike existing speech repair methods, ScratchThat is able to automatically infer query parameters and intelligently select entities in a correction clause for editing. We conducted three evaluations to (1) elicit natural forms of speech repair in voice commands, (2) compare the interaction speed and NASA TLX score of the system to existing voice-based correction methods, and (3) assess the accuracy of the ScratchThat algorithm. Our results show that (1) speech repair for voice commands differ from previous models for conversational speech repair, (2) methods for command correction based on speech repair are significantly faster than other voice-based methods, and (3) the ScratchThat algorithm facilitates accurate command repair as rated by humans (77% accuracy) and machines (0.94 BLEU score). Finally, we present several ScratchThat use cases, which collectively demonstrate its utility across many applications.
Interpretation of Geological Structures Hosting Potential Gold Deposits in the Konongo Gold Mine Using Airborne Magnetic, Electromagnetic and Radiometric Datasets

The renewed interest in the reactivation of the defunct National Konongo Gold Mine located in the Ashanti Greenstone Belt, calls for a further probe into its geology and the associated mineral hosting structures to discover its mineralization potential.In order to achieve this, airborne magnetic, radiometric and electromagnetic datasets were interpreted to determine the potential gold hosting features in the studied area.The results show the area to consist of the metasediment, the metavolcanic, Tartwaian Formation and their associated granitoids.Results also show that the Tarkwaian sediments, observed largely in the north-eastern side of the site; widen out substantially and truncate in the south.The analysis of the structure lineaments using a rose diagram reveals three main tectonic structures trending in N-S, NNW-SSE, and third the structure trending in the NNE-SSW to NE-SW directions in the area.The dominant structures in the area, form 90% of all the delineated structures and trend in the NE-SW and NNE-SSW direction with the remaining 10% trending in the N-S and NNW-SSE.These structures are associated with the major shear and fracture zones located mainly at the contact between the basin sediments and volcanic belt and also associated with the Tarkwaian Formation.The mapped potential gold mineralization zones located mainly at the contact between the metasediment and the metavolcanic units of the Birimian Supergroup, as well as the Tarkwaian Formation, were mapped by integrating the structures, alteration zones as well as the complex dyke systems.This paper delineates the prominent geological structures with the potential of hosting economic gold mineralization in and around the Ko-
Time Series Anomaly Detection with Variational Autoencoders

Anomaly detection is a very worthwhile question. However, the anomaly is not a simple two-category in reality, so it is difficult to give accurate results through the comparison of similarities. There are already some deep learning models based on GAN for anomaly detection that demonstrate validity and accuracy on time series data sets. In this paper, we propose an unsupervised model-based anomaly detection named LVEAD, which assumpts that the anomalies are objects that do not fit perfectly with the model. For better handling the time series, we use the LSTM model as the encoder and decoder part of the VAE model. Considering to better distinguish the normal and anomaly data, we train a re-encoder model to the latent space to generate new data. Experimental results of several benchmarks show that our method outperforms state-of-the-art anomaly detection techniques.
The invisible hole of information on SMB's cybersecurity

Small and Medium Businesses (SMB) use Internet and computer-based tools in their daily processes, sometimes without being aware to the cyber threats, or without knowing how to be prepared in case of a cyber-attack, although they are a major target for cyber-attacks. Specific information about cybersecurity needed by SMBs, in order to cope with cyber threats, is not always available or easily accessible. In this study, a vast search of different types of information about SMBs’ cybersecurity was performed, in order to find whether a hole of accessible information exists in this area. This exploratory research covered general mass communication media channels, technological and professional cybersecurity websites, and academic journals, and found that indeed very few studies, articles and news items were published in this matter. Leveraging knowledge and awareness, diminishing the shame for reporting cyber-attacks, and increasing mass communication media interest and public attention, may be activities to cover this “invisible hole”.
Using Smartwatches as an Interactive Movie Controller: A Case Study with the Bandersnatch Movie

This work proposes the development of a method that allows the use of smartwatch as a control for interactive films using continuous recognition of gestures and conducts a case study with the film "Black Mirror: Bandersnatch". We developed two prototypes to interact with interactive movies. The first one uses gesture recognition on the smartwatch screen. Thus, we created a set of gestures composed of straight lines that represent the actions in the interactive movie. In this prototype, the recognition of gestures is performed by the algorithm of continuous recognition of gestures, in this way, a gesture can be recognized before the user finalize, and action is sent quickly to the movie. The second prototype uses a touch of pressure in the smartwatch to control the film. Thus, the actions to be performed in the smartwatch that represents the actions in the interactive film is proposed, the recognition of the gestures is determined by the change of the values obtained by the accelerometer. The prototypes communicate with a simulation service responsible for running the movie. The prototypes were used in a study with users, as well as in usability and experience tests, and the results show that the method has the potential to be used in everyday users to control interactive films efficiently and effectively.
Tails of Triangular Flows.

Triangular maps are a construct in probability theory that allows the transformation of any source density to any target density. We consider flow based models that learn these triangular transformations which we call triangular flows and study the properties of these triangular flows with the goal of capturing heavy tailed target this http URL one dimension, we prove that the density quantile functions of the source and target density can characterize properties of the increasing push-forward transformation and show that no Lipschitz continuous increasing map can transform a light-tailed source to a heavy-tailed target density. We further precisely relate the asymptotic behavior of these density quantile functions with the existence of certain function moments of distributions. These results allow us to give a precise asymptotic rate at which an increasing transformation must grow to capture the tail properties of a target given the source distribution. In the multivariate case, we show that any increasing triangular map transforming a light-tailed source density to a heavy-tailed target density must have all eigenvalues of the Jacobian to be unbounded. Our analysis suggests the importance of source distribution in capturing heavy-tailed distributions and we discuss the implications for flow based models.