Reviewerly/exHarmony huggingface dataset:
The quaternion-based anisotropic gradient for the color images

Image gradient, as a preprocessing step is an essential tool in image processing in many research areas such as edge detection, segmentation, smoothing, inpainting, etc. In the present paper, we develop a new gradient by integrated the quaternion framework with LPA-ICI (local polynomial approximation &#x2013; the intersection of confidence intervals) based on anisotropic gradient concepts for the color image processing applications. Computer simulations on the Berkeley Segmentation Dataset show that the new quaternion anisotropic gradient exhibits fewer color artifacts compared to state-of-the-art techniques including well-knowledge gradient operators.
Analysis of Granular Acousmatic Music: Representation of sound flux and emergence

Analysing electroacoustic music is a challenging task that can be approached by different strategies. In the last few decades, newly emerging computer environments have enabled analysts to examine the sound spectrum content in greater detail. This has resulted in new graphical representation of features extracted from audio recordings. In this article, we propose the use of representations from complex dynamical systems such as phase space graphics in musical analysis to reveal emergent timbre features in granular technique-based acousmatic music. It is known that granular techniques applied to musical composition generate considerable sound flux, regardless of the adopted procedures and available technological equipment. We investigate points of convergence between different aesthetics of the so-called Granular Paradigm in electroacoustic music, and consider compositions employing different methods and techniques. We analyse three works: Concret PH (1958) by Iannis Xenakis, Riverrun (1986) by Barry Truax, and Schall (1996) by Horacio Vaggione. In our analytical methodology, we apply such concepts as volume and emergence, as well as their graphical representation to the pieces. In conclusion we compare our results and discuss how they relate to the three composers’ specific procedures creating sound flux as well as to their compositional epistemologies and ontologies.
Quality of Experience of hand controller latency in a virtual reality simulator

In this study, we investigate a VR simulator of a forestry crane used for loading logs onto a truck, mainly looking at Quality of Experience (QoE) aspects that may be relevant for task completion, but also whether there are any discomfort related symptoms experienced during task execution. A QoE test has been designed to capture both the general subjective experience of using the simulator and to study task performance. Moreover, a specific focus has been to study the effects of latency on the subjective experience, with regards to delays in the crane control interface. A formal subjective study has been performed where we have added controlled delays to the hand controller (joystick) signals. The added delays ranged from 0 ms to 800 ms. We found no significant effects of delays on the task performance on any scales up to 200 ms. A significant negative effect was found for 800 ms added delay. The Symptoms reported in the Simulator Sickness Questionnaire (SSQ) was significantly higher for all the symptom groups, but a majority of the participants reported only slight symptoms. Two out of thirty test persons stopped the test before finishing due to their symptoms.
Fuzzy Gaussian Lasso clustering with application to cancer data

Recently, Yang et al. (2019) proposed a fuzzy model-based Gaussian (F-MB-Gauss) clustering that combines a model-based Gaussian with fuzzy membership functions for clustering. In this paper, we further consider the F-MB-Gauss clustering with the least absolute shrinkage and selection operator (Lasso) for feature (variable) selection, termed a fuzzy Gaussian Lasso (FG-Lasso) clustering algorithm. We demonstrate that the proposed FG-Lasso is a good clustering algorithm with better choice for feature subset selection. Experimental results and comparisons actually present these good aspects of the proposed FG-Lasso clustering algorithm. Cancer is a disease with growth of abnormal cells in a body. WHO reported that it is the first or second main leading cause of death. It spreads and affects the other parts of body if there is not properly diagnosed. In the paper, we apply the proposed FG-Lasso to cancer data with good feature selection and clustering results.
Reasoning over Arabic WordNet Relations with Neural Tensor Network

Arabic WordNet is an important resource for many tasks of natural language processing. However, it suffers from many problems. In this paper, we address the problem of the unseen relationships between words in Arabic WordNet. More precisely, we focus on the ability for new relationships to be learned ‘automatically’ in Arabic WordNet from existing relationships. Using the Neural Tensor Network, we investigate how it can be an advantageous technique to fill the relationship gaps between Arabic WordNet words. With minimum resources, this model delivers meaningful results. The critical component is how to represent the entities of Arabic WordNet. For that, we use AraVec, a set of pre-trained distributed word representation for the Arabic language. We show how much it helps to use these vectors for initialization. We evaluated the model, using a number of tests which reveal that semantically-initialized vectors provide considerable greater accuracy than randomly initialized ones.
Unbalanced Biclique Cryptanalysis of Full-Round GIFT

GIFT is a family of lightweight block ciphers presented at CHES 2017. Biclique cryptanalysis is proposed to attack the full AES by Bogdanov et al. in ASIACRYPT 2011. The attack can decrease computation complexity using the technology of meet-in-the-middle and reduce data complexity utilising the biclique structure. In this paper, we first provide an unbalanced biclique attack on full round GIFT. The master key has been recovered for the full round GIFT-64 by a 5-round <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times 16$ </tex-math></inline-formula> unbalanced biclique with data complexity of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">16</sup> and time complexity of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">122.95</sup> . Furthermore, a 4-round <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times 24$ </tex-math></inline-formula> unbalanced biclique is constructed on GIFT-128 to recover the master key with data complexity of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">80</sup> and computational complexity of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">118.38</sup> , respectively. The research results show GIFT algorithm has weak immunity to biclique cryptanalysis.
Evaluation of Asana, Odoo, and ProjectLibre Project Management Tools using the OSSpal Methodology

In order to successfully complete projects it is essential for companies to acquire a project management tool that assists in their planning, cost and resource management. Currently, there are several open source project management tools on the market that have much of the functionality required. Thus, it is important to choose the most appropriate one according to the needs of the user. To help with this choice, open source software evaluation methodologies can be used. In this paper, we use the OSSpal methodology to evaluate three popular open source project management tools: Asana, Odoo, and ProjectLibre.
A New Linear-Time Algorithm for Centroid Decomposition

The centroid of a tree is a node that, when removed, breaks the tree in connected components of size at most half of that of the original tree. By recursing this procedure on the components, one obtains the centroid decomposition of the tree, also known as centroid tree. The centroid tree has logarithmic height and its construction is a powerful pre-processing step in several tree-processing algorithms. The folklore recursive algorithm for computing the centroid tree runs in $$O(n\log n)$$ time. To the best of our knowledge, the only result claiming O(n) time is unpublished and relies on (dynamic) heavy path decomposition of the original tree. In this short paper, we describe a new simple and practical linear-time algorithm for the problem based on the idea of applying the folklore algorithm to a suitable decomposition of the original tree.
Spatiotemporal Breast Mass Detection Network (MD-Net) in 4D DCE-MRI Images

Automatic mass detection in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) helps to reduce the workload of radiologists and improves diagnostic accuracy. However, most of the existing methods rely on hand-crafted features followed by rule-based or shallow machine learning based detection methods. Due to the limited expressive power of hand-crafted features, the diagnostic performances of existing methods are usually unsatisfactory. In this work, we aim to leverage recent deep learning techniques for breast lesion detection and propose the Spatiotemporal Breast Mass Detection Networks (MD-Nets) to detect the masses in the 4D DCE-MRI images automatically. Simulating the clinical diagnosis process, we initially generate image-based candidates from all individual images and then construct a spatiotemporal 4D data to classify mass by using the convolutional long short-term memory network (ConvLSTM) to incorporate kinetic and spatial characteristics. Moreover, we collect a DCE-MRI dataset containing 21,294 annotated images from 172 studies. In experiments, we achieve an AUC of 0.9163 with a sensitivity of 0.8655 and a specificity of 0.8452, which verifies the effectiveness of our method.
A type of block withholding delay attack and the countermeasure based on type-2 fuzzy inference

We proposed a new type of bitcoin withholding attack named block withholding delay (BWD). It is different from the traditional withholding attacks which always drop valid blocks. BWD attackers never discard blocks but they delay the submissions of blocks to the pool managers, resulting the pool failed in the mining competitions and loss of rewards. We analyzed the optimum strategy of a BWD attacker who split its computing power into two parts, one was utilized to launch BWD attacks on the victim pools, while the other part was used for solo mining. We present detailed quantitative analysis of the maximum incentive that an attacker can earn by carefully splitting its computing power, and demonstrated that the attacker can obtain higher incentives than its contribution to the network in different conditions. Furthermore, we proposed a countermeasure against BWD based on the interval type-2 Takagi-Sugeno-Kang fuzzy inference system (IT2-TSK-FIS). The principle is to modify the private payoff scheme of pools to increase the risk of losing revenues of the rogue miners who deliberately delay block submissions. The scheme dealing the uncertain cause of block delay using fuzzy inference, and it is so designed that it does not require modifications of public mining protocols or data structures of the bitcoin network, which makes it applicable in practical pools.
A New Incremental Cluster Validity Index for Streaming Clustering Analysis

In this paper, we present an incremental version of the Partition Coefficient and Exponential Separation (PCAES) cluster validity index in the context of streaming data analysis. Incremental PCAES (iPCAES) can be used to monitor evolving structures in streaming data. We investigate the use of the proposed index to understand and analyze the performance of the MU Streaming Clustering (MUSC) algorithm. Synthetic and real-life streaming datasets are used to demonstrate the benefits that can be drawn from such indices such as the appearance of a new structure in the data stream, handling of outlier data samples, and the effect of the streaming sample order on the resultant cluster history. We compare the performance of iPCAES index with the incremental Davies-Boudin index (iDB) because iDB was found to be the most stable among other incremental indices that offer comparable approaches.
Improving Data Privacy Using Fuzzy Logic and Autoencoder Neural Network

Data privacy is a very important problem to address while sharing data among multiple organizations and has become very crucial in the health sectors since multiple organizations such as hospitals are storing data of patients in the form of Electronic Health Records. Stored data is used with other organizations or research analysts to improve the health care of patients. However, the data records contain sensitive information such as age, sex, and date of birth of the patients. Revealing sensitive data can cause a privacy breach of the individuals. This has triggered research that has led to many different privacy preserving techniques being introduced. Thus, we designed a technique that not only encrypts / hides the sensitive information but also sends the data to different organizations securely. To encrypt sensitive data we use different fuzzy logic membership functions. We then use an autoencoder neural network to send the modified data. The output data of the autoencoder can then be used by different organizations for research analysis.
Formal reasoning on knowledge and commitments in multi-agent systems using T<scp>heatre</scp>

This paper proposes a formal method based on the Theatre framework for modeling and analysis of knowledge and commitments in multi-agent systems (MASs). Theatre centers on actors and a reduction on to Uppaal, which enables both non-deterministic analysis (that is, qualitative analysis by exhaustive verification, or showing that something can possibly occur) and quantitative analysis (that is, estimating probability measures of event occurrence through simulations) of the same model. The article describes the modeling and analysis approach based on Theatre and Uppaal, and shows its application to the modeling and property checking of the NetBill protocol used in web-based MAS applications when selling/buying goods. Properties of the NetBill protocol are demonstrated by experimental results.
Wave Prediction in the Sea of Japan by Deep Learning Using Meteorological Data

Numerical wave prediction models require a large amount of computational power to timely complete the required calculations. Artificial Neural Networks (NN) have been introduced to perform predictions at a lesser computational cost and increased processing speed. Deep learning and specifically Convolutional Neural Networks (CNNs) have become accepted for various image recognition applications. Motivation for the examination of wave prediction by deep learning came from the success of CNNs in vision applications and the similarity of meteorological weather grid data to visual images. This study investigates a deep learning technique using the Japan Meteorological Agency’s Grid Point Value Mesoscale Model to predict wave height and period along Japanese coasts of the Sea of Japan. In particular, this study uses the Xception deep learning architecture with depthwise separable convolution to obtain improved wave height and period prediction over artificial neural networks, and gets overall success results.
Intelligent Health Care Data Management Using Blockchain: Current Limitation and Future Research Agenda

Health care is undergoing a big data revolution, with vast amounts of information supplied from numerous sources, leading to major paradigm shifts including precision medicine and AI-driven health care among others. Yet, there still exist significant barriers before such approaches could be adopted in practice, including data integration and interoperability, data sharing, security and privacy protection, scalability, and policy and regulatory issues. Blockchain provides a unique opportunity to tackle major challenges in health care and biomedical research, such as enabling data sharing and integration for patient-centered care, data provenance allowing verification authenticity of the data, and optimization of some of the health care processes among others. Nevertheless, technological constraints of current blockchain technologies necessitate further research before mass adoption of blockchain-based health care data management is possible. We analyze context-based requirements and capabilities of the available technology and propose a research agenda and new approaches towards achieving intelligent health care data management using blockchain.
Gender Bias in Pretrained Swedish Embeddings

This paper investigates the presence of gender bias in pretrained Swedish embeddings. We focus on a scenario where names are matched with occupations, and we demonstrate how a number of standard pretrained embeddings handle this task. Our experiments show some significant differences between the pretrained embeddings, with word-based methods showing the most bias and contextualized language models showing the least. We also demonstrate that the previously proposed debiasing method does not affect the performance of the various embeddings in this scenario.
Bornholmsk Natural Language Processing: Resources and Tools.

This paper introduces language processing resources and tools for Bornholmsk, a language spoken on the island of Bornholm, with roots in Danish and closely related to Scanian. This presents an overview of the language and available data, and the first NLP models for this living, minority Nordic language. Sammenfattnijng pa borrijnholmst: Daejnna artikkelijn introduserer naturspragsresurser a varktoi for borrijnholmst, ed sprag a daer snakkes pa on Borrijnholm me rodder i danst a i naer familia me skanst. Artikkelijn gjer ed âuersyn âuer spraged a di datan som fijnnes, a di fosste NLP modaellarna for daetta laewenes nordiska minnretâlsspraged.
A Hierarchical Model for Virtualized Data Center Availability Evaluation

Availability events like hangs, failures, and interruptions are usually unexpected and unpredictable. Due to the involving uncertainty, setting up a consolidated approach for availability evaluation is a hard endeavor. The availability evaluation becomes even more complicated in complex systems like cloud computing. Previous works fill such a gap using availability models. However, the majority of those papers proposes models for specific scenarios, which impairs their scalability and flexibility. In this paper, we present a scalable availability model for virtualized datacenters. From the model's design point-of-view, we provide: i) a hierarchical model based on Reliability Block Diagrams and Stochastic Reward Nets, and ii) an evaluation of the time to compute the model's results. We tested different architectures regarding system availability, capacity-oriented availability, and power consumption. As our model considers the fundamental premises of virtualized environments, it can be extended to specific scenarios. We also used a multiple-criteria decision-making process to highlight the best architectures for different users' needs, considering system downtime, capacity-oriented availability, and power consumption.
Finding optimal Bayesian networks by a layered learning method

It is unpractical to learn the optimal structure of a big Bayesian network (BN) by exhausting the feasible structures, since the number of feasible structures is super exponential on the number of nodes.This paper proposes an approach to layer nodes of a BN by using the conditional independence testing.The parents of a node layer only belong to the layer, or layers who have priority over the layer.When a set of nodes has been layered, the number of feasible structures over the nodes can be remarkably reduced, which makes it possible to learn optimal BN structures for bigger sizes of nodes by accurate algorithms.Integrating the dynamic programming (DP) algorithm with the layering approach, we propose a hybrid algorithm -layered optimal learning (LOL) to learn BN structures.Benefitted by the layering approach, the complexity of the DP algorithm reduces to O(ρ2 n-1 ) from O(n2 n-1 ), where ρ < n.Meanwhile, the memory requirements for storing intermediate results are limited to O(Cwhere k # < n.A case study on learning a standard BN with 50 nodes is conducted.The results demonstrate the superiority of the LOL algorithm, with respect to the Bayesian information criterion (BIC) score criterion, over the hill-climbing, max-min hill-climbing, PC, and three-phrase dependency analysis algorithms.
Estimated Vehicle Fuel Calculation Based on Google Map Realtime Distance

Vehicles are very important tools in our daily lives. Almost everyone in Indonesia has a vehicle, but not all know how much it costs to spend one trip using their vehicle which can cause uncontrolled budgetary expenditure. In this study aims to calculate the estimated fuel of the vehicle based on the distance of Google Maps using the android application. This application displays 2 types of vehicles, namely motorbikes and cars with several vendors and vehicle brands. This application uses the Google Maps API which is used to find distances at two points, then calculate them with the formula that has been made. To test the performance of the proposed formula, we compared the data we obtained with direct testing. The results showed that the accuracy obtained was around 52%.
Standardization of the size and shape of virtual human body for apparel products

Abstract International virtual human body (VHB) standards from the International Organization for Standardization (ISO) specifically used in virtual garment systems in the apparel field, as suggested in ISO/TC 133/WG 2 (Working group 2), contain fundamental content regarding definitions of terms, attributes of composition, and the expression and alteration of VHBs. As the first attempt in the series of international standards dealing with VHBs, this study has dealt with fundamental content related to VHB size. Additional standardization is required to allow the size and shape of VHB to be reproducible. Therefore, this study suggests academic and industrial requirements from the perspective of standardization to identify and solve issues regarding the reproduction of human bodies in terms of VHB size and shape. This study is meaningful in that it provides an overview of current VHB standardization efforts, related proceedings, and additionally required assignments. The suggested industrial and academic requirements are anticipated to be helpful in the systematic development and utilization of VHB and general standardization work.