Reviewerly/exHarmony huggingface dataset:
Discrete Laplacian Operator and Its Applications in Signal Processing

Fractional calculus has increased in popularity in recent years, as the number of its applications in different fields has increased.Compared to the traditional operations in calculus (integration and differentiation) which are uniquely defined, the fractional-order operators have numerous definitions.Furthermore, a consensus on the most suitable definition for a given task is yet to be reached.Fractional operators are defined as continuous operators and their implementation requires a discretization step.In this article, we propose a discrete fractional Laplacian as a matrix operator.The proposed operator is real (non-complex) which makes it computationally efficient.The construction of the proposed fractional Laplacian utilizes the DCT transform avoiding the complexity associated with the discretization step which is typical in the constructions based on signal processing.We demonstrate the utility of the proposed operator on a number of data modeling and image processing tasks.
Quality of Service (QoS) Management for Local Area Network (LAN) Using Traffic Policy Technique to Secure Congestion

This study presents the proposed testbed implementation for the Advanced Technology Training Center (ADTEC) Batu Pahat, one of Malaysia’s industrial training institutes. The objectives of this study are to discover the issues regarding network congestion, propose a suitable method to overcome such issues, and generate output data for the comparison of the results before and after the proposed implementation. The internet is directly connected to internet service providers (ISPs), which neither impose any rule nor filter the traffic components; all connections comply on the basis of the base effort services provided by the ISP. The congestion problem has been raised several times and the information technology (IT) department has been receiving complaints about poor and sometimes intermittent internet connection. Such issues provide some ideas for a possible solution because the end client is a human resource core business. In addition, budget constraints contribute to this problem. After a comprehensive review of related literature and discussion with experts, the implementation of quality of service through add-on rules, such as traffic policing on network traffic, was proposed. The proposed testbed also classified the traffic. Results show that the proposed testbed is stable. After the implementation of the generated solution, the IT department no longer receives any complaints, and thus fulfills the goal of having zero internet connection issues.
Why Can Computers Understand Natural Language?

The present paper intends to draw the conception of language implied in the technique of word embeddings that supported the recent development of deep neural network models in computational linguistics. After a preliminary presentation of the basic functioning of elementary artificial neural networks, we introduce the motivations and capabilities of word embeddings through one of its pioneering models, word2vec. To assess the remarkable results of the latter, we inspect the nature of its underlying mechanisms, which have been characterized as the implicit factorization of a word-context matrix. We then discuss the ordinary association of the “distributional hypothesis” with a “use theory of meaning,” often justifying the theoretical basis of word embeddings, and contrast them to the theory of meaning stemming from those mechanisms through the lens of matrix models (such as vector space models and distributional semantic models). Finally, we trace back the principles of their possible consistency through Harris’s original distributionalism up to the structuralist conception of language of Saussure and Hjelmslev. Other than giving access to the technical literature and state of the art in the field of natural language processing to non-specialist readers, the paper seeks to reveal the conceptual and philosophical stakes involved in the recent application of new neural network techniques to the computational treatment of language.
Tools for Teaching Mining Students in Virtual Reality based on 360°Video Experiences

In recent years, Virtual Reality (VR) technology has found their way into higher education. Its power lays in its ability to provide immersive three-dimensional (3D) experiences that help conveying educational content whilst providing rich interaction possibilities. Especially in mining engineering education, VR has high potential to reshape the provided learning content. Field trips, i.e. mine visits, are an integral part of the education and necessary to transfer knowledge to students. However, field trips are time and cost intensive and mines often have tight entry regulations. As a result, the number of field trips is limited. VR-based field trips offer a considerable alternative presupposed they replicate the complex mining environment realistically. In addition, VR mines have the advantage of taking students close to events (e.g. explosions) that are impossible to demonstrate in a real mine. However, generating realistic 3D content for VR still involves complex, and thus time consuming tasks. Therefore, we present the design of a VR Framework for teaching mining students based on 360° video data, its evaluation in three different lectures, and its extension based on the feedback we received from students and teachers from four different universities.
Demystifying elliptic curve cryptography: Curve selection, implementation and countermeasures to attacks

Abstract Abstract Elliptic curve cryptography (ECC) has been the main choice for the security experts from some time now. As the IoT has enveloped the world around us; ECC has became more popular as security solution for the resource-constrained devices which covers the physical layer of IoT. Different libraries are available for ECC. In this paper we have discussed various important aspects in the implementation of ECC, such as finite fields, selection of curve model and curves along with the attacks on ECC. Implementation of ECC has been carried out on NIST curve P-256 with ElGamal encryption. Subject Classification: 68U99Keywords: ECCFinite fieldsECDHECDSACurve selectionsecurity
Context-aware colorization of gray-scale images utilizing a cycle-consistent generative adversarial network architecture

Converting gray-scale images to colorful ones is one of the challenging tasks in the Computer Vision area, and various approaches based on neural network architectures have been proposed to generate colorful images. However, most of the suggested colorization frameworks use a single model for colorization regardless of the diversity of colors in images of various datasets. We claim that since such a structure is responsible for generating all types of images, it results in producing either faint-colored images or some artifacts in the images. We addressed this issue by proposing parallel colorization models, each of which is customized for generating images with similar contexts or color themes. Our proposed architecture consists of two stages as follows. The first stage includes a single colorization network that generates an initial colored image and simultaneously selects one of the specialist networks of the second stage for each image. The second stage consists of several specialist networks, whose tasks are improving the quality of the initial colored image and produce real-looking, plausible images. Our goal is to assign images to specialist networks in a way that each one of the specialist networks deals with contextually similar images so that the networks become more confident in generating vibrant colors and high-quality images. Cycle-Consistent Generative Adversarial Network structure is utilized in our architecture since it has demonstrated great potential in creating plausible images in the literature. The proposed model is evaluated on the ImageNet dataset, and the results stand for the effectiveness of employing context-aware specialists for colorization.
Intelligent English translation mobile platform and recognition system based on support vector machine

In the process of learning English, the status of spoken language is particularly important, and it is also the most concerned aspect of most English learners. However, the current situation is that due to the limited resources of traditional teachers and the lack of oral practice environment, it i s difficult for many learners to effectively improve their English level. Based on this, this study builds a smart English recognition system based on support vector machine. Moreover, this paper introduces a support vector machine to characterize speech signals. In addition, this paper uses feature fusion to map complex nonlinear relationships between features based on support vector machines and establishes a smart English recognition system based on support vector machine. The model can accurately identify the syllables and pronunciations in the words. Moreover, the use of a large-scale corpus based on non-specific people in this article can represent the generality of spoken learner.
Markers tracking and extracting structural vibration utilizing Randomized Hough transform

The structural vibrations data may be used for health-monitoring of structures, determination of the structural characteristics, or up-date the structural analysis model. Therefore, accuracy in the measurement of these vibrations is crucial. Extracting the structural vibrations by utilizing accelerometers or LVDT1 sensors has different limitations, such as wiring restrictions, accessibility, and sometimes a change in the mass and stiffness of the structure. In recent years, we have witnessed an increase in the use of remote sensing. One of these methods is the use of camera and image processing. In this paper, a new algorithm is used to track marker points, which are attached to the object to extract structural dynamic characteristics like mode shapes and natural frequencies. For this purpose, the Randomized Hough detection algorithm is used to observe the ellipse markers movement on the structure. So, a three-story frame was considered and the vibration, mode shape and the natural frequency were derived from the new algorithm. The results have been compared with other measurement techniques like the Lucas-Kanade algorithm, the accelerometer sensors, and finite element method. The consequences indicate that the new algorithm improves computer vision in tracking points for extracting structural dynamic properties.
Cross-Time and Orientation-Invariant Overhead Image Geolocalization Using Deep Local Features

Overhead image geolocalization is becoming increasingly important due to the growing collection of drone imagery without location information. In this paper, we perform large-scale overhead image geolocalization by matching a query image to wide-area reference imagery with known location. We use deep local features so that the query image need not align with but only overlap the tiled reference imagery. We further address two key challenges. For when the query and reference imagery are from different dates, we perform cross-time geolocalization using time invariant features learned using a Siamese network. For when the query and reference imagery are oriented differently, we introduce an orientation normalization network. We demonstrate our contributions on two new high-resolution overhead image datasets. Our method significantly outperforms strong baselines on cross-time geolocalization and is shown to exhibit promising orientation invariance.
Informed Anytime Fast Marching Tree for Asymptotically Optimal Motion Planning

In many applications, it is necessary for motion planning planners to get high-quality solutions in high-dimensional complex problems. In this article, we propose an anytime asymptotically optimal sampling-based algorithm, namely, informed anytime fast marching tree (IAFMT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> ), designed for solving motion planning problems. Employing a hybrid incremental search and a dynamic optimal search, the IAFMT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> fast finds a feasible solution; if time permits, it can efficiently improve the solution toward the optimal solution. This article also presents the theoretical analysis of probabilistic completeness, asymptotic optimality, and computational complexity on the proposed algorithm. Its ability to converge to a high-quality solution with efficiency, stability, and self-adaptability has been tested by challenging simulations and a humanoid mobile robot.
Critical digital literacy of future teachers in the Twitter Age (La alfabetización crítica digital del futuro profesorado en tiempos de Twitter) (<i>La alfabetización crítica digital del futuro profesorado en tiempos de Twitter</i>)

The transformations caused by the proliferation of social media in the Digital Age are having a huge impact on the field of education. Teacher training programmes are faced with the challenge of preparing future teachers to interpret digital media critically. Creating an informed, reflective citizenry depends on this training. This leads to the question: Are future teachers critical thinkers? This study inquires into the capacities of students in their third and fourth years of the Bachelor's degree in Primary Education at five Spanish universities (n = 322) to construct critical discourses. This study examines their capacities to analyse and discuss information from social media on controversial issues around gender violence, machismo, LGBTIQ persons, homophobia, intolerance and undemocratic attitudes. The results reveal the future teachers' difficulties developing critical discourses based on online news items on controversial issues. Only a minority doubt, check, analyse and argue critically.
Group anomaly detection based on Bayesian framework with genetic algorithm

Anomaly detection is an important application field of evolutionary algorithm. Unlike traditionly anomaly detection, group anomaly detection aims to discover the anomalous aggregate behaviors in data points. Over past decades, a large number of promising methods have been successfully applied for group anomaly detection. However, they inherently neglect the correlations among groups in data points, limiting their abilities. This paper presents a correlated hierarchical generative model, which can model the intricate correlations hidden in groups by introducing a logistic normal distribution to capture the correlations among groups. With the proposed model, we construct a full variational Bayesian framework, which can data-adaptively optimize the model parameters of the proposed model. The model is designed and trained using Genetic Algorithm (GA), which helps automating the use of generative model. Further, a new score function is proposed as an anomaly criterion to estimate final anomaly groups in data points. Several experiments on synthetic data and real astronomical star data from Sloan Digital Sky Survey demonstrate the effectiveness of proposed method compared with the-state-of-art methods, in terms of average accurac (AP) and area under the Receiver Operating Characteristic(ROC) curve(AUC).
Online tuning of predictor weights for relevant data selection in just-in-time-learning

Just-in-Time-Learning (JITL) is one of the most frequently used adaptive methods in data-based soft sensor design for chemical processes. While JITL is an effective method to combat concept drifts, samples selected via similarity metric in a Euclidean space consisting of a large number of equally-weighted predictors may diminish the performance of the JITL, due to curse of dimensionality and the varying degrees of nonlinearity between the predictor and response variables. Algorithms involving offline tuning of predictor weights were developed to tackle this issue, but changes in process conditions may depreciate a currently used set of weights for measuring similarity. In the current study, an adaptive method is developed for adjusting predictor weights in the Euclidean space used in measuring similarity between samples, named JITL via Online Weighted Euclidean Distance (JITL-OWED). JITL-OWED mainly consists of four steps: i) Relevant data is selected from an online weighted Euclidean space, and multiple models using different weights in their similarity measures are simultaneously constructed. ii) Control charts are used to detect changes in the prediction accuracy of the multiple models constructed online, hence triggering new subset searches. iii) A small number of search steps is used in subset selection, akin to early stopping in gradient search, with the "modest" aim of moving towards a local minimum for feasible online implementation. Exponentially Weighted Moving Average (EWMA) filtering is employed to stabilize the results from feature searches. iv) Final predictions are obtained via stacking multiple models. Employing JITL-OWED on one synthetic and four publicly available real datasets shows that online predictor weighting may indeed improve the prediction accuracy of the traditional JITL up to 80%, and JITL-OWED is both easy to implement and tune.
Deep Position-Aware Hashing for Semantic Continuous Image Retrieval

Preserving the semantic similarity is one of the most important goals of hashing. Most existing deep hashing methods employ pairs or triplets of samples in training stage, which only consider the semantic similarity within a minibatch and depict the local positional relationship in Hamming space, leading to intermittent semantic similarity preservation. In this paper, we propose Deep Position-Aware Hashing (DPAH) to ensure continuous semantic similarity in Hamming space by modeling global positional relationship. Specifically, we introduce a set of learnable class centers as the global proxies to represent the global information and generate discriminative binary codes by constraining the distance between data points and class centers. In addition, in order to reduce the information loss caused by relaxing the binary codes to real-values in optimization, we propose kurtosis loss (KT loss) to handle the distribution of real-valued features before thresholding to be double-peak, and then enable the real-valued features to be more binarylike. Comprehensive experiments on three datasets show that our DPAH outperforms state-of-the-art methods.
DMAKit: A user-friendly web platform for bringing state-of-the-art data analysis techniques to non-specific users

Tremendous advances in different areas of knowledge are producing vast volumes of data, a quantity so large that it has made necessary the development of new computational algorithms. Among the algorithms developed, we find Machine Learning models and specific data mining techniques that might be useful for all areas of knowledge. The use of computational tools for data analysis is increasingly required, given the need to extract meaningful information from such large volumes of data. However, there are no free access libraries, modules, or web services that comprise a vast array of analytical techniques in a user-friendly environment for non-specific users. Those that exist raise high usability barriers for those untrained in the field as they usually have specific installation requirements and require in-depth programming knowledge, or may result expensive. As an alternative, we have developed DMAKit, a user-friendly web platform powered by DMAKit-lib, a new library implemented in Python, which facilitates the analysis of data of different kind and origins. Our tool implements a wide array of state-of-the-art data mining and pattern recognition techniques, allowing the user to quickly implement classification, prediction or clustering models, statistical evaluation, and feature analysis of different attributes in diverse datasets without requiring any specific programming knowledge. DMAKit is especially useful for users who have large volumes of data to be analyzed but do not have the informatics, mathematical, or statistical knowledge to implement models. We expect this platform to provide a way to extract information and analyze patterns through data mining techniques for anyone interested in applying them with no specific knowledge required. Particularly, we present several cases of study in the areas of biology, biotechnology, and biomedicine, where we highlight the applicability of our tool to ease the labor of non-specialist users to apply data analysis and pattern recognition techniques. DMAKit is available for non-commercial use as an open-access library, licensed under the GNU General Public License, version GPL 3.0. The web platform is publicly available at https://pesb2.cl/dmakitWeb. Demonstrative and tutorial videos for the web platform are available in https://pesb2.cl/dmakittutorials/. Complete urls for relevant content are listed in the Data Availability section.
SABlockFL: a blockchain-based smart agent system architecture and its application in federated learning

Purpose The purpose of this work is to bridge FL and blockchain technology through designing a blockchain-based smart agent system architecture and applying in FL. and blockchain technology through designing a blockchain-based smart agent system architecture and applying in FL. FL is an emerging collaborative machine learning technique that trains a model across multiple devices or servers holding private data samples without exchanging their data. The locally trained results are aggregated by a centralized server in a privacy-preserving way. However, there is an assumption where the centralized server is trustworthy, which is impractical. Fortunately, blockchain technology has opened a new era of data exchange among trustless strangers because of its decentralized architecture and cryptography-supported techniques. Design/methodology/approach In this study, the author proposes a novel design of a smart agent inspired by the smart contract concept. Specifically, based on the proposed smart agent, a fully decentralized, privacy-preserving and fair deep learning blockchain-FL framework is designed, where the agent network is consistent with the blockchain network and each smart agent is a participant in the FL task. During the whole training process, both the data and the model are not at the risk of leakage. Findings A demonstration of the proposed architecture is designed to train a neural network. Finally, the implementation of the proposed architecture is conducted in the Ethereum development, showing the effectiveness and applicability of the design. Originality/value The author aims to investigate the feasibility and practicality of linking the three areas together, namely, multi-agent system, FL and blockchain. A blockchain-FL framework, which is based on a smart agent system, has been proposed. The author has made several contributions to the state-of-the-art. First of all, a concrete design of a smart agent model is proposed, inspired by the smart contract concept in blockchain. The smart agent is autonomous and is able to disseminate, verify the information and execute the supported protocols. Based on the proposed smart agent model, a new architecture composed by these agents is formed, which is a blockchain network. Then, a fully decentralized, privacy-preserving and smart agent blockchain-FL framework has been proposed, where a smart agent acts as both a peer in a blockchain network and a participant in a FL task at the same time. Finally, a demonstration to train an artificial neural network is implemented to prove the effectiveness of the proposed framework.