Reviewerly/exHarmony huggingface dataset:
Robust fusion steady‐state estimators for networked stochastic uncertain systems with packet dropouts and missing measurements

Abstract In this article, the robust fusion steady‐state filtering problem is investigated for a class of multisensor networked systems with mixed uncertainties. The uncertainties include state‐dependent and noise‐dependent multiplicative noises, missing measurements, packet dropouts, and uncertain noise variances, the phenomena of missing measurements and packet dropouts occur in a random way, and are described by two Bernoulli distributed random variables with known conditional probabilities. Using a model transformation approach, which consists of augmented approach, de‐randomization approach and fictitious noise approach, the original multisensor system under study is converted into a multi‐model multisensor system with only uncertain noise variances. According to the minimax robust estimation principle, based on the worst‐case subsystems with conservative upper bounds of uncertain noise variances, the robust local steady‐state Kalman estimators (predictor, filter, and smoother) are presented in a unified framework. Applying the optimal fusion algorithm weighted by matrices, the robust distributed weighted state fusion steady‐state Kalman estimators are derived for the considered system. The robustness of the proposed estimators is proved by using a combination method consisting of augmented noise approach, decomposition approach of non‐negative definite matrix, matrix representation approach of quadratic form, and Lyapunov equation approach, such that for all admissible uncertainties, the actual steady‐state estimation error variances of the estimators are guaranteed to have the corresponding minimal upper bounds. The accuracy relations among the robust local and fused steady‐state Kalman estimators are proved. An example with application to autoregressive moving average (ARMA) signal processing is proposed, which shows that the robust local and fusion signal estimation problems can be solved by the state estimation problems. Simulation example verifies the effectiveness and correctness of the proposed results.
THE PHENOMENON OF CYBERCRIME: FROM THE TRANSNATIONAL CONNOTATION TO THE NEED FOR GLOBALIZATION OF JUSTICE

Information technology has brought about epochal changes in every area of society, offering opportunities for development on a social, cultural, and economic level, but also a fertile ground for activities with criminal purposes that take place in cyberspace. The threat present in the intangible world of cyberspace is extremely concrete and is one of the major sources of concern and investment by States, given that the Internet is considered the critical infrastructure for excellence. Cybercrime, characterized by a transnational connotation of borderless or aspatial crime, provides a position of advantage to the cybercriminal compared to the traditional criminal. The development of information technologies has led to the digitization of organized crime which thus succeeds in maximizing profits by exploiting the opportunities offered by new communication technologies and minimizing the risk of being identified, arrested, convicted and of suffering the seizure of the proceeds of criminal activities. Considering the contradictory and inhomogeneous international legal framework due to the transnational scope of cybercrime, the identification of the locus commissi delicti is difficult and the legal prosecution of cybercrimes is complex; therefore, cybercriminals could operate without an adequate response from some states in terms of prevention, sanction, containment and contrast. The sovereignty of States, in the context of cybercrime repression, is identified as an insurmountable obstacle in the creation of a supranational union of law. Therefore, to put legal operators in the real conditions to suppress transnational cybercrime, globalization of justice is needed.&#x0D; &#x0D; &lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0729/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Modeling Voice Pathology Detection Using Imbalanced Learning

To solve the problem of imbalanced data distribution of MEEI database, a modeling voice pathology detection method use imbalanced learning algorithm is presented in this paper. The method is based on support vector machine (SVM), decision tree (DT) and random forest (RF) to build voice pathology detection(VPD) model. Three imbalanced learning algorithms, SMOTE, Borderline-SMOTE and ADASYN, oversample a minority class (normal voice samples) and finally a training set of class-balanced is obtained. 10-fold cross validation is used in the experiment, and Precision, Recall and AUC/PRC are selected as model evaluation measures. Experimental results show that the imbalanced learning algorithm improves the recognition ability of pathological voice detection model for a minority class. RF as an ensemble model can combine better with imbalanced algorithms and performance slightly better than single classifier models. Its originality lies in the consideration of MEEI database as training set of pathological voice model, the effect of class-imbalanced on model performance, and Accuracy (Acc) as the main evaluation measure of VPD model is not suitable for training set of class-imbalanced.
The Generative Tool: a design journey in fashion education

The Ideation Tool (IT) emerged from a doctoral project, as a pedagogical strategy to assist students navigate and frame wicked design problems and design process activities. Subsequently, the IT was renamed as the Generative Tool (GT). Guided by action research, two teaching and learning iteration cycles with fashion design students at a South African higher education institution aimed to evaluate and refine the tool. The first iteration (Phase 1) focusses on two fourth-year fashion design students. For the second iteration (Phase 2), a refined GT was used by third-year fashion design students as a guide for the design process and a tool for reflection. This research explores the two independent student groups' experiences regarding their application of the tool, and propose strategies for refinement. The findings reveal that the GT is a useful pedagogical strategy in fashion design education to navigate the fuzzy front-end of the design journey.
Decoder-Side Quality Enhancement of JPEG Images Using Deep Learning-Based Prediction Models for Quantized DCT Coefficients

Many recent works have successfully applied some types of Convolutional Neural Networks (CNNs) to reduce the noticeable distortion resulting from the lossy JPEG compression technique. Most of them are built upon the processing made on the spatial domain. In this work, we propose a JPEG image decoder that is purely based on the frequency-to-frequency domain: it reads the quantized DCT coefficients received from a low-quality image bitstream and, using a deep learning-based model, predicts the missing coefficients in order to recompose the same image with enhanced quality. In experiments with two datasets, our best model was able to improve from images with quantized DCT coefficients corresponding to a Qualityz Factor (QF) of 10 to enhanced quality images with QF slightly higher than 20.
Interpretable Signed Link Prediction With Signed Infomax Hyperbolic Graph

Signed link prediction in social networks aims to reveal the underlying relationships (i.e., links) among users (i.e., nodes) given their existing positive and negative interactions observed. Most of the prior efforts are devoted to learning node embeddings with graph neural networks (GNNs), which preserve the signed network topology by message-passing along edges to facilitate the downstream link prediction task. Nevertheless, the existing graph-based approaches could hardly provide human-intelligible explanations for the following three questions: (1) which neighbors to aggregate, (2) which path to propagate along, and (3) which social theory to follow in the learning process. To answer the aforementioned questions, in this paper, we investigate how to reconcile the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">balance</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">status</i> social rules with information theory and develop a unified framework, termed as Signed Infomax Hyperbolic Graph ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SIHG</b> ). By maximizing the mutual information between edge polarities and node embeddings, one can identify the most representative neighboring nodes that support the inference of edge sign. Different from existing GNNs that could only group features of friends in the subspace, the proposed SIHG incorporates the signed attention module, which is also capable of pushing hostile users far away from each other to preserve the geometry of antagonism. The polarity of the learned edge attention maps, in turn, provides interpretations of the social theories used in each aggregation. In order to model high-order user relations and complex hierarchies, the node embeddings are projected and measured in a hyperbolic space with a lower distortion. Extensive experiments on four signed network benchmarks demonstrate that the proposed SIHG framework significantly outperforms the state-of-the-arts in signed link prediction.
Accelerating the Development of NTRU Algorithm on Embedded Systems

The advent of quantum computers represents a serious threat to current public key cryptosystems. To face this problem the so-called Post-Quantum (PQ) cryptographic solutions are being developed, many of which have been presented to the competition launched by NIST to evaluate proposals of PQ cryptography for standardization and deployment. This paper addresses the implementation of the NTRU PQ cryptographic algorithm on embedded systems. Using a Python-based development framework to accelerate the design process, software-only and hybrid (HW/SW) implementations of NTRU are evaluated in terms of operation speed and resource consumption on a System-on-Chip (SoC). Results show that hardware implementation of critical operations in conjuction with a Python+C programming allows an increase in performance that ranges from 130 to 450 depending on the selected scenario to use the algorithm.
A Forensically Sound Method of Identifying Downloaders and Uploaders in Freenet

The creation and distribution of child sexual abuse materials (CSAM) involves a continuing violation of the victims? privacy beyond the original harms they document. A large volume of these materials is distributed via the Freenet anonymity network: in our observations, nearly one third of requests on Freenet were for known CSAM. In this paper, we propose and evaluate a novel approach for investigating these violations of exploited childrens' privacy. Our forensic method distinguishes whether or not a neighboring peer is the actual uploader or downloader of a file or merely a relayer. Our method requires analysis of the traffic sent to a single, passive node only. We evaluate our method extensively. Our in situ measurements of actual CSAM requests show an FPR of 0.002 ± 0.003 for identifying downloaders. And we show an FPR of 0.009 ± 0.018, a precision of 1.00 ± 0.01, and a TPR of 0.44 ± 0.01 for identifying uploaders based on in situ tests. Further, we derive expressions for the FPR and Power of our hypothesis test; perform simulations of single and concurrent downloaders; and characterize the Freenet network to inform parameter selection. We were participants in several United States Federal Court cases in which the use of our method was uniformly upheld.
Experimenting Deep Convolutional Visual Feature Learning using Compositional Subspace Representation and Fashion-MNIST

This paper introduces a formal framework to model the convolutional visual feature learning in a convolutional neural network, which is called compositional subspace representation. The objective is to explain the convolutional visual feature learning computation using a rigid and structural method. The theoretical basis of the proposed framework is, the best way for representation to model a complex learning function is by using a composition of simple two-dimensional piecewise-linear functions to form a multilayers successive cascaded projection function for complex representation. Under the same hypothesis, the proposed framework also explains the hierarchical feature learning representation in a convolutional neural network, the well-acknowledged significant advantage of convolutional neural networks in visual computing. The proposed framework has experimented with image classification using the Fashion-MNIST dataset. Experimental assessments using learning curves analysis, confusion matrix, and visual assessment are presented and discussed. The experimental results were consistent with the theoretical expectation.
VAN: Versatile Affinity Network for End-to-End Online Multi-object Tracking

In recent years, tracking-by-detection has become the most popular multi-object tracking (MOT) method, and deep convolutional neural networks (CNNs)-based appearance features have been successfully applied to enhance the performance of candidate association. Several MOT methods adopt single-object tracking (SOT) and handcrafted rules to deal with incomplete detection, resulting in numerous false positives (FPs) and false negatives (FNs). However, a separately trained SOT network is not directly adaptable because domains can differ, and handcrafted rules contain a considerable number of hyperparameters, thus making it difficult to optimize the MOT method. To address this issue, we propose a versatile affinity network (VAN) that can perform the entire MOT process in a single network including target specific SOT to handle incomplete detection issues, affinity computation between target and candidates, and decision of tracking termination. We train the VAN in an end-to-end manner by using event-aware learning that is designed to reduce the potential error caused by FNs, FPs, and identity switching. The proposed VAN significantly reduces the number of hyperparameters and handcrafted rules required for the MOT framework and successfully improves the MOT performance. We implement the VAN using two baselines with different candidate refinement methods to demonstrate the effects of the proposed VAN. We also conduct extensive experiments including ablation studies on three public benchmark datasets: 2D MOT2015, MOT2016, and MOT2017. The results indicate that the proposed method successfully improves the object tracking performance compared with that of baseline methods, and outperforms recent state-of-the-art MOT methods in terms of several tracking metrics including MOT accuracy (MOTA), identity F1 score (IDF1), percentage of mostly tracked targets (MT), and FP.
The effect of gender, religiosity and personality on the interpersonal distance preference: a virtual reality study

Interpersonal distance (ID) is an important aspect of human nonverbal behavior. Both situational and dispositional factors play roles in interpersonal distance preference (IDP). In the present study, we investigated the effect of gender, religiosity, and Big Five personality factors on IDP in an Iranian sample. Forty-six university students watched fourteen 360-degree video clips via a virtual reality headset. In each video-clip, a male or a female actor spoke toward the camera from a particular distance and participants rated the favorability of each distance. Results showed that at the middle-range distances, religious individuals exhibited a pronounced gender bias by rating their distance from the same gender actor more favorable than the same distance from the opposite gender actor. Furthermore, openness to experience was correlated with the favorability of the middle-range distances. These findings can be justified by dominant Islamic values in Iran and specific characteristics of openness to experience.
Never Waste a Good Crisis? Post-Pandemic Implications for Human-Computer Interaction

So far 137 million people have been infected by the COVID-19 virus worldwide, and over 2.96 million lives have been lost. The urgent and sustained response to COVID-19 has impacted on everyone’s life in one way or another. This paper contributes to the collective and reflective appraisal of COVID-19’s impact by conducting a horizon scan in search for some of the post-pandemic implications for design research. After reviewing some general implications, we investigate two focus areas: (1) supply chain disruptions, and; (2) post-pandemic cities. While some measures may remain temporary and ad hoc, our analysis suggests it is imperative to derive a learning dividend and legacy from this traumatic and profound experience. We conclude with some remarks on community activism and ways for design to evolve in order to create more socially and ecologically just practices and outcomes in the aftermath of this global crisis.
Convolutional Neural Networks for Literature Retrieval

Recently, the amount of literature has increased rapidly. The traditional literature retrieval technology can no longer meet the requirements of massive document retrieval. With the rapid development of deep learning (especially convolutional neural networks), many results have been achieved in image, video and natural language processing. Here we proposed a literature retrieval framework with convolutional neural network. Specifically, we first use word vector representation method to transform text. Then we input text into the convolutional neural network with hash layer to extract textual hash features and complete the literature retrieval. Experimental results show that the literature retrieval framework with convolutional neural network improves the accuracy of retrieval, and proves that the application of convolutional neural network in document retrieval can simplify the retrieval process and improve the document retrieval system.
Potential assessment of solar power plant: A case study of a small island in Eastern Indonesia

Abstract Indonesia as a tropical country has high solar energy potential to generate power. Solar power plant (SPP) using Photovoltaic (PV) is suitable for power generation in a small power system, such as in several small islands in Eastern Indonesia since solar energy is easy to obtain there. The implementation of SPP for power generation needs an examination of energy yield to observe the performance ratio of SPP. Those parameters are important to study to further analyze the economic potential and feasibility of SPP development. This study estimates the performance ratio (PR) and potential energy yield of SPP in a small island in Eastern Indonesia through a self-developed program using MATLAB based Graphical User Interface (GUI). Moreover, this program also provides the levelized cost of energy (LCOE) calculation and land use optimization by readjusting the PV array configuration. According to the simulation result, the implementation of SPP in Eastern Indonesia has 85.4% and 23.54% for annual PR and capacity factor (CF) respectively. The simulation also shows that the optimum distance between PV strings produces higher annual energy yield and CF.
Live RF Image Transmission using OFDM with RPi and PlutoSDR

Orthogonal frequency-division multiplexing (OFDM) is a multi-carrier digital communication technique which addresses digital communication problems like intersymbol interference (ISI), low data rates, and inter-carrier interference (ICI). It uses multiple low data rate carriers to assemble a high data rate communication system. Each low data rate carrier has enough long symbol periods which then eliminates ISI. Orthogonality allows each carrier frequency to be closely spaced, although overlapped, without ICI. The contribution of this paper is to show that real-time OFDM RF communications is possible and assess its performance. This was achieved by integrating several embedded platforms: a light-weight processor, web camera, and software define radio using the Mathworks MATLAB environment. This is an efficient way to transmit live images or video feeds for monitoring and surveillance purposes. The work focused on transmitting live images at a constant rate from the web camera to the software defined radio, which relayed it to another software defined radio at a remote location.
PRICE PREDICTION OF DIFFERENT CRYPTOCURRENCIES USING TECHNICAL TRADE INDICATORS AND MACHINE LEARNING

Abstract From the past two years with increasing geopolitical and economic issues, global currency values have been falling and stock markets have been having a poor run &amp; investors losing wealth. This has led to a renewal of interest in digital currencies. Cryptocurrency one of the most prominent digital currency has found itself in spotlight with investors wanting a piece of it and business establishments accepting it as a source of payment due to its stable performance in the last few years. This research has been done on predicting cryptocurrency prices using machine learning based neural network which has a lowest the model loss over 100 epochs during training and Technical Trade Indicators (TTI) graphs depicts a real BTC value 5 to 10 times in 300-days of current fiscal year has further supported this increasing trader confidence and a shift in global cryptocurrency graph by predicted BTC values. On the same lines, we are analyzing bitcoin prices using Machine Learning and Sentiment Analysis. We also study stock market trends in order to better predict bitcoin prices quantitively. In this work we analyze the impact of global currencies like US Dollar, foreign exchanges on Bitcoin prices and whether Bitcoin has the stability to dethrone global currencies and become the single medium of transaction. This work is adequate enough to aid in predicting price and with results obtained from predicting Bitcoin prices using machine learning based neural network achieving an accuracy of 94.89% under all circumstances of technical trade indication thereby bringing down its price prediction by over 13.7% in April 2020 itself during evaluation.
A Computational Approach to Measuring the Semantic Divergence of Cognates

Meaning is the foundation stone of intercultural communication. Languages are continuously changing, and words shift their meanings for various reasons. Semantic divergence in related languages is a key concern of historical linguistics. In this paper we investigate semantic divergence across languages by measuring the semantic similarity of cognate sets in multiple languages. The method that we propose is based on cross-lingual word embeddings. In this paper we implement and evaluate our method on English and five Romance languages, but it can be extended easily to any language pair, requiring only large monolingual corpora for the involved languages and a small bilingual dictionary for the pair. This language-agnostic method facilitates a quantitative analysis of cognates divergence – by computing degrees of semantic similarity between cognate pairs – and provides insights for identifying false friends. As a second contribution, we formulate a straightforward method for detecting false friends, and introduce the notion of “soft false friend” and “hard false friend”, as well as a measure of the degree of “falseness” of a false friends pair. Additionally, we propose an algorithm that can output suggestions for correcting false friends, which could result in a very helpful tool for language learning or translation.