Reviewerly/exHarmony huggingface dataset:
gRDF: An Efficient Compressor with Reduced Structural Regularities That Utilizes gRePair

The explosive volume of semantic data published in the Resource Description Framework (RDF) data model demands efficient management and compression with better compression ratio and runtime. Although extensive work has been carried out for compressing the RDF datasets, they do not perform well in all dimensions. However, these compressors rarely exploit the graph patterns and structural regularities of real-world datasets. Moreover, there are a variety of existing approaches that reduce the size of a graph by using a grammar-based graph compression algorithm. In this study, we introduce a novel approach named gRDF (graph repair for RDF) that uses gRePair, one of the most efficient grammar-based graph compression schemes, to compress the RDF dataset. In addition to that, we have improved the performance of HDT (header-dictionary-triple), an efficient approach for compressing the RDF datasets based on structural properties, by introducing modified HDT (M-HDT). It can detect the frequent graph pattern by employing the data-structure-oriented approach in a single pass from the dataset. In our proposed system, we use M-HDT for indexing the nodes and edge labels. Then, we employ gRePair algorithm for identifying the grammar from the RDF graph. Afterward, the system improves the performance of k2-trees by introducing a more efficient algorithm to create the trees and serialize the RDF datasets. Our experiments affirm that the proposed gRDF scheme can substantially achieve at approximately 26.12%, 13.68%, 6.81%, 2.38%, and 12.76% better compression ratio when compared with the most prominent state-of-the-art schemes such as HDT, HDT++, k2-trees, RDF-TR, and gRePair in the case of real-world datasets. Moreover, the processing efficiency of our proposed scheme also outperforms others.
Rules embedded harris hawks optimizer for large-scale optimization problems

Harris Hawks Optimizer (HHO) is a recent optimizer that was successfully applied for various real-world problems. However, working under large-scale problems requires an efficient exploration/exploitation balancing scheme that helps HHO to escape from possible local optima stagnation. To achieve this objective and boost the search efficiency of HHO, this study develops embedded rules used to make adaptive switching between exploration/exploitation based on search performances. These embedded rules were formulated based on several parameters such as population status, success rate, and the number of consumed search iterations. To verify the effectiveness of these embedded rules in improving HHO performances, a total of six standard high-dimensional functions ranging from 1000-D to 10,000-D and CEC'2010 large-scale benchmark were employed in this study. In addition, the proposed Rules Embedded Harris Hawks Optimizer (REHHO) applied for one real-world high dimensional wavelength selection problem. Conducted experiments showed that these embedded rules significantly improve HHO in terms of accuracy and convergence curve. In particular, REHHO was able to achieve superior performances against HHO in all conducted benchmark problems. Besides that, results showed that faster convergence was obtained from the embedded rules. Furthermore, REHHO was able to outperform several recent and state-of-the-art optimization algorithms.
Adaptive Fusion Based Method for Imbalanced Data Classification

The imbalance problem is widespread in real-world applications. When training a classifier on the imbalance datasets, the classifier is hard to learn an appropriate decision boundary, which causes unsatisfying classification performance. To deal with the imbalance problem, various ensemble algorithms are proposed. However, conventional ensemble algorithms do not consider exploring an effective feature space to further improve the performance. In addition, they treat the base classifiers equally and ignore the different contributions of each base classifier to the ensemble result. In order to address these problems, we propose a novel ensemble algorithm that combines effective data transformation and an adaptive weighted voting scheme. First, we utilize modified metric learning to obtain an effective feature space based on imbalanced data. Next, the base classifiers are assigned different weights adaptively. The experiments on multiple imbalanced datasets, including images and biomedical datasets verify the superiority of our proposed ensemble algorithm.
Platformed antisemitism on Twitter: Anti-Jewish rhetoric in political discourse surrounding the 2018 US midterm election

What does antisemitism look like in the context of political discussions on Twitter? In this article, we introduce the notion of platformed antisemitism. We first define it as a platform-agnostic concept, and then explore it through an exemplary case study of Twitter and its affordances by way of a mixed-methods analysis of discourse surrounding the 2018 US midterm election. Via qualitative textual analysis, we document how political discourse on Twitter is marred by antisemitic conspiracy theories that intersect with QAnon and Trump/MAGA support. Through quantitative content analysis of a sample of 99,062 tweets, we highlight a list of terms and hashtags most often associated with antisemitic speech on Twitter and showcase how specific affordances on the platform (quote-tweets, hashtags) amplify and/or diminutize antisemitic speech. Via Lasso regression, we introduce an antisemitism classifier that can be used to further refine future detection efforts of antisemitic speech.
Octopus Sensing: A Python library for human behavior studies

Designing user studies and collecting data is critical to exploring and automatically recognizing human behavior.It is currently possible to use a range of sensors to capture heart rate, brain activity, skin conductance, and a variety of different physiological cues (Seneviratne et al., 2017).These data can be combined to provide information about a user's emotional state (Dzedzickis et al., 2020;Egger et al., 2019), cognitive load (Mangaroska et al., 2022;Vanneste et al., 2021), or other factors.However, even when data are collected correctly, synchronizing data from multiple sensors is time-consuming and prone to errors.Failure to record and synchronize data is likely to result in errors in analysis and results, as well as the need to repeat the time-consuming experiments several times.To overcome these challenges, Octopus Sensing facilitates synchronous data acquisition from various sources and provides some utilities for designing user studies, real-time monitoring, and offline data visualization.The primary aim of Octopus Sensing is to provide a simple scripting interface so that people with basic or no software development skills can define sensor-based experiment scenarios with less effort.
Robust Masked Face Recognition via Balanced Feature Matching

Wearing a facial mask has become a must in our daily life due to the global COVID-19 pandemic. However, the performance of conventional face recognition systems severely degrades for faces occluded by masks. How to combat the effect of occlusion on face recognition is an important issue. However, the performance of existing methods developed for masked face recognition unpleasantly degrades when dealing with unmasked faces. To address this issue for real-world applications, where the gallery image or the probe image may be a masked or unmasked face, we propose the concept of balanced facial feature matching and, based on it, design a robust masked face recognition system. The matching is balanced because it is performed on features extracted from corresponding facial regions. The system consists of a classification network and two feature extractors. The classification network classifies an input face image into a masked face or an unmasked face. One feature extractor extracts the feature of a full face, and the other uses a guided perceptual loss to focus the feature extraction on the non-occluded part of the face. The system is tested on both synthetic and real data. The face verification accuracy is improved by 2.4&#x0025; for the synthetically masked LFW dataset, 1.9&#x0025; for the MFR2 dataset, and 5.4&#x0025; for the RMFD dataset. The results further show that the system improves masked face recognition while preserving the performance of unmasked face recognition.
AI-based user authentication reinforcement by continuous extraction of behavioral interaction features

Abstract In this work, we conduct an experiment to analyze the feasibility of a continuous authentication method based on the monitorization of the users’ activity to verify their identities through specific user profiles modeled via Artificial Intelligence techniques. In order to conduct the experiment, a custom application was developed to gather user records in a guided scenario where some predefined actions must be completed. This dataset has been anonymized and will be available to the community. Additionally, a public dataset was also used for benchmarking purposes so that our techniques could be validated in a non-guided scenario. Such data were processed to extract a number of key features that could be used to train three different Artificial Intelligence techniques: Support Vector Machines, Multi-Layer Perceptrons, and a Deep Learning approach. These techniques demonstrated to perform well in both scenarios, being able to authenticate users in an effective manner. Finally, a rejection test was conducted, and a continuous authentication system was proposed and tested using weighted sliding windows, so that an impostor could be detected in a real environment when a legitimate user session is hijacked.
Adaptive dictionary and structure learning for unsupervised feature selection

Unsupervised feature selection is very attractive in many practical applications, as it needs no semantic labels during the learning process. However, the absence of semantic labels makes the unsupervised feature selection more challenging, as the method can be affected by the noise, redundancy, or missing in the originally extracted features. Currently, most methods either consider the influence of noise for sparse learning or think over the internal structure information of the data, leading to suboptimal results. To relieve these limitations and improve the effectiveness of unsupervised feature selection, we propose a novel method named Adaptive Dictionary and Structure Learning (ADSL) that conducts spectral learning and sparse dictionary learning in a unified framework. Specifically, we adaptively update the dictionary based on sparse dictionary learning. And, we also introduce the spectral learning method of adaptive updating affinity matrix. While removing redundant features, the intrinsic structure of the original data can be retained. In addition, we adopt matrix completion in our framework to make it competent for fixing the missing data problem. We validate the effectiveness of our method on several public datasets. Experimental results show that our model not only outperforms some state-of-the-art methods on complete datasets but also achieves satisfying results on incomplete datasets.
A strategy to estimate the optimal low-rank in incremental SVD-based algorithms for recommender systems

Recommender systems apply machine learning and data mining techniques for filtering unseen information, and they can provide an opportunity to predict whether a user would be interested in a given item. The main types of recommender systems are collaborative filtering (CF) and content-based filtering, which suffer from scalability and data sparsity resulting in poor quality recommendations and reduced coverage. There are two incremental algorithms based on Singular Value Decomposition (SVD) with high scalability for recommender systems which are named the incremental SVD algorithm and incremental Approximating the Singular Value Decomposition (ApproSVD) algorithm. In both mentioned methods, the estimated value of rank for approximating the recommender systems’ data matrix is chosen experimentally in the related literature. In this paper, we investigate the role of singular values for estimating a more reliable amount of rank in the mentioned dimensionality reduction techniques to improve the recommender systems’ performance. In other words, we offered a strategy for choosing the optimal rank that approximates the data matrix more accurately in incremental algorithms with the help of singular values. The numerical results illustrate that the suggested strategy improves the accuracy of the recommendations and run times of both algorithms when employs for Movielens, Netflix, and Jester dataset.
Classifying the Severity of Cyberbullying Incidents by Using a Hierarchical Squashing-Attention Network

Cyberbullying has become more prevalent in online social media platforms. Natural language processing and machine learning techniques have been employed to develop automatic cyberbullying detection models, which are only designed for binary classification tasks that can only detect whether the text contains cyberbullying content. Cyberbullying severity is a critical factor that can provide organizations with valuable information for developing cyberbullying prevention strategies. This paper proposes a hierarchical squashing-attention network (HSAN) for classifying the severity of cyberbullying incidents. Therefore, the study aimed to (1) establish a Chinese-language cyberbullying severity dataset marked with three severity ratings (slight, medium, and serious) and (2) develop a new squashing-attention mechanism (SAM) of HSAN according to the squashing function, which uses vector length to estimate the weight of attention. Experiments indicated that the SAM could sufficiently analyze sentences to determine cyberbullying severity. The proposed HSAN model outperformed other machine-learning-based and deep-learning-based models in determining the severity of cyberbullying incidents.
Controller Design of Tracking WMR System Based on Deep Reinforcement Learning

Traditional PID controllers are widely used in industrial applications due to their simple computational architecture. However, the gain parameters of this simple computing architecture are fixed, and in response to environmental changes, the PID parameters must be continuously adjusted until the system is optimized. This research proposes to use the most important deep reinforcement learning (DRL) algorithm in deep learning as the basis and to modulate the gain parameters of the PID controller with fuzzy control. The research has the ability and advantages of reinforcement learning and fuzzy control and constructs a tracking unmanned wheel system. The mobile robotic platform uses a normalization system during computation to reduce the effects of reading errors caused by the wheeled mobile robot (WMR) of environment and sensor processes. The DRL-Fuzzy-PID controller architecture proposed in this paper utilizes degree operation to avoid the data error of negative input in the absolute value judgment, thereby reducing the amount of calculation. In addition to improving the accuracy of fuzzy control, it also uses reinforcement learning to quickly respond and minimize steady-state error to achieve accurate calculation performance. The experimental results of this study show that in complex trajectory sites, the tracking stability of the system using DRL-fuzzy PID is improved by 15.2% compared with conventional PID control, the maximum overshoot is reduced by 35.6%, and the tracking time ratio is shortened by 6.78%. If reinforcement learning is added, the convergence time of the WMR system will be about 0.5 s, and the accuracy rate will reach 95%. This study combines the computation of deep reinforcement learning to enhance the experimentally superior performance of the WMR system. In the future, intelligent unmanned vehicles with automatic tracking functions can be developed, and the combination of IoT and cloud computing can enhance the innovation of this research.
Research on Keyword Extraction Algorithm in English Text Based on Cluster Analysis

How to facilitate users to quickly and accurately search for the text information they need is a current research hotspot. Text clustering can improve the efficiency of information search and is an effective text retrieval method. Keyword extraction and cluster center point selection are key issues in text clustering research. Common keyword extraction algorithms can be divided into three categories: semantic-based algorithms, machine learning-based algorithms, and statistical model-based algorithms. There are three common methods for selecting cluster centers: randomly selecting the initial cluster center point, manually specifying the cluster center point, and selecting the cluster center point according to the similarity between the points to be clustered. The randomly selected initial cluster center points may contain "outliers," and the clustering results are locally optimal. Manually specifying the cluster center points will be very subjective because each person's understanding of the text set is different, and it is not suitable for the case of a large number of text sets. Selecting the cluster center points according to the similarity between the points to be clustered can make the selected cluster center points distributed in each class and be as close as possible to the class center points, but it takes a long time to calculate the cluster centers. Aiming at this problem, this paper proposes a keyword extraction algorithm based on cluster analysis. The results show that the algorithm does not rely on background knowledge bases, dictionaries, etc., and obtains statistical parameters and builds models through training. Experiments show that the keyword extraction algorithm has high accuracy and can quickly extract the subject content of an English translation.
Design of Distance Assistance System for Intelligent Education by Web-based Applications

Abstract In order to improve the quality of distance education and solve the problem of slow data processing of the teaching system, an intelligent distance education assistance system based on WEB is developed in this paper. After verification, students, teachers and administrators log into the distance intelligent teaching assistance system and transmit various information to the interface of the teaching and administration subsystems. The submitted information is merged using the Bayesian model for integrating educational resources in the digital cloud to create a distance education database that supports the system with data. At the same time, the evaluation of the business logic of the data is performed. After the data is converted to other formats in the subsequent convertible database, it returns to the user interface to provide browsing and consulting functions for users. The experimental results show that the designed system can realize the remote auxiliary function of intelligent education and effectively improve the quality of teaching. The real-time data acquisition rate of the system in this paper is always equal to the set value. The average acceleration of the system in this paper is 5.5 and the data processing efficiency is higher. The minimum safety factor of the system is between 7.8 and 8.5, and the system has high stability. The user satisfaction of the system is over 93%, and the accuracy of the collected data is relatively high. The auxiliary system designed in this paper can provide a stable and efficient application environment for distance education.
3D LiDAR Automatic Driving Environment Detection System Based on MobileNetv3-YOLOv4

In this paper, we proposed 3D LiDAR Automatic Driving Environment Detection System Based on MobileNetv3-YOLOv4. In recent years, artificial intelligence and automatic driving technology have developed very rapidly. Automatic driving has the advantages of law-abiding and fast response, which can significantly reduce driver and passenger casualties. However, due to the large number of parameters and complexity of most object detection neural networks, the computation time required is huge. To solve this problem, this paper applies the lightweight technique of Mobilenetv3 to significantly improve the original object detection neural network, and finds the region of interest by using point cloud de-grounding and clustering algorithms. The data from the region of interest is fed into the Mobilenetv3-YOLOv4 neural network for detection to perform the high accuracy of object detection.
Parametric building forms rationalizing the incident direct solar irradiation

The article presents an innovative procedure that allows for an analysis of the influence of the shape and mutual arrangement of external building partitions on the amount of the direct solar irradiation incident on these partitions during a heating season. The conducted analysis related interdisciplinary issues in the field of solar radiation, unconventional forms of buildings and artificial neural networks. The developed algorithm made it possible to obtain a relationship between the general building form and the amount of their radiation falling on the facade walls and roof of the examined building type. The developed artificial network allows the above irradiation to be calculated during the changes in the geometrical characteristics of the utilized type of buildings and design of effective building forms and rational arrangements of their external solar partitions. The satellite data and online tools published on the website of the European Commission are used. The practicable ranges of the relative changes in the amount of the incident direct irradiation and the corresponding values of the independent variables defining the considered building forms are given, which is an important hint for the application of the obtained results in the design and further research works on more complex relationships.
Big data ordination towards intensive care event count cases using fast computing GLLVMS

In heart data mining and machine learning, dimension reduction is needed to remove multicollinearity. Meanwhile, it has been proven to improve the interpretation of the parameter model. In addition, dimension reduction can also increase the time of computing in high dimensional data.In this paper, we perform high dimensional ordination towards event counts in intensive care hospital for Emergency Department (ED 1), First Intensive Care Unit (ICU1), Second Intensive Care Unit (ICU2), Respiratory Care Intensive Care Unit (RICU), Surgical Intensive Care Unit (SICU), Subacute Respiratory Care Unit (RCC), Trauma and Neurosurgery Intensive Care Unit (TNCU), Neonatal Intensive Care Unit (NICU) which use the Generalized Linear Latent Variable Models (GLLVM's).During the analysis, we measure the performance and calculate the time computing of GLLVM by employing variational approximation and Laplace approximation, and compare the different distributions, including Negative Binomial, Poisson, Gaussian, ZIP, and Tweedie, respectively. GLLVMs (Generalized Linear Latent Variable Models), an extended version of GLMs (Generalized Linear Models) with latent variables, have fast computing time. The major challenge in latent variable modelling is that the function [Formula: see text] is not trivial to solve since the marginal likelihood involves integration over the latent variable u.In a nutshell, GLLVMs lead as the best performance reaching the variance of 98% comparing other methods. We get the best model negative binomial and Variational approximation, which provides the best accuracy by accuracy value of AIC, AICc, and BIC. In a nutshell, our best model is GLLVM-VA Negative Binomial with AIC 7144.07 and GLLVM-LA Negative Binomial with AIC 6955.922.