Reviewerly/exHarmony huggingface dataset:
The impact of eye gaze on social interactions of females in virtual reality: The mediating role of the uncanniness of avatars and the moderating role of task type

The introduction of the Metaverse and the rise of social virtual reality platforms such as VRChat has led to increased communication and collaboration in virtual reality. As interactions in social virtual reality take place through avatars, the behaviour, and in particular the eye gaze of an avatar can have an impact on the user experience. However, it is still unclear which features of the user experience are most influenced. Therefore, this study used data from 44 female participants to investigate how avatar gaze behaviour (static eyes vs. real-time tracked eyes) affects their perceived quality of communication and the amount of one-sided eye contact participants make during a communicative or collaborative task in virtual reality. In addition, the study investigated the mediating role of perceived avatar "uncanniness" (i.e., the finding that humanoid objects that imperfectly resemble real people evoke feelings of discomfort) in this relationship and the moderation effect of two multi-user scenarios (collaboration vs. communication). The results showed that uncanniness directly affected the perceived quality of communication. However, it did not significantly mediate the relationship between avatar eye gaze behaviour and the quality of communication or the amount of one-sided eye contact. Finally, there were no significant differences in user experience between the two scenarios. From this, we can conclude that the uncanniness of an avatar being interacted with in VR is not enough to hinder communication and collaboration in an immersive medium. And even if an avatar is perceived as uncanny, normative communication cues such as eye contact are still present. Notably, due to sample availability, the results of this study are based on a female-only sample. Thus, future research can benefit from exploring the outlined effects in a more gender-balanced sample.
Diagnosis of Alzheimer’s disease via Intuitionistic fuzzy least squares twin SVM

Neurodegenerative disorders like Alzheimer's disease (AD) are irreversible and show atrophies in the area of the cerebral cortex of brain. AD leads to loss of memory and other cognitive impairments. The AD subjects are evaluated based on magnetic resonance imaging scans. The data may have the problem of class imbalance, noise and outliers which is a great challenge for classification. Support vector machines and twin support vector machine-based classifiers may not effectively deal with these problems as both these models assume that all the samples are equally important for the separating hyperplane. To overcome these issues, we propose intuitionistic fuzzy least square twin support vector machine for class imbalance problems (IFLSTSVM) and class specific-IFLSTSVM (CS-IFLSTSVM). To minimize the effects of class imbalance, the samples are appropriately weighted to minimize their effect on the optimal hyperplane. Moreover, we use intuitionistic fuzzy scores to overcome the issues of noise and outliers. Intuitionistic fuzzy score values generate appropriate weights by considering both the distance of the samples from the class centroid as well as the heterogeneity of the samples. The proposed models IFLSTSVM and CS-IFLSTSVM are efficient as they need to solve a system of linear equations. In Alzheimer's disease diagnosis, the proposed IFLSTSVM and CS-IFLSTSVM models showed better performance in MCI_vs_AD and CN_vs_MCI cases, respectively. Moreover, the proposed models showed better performance in the diagnosis of breast cancer classification. The statistical analysis carried out over KEEL and UCI data leads to the superiority of the proposed models. The source code of the proposed model is available at https://github.com/mtanveer1/Diagnosis-of-Alzheimer-s-disease-via-Intuitionistic-fuzzy-least-squares-twin-SVM.
Improving Estimation of Layer Thickness and Identification of Slicer for 3D Printing Forensics

This study emphasizes the significance of estimating the layer thickness and identifying slicer programs in the realm of 3D printing forensics. With the progress in 3D printing technology, precise estimation of the layer thickness has become crucial. However, previous research on layer thickness estimation has mainly treated the problem as a classification task, which is inadequate for continuous layer thickness parameters. Furthermore, previous studies have concentrated on hardware-based printer identification, but the identification of slicer programs through 3D objects is a vital aspect of the software domain and can provide valuable clues for 3D printing forensics. In this study, a regression-based approach utilizing a vision transformer model was proposed. Experiments conducted on the SI3DP++ dataset demonstrated that the proposed model could handle a broad range of data and outperform the current classification models. Additionally, this study proposed a new research direction by introducing slicer program identification, which significantly contributes to the field of 3D printing forensics.
Machine Learning Method for Changepoint Detection in Short Time Series Data

Analysis of data is crucial in waste management to improve effective planning from both short- and long-term perspectives. Real-world data often presents anomalies, but in the waste management sector, anomaly detection is seldom performed. The main goal and contribution of this paper is a proposal of a complex machine learning framework for changepoint detection in a large number of short time series from waste management. In such a case, it is not possible to use only an expert-based approach due to the time-consuming nature of this process and subjectivity. The proposed framework consists of two steps: (1) outlier detection via outlier test for trend-adjusted data, and (2) changepoints are identified via comparison of linear model parameters. In order to use the proposed method, it is necessary to have a sufficient number of experts’ assessments of the presence of anomalies in time series. The proposed framework is demonstrated on waste management data from the Czech Republic. It is observed that certain waste categories in specific regions frequently exhibit changepoints. On the micro-regional level, approximately 31.1% of time series contain at least one outlier and 16.4% exhibit changepoints. Certain groups of waste are more prone to the occurrence of anomalies. The results indicate that even in the case of aggregated data, anomalies are not rare, and their presence should always be checked.
IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities

This study addresses anomaly detection in smart city environments driven by the Internet of Things. In these cities, digital interconnection and the extensive network of sensors generate enormous amounts of data, which are essential to improving citizens’ efficiency and quality of life. However, this data may also contain strange events that require early detection to ensure the proper functioning of urban systems. For this, anomaly detection models are explored to identify unusual patterns in urban data. The work focuses on the applicability and effectiveness of these models in different urban scenarios supported by the Internet of Things. Furthermore, its performance is evaluated by comparing it with existing approaches, and its advantages and limitations are analyzed. The results show that the proposed models, including Isolation Forest, recurrent neural network, and variational autoencoder, are highly effective in detecting anomalies in urban data. This work contributes to the field of smart cities by improving the safety and efficiency of urban systems. Early detection of anomalies makes it possible to prevent unplanned interruptions, ensure the safety of citizens, and maintain the integrity of urban systems. Furthermore, the relevance of this work in the existing literature and its importance for the evolution of smart cities supported by the Internet of Things are highlighted.
Voltage-activated parametric entangling gates based on gatemon qubits

We describe the generation of entangling gates on superconductor-semiconductor hybrid qubits by ac voltage modulation of the Josephson energy. Our numerical simulations demonstrate that the unitary error can be below ${10}^{\ensuremath{-}5}$ in a variety of 75-ns-long two-qubit gates (CZ, $i\mathrm{SWAP}$, and $\sqrt{i\mathrm{SWAP}}$) implemented using parametric resonance. We analyze the conditional $ZZ$ phase and demonstrate that the CZ gate needs no further phase-correction steps, while the $ZZ$ phase error in swap-type gates can be compensated by choosing pulse parameters. With decoherence considered, we estimate that qubit relaxation time needs to exceed $70\phantom{\rule{0.2em}{0ex}}\text{\ensuremath{\mu}}\mathrm{s}$ to achieve the 99.9% fidelity threshold.
PENGARUH MEDIA POP UP BOOK TERHADAP MINAT BACA PESERTA DIDIK SDN 2 LENDANG KUNYIT TAHUN AJARAN 2022/2023

One of the low reading skills is caused by reduced interest in reading students, this low interest in reading has an impact on the low enthusiasm of students in participating in learning. The purpose of this study is to determine the influence of pop up book media on the reading interest of SDN 2 Lendang Kunyit students for the 2022/2023 school year. This research is a quantitative research with a type of experimental research with a pre-experimental design. The population in this study was 27 grade V students of SDN 2 Lendang Kunyit. Sampling in this study used saturated sampling. The data collection technique used in this study to measure reading interest is questionnaire / questionnaire as pre-test and post test. based on the results of the analysis with the help of SPSS V29 The average pre-test result was 87.07 with the most categories, namely medium with a range of values of 67.27 &lt; X &lt; 106.86. In the post-test results, students' reading interest obtained an average post-test result of 119.37 with the most categories, namely medium with a value range of 103.03 &lt; X &lt; 135.70. From the results of the study, it was found that the significance value was &lt;0.05 (0.001 &lt; 0.05). If the significance &lt; 0.05 then Ha is accepted and if the significance &gt; 0.05 then Ho is rejected. It can be concluded that there is an influence of Pop Up Book media on the reading interest of SDN 2 Lendang Kunyit students for the 2022/2023 school year.
A Survey of Detection and Mitigation for Fake Images on Social Media Platforms

Recently, the spread of fake images on social media platforms has become a significant concern for individuals, organizations, and governments. These images are often created using sophisticated techniques to spread misinformation, influence public opinion, and threaten national security. This paper begins by defining fake images and their potential impact on society, including the spread of misinformation and the erosion of trust in digital media. This paper also examines the different types of fake images and their challenges for detection. We then review the recent approaches proposed for detecting fake images, including digital forensics, machine learning, and deep learning. These approaches are evaluated in terms of their strengths and limitations, highlighting the need for further research. This paper also highlights the need for multimodal approaches that combine multiple sources of information, such as text, images, and videos. Furthermore, we present an overview of existing datasets, evaluation metrics, and benchmarking tools for fake image detection. This paper concludes by discussing future directions for fake image detection research, such as developing more robust and explainable methods, cross-modal fake detection, and the integration of social context. It also emphasizes the need for interdisciplinary research that combines computer science, digital forensics, and cognitive psychology experts to tackle the complex problem of fake images. This survey paper will be a valuable resource for researchers and practitioners working on fake image detection on social media platforms.
Ultraviolet Erythemal Irradiance (UVER) under Different Sky Conditions in Burgos, Spain: Multilinear Regression and Artificial Neural Network Models

Different strategies for modeling Global Horizontal UltraViolet Erythemal irradiance (GHUVE) based on meteorological parameters measured in Burgos (Spain) have been developed. The experimental campaign ran from September 2020 to June 2022. The selection of relevant variables for modeling was based on Pearson’s correlation coefficient. Multilinear Regression Model (MLR) and artificial neural network (ANN) techniques were employed to model GHUVE under different sky conditions (all skies, overcast, intermediate, and clear skies), classified according to the CIE standard on a 10 min basis. ANN models of GHUVE outperform those based on MLR according to the traditional statistical indices used in this study (R2, MBE, and nRMSE). Moreover, the work proposes a simple all-sky ANN model of GHUVE based on usually recorded variables at ground meteorological stations.
A Hybrid Deep Ranking Weighted Multi-Hashing Recommender System

In countries where there is a low availability of resources for language, businesses face the challenge of overcoming language barriers to reach their customers. One possible solution is to use collaborative filtering-based recommendation systems in their native languages. These systems employ algorithms that understand the customers’ preferences and suggest products or services in their native language. Collaborative filtering (CF) is a popular recommendation technique that simulates word-of-mouth phenomena. However, the accuracy of a CF recommendation can be affected by sparse data. In this research paper, we present a novel hybrid weighted multi-deep ranking supervised hashing (HWMDRH) approach. Our method leverages both user-based and item-based CF by merging the item-based deep ranking weighted multi-hash recommender system prediction with the user-based deep ranking weighted multi-hash recommender system prediction to generate Top-N prediction. We conducted extensive experiments on the MovieLens 1M dataset, and our results show that the proposed HWMDRH model outperforms existing models and achieves state-of-the-art performance across recall, precision, RMSE, and F1-score metrics.
Structure Estimation of Adversarial Distributions for Enhancing Model Robustness: A Clustering-Based Approach

In this paper, we propose an advanced method for adversarial training that focuses on leveraging the underlying structure of adversarial perturbation distributions. Unlike conventional adversarial training techniques that consider adversarial examples in isolation, our approach employs clustering algorithms in conjunction with dimensionality reduction techniques to group adversarial perturbations, effectively constructing a more intricate and structured feature space for model training. Our method incorporates density and boundary-aware clustering mechanisms to capture the inherent spatial relationships among adversarial examples. Furthermore, we introduce a strategy for utilizing adversarial perturbations to enhance the delineation between clusters, leading to the formation of more robust and compact clusters. To substantiate the method’s efficacy, we performed a comprehensive evaluation using well-established benchmarks, including MNIST and CIFAR-10 datasets. The performance metrics employed for the evaluation encompass the adversarial clean accuracy trade-off, demonstrating a significant improvement in both robust and standard test accuracy over traditional adversarial training methods. Through empirical experiments, we show that the proposed clustering-based adversarial training framework not only enhances the model’s robustness against a range of adversarial attacks, such as FGSM and PGD, but also improves generalization in clean data domains.
Improved dispersion-encoded full-range spectral interferometry for large depth, large inclination and rough samples

In this study, we provide and validate an enhanced dispersion-encoded full-range spectral interferometry that offers improved measurement accuracy for samples characterized by significant depth, inclination, and roughness. Due to the low intensity of the optical signal in these samples, the optical signal is easy to be overwhelmed by the disturbance terms, demanding a more precise phase compensation. To address this issue, the direct current is removed by subtracting the envelope of the interference spectrum, allowing the weak signal near zero optical delay can be identified. Besides, considering the unwrapped phase error caused by the spectrometer aberrations, only the data with good phase continuity are preserved, which improves the accuracy of phase compensation. Using the proposed technique, the depth measurement range of the system is extended from 6 mm to 12 mm, while achieving an axial measurement precision of 2.5µm. In addition, the full-depth images of a ceramic standard step block and a 3D-printed sample are measured. It shows that our system exhibits superior adaptation to complex surfaces compared to the laser line profiler.
Sentiment Analysis Using Text Mining Techniques On Social Media Using the Support Vector Machine Method Case Study Seagames 2023 Football Final

This thesis aims to analyze sentiment on text data from social media related to the 2023 SEA Games, especially in the final match of the soccer sport. The method used is the Text Mining Technique with the SVM (Support Vector Machine) algorithm to classify user sentiment as positive or negative regarding the match. Text data is retrieved from various social media platforms during and after the match. The results of the sentiment analysis are expected to provide insight into the public's view of the sporting event. This research can contribute to the understanding of public sentiment towards the 2023 SEA Games final football match through the analysis of text data from social media.
FlowFormer: 3D scene flow estimation for point clouds with transformers

Since estimating scene flow from point clouds is challenging, some methods involve the robust Transformer. However, there are two problems with these methods: (1) Dense connectivity of global attention reaches the efficiency bottleneck. (2) The lack of adaptive flow encoding in local neighborhoods results in inaccurate flow values. To this end, we propose a new Transformer based scene flow estimation network. Specifically, we first develop a lightweight feature enhancement module with seed points guided attention layers to encode the global contexts of point clouds into local descriptors, so that discriminative descriptors are generated for reliable correlation construction and initial flow estimation. Then, we construct a Transformer based flow refinement module for more accurate flow, which includes a flow aggregation layer at the coarse level and a flow propagation layer at the finer level. The flow aggregation layer encodes the local flow smoothness via adaptively aggregating the flow of neighbors to central points. The flow propagation layer can softly upsample the coarse-level flow to the finer level, which directly takes the coarse-level flow as the “value” and adaptively learns the transform weights between the coarse-level points and fine-level points. The experiments on extensive datasets demonstrate that our method can yield outstanding performance.
The Scientometric Overview of the Economics Disciplines Publications in Kazakhstan in 1997-2022

This article gives an overview of the economics disciplines publications in Kazakhstan indexed in the Scopus database in the period from 1997 to 2022. The literature provides an outlook on scientometrics research that can be used for the analysis of specific research fields. Scientometrics methods used in this article identify the state of the research in economics disciplines in Kazakhstan, explicitly focusing on descriptive analysis such as distribution by number of articles, number of journals, number of authors per article, number of citations, number of citations per article and distribution of articles across journals. By using the network analysis and VosViewer software, links and connections within economics discipline publications were identified. Descriptive results of the study identify that across all economics disciplines “Economics, Econometrics, and Finance” has the largest number of publications. In contrast “Business, Management, and Accounting” has the most significant number of authors. The network analysis demonstrated weaker links in “Decision sciences” while in “Economics, Econometrics, and Finance” and “Business, Management, and Accounting” the relationship is closer and more interconnected in the subject areas. The analysis of scientific journals identified the quality of publications and type of journals where papers are published as well as the highest cited papers in the economic disciplines affiliated with Kazakhstan. Conclusions and recommendations suggest that internal processes and scientific infrastructure at universities aimed to improve the quality of publications will increase the interest in research published in Kazakhstan in the global scientific community and have practical implications for the economy.
An algorithmic approach to identification of gray areas: Analysis of sleep scoring expert ensemble non agreement areas using a multinomial mixture model

Machine learning (ML) models have become a key component in modern world services. In decision-making domains where human expertise is crucial, for example, for manually scoring biological signal data, human uncertainties undermine experts’ trust in the outcomes of these models. The field of sleep staging in particular, which requires experts to score complex biological signal is notably impacted by scoring uncertainties. Data consisting of an ensemble of independent scorers are collected to estimate inter scorer agreement and the uncertainty associated with manual scoring. However, scorers’ uncertainty lacks statistical modeling, which poses difficulties in validating ML algorithms and leads to issues of reliability and explainability. From the ensemble of scorers, uncertainty zones, called gray areas, are highlighted by samples where scorers disagree. The objective of our work is to provide a framework introducing and inferring gray areas. We present a flexible and easy-to-use multi-objective method based on multinomial mixture models clustering the different levels of scorer agreement and summarize the results into two sets of high-agreement and gray area clusters, which are called supra-clusters. The threshold is selected according to the maximization of the distance between two distributions of scorers agreement measure. Effective results were obtained by the method after it was fitted on simulated data. Additionally, the method was applied to a real case of uncertainty analysis in the sleep staging domain. A series of actual sleep stages scored by an ensemble of 10 independent scorers for a dataset of 50 participants was used.