Reviewerly/exHarmony huggingface dataset:
Physiological Data for User Experience and Quality of Experience: A Systematic Review (2018–2022)

The evaluation of human responses in multimedia experiences using physiological data has a well-established presence in the academic literature. However, this field is currently undergoing transformative changes, driven by the accessibility of diverse and cost-effective devices, innovative software analysis methods, and the emergence of novel application domains such as Virtual and Augmented Reality and mulsemedia. To address the imperative of contextualizing these evolving trends in a contemporary context, this paper presents a systematic review with the objective of delineating the array of physiological data utilized in assessing Quality of Experience (QoE) and User Experience (UX) in multimedia studies. It also examines the devices employed for data collection and the analytical techniques applied to interpret the acquired data. While our review exposes both constraints and promising discoveries in these domains, it also emphasizes the escalating significance and practicality of leveraging physiological data in user assessments, especially as the boundaries between the physical and digital domains continue to blur.
Transformative Virtual Reality Games for Adaptive Sports Training: Exploring the Perceptions of Individuals with Mobility Impairments

The rise of virtual reality (VR) technology has opened up new opportunities for individuals with mobility impairments to participate in adaptive sports training. This paper reports on a survey study of individuals with mobility impairments to explore their expectations and needs when designing and developing VR games for adaptive sports training. Results indicate that participants are highly motivated to engage in VR adaptive sports training due to its promising potential and have specific requirements for game design and development, including ease of use, realistic gameplay, and accessible controls. This study highlights the transformative potential of VR technology in providing equal opportunities for individuals with mobility impairments to participate in adaptive sports training and promotes the development of inclusive VR games for disable users. Our findings have actionable implications for game designers, developers, and researchers, as they underscore the importance of accessibility and inclusiveness in the design and development of VR games for adaptive sports training.
Artificial intelligence and predictive marketing: an ethical framework from managers’ perspective

Purpose Artificial intelligence (AI) offers many benefits to improve predictive marketing practice. It raises ethical concerns regarding customer prioritization, market share concentration and consumer manipulation. This paper explores these ethical concerns from a contemporary perspective, drawing on the experiences and perspectives of AI and predictive marketing professionals. This study aims to contribute to the field by providing a modern perspective on the ethical concerns of AI usage in predictive marketing, drawing on the experiences and perspectives of professionals in the area. Design/methodology/approach The study conducted semistructured interviews for 6 weeks with 14 participants experienced in AI-enabled systems for marketing, using purposive and snowball sampling techniques. Thematic analysis was used to explore themes emerging from the data. Findings Results reveal that using AI in marketing could lead to unintended consequences, such as perpetuating existing biases, violating customer privacy, limiting competition and manipulating consumer behavior. Originality/value The authors identify seven unique themes and benchmark them with Ashok’s model to provide a structured lens for interpreting the results. The framework presented by this research is unique and can be used to support ethical research spanning social, technological and economic aspects within the predictive marketing domain.
Vernacular constructions of the relationship between freedom of speech and (potential) hate speech: The case of Finland

Abstract The blurred distinction between freedom of expression and hate speech in ever more polarised public debates across Europe and beyond has prompted research on hate speech, particularly focusing on right‐wing populist politicians. Little is known, however, about how this distinction is construed by ordinary citizens. Deploying the concept of retrogressive mobilisation, this study examines how cases of (potential) political hate speech – one targeting racialised minorities, the other the LGBTQ+ community – are interpreted and negotiated by ordinary citizens through their comments on online news in Finland. Deploying a critical discursive psychological approach, we analyse the vernacular meanings that ordinary citizens attach to the notions of political hate speech, thereby highlighting the dynamic relationship between political and everyday discourse. We evidence three discursive constructions of the relationship between freedom of expression and (potential) hate speech. In these constructions, the same rhetorical resources, especially the liberal arguments of equality and freedom of expression, were deployed to service the opposite discursive functions – that is, for both ‘liberal’ and ‘illiberal’ ends – to condemn and justify discrimination against minoritised groups. Our study contributes to the social psychological understanding of contemporary hate speech and builds a bridge between social psychology and the more recent field of anti‐gender research.
A Novel Tracking Algorithm Based on Waveform Selection for Maneuvering Targets in Clutter

Abstract The cognition radar utilizes the dynamic waveform configuration to adapt to the environment to improve tracking performance. A novel tracking algorithm based on waveform selection is proposed to address the maneuvering aircraft tracking problem in clutter. Based on the modified current statistical (MCS) model, the modified probabilistic data association filter (MPDAF) is integrated with the square-root cubature Kalman filter (SCKF) to deal with the nonlinear measurement and the clutter. Additionally, the waveform library is established by applying the fractional Fourier transform (FRFT) to a base waveform, and a direct method is proposed to select the optimal waveform to minimize the posterior state error covariance matrix. The simulation results showed that, compared with the filters with the fixed waveform as well as the state-of-the-art algorithms, the proposed algorithm achieved higher estimation precision and lower track loss percentage while maintaining a low computational burden.
Self‐attention residual network‐based spatial super‐resolution synthesis for time‐varying volumetric data

Abstract In the field of scientific visualization, the upscaling of time‐varying volume is meaningful. It can be used in in situ visualization to help scientists overcome the limitations of I/O speed and storage capacity when analysing and visualizing large‐scale, time‐varying simulation data. This paper proposes self‐attention residual network‐based spatial super‐resolution (SARN‐SSR), a spatial super‐resolution model based on self‐attention residual networks that can generate time‐varying data with temporal coherence. SARN‐SSR consists of two components: a generator and a discriminator. The generator takes the low‐resolution volume sequences as the input and gives the corresponding high‐resolution volume sequences as the output. The discriminator takes both synthesized and real high‐resolution volume sequence as the input and gives a matrix to predict the realness as the output. To verify the validity of SARN‐SSR, four sets of time‐varying volume datasets are applied from scientific simulation. In addition, SARN‐SSR is compared on these datasets, both qualitatively and quantitatively, with two deep learning‐based techniques and one traditional technique. The experimental results show that by using this method, the closest time‐varying data to the ground truth can be obtained.
Calculation and Analysis of Specific Losses of Active Power in Overhead Power Lines due to Corona in View of Climatic Data

. It is a very challenging task to correctly use weather data to calculate the active power loss due to corona in overhead power lines. One of the significant reasons for this was the problem of accuracy of determining weather conditions and the use of the forecast for calculating extended overhead lines. In the USSR (Union of Soviet Socialist Republics), based on field tests and experimental data, recommendations were developed for accounting for losses of electric energy due to corona and interference in overhead lines, the average values of which were given in the relevant instruction for the regulation and justification of the norm of electricity consumption for its transmission over electric networks. According to this regulatory document, the specific cost of active power due to corona in overhead lines, averaged by design, was determined depending on weather conditions, which were divided into four groups. In this paper, the effect of active power losses on corona is estimated using various factors on the example of high-voltage overhead lines in the main electrical networks of the Republic of Belarus of various rated voltages. Dependences of active power losses due to corona have been constructed and modeled, taking into account different climatic and weather conditions observed in the country. The determination of metrological parameters was based on the analysis of weather data and patterns of types of weather conditions. In order to improve the accuracy of calculating the specific losses of active power due to corona, all possible weather conditions were considered, as well as their combinations. Afterwards, the data on weather conditions were processed and their types were reduced to one of the four existing types of weather. A method for determining electric power losses due to corona in overhead lines based on the average relative air density and geometric parameters of the predominant section of overhead lines has been proposed and presented. It is shown that the usage of exclusively rated voltage can cause errors in determining both the specific and actual values of active power losses for corona.
Modelling Exchange with Blockchain for the Collaborative Design of a Building Envelope in BIM

Introduction The main challenge in using building information modelling (BIM) is the centralised collaborative framework in the various design software ( e.g ., AutoCAD, Revit, RHINO, and ArchiCAD) used by professionals across various disciplines, which causes difficulties during data exchange. Methods The existing framework may increase the project lead time, require more effort, and result in design conflicts and misunderstandings. Thus, this paper has proposed a dynamic information storage with blockchain system (DISBS) using a physical curtain wall project for practical implementation. In addition, this paper has further discussed the linkages and verified and analysed the effectiveness of the proposed methodology. Results Through the DISBS proposed in this research, the characteristics and achievable functions of blockchain technology are presented. Various applications for the field of construction engineering are highly developable within this research. Conclusion The DISBS can improve not only the management quality of construction projects, but also promote mutual trust in the construction industry and optimize architecture, engineering, and construction (AECO) productivity.
Enhancing biomechanical machine learning with limited data: generating realistic synthetic posture data using generative artificial intelligence

Objective: Biomechanical Machine Learning (ML) models, particularly deep-learning models, demonstrate the best performance when trained using extensive datasets. However, biomechanical data are frequently limited due to diverse challenges. Effective methods for augmenting data in developing ML models, specifically in the human posture domain, are scarce. Therefore, this study explored the feasibility of leveraging generative artificial intelligence (AI) to produce realistic synthetic posture data by utilizing three-dimensional posture data. Methods: Data were collected from 338 subjects through surface topography. A Variational Autoencoder (VAE) architecture was employed to generate and evaluate synthetic posture data, examining its distinguishability from real data by domain experts, ML classifiers, and Statistical Parametric Mapping (SPM). The benefits of incorporating augmented posture data into the learning process were exemplified by a deep autoencoder (AE) for automated feature representation. Results: Our findings highlight the challenge of differentiating synthetic data from real data for both experts and ML classifiers, underscoring the quality of synthetic data. This observation was also confirmed by SPM. By integrating synthetic data into AE training, the reconstruction error can be reduced compared to using only real data samples. Moreover, this study demonstrates the potential for reduced latent dimensions, while maintaining a reconstruction accuracy comparable to AEs trained exclusively on real data samples. Conclusion: This study emphasizes the prospects of harnessing generative AI to enhance ML tasks in the biomechanics domain.
Geometric dual and sum‐rank minimal codes

Abstract The main purpose of this paper is to further study the structure, parameters and constructions of the recently introduced minimal codes in the sum‐rank metric. These objects form a bridge between the classical minimal codes in the Hamming metric, the subject of intense research over the past three decades partly because of their cryptographic properties, and the more recent rank‐metric minimal codes. We prove some bounds on their parameters, existence results, and, via a tool that we name geometric dual, we manage to construct minimal codes with few weights. A generalization of the celebrated Ashikhmin–Barg condition is proved and used to ensure the minimality of certain constructions.
Optimization of Internet of Things Remote Desktop Protocol for Low-Bandwidth Environments Using Convolutional Neural Networks

This paper discusses optimizing desktop image quality and bandwidth consumption in remote IoT GUI desktop scenarios. Remote desktop tools, which are crucial for work efficiency, typically employ image compression techniques to manage bandwidth. Although JPEG is widely used for its efficiency in eliminating redundancy, it can introduce quality loss with increased compression. Recently, deep learning-based compression techniques have emerged, challenging traditional methods like JPEG. This study introduces an optimized RFB (Remote Frame Buffer) protocol based on a convolutional neural network (CNN) image compression algorithm, focusing on human visual perception in desktop image processing. The improved RFB protocol proposed in this paper, compared to the unoptimized RFB protocol, can save 30-80% of bandwidth consumption and enhances remote desktop image quality, as evidenced by improved PSNR and MS-SSIM values between the remote desktop image and the original image, thus providing superior desktop image transmission quality.
Diving into Clarity: Restoring Underwater Images using Deep Learning

Abstract In this paper we propose a learning-based restoration approach to learn the optimal parameters for enhancing the quality of different types of underwater images and apply a set of intensity transformation techniques to process raw underwater images. The methodology comprises two steps. Firstly, a Convolutional Neural Network (CNN) Regression model is employed to learn enhancing parameters for each underwater image type. Trained on a diverse dataset, the CNN captures complex relationships, enabling generalization to various underwater conditions. Secondly, we apply intensity transformation techniques to raw underwater images. These transformations collectively compensate for visual information loss due to underwater degradation, enhancing overall image quality. In order to evaluate the performance of our proposed approach, we conducted qualitative and quantitative experiments using well-known underwater image datasets (U45 and UIEB), and using the proposed challenging dataset composed by 276 underwater images from the Amazon region (AUID). The results demonstrate that our approach achieves an impressive accuracy rate in different underwater image datasets. For U45 and UIEB datasets, regarding PSNR and SSIM quality metrics, we achieved 26.967, 0.847, 27.299 and 0.793, respectively. Meanwhile, the best comparison techniques achieved 26.879, 0.831, 27.157 and 0.788, respectively.
Computational investigation and validation of new MEXICO experiment

A computational investigation of New MEXICO test cases operating under axial flow conditions is reported. Three wind speed cases (10, 15, 24 m/s) corresponding to three different tip speed ratios (10, 6.67, 4.17) when the turbine operates at 425.1 rpm were considered. ANSYS CFX 2021R1 was employed to perform simulations using Single Reference Frame (SRF) and Multiple Reference Frame (MRF) approaches. The flow field is computed by solving unsteady Reynolds Averaged Navier-Stokes (uRANS) equations coupled with SST k-ω turbulence model and Gamma-Theta transition model. Validation involved comparing CFD-predicted integral quantities, static pressure distributions, and loads with corresponding experimental values demonstrating reasonably good agreement at all three wind speeds. Overall, SRF exhibited slightly better wake predictions (hypothetical), while MRF predictions were closer to measurements for integral quantities, static pressure and loads. This study demonstrates the utility of uRANS-based 3D CFD computations in wind turbine aerodynamics studies.
A novel adaptive maximum correntropy cubature Kalman filter based on multiple fading factors

In this paper, an adaptive maximum correntropy cubature Kalman filter based on multiple fading factors (MAMCKF) is proposed to address the problem of inaccurate process noise covariance and unknown measurement noise covariance together with outliers in target tracking. Although there are many adaptive filters and robust filters have been proposed to handle unknown measurement noise covariance or measurement outliers, most filters cannot deal with both unknown noise covariance and outliers simultaneously. In this article, we propose an adaptive and robust cubature Kalman filter. The modified measurement noise covariance matrix (MNCM) and innovation covariance matrix are used to construct multiple fading factors for correcting the prediction error covariance matrix (PECM), which can achieve adaptability. Then, the maximum correntropy criterion (MCC) is introduced to suppress outliers, which further enhances the robustness. Compared with the existing approaches, the proposed approach improves the performance by at least 5% in unknown time-varying noise, unknown time-varying heavy-tailed noise, and non-Gaussian heavy-tailed noise scenarios. The simulation results show that the proposed approach can effectively suppress inaccurate process noise covariance and unknown time-varying measurement noise together with outliers. Compared with the existing filtering approaches, the proposed approach exhibits both adaptability and robustness.
Enhanced mechanism to prioritize the cloud data privacy factors using AHP and TOPSIS: a hybrid approach

Abstract Cloud computing is a new paradigm in this new cyber era. Nowadays, most organizations are showing more reliability in this environment. The increasing reliability of the Cloud also makes it vulnerable. As vulnerability increases, there will be a greater need for privacy in terms of data, and utilizing secure services is highly recommended. So, data on the Cloud must have some privacy mechanisms to ensure personal and organizational privacy. So, for this, we must have an authentic way to increase the trust and reliability of the organization and individuals The authors have tried to create a way to rank things that uses the Analytical Hieratical Process (AHP) and the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). Based on the result and comparison, produce some hidden advantages named cost, benefit, risk and opportunity-based outcomes of the result. In this paper, we are developing a cloud data privacy model; for this, we have done an intensive literature review by including Privacy factors such as Access Control, Authentication, Authorization, Trustworthiness, Confidentiality, Integrity, and Availability. Based on that review, we have chosen a few parameters that affect cloud data privacy in all the phases of the data life cycle. Most of the already available methods must be revised per the industry’s current trends. Here, we will use Analytical Hieratical Process and Technique for Order Preference by Similarity to the Ideal Solution method to prove that our claim is better than other cloud data privacy models. In this paper, the author has selected the weights of the individual cloud data privacy criteria and further calculated the rank of individual data privacy criteria using the AHP method and subsequently utilized the final weights as input of the TOPSIS method to rank the cloud data privacy criteria.
Interval Type-2 Mutual Subsethood Cauchy Fuzzy Neural Inference System (IT2MSCFuNIS)

Abstract An interval type-2 (IT2) mutual subsethood Cauchy fuzzy neural inference system has been proposed in this paper. The network architecture consists of 3-layers with all connection weights being IT2 Cauchy fuzzy membership functions (CMFs). The crisp inputs to the system are fuzzified into IT2CMFs with fixed centers and uncertain spreads. The hidden layer represents the rule-based knowledge. The firing degree of the antecedent part of each rule at the hidden layer is computed by aggregating the product of the mutual subsethood similarity measures between the inputs and the connection weights. A volume defuzzification is used to compute the numeric output. A gradient descent back-propagation algorithm is used to train the model. The novelty of the proposed model is threefold. First, is enriching the theory of the mutual subsethood fuzzy neural models by adopting the Cauchy membership function (CMF) as another powerful fuzzy basis function (FBF) rather than the classical choice of Gaussian fuzzy membership functions (GMFs). Second, is the success of computing the mutual subsethood similarity measure between the IT2CMFs and all the model parameters’ updating equations in analytic closed-form formulas, not numerically or approximately. Third, is the ability to extract the type-1 (T1) mutual subsethood Cauchy fuzzy neural inference system (T1MSCFuNIS) with all its analytic closed-form formulas directly as a special case from the general formulas of IT2MSCFuNIS model. Such a novelty makes the proposed model a concrete and effective development of the theory of mutual subsethood fuzzy neural models. Both IT2MSCFuNIS and T1MSCFuNIS models have been tested using different examples from the domains of function approximation, classification, and prediction. The results ensure the efficacy of both models compared with other models reported in the literature.