Reviewerly/exHarmony huggingface dataset:
Reputation of Electric Vehicles in the Environment of Carbon Reduction and Accelerated Digitization

The accelerated digitization of the third decade of the twenty-first century poses a challenge both for science and for practice. The study presents partial results of continuous research on online reputation management of entities operating in the environment of low-carbon economy. The aim of the study is the application of a standardized methodology for calculating the Total level of Online Reputation (TOR) to determine the market position of selected Electric Vehicles (EVs) compared to the market position of conventional Vehicles with Internal Combustion Engines (ICEVs) in the online environment. The research sample consists of the ten best-selling Vehicles and the ten best-selling Electric Vehicles in the world by sales in the year 2021. Based on the measurement results and the subsequent analysis of the context, it can be concluded that the EV market shows the parameters of a developing market not only from the point of view of sales but also in terms of the overall level of Online Reputation as such. At the same time, it is possible to point out a high geographical specificity and significant disproportionality of the EV market compared to ICEVs. From the overall market perspective, the future of cars in the EV category is still unclear, as building trust in low-carbon products is limited by historical tradition. The main representatives of the EV industry thus represent the first forays of the onset of the low-carbon era in individual transport. The description of the issue will require the monitoring of status indicators over time. The results of the presented study can thus serve as a baseline and methodological framework for further research of the adoption of low-carbon policies in common practice.
Aided Imaging Phase Measuring Deflectometry Based on Concave Focusing Mirror

With the rapid development of aerospace, high-speed train, and automotive industries, the demand for the measurement of high-precision specular components is increasing. The acquisition of high-precision three-dimensional (3D) data is conducive to improving the performance of and extending the service life of these components. However, the existing 3D measurement methods of specular surfaces are affected by the inherent limitation of the depth of field (DOF) of camera lenses. Based on the principle of paraxial reflection imaging of a concave mirror, this paper introduces a concave mirror into a phase measuring deflectometry (PMD) system and proposes an aided imaging PMD (AIPMD) based on a concave focusing mirror. The proposed system realizes the clear imaging of the encoded patterns and the surface under test in the DOF of the camera lens, simultaneously. Meanwhile, the iterative coefficient specular reconstruction algorithm is studied based on this system. The feasibility and accuracy of the proposed method are verified by simulations and experiments.
Research on Gesture Recognition System Using Multiple Sensors Based on Earth’s Magnetic Field and 1D Convolution Neural Network

A commonly used method of gesture recognition is the use of sensor technology. Typically, technology detecting Earth’s magnetic field is used in indoor positioning, and magnetic detection technology serves as a redundant method for gesture recognition devices. In this paper, we propose a novel system that utilizes multiple sensors measuring Earth’s magnetic field to collect data and perform gesture recognition through a one-dimensional convolutional neural network algorithm. By applying the detection of Earth’s magnetic field to gesture recognition, our system significantly improves the accuracy of recognition through a one-dimensional (1D) neural network algorithm. We conducted experiments where we collected and recognized American Sign Language standard letters, and achieved an accuracy rate close to 97%. Our experimental results demonstrate that this gesture recognition system using magnetic field sensors and a one-dimensional neural network algorithm is feasible for practical applications. Furthermore, our approach reduces the complexity of the device compared to the gesture recognition method based on artificial magnetic fields, while maintaining high recognition accuracy and not limiting the user’s hand movements. This technology holds great promise for the field of human–computer interaction.
An Ensemble-Learning-Based Technique for Bimodal Sentiment Analysis

Human communication is predominantly expressed through speech and writing, which are powerful mediums for conveying thoughts and opinions. Researchers have been studying the analysis of human sentiments for a long time, including the emerging area of bimodal sentiment analysis in natural language processing (NLP). Bimodal sentiment analysis has gained attention in various areas such as social opinion mining, healthcare, banking, and more. However, there is a limited amount of research on bimodal conversational sentiment analysis, which is challenging due to the complex nature of how humans express sentiment cues across different modalities. To address this gap in research, a comparison of multiple data modality models has been conducted on the widely used MELD dataset, which serves as a benchmark for sentiment analysis in the research community. The results show the effectiveness of combining acoustic and linguistic representations using a proposed neural-network-based ensemble learning technique over six transformer and deep-learning-based models, achieving state-of-the-art accuracy.
An Improved Genetic Algorithm with Swarm Intelligence for Security-Aware Task Scheduling in Hybrid Clouds

The hybrid cloud has attracted more and more attention from various fields by combining the benefits of both private and public clouds. Task scheduling is still a challenging open issue to optimize user satisfaction and resource efficiency for providing services by a hybrid cloud. Thus, in this paper, we focus on the task scheduling problem with deadline and security constraints in hybrid clouds. We formulate the problem into mixed-integer non-linear programming, and propose a polynomial time algorithm by integrating swarm intelligence into the genetic algorithm, which is named SPGA. Specifically, SPGA uses the self and social cognition exploited by particle swarm optimization in the population evolution of GA. In each evolutionary iteration, SPGA performs the mutation operator on an individual with not only another individual, as in GA, but also the individual’s personal best code and the global best code. Extensive experiments are conducted for evaluating the performance of SPGA, and the results show that SPGA achieves up to a 53.2% higher accepted ratio and 37.2% higher resource utilization, on average, compared with 12 other scheduling algorithms.
Fast iterative reverse filters using fixed-point acceleration

Abstract Iterative reverse filters have been recently developed to address the problem of removing effects of a black box image filter. Because numerous iterations are usually required to achieve the desired result, the processing speed is slow. In this paper, we propose to use fixed-point acceleration techniques to tackle this problem. We present an interpretation of existing reverse filters as fixed-point iterations and discuss their relationship with gradient descent. We then present extensive experimental results to demonstrate the performance of fixed-point acceleration techniques named after: Anderson, Chebyshev, Irons, and Wynn. We also compare the performance of these techniques with that of gradient descent acceleration. Key findings of this work include: (1) Anderson acceleration can make a non-convergent reverse filter convergent, (2) the T-method with an acceleration technique is highly efficient and effective, and (3) in terms of processing speed, all reverse filters can benefit from one of the acceleration techniques.
Modelling Sign Language with Encoder-Only Transformers and Human Pose Estimation Keypoint Data

We present a study on modelling American Sign Language (ASL) with encoder-only transformers and human pose estimation keypoint data. Using an enhanced version of the publicly available Word-level ASL (WLASL) dataset, and a novel normalisation technique based on signer body size, we show the impact model architecture has on accurately classifying sets of 10, 50, 100, and 300 isolated, dynamic signs using two-dimensional keypoint coordinates only. We demonstrate the importance of running and reporting results from repeated experiments to describe and evaluate model performance. We include descriptions of the algorithms used to normalise the data and generate the train, validation, and test data splits. We report top-1, top-5, and top-10 accuracy results, evaluated with two separate model checkpoint metrics based on validation accuracy and loss. We find models with fewer than 100k learnable parameters can achieve high accuracy on reduced vocabulary datasets, paving the way for lightweight consumer hardware to perform tasks that are traditionally resource-intensive, requiring expensive, high-end equipment. We achieve top-1, top-5, and top-10 accuracies of 97%, 100%, and 100%, respectively, on a vocabulary size of 10 signs; 87%, 97%, and 98% on 50 signs; 83%, 96%, and 97% on 100 signs; and 71%, 90%, and 94% on 300 signs, thereby setting a new benchmark for this task.
Identifying Strategies to Mitigate Cybersickness in Virtual Reality Induced by Flying with an Interactive Travel Interface

As Virtual Reality (VR) technology has improved in hardware, accessibility of development and availability of applications, its interest has increased. However, the problem of Cybersickness (CS) still remains, causing uncomfortable symptoms in users. Therefore, this research seeks to identify and understand new CS mitigation strategies that can contribute to developer guidelines. Three hypotheses for strategies were devised and tested in an experiment. This involved a physical travel interface for flying through a Virtual Environment (VE) as a Control (CT) condition. On top of this, three manipulation conditions referred to as Gaze-tracking Vignette (GV), First-person Perspective with members representation (FP) and Fans and Vibration (FV) were applied. The experiment was between subjects, with 37 participants randomly allocated across conditions. According to the Simulator Sickness Questionnaire (SSQ) scores, significant evidence was found that GV and FP made CS worse. Evidence was also found that FV did not have an effect on CS. However, from the physiological data recorded, an overall lowering of heart rate for FV indicated that it might have some effect on the experience, but cannot be strongly linked with CS. Additionally, comments from some participants identified that they experienced symptoms consistent with CS. Amongst these, dizziness was the most common, with a few having issues with the usability of the travel interface. Despite some CS symptoms, most participants reported little negative impact of CS on the overall experience and feelings of immersion.
SlowFast Multimodality Compensation Fusion Swin Transformer Networks for RGB-D Action Recognition

RGB-D-based technology combines the advantages of RGB and depth sequences which can effectively recognize human actions in different environments. However, the spatio-temporal information between different modalities is difficult to effectively learn from each other. To enhance the information exchange between different modalities, we introduce a SlowFast multimodality compensation block (SFMCB) which is designed to extract compensation features. Concretely, the SFMCB fuses features from two independent pathways with different frame rates into a single convolutional neural network to achieve performance gains for the model. Furthermore, we explore two fusion schemes to combine the feature from two independent pathways with different frame rates. To facilitate the learning of features from independent multiple pathways, multiple loss functions are utilized for joint optimization. To evaluate the effectiveness of our proposed architecture, we conducted experiments on four challenging datasets: NTU RGB+D 60, NTU RGB+D 120, THU-READ, and PKU-MMD. Experimental results demonstrate the effectiveness of our proposed model, which utilizes the SFMCB mechanism to capture complementary features of multimodal inputs.
Affect Analysis in Arabic Text: Further Pre-Training Language Models for Sentiment and Emotion

One of the main tasks in the field of natural language processing (NLP) is the analysis of affective states (sentiment and emotional) based on written text, and attempts have improved dramatically in recent years. However, in studies on the Arabic language, machine learning or deep learning algorithms were utilised to analyse sentiment and emotion more often than current pre-trained language models. Additionally, further pre-training the language model on specific tasks (i.e., within-task and cross-task adaptation) has not yet been investigated for Arabic in general, and for the sentiment and emotion task in particular. In this paper, we adapt a BERT-based Arabic pretrained language model for the sentiment and emotion tasks by further pre-training it on a sentiment and emotion corpus. Hence, we developed five new Arabic models: QST, QSR, QSRT, QE3, and QE6. Five sentiment and two emotion datasets spanning both small- and large-resource settings were used to evaluate the developed models. The adaptation approaches significantly enhanced the performance of seven Arabic sentiment and emotion datasets. The developed models showed excellent improvements over the sentiment and emotion datasets, which ranged from 0.15–4.71%.
Evolutionary Optimization of Energy Consumption and Makespan of Workflow Execution in Clouds

Making sound trade-offs between the energy consumption and the makespan of workflow execution in cloud platforms remains a significant but challenging issue. So far, some works balance workflows’ energy consumption and makespan by adopting multi-objective evolutionary algorithms, but they often regard this as a black-box problem, resulting in the low efficiency of the evolutionary search. To compensate for the shortcomings of existing works, this paper mathematically formulates the cloud workflow scheduling for an infrastructure-as-a-service (IaaS) platform as a multi-objective optimization problem. Then, this paper tailors a knowledge-driven energy- and makespan-aware workflow scheduling algorithm, namely EMWSA. Specifically, a critical task adjustment-based local search strategy is proposed to intelligently adjust some critical tasks to the same resource of their successor tasks, striving to simultaneously reduce workflows’ energy consumption and makespan. Further, an idle gap reuse strategy is proposed to search the optimal energy consumption of each non-critical task without affecting the operation of other tasks, so as to further reduce energy consumption. Finally, in the context of real-world workflows and cloud platforms, we carry out comparative experiments to verify the superiority of the proposed EMWSA by significantly outperforming 4 representative baselines on 19 out of 20 workflow instances.
Sulfide Trace Element Signatures and S- and Pb-Isotope Geochemistry of Porphyry Copper and Epithermal Gold-Base Metal Mineralization in the Elatsite–Chelopech Ore Field (Bulgaria)

The Elatsite–Chelopech ore field in the northern part of the Panagyurishte district in Central Bulgaria comprises numerous spatially associated porphyry copper and epithermal gold deposits and prospects. In addition to the mineralization and alteration features, trace elements, lead and sulfur isotope signatures of sulfide minerals from porphyry copper, base metal and gold-base metal deposits/prospects have been studied. LA-ICP-MS analyses of pyrite, arsenopyrite and sulfosalt minerals validate them as major carriers for Au, Ag, Sb, Se and Co. Pyrite from the three types of mineralization has specific geochemical characteristics. Pyrite from the porphyry copper deposits/prospects has generally lower total trace element content compared to pyrite from the epithermal prospects, except for Se, Co and Ni. Pyrite from the base metal and gold-base metal veins is enriched in As, Au, Ag, Sb and Pb. In pyrite from the base metal deposits, Co and Ni have contents comparable to the pyrite from the porphyry copper deposits, while pyrite from the gold-base metal veins shows lower Co and Ni. Arsenopyrite from these deposits shows similar features. Similarly, sphalerite from the gold-base metal veins also has lower Co content compared to sphalerite from the base metal veins but higher In and Cu contents. In addition to the close spatial relationships between the Elatsite and Gorna Kamenitsa porphyry Cu deposits and Negarstitsa-West and Dolna Kamenitsa base metal prospects, as well as similarities in the mineralization and alteration styles, the lead isotopic (206Pb/204Pb = 18.61–18.68, 207Pb/204Pb = 15.64–15.65 for porphyry and 206Pb/204Pb = 18.55–18.67, 207Pb/204Pb = 15.64–15.68 for base metal) and sulfur isotopic (δ34S values of −3 to +1‰ for porphyry and δ34S values of −1.7 to +3.5‰ for base metal) signatures of sulfides support the idea of a genetic link between these two types of deposits. The porphyry and base-metal mineralization result from a common major ore-forming event during the Late Cretaceous, corresponding to deep/higher-temperature and shallower/distal/lower-temperature environments, respectively. In particular, more radiogenic lead (206Pb/204Pb = 18.41–18.47, 207Pb/204Pb = 15.67–15.76) and slightly different sulfur isotopic compositions (δ34S values of +3.5 to +10.6‰) of sulfides from the distal gold-base metal veins of Kordunsko Dere, Svishti Plaz and Shipkite might be a consequence of the interaction of the ore-forming fluids with an external older crustal and isotopically positive S source. Alternatively, a different fluid source/event for the formation of these gold-base metal veins may be suggested.
Confidential computing and related technologies: a critical review

Abstract This research critically reviews the definition of confidential computing (CC) and the security comparison of CC with other related technologies by the Confidential Computing Consortium (CCC). We demonstrate that the definitions by CCC are ambiguous, incomplete and even conflicting. We also demonstrate that the security comparison of CC with other technologies is neither scientific nor fair. We highlight the issues in the definitions and comparisons and provide initial recommendations for fixing the issues. These recommendations are the first step towards more precise definitions and reliable comparisons in the future.
Le nuove vie della giuridificazione del corpo. | The new ways of legalising the body.

This paper aims to analyse the relationship between the law and the body from a legal and philosophical perspective. The presupposition of the following remarks is that the technological development is the defining feature of the modern societies. This phenomenon has relevant implications on the languages describing the body and its disposability. The enigma of the body, which has always concerned the philosophical thought, is nowadays of great importance, since it is a privileged position from which to look at the modernity. The law, that has always taken the corporeity into account, is concerned with the emersion of the digital persona, which now more than ever calls for appropriate protection. The investigated matter recalls the distinction between personality rights and property rights. The existential situations can be subject of agreements that entail a non-contractual nature. Yet, market-alienability and non-patrimonial interest get to be strictly entangled in the case of contracts regulating aspects of one’s identity, in which case existential components remain.
Can Gender and Motion Sickness Susceptibility Predict Cybersickness in VR ?

VR headsets are becoming part of our everyday lives, however, cy-bersickness is a common side effect when using these devices. The experience and intensity of symptoms varies greatly between indi-viduals with some seemingly resistant to cybersickness and other suffering greatly. It is still not clear why some individuals suffer more than others and what characteristics might contribute to such a resistance. In this study, we investigate two individual characteris-tics that have been commonly discussed in relation to cybersickness with no clear consensus being established so far: gender and motion sickness history. Findings support the notion that females are more susceptible to motion sickness. However, one's motion sickness history did not relate to symptoms of cybersickness. In line with this, the difference in motion sickness history between genders did not translate to their experience of cybersickness, with males and females experiencing similar levels of cybersickness. These findings demonstrate the complexity of the relationship between gender and motion sickness history and their impacts on the experience of cybersickness. This work highlights the need for future explo-ration of individual characteristics and their effects on cybersickness susceptibility.
SPHinX Authentication Technique: Secure Painting autHentication in eXtended reality

Authentication in immersive virtual reality, and more in general, into extended reality applications is a growing need. The crude adoption of 2D authentication schemes into virtual environments could raise security and usability issues. This paper proposes SPHinX, a novel three-dimensional (3D) authentication scheme based on unlocking patterns. Users can authenticate by painting (tracing) patterns on a 3D object. In this way, 3D interactions can be leveraged to make the authentication scheme less vulnerable to different types of attacks, e.g., shoulder surfing. A user study was carried out by involving 16 participants with the aim to compare a number of alternatives concerning the shape of the object to be painted, the use of single or multiple patterns, and multi-factor authentication.
Examining the Fine Motor Control Ability of Linear Hand Movement in Virtual Reality

Linear hand movement in mid-air is one of the most fundamental interactions in virtual reality (e.g., when dragging/scaling/manipulating objects and drawing shapes). However, the lack of tactile feedback makes it difficult to precisely control the direction and amplitude of hand movement. In this paper, we conducted three user studies to progressively examine users' ability of fine motor control in 3D linear hand movement tasks. In Study 1, we examined participants' behavioural patterns when drawing straight lines in various directions and lengths, using both the hand and the controller. Results showed that the exhibited stroke length tended to be longer than perceived, regardless of the interaction tool. While displaying the trajectory could help reduce directional and length errors. In Study 2, we further tested the effect of different visual references and found that, compared with an empty room or cluttered scenarios, providing only a virtual table yielded higher input precision and user preference. In Study 3, we repeated Study 2 in real dragging and scaling tasks and verified the generalizability of the findings in terms of input error. Our core finding is that the user's hand moves significantly longer than the task length due to the underestimation of stroke length, yet the error of the Z-axis movement is smaller than that of the X-axis and the Y-axis, and a simple virtual desktop can effectively reduce errors.