Reviewerly/exHarmony huggingface dataset:
Effects of Cybersickness Caused by Head-Mounted Display–Based Virtual Reality on Physiological Responses: Cross-sectional Study

Although more people are experiencing cybersickness due to the popularization of virtual reality (VR), no official standard for the cause and reduction of cybersickness exists to date. One of the main reasons is that an objective method to assess cybersickness has not been established. To resolve this, research on evaluating cybersickness with physiological responses that can be measured in real time is required. Since research on deriving physiological responses that can assess cybersickness is at an early stage, further studies examining various physiological responses are needed.This study analyzed the effects of cybersickness caused by head-mounted display-based VR on physiological responses.We developed content that provided users with a first-person view of an aircraft that moved (with translation and combined rotation) over a city via a predetermined trajectory. In the experiment, cybersickness and the physiological responses of participants were measured. Cybersickness was assessed by the Simulator Sickness Questionnaire (SSQ). The measured physiological responses were heart rate, blood pressure, body temperature, and cortisol level.Our measurement confirmed that all SSQ scores increased significantly (all Ps<.05) when participants experienced cybersickness. Heart rate and cortisol level increased significantly (P=.01 and P=.001, respectively). Body temperature also increased, but there was no statistically significant difference (P=.02). Systolic blood pressure and diastolic blood pressure decreased significantly (P=.001).Based on the results of our analysis, the following conclusions were drawn: (1) cybersickness causes significant disorientation, and research on this topic should focus on factors that affect disorientation; and (2) the physiological responses that are suitable for measuring cybersickness are heart rate and cortisol level.
Underwater Image Enhancement Based on Color Correction and Detail Enhancement

To solve the problems of underwater image color deviation, low contrast, and blurred details, an algorithm based on color correction and detail enhancement is proposed. First, the improved nonlocal means denoising algorithm is used to denoise the underwater image. The combination of Gaussian weighted spatial distance and Gaussian weighted Euclidean distance is used as the index of nonlocal means denoising algorithm to measure the similarity of structural blocks. The improved algorithm can retain more edge features and texture information while maintaining noise reduction ability. Then, the improved U-Net is used for color correction. Introducing residual structure and attention mechanism into U-Net can effectively enhance feature extraction ability and prevent network degradation. Finally, a sharpening algorithm based on maximum a posteriori is proposed to enhance the image after color correction, which can increase the detailed information of the image without expanding the noise. The experimental results show that the proposed algorithm has a remarkable effect on underwater image enhancement.
Simple, Reliable, and Noise-Resilient Continuous-Variable Quantum State Tomography with Convex Optimization

Precise reconstruction of unknown quantum states from measurement data, a process commonly called quantum state tomography, is a crucial component in the development of quantum information processing technologies. Many different tomography methods have been proposed over the years. Maximum-likelihood estimation is a prominent example, being the most popular method for a long period of time. Recently, more advanced neural-network methods have started to emerge. Here, we go back to basics and present a method for continuous-variable state reconstruction that is both conceptually and practically simple, based on convex optimization. Convex optimization has been used for process tomography and qubit-state tomography, but seems to have been overlooked for continuous-variable quantum-state tomography. We demonstrate high-fidelity reconstruction of an underlying state from data corrupted by thermal noise and imperfect detection, for both homodyne and heterodyne measurements. A major advantage over other methods is that convex optimization algorithms are guaranteed to converge to the optimal solution.
Loose Ends: A Mixed-Initiative Creative Interface for Playful Storytelling

We present Loose Ends, a mixed-initiative co-creative storytelling play experience in which a human player and an AI system work together to compose a story. Loose Ends specifically aims to provide computational support for managing multiple parallel plot threads and bringing these threads to satisfying conclusions—something that has proven difficult in past attempts to facilitate playful mixed-initiative storytelling. We describe the overall human-AI interaction loop in Loose Ends, including the implementation of the rules-based AI system that enables this interaction loop; discuss four examples of desirable mixed-initiative interactions that are possible in Loose Ends, but not in similar systems; and present results from a preliminary expert evaluation of Loose Ends. Altogether, we find that Loose Ends shows promise for creating a sense of coauthorship in the player while also mitigating the directionlessness reported by players of earlier systems.
Blockchain-Based Multiple Authorities Attribute-Based Encryption for EHR Access Control Scheme

The Internet of Medical Things (IOMT) is critical in improving electronic device precision, dependability, and productivity. Researchers are driving the development of digital healthcare systems by connecting available medical resources and healthcare services. However, there are concerns about the security of sharing patients’ electronic health records. In response to the prevailing problems such as difficulties in sharing medical records between different hospitals and patients’ inability to grasp the usage of their medical records, we propose a patient-controlled and cloud-chain collaborative multi-authority attribute-based encryption for EHR sharing with verifiable outsourcing decryption and hiding access policies (VO-PH-MAABE). This scheme uses blockchain to store the validation parameters by utilizing its immutable, which data users use to verify the correctness of third-party outsourcing decryption results. In addition, we use policy-hiding technology to protect data privacy so that data security is guaranteed. Moreover, we use blockchain technology to establish trust among multiple authorities and utilize Shamir secret sharing and smart contracts to compute keys or tokens for attributes managed across multiple administrative domains, which avoids a single point of failure and reduces communication and computation overhead on the data user side. Finally, the ciphertext indistinguishability security under the chosen plaintext attack is demonstrated under the stochastic prediction model and compared with other schemes in terms of functionality, communication overhead, and computation overhead. The experimental results show the effectiveness of this scheme.
An analysis of the effects of limited training data in distributed learning scenarios for brain age prediction

Abstract Objective Distributed learning avoids problems associated with central data collection by training models locally at each site. This can be achieved by federated learning (FL) aggregating multiple models that were trained in parallel or training a single model visiting sites sequentially, the traveling model (TM). While both approaches have been applied to medical imaging tasks, their performance in limited local data scenarios remains unknown. In this study, we specifically analyze FL and TM performances when very small sample sizes are available per site. Materials and Methods 2025 T1-weighted magnetic resonance imaging scans were used to investigate the effect of sample sizes on FL and TM for brain age prediction. We evaluated models across 18 scenarios varying the number of samples per site (1, 2, 5, 10, and 20) and the number of training rounds (20, 40, and 200). Results Our results demonstrate that the TM outperforms FL, for every sample size examined. In the extreme case when each site provided only one sample, FL achieved a mean absolute error (MAE) of 18.9 ± 0.13 years, while the TM achieved a MAE of 6.21 ± 0.50 years, comparable to central learning (MAE = 5.99 years). Discussion Although FL is more commonly used, our study demonstrates that TM is the best implementation for small sample sizes. Conclusion The TM offers new opportunities to apply machine learning models in rare diseases and pediatric research but also allows even small hospitals to contribute small datasets.
Use of augmented reality in chromatography learning: How is this dynamic visual artifact fostering the visualization capacities of chemistry undergraduate students?

Developing chemistry students' capability to use representations to explain phenomena is a challenging task for educators. To overcome chemistry students' learning difficulties, strategies that favor the development of visualization capabilities have been identified as productive. We are particularly interested in exploring the use of augmented reality in chemistry education to foster the development of those capabilities in undergraduate students. Our research objective was to analyze the contribution of augmented reality to support undergraduate chemistry students' visualization capacities while explaining the physical-kinetic processes of chromatography. Using an exploratory case study approach, we designed and conducted four task teaching and learning sequences, with seven Augmented Reality markers embedded. Thirty-eight undergraduate students, who voluntarily agreed to participate, explained the different elution rates of pigments in a chromatographic column. Their written accounts were analyzed to identify the level of sophistication of their representations. After using Augmented Reality, students' representations progressed from simple macroscopic descriptions of observed phenomena to explanations of processes where scientific ideas and microscopic representations were used as supporting evidence. Our study shows that the use of Augmented Reality has the potential to favor a more sophisticated use of representations when undergraduate students explain chromatographic processes. However, there are still limitations in reaching the highest levels of performance described in the literature.
A Robust Learning Algorithm Based on Particle Swarm Optimization for Pi-Sigma Artificial Neural Networks

Artificial neural networks (ANNs) have been frequently used in forecasting problems in recent years. One of the most popular types of ANNs in these days is Pi-Sigma artificial neural networks (PS-ANNs). PS-ANNs have a high order ANN structure and they use both multiplicative and additive neuron models in their architecture. PS-ANNs produce superior forecasting performance because of their high order structure. PS-ANNs are affected negatively by an outlier or outliers in a data set because of having a multiplicative neuron model in their architecture. In this study, a new robust learning algorithm based on particle swarm optimization and Huber's loss function for PS-ANNs is proposed. To evaluate the performance of the proposed method, Dow Jones stock exchange and Australian beer consumption data sets are analyzed and the obtained results are compared with many ANNs types proposed in the literature. Besides, the performance of the proposed method in outlier cases is also investigated by injecting outliers into these data sets. It is seen that the proposed learning algorithm has a satisfying performance both the data have an outlier or outliers' case and original case.
Temporal Feature Learning and Pulse Prediction for Radars with Variable Parameters

Many modern radars use variable pulse repetition intervals (PRI) to improve anti-reconnaissance and anti-jamming performance. Their PRI features are probably software-defined, but the PRI values at different time instants are variable. Previous statistical pattern analyzing methods are unable to extract such undetermined PRI values and features, which greatly increases the difficulty of Electronic Support Measures (ESM) against such radars. In this communication, we first establish a model to describe the temporal patterns of software-defined radar pulse trains, then introduce the recurrent neural network (RNN) to mine high-order relationships between successive pulses, and finally exploit the temporal features to predict the time of arrival of upcoming pulses. In the simulation part, we compare different time series prediction models to verify the RNN’s adaptability for pulse sequences of variable parameter radars. Moreover, behaviors of different RNN units in this task are compared, and the results show that the proposed method can learn complex PRI features in pulse trains even in the presence of significant data noises and agile PRIs.
An IoT Platform for the Development of Low-cost Emergencies Detection Units based on Soft Sensors

For centuries, emergencies have been a recurring situation in cities. Natural or human-induced emergency situations can cause infrastructural damages in cities, potentially resulting in injuries, death, and economic losses. In this context, the use of Internet of Things technologies can support more efficient detection of emergencies such as floods, landslides, fire, and terrorism, being possible to alert people and trigger mitigation actions on time. Therefore, this work proposes an IoT platform for the development of sensors-based emergencies detection units, comprising a hardware architecture and a Real Time Operating System based firmware. By combining the concept of Soft Sensors with the parallelism of tasks on multi-core microcontrolers, as well as taking into consideration low-cost and energy efficient demands, it is expected an improvement in the development of such units in smart cities.
Evaluating geostatistical methods along with semi-destructive analysis for forensic provenancing organic-rich soils in humid subtropical climate

When no samples are available for direct comparisons during a criminal investigation, forensic scientists must resort to georeferenced soil databases in order to find the source of a single questioned evidence. To this end, several authors addressed many methods to infer the origin of soil samples, such as establishing search range intervals or defining statistical similarities. However, little is currently known about the efficiency of these methods when it comes to organic-rich and deep weathered subtropical soils. Therefore, this study attempts to contribute to this subject by evaluating the predictability of a soil database built in a 100 km² area in the Curitiba Metropolitan Region, Brazil, where 232 topsoil (<5 cm), 14 validation, and 38 subsoil samples (B horizon) were collected. To determine their physical, chemical, and colorimetric features, samples were subjected to magnetic susceptibility analysis, gamma-ray spectrometry, color analysis, pXRF, and ATR-FTIR. Two prediction methods were selected: search range intervals (SR) and n-dimensional Euclidean distances (ED). After the descriptive and multivariate statistical analyses, geostatistical models were generated for the 28 obtained variables using the empirical Bayesian kriging (EBK) with regression prediction method, where geological and airborne geophysical data were used as explanatory matrix. For the ED provenancing method, dissimilarity values were calculated and interpolated by applying the inverse distance weighting (IDW) approach. Findings, while preliminary, suggest that magnetic susceptibility, lightness, Fe, Mn, Cu, Ni, and the light elements group (LE) may be the most significant variables to discriminate parent rock and to correlate with subsurface data. The SR method managed to correctly predict the origin of 4 of 14 validation samples, reducing up to 97.5% of the original area. The ED method, on the other hand, accurately estimated the provenance of 8 out of 14 samples, excluding up to 63.4% of the area. Despite the limitations and errors inherent to semi-destructive analyses, the moderate chemical weathering, and the high organic matter content, overall soil provenancing results were suitable, demonstrating that spatial variability of topsoils developed under humid subtropical environments can be modeled and used in forensic contexts, as long as the right methods are applied.
A collaborative semantic framework based on activities for the development of applications in Smart Home living labs

An essential element to improve the quality of life at home is to understand the behavior of its inhabitants and the activities they perform on a daily basis. The collection and analysis of this information in Smart Home environments poses several challenges, mainly data variability, and the management and synchronization of devices, which have limited the adoption of this kind of solutions. Researchers tend to work focused on providing solutions to specific issues and, in research environments that share infrastructure, such as living labs, it is common that previous developments of other teams are not reused. This process is suboptimal and does not allow understanding the Smart Home as a single space, but as a set of functional islands. To overcome these obstacles, a novel collaborative semantic framework is proposed for the extraction, characterization, and formalization of data to enable its reutilization and interpretation. The framework is based on the concept of activity, decomposed as a series of actions and events, and follows current trends in the domain of Activities of Daily Living and Smart Homes. A set of interoperability enablers based on JSON schemas and formalized descriptions of the domain have been proposed as the main facilitators of collaboration and the integration of elements coexisting in the Smart Home environment. The evaluation of the framework through the development of two research projects showed significant benefits, improving the overall quality of the solutions and enriching the extraction of insights through the correlated analysis of data.
A Brief Review on Mathematical Tools Applicable to Quantum Computing for Modelling and Optimization Problems in Engineering

Since its emergence, quantum computing has enabled a wide spectrum of new possibilities and advantages, including its efficiency in accelerating computational processes exponentially. This has directed much research towards completely novel ways of solving a wide variety of engineering problems, especially through describing quantum versions of many mathematical tools such as Fourier and Laplace transforms, differential equations, systems of linear equations, and optimization techniques, among others. Exploration and development in this direction will revolutionize the world of engineering. In this manuscript, we review the state of the art of these emerging techniques from the perspective of quantum computer development and performance optimization, with a focus on the most common mathematical tools that support engineering applications. This review focuses on the application of these mathematical tools to quantum computer development and performance improvement/optimization. It also identifies the challenges and limitations related to the exploitation of quantum computing and outlines the main opportunities for future contributions. This review aims at offering a valuable reference for researchers in fields of engineering that are likely to turn to quantum computing for solutions. Doi: 10.28991/ESJ-2023-07-01-020 Full Text: PDF
Enhancing the 4Cs among college students of a communication skills course in Tanzania through a project-based learning model

The development of technology has changed the way people communicate in academic contexts as well as working places, for example from print messages to screened messages, and from face-to-face classroom and office meetings to virtual classes and offices. This has prompted the shift from traditional teaching practices to student-centered in which students are guided by their teachers to develop skills for 21st Century careers through the Project-Based Learning model. However, in Tanzania colleges, the teaching of a Communication Skills Course perpetuates traditional teaching practices, which could reduce the chance for students to participate in global democratic activities. Consequently, the present study aimed at exploring how to enhance the 4 C's (critical thinking, communication, collaboration, and creativity) among the college students of the Communication Skills Course in Tanzania through the Project-Based Learning model. Qualitative participatory action research with two cycles was adopted. Data from ten students, one teacher, and the curriculum were collected through observation, document analysis, and reflective journals. Data were analyzed thematically. Findings show that current teaching practices for Communication Skills Course in Tanzania colleges hardly enhance the development of the 4 C's among students. It was also observed that the implementation of PBL resulted in enhancing the 4 C's among college students in Tanzania colleges. However, large class sizes, poor technological infrastructure, and lack of facilities are among the challenges experienced during the implementation of PBL in enhancing the 4Cs. The study recommends the reformation of current teaching practices, to focus on developing skills needed to cope with students' real-life contexts, instead of focusing on content knowledge. Also, the study calls for the adoption of PBL in Tanzania colleges in teaching Communication Skills to develop necessary skills for the 21st Century. To cope with any potential challenges, the study suggests that there should be collaborative efforts among education stakeholders.