Reviewerly/exHarmony huggingface dataset:
What do university students think about the metaverse?

Metaverse is a new concept which is frequently discussed in many fields such as health, art, sports, entertainment, education, trade, and marketing. The scope and actuality of this concept, which expresses a new digital world beyond the universe, necessitated this study. In this study; gender, age, duration of social media usage and levels of metaverse knowledge, awareness and attitude of university students have been tried to be analyzed statistically in terms of variables such as playing digital games, using e-commerce, communicating with other individuals in virtual channels and the availability of crypto money. The survey model, one of the quantitative research methods, was preferred in the study. Independent Sample T Test and Pearson Correlation tests were used to analyze data with a normal distribution. The universe of the research includes students studying at Ağrı İbrahim Çeçen University in the 2021-2022 academic year, and the sample of the research includes 271 students (122 females and 149 males), studying in different departments of the same university. Apart from the personal information form, the 'Metaverse Scale’ developed by Süleymanoğulları et al. was chosen as a data collection tool. According to the results, male participants' metaverse knowledge, attitude, and awareness levels are higher than females in the sub-dimensions of digitalization and lifestyle. While a positive and weak relationship was found between the participants’ average daily social media usage time and the technology sub-dimension average score, the analysis results indicate that the levels of the participants who engage in e-commerce in the technology, digitalization, and lifestyle sub-dimensions are higher than the others. Finally, it has been found that the metaverse knowledge, attitude and awareness levels of the students with crypto money in the sub-dimensions of technology, digitalization and lifestyle are higher than those in the other group. According to the results of this study, it can be said that the metaverse attitudes, awareness and knowledge levels of the university students who constitute the research sample group differ depending on sociocultural and socioeconomic factors.
RemoteLab: A VR Remote Study Toolkit

User studies play a critical role in human subject research, including human-computer interaction. Virtual reality (VR) researchers tend to conduct user studies in-person at their laboratory, where participants experiment with novel equipment to complete tasks in a simulated environment, which is often new to many. However, due to social distancing requirements in recent years, VR research has been disrupted by preventing participants from attending in-person laboratory studies. On the other hand, affordable head-mounted displays are becoming common, enabling access to VR experiences and interactions outside traditional research settings. Recent research has shown that unsupervised remote user studies can yield reliable results, however, the setup of experiment software designed for remote studies can be technically complex and convoluted. We present a novel open-source Unity toolkit, RemoteLab, designed to facilitate the preparation of remote experiments by providing a set of tools that synchronize experiment state across multiple computers, record and collect data from various multimedia sources, and replay the accumulated data for analysis. This toolkit facilitates VR researchers to conduct remote experiments when in-person experiments are not feasible or increase the sampling variety of a target population and reach participants that otherwise would not be able to attend in-person.
An Enhanced Architecture to Resolve Public-Key Cryptographic Issues in the Internet of Things (IoT), Employing Quantum Computing Supremacy

The Internet of Things (IoT) strongly influences the world economy; this emphasizes the importance of securing all four aspects of the IoT model: sensors, networks, cloud, and applications. Considering the significant value of public-key cryptography threats on IoT system confidentiality, it is vital to secure it. One of the potential candidates to assist in securing public key cryptography in IoT is quantum computing. Although the notion of IoT and quantum computing convergence is not new, it has been referenced in various works of literature and covered by many scholars. Quantum computing eliminates most of the challenges in IoT. This research provides a comprehensive introduction to the Internet of Things and quantum computing before moving on to public-key cryptography difficulties that may be encountered across the convergence of quantum computing and IoT. An enhanced architecture is then proposed for resolving these public-key cryptography challenges using SimuloQron to implement the BB84 protocol for quantum key distribution (QKD) and one-time pad (OTP). The proposed model prevents eavesdroppers from performing destructive operations in the communication channel and cyber side by preserving its state and protecting the public key using quantum cryptography and the BB84 protocol. A modified version is introduced for this IoT situation. A traditional cryptographic mechanism called "one-time pad" (OTP) is employed in hybrid management.
Fall Detection System Based on Simple Threshold Method and Long Short-Term Memory: Comparison with Hidden Markov Model and Extraction of Optimal Parameters

In an aging global society, a few complex problems have been occurring due to falls among the increasing elderly population. Therefore, falls are detected using a pendant-type sensor that can be worn comfortably for fall detection. The sensed data are processed by the embedded environment and classified by a long-term memory (LSTM). A fall detection system that combines a simple threshold method (STM) and LSTM, the STM-LSTM-based fall detection system, is introduced. In terms of training data accuracy, the proposed STM-LSTM-based fall detection system is compared with the previously reported STM-hidden Markov model (HMM)-based fall detection system. The training accuracy of the STM-LSTM fall detection system is 100%, while the highest training accuracy by the STM-HMM-based one is 99.5%, which is 0.5% less than the best of the STM-LSTM-based system. In addition, in the optimized LSTM fall detection system, this may be overfitted because all data are trained without separating any validation data. In order to resolve the possible overfitting issue, training and validation data are evaluated separately in 4:1, and then in terms of validation data accuracy of the STM-LSTM-based fall detection system, optimal values of the parameters in LSTM and normalization method are found as follows: best accuracy of 98.21% at no-normalization, no-sampling, 128hidden layer nodes, and regularization rate of 0.015. It is also observed that as the number of hidden layer nodes or sampling interval increases, the regularization rate at the highest value of accuracy increases. This means that overfitting can be suppressed by increasing the regularization, and thus an appropriate number of hidden layer nodes and a regularization rate must be selected to improve the fall detection efficiency.
Coding with Block Programming Languages in Educational Robotics and Mobiles, Improve Problem Solving, Creativity &amp; Critical Thinking Skills

The purpose of the article is to highlight how students' computational thinking, which is a critical thinking skill, can be developed through educational robotics and programming. It is a fun and engaging learning activity that encourages students to collaborate, delve into a problem, construct knowledge, and cultivate critical thinking. Educational robotics is therefore an innovative teaching tool, which contributes to the implementation of the above goal and follows the principles of building, specifically the construction of knowledge (Mikropoulos &amp; Bellou, 2010). It develops students' critical thinking, strengthens their mental models, and activates known learning mechanisms leading them to a deeper level of understanding and assimilation of knowledge, which cannot be accomplished with traditional teaching methodologies. It has also been proven to be able to help students solve complex problems as well as contribute to the development of computational thinking skills (Atmatzidou &amp; Demetriadis, 2014), which should characterize the entire literate population and complement the other three basic reading skills, writing, and mathematics (Mavroudis, Petrou &amp; Fesakis, 2014).
Beyond Seasickness: A Motivated Call for a New Motion Sickness Standard across Motion Environments

Motion sickness is known under several names in different domains, such as seasickness, carsickness, cybersickness, and simulator sickness. As we will argue, these can all be considered manifestations of one common underlying mechanism. In recent years, it has received renewed interest, largely due to the advent of automated vehicles and developments in virtual reality, in particular using head-mounted displays. Currently, the most widely accepted standard to predict motion sickness is ISO 2631-1 (1997), which is based on studies on seasickness and has limited applicability to these newer domains. Therefore, this paper argues for extending the ISO standard to cover all forms of motion sickness, to incorporate factors affecting motion sickness, and to consider various degrees of severity of motion sickness rather than just emesis. This requires a dedicated standard, separate from other effects of whole-body vibration as described in the current ISO 2631-1. To that end, we first provide a sketch of the historical origins of the ISO 2631-1 standard regarding motion sickness and discuss the evidence for a common mechanism underlying various forms of motion sickness. After discussing some methodological issues concerning the measurement of motion sickness, we outline the main knowledge gaps that require further research.
IoT-Chain and Monitoring-Chain Using Multilevel Blockchain for IoT Security

In general, the Internet of Things (IoT) relies on centralized servers due to limited computing power and storage capacity. These server-based architectures have vulnerabilities such as DDoS attacks, single-point errors, and data forgery, and cannot guarantee stability and reliability. Blockchain technology can guarantee reliability and stability with a P2P network-based consensus algorithm and distributed ledger technology. However, it requires the high storage capacity of the existing blockchain and the computational power of the consensus algorithm. Therefore, blockchain nodes for IoT data management are maintained through an external cloud, an edge node. As a result, the vulnerability of the existing centralized structure cannot be guaranteed, and reliability cannot be guaranteed in the process of storing IoT data on the blockchain. In this paper, we propose a multi-level blockchain structure and consensus algorithm to solve the vulnerability. A multi-level blockchain operates on IoT devices, and there is an IoT chain layer that stores sensor data to ensure reliability. In addition, there is a hyperledger fabric-based monitoring chain layer that operates the access control for the metadata and data of the IoT chain to lighten the weight. We propose an export consensus method between the two blockchains, the Schnorr signature method, and a random-based lightweight consensus algorithm within the IoT-Chain. Experiments to measure the blockchain size, propagation time, consensus delay time, and transactions per second (TPS) were conducted using IoT. The blockchain did not exceed a certain size, and the delay time was reduced by 96% to 99% on average compared to the existing consensus algorithm. In the throughput tests, the maximum was 1701 TPS and the minimum was 1024 TPS.
Strong–weak duality via Jordan–Wigner transformation: Using fermionic methods for strongly correlated <i>su</i>(2) spin systems

The Jordan-Wigner transformation establishes a duality between su(2) and fermionic algebras. We present qualitative arguments and numerical evidence that when mapping spins to fermions, the transformation makes strong correlation weaker, as demonstrated by the Hartree-Fock approximation to the transformed Hamiltonian. This result can be rationalized in terms of rank reduction of spin shift terms when transformed to fermions. Conversely, the mapping of fermions to qubits makes strong correlation stronger, complicating its solution when one uses qubit-based correlators. The presence of string operators poses challenges to the implementation of quantum chemistry methods on classical computers, but these can be dealt with using established techniques of low computational cost. Our proof of principle results for XXZ and J1-J2 Heisenberg (in 1D and 2D) indicates that the JW transformed fermionic Hamiltonian has reduced complexity in key regions of their phase diagrams and provides a better starting point for addressing challenging spin problems.
Earthquake Location and Magnitude Estimation with Graph Neural Networks

We solve the traditional problems of earthquake location and magnitude estimation through a supervised learning approach, where we train a Graph Neural Network to predict estimates directly from input pick data, and each input allows a distinct seismic network with variable number of stations and positions. We train the model using synthetic simulations from assumed travel-time and amplitude-distance attenuation models. The architecture uses one graph to represent the station set, and another to represent the model space. The input includes theoretical predictions of data, given model parameters, and the adjacency matrices of the graphs defined link spatially local elements. As we show, graph convolutions on this combined representation are highly effective at inference, data fusion, and outlier suppression. We compare our results with traditional methods and observe favorable performance.
Optimizing counterdiabaticity by variational quantum circuits

Using counterdiabatic (CD) driving-aiming at suppression of diabatic transition-in digitized adiabatic evolution has garnered immense interest in quantum protocols and algorithms. However, improving the approximate CD terms with a nested commutator ansatz is a challenging task. In this work, we propose a technique of finding optimal coefficients of the CD terms using a variational quantum circuit. By classical optimization routines, the parameters of this circuit are optimized to provide the coefficients corresponding to the CD terms. Then their improved performance is exemplified in Greenberger-Horne-Zeilinger state preparation on the nearest-neighbour Ising model. Finally, we also show the advantage over the usual quantum approximation optimization algorithm, in terms of fidelity with bounded time. This article is part of the theme issue 'Shortcuts to adiabaticity: theoretical, experimental and interdisciplinary perspectives'.
Unsupervised Image-to-Image Translation: A Review

Supervised image-to-image translation has been proven to generate realistic images with sharp details and to have good quantitative performance. Such methods are trained on a paired dataset, where an image from the source domain already has a corresponding translated image in the target domain. However, this paired dataset requirement imposes a huge practical constraint, requires domain knowledge or is even impossible to obtain in certain cases. Due to these problems, unsupervised image-to-image translation has been proposed, which does not require domain expertise and can take advantage of a large unlabeled dataset. Although such models perform well, they are hard to train due to the major constraints induced in their loss functions, which make training unstable. Since CycleGAN has been released, numerous methods have been proposed which try to address various problems from different perspectives. In this review, we firstly describe the general image-to-image translation framework and discuss the datasets and metrics involved in the topic. Furthermore, we revise the current state-of-the-art with a classification of existing works. This part is followed by a small quantitative evaluation, for which results were taken from papers.
Secular variability in zircon phosphorus concentrations prevents simple petrogenetic classification

Phosphorus (P) concentrations in zircon have been used to discriminate their derivation from metaluminous versus strongly peraluminous granites (SPGs) based on the empirical observation of lower P concentrations in zircon from Phanerozoic metaluminous versus peraluminous granites.Higher P concentrations in zircon from Phanerozoic SPGs reflect enhanced apatite solubility in peraluminous melts and overall higher P concentrations in peraluminous granites.However, SPGs derived from partial melting of Precambrian sedimentary rocks have lower P concentrations compared to Phanerozoic metaluminous granites, reflecting lower P concentrations in Precambrian versus Phanerozoic sedimentary sources.We demonstrate that zircons from Precambrian SPGs also have lower P concentrations compared to Phanerozoic counterparts, likely reflecting lower P concentrations in their parental melts.Applying the P-in-zircon proxy to the detrital zircon record does not effectively discriminate between metaluminous and peraluminous sources and underestimates contributions from peraluminous granites.Although detrital zircons are an important early Earth archive, a uniformitarian perspective cannot always be applied when using trace element proxies developed on Phanerozoic samples.
Implementation of the internet of things on smart posters using near field communication technology in the tourism sector

Tourism promotion in Pekanbaru is one step in increasing the number of tourists visiting Pekanbaru City. Through tourism promotion, tourists will find out where the locations are in Pekanbaru and information related to these tourist objects. This research aims to design a tourism promotion system using near-field communication (NFC) smart posters using smartphones in the city of Pekanbaru and apply NFC technology to Android smartphones in the city of Pekanbaru. Promote tourism in the city of Pekanbaru. They were testing this application with the System Usability Score, which had a good score of 74.30. This study shows that the planning and modeling of the smart poster system using NFC technology makes it easier to identify important information for every tourism activity in Pekanbaru. The results of this study are the design and product of an intelligent poster using NFC on an Android smartphone that can help users achieve information so that it is more effective and efficient.
Indonesian Hoax News Classification with Multilingual Transformer Model and BERTopic

Technology and information growth make all internet users can play a role in disseminating information, including hoax news. One way that can be done to avoid hoax news is to look for sources of information, but valid news is not always perceived as 'true' by individuals because human judgments can lead to bias. Several studies on automatic hoax news classification have been carried out using various deep learning approaches such as the pre-trained multilingual transformer model. This study focuses on classifying Indonesian hoax news using the pre-trained transformer multilingual model (XLM-R and mBERT) combined with a BERTopic model as a topic distribution model. The result shows that the proposed method outperforms the baseline model in classifying fake news in the low-resource language (Indonesian) with accuracy, precision, recall, and F1 results of 0.9051, 0.9515, 0.8233, and 0.8828 respectively.
Presence in video games: A systematic review and meta-analysis of the effects of game design choices

Sense of presence is a widely assessed dimension of video game player experience. A systematic literature review and a meta-analysis were conducted to provide a more comprehensive view of the elements of game design that have an effect on the sense of presence, as well as its different dimensions studied and assessment techniques. The review revealed that many major categories of game design aspects were well represented. The meta-analysis revealed that several game design factors have significant effects on different dimensions of presence. The largest revealed effects were that playing games with a head-mounted display and motion controller rather than a monitor display and non-motion controller has a large effect on global presence. Also, playing with human co-players rather than computer-controlled co-players and playing cooperatively rather than competitively have a very large and large effect on social presence, respectively. Implications for future research are discussed, such as investigating the effects of design factors on presence in a more targeted manner, systematically assessing presence with its most relevant sub-dimensions, and using more similar rating scales. Design recommendations, with their expected impact on players' sense of presence, are proposed.
Solar Irradiance Probabilistic Forecasting Using Machine Learning, Metaheuristic Models and Numerical Weather Predictions

Solar-power-generation forecasting tools are essential for microgrid stability, operation, and planning. The prediction of solar irradiance (SI) usually relies on the time series of SI and other meteorological data. In this study, the considered microgrid was a combined cold- and power-generation system, located in Tahiti. Point forecasts were obtained using a particle swarm optimization (PSO) algorithm combined with three stand-alone models: XGboost (PSO-XGboost), the long short-term memory neural network (PSO-LSTM), and the gradient boosting regression algorithm (PSO-GBRT). The implemented daily SI forecasts relied on an hourly time-step. The input data were composed of outputs from the numerical forecasting model AROME (Météo France) combined with historical meteorological data. Our three hybrid models were compared with other stand-alone models, namely, artificial neural network (ANN), convolutional neural network (CNN), random forest (RF), LSTM, GBRT, and XGboost. The probabilistic forecasts were obtained by mapping the quantiles of the hourly residuals, which enabled the computation of 38%, 68%, 95%, and 99% prediction intervals (PIs). The experimental results showed that PSO-LSTM had the best accuracy for day-ahead solar irradiance forecasting compared with the other benchmark models, through overall deterministic and probabilistic metrics.
PAL: Program-aided Language Models

Large language models (LLMs) have recently demonstrated an impressive ability to perform arithmetic and symbolic reasoning tasks, when provided with a few examples at test time ("few-shot prompting"). Much of this success can be attributed to prompting methods such as "chain-of-thought'', which employ LLMs for both understanding the problem description by decomposing it into steps, as well as solving each step of the problem. While LLMs seem to be adept at this sort of step-by-step decomposition, LLMs often make logical and arithmetic mistakes in the solution part, even when the problem is decomposed correctly. In this paper, we present Program-Aided Language models (PAL): a novel approach that uses the LLM to read natural language problems and generate programs as the intermediate reasoning steps, but offloads the solution step to a runtime such as a Python interpreter. With PAL, decomposing the natural language problem into runnable steps remains the only learning task for the LLM, while solving is delegated to the interpreter. We demonstrate this synergy between a neural LLM and a symbolic interpreter across 13 mathematical, symbolic, and algorithmic reasoning tasks from BIG-Bench Hard and other benchmarks. In all these natural language reasoning tasks, generating code using an LLM and reasoning using a Python interpreter leads to more accurate results than much larger models. For example, PAL using Codex achieves state-of-the-art few-shot accuracy on the GSM8K benchmark of math word problems, surpassing PaLM-540B which uses chain-of-thought by absolute 15% top-1. Our code and data are publicly available at http://reasonwithpal.com/ .