Reviewerly/exHarmony huggingface dataset:
Predictors to Use Mobile Apps for Monitoring COVID-19 Symptoms and Contact Tracing: Survey Among Dutch Citizens

eHealth apps have been recognized as a valuable tool to reduce COVID-19's effective reproduction number. The factors that determine the acceptance of COVID-19 apps remain unknown. The exception here is privacy.The aim of this article was to identify antecedents of acceptance of (1) a mobile app for COVID-19 symptom recognition and monitoring and (2) a mobile app for contact tracing, both by means of an online survey among Dutch citizens.Next to the demographics, the online survey contained questions focusing on perceived health, fear of COVID-19, and intention to use. We used snowball sampling via posts on social media and personal connections. To identify antecedents of the model for acceptance of the 2 mobile apps, we conducted multiple linear regression analyses.In total, 238 Dutch adults completed the survey; 59.2% (n=141) of the responders were female and the average age was 45.6 years (SD 17.4 years). For the symptom app, the final model included the predictors age, attitude toward technology, and fear of COVID-19. The model had an r2 of 0.141. The final model for the tracing app included the same predictors and had an r2 of 0.156. The main reason to use both mobile apps was to control the spread of the COVID-19 virus. Concerns about privacy was mentioned as the main reason to not use the mobile apps.Age, attitude toward technology, and fear of COVID-19 are important predictors of the acceptance of COVID-19 mobile apps for symptom recognition and monitoring and for contact tracing. These predictors should be taken into account during the development and implementation of these mobile apps to secure acceptance.
A Novel Sparrow Search Algorithm for the Traveling Salesman Problem

The sparrow search algorithm (SSA) tends to fall into local optima and to have insufficient stagnation when applied to the traveling salesman problem (TSP). To address this issue, we propose a novel greedy genetic sparrow search algorithm based on a sine and cosine search strategy (GGSC-SSA). First, the greedy algorithm is introduced to initialize the population and to increase the diversity of the population. Second, genetic operators are used to update the population, balancing global search and local development capabilities. Finally, the adaptive weight is introduced in the producer update to increase the adaptability of the algorithm and to optimize the quality of the solution, and a sin-cosine search strategy is introduced to update the scroungers. In addition, the GGSC-SSA is compared with the genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO), grey wolf optimization (GWO), ant colony optimization (ACO) and the artificial fish (AF) algorithm on TSP datasets for performance testing. We also compare it with some recently improved algorithms. The results of the simulations are encouraging; the GGSC-SSA significantly enhances the solution precision, optimization speed and robustness.
Natural Language to Visualization by Neural Machine Translation

Supporting the translation from natural language (NL) query to visualization (NL2VIS) can simplify the creation of data visualizations because if successful, anyone can generate visualizations by their natural language from the tabular data. The state-of-the-art NL2VIS approaches (e.g., NL4DV and FlowSense) are based on semantic parsers and heuristic algorithms, which are not end-to-end and are not designed for supporting (possibly) complex data transformations. Deep neural network powered neural machine translation models have made great strides in many machine translation tasks, which suggests that they might be viable for NL2VIS as well. In this paper, we present ncNet, a Transformer-based sequence-to-sequence model for supporting NL2VIS, with several novel visualization-aware optimizations, including using attention-forcing to optimize the learning process, and visualization-aware rendering to produce better visualization results. To enhance the capability of machine to comprehend natural language queries, ncNet is also designed to take an optional chart template (e.g., a pie chart or a scatter plot) as an additional input, where the chart template will be served as a constraint to limit what could be visualized. We conducted both quantitative evaluation and user study, showing that ncNet achieves good accuracy in the nvBench benchmark and is easy-to-use.
Short term solar irradiance forecasting using sky images based on a hybrid CNN–MLP model

High penetration of photovoltaics (PV) has been observed in the energy market over the last decade. However, its integration into electrical grids is challenging, as solar energy is highly fluctuating given its dependence on different weather variables. Consequently, short-term forecasting of solar irradiance provides a pivotal solution to ensure optimal use of the produced energy and reduce its uncertainty. This study proposes a hybrid convolutional neural network and Multilayer perceptron (CNN–MLP) model to forecast the global irradiance 15 min ahead. The model uses images from a hemispherical sky imager, time series of GHI, and weather variables collected from a ground meteorological station in Morocco. The evaluation of the proposed model under clear, mixed, and overcast days shows that the proposed model performs better than the persistence model. The root mean square error (RMSE) varies between 13.05 W/m2 and 49.16 W/m2 for CNN–MLP and between 45.76 W/m2 and 114.19 W/m2 for persistence. The coefficient of determination (R2) varies between 0.99 and 0.94 for the MLP–CNN and between 0.98 and 0.79 for persistence. The results show that the proposed model could be an appropriate choice for short-term forecasting even under cloudy conditions.
Wireless Edge-Empowered Metaverse: A Learning-Based Incentive Mechanism for Virtual Reality

The Metaverse is regarded as the next-generation Internet paradigm that allows humans to play, work, and socialize in an alternative virtual world with an immersive experience, for instance, via head-mounted displays for Virtual Reality (VR) rendering. With the help of ubiquitous wireless connections and powerful edge computing technologies, VR users in the wireless edge-empowered Metaverse can immerse themselves in the virtual through the access of VR services offered by different providers. However, VR applications are computation- and communication-intensive. The VR service providers (SPs) have to optimize the VR service delivery efficiently and economically given their limited communication and computation resources. An incentive mechanism can be thus applied as an effective tool for managing VR services between providers and users. Therefore, in this paper, we propose a learning-based Incentive Mechanism framework for VR services in the Metaverse. First, we propose the quality of perceptual experience as the metric for VR users immersing in the virtual world. Second, for quick trading of VR services between VR users (i.e., buyers) and VR SPs (i.e., sellers), we design a double Dutch auction mechanism to determine optimal pricing and allocation rules in this market. Third, for auction information exchange cost reduction, we design a deep reinforcement learning-based auctioneer to accelerate this auction process. Experimental results demonstrate that the proposed framework can achieve near-optimal social welfare while reducing at least half of the auction information exchange cost than baseline methods.
A Performance Comparison of Optimization Algorithms on a Generated Dataset

Optimization Algorithms are one of the important machine learning techniques, they play a vital role in improving the performance of the model. In supervised learning, data samples are given, to train a model whose outcomes are already known. The dataset of 5000 samples with 2 features and 4 classes have been generated. We've trained the dataset with different Optimization algorithms such as Gradient descent, Mini Batch gradient descent, Momentum, NAG, RMSprop, Adagrad and Adam. To check the performance, these algorithms have been employed on the proposed model and compared the accuracy obtained by each algorithm. The presented work provides a clear understanding as well as the working of algorithms. The proposed model effectiveness can be seen by comparing different optimization algorithms. The developed model is able to obtain the maximum accuracy of 0.926 on Adam at testing phase with a learning rate of 0.5. The test result shows that Adam performs better than the other algorithms.
Causal inference for time series analysis: problems, methods and evaluation

Time series data are a collection of chronological observations which are generated by several domains such as medical and financial fields. Over the years, different tasks such as classification, forecasting and clustering have been proposed to analyze this type of data. Time series data have been also used to study the effect of interventions overtime. Moreover, in many fields of science, learning the causal structure of dynamic systems and time series data is considered an interesting task which plays an important role in scientific discoveries. Estimating the effect of an intervention and identifying the causal relations from the data can be performed via causal inference. Existing surveys on time series discuss traditional tasks such as classification and forecasting or explain the details of the approaches proposed to solve a specific task. In this paper, we focus on two causal inference tasks, i.e., treatment effect estimation and causal discovery for time series data and provide a comprehensive review of the approaches in each task. Furthermore, we curate a list of commonly used evaluation metrics and datasets for each task and provide an in-depth insight. These metrics and datasets can serve as benchmark for research in the field.
Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective

Federated learning (FL) becomes popular and has shown great potentials in training large-scale machine learning (ML) models without exposing the owners’ raw data. In FL, the data owners can train ML models based on their local data and only send the model updates rather than raw data to the model owner for aggregation. To improve learning performance in terms of model accuracy and training completion time, it is essential to recruit sufficient participants. Meanwhile, the data owners are rational and may be unwilling to participate in the collaborative learning process due to the resource consumption. To address the issues, there have been various works recently proposed to motivate the data owners to contribute their resources. In this paper, we provide a comprehensive review for the economic and game theoretic approaches proposed in the literature to design various schemes for incentivizing data owners to participate in FL training process. In particular, we first present the fundamentals and background of FL, economic theories commonly used in incentive mechanism design. Then, we review applications of game theory and economic approaches applied for incentive mechanisms design of FL. Finally, we highlight some open issues and future research directions concerning incentive mechanism design of FL.
VPFNet: Improving 3D Object Detection With Virtual Point Based LiDAR and Stereo Data Fusion

It has been well recognized that fusing the complementary information from depth-aware LiDAR point clouds and semantic-rich stereo images would benefit 3D object detection. Nevertheless, it is non-trivial to explore the inherently unnatural interaction between sparse 3D points and dense 2D pixels. To ease this difficulty, the recent approaches generally project the 3D points onto the 2D image plane to sample the image data and then aggregate the data at the points. However, these approaches often suffer from the mismatch between the resolution of point clouds and RGB images, leading to sub-optimal performance. Specifically, taking the sparse points as the multi-modal data aggregation locations causes severe information loss for high-resolution images, which in turn undermines the effectiveness of multi-sensor fusion. In this paper, we present <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VPFNet</monospace> —a new architecture that cleverly aligns and aggregates the point cloud and image data at the “virtual” points. Particularly, with their density lying between that of the 3D points and 2D pixels, the virtual points can nicely bridge the resolution gap between the two sensors, and thus preserve more information for processing. Moreover, we also investigate the data augmentation techniques that can be applied to both point clouds and RGB images, as the data augmentation has made non-negligible contribution towards 3D object detectors to date. We have conducted extensive experiments on KITTI dataset, and have observed good performance compared to the state-of-the-art methods. Remarkably, our <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VPFNet</monospace> achieves 83.21% moderate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$AP_{3D}$</tex-math></inline-formula> and 91.86% moderate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$AP_{BEV}$</tex-math></inline-formula> on the KITTI test set. The network design also takes computation efficiency into consideration – we can achieve a FPS of 15 on a single NVIDIA RTX 2080Ti GPU.
A Multi-Strategy Whale Optimization Algorithm and Its Application

Whale Optimization Algorithm (WOA) is a key tool for solving complex engineering optimization problems, aiming at adjusting important parameters to satisfy constraints and optimal objectives. WOA has a simple structure, few parameters, high search capability, and easy implementation. However, it suffers from the same problems as other metaheuristic algorithms of being prone to local optima and slow convergence, for which the Multi-Strategy Whale Optimization Algorithm (MSWOA) is proposed. Four strategies are introduced in MSWOA. Firstly, a highly randomized chaotic logistic map is used to generate a high-quality initial population. Secondly, exploitation and exploration are enhanced by setting adaptive weights and dynamic convergence factors. Further, a Lévy flight mechanism is introduced to maintain the population diversity in each iteration. Finally, the Evolutionary Population Dynamics (EPD) mechanism is introduced to improve the efficiency of search agents in finding the optimum. Another problem lies in the Semi-Supervised Extreme Learning Machine (SSELM) based on manifold regularization is an effective classification and regression model, but the random generation of input weights and hidden layer thresholds and the grid selection of hyperparameters lead to unsatisfactory classification performance. To this end, we developed the MSWOA-SSELM model, optimally selected the parameters of SSLEM using MSWOA, and applied it to logging layer recognition, which effectively improved the accuracy of logging interpretation. By comparing the experiments with 14 swarm intelligence algorithms on 18 benchmark test functions, the CEC2017 benchmark suite, and an engineering application problem, the experimental results show that MSWOA is significantly superior and effective in solving global optimization problems. Finally, the proposed MSWOA-SSELM is applied in three wells and outperforms other classification models in terms of Accuracy (ACC), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). It obtained the best results with 96.2567% ACC, MAE of 0.0749, and RMSE of 0.3870.
Motion sickness: current concepts and management

Motion sickness is an ancient phenomenon that affects many people. Nausea, vomiting, disorientation, sweating, fatigue, and headache are just few of the many signs and symptoms that are commonly experienced during an episode of motion sickness. In the present review, we will provide an overview of the current research trends and topics in the domain of motion sickness, including theoretical considerations, physiological and neural mechanisms, individual risk factors, and treatment options, as well as recommendations for future research directions.More recently, motion sickness has been in the focus of attention in the context of two global technological trends, namely automated vehicles and virtual reality. Both technologies bear the potential to revolutionize our daily lives in many ways; however, motion sickness is considered a serious concern that threatens their success and acceptance. The majority of recent research on motion sickness focuses on one of these two areas.Aside from medication (e.g. antimuscarinics, antihistamines), habituation remains the most effective nonpharmacological method to reduce motion sickness. A variety of novel techniques has been investigated with promising results, but an efficient method to reliably prevent or minimize motion sickness has yet to emerge.
Fluxonium: An Alternative Qubit Platform for High-Fidelity Operations

Superconducting qubits provide a promising path toward building large-scale quantum computers. The simple and robust transmon qubit has been the leading platform, achieving multiple milestones. However, fault-tolerant quantum computing calls for qubit operations at error rates significantly lower than those exhibited in the state of the art. Consequently, alternative superconducting qubits with better error protection have attracted increasing interest. Among them, fluxonium is a particularly promising candidate, featuring large anharmonicity and long coherence times. Here, we engineer a fluxonium-based quantum processor that integrates high qubit coherence, fast frequency tunability, and individual-qubit addressability for reset, readout, and gates. With simple and fast gate schemes, we achieve an average single-qubit gate fidelity of 99.97% and a two-qubit gate fidelity of up to 99.72%. This performance is comparable to the highest values reported in the literature of superconducting circuits. Thus our work, within the realm of superconducting qubits, reveals an alternative qubit platform that is competitive with the transmon system.Received 12 January 2022Accepted 1 June 2022DOI:https://doi.org/10.1103/PhysRevLett.129.010502© 2022 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum computationPhysical SystemsSuperconducting qubitsQuantum Information, Science & Technology
Cloud computing security: A survey of service-based models

Cloud computing has recently attracted significant attention due to its economical and high-quality services. In the last decade, cloud services have inevitably entangled with businesses’ and individuals’ daily lives through products and services. On-demand, pay-per-use characteristics encourage corporations to outsource part of their businesses to accelerate their services and multiply value. The latest market tendency toward migration to cloud environments, started in 2019, indicates a flourishing trend in the next few years. Despite the numerous benefits of the cloud computing model for businesses or individuals, security issues still have been stated as the top cloud challenge in 2020. Although various factors affect security, technologies enabling cloud computing such as virtualization and multitenancy, in addition to on-demand characteristics, initiate new security entrances for malevolent activities. In this study, we surveyed service-based cloud computing security issues to establish the current state of the field. The main contribution of this paper is to analyze the state of cloud security in the last decade and provide a unified taxonomy of security issues over the three-layer model, i.e., IaaS, PaaS, and SaaS.
Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs

Unmanned aerial vehicles are prone to several cyber-attacks, including Global Positioning System spoofing. Several techniques have been proposed for detecting such attacks. However, the recurrence and frequent Global Positioning System spoofing incidents show a need for effective security solutions to protect unmanned aerial vehicles. In this paper, we propose two dynamic selection techniques, Metric Optimized Dynamic selector and Weighted Metric Optimized Dynamic selector, which identify the most effective classifier for the detection of such attacks. We develop a one-stage ensemble feature selection method to identify and discard the correlated and low importance features from the dataset. We implement the proposed techniques using ten machine-learning models and compare their performance in terms of four evaluation metrics: accuracy, probability of detection, probability of false alarm, probability of misdetection, and processing time. The proposed techniques dynamically choose the classifier with the best results for detecting attacks. The results indicate that the proposed dynamic techniques outperform the existing ensemble models with an accuracy of 99.6%, a probability of detection of 98.9%, a probability of false alarm of 1.56%, a probability of misdetection of 1.09%, and a processing time of 1.24 s.
Convolutional attention neural network over graph structures for improving the performance of aspect-level sentiment analysis

Recently, aspect-level sentiment analysis methods using graph convolutional network (GCN)-based structures with fairly good performance have been introduced. However, previous GCN-based methods often experience one of the following limitations. First, GCNs usually use edges with binary weights. However, binary weights are not helpful in many tasks. Second, these GCNs only focus on extracting node features from some single words or phrases and ignore their context in the entire sentence or paragraph or only consider the information of independent phrases when determining the relation between two graph edges overlooking the semantic relation among these phrases. Finally, no studies simultaneously use the information on the context, the semantic relation, and the sentiment knowledge among words or phrases to build GCNs for aspect-level sentiment analysis. Therefore, to resolve these limitations, in this study, we propose a new method, the CANN-SSCG model, as follows. First, we built three separate heterogeneous graphs, namely, syntax-based, semantic-based, and context-based graphs. Second, we constructed a general heterogeneous graph (SSC graph) by combining the three constructed graphs. We then converted the nodes of the SSC graph into sentence vectors using a GCN with two layers (creating an SSC-GCN). Finally, we used a convolutional neural network algorithm with attention to position embeddings (CANN) on the output of the SSC-GCN model for aspect-level sentiment analysis. The experiments, which used three different datasets, including reviews and tweets, showed that the proposed method yields promising results based on the F1score.
Can people experience romantic love for artificial intelligence? An empirical study of intelligent assistants

Along with the development of artificial intelligence (AI), more IT applications based on AI are being created. A personal intelligent assistant is an AI application that provides information, education, consulting, or entertainment to users. Due to their high levels of cognitive and emotional capabilities, we assume that users can form humanlike relationships with intelligent assistants, therefore, we develop a research model based on the theory of love. Data were collected from users of intelligent assistants through a survey. The results indicate that users can develop intimacy and passion for an AI application similar to that experienced with human beings. These feelings are related to users’ commitment, promoting the usage of an intelligent assistant, influenced by AI factors (performance efficacy and emotional capability), and moderated by human trust disposition.