Reviewerly/exHarmony huggingface dataset:
Effects of virtual reality and test environment on user experience, usability, and mental workload in the evaluation of a blood pressure monitor

User experience and user acceptance of a product are essential for the product’s success. Virtual reality (VR) technology has the potential to assess these parameters early in the development process. However, research is scarce on whether the evaluation of the user experience and user acceptance of prototypes in VR, as well as the simulation of the usage environment, lead to comparable results to reality. To investigate this, a digital twin of a blood pressure monitor (BPM) was created using VR. In a 2 × 2 factorial between-subjects design, 48 participants tested the real or VR BPM. The tests were performed either in a low-detail room at a desk or in a detailed operating room (OR) environment. Participants executed three use scenarios with the BPM and rated their user experience and acceptance with standardized questionnaires. A test leader evaluated the performance of the participants’ actions using a three-point scheme. The number of user interactions, task time, and perceived workload were assessed. The participants rated the user experience of the BPM significantly ( p &amp;lt; .05) better in VR. User acceptance was significantly higher when the device was tested in VR and in a detailed OR environment. Participant performance and time on task did not significantly differ between VR and reality. However, there was significantly less interaction with the VR device ( p &amp;lt; .001). Participants who tested the device in a detailed OR environment rated their performance significantly worse. In reality, the participants were able to haptically experience the device and thus better assess its quality. Overall, this study shows that user evaluations in VR should focus on objective criteria, such as user errors. Subjective criteria, such as user experience, are significantly biased by VR.
Explaining cybercrime victimization using a longitudinal population-based survey experiment. Are personal characteristics, online routine activities, and actual self-protective online behavior related to future cybercrime victimization?

With the increasing prevalence of cybercrime victimization there is a growing need for prevention. Previous studies have attempted to uncover risk factors associated with cybercrime victimization in the areas of personal characteristics and online routine activities. This article aims to take the field a step further by including actual self-protective online behavior, obtained through a population-based survey experiment (N = 1886), as a risk factor for cybercrime victimization. In wave 1 of our longitudinal design, personal characteristics, online routine activities, and actual self-protective online behavior concerning password strength, clicking behavior, sharing personal information, and handling phishing emails were measured. In wave 2, cybercrime victimization of several types of cyber-enabled and cyber-dependent cybercrimes was measured one year later. Results indicate that few personal characteristics, online routine activities, and self-protective online behaviors are related to the odds of becoming a cybercrime victim. This furthermore illustrates the heterogeneity of cybercrime victimization, since most significant factors only seem to be related to the risk of one particular type of cybercrime. These results indicate that to explain cybercrime victimization, the research field needs to shift its focus and adapt to new online developments.
Ranking nodes in directed networks via continuous-time quantum walks

Four new centrality measures for directed networks based on unitary, continuous-time quantum walks (CTQW) in n dimensions—where n is the number of nodes—are presented, tested and discussed. The main idea behind these methods consists in re-casting the classical HITS and PageRank algorithms as eigenvector problems for symmetric matrices, and using these symmetric matrices as Hamiltonians for CTQWs, in order to obtain a unitary evolution operator. The choice of the initial state is also crucial. Two options were tested: a vector with uniform occupation and a vector weighted w.r.t. in- or out-degrees (for authority and hub centrality, respectively). Two methods are based on a HITS-derived Hamiltonian, and two use a PageRank-derived Hamiltonian. Centrality scores for the nodes are defined as the average occupation values. All the methods have been tested on a set of small, simple graphs in order to spot possible evident drawbacks, and then on a larger number of artificially generated larger-sized graphs, in order to draw a comparison with classical HITS and PageRank. Numerical results show that, despite some pathologies found in three of the methods when analyzing small graphs, all the methods are effective in finding the first and top ten nodes in larger graphs. We comment on the results and offer some insight into the good accordance between classical and quantum approaches.
Evaluation and Comparison of Lattice-Based Cryptosystems for a Secure Quantum Computing Era

The rapid development of quantum computing devices promises powerful machines with the potential to confront a variety of problems that conventional computers cannot. Therefore, quantum computers generate new threats at unprecedented speed and scale and specifically pose an enormous threat to encryption. Lattice-based cryptography is regarded as the rival to a quantum computer attack and the future of post-quantum cryptography. So, cryptographic protocols based on lattices have a variety of benefits, such as security, efficiency, lower energy consumption, and speed. In this work, we study the most well-known lattice-based cryptosystems while a systematic evaluation and comparison is also presented.
Higher Education in Mexico: The Effects and Consequences of the COVID-19 Pandemic

This review examines the impact of the COVID-19 pandemic on higher education in Mexico. It acknowledges the difficulties and disruptions caused by the global emergency but also emphasizes the opportunities for reflection and learning that have emerged. This work employed a comprehensive methodology, including a thorough literature search across multiple academic databases and consultation with national statistical sources and newspapers. Sixty-nine articles were selected based on predetermined keywords and criteria, leading to the identification of two central themes: impacts and implications on higher education and innovation in teaching and learning experiences. The findings shed light on the effects of the pandemic and highlight the need for resources, pedagogical considerations, and a reevaluation of priorities in the education sector. The review concludes by emphasizing the importance of improving equity, quality, and long-term sustainability in higher education in Mexico while recognizing the opportunity for educational reform in the post-pandemic era.
Deep learning approach for one-hour ahead forecasting of weather data

ABSTRACTABSTRACTWeather is made up of multiple parameters, including solar radiation (SR), atmospheric pressure (AP), soil temperature (ST), atmospheric temperature (AT), wind speed (WS), relative humidity (RH), and sunshine duration (SD). These factors are also crucial for the renewable energy sector, solar simulation, agriculture, air pollution, water supply and distribution, avalanche warning, forestry, and town and regional planning. A deep learning method based on a neural network with Long Short-Term Memory (LSTM) was employed in this investigation for one-hour-ahead weather data forecasting. The ability of the LSTM model was compared with the Adaptive Neuro-Fuzzy Inference System (ANFIS) with that of the fuzzy c-means (FCM), Autoregressive Integrated Moving Average (ARIMA) model, and the Autoregressive Moving Average (ARMA) model. Mean absolute error (MAE), correlation coefficient (R), root means square error (RMSE), average bias, Nash – Sutcliffe efficiency coefficient (NSE), and mean absolute percentage error (MAPE) were selected as evaluation criteria. Results indicated that the proposed LSTM model presented good enough results compared to other used methods. 7 different types of meteorological data from a total of 4 years (35040 hours) were divided into 25% test data and 75% training data for the models. The best result was obtained for the hourly ST estimation of Adana province using the LSTM method, the MAE, RMSE, R, bias, NSE, and MAPE values were computed as 0.016°C, 0.078°C, 0.9999, −0.00018°C, 0.0805%, and 0.9999, respectively. On the other hand, the worst result was obtained for the hourly SD for Mardin province when ARIMA was used, and the statistical measures were derived as 0.128 hours for MAE, 0.215 hours for RMSE, 0.8851 for R, 0.00091 hours for bias, and 0.7657 for NSE. In this regard, it is demonstrated that the LSTM technique outperformed the other models in terms of all-weather data estimates and delivered highly sensitive outcomes.KEYWORDS: Atmospheric parametersestimationARMAARIMALSTMANFIS FCM AcknowledgmentThe State Meteorological Department of Türkiye provided data for which the author is exceedingly appreciative.Disclosure statementNo potential conflict of interest was reported by the author.Additional informationNotes on contributorsArif OzbekArif Ozbek is an associate professor in Mechanical Engineering Department, Cukurova University, Türkiye. He has been a member of the Chamber of Mechanical Engineers of Turkey since 2000. His research interests are thermodynamics, renewable energy, heat transfer and air-conditioning systems.
Investigating the Dynamics of Cryptocurrencies with Financial Markets: Evidence from an ARDL Approach

This article focuses on the returns of four major cryptocurrencies. It covers a complete study of the dynamics of cryptocurrencies with traditional financial markets, including stocks, gold and oil prices and the technology proxied by hashrates and social sentiments gauged by Twitter data. The article fits the time series model using the Autoregressive Distributed Lag framework and selects the fitted model using penalized least-square estimates. The study also encompasses a set of control variables to improve the model’s accuracy. The findings confirm the existence of a dynamic relationship between the cryptocurrency market and the traditional financial markets. Our results imply that cryptocurrencies can be used as a tool for portfolio diversification. This study is a unique attempt at identifying the major drivers of the cryptocurrency market at large, by disentangling and modelling the dynamic relationships shared by the leading four cryptocurrencies with the relevant components of the financial markets. This article is a significant contribution to the emerging literature on cryptocurrencies and has implications for practitioners.
Towards Secure MicroPython on Morello (WIP)

The Arm Morello platform is a prototype system that supports hardware capabilities for improving runtime security. Although Morello is a server class compute component, there is ongoing work aimed at bringing architectural capabilities to embedded scale devices. For this reason, we are porting the MicroPython framework to Morello. Our intention is to understand the impact of hardware capabilities on lightweight runtime execution environments, like MicroPython, that target embedded devices. In this work-in-progress report, we describe the minimal modifications required to compile the C source code of MicroPython for Morello. We show that this approach gives a working, but not necessarily more secure, version of MicroPython. Our paper proceeds to outline how capabilities could be used to improve runtime system security for MicroPython runtime and hosted applications.
DualDA-Net: Dual-Head Rectification for Cross-Domain Object Detection of Remote Sensing

Object detectors of remote sensing (RS) imagery with deep learning have become increasingly popular and rely heavily on extensive labeled data. The source-only detectors, which are trained on massive labeled data in a source domain, in some cases fail to get satisfactory performance on a target domain due to the domain shift. To alleviate the domain shift, popular approaches consider feature distribution alignment, but the target domain with massive unlabeled data is under-utilized. Some methods use the source-only model to generate pseudo labels for target domain data, but the variation of different remote sensing scenarios produces the domain shift, which is injected directly into the pseudo labels. Therefore, We propose the Dual-head rectification Domain Adaptation network (DualDA-Net) to alleviate the domain shift and exploit the potential of unlabeled target domain data. DualDA-Net cooperates the coarse-to-fine consistency alignment (CCA) with dual-head co-training (DHCT) to align the distribution and generate pseudo labels progressively. Specifically, the CCA focuses on source and target domain feature distribution alignment via coarse-to-fine consistency alignment on multi-level features. Moreover, the DHCT with dual detection heads is deployed in the teacher-student framework, where one of the heads complements the other with high-quality predictions to rectify the pseudo labels as supervision and alleviate the biased information. Sufficient experiments have been conducted on several domain adaptation settings. The experimental results demonstrate that our DualDA-Net achieves success in the target domain for cross domain object detection of RS imagery.
Asynchronous Curriculum Experience Replay: A Deep Reinforcement Learning Approach for UAV Autonomous Motion Control in Unknown Dynamic Environments

Unmanned aerial vehicles (UAVs) have been widely used in military warfare, and realizing safely autonomous motion control (AMC) in complex unknown environments is a challenge to face. In this paper, we formulate the AMC problem as a Markov decision process (MDP) and propose an advanced deep reinforcement learning (DRL) method that allows UAVs to execute complex tasks in different environments. Aiming to overcome the limitations of the prioritized experience replay (PER), the proposed asynchronous curriculum experience replay (ACER) uses multithreads to asynchronously update the priorities and assigns the true priorities to increase the diversity of experiences. It also applies a temporary pool to enhance learning from new experiences and changes the fashion of experience pool to first-in-useless-out (FIUO) to make better use of old experiences. In addition, combined with curriculum learning (CL), a more reasonable training paradigm is designed for ACER to train UAV agents smoothly. By training in a large-scale dynamic environment constructed based on the parameters of a real UAV, ACER improves the convergence speed by 24.66% and the convergence result by 5.59% compared to the twin delayed deep deterministic policy gradient (TD3) algorithm. The testing experiments carried out in environments with different complexities further demonstrate the strong robustness and generalization ability of the ACER agents.
sChain: An Efficient and Secure Solution for Improving Blockchain Storage

Emerging blockchain technology has become the cornerstone of many applications providing trusted data services. However, existing blockchain platforms cannot meet the growing demand for big data storage. Blockchain should duplicate both transactions and other user-defined data across nodes for integrity assurance. The rapid expansion of data on blockchain (on-chain data) increases the difficulty of deploying a full node, resulting in decreasing the degree of decentralization and adding the risk of broken data. To tackle these problems, we propose sChain, a novel framework for improving blockchain storage capacity, which does not revise blockchain implementation and can be applied to almost all the existing blockchain platforms. sChain outsources the user data to storage devices that are structurally external to the blockchain network. In theory, a user can outsource unlimited data to sChain. However, those off-chain data may suffer from corruption. To verify the data integrity, we propose a new provable data possession (PDP) scheme, which does not need a centralized entity to maintain any secret keys and therefore eliminates a single point of failure. What is more, we also design a prototype to accelerate the proposed PDP scheme through Intel SGX technology and parallel processing. Security analysis and evaluation results show that sChain can protect data security and effectively improve the blockchain storage capacity, respectively.
Development of Optimization-based Path Planning Method with Virtual Environment

During autonomous driving, unmanned vehicles encounter various environmental changes. Because such environmental changes degrade the overall autonomous driving performance, an optimal path that considers changes in the surrounding environment is required. In this paper, we propose an optimization-based path planning technique that comprehensively reflects changing traversable areas, vehicle dynamic models, static obstacles, and state/input constraints. To this end, the optimization path planning problem is formulated by considering all the aforementioned constraints and target point following. In addition, the pseudo code for obtaining a numerical optimal solution is derived using the first-order gradient descent method. On the other hand, development of autonomous driving system is time-consuming and expensive. To handle the difficulty, this study constructs a virtual environment autonomous driving system that integrates a robot operating system (ROS) and CarMaker. To verify the performance of the proposed path planning method, autonomous driving simulation is conducted in the developed virtual environment and the results are analyzed.
Mapping cyber insurance: a taxonomical study using bibliometric visualization and systematic analysis

Purpose This study aims to examine the research output on cyber insurance from 2002 to 2021 through an extensive bibliometric analysis. It examines the cyber insurance resources and how the process of cyber insurance works. Design/methodology/approach This paper uses Scopus and VOSviewer to analyze cyber insurance papers. Using 503 papers from Scopus, this paper enhances the understanding of cyber insurance through collaborative network maps of experts and researchers. Findings The study comprehensively evaluates the development of cyber research. The results show that the number of research articles on cyber insurance has significantly increased since 2009. Practical implications The study's results offer practical implications for researchers to gain knowledge on the latest trends and developments in the domain. In addition, the study highlights the significance of cyber insurance in mitigating financial risks linked to cyberattacks, potentially boosting the investment of more organizations in such policies. Furthermore, practitioners can enhance their understanding of the various types of cyber insurance policies and their coverage. Originality/value Our results are likely to encourage practitioners, computer scientists, auditors, accountants and lawyers to contribute further to corporate strategies, data analytics and business operations to mitigate cyber risk consequences. In addition, understanding regarding the cyber insurance concept formed between experts and researchers is limited. This paper fills this gap by evaluating and identifying the development of cyber insurance literature.
MUI-TARE: Cooperative Multi-Agent Exploration With Unknown Initial Position

Multi-agent exploration of a bounded 3D environment with the unknown initial poses of agents is a challenging problem. It requires both quickly exploring the environments and robustly merging the sub-maps built by the agents. Most existing exploration strategies directly merge two sub-maps built by different agents when a single frame observation is matched, which can lead to incorrect merging due to the false-positive detection of the overlap and is thus not robust. In the meanwhile, some recent place recognition methods use sequence matching for robust data association. However, naively applying these sequence matching methods to multi-agent exploration may require one agent to repeat a large amount of another agent's history trajectory so that a sequence of matched observation can be established, which reduces the overall exploration time efficiency. To intelligently balance the robustness of sub-map merging and exploration efficiency, we develop a new approach for lidar-based multi-agent exploration, which can direct one agent to repeat another agent's trajectory in an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adaptive</i> manner based on the quality indicator of the sub-map merging process. Additionally, our approach extends the recent single-agent hierarchical exploration strategy to multiple agents in a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cooperative</i> manner for agents whose sub-maps are merged, to improve exploration efficiency. Our experiments show that our approach is up to 50% more efficient than the baselines while merging sub-maps robustly.
Occluded Person Re-Identification by Multi-Granularity Generation Adversarial Network

In order to address the problem that the detailed features of pedestrians are not prominent and the pedestrian pictures are obscured in unique environments in the process of person re-recognition, we propose a person re-recognition method with a multi-grain size generative adversarial network. Firstly, we use the generative adversarial network to recover the occluded pedestrian pictures; secondly, we improve the traditional multi-granularity network by adding an Efficient Channel Attention for Deep Convolutional Neural Networks (ECA-Net) on the coarse-grained branch to focus on the feature information in the pedestrian pictures and use the High-Resolution Net (HRNet) for pose estimation on the fine-grained branch to divide the pedestrian pictures into nine parts, to enhance the network’s learning of more detailed features of pedestrians, and thus improve the accuracy of pedestrian re-recognition learning, which in turn improves the accuracy of person re-identification.
Novel CNN Accelerator Design With Dual Benes Network Architecture

We presented a novel hardware architecture that uses dual Benes networks to accelerate Convolutional Neural Network (CNN) algorithms.This architecture can reduce the need for high-speed buses and maintain a high-speed connection between execution units and memories.Also, this design can work with multiple neural network models by changing the Benes network configuration only due to the reorderability of the non-blocking networks.The proposed architecture can save the time of re-implementing the hardware design.Thus, this architecture can act as a general CNN accelerator.We successfully implemented our CNN accelerator on the Xilinx Virtex UltraScale+ VU19P and evaluated it by running the CNN model LeNet-5.