Reviewerly/exHarmony huggingface dataset:
Parameter estimation calibration of discretized polar modulation continuous-variable quantum key distribution

In experimental setups of continuous-variable quantum key distribution (CV-QKD), the ideal Gaussian modulation will suffer from discretization and degrade into discretized polar modulation (DPM), which deteriorates the accuracy of parameter estimation and results in an overestimation of excess noise. We demonstrate that in the asymptotic case, the DPM-induced estimation bias is determined exclusively by the modulation resolutions and can be modeled as a quadratic function. To obtain an accurate estimation, a calibration on the estimated excess noise is implemented based on the closed-form expression of the quadratic bias model, while statistical analysis of the model residuals defines the upper bound of estimated excess noise and the lower bound of secret key rate. Simulation results show that when modulation variance is 25 and excess noise is 0.02, the proposed calibration scheme can eliminate an estimation bias of 14.5%, thus enhancing the efficiency and feasibility of DPM CV-QKD.
The different impact of attention, movement, and sensory information on body metric representation

A growing body of research investigating the relationship between body representation and tool-use has shown that body representation is highly malleable. The nature of the body representation does not consist only of sensory attributes but also of motor action–oriented qualities, which may modulate the subjective experience of our own body. However, how these multisensory factors and integrations may specifically guide and constrain body reorientation’s plasticity has been under-investigated. In this study, we used a forearm bisection task to selectively investigate the contribution of motor, sensory, and attentional aspects in guiding body representation malleability. Results show that the perceived forearm midpoint deviates from the real one. This shift is further modulated by a motor task but not by a sensory task, whereas the attentional task generates more uncertain results. Our findings provide novel insight into the individual role of movement, somatosensation, and attention in modulating body metric representation.
The Effect of Stress on Happiness of University Students: Mediating Effect of Resilience

The Purpose of this study is to verify the mediating effect of resilience in the relationship between happiness and stress of university students.Therefore, it is intended to improve the mental health level and the quality of life of university students.To verify the research model, we conducted an online survey of university students in B, C regions from November 2020 to April 2021 and used data from 261 people.The main measurement variables are stress, happiness, and resilience, and descriptive statistics, correlation analysis, and regression analysis were conducted using SPSS 25.0.In addition, a three-step analysis of the mediating effect of resilience was conducted, and statistical significance was confirmed through Sobel-Test.As a result, first, it was analyzed that the stress of university students had negative(-) effect on resilience.Second, it was confirmed that stress had a negative(-) effect on happiness.Third, it was analyzed that resilience has a partial mediating effect on the relationship between stress and happiness.Therefore, this study intends to raise a plan to improves the level of happiness of university students and suggestions for follow-up research based on the this research results.
Effect of Cognitive Learning Design in An Online Environment on Learning Achievement and the Mediating Effect of Cognitive Load

This study aimed to investigate the mediating effect of cognitive load in the relationship between cognitive learning design uses and learning achievement in an online learning environment for adult learners.383 samples from cyber university were collected for the analysis.The findings of this study were as follows: (1) the use of cognitive learning design had the significantly positive effect on germane cognitive load, while negative effects on extraneous and intrinsic cognitive load.(2) the use of cognitive learning design positively influenced learning achievement, while intrinsic cognitive load had a significant negative effect on learning achievement.(3) there are no significant effects of extraneous and intrinsic cognitive load on learning achievement while controlling for the use of cognitive learning design on online learning environment.(4) Lastly, there is a mediation effect of intrinsic cognitive load on the relationship between the use of cognitive learning design and learning achievement.This study implies that the use of cognitive learning design on online learning environment is an important factor for improving learning and intrinsic cognitive load should be considered when designing online learning system.Also, although all cognitive loads are importantly taken into accounted for the effective online learning system, the effective learning environment based on cognitive learning design can be established through considering the intrinsic cognitive load.
Chamfer Distance on the Four-dimensional Face-centred Cubic Grid

Chamfer distances are step-based distances on various grids. The fourdimensional FCC grid is the extension of the usual face-centred cubic grid into four dimensions. There are two types of steps, and thus, two weights are used. Operational research, namely linear programming and Gomory cut are applied to describe optimal paths and hence, their weighted lengths, the chamfer distance.
People counting in railway environment using computer vision

Ensuring passengers' safety and comfort is a daily comfort for railway operators.In order to correctly size their transport plan, it is necessary to know in real time the number of users present in the trains.With the advent of artificial intelligence, several techniques now make it possible to detect and track passengers effectively.The combination of these 2 techniques can then make it possible to carry out a relevant count.In this article, we compare 2 counting methods based on the one hand on the detection of faces, on the other hand on the segmentation of shapes and their reidentification.We will show the promising results obtained, as well as the impact of physical phenomena such as occultation on the precision of the counting performed.
Development of a Framework for Evaluating Digital Literacy Competencies of Primary and Middle School Students

Objectives The purpose of this study is to develop a framework for evaluating the digital literacy competencies of primary and middle school students in South Korea.&#x0D; Methods To achieve this, the researchers analyzed previous domestic and international studies and cases to develop a draft evaluation framework that includes definitions of digital literacy, evaluation areas, sub-elements, definitions of sub-elements, and examples of achievement. The developed draft was revised and validated through two rounds of the Delphi survey.&#x0D; Results The final proposed digital literacy evaluation framework consists of five evaluation areas and 11 sub-elements, including ① digital tools (understanding of digital tools, utilization of digital tools), ② digital information and data (information search and data collection, analysis and evaluation of information and data, management of information and data), ③ digital communication and collaboration (digital communication, digital collaboration), ④ digital resource production (content production, problem-solving based on computational thinking), and ⑤ digital safety and health (digital safety, digital health).&#x0D; Conclusions The significance of the evaluation framework for the digital literacy developed in this study is that it systematizes the construct for measurement of the digital literacy required by primary and middle school students. In the future, this framework can be used to develop evaluation tools for diagnosing the level of digital literacy of students.
Visualization, Analysis and Collaborative Discussion of Severe Weather Events

Designing effective collaboration, visualization, and analysis of severe weather events (e.g., excessive rainfall accumulation, floods, severe thunderstorms, tornadoes, and droughts) for meteorologists on the web is challenging because it requires processing huge amounts of data from different sources, diverse technologies, as well as using appropriate visualization techniques. This paper presents an innovative web toolset for visualization, collaborative discussion, and analysis of severe weather events. It is composed of the tool Viewing events on Maps (VIMAPS), which allows users to visualize and analyze severe weather events using maps enriched with multivariate data glyphs to observe spatial relations from any desired angle; and the tool called Collaborative system for Meteorological Data Analysis (COMETA) that allows the collaborative analysis of severe weather events between remote users synchronously or asynchronously. These tools were tested using real data and showed that they fulfill the usability requirements of this class of application.
eXplainable Artificial Intelligence - A Study of Sentiments About Vaccination in Brazil

Sentiment analysis in social networks is a focus in several studies on Machine Learning, this happens because the scope and speed with which opinions and emotions about events, controversial issues and products and services are treated on the Internet make it attractive to analyze this medium to obtain relevant information and of interest. Based on this context, this paper presents a sentiment analysis on social networks, focusing on Twitter, about the COVID-19 vaccination campaign in Brazil, using Machine Learning techniques, more specifically, logistic regression, and subsequently the eXplainable Artificial Intelligence (XAI) with the methods LIME, SHAP and Eli5 to interpret the model output. Although there are several applications in the field of sentiment analysis, this study focuses on using real Twitter data, extracted according to the desired context, for five months, processing, analyzing and preparing them for training, and on the explainability of the results obtained during the analysis. The results obtained show that the sample population was mostly in favor of vaccination for issues such as health and the collective good of the population, while those who were against wondered about compulsion and the power of freedom of choice, and expressed fear of being part of an experiment, given the design time of vaccine development.
A Software Architecture Based on the Blockchain-Database Hybrid for Electronic Health Records

Blockchain applications are regarded as a disruptive technology in the health area for working in a decentralized manner, with inherited features such as immutability, privacy, scalability, and security, and with users managing their data. We present a software architecture based on blockchain and a document-oriented database to manage and store electronic health records (EHRs). This software architecture was designed and implemented to manage EHRs of patients in the process of motor function recovery and assist the research on virtual reality and neurofunctional recovery (BRAINN_VR) at the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN). This solution allows BRAINN_VR researchers to analyze data while respecting the patient’s privacy. We evaluate the network performance of our solution to validate EHR insertions using four scenarios with varying data block size, network latency, and jitter. The consensus performance reached shows the feasibility of our architecture. Our software architecture has the potential to provide seamless interaction between multi-institutional research laboratories, health institutions, and patients.
Implementation of eXplainable Artificial Intelligence

Solutions based on Artificial Intelligence are being used to solve problems in various domains. However, many people feel uncomfortable with this type of solution because they must understand how it works. In the face of this, the so-called eXplainable Artificial Intelligence arises, seeking not only to provide the answers produced by Artificial Intelligence but also to offer aspects of explainability, detailing the decision process and generating confidence. In this context, a literature review on eXplainable Artificial Intelligence has presented a brief comparative study between the most popular libraries for this implementation and a deepening of the theme of explainability evaluation and the comprehension process. A proposal for the implementation and evaluation of eXplainable Artificial Intelligence in the context of movement classification to support neuromotor rehabilitation was built from the results obtained. The first experiments performed showed to be promising. The proposal is expected to be relevant for addressing a growing theme in a context, the health area, that demands explicability and transparency in decisions.
Correct Assignment of Rayleigh Waves to Seismic Events

Petra Buchholz and Siegried Wendt from Collm Geopysical Observatory, Germany, report on the correct assignment of Rayleigh waves to seismic events for the Summary of the Bulletin of the ISC.
Kultúrno-spoločenské podmienky pre vznik prvej samostatne fungujúcej galérie na Slovensku

The work describes the evolution of cultural and social conditions in the Slovak territory, respectively, in Upper Hungary, in the possible establishment of a public institution collecting exclusively artistic material.The example of the development of art associations and local museums offers a view of this situation through particular historical periods, more precisely from the middle of the nineteenth century to the gallery's founding in 1948.However, the purpose of the work is not only to outline the social discourse on the possibility of establishing a regional gallery or a public art museum.In addition to concrete examples and evidence of this debate, the work also offers insight into the political orientation of individual institutions that could have been potential players during this development.The work points to a complicated tangle of individual opinions, values and beliefs.
Conflict Evidence Fusion Algorithm Based on Cosine Distance and Information Entropy

&lt;p&gt;Dealing with high conflict evidence, traditional evidence theory sometimes has certain limitations, and results in fusion results contrary to common sense. In order to solve the problem of high conflict evidence fusion, this paper analyzes traditional evidence theory and proposes an evidence fusion method that combines cosine distance and information entropy. Cosine distance can measure the directionality between two vectors. The better the directionality, the more similar the two vectors are. Therefore, this article uses cosine distance to determine the similarity between evidences, and then calculates the credibility of each piece of evidence. Information entropy can calculate the amount of information for each evidence. The greater the information entropy, the greater the uncertainty of the evidence. Therefore, this article uses information entropy to measure the uncertainty of the evidence. Then, the credibility and uncertainty of the evidence are fused to calculate the weight of the evidence. Then we use d-s evidence theory for evidence fusion. The numerical example shows that the method is feasible and effective in dealing with conflict evidence. &lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
GPS-Aided Auto Navigation System for Autonomous Vehicles

Autonomous Vehicles (AV) are the future of the smart digital world. Auto Navigation is the heart of AVs. However, researchers have been working on precise auto navigation control of AVs for many years. Hence, In this paper, a GPS-aided auto navigation system is proposed with position control and heading control of AV. Further, to test the proposed algorithm an AV is designed and developed with low-cost sensors and actuators. The real-time testing of the proposed adaptive control auto navigation mechanism has been performed with three test cases such as straight line, L-shaped and Semi-circular. The results shown in this paper are the deviation between the travelled path and the defined path of the vehicle in different trajectories by assuming no obstacles in the path. The deviation of the path travelled by AV with respect to the defined path using the proposed algorithm is less than 0.5m. Finally, the telemetry of AV has been monitored by the developed Graphical User Interface (GUI). This work is very useful in auto navigation of vehicles where humans can not be sustained.
A new band selection method for multispectral data based on criterion function of information capability

Multispectral remote sensing is one of the most popular techniques in the earth observation, because this technique can provide information of ground objects on Earth’s surface using hundreds of narrow bands. However, multispectral images produces a very large volume of data. Processing the huge volume of information is one of most important and actual problems of remote sensing. The rapid development of the remote sensing demand to develop the data processing algorithms. But at present data processing techniques cannot give accurate results. If we use traditional methods to process multispectral images, the volume of the data increases. The main goal of the band selection is to choose the optimal combination of spectral bands for the solution of the particular remote sensing task. This process is important because different bands are sensitive to different objects. Selecting the right bands can help to optimize the detection of different ground objects. Some spectral bands are more sensitive to minerals, while others are more sensitive to vegetation or water bodies. Under a small number of training samples, the classification accuracy of multispectral images decreases when the volume of multispectral data increases. Usually adjacent bands are highly correlated, and some spectral bands may not carry unique information. That’s why it is necessarily to reduce the dimensionality of multispectral data. It helps to store, process, transmit information more efficiently and to reduce the computational costs while processing images. The different modern methods of multispectral band selection are also considered and analyzed in this work. It also is proposed a new method to select spectral bands, which is based on the concept of criterion function of information capability of spectral bands. In this article some examples using criterion function of information capability are considered too.
The Connection Framework: How designers and developers can impact social connection

This paper outlines the importance of social connection and its relationship with connective technologies, defined as computer applications with the goal of creating or sustaining a social experience or network, including digital social media.We investigate how the choices made in constructing the design and algorithms used in connective technologies create both positive and negative effects.Especially as an explosion of AI content creation and metaverse technologies are rapidly entering the market, this becomes a critical conversation to avoid a repetition of previously made damaging effects.We propose a framework which designers and developers can use to better facilitate social connection, applying lessons learned from the rise of social media to new forms of interactive and immersive media.Social connection.Belonging.Design responsibility.
3D Tracking of Multiple Drones Based on Particle Swarm Optimization

This paper presents a method for the tracking of multiple drones in three-dimensional space based on data from a multi-camera system. It uses the Particle Swarm Optimization (PSO) algorithm and methods for background/foreground detection. In order to evaluate the developed tracking algorithm, the dataset consisting of three simulation sequences and two real ones was prepared. The sequences contain from one to ten drones moving with different flight patterns. The simulation sequences were created using the Unreal Engine and the AirSim plugin, whereas the real sequences were registered in the Human Motion Lab at the Polish-Japanese Academy of Information Technology. The lab is equipped with the Vicon motion capture system, which was used to acquire ground truth data. The conducted experiments show the high efficiency and accuracy of the proposed method. For the simulation data, tracking errors from 0.086 m to 0.197 m were obtained, while for real data, the error was 0.101–0.124 m. The system was developed for augmented reality applications, especially games. The dataset is available at http://bytom.pja.edu.pl/drones/ .
Monocular Road Planar Parallax Estimation

Estimating the 3D structure of the drivable surface and surrounding environment is a crucial task for assisted and autonomous driving. It is commonly solved either by using 3D sensors such as LiDAR or directly predicting the depth of points via deep learning. However, the former is expensive, and the latter lacks the use of geometry information for the scene. In this paper, instead of following existing methodologies, we propose Road Planar Parallax Attention Network (RPANet), a new deep neural network for 3D sensing from monocular image sequences based on planar parallax, which takes full advantage of the omnipresent road plane geometry in driving scenes. RPANet takes a pair of images aligned by the homography of the road plane as input and outputs a γ map (the ratio of height to depth) for 3D reconstruction. The γ map has the potential to construct a two-dimensional transformation between two consecutive frames. It implies planar parallax and can be combined with the road plane serving as a reference to estimate the 3D structure by warping the consecutive frames. Furthermore, we introduce a novel cross-attention module to make the network better perceive the displacements caused by planar parallax. To verify the effectiveness of our method, we sample data from the Waymo Open Dataset and construct annotations related to planar parallax. Comprehensive experiments are conducted on the sampled dataset to demonstrate the 3D reconstruction accuracy of our approach in challenging scenarios.
HOMOMORPHIC AGGREGATE SIGNATURE SCHEME WITH OUTSOURCING ALGORITHM BASED PRIVACY PRESERVING IN VANETS

One of the important paradigms of recent technological development has known to be Vehicular Ad-hoc Networks (VANETs) due to its current intelligent transportation network. Moreover, leakage of communication information or sensitive data in VANETs can leads to vulnerable attacks that may affect many properties and lives. For this reason, VANET systems need to have high-level security aspects. In addition, devices with less computing resources expected for fast computation power. Several researchers have analysed various privacy preserving models for VANETs to protect from different attacks, which is not enough to prevent from new attackers. In this research, a Homomorphic Aggregate Signature (HAS) scheme proposed for achieving unforgeable message attacks without having random oracle and new outsourcing algorithm is designed efficiently for exponential operations to reduce the computational cost in which matrices conjugate operation based on homomorphic mapping is used for achieving the security of both base and exponent numbers. In addition, a protocol based on privacy preserving is constructed for VANETs by using the proposed HAS scheme and outsourcing computing, in which the authentication is processed by presenting a proxy re-signature scheme. In this proposed protocol, RSU will act as an agent when TA authorizes it and OBU’s signatures are converted to TA’s signature by using RSU. Real-identity of OBU is traced by TA using its secret key when an unknown/malicious message is detected then the proposed protocol will provide traceability, privacy, and anonymity. Furthermore, pairing and exponential operations are not needed by the proposed scheme as ` complexity of calculations is reduced significantly for VANET system.