Reviewerly/exHarmony huggingface dataset:
COVID-19 passports and refugees: an emerging border technology of exclusion and mobility injustice

The global COVID-19 pandemic has seen unprecedented state border closures and a proliferation of novel restrictions on human mobility both within and between states. This article examines the human rights implications for refugees and asylum seekers of one COVID-19 response measure within and beyond Australia: namely, the adoption of COVID-19 vaccination passport systems. We argue that the use of COVID-19 passport systems in 2021 intensified and entrenched the growing inequalities between states and people in the vaccine-rich Global North and vaccine-deprived Global South as well as between citizens and non-citizens within particular states. Using the concepts of ‘mobility injustice’ and ‘immunoprivilege’, we explore how COVID-19 passport systems created particular additional barriers for refugees to access asylum, to exercise their right to mobility and to realise their right to health. We thus call for ongoing vigilance against the potential for COVID-19 passport systems to be redeployed in future times of global pandemics or emergencies to the detriment of refugees, asylum seekers and undocumented people, both in Australia and globally, even while being touted as a means of protecting populations, opening international travel and granting greater freedoms.
Detection and Recognition Algorithm of Arbitrary-Oriented Oil Replenishment Target in Remote Sensing Image

In view of the fact that the aerial images of UAVs are usually taken from a top-down perspective, there are large changes in spatial resolution and small targets to be detected, and the detection method of natural scenes is not effective in detecting under the arbitrary arrangement of remote sensing image direction, which is difficult to apply to the detection demand scenario of road technology status assessment, this paper proposes a lightweight network architecture algorithm based on MobileNetv3-YOLOv5s (MR-YOLO). First, the MobileNetv3 structure is introduced to replace part of the backbone network of YOLOv5s for feature extraction so as to reduce the network model size and computation and improve the detection speed of the target; meanwhile, the CSPNet cross-stage local network is introduced to ensure the accuracy while reducing the computation. The focal loss function is improved to improve the localization accuracy while increasing the speed of the bounding box regression. Finally, by improving the YOLOv5 target detection network from the prior frame design and the bounding box regression formula, the rotation angle method is added to make it suitable for the detection demand scenario of road technology status assessment. After a large number of algorithm comparisons and data ablation experiments, the feasibility of the algorithm was verified on the Xinjiang Altay highway dataset, and the accuracy of the MR-YOLO algorithm was as high as 91.1%, the average accuracy was as high as 92.4%, and the detection speed reached 96.8 FPS. Compared with YOLOv5s, the p-value and mAP values of the proposed algorithm were effectively improved. It can be seen that the proposed algorithm improves the detection accuracy and detection speed while greatly reducing the number of model parameters and computation.
Recommender System Based on Tweets with Singular Value Decomposition and Support Vector Machine Classification

In modern times, the movie industry is growing rapidly. Netflix is one of the platforms that can be used to watch movies and provides many types of genres and movie titles. With so many genres and movie titles sometimes making it difficult for people to choose a movie to watch, one solution to the problem is a recommendation system that can recommend movies based on user ratings. One method in the recommendation system is collaborative filtering. One of the algorithms contained in collaborative filtering is singular value decomposition. Twitter is one of the places where people often write their opinions about the movies they have watched, from people's tweets on Twitter will be processed into rating value data. In this system, tweets become input that is processed into data that has a rating. This research implements a user-based recommendation system based on ratings from tweets using collaborative filtering combined with the Singular Value Decomposition (SVD) algorithm and Support Vector Machine (SVM) classification and implemented it on user-based and item-based. This research aims to implement a system that combines collaborative filtering techniques with the Singular Value Decomposition (SVD) algorithm and Support Vector Machine (SVM) classification. With the hope of producing a good movie recommendation model and providing accurate predictions for recommended and non-recommended movies. The test results in this study show that Collaborative Filtering gets the best RMSE value of 0.8162 on user-based and 0.5911 on item-based. The combination of Singular Value Decomposition (SVD) algorithm and Support Vector Machine (SVM) classification using hyperparameter tuning resulted in 81% precision and 81% recall for user-based while 80% precision and 80% recall for item-based.
PHENOMENOLOGY OF MUSICAL UNDERSTANDING: THE CASE OF ROCK MUSIC

The article deals with the problem of understanding the meaning of music on the example of rock music. The purpose of the article is to find a criterion for such an understanding. For this purpose, the question is considered as to what musical meaning is. Since rock compositions usually have a short length and one explicit melody, it can be said that musical understanding is the grasping of this melody. The question arises as to what it means to grab a melody, and whether it means the ability to sing it. Not necessarily, it turns out–rather, it means capturing its pattern. The idea that music is a language is rejected: music does not have external meaning. The article discusses several examples of the “translation” of one melody into another style or mode. In such a translation the hyletic data changes, but the pattern remains. This is associated with the preservation of musical meaning and musical thought. The most important criterion of musical understanding is the existential experience of the melody. To understand music, to grasp its meaning, the listener must be open, that is, be ready to perceive music as an aesthetic object. At the moment of listening, music is a saturated phenomenon, because it completely fills its horizon. Any other, alternative music is impossible. The problem of corporeality in the perception of music is examined. It is especially important in rock music, which usually has a clear rhythm. The pattern of the melody also has a spatial character, which refers to corporality. Here we can talk about a certain “resonance” of external music with the inner musical world of the listener.
Home Learning Experiences Through the Covid-19 Pandemic

The COVID-19 pandemic has drastically affected the education process almost all over the world.Some countries closed their schools, Slovakia was among them (schools in Slovakia were closed intermittently for almost two years).Teachers faced the challenge of developing alternative educational practices through digital technologies.Students also faced personal, technological, and social challenges.Distance education, as a replacement of imparting and receiving knowledge, was in many aspects also very demanding for parents.It was necessary to overcome several technical problems (availability of appropriate and reliable Internet connection, provision of appropriate computer equipment and sufficient personal educational space for each member of family).An important role was also played by the student's ability to mobilize his own motivation for asynchronous and autonomous learning.The discussion with the professional public and the review of the relevant literature indicated that the teaching of mathematics is more sensitive to the interruption of attendance education.As the students themselves expressed: for the understanding of mathematical concepts, the personal presence of the teacher necessary and fundamentally affects the student's ability to obtain new knowledge and understand it.The testing of knowledge of students in Slovakia in 2022 at all levels of schools (after almost two years of distance learning) indicates that in the field of mathematics education there has been the biggest drop in knowledge compared to other subjects.Our study focused on the analysis and uncovering of negative but also positive factors operating in the online teaching of mathematics, which significantly affect the results and level of knowledge of students at the university.Mapping and identification of problematic moments in this process helped us reveal the results of a survey (study) conducted among students of the 1st year of bachelor's studies at the University of Žilina.
On the Accuracy of the One-step UKF and the Two-step UKF

The most accurate version of the unscented Kalman filter (UKF) involves the construction of two ensembles. To reduce computational cost, however, UKF is often implemented without the second ensemble. This simplification comes at a price, however, since, for linear systems, the one-step variation of the two-step UKF does not specialize to the classical Kalman filter, with an associated loss of accuracy. This paper remedies this drawback by developing a modified one-step UKF that recovers the classical Kalman filter for linear systems. Numerical examples show that the modified one-step UKF also recovers the accuracy of the two-step UKF in nonlinear systems with linear outputs.
Two-stage solution of quantum process tomography in the natural basis

Quantum process tomography (QPT) is a critical task for characterizing the dynamics of quantum systems and achieving precise quantum control. In this paper, we propose an effective two-stage solution (TSS) for QPT in the natural basis. Our algorithm has O(d <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> ) computational complexity where d is the dimension of the quantum system. We establish an error bound $O\left( {\frac{{{d^5}}}{{\sqrt N }}} \right)$ where N is the copy number for each output state. The numerical examples compared with a convex optimization algorithm demonstrate the effectiveness of our algorithm.
WikiMulti: A Corpus for Cross-Lingual Summarization

Cross-lingual summarization (CLS) is the task to produce a summary in one particular language for a source document in a different language. We introduce WikiMulti - a new dataset for cross-lingual summarization based on Wikipedia articles in 15 languages. As a set of baselines for further studies, we evaluate the performance of existing cross-lingual abstractive summarization methods on our dataset. We make our dataset publicly available here: https://github.com/tikhonovpavel/wikimulti .
An investigation of jamming in free-space quantum key distribution

Quantum key distribution (QKD) is a quantum communications protocol which provides the growth of encryption keys under guaranteed security. Due to the single-photon nature of many QKD protocols, QKD systems can be optically jammed by overwhelming a receiver with many photons at wavelengths at which the single photon detectors are responsive, causing a prohibitively high quantum bit error rate (QBER). In satellite QKD (SatQKD), which relies on communication during brief satellite visual contact, short jamming periods could prevent access to secure communications for much longer periods of time. Optical jamming (OJ) can be achieved both from within line-of-sight by targeting the receiver with a light source, or, in the case of downlink SatQKD, from without line-of-sight by reflecting OJ light off the transmitting satellite.1 The latter attack can be effective 1000km from the ground station, which presents challenges to the deployment of SatQKD in mission-critical applications. In this work, we present two investigations for OJ attacks on SatQKD. Firstly, we present an experimental demonstration utilizing SPAD array technology to locate and mitigate within line-of-sight OJ at long range. Secondly, we present simulations quantifying the effectiveness of without line-of-sight OJ against SatQKD systems and outline mitigation techniques inspired by RF communications. Implementation of the mitigation techniques will be essential for defence applications.
Self-calibration phase demodulation scheme for stabilization based on an auxiliary reference fiber-optic interferometer and ellipse fitting algorithm

To solve the problem of light source jitter and asymmetric 3 × 3 coupler, a phase demodulation method with the combination of an auxiliary reference interferometer and elliptic fitting algorithm is proposed, which is verified by simulation and experiment. By introducing additional phase modulation in the auxiliary reference interferometer, the parameters of the sensing arm can be calibrated in real time, which ensures the effective operation of elliptic fitting algorithm in small signal measurement. Consequently, the experiments show that the self-calibration scheme enables a higher signal to noise and distortion ratio with an average increase of 1.65 dB and 10.47 dB compared with the traditional Arctan and cross multiplication differential, respectively. Meanwhile, the self-calibration scheme can also effectively suppress the harmonic distortion, with a total harmonic distortion of -33.64 dB in the case of small signal.
Hate in the time of the Covid-19 pandemic: dehumanisation as a side effect; re-humanisation as a remedy

Abstract This article is about denouncing the dehumanisation process that took place in the time of Covid-19. It recognises that governments have a vital role to play in setting national directions to tackle racist violence and that the value of having hate crime laws should not be underestimated. However, it argues that a broader approach is needed to embark upon a re -humanisation initiative and effectively combat racist violence. It emphasises that, to get people truly devoted to a course of action, they must develop a greater understanding of the sources of the problem. Accordingly, this article suggests that academia has a key role to play in shedding light on the occurrence of de -humanisation and the potential for re -humanisation.
Identifying risk factors of developing type 2 diabetes from an adult population with initial prediabetes using a Bayesian network

It is known that people with prediabetes increase their risk of developing type 2 diabetes (T2D), which constitutes a global public health concern, and it is associated with other diseases such as cardiovascular disease.This study aimed to determine those factors with high influence in the development of T2D once prediabetes has been diagnosed, through a Bayesian network (BN), which can help to prevent T2D. Furthermore, the set of features with the strongest influences on T2D can be determined through the Markov blanket. A BN model for T2D was built from a dataset composed of 12 relevant features of the T2D domain, determining the dependencies and conditional independencies from empirical data in a multivariate context. The structure and parameters were learned with the bnlearn package in R language introducing prior knowledge. The Markov blanket was considered to find those features (variables) which increase the risk of T2D.The BN model established the different relationships among features (variables). Through inference, a high estimated probability value of T2D was obtained when the body mass index (BMI) was instantiated to obesity value, the glycosylated hemoglobin (HbA1c) to more than 6 value, the fatty liver index (FLI) to more than 60 value, physical activity (PA) to no state, and age to 48-62 state. The features increasing T2D in specific states (warning factors) were ranked.The feasibility of BNs in epidemiological studies is shown, in particular, when data from T2D risk factors are considered. BNs allow us to order the features which influence the most the development of T2D. The proposed BN model might be used as a general tool for prevention, that is, to improve the prognosis.
COMPARISON OF DATA MINING CLASSIFICATION METHODS TO DETECT HEART DISEASE

Heart disease is a disease that is deadly and must be treated as soon as possible because if it is too late, it has a big risk to one's life. Factors causing the disease of the heart is the use of tobacco, the physical who are less active, and an unhealthy diet. With existing data, the study is to compare the three algorithms, namely: Naive Bayes, Logistic Regression, and Support Vector Machine (SVM) which aims to determine the level of accuracy of the best of the dataset that is used to predict disease heart. This research produces the best accuracy of 87%, which is generated by the Naive Bayes method
Digital contact tracing during the COVID-19 pandemic in France: Associated factors and reasons for non-use

To estimate the proportion of users of the TousAntiCovid app(lication) and identify factors associated with its non-use for contact tracing.We conducted an online survey of a quota sample of French adults between 8 and 18 January 2021. Three categories of TousAntiCovid use were considered: contact tracing, other or temporary usage, and no use. A weighted multiple logistic regression was performed to analyze the factors associated with these different uses.Among the 1000 respondents, 63.3% declared they had never downloaded the TousAntiCovid app, 23.5% used it for contact tracing. The remaining 13.2% did not enable contact tracing, mainly because of excessive battery consumption and fear of misuse of personal data. Trust in political representatives, financial deprivation and other factors were associated with never downloading the app.This study confirms the previously suggested links between trust in political representatives, financial deprivation and the use of contact tracing apps in France.
High-accuracy calibration method for an underwater one-mirror galvanometric laser scanner

Three-dimensional (3D) perception of deep-sea targets is the key to autonomous operation of underwater equipment (e.g., underwater robots). Underwater one-mirror galvanometric line-laser scanner has advantages for short-range measurement, but it is difficult to achieve high calibration accuracy due to installation errors and refraction effects. For this reason, in this paper, a high-accuracy refraction-considered and installation-error-independent calibration method is proposed for the vision system. Firstly, to address the difficulty of aligning the incident light plane with the galvanometer shaft, a high-accuracy land-based installation-error-independent model is proposed, which avoids the influence of the installation errors and allows the real shaft axis and the light-plane cluster poses to be calculated using only three light planes. Subsequently, considering the underwater refraction, a 3D model is established for simulating refractive behaviors of the light-plane cluster, and then a partition-based method is proposed for calibrating the underwater light-plane cluster, which further improves the calibration accuracy of the scanner in underwater measurement scenarios. Finally, a one-mirror galvanometric laser scanner is developed in the laboratory to verify the calibration accuracy and to perform the 3D measurement experiments of underwater targets. The results show that the calibration accuracy of the proposed land-based installation-error-independent model is improved 2 times more compared with the traditional installation-error-dependent model. Additionally, the measurement accuracy of the scanner for the standard sphere is 11.98 µm and 12.75 µm in the air and underwater measurement scenarios, and the two measurements are in good agreement. The above results comprehensively verify the high accuracy of the calibration method proposed in this paper.
A certificateless Multi-receiver Encryption scheme based on SM2 signature algorithm

The Multi-receiver Encryption (MRE) scheme can meet the secure data transmission requirements in multicast and broadcast scenarios. To meet compliance, critical information infrastructure in China should be protected with Chinese national commercial cryptographic algorithms. Designing an MRE scheme based on Elliptic Curve Cryptography (ECC) is one of the current design methods with better flexibility and performance. However, the research on MRE schemes based on SM2 elliptic curve public-key cryptography is still in a blank state. This paper proposes a Certificateless SM2-based Multi-receiver Encryption (CL-SM2-MRE) scheme. We prove the security of the CL-SM2-MRE scheme under the Random Oracle Model (ROM) and analyze the performance.
RJA-Star Algorithm for UAV Path Planning Based on Improved R5DOS Model

To improve the obstacle avoidance ability of agricultural unmanned aerial vehicles (UAV) in farmland settings, a three-dimensional space path planning model based on the R5DOS model is proposed in this paper. The direction layer of the R5DOS intersection model is improved, and the RJA-star algorithm is constructed with the improved jump point search A-star algorithm in our paper. The R5DOS model is simulated in MATLAB. The simulation results show that this model can reduce the computational complexity, computation time, the number of corners and the maximum angles of the A-star algorithm. Compared with the traditional algorithm, the model can avoid obstacles effectively and reduce the reaction times of the UAV. The final fitting results show that compared with A-star algorithm, the RJA-star algorithm reduced the total distance by 2.53%, the computation time by 97.65%, the number of nodes by 99.96% and the number of corners by 96.08% with the maximum corners reduced by approximately 63.30%. Compared with the geometric A-star algorithm, the running time of the RJA-star algorithm is reduced by 95.84%, the number of nodes is reduced by 99.95%, and the number of turns is reduced by 67.28%. In general, the experimental results confirm the effectiveness and feasibility of RJA star algorithm in three-dimensional space obstacle avoidance.