Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of Meditation in Ameliorating Examination Stress Induced Changes in Cardiovascular and Autonomic Functions

Background Examination stress is a very well-known model of psychological stress in students. It induces changes in systolic (SBP) and diastolic blood pressure (DBP), along with changes in heart rate variability (HRV) and baroreflex sensitivity (BRS), due to autonomic perturbations. Purpose To find out if Raj Yoga meditation (RYM) practice affects autonomic and cardiovascular function in healthy young subjects during periods of examination stress. Our primary objective was to evaluate the effect of one month of supervised RYM practice on ameliorating examination-induced changes in cardiovascular and autonomic function. The secondary objective was to measure the stress levels of medical students before and after RYM. Methods Pre-training measurements of SBP, DBP, HRV, and BRS were done, and the Medical Student Stressor Questionnaire (MSSQ) was administered to 80 participants one month before examinations. They were then trained in RYM. Post-training assessment of the same parameters was done after examinations and also after two months. Results In our study, RYM training decreased DBP (p = 0.01) but not SBP. BRS showed a trend towards an increase after RYM practice, but it was not statistically significant (p = 0.44). The standard deviation of the NN interval (SDNN) (p = 0.03), low-frequency (LF) nu (0.003), and high-frequency (HF) nu (0.04) showed a statistically significant change. Average RR, median RR, average rate, square root of the mean squared differences of successive NN intervals (RMSSD), pRR, total power, LF (µs2), and LF/HF ratio were not statistically significantly different between the three groups. There was a statistically significant decline in MSSQ scores for MSSQ I (p = 0.04), MSSQ II (p = 0.04), and MSSQ IV (p = 0.03). Conclusion Short-term practice of supervised RYM during stressful periods is protective for the cardiovascular and autonomic systems and decreases stress in medical students.
A Semisupervised Approach for Industrial Anomaly Detection via Self-Adaptive Clustering

With the rapid development of the Industrial Internet of Things, log-based anomaly detection has become vital for smart industrial construction that has prompted many researchers to contribute. To detect anomalies based on log data, semisupervised approaches stand out from supervised and unsupervised approaches because they only require a portion of labeled data and are relatively stable. However, the state-of-the-art semisupervised approaches still suffer from two main problems: manual parameter setting and unsatisfactory performance with high false positives. We propose AdaLog, an integrated semisupervised approach based on self-adaptive clustering, for industrial anomaly detection. In particular, the clustering step performs automatic label probability estimation by distinguishing 12 situations so that the label probability of each unlabeled data can be carefully calculated, leading to high accuracy. In addition, AdaLog employs a pretrained model to learn contextual information comprehensively and a transformer-based model to detect anomalies efficiently. To alleviate class imbalance, an undersampling method is incorporated. The results on three popular datasets demonstrate that AdaLog significantly outperforms three state-of-the-art semisupervised approaches by 17.8%–2489.8% on average in terms of F1-score, and is even superior to two supervised approaches in most cases with average improvements of 10.9%–23.8%.
Optimal Attack Strategy in Random Scale-Free Networks Based on Incomplete Information

We introduce an attack model based on incomplete information, which means that we can obtain the information from partial nodes. We investigate the optimal attack strategy in random scale-free networks both analytically and numerically. We show that the attack strategy can affect the attack effect remarkably and the OAS can achieve better attack effect than other typical attack strategies. It is found that when the attack intensity is small, the attacker should attack more nodes in the “white area" in which we can obtain attack information; when the attack intensity is greater, the attacker should attack more nodes in the “black area" in which we can not obtain attack information. Moreover, we show that there is an inflection point in the curve of optimal attack proportion. For a given magnitude of attack information, the optimal attack proportion decreases with the attack intensity before the inflection point and then increases after the inflection point.
Computational valency lexica for Latin and Greek in use: a case study of syntactic ambiguity

Abstract We have built a corpus-driven valency lexicon for Greek verbs by following an approach devised for Latin data. We have then used the lexicon to detect a specific type of potentially ambiguous syntactic patterns in Latin and Greek hexametric poetry, which can consistently be disambiguated by prosodic breaks. Such disambiguating breaks were then mapped onto the metrical structure of the lines containing the ambiguous patterns, in order to assess their correspondence to metrical boundaries and to gather independent evidence on the phonetic nature of the boundaries themselves in view of further investigation. From a methodological point of view, the lexica have enabled us to draw on a relatively large set of texts to study a rare phenomenon and to establish a semi-automatic procedure that can be replicated on larger and compatible corpora.
Heliospheric Energetic Particles and Galactic Cosmic Ray Modulation

The paper presents an overview of the SH ‘Solar and Heliospheric cosmic rays’ session of the 24th European Cosmic Ray Symposium (ECRS), Kiel, Germany, 2014. It covers the topics of Solar Energetic Particle (SEP) origin, acceleration and transport at the Sun and in the interplanetary medium, also from the aspect of multi-spacecraft observations, as well as the Galactic Cosmic Ray (GCR) short- and long-term variations and the Jovian electron variations in the heliosphere. Relevant instruments and methods presented are also covered by this review. The paper is written from a personal perspective, emphasizing those results that the author found most interesting.
Brain Computer Interface System for UAV Target Searching

This paper proposes a Brain Computer Interface (BCI) system for unmanned aerial vehicle (UAV) indoor space target searching. The monocular vision navigation subsystem provides three-dimensional (3D) space feasible flight directions. To realize four-class motor imagery (MI) electroencephalogram (EEG) features extraction (left/right-hand, feet and tongue), the decision subsystem adopts a mathematical paradigm combining “one versus one” (OVO) and “one versus rest” (OVR) to traditional Common Spatial Pattern (CSP). Simultaneously, it utilizes a 2-layer hierarchical support vector machine (HSVM) to realize the feature classification and final feasible flight direction decision. The actual indoor space target searching experiment verifies that this BCI system has good adaptability.
Insight into seasonal aerosol concentrations and meteorological influence over a flood affected area using satellite data and statistical model simulation

Aerosol particles in the atmosphere are one of the many factors that might affect floods. One of India's most flood‐prone regions, Muzaffarpur district in Bihar, has been chosen as the study area. The time period considered for the satellite data on meteorological influence is from the year 2000 to 2021. The correlation coefficient between past forecasts and its confirming data is often used to assess the effectiveness of weather and climate forecasting systems. Therefore, changes in correlation can be utilized to gauge advancements in forecasting ability. In this study, the relationship between aerosol optical depth (AOD) 550 nm, temperature, relative humidity, wind speed, PM2.5 and precipitation are carried out with correlation co‐efficient. The average rainfall throughout the monsoon was found to be 162.5 mm, during the period 2000–2021. The descriptive statistical data for seasonal data were determined for AOD (550 nm), PM2.5, wind speed, relative humidity, and temperature values. It is inferred that there is a positive correlation between precipitation and AOD for the monsoon season. Hence the fitting of meteorological parameter AOD for the monsoon season are done with six different probability distributions Gamma (3P), Logistics, Lognormal (3P), Normal, Rayleigh (2P) and Weibull (3P). The parameters of these distributions are evaluated using the Maximum Likelihood Estimation and the goodness of fit is tested using the test statistics Kolmogorov Smirnov and Anderson Darling test. Among the six discussed distributions, Weibull (3P) distribution provides the best fit to model the meteorological data from the satellite, the flood prediction would be obtained from the model proposed.
Un théorème de fonctions implicites. Applications

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Relative Localization Estimation for Multiple Robots via the Rotating Ultra-Wideband Tag

Most distributed algorithms for robot coordination require relative location information, but how to obtain relative locations in a distributed manner is still a primary problem to address in multi-robot applications. In order to obtain the relative locations between robots, no matter whether they are in relative motion or stationary situation, we design a rotating ultra-wideband tag to provide the persistency of excitation condition and two estimation algorithms to estimate the relative locations in a distributed manner. Moreover, our approach relies only on on-board sensors and requires only one ultra-wideband tag per robot, eliminating the need for any ground anchors, thus allowing deployment in GNSS-denied environments without range restrictions. The proposed approach in this letter is also tested in simulations and experiments to verify the theoretical findings and effectiveness in practice.
Decoding Contextual Factors Differentiating Adolescents’ High, Average, and Low Digital Reading Performance Through Machine-Learning Methods

The prevalence of information and communication technologies (ICTs) has brought about profound changes in the field of reading, resulting in a large and rapidly growing number of young digital readers. The article intends to identify key contextual factors that synergistically differentiate high and low performers, high and average performers, and low and average performers in digital reading, through the utilization of machine-learning methods, namely, support vector machine (SVM) and SVM recursive feature elimination. In addition, the Shapley additive explanations (SHAP) method was applied to augment the machine-learning models and detect the features impact on the final output. The latest-released Programme for International Student Assessment reading data were analyzed, and the samples included 276 269 15-year-old students from 38 Organization for Economic Cooperation and Development countries. The results show that an optimal feature set of contextual factors at the school, classroom, and student levels in the high–low model, high–average model, and low–average model boast high accuracy. Compared with average-performing students, high-performing students spend more time reading emails and are associated with high-quality teaching that incorporates digital literacy, and low-performing students are characterized by a lack of interest in ICT use and are more susceptible to the abuse of ICT resources, classroom disorder, and discrimination at school. The use of machine-learning algorithms for pairwise comparisons provides new perspectives for personalized digital reading education, and the evaluation of the effect of every factor using SHAP method offers a clear view for educational researchers. This article sheds light on factors that may contribute to the development of students’ digital reading literacy and the practice of adopting an individualized approach to digital reading pedagogy for educators and instructors.
Global Synthesis of Regional Holocene Hydroclimate Variability Using Proxy and Model Data

Substantial changes in terrestrial hydroclimate during the Holocene are recorded in geological archives and simulated by computer models. To identify spatial and temporal patterns during the past 12 ka, proxy records sensitive to changing precipitation and effective moisture (precipitation minus evaporation) were compiled from across the globe (n = 813). Proxy composite timeseries were computed for 30 of the IPCC AR6 regions and compared to two full‐Holocene transient model simulations (TraCE‐21ka and HadCM3) and twelve mid‐Holocene CMIP6 simulations. We find that throughout Northern Hemisphere monsoon regions, proxy and model simulations indicate wetter‐than‐modern conditions during the early and mid‐Holocene while Southern Hemisphere monsoon regions were drier. This insolation driven trend toward modern values began approximately 6,000 years ago, and the clear agreement among proxy records and models may reflect the large magnitude of precipitation change and consistent atmospheric circulation forcing mechanism for these regions. In the midlatitudes, the pattern of change is less certain. Generally, proxy composites show a wetting trend throughout the Holocene for the northern midlatitudes, possibly due to strengthening westerlies from an increasing latitudinal temperature gradient. However, simulations indicate that the magnitude of change was relatively low, and for portions of North America, there is a proxy‐model disagreement. At high latitudes, hydroclimate is positively correlated with temperature in both proxies and models, consistent with projected wetting as temperatures rise. Overall, this large proxy database reveals a coherent pattern of hydroclimate variability despite the challenges associated with reconstructing hydroclimate fields.
Towards Semi-Supervised Universal Graph Classification

Graph neural networks have pushed state-of-the-arts in graph classifications recently. Typically, these methods are studied within the context of supervised end-to-end training, which necessities copious task-specific labels. However, in real-world circumstances, labeled data could be limited, and there could be a massive corpus of unlabeled data, even from unknown classes as a complementary. Towards this end, we study the problem of semi-supervised universal graph classification, which not only identifies graph samples which do not belong to known classes, but also classifies the remaining samples into their respective classes. This problem is challenging due to a severe lack of labels and potential class shifts. In this paper, we propose a novel graph neural network framework named UGNN, which makes the best of unlabeled data from the subgraph perspective. To tackle class shifts, we estimate the certainty of unlabeled graphs using multiple subgraphs, which facilities the discovery of unlabeled data from unknown categories. Moreover, we construct semantic prototypes in the embedding space for both known and unknown categories and utilize posterior prototype assignments inferred from the Sinkhorn-Knopp algorithm to learn from abundant unlabeled graphs across different subgraph views. Extensive experiments on six datasets verify the effectiveness of UGNN in different settings.
The energetics of large-scale internal wave degeneration in lakes

Field observations in lakes, where the effects of the Earth's rotation can be neglected, suggest that the basin-scale internal wave field may be decomposed into a standing seiche, a progressive nonlinear surge and a dispersive solitary wave packet. In this study we use laboratory experiments to quantify the temporal energy distribution and flux between these three component internal wave modes. The system is subjected to a single forcing event creating available potential energy at time zero (APE). During the first horizontal mode one basin-scale wave period ($T_i$), as much as 10% and 20% of the APE may be found in the solitary waves and surge, respectively. The remainder is contained in the horizontal mode one seiche or lost to viscous dissipation. These findings suggest that linear analytical solutions, which consider only basin-scale wave motions, may significantly underestimate the total energy contained in the internal wave field. Furthermore, linear theories prohibit the development of the progressive nonlinear surge, which serves as a vital link between basin-scale and sub-basin-scale motions. The surge receives up to 20% of the APE during a nonlinear steepening phase and, in turn, conveys this energy to the smaller-scale solitary waves as dispersion becomes significant. This temporal energy flux may be quantified in terms of the ratio of the linear and nonlinear terms in the nonlinear non-dispersive wave equation. Through estimation of the viscous energy loss, it was established that all inter-modal energy flux occurred before 2$T_i$; the modes being independently damped thereafter. The solitary wave energy remained available to propagate to the basin perimeter, where although it is beyond the scope of this study, wave breaking is expected. These results suggest that a periodically forced system with sloping topography, such as a typical lake, may sustain a quasi-steady flux of 20% of APE to the benthic boundary layer at the depth of the metalimnion.
A brightness classification and improved dichotomy based license plate segmentation algorithm

License plate(LP) segmentation can easily be affected by rim,noise and LP rotation,which causes low segmentation accuracy.We presented a good-robustness segmentation algorithm for the issue.The algorithm performed brightness classification and grayscale enhancement in the preprocessing stage before tilt correction and localization of the upper and lower boundaries.An improved dichotomy algorithm was then applied to segment LP characters.We implemented precise border localization for characters 1,2,6 and 7.Experiment shows that the algorithm has low computational burden and can effectively overcome such negative influences as low contrast,blurness,character adhesion and LP tilt.
The basic reproduction number of discrete SIR and SEIS models with periodic parameters

Seasonal fluctuations have been observed in many infectious diseases. Discrete epidemic models with periodic epidemiological parameters are formulated and studied to take into account seasonal variations of infectious diseases. The definition and the formula of the basic reproduction number $R_0$ are given by following the framework in [1,2,3,4,5]. Threshold results for a general model are obtained which show that the magnitude of $R_0$ determines whether the disease will go extinct (when $R_0 1$) in the population. Applications of these general results to discrete periodic SIR and SEIS models are demonstrated. The disease persistence and the existence of the positive periodic solution are established. Numerical explorations of the model properties are also presented via several examples including the calculations of the basic reproduction number, conditions for the disease extinction or persistence, and the existence of periodic solutions as well as its stability.
Security-Aware Provenance for Transparency in IoT Data Propagation

A successful application of an Internet of Things (IoT) based network depends on the accurate and successful delivery of data collected from numerous sources. A significant concern in IoT systems arises when end-users do not have sufficient transparency and are unaware of any potential data manipulation and risk in each step involved in data propagation. One potential solution is to integrate security metadata in IoT-based security-aware provenance graphs that provides better transparency with security awareness at each step of data propagation. In this paper, we integrate security metadata into the provenance graph with predefined security policies. We design a hypothetical IoT-Health scenario with possible threats: node cloning, fault packet injection, denial of service, unauthorized access, and malicious code injection. We simulate these threats in six cases to identify relevant risks. Our findings show how a security-aware provenance graph can offer end users greater transparency and security awareness by identifying failed signature verification (case 1), denial of service (case 2), unauthorized access (case 3), intrusion detection (case 4), missing WAF (case 5), and permission violation (case 6). We evaluate the transparency through obtaining authentication, integrity, availability and detecting underlying threats. Accordingly, this study promotes better risk assessment and decision-making for users with negligible performance overhead.
Defining Software Test Architectures with the UML Testing Profile

The concept of software test architecture (STA) has been discussed within industry meetings with little universal agreement on its usage. This paper proposes the use of Test Architecture (TA) as defined in the OMG UTP in place of STA and refines some earlier paper definitions and usage of STA to align with UTP and ISO concepts. Further, the paper considers TA styles of test environments and how they can use UTP modeling, which can lead to test automation as well as to better testing outcomes.
Malaria: An Overview

Abstract Malaria is a global public health burden with an estimated 229 million cases reported worldwide in 2019. About 94% of the reported cases were recorded in the African region. About 200 different species of protozoa have been identified so far and among them, at least 13 species are known to be pathogenic to humans. The life cycle of the malaria parasite is a complex process comprising an Anopheles mosquito and a vertebrate host. Its pathophysiology is characterized by fever secondary to the rupture of erythrocytes, macrophage ingestion of merozoites, and/or the presence of antigen-presenting trophozoites in the circulation or spleen which mediates the release of tumor necrosis factor α (TNF-α). Malaria can be diagnosed through clinical observation of the signs and symptoms of the disease. Other diagnostic techniques used to diagnose malaria are the microscopic detection of parasites from blood smears and antigen-based rapid diagnostic tests. The management of malaria involves preventive and/or curative approaches. Since untreated uncomplicated malaria can progress to severe malaria. To prevent or delay the spread of antimalarial drug resistance, WHO recommends the use of combination therapy for all episodes of malaria with at least two effective antimalarial agents having a different mechanism of action. The Centers for Disease Control (CDC) emphasizes that there is no prophylactic agent that can prevent malaria 100%. Therefore, prophylaxis shall be augmented with the use of personal protective measures.
Preliminary Results in Using Attention for Increasing Attack Identification Efficiency

In previous work, we proposed an end-to-end intrusion early detection system to identify network attacks in real-time before they complete and could cause more damage to the system under attack. To implement the approach, we have used a deep neural network which was trained in a supervised manner to extract relevant features from raw network traffic in order to classify network flows into different types of attacks. In this work, we discuss the initial results of the benefits that an attention mechanism brings to the classification performance and the capacity of the network to detect attacks earlier. We empirically evaluate our approach on the CICIDS2017 dataset. Preliminary results show that the attention mechanism improves both the balanced accuracy of the classifier as well as the early detection of attacks.