Miracle-dz/UniFAR-FTUs huggingface dataset:
Information Security Risk Assessment Method for Ship Control System Based on Fuzzy Sets and Attack Trees

Information security risk assessment for industrial control system is usually influenced by uncertain factors. For effectively dealing with problem that the uncertainty and quantification difficulties are caused by subjective and objective factors in the assessment process, an information security risk assessment method based on attack tree model with fuzzy set theory and probability risk assessment technology is proposed, which is applied in a risk scenario of ship control system. Firstly, potential risks of the control system are analyzed and the attack tree model is established. Then triangular fuzzy numbers and expert knowledge are used to determine the factors that influence the probability of a leaf node and the leaf nodes are quantified to obtain the interval probability. Finally, the fuzzy arithmetic is used to determine the interval probability of the root node and the attack path. After defuzzification, the potential risks of the system and the probability of occurrence of each attack path are obtained. Compared with other methods, the proposed method can greatly reduce the impact of subjectivity on the risk assessment of industrial control systems and get more stable, reliable, and scientific evaluation results.
A Comparison of the Signal from Diverse Optical Sensors for Monitoring Alpine Grassland Dynamics

Grasslands cover up to 40% of the mountain areas globally and 23% of the European Alps and affect numerous key ecological processes. An increasing number of optical sensors offer a great opportunity to monitor and address dynamic changes in the growth and status of grassland vegetation due to climatic and anthropogenic influences. Vegetation indices (VI) calculated from optical sensor data are a powerful tool in analyzing vegetation dynamics. However, different sensors have their own characteristics, advantages, and challenges in monitoring vegetation over space and time that require special attention when compared to or combined with each other. We used the Normalized Difference Vegetation Index (NDVI) derived from handheld spectrometers, station-based Spectral Reflectance Sensors (SRS), and Phenocams as well as the spaceborne Sentinel-2 Multispectral Instrument (MSI) for assessing growth and dynamic changes in four alpine meadows. We analyzed the similarity of the NDVI on diverse spatial scales and to what extent grassland dynamics of alpine meadows can be detected. We found that NDVI across all sensors traces the growing phases of the vegetation although we experienced a notable variability in NDVI signals among sensors and differences among the sites and plots. We noticed differences in signal saturation, sensor specific offsets, and in the detectability of short-term events. These NDVI inconsistencies depended on sensor-specific spatial and spectral resolutions and acquisition geometries, as well as on grassland management activities and vegetation growth during the year. We demonstrated that the combination of multiple-sensors enhanced the possibility for detecting short-term dynamic changes throughout the year for each of the stations. The presented findings are relevant for building and evaluating a combined sensor approach for consistent vegetation monitoring.
Stress Management and Resilience Intervention in a Women's Heart Clinic: A Pilot Study.

BACKGROUND In general, women report higher stress levels than men. High baseline anxiety, depression, and stress levels are associated with greater risk of cardiovascular diseases. Current evidence for efficacy of stress management interventions for women is limited. This study aimed at assessing the effect of a stress management and resiliency training (SMART) program for decreasing stress, anxiety, and depressive symptoms. METHODS Fifty moderately or severely stressed Women's Heart/Preventive Cardiology Clinic patients consented to the SMART intervention delivered online (n = 36) or in-person (n = 9). Primary outcome measures were the observed changes between baseline and at 12 weeks for the following psychometric tools: General Anxiety Disorder-7 (GAD-7), Patient Health Questionnaires (PHQ-9), Perceived Stress Scale (PSS), and Brief Resiliency Scale (BRS). RESULTS Forty-five patients completed the study. We observed significant improvements in PSS and GAD-7, but not in PHQ-9 or BRS, after the SMART intervention. When assessing outcomes among those with depressive symptoms at baseline (PHQ-9 > 15), we observed significant changes in PSS, GAD-7, and PHQ-9. No differences between online and in-person program delivery methods were found (all p-values >0.05). CONCLUSIONS Training exposure using the SMART program to decrease stress and anxiety in women seeking preventive cardiology services was feasible and similarly effective, whether delivered online or in a single in-person session. Impacts on depression and resilience likely require a more intensive approach. In the future, larger randomized clinical trials with additional training and longer follow-up are warranted.
Object Detection in Remote Sensing Images Based on a Scene-Contextual Feature Pyramid Network

Object detection has attracted increasing attention in the field of remote sensing image analysis. Complex backgrounds, vertical views, and variations in target kind and size in remote sensing images make object detection a challenging task. In this work, considering that the types of objects are often closely related to the scene in which they are located, we propose a convolutional neural network (CNN) by combining scene-contextual information for object detection. Specifically, we put forward the scene-contextual feature pyramid network (SCFPN), which aims to strengthen the relationship between the target and the scene and solve problems resulting from variations in target size. Additionally, to improve the capability of feature extraction, the network is constructed by repeating a building aggregated residual block. This block increases the receptive field, which can extract richer information for targets and achieve excellent performance with respect to small object detection. Moreover, to improve the proposed model performance, we use group normalization, which divides the channels into groups and computes the mean and variance for normalization within each group, to solve the limitation of the batch normalization. The proposed method is validated on a public and challenging dataset. The experimental results demonstrate that our proposed method outperforms other state-of-the-art object detection models.
The Influence of Spectral Pretreatment on the Selection of Representative Calibration Samples for Soil Organic Matter Estimation Using Vis-NIR Reflectance Spectroscopy

In constructing models for predicting soil organic matter (SOM) by using visible and near-infrared (vis–NIR) spectroscopy, the selection of representative calibration samples is decisive. Few researchers have studied the inclusion of spectral pretreatments in the sample selection strategy. We collected 108 soil samples and applied six commonly used spectral pretreatments to preprocess soil spectra, namely, Savitzky–Golay (SG) smoothing, first derivative (FD), logarithmic function log(1/R), mean centering (MC), standard normal variate (SNV), and multiplicative scatter correction (MSC). Then, the Kennard–Stone (KS) strategy was used to select calibration samples based on the pretreated spectra, and the size of the calibration set varied from 10 samples to 86 samples (80% of the total samples). These calibration sets were employed to construct partial least squares regression models (PLSR) to predict SOM, and the built models were validated by a set of 21 samples (20% of the total samples). The results showed that 64−78% of the calibration sets selected by the inclusion of pretreatment demonstrated significantly better performance of SOM estimation. The average improved residual predictive deviations (ΔRPD) were 0.06, 0.13, 0.19, and 0.13 for FD, log(1/R), MSC, and SNV, respectively. Thus, we concluded that spectral pretreatment improves the sample selection strategy, and the degree of its influence varies with the size of the calibration set and the type of pretreatment.
A Generalised Bayesian Inference Method for Maritime Surveillance Using Historical Data

In practice, maritime monitoring systems rely on manual work to identify the authenticities, risks, behaviours and importance of moving objects, which cannot be obtained directly through sensors, especially from marine radar. This paper proposes a generalised Bayesian inference-based artificial intelligence that is capable of identifying these patterns of moving objects based on their dynamic attributes and historical data. First of all, based on dependable prior data, likelihood information about objects of interest is obtained in terms of dynamic attributes, such as speed, direction and position. Observations on these attributes of a new object can be obtained as pieces of evidence profiled as probability distributions or generally belief distributions if ambiguity appears in the observations. Using likelihood modelling, the observed pieces of evidence are independent of the prior distribution patterns. Subsequently, Dempster’s rule is used to combine the pieces of evidence under consideration of their weight and reliability to identify the moving object. A real world case study of maritime radar surveillance is conducted to validate and prove the efficiency of the proposed approach. Overall, this approach is capable of providing a probabilistic and rigorous recognition result for pattern recognition of moving objects, which is suitable for any other actively detecting applications in transportation systems.
Improving WCET Evaluation using Linear Relation Analysis

The precision of a worst case execution time (WCET) evaluation tool on a given program is highly dependent on how the tool is able to detect and discard semantically infeasible executions of the program. In this paper, we propose to use the classical abstract interpretation-based method of linear relation analysis to discover and exploit relations between execution paths. For this purpose, we add auxiliary variables (counters) to the program to trace its execution paths. The results are easily incorporated in the classical workflow of a WCET evaluator, when the evaluator is based on the popular implicit path enumeration technique. We use existing tools - a WCET evaluator and a linear relation analyzer - to build and experiment a prototype implementation of this idea.
Probing nuclear bubble configurations by proton-induced reactions

In the framework of the isospin-dependent Boltzmann-Uehling-Uhlenbeck transport model, nuclear bubble configuration in the hypothetical $^{48}$Si nucleus is studied by proton induced central reaction at an incident beam energy of 0.8 GeV/nucleon. It is found that along the beam direction more energetic protons are emitted with bubble configuration in the target. In the forward angles, compared with the case without bubble configuration, less scattered energetic protons are emitted with bubble configuration in the target. We thus provide a new way to probe the bubble configuration in nuclei.
Scale-Adaptive Neural Dense Features: Learning via Hierarchical Context Aggregation

How do computers and intelligent agents view the world around them? Feature extraction and representation constitutes one the basic building blocks towards answering this question. Traditionally, this has been done with carefully engineered hand-crafted techniques such as HOG, SIFT or ORB. However, there is no "one size fits all" approach that satisfies all requirements. In recent years, the rising popularity of deep learning has resulted in a myriad of end-to-end solutions to many computer vision problems. These approaches, while successful, tend to lack scalability and can't easily exploit information learned by other systems. Instead, we propose SAND features, a dedicated deep learning solution to feature extraction capable of providing hierarchical context information. This is achieved by employing sparse relative labels indicating relationships or similarity of dissimilarity between image locations. The nature of these labels results in an almost infinite set of dissimilar examples to choose from. We demonstrate how the selection of negative examples during training can be used to modify the feature space and vary it's learned properties. To demonstrate the generality of this approach, we apply the proposed features to a multitude of tasks, each requiring different properties. This includes disparity estimation, semantic segmentation, self-localisation and SLAM. In all cases, we show how incorporating SAND features results in better or comparable results to the baseline, whilst requiring little to no additional training.
Prediction/discussion-based learning cycle versus conceptual change text: comparative effects on students’ understanding of genetics

Background and purpose: The purpose of this study was to investigate the comparative effects of a prediction/discussion-based learning cycle (HPD-LC), conceptual change text (CCT) and traditional instruction on 10th grade students’ understanding of genetics concepts. Sample: Participants were 112 10th basic grade male students in three classes of the same school located in an urban area. The three classes taught by the same biology teacher were randomly assigned as a prediction/discussion-based learning cycle class (n = 39), conceptual change text class (n = 37) and traditional class (n = 36). Design and method: A quasi-experimental research design of pre-test–post-test non-equivalent control group was adopted. Participants completed the Genetics Concept Test as pre-test–post-test, to examine the effects of instructional strategies on their genetics understanding. Pre-test scores and Test of Logical Thinking scores were used as covariates. Results: The analysis of covariance showed a statistically significant difference between the experimental and control groups in the favor of experimental groups after treatment. However, no statistically significant difference between the experimental groups (HPD-LC versus CCT instruction) was found. Conclusions: Overall, the findings of this study support the use of the prediction/discussion-based learning cycle and conceptual change text in both research and teaching. The findings may be useful for improving classroom practices in teaching science concepts and for the development of suitable materials promoting students’ understanding of science.
Pex13 and Pex14, the key components of the peroxisomal docking complex, are required for peroxisome formation, host infection and pathogenicity-related morphogenesis in Magnaporthe oryzae

ABSTRACT Peroxisomes are ubiquitous organelles in eukaryotic cells that fulfill multiple important metabolisms. Pex13 and Pex14 are key components of the peroxisomal docking complex in yeasts and mammals. In the present work, we functionally characterized the homologues of Pex13 and Pex14 (Mopex13 and Mopex14) in the rice blast fungus Magnaporthe oryzae. Mopex13 and Mopex14 were peroxisomal membrane distributed and were both essential for the maintenance of Mopex14/17 on the peroxisomal membrane. Mopex13 and Mopex14 interacted with each other, and with Mopex14/17 and peroxisomal matrix protein receptors. Disruption of Mopex13 and Mopex14 resulted in a cytoplasmic distribution of peroxisomal matrix proteins and the Woronin body protein Hex1. In the ultrastructure of Δmopex13 and Δmopex14 cells, peroxisomes were detected on fewer occasions, and the Woronin bodies and related structures were dramatically affected. The Δmopex13 and Δmopex14 mutants were reduced in vegetative growth, conidial generation and mycelial melanization, in addition, Δmopex13 showed reduced conidial germination and appressorial formation and abnomal appressorial morphology. Both Δmopex13 and Δmopex14 were deficient in appressorial turgor and nonpathogenic to their hosts. The infection failures in Δmopex13 and Δmopex14 were also due to their reduced ability to degrade fatty acids and to endure reactive oxygen species and cell wall-disrupting compounds. Additionally, Mopex13 and Mopex14 were required for the sexual reproduction of the fungus. These data indicate that Mopex13 and Mopex14, as key components of the peroxisomal docking complex, are indispensable for peroxisomal biogenesis, fungal development and pathogenicity in the rice blast fungus.
A Contingent/Assimilation Framework for Public Interorganizational Systems Decisions: Should the City of Pittsburgh and Allegheny County Consolidate Information Technology Services?

Many times, benefit-opportunity-cost-risk (BOCR) information system (IS)-related decisions are based mainly on short-term financial variables. We propose a broader decision-making framework, using contingency theory to identify environmental specific factors and assimilation theory to identify post-implementation factors to be considered in the decision. This approach is demonstrated within the context of a case study: the decision to merge the City of Pittsburgh and Allegheny County IS functions using an analytic network process (ANP)/BOCR analysis. ANP is a multi-criteria decision-making approach toward making decisions taking into account multiple criteria, alternatives and their interrelationships. The contingency/assimilation approach proposed here can be used in all sorts of IS decisions while the proposed IS merge decision model can be applied, with some contextual modifications, to similar public IS merge decisions. In addition, the ANP/BOCR approach is meticulously explained to serve as a useful reference for prospective users.
Robust Two-Dimensional Spatial-Variant Map-Drift Algorithm for UAV SAR Autofocusing

Autofocus has attracted wide attention for unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) systems, because autofocus process is crucial and difficult when the phase error is spatially dependent on both range and azimuth directions. In this paper, a novel two-dimensional spatial-variant map-drift algorithm (2D-SVMDA) is developed to provide robust autofocusing performance for UAV SAR imagery. This proposed algorithm combines two enhanced map-drift kernels. On the one hand, based on the azimuth-dependent phase correction, a novel azimuth-variant map-drift algorithm (AVMDA) is established to model the residual phase error as a linear function in the azimuth direction. Then the model coefficients are efficiently estimated by a quadratic Newton optimization with modified maximum cross-correlation. On the other hand, by concatenating the existing range-dependent map-drift algorithm (RDMDA) and the proposed AVMDA in this paper, a phase autofocus procedure of 2D-SVMDA is finally established. The proposed 2D-SVMDA can handle spatial-variance problems induced by strong phase errors. Simulated and real measured data are employed to demonstrate that the proposed algorithm compensates both the range- and azimuth-variant phase errors effectively.
The Spectral-Spatial Joint Learning for Change Detection in Multispectral Imagery

Change detection is one of the most important applications in the remote sensing domain. More and more attention is focused on deep neural network based change detection methods. However, many deep neural networks based methods did not take both the spectral and spatial information into account. Moreover, the underlying information of fused features is not fully explored. To address the above-mentioned problems, a Spectral-Spatial Joint Learning Network (SSJLN) is proposed. SSJLN contains three parts: spectral-spatial joint representation, feature fusion, and discrimination learning. First, the spectral-spatial joint representation is extracted from the network similar to the Siamese CNN (S-CNN). Second, the above-extracted features are fused to represent the difference information that proves to be effective for the change detection task. Third, the discrimination learning is presented to explore the underlying information of obtained fused features to better represent the discrimination. Moreover, we present a new loss function that considers both the losses of the spectral-spatial joint representation procedure and the discrimination learning procedure. The effectiveness of our proposed SSJLN is verified on four real data sets. Extensive experimental results show that our proposed SSJLN can outperform the other state-of-the-art change detection methods.
Relative activation patterns associated with self-transcendent and self-enhancement core values: An fMRI study of basic human values theory concepts in males

ABSTRACT Core values have been shown to influence a variety of social behaviors, but research on the brain networks supporting their effects is sparse. While undergoing fMRI scanning, twenty male participants evaluated descriptions of real-world activities according to how worthwhile they were and how likely they were to participate in them. Each activity was categorized according to contexts conceptualized in the Basic Human Values Theory (BHVT) model of core values. We investigated two Self-enhancement values (Power and Achievement) and two Self-transcendent values (Benevolence and Universalism). Behavioral results indicated that Achievement and Benevolence activities were rated higher on both worthiness and participation willingness than Power and Universalism activities. Neuroimaging results revealed that self-transcendence activities elicited greater medial prefrontal cortex and anterior cingulate activation relative to self-enhancement activities during participation rated trials. Contrasting Power, Benevolence, and Universalism activities against Achievement activities during participation rated trials revealed a network of regions critical for moral processing, suggesting that activities corresponding to these three values were considered within a moral framework. No brain regions demonstrated activity that tracked behavioral ratings associated with specific values. This study expands upon previous core values research by demonstrating that real-world contexts related to different BHVT values elicit different brain regions.
An intensity model for credit risk with switching Lévy processes

We develop a switching regime version of the intensity model for credit risk pricing. The default event is specified by a Poisson process whose intensity is modeled by a switching Lévy process. This model presents several interesting features. First, as Lévy processes encompass numerous jump processes, our model can duplicate the sudden jumps observed in credit spreads. Also, due to the presence of jumps, probabilities do not vanish at very short maturities, contrary to models based on Brownian dynamics. Furthermore, as the parameters of the Lévy process are modulated by a hidden Markov chain, our approach is well suited to model changes of volatility trends in credit spreads, related to modifications of unobservable economic factors.