Enhancing PDF Malware Detection through Logistic Model Trees

Malware is an ever-present and dynamic threat to networks and computer systems in cybersecurity,and because of its complexity and evasiveness,it is challenging to identify using traditional signature-based detection approaches.The study article discusses the growing danger to cybersecurity that malware hidden in PDF files poses,highlighting the shortcomings of conventional detection techniques and the difficulties presented by adversarial methodologies.The article presents a new method that improves PDF virus detection by using document analysis and a Logistic Model Tree.Using a dataset from the Canadian Institute for Cybersecurity,a comparative analysis is carried out with well-known machine learning models,such as Credal Decision Tree,Naïve Bayes,Average One Dependency Estimator,Locally Weighted Learning,and Stochastic Gradient Descent.Beyond traditional structural and JavaScript-centric PDF analysis,the research makes a substantial contribution to the area by boosting precision and resilience in malware detection.The use of Logistic Model Tree,a thorough feature selection approach,and increased focus on PDF file attributes all contribute to the efficiency of PDF virus detection.The paper emphasizes Logistic Model Tree’s critical role in tackling increasing cybersecurity threats and proposes a viable answer to practical issues in the sector.The results reveal that the Logistic Model Tree is superior,with improved accuracy of 97.46%when compared to benchmark models,demonstrating its usefulness in addressing the ever-changing threat landscape.

Statistical Modeling with a Hidden Markov Tree and High-resolution Interpolation for Spaceborne Radar Reflectivity in the Wavelet Domain

With the increasing availability of precipitation radar data from space,enhancement of the resolution of spaceborne precipitation observations is important,particularly for hazard prediction and climate modeling at local scales relevant to extreme precipitation intensities and gradients.In this paper,the statistical characteristics of radar precipitation reflectivity data are studied and modeled using a hidden Markov tree(HMT)in the wavelet domain.Then,a high-resolution interpolation algorithm is proposed for spaceborne radar reflectivity using the HMT model as prior information.Owing to the small and transient storm elements embedded in the larger and slowly varying elements,the radar precipitation data exhibit distinct multiscale statistical properties,including a non-Gaussian structure and scale-to-scale dependency.An HMT model can capture well the statistical properties of radar precipitation,where the wavelet coefficients in each sub-band are characterized as a Gaussian mixture model(GMM),and the wavelet coefficients from the coarse scale to fine scale are described using a multiscale Markov process.The state probabilities of the GMM are determined using the expectation maximization method,and other parameters,for instance,the variance decay parameters in the HMT model are learned and estimated from high-resolution ground radar reflectivity images.Using the prior model,the wavelet coefficients at finer scales are estimated using local Wiener filtering.The interpolation algorithm is validated using data from the precipitation radar onboard the Tropical Rainfall Measurement Mission satellite,and the reconstructed results are found to be able to enhance the spatial resolution while optimally reproducing the local extremes and gradients.

Can a multistage approach improve individual tree mortality predictions across the complex mixed-species and managed forests of eastern North America?

Tree mortality plays a fundamental role in the dynamics of forest ecosystems,yet it is one of the most difficult phenomena to accurately predict.Various modeling strategies have been developed to improve individual tree mortality predictions.One less explored strategy is the use of a multistage modeling approach.Potential improvements from this approach have remained largely unknown.In this study,we developed a novel multistage approach and compared its performance in individual tree mortality predictions with a more conventional approach using an identical individual tree mortality model formulation.Extensive permanent plot data(n=9442)covering the Acadian Region of North America and over multiple decades(1965–2014)were used in this study.Our results indicated that the model behavior with the multistage approach better depicted the observed mortality and showed a notable improvement over the conventional approach.The difference between the observed and predicted numbers of dead trees using the multistage approach was much smaller when compared with the conventional approach.In addition,tree survival probabilities predicted by the multistage approach generally were not significantly different from the observations,whereas the conventional approach consistently underestimated mortality across species and overestimated tree survival probabilities over the large range of DBH in the data.The new multistage approach also predictions of zero mortality in individual plots,a result not possible in conventional models.Finally,the new approach was more tolerant of modeling errors because it based estimates on ranked tree mortality rather than error-prone predicted values.Overall,this new multistage approach deserves to be considered and tested in future studies.

Individual tree segmentation and biomass estimation based on UAV Digital aerial photograph

Digital aerial photograph(DAP)data is processed based on Structure from Motion(Sf M)algorithm and regional net adjustment method to generate digital surface discrete point clouds similar to Light Detection and Ranging(LiDAR)and digital orthophoto mosaic(DOM)similar to optical remote sensing image.In this study,we obtained highresolution images of mature forests of Chinese fir by unmanned aerial vehicle(UAV)flying through crossroute flight,and then reconstructed the threedimensional point clouds in the UAV aerial area by SfM technique.The point cloud segmentation(PCS)algorithm was used for the individual tree segmentation,and the F-score of the three sample plots were 0.91,0.94,and 0.94,respectively.Individual tree biomass modeling was conducted using 155 mature Chinese fir forests which were correctly segmented.The relative root mean squared error(rRMSE)values of random forest(RF),bagged tree(BT)and support vector regression(SVR)were 34.48%,35.74%and 40.93%,respectively.Our study demonstrated that DAP point clouds had great potential to extract forest vertical parameters and could be applied successfully in individual tree segmentation and individual tree biomass modeling.

Mathematical Modeling of Carcinogenesis Based on Chromosome Aberration Data

Objective： The progression of human cancer is characterized by the accumulation of genetic instability. An increasing number of experimental genetic molecular techniques have been used to detect chromosome aberrations. Previous studies on chromosome abnormalities often focused on identifying the frequent loci of chromosome alterations, but rarely addressed the issue of interrelationship of chromosomal abnormalities. In the last few years, several mathematical models have been employed to construct models of carcinogenesis, in an attempt to identify the time order and cause-and-effect relationship of chromosome aberrations. The principles and applications of these models are reviewed and compared in this paper. Mathematical modeling of carcinogenesis can contribute to our understanding of the molecular genetics of tumor development, and identification of cancer related genes, thus leading to improved clinical practice of cancer.

Classifying Machine Learning Features Extracted from Vibration Signal with Logistic Model Tree to Monitor Automobile Tyre Pressure

Tyre pressure monitoring system(TPMS)is compulsory in most countries like the United States and European Union.The existing systems depend on pressure sensors strapped on the tyre or on wheel speed sensor data.A difference in wheel speed would trigger an alarm based on the algorithm implemented.In this paper,machine learning approach is proposed as a new method to monitor tyre pressure by extracting the vertical vibrations from a wheel hub of a moving vehicle using an accelerometer.The obtained signals will be used to compute through statistical features and histogram features for the feature extraction process.The LMT(Logistic Model Tree)was used as the classifier and attained a classification accuracy of 92.5%with 10-fold cross validation for statistical features and 90.5% with 10-fold cross validation for histogram features.The proposed model can be used for monitoring the automobile tyre pressure successfully.

STUDY ON THE TREE GROWTH, ARCHITECTURE AND STAND STRUCTURE OF KOREAN PINE PLANTATION

The artificial pure and mixed Korean pine (Pinus koraiensis) forests were investigated at Dailing Forestry Bureau in Xiaoxing'an mountains from 1990 to 1992. Depending on the distance between the samplings of Korean pine and their neighbor trees, the neighbor tree height, the size of neighbor tree canopy, and dimension of neighbor tree. The forest structure was classified into three types: (1) prowth of a tree in the light (open), (2) Growth of a tree in the canopy gap (Gap), (3)Growth of a tree under broad-leaved tree canopy. The frequeney, height, and age of stem divergence of Korean pine tree were investigated by sampling trees. The temporal and spatial model of the tree growth was applied on basis of the height of stem divergence, ratio of height and DBH, and character of tree stem.The morphology and growth character of Korean pine trees during different development stage were forecasted.

An imputation/copula-based stochastic individual tree growth model for mixed species Acadian forests： a case study using the Nova Scotia permanent sample plot network

Background： A novel approach to modelling individual tree growth dynamics is proposed. The approach combines multiple imputation and copula sampling to produce a stochastic individual tree growth and yield projection system. Methods： The Nova Scotia, Canada permanent sample plot network is used as a case study to develop and test the modelling approach. Predictions from this model are compared to predictions from the Acadian variant of the Forest Vegetation Simulator, a widely used statistical individual tree growth and yield model. Results： Diameter and height growth rates were predicted with error rates consistent with those produced using statistical models. Mortality and ingrowth error rates were higher than those observed for diameter and height, but also were within the bounds produced by traditional approaches for predicting these rates. Ingrowth species composition was very poorly predicted. The model was capable of reproducing a wide range of stand dynamic trajectories and in some cases reproduced trajectories that the statistical model was incapable of reproducing. Conclusions： The model has potential to be used as a benchmarking tool for evaluating statistical and process models and may provide a mechanism to separate signal from noise and improve our ability to analyze and learn from large regional datasets that often have underlying flaws in sample design.

Development and evaluation of an individual tree growth and yield model for the mixed species forest of the Adirondacks Region of New York, USA

Background： Growth and yield models are important tools for forest planning. Due to its geographic location, topology, and history of management, the forests of the Adirondacks Region of New York are unique and complex. However, only a relatively limited number of growth and yield models have been developed and/or can be reasonably extended to this region currently. Methods： in this analysis, 571 long-term continuous forest inventory plots with a total of 10 - 52 years of measurement data from four experimental forests maintained by the State University of New York College of Environmental Science and Forestry and one nonindustrial private forest were used to develop an individual tree growth model for the primary hardwood and softwood species in the region. Species-specific annualized static and dynamic equations were developed using the available data and the system was evaluated for long-term behavior. Results： Equivalence tests indicated that the Northeast Variant of the Forest Vegetation Simulator （FVS-NE） was biased in its estimation of tree total and bole height, diameter and height increment, and mortality for most species examined. In contrast, the developed static and annualized dynamic, species-specific equations performed quite well given the underlying variability in the data. Long-term model projections were consistent with the data and suggest a relatively robust system for prediction. Conclusions： Overall, the developed growth model showed reasonable behavior and is a significant improvement over existing models for the region. The model also highlighted the complexities of forest dynamics in the region and should help improve forest planning efforts there.

M5 Model Tree to Predict Temporal Evolution of Clear-Water Abutment Scour

Scour is a natural phenomenon that is created by the rivers streams or the flood which brings about transferring or eroding of bed materials. To have accurate and safe erosion control structures design, maximum scour depth in downstream of the structures gains specific significance. In the current study, M5 model tree as remedy data mining approaches is suggested to estimate the scour depth around the abutments. To do this, Kayaturk laboratory data (2005), with different hydraulic conditions, are used. Then, the results of M5 model were also compared with genetic programming (GP) and pervious empirical results to investigate the applicability, ability, and accuracy of these procedures. To examine the accuracy of the results yielded from the M5 and GP procedures, two performance indicators (determination coefficient (R2) and root mean square error (RMSE)) were used. The comparison test of results clearly shows that the implementation of M5 technique sounds satisfactory regarding the performance indicators (R2 = 0.944 and RMSE = 0.126) with less deviation from the numerical values. In addition, M5 tree model, by presenting relationships based on liner regression, has good capability to estimate the depth of scour abutment for engineers in practical terms.

上海市居家康复治疗项目实施现况及影响因素

目的了解上海市居家康复治疗项目实施现状,探究影响社区开展居家康复的因素,为进一步提升居家康复服务质量及利用率提出可行建议。方法2021年8—12月对上海市3个区的所有社区卫生服务中心(共36家)进行问卷调查,分别建立决策树模型、神经网络模型分析社区开展居家康复服务的影响因素。结果总计发放机构调查问卷36份,回收36份,回收率100%。无论是西医康复治疗项目还是中医康复治疗项目,社区开展率均高于居家开展率。康复床位数、强制性运动疗法、轮椅操作训练等是影响社区开展居家康复的因素。结论上海市居家康复服务开展率低于社区,建议通过增加机构康复床位数量、探索适宜的居家康复治疗项目、增加康复人员轮椅操作培训等方式提高居家康复服务的数量和质量。

Fatigue Life Estimation of High Strength 2090-T83 Aluminum Alloy under Pure Torsion Loading Using Various Machine Learning Techniques

The ongoing effort to create methods for detecting and quantifying fatigue damage is motivated by the high levels of uncertainty in present fatigue-life prediction approaches and the frequently catastrophic nature of fatigue failure.The fatigue life of high strength aluminum alloy 2090-T83 is predicted in this study using a variety of artificial intelligence and machine learning techniques for constant amplitude and negative stress ratios(R?1).Artificial neural networks(ANN),adaptive neuro-fuzzy inference systems(ANFIS),support-vector machines(SVM),a random forest model(RF),and an extreme-gradient tree-boosting model(XGB)are trained using numerical and experimental input data obtained from fatigue tests based on a relatively low number of stress measurements.In particular,the coefficients of the traditional force law formula are found using relevant numerical methods.It is shown that,in comparison to traditional approaches,the neural network and neuro-fuzzy models produce better results,with the neural network models trained using the boosting iterations technique providing the best performances.Building strong models from weak models,XGB helps to predict fatigue life by reducing model partiality and variation in supervised learning.Fuzzy neural models can be used to predict the fatigue life of alloys more accurately than neural networks and traditional methods.

Modelling the dead fuel moisture content in a grassland of Ergun City,China

The dead fuel moisture content(DFMC)is the key driver leading to fire occurrence.Accurately estimating the DFMC could help identify locations facing fire risks,prioritise areas for fire monitoring,and facilitate timely deployment of fire-suppression resources.In this study,the DFMC and environmental variables,including air temperature,relative humidity,wind speed,solar radiation,rainfall,atmospheric pressure,soil temperature,and soil humidity,were simultaneously measured in a grassland of Ergun City,Inner Mongolia Autonomous Region of China in 2021.We chose three regression models,i.e.,random forest(RF)model,extreme gradient boosting(XGB)model,and boosted regression tree(BRT)model,to model the seasonal DFMC according to the data collected.To ensure accuracy,we added time-lag variables of 3 d to the models.The results showed that the RF model had the best fitting effect with an R2value of 0.847 and a prediction accuracy with a mean absolute error score of 4.764%among the three models.The accuracies of the models in spring and autumn were higher than those in the other two seasons.In addition,different seasons had different key influencing factors,and the degree of influence of these factors on the DFMC changed with time lags.Moreover,time-lag variables within 44 h clearly improved the fitting effect and prediction accuracy,indicating that environmental conditions within approximately 48 h greatly influence the DFMC.This study highlights the importance of considering 48 h time-lagged variables when predicting the DFMC of grassland fuels and mapping grassland fire risks based on the DFMC to help locate high-priority areas for grassland fire monitoring and prevention.

Easy modeling of realistic trees from freehand sketches

Creating realistic 3D tree models in a convenient way is a challenge in game design and movie making due to diversification and occlusion of tree structures. Current sketch-based and imagebased approaches for fast tree modeling have limitations in effect and speed, and they generally include complex parameter adjustment, which brings difficulties to novices. In this paper, we present a simple method for quickly generating various 3D tree models from freehand sketches without parameter adjustment. On two input images, the user draws strokes representing the main branches and crown silhouettes of a tree. The system automatically produces a 3D tree at high speed. First, two 2D skeletons are built from strokes, and a 3D tree structure resembling the input sketches is built by branch retrieval from the 2D skeletons. Small branches are generated within the sketched 2D crown silhouettes based on self-similarity and angle restriction. This system is demonstrated on a variety of examples. It maintains the main features of a tree： the main branch structure and crown shape, and can be used as a convenient tool for tree simulation and design.

When a tree model meets texture baking:an approach for quality-preserving lightweight visualization in virtual 3D scene construction

Three-dimensional(3D)high-fidelity surface models play an important role in urban scene construction.However,the data quantity of such models is large and places a tremendous burden on rendering.Many applications must balance the visual quality of the models with the rendering efficiency.The study provides a practical texture baking processing pipeline for generating 3D models to reduce the model complexity and preserve the visually pleasing details.Concretely,we apply a mesh simplification to the original model and use texture baking to create three types of baked textures,namely,a diffuse map,normal map and displacement map.The simplified model with the baked textures has a pleasing visualization effect in a rendering engine.Furthermore,we discuss the influence of various factors in the process on the results,as well as the functional principles and characteristics of the baking textures.The proposed approach is very useful for real-time rendering with limited rendering hardware as no additional memory or computing capacity is required for properly preserving the relief details of the model.Each step in the pipeline is described in detail to facilitate the realization.

Towards Efficient Robotic Software Development by Reusing Behavior Tree Structures for Task Planning Paradigms

interaction pipelines while maintaining interfaces for task-specific customization.The Structural-BT framework supports the modular design of structure functionalities and allows easy extensibility of the inner planning flows between BT components.With the Structural-BT framework,software engineers can develop robotic software by flexibly composing BT structures to formulate the skeleton software architecture and implement task-specific algorithms when necessary.In the experiment,this paper develops robotic software for diverse task scenarios and selects the baseline approaches of Robot Operating System(ROS)and classical BT development frameworks for comparison.By quantitatively measuring the reuse frequencies and ratios of BT structures,the Structural-BT framework has been shown to be more efficient than the baseline approaches for robotic software development.

Comparison of machine learning models for gully erosion susceptibility mapping

Gully erosion is a disruptive phenomenon which extensively affects the Iranian territory,especially in the Northern provinces.A number of studies have been recently undertaken to study this process and to predict it over space and ultimately,in a broader national effort,to limit its negative effects on local communities.We focused on the Bastam watershed where 9.3%of its surface is currently affected by gullying.Machine learning algorithms are currently under the magnifying glass across the geomorphological community for their high predictive ability.However,unlike the bivariate statistical models,their structure does not provide intuitive and quantifiable measures of environmental preconditioning factors.To cope with such weakness,we interpret preconditioning causes on the basis of a bivariate approach namely,Index of Entropy.And,we performed the susceptibility mapping procedure by testing three extensions of a decision tree model namely,Alternating Decision Tree(ADTree),Naive-Bayes tree(NBTree),and Logistic Model Tree(LMT).We dichotomized the gully information over space into gully presence/absence conditions,which we further explored in their calibration and validation stages.Being the presence/absence information and associated factors identical,the resulting differences are only due to the algorithmic structures of the three models we chose.Such differences are not significant in terms of performances;in fact,the three models produce outstanding predictive AUC measures(ADTree=0.922;NBTree=0.939;LMT=0.944).However,the associated mapping results depict very different patterns where only the LMT is associated with reasonable susceptibility patterns.This is a strong indication of what model combines best performance and mapping for any natural hazard-oriented application.

Analysis of hospitalization costs related to fall injuries in elderly patients

BACKGROUND With the aging world population,the incidence of falls has intensified and fallrelated hospitalization costs are increasing.Falls are one type of event studied in the health economics of patient safety,and many developed countries have conducted such research on fall-related hospitalization costs.However,China,a developing country,still lacks large-scale studies in this area.AIM To investigate the factors related to the hospitalization costs of fall-related injuries in elderly inpatients and establish factor-based,cost-related groupings.METHODS A retrospective study was conducted.Patient information and cost data for elderly inpatients(age≥60 years,n=3362)who were hospitalized between 2016 and 2019 due to falls was collected from the medical record systems of two grade-A tertiary hospitals in China.Quantile regression(QR)analysis was used to identify the factors related to fall-related hospitalization costs.A decision tree model based on the chi-squared automatic interaction detector algorithm for hospitalization cost grouping was built by setting the factors in the regression results as separation nodes.RESULTS The total hospitalization cost of fall-related injuries in the included elderly patients was 180479203.03 RMB,and the reimbursement rate of medical benefit funds was 51.0%(92039709.52 RMB/180479203.03 RMB).The medical material costs were the highest component of the total hospitalization cost,followed(in order)by drug costs,test costs,treatment costs,integrated medical service costs and blood transfusion costs The QR results showed that patient age,gender,length of hospital stay,payment method,wound position,wound type,operation times and operation type significantly influenced the inpatient cost(P<0.05).The cost grouping model was established based on the QR results,and age,length of stay,operation type,wound position and wound type were the most important influencing factors in the model.Furthermore,the cost of each combination varied significantly.CONCLUSION Our grouping model of hospitalization costs clearly reflected the key factors affecting hospitalization costs and can be used to strengthen the reasonable control of these costs.

Forecasting Multi-Step Ahead Monthly Reference Evapotranspiration Using Hybrid Extreme Gradient Boosting with Grey Wolf Optimization Algorithm

It is important for regional water resources management to know the agricultural water consumption information several months in advance.Forecasting reference evapotranspiration(ET_(0))in the next few months is important for irrigation and reservoir management.Studies on forecasting of multiple-month ahead ET_(0) using machine learning models have not been reported yet.Besides,machine learning models such as the XGBoost model has multiple parameters that need to be tuned,and traditional methods can get stuck in a regional optimal solution and fail to obtain a global optimal solution.This study investigated the performance of the hybrid extreme gradient boosting(XGBoost)model coupled with the Grey Wolf Optimizer(GWO)algorithm for forecasting multi-step ahead ET_(0)(1-3 months ahead),compared with three conventional machine learning models,i.e.,standalone XGBoost,multi-layer perceptron(MLP)and M5 model tree(M5)models in the subtropical zone of China.The results showed that theGWO-XGB model generally performed better than the other three machine learning models in forecasting 1-3 months ahead ET_(0),followed by the XGB,M5 and MLP models with very small differences among the three models.The GWO-XGB model performed best in autumn,while the MLP model performed slightly better than the other three models in summer.It is thus suggested to apply the MLP model for ET_(0) forecasting in summer but use the GWO-XGB model in other seasons.

Distributed privacy protection strategy for MEC enhanced wireless body area networks

With the rapid development and widespread application of Wireless Body Area Networks(WBANs),the traditional centralized system architecture cannot handle the massive data generated by the edge devices.Meanwhile,in order to ensure the security of physiological privacy data and the identity privacy of patients,this paper presents a privacy protection strategy for Mobile Edge Computing(MEC)enhanced WBANs,which leverages the blockchain-based decentralized MEC paradigm to support efficient transmission of privacy information with low latency,high reliability within a high-demand data security scenario.On this basis,the Merkle tree optimization model is designed to authenticate nodes and to verify the source of physiological data.Furthermore,a hybrid signature algorithm is devised to guarantee the node anonymity with unforgeability,data integrity and reduced delay.The security performance analysis and simulation results show that our proposed strategy not only reduces the delay,but also secures the privacy and transmission of sensitive WBANs data.