A User Participation Behavior Prediction Model of Social Hotspots Based on Influence and Markov Random Field

Hotspot topic trends can be captured by analyzing user attributes and historical behavior in social network. In this paper, we propose a user participation behavior prediction model for social hotspots, based on user behavior and relationship data, to predict user participation behavior and topic development trends. Firstly, for the complex factors of user behavior, three dynamic influence factor functions are defined, including individual, peer and community influence. These functions take timeliness into account using a time discretization method. Secondly, to determine laws of individual behavior and group behavior within a social topic, a hotspot user participation behavior prediction model is proposed and associated with the basic concepts of randora field and Markov property in information diffusion. The experimental results show that the model can not only dynamically predict the individual behavior, but also grasp the development trends of topics.

A Novel User Behavior Prediction Model Based on Automatic Annotated Behavior Recognition in Smart Home Systems

User behavior prediction has become a core element to Internet of Things(IoT)and received promising attention in the related fields.Many existing IoT systems(e.g.smart home systems)have been deployed various sensors and the user’s behavior can be predicted through the sensor data.However,most of the existing sensor-based systems use the annotated behavior data which requires human intervention to achieve the behavior prediction.Therefore,it is a challenge to provide an automatic behavior prediction model based on the original sensor data.To solve the problem,this paper proposed a novel automatic annotated user behavior prediction(AAUBP)model.The proposed AAUBP model combined the Discontinuous Solving Order Sequence Mining(DVSM)behavior recognition model and behavior prediction model based on the Long Short Term Memory(LSTM)network.To evaluate the model,we performed several experiments on a real-world dataset tuning the parameters.The results showed that the AAUBP model can effectively recognize behaviors and had a good performance for behavior prediction.

Predicting Purchasing Behavior on E-Commerce Platforms: A Regression Model Approach for Understanding User Features that Lead to Purchasing

This research introduces a novel approach to improve and optimize the predictive capacity of consumer purchase behaviors on e-commerce platforms. This study presented an introduction to the fundamental concepts of the logistic regression algorithm. In addition, it analyzed user data obtained from an e-commerce platform. The original data were preprocessed, and a consumer purchase prediction model was developed for the e-commerce platform using the logistic regression method. The comparison study used the classic random forest approach, further enhanced by including the K-fold cross-validation method. Evaluation of the accuracy of the model’s classification was conducted using performance indicators that included the accuracy rate, the precision rate, the recall rate, and the F1 score. A visual examination determined the significance of the findings. The findings suggest that employing the logistic regression algorithm to forecast customer purchase behaviors on e-commerce platforms can improve the efficacy of the approach and yield more accurate predictions. This study serves as a valuable resource for improving the precision of forecasting customers’ purchase behaviors on e-commerce platforms. It has significant practical implications for optimizing the operational efficiency of e-commerce platforms.

ICS-SVM:A user retweet prediction method for hot topics based on improved SVM

In social networks,many complex factors affect the prediction of user forwarding behavior.This paper proposes an improved SVM prediction method for user forwarding behavior of hot topics to improve prediction accuracy.Firstly,we consider that the improved Cuckoo Search algorithm can select the optimal penalty parameters and kernel function parameters to optimize the SVM and thus predict the user's forwarding behavior.Secondly,this paper considers the factors that affect the user forwarding behavior comprehensively from the user's own factors and external factors.Finally,based on the characteristics of the user's forwarding behavior changing over time,the time-slicing method is used to predict the trend of hot topics.Experiments show that the method can accurately predict the user's forwarding behavior and can sense the trend of hot topics.

Prediction of the dynamic effective properties of particle-reinforced composite materials

The prediction behaviors of some coherent plane wave equations for the effective velocities and attenuations of the coherent plane waves propagating through a composite material and for the effective elastic moduli of the composites are studied. The numerical results obtained by Waterman & Truell's, Twersky's and Gubernatis's equations for Glass-Epoxy composites with various volume fractions are compared. It is found that the predictions by both Twersky's and Gubernatis's equations underestimate the effective velocities and the effective elastic moduli when compare with the predictions by Waterman & Truell's equation. Furthermore, the deviations are more evident for the shear wave than that for the longitudinal wave. But these deviations decrease gradually with the increase of the frequency and increase gradually with the increase of the volume fraction.

Energy Balance-related Behaviors Are Related to Cardiometabolic Parameters and Predict Adiposity in 8-14-year-old Overweight Chinese Children One Year Later

To identify target energy balance-related behaviors（ERBs）,baseline data from 141overweight or obese schoolchildren（aged 8-14years old）was used to predict adiposity[body mass index（BMI）and fat percentage]one year later.The ERBs included a modified Dietary Approach to Stop Hypertension diet score（DASH score）,leisure-time physical activity（PA,days/week）,and leisure screen time（minutes/day）.Several cardiometabolic variables were measured in the fasting state, including systolic blood pressure （SBP）, diastolic blood pressure （DBP）, blood glucose （GLU）, total cholesterol （TC）, triglycerides （TG）, low-density lipoprotein （LDL-C）, and high-density lipoprotein （HDL-C）.

用于学习和解释行人预期行为的群体交互场

Anticipating others’actions is innate and essential in order for humans to navigate and interact well with others in dense crowds.This ability is urgently required for unmanned systems such as service robots and self-driving cars.However,existing solutions struggle to predict pedestrian anticipation accurately,because the influence of group-related social behaviors has not been well considered.While group relationships and group interactions are ubiquitous and significantly influence pedestrian anticipation,their influence is diverse and subtle,making it difficult to explicitly quantify.Here,we propose the group interaction field(GIF),a novel group-aware representation that quantifies pedestrian anticipation into a probability field of pedestrians’future locations and attention orientations.An end-to-end neural network,GIFNet,is tailored to estimate the GIF from explicit multidimensional observations.GIFNet quantifies the influence of group behaviors by formulating a group interaction graph with propagation and graph attention that is adaptive to the group size and dynamic interaction states.The experimental results show that the GIF effectively represents the change in pedestrians’anticipation under the prominent impact of group behaviors and accurately predicts pedestrians’future states.Moreover,the GIF contributes to explaining various predictions of pedestrians’behavior in different social states.The proposed GIF will eventually be able to allow unmanned systems to work in a human-like manner and comply with social norms,thereby promoting harmonious human-machine relationships.

Numerical simulation and experimental verification of bubble size distribution in an air dense medium fluidized bed

Bubble size distribution is the basic apparent performance and obvious characteristics in the air dense medium fluidized bed (ADMFB). The approaches of numerical simulation and experimental verification were combined to conduct the further research on the bubble generation and movement behavior. The results show that ADMFB could display favorable expanded characteristics after steady fluidization. With different particle size distributions of magnetite powder as medium solids, we selected an appropriate prediction model for the mean bubble diameter in ADMFB. The comparison results indicate that the mean bubble diameters along the bed heights are 35 mm < D b < 66 mm and 40 mm < D b < 69 mm with the magnetite powder of 0.3 mm+0.15mm and 0.15mm+0.074mm, respectively. The prediction model provides good agreements with the experimental and simulation data. Based on the optimal operating gas velocity distribution, the mixture of magnetite powder and <1mm fine coal as medium solids were utilized to carry out the separation experiment on 6-50mm raw coal. The results show that an optimal separation density d P of 1.73g/cm 3 with a probable error E of 0.07g/cm 3 and a recovery efficiency of 99.97% is achieved, which indicates good separation performance by applying ADMFB.

Fractal and chaotic laws on seismic dissipated energy in an energy system of enginering structures

Fractal and chaotic laws of engineering structures are discussed in this paper, it means that the intrinsic essences and laws on dynamic systems which are made from seismic dissipated energy intensity E d and intensity of seismic dissipated energy moment I e are analyzed. Based on the intrinsic characters of chaotic and fractal dynamic system of E d and I e, three kinds of approximate dynamic models are rebuilt one by one: index autoregressive model, threshold autoregressive model and local-approximate autoregressive model. The innate laws, essences and systematic error of evolutional behavior I e are explained over all, the short-term behavior predictability and long-term behavior probability of which are analyzed in the end. That may be valuable for earthquake-resistant theory and analysis method in practical engineering structures.

A Novel Cultural Crowd Model Toward Cognitive Artificial Intelligence

Existing literature shows cultural crowd management has unforeseen issues due to four dynamic elements;time,capacity,speed,and culture.Crosscultural variations are increasing the complexity level because each mass and event have different characteristics and challenges.However,no prior study has employed the six Hofstede Cultural Dimensions(HCD)for predicting crowd behaviors.This study aims to develop the Cultural Crowd-Artificial Neural Network(CC-ANN)learning model that considers crowd’s HCD to predict their physical(distance and speed)and social(collectivity and cohesion)characteristics.The model was developed towards a cognitive intelligent decision support tool where the predicted characteristics affect the estimated regulation plan’s time and capacity.We designed the experiments as four groups to analyze the proposed model’s outcomes and extract the interrelations between the HCD of crowd’s grouped individuals and their physical and social characteristics.Furthermore,the extracted interrelations were verified with the dataset’s statistical correlation analysis with a P-value<0.05.Results demonstrate that the predicted crowd’s characteristics were positively/negatively affected by their considered cultural features.Similarly,analyzing outcomes identified the most influential HCD for predicting crowd behavior.The results also show that the CC-ANN model improves the prediction and learning performance for the crowd behavior because the achieved accepted level of accuracy does not exceed 10 to 20 epochs in most cases.Moreover,the performance improved by 90%,93%respectively in some cases.Finally,all prediction best cases were related to one or more cultural features with a low error of 0.048,0.117,0.010,and 0.014 mean squared error,indicating a novel cultural learning model.

Experimental and simulated characterization of creep behavior of P92 steel with prior cyclic loading damage

The effect of prior cyclic loading on creep behavior of P92 steel was investigated. Creep tests on prior cyclic loading exposure specimens were performed at 650？C and 130 MPa. In order to clarify the influence of prior cyclic loading on creep behavior, optical microscope, scanning electron microscope and transmission electron microscope were used. Experimental results indicate that the prior cyclic loading degrades the creep strength significantly. However, the degradation tends to be saturated with further increase in prior cyclic loading. From the view of microstructural evolution, the recovery of martensite laths takes place during prior cyclic loading exposure. This facilitates the dislocation movement during the following creep process. Therefore, premature rupture of creep test occurs. Additionally, saturated behavior of degradation can be attributed to the near completed recovery of martensite laths. Based on the effect of prior cyclic loading, a newly modified Hayhurst creep damage model was proposed to consider the prior cyclic loading damage. The main advantage of the proposed model lies in its ability to directly predict creep behavior with different levels of prior cyclic loading damage. Comparison of the predicted and experimental results shows that the proposed model can give a reasonable prediction for creep behavior of P92 steel with different level of prior cyclic loading damage.

On-Line Predicting Behaviors of Jobs in Dynamic Load Balancing

A key issue of dynamic load balancing in a loosely coupled distributed systemis selecting appropriate jobs to transfer. In this paper, a job selection policybased on on-line predicting behaviors of jobs is proposed. Thacing is used atthe beginning of execution of a job to predict the approkimate execution timeand resource requirements of the job so as to make a correct decision aboutwhether transferring the job is worthwhile. A dynamic load balancer using thejob selection policy has been implemelited. Experimelital measurement resultsshow that the policy proposed is able to improve mean response time of jobsand resource utilization of systems substantially.

cffdrs: an R package for the Canadian Forest Fire Danger Rating System

Introduction:The Canadian Forest Fire Danger Rating System(CFFDRS)is a globally known wildland fire risk assessment system,and two major components,the fire weather index system and the fire behavior prediction system,have been extensively used both nationally and internationally to aid operational wildland fire decision making.Methods:In this paper,we present an overview of an R package cffdrs,which is developed to calculate components of the CFFDRS,and highlight some of its functionality.In particular,we demonstrate how these functions could be used for large data analysis.Results and Discussion:With this cffdrs package,we provide a portal for not only a collection of R functions dealing with all available components in CFFDRS but also a platform for various additional developments that are useful for the understanding of fire occurrence and behavior.This is the first time that all relevant CFFDRS methods are incorporated into the same platform,which can be accessed by both the management and research communities.

A novel spatio-temporal trajectory data-driven development approach for autonomous vehicles

Nowadays,autonomous driving has been attracted widespread attention from academia and industry.As we all know,deep learning is effective and essential for the development of AI components of Autonomous Vehicles(AVs).However,it is challenging to adopt multi-source heterogenous data in deep learning.Therefore,we propose a novel data-driven approach for the delivery of high-quality Spatio-Temporal Trajectory Data(STTD)to AVs,which can be deployed to assist the development of AI components with deep learning.The novelty of our work is that the meta-model of STTD is constructed based on the domain knowledge of autonomous driving.Our approach,including collection,preprocessing,storage and modeling of STTD as well as the training of AI components,helps to process and utilize huge amount of STTD efficiently.To further demonstrate the usability of our approach,a case study of vehicle behavior prediction using Long Short-Term Memory(LSTM)networks is discussed.Experimental results show that our approach facilitates the training process of AI components with the STTD.