A malware propagation prediction model based on representation learning and graph convolutional networks

The traditional malware research is mainly based on its recognition and detection as a breakthrough point,without focusing on its propagation trends or predicting the subsequently infected nodes.The complexity of network structure,diversity of network nodes,and sparsity of data all pose difficulties in predicting propagation.This paper proposes a malware propagation prediction model based on representation learning and Graph Convolutional Networks(GCN)to address the aforementioned problems.First,to solve the problem of the inaccuracy of infection intensity calculation caused by the sparsity of node interaction behavior data in the malware propagation network,a mechanism based on a tensor to mine the infection intensity among nodes is proposed to retain the network structure information.The influence of the relationship between nodes on the infection intensity is also analyzed.Second,given the diversity and complexity of the content and structure of infected and normal nodes in the network,considering the advantages of representation learning in data feature extraction,the corresponding representation learning method is adopted for the characteristics of infection intensity among nodes.This can efficiently calculate the relationship between entities and relationships in low dimensional space to achieve the goal of low dimensional,dense,and real-valued representation learning for the characteristics of propagation spatial data.We also design a new method,Tensor2vec,to learn the potential structural features of malware propagation.Finally,considering the convolution ability of GCN for non-Euclidean data,we propose a dynamic prediction model of malware propagation based on representation learning and GCN to solve the time effectiveness problem of the malware propagation carrier.The experimental results show that the proposed model can effectively predict the behaviors of the nodes in the network and discover the influence of different characteristics of nodes on the malware propagation situation.

Design Property Network-Based Change Propagation Prediction Approach for Mechanical Product Development

Design changes are unavoidable during mechanical product development; whereas the avalanche propagation of design change imposes severely negative impacts on the design cycle. To improve the validity of the change propagation prediction, a mathematical program- ming model is presented to predict the change propagation impact quantitatively. As the foundation of change propa- gation prediction, a design change analysis model（DCAM） is built in the form of design property network. In DCAM, the connections of the design properties are identified as the design specification, which conform to the small-world network theory. To quantify the change propagation impact, change propagation intensity（CPI） is defined as a quantitative and much more objective assessment metric. According to the characteristics of DCAM, CPI is defined and indicated by tour assessment factors： propagation likelihood, node degree, long-chain linkage, and design margin. Furthermore, the optimal change propagation path is searched with the evolutionary ant colony optimiza- tion（ACO） algorithm, which corresponds to the minimized maximum of accumulated CPI. In practice, the change impact of a gear box is successfully analyzed. The pro- posed change propagation prediction method is verified tobe efficient and effective, which could provide different results according to various the initial changes.

STUDY ON GIS BASED PREDICTION SYSTEM FOR RADIO WAVE PROPAGATION IN URBAN REGION

In this letter, an integrated application of the prediction for radio wave propagation with the Geographic Information System (GIS) is presented and a real prediction system based on GIS is implemented.

3-D FRACTURE PROPAGATION SIMULATION AND PRODUCTION PREDICTION IN COALBED

In accordance with the fracturing and producing mechanism in coalbed methane well, and combining the knowledge of fluid mechanics, linear elastic fracture mechanics, thermal transfer, computing mathematics and software engineering, the three-dimensional hydraulic fracture propagating and dynamical production predicting models for coalbed methane well is put forward. The fracture propagation model takes the variation of rock mechanical properties and in-situ stress distribution into consideration. The dynamic performance prediction model takes the gas production mechanism into consideration. With these models, a three-dimensional hydraulic fracturing optimum design software for coalbed methane well is developed, and its practicality and reliability have been proved by ex-ample computation.

A Predictive 6G Network with Environment Sensing Enhancement:From Radio Wave Propagation Perspective

In order to support the future digital society,sixth generation(6G)network faces the challenge to work efficiently and flexibly in a wider range of scenarios.The traditional way of system design is to sequentially get the electromagnetic wave propagation model of typical scenarios firstly and then do the network design by simulation offline,which obviously leads to a 6G network lacking of adaptation to dynamic environments.Recently,with the aid of sensing enhancement,more environment information can be obtained.Based on this,from radio wave propagation perspective,we propose a predictive 6G network with environment sensing enhancement,the electromagnetic wave propagation characteristics prediction enabled network(EWave Net),to further release the potential of 6G.To this end,a prediction plane is created to sense,predict and utilize the physical environment information in EWave Net to realize the electromagnetic wave propagation characteristics prediction timely.A two-level closed feedback workflow is also designed to enhance the sensing and prediction ability for EWave Net.Several promising application cases of EWave Net are analyzed and the open issues to achieve this goal are addressed finally.

Performance prediction of gravity concentrator by using artificial neural network-a case study

In conventional chromite beneficiation plant, huge quantity of chromite is used to loss in the form of tailing. For recovery these valuable mineral, a gravity concentrator viz. wet shaking table was used.Optimisation along with performance prediction of the unit operation is necessary for efficient recovery.So, in this present study, an artificial neural network(ANN) modeling approach was attempted for predicting the performance of wet shaking table in terms of grade(%) and recovery(%). A three layer feed forward neural network(3:3–11–2:2) was developed by varying the major operating parameters such as wash water flow rate(L/min), deck tilt angle(degree) and slurry feed rate(L/h). The predicted value obtained by the neural network model shows excellent agreement with the experimental values.

Interpretation and Classification of P-Series Recommendations in ITU-R

As ITU-R Recommendations is widely implemented for countries all over the world, the role and status of ITU-R Recommendations are increasingly prominent in the field of radio engineering. ITU and ITU-R Study Groups are summarized. Furthermore, the operating mode of the third study group, and the input documents are interpreted in detail. Lastly, from both wireless system design and electromagnetic compatibility analysis perspective, all of 79 P-series Recommendations are analyzed and classified, and the main contents of each Recommendation are summarized. The above research promote P-series Recommendations are widely used in China.

Research on Influencing Factors of Information Dissemination Based on User Preference

The rapid development of the Intemet makes the social network of information dissemination has undergone tremendous changes. Based on the introduction of social network information dissemination mode, this paper analyzes the influencing factors of information dissemination, establishes the user preference model through CP-nets tool, and combines the AHP principle to mine the user＇s preference order, and obtain the user＇s optimal preference feature Portfolio, and finally collect the user in the microblogging platform in the historical behavior data. the use of NetLogo different users of information dissemination decision to predict.

Generation and Application of A Standardized Load-Time History to Tubular T-joints in Offshore Platforms

Marine structures are mostly made of metals and always experience complex random loading during their service periods. The fatigue crack growth behaviors of metal materials have been proved from laboratory tests to be sensitive to the loading sequence encountered. In order to take account of the loading sequence effect, fatigue life prediction should be based on fatigue crack propagation（FCP） theory rather than the currently used cumulative fatigue damage（CFD） theory. A unified fatigue life prediction（UFLP） method for marine structures has been proposed by the authors＇ group. In order to apply the UFLP method for newly designed structures, authorities such as the classification societies should provide a standardized load-time history（SLH） such as the TWIST and FALSTAFF sequences for transport and fighter aircraft. This paper mainly aims at proposing a procedure to generate the SLHs for marine structures based on a short-term loading sample and to provide an illustration on how to use the presented SLH to a typical tubular T-joint in an offshore platform based on the UFLP method.

Characterization of Organic-Rich Shales for Petroleum Exploration & Exploitation: A Review-Part 3: Applied Geomechanics, Petrophysics and Reservoir Modeling

Modeling geomechanical properties of shales to make sense of their complex properties is at the forefront of petroleum exploration and exploitation application and has received much re- search attention in recent years. A shale＇s key geomechanical properties help to identify its ＂fracibility＂ its fluid flow patterns and rates, and its in-place petroleum resources and potential commercial re- serves. The models and the information they provide, in turn, enable engineers to design drilling pat- terns, fracture-stimulation programs and materials selection that will avoid formation damage and op- timize recovery of petroleum. A wide-range of tools, technologies, experiments and mathematical techniques are deployed to achieve this. Characterizing the interconnected fracture, permeability and porosity network is an essential step in understanding a shales highly-anisotropic features on multiple scales （nano to macro）. Weli-log data, and its petrophysical interpretation to calibrate many geome- chanical metrics to those measured in rock samples by laboratory techniques plays a key role in pro- viding affordable tools that can be deployed cost-effectively in multiple well bores. Likewise, micro- seismic data helps to match fracture density and propagation observed on a reservoir scale with pre- dictions from simulations and laboratory tests conducted on idealised/simplified discrete fracture net- work models. Shales complex wettability, adsorption and water imbibition characteristics have a sig- nificant influence on potential formation damage during stimulation and the short-term and long-term flow of petroleum achievable. Many gas flow mechanisms and models are proposed taking into ac- count the multiple flow mechanisms involved （e.g., desorption, diffusion, slippage and viscous flow op- erating at multiple porosity levels from nano- to macro-scales）. Fitting historical production data and well decline curves to model predictions helps to verify whether model＇s geomechanical assumptions are realistic or not. This review discusses the techniques applied and the models developed that are relevant to applied geomechanics, highlighting examples of their application and the numerous out- standin~ questions associated with them.