90,888 research outputs found
Comprehensive Security Framework for Global Threats Analysis
Cyber criminality activities are changing and becoming more and more professional. With the growth of financial flows through the Internet and the Information System (IS), new kinds of thread arise involving complex scenarios spread within multiple IS components. The IS information modeling and Behavioral Analysis are becoming new solutions to normalize the IS information and counter these new threads. This paper presents a framework which details the principal and necessary steps for monitoring an IS. We present the architecture of the framework, i.e. an ontology of activities carried out within an IS to model security information and User Behavioral analysis. The results of the performed experiments on real data show that the modeling is effective to reduce the amount of events by 91%. The User Behavioral Analysis on uniform modeled data is also effective, detecting more than 80% of legitimate actions of attack scenarios
Dynamic hybrid simulation of batch processes driven by a scheduling module
Simulation is now a CAPE tool widely used by practicing engineers for process design and control. In particular, it allows various offline analyses to improve system performance such as productivity, energy efficiency, waste reduction, etc. In this framework, we have developed the dynamic hybrid simulation environment PrODHyS whose particularity is to provide general and reusable object-oriented components dedicated to the modeling of devices and operations found in chemical processes. Unlike continuous processes, the dynamic simulation of batch processes requires the execution of control recipes to achieve a set of production orders. For these reasons, PrODHyS is coupled to a scheduling module (ProSched) based on a MILP mathematical model in order to initialize various operational parameters and to ensure a proper completion of the simulation. This paper focuses on the procedure used to generate the simulation model corresponding to the realization of a scenario described through a particular scheduling
Model validation of simple-graph representations of metabolism
The large-scale properties of chemical reaction systems, such as the
metabolism, can be studied with graph-based methods. To do this, one needs to
reduce the information -- lists of chemical reactions -- available in
databases. Even for the simplest type of graph representation, this reduction
can be done in several ways. We investigate different simple network
representations by testing how well they encode information about one
biologically important network structure -- network modularity (the propensity
for edges to be cluster into dense groups that are sparsely connected between
each other). To reach this goal, we design a model of reaction-systems where
network modularity can be controlled and measure how well the reduction to
simple graphs capture the modular structure of the model reaction system. We
find that the network types that best capture the modular structure of the
reaction system are substrate-product networks (where substrates are linked to
products of a reaction) and substance networks (with edges between all
substances participating in a reaction). Furthermore, we argue that the
proposed model for reaction systems with tunable clustering is a general
framework for studies of how reaction-systems are affected by modularity. To
this end, we investigate statistical properties of the model and find, among
other things, that it recreate correlations between degree and mass of the
molecules.Comment: to appear in J. Roy. Soc. Intefac
Deep Interest Evolution Network for Click-Through Rate Prediction
Click-through rate~(CTR) prediction, whose goal is to estimate the
probability of the user clicks, has become one of the core tasks in advertising
systems. For CTR prediction model, it is necessary to capture the latent user
interest behind the user behavior data. Besides, considering the changing of
the external environment and the internal cognition, user interest evolves over
time dynamically. There are several CTR prediction methods for interest
modeling, while most of them regard the representation of behavior as the
interest directly, and lack specially modeling for latent interest behind the
concrete behavior. Moreover, few work consider the changing trend of interest.
In this paper, we propose a novel model, named Deep Interest Evolution
Network~(DIEN), for CTR prediction. Specifically, we design interest extractor
layer to capture temporal interests from history behavior sequence. At this
layer, we introduce an auxiliary loss to supervise interest extracting at each
step. As user interests are diverse, especially in the e-commerce system, we
propose interest evolving layer to capture interest evolving process that is
relative to the target item. At interest evolving layer, attention mechanism is
embedded into the sequential structure novelly, and the effects of relative
interests are strengthened during interest evolution. In the experiments on
both public and industrial datasets, DIEN significantly outperforms the
state-of-the-art solutions. Notably, DIEN has been deployed in the display
advertisement system of Taobao, and obtained 20.7\% improvement on CTR.Comment: 9 pages. Accepted by AAAI 201
Decision system based on neural networks to optimize the energy efficiency of a petrochemical plant
The energy efficiency of industrial plants is an important issue in any type of business but particularly in
the chemical industry. Not only is it important in order to reduce costs, but also it is necessary even more
as a means of reducing the amount of fuel that gets wasted, thereby improving productivity, ensuring
better product quality, and generally increasing profits. This article describes a decision system developed
for optimizing the energy efficiency of a petrochemical plant. The system has been developed after
a data mining process of the parameters registered in the past. The designed system carries out an optimization
process of the energy efficiency of the plant based on a combined algorithm that uses the following
for obtaining a solution: On the one hand, the energy efficiency of the operation points occurred in
the past and, on the other hand, a module of two neural networks to obtain new interpolated operation
points. Besides, the work includes a previous discriminant analysis of the variables of the plant in order to
select the parameters most important in the plant and to study the behavior of the energy efficiency
index. This study also helped ensure an optimal training of the neural networks. The robustness of the
system as well as its satisfactory results in the testing process (an average rise in the energy efficiency
of around 7%, reaching, in some cases, up to 45%) have encouraged a consulting company (ALIATIS) to
implement and to integrate the decision system as a pilot software in an SCADA
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Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information
The complex temporal heterogeneity of rainfall coupled with mountainous physiographic context makes a great challenge in the development of accurate short-term rainfall forecasts. This study aims to explore the effectiveness of multiple rainfall sources (gauge measurement, and radar and satellite products) for assimilation-based multi-sensor precipitation estimates and make multi-step-ahead rainfall forecasts based on the assimilated precipitation. Bias correction procedures for both radar and satellite precipitation products were first built, and the radar and satellite precipitation products were generated through the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), respectively. Next, the synthesized assimilated precipitation was obtained by merging three precipitation sources (gauges, radars and satellites) according to their individual weighting factors optimized by nonlinear search methods. Finally, the multi-step-ahead rainfall forecasting was carried out by using the adaptive network-based fuzzy inference system (ANFIS). The Shihmen Reservoir watershed in northern Taiwan was the study area, where 641 hourly data sets of thirteen historical typhoon events were collected. Results revealed that the bias adjustments in QPESUMS and PERSIANN-CCS products did improve the accuracy of these precipitation products (in particular, 30-60% improvement rates for the QPESUMS, in terms of RMSE), and the adjusted PERSIANN-CCS and QPESUMS individually provided about 10% and 24% contribution accordingly to the assimilated precipitation. As far as rainfall forecasting is concerned, the results demonstrated that the ANFIS fed with the assimilated precipitation provided reliable and stable forecasts with the correlation coefficients higher than 0.85 and 0.72 for one- and two-hour-ahead rainfall forecasting, respectively. The obtained forecasting results are very valuable information for the flood warning in the study watershed during typhoon periods. © 2013 Elsevier B.V
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