Modeling Software Tools Research Articles

Detecting web attacks with end-to-end deep learning

Web applications are popular targets for cyber-attacks because they are network-accessible and often contain vulnerabilities. An intrusion detection system monitors web applications and issues alerts when an attack attempt is detected. Existing implementations of intrusion detection systems usually extract features from network packets or string characteristics of input that are manually selected as relevant to attack analysis. Manually selecting features, however, is time-consuming and requires in-depth security domain knowledge. Moreover, large amounts of labeled legitimate and attack request data are needed by supervised learning algorithms to classify normal and abnormal behaviors, which is often expensive and impractical to obtain for production web applications.This paper provides three contributions to the study of autonomic intrusion detection systems. First, we evaluate the feasibility of an unsupervised/semi-supervised approach for web attack detection based on the Robust Software Modeling Tool (RSMT), which autonomically monitors and characterizes the runtime behavior of web applications. Second, we describe how RSMT trains a stacked denoising autoencoder to encode and reconstruct the call graph for end-to-end deep learning, where a low-dimensional representation of the raw features with unlabeled request data is used to recognize anomalies by computing the reconstruction error of the request data. Third, we analyze the results of empirically testing RSMT on both synthetic datasets and production applications with intentional vulnerabilities. Our results show that the proposed approach can efficiently and accurately detect attacks, including SQL injection, cross-site scripting, and deserialization, with minimal domain knowledge and little labeled training data.

Computational Photography, 3-D Modeling, and Online Publication of Basketry for Cache Cave, California

ABSTRACTAdvanced 2-D and 3-D computer visualizations are increasingly being used for recording and documentation, analysis, dissemination, and public engagement purposes. Recent technological advances not only considerably improve data acquisition, processing, and analysis but also enable easy and efficient online presentation. This article evaluates the contributions of advanced 2-D and 3-D computer visualization and discusses the potential of 3-D modeling for recording basketry technology and documenting the state of preservation of baskets. It explores the available analysis, integration, and online dissemination tools, using as case studies recently excavated baskets from Cache Cave in southern California. Results indicate that the proposed methodology, which incorporates reflectance transformation imaging visualizations and photogrammetric 3-D models, which are further processed using 3-D modeling software and integrated analysis tools and then transformed to a Web-based format, is a useful addition to the basketry analysis toolkit.

New method for calculating the susceptances of a balancing capacitive compensator for a three-phase four-wire distribution network

Nowadays, there is an increasing development of equipment type Custom Power Devices (CPDs) designed to ensure a high level of electrical power quality at the Point of Common Coupling (PCC). Most of these devices are built as advanced power electronics applications, such as the Switching Power Converters (SPCs) category. However, for a wide range of applications in consumer installations and electricity distribution operators, such as power factor correction, load balancing, voltage regulation, or flicker mitigation, the reactive power compensators based on passive circuit elements, Reactive Power Compensators (RPCs) are successfully used. The most efficient RPCs are those built so that they may adapt to variable regimes, known as Static Var Compensators (SVCs). Two of the usual functions of a SVC, namely power factor improvement and load balancing in a three-phase distribution network, can be achieved simultaneously by using an unbalanced SVC built in the version of Adaptive Balancing Reactive Compensator (ABRC). The paper briefly–outlines the mathematical model and operation of a Balancing Reactive Compensator (BRC) for a three-phase four-wire network. Then, moves on to develop a direct method of sizing based on equations that directly link the susceptances’ values of the compensator with the real and imaginary components of load sequence currents. The problem is to determine the weight of these components, meaning their degree of compensation, by requiring only capacitive or null susceptances and avoiding capacitive over-compensation of positive sequence currents. Thus, a particular case of BRC, i.e. a Balancing Capacitive Compensator (BCC) is obtained, which keeps the same functions, but has the advantage of reducing costs by eliminating high-power single-phase coils and by simplifying the compensation control system. The paper also presents the numerical results and the conclusions of a case study on the application of the method, performed using two computational and modeling software tools. Experimental laboratory determinations and modeling under nonsinusoidal conditions complete the analysis. The results demonstrate the correctness of the new method of solving the sizing problem and once again validate the unbalanced capacitive compensation as an efficient method of power factor improvement and load balancing at a PCC belonging to a three-phase four-wire distribution network.

Techno‐economic and life‐cycle assessments of small‐scale biorefineries for isobutene and xylo‐oligosaccharides production: a comparative study in Portugal and Chile

AbstractThis work presents a comparative simulation study involving the techno‐economic and environmental assessment of lignocellulosic‐based small‐scale biorefineries, integrated with a piggery waste‐based anaerobic digestion platform (ADB), located in Portugal and Chile. Two main products are obtained: isobutene and xylo‐oligosaccharides (XOS). The bioproduction of isobutene using a genetically engineered organism (Escherichia coli), coupled with the removal and purification of high added‐value XOS, obtained after a feedstock hydrothermal pre‐treatment, was evaluated. Two lignocellulosic agricultural wastes were used: corn stover in the Portuguese case study and wheat straw in Chilean case study. Both processes were simulated using the Aspen Plus modeling software tool, while the Aspen Process Economic Analyzer was used to carry out the economic evaluation. The simulation results were validated with experimental data from the laboratory and the literature. An economic assessment was performed considering the different locations of both biorefineries. A life‐cycle analysis (LCA) was also applied to evaluate the differences in environmental impacts on both locations. The results showed that the isobutene / XOS biorefinery concept was economically viable in both Portugal and Chile, mainly due to the high market value of XOS. The biorefinery has lower production costs for isobutene and XOS (1 US$/kg of isobutene and 1.18 US$/kg of XOS) when located in Portugal, as compared with Chile (1.14 US$/kg of isobutene and 1.56 US$/kg of XOS). Conversely, it leads to less environmental impact when located in Chile: 48.8 kgCO2eq./GJisobutene, in comparison to 60.7 kgCO2eq./GJisobutene in Portugal. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd

Comprehensive Review of 3D Segmentation Software Tools for MRI Usable for Pelvic Surgery Planning.

Patient-specific 3D modeling is the first step towards image-guided surgery, the actual revolution in surgical care. Pediatric and adolescent patients with rare tumors and malformations should highly benefit from these latest technological innovations, allowing personalized tailored surgery. This study focused on the pelvic region, located at the crossroads of the urinary, digestive, and genital channels with important vascular and nervous structures. The aim of this study was to evaluate the performances of different software tools to obtain patient-specific 3D models, through segmentation of magnetic resonance images (MRI), the reference for pediatric pelvis examination. Twelve software tools freely available on the Internet and two commercial software tools were evaluated using T2-w MRI and diffusion-weighted MRI images. The software tools were rated according to eight criteria, evaluated by three different users: automatization degree, segmentation time, usability, 3D visualization, presence of image registration tools, tractography tools, supported OS, and potential extension (i.e., plugins). A ranking of software tools for 3D modeling of MRI medical images, according to the set of predefined criteria, was given. This ranking allowed us to elaborate guidelines for the choice of software tools for pelvic surgical planning in pediatric patients. The best-ranked software tools were Myrian Studio, ITK-SNAP, and 3D Slicer, the latter being especially appropriate if nerve fibers should be included in the 3D patient model. To conclude,this study proposed a comprehensive review of software tools for 3D modeling of the pelvis according to a set of eight criteria and delivered specific conclusions for pediatric and adolescent patients that can be directly applied to clinical practice.

Demand prediction and regulation zoning of urban-industrial land: Evidence from Beijing-Tianjin-Hebei Urban Agglomeration, China.

As a main type of urban construction land, urban-industrial land is used to provide the judging criteria for construction land scale in the planning period when urban population, industrial development, investment scale, and other conditions are uncertain in China; however, research on expected indicator such as urban-industrial land in overall land use plan mainly focuses on qualitative analysis; quantitative analysis research has not yet been carried out. Using MATLAB R2016a software modeling tools to establish GM (1, 1) model and RBF neural network model, respectively, this paper predicted the demand of urban-industrial land in Beijing-Tianjin-Hebei Urban Agglomeration. Comparing the predicated results with the actual value of urban-industrial land in Beijing, Tianjin, and 11 prefecture-level cities in Hebei Province, we determined the reasonable prediction model for urban-industrial land after testing the accuracy of the two prediction models. The results showed that the RBF neural network model was the more reasonable prediction model for urban-industrial land. Using the predicted results of the RBF neural network model, combining expected indicators of overall land use plan (2006-2020) in Beijing and Tianjin, as well as 11 prefecture-level cities in Hebei Province in the planning target year, determined remaining usable time of urban-industrial land. Finally, combined with the actual scale of urban-industrial land in 2015 and the predicated scale of urban-industrial land in 2020, the remaining usable time of each city's urban-industrial land was calculated in terms of the average annual growth rate of urban-industrial land from 2009 to 2015. According to the comparative relationship between the remaining usable time and the remaining time of the overall land use plan (5 years), urban-industrial lands were divided into three kinds of regulation zones: reasonable reduction zone, optimized adjustment zone, and core development zone. The policy implications for urban-industrial land in each regulation zone were also provided. This paper can provide reference for regulation zoning of urban-industrial land in developing countries and regions.

Modeling of dynamic mass coupled system with Runge-Kutta fourth order

This paper shows the mathematical modeling process of a mechanical system of masses coupled by two springs and a shock absorber. The process of capture of movement of the mass system coupled with springs was done with software Tracker video analysis and modeling tool. The motion capture of the coupled masses A and B was made with a duration of 20 seconds. A comparison of the results of the movement of the two masses will be established as a first instance, by adjusting trajectories of curves in time using Matlab software and as a second instance a numerical solution of the Mass-Spring model will be established using the Runge Kutta method of 4 order. With this last method, it is expected to achieve a better precision modeling of the initial value problem. Finally, a comparison of the two mathematical models will be made analyzing.

How to use neural network and web technologies in modeling complex technical systems

This paper discusses the problem of integrating modern methods of forecasting and modeling complex technical objects into the learning process. As an example, the problem of solving a system of ordinary differential equations is considered, which has significant practical application. In particular, solving a system of differential equations can be an essential part of patents. The neural network method to solve this problem by using Matlab simulation software and visual modeling tool Simulink is considered. Efficient cloud-based solution to ordinary differential equations is presented.

STAlagmite dating by radiocarbon (star): A software tool for reliable and fast age depth modelling

Abstract Speleothems, secondary cave carbonates, are important tools for climate reconstruction, especially as they often can be very precisely dated with the UTh method. If the U-Th method fails, dating becomes difficult, and often results in abandonment of samples and study sites. Radiocarbon dating is the only other radiometric dating technique applicable to the last ∼50 ka, but presents complexities related to temporal variability of the reservoir effect in speleothems. Thus, radiocarbon dating of speleothems is not straightforward, and there are currently no publicly available tools to define proper age-depth relationships with this method. Here, we present an improved version of a previously published radiocarbon based age-depth modelling approach (star, Lechleitner et al., 2016b), which is now made freely available. The software is easy to use and provides the possibility to obtain reliable age-depth relationships, without prior knowledge of reservoir effects and their variability. In addition, star is able to detect and handle growth stops and phases with different growth rates. We test star on artificially constructed data sets and illustrate steps to improve the model performance. Furthermore, we apply the new approach to published radiocarbon data of U-Th dated stalagmites. This offers the possibility to investigate the strengths and weaknesses of the new approach with respect to potentially significant long term trends in the radiocarbon reservoir effect, which might otherwise remain undetected. In summary, we have produced a valuable software, which easily enables to construct age-depth relationships on the basis of reservoir effect disturbed radiocarbon measurements.

Metamodelling for Design of Mechatronic and Cyber-Physical Systems

The paper presents the issue of metamodeling of Domain-Specific Languages (DSL) for the purpose of designing complex mechatronics systems. Usually, one of the problems during the development of such projects is an interdisciplinary character of the team that is involved in this endeavour. The success of a complex machine project (e.g., Computer Numerically Controlled machine (CNC), loading crane, forestry crane) often depends on a proper communication between team members. The domain-specific modelling languages developed using one of the two approaches discussed in the work, lead to a machine design that can be carried out much more efficiently than with conventional approaches. Within the paper, the Meta-Object Facility (MOF) approach to metamodeling is presented; it is much more prevalent in modern modelling software tools than Graph-Object-Property-Relationship-Role (GOPRR). The main outcome of this work is the first presentation of researchML modelling language that is the result of more than twenty ambitious research and development projects. It is effectively used within new enterprises and leads to improved traceability of the project goals. It enables for fully-featured automatic code generation which is one of the main pillars of the agile management within mechatronic system design projects.

Ambit-GCM: An Open-source Software Tool for Group Contribution Modelling.

Ambit-GCM is a new software tool for group contribution modelling (GCM), developed as a part of the chemoinformatics platform AMBIT. It is an open-source tool distributed under LGPL license, written in Java and based on the Chemistry Development Kit. Ambit-GCM provides an environment for creating models of molecular properties using additive schemes of zero, first or second orders. Ambit-GCM supports a set of local atomic attributes used for dynamic configuration of desired atom descriptions, which are applied to define fragments of different sizes. All defined groups are exhaustively generated for each molecule from a training set of compounds and combined to form the basic set of GCM fragments. Additionally, Ambit-GCM users can define correction factors via custom SMARTS notations or add externally calculated molecular descriptors. A molecular property model is obtained as a sum over all found groups by multiplying each group or correction factor frequency to its corresponding contribution. Multiple linear regression analysis (MLRA) is used for group contributions calculation. Ambit-GCM performs full statistical characterization of the obtained MLRA models via various validation techniques: external tests validation, cross validation, y-scrambling, etc. The software can be optionally used only for molecule fragmentation combined with an external statistical modelling package for further processing. Ambit-GCM example usage and test cases are given.

Student experience with software modeling tools

Modeling is a key concept in software engineering education, since students need to learn it in order to be able to produce large-scale and reliable software. Quality tools are needed to support modeling in education, but existing tools vary considerably both in their features and in their strengths and weaknesses. The objective of the research presented in this paper was to help professors and students choose tools by determining which strengths and weaknesses matter most to students, which tools exhibit which of these strengths and weaknesses, and how difficult to use are various tools for students. To achieve this objective, we conducted a survey of the use of modeling tools among students in software engineering courses from Brazil, Canada, USA, Spain, Denmark, UK and China. We report the results regarding the 31 UML tools that 117 participants have used, focusing on the nine tools that the students have used most heavily. Common benefits quoted by students in choosing a tool include simplicity of installing and learning, being free, supporting the most important notations and providing code generation. The most cited complaints about tools include lack of feedback, being slow to use, difficulty drawing the diagrams, not interacting well with other tools and being complex to use. This research also compares the results with the findings of another survey conducted among professors who taught modeling. The results should benefit tool developers by suggesting ways they could improve their tools. The results should also help inform the selection of tools by educators and students.

Safer Predictions

Where is it safest to design access points for highways and express lanes? James Allen reports on a unique traffic modeling software tool that has helped answer this question in Minnesota, and is even influencing policy across the USA

Software tools for complex modeling in monitoring and forecasting of emergencies using the Earth remote sensing data

Software tools for complex modeling in monitoring and forecasting of emergencies using the Earth remote sensing data

Development and assessment of a solar, wind and hydrogen hybrid trigeneration system

A solar-wind hybrid trigeneration system is proposed and analyzed thermodynamically through energy and exergy approaches in this paper. Hydrogen, electricity and heat are the useful products generated by the hybrid system. The system consists of a solar heliostat field, a wind turbine and a thermochemical copper-chlorine (Cu-Cl) cycle for hydrogen production linked with a hydrogen compression system. A solar heliostat field is employed as a source of thermal energy while the wind turbine is used to generate electricity. Electric power harvested by the wind turbine is supplied to the electrolyzer and compressors and provides an additional excess of electricity. Hydrogen produced by the thermochemical copper-chlorine (Cu-Cl) cycle is compressed in a hydrogen compression system for storage purposes. Both Aspen Plus 9.0 and EES are employed as software tools for the system modeling and simulation. The system is designed to achieve high hydrogen production rate of 455.1 kg/h. The overall energy and exergy efficiencies of the hybrid system are 49% and 48.2%, respectively. Some additional results about the system performance are obtained, presented and discussed in the paper.

Development and validation of an HVAC on/off controller in EnergyPlus for energy simulation of residential and small commercial buildings

Heating, ventilation, and air-conditioning (HVAC) systems are sized using design conditions, conditions that occur only a small percentage of time each year. As a result, HVAC systems frequently operate at part-load conditions since their capacity is larger than necessary except in the most extreme weather conditions. Most HVAC systems for residential and small commercial buildings in the U.S. use on/off controls, however, most energy modeling software tools do not simulate the on/off nature of this type of HVAC controls. This paper presents the development and validation of an on/off controller for residential applications in EnergyPlus using a custom EMS (energy management system). This controller is validated using minute-level field data collected for a house located in Sacramento, California with simulated occupancy and internal loads. The results obtained from EnergyPlus with and without the use of the developed on/off controller are also compared. The application of the developed on/off controller improves the HVAC energy use results accuracy around 19% in terms of the Normalized Mean Bias Error (NMBE) at the one-minute level compared to the results without the application of the on/off controller. It also makes the nature of operation and associated energy use signal of the HVAC system more realistic in terms of the on/off nature of the residential direct expansion coils. Furthermore, in this paper, the accuracy improvement of the results dominated by the internal loads is investigated by applying the one-minute schedule compared to the usual hourly schedules for the internal thermal loads.

FCM Expert: Software Tool for Scenario Analysis and Pattern Classification Based on Fuzzy Cognitive Maps

Fuzzy Cognitive Maps (FCMs) have become a suitable and proven knowledge-based methodology for systems modeling and simulation. This technique is especially attractive when modeling systems characterized by ambiguity, and/or non-trivial causalities among its variables. The rich literature that is found related to FCMs reports very clearly many successful studies solved through the use of FCMs; however, when it comes to software implementations, where domain experts can design FCM-based systems, run simulations or perform more advanced experiments, not much is found or documented. The few existing implementations are not proficient in providing options for experimentation. Therefore, we believe that a gap exists, specifically between the theoretical advances and the development of accurate, transparent and sound FCM-based systems; and we advocate for the creation of more complete and exible software products. The goal of this paper is to introduce “FCM Expert”, a software tool for fuzzy cognitive modeling, where we focus on scenario analysis and pattern classification. The main features of FCM Expert rely on Machine Learning algorithms to compute the parameters that might define a model, optimize its network topology and improve the system convergence without losing information. Also, FCM Expert allows performing WHAT-IF simulations and studying the system behavior through a friendly, intuitive and easy-to-use graphical user interface.

Iterative Method for Determining the Values of the Susceptances of a Balancing Capacitive Compensator

To increase the electrical power quality, in the last decades, an intense development in the last decades of high-performance equipment built as advanced power electronics applications, such as the compensators from Switching Power Converter category, has taken place. For all that, Reactive Power Compensators (RPC) based on passive circuit elements, such as Static var Compensators (SVCs), still occupy a wide range of applications in customer and installations of the distribution system installations. The functions of power factor (PF) improvement and load balancing in a three-phase distribution network can be achieved with an unbalanced SVC, known as the Adaptive Balancing Reactive Compensator (ABRC). Presenting first the mathematical model of the initial sizing and the working mechanism of a Balancing Reactive Compensator (BRC) for a three-phase four-wire network, this article develops a compensator resizing algorithm through an iterative change of the initial sizing to transform the compensator into a Balancing Capacitive Compensator (BCC), which keeps the same functions. By using two computational and modeling software tools, a case study on the application of the method was carried out, demonstrating the availability of the sizing problem solution and validating the unbalanced capacitive compensation as an efficient way to PF improving and load balancing in a PCC (Point of Common Coupling).

Exploring the Interplay between CAD and FreeFem++ as an Energy Decision-Making Tool for Architectural Design

The energy modelling software tools commonly used for architectural purposes do not allow a straightforward real-time implementation within the architectural design programs. In addition, the surrounding exterior spaces of the building, including the inner courtyards, hardly present a specific treatment distinguishing these spaces from the general external temperature in the thermal simulations. This is a clear disadvantage when it comes to streamlining the design process in relation to the whole-building energy optimization. In this context, the present study aims to demonstrate the advantages of the FreeFem++ open source program for performing simulations in architectural environments. These simulations include microclimate tests that describe the interactions between a building architecture and its local exterior. The great potential of this mathematical tool can be realized through its complete system integration within CAD (Computer-Aided Design) software such as SketchUp or AutoCAD. In order to establish the suitability of FreeFem++ for the performance of simulations, the most widely employed energy simulation tools able to consider a proposed architectural geometry in a specific environment are compared. On the basis of this analysis, it can be concluded that FreeFem++ is the only program displaying the best features for the thermal performance simulation of these specific outdoor spaces, excluding the currently unavailable easy interaction with architectural drawing programs. The main contribution of this research is, in fact, the enhancement of FreeFem++ usability by proposing a simple intuitive method for the creation of building geometries and their respective meshing (pre-processing). FreeFem++ is also considered a tool for data analysis (post-processing) able to help engineers and architects with building energy-efficiency-related tasks.

Estimating Attractor Reachability in Asynchronous Logical Models.

Logical models are well-suited to capture salient dynamical properties of regulatory networks. For networks controlling cell fate decisions, cell fates are associated with model attractors (stable states or cyclic attractors) whose identification and reachability properties are particularly relevant. While synchronous updates assume unlikely instantaneous or identical rates associated with component changes, the consideration of asynchronous updates is more realistic but, for large models, may hinder the analysis of the resulting non-deterministic concurrent dynamics. This complexity hampers the study of asymptotical behaviors, and most existing approaches suffer from efficiency bottlenecks, being generally unable to handle cyclical attractors and quantify attractor reachability. Here, we propose two algorithms providing probability estimates of attractor reachability in asynchronous dynamics. The first algorithm, named Firefront, exhaustively explores the state space from an initial state, and provides quasi-exact evaluations of the reachability probabilities of model attractors. The algorithm progresses in breadth, propagating the probabilities of each encountered state to its successors. Second, Avatar is an adapted Monte Carlo approach, better suited for models with large and intertwined transient and terminal cycles. Avatar iteratively explores the state space by randomly selecting trajectories and by using these random walks to estimate the likelihood of reaching an attractor. Unlike Monte Carlo simulations, Avatar is equipped to avoid getting trapped in transient cycles and to identify cyclic attractors. Firefront and Avatar are validated and compared to related methods, using as test cases logical models of synthetic and biological networks. Both algorithms are implemented as new functionalities of GINsim 3.0, a well-established software tool for logical modeling, providing executable GUI, Java API, and scripting facilities.