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The Ship Applied Fire Engineering (SAFE) computer program evaluates the probability of limiting the spread of fire from one compartment to another throughout a vessel. The results from SAFE are expressed in terms of the frequency of expected loss of each compartment. To evaluate these frequencies, it is necessary to determine the cumulative probability of loss of each compartment in the ship as a target from all possible fire paths from all possible rooms of origin. The existing algorithm in SAFE for calculating the loss of target compartments, referred to as the Cumulative L-Curve Algorithm," errs on the conservative side since it permits the contribution of a particular compartment to be counted more than once toward the total probability of loss for a target compartment. The approach selected to develop a mathematically valid cumulative L-Curve algorithm utilizes the mathematical tools of reliability theory, specifically for interconnected systems. The implication of this research is that with the new algorithm, SAFE will be able to more accurately calculate the fire safety levels of all compartments. Performance-based fire protection results must accurately indicate whether compartments are under- or over-protected relative to desired objectives. The improved algorithm will help achieve this goal.

Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(exp 15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.

Systems engineering decision algorithm related to Apollo Applications Program integration problems

Particle swarm optimization (PSO) is a widely recognized bio-inspired algorithm for systematically exploring solution spaces and iteratively iden-tifying optimal points. Through updating local and global best solutions, PSO effectively explores the search process, enabling the discovery of the most advantageous outcomes. This study proposes a novel Smith chart-based particle swarm optimization to solve convex and nonconvex multi-objective engineering problems by representing complex plane values in a polar coordinate system. The main contribution of this paper lies in the utilization of the Smith chart’s impedance and admittance circles to dynamically update the location of each particle, thereby effectively deter-mining the local best particle. The proposed method is applied to three test functions with different behaviors, namely concave, convex, noncon-tinuous, and nonconvex, and performance parameters are examined. The simulation results show that the proposed strategy offers successful conver-gence performance for multi-objective optimization applications and meets performance expectations with a well-distributed solution set.

A new optimization algorithm published on Knowledge-Based System in 2023.

Under the Engineering Analysis Task of Contract F08635- 87-C-0074, two development efforts pertaining to GADS (Graphic Attitude Determination System) algorithms were pursued. These efforts resulted in two upgrades to the GADS algorithms. The first upgrade resulted from a direct linear algebra solution for the model drawing equations for a body of revolution. The second upgrade resulted from developing an error analysis that is driven by the model being displayed to determine an estimated covariance for the errors in the position and attitude of the model being fit to the displayed image.

Under the Engineering Analysis Task of Contract F08635- 87-C-0074, two development efforts pertaining to GADS (Graphic Attitude Determination System) algorithms were pursued. These efforts resulted in two upgrades to the GADS algorithms. The first upgrade resulted from a direct linear algebra solution for the model drawing equations for a body of revolution. The second upgrade resulted from developing an error analysis that is driven by the model being displayed to determine an estimated covariance for the errors in the position and attitude of the model being fit to the displayed image.

Under the Engineering Analysis Task of Contract F08635- 87-C-0074, two development efforts pertaining to GADS (Graphic Attitude Determination System) algorithms were pursued. These efforts resulted in two upgrades to the GADS algorithms. The first upgrade resulted from a direct linear algebra solution for the model drawing equations for a body of revolution. The second upgrade resulted from developing an error analysis that is driven by the model being displayed to determine an estimated covariance for the errors in the position and attitude of the model being fit to the displayed image.

Protein structural alignment is an important problem in computational biology. In this paper, we present first successes on provably optimal pairwise alignment of protein inter-residue distance matrices, using the popular Dali scoring function. We introduce the structural alignment problem formally, which enables us to express a variety of scoring functions used in previous work as special cases in a unified framework. Further, we propose the first mathematical model for computing optimal structural alignments based on dense inter-residue distance matrices. We therefore reformulate the problem as a special graph problem and give a tight integer linear programming model. We then present algorithm engineering techniques to handle the huge integer linear programs of real-life distance matrix alignment problems. Applying these techniques, we can compute provably optimal Dali alignments for the very first time.

We present an adinkra-based computer algorithm implemented in a Mathematica code and use it in a limited demonstration of how to engineer off-shell, arbitrary N-extended world-sheet supermultiplets. Using one of the outputs from this algorithm, we present evidence for the unexpected discovery of a previously unknown 8 - 8 representation of N = 2 world sheet supersymmetry. As well, we uncover a menagerie of (p, q) = (3, 1) world sheet supermultiplets.

We present an adinkra-based computer algorithm implemented in a Mathematica code and use it in a limited demonstration of how to engineer off-shell, arbitrary N-extended world-sheet supermultiplets. Using one of the outputs from this algorithm, we present evidence for the unexpected discovery of a previously unknown 8 - 8 representation of N = 2 world sheet supersymmetry. As well, we uncover a menagerie of (p, q) = (3, 1) world sheet supermultiplets.

This report proposes Pattern Theory as a basis for an engineering theory of algorithms design. Pattern Theory (PT) begins with a general statement of the problem and then makes deliberate specializations. The problem of finding a pattern in a function is the essence of algorithm design. The key to PT is its measure of pattern-ness: Decomposed Function Cardinality (SFC). Low DFC indicates pattern-ness. The principal result is a demonstration of the generality with which DFC measures pattern-ness. This generality is supported theoretically by relating DFC to time complexity, program length and circuit complexity. A test is developed, based on DFC, for whether or not a function will decompose. This test is used in Ada Function Decomposition (AFD) programs. AFD produces a decomposition (i.e. an algorithm in combinational form) and DFC. The generality of DFC is also supported experimentally. The Pattern Theory approach to machine learning and data compression demonstrated greater generality than other approaches. The DFC's of over 800 nonrandom functions (numeric, symbolic, string based, graph based, images and files) were measured. Roughly 98% of the nonrandom functions had low DFC versus less than 1% for random functions. AFD found the classical algorithms for several functions.

A data mining based procedure for automated reverse engineering and defect discovery has been developed. The data mining algorithm for reverse engineering uses a genetic program (GP) as a data mining function. A GP is an evolutionary algorithm that automatically evolves populations of computer programs or mathematical expressions, eventually selecting one that is optimal in the sense that it maximizes a fitness function. The system to be reverse engineered is typically a subcomponent of a sensor that may not be disassembled and for which there are no design documents. The sensor is used to create a database of input signals and output measurements. Rules about the likely design properties of the sensor are collected from experts. The rules are used to create a fitness function for the GP, allowing GP-based data mining. This procedure incorporates not only the experts' rules into the fitness function, but also the information in the database. The information extracted through this process is the internal design specifications of the sensor. These design properties can be used to create a fitness function for a genetic algorithm (GA), which is in turn used to search for defects in the digital logic (DL) design. In this report, design flaws in two different sensor systems are detected using a GA. One of these systems makes passive detections, the other makes up part of a radar. In the second case, detecting the flaw allows the design of a radar jamming signal. Uncertainty related to the input-output database and the expert-based rule set can significantly alter the reverse engineering results. This report provides significant experimental and theoretical results related to GP-based data mining for reverse engineering. It presents methods of quantifying uncertainty. Finally, it examines methods for reducing the uncertainty.

A new method for developing tradeoffs in the engineering of complex systems is described. The Genetic-Algorithm-Based Normal Boundary Intersection (GANBI) method serves as a preprocessor for conventional genetic-algorithm-based Pareto optimization solvers. The algorithm is based on applying the normal boundary intersection approach of Pareto optimization to genetic solvers. The approach is shown to provide rapid convergence and to provide a better estimate of the Pareto set than existing state-of-the-art methods. A description of Pareto optimization methods for engineering design is included to put the new method in the context of existing solution approaches. The algorithm for the GANBI method is derived and detailed in the report, and numerical examples showing its efficiency in solving an academic problem are presented. The report concludes with an example of how the GANBI method has been used to make tradeoff decisions in the design of large-scale distributed undersea sensor networks.

A general purpose computer program is developed to perform nonlinear constrained optimization of engineering design problems. The program is developed especially for use on microcomputers and is called Microcomputer Software for Constrained Optimization Problems (MSCOP) . It will accept a nonlinear objective function and up to 50 inequality constraint functions and up to 20 bounded design variables. MSCOP employs the method of feasible directions. Although developed for microcomputers, for speed of development, the MSCOP was implemented on an IBM 3033 using standard basic language, Waterloo BASIC Version 2.0. It is directly transportable to a variety of microcomputers. Typical applications of MSCOP program are in the design of machine components and simple beam and truss structures. Solutions to three sample problems are given.

One mission of the National Air Intelligence Center (NAIC) is the reverse engineering of foreign missile weapon systems from incomplete observational data. In the past, intuition and repeated runs of a missile performance model were required to converge to a solution compatible with observed flight characteristics. This approach can be cumbersome and time consuming, as well as being subject to undesirable influences from the analyst's preconceptions and biases. An alternative approach has been created to apply genetic algorithm (GA) techniques to allow automation of the process, wider exploration of the design space, and more optimal solutions matching the observational data. The GA, when interfaced with a missile performance model, was able to identify a set of missiles that very closely matched the observed performance of a given sample missile. The approach was able to provide the analyst with multiple candidate missiles for further analysis that would have been missed by the previous trial and error approach.

In this paper we present a search algorithm that finds useful optical quantum states which can be created with current technology. We apply the algorithm to the field of quantum metrology with the goal of finding states that can measure a phase shift to a high precision. Our algorithm efficiently produces a number of novel solutions: we find experimentally-ready schemes to produce states that show significant improvements over the state-of-the-art, and can measure with a precision that beats the shot noise limit by over a factor of 4. Furthermore, these states demonstrate a robustness to moderate/high photon losses, and we present a conceptually simple measurement scheme that saturates the Cram\'er-Rao bound.

A general purpose computer program is developed to perform nonlinear constrained optimization of engineering design problems. The program is developed especially for use on microcomputers and is called Microcomputer Software for Constrained Optimization Problems (MSCOP). It will accept a nonlinear objective function and up to 50 inequality constraint functions and up to 20 bounded design variables. MSCOP employs the method of feasible directions. Although developed for microcomputers, for speed of development, the MSCOP was implemented on an IBM 3033 using standard basic language, Waterloo BASIC Version 2.0. It is directly transportable to a variety of microcomputers. Typical applications of MSCOP program are in the design of machine components and simple beam and truss structures. Solutions to three sample problems are given. Additional keywords: Theses; Fortran. (Author)

This paper aims to develop and verify the efficiency of algorithm aplications and compare them with the numerical techniques used in engineering areas, stem from structures of neural network for function approximation. For that, function modeling were used, based on complex formulas, making approximation mandatory. The statement investigated is that neural networks are capable to solve with great accuracy problems of this kind. As a test model, a measurement approximation of thermodynamics tables (saturated water) was carried and it was compared with the results of traditional methods (linear and quadratic functions), confirming the efficiency and possibility of its use.

This paper aims to develop and verify the efficiency of algorithm aplications and compare them with the numerical techniques used in engineering areas, stem from structures of neural network for function approximation. For that, function modeling were used, based on complex formulas, making approximation mandatory. The statement investigated is that neural networks are capable to solve with great accuracy problems of this kind. As a test model, a measurement approximation of thermodynamics tables (saturated water) was carried and it was compared with the results of traditional methods (linear and quadratic functions), confirming the efficiency and possibility of its use.

Although the exploitation of GWO advances sharply, it has limitations for continuous implementing exploration. On the other hand, the EHO algorithm easily has shown its capability to prevent local optima. For hybridization and by considering the advantages of GWO and the abilities of EHO, it would be impressive to combine these two algorithms. In this respect, the exploitation and exploration performances and the convergence speed of the GWO algorithm are improved by combining it with the EHO algorithm. Therefore, this paper proposes a new hybrid Grey Wolf Optimizer (GWO) combined with Elephant Herding Optimization (EHO) algorithm. Twenty-three benchmark mathematical optimization challenges and six constrained engineering challenges are used to validate the performance of the suggested GWOEHO compared to both the original GWO and EHO algorithms and some other well-known optimization algorithms. Wilcoxon's rank-sum test outcomes revealed that GWOEHO outperforms others in most function minimization. The results also proved that the convergence speed of GWOEHO is faster than the original algorithms.

Although the exploitation of GWO advances sharply, it has limitations for continuous implementing exploration. On the other hand, the EHO algorithm easily has shown its capability to prevent local optima. For hybridization and by considering the advantages of GWO and the abilities of EHO, it would be impressive to combine these two algorithms. In this respect, the exploitation and exploration performances and the convergence speed of the GWO algorithm are improved by combining it with the EHO algorithm. Therefore, this paper proposes a new hybrid Grey Wolf Optimizer (GWO) combined with Elephant Herding Optimization (EHO) algorithm. Twenty-three benchmark mathematical optimization challenges and six constrained engineering challenges are used to validate the performance of the suggested GWOEHO compared to both the original GWO and EHO algorithms and some other well-known optimization algorithms. Wilcoxon's rank-sum test outcomes revealed that GWOEHO outperforms others in most function minimization. The results also proved that the convergence speed of GWOEHO is faster than the original algorithms.

The first decade of the 21st century has seen tremendous improvements in mobile internet and its technologies. The high traffic volume of services such as video conference and other real-time traffic applications are imposing a great challenge on networks. In the meantime, demand for the use of mobile devices in computation and communication such as smart phones, personal digital assistants, and mobile-enabled laptops has grown rapidly. These services have driven the demand for increasing and guaranteing bandwidth requirements in the network. A direction of this paper is in the case of resource reservation protocol (RSVP) over mobile IPv6 networks. There are numbers of proposed solutions for RSVP and quality of service provision over mobile IPv6 networks, but most of them using advanced resource reservation. In this paper, we propose a mathematical model to determine maximum end-to-end delay bound through intermediate routers along the network. These bounds are sent back to the home agent for further processing. Once the home agent receives maximum end-to-end delay bounds, it calculates cumulative bound and compares this bound with the desired application end-to-end delay bound to make final decision on resource reservation. This approach improves network resource utilization.

The first decade of the 21st century has seen tremendous improvements in mobile internet and its technologies. The high traffic volume of services such as video conference and other real-time traffic applications are imposing a great challenge on networks. In the meantime, demand for the use of mobile devices in computation and communication such as smart phones, personal digital assistants, and mobile-enabled laptops has grown rapidly. These services have driven the demand for increasing and guaranteing bandwidth requirements in the network. A direction of this paper is in the case of resource reservation protocol (RSVP) over mobile IPv6 networks. There are numbers of proposed solutions for RSVP and quality of service provision over mobile IPv6 networks, but most of them using advanced resource reservation. In this paper, we propose a mathematical model to determine maximum end-to-end delay bound through intermediate routers along the network. These bounds are sent back to the home agent for further processing. Once the home agent receives maximum end-to-end delay bounds, it calculates cumulative bound and compares this bound with the desired application end-to-end delay bound to make final decision on resource reservation. This approach improves network resource utilization.

Measures of wealth and production have been found to scale superlinearly with the population of a city. Therefore, it makes economic sense for humans to congregate together in dense settlements. A recent model of population dynamics showed that population growth can become superexponential due to the superlinear scaling of production with population in a city. Here, we generalize this population dynamics model and demonstrate the existence of multiple stable equilibrium points, showing how population growth can be stymied by a poor economic environment. This occurs when the goods and services produced by the city become less profitable due to a lack of diversification in the city's economy. Then, relying on critical slowing down signals related to the stability of an equilibrium point, we present an algorithm for engineering regime shifts such that a city at a stable equilibrium point may continue to grow again. The generality of the model and the algorithm used here implies that the model and algorithm need not be restricted to urban systems; they are easily applicable to other types of systems where the assumptions used are valid.

Get the class materials to follow along at http://www.OpenSecurityTraining.info/IntroductionToReverseEngineering.html Follow us on Twitter for class news @OpenSecTraining  or via RSS at http://www.OpenSecurityTraining.info/ChangeBlog/ChangeBlog.html Throughout the history of invention curious minds have sought to understand the inner workings of their gadgets. Whether investigating a broken watch, or improving an engine, these people have broken down their goods into their elemental parts to understand how they work. This is Reverse Engineering (RE), and it is done every day from recreating outdated and incompatible software, understanding malicious code, or exploiting weaknesses in software. In this course by Frank Poz, we will explore what drives people to reverse engineer software and the methodology and tools used to do it. Topics include, but are not limited to: * Uses for RE * The tricks and pitfalls of analyzing compiled code * Identifying calling conventions * How to navigate x86 assembly using IDA Pro * Identifying Control Flows * Identifying the Win32 API * Using a debugger to aid RE * Dynamic Analysis tools and techniques for RE During the course students will complete many hands on exercises. Introductory Intel x86 ( http://www.OpenSecurityTraining.info/IntroX86.html ) is a direct and very necessary prerequisite which should be taken before this class. This class will serve as a prerequisite for a later class on malware static analysis.

Recent advances bring within reach the viability of solving combinatorial problems using a quantum annealing algorithm implemented on a purpose-built platform that exploits quantum properties. However, the question of how to tune the algorithm for most effective use in this framework is not well understood. In this paper we describe some operational parameters that drive performance, discuss approaches for mitigating sources of error, and present experimental results from a D-Wave Two quantum annealing processor.

Recent advances bring within reach the viability of solving combinatorial problems using a quantum annealing algorithm implemented on a purpose-built platform that exploits quantum properties. However, the question of how to tune the algorithm for most effective use in this framework is not well understood. In this paper we describe some operational parameters that drive performance, discuss approaches for mitigating sources of error, and present experimental results from a D-Wave Two quantum annealing processor.

Recent advances bring within reach the viability of solving combinatorial problems using a quantum annealing algorithm implemented on a purpose-built platform that exploits quantum properties. However, the question of how to tune the algorithm for most effective use in this framework is not well understood. In this paper we describe some operational parameters that drive performance, discuss approaches for mitigating sources of error, and present experimental results from a D-Wave Two quantum annealing processor.

The "Masterpiece Optimization Algorithm (MOA)" is introduced as a new nature-inspired meta-heuristic optimization method that mimics the masterpiece of Japanese Male Puffer Fishes (MPFs) that succeed in mating. Actually, the nest prepared by the MPFs for spawning is known as the masterpiece. Due to the importance of seabed fine sand to fish reproduction, the proposed algorithm is based on movement and accumulation of seabed fine sand particles. In order to find a place with a sufficient amount of fine sand for spawning, the MPF starts a search on the seabed. It then constructs the nest so that fine sand covers the eggs. The location with the greatest quantity of fine sand is where the majority of egg protection occurs. In the algorithm, this point is considered the global optimum. Now, to handle this content, in the first iteration of the proposed algorithm, different points in the answer zone are introduced for the MPF. Then, the MPF selects a point for the nest location using the Evaluator Function ( EF ). In continuation of the first iteration, three steps are executed. The point acquired in the third step represents a local optimum. For the next iteration, this point is deemed the nest location, and the aforementioned three steps are also executed. This process is done iteratively during each iteration. The best point of the last iteration is the global optimum of the problem in the answer zone. The obtained solutions of the MOA were then compared with some available techniques in the literature.

Genetic Algorithm Lecture By 5 Minutes Engineering

This paper provides a novel instructional methodology that is a unique E-Learning engineered "4A Metric Algorithm" designed to conceptually address the four main challenges faced by 21st century students, who are tempted to cheat in a myriad of higher education settings (face to face, hybrid, and online). The algorithmic online neuroscience supported instructional methodology detailed in the narrative also provides an active solution that when implemented addresses the needs of Colleges and Universities who desire new methods to achieve their academic goals (such as increase student retention and graduation rates, and respond to their high standards of student success without diminishing academic rigor). A case study is presented that details how to effectively and successfully implement the algorithm in an online graduate statistics course. Additional research into the infrastructure of the 4A Metric Algorithm as a dynamic neuroengineering online solution will further advance the 4A Metric Algorithm as a standard for in-depth online instruction that is also an interactive solution designed to effectively counter cheating and reduce its recidivism.

Get the class materials to follow along at http://www.OpenSecurityTraining.info/IntroductionToReverseEngineering.html Follow us on Twitter for class news @OpenSecTraining  or via RSS at http://www.OpenSecurityTraining.info/ChangeBlog/ChangeBlog.html Throughout the history of invention curious minds have sought to understand the inner workings of their gadgets. Whether investigating a broken watch, or improving an engine, these people have broken down their goods into their elemental parts to understand how they work. This is Reverse Engineering (RE), and it is done every day from recreating outdated and incompatible software, understanding malicious code, or exploiting weaknesses in software. In this course by Frank Poz, we will explore what drives people to reverse engineer software and the methodology and tools used to do it. Topics include, but are not limited to: * Uses for RE * The tricks and pitfalls of analyzing compiled code * Identifying calling conventions * How to navigate x86 assembly using IDA Pro * Identifying Control Flows * Identifying the Win32 API * Using a debugger to aid RE * Dynamic Analysis tools and techniques for RE During the course students will complete many hands on exercises. Introductory Intel x86 ( http://www.OpenSecurityTraining.info/IntroX86.html ) is a direct and very necessary prerequisite which should be taken before this class. This class will serve as a prerequisite for a later class on malware static analysis.

Robust optimization is a young and emerging field of research having received a considerable increase of interest over the last decade. In this paper, we argue that the the algorithm engineering methodology fits very well to the field of robust optimization and yields a rewarding new perspective on both the current state of research and open research directions. To this end we go through the algorithm engineering cycle of design and analysis of concepts, development and implementation of algorithms, and theoretical and experimental evaluation. We show that many ideas of algorithm engineering have already been applied in publications on robust optimization. Most work on robust optimization is devoted to analysis of the concepts and the development of algorithms, some papers deal with the evaluation of a particular concept in case studies, and work on comparison of concepts just starts. What is still a drawback in many papers on robustness is the missing link to include the results of the experiments again in the design.

This guide is intended to begin the process of standardizing the way in which algorithms are documented within the Advanced Development community at the Naval Undersea Warfare Center Division. The sections of this guide provide the outline for an Algorithm Description Document to be created for each algorithm. The format of the generated document should follow that of this guide as closely as is practicable. (MM)

Get the class materials to follow along at http://www.OpenSecurityTraining.info/ReverseEngineeringMalware.html Follow us on Twitter for class news @OpenSecTraining . An email arrives in your inbox: "You have to check out this picture!" It came from your friends address, which you know and trust. It beckons you to open it. Maybe you weren't fooled this time, but it's likely at least one of the 50 other recipients couldn't resist. As we store more of our confidential information on our computers, from bank account credentials, to company secrets, the reward to risk ratio increases as has the number malware (malicious software) threats. While anti-virus and intrusion detection systems have improved over the years, nothing can substitute a skilled malware analyst when a business needs to understand and mitigate a network intrusion. This class picks up where the Introduction to Reverse Engineering of Software course left off, exploring how static reverse engineering techniques can be used to understand what a piece of malware does and how it can be removed. Topics include: - Understanding common malware features and behavior  - Defeating code armoring and obfuscation  - Signature creation and applying prior analysis  - Dynamic analysis tools and how they can aid static analysis  During the course students will complete many hands on exercises. Before taking this class you should take Introduction to Intel x86 and Introduction to Reverse Engineering Software or have equivalent knowledge.

Get the class materials to follow along at http://www.OpenSecurityTraining.info/ReverseEngineeringMalware.html Follow us on Twitter for class news @OpenSecTraining . An email arrives in your inbox: "You have to check out this picture!" It came from your friends address, which you know and trust. It beckons you to open it. Maybe you weren't fooled this time, but it's likely at least one of the 50 other recipients couldn't resist. As we store more of our confidential information on our computers, from bank account credentials, to company secrets, the reward to risk ratio increases as has the number malware (malicious software) threats. While anti-virus and intrusion detection systems have improved over the years, nothing can substitute a skilled malware analyst when a business needs to understand and mitigate a network intrusion. This class picks up where the Introduction to Reverse Engineering of Software course left off, exploring how static reverse engineering techniques can be used to understand what a piece of malware does and how it can be removed. Topics include: - Understanding common malware features and behavior  - Defeating code armoring and obfuscation  - Signature creation and applying prior analysis  - Dynamic analysis tools and how they can aid static analysis  During the course students will complete many hands on exercises. Before taking this class you should take Introduction to Intel x86 and Introduction to Reverse Engineering Software or have equivalent knowledge.

The success or failure of students is a concern for every academic institution, college, university, governments and students themselves. This paper presents a model to determine the propensity of a student to succeed in the Electrical Engineering Department at Vaal University of Technology. Firstly, various machine learning algorithms which can be used in modelling and in predicting student success or failure are discussed as well a new algorithm called the date band algorithm. Secondly, the concept of an academic model is also discussed. This model defines the domain and focus of data used to make preditcions. The academic model consists of the subject, the lecturer and the sudent each of which has various attributes. One of the attributes discussed in this paper is the popularity index which is a measure of cohesiveness of the model.The Date Band Algorithm is presented among others in the development of the model. In this algorithm, predictions are made to optimize the performance of academic environment, thereby impacting on the choices of funders when they support students.

Bibliography: l. 55

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The most well-known evolutionary search methods are Genetic Algorithms (GAs). Despite the fact that they are often used in control engineering issues, they are not presently a conventional item in the regulator engineer's toolbox. This may be due in portion to the datum that there are few broad overviews of the use of GAs to regulator production issues now available, as well as the fact that they are often reported on at computer science conferences rather than control engineering conferences. This article tries to address this gap by providing an overview of current GA applications in the area of control engineering. The hereditary calculation (GA) is a model or deliberation of organic development in light of Charles Darwin's hypothesis of normal choice, made by John Holland and his associates during the 1960s and 1970s. Holland is broadly attributed similar to the first to utilize hybrid and recombination, change, and choice to the investigation of versatile and counterfeit frameworks. The hereditary calculation as a critical thinking strategy is deficient without these hereditary administrators. From that point forward, an assortment of hereditary calculation variations have been created and applied to an assortment of advancement issues, going from chart shading to design acknowledgment, from discrete frameworks (like the mobile sales rep issue) to persistent frameworks (e.g., the effective plan of airfoils in advanced plane design), and from monetary business sectors to multi-objective designing streamlining

The most well-known evolutionary search methods are Genetic Algorithms (GAs). Despite the fact that they are often used in control engineering issues, they are not presently a conventional item in the regulator engineer's toolbox. This may be due in portion to the datum that there are few broad overviews of the use of GAs to regulator production issues now available, as well as the fact that they are often reported on at computer science conferences rather than control engineering conferences. This article tries to address this gap by providing an overview of current GA applications in the area of control engineering. The hereditary calculation (GA) is a model or deliberation of organic development in light of Charles Darwin's hypothesis of normal choice, made by John Holland and his associates during the 1960s and 1970s. Holland is broadly attributed similar to the first to utilize hybrid and recombination, change, and choice to the investigation of versatile and counterfeit frameworks. The hereditary calculation as a critical thinking strategy is deficient without these hereditary administrators. From that point forward, an assortment of hereditary calculation variations have been created and applied to an assortment of advancement issues, going from chart shading to design acknowledgment, from discrete frameworks (like the mobile sales rep issue) to persistent frameworks (e.g., the effective plan of airfoils in advanced plane design), and from monetary business sectors to multi-objective designing streamlining

The most well-known evolutionary search methods are Genetic Algorithms (GAs). Despite the fact that they are often used in control engineering issues, they are not presently a conventional item in the regulator engineer's toolbox. This may be due in portion to the datum that there are few broad overviews of the use of GAs to regulator production issues now available, as well as the fact that they are often reported on at computer science conferences rather than control engineering conferences. This article tries to address this gap by providing an overview of current GA applications in the area of control engineering. The hereditary calculation (GA) is a model or deliberation of organic development in light of Charles Darwin's hypothesis of normal choice, made by John Holland and his associates during the 1960s and 1970s. Holland is broadly attributed similar to the first to utilize hybrid and recombination, change, and choice to the investigation of versatile and counterfeit frameworks. The hereditary calculation as a critical thinking strategy is deficient without these hereditary administrators. From that point forward, an assortment of hereditary calculation variations have been created and applied to an assortment of advancement issues, going from chart shading to design acknowledgment, from discrete frameworks (like the mobile sales rep issue) to persistent frameworks (e.g., the effective plan of airfoils in advanced plane design), and from monetary business sectors to multi-objective designing streamlining

The most well-known evolutionary search methods are Genetic Algorithms (GAs). Despite the fact that they are often used in control engineering issues, they are not presently a conventional item in the regulator engineer's toolbox. This may be due in portion to the datum that there are few broad overviews of the use of GAs to regulator production issues now available, as well as the fact that they are often reported on at computer science conferences rather than control engineering conferences. This article tries to address this gap by providing an overview of current GA applications in the area of control engineering. The hereditary calculation (GA) is a model or deliberation of organic development in light of Charles Darwin's hypothesis of normal choice, made by John Holland and his associates during the 1960s and 1970s. Holland is broadly attributed similar to the first to utilize hybrid and recombination, change, and choice to the investigation of versatile and counterfeit frameworks. The hereditary calculation as a critical thinking strategy is deficient without these hereditary administrators. From that point forward, an assortment of hereditary calculation variations have been created and applied to an assortment of advancement issues, going from chart shading to design acknowledgment, from discrete frameworks (like the mobile sales rep issue) to persistent frameworks (e.g., the effective plan of airfoils in advanced plane design), and from monetary business sectors to multi-objective designing streamlining