Visualizing “typical” and “exotic” Internet traffic data

The threat of cyber attacks motivates the need to monitor Internet traffic data for potentially abnormal behavior. Due to the enormous volumes of such data, statistical process monitoring tools, such as those traditionally used on data in the product manufacturing arena, are inadequate. “Exotic” data may indicate a potential attack; detecting such data requires a characterization of “typical” data. We devise some new graphical displays, including a “skyline plot,” that permit ready visual identification of unusual Internet traffic patterns in “streaming” data, and use appropriate statistical measures to help identify potential cyberattacks. These methods are illustrated on a moderate-sized data set (135,605 records) collected at George Mason University.     

“Beating” Fitts’ law: virtual enhancements for pointing facilitation

We survey recent research into new techniques for artificially facilitating pointing at targets in graphical user interfaces. While pointing in the physical world is governed by Fitts’ law and constrained by physical laws, pointing in the virtual world does not necessarily have to abide by the same constraints, opening the possibility for “beating” Fitts’ law with the aid of the computer by artificially reducing the target distance, increasing the target width, or both. The survey suggests that while the techniques developed to date are promising, particularly when applied to the selection of single isolated targets, many of them do not scale well to the common situation in graphical user interfaces where multiple targets are located in close proximity.     

Credit scoring with a data mining approach based on support vector machines

The credit card industry has been growing rapidly recently, and thus huge numbers of consumers’ credit data are collected by the credit department of the bank. The credit scoring manager often evaluates the consumer’s credit with intuitive experience. However, with the support of the credit classification model, the manager can accurately evaluate the applicant’s credit score. Support Vector Machine (SVM) classification is currently an active research area and successfully solves classification problems in many domains. This study used three strategies to construct the hybrid SVM-based credit scoring models to evaluate the applicant’s credit score from the applicant’s input features. Two credit datasets in UCI database are selected as the experimental data to demonstrate the accuracy of the SVM classifier. Compared with neural networks, genetic programming, and decision tree classifiers, the SVM classifier achieved an identical classificatory accuracy with relatively few input features. Additionally, combining genetic algorithms with SVM classifier, the proposed hybrid GA-SVM strategy can simultaneously perform feature selection task and model parameters optimization. Experimental results show that SVM is a promising addition to the existing data mining methods.     

Evaluation of sub-component alternatives in product design processes

In this paper, a sub-component selection methodology for product design is described. The described technique incorporates the analytic hierarchy process and linear goal programming into the process of evaluating alternatives for sub-components and parts, which enables the design of products by satisfying customer, technical, and financial requirements. Also, an additional comparison technique for comparing sub-component alternatives is developed, called “scoring matrix”. In this technique, pair-wise comparisons are performed within one matrix for all possible criteria to measure the strength of one-to-one relationship between sub-component alternatives. This technique is more appropriate than traditional analytical hierarchy process in addressing problems such as the comparison of sub-component alternatives. An illustrative example demonstrates the application of our methodology to the design of a computer system.     

The formation of learners’ semiosphere by authentic inquiry with an integrated learning object “Young Scientist”

An integrated learning object, a web-based inquiry environment “Young Scientist” for basic school level is introduced by applying the semiosphere conception for explaining learning processes. The study focused on the development of students’ (n = 30) awareness of the affordances of learning objects (LO) during the 3 inquiry tasks, and their ability of dynamically reconstructing meanings in the inquiry subtasks through exploiting these LO affordances in “Young Scientist”. The problem-solving data recorded by the inquiry system and the awareness questionnaire served as the data-collection methods.It was demonstrated that learners obtain complete awareness of the LO affordances in an integrated learning environment only after several problem-solving tasks. It was assumed that the perceived task-related properties and functions of LOs depend on students’ interrelations with LOs in specific learning contexts. Learners’ overall awareness of certain LO affordances, available in the inquiry system “Young Scientist”, developed with three kinds of patterns, describing the hierarchical development of the semiosphere model for learners. The better understanding of the LO affordances, characteristic to the formation of the functioning semiosphere, was significantly related to the advanced knowledge construction during these inquiry subtasks that presumed translation of information from one semiotic system to another. The implications of the research are discussed in the frames of the development of new contextual gateways for learning with virtual objects. It is assumed that effective LO-based learning has to be organized through pedagogically constrained gateways by manifesting certain LO affordances in the context in order to build up the dynamic semiosphere model for learners.     

Duality theory in fuzzy mathematical programming problems with fuzzy coefficients

In this paper, the notions of subgradient, subdifferential, and differential with respect to convex fuzzy mappings are investigated, which provides the basis for the fuzzy extremum problem theory. We consider the problems of minimizing or maximizing a convex fuzzy mapping over a convex set and develop the necessary and/or sufficient optimality conditions. Furthermore, the concept of saddle-points and minimax theorems under fuzzy environment is discussed. The results obtained are used to formulate the Lagrangian dual of fuzzy programming. Under certain fuzzy convexity assumptions, KKT conditions for fuzzy programming are derived, and the “perturbed” convex fuzzy programming is considered. Finally, these results are applied to fuzzy linear programming and fuzzy quadratic programming.     

Advertisement clicking prediction by using multiple criteria mathematical programming

In online advertisement industry, it is important to predict potentially profitable users who will click target ads (i.e., Behavioral targeting). The task selects the potential users that are likely to click the ads by analyzing user’s clicking/web browsing information and displaying the most relevant ads to them. This paper proposes four multiple criteria mathematical programming models for advertisement clicking problems. First two are multi-criteria linear regression (MCLR) and kernel-based multiple criteria regression (KMCR) algorithms for click-through rate (CTR) prediction. The second two are multi-criteria linear programming (MCLP) and kernel-based multiple criteria programming (KMCP) algorithms, which are used to predict ads clicking events, such as identifying clicked ads in a set of ads. Using the experimental datasets from KDD Cup 2012, the paper first conducts a comparison of the proposed MCLR and KMCR with the methods of support vector regression (SVR) and logistic regression (LR), which shows that both MCLR and KMCR are good alternatives. Then the paper further studies the performance between the proposed MCLP and KMCP algorithms with known algorithms, including support vector machines (SVM), LR, radial basis function network (RBFN), k-nearest neighbor algorithm (KNN) and Naïve Bayes (NB) in both prediction and selection processes. The studies show that the MCLP and KMCP models have better performance stability and can be used to effectively handle behavioral targeting application for online advertisement problems.     

Internal reinforcement in a connectionist genetic programming approach

Genetic programming (GP) can learn complex concepts by searching for the target concept through evolution of a population of candidate hypothesis programs. However, unlike some learning techniques, such as Artificial Neural Networks (ANNs), GP does not have a principled procedure for changing parts of a learned structure based on that structure's performance on the training data. GP is missing a clear, locally optimal update procedure, the equivalent of gradient-descent backpropagation for ANNs. This article introduces a new algorithm, “internal reinforcement”, for defining and using performance feedback on program evolution. This internal reinforcement principled mechanism is developed within a new connectionist representation for evolving parameterized programs, namely “neural programming”. We present the algorithms for the generation of credit and blame assignment in the process of learning programs using neural programming and internal reinforcement. The article includes a comprehensive overview of genetic programming and empirical experiments that demonstrate the increased learning rate obtained by using our principled program evolution approach.     

A rough set-based multiple criteria linear programming approach for the medical diagnosis and prognosis

It is well known that data mining is a process of discovering unknown, hidden information from a large amount of data, extracting valuable information, and using the information to make important business decisions. And data mining has been developed into a new information technology, including regression, decision tree, neural network, fuzzy set, rough set, and support vector machine. This paper puts forward a rough set-based multiple criteria linear programming (RS-MCLP) approach for solving classification problems in data mining. Firstly, we describe the basic theory and models of rough set and multiple criteria linear programming (MCLP) and analyse their characteristics and advantages in practical applications. Secondly, detailed analysis about their deficiencies are provided, respectively. However, because of the existing mutual complementarities between them, we put forward and build the RS-MCLP methods and models which sufficiently integrate their virtues and overcome the adverse factors simultaneously. In addition, we also develop and implement these algorithm and models in SAS and Windows system platforms. Finally, many experiments show that the RS-MCLP approach is prior to single MCLP model and other traditional classification methods in data mining, and remarkably improve the accuracy of medical diagnosis and prognosis simultaneously.     

Practical optimization by OR and simulation

This paper aims to promote and illustrate that the combination of classical operations research (queueing, linear and stochastic dynamic programming) and simulation (techniques and tools) can be most beneficial.First, an instructive example of parallel queues will be studied to address the question whether these queues should be pooled or not. This simple example already shows the necessary and fruitful combination of queueing and simulation. Next, the combined approach will be applied to and illustrated for:

• pooling (or not) of call centers,

• pooling (or not) in hospitals,

• checking-in at airports,

• flight catering, and

• assembly lines.

The applications show that “to pool or not” is not the only question for which further research and application of a combined OR-Simulation approach can be most fruitful for ‘practical optimization’.     

Programmable Rewriting Strategies in Haskell: — White Paper —

Programmable rewriting strategies provide a valuable tool for implementing traversal functionality in grammar-driven (or schema-driven) tools. The working Haskell programmer has access to programmable rewriting strategies via two similar options: (i) the Strafunski bundle for generic functional programming and language processing, and (ii) the “Scrap Your Boilerplate” approach to generic functional programming. Basic rewrite steps are encoded as monomorphic functions on datatypes. Rewriting strategies are polymorphic functions composed from appropriate basic strategy combinators.We will briefly review programmable rewriting strategies in Haskell. We will address the following questions:

• What are the merits of Haskellish strategies?

• What is the relation between strategic programming and generic programming?

• What are the challenges for future work on functional strategies?

A nexus of Cyber-Geography and Cyber-Psychology: Topos/“Notopia” and identity in hacking

This paper explores the Cyber-Psychological and Cyber-Geographic aspects of hacking and hacktivism. An examination of the literature related to hackers and hacking reveals a complex nexus of spatial (including cyber-spatial such as “Notopia”) and psychological aspects of hacking, from which emerges a central question of how humans perceive and manipulate their cyber-identities. Concealing (real and cyber) identities is typical in hacking. With our progressive acculturation with identity-less and place-less modes of existence, our cyber-identities through time may be studied from within John Locke’s criterion of “memory” and the spatial-geographical criterion of identity.     

Several multi-criteria programming methods for classification

In this paper, we propose a general optimization-based model for classification. Then we show that some well-known optimization-based methods for classification, which were developed by Shi et al. [Data mining in credit card portfolio management: a multiple criteria decision making approac. In: Koksalan M, Zionts S, editors. Multiple criteria decision making in the new millennium. Berlin: Springer; 2001. p. 427–36] and Freed and Glover [A linear programming approach to the discriminant problem. Decision Sciences 1981; 12: 68–79; Simple but powerful goal programming models for discriminant problems. European Journal of Operational Research 1981; 7: 44–60], are special cases of our model. Moreover, three new models, MCQP (multi-criteria indefinite quadratic programming), MCCQP (multi-criteria concave quadratic programming) and MCVQP (multi-criteria convex programming), are developed based on the general model. We also propose algorithms for MCQP and MCCQP, respectively. Then we apply these models to three real-life problems: credit card accounts, VIP mail-box and social endowment insurance classification. Extensive experiments are done to compare the efficiency of these methods.     

Well-posedness and attainability of indefinite stochastic linear quadratic control in infinite time horizon

This paper is concerned with a stochastic linear-quadratic (LQ) problem in an infinite time horizon with multiplicative noises both in the state and the control. A distinctive feature of the problem under consideration is that the cost weighting matrices for the state and the control are allowed to be indefinite. A new type of algebraic Riccati equation – called a generalized algebraic Riccati equation (GARE) – is introduced which involves a matrix pseudo-inverse and two additional algebraic equality/inequality constraints. It is then shown that the well-posedness of the indefinite LQ problem is equivalent to a linear matrix inequality (LMI) condition, whereas the attainability of the LQ problem is equivalent to the existence of a “stabilizing solution” to the GARE. Moreover, all possible optimal controls are identified via the solution to the GARE. Finally, it is proved that the solution to the GARE can be obtained via solving a convex optimization problem called semidefinite programming.     

A closed-loop logistic model with a spanning-tree based genetic algorithm

Due to the problem of global warming, the green supply chain management, in particular, closed-loop logistics, has drawn the attention of researchers. Although there were logistics models that were examined in the literatures, most of them were case based and not in a closed-loop. Therefore, they lacked generality and could not serve the purposes of recycling, reuse and recovery required in a green supply chain. In this study, the integration of forward and reverse logistics was investigated, and a generalized closed-loop model for the logistics planning was proposed by formulating a cyclic logistics network problem into an integer linear programming model. Moreover, the decisions for selecting the places of manufactories, distribution centers, and dismantlers with the respective operation units were supported with the minimum cost. A revised spanning-tree based genetic algorithm was also developed by using determinant encoding representation for solving this NP model. Numerical experiments were presented, and the results showed that the proposed model and algorithms were able to support the logistic decisions in a closed-loop supply chain efficiently and accurately.

Statement of scope and purposes

This study concerns with operations of 3R in the green supply chain logistics and the location selection optimization. Based on ‘cradle to cradle’ principle of a green product, a “closed-loop” structure of a network was proposed in order to integrate the environmental issues into a traditional logistic system. Due to NP-hard nature of the model, a Genetic Algorithm, which is based on spanning tree structure was developed. Test problems from the small size for accuracy to the large scale for efficiency have been demonstrated with comparison. The promising results have shown the applicability of the proposed model with the solution procedure.     

Performance analysis of GAME: A generic automated marking environment

This paper describes the Generic Automated Marking Environment (GAME) and provides a detailed analysis of its performance in assessing student programming projects and exercises. GAME has been designed to automatically assess programming assignments written in a variety of languages based on the “structure” of the source code and the correctness of the program’s output. Currently, the system is able to mark programs written in Java, C++ and the C language. To use the system, instructors are required to provide a simple “marking schema” for each given assessment item, which includes pertinent information such as the location of files and the model solution. In this research, GAME has been tested on a number of student programming exercises and assignments and its performance has been compared against that of a human marker. An in-depth statistical analysis of the comparison is presented, providing encouraging results and directions for employing GAME as a tool for teaching and learning.     

A class of classification and regression methods by multiobjective programming

An extensive review for the recent developments of multiple criteria linear programming data mining models is provided in this paper. These researches, which include classification and regression methods, are introduced in a systematic way. Some applications of these methods to real-world problems are also involved in this paper. This paper is a summary and reference of multiple criteria linear programming methods that might be helpful for researchers and applications in data mining.     

LP-based bounds for the container and multi-container loading problem

New relaxations are developed in this paper for problems of optimal packing of small (rectangular-shaped) pieces within one or several larger containers. Based on these relaxations tighter bounds for the Container Loading Problem (CLP) and the Multi-Container Loading Problem (MCLP) are obtained.
The new relaxations for the CLP and MCLP lead to linear programming problems. A corresponding solution approach is discussed which is based on a column generation technique. Results of computational tests are also given.     

A “mixed” small gain and passivity theorem in the frequency domain

We show that the negative feedback interconnection of two causal, stable, linear time-invariant systems, with a “mixed” small gain and passivity property, is guaranteed to be finite-gain stable. This “mixed” small gain and passivity property refers to the characteristic that, at a particular frequency, systems in the feedback interconnection are either both “input and output strictly passive”; or both have “gain less than one”; or are both “input and output strictly passive” and simultaneously both have “gain less than one”. The “mixed” small gain and passivity property is described mathematically using the notion of dissipativity of systems, and finite-gain stability of the interconnection is proven via a stability result for dissipative interconnected systems.     

A note on “Solving linear programming problems under fuzziness and randomness environment using attainment values”

This paper is an amendment to Hop’s paper [N.V. Hop, Solving linear programming problems under fuzziness and randomness environment using attainment values, Information Sciences 177 (2007) 2971-2984], in solving linear programming problems under fuzziness and randomness environments. Hop introduced a new characterization of relationship, attainment values, to enable the conversion of fuzzy (stochastic) linear programming models into corresponding deterministic linear programming models. The purpose of this paper is to provide a correction and an improvement of Hop’s analytical work through rationalization and simplification. More importantly, it is shown that Hop’s analysis does not support his demonstration or the solution-finding mechanism; the attainment values approach as he had proposed does not result in superior performance as compared to other existing approaches because it neglects some relevant and inevitable theoretical essentials. Two numerical examples from Hop’s paper are also employed to show that his approach, in the conversion of fuzzy (stochastic) linear programming problems to corresponding problems, is questionable and can neither find the maximum nor the minimum in the examples. The models of the examples are subsequently amended in order to derive the correct optimal solutions.