Konstantinos G. Margaritis

Using SVD and demographic data for the enhancement of generalized Collaborative Filtering

In this paper we examine how Singular Value Decomposition (SVD) along with demographic information can enhance plain Collaborative Filtering (CF) algorithms. After a brief introduction to SVD, where some of its previous applications in Recommender Systems are revisited, we proceed with a full description of our proposed method which utilizes SVD and demographic data at various points of the filtering procedure in order to improve the quality of the generated predictions. We test the efficiency of the resulting approach on two commonly used CF approaches (User-based and Item-based CF). The experimental part of this work involves a number of variations of the proposed approach. The results show that the combined utilization of SVD with demographic data is promising, since it does not only tackle some of the recorded problems of Recommender Systems, but also assists in increasing the accuracy of systems employing it.

An Experimental Study of Benchmarking Functions for Genetic Algorithms

This paper presents a review and experimental results on the major benchmarking functions used for performance control of Genetic Algorithms (GAs). Parameters considered include the effect of population size, crossover probability and pseudo-random number generators (PNGs). The general computational behavior of two basic GAs models, the Generational Replacement Model (GRM) and the Steady State Replacement Model (SSRM) is evaluated.

ON BENCHMARKING FUNCTIONS FOR GENETIC ALGORITHMS

This paper presents experimental results on the major benchmarking functions used for performance evaluation of Genetic Algorithms (GAs). Parameters considered include the effect of population size, crossover probability, mutation rate and pseudorandom generator. The general computational behavior of two basic GAs models, the Generational Replacement Model (GRM) and the Steady State Replacement Model (SSRM) is evaluated.

Cognitive mapping and certainty neuron fuzzy cognitive maps

Cognitive maps (CMs) and fuzzy cognitive maps (FCMs) are well-established techniques that attempt to emulate the cognitive process of human experts on specific domains by creating causal models as signed/weighted directed graphs of concepts and the various causal relationships that exist between the concepts. They are mainly used for decision making and prediction. A number of extensions are proposed to increase their inference and representation capabilities. Certainty neuron fuzzy cognitive maps (CNFCMs) are proposed by the authors. This structure can be considered as a recurrent neural network with certainty neurons to be used that are neurons using a special kind of transfer function of two variables. The new transfer function employs the certainty factor handling function that was used in the MYCIN expert system, and also imposes a decay mechanism. In CMs and most FCMs, the activation level of every concept of the model, in a crisp on/off manner, can take one value among the two allowed values, −1 or 1. CNFCM allows the activation level to be any decimal in the interval [−1,1] increasing the representation capabilities of the model. The equations that are applied at the equilibrium points of the CNFCM are found. Through simulations, the dynamical behavior of CNFCMs is presented and the inference capabilities are illustrated in comparison to that of the classical FCM by means of an example.

The MYCIN certainty factor handling function as uninorm operator and its use as a threshold function in artificial neurons

Abstract The uninorm operator class as defined by Yager, unifies the t-norm and t-conorm operator classes and allows special kind of aggregation that depends on the identity element. In the paper, the MYCIN certainty factor handling function f M is proved to belong to the uninorm operator class. The shape of function f M is studied and its resemblance to artificial neurons threshold functions is established. The two variable function f M can be seen as an extension of typical neuron threshold functions to the three-dimensional space. The use of one of the two variables as a parameter gives the neuron tuning capabilities. A specific class of Artificial Neural Networks that cope with uncertainty and can use M as threshold function is also proposed.

An experimental study of the dynamics of the certainty neuron fuzzy cognitive maps

Abstract Certainty Neurons have been introduced as a new type of artificial neurons that use a two variable transfer function that provides them with memory capabilities and decay mechanism. They are used in fuzzy cognitive maps which is an artificial neural network structure that creates models as collections of concepts – neurons and the various causal relationships – weighted arcs that exist between them. An experimental study of the certainty neuron fuzzy cognitive maps (CNFCMs) dynamical behaviour is presented as this appears through simulations. Two control parameters are used: the symmetry of the systems weight matrix and the strength of the decay mechanism. The values of these two parameters can lead the system to exhibit stable fixed point behaviour, limit cycle behaviour or to collapse. The ways that the two control parameters cause the change of the systems dynamical behaviour from fixed point to limit cycle are also presented. The areas where the systems exhibit specific dynamical behaviour are identified.

String Matching on a Multicore GPU Using CUDA

Graphics Processing Units (GPUs) have evolved over the past few years from dedicated graphics rendering devices to powerful parallel processors, outperforming traditional Central Processing Units (CPUs) in many areas of scientific computing. The use of GPUs as processing elements was very limited until recently, when the concept of General-Purpose computing on Graphics Processing Units (GPGPU) was introduced. GPGPU made possible to exploit the processing power and the memory bandwidth of the GPUs with the use of APIs that hide the GPU hardware from programmers. This paper presents experimental results on the parallel processing for some well known on-line string matching algorithms using one such GPU abstraction API, the Compute Unified Device Architecture (CUDA).

Performance comparison of memetic algorithms

Local search techniques have been applied in optimization methods. The effect of local search to the memetic algorithms can make multimodal and non-linear problems easier to solve. Parameters considered include the effect of population size and recombination mechanisms. Experiments comparing three local search techniques for a memetic algorithm are represent. Further, we have adopted a global parallelization approach that preserves the properties, behavior, and fundamental of the sequential algorithm.

A multipopulation cultural algorithm for the electrical generator scheduling problem

The electrical generator maintenance scheduling problem has been tackled by a variety of traditional optimisation techniques over the years. This paper proposes a method to solve the maintenance scheduling problem, called the parallel co-operating cultural algorithm (PARCA). In the proposed model, a variety of selection mechanisms, operators, communication methods, and local search procedures are applied to each solution generated by genetic operators and parameters as explained in the sequel. Our cultural algorithm framework combines the weak search method with the knowledge representation scheme for collecting and reasoning knowledge about individual experience.

ON-LINE STRING MATCHING ALGORITHMS: SURVEY AND EXPERIMENTAL RESULTS

In this paper we present a short survey and experimental results for well known sequential string matching algorithms. We consider algorithms based on different approaches including classical, suffix automata, bit-parallelism and hashing. We put special emphasis on algorithms recently presented such as Shift-Or and BNDM algorithms. We compare these algorithms in terms of the number of character comparisons and the running time for four different types of text: binary alphabet, alphabet of size 8, English alphabet and DNA alphabet.