Search

We present a time domain approach to explore a sound transformation paradigm for musical performance. Given a set of sounds containing a priori desired qualities and a population of agents interacting locally, the method generates both musical form and matter resulting from sonic trajectories. This proposal involves the use of bio-inspired algorithms, which possess intrinsic features of adaptive, self-organizing systems, as definers of generating and structuring processes of sound elements. Self-organization makes viable the temporal emergence of stable structures without an external organizing element. Regarding musical performance as a creative process that can be described using trajectories through the compositional space, and having the simultaneous emergence of musical matter and form resulting from the process itself as the final objective, the conception of a generative paradigm in computer music that does not contemplate a priori external organizing elements is the main focus of this proposal. [ABSTRACT FROM AUTHOR]

Plant breeding is sometimes described as an art, but the practice has been influenced a lot by the mendelian and DNA theories. Recently, quantitative geneticists have put forward provocative conclusions about how breeding should be done, and those ideas went against quite a lot of the current practice, and against some aspects of the old dogmas. Most traits are correlated to some extent; therefore, genetic improvement based on single traits or even relatively simplified approaches is counter-productive. Meanwhile one scientist from Brazil had developed empirically, between 1969 and 2005, a multi-trait based breeding and selection system by which genetic progress could be accelerated. This complex approach was enhanced by systematic use of carefully chosen complex stresses. Some Canadian scientists recently adopted a modified version of the process. Results could be imitated from a completely different «germplasm basis». Those results suggest a need to reconsider both the theory and the practice of plant breeding. The diverse methodological components can be grouped under the name of systemic approaches. Such methods are in line with the postulates and conclusions of quantitative genetics, and also with the most recent evidence about the system of network interrelations that controls the functions of the genome and of the cellular machinery. The new approach is also allowing a more significant role of genotype x environment interaction (GE) and genotype main effect plus genotype x environment interaction (GGE) in decision-making, Moreover, the systemic approach is compatible with a less static view of heredity, which includes for example epigenesis and various effects of stress on the natural recombination and mutation rate [ABSTRACT FROM AUTHOR]

Business process models assist business and information technology managers while adapting, reengineering and optimizing the organizational processes through analysis, visualization and simulation. Despite the number of notations and techniques to support business process modeling, there is no agreement on the modeling criteria to be used by different organizational stakeholders. This paper describes a method to infer business activities in order to facilitate the consistent representation of business processes, thus facilitating their sharing, dissemination and analysis. The method relies on using a number of properties derived from the dimensions of the Zachman framework. [ABSTRACT FROM AUTHOR]

In the paper we combine a Bayesian Network model for encoding forensic evidence during a given time interval with a Hidden Markov Model (EBN-HMM) for tracking and predicting the degree of criminal activity as it evolves over time. The model is evaluated with 500 randomly produced digital forensic scenarios and two specific forensic cases. The experimental results indicate that the model fits well with expert classification of forensic data. Such initial results point out the potential of such Dynamical Bayesian Network methods for the analysis of digital forensic data. [ABSTRACT FROM AUTHOR]

A challenge already opened for a long time concerning Content-based Image Retrieval (CBIR) systems is how to define a suitable distance function to measure the similarity between images regarding an application context, which complies with the human specialist perception of similarity. In this paper, we present a new family of distance functions, namely, Attribute Interaction Influence Distances (AID), aiming at retrieving images by similarity. Such measures address an important aspect of psychophysical comparison between images: the effect in the interaction on the variations of the image features. The AID functions allow comparing feature vectors using two parameterized expressions: one targeting weak feature interaction; and another for strong interaction. This paper also presents experimental results with medical images, showing that when the reference is the radiologist perception, AID works better than the distance functions most commonly used in CBIR. [ABSTRACT FROM AUTHOR]

In this work we propose an immune-based approach for designing of fuzzy systems. From numerical data and with membership function previously defined, the immune algorithm evolves a population of fuzzy classification rules based on the clonal selection, hypermutation and immune network principles. Once AIS are able to find multiple good solutions of the problem, accurate and diverse fuzzy systems are built in a single run. Hence, we construct an ensemble of these classifier in order to achieve better results. An ensemble of classifiers consists of a set of individual classifiers whose outputs are combined when classifying novel patterns. The good performance of an ensemble is strongly dependent of individual accuracy and diversity of its components. We evaluate the proposed methodology through computational experiments on some datasets. The results demonstrate that the performance of the obtained fuzzy systems in isolation is very good. However when we combine these systems, a significant improvement is obtained in the correct classification rate, outperforming the single best classifier. [ABSTRACT FROM AUTHOR]

Computer generated sounds for music applications have many facets, of which timbre design is of groundbreaking significance. Timbre is a remarkable and rather complex phenomenon that has puzzled researchers for a long time. Actually, the nature of musical signals is not fully understood yet. In this paper, we present a sound synthesis method using an artificial immune network for data clustering, denoted aiNet. Sounds produced by the method are referred to as immunological sounds. Basically, antibody-sounds are generated to recognize a fixed and predefined set of antigen-sounds, thus producing timbral variants with the desired characteristics. The aiNet algorithm provides maintenance of diversity and an adaptive number of resultant antibody-sounds (memory cells), so that the intended aesthetical result is properly achieved by avoiding the formal definition of the timbral attributes. The initial set of antibody-sounds may be randomly generated vectors, sinusoidal waves with random frequency, or a set of loaded waveforms. To evaluate the obtained results we propose an affinity measure based on the average spectral distance from the memory cells to the antigen-sounds. With the validation of the affinity criterion, the experimental procedure is outlined, and the results are depicted and analyzed. [ABSTRACT FROM AUTHOR]

In this paper, we propose an organizational model to describe the execution of business activities. The model offers a dynamic, actor centered, context based and business process oriented perspective of the organization that explicitly addresses the information and collaboration requirements derived from human multi-tasking capabilities. Actors are approached as a network of contexts managed by an "operating system". Three kinds of actors are defined; human, business process and organization actor. Two context types are introduced. An action context defines the specific behavior and information needs of a human actor performing a task under a given role, at particular time intervals. Interaction contexts support and regulate activity-related interactions among action contexts. This modeling approach seeks to facilitate a personalized, proactive and timely knowledge support to human business actors. We illustrate these ideas with working examples. [ABSTRACT FROM AUTHOR]

This work discusses the issue of approximation in point set matching problems. In general, one may have two classes of approximations when tackling a matching problem: a representational approximation, which involves a simplified and suboptimal modeling for the original problem, and algorithmic approximation, which consists in using suboptimal algorithms to infer the assignment. Matching techniques in general have relied on the second approach: to keep a complete model of the original problem and use suboptimal techniques to solve it. In this paper, we show how a technique based on using exact inference in simple graphical models, which is an instance of the first class, can significantly outperform instances of techniques from the second class. We give theoretical insights of why this happens, and experimentally compare our approach with the well-known Shapiro and Brady and Christmas et al. methods, which are exemplars of the second class. We perform experiments with synthetic and real-world data sets, which reveal a significant accuracy improvement of the proposed technique both under point position jitter and size increasing of the point sets. The main conclusion is that techniques based on optimal algorithms with appropriate suboptimal representations may lead to better results than their counterparts which consist in using optimal representations, but approximate algorithms. [ABSTRACT FROM AUTHOR]

In this paper we tackle the main problem presented by the majority of Information Visualization techniques, that is, the limited number of data items that can be visualized simultaneously. Our approach proposes an innovative and interactive systematization that can augment the potential for data presentation by utilizing multiple views. These multiple presentation views are kept linked according to the analytical decisions took by the user and are tracked in a tree-like structure. Our emphasis is on developing an intuitive yet powerful system that helps the user to browse the information and to make decisions based both on overview and on detailed perspectives of the data under analysis. The visualization tree keeps track of the interactive actions taken by the user without losing context. [ABSTRACT FROM AUTHOR]

The cellulose obtained of the natural fibre used as reinforced in the composites materials have found an increasing number of applications in recent years[1-3]. Car manufacturers have shown special interest in these materials. Advantages of natural fibres over the traditional counterparts include: relatively low cost, low weight, less damage to processing equipment, improved surface finish of moulded parts, good mechanical properties. Another important advantage of natural fibres is the relatively abundant in nature and, therefore, can be obtained from renewable resources, and been also recycled. [ABSTRACT FROM AUTHOR]