Metaheuristic Design Patterns: New Perspectives for Larger-Scale Search Architectures

Design patterns capture the essentials of recurring best practice in an abstract form. Their merits are well established in domains as diverse as architecture and software development. They offer significant benefits, not least a common conceptual vocabulary for designers, enabling greater communication of high-level concerns and increased software reuse. Inspired by the success of software design patterns, this chapter seeks to promote the merits of a pattern-based method to the development of metaheuristic search software components. To achieve this, a catalog of patterns is presented, organized into the families of structural, behavioral, methodological and component-based patterns. As an alternative to the increasing specialization associated with individual metaheuristic search components, the authors encourage computer scientists to embrace the ‘cross cutting' benefits of a pattern-based perspective to optimization algorithms. Some ways in which the patterns might form the basis of further larger-scale metaheuristic component design automation are also discussed

Tag-Cloud Drawing: Algorithms for Cloud Visualization

Tag clouds provide an aggregate of tag-usage statistics. They are typically sent as in-line HTML to browsers. However, display mechanisms suited for ordinary text are not ideal for tags, because font sizes may vary widely on a line. As well, the typical layout does not account for relationships that may be known between tags. This paper presents models and algorithms to improve the display of tag clouds that consist of in-line HTML, as well as algorithms that use nested tables to achieve a more general 2-dimensional layout in which tag relationships are considered. The first algorithms leverage prior work in typesetting and rectangle packing, whereas the second group of algorithms leverage prior work in Electronic Design Automation. Experiments show our algorithms can be efficiently implemented and perform well.Comment: To appear in proceedings of Tagging and Metadata for Social Information Organization (WWW 2007

A tutorial for competent memetic algorithms: Model, taxonomy and design issues

The combination of evolutionary algorithms with local search was named "memetic algorithms" (MAs) (Moscato, 1989). These methods are inspired by models of natural systems that combine the evolutionary adaptation of a population with individual learning within the lifetimes of its members. Additionally, MAs are inspired by Richard Dawkin's concept of a meme, which represents a unit of cultural evolution that can exhibit local refinement (Dawkins, 1976). In the case of MA's, "memes" refer to the strategies (e.g., local refinement, perturbation, or constructive methods, etc.) that are employed to improve individuals. In this paper, we review some works on the application of MAs to well-known combinatorial optimization problems, and place them in a framework defined by a general syntactic model. This model provides us with a classification scheme based on a computable index D, which facilitates algorithmic comparisons and suggests areas for future research. Also, by having an abstract model for this class of metaheuristics, it is possible to explore their design space and better understand their behavior from a theoretical standpoint. We illustrate the theoretical and practical relevance of this model and taxonomy for MAs in the context of a discussion of important design issues that must be addressed to produce effective and efficient MAs

Route planning methods for a modular warehouse system

In this study, procedures are presented that can be used to determine the routes of the packages transported within a modular storage system. The problem is a variant of robot motion planning problem. The structures of the procedures are developed in three steps for the simultaneous movement of multiple unit-sized packages in a modular warehouse. The proposed heuristic methods consist of route planning, tagging, and main control components. In order to demonstrate the solution performance of the methods, various experiments were conducted with different data sets and the solution times and qualities of the proposed methods were compared with previous studies. It was found that the proposed methods provide better solutions when taking the number of steps and solution time into consideration

A Fine Grain Sentiment Analysis with Semantics in Tweets

Social networking is nowadays a major source of new information in the world. Microblogging sites like Twitter have millions of active users (320 million active users on Twitter on the 30th September 2015) who share their opinions in real time, generating huge amounts of data. These data are, in most cases, available to any network user. The opinions of Twitter users have become something that companies and other organisations study to see whether or not their users like the products or services they offer. One way to assess opinions on Twitter is classifying the sentiment of the tweets as positive or negative. However, this process is usually done at a coarse grain level and the tweets are classified as positive or negative. However, tweets can be partially positive and negative at the same time, referring to different entities. As a result, general approaches usually classify these tweets as “neutral”. In this paper, we propose a semantic analysis of tweets, using Natural Language Processing to classify the sentiment with regards to the entities mentioned in each tweet. We offer a combination of Big Data tools (under the Apache Hadoop framework) and sentiment analysis using RDF graphs supporting the study of the tweet’s lexicon. This work has been empirically validated using a sporting event, the 2014 Phillips 66 Big 12 Men’s Basketball Championship. The experimental results show a clear correlation between the predicted sentiments with specific events during the championship

Temporal Segmentation of Video Lectures: a speech-based optimization framework

Video lectures are very popular nowadays. Following the new teaching trends, students are increasingly seeking educational videos on the web for the most different purposes: learn something new, review content for exams or just out of curiosity. Unfortunately, finding specific content in this type of video is not an easy task. Many video lectures are extensive and cover several topics, and not all of these topics are relevant to the user who has found the video. The result is that the user spends so much time trying to find a topic of interest in the middle of content irrelevant to him. The temporal segmentation of video lectures in topics can solve this problem allowing users to navigate of a non-linear way through all topics of a video lecture. However, temporal video lecture segmentation is a time-consuming task and must be automatized. For this reason, in this work we propose an optimization framework for the temporal video lecture segmentation problem. Our proposal only uses information from the teacher’s speech, therefore it does not depend on any additional resources such as slides, textbooks or manually generated subtitles. This makes our proposal versatile, as we can apply it to a wide range of different video lectures, as it only requires the teacher’s speech on the video. To do this, we formulate this problem as a linear programming model where we combine prosodic and semantic features from speech that may indicate topic transitions. To optimize this model, we use a elitist genetic algorithm with local search. Through the experiments, we were able to evaluate different aspects of our approach such as sensibility to parameter variation and convergence behavior. Also, we show that our method was capable of overcoming state-of-the-art methods, both in Recall and in F1-Score, in two different datasets of video lectures. Finally, we provide the implementation of our framework so that other researchers can contribute and reproduce our results.As videoaulas são muito populares hoje em dia. Seguindo as novas tendências de ensino, estudantes procuram cada vez mais por vídeos educacionais na Web com os mais diferentes propósitos: aprender algo novo, revisar conteúdo para exames ou apenas por curiosidade. Infelizmente, encontrar conteúdo específico nesse tipo de vídeo não é uma tarefa fácil. Muitas videoaulas são extensas e abrangem vários tópicos, sendo que nem todos são relevantes para o usuário que encontrou o vídeo. O resultado disso é que o usuário acaba gastando muito tempo ao tentar encontrar um tópico de interesse em meio a conteúdo que é irrelevante para ele. A segmentação temporal de videoaulas em tópicos pode resolver esse problema ao permitir que os usuários naveguem de maneira não-linear entre os tópicos existentes em uma videoaula. No entanto, se trata de uma tarefa dispendiosa que precisa ser automatizada. Por esse motivo, neste trabalho, propomos um framework de otimização para o problema de segmentação temporal de videoaulas. Nossa proposta utiliza apenas informações da fala do professor, portanto, não depende de recursos adicionais, como slides, livros didáticos ou legendas geradas manualmente. Isso a torna versátil, pois podemos aplicá-la a uma ampla variedade de videoaulas, uma vez que requer apenas que o discurso do professor esteja presente. Para fazer isso, formulamos o problema como um modelo de programação linear, onde combinamos recursos prosódicos e semânticos da fala que podem indicar transições de tópicos. Para otimizar esse modelo, usamos um algoritmo genético elitista com busca local. Através dos experimentos, fomos capazes de avaliar diferentes aspectos de nossa abordagem, como sua sensibilidade à variação de parâmetros e comportamento de convergência. Além disso, mostramos que nosso método foi capaz de superar métodos do estado da arte, tanto em Recall quanto em F1-Score, em dois conjuntos diferentes de videoaulas. Por fim, disponibilizamos a implementação de nosso framework para que outros pesquisadores possam contribuir e reproduzir nossos resultados.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superio

Crowdsourcing Emotions in Music Domain

An important source of intelligence for music emotion recognition today comes from user-provided community tags about songs or artists. Recent crowdsourcing approaches such as harvesting social tags, design of collaborative games and web services or the use of Mechanical Turk, are becoming popular in the literature. They provide a cheap, quick and efficient method, contrary to professional labeling of songs which is expensive and does not scale for creating large datasets. In this paper we discuss the viability of various crowdsourcing instruments providing examples from research works. We also share our own experience, illustrating the steps we followed using tags collected from Last.fm for the creation of two music mood datasets which are rendered public. While processing affect tags of Last.fm, we observed that they tend to be biased towards positive emotions; the resulting dataset thus contain more positive songs than negative ones