Evolutionary methods are well-known techniques for solving nonlinear constrained optimization problems. Due to the exploration power of evolution-based optimizers, population usually converges to a region around global optimum after several generations. Although this convergence can be efficiently used to reduce search space, in most of the existing optimization methods, search is still continued over original space and considerable time is wasted for searching ineffective regions. This paper proposes a simple and general approach based on search space reduction to improve the exploitation power of the existing evolutionary methods without adding any significant computational complexity. After a number of generations when enough exploration is performed, search space is reduced to a small subspace around the best individual, and then search is continued over this reduced space. If the space reduction parameters (red&#x5f;gen and red&#x5f;factor) are adjusted properly, reduced space will include global optimum. The proposed scheme can help the existing evolutionary methods to find better near-optimal solutions in a shorter time. To demonstrate the power of the new approach, it is applied to a set of benchmark constrained optimization problems and the results are compared with a previous work in the literature.

Alireza Rowhanimanesh

Sohrab Efati

A Novel Approach to Improve the Performance of Evolutionary Methods for Nonlinear Constrained Optimization

This paper focuses on designing a tool for guiding a group of people out of a public building when they are faced with dangerous situations that require immediate evacuation. Despite architectural attempts to produce safe floor plans and exit door placements, people will still commit to fatal route decisions. Since they have access to global views, we believe supervisory people in the control room can use our simulation tools to determine the best courses of action for people. Accordingly, supervisors can guide people to safety. In this paper, we combine Coulomb&#x2019;s electrical law, graph theory, and convex and centroid concepts to demonstrate a computer-generated evacuation scenario that divides the environment into different safe boundaries around the locations of each exit door in order to guide people through exit doors safely and in the most expedient time frame. Our mechanism continually updates the safe boundaries at each moment based on the latest location of individuals who are present inside the environment. Guiding people toward exit doors depends on the momentary situations in the environment, which in turn rely on the specifications of each exit door. Our mechanism rapidly adapts to changes in the environment in terms of moving agents and changes in the environmental layout that might be caused by explosions or falling walls.

Pejman Kamkarian

Henry Hexmoor

Crowd Evacuation for Indoor Public Spaces Using Coulomb&#x2019;s Law

We propose a Cooperative Question Answering System that takes as input natural language queries and is able to return a cooperative answer based on semantic web resources, more specifically DBpedia represented in OWL/RDF as knowledge base and WordNet to build similar questions. Our system resorts to ontologies not only for reasoning but also to find answers and is independent of prior knowledge of the semantic resources by the user. The natural language question is translated into its semantic representation and then answered by consulting the semantics sources of information. The system is able to clarify the problems of ambiguity and helps finding the path to the correct answer. If there are multiple answers to the question posed (or to the similar questions for which DBpedia contains answers), they will be grouped according to their semantic meaning, providing a more cooperative and clarified answer to the user.

Dora Melo

Irene Pimenta Rodrigues

Vitor Beires Nogueira

Work Out the Semantic Web Search: The Cooperative Way

A qualia exploitation of sensor technology (QUEST) motivated architecture using algorithm fusion and adaptive feedback loops for face recognition for hyperspectral imagery (HSI) is presented. QUEST seeks to develop a general purpose computational intelligence system that captures the beneficial engineering aspects of qualia-based solutions. Qualia-based approaches are constructed from subjective representations and have the ability to detect, distinguish, and characterize entities in the environment  Adaptive feedback loops are implemented that enhance performance by reducing candidate subjects in the gallery and by injecting additional probe images during the matching process. The architecture presented provides a framework  for exploring more advanced integration strategies beyond those presented. Algorithmic results and performance improvements are presented as spatial, spectral, and temporal effects are utilized; additionally, a Matlab-based graphical user interface (GUI) is developed to aid processing, track performance, and to display results.

David M. Ryer

Trevor J. Bihl

Kenneth W. Bauer

Steven K. Rogers

QUEST Hierarchy for Hyperspectral Face Recognition

We have developed a method for spatiotemporally integrating databases of shop and company information, such as from a digital telephone directory, spatiotemporally, in order to monitor dynamic urban transformations in a detailed manner. To realize this, an additional method is necessary to verify the identicalness of different instances of Japanese shop and company names that might contain fluctuations of description. In this paper, we discuss a method that utilizes an <svg style="vertical-align:-0.1638pt;width:8.6625004px;" id="M1" height="7.9499998" version="1.1" viewBox="0 0 8.6625004 7.9499998" width="8.6625004" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(.017,0,0,-.017,.062,7.675)"><path id="x1D45B" d="M495 86q-46 -47 -87 -72.5t-63 -25.5q-43 0 -16 107l49 210q7 34 8 50.5t-3 21t-13 4.5q-35 0 -109.5 -72.5t-115.5 -140.5q-21 -75 -38 -159q-50 -10 -76 -21l-6 8l84 340q8 35 -4 35q-17 0 -67 -46l-15 26q44 44 85.5 70.5t64.5 26.5q35 0 10 -103l-24 -98h2&#xD;&#xA;q42 56 97 103.5t96 71.5q46 26 74 26q9 0 16 -2.5t14 -11.5t9.5 -24.5t-1 -44t-13.5 -68.5q-30 -117 -47 -200q-4 -19 -3.5 -25t6.5 -6q21 0 70 48z" /></g></svg>-gram model for comparing and identifying Japanese words. The processing accuracy was improved through developing various kinds of libraries for frequently appearing words, and using these libraries to clean shop and company names. In addition, the accuracy was greatly and novelty improved through the detection of those frequently appearing words that appear eccentrically across both space and time. By utilizing natural language processing (NLP), our method incorporates a novel technique for the advanced processing of spatial and temporal data.

Yuki Akiyama

Ryosuke Shibasaki

A Method for Identifying Japanese Shop and Company Names by Spatiotemporal Cleaning of Eccentrically Located Frequently Appearing Words

We present an extensive experimental study of Phrase-based Statistical Machine Translation, from the point of view of its learning capabilities. Very accurate Learning Curves are obtained, using high-performance computing, and extrapolations of the projected performance of the system under different conditions are provided. Our experiments confirm existing and mostly unpublished beliefs about the learning capabilities of statistical machine translation systems. We also provide insight into the way statistical machine translation learns from data, including the respective influence of translation and language models, the impact of phrase length on performance, and various unlearning and perturbation analyses. Our results support and illustrate the fact that performance improves by a constant amount for each doubling of the data, across different language pairs, and different systems. This fundamental limitation seems to be a direct consequence of Zipf law governing textual data. Although the rate of improvement may depend on both the data and the estimation method, it is unlikely that the general shape of the learning curve will change without major changes in the modeling and inference phases. Possible research directions that address this issue include the integration of linguistic rules or the development of active learning procedures.

Marco Turchi

Tijl De Bie

Cyril Goutte

Nello Cristianini

Learning to Translate: A Statistical and Computational Analysis

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Advances in Artificial Intelligence