Visual Chunking: A List Prediction Framework for Region-Based Object Detection

We consider detecting objects in an image by iteratively selecting from a set of arbitrarily shaped candidate regions. Our generic approach, which we term visual chunking, reasons about the locations of multiple object instances in an image while expressively describing object boundaries. We design an optimization criterion for measuring the performance of a list of such detections as a natural extension to a common per-instance metric. We present an efficient algorithm with provable performance for building a high-quality list of detections from any candidate set of region-based proposals. We also develop a simple class-specific algorithm to generate a candidate region instance in near-linear time in the number of low-level superpixels that outperforms other region generating methods. In order to make predictions on novel images at testing time without access to ground truth, we develop learning approaches to emulate these algorithms' behaviors. We demonstrate that our new approach outperforms sophisticated baselines on benchmark datasets.Comment: to appear at ICRA 201

Conventionalism, structuralism and neo-Kantianism in Poincaré׳s philosophy of science

Poincaré is well known for his conventionalism and structuralism. However, the relationship between these two theses and their place in Poincaré’s epistemology of science remain puzzling. In this paper I show the scope of Poincaré’s conventionalism and its position in Poincaré’s hierarchical approach to scientific theories. I argue that for Poincaré scientific knowledge is relational and made possible by synthetic a priori, empirical and conventional elements, which, however, are not chosen arbitrarily. By examining his geometric conventionalism, his hierarchical account of science and defence of continuity in theory change, I argue that Poincaré defends a complex structuralist position based on synthetic a priori and conventional elements, the mind-dependence of which departs him from metaphysical realism

Harmony Search Method: Theory and Applications

The Harmony Search (HS) method is an emerging metaheuristic optimization algorithm, which has been employed to cope with numerous challenging tasks during the past decade. In this paper, the essential theory and applications of the HS algorithm are first described and reviewed. Several typical variants of the original HS are next briefly explained. As an example of case study, a modified HS method inspired by the idea of Pareto-dominance-based ranking is also presented. It is further applied to handle a practical wind generator optimal design problem

Mathematical Model Of Energy Saving Glass Coating Shape Design Using Binary Harmony Search For Better Signal Transmission

In recent years, buildings are designed using a special coated glass window. This glass serves as the outer shell that avoids the exposure of dangerous Ultra-violet rays; hazard light, direct sunlight and heat. Instead of being a normal window, this glass also acts to maintain the internal temperature of a building. Besides, this glass is known with the ability to save energy, which is an extensive technology of low-emissivity glasses that assist in reducing electricity usage. However, the use of this glass has impacted signal transmissions such as electromagnetic signal use in mobile communication by causing attenuation to the useful signals such as mobile phone (GSM, 3G), global positioning system (GPS), wireless network (Wi-Fi) and wireless broadband (LTE) due to the fabricated layer made of metallic-oxide on the window. Thus, engraving approach using a symmetrical shape design on the surface layer has shown improvement in reducing the attenuation problem. A well-designed algorithm is able to generate an optimized irregular shape design that can result in less attenuation on the transmission signal. Therefore, this study was conducted to propose a practical and effective irregular shape design that considers the property of coated layer and transmission signal. This approach is able to provide less attenuation problem and high efficiency of the signals through the coated glass. A model was developed to specify the requirement of coated glass on the energy saving glass. It determines the optimum irregular shape design, which is then integrated with Harmony Search (HS) optimization technique. By applying HS, an optimized shape design was generated, which met the objective of this study. HS generates a binary design representing bit ‘1’ and ‘0’. The obtained result were then simulated into a CST (Microwave) and tested on the S-parameter aspects, which are the return loss (S11) and transmission coefficient (S21). The efficiency of the irregular shape design was attained after the simulation process. Meanwhile, experimental result obtained in this study showed an irregular shape design generated by HS, hence showing an improvement in reducing the attenuation problem by 99.88% efficiency. The coated glass with optimized irregular shape design engraved on it can give a better signal transmission for mobile device, tracking system, wireless network and wireless broadband

Incorporating characteristics of human creativity into an evolutionary art algorithm (journal article)

A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how computer generated art and design can become more creatively human-like with respect to both process and outcome. As an example of a step in this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The goal is to evolve abstract portraits of Darwin, using our 2nd generation fitness function which rewards genomes that not just produce a likeness of Darwin but exhibit certain strategies characteristic of human artists. We note that in human creativity, change is less choosing amongst randomly generated variants and more capitalizing on the associative structure of a conceptual network to hone in on a vision. We discuss how to achieve this fluidity algorithmically

Repulsive Particle Swarm Method on Some Difficult Test Problems of Global Optimization

In this paper we test a particular variant of the (Repulsive) Particle Swarm method on some rather difficult global optimization problems. A number of these problems are collected from the extant literature and a few of them are newly introduced. First, we introduce the Particle Swarm method of global optimization and its variant called the 'Repulsive Particle Swarm' (RPS) method. Then we endow the particles with some stronger local search abilities - much like tunneling - so that each particle can make a search in its neighborhood to optimize itself. Next, we introduce the test problems, the existing as well as the new ones. We also give plots of some of these functions to help appreciation of the optimization problem. Finally, we present the results of the RPS optimization exercise and compare the results with those obtained by using the Genetic algorithm (GA)and/or Simulated annealing (SA) method. We append the (Fortran) computer program that we have developed and used in this exercise. Our findings indicate that neither the RPS nor the GA/SA method can assuredly find the optimum of an arbitrary function. In case of the Needle-eye and the Corana functions both methods perform equally well while in case of Bukin's 6th function both yield the values of decision variables far away from the right ones. In case of zero-sum function, GA performs better than the RPS. In case of the Perm #2 function, both of the methods fail when the dimension grows larger. In several cases, GA falters or fails while RPS succeeds. In case of N#1 through N#5 and the ANNs XOR functions the RPS performs better than the Genetic algorithm. It is needed that we find out some criteria to classify the problems that suit (or does not suit) a particular method. This classification will highlight the comparative advantages of using a particular method for dealing with a particular class of problems.Repulsive Particle Swarm; Global optimization; non-convex functions; Bounded rationality; local optima; Bukin; Corana; Rcos; Freudenstein Roth; Goldenstein Price; ANNs XOR; Perm; Power sum; Zero sum; Needle-eye; Genetic algorithms; variants; Fortran; computer program; benchmark; test

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system