Towards Swarm Calculus: Urn Models of Collective Decisions and Universal Properties of Swarm Performance

Methods of general applicability are searched for in swarm intelligence with the aim of gaining new insights about natural swarms and to develop design methodologies for artificial swarms. An ideal solution could be a `swarm calculus' that allows to calculate key features of swarms such as expected swarm performance and robustness based on only a few parameters. To work towards this ideal, one needs to find methods and models with high degrees of generality. In this paper, we report two models that might be examples of exceptional generality. First, an abstract model is presented that describes swarm performance depending on swarm density based on the dichotomy between cooperation and interference. Typical swarm experiments are given as examples to show how the model fits to several different results. Second, we give an abstract model of collective decision making that is inspired by urn models. The effects of positive feedback probability, that is increasing over time in a decision making system, are understood by the help of a parameter that controls the feedback based on the swarm's current consensus. Several applicable methods, such as the description as Markov process, calculation of splitting probabilities, mean first passage times, and measurements of positive feedback, are discussed and applications to artificial and natural swarms are reported

Active Discovery of Network Roles for Predicting the Classes of Network Nodes

Nodes in real world networks often have class labels, or underlying attributes, that are related to the way in which they connect to other nodes. Sometimes this relationship is simple, for instance nodes of the same class are may be more likely to be connected. In other cases, however, this is not true, and the way that nodes link in a network exhibits a different, more complex relationship to their attributes. Here, we consider networks in which we know how the nodes are connected, but we do not know the class labels of the nodes or how class labels relate to the network links. We wish to identify the best subset of nodes to label in order to learn this relationship between node attributes and network links. We can then use this discovered relationship to accurately predict the class labels of the rest of the network nodes. We present a model that identifies groups of nodes with similar link patterns, which we call network roles, using a generative blockmodel. The model then predicts labels by learning the mapping from network roles to class labels using a maximum margin classifier. We choose a subset of nodes to label according to an iterative margin-based active learning strategy. By integrating the discovery of network roles with the classifier optimisation, the active learning process can adapt the network roles to better represent the network for node classification. We demonstrate the model by exploring a selection of real world networks, including a marine food web and a network of English words. We show that, in contrast to other network classifiers, this model achieves good classification accuracy for a range of networks with different relationships between class labels and network links

Statistical interaction modeling of bovine herd behaviors

While there has been interest in modeling the group behavior of herds or flocks, much of this work has focused on simulating their collective spatial motion patterns which have not accounted for individuality in the herd and instead assume a homogenized role for all members or sub-groups of the herd. Animal behavior experts have noted that domestic animals exhibit behaviors that are indicative of social hierarchy: leader/follower type behaviors are present as well as dominance and subordination, aggression and rank order, and specific social affiliations may also exist. Both wild and domestic cattle are social species, and group behaviors are likely to be influenced by the expression of specific social interactions. In this paper, Global Positioning System coordinate fixes gathered from a herd of beef cows tracked in open fields over several days at a time are utilized to learn a model that focuses on the interactions within the herd as well as its overall movement. Using these data in this way explores the validity of existing group behavior models against actual herding behaviors. Domain knowledge, location geography and human observations, are utilized to explain the causes of these deviations from this idealized behavior

Collective estimation of multiple bivariate density functions with application to angular-sampling-based protein loop modeling

This article develops a method for simultaneous estimation of density functions for a collection of populations of protein backbone angle pairs using a data-driven, shared basis that is constructed by bivariate spline functions defined on a triangulation of the bivariate domain. The circular nature of angular data is taken into account by imposing appropriate smoothness constraints across boundaries of the triangles. Maximum penalized likelihood is used to fit the model and an alternating blockwise Newton-type algorithm is developed for computation. A simulation study shows that the collective estimation approach is statistically more efficient than estimating the densities individually. The proposed method was used to estimate neighbor-dependent distributions of protein backbone dihedral angles (i.e., Ramachandran distributions). The estimated distributions were applied to protein loop modeling, one of the most challenging open problems in protein structure prediction, by feeding them into an angular-sampling-based loop structure prediction framework. Our estimated distributions compared favorably to the Ramachandran distributions estimated by fitting a hierarchical Dirichlet process model; and in particular, our distributions showed significant improvements on the hard cases where existing methods do not work well

Colour consistency in computer vision : a multiple image dynamic exposure colour classification system : a thesis presented to the Institute of Natural and Mathematical Sciences in fulfilment of the requirements for the degree of Master of Science in Computer Science at Massey University, Albany, Auckland, New Zealand

Colour classification vision systems face difficulty when a scene contains both very bright and dark regions. An indistinguishable colour at one exposure may be distinguishable at another. The use of multiple cameras with varying levels of sensitivity is explored in this thesis, aiding the classification of colours in scenes with high illumination ranges. Titled the Multiple Image Dynamic Exposure Colour Classification (MIDECC) System, pie-slice classifiers are optimised for normalised red/green and cyan/magenta colour spaces. The MIDECC system finds a limited section of hyperspace for each classifier, resulting in a process which requires minimal manual input with the ability to filter background samples without specialised training. In experimental implementation, automatic multiple-camera exposure, data sampling, training and colour space evaluation to recognise 8 target colours across 14 different lighting scenarios is processed in approximately 30 seconds. The system provides computationally effective training and classification, outputting an overall true positive score of 92.4% with an illumination range between bright and dim regions of 880 lux. False positive classifications are minimised to 4.24%, assisted by heuristic background filtering. The limited search space classifiers and layout of the colour spaces ensures the MIDECC system is less likely to classify dissimilar colours, requiring a certain ‘confidence’ level before a match is outputted. Unfortunately the system struggles to classify colours under extremely bright illumination due to the simplistic classification building technique. Results are compared to the common machine learning algorithms Naïve Bayes, Neural Networks, Random Tree and C4.5 Tree Classifiers. These algorithms return greater than 98.5% true positives and less than 1.53% false positives, with Random Tree and Naïve Bayes providing the best and worst comparable algorithms, respectively. Although resulting in a lower classification rate, the MIDECC system trains with minimal user input, ignores background and untrained samples when classifying and trains faster than most of the studied machine learning algorithms.Colour classification vision systems face difficulty when a scene contains both very bright and dark regions. An indistinguishable colour at one exposure may be distinguishable at another. The use of multiple cameras with varying levels of sensitivity is explored in this thesis, aiding the classification of colours in scenes with high illumination ranges. Titled the Multiple Image Dynamic Exposure Colour Classification (MIDECC) System, pie-slice classifiers are optimised for normalised red/green and cyan/magenta colour spaces. The MIDECC system finds a limited section of hyperspace for each classifier, resulting in a process which requires minimal manual input with the ability to filter background samples without specialised training. In experimental implementation, automatic multiple-camera exposure, data sampling, training and colour space evaluation to recognise 8 target colours across 14 different lighting scenarios is processed in approximately 30 seconds. The system provides computationally effective training and classification, outputting an overall true positive score of 92.4% with an illumination range between bright and dim regions of 880 lux. False positive classifications are minimised to 4.24%, assisted by heuristic background filtering. The limited search space classifiers and layout of the colour spaces ensures the MIDECC system is less likely to classify dissimilar colours, requiring a certain ‘confidence’ level before a match is outputted. Unfortunately the system struggles to classify colours under extremely bright illumination due to the simplistic classification building technique. Results are compared to the common machine learning algorithms Naïve Bayes, Neural Networks, Random Tree and C4.5 Tree Classifiers. These algorithms return greater than 98.5% true positives and less than 1.53% false positives, with Random Tree and Naïve Bayes providing the best and worst comparable algorithms, respectively. Although resulting in a lower classification rate, the MIDECC system trains with minimal user input, ignores background and untrained samples when classifying and trains faster than most of the studied machine learning algorithms