Pollen recognition has a wide range of industrial and scientific applications. It guides the energy industry to potential oil and gas deposits, it is proxy data for climate-change scien- tists, and it increases agricultural production. However, pollen recognition is time consuming because it is usually done by visual inspection. Current automated solutions rely on pre-designed measurements of texture and contours, which require tuning for optimal features of a dataset. Also, most methods classify pollen using single-focus images, which require pollen grains to be captured at specific focal planes. We take a difference approach. Instead of using single-focus images, we use stacks of multifocal images (i.e., z-stack) to account for both visual characteristics and 3-D information. We automatically learn from the data the best visual characteristics for classifying pollen using deep-learning methods. Here, we train convolutional and recurrent neural networks (CNN and RNN) to learn the optimal features and recognize a pollen grain as a sequence of multifocal images acquired by an optical microscope. Additionally, we transfer the knowledge pre-trained network to ours to improve its classification and convergence speed. We evaluated our method using 392 stack sequences of 10 types of pollen grains with 10 images for each sequence. Our method achieved a remarkable classi- fication rate of 100%.

Model-free reinforcement learning techniques have been successfully used in diverse robotic applications. In this paper, we design and implement the Q-learning algorithm, a widely used model-free algorithm to find the optimal speed control function for a fast moving train on a fixed track. The goal is to allow for the train to learn the fastest speed profile it may use on a track, without derailment. We contrast the performance of the learning algorithm with the performance of the human controlling trying to perform the same task. In order the test the proposed algorithm, a complete hardware and software testbed has been designed and implemented, allowing for the evaluation of the learning models over a physical environment. We conclude that in simple tasks, the performance on humans in speed control is similar to the performance of the reinforcement learning algorithm, but when a more complex track is considered, the proposed reinforcement learning learning models outperforms the humans.

We devise an anomaly detection system that detects stolen phones. In this system, we use a mining algorithm to extract sequential patterns from a user’s past behavior to construct a personalized model. We then put forward scoring functions and threshold setting strategies to detect stealing events. We evaluate our approach with a data set from the MIT Reality Mining project. Experimental results indicate that our approach can detect 87% of simulated stealing events with an average false positive rate of 0.9%.

The Multi-Objective Binary Fish School Search (MOBFSS) algorithm was proposed to solve optimization problems with two or three conflicting objectives and operating on discrete binary variables. The original proposal revealed good accuracy but it also exhibited a high computational cost. Here, we present strategies to obtain an improved version of MOBFSS that reaches lower Pareto fronts for minimization problems at a better computational cost. We also deploy local search procedures as proposed in BMOPSO-CDRLS to find solutions closer to the optimal solution. The achieved results outperform the state-of-art algorithms BMOPSO-CDR and BMOPSO-CDRLS in feature selection problems for hypervolume optimization. Hence, this paper contributes to the literature in Swarm Intelligence by introducing several algorithms that can be applied to improve feature selection in the context of classification programs.

The accuracy of pattern-classification methods depends on how well the measured characteristics (i.e., features) represent the object to be classified. When using pre-designed features as it is the case of many pattern classifiers, one can try to enhance the features' discriminative power by inserting high-level semantic information into the feature vectors. In this paper, we propose a method that increases the discriminative power of features by augmenting them with high-level semantic information learned from training data. Our method combines the advantages of dimensionality reduction techniques and feature-selection techniques. Instead of augmenting feature vectors, we map them using a modified neural network that has been trained to categorize the data into target groups. This neural network embeds categorization information. We tested the method on classification tasks for pollen species, human action, and acoustic signals. In all these tasks, our feature-enhancing method improved classification rates.

We tackle the problem of providing engineering decision makers with relevant information extracted from data obtained via a process model based on deliberation and voting. We list examples of potential applications from the area of bug-fix scheduling for software, as well as space-vehicles 'go'-'no-go' decision making. In such application domains, important decisions have to be made hastily and therefore the decision factors have to be informed timely of the main issues discovered by the teams. A logic is proposed for reasoning with comments available in such deliberations. Search based algorithms are proposed which recommend the best justifications for a decision and retain the voting decisions for interested parties to tally. We have developed a Bayesian network for generating data by simulation based on probabilistic models that we can train from collected deliberation databases. The data generated in this way was used for evaluating the proposed search algorithm, showing how it can provide better than random recommendations of arguments to decision makers.

The mining of justifications to be recommended to visitors of deliberation for a used in decision making by constituents raises specific challenges. Graph-based representations can improve our understanding of the problem and enable reasoning with the available data. The addressed technical problem consists in recommending sets of texts containing comprehensive arguments supporting or opposing poll alternatives, as mined from submissions of opinions in electronic deliberative polls. A graphical framework is proposed to enable the development of techniques for identification of relevant/encompassing arguments in debates following the Alternative-Based Information System (ABIS) model, a competitor of the IBIS model. Bipolar argumentation frameworks are extended with votes, enhance relations and argument coalitions, proposing the BAPDF family of frameworks.

Forming effective coalitions is a major research challenge in AI and multi-agent systems (MAS). Coalition Structure generation (CSG) involves partitioning a set of agents into coalitions so that social surplus (the sum of the rewards of all coalitions) is maximized. A partition is called a Coalition Structure (CS). In traditional works, the value of a coalition is given by a black box function called a characteristic function. In this paper, we propose a novel formalization of CSG, i.e., we assume the value of a characteristic function is given by an optimal solution of a distributed constraint optimization problem (DCOP) among the agents of a coalition. A DCOP is a popular approach for modeling cooperative agents, since it is quite general and can formalize various application problems in MAS. At first glance, one might assume that the computational costs required in this approach would be too expensive, since we need to solve an NP-hard problem just to obtain the value of a single coalition. To optimally solve a CSG, we might need to solve n-th power of 2 DCOP problem instances, where n is the number of agents. However, quite surprisingly, we show that an approximation algorithm, whose computational cost is about the same as solving just one DCOP, can find a CS with quality guarantees. More specifically, we develop an algorithm with parameter k that can find a CS whose social surplus is at least max(k/(w*+1), 2k/n) of the optimal CS, where w* is the tree width of a constraint graph. When k=1, the complexity of this algorithm is about the same as solving just one DCOP. These results illustrate that the locality of interactions among agents, which is explicitly modeled in the DCOP formalization, is quite useful in developing an efficient CSG algorithm with quality guarantees.