Much of opinion mining research focuses on product reviews because reviews are opinion-rich and contain little irrelevant information. However, this cannot be said about online discussions and comments. In such postings, the discussions can get highly emotional and heated with many emotional statements, and even personal attacks. As a result, many of the postings and sentences do not express positive or negative opinions about the topic being discussed. To find people’s opinions on a topic and its different aspects, which we call evaluative opinions, those irrelevant sentences should be removed. The goal of this research is thus to identify evaluative opinion sentences. A novel unsupervised approach is proposed to solve the problem, and our experimental results show that it performs well.

Affine-invariant feature matching plays an important role in many robot vision applications, such as robot visual navigation, object detection, visual tracking and visual SLAM, etc. In the early stages, invariant keypoints are used to detect the affine transformation. But the accuracy is very low. In recent years, some people introduce SIFT method into robot vision field, which greatly enhances the accuracy. But it is too time-consuming to meet the requirements of real-time robot vision applications. In this paper, we propose a novel learning-based feature matching approach to address the problem. First, it uses a fast algorithm to extract keypoints. Then, our method identifies keypoints that belong to different objects or background by color and texture representation. The keypoints are clustered into corresponding groups. At last, a two-stage multilayer ferns classifier is trained to recognize the local patches and get the estimate of viewpoint. We test our approach on public datasets and apply it in a visual SLAM application. The result demonstrates that our method can provide robust and powerful matching ability. Even on some difficult matching cases, it also performs remarkably well. Further more, because there is no need to compute descriptors for the image, our method is very fast at run-time.

In sentiment analysis of product reviews, one important problem is to produce a summary of opinions based on product features/attributes (also called aspects). However, for the same feature, people can express it with many different words or phrases. To produce a useful summary, these words and phrases, which are domain synonyms, need to be grouped under the same feature group. Although several methods have been proposed to extract product features from reviews, limited work has been done on clustering or grouping of synonym features. This paper focuses on this task. Classic methods for solving this problem are based on unsupervised learning using some forms of distributional similarity. However, we found that these methods do not do well. We then model it as a semi-supervised learning problem. Lexical characteristics of the problem are exploited to automatically identify some labeled examples. Empirical evaluation shows that the proposed method outperforms existing state-of-the-art methods by a large margin.

The Bayesian optimization algorithm (BOA) uses Bayesian networks to explore the dependencies between decision variables of an optimization problem in pursuit of both faster speed of convergence and better solution quality. In this paper, a novel method that learns the structure of Bayesian networks for BOA is proposed. The proposed method, called L1BOA, uses L1-regularized regression to find the candidate parents of each variable, which leads to a sparse but nearly optimized network structure. The proposed method improves the efficiency of the structure learning in BOA due to the reduction and automated control of network complexity introduced with L1-regularized learning. Experimental studies on different types of benchmark problems are carried out, which show that L1BOA outperforms the standard BOA when no a-priori knowledge about the problem structure is available, and nearly achieves the best performance of BOA that applies explicit complexity controls.

While local patches recognition is a key component of modern approaches to affine transformation detection and object detection, existing learning-based approaches just identify the patches based on a set of randomly picked and combined binary features, which will lose some strong correlations between features and can not provide stable and remarkable identification ability. In this paper, we proposed a method that select and organize the features in a Multilayer Ferns structure, and show that it is both faster in the run-time processing and more powerful in the identification ability than state-of-the-art ad hoc approaches.

The stock market is considered as a high complex and dynamic system. Many machine learning and data mining technologies are used for stock analysis, but it still leaves an open question about how to integrate these methods with the plentiful knowledge and techniques accumulated in stock investment which are critical to the successful stock analysis. In this paper, we propose an intelligent stock trading system by combining support vector machine (SVM) algorithm and box theory of stock. The box theory believes a successful stock buying/selling generally occurs when the price effectivley breaks out the original oscillation box into another new box. In the system, support vector machine algorithm is utilized to make forecasts of the top and bottom of the oscillation box. Then a trading strategy based on the box theory is constructed to make trading decisions. The different stock movement patterns, i.e. bull, bear and fluctuant market, are used to test the feasibility of the system. The experiments on S&P500 components show a promising performance is achieved.

An aanytime algorithm based on modified genetic algorithm (GA) for dynamic WTA problem, subject to temporal constraints, is developed in the paper. In the algorithm the weapons are assigned to targets one by one before the deadline of each target comes. After a target is assigned with some weapon, the target is replaced by a new one in all chromosomes in the population while the optimization process will not undergo any restart. The algorithm has three main advantages: 1) a new target can be dynamically accommodated in the allocation process without losing previous optimizing information, 2) the quality of the pairing decisions may be improved in the evolving process with a prolonged computation time, and 3) it optimally deploys weapons to targets where a weapon can be assigned to more than one target asynchronously without missing any deadline of the targets, under the precondition that the weapon can be allocated to only one target at one time. The feasibility and the validity of the modified GA are verified in simulations.

In this paper, an anytime algorithm based on the modified genetic algorithm (GA) is presented to solve the dynamic weapon-target allocation (DWTA) problem. With this algorithm, the targets are assigned with weapons one-by-one according to when each target flies away from the launching zone of the weapons, which is also a deadline for completion of pairing weapon for the target. In this paper, the modified GA is applied to the case that the targets are reduced by continuous interception until all of them are neutralized, and along with it in the process, the number of weapons put in use is decreasing. This is a marginal case for an integrated DWTA problem, and the investigation on it provides a basis for solving more complex DWTA problem.

Sensor registration is a basis for well-organized sensor network, and a precondition for data fusion. In cases of constant registration errors, batch processing methods are always applied, where the registration is actually viewd as an optimization problem. Such methods are fast convergent, but sometimes they are not flexible in different cases and the optimal techniques used in batch processing methods may provide the suboptimum as solution. What’s more, when dealing with a large number of sensors, the batch processing methods may come across numeric problems. To address the registration problem in some practical cases, the evolutionary algorithm based method can be explored. A method based on genertic algorithm, as well as the least squares method, are developed for sensor registration in different simulation scenarios and compared. Simulation results are analyzed to make clear the advantages and disadvantages of the methods.

This paper deals with the adaptive variance scaling issue incontinuous Estimation of Distribution Algorithms. A phenomenon is discovered that current adaptive variance scaling method in EDA suffers from imprecise structure learning. A new type of adaptation method is proposed to overcome this defect. The method tries to measure the difference between the obtained population and the prediction of the probabilistic model, then calculate the scaling factor by minimizing the cross entropy between these two distributions. This approach calculates the scaling factor immediately rather than adapts it incrementally. Experiments show that this approach extended the class of problems that can be solved, and improve the search efficiency in some cases. Moreover, the proposed approach features in that each decomposed subspace can be assigned an individual scaling factor, which helps to solve problems with special dimension property.

A quasi-passive dynamic walking robot is built to study natural and energy-efficient biped walking. The robot is actuated by MACCEPA actuators. A reinforcement learning based control method is proposed to enhance the robustness and stability of the robot's walking. The proposed method first learns the desired gait for the robot's walking on a flat floor. Then a fuzzy advantage learning method is used to control it to walk on uneven floor. The effectiveness of the method is verified by simulation results.

This paper extends the Boltzmann Selection, a method in EDA with theoretical importance, from discrete domain to the continuous one. The difficulty of estimating the exact Boltzmann distribution in continuous state space is circumvented by adopting the multivariate Gaussian model, which is popular in continuous EDA, to approximate only the final sampling distribution. With the minimum Kullback-Leibeler divergence principle, both the mean vector and the covariance matrix of the Gaussian model can be calibrated to preserve the features of Boltzmann selection reflecting desired selection pressure. A method is proposed to adapt the selection pressure based on measuring the successfulness of the past evolution process. These works established a formal basis that helps to build probabilistic models in continuous EDA algorithms with adaptive parameters. The framework is incorporated in both the continuous UMDA and the EMNA algorithm, and tested in several benchmark problems. The experiment results are compared with some existing EDA versions and the benefit of the proposed approach is discussed.

An open robot control system pursues easy extension, flexible reconfiguration, facile portability and jointless interoperation. Therefore, the system elements from multi-disciplinary areas can be integrated and reconfigured easily in such a system. Also the system modules can be ported flexibly. In this paper, a Rt-Linux based open robot controller (RTOC) is investigated. A reference model for robot controlling is proposed, in which hardware platform, operating system module and application modules are included. Then for the implementation of RTOC, two critical implementation problems - layered architecture and the intra-layer interfaces are discussed on the base of its reference model. The RTOC openness is also analyzed. Consequently, the proposed RTOC is applied to an industrial arc welding robot