A neuro-genetic-simulated annealing approach to the inverse kinematics solution of robots: a simulation based study

In this study, a hybrid intelligent solution system including neural networks, genetic algorithms and simulated annealing has been proposed for the inverse kinematics solution of robotic manipulators. The main purpose of the proposed system is to decrease the end effector error of a neural network based inverse kinematics solution. In the designed hybrid intelligent system, simulated annealing algorithm has been used as a genetic operator to decrease the process time of the genetic algorithm to find the optimum solution. Obtained best solution from the neural network has been included in the initial solution of genetic algorithm with randomly produced solutions. The end effector error has been reduced micrometer levels after the implementation of the hybrid intelligent solution system.     

Multi‐level reranking approach for bug localization

Bug fixing has a key role in software quality evaluation. Bug fixing starts with the bug localization step, in which developers use textual bug information to find location of source codes which have the bug. Bug localization is a tedious and time consuming process. Information retrieval requires understanding the programme's goal, coding structure, programming logic and the relevant attributes of bug. Information retrieval (IR) based bug localization is a retrieval task, where bug reports and source files represent the queries and documents, respectively. In this paper, we propose BugCatcher, a newly developed bug localization method based on multi‐level re‐ranking IR technique. We evaluate BugCatcher on three open source projects with approximately 3400 bugs. Our experiments show that multi‐level reranking approach to bug localization is promising. Retrieval performance and accuracy of BugCatcher are better than current bug localization tools, and BugCatcher has the best Top N, Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR) values for all datasets.     

Inner approximations of completely positive reformulations of mixed binary quadratic programs: a unified analysis

Every quadratic programming problem with a mix of continuous and binary variables can be equivalently reformulated as a completely positive optimization problem, that is, a linear optimization problem over the convex but computationally intractable cone of completely positive matrices. In this paper, we focus on general inner approximations of the cone of completely positive matrices on instances of completely positive optimization problems that arise from the reformulation of mixed binary quadratic programming problems. We provide a characterization of the feasibility of such an inner approximation as well as the optimal value of a feasible inner approximation. In particular, our results imply that polyhedral inner approximations are equivalent to a finite discretization of the feasible region of the original completely positive optimization problem. Our characterization yields, as a byproduct, an upper bound on the gap between the optimal value of an inner approximation and that of the original instance. We discuss the implications of this error bound for standard and box-constrained quadratic programs as well as general mixed binary quadratic programs with a bounded feasible region.     

A comprehensive study on mid-level representation and ensemble learning for emotional analysis of video material

Multimedia Tools and Applications - In today’s society where audio-visual content such as professionally edited and user-generated videos is ubiquitous, automatic analysis of this content is...     

Evaluation of a university website’s usability for visually impaired students

Today websites are the tools most commonly used to access information. People with disabilities face difficulties accessing or using information, and the importance of website usability in their lives needs to be recognized. Visually impaired students need to be able to use university websites that inform them about the opportunities and events taking place on campus. This study aims to evaluate the usability of a university website by visually impaired students. In this research, six visually impaired students were interviewed. The assistive technologies they use, as well as the various web pages they wished to use unaided were identified. Following data collection, usability tests were conducted and satisfaction surveys were completed. The usability test was done with five visually impaired students. They were asked to think aloud while performing 11 tasks involving their university’s web pages, including the main page and the pages of student affairs, library and departments and then to accomplish these tasks. In this test, five tasks were not successfully completed by all students. According to the test results, finding final exam dates on the academic calendar posed major difficulties, and accessing the course schedule web page was the task that required the most time. The test results indicated the need for a search engine on each page, a text version for all pages, rearrangement of the web link sequences with tabs and more information about visuals. Suggestions related to the visually impaired students’ needs were offered.     

Privacy-preserving boosting

We describe two algorithms, BiBoost (Bipartite Boosting) and MultBoost (Multiparty Boosting), that allow two or more participants to construct a boosting classifier without explicitly sharing their data sets. We analyze both the computational and the security aspects of the algorithms. The algorithms inherit the excellent generalization performance of AdaBoost. Experiments indicate that the algorithms are better than AdaBoost executed separately by the participants, and that, independently of the number of participants, they perform close to AdaBoost executed using the entire data set. Responsible Editor: Charu Aggarwal.     

Multirobot coordination in pick-and-place tasks on a moving conveyor

This paper considers the problem of multirobot coordination in pick-and-place tasks on a conveyor band. The robot team is composed of identical robots with mutually exclusive, but neighboring workspaces. The products are fed in at one end of the band, move through each workspace sequentially until being picked up and are collected at the other end—if not picked up interim. Each robot has the same task—that is picking up and packaging as many products as possible. We propose an approach based on noncooperative game theory where each robot uses local observations of the conveyor band and their neighbors' actions in order to decide on its actions. The developed algorithm has been implemented and tested in a simulated manufacturing environment using Webots. Results obtained from the simulations are analyzed using a variety of statistical performance measures.     

Eigenclassifiers for combining correlated classifiers

In practice, classifiers in an ensemble are not independent. This paper is the continuation of our previous work on ensemble subset selection [A. Ula?, M. Semerci, O.T. Y?ld?z, E. Alpayd?n, Incremental construction of classifier and discriminant ensembles, Information Sciences, 179 (9) (2009) 1298–1318] and has two parts: first, we investigate the effect of four factors on correlation: (i) algorithms used for training, (ii) hyperparameters of the algorithms, (iii) resampled training sets, (iv) input feature subsets. Simulations using 14 classifiers on 38 data sets indicate that hyperparameters and overlapping training sets have higher effect on positive correlation than features and algorithms. Second, we propose postprocessing before fusing using principal component analysis (PCA) to form uncorrelated eigenclassifiers from a set of correlated experts. Combining the information from all classifiers may be better than subset selection where some base classifiers are pruned before combination, because using all allows redundancy.     

Cost-conscious comparison of supervised learning algorithms over multiple data sets

In the literature, there exist statistical tests to compare supervised learning algorithms on multiple data sets in terms of accuracy but they do not always generate an ordering. We propose Multi²Test, a generalization of our previous work, for ordering multiple learning algorithms on multiple data sets from “best” to “worst” where our goodness measure is composed of a prior cost term additional to generalization error. Our simulations show that Multi²Test generates orderings using pairwise tests on error and different types of cost using time and space complexity of the learning algorithms.