This paper proposes a new visual control approach based on sinusoidal inputs to be used on a nonholonomic robot. We present several contributions: In our method, developed considering a unicycle kinematic model, sinusoids are used in such a way that the generated vehicle trajectories are feasible, smooth and versatile. Our technique improves previous sinusoidal-based control works in terms of efficiency and flexibility. As further contributions, we present analytical expressions for the evolution of the robot's state, and propose a new state-feedback control law based on these expressions. All the information used in the control scheme is obtained from omnidirectional vision by means of the one-dimensional trifocal tensor. Stability analysis of the proposed approach is presented, and its performance is illustrated through experiments.

Research into the online informal learning of English has already shown it to be a widespread phenomenon involving a range of comprehension and production activities such as viewing original version television series, listening to music on demand and social networking with other English users.

Dynamic systems theory provides a suitable framework within which to study informal learning because it emphasises the unique range of resources, strategies and relationships which contribute to non-linear language development for each learner.

While research into the impact of these activities for language development has yielded some data regarding vocabulary gains and improvements in fluency and comprehension skills, the mechanisms at work in such language development have proved difficult to study because of the private nature of most online informal learning. In this study, an emic approach is adopted, involving a group of students experienced in second language acquisition research, who used blogs to report on their own online informal learning of English over a three-month period. Extensive examples from these blogs are used to build up a picture of the learning processes at work, within a framework suggested by the literature of complex dynamic systems. These results allow conclusions to be drawn regarding the way in which these processes are used by informal learners in different phases of real communicative tasks.

This paper addresses the task of automatic extraction of definitions by thoroughly exploring an approach that solely relies on machine learning techniques, and by focusing on the issue of the imbalance of relevant datasets. We obtained a breakthrough in terms of the automatic extraction of definitions, by extensively and systematically experimenting with different sampling techniques and their combination, as well as a range of different types of classifiers. Performance consistently scored in the range of 0.95–0.99 of area under the receiver operating characteristics, with a notorious improvement between 17 and 22 percentage points regarding the baseline of 0.73–0.77, for datasets with different rates of imbalance. Thus, the present paper also represents a contribution to the seminal work in natural language processing that points toward the importance of exploring the research path of applying sampling techniques to mitigate the bias induced by highly imbalanced datasets, and thus greatly improving the performance of a large range of tools that rely on them.

We consider a dependency-parsed text corpus as an instance of a labeled directed graph, where nodes represent words and weighted directed edges represent the syntactic relations between them. We show that graph walks, combined with existing techniques of supervised learning that model local and global information about the graph walk process, can be used to derive a task-specific word similarity measure in this graph. We also propose and evaluate a new learning method in this framework, a path-constrained graph walk variant, in which the walk process is guided by high-level knowledge about meaningful edge sequences (paths) in the graph. Empirical evaluation on the tasks of named entity coordinate term extraction and general word synonym extraction show that this framework is preferable to, or competitive with, vector-based models when learning is applied, and using small to moderate size text corpora.

The majority of current robotic joints are primarily actuated by rotational mechanisms. These electrical drives have substantially different features than the features found in human muscular systems. This paper presents a cost-effective solution to the backlash of a phenomenon known to cause positioning errors and other undesirable dynamic effects in drives. These errors are particularly pronounced when relatively major changes appear in the pre-load conditions of the motor such as in the case of a robotic leg or arm with a high degree of freedom. Current solutions require an accurate time-varying model of drives that is not available in the majority of practical cases. Therefore, in this paper a digitally controlled mechanical solution is proposed which is inspired by the human flexor–extensor mechanism. The idea is to construct an antagonistic actuator pair analogous to the flexor and extensor muscles. In order to obtain good control performance even in the low-speed range, permanent magnet stepper motors were chosen as actuators that are commutated in a digitally closed-loop fashion. The operation of the controlled structure has been verified in a real experimental environment where measurements showed good results and match with previous simulations.

This paper presents a general approach to automatically compile e-learning models to planning, allowing us to easily generate plans, in the form of learning designs, by using existing domain-independent planners. The idea is to compile, first, a course defined in a standard e-learning language into a planning domain, and, second, a file containing students learning information into a planning problem. We provide a common compilation and extend it to three particular approaches that cover a full spectrum of planning paradigms, which increases the possibilities of using current planners: (i) hierarchical, (ii) including PDDL (Planning Domain Definition Language) actions with conditional effects and (iii) including PDDL durative actions. The learning designs are automatically generated from the plans and can be uploaded, and subsequently executed, by learning management platforms. We also provide an extensive analysis of the e-learning metadata specification required for planning, and the pros and cons on the knowledge engineering procedures used in each of the three compilations. Finally, we include some qualitative and quantitative experimentation of the compilations in several domain-independent planners to measure its scalability and applicability.

In most of our daily motion tasks, the coordination between limbs is very crucial for successful execution of the tasks. In this paper, coordination among oscillators controlling in Cartesian space is studied to control bipedal walking. In our method, phase adjustment among oscillators is considered as one of the key issues to achieve coordination. A new phase adjustment method is proposed. With this method, an oscillator is able to coordinate other oscillators and maintain a desired phase relationship. This property is important for the walking control especially when external perturbations are given. To simplify the relationship between oscillators in a central pattern generator (CPG), a hierarchical CPG structure is adopted, where a main oscillator will be used to adjust other oscillators. In the simulation, the walking motion controlled by the CPG controller converges to a stable pattern even with external perturbations. We have implemented the controller in both the simulation model and real hardware robot.

We consider the connectivity of autonomous mobile robots. The robots navigate using simple local steering rules without requiring explicit communication among themselves. We show that using only position information of neighbors, the group connectivity can be sustained even in the case of bounded position measurement errors and the occlusion of robots by other robots in the group. In implementing the proposed scheme, sub-optimal solutions are invoked to avoid an excessive computational burden. We also discuss the possibility of deadlock which may bring the group to a standstill and show that the proposed methodology avoids such a scenario in real-life settings.

This paper presents a distributed architecture for automating data mining (DM) processes using standard languages. DM is a difficult task that relies on an exploratory and analytic process of processing large quantities of data in order to discover meaningful patterns. The increasing heterogeneity and complexity of available data requires some expert knowledge on how to combine the multiple and alternative DM tasks to process the data. Here, we describe DM tasks in terms of Automated Planning, which allows us to automate the DM knowledge flow construction. The work is based on the use of standards that have been defined in both DM and automated-planning communities. Thus, we use PMML (Predictive Model Markup Language) to describe DM tasks. From the PMML, a problem description in PDDL (Planning Domain Definition Language) can be generated, so any current planning system can be used to generate a plan. This plan is, again, translated to a DM workflow description, Knowledge Flow for Machine Learning format (Knowledge Flow file for the WEKA (Waikato Environment for Knowledge Analysis) tool), so the plan or DM workflow can be executed in WEKA.

Clinical outcomes have shown that robot-assisted rehabilitation is potential of enhancing quantification of therapeutic process for patients with stroke. During robotic rehabilitation exercise, the assistive robot must guarantee subject's safety in emergency situations, e.g., sudden spasm or twitch, abruptly severe tremor, etc. This paper presents a hierarchical control strategy, which is proposed to improve the safety and robustness of the rehabilitation system. The proposed hierarchical architecture is composed of two main components: a high-level safety supervisory controller (SSC) and low-level position-based impedance controller (PBIC). The high-level SSC is used to automatically regulate the desired force for a reasonable disturbance or timely put the emergency mode into service according to the evaluated physical state of training impaired limb (PSTIL) to achieve safety and robustness. The low-level PBIC is implemented to achieve compliance between the robotic end-effector and the impaired limb during the robot-assisted rehabilitation training. The results of preliminary experiments demonstrate the effectiveness and potentiality of the proposed method for achieving safety and robustness of the rehabilitation robot.

We consider the conventional techniques of vision robot system calibration where camera parameters and robot hand–eye parameters are computed separately, i.e., first performing camera calibration and then carrying out hand–eye calibration based on the calibrated parameters of cameras. In this paper we propose a joint algorithm that combines the camera calibration and the hand–eye calibration together. The proposed algorithm gives the solutions of the cameras' parameters and the hand–eye parameters simultaneously by using nonlinear optimization. Both simulations and real experiments show the superiority of our algorithm. We also apply our algorithm in the real application of the robot-assisted surgical system, and very good results have been obtained.

In this paper we describe the many steps involved in building a production quality Machine Translation system for translating weather warnings between French and English. Although in principle this task may seem straightforward, the details, especially corpus preparation and final text presentation, involve many difficult aspects that are often glossed over in the literature. On top of the classic Statistical Machine Translation evaluation metric results, four manual evaluations have been performed to assess and improve translation quality. We also show the usefulness of the integration of out-of-domain information sources in a Statistical Machine Translation system to produce high quality translated text.

A partial Steiner (n,r,l)-system is an r-uniform hypergraph on n vertices in which every set of l vertices is contained in at most one edge. A partial Steiner (n,r,l)-system is complete if every set of l vertices is contained in exactly one edge. In a hypergraph , the independence number α() denotes the maximum size of a set of vertices in containing no edge. In this article we prove the following. Given integers r,l such that r ≥ 2l − 1 ≥ 3, we prove that there exists a partial Steiner (n,r,l)-system such that

$$\alpha(\HH) \lesssim \biggl(\frac{l-1}{r-1}(r)_l\biggr)^{\frac{1}{r-1}}n^{\frac{r-l}{r-1}} (\log n)^{\frac{1}{r-1}} \quad \mbox{ as }n \rightarrow \infty.$$