The paper presents physical modeling, design, simulations, and experimentation on a novel Soft Underwater Artificial Skin (SUAS) used as tactile sensor. The SUAS functions as an electrostatic capacitive sensor, and it is composed of a hyperelastic membrane used as external cover and oil inside it used to compensate the marine pressure. Simulation has been performed studying and modeling the behavior of the external interface of the SUAS in contact with external concentrated loads in marine environment. Experiments on the external and internal components of the SUAS have been done using two different conductive layers in oil. A first prototype has been realized using a 3D printer. The results of the paper underline how the soft materials permit better adhesion of the conductive layer to the transducers of the SUAS obtaining higher capacitance. The results here presented confirmed the first hypotheses presented in a last work and opened new ways in the large-scale underwater tactile sensor design and development. The investigations are performed in collaboration with a national Italian project named MARIS, regarding the possible extension to the underwater field of the technologies developed within the European project ROBOSKIN.

The law has language at its heart, so it’s not surprising that software that operates on natural language has played a role in some areas of the legal profession for a long time. But the last few years have seen an increased interest in applying modern techniques to a wider range of problems, so I look here at how natural language processing is being used in the legal sector today.

We analyze resources and models for Arabic community Question Answering (cQA). In particular, we focus on CQA-MD, our cQA corpus for Arabic in the domain of medical forums. We describe the corpus and the main challenges it poses due to its mix of informal and formal language, and of different Arabic dialects, as well as due to its medical nature. We further present a shared task on cQA at SemEval, the International Workshop on Semantic Evaluation, based on this corpus. We discuss the features and the machine learning approaches used by the teams who participated in the task, with focus on the models that exploit syntactic information using convolutional tree kernels and neural word embeddings. We further analyze and extend the outcome of the SemEval challenge by training a meta-classifier combining the output of several systems. This allows us to compare different features and different learning algorithms in an indirect way. Finally, we analyze the most frequent errors common to all approaches, categorizing them into prototypical cases, and zooming into the way syntactic information in tree kernel approaches can help solve some of the most difficult cases. We believe that our analysis and the lessons learned from the process of corpus creation as well as from the shared task analysis will be helpful for future research on Arabic cQA.

Natural Language Engineering really came about from a meeting between Roberto Garigliano (then of Durham University) and myself in his office in late 1992 or early 1993. I had returned to academia the previous year after a spell doing a variety of jobs in industry, and had become aware of Roberto and the Natural Language Group at Durham (just about 15 miles from the University of Sunderland where I was working). Roberto and I discussed several possible avenues of cooperation, including sponsorship by Durham of students on existing Sunderland masters degrees, a joint Durham/Sunderland specialist Masters in Language Engineering (which came to nothing) and a new journal focused on practical, engineering work in the language domain. Incidentally, one of the sponsored master’s students was Siobhan Devlin, now Head of Computing at Sunderland.

The objective of the presented work is to take advantage of the precision capabilities of tailor-fiber-placement (TFP) embroidery processes in order to qualify carbon-fiber parts as viable antennas for wireless power transfer applications in multifunctional carbon-fiber-reinforced plastic (CFRP) composites. The solution comes first from a literature study of electrical, high-frequency, and textile engineering concepts. This review built familiarity with the technological challenges and state-of-the-art of the presented technology. Next step was iterative experimentation of machine capabilities for the production of carbon-fiber antennas. Finally, antenna prototypes were produced and their physical and electrical characteristics were evaluated through several test methods. The results showed that TFP embroidery machines were capable of producing quality, carbon antennas. Induction values of the antennas from 0.5 to 3.5 ‘H were achieved. Signal transfer efficiencies from carbon-antenna transmitters to an aftermarket receiver show promise in commercial application.

This paper presents a robust adaptive impedance controller for robot manipulators using function approximation techniques (FAT). Recently, FAT-based robust impedance controllers have been presented using Fourier series expansion for uncertainty estimation. In fact, sinusoidal functions can approximate nonlinear functions with arbitrary small approximation error based on the orthogonal functions theorem. The novelty of this paper in comparison with previous related works is that the number of required regressor matrices in this paper has been reduced. This superiority becomes more dominant when the manipulator degrees of freedom (DOFs) are increased. First, the desired signals for motor currents are calculated, and then the desired voltages are obtained. In the proposed approach, only a simple model of the actuator and manipulator dynamics is used in the controller design and all the rest dynamics are treated as external disturbance. The external disturbances can then be approximated by Fourier series expansion. The adaptation laws for Fourier series coefficients are derived from a Lyapunov-based stability analysis. Simulation results on a 2-DOF planar robot manipulator including the actuator dynamics indicate the efficiency of proposed method.

In this paper, I motivate a cut free sequent calculus for classical logic with first order quantification, allowing for singular terms free of existential import. Along the way, I motivate a criterion for rules designed to answer Prior’s question about what distinguishes rules for logical concepts, like conjunction from apparently similar rules for putative concepts like Prior’s tonk, and I show that the rules for the quantifiers—and the existence predicate—satisfy that condition.

We consider task planning for long-living intelligent agents situated in dynamic environments. Specifically, we address the problem of incomplete knowledge of the world due to the addition of new objects with unknown action models. We propose a multilayered agent architecture that uses meta-reasoning to control hierarchical task planning and situated learning, monitor expectations generated by a plan against world observations, forms goals and rewards for the situated reinforcement learner, and learns the missing planning knowledge relevant to the new objects. We use occupancy grids as a low-level representation for the high-level expectations to capture changes in the physical world due to the additional objects, and provide a similarity method for detecting discrepancies between the expectations and the observations at run-time; the meta-reasoner uses these discrepancies to formulate goals and rewards for the learner, and the learned policies are added to the hierarchical task network plan library for future re-use. We describe our experiments in the Minecraft and Gazebo microworlds to demonstrate the efficacy of the architecture and the technique for learning. We test our approach against an ablated reinforcement learning (RL) version, and our results indicate this form of expectation enhances the learning curve for RL while being more generic than propositional representations.

This paper presents a robust tracking controller for electrically driven robots, without the need for velocity measurements of joint variables. Many observers require the system dynamics or nominal models, while a model-free observer is presented in this paper. The novelty of this paper is presenting a new observer–controller structure based on function approximation techniques and Stone–Weierstrass theorem using differential equations. In fact, it is assumed that the lumped uncertainty can be modeled by linear differential equations. Then, using Stone–Weierstrass theorem, it is verified that these differential equations are universal approximators. The advantage of proposed approach in comparison with previous related works is simplicity and reducing the dimensions of regressor matrices without the need for any information of the systems’ dynamic. Simulation results on a 6-degrees of freedom robot manipulator driven by geared permanent magnet DC motors indicate the satisfactory performance of the proposed method in overcoming uncertainties and reducing the tracking error. To evaluate the performance of proposed controller in practical implementations, experimental results on an SCARA manipulator are presented.