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It is well known that the sense of presence in a tele-robot system for both home-based tele-rehabilitation and rescue operations is enhanced by haptic feedback. Beyond several advantages, in the presence of communication delay haptic feedback can lead to an unstable teleoperation system. During the last decades, several control techniques have been proposed to ensure a good trade-off between transparency and stability in bilateral teleoperation systems under time delays. These proposed control approaches have been extensively tested with teleoperation systems based on identical master and slave robots having few degrees of freedom (DoF). However, a small number of DoFs cannot ensure both an effective restoration of the multi-joint coordination in tele-rehabilitation and an adequate dexterity during manipulation tasks in rescue scenario. Thus, a deep understanding of the applicability of such control techniques on a real bilateral teleoperation setup is needed. In this work, we investigated the behavior of the time-domain passivity approach (TDPA) applied on an asymmetrical teleoperator system composed by a 5-DoFs impedance designed upper-limb exoskeleton and a 4-DoFs admittance designed anthropomorphic robot. The conceived teleoperation architecture is based on a velocity–force (measured) architecture with position drift compensation and has been tested with a representative set of tasks under communication delay (80 ms round-trip). The results have shown that the TDPA is suitable for a multi-DoFs asymmetrical setup composed by two isomorphic haptic interfaces characterized by different mechanical features. The stability of the teleoperator has been proved during several (1) high-force contacts against stiff wall that involve more Cartesian axes simultaneously, (2) continuous contacts with a stiff edge tests, (3) heavy-load handling tests while following a predefined path and (4) high-force contacts against stiff wall while handling a load. The found results demonstrated that the TDPA could be used in several teleoperation scenarios like home-based tele-rehabilitation and rescue operations.
In this paper, we are interested in high-level programming languages to implement the core components of an interactive theorem prover for a dependently typed language: the kernel – responsible for type-checking closed terms – and the elaborator – that manipulates open terms, that is terms containing unresolved unification variables.
In this paper, we confirm that λProlog, the language developed by Miller and Nadathur since the 80s, is extremely suitable for implementing the kernel. Indeed, we easily obtain a type checker for the Calculus of Inductive Constructions (CIC). Even more, we do so in an incremental way by escalating a checker for a pure type system to the full CIC.
We then turn our attention to the elaborator with the objective to obtain a simple implementation thanks to the features of the programming language. In particular, we want to use λProlog’s unification variables to model the object language ones. In this way, scope checking, carrying of assignments and occur checking are handled by the programming language.
We observe that the eager generative semantics inherited from Prolog clashes with this plan. We propose an extension to λProlog that allows to control the generative semantics, suspend goals over flexible terms turning them into constraints, and finally manipulate these constraints at the meta-meta level via constraint handling rules.
We implement the proposed language extension in the Embedded Lambda Prolog Interpreter system and we discuss how it can be used to extend the kernel into an elaborator for CIC.
For an edge-coloured graph G, the minimum colour degree of G means the minimum number of colours on edges which are incident to each vertex of G. We prove that if G is an edge-coloured graph with minimum colour degree at least 5, then V(G) can be partitioned into two parts such that each part induces a subgraph with minimum colour degree at least 2. We show this theorem by proving amuch stronger form. Moreover, we point out an important relationship between our theorem and Bermond and Thomassen’s conjecture in digraphs.
This paper presents a modified genetic algorithm (GA) using a new crossover operator (ADX) and a novel statistic correlation mutation algorithm (CAM). Both ADX and CAM work with population information to improve existing individuals of the GA and increase the exploration potential via the correlation mutation. Solution-based methods offer better local improvement of already known solutions while lacking at exploring the whole search space; in contrast, evolutionary algorithms provide better global search in exchange of exploitation power. Hybrid methods are widely used for constrained optimization problems due to increased global and local search capabilities. The modified GA improves results of constrained problems by balancing the exploitation and exploration potential of the algorithm. The conducted tests present average performance for various CEC’2015 benchmark problems, while offering better reliability and superior results on path planning problem for redundant manipulator and most of the constrained engineering design problems tested compared with current works in the literature and classic optimization algorithms.
Microwave power transfer (MPT) can solve certain types of problems. For example, Internet of Things requires a flexible configuration of sensor networks, which is hindered by wired-charging sensors. This problem can be overcome by MPT techniques. However, the transmission efficiency of MPT is lower than that of wired transmission. This study focuses on the operation of rectifiers having a pulse-modulated input signal. Although a pulse-modulated wave is effective for improving the RF-DC conversion efficiency, the output voltage waves of rectifiers have a high ripple content. Moreover, the harmonic balance method cannot be used to simulate the operation of a pulse-modulated rectifier. To reduce the ripple content, a smoothing capacitor should be connected in parallel to an output load. We investigated the influence of a smoothing capacitor, the general characteristics of rectifiers under pulse-modulated waves, and the effectiveness of using pulse-modulated waves for improving RF-DC conversion efficiency. In conclusion, we reveal a necessary condition of the smoothing capacitor for improvement, demonstrate the effectiveness of pulse modulation, and show that the optimum impedance with a pulse-modulated wave input is an inverse of duty ratio times as compared to that with continuous wave input.
More mobile devices such as mobile phones and robots are wirelessly charged for convenience, simplicity, and safety, and it would be desirable to achieve three-dimensional (3D) wireless charging with high spatial freedom and long range. This paper proposes a 3D wireless charging cube with three orthogonal coils and supporting magnetic cores to enhance the magnetic flux outside the cube. The proposed system is simulated by Ansoft Maxwell and implemented by a downsized prototype. Both simulation and experimental results show that the magnetic cores can strengthen the magnitude of B-field outside the cube. The final prototype demonstrates that the power transfer distance outside the cube for getting the same induced electromotive force in the receiver coil is extended approximately by 50 mm using magnetic cores with a permeability of 2800. It is found that the magnitude of B-field outside the cube can be increased by increasing the width and the permeability of the magnetic cores. The measured results show that when the permeability of the magnetic cores is fixed, the induced electromotive force in the receiver coil at a point 300 mm away from the center of the cube is increased by about 2V when the width of the magnetic cores is increased from 50 to 100 mm. The increase in the induced electromotive force at an extended point implies a greater potential of wireless power transfer capability to the power pickup.
Answer Set Programming (ASP) is a purely declarative formalism developed in the field of logic programming and non-monotonic reasoning: computational problems are encoded by logic programs whose answer sets, corresponding to solutions, are computed by an ASP system. Different, semantically equivalent, programs can be defined for the same problem; however, performance of systems evaluating them might significantly vary. We propose an approach for automatically transforming an input logic program into an equivalent one that can be evaluated more efficiently. One can make use of existing tree-decomposition techniques for rewriting selected rules into a set of multiple ones; the idea is to guide and adaptively apply them on the basis of proper new heuristics, to obtain a smart rewriting algorithm to be integrated into an ASP system. The method is rather general: it can be adapted to any system and implement different preference policies. Furthermore, we define a set of new heuristics tailored at optimizing grounding, one of the main phases of the ASP computation; we use them in order to implement the approach into the ASP system DLV, in particular into its grounding subsystem ℐ-DLV, and carry out an extensive experimental activity for assessing the impact of the proposal.
Ramsey quantifiers are a natural object of study not only for logic and computer science but also for the formal semantics of natural language. Restricting attention to finite models leads to the natural question whether all Ramsey quantifiers are either polynomial-time computable or NP-hard, and whether we can give a natural characterization of the polynomial-time computable quantifiers. In this paper, we first show that there exist intermediate Ramsey quantifiers and then we prove a dichotomy result for a large and natural class of Ramsey quantifiers, based on a reasonable and widely believed complexity assumption. We show that the polynomial-time computable quantifiers in this class are exactly the constant-log-bounded Ramsey quantifiers.
Wearable devices are fast evolving to address mobility and autonomy needs of elderly people who would benefit from physical assistance. Recent developments in soft robotics provide important opportunities to develop soft exoskeletons (also called exosuits) to enable both physical assistance and improved usability and acceptance for users. The XoSoft EU project has developed a modular soft lower limb exoskeleton to assist people with low mobility impairments. In this paper, we present the design of a soft modular lower limb exoskeleton to improve person’s mobility, contributing to independence and enhancing quality of life. The novelty of this work is the integration of quasi-passive elements in a soft exoskeleton. The exoskeleton provides mechanical assistance for subjects with low mobility impairments reducing energy requirements between 10% and 20%. Investigation of different control strategies based on gait segmentation and actuation elements is presented. A first hip–knee unilateral prototype is described, developed, and its performance assessed on a post-stroke patient for straight walking. The study presents an analysis of the human–exoskeleton energy patterns by way of the task-based biological power generation. The resultant assistance, in terms of power, was 10.9% ± 2.2% for hip actuation and 9.3% ± 3.5% for knee actuation. The control strategy improved the gait and postural patterns by increasing joint angles and foot clearance at specific phases of the walking cycle.
The sentences employed in semantic paradoxes display a wide range of semantic behaviours. However, the main theories of truth currently available either fail to provide a theory of paradox altogether, or can only account for some paradoxical phenomena by resorting to multiple interpretations of the language, as in (Kripke, 1975). In this article, I explore the wide range of semantic behaviours displayed by paradoxical sentences, and I develop a unified theory of truth and paradox, that is a theory of truth that also provides a unified account of paradoxical sentences. The theory I propose here yields a threefold classification of paradoxical sentences—liar-like sentences, truth-teller–like sentences, and revenge sentences. Unlike existing treatments of semantic paradox, the theory put forward in this article yields a way of interpreting all three kinds of paradoxical sentences, as well as unparadoxical sentences, within a single model.
We present an embedding of the Lambek–Grishin calculus into an extension of the nonassociative Lambek calculus with negation. The embedding is based on the De Morgan interpretation of the dual Grishin connectives.
We study a restricted form of list colouring, for which every pair of lists that correspond to adjacent vertices may not share more than one colour. The optimal list size such that a proper list colouring is always possible given this restriction, we call separation choosability. We show for bipartite graphs that separation choosability increases with (the logarithm of) the minimum degree. This strengthens results of Molloy and Thron and, partially, of Alon. One attempt to drop the bipartiteness assumption precipitates a natural class of Ramsey-type questions, of independent interest. For example, does every triangle-free graph of minimum degree d contain a bipartite induced subgraph of minimum degree Ω(log d) as d→∞?
Processing programs as data is one of the successes of functional and logic programming. Higher-order functions, as program-processing programs are called in functional programming, and meta-programs, as they are called in logic programming, are widespread declarative programming techniques. In logic programming, there is a gap between the meta-programming practice and its theory: The formalizations of meta-programming do not explicitly address its impredicativity and are not fully adequate. This article aims at overcoming this unsatisfactory situation by discussing the relevance of impredicativity to meta-programming, by revisiting former formalizations of meta-programming, and by defining Reflective Predicate Logic, a conservative extension of first-order logic, which provides a simple formalization of meta-programming.
We shall be concerned with the modal logic BK—which is based on the Belnap–Dunn four-valued matrix, and can be viewed as being obtained from the least normal modal logic K by adding ‘strong negation’. Though all four values ‘truth’, ‘falsity’, ‘neither’ and ‘both’ are employed in its Kripke semantics, only the first two are expressible as terms. We show that expanding the original language of BK to include constants for ‘neither’ or/and ‘both’ leads to quite unexpected results. To be more precise, adding one of these constants has the effect of eliminating the respective value at the level of BK-extensions. In particular, if one adds both of these, then the corresponding lattice of extensions turns out to be isomorphic to that of ordinary normal modal logics.
This paper presents a simple and intuitive syntax for proof nets of the multiplicative cyclic fragment (McyLL) of linear logic (LL). The main technical achievement of this work is to propose a correctness criterion that allows for sequentialization (recovering a proof from a proof net) for all McyLL proof nets, including those containing cut links. This is achieved by adapting the idea of contractibility (originally introduced by Danos to give a quadratic time procedure for proof nets correctness) to cyclic LL. This paper also gives a characterization of McyLL proof nets for Lambek Calculus and thus a geometrical (i.e., non-inductive) way to parse phrases or sentences by means of Lambek proof nets.
In the present communication, we introduce quantile-based (dynamic) inaccuracy measures and study their properties. Such measures provide an alternative approach to evaluate inaccuracy contained in the assumed statistical models. There are several models for which quantile functions are available in tractable form, though their distribution functions are not available in explicit form. In such cases, the traditional distribution function approach fails to compute inaccuracy between two random variables. Various examples are provided for illustration purpose. Some bounds are obtained. Effect of monotone transformations and characterizations are provided.
Machine learning algorithms provide the ability to quickly adapt and find patterns in large diverse data sources and therefore are a potential asset to application developers in enterprise systems, networks, and security domains. They make analyzing the security implications of these tools a critical task for machine learning researchers and practitioners alike, spawning a new subfield of research into adversarial learning for security-sensitive domains. The work presented in this book advanced the state of the art in this field of study with five primary contributions: a taxonomy for qualifying the security vulnerabilities of a learner, two novel practical attack/defense scenarios for learning in real-world settings, learning algorithms with theoretical guarantees on training-data privacy preservation, and a generalization of a theoretical paradigm for evading detection of a classifier. However, research in adversarial machine learning has only begun to address the field's complex obstacles—many challenges remain. These challenges suggest several new directions for research within both fields of machine learning and computer security. In this chapter we review our contributions and list a number of open problems in the area.
Above all, we investigated both the practical and theoretical aspects of applying machine learning in security domains. To understand potential threats, we analyzed the vulnerability of learning systems to adversarial malfeasance. We studied both attacks designed to optimally affect the learning system and attacks constrained by real-world limitations on the adversary's capabilities and information.We further designed defense strategies, which we showed significantly diminish the effect of these attacks. Our research focused on learning tasks in virus, spam, and network anomaly detection, but also is broadly applicable across many systems and security domains and has farreaching implications to any system that incorporates learning. Here is a summary of the contributions of each component of this book followed by a discussion of open problems and future directions for research.
Framework for Secure Learning
The first contribution discussed in this book was a framework for assessing risks to a learner within a particular security context (see Table 3.1). The basis for this work is a taxonomy of the characteristics of potential attacks. From this taxonomy (summarized in Table 9.1), we developed security games between an attacker and defender tailored to the particular type of threat posed by the attacker.