Steep slope vineyards are a complex scenario for the development of ground robots. Planning a safe robot trajectory is one of the biggest challenges in this scenario, characterized by irregular surfaces and strong slopes (more than 35°). Moving the robot through a pile of stones, spots with high slope or/and with wrong robot yaw may result in an abrupt fall of the robot, damaging the equipment and centenary vines, and sometimes imposing injuries to humans. This paper presents a novel approach for path planning aware of center of mass of the robot for application in sloppy terrains. Agricultural robotic path planning (AgRobPP) is a framework that considers the A* algorithm by expanding inner functions to deal with three main inputs: multi-layer occupation grid map, altitude map and robot’s center of mass. This multi-layer grid map is updated by obstacles taking into account the terrain slope and maximum robot posture. AgRobPP is also extended with algorithms for local trajectory replanning during the execution of a trajectory that is blocked by the presence of an obstacle, always assuring the safety of the re-planned path. AgRobPP has a novel PointCloud translator algorithm called PointCloud to grid map and digital elevation model (PC2GD), which extracts the occupation grid map and digital elevation model from a PointCloud. This can be used in AgRobPP core algorithms and farm management intelligent systems as well. AgRobPP algorithms demonstrate a great performance with the real data acquired from AgRob V16, a robotic platform developed for autonomous navigation in steep slope vineyards.

The paper presents experiments on part-of-speech and full morphological tagging of the Slavic minority language Rusyn. The proposed approach relies on transfer learning and uses only annotated resources from related Slavic languages, namely Russian, Ukrainian, Slovak, Polish, and Czech. It does not require any annotated Rusyn training data, nor parallel data or bilingual dictionaries involving Rusyn. Compared to earlier work, we improve tagging performance by using a neural network tagger and larger training data from the neighboring Slavic languages. We experiment with various data preprocessing and sampling strategies and evaluate the impact of multitask learning strategies and of pretrained word embeddings. Overall, while genre discrepancies between training and test data have a negative impact, we improve full morphological tagging by 9% absolute micro-averaged F1 as compared to previous research.

For given graphs G1,…, Gk, the size-Ramsey number $\hat R({G_1}, \ldots ,{G_k})$ is the smallest integer m for which there exists a graph H on m edges such that in every k-edge colouring of H with colours 1,…,k, H contains a monochromatic copy of Gi of colour i for some 1 ≤ i ≤ k. We denote $\hat R({G_1}, \ldots ,{G_k})$ by ${\hat R_k}(G)$ when G1 = ⋯ = Gk = G.

Haxell, Kohayakawa and Łuczak showed that the size-Ramsey number of a cycle Cn is linear in n, ${\hat R_k}({C_n}) \le {c_k}n$ for some constant ck. Their proof, however, is based on Szemerédi’s regularity lemma so no specific constant ck is known.

In this paper, we give various upper bounds for the size-Ramsey numbers of cycles. We provide an alternative proof of ${\hat R_k}({C_n}) \le {c_k}n$, avoiding use of the regularity lemma, where ck is exponential and doubly exponential in k, when n is even and odd, respectively. In particular, we show that for sufficiently large n we have ${\hat R_2}({C_n}) \le {10^5} \times cn$, where c = 6.5 if n is even and c = 1989 otherwise.

We study, in an abstract and general framework, formal representations of dependence and groundedness which occur in semantic theories of truth. Our goals are (a) to relate the different ways in which groundedness is defined according to the way dependence is represented and (b) to represent different notions of dependence as instances of a suitable generalisation of the mathematical notion of functional dependence.

Virtual patient software allows health professionals to practise their skills by interacting with tools simulating clinical scenarios. A natural language dialogue system can provide natural interaction for medical history-taking. However, the large number of concepts and terms in the medical domain makes the creation of such a system a demanding task. We designed a dialogue system that stands out from current research by its ability to handle a wide variety of medical specialties and clinical cases. To address the task, we designed a patient record model, a knowledge model for the task and a termino-ontological model that hosts structured thesauri with linguistic, terminological and ontological knowledge. We used a frame- and rule-based approach and terminology-rich resources to handle the medical dialogue. This work focuses on the termino-ontological model, the challenges involved and how the system manages resources for the French language. We adopted a comprehensive approach to collect terms and ontological knowledge, and dictionaries of affixes, synonyms and derivational variants. Resources include domain lists containing over 161,000 terms, and dictionaries with over 959,000 word/concept entries. We assessed our approach by having 71 participants (39 medical doctors and 32 non-medical evaluators) interact with the system and use 35 cases from 18 specialities. We conducted a quantitative evaluation of all components by analysing interaction logs (11,834 turns). Natural language understanding achieved an F-measure of 95.8%. Dialogue management provided on average 74.3 (±9.5)% of correct answers. We performed a qualitative evaluation by collecting 171 five-point Likert scale questionnaires. All evaluated aspects obtained mean scores above the Likert mid-scale point. We analysed the vocabulary coverage with regard to unseen cases: the system covered 97.8% of their terms. Evaluations showed that the system achieved high vocabulary coverage on unseen cases and was assessed as relevant for the task.

The Univalence axiom, due to Vladimir Voevodsky, is often taken to be one of the most important discoveries arising from the Homotopy Type Theory (HoTT) research programme. It is said by Steve Awodey that Univalence embodies mathematical structuralism, and that Univalence may be regarded as ‘expanding the notion of identity to that of equivalence’. This article explores the conceptual, foundational and philosophical status of Univalence in Homotopy Type Theory. It extends our Types-as-Concepts interpretation of HoTT to Universes, and offers an account of the Univalence axiom in such terms. We consider Awodey’s informal argument that Univalence is motivated by the principle that reasoning should be invariant under isomorphism, and we examine whether an autonomous and rigorous justification along these lines can be given. We consider two problems facing such a justification. First, there is a difference between equivalence and isomorphism and Univalence must be formulated in terms of the former. Second, the argument as presented cannot establish Univalence itself but only a weaker version of it, and must be supplemented by an additional principle. The article argues that the prospects for an autonomous justification of Univalence are promising.

Light verb constructions (LVCs) are verb and noun combinations in which the verb has lost its meaning to some degree and the noun is used in one of its original senses, typically denoting an event or an action. They exhibit special linguistic features, especially when regarded in a multilingual context. In this paper, we focus on the automatic detection of LVCs in raw text in four different languages, namely, English, German, Spanish, and Hungarian. First, we analyze the characteristics of LVCs from a linguistic point of view based on parallel corpus data. Then, we provide a standardized (i.e., language-independent) representation of LVCs that can be used in machine learning experiments. After, we experiment on identifying LVCs in different languages: we exploit language adaptation techniques which demonstrate that data from an additional language can be successfully employed in improving the performance of supervised LVC detection for a given language. As there are several annotated corpora from several domains in the case of English and Hungarian, we also investigate the effect of simple domain adaptation techniques to reduce the gap between domains. Furthermore, we combine domain adaptation techniques with language adaptation techniques for these two languages. Our results show that both out-domain and additional language data can improve performance. We believe that our language adaptation method may have practical implications in several fields of natural language processing, especially in machine translation.

Graded epistemic logic is a logic for reasoning about uncertainties. Graded epistemic logic is interpreted on graded models. These models are generalizations of Kripke models. We obtain completeness of some graded epistemic logics. We further develop dynamic extensions of graded epistemic logics, along the framework of dynamic epistemic logic. We give an extension with public announcements, i.e., public events, and an extension with graded event models, a generalization also including nonpublic events. We present complete axiomatizations for both logics.

Working with mobile robots, prior to execute the local planning stage, they must know the environment where they are moving. For that reason the perception and mapping stages must be performed previously. This paper presents a survey in the state of the art in detection and tracking of moving obstacles (DATMO). The aim of what follows is to provide an overview of the most remarkable methods at each field specially in indoor environments where dynamic obstacles can be potentially more dangerous and unpredictable. We are going to show related DATMO methods organized in three approaches: model-free, model-based and grid-based. In addition, a comparison between them and conclusions will be presented.

We study effectively inseparable (abbreviated as e.i.) prelattices (i.e., structures of the form $L = \langle \omega , \wedge , \vee ,0,1,{ \le _L}\rangle$ where ω denotes the set of natural numbers and the following four conditions hold: (1) $\wedge , \vee$ are binary computable operations; (2) ${ \le _L}$ is a computably enumerable preordering relation, with $0{ \le _L}x{ \le _L}1$ for every x; (3) the equivalence relation ${ \equiv _L}$ originated by ${ \le _L}$ is a congruence on L such that the corresponding quotient structure is a nontrivial bounded lattice; (4) the ${ \equiv _L}$-equivalence classes of 0 and 1 form an effectively inseparable pair of sets). Solving a problem in (Montagna & Sorbi, 1985) we show (Theorem 4.2), that if L is an e.i. prelattice then ${ \le _L}$ is universal with respect to all c.e. preordering relations, i.e., for every c.e. preordering relation R there exists a computable function f reducing R to ${ \le _L}$, i.e., $xRy$ if and only if $f\left( x \right){ \le _L}f\left( y \right)$, for all $x,y$. In fact (Corollary 5.3) ${ \le _L}$ is locally universal, i.e., for every pair $a{ < _L}b$ and every c.e. preordering relation R one can find a reducing function f from R to ${ \le _L}$ such that the range of f is contained in the interval $\left\{ {x:a{ \le _L}x{ \le _L}b} \right\}$. Also (Theorem 5.7) ${ \le _L}$ is uniformly dense, i.e., there exists a computable function f such that for every $a,b$ if $a{ < _L}b$ then $a{ < _L}f\left( {a,b} \right){ < _L}b$, and if $a{ \equiv _L}a\prime$ and $b{ \equiv _L}b\prime$ then $f\left( {a,b} \right){ \equiv _L}f\left( {a\prime ,b\prime } \right)$. Some consequences and applications of these results are discussed: in particular (Corollary 7.2) for $n \ge 1$ the c.e. preordering relation on ${{\rm{\Sigma }}_n}$ sentences yielded by the relation of provable implication of any c.e. consistent extension of Robinson’s system R or Q is locally universal and uniformly dense; and (Corollary 7.3) the c.e. preordering relation yielded by provable implication of any c.e. consistent extension of Heyting Arithmetic is locally universal and uniformly dense.

This paper proposes four novel term evaluation metrics to represent documents in the text categorization where class distribution is imbalanced. These metrics are achieved from the revision of the four common term evaluation metrics: chi-square, information gain, odds ratio, and relevance frequency. While the common metrics require a balanced class distribution, our proposed metrics evaluate the document terms under an imbalanced distribution. They calculate the degree of relatedness of terms with respect to minor and major classes by considering their imbalanced distribution. Using these metrics in the document representation makes a better distinction between the documents of the minor and major classes and improves the performance of machine learning algorithms. The proposed metrics are assessed over three popular benchmarks (two subsets of Reuters-21578 and WebKB) by using four classification algorithms: support vector machines, naive Bayes, decision trees, and centroid-based classifiers. Our empirical results indicate that the proposed metrics outperform the common metrics in the imbalanced text categorization.