1,720,984 research outputs found
A causal view on non-interference
No full textThe concept of non-interference has been introduced to characterise the absence of undesired information flows in a computing system. Although it is often explained referring to an informal notion of causality - the activity involving the part of the system with higher level of confidentiality should not cause any observable effect at lower levels - it is almost invariably formalised in terms of interleaving semantics. Here we focus on Petri nets and on the BNDC (Bisimilarity-based Non-Deducibility on Composition) property, a formalisation of non-interference widely studied in the literature. We show that BNDC admits natural characterisations based on the unfolding semantics - a classical true concurrent semantics for Petri nets - in terms of causalities and conflicts between high and low level activities. This leads to algorithms for checking BNDC on various classes of Petri nets, based on the construction of suitable complete prefixes of the unfolding. We also developed a prototype tool UBIC (Unfolding-Based Interference Checker), working on safe Petri nets, which provides promising results in terms of efficiency
The Segment Anything Model (SAM) for accelerating the smart farming revolution
Full text linkPrecision agriculture uses accurate identification and mapping of crop features by automated mechanisms. Using computer vision techniques implemented by supervised deep learning systems to solve many precision agricultural problems necessitates large-scale data collection and prolonged ground truth annotation by humans. The so-called foundation models in Artificial Intelligence (AI) are becoming increasingly significant. Meta AI Research is working on a project called Segment Anything to provide a base model for image segmentation. It can accomplish zero-shot generalisation to strange objects and images without additional training. This study evaluates the performance of the Segment Anything Model (SAM) for the problem of semantic segmentation of objects in the context of precision agriculture
Towards rigorous dataset quality standards for deep learning tasks in precision agriculture: A case study exploration
Full text linkDeep Learning (DL) through Convolutional Neural Networks (CNNs) has emerged as a critical player in classifying plant diseases from images. This prominence has intensified the demand for a substantial volume of annotated training data. However, acquiring such data is costly and intricate, fraught with subtle challenges. In the domain of plants, where data collection can be even more complex, this study scrutinises how one dataset was gathered. Specifically, it delves into the nuances of collecting images of grapevine leaves in an open field for a binary classification task, discerning the presence or absence of Esca disease. Adherence to rigorous dataset quality standards during image collection is paramount in precision agriculture. Errors made in this phase can have devastating repercussions on all subsequent work. For instance, collections of photos may exhibit a consistent disparity in background characteristics between images belonging to different classes. This persistent difference can lead a deep-learning algorithm to learn undesired correlations, even though the algorithm's performances are excellent because the train and test sets possess the same kind of disparity
Easy lambda-terms are not always simple
Full text linkA closed λ-term M is easy if, for any other closed term N, the lambda theory generated by M = N is consistent. Recently, it has been introduced a general technique to prove the easiness of λ-terms through the semantical notion of simple easiness. Simple easiness implies easiness and allows to prove consistency results via construction of suitable filter models of λ-calculus living in the category of complete partial orderings: given a simple easy term M and an arbitrary closed term N, it is possible to build (in a canonical way) a non-trivial filter model which equates the interpretation of M and N. The question whether easiness implies simple easiness constitutes Problem 19 in the TLCA list of open problems. In this paper we negatively answer the question providing a non-empty co-r.e. (complement of a recursively enumerable) set of easy, but not simple easy, λ-terms
Graph easy sets of mute lambda terms
Full text linkAmong the unsolvable terms of the lambda calculus, the mute ones are those having the highest degree of undefinedness. In this paper, we define for each natural number n, an infinite and recursive set M_n of mute terms, and show that it is graph-easy: for any closed term t of the lambda calculus there exists a graph model equating all the terms of M_n to t. Alongside, we provide a brief survey of the notion of undefinedness in the lambda calculus
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
A graph easy set of mute lambda terms
No full textAmong the unsolvable terms of the lambda calculus, the mute (or root-active) ones are those having the highest degree of un-definedness. In this paper, we define an infinite set S of mute terms, and show that it is graph-easy: for any closed term t of the lambda calculus there exists a graph model equating all the terms of S to t
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