1,721,029 research outputs found
Spatial GNSS Spoofing against Drone Swarms with Multiple Antennas and Wiener Filter
No full textSpoofing of global-navigation satellite system (GNSS) signals to induce a target position estimate is a relevant security threat to the navigation of drones. However, spoofing multiple drones simultaneously as they move in a swarm, without disrupting their formation, is a complex task. In this paper, we propose to transmit spoofing signals from the ground, such that the fake position can be estimated in any point of an area of the plane where the swarm is moving. To this end we filter the satellite-generated GNSS signals with a multidimensional linear filter, and transmit the filtered signal with multiple ground antennas. The multidimensional filter is designed according to a generalized Wiener-filter criterion, such that the fake signal is accurately reproduced (in terms of mean squared error (MSE)) in the whole spoofed area. We investigate the impact of various design parameters (among others, the size of the spoofed area, the number of ground antennas, and the number of spoofed satellites) on both the MSE, and the probability of acquisition of the spoofed signal by the drones
A federated society of bots for smart contract testing
Full text linkSmart contracts are a new type of software that allows its users to perform irreversible transactions on a distributed persistent data storage called the blockchain. The nature of such contracts and the technical details of the blockchain architecture give raise to new kinds of faults, which require specific test behaviours to be exposed. In this paper we present SoCRATES, a generic and extensible framework to test smart contracts running in a blockchain. The key properties of SoCRATES are: (1) it comprises bots that interact with the blockchain according to a set of composable behaviours; (2) it can instantiate a society of bots, which can trigger faults due to multi-user interactions that are impossible to expose with a single bot. Our experimental results show that SoCRATES can expose known faults and detect previously unknown faults in contracts currently published in the Ethereum blockchain. They also show that a society of bots is often more effective than a single bot in fault exposure. (C) 2020 Elsevier Inc. All rights reserved
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
Surface modification of textile substrates with fluorinated acrylic lattices as water- and oil-repellent finishes
No full textDeep Reinforcement Learning for Black-box Testing of Android Apps
No full textThe state space of Android apps is huge, and its thorough exploration during testing remains a significant challenge. The best exploration strategy is highly dependent on the features of the app under test. Reinforcement Learning (RL) is a machine learning technique that learns the optimal strategy to solve a task by trial and error, guided by positive or negative reward, rather than explicit supervision. Deep RL is a recent extension of RL that takes advantage of the learning capabilities of neural networks. Such capabilities make Deep RL suitable for complex exploration spaces such as one of Android apps. However, state-of-the-art, publicly available tools only support basic, Tabular RL. We have developed ARES, a Deep RL approach for black-box testing of Android apps. Experimental results show that it achieves higher coverage and fault revelation than the baselines, including state-of-the-art tools, such as TimeMachine and Q-Testing. We also investigated the reasons behind such performance qualitatively, and we have identified the key features of Android apps that make Deep RL particularly effective on them to be the presence of chained and blocking activities. Moreover, we have developed FATE to fine-tune the hyperparameters of Deep RL algorithms on simulated apps, since it is computationally expensive to carry it out on real apps
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