1,852,828 research outputs found
Drones on the horizon: Transforming Africa’s agriculture
This report provides a contextualized review of drones as a vital precision agriculture-enabling technology and its range of relevant
uses for providing detailed and on-demand data in order to enhance decision-making by farmers and hence facilitate much needed
support
Learning robot policies using a high-level abstraction persona-behaviour simulator
2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksCollecting data in Human-Robot Interaction for training learning agents might be a hard task to accomplish. This is especially true when the target users are older adults with dementia since this usually requires hours of interactions and puts quite a lot of workload on the user. This paper addresses the problem of importing the Personas technique from HRI to create fictional patients’ profiles. We propose a Persona-Behaviour Simulator tool that provides, with high-level abstraction, user’s actions during an HRI task, and we apply it to cognitive training exercises for older adults with dementia. It consists of a Persona Definition that characterizes a patient along four dimensions and a Task Engine that provides information regarding the task complexity. We build a simulated environment where the high-level user’s actions are provided by the simulator and the robot initial policy is learned using a Q-learning algorithm. The results show that the current simulator provides a reasonable initial policy for a defined Persona profile. Moreover, the learned robot assistance has proved to be robust to potential changes in the user’s behaviour. In this way, we can speed up the fine-tuning of the rough policy during the real interactions to tailor the assistance to the given user. We believe the presented approach can be easily extended to account for other types of HRI tasks; for example, when input data is required to train a learning algorithm, but data collection is very expensive or unfeasible. We advocate that simulation is a convenient tool in these cases.Peer ReviewedPostprint (author's final draft
High-level Cryptographic Abstractions
The interfaces exposed by commonly used cryptographic libraries are clumsy,
complicated, and assume an understanding of cryptographic algorithms. The
challenge is to design high-level abstractions that require minimum knowledge
and effort to use while also allowing maximum control when needed.
This paper proposes such high-level abstractions consisting of simple
cryptographic primitives and full declarative configuration. These abstractions
can be implemented on top of any cryptographic library in any language. We have
implemented these abstractions in Python, and used them to write a wide variety
of well-known security protocols, including Signal, Kerberos, and TLS.
We show that programs using our abstractions are much smaller and easier to
write than using low-level libraries, where size of security protocols
implemented is reduced by about a third on average. We show our implementation
incurs a small overhead, less than 5 microseconds for shared key operations and
less than 341 microseconds (< 1%) for public key operations. We also show our
abstractions are safe against main types of cryptographic misuse reported in
the literature
The CMS High Level Trigger
The CMS experiment has been designed with a 2-level trigger system: the Level
1 Trigger, implemented on custom-designed electronics, and the High Level
Trigger (HLT), a streamlined version of the CMS offline reconstruction software
running on a computer farm. A software trigger system requires a tradeoff
between the complexity of the algorithms running on the available computing
power, the sustainable output rate, and the selection efficiency. Here we will
present the performance of the main triggers used during the 2012 data taking,
ranging from simpler single-object selections to more complex algorithms
combining different objects, and applying analysis-level reconstruction and
selection. We will discuss the optimisation of the triggers and the specific
techniques to cope with the increasing LHC pile-up, reducing its impact on the
physics performance.Comment: PIC2013 conferenc
Is There High-Level Causation?
The discovery of high-level causal relations seems a central activity of the special sciences. Those same sciences are less successful in formulating strict laws. If causation must be underwritten by strict laws, we are faced with a puzzle (first noticed by Donald Davidson), which might be dubbed the 'no strict laws' problem for high-level causation. Attempts have been made to dissolve this problem by showing that leading theories of causation do not in fact require that causation be underwritten by strict laws. But this conclusion has been too hastily drawn. Philosophers have tended to equate non-strict laws with ceteris paribus laws. I argue that there is another category of non-strict law that has often not been properly distinguished: namely, (what I will call) minutiae rectus laws. If, as it appears, many special science laws are minutiae rectus laws, then this poses a problem for their ability to underwrite causal relations in a way that their typically ceteris paribus nature does not. I argue that the best prospect for resolving the resurgent 'no strict laws' problem is to argue that special science laws are in fact typically probabilistic (and thus able to support probabilistic causation), rather than being minutiae rectus laws
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Benchmarking for high-level synthesis
This paper discusses issues in benchmarking for synthesis, and suggests techniques for the comparison of benchmark descriptions, the synthesis tools used, as well as the synthesized designs finally generated. We propose a classification scheme for the assumptions made for the comparison of different synthesis tools, and present an Assumptions Chart that can be used to visualize different benchmarks, tools and synthesis results. We illustrate application of this Assumptions Chart using synthesis experiments that were conducted on some sample High-Level Synthesis Workshop bench-marks
High Level Tracker Triggers for CMS
Two fast trigger algorithms based on 3 innermost hits in the CMS Inner
Tracker are presented. One of the algorithms will be applied at LHC low
luminosity to select B decay channels. Performance of the algorithm is
demonstrated for the decay channel Bs->Ds+pi. The second algorithm will be used
to select tau-jets at LHC high luminosity.Comment: 10 pages, 10 figures, to be published in the Vertex 2001 Conference
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