16,982 research outputs found
A Conceptual Model for Scholarly Research Activity
This paper presents a conceptual model for scholarly research
activity, developed as part of the conceptual modelling work
within the ???Preparing DARIAH??? European e-Infrastructures
project. It is inspired by cultural-historical activity theory,
and is expressed in terms of the CIDOC Conceptual Reference
Model, extending its notion of activity so as to also
account, apart from historical practice, for scholarly research
planning. It is intended as a framework for structuring and
analyzing the results of empirical research on scholarly practice
and information requirements, encompassing the full
research lifecycle of information work and involving both
primary evidence and scholarly objects; also, as a framework
for producing clear and pertinent information requirements,
and specifications of digital infrastructures, tools and services
for scholarly research. We plan to use the model to tag interview
transcripts from an empirical study on scholarly information
work, and thus validate its soundness and fitness for
purpose
Problems in the design and implementation of a GKS-based user interface for a graphical information system
CISRG discussion paper ;
Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing
Within the context of autonomous driving a model-based reinforcement learning
algorithm is proposed for the design of neural network-parameterized
controllers. Classical model-based control methods, which include sampling- and
lattice-based algorithms and model predictive control, suffer from the
trade-off between model complexity and computational burden required for the
online solution of expensive optimization or search problems at every short
sampling time. To circumvent this trade-off, a 2-step procedure is motivated:
first learning of a controller during offline training based on an arbitrarily
complicated mathematical system model, before online fast feedforward
evaluation of the trained controller. The contribution of this paper is the
proposition of a simple gradient-free and model-based algorithm for deep
reinforcement learning using task separation with hill climbing (TSHC). In
particular, (i) simultaneous training on separate deterministic tasks with the
purpose of encoding many motion primitives in a neural network, and (ii) the
employment of maximally sparse rewards in combination with virtual velocity
constraints (VVCs) in setpoint proximity are advocated.Comment: 10 pages, 6 figures, 1 tabl
Self-organization of action hierarchy and compositionality by reinforcement learning with recurrent neural networks
Recurrent neural networks (RNNs) for reinforcement learning (RL) have shown
distinct advantages, e.g., solving memory-dependent tasks and meta-learning.
However, little effort has been spent on improving RNN architectures and on
understanding the underlying neural mechanisms for performance gain. In this
paper, we propose a novel, multiple-timescale, stochastic RNN for RL. Empirical
results show that the network can autonomously learn to abstract sub-goals and
can self-develop an action hierarchy using internal dynamics in a challenging
continuous control task. Furthermore, we show that the self-developed
compositionality of the network enhances faster re-learning when adapting to a
new task that is a re-composition of previously learned sub-goals, than when
starting from scratch. We also found that improved performance can be achieved
when neural activities are subject to stochastic rather than deterministic
dynamics
Checking Interaction-Based Declassification Policies for Android Using Symbolic Execution
Mobile apps can access a wide variety of secure information, such as contacts
and location. However, current mobile platforms include only coarse access
control mechanisms to protect such data. In this paper, we introduce
interaction-based declassification policies, in which the user's interactions
with the app constrain the release of sensitive information. Our policies are
defined extensionally, so as to be independent of the app's implementation,
based on sequences of security-relevant events that occur in app runs. Policies
use LTL formulae to precisely specify which secret inputs, read at which times,
may be released. We formalize a semantic security condition, interaction-based
noninterference, to define our policies precisely. Finally, we describe a
prototype tool that uses symbolic execution to check interaction-based
declassification policies for Android, and we show that it enforces policies
correctly on a set of apps.Comment: This research was supported in part by NSF grants CNS-1064997 and
1421373, AFOSR grants FA9550-12-1-0334 and FA9550-14-1-0334, a partnership
between UMIACS and the Laboratory for Telecommunication Sciences, and the
National Security Agenc
Ongoing Emergence: A Core Concept in Epigenetic Robotics
We propose ongoing emergence as a core concept in
epigenetic robotics. Ongoing emergence refers to the
continuous development and integration of new skills
and is exhibited when six criteria are satisfied: (1)
continuous skill acquisition, (2) incorporation of new
skills with existing skills, (3) autonomous development
of values and goals, (4) bootstrapping of initial skills, (5)
stability of skills, and (6) reproducibility. In this paper
we: (a) provide a conceptual synthesis of ongoing
emergence based on previous theorizing, (b) review
current research in epigenetic robotics in light of ongoing
emergence, (c) provide prototypical examples of ongoing
emergence from infant development, and (d) outline
computational issues relevant to creating robots
exhibiting ongoing emergence
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