48,015 research outputs found
A Revised Publication Model for ECML PKDD
ECML PKDD is the main European conference on machine learning and data
mining. Since its foundation it implemented the publication model common in
computer science: there was one conference deadline; conference submissions
were reviewed by a program committee; papers were accepted with a low
acceptance rate. Proceedings were published in several Springer Lecture Notes
in Artificial (LNAI) volumes, while selected papers were invited to special
issues of the Machine Learning and Data Mining and Knowledge Discovery
journals. In recent years, this model has however come under stress. Problems
include: reviews are of highly variable quality; the purpose of bringing the
community together is lost; reviewing workloads are high; the information
content of conferences and journals decreases; there is confusion among
scientists in interdisciplinary contexts. In this paper, we present a new
publication model, which will be adopted for the ECML PKDD 2013 conference, and
aims to solve some of the problems of the traditional model. The key feature of
this model is the creation of a journal track, which is open to submissions all
year long and allows for revision cycles.Comment: 13 page
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Transforming failure into success through organisational learning: An analysis of a manufacturing information system
This paper describes the idiosyncracies of a case study company, through highlighting issues and problems
experienced during their attempts to evaluate, implement and realise the holistic implications of a manufacturing
information system. Although the Information System (IS) was operational for a period of time, it
was eventually deemed a failure. The reason for this was that a range of human and organisational factors
prevented the organisation from embracing the full impact of the system. The eventual success of their
information system was realised through a bespoke implementation, based upon a traditional systems development
lifecycle that indirectly addressed learning issues following the earlier failed deployment. The
paper highlights key issues relating to business success and failure, and then contrasts them alongside the
presented case study. In doing so, the authors conclude by proposing methods through which manufacturing
information systems can be transformed for business success. This is described achievable through both a
realisation in the positioning of the organisation relative to technology management, and the related mapping
of human and technological constructs that support information systems related succes
IEEE ACCESS SPECIAL SECTION EDITORIAL: REAL-TIME MACHINE LEARNING APPLICATIONS IN MOBILE ROBOTICS
In the last ten years, advances in machine learning methods have brought tremendous developments to the field of robotics. The performance in many robotic applications such as robotics grasping, locomotion, humanârobot interaction, perception and control of robotic systems, navigation, planning, mapping, and localization has increased since the appearance of recent machine learning methods. In particular, deep learning methods have brought significant improvements in a broad range of robot applications including drones, mobile robots, robotics manipulators, bipedal robots, and self-driving cars. The availability of big data and more powerful computational resources, such as graphics processing units (GPUs), has made numerous robotic applications feasible which were not possible previously
Indexing of fictional video content for event detection and summarisation
This paper presents an approach to movie video indexing that utilises audiovisual analysis to detect important and meaningful temporal video segments, that we term events. We consider three event classes, corresponding to dialogues, action sequences, and montages, where the latter also includes musical sequences. These three event classes are intuitive for a viewer to understand and recognise whilst accounting for over 90% of the content of most movies. To detect events we leverage traditional filmmaking principles and map these to a set of computable low-level audiovisual features. Finite state machines (FSMs) are used to detect when temporal sequences of specific features occur. A set of heuristics, again inspired by filmmaking conventions, are then applied to the output of multiple FSMs to detect the required events. A movie search system, named MovieBrowser, built upon this approach is also described. The overall approach is evaluated against a ground truth of over twenty-three hours of movie content drawn from various genres and consistently obtains high precision and recall for all event classes. A user experiment designed to evaluate the usefulness of an event-based structure for both searching and browsing movie archives is also described and the results indicate the usefulness of the proposed approach
Securing the Participation of Safety-Critical SCADA Systems in the Industrial Internet of Things
In the past, industrial control systems were âair gappedâ and
isolated from more conventional networks. They used
specialist protocols, such as Modbus, that are very different
from TCP/IP. Individual devices used proprietary operating
systems rather than the more familiar Linux or Windows.
However, things are changing. There is a move for greater
connectivity â for instance so that higher-level enterprise
management systems can exchange information that helps
optimise production processes. At the same time, industrial
systems have been influenced by concepts from the Internet
of Things; where the information derived from sensors and
actuators in domestic and industrial components can be
addressed through network interfaces. This paper identifies a
range of cyber security and safety concerns that arise from
these developments. The closing sections introduce potential
solutions and identify areas for future research
Special Libraries, December 1954
Volume 45, Issue 10https://scholarworks.sjsu.edu/sla_sl_1954/1009/thumbnail.jp
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