1,723,808 research outputs found
Reservation-Based Federated Scheduling for Parallel Real-Time Tasks
This paper considers the scheduling of parallel real-time tasks with
arbitrary-deadlines. Each job of a parallel task is described as a directed
acyclic graph (DAG). In contrast to prior work in this area, where
decomposition-based scheduling algorithms are proposed based on the
DAG-structure and inter-task interference is analyzed as self-suspending
behavior, this paper generalizes the federated scheduling approach. We propose
a reservation-based algorithm, called reservation-based federated scheduling,
that dominates federated scheduling. We provide general constraints for the
design of such systems and prove that reservation-based federated scheduling
has a constant speedup factor with respect to any optimal DAG task scheduler.
Furthermore, the presented algorithm can be used in conjunction with any
scheduler and scheduling analysis suitable for ordinary arbitrary-deadline
sporadic task sets, i.e., without parallelism
Applying Prolog to Develop Distributed Systems
Development of distributed systems is a difficult task. Declarative
programming techniques hold a promising potential for effectively supporting
programmer in this challenge. While Datalog-based languages have been actively
explored for programming distributed systems, Prolog received relatively little
attention in this application area so far. In this paper we present a
Prolog-based programming system, called DAHL, for the declarative development
of distributed systems. DAHL extends Prolog with an event-driven control
mechanism and built-in networking procedures. Our experimental evaluation using
a distributed hash-table data structure, a protocol for achieving Byzantine
fault tolerance, and a distributed software model checker - all implemented in
DAHL - indicates the viability of the approach
Earth resources data analysis program, phase 3
Tasks were performed in two areas: (1) systems analysis and (2) algorithmic development. The major effort in the systems analysis task was the development of a recommended approach to the monitoring of resource utilization data for the Large Area Crop Inventory Experiment (LACIE). Other efforts included participation in various studies concerning the LACIE Project Plan, the utility of the GE Image 100, and the specifications for a special purpose processor to be used in the LACIE. In the second task, the major effort was the development of improved algorithms for estimating proportions of unclassified remotely sensed data. Also, work was performed on optimal feature extraction and optimal feature extraction for proportion estimation
A critical analysis of the EU legal provisions on terrorism
Terrorism activities within the EU are essentially transnational. Given that counter-terrorism operations and prosecutions have been allocated to different actors in different EU member states, operating within quite differing legal provisions and systems, co-ordination by the EU in this area is to be welcomed. In addition to critically examining the new EU laws in this area, this paper focuses on legal framework for the interaction of police, security services, and judiciary across Europe in this task, to include Europol and Schengen provisions. It also pays particular attention to the divergent positions of the UK and Ireland within this framework
Dynamic systems as tools for analysing human judgement
With the advent of computers in the experimental labs, dynamic systems have become a new tool for research on problem solving and decision making. A short review on this research is given and the main features of these systems (connectivity and dynamics) are illustrated. To allow systematic approaches to the influential variables in this area, two formal frameworks (linear structural equations and finite state automata) are presented. Besides the formal background, it is shown how the task demands of system identification and system control can be realized in these environments and how psychometrically acceptable dependent variables can be derived
Identification of Hessian matrix in distributed gradient-based multi-agent coordination control systems
Multi-agent coordination control usually involves a potential function that
encodes information of a global control task, while the control input for
individual agents is often designed by a gradient-based control law. The
property of Hessian matrix associated with a potential function plays an
important role in the stability analysis of equilibrium points in
gradient-based coordination control systems. Therefore, the identification of
Hessian matrix in gradient-based multi-agent coordination systems becomes a key
step in multi-agent equilibrium analysis. However, very often the
identification of Hessian matrix via the entry-wise calculation is a very
tedious task and can easily introduce calculation errors. In this paper we
present some general and fast approaches for the identification of Hessian
matrix based on matrix differentials and calculus rules, which can easily
derive a compact form of Hessian matrix for multi-agent coordination systems.
We also present several examples on Hessian identification for certain typical
potential functions involving edge-tension distance functions and
triangular-area functions, and illustrate their applications in the context of
distributed coordination and formation control
DCASE 2018 Challenge Surrey Cross-Task convolutional neural network baseline
The Detection and Classification of Acoustic Scenes and Events (DCASE)
consists of five audio classification and sound event detection tasks: 1)
Acoustic scene classification, 2) General-purpose audio tagging of Freesound,
3) Bird audio detection, 4) Weakly-labeled semi-supervised sound event
detection and 5) Multi-channel audio classification. In this paper, we create a
cross-task baseline system for all five tasks based on a convlutional neural
network (CNN): a "CNN Baseline" system. We implemented CNNs with 4 layers and 8
layers originating from AlexNet and VGG from computer vision. We investigated
how the performance varies from task to task with the same configuration of
neural networks. Experiments show that deeper CNN with 8 layers performs better
than CNN with 4 layers on all tasks except Task 1. Using CNN with 8 layers, we
achieve an accuracy of 0.680 on Task 1, an accuracy of 0.895 and a mean average
precision (MAP) of 0.928 on Task 2, an accuracy of 0.751 and an area under the
curve (AUC) of 0.854 on Task 3, a sound event detection F1 score of 20.8% on
Task 4, and an F1 score of 87.75% on Task 5. We released the Python source code
of the baseline systems under the MIT license for further research.Comment: Accepted by DCASE 2018 Workshop. 4 pages. Source code availabl
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