147,137 research outputs found
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
Area-energy aware dataflow optimisation of visual tracking systems
This paper presents an orderly dataflow-optimisation approach suitable for area-energy aware computer vision applications on FPGAs. Vision systems are increasingly being deployed in power constrained scenarios, where the
dataflow model of computation has become popular for describing complex algorithms. Dataflow model allows processing datapaths comprised of several independent
and well defined computations. However, compilers are often unsuccessful in identifying domain-specific optimisation opportunities resulting in wasted
resources and power consumption. We present a methodology for the optimisation of dataflow networks, according to patterns often found in computer vision systems, focusing on identifying optimisations which are not discovered automatically by an optimising compiler. Code transformation using profiling and refactoring
provides opportunities to optimise the design, targeting FPGA implementations and focusing on area and power abatement. Our refactoring methodology, applying transformations to a complex algorithm for visual tracking resulted in significant reduction in power consumption and resource usage
Alter ego, state of the art on user profiling: an overview of the most relevant organisational and behavioural aspects regarding User Profiling.
This report gives an overview of the most relevant organisational and\ud
behavioural aspects regarding user profiling. It discusses not only the\ud
most important aims of user profiling from both an organisation’s as\ud
well as a user’s perspective, it will also discuss organisational motives\ud
and barriers for user profiling and the most important conditions for\ud
the success of user profiling. Finally recommendations are made and\ud
suggestions for further research are given
Dealing with temporal inconsistency in automated computer forensic profiling
Computer profiling is the automated forensic examination of a computer system in order to provide a human investigator with a characterisation of the activities that have taken place on that system. As part of this process, the logical components of the computer system – components such as users, files and applications - are enumerated and the relationships between them discovered and reported. This information is enriched with traces of historical activity drawn from system logs and from evidence of events found in the computer file system. A potential problem with the use of such information is that some of it may be inconsistent and contradictory thus compromising its value. This work examines the impact of temporal inconsistency in such information and discusses two types of temporal inconsistency that may arise – inconsistency arising out of the normal errant behaviour of a computer system, and inconsistency arising out of deliberate tampering by a suspect – and techniques for dealing with inconsistencies of the latter kind. We examine the impact of deliberate tampering through experiments conducted with prototype computer profiling software. Based on the results of these experiments, we discuss techniques which can be employed in computer profiling to deal with such temporal inconsistencies
Near-bottom seismic profiling: High lateral variability, anomalous amplitudes, and estimates of attenuation
For almost a decade the Marine Physical Laboratory of Scripps Institution of Oceanography has been conducting near‐bottom geophysical surveys involving quantitative seismic profiling. Operating initially at 4 kHz and more recently at 6 kHz, this system has provided a wealth of fine scale quantitative data on the acoustic properties of ocean sediments. Over lateral distances of a few meters, 7‐dB changes in overall reflected energy as well as 10‐dB changes from individual reflectors have been observed. Anomalously high amplitudes from deep reflectors have been commonly observed, suggesting that multilayer interference is prevalent in records from such pulsed cw profilers. This conclusion is supported by results from sediment core physical property work and related convolution modeling, as well as by the significant differences observed between 4‐ and 6‐kHz profiles. In general, however, lateral consistency has been adequate in most areas surveyed to permit good estimates of acoustic attenuation from returns from dipping reflectors and sediment wedges
RPPM : Rapid Performance Prediction of Multithreaded workloads on multicore processors
Analytical performance modeling is a useful complement to detailed cycle-level simulation to quickly explore the design space in an early design stage. Mechanistic analytical modeling is particularly interesting as it provides deep insight and does not require expensive offline profiling as empirical modeling. Previous work in mechanistic analytical modeling, unfortunately, is limited to single-threaded applications running on single-core processors.
This work proposes RPPM, a mechanistic analytical performance model for multi-threaded applications on multicore hardware. RPPM collects microarchitecture-independent characteristics of a multi-threaded workload to predict performance on a previously unseen multicore architecture. The profile needs to be collected only once to predict a range of processor architectures. We evaluate RPPM's accuracy against simulation and report a performance prediction error of 11.2% on average (23% max). We demonstrate RPPM's usefulness for conducting design space exploration experiments as well as for analyzing parallel application performance
Towards engineering ontologies for cognitive profiling of agents on the semantic web
Research shows that most agent-based collaborations
suffer from lack of flexibility. This is due to the fact that
most agent-based applications assume pre-defined
knowledge of agents’ capabilities and/or neglect basic
cognitive and interactional requirements in multi-agent
collaboration. The highlight of this paper is that it brings
cognitive models (inspired from cognitive sciences and HCI)
proposing architectural and knowledge-based requirements
for agents to structure ontological models for cognitive
profiling in order to increase cognitive awareness between
themselves, which in turn promotes flexibility, reusability
and predictability of agent behavior; thus contributing
towards minimizing cognitive overload incurred on humans.
The semantic web is used as an action mediating space,
where shared knowledge base in the form of ontological
models provides affordances for improving cognitive
awareness
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