34,243 research outputs found
Latest trends in hybrid machine translation and its applications
This survey on hybrid machine translation (MT) is motivated by the fact that hybridization techniques have become popular as they attempt to combine the best characteristics of highly advanced pure rule or corpus-based MT approaches. Existing research typically covers either simple or more complex architectures guided by either rule or corpus-based approaches. The goal is to combine the best properties of each type.
This survey provides a detailed overview of the modification of the standard rule-based architecture to include statistical knowl- edge, the introduction of rules in corpus-based approaches, and the hybridization of approaches within this last single category. The principal aim here is to cover the leading research and progress in this field of MT and in several related applications.Peer ReviewedPostprint (published version
Hybrid image representation methods for automatic image annotation: a survey
In most automatic image annotation systems, images are represented with low level features using either global
methods or local methods. In global methods, the entire image is used as a unit. Local methods divide images into blocks where fixed-size sub-image blocks are adopted as sub-units; or into regions by using segmented regions as sub-units in images. In contrast to typical automatic image annotation methods that use either global or local features exclusively, several recent methods have considered incorporating the two kinds of information, and believe that the combination of the two levels of features is
beneficial in annotating images. In this paper, we provide a
survey on automatic image annotation techniques according to
one aspect: feature extraction, and, in order to complement
existing surveys in literature, we focus on the emerging image annotation methods: hybrid methods that combine both global and local features for image representation
Discrete event simulation and virtual reality use in industry: new opportunities and future trends
This paper reviews the area of combined discrete
event simulation (DES) and virtual reality (VR) use within industry.
While establishing a state of the art for progress in this
area, this paper makes the case for VR DES as the vehicle of choice
for complex data analysis through interactive simulation models,
highlighting both its advantages and current limitations. This paper
reviews active research topics such as VR and DES real-time
integration, communication protocols, system design considerations,
model validation, and applications of VR and DES. While
summarizing future research directions for this technology combination,
the case is made for smart factory adoption of VR DES as
a new platform for scenario testing and decision making. It is put
that in order for VR DES to fully meet the visualization requirements
of both Industry 4.0 and Industrial Internet visions of digital
manufacturing, further research is required in the areas of lower
latency image processing, DES delivery as a service, gesture recognition
for VR DES interaction, and linkage of DES to real-time data streams and Big Data sets
A Framework for Developing Real-Time OLAP algorithm using Multi-core processing and GPU: Heterogeneous Computing
The overwhelmingly increasing amount of stored data has spurred researchers
seeking different methods in order to optimally take advantage of it which
mostly have faced a response time problem as a result of this enormous size of
data. Most of solutions have suggested materialization as a favourite solution.
However, such a solution cannot attain Real- Time answers anyhow. In this paper
we propose a framework illustrating the barriers and suggested solutions in the
way of achieving Real-Time OLAP answers that are significantly used in decision
support systems and data warehouses
Next Generation Cloud Computing: New Trends and Research Directions
The landscape of cloud computing has significantly changed over the last
decade. Not only have more providers and service offerings crowded the space,
but also cloud infrastructure that was traditionally limited to single provider
data centers is now evolving. In this paper, we firstly discuss the changing
cloud infrastructure and consider the use of infrastructure from multiple
providers and the benefit of decentralising computing away from data centers.
These trends have resulted in the need for a variety of new computing
architectures that will be offered by future cloud infrastructure. These
architectures are anticipated to impact areas, such as connecting people and
devices, data-intensive computing, the service space and self-learning systems.
Finally, we lay out a roadmap of challenges that will need to be addressed for
realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201
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