1,956 research outputs found
Measuring the similarity of PML documents with RFID-based sensors
The Electronic Product Code (EPC) Network is an important part of the
Internet of Things. The Physical Mark-Up Language (PML) is to represent and
de-scribe data related to objects in EPC Network. The PML documents of each
component to exchange data in EPC Network system are XML documents based on PML
Core schema. For managing theses huge amount of PML documents of tags captured
by Radio frequency identification (RFID) readers, it is inevitable to develop
the high-performance technol-ogy, such as filtering and integrating these tag
data. So in this paper, we propose an approach for meas-uring the similarity of
PML documents based on Bayesian Network of several sensors. With respect to the
features of PML, while measuring the similarity, we firstly reduce the
redundancy data except information of EPC. On the basis of this, the Bayesian
Network model derived from the structure of the PML documents being compared is
constructed.Comment: International Journal of Ad Hoc and Ubiquitous Computin
Multi modal multi-semantic image retrieval
PhDThe rapid growth in the volume of visual information, e.g. image, and video can
overwhelm users’ ability to find and access the specific visual information of interest
to them. In recent years, ontology knowledge-based (KB) image information retrieval
techniques have been adopted into in order to attempt to extract knowledge from these
images, enhancing the retrieval performance. A KB framework is presented to
promote semi-automatic annotation and semantic image retrieval using multimodal
cues (visual features and text captions). In addition, a hierarchical structure for the KB
allows metadata to be shared that supports multi-semantics (polysemy) for concepts.
The framework builds up an effective knowledge base pertaining to a domain specific
image collection, e.g. sports, and is able to disambiguate and assign high level
semantics to ‘unannotated’ images.
Local feature analysis of visual content, namely using Scale Invariant Feature
Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’
model (BVW) as an effective method to represent visual content information and to
enhance its classification and retrieval. Local features are more useful than global
features, e.g. colour, shape or texture, as they are invariant to image scale, orientation
and camera angle. An innovative approach is proposed for the representation,
annotation and retrieval of visual content using a hybrid technique based upon the use
of an unstructured visual word and upon a (structured) hierarchical ontology KB
model. The structural model facilitates the disambiguation of unstructured visual
words and a more effective classification of visual content, compared to a vector
space model, through exploiting local conceptual structures and their relationships.
The key contributions of this framework in using local features for image
representation include: first, a method to generate visual words using the semantic
local adaptive clustering (SLAC) algorithm which takes term weight and spatial
locations of keypoints into account. Consequently, the semantic information is
preserved. Second a technique is used to detect the domain specific ‘non-informative
visual words’ which are ineffective at representing the content of visual data and
degrade its categorisation ability. Third, a method to combine an ontology model with
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a visual word model to resolve synonym (visual heterogeneity) and polysemy
problems, is proposed. The experimental results show that this approach can discover
semantically meaningful visual content descriptions and recognise specific events,
e.g., sports events, depicted in images efficiently.
Since discovering the semantics of an image is an extremely challenging problem, one
promising approach to enhance visual content interpretation is to use any associated
textual information that accompanies an image, as a cue to predict the meaning of an
image, by transforming this textual information into a structured annotation for an
image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct
types of information representation and modality, there are some strong, invariant,
implicit, connections between images and any accompanying text information.
Semantic analysis of image captions can be used by image retrieval systems to
retrieve selected images more precisely. To do this, a Natural Language Processing
(NLP) is exploited firstly in order to extract concepts from image captions. Next, an
ontology-based knowledge model is deployed in order to resolve natural language
ambiguities. To deal with the accompanying text information, two methods to extract
knowledge from textual information have been proposed. First, metadata can be
extracted automatically from text captions and restructured with respect to a semantic
model. Second, the use of LSI in relation to a domain-specific ontology-based
knowledge model enables the combined framework to tolerate ambiguities and
variations (incompleteness) of metadata. The use of the ontology-based knowledge
model allows the system to find indirectly relevant concepts in image captions and
thus leverage these to represent the semantics of images at a higher level.
Experimental results show that the proposed framework significantly enhances image
retrieval and leads to narrowing of the semantic gap between lower level machinederived
and higher level human-understandable conceptualisation
The Research of Information Filtering Technology Based on Bayesian Network
AbstractInformation filtering research is currently a hot topic. The screening and filter information is for information according to the predetermined standard classification. The paper gives a use of Bayesian network method for information on the objective of classification, thereby, make the information filtering accuracy obtained improve greatly
A brief network analysis of Artificial Intelligence publication
In this paper, we present an illustration to the history of Artificial
Intelligence(AI) with a statistical analysis of publish since 1940. We
collected and mined through the IEEE publish data base to analysis the
geological and chronological variance of the activeness of research in AI. The
connections between different institutes are showed. The result shows that the
leading community of AI research are mainly in the USA, China, the Europe and
Japan. The key institutes, authors and the research hotspots are revealed. It
is found that the research institutes in the fields like Data Mining, Computer
Vision, Pattern Recognition and some other fields of Machine Learning are quite
consistent, implying a strong interaction between the community of each field.
It is also showed that the research of Electronic Engineering and Industrial or
Commercial applications are very active in California. Japan is also publishing
a lot of papers in robotics. Due to the limitation of data source, the result
might be overly influenced by the number of published articles, which is to our
best improved by applying network keynode analysis on the research community
instead of merely count the number of publish.Comment: 18 pages, 7 figure
An Agent-Based Intrusion Detection System for Local Area Networks
Since it is impossible to predict and identify all the vulnerabilities of a
network beforehand, and penetration into a system by malicious intruders cannot
always be prevented, intrusion detection systems (IDSs) are essential entities
to ensure the security of a networked system. To be effective in carrying out
their functions, the IDSs need to be accurate, adaptive, and extensible. Given
these stringent requirements and the high level of vulnerabilities of the
current days' networks, the design of an IDS has become a very challenging
task. Although, an extensive research has been done on intrusion detection in a
distributed environment, distributed IDSs suffer from a number of drawbacks
e.g., high rates of false positives, low detection efficiency etc. In this
paper, the design of a distributed IDS is proposed that consists of a group of
autonomous and cooperating agents. In addition to its ability to detect
attacks, the system is capable of identifying and isolating compromised nodes
in the network thereby introducing fault-tolerance in its operations. The
experiments conducted on the system have shown that it has a high detection
efficiency and low false positives compared to some of the currently existing
systems.Comment: 13 pages, 5 figures, 2 table
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