Abstract: The paper presents a multimodal approach for biometric authentication, based on multiple classifiers. The proposed solution uses a post-classification biometric fusion method in which the biometric data classifiers outputs are combined in order to improve the overall biometric system performance by decreasing the classification error rates. The paper shows also the biometric recognition task improvement by means of a carefully feature selection, as much as not all of the feature vectors components support the accuracy improvement.
Abstract: Needs of an efficient information retrieval in recent
years in increased more then ever because of the frequent use of
digital information in our life. We see a lot of work in the area of
textual information but in multimedia information, we cannot find
much progress. In text based information, new technology of data
mining and data marts are now in working that were started from the
basic concept of database some where in 1960.
In image search and especially in image identification,
computerized system at very initial stages. Even in the area of image
search we cannot see much progress as in the case of text based
search techniques. One main reason for this is the wide spread roots
of image search where many area like artificial intelligence,
statistics, image processing, pattern recognition play their role. Even
human psychology and perception and cultural diversity also have
their share for the design of a good and efficient image recognition
and retrieval system.
A new object based search technique is presented in this paper
where object in the image are identified on the basis of their
geometrical shapes and other features like color and texture where
object-co-relation augments this search process.
To be more focused on objects identification, simple images are
selected for the work to reduce the role of segmentation in overall
process however same technique can also be applied for other
images.
Abstract: The Tropical Data Hub (TDH) is a virtual research environment that provides researchers with an e-research infrastructure to congregate significant tropical data sets for data reuse, integration, searching, and correlation. However, researchers often require data and metadata synthesis across disciplines for crossdomain analyses and knowledge discovery. A triplestore offers a semantic layer to achieve a more intelligent method of search to support the synthesis requirements by automating latent linkages in the data and metadata. Presently, the benchmarks to aid the decision of which triplestore is best suited for use in an application environment like the TDH are limited to performance. This paper describes a new evaluation tool developed to analyze both features and performance. The tool comprises a weighted decision matrix to evaluate the interoperability, functionality, performance, and support availability of a range of integrated and native triplestores to rank them according to requirements of the TDH.
Abstract: This study proposes novel hybrid social network analysis and collaborative filtering approach to enhance the performance of recommender systems. The proposed model selects subgroups of users in Internet community through social network analysis (SNA), and then performs clustering analysis using the information about subgroups. Finally, it makes recommendations using cluster-indexing CF based on the clustering results. This study tries to use the cores in subgroups as an initial seed for a conventional clustering algorithm. This model chooses five cores which have the highest value of degree centrality from SNA, and then performs clustering analysis by using the cores as initial centroids (cluster centers). Then, the model amplifies the impact of friends in social network in the process of cluster-indexing CF.
Abstract: Considering the theory of attribute grammars, we use
logical formulas instead of traditional functional semantic rules.
Following the decoration of a derivation tree, a suitable algorithm
should maintain the consistency of the formulas together with the
evaluation of the attributes. This may be a Prolog-like resolution, but
this paper examines a somewhat different strategy, based on
production specialization, local consistency and propagation: given a
derivation tree, it is interactively decorated, i.e. incrementally
checked and evaluated. The non-directed dependencies are
dynamically directed during attribute evaluation.
Abstract: In this paper, a new Genetic Algorithm (GA) based
methodology is proposed to optimize the Degree of Hybridization
(DOH) in a passenger parallel hybrid car. At first step, target
parameters for the vehicle are decided and then using ADvanced
VehIcle SimulatOR (ADVISOR) software, the variation pattern of
these target parameters, across the different DOHs, is extracted. At
the next step, a suitable cost function is defined and is optimized
using GA. In this paper, also a new technique has been proposed for
deciding the number of battery modules for each DOH, which leads
to a great improvement in the vehicle performance. The proposed
methodology is so simple, fast and at the same time, so efficient.
Abstract: Data mining and knowledge engineering have become a tough task due to the availability of large amount of data in the web nowadays. Validity and reliability of data also become a main debate in knowledge acquisition. Besides, acquiring knowledge from different languages has become another concern. There are many language translators and corpora developed but the function of these translators and corpora are usually limited to certain languages and domains. Furthermore, search results from engines with traditional 'keyword' approach are no longer satisfying. More intelligent knowledge engineering agents are needed. To address to these problems, a system known as Multilingual Word Semantic Network is proposed. This system adapted semantic network to organize words according to concepts and relations. The system also uses open source as the development philosophy to enable the native language speakers and experts to contribute their knowledge to the system. The contributed words are then defined and linked using lexical and semantic relations. Thus, related words and derivatives can be identified and linked. From the outcome of the system implementation, it contributes to the development of semantic web and knowledge engineering.
Abstract: Detecting object in video sequence is a challenging
mission for identifying, tracking moving objects. Background
removal considered as a basic step in detected moving objects tasks.
Dual static cameras placed in front and rear moving platform
gathered information which is used to detect objects. Background
change regarding with speed and direction moving platform, so
moving objects distinguished become complicated. In this paper, we
propose framework allows detection moving object with variety of
speed and direction dynamically. Object detection technique built on
two levels the first level apply background removal and edge
detection to generate moving areas. The second level apply Moving
Areas Filter (MAF) then calculate Correlation Score (CS) for
adjusted moving area. Merging moving areas with closer CS and
marked as moving object. Experiment result is prepared on real scene
acquired by dual static cameras without overlap in sense. Results
showing accuracy in detecting objects compared with optical flow
and Mixture Module Gaussian (MMG), Accurate ratio produced to
measure accurate detection moving object.
Abstract: There have been many games developing simulation
of soccer games. Many of these games have been designed with
highly realistic features to attract more users. Many have also
incorporated better artificial intelligent (AI) similar to that in a real
soccer game. One of the challenging issues in a soccer game is the
cooperation, coordination and negotiation among distributed agents
in a multi-agent system. This paper focuses on the incorporation of
multi-agent technique in a soccer game domain. The better the
cooperation of a multi-agent team, the more intelligent the game will
be. Thus, past studies were done on the robotic soccer game because
of the better multi-agent system implementation. From this study, a
better approach and technique of multi-agent behavior could be
select to improve the author-s 2D online soccer game.
Abstract: Message Passing Interface is widely used for Parallel
and Distributed Computing. MPICH and LAM are popular open
source MPIs available to the parallel computing community also
there are commercial MPIs, which performs better than MPICH etc.
In this paper, we discuss a commercial Message Passing Interface, CMPI
(C-DAC Message Passing Interface). C-MPI is an optimized
MPI for CLUMPS. It is found to be faster and more robust compared
to MPICH. We have compared performance of C-MPI and MPICH
on Gigabit Ethernet network.
Abstract: This article deals to describe the simulation
investigation of the digital phase locked loop implemented in
software (SDPLL). SDPLL has been developed for speed drives of an
induction motor in scalar strategy. A drive was implemented and
simulation results are presented to verify the robustness against motor
parameter variation and regulation speed.
Abstract: Most neural network (NN) models of human category learning use a gradient-based learning method, which assumes that locally-optimal changes are made to model parameters on each learning trial. This method tends to under predict variability in individual-level cognitive processes. In addition many recent models of human category learning have been criticized for not being able to replicate rapid changes in categorization accuracy and attention processes observed in empirical studies. In this paper we introduce stochastic learning algorithms for NN models of human category learning and show that use of the algorithms can result in (a) rapid changes in accuracy and attention allocation, and (b) different learning trajectories and more realistic variability at the individual-level.
Abstract: The current speech interfaces in many military
applications may be adequate for native speakers. However,
the recognition rate drops quite a lot for non-native speakers
(people with foreign accents). This is mainly because the nonnative
speakers have large temporal and intra-phoneme
variations when they pronounce the same words. This
problem is also complicated by the presence of large
environmental noise such as tank noise, helicopter noise, etc.
In this paper, we proposed a novel continuous acoustic feature
adaptation algorithm for on-line accent and environmental
adaptation. Implemented by incremental singular value
decomposition (SVD), the algorithm captures local acoustic
variation and runs in real-time. This feature-based adaptation
method is then integrated with conventional model-based
maximum likelihood linear regression (MLLR) algorithm.
Extensive experiments have been performed on the NATO
non-native speech corpus with baseline acoustic model trained
on native American English. The proposed feature-based
adaptation algorithm improved the average recognition
accuracy by 15%, while the MLLR model based adaptation
achieved 11% improvement. The corresponding word error
rate (WER) reduction was 25.8% and 2.73%, as compared to
that without adaptation. The combined adaptation achieved
overall recognition accuracy improvement of 29.5%, and
WER reduction of 31.8%, as compared to that without
adaptation.
Abstract: Congestion control is one of the fundamental issues in computer networks. Without proper congestion control mechanisms there is the possibility of inefficient utilization of resources, ultimately leading to network collapse. Hence congestion control is an effort to adapt the performance of a network to changes in the traffic load without adversely affecting users perceived utilities. AIMD (Additive Increase Multiplicative Decrease) is the best algorithm among the set of liner algorithms because it reflects good efficiency as well as good fairness. Our control model is based on the assumption of the original AIMD algorithm; we show that both efficiency and fairness of AIMD can be improved. We call our approach is New AIMD. We present experimental results with TCP that match the expectation of our theoretical analysis.
Abstract: Providing Services at Home has become over the last
few years a very dynamic and promising technological domain. It is
likely to enable wide dissemination of secure and automated living
environments. We propose a methodology for identifying threats to
Services at Home Delivery systems, as well as a threat analysis
of a multi-provider Home Gateway architecture. This methodology
is based on a dichotomous positive/preventive study of the target
system: it aims at identifying both what the system must do, and
what it must not do. This approach completes existing methods with
a synthetic view of potential security flaws, thus enabling suitable
measures to be taken into account. Security implications of the
evolution of a given system become easier to deal with. A prototype
is built based on the conclusions of this analysis.
Abstract: Databases have become ubiquitous. Almost all IT applications are storing into and retrieving information from databases. Retrieving information from the database requires knowledge of technical languages such as Structured Query Language (SQL). However majority of the users who interact with the databases do not have a technical background and are intimidated by the idea of using languages such as SQL. This has led to the development of a few Natural Language Database Interfaces (NLDBIs). A NLDBI allows the user to query the database in a natural language. This paper highlights on architecture of new NLDBI system, its implementation and discusses on results obtained. In most of the typical NLDBI systems the natural language statement is converted into an internal representation based on the syntactic and semantic knowledge of the natural language. This representation is then converted into queries using a representation converter. A natural language query is translated to an equivalent SQL query after processing through various stages. The work has been experimented on primitive database queries with certain constraints.
Abstract: The purpose of this article is to introduce an advanced
system for the support of processing of medical image information,
and the terminology related to this system, which can be an important
element to a faster transition to a fully digitalized hospital.
The core of the system is a set of DICOM compliant applications
running over a dedicated computer network. The whole integrated
system creates a collaborative platform supporting daily routines in
the radiology community, developing communication channels,
supporting the exchange of information and special consultations
among various medical institutions as well as supporting medical
training for practicing radiologists and medical students. It gives the
users outside of hospitals the tools to work in almost the same
conditions as in the radiology departments.
Abstract: The localization of software products is essential for reaching the users of the international market. An important task for this is the translation of the user interface into local national languages. As graphical interfaces are usually optimized for the size of the texts in the original language, after the translation certain user controls (e.g. text labels and buttons in dialogs) may grow in such a manner that they slip above each other. This not only causes an unpleasant appearance but also makes the use of the program more difficult (or even impossible) which implies that the arrangement of the controls must be corrected subsequently. The correction should preserve the original structure of the interface (e.g. the relation of logically coherent controls), furthermore, it is important to keep the nicely proportioned design: the formation of large empty areas should be avoided. This paper describes an algorithm that automatically rearranges the controls of a graphical user interface based on the principles above. The algorithm has been implemented and integrated into a translation support system and reached results pleasant for the human eye in most test cases.
Abstract: The present paper is oriented to classification and application of agent technique in simulation of anticipatory systems, namely those that use simulation models for the aid of anticipation. The main ideas root in the fact that the best way for description of computer simulation models is the technique of describing the simulated system itself (and the translation into the computer code is provided as automatic), and that the anticipation itself is often nested.
Abstract: Jayanti-s algorithm is one of the best known abortable mutual exclusion algorithms. This work is an attempt to overcome an already known limitation of the algorithm while preserving its all important properties and elegance. The limitation is that the token number used to assign process identification number to new incoming processes is unbounded. We have used a suitably adapted alternative data structure, in order to completely eliminate the use of token number, in the algorithm.