Abstract: One of the major challenges in the Information
Retrieval field is handling the massive amount of information
available to Internet users. Existing ranking techniques and strategies
that govern the retrieval process fall short of expected accuracy.
Often relevant documents are buried deep in the list of documents
returned by the search engine. In order to improve retrieval accuracy
we examine the issue of language effect on the retrieval process.
Then, we propose a solution for a more biased, user-centric relevance
for retrieved data. The results demonstrate that using indices based
on variations of the same language enhances the accuracy of search
engines for individual users.
Abstract: Automatic reusability appraisal could be helpful in
evaluating the quality of developed or developing reusable software
components and in identification of reusable components from
existing legacy systems; that can save cost of developing the software
from scratch. But the issue of how to identify reusable components
from existing systems has remained relatively unexplored. In this
paper, we have mentioned two-tier approach by studying the
structural attributes as well as usability or relevancy of the
component to a particular domain. Latent semantic analysis is used
for the feature vector representation of various software domains. It
exploits the fact that FeatureVector codes can be seen as documents
containing terms -the idenifiers present in the components- and so
text modeling methods that capture co-occurrence information in
low-dimensional spaces can be used. Further, we devised Neuro-
Fuzzy hybrid Inference System, which takes structural metric values
as input and calculates the reusability of the software component.
Decision tree algorithm is used to decide initial set of fuzzy rules for
the Neuro-fuzzy system. The results obtained are convincing enough
to propose the system for economical identification and retrieval of
reusable software components.
Abstract: Gabor-based face representation has achieved enormous success in face recognition. This paper addresses a novel algorithm for face recognition using neural networks trained by Gabor features. The system is commenced on convolving a face image with a series of Gabor filter coefficients at different scales and orientations. Two novel contributions of this paper are: scaling of rms contrast and introduction of fuzzily skewed filter. The neural network employed for face recognition is based on the multilayer perceptron (MLP) architecture with backpropagation algorithm and incorporates the convolution filter response of Gabor jet. The effectiveness of the algorithm has been justified over a face database with images captured at different illumination conditions.
Abstract: In this paper test generation methods and appropriate fault models for testing and analysis of embedded systems described as (extended) finite state machines ((E)FSMs) are presented. Compared to simple FSMs, EFSMs specify not only the control flow but also the data flow. Thus, we define a two-level fault model to cover both aspects. The goal of this paper is to reuse well-known FSM-based test generation methods for automation of embedded system testing. These methods have been widely used in testing and validation of protocols and communicating systems. In particular, (E)FSMs-based specification and testing is more advantageous because (E)FSMs support the formal semantic of already standardised formal description techniques (FDTs) despite of their popularity in the design of hardware and software systems.
Abstract: As open innovation has received increasingly attention
in the management of innovation, the importance of identifying
potential partnership is increasing. This paper suggests a methodology
to identify the interested parties as one of Innovation intermediaries to
enable open innovation with patent network. To implement the
methodology, multi-stage patent citation analysis such as
bibliographic coupling and information visualization method such as
keyword vector mapping are utilized. This paper has contribution in
that it can present meaningful collaboration keywords to identified
potential partners in network since not only citation information but
also patent textual information is used.
Abstract: Data mining techniques have been used in medical
research for many years and have been known to be effective. In order
to solve such problems as long-waiting time, congestion, and delayed
patient care, faced by emergency departments, this study concentrates
on building a hybrid methodology, combining data mining techniques
such as association rules and classification trees. The methodology is
applied to real-world emergency data collected from a hospital and is
evaluated by comparing with other techniques. The methodology is
expected to help physicians to make a faster and more accurate
classification of chest pain diseases.
Abstract: With the rapid development of wireless mobile communication, applications for mobile devices must focus on network security. In 2008, Chang-Chang proposed security improvements on the Lu et al.-s elliptic curve authentication key agreement protocol for wireless mobile networks. However, this paper shows that Chang- Chang-s improved protocol is still vulnerable to off-line password guessing attacks unlike their claims.
Abstract: With advances in computer vision, non-contact gaze tracking systems are heading towards being much easier to operate and more comfortable for use, the technique proposed in this paper is specially designed for achieving these goals. For the convenience in operation, the proposal aims at the system with simple configuration which is composed of a fixed wide angle camera and dual infrared illuminators. Then in order to enhance the usability of the system based on single camera, a self-adjusting method which is called Real-time gaze Tracking Algorithm with head movement Compensation (RTAC) is developed for estimating the gaze direction under natural head movement and simplifying the calibration procedure at the same time. According to the actual evaluations, the average accuracy of about 1° is achieved over a field of 20×15×15 cm3.
Abstract: Number of documents being created increases at an
increasing pace while most of them being in already known topics
and little of them introducing new concepts. This fact has started a
new era in information retrieval discipline where the requirements
have their own specialties. That is digging into topics and concepts
and finding out subtopics or relations between topics. Up to now IR
researches were interested in retrieving documents about a general
topic or clustering documents under generic subjects. However these
conventional approaches can-t go deep into content of documents
which makes it difficult for people to reach to right documents they
were searching. So we need new ways of mining document sets
where the critic point is to know much about the contents of the
documents. As a solution we are proposing to enhance LSI, one of
the proven IR techniques by supporting its vector space with n-gram
forms of words. Positive results we have obtained are shown in two
different application area of IR domain; querying a document
database, clustering documents in the document database.
Abstract: An effective method for the early detection of breast
cancer is the mammographic screening. One of the most important
signs of early breast cancer is the presence of microcalcifications. For
the detection of microcalcification in a mammography image, we
propose to conceive a multiagent system based on a dual irregular
pyramid.
An initial segmentation is obtained by an incremental approach;
the result represents level zero of the pyramid. The edge information
obtained by application of the Canny filter is taken into account to
affine the segmentation. The edge-agents and region-agents cooper
level by level of the pyramid by exploiting its various characteristics
to provide the segmentation process convergence.
Abstract: Functioning of a biometric system in large part
depends on the performance of the similarity measure function.
Frequently a generalized similarity distance measure function such as
Euclidian distance or Mahalanobis distance is applied to the task of
matching biometric feature vectors. However, often accuracy of a
biometric system can be greatly improved by designing a customized
matching algorithm optimized for a particular biometric application.
In this paper we propose a tailored similarity measure function for
behavioral biometric systems based on the expert knowledge of the
feature level data in the domain. We compare performance of a
proposed matching algorithm to that of other well known similarity
distance functions and demonstrate its superiority with respect to the
chosen domain.
Abstract: Logic based methods for learning from structured data
is limited w.r.t. handling large search spaces, preventing large-sized
substructures from being considered by the resulting classifiers. A
novel approach to learning from structured data is introduced that
employs a structure transformation method, called finger printing, for
addressing these limitations. The method, which generates features
corresponding to arbitrarily complex substructures, is implemented in
a system, called DIFFER. The method is demonstrated to perform
comparably to an existing state-of-art method on some benchmark
data sets without requiring restrictions on the search space.
Furthermore, learning from the union of features generated by finger
printing and the previous method outperforms learning from each
individual set of features on all benchmark data sets, demonstrating
the benefit of developing complementary, rather than competing,
methods for structure classification.
Abstract: This paper presents a fuzzy control system for a three degree of freedom (3-DOF) stabilized platform with explicit decoupling scheme. The system under consideration is a system with strong interactions between three channels. By using the concept of decentralized control, a control structure is developed that is composed of three control loops, each of which is associated with a single-variable fuzzy controller and a decoupling unit. Takagi-Sugeno (TS) fuzzy control algorithm is used to implement the fuzzy controller. The decoupling units design is based on the adaptive theory reasoning. Simulation tests were established using Simulink of Matlab. The obtained results have demonstrated the feasibility and effectiveness of the proposed approach. Simulation results are represented in this paper.
Abstract: Well-developed strategic marketing planning is the essential
prerequisite for establishment of the right and unique competitive
advantage. Typical market, however, is a heterogeneous
and decentralized structure with natural involvement of individual
or group subjectivity and irrationality. These features cannot be
fully expressed with one-shot rigorous formal models based on,
e.g. mathematics, statistics or empirical formulas. We present an
innovative solution, extending the domain of agent based computational
economics towards the concept of hybrid modeling in service
provider and consumer market such as telecommunications. The
behavior of the market is described by two classes of agents -
consumer and service provider agents - whose internal dynamics
are fundamentally different. Customers are rather free multi-state
structures, adjusting behavior and preferences quickly in accordance
with time and changing environment. Producers, on the contrary,
are traditionally structured companies with comparable internal processes
and specific managerial policies. Their business momentum is
higher and immediate reaction possibilities limited. This limitation
underlines importance of proper strategic planning as the main
process advising managers in time whether to continue with more
or less the same business or whether to consider the need for future
structural changes that would ensure retention of existing customers
or acquisition of new ones.
Abstract: A generic and extendible Multi-Agent Data Mining
(MADM) framework, MADMF (the Multi-Agent Data Mining
Framework) is described. The central feature of the framework is that
it avoids the use of agreed meta-language formats by supporting a
framework of wrappers.
The advantage offered is that the framework is easily extendible,
so that further data agents and mining agents can simply be added to
the framework. A demonstration MADMF framework is currently
available. The paper includes details of the MADMF architecture and
the wrapper principle incorporated into it. A full description and
evaluation of the framework-s operation is provided by considering
two MADM scenarios.
Abstract: Determining how many virtual machines a Linux host
could run can be a challenge. One of tough missions is to find the
balance among performance, density and usability. Now KVM
hypervisor has become the most popular open source full
virtualization solution. It supports several ways of running guests with
more memory than host really has. Due to large differences between
minimum and maximum guest memory requirements, this paper
presents initial results on same-page merging, ballooning and live
migration techniques that aims at optimum memory usage on
KVM-based cloud platform. Given the design of initial experiments,
the results data is worth reference for system administrators. The
results from these experiments concluded that each method offers
different reliability tradeoff.
Abstract: There are a many of needs for the development of
SiC-based hydrogen sensor for harsh environment applications. We
fabricated and investigated Pd/Ta2O5/SiC-based hydrogen sensors
with MOS capacitor structure for high temperature process monitoring
and leak detection applications in such automotive, chemical and
petroleum industries as well as direct monitoring of combustion
processes. In this work, we used silicon carbide (SiC) as a substrate to
replace silicon which operating temperatures are limited to below
200°C. Tantalum oxide was investigated as dielectric layer which has
high permeability for hydrogen gas and high dielectric permittivity,
compared with silicon dioxide or silicon nitride. Then, electrical
response properties, such as I-V curve and dependence of capacitance
on hydrogen concentrations were analyzed in the temperature ranges
of room temperature to 500°C for performance evaluation of the
sensor.
Abstract: This paper presents a subjective job scheduler based
on a 3-layer Backpropagation Neural Network (BPNN) and a greedy
alignment procedure in order formulates a real-life situation. The
BPNN estimates critical values of jobs based on the given subjective
criteria. The scheduler is formulated in such a way that, at each time
period, the most critical job is selected from the job queue and is
transferred into a single machine before the next periodic job arrives.
If the selected job is one of the oldest jobs in the queue and its
deadline is less than that of the arrival time of the current job, then
there is an update of the deadline of the job is assigned in order to
prevent the critical job from its elimination. The proposed
satisfiability criteria indicates that the satisfaction of the scheduler
with respect to performance of the BPNN, validity of the jobs and the
feasibility of the scheduler.
Abstract: This paper presents data annotation models at
five levels of granularity (database, relation, column, tuple, and cell) of relational data to address the problem of unsuitability of most relational databases to express annotations. These models
do not require any structural and schematic changes to the
underlying database. These models are also flexible, extensible,
customizable, database-neutral, and platform-independent. This paper also presents an SQL-like query language, named Annotation Query Language (AnQL), to query annotation documents. AnQL is simple to understand and exploits the already-existent wide knowledge and skill set of SQL.
Abstract: In this work a visual and reactive contour following
behaviour is learned by reinforcement. With artificial vision the
environment is perceived in 3D, and it is possible to avoid obstacles
that are invisible to other sensors that are more common in mobile
robotics. Reinforcement learning reduces the need for intervention in
behaviour design, and simplifies its adjustment to the environment,
the robot and the task. In order to facilitate its generalisation to other
behaviours and to reduce the role of the designer, we propose a
regular image-based codification of states. Even though this is much
more difficult, our implementation converges and is robust. Results
are presented with a Pioneer 2 AT on a Gazebo 3D simulator.