Abstract: An additive fuzzy system comprising m rules with
n inputs and p outputs in each rule has at least t m(2n + 2 p + 1)
parameters needing to be tuned. The system consists of a large
number of if-then fuzzy rules and takes a long time to tune its
parameters especially in the case of a large amount of training data
samples. In this paper, a new learning strategy is investigated to cope
with this obstacle. Parameters that tend toward constant values at the
learning process are initially fixed and they are not tuned till the end
of the learning time. Experiments based on applications of the
additive fuzzy system in function approximation demonstrate that the
proposed approach reduces the learning time and hence improves
convergence speed considerably.
Abstract: The SOM has several beneficial features which make
it a useful method for data mining. One of the most important
features is the ability to preserve the topology in the projection.
There are several measures that can be used to quantify the goodness
of the map in order to obtain the optimal projection, including the
average quantization error and many topological errors. Many
researches have studied how the topology preservation should be
measured. One option consists of using the topographic error which
considers the ratio of data vectors for which the first and second best
BMUs are not adjacent. In this work we present a study of the
behaviour of the topographic error in different kinds of maps. We
have found that this error devaluates the rectangular maps and we
have studied the reasons why this happens. Finally, we suggest a new
topological error to improve the deficiency of the topographic error.
Abstract: One major difficulty that faces developers of
concurrent and distributed software is analysis for concurrency based
faults like deadlocks. Petri nets are used extensively in the
verification of correctness of concurrent programs. ECATNets are a
category of algebraic Petri nets based on a sound combination of
algebraic abstract types and high-level Petri nets. ECATNets have
'sound' and 'complete' semantics because of their integration in
rewriting logic and its programming language Maude. Rewriting
logic is considered as one of very powerful logics in terms of
description, verification and programming of concurrent systems We
proposed previously a method for translating Ada-95 tasking
programs to ECATNets formalism (Ada-ECATNet) and we showed
that ECATNets formalism provides a more compact translation for
Ada programs compared to the other approaches based on simple
Petri nets or Colored Petri nets. We showed also previously how the
ECATNet formalism offers to Ada many validation and verification
tools like simulation, Model Checking, accessibility analysis and
static analysis. In this paper, we describe the implementation of our
translation of the Ada programs into ECATNets.
Abstract: This paper presents a VLSI design approach of a highspeed
and real-time 2-D Discrete Wavelet Transform computing. The
proposed architecture, based on new and fast convolution approach,
reduces the hardware complexity in addition to reduce the critical
path to the multiplier delay. Furthermore, an advanced twodimensional
(2-D) discrete wavelet transform (DWT)
implementation, with an efficient memory area, is designed to
produce one output in every clock cycle. As a result, a very highspeed
is attained. The system is verified, using JPEG2000
coefficients filters, on Xilinx Virtex-II Field Programmable Gate
Array (FPGA) device without accessing any external memory. The
resulting computing rate is up to 270 M samples/s and the (9,7) 2-D
wavelet filter uses only 18 kb of memory (16 kb of first-in-first-out
memory) with 256×256 image size. In this way, the developed design
requests reduced memory and provide very high-speed processing as
well as high PSNR quality.
Abstract: For complete support of Quality of Service, it is better that environment itself predicts resource requirements of a job by using special methods in the Grid computing. The exact and correct prediction causes exact matching of required resources with available resources. After the execution of each job, the used resources will be saved in the active database named "History". At first some of the attributes will be exploit from the main job and according to a defined similarity algorithm the most similar executed job will be exploited from "History" using statistic terms such as linear regression or average, resource requirements will be predicted. The new idea in this research is based on active database and centralized history maintenance. Implementation and testing of the proposed architecture results in accuracy percentage of 96.68% to predict CPU usage of jobs and 91.29% of memory usage and 89.80% of the band width usage.
Abstract: Text categorization is the problem of classifying text
documents into a set of predefined classes. In this paper, we
investigated three approaches to build a meta-classifier in order to
increase the classification accuracy. The basic idea is to learn a metaclassifier
to optimally select the best component classifier for each
data point. The experimental results show that combining classifiers
can significantly improve the accuracy of classification and that our
meta-classification strategy gives better results than each individual
classifier. For 7083 Reuters text documents we obtained a
classification accuracies up to 92.04%.
Abstract: Clustering algorithms help to understand the hidden
information present in datasets. A dataset may contain intrinsic and
nested clusters, the detection of which is of utmost importance. This
paper presents a Distributed Grid-based Density Clustering algorithm
capable of identifying arbitrary shaped embedded clusters as well as
multi-density clusters over large spatial datasets. For handling
massive datasets, we implemented our method using a 'sharednothing'
architecture where multiple computers are interconnected
over a network. Experimental results are reported to establish the
superiority of the technique in terms of scale-up, speedup as well as
cluster quality.
Abstract: Character segmentation is an important preprocessing
step for text recognition. In degraded documents, existence of
touching characters decreases recognition rate drastically, for any
optical character recognition (OCR) system. In this paper we have
proposed a complete solution for segmenting touching characters in
all the three zones of printed Gurmukhi script. A study of touching
Gurmukhi characters is carried out and these characters have been
divided into various categories after a careful analysis. Structural
properties of the Gurmukhi characters are used for defining the
categories. New algorithms have been proposed to segment the
touching characters in middle zone, upper zone and lower zone.
These algorithms have shown a reasonable improvement in
segmenting the touching characters in degraded printed Gurmukhi
script. The algorithms proposed in this paper are applicable only to
machine printed text. We have also discussed a new and useful
technique to segment the horizontally overlapping lines.
Abstract: Static analysis of source code is used for auditing web
applications to detect the vulnerabilities. In this paper, we propose a
new algorithm to analyze the PHP source code for detecting LFI and
RFI potential vulnerabilities. In our approach, we first define some
patterns for finding some functions which have potential to be abused
because of unhandled user inputs. More precisely, we use regular
expression as a fast and simple method to define some patterns for
detection of vulnerabilities. As inclusion functions could be also used
in a safe way, there could occur many false positives (FP). The first
cause of these FP-s could be that the function does not use a usersupplied
variable as an argument. So, we extract a list of usersupplied
variables to be used for detecting vulnerable lines of code.
On the other side, as vulnerability could spread among the variables
like by multi-level assignment, we also try to extract the hidden usersupplied
variables. We use the resulted list to decrease the false
positives of our method. Finally, as there exist some ways to prevent
the vulnerability of inclusion functions, we define also some patterns
to detect them and decrease our false positives.
Abstract: Scheduling algorithms are used in operating systems
to optimize the usage of processors. One of the most efficient
algorithms for scheduling is Multi-Layer Feedback Queue (MLFQ)
algorithm which uses several queues with different quanta. The most
important weakness of this method is the inability to define the
optimized the number of the queues and quantum of each queue. This
weakness has been improved in IMLFQ scheduling algorithm.
Number of the queues and quantum of each queue affect the response
time directly. In this paper, we review the IMLFQ algorithm for
solving these problems and minimizing the response time. In this
algorithm Recurrent Neural Network has been utilized to find both
the number of queues and the optimized quantum of each queue.
Also in order to prevent any probable faults in processes' response
time computation, a new fault tolerant approach has been presented.
In this approach we use combinational software redundancy to
prevent the any probable faults. The experimental results show that
using the IMLFQ algorithm results in better response time in
comparison with other scheduling algorithms also by using fault
tolerant mechanism we improve IMLFQ performance.
Abstract: SoftBoost is a recently presented boosting algorithm,
which trades off the size of achieved classification margin and
generalization performance. This paper presents a performance
evaluation of SoftBoost algorithm on the generic object recognition
problem. An appearance-based generic object recognition
model is used. The evaluation experiments are performed using
a difficult object recognition benchmark. An assessment with respect
to different degrees of label noise as well as a comparison to
the well known AdaBoost algorithm is performed. The obtained
results reveal that SoftBoost is encouraged to be used in cases
when the training data is known to have a high degree of noise.
Otherwise, using Adaboost can achieve better performance.
Abstract: In current common research reports, salient regions
are usually defined as those regions that could present the main
meaningful or semantic contents. However, there are no uniform
saliency metrics that could describe the saliency of implicit image
regions. Most common metrics take those regions as salient regions,
which have many abrupt changes or some unpredictable
characteristics. But, this metric will fail to detect those salient useful
regions with flat textures. In fact, according to human semantic
perceptions, color and texture distinctions are the main characteristics
that could distinct different regions. Thus, we present a novel saliency
metric coupled with color and texture features, and its corresponding
salient region extraction methods. In order to evaluate the
corresponding saliency values of implicit regions in one image, three
main colors and multi-resolution Gabor features are respectively used
for color and texture features. For each region, its saliency value is
actually to evaluate the total sum of its Euclidean distances for other
regions in the color and texture spaces. A special synthesized image
and several practical images with main salient regions are used to
evaluate the performance of the proposed saliency metric and other
several common metrics, i.e., scale saliency, wavelet transform
modulus maxima point density, and important index based metrics.
Experiment results verified that the proposed saliency metric could
achieve more robust performance than those common saliency
metrics.
Abstract: Knowledge discovery from text and ontology learning
are relatively new fields. However their usage is extended in many
fields like Information Retrieval (IR) and its related domains. Human
Plausible Reasoning based (HPR) IR systems for example need a
knowledge base as their underlying system which is currently made
by hand. In this paper we propose an architecture based on ontology
learning methods to automatically generate the needed HPR
knowledge base.
Abstract: The belief decision tree (BDT) approach is a decision
tree in an uncertain environment where the uncertainty is represented
through the Transferable Belief Model (TBM), one interpretation
of the belief function theory. The uncertainty can appear either in
the actual class of training objects or attribute values of objects to
classify. In this paper, we develop a post-pruning method of belief
decision trees in order to reduce size and improve classification
accuracy on unseen cases. The pruning of decision tree has a
considerable intention in the areas of machine learning.
Abstract: The first generation of Mobile Agents based Intrusion
Detection System just had two components namely data collection
and single centralized analyzer. The disadvantage of this type of
intrusion detection is if connection to the analyzer fails, the entire
system will become useless. In this work, we propose novel hybrid
model for Mobile Agent based Distributed Intrusion Detection
System to overcome the current problem. The proposed model has
new features such as robustness, capability of detecting intrusion
against the IDS itself and capability of updating itself to detect new
pattern of intrusions. In addition, our proposed model is also capable
of tackling some of the weaknesses of centralized Intrusion Detection
System models.
Abstract: In this paper, an extended study is performed on the
effect of different factors on the quality of vector data based on a
previous study. In the noise factor, one kind of noise that appears in
document images namely Gaussian noise is studied while the previous
study involved only salt-and-pepper noise. High and low levels of
noise are studied. For the noise cleaning methods, algorithms that were
not covered in the previous study are used namely Median filters and
its variants. For the vectorization factor, one of the best available
commercial raster to vector software namely VPstudio is used to
convert raster images into vector format. The performance of line
detection will be judged based on objective performance evaluation
method. The output of the performance evaluation is then analyzed
statistically to highlight the factors that affect vector quality.
Abstract: Quality of Service (QoS) Routing aims to find path between source and destination satisfying the QoS requirements which efficiently using the network resources and underlying routing algorithm and to fmd low-cost paths that satisfy given QoS constraints. One of the key issues in providing end-to-end QoS guarantees in packet networks is determining feasible path that satisfies a number of QoS constraints. We present a Optimized Multi- Constrained Routing (OMCR) algorithm for the computation of constrained paths for QoS routing in computer networks. OMCR applies distance vector to construct a shortest path for each destination with reference to a given optimization metric, from which a set of feasible paths are derived at each node. OMCR is able to fmd feasible paths as well as optimize the utilization of network resources. OMCR operates with the hop-by-hop, connectionless routing model in IP Internet and does not create any loops while fmding the feasible paths. Nodes running OMCR not necessarily maintaining global view of network state such as topology, resource information and routing updates are sent only to neighboring nodes whereas its counterpart link-state routing method depend on complete network state for constrained path computation and that incurs excessive communication overhead.
Abstract: The complexity of today-s software systems makes
collaborative development necessary to accomplish tasks.
Frameworks are necessary to allow developers perform their tasks
independently yet collaboratively. Similarity detection is one of the
major issues to consider when developing such frameworks. It allows
developers to mine existing repositories when developing their own
views of a software artifact, and it is necessary for identifying the
correspondences between the views to allow merging them and
checking their consistency. Due to the importance of the
requirements specification stage in software development, this paper
proposes a framework for collaborative development of Object-
Oriented formal specifications along with a similarity detection
approach to support the creation, merging and consistency checking
of specifications. The paper also explores the impact of using
additional concepts on improving the matching results. Finally, the
proposed approach is empirically evaluated.
Abstract: In this paper, we explore a new scheme for filtering spoofed packets (DDOS attack) which is a combination of path fingerprint and client puzzle concepts. In this each IP packet has a unique fingerprint is embedded that represents, the route a packet has traversed. The server maintains a mapping table which contains the client IP address and its corresponding fingerprint. In ingress router, client puzzle is placed. For each request, the puzzle issuer provides a puzzle which the source has to solve. Our design has the following advantages over prior approaches, 1) Reduce the network traffic, as we place a client puzzle at the ingress router. 2) Mapping table at the server is lightweight and moderate.
Abstract: The main idea behind in network aggregation is that,
rather than sending individual data items from sensors to sinks,
multiple data items are aggregated as they are forwarded by the
sensor network. Existing sensor network data aggregation techniques
assume that the nodes are preprogrammed and send data to a central
sink for offline querying and analysis. This approach faces two major
drawbacks. First, the system behavior is preprogrammed and cannot
be modified on the fly. Second, the increased energy wastage due to
the communication overhead will result in decreasing the overall
system lifetime. Thus, energy conservation is of prime consideration
in sensor network protocols in order to maximize the network-s
operational lifetime. In this paper, we give an energy efficient
approach to query processing by implementing new optimization
techniques applied to in-network aggregation. We first discuss earlier
approaches in sensors data management and highlight their
disadvantages. We then present our approach “Energy Efficient
Indexed Aggregation" (EEIA) and evaluate it through several
simulations to prove its efficiency, competence and effectiveness.