Abstract: A key aspect of the design of any software system is
its architecture. An architecture description provides a formal model
of the architecture in terms of components and connectors and how
they are composed together. COSA (Component-Object based
Software Structures), is based on object-oriented modeling and
component-based modeling. The model improves the reusability by
increasing extensibility, evolvability, and compositionality of the
software systems. This paper presents the COSA modelling tool
which help architects the possibility to verify the structural coherence
of a given system and to validate its semantics with COSA approach.
Abstract: SIP (Session Initiation Protocol), using HTML based
call control messaging which is quite simple and efficient, is being
replaced for VoIP networks recently. As for authentication and
authorization purposes there are many approaches and considerations
for securing SIP to eliminate forgery on the integrity of SIP
messages. On the other hand Elliptic Curve Cryptography has
significant advantages like smaller key sizes, faster computations on
behalf of other Public Key Cryptography (PKC) systems that obtain
data transmission more secure and efficient. In this work a new
approach is proposed for secure SIP authentication by using a public
key exchange mechanism using ECC. Total execution times and
memory requirements of proposed scheme have been improved in
comparison with non-elliptic approaches by adopting elliptic-based
key exchange mechanism.
Abstract: In this paper we propose a novel approach for ascertaining human identity based on fusion of profile face and gait biometric cues The identification approach based on feature learning in PCA-LDA subspace, and classification using multivariate Bayesian classifiers allows significant improvement in recognition accuracy for low resolution surveillance video scenarios. The experimental evaluation of the proposed identification scheme on a publicly available database [2] showed that the fusion of face and gait cues in joint PCA-LDA space turns out to be a powerful method for capturing the inherent multimodality in walking gait patterns, and at the same time discriminating the person identity..
Abstract: Data Envelopment Analysis (DEA) is one of the most
widely used technique for evaluating the relative efficiency of a set
of homogeneous decision making units. Traditionally, it assumes that
input and output variables are known in advance, ignoring the critical
issue of data uncertainty. In this paper, we deal with the problem
of efficiency evaluation under uncertain conditions by adopting the
general framework of the stochastic programming. We assume that
output parameters are represented by discretely distributed random
variables and we propose two different models defined according to a
neutral and risk-averse perspective. The models have been validated
by considering a real case study concerning the evaluation of the
technical efficiency of a sample of individual firms operating in
the Italian leather manufacturing industry. Our findings show the
validity of the proposed approach as ex-ante evaluation technique
by providing the decision maker with useful insights depending on
his risk aversion degree.
Abstract: Mixed-traffic (e.g., pedestrians, bicycles, and vehicles)
data at an intersection is one of the essential factors for intersection
design and traffic control. However, some data such as pedestrian
volume cannot be directly collected by common detectors (e.g.
inductive loop, sonar and microwave sensors). In this paper, a video
based detection algorithm is proposed for mixed-traffic data collection
at intersections using surveillance cameras. The algorithm is derived
from Gaussian Mixture Model (GMM), and uses a mergence time
adjustment scheme to improve the traditional algorithm. Real-world
video data were selected to test the algorithm. The results show that
the proposed algorithm has the faster processing speed and more
accuracy than the traditional algorithm. This indicates that the
improved algorithm can be applied to detect mixed-traffic at
signalized intersection, even when conflicts occur.
Abstract: The paper focuses on the area of context modeling with respect to the specification of context-aware systems supporting ubiquitous applications. The proposed approach, followed within the SIMPLICITY IST project, uses a high-level system ontology to derive context models for system components which consequently are mapped to the system's physical entities. For the definition of user and device-related context models in particular, the paper suggests a standard-based process consisting of an analysis phase using the Common Information Model (CIM) methodology followed by an implementation phase that defines 3GPP based components. The benefits of this approach are further depicted by preliminary examples of XML grammars defining profiles and components, component instances, coupled with descriptions of respective ubiquitous applications.
Abstract: When binary decision diagrams are formed from
uniformly distributed Monte Carlo data for a large number of
variables, the complexity of the decision diagrams exhibits a
predictable relationship to the number of variables and minterms. In
the present work, a neural network model has been used to analyze the
pattern of shortest path length for larger number of Monte Carlo data
points. The neural model shows a strong descriptive power for the
ISCAS benchmark data with an RMS error of 0.102 for the shortest
path length complexity. Therefore, the model can be considered as a
method of predicting path length complexities; this is expected to lead
to minimum time complexity of very large-scale integrated circuitries
and related computer-aided design tools that use binary decision
diagrams.
Abstract: Document image processing has become an
increasingly important technology in the automation of office
documentation tasks. During document scanning, skew is inevitably
introduced into the incoming document image. Since the algorithm
for layout analysis and character recognition are generally very
sensitive to the page skew. Hence, skew detection and correction in
document images are the critical steps before layout analysis. In this
paper, a novel skew detection method is presented for binary
document images. The method considered the some selected
characters of the text which may be subjected to thinning and Hough
transform to estimate skew angle accurately. Several experiments
have been conducted on various types of documents such as
documents containing English Documents, Journals, Text-Book,
Different Languages and Document with different fonts, Documents
with different resolutions, to reveal the robustness of the proposed
method. The experimental results revealed that the proposed method
is accurate compared to the results of well-known existing methods.
Abstract: In illumination variant face recognition, existing
methods extracting face albedo as light normalized image may lead to
loss of extensive facial details, with light template discarded. To
improve that, a novel approach for realistic facial texture
reconstruction by combining original image and albedo image is
proposed. First, light subspaces of different identities are established
from the given reference face images; then by projecting the original
and albedo image into each light subspace respectively, texture
reference images with corresponding lighting are reconstructed and
two texture subspaces are formed. According to the projections in
texture subspaces, facial texture with normal light can be synthesized.
Due to the combination of original image, facial details can be
preserved with face albedo. In addition, image partition is applied to
improve the synthesization performance. Experiments on Yale B and
CMUPIE databases demonstrate that this algorithm outperforms the
others both in image representation and in face recognition.
Abstract: Structural representation and technology mapping of
a Boolean function is an important problem in the design of nonregenerative
digital logic circuits (also called combinational logic
circuits). Library aware function manipulation offers a solution to
this problem. Compact multi-level representation of binary networks,
based on simple circuit structures, such as AND-Inverter Graphs
(AIG) [1] [5], NAND Graphs, OR-Inverter Graphs (OIG), AND-OR
Graphs (AOG), AND-OR-Inverter Graphs (AOIG), AND-XORInverter
Graphs, Reduced Boolean Circuits [8] does exist in
literature. In this work, we discuss a novel and efficient graph
realization for combinational logic circuits, represented using a
NAND-NOR-Inverter Graph (NNIG), which is composed of only
two-input NAND (NAND2), NOR (NOR2) and inverter (INV) cells.
The networks are constructed on the basis of irredundant disjunctive
and conjunctive normal forms, after factoring, comprising terms with
minimum support. Construction of a NNIG for a non-regenerative
function in normal form would be straightforward, whereas for the
complementary phase, it would be developed by considering a virtual
instance of the function. However, the choice of best NNIG for a
given function would be based upon literal count, cell count and
DAG node count of the implementation at the technology
independent stage. In case of a tie, the final decision would be made
after extracting the physical design parameters.
We have considered AIG representation for reduced disjunctive
normal form and the best of OIG/AOG/AOIG for the minimized
conjunctive normal forms. This is necessitated due to the nature of
certain functions, such as Achilles- heel functions. NNIGs are found
to exhibit 3.97% lesser node count compared to AIGs and
OIG/AOG/AOIGs; consume 23.74% and 10.79% lesser library cells
than AIGs and OIG/AOG/AOIGs for the various samples considered.
We compare the power efficiency and delay improvement achieved
by optimal NNIGs over minimal AIGs and OIG/AOG/AOIGs for
various case studies. In comparison with functionally equivalent,
irredundant and compact AIGs, NNIGs report mean savings in power
and delay of 43.71% and 25.85% respectively, after technology
mapping with a 0.35 micron TSMC CMOS process. For a
comparison with OIG/AOG/AOIGs, NNIGs demonstrate average
savings in power and delay by 47.51% and 24.83%. With respect to
device count needed for implementation with static CMOS logic
style, NNIGs utilize 37.85% and 33.95% lesser transistors than their
AIG and OIG/AOG/AOIG counterparts.
Abstract: In many applications, it is a priori known that the
target function should satisfy certain constraints imposed by, for
example, economic theory or a human-decision maker. Here we
consider partially monotone problems, where the target variable
depends monotonically on some of the predictor variables but not all.
We propose an approach to build partially monotone models based
on the convolution of monotone neural networks and kernel
functions. The results from simulations and a real case study on
house pricing show that our approach has significantly better
performance than partially monotone linear models. Furthermore, the
incorporation of partial monotonicity constraints not only leads to
models that are in accordance with the decision maker's expertise,
but also reduces considerably the model variance in comparison to
standard neural networks with weight decay.
Abstract: The problem of frequent itemset mining is considered in this paper. One new technique proposed to generate frequent patterns in large databases without time-consuming candidate generation. This technique is based on focusing on transaction instead of concentrating on itemset. This algorithm based on take intersection between one transaction and others transaction and the maximum shared items between transactions computed instead of creating itemset and computing their frequency. With applying real life transactions and some consumption is taken from real life data, the significant efficiency acquire from databases in generation association rules mining.
Abstract: This paper presents a solution for the behavioural animation of autonomous virtual agent navigation in virtual environments. We focus on using Dempster-Shafer-s Theory of Evidence in developing visual sensor for virtual agent. The role of the visual sensor is to capture the information about the virtual environment or identifie which part of an obstacle can be seen from the position of the virtual agent. This information is require for vitual agent to coordinate navigation in virtual environment. The virual agent uses fuzzy controller as a navigation system and Fuzzy α - level for the action selection method. The result clearly demonstrates the path produced is reasonably smooth even though there is some sharp turn and also still not diverted too far from the potential shortest path. This had indicated the benefit of our method, where more reliable and accurate paths produced during navigation task.
Abstract: In this paper, we propose an adaptation of the Patricia-Tree for sparse datasets to generate non redundant rule associations. Using this adaptation, we can generate frequent closed itemsets that are more compact than frequent itemsets used in Apriori approach. This adaptation has been experimented on a set of datasets benchmarks.
Abstract: In this paper a new approach to face recognition is presented that achieves double dimension reduction making the system computationally efficient with better recognition results. In pattern recognition techniques, discriminative information of image increases with increase in resolution to a certain extent, consequently face recognition results improve with increase in face image resolution and levels off when arriving at a certain resolution level. In the proposed model of face recognition, first image decimation algorithm is applied on face image for dimension reduction to a certain resolution level which provides best recognition results. Due to better computational speed and feature extraction potential of Discrete Cosine Transform (DCT) it is applied on face image. A subset of coefficients of DCT from low to mid frequencies that represent the face adequately and provides best recognition results is retained. A trade of between decimation factor, number of DCT coefficients retained and recognition rate with minimum computation is obtained. Preprocessing of the image is carried out to increase its robustness against variations in poses and illumination level. This new model has been tested on different databases which include ORL database, Yale database and a color database. The proposed technique has performed much better compared to other techniques. The significance of the model is two fold: (1) dimension reduction up to an effective and suitable face image resolution (2) appropriate DCT coefficients are retained to achieve best recognition results with varying image poses, intensity and illumination level.
Abstract: The human friendly interaction is the key function of a human-centered system. Over the years, it has received much attention to develop the convenient interaction through intention recognition. Intention recognition processes multimodal inputs including speech, face images, and body gestures. In this paper, we suggest a novel approach of intention recognition using a graph representation called Intention Graph. A concept of valid intention is proposed, as a target of intention recognition. Our approach has two phases: goal recognition phase and intention recognition phase. In the goal recognition phase, we generate an action graph based on the observed actions, and then the candidate goals and their plans are recognized. In the intention recognition phase, the intention is recognized with relevant goals and user profile. We show that the algorithm has polynomial time complexity. The intention graph is applied to a simple briefcase domain to test our model.
Abstract: Human perceives color in categories, which may be
identified using color name such as red, blue, etc. The categorization
is unique for each human being. However despite the individual
differences, the categorization is shared among members in society.
This allows communication among them, especially when using
color name. Sociable robot, to live coexist with human and become
part of human society, must also have the shared color
categorization, which can be achieved through learning. Many
works have been done to enable computer, as brain of robot, to learn
color categorization. Most of them rely on modeling of human color
perception and mathematical complexities. Differently, in this work,
the computer learns color categorization through interaction with
humans. This work aims at developing the innate ability of the
computer to learn the human-like color categorization. It focuses on
the representation of color categorization and how it is built and
developed without much mathematical complexity.
Abstract: This paper describes a segmentation algorithm based
on the cooperation of an optical flow estimation method with edge
detection and region growing procedures.
The proposed method has been developed as a pre-processing
stage to be used in methodologies and tools for video/image indexing
and retrieval by content. The addressed problem consists in
extracting whole objects from background for producing images of
single complete objects from videos or photos. The extracted images
are used for calculating the object visual features necessary for both
indexing and retrieval processes.
The first task of the algorithm exploits the cues from motion
analysis for moving area detection. Objects and background are then
refined using respectively edge detection and region growing
procedures. These tasks are iteratively performed until objects and
background are completely resolved.
The developed method has been applied to a variety of indoor and
outdoor scenes where objects of different type and shape are
represented on variously textured background.
Abstract: Wireless sensor networks (WSNs) have gained
tremendous attention in recent years due to their numerous
applications. Due to the limited energy resource, energy efficient
operation of sensor nodes is a key issue in wireless sensor networks.
Cooperative caching which ensures sharing of data among various
nodes reduces the number of communications over the wireless
channels and thus enhances the overall lifetime of a wireless sensor
network. In this paper, we propose a cooperative caching scheme
called ZCS (Zone Cooperation at Sensors) for wireless sensor
networks. In ZCS scheme, one-hop neighbors of a sensor node form a
cooperative cache zone and share the cached data with each other.
Simulation experiments show that the ZCS caching scheme achieves
significant improvements in byte hit ratio and average query latency
in comparison with other caching strategies.
Abstract: Currently, there has been a 3G mobile networks data
traffic explosion due to the large increase in the number of smartphone
users. Unlike a traditional wired infrastructure, 3G mobile networks
have limited wireless resources and signaling procedures for complex
wireless resource management. And mobile network security for
various abnormal and malicious traffic technologies was not ready. So
Malicious or potentially malicious traffic originating from mobile
malware infected smart devices can cause serious problems to the 3G
mobile networks, such as DoS and scanning attack in wired networks.
This paper describes the DoS security threat in the 3G mobile network
and proposes a detection technology.