Abstract: Safety analysis for multi-agent systems is complicated by the, potentially nonlinear, interactions between agents. This paper proposes a method for analyzing the safety of multi-agent systems by explicitly focusing on interactions and the accident data of systems that are similar in structure and function to the system being analyzed. The method creates a Bayesian network using the accident data from similar systems. A feature of our method is that the events in accident data are labeled with HAZOP guide words. Our method uses an Ontology to abstract away from the details of a multi-agent implementation. Using the ontology, our methods then constructs an “Interaction Map,” a graphical representation of the patterns of interactions between agents and other artifacts. Interaction maps combined with statistical data from accidents and the HAZOP classifications of events can be converted into a Bayesian Network. Bayesian networks allow designers to explore “what it” scenarios and make design trade-offs that maintain safety. We show how to use the Bayesian networks, and the interaction maps to improve multi-agent system designs.
Abstract: Advances in spatial and spectral resolution of satellite
images have led to tremendous growth in large image databases. The
data we acquire through satellites, radars, and sensors consists of
important geographical information that can be used for remote
sensing applications such as region planning, disaster management.
Spatial data classification and object recognition are important tasks
for many applications. However, classifying objects and identifying
them manually from images is a difficult task. Object recognition is
often considered as a classification problem, this task can be
performed using machine-learning techniques. Despite of many
machine-learning algorithms, the classification is done using
supervised classifiers such as Support Vector Machines (SVM) as the
area of interest is known. We proposed a classification method,
which considers neighboring pixels in a region for feature extraction
and it evaluates classifications precisely according to neighboring
classes for semantic interpretation of region of interest (ROI). A
dataset has been created for training and testing purpose; we
generated the attributes by considering pixel intensity values and
mean values of reflectance. We demonstrated the benefits of using
knowledge discovery and data-mining techniques, which can be on
image data for accurate information extraction and classification from
high spatial resolution remote sensing imagery.
Abstract: The main purpose of this study is to assess the
sediment quality and potential ecological risk in marine sediments in
Gymea Bay located in south Sydney, Australia. A total of 32 surface
sediment samples were collected from the bay. Current track
trajectories and velocities have also been measured in the bay. The
resultant trace elements were compared with the adverse biological
effect values Effect Range Low (ERL) and Effect Range Median
(ERM) classifications. The results indicate that the average values of
chromium, arsenic, copper, zinc, and lead in surface sediments all
reveal low pollution levels and are below ERL and ERM values. The
highest concentrations of trace elements were found close to
discharge points and in the inner bay, and were linked with high
percentages of clay minerals, pyrite and organic matter, which can
play a significant role in trapping and accumulating these elements.
The lowest concentrations of trace elements were found to be on the
shoreline of the bay, which contained high percentages of sand
fractions. It is postulated that the fine particles and trace elements are
disturbed by currents and tides, then transported and deposited in
deeper areas. The current track velocities recorded in Gymea Bay had
the capability to transport fine particles and trace element pollution
within the bay. As a result, hydrodynamic measurements were able to
provide useful information and to help explain the distribution of
sedimentary particles and geochemical properties. This may lead to
knowledge transfer to other bay systems, including those in remote
areas. These activities can be conducted at a low cost, and are
therefore also transferrable to developing countries. The advent of
portable instruments to measure trace elements in the field has also
contributed to the development of these lower cost and easily applied
methodologies available for use in remote locations and low-cost
economies.
Abstract: Mobile Adhoc Networks (MANETs) are
infrastructure-less, dynamic network of collections of wireless mobile
nodes communicating with each other without any centralized
authority. A MANET is a mobile device of interconnections through
wireless links, forming a dynamic topology. Routing protocols have a
big role in data transmission across a network. Routing protocols,
two major classifications are unipath and multipath. This study
evaluates performance of an on-demand multipath routing protocol
named Adhoc On-demand Multipath Distance Vector routing
(AOMDV). This study proposes Energy Aware AOMDV (EAAOMDV)
an extension of AOMDV which decreases energy
consumed on a route.
Abstract: Given the limited research on Small and Mediumsized
Enterprises’ (SMEs) contribution to Corporate Social
Responsibility (CSR) and even scarcer research on Swiss SMEs, this
paper helps to fill these gaps by enabling the identification of supranational
SME parameters. Thus, the paper investigates the current
state of SME practices in Switzerland and across 15 other countries.
Combining the degree to which SMEs demonstrate an explicit (or
business case) approach or see CSR as an implicit moral activity with
the assessment of their attributes for “variety of capitalism” defines
the framework of this comparative analysis. To outline Swiss small
business CSR patterns in particular, 40 SME owner-managers were
interviewed. A secondary data analysis of studies from different
countries laid groundwork for this comparative overview of small
business CSR. The paper identifies Swiss small business CSR as
driven by norms, values, and by the aspiration to contribute to
society, thus, as an implicit part of the day-to-day business. Similar to
most Central European, Mediterranean, Nordic, and Asian countries,
explicit CSR is still very rare in Swiss SMEs. Astonishingly, also
British and American SMEs follow this pattern in spite of their strong
and distinctly liberal market economies. Though other findings show
that nationality matters this research concludes that SME culture and
an informal CSR agenda are strongly formative and superseding even
forces of market economies, nationally cultural patterns, and
language. Hence, classifications of countries by their market system,
as found in the comparative capitalism literature, do not match the
CSR practices in SMEs as they do not mirror the peculiarities of their
business. This raises questions on the universality and
generalisability of unmediated, explicit management concepts,
especially in the context of small firms.
Abstract: The security of cloud services is the concern of cloud
service providers. In this paper, we will mention different
classifications of cloud attacks referred by specialized organizations.
Each agency has its classification of well-defined properties. The
purpose is to present a high-level classification of current research in
cloud computing security. This classification is organized around
attack strategies and corresponding defenses.
Abstract: The main function of Medium Access Control (MAC) is to share the channel efficiently between all nodes. In the real-time scenario, there will be certain amount of wastage in bandwidth due to back-off periods. More bandwidth will be wasted in idle state if the back-off period is very high and collision may occur if the back-off period is small. So, an optimization is needed for this problem. The main objective of the work is to reduce delay due to back-off period thereby reducing collision and increasing throughput. Here a method, called the virtual back-off algorithm (VBA) is used to optimize the back-off period and thereby it increases throughput and reduces collisions. The main idea is to optimize the number of transmission for every node. A counter is introduced at each node to implement this idea. Here counter value represents the sequence number. VBA is classified into two types VBA with counter sharing (VBA-CS) and VBA with no counter sharing (VBA-NCS). These two classifications of VBA are compared for various parameters. Simulation is done in NS-2 environment. The results obtained are found to be promising.
Abstract: Molluca Collision Zone is located at the junction of
the Eurasian, Australian, Pacific and the Philippines plates. Between
the Sangihe arc, west of the collision zone, and to the east of
Halmahera arc is active collision and convex toward the Molluca Sea.
This research will analyze the behavior of earthquake occurrence in
Molluca Collision Zone related to the distributions of an earthquake
in each partition regions, determining the type of distribution of a
occurrence earthquake of partition regions, and the mean occurence
of earthquakes each partition regions, and the correlation between the
partitions region. We calculate number of earthquakes using partition
method and its behavioral using conventional statistical methods. In
this research, we used data of shallow earthquakes type and its
magnitudes ≥4 SR (period 1964-2013). From the results, we can
classify partitioned regions based on the correlation into two classes:
strong and very strong. This classification can be used for early
warning system in disaster management.
Abstract: The sub-task pattern in terms of deviations and defects
should be identified and understood in order to improve the quality of
practices in construction projects. Therefore, sub-task susceptibility
to exposure to deviations and defects has been evaluated and
classified via six classifications proposed in this study. Thirty-four
case studies of specific sub-tasks (from compression members in
constructed concrete structures) were collected from seven
construction projects in order to examine the study’s proposed
classifications. The study revealed that the sub-task has a high
sensitivity to deviation, where 91% of the cases were recorded as
deviations; however, only 19% of cases were recorded as defects.
Other findings were that the actual work during the execution process
is a high source of deviation for this sub-task (74%), while only 26%
of the source of deviation was due to both design documentation and
the actual work. These findings significantly imply that the study’s
proposed classifications could be used to determine the pattern of
each sub-task and develop proactive actions to overcome issues of
sub-task deviations and defects.
Abstract: One of the most essential issues in software products is to maintain it relevancy to the dynamics of the user’s requirements and expectation. Many studies have been carried out in quality aspect of software products to overcome these problems. Previous software quality assessment models and metrics have been introduced with strengths and limitations. In order to enhance the assurance and buoyancy of the software products, certification models have been introduced and developed. From our previous experiences in certification exercises and case studies collaborating with several agencies in Malaysia, the requirements for user based software certification approach is identified and demanded. The emergence of social network applications, the new development approach such as agile method and other varieties of software in the market have led to the domination of users over the software. As software become more accessible to the public through internet applications, users are becoming more critical in the quality of the services provided by the software. There are several categories of users in web-based systems with different interests and perspectives. The classifications and metrics are identified through brain storming approach with includes researchers, users and experts in this area. The new paradigm in software quality assessment is the main focus in our research. This paper discusses the classifications of users in web-based software system assessment and their associated factors and metrics for quality measurement. The quality model is derived based on IEEE structure and FCM model. The developments are beneficial and valuable to overcome the constraints and improve the application of software certification model in future.
Abstract: With the increase in population along with economic prosperity, an enormous increase in the number and types of vehicles on the roads occurred. This fact brings a growing need for efficiently yet effectively classifying vehicles into their corresponding categories, which play a crucial role in many areas of infrastructure planning and traffic management.
This paper presents two vehicle-type classification approaches; 1) geometric-based and 2) appearance-based. The two classification approaches are used for two tasks: multi-class and intra-class vehicle classifications. For the evaluation purpose of the proposed classification approaches’ performance and the identification of the most effective yet efficient one, 10-fold cross-validation technique is used with a large dataset. The proposed approaches are distinguishable from previous research on vehicle classification in which: i) they consider both geometric and appearance attributes of vehicles, and ii) they perform remarkably well in both multi-class and intra-class vehicle classification. Experimental results exhibit promising potentials implementations of the proposed vehicle classification approaches into real-world applications.
Abstract: Reflecting the concepts of the development of the whole child, it is claimed that, purposeful engagement in Physical activities or exercise involved ritual dances has the potential to engender in young people, The purpose of the present study was to analyze school children and their personal adjustment based on Ritual dance participation. For the purpose, two thousand and three hundred school children of Kerala were analyzed. AISS manual of A.K.P Sinha and R.P Singh was used to collect the data for adjustments. The adjustment qualities classifies as Excellent, Good, Average, Unsatisfactory and Very unsatisfactory. The total performance denotes the state of adjustment based on the classifications. Findings of the study were subjected to percentages and ‘t’ ratio. The study enlightened that, the emotional, social and overall adjustments are better than non-athletes. But the study elucidated that, there is no difference in educational adjustment of school athletes and non athletes among school children.
Abstract: MM-Path, an acronym for Method/Message Path, describes the dynamic interactions between methods in object-oriented systems. This paper discusses the classifications of MM-Path, based on the characteristics of object-oriented software. We categorize it according to the generation reasons, the effect scope and the composition of MM-Path. A formalized representation of MM-Path is also proposed, which has considered the influence of state on response method sequences of messages. .Moreover, an automatic MM-Path generation approach based on UML Statechart diagram has been presented, and the difficulties in identifying and generating MM-Path can be solved. . As a result, it provides a solid foundation for further research on test cases generation based on MM-Path.
Abstract: The goal of a network-based intrusion detection
system is to classify activities of network traffics into two major
categories: normal and attack (intrusive) activities. Nowadays, data
mining and machine learning plays an important role in many
sciences; including intrusion detection system (IDS) using both
supervised and unsupervised techniques. However, one of the
essential steps of data mining is feature selection that helps in
improving the efficiency, performance and prediction rate of
proposed approach. This paper applies unsupervised K-means
clustering algorithm with information gain (IG) for feature selection
and reduction to build a network intrusion detection system. For our
experimental analysis, we have used the new NSL-KDD dataset,
which is a modified dataset for KDDCup 1999 intrusion detection
benchmark dataset. With a split of 60.0% for the training set and the
remainder for the testing set, a 2 class classifications have been
implemented (Normal, Attack). Weka framework which is a java
based open source software consists of a collection of machine
learning algorithms for data mining tasks has been used in the testing
process. The experimental results show that the proposed approach is
very accurate with low false positive rate and high true positive rate
and it takes less learning time in comparison with using the full
features of the dataset with the same algorithm.
Abstract: Fishing has always been an essential component of
the Polynesians- life. Fishhooks, mostly in pearl shell, found during
archaeological excavations are the artifacts related to this activity the
most numerous. Thanks to them, we try to reconstruct the ancient
techniques of resources exploitation, inside the lagoons and offshore.
They can also be used as chronological and cultural indicators. The
shapes and dimensions of these artifacts allow comparisons and
classifications used in both functional approach and chrono-cultural
perspective. Hence it is very important for the ethno-archaeologists
to dispose of reliable methods and standardized measurement of
these artifacts. Such a reliable objective and standardized method
have been previously proposed. But this method cannot be envisaged
manually because of the very important time required to measure
each fishhook manually and the quantity of fishhooks to measure
(many hundreds). We propose in this paper a detailed acquisition
protocol of fishhooks and an automation of every step of this method.
We also provide some experimental results obtained on the fishhooks
coming from three archaeological excavations sites.
Abstract: This paper proposed classification models that would
be used as a proxy for hard disk drive (HDD) functional test equitant
which required approximately more than two weeks to perform the
HDD status classification in either “Pass" or “Fail". These models
were constructed by using committee network which consisted of a
number of single neural networks. This paper also included the
method to solve the problem of sparseness data in failed part, which
was called “enforce learning method". Our results reveal that the
constructed classification models with the proposed method could
perform well in the sparse data conditions and thus the models,
which used a few seconds for HDD classification, could be used to
substitute the HDD functional tests.
Abstract: In this note first we define the notions of intuitionistic
fuzzy dual positive implicative hyper K-ideals of types
1,2,3,4 and intuitionistic fuzzy dual hyper K-ideals. Then we
give some classifications about these notions according to the
level subsets. Also by given some examples we show that these
notions are not equivalent, however we prove some theorems
which show that there are some relationships between these
notions. Finally we define the notions of product and antiproduct
of two fuzzy subsets and then give some theorems
about the relationships between the intuitionistic fuzzy dual
positive implicative hyper K-ideal of types 1,2,3,4 and their
(anti-)products, in particular we give a main decomposition
theorem.
Abstract: The method of gait identification based on the nearest neighbor classification technique with motion similarity assessment by the dynamic time warping is proposed. The model based kinematic motion data, represented by the joints rotations coded by Euler angles and unit quaternions is used. The different pose distance functions in Euler angles and quaternion spaces are considered. To evaluate individual features of the subsequent joints movements during gait cycle, joint selection is carried out. To examine proposed approach database containing 353 gaits of 25 humans collected in motion capture laboratory is used. The obtained results are promising. The classifications, which takes into consideration all joints has accuracy over 91%. Only analysis of movements of hip joints allows to correctly identify gaits with almost 80% precision.
Abstract: Due to a high unemployment rate among local people
and a high reliance on expatriate workers, the governments in the
Gulf Co-operation Council (GCC) countries have been implementing
programmes of localisation (replacing foreign workers with GCC
nationals). These programmes have been successful in the public
sector but much less so in the private sector. However, there are now
insufficient jobs for locals in the public sector and the onus to provide
employment has fallen on the private sector. This paper is concerned
with a study, which is a work in progress (certain elements are
complete but not the whole study), investigating the effective
implementation of localisation policies in four- and five-star hotels in
the Kingdom of Saudi Arabia (KSA) and the United Arab Emirates
(UAE). The purpose of the paper is to identify the research gap, and
to present the need for the research. Further, it will explain how this
research was conducted.
Studies of localisation in the GCC countries are under-represented
in scholarly literature. Currently, the hotel sectors in KSA and UAE
play an important part in the countries’ economies. However, the
total proportion of Saudis working in the hotel sector in KSA is
slightly under 8%, and in the UAE, the hotel sector remains highly
reliant on expatriates. There is therefore a need for research on
strategies to enhance the implementation of the localisation policies
in general and in the hotel sector in particular.
Further, despite the importance of the hotel sector to their
economies, there remains a dearth of research into the
implementation of localisation policies in this sector. Indeed, as far as
the researchers are aware, there is no study examining localisation in
the hotel sector in KSA, and few in the UAE. This represents a
considerable research gap.
Regarding how the research was carried out, a multiple case study
strategy was used. The four- and five-star hotel sector in KSA is one
of the cases, while the four- and five-star hotel sector in the UAE is
the other case. Four- and five-star hotels in KSA and the UAE were
chosen as these countries have the longest established localisation
policies of all the GCC states and there are more hotels of these
classifications in these countries than in any of the other Gulf
countries. A literature review was carried out to underpin the
research. The empirical data were gathered in three phases. In order
to gain a pre-understanding of the issues pertaining to the research
context, Phase I involved eight unstructured interviews with officials
from the Saudi Commission for Tourism and Antiquities (three
interviewees); the Saudi Human Resources Development Fund (one);
the Abu Dhabi Tourism and Culture Authority (three); and the Abu
Dhabi Development Fund (one).
In Phase II, a questionnaire was administered to 24 managers and
24 employees in four- and five-star hotels in each country to obtain
their beliefs, attitudes, opinions, preferences and practices concerning
localisation.
Unstructured interviews were carried out in Phase III with six
managers in each country in order to allow them to express opinions
that may not have been explored in sufficient depth in the
questionnaire. The interviews in Phases I and III were analysed using
thematic analysis and SPSS will be used to analyse the questionnaire
data.
It is recommended that future research be undertaken on a larger
scale, with a larger sample taken from all over KSA and the UAE
rather than from only four cities (i.e., Riyadh and Jeddah in KSA and
Abu Dhabi and Sharjah in the UAE), as was the case in this research.
Abstract: Least Development Countries (LDC) like
Bangladesh, whose 25% revenue earning is achieved from Textile
export, requires producing less defective textile for minimizing
production cost and time. Inspection processes done on these
industries are mostly manual and time consuming. To reduce error
on identifying fabric defects requires more automotive and
accurate inspection process. Considering this lacking, this research
implements a Textile Defect Recognizer which uses computer
vision methodology with the combination of multi-layer neural
networks to identify four classifications of textile defects. The
recognizer, suitable for LDC countries, identifies the fabric defects
within economical cost and produces less error prone inspection
system in real time. In order to generate input set for the neural
network, primarily the recognizer captures digital fabric images by
image acquisition device and converts the RGB images into binary
images by restoration process and local threshold techniques.
Later, the output of the processed image, the area of the faulty
portion, the number of objects of the image and the sharp factor of
the image, are feed backed as an input layer to the neural network
which uses back propagation algorithm to compute the weighted
factors and generates the desired classifications of defects as an
output.