Abstract: This paper presents an approach for identifying interactive effects using Network Science (NS) supported by Social Network Analysis (SNA) techniques. Based on general observations that learning processes and behaviors are shaped by the social relationships and influenced by learning environment, the central idea was to understand both the human and non-human interactive effects for a blended learning mode of delivery of computer science modules. Important findings include (a) the importance of non-human nodes to influence the centrality and transfer; (b) the degree of non-human and human connectivity impacts learning. This project reveals that the NS pattern and connectivity as measured by node relationships offer alternative approach for hypothesis generation and design of qualitative data collection. An iterative process further reinforces the analysis, whereas the experimental simulation option itself is an interesting alternative option, a hybrid combination of both experimental simulation and qualitative data collection presents itself as a promising and viable means to study complex scenario such as blended learning delivery mode. The primary value of this paper lies in the design of the approach for studying interactive effects of human (social nodes) and non-human (learning/study environment, Information and Communication Technologies (ICT) infrastructures nodes) components. In conclusion, this project adds to the understanding and the use of SNA to model and study interactive effects in blended social learning.
Abstract: 2016 has become the year of the Artificial Intelligence explosion. AI technologies are getting more and more matured that most world well-known tech giants are making large investment to increase the capabilities in AI. Machine learning is the science of getting computers to act without being explicitly programmed, and deep learning is a subset of machine learning that uses deep neural network to train a machine to learn features directly from data. Deep learning realizes many machine learning applications which expand the field of AI. At the present time, deep learning frameworks have been widely deployed on servers for deep learning applications in both academia and industry. In training deep neural networks, there are many standard processes or algorithms, but the performance of different frameworks might be different. In this paper we evaluate the running performance of two state-of-the-art distributed deep learning frameworks that are running training calculation in parallel over multi GPU and multi nodes in our cloud environment. We evaluate the training performance of the frameworks with ResNet-50 convolutional neural network, and we analyze what factors that result in the performance among both distributed frameworks as well. Through the experimental analysis, we identify the overheads which could be further optimized. The main contribution is that the evaluation results provide further optimization directions in both performance tuning and algorithmic design.
Abstract: Urban public transportation in Rio de Janeiro is based on bus lines, powered by diesel, and four limited metro lines that support only some neighborhoods. This work presents an infrastructure built to better understand microclimate variations related to massive urban transportation in some specific areas of the city. The use of sensor nodes with small analytics capacity provides environmental information to population or public services. The analyses of data collected from a few small sensors positioned near some heavy traffic streets show the harmful impact due to poor bus route plan.
Abstract: Since last decade, wireless sensor networks (WSNs) have been used in many areas like health care, agriculture, defense, military, disaster hit areas and so on. Wireless Sensor Networks consist of a Base Station (BS) and more number of wireless sensors in order to monitor temperature, pressure, motion in different environment conditions. The key parameter that plays a major role in designing a protocol for Wireless Sensor Networks is energy efficiency which is a scarcest resource of sensor nodes and it determines the lifetime of sensor nodes. Maximizing sensor node’s lifetime is an important issue in the design of applications and protocols for Wireless Sensor Networks. Clustering sensor nodes mechanism is an effective topology control approach for helping to achieve the goal of this research. In this paper, the researcher presents an energy efficiency protocol to prolong the network lifetime based on Energy efficient clustering algorithm. The Low Energy Adaptive Clustering Hierarchy (LEACH) is a routing protocol for clusters which is used to lower the energy consumption and also to improve the lifetime of the Wireless Sensor Networks. Maximizing energy dissipation and network lifetime are important matters in the design of applications and protocols for wireless sensor networks. Proposed system is to maximize the lifetime of the Wireless Sensor Networks by choosing the farthest cluster head (CH) instead of the closest CH and forming the cluster by considering the following parameter metrics such as Node’s density, residual-energy and distance between clusters (inter-cluster distance). In this paper, comparisons between the proposed protocol and comparative protocols in different scenarios have been done and the simulation results showed that the proposed protocol performs well over other comparative protocols in various scenarios.
Abstract: This paper deals with automatic extraction of 20 ‘adjective + noun’ collocations using four different association measures: T-score, MI, Log Dice, and Log Likelihood with most emphasis on mainly Log Likelihood and Log Dice scores for which an argument for their suitability in this experiment is to be presented. The nodes of the chosen collocates are 20 adjectival false friends between English and French. The noun candidate to be chosen needs to occur with a threshold of top ten collocates in two lists in which the results are sorted by Log Likelihood and Log Dice. The fulfillment of this criterion will guarantee that the chosen candidates are both exclusive and significant noun collocates and thereby, they make perfect noun candidates for the nodes. The results of the top 10 collocates sorted by Log Dice and Log Likelihood are not to be filtered. Thereby technical terms, function words, and stop words are not to be removed for the purposes of the analysis. Out of 20 adjectives, 15 ‘adjective + noun’ collocations have been extracted by the means of consensus of Log Likelihood and Log Dice scores on the top 10 noun collocates. The generated list of the automatic extracted ‘adjective + noun’ collocations will serve as the bulk of a translation test in which Algerian students of translation are asked to render these collocations into Arabic. The ultimate goal of this test is to test French influence as a Second Language on English as a Foreign Language in the Algerian context.
Abstract: Networks can be utilized to represent project planning problems, using nodes for activities and arcs to indicate precedence relationship between them. For fixed activity duration, a simple algorithm calculates the amount of time required to complete a project, followed by the activities that comprise the critical path. Program Evaluation and Review Technique (PERT) generalizes the above model by incorporating uncertainty, allowing activity durations to be random variables, producing nevertheless a relatively crude solution in planning problems. In this paper, based on the findings of the relevant literature, which strongly suggests that a Beta distribution can be employed to model earthmoving activities, we utilize Monte Carlo simulation, to estimate the project completion time distribution and measure the influence of skewness, an element inherent in activities of modern technical projects. We also extract the activity criticality index, with an ultimate goal to produce more accurate planning estimations.
Abstract: Vehicular Ad-hoc Network (VANET) is an emerging and very promising technology that has great demand on the access capability of the existing wireless technology. VANETs help improve traffic safety and efficiency. Each vehicle can exchange their information to inform the other vehicles about the current status of the traffic flow or a dangerous situation such as an accident. To achieve these, a reliable and efficient Medium Access Control (MAC) protocol with minimal transmission collisions is required. High speed nodes, absence of infrastructure, variations in topology and their QoS requirements makes it difficult for designing a MAC protocol in vehicular networks. There are several MAC protocols proposed for VANETs to ensure that all the vehicles could send safety messages without collisions by reducing the end-to-end delay and packet loss ratio. This paper gives an overview of the several proposed MAC protocols for VANETs along with their benefits and limitations and presents an overall classification based on their characteristics.
Abstract: The Semi Interlocking Masonry (SIM) system has been developed in Masonry Research Group at the University of Newcastle, Australia. The main purpose of this system is to enhance the seismic resistance of framed structures with masonry panels. In this system, SIM panels dissipate energy through the sliding friction between rows of SIM units during earthquake excitation. This paper aimed to find the applicability of artificial neural network (ANN) to predict the displacement behaviour of the SIM panel under out-of-plane loading. The general concept of ANN needs to be trained by related force-displacement data of SIM panel. The overall data to train and test the network are 70 increments of force-displacement from three tests, which comprise of none input nodes. The input data contain height and length of panels, height, length and width of the brick and friction and geometry angle of brick along the compressive strength of the brick with the lateral load applied to the panel. The aim of designed network is prediction displacement of the SIM panel by Multi-Layer Perceptron (MLP). The mean square error (MSE) of network was 0.00042 and the coefficient of determination (R2) values showed the 0.91. The result revealed that the ANN has significant agreement to predict the SIM panel behaviour.
Abstract: Recent innovations in the field of technology led to the use of wireless sensor networks in various applications, which consists of a number of small, very tiny, low-cost, non-tamper proof and resource constrained sensor nodes. These nodes are often distributed and deployed in an unattended environment, so as to collaborate with each other to share data or information. Amidst various applications, wireless sensor network finds a major role in monitoring battle field in military applications. As these non-tamperproof nodes are deployed in an unattended location, they are vulnerable to many security attacks. Amongst many security attacks, the node replication attack seems to be more threatening to the network users. Node Replication attack is caused by an attacker, who catches one true node, duplicates the first certification and cryptographic materials, makes at least one or more copies of the caught node and spots them at certain key positions in the system to screen or disturb the network operations. Preventing the occurrence of such node replication attacks in network is a challenging task. In this survey article, we provide the classification of detection schemes and also explore the various schemes proposed in each category. Also, we compare the various detection schemes against certain evaluation parameters and also its limitations. Finally, we provide some suggestions for carrying out future research work against such attacks.
Abstract: Various processes are modelled using a discrete phase,
where particles are seeded from a source. Such particles can represent
liquid water droplets, which are affecting the continuous phase by
exchanging thermal energy, momentum, species etc. Discrete phases
are typically modelled using parcel, which represents a collection of
particles, which share properties such as temperature, velocity etc.
When coupling the phases, the exchange rates are integrated over
the cell, in which the parcel is located. This can cause spikes and
fluctuating exchange rates. This paper presents an alternative method of coupling a discrete
and a continuous plug flow phase. This is done using triangular
parcels, which span between nodes following the dynamics of single
droplets. Thus, the triangular parcels are propagated using the corner
nodes. At each time step, the exchange rates are spatially integrated
over the surface of the triangular parcels, which yields a smooth
continuous exchange rate to the continuous phase. The results shows that the method is more stable, converges
slightly faster and yields smooth exchange rates compared with
the steam tube approach. However, the computational requirements
are about five times greater, so the applicability of the alternative
method should be limited to processes, where the exchange rates are
important. The overall balances of the exchanged properties did not
change significantly using the new approach.
Abstract: A critical problem in wireless sensor networks is limited battery and memory of nodes. Therefore, each node in the network could maintain only a subset of its neighbors to communicate with. This will increase the battery usage in the network because each packet should take more hops to reach its destination. In order to tackle these problems, spanner graphs are defined. Since each node has a small degree in a spanner graph and the distance in the graph is not much greater than its actual geographical distance, spanner graphs are suitable candidates to be used for the topology of a wireless sensor network. In this paper, we study Yao graphs and their behavior for a randomly selected set of points. We generate several random point sets and compare the properties of their Yao graphs with the complete graph. Based on our data sets, we obtain several charts demonstrating how Yao graphs behave for a set of randomly chosen point set. As the results show, the stretch factor of a Yao graph follows a normal distribution. Furthermore, the stretch factor is in average far less than the worst case stretch factor proved for Yao graphs in previous results. Furthermore, we use Yao graph for a realistic point set and study its stretch factor in real world.
Abstract: The saltatory conduction is the way the action potential
is transmitted along a myelinated axon. The potential diffuses along
the myelinated compartments and it is regenerated in the Ranvier
nodes due to the ion channels allowing the flow across the membrane.
For an efficient simulation of populations of neurons, it is important
to use reduced order models both for myelinated compartments
and for Ranvier nodes and to have control over their accuracy and
inner parameters. The paper presents a reduced order model of this
neural system which allows an efficient simulation method for the
saltatory conduction in myelinated axons. This model is obtained
by concatenating reduced order linear models of 1D myelinated
compartments and nonlinear 0D models of Ranvier nodes. The
models for the myelinated compartments are selected from a series of
spatially distributed models developed and hierarchized according to
their modeling errors. The extracted model described by a nonlinear
PDE of hyperbolic type is able to reproduce the saltatory conduction
with acceptable accuracy and takes into account the finite propagation
speed of potential. Finally, this model is again reduced in order to
make it suitable for the inclusion in large-scale neural circuits.
Abstract: Lung CT image segmentation is a prerequisite in lung
CT image analysis. Most of the conventional methods need a
post-processing to deal with the abnormal lung CT scans such as
lung nodules or other lesions. The simplest similarity measure in
the standard Graph Cuts Algorithm consists of directly comparing
the pixel values of the two neighboring regions, which is not
accurate because this kind of metrics is extremely sensitive to minor
transformations such as noise or other artifacts problems. In this work,
we propose an improved version of the standard graph cuts algorithm
based on the Patch-Based similarity metric. The boundary penalty
term in the graph cut algorithm is defined Based on Patch-Based
similarity measurement instead of the simple intensity measurement
in the standard method. The weights between each pixel and its
neighboring pixels are Based on the obtained new term. The graph
is then created using theses weights between its nodes. Finally,
the segmentation is completed with the minimum cut/Max-Flow
algorithm. Experimental results show that the proposed method is
very accurate and efficient, and can directly provide explicit lung
regions without any post-processing operations compared to the
standard method.
Abstract: The north-eastern, Himalayan, and Eastern Ghats Belt
of India comprise of earthquake-prone, remote, and hilly terrains.
Earthquakes have caused enormous damages in these regions in the
past. A wireless sensor network based earthquake early warning
system (EEWS) is being developed to mitigate the damages caused
by earthquakes. It consists of sensor nodes, distributed over the
region, that perform majority voting of the output of the seismic
sensors in the vicinity, and relay a message to a base station to alert
the residents when an earthquake is detected. At the heart of the
EEWS is a low-power two-stage seismic sensor that continuously
tracks seismic events from incoming three-axis accelerometer signal
at the first-stage, and, in the presence of a seismic event, triggers
the second-stage P-wave detector that detects the onset of P-wave
in an earthquake event. The parameters of the P-wave detector have
been optimized for minimizing detection time and maximizing the
accuracy of detection.Working of the sensor scheme has been verified
with seven earthquakes data retrieved from IRIS. In all test cases, the
scheme detected the onset of P-wave accurately. Also, it has been
established that the P-wave onset detection time reduces linearly with
the sampling rate. It has been verified with test data; the detection
time for data sampled at 10Hz was around 2 seconds which reduced
to 0.3 second for the data sampled at 100Hz.
Abstract: Wireless sensor network finds role in environmental monitoring, industrial applications, surveillance applications, health monitoring and other supervisory applications. Sensing devices form the basic operational unit of the network that is self-battery powered with limited life time. Sensor node spends its limited energy for transmission, reception, routing and sensing information. Frequent energy utilization for the above mentioned process leads to network lifetime degradation. To enhance energy efficiency and network lifetime, we propose a modified energy optimization and node recovery post failure method, Energy-Link Failure Recovery Routing (E-LFRR) algorithm. In our E-LFRR algorithm, two phases namely, Monitored Transmission phase and Replaced Transmission phase are devised to combat worst case link failure conditions. In Monitored Transmission phase, the Actuator Node monitors and identifies suitable nodes for shortest path transmission. The Replaced Transmission phase dispatches the energy draining node at early stage from the active link and replaces it with the new node that has sufficient energy. Simulation results illustrate that this combined methodology reduces overhead, energy consumption, delay and maintains considerable amount of alive nodes thereby enhancing the network performance.
Abstract: This work approaches the automatic planning of paths
for Unmanned Aerial Vehicles (UAVs) through the application of the
Rapidly Exploring Random Tree Star-Smart (RRT*-Smart) algorithm.
RRT*-Smart is a sampling process of positions of a navigation
environment through a tree-type graph. The algorithm consists of
randomly expanding a tree from an initial position (root node) until
one of its branches reaches the final position of the path to be
planned. The algorithm ensures the planning of the shortest path,
considering the number of iterations tending to infinity. When a
new node is inserted into the tree, each neighbor node of the
new node is connected to it, if and only if the extension of the
path between the root node and that neighbor node, with this new
connection, is less than the current extension of the path between
those two nodes. RRT*-smart uses an intelligent sampling strategy
to plan less extensive routes by spending a smaller number of
iterations. This strategy is based on the creation of samples/nodes
near to the convex vertices of the navigation environment obstacles.
The planned paths are smoothed through the application of the
method called quintic pythagorean hodograph curves. The smoothing
process converts a route into a dynamically-viable one based on the
kinematic constraints of the vehicle. This smoothing method models
the hodograph components of a curve with polynomials that obey
the Pythagorean Theorem. Its advantage is that the obtained structure
allows computation of the curve length in an exact way, without the
need for quadratural techniques for the resolution of integrals.
Abstract: Tire noise has a significant impact on ride quality
and vehicle interior comfort, even at low frequency. Reduction of
tire noise is especially important due to strict state and federal
environmental regulations. The primary sources of tire noise are the
low frequency structure-borne noise and the noise that originates from
the release of trapped air between the tire tread and road surface
during each revolution of the tire. The frequency response of the tire
changes at low and high frequency. At low frequency, the tension
and bending moment become dominant, while the internal structure
and local deformation become dominant at higher frequencies. Here,
we analyze tire response in terms of deformation and rolling velocity
at low revolution frequency. An Abaqus FEA finite element model
is used to calculate the static and dynamic response of a rolling tire
under different rolling conditions. The natural frequencies and mode
shapes of a deformed tire are calculated with the FEA package where
the subspace-based steady state dynamic analysis calculates dynamic
response of tire subjected to harmonic excitation. The analysis was
conducted on the dynamic response at the road (contact point of tire
and road surface) and side nodes of a static and rolling tire when
the tire was excited with 200 N vertical load for a frequency ranging
from 20 to 200 Hz. The results show that frequency has little effect on
tire deformation up to 80 Hz. But between 80 and 200 Hz, the radial
and lateral components of displacement of the road and side nodes
exhibited significant oscillation. For the static analysis, the fluctuation
was sharp and frequent and decreased with frequency. In contrast, the
fluctuation was periodic in nature for the dynamic response of the
rolling tire. In addition to the dynamic analysis, a steady state rolling
analysis was also performed on the tire traveling at ground velocity
with a constant angular motion. The purpose of the computation
was to demonstrate the effect of rotating motion on deformation and
rolling velocity with respect to a fixed Newtonian reference point.
The analysis showed a significant variation in deformation and rolling
velocity due to centrifugal and Coriolis acceleration with respect to
a fixed Newtonian point on ground.
Abstract: Minimizing the weight in flexible structures means
reducing material and costs as well. However, these structures could
become prone to vibrations. Attenuating these vibrations has become
a pivotal engineering problem that shifted the focus of many research
endeavors. One technique to do that is to design and implement
an active control system. This system is mainly composed of a
vibrating structure, a sensor to perceive the vibrations, an actuator
to counteract the influence of disturbances, and finally a controller to
generate the appropriate control signals. In this work, two different
techniques are explored to create two different mathematical models
of an active control system. The first model is a finite element model
with a reduced number of nodes and it is called a super-element.
The second model is in the form of state-space representation, i.e.
a set of partial differential equations. The damping coefficients are
calculated and incorporated into both models. The effectiveness of
these models is demonstrated when the system is excited by its first
natural frequency and an active control strategy is developed and
implemented to attenuate the resulting vibrations. Results from both
modeling techniques are presented and compared.
Abstract: Distributed database is a collection of logically related databases that cooperate in a transparent manner. Query processing uses a communication network for transmitting data between sites. It refers to one of the challenges in the database world. The development of sophisticated query optimization technology is the reason for the commercial success of database systems, which complexity and cost increase with increasing number of relations in the query. Mariposa, query trading and query trading with processing task-trading strategies developed for autonomous distributed database systems, but they cause high optimization cost because of involvement of all nodes in generating an optimal plan. In this paper, we proposed a modification on the autonomous strategy K-QTPT that make the seller’s nodes with the lowest cost have gradually high priorities to reduce the optimization time. We implement our proposed strategy and present the results and analysis based on those results.
Abstract: Wireless Sensor Networks (WSNs) consist of a set of sensor nodes with limited capability. WSNs may suffer from multiple node failures when they are exposed to harsh environments such as military zones or disaster locations and lose connectivity by getting partitioned into disjoint segments. Relay nodes (RNs) are alternatively introduced to restore connectivity. They cost more than sensors as they benefit from mobility, more power and more transmission range, enforcing a minimum number of them to be used. This paper addresses the problem of RN placement in a multiple disjoint network by developing a genetic algorithm (GA). The problem is reintroduced as the Steiner tree problem (which is known to be an NP-hard problem) by the aim of finding the minimum number of Steiner points where RNs are to be placed for restoring connectivity. An upper bound to the number of RNs is first computed to set up the length of initial chromosomes. The GA algorithm then iteratively reduces the number of RNs and determines their location at the same time. Experimental results indicate that the proposed GA is capable of establishing network connectivity using a reasonable number of RNs compared to the best existing work.