Abstract: This paper introduces a new dataset (and the methodology used to generate it) based on a wide range of historical Arabic documents containing clean data simple and homogeneous-page layouts. The experiments are implemented on printed and handwritten documents obtained respectively from some important libraries such as Qatar Digital Library, the British Library and the Library of Congress. We have gathered and commented on 150 archival document images from different locations and time periods. It is based on different documents from the 17th-19th century. The dataset comprises differing page layouts and degradations that challenge text line segmentation methods. Ground truth is produced using the Aletheia tool by PRImA and stored in an XML representation, in the PAGE (Page Analysis and Ground truth Elements) format. The dataset presented will be easily available to researchers world-wide for research into the obstacles facing various historical Arabic documents such as geometric correction of historical Arabic documents.
Abstract: Risk assessment and the knowledge provided through this process is a crucial part of any decision-making process in the management of risks and uncertainties. Failure in assessment of risks can cause inadequacy in the entire process of risk management, which in turn can lead to failure in achieving organisational objectives as well as having significant damaging consequences on populations affected by the potential risks being assessed. The choice of tools and techniques in risk assessment can influence the degree and scope of decision-making and subsequently the risk response strategy. There are various available qualitative and quantitative tools and techniques that are deployed within the broad process of risk assessment. The sheer diversity of tools and techniques available to practitioners makes it difficult for organisations to consistently employ the most appropriate methods. This tools and techniques adaptation is rendered more difficult in public risk regulation organisations due to the sensitive and complex nature of their activities. This is particularly the case in areas relating to the environment, food, and human health and safety, when organisational goals are tied up with societal, political and individuals’ goals at national and international levels. Hence, recognising, analysing and evaluating different decision support tools and techniques employed in assessing risks in public risk management organisations was considered. This research is part of a mixed method study which aimed to examine the perception of risk assessment and the extent to which organisations practise risk assessment’ tools and techniques. The study adopted a semi-structured questionnaire with qualitative and quantitative data analysis to include a range of public risk regulation organisations from the UK, Germany, France, Belgium and the Netherlands. The results indicated the public risk management organisations mainly use diverse tools and techniques in the risk assessment process. The primary hazard analysis; brainstorming; hazard analysis and critical control points were described as the most practiced risk identification techniques. Within qualitative and quantitative risk analysis, the participants named the expert judgement, risk probability and impact assessment, sensitivity analysis and data gathering and representation as the most practised techniques.
Abstract: The aim of this study was to examine the effect of
cooperative learning method on student’s academic achievement and
on the achievement level over a usual method in teaching different
topics of mathematics. The study also examines the perceptions of
students towards cooperative learning. Cooperative learning is the
instructional strategy in which pairs or small groups of students with
different levels of ability work together to accomplish a shared goal.
The aim of this cooperation is for students to maximize their own
and each other learning, with members striving for joint benefit.
The teacher’s role changes from wise on the wise to guide on
the side. Cooperative learning due to its influential aspects is the
most prevalent teaching-learning technique in the modern world.
Therefore the study was conducted in order to examine the effect
of cooperative learning on the academic achievement of grade 9
students in Mathematics in case of Mettu secondary school. Two
sample sections are randomly selected by which one section served
randomly as an experimental and the other as a comparison group.
Data gathering instruments are achievement tests and questionnaires.
A treatment of STAD method of cooperative learning was provided
to the experimental group while the usual method is used in the
comparison group. The experiment lasted for one semester. To
determine the effect of cooperative learning on the student’s academic
achievement, the significance of difference between the scores of
groups at 0.05 levels was tested by applying t test. The effect size
was calculated to see the strength of the treatment. The student’s
perceptions about the method were tested by percentiles of the
questionnaires. During data analysis, each group was divided into
high and low achievers on basis of their previous Mathematics result.
Data analysis revealed that both the experimental and comparison
groups were almost equal in Mathematics at the beginning of the
experiment. The experimental group out scored significantly than
comparison group on posttest. Additionally, the comparison of mean
posttest scores of high achievers indicates significant difference
between the two groups. The same is true for low achiever students
of both groups on posttest. Hence, the result of the study indicates
the effectiveness of the method for Mathematics topics as compared
to usual method of teaching.
Abstract: The mobile cloud computing (MCC) with wireless sensor networks (WSNs) technology gets more attraction by research scholars because its combines the sensors data gathering ability with the cloud data processing capacity. This approach overcomes the limitation of data storage capacity and computational ability of sensor nodes. Finally, the stored data are sent to the mobile users when the user sends the request. The most of the integrated sensor-cloud schemes fail to observe the following criteria: 1) The mobile users request the specific data to the cloud based on their present location. 2) Power consumption since most of them are equipped with non-rechargeable batteries. Mostly, the sensors are deployed in hazardous and remote areas. This paper focuses on above observations and introduces an approach known as collaborative location-based sleep scheduling (CLSS) scheme. Both awake and asleep status of each sensor node is dynamically devised by schedulers and the scheduling is done purely based on the of mobile users’ current location; in this manner, large amount of energy consumption is minimized at WSN. CLSS work depends on two different methods; CLSS1 scheme provides lower energy consumption and CLSS2 provides the scalability and robustness of the integrated WSN.
Abstract: Debts reconstruction under some of moratorium
projects is one of important method that highly benefits to both the
Banks and farmers. The method can reduce probabilities for nonprofits
loan. This paper discuss about debts reconstruction and career
development training for farmers in Thailand between 2011 and
2013. The research designed is mix-method between quantitative
survey and qualitative survey. Sample size for quantitative method is
1003 cases. Data gathering procedure is between October and
December 2013. Main results affirmed that debts reconstruction is
needed. And there are numerous benefits from farmers’ career
development training. Many of farmers who attend field school
activities able to bring knowledge learned to apply for the farms’
work. They can reduce production costs. Framers’ quality of life and
their household well-being also improve. This program should apply
in any countries where farmers have highly debts and highly risks for
not return the debts.
Abstract: Many cluster based routing protocols have been
proposed in the field of wireless sensor networks, in which a group of
nodes are formed as clusters. A cluster head is selected from one
among those nodes based on residual energy, coverage area, number
of hops and that cluster-head will perform data gathering from
various sensor nodes and forwards aggregated data to the base station
or to a relay node (another cluster-head), which will forward the
packet along with its own data packet to the base station. Here a
Game Theory based Diligent Energy Utilization Algorithm (GTDEA)
for routing is proposed. In GTDEA, the cluster head selection is done
with the help of game theory, a decision making process, that selects
a cluster-head based on three parameters such as residual energy
(RE), Received Signal Strength Index (RSSI) and Packet Reception
Rate (PRR). Finding a feasible path to the destination with minimum
utilization of available energy improves the network lifetime and is
achieved by the proposed approach. In GTDEA, the packets are
forwarded to the base station using inter-cluster routing technique,
which will further forward it to the base station. Simulation results
reveal that GTDEA improves the network performance in terms of
throughput, lifetime, and power consumption.
Abstract: The decision-making process is theoretically clearly
defined. Generally, it includes the problem identification and
analysis, data gathering, goals and criteria setting, alternatives
development and optimal alternative choice and its implementation.
In practice however, various modifications of the theoretical
decision-making process can occur. The managers can consider some
of the phases to be too complicated or unfeasible and thus they do not
carry them out and conversely some of the steps can be
overestimated.
The aim of the paper is to reveal and characterize the perception of
the individual phases of decision-making process by the managers.
The research is concerned with managers in the military environment
– commanders. Quantitative survey is focused cross-sectionally in the
individual levels of management of the Ministry of Defence of the
Czech Republic. On the total number of 135 respondents the analysis
focuses on which of the decision-making process phases are
problematic or not carried out in practice and which are again
perceived to be the easiest. Then it is examined the reasons of the
findings.
Abstract: Traditional Wireless Sensor Networks (WSNs) generally use static sinks to collect data from the sensor nodes via multiple forwarding. Therefore, network suffers with some problems like long message relay time, bottle neck problem which reduces the performance of the network.
Many approaches have been proposed to prevent this problem with the help of mobile sink to collect the data from the sensor nodes, but these approaches still suffer from the buffer overflow problem due to limited memory size of sensor nodes. This paper proposes an energy efficient scheme for data gathering which overcomes the buffer overflow problem. The proposed scheme creates virtual grid structure of heterogeneous nodes. Scheme has been designed for sensor nodes having variable sensing rate. Every node finds out its buffer overflow time and on the basis of this cluster heads are elected. A controlled traversing approach is used by the proposed scheme in order to transmit data to sink. The effectiveness of the proposed scheme is verified by simulation.
Abstract: Voice over Internet Protocol (VoIP) products is an emerging technology that can contain forensically important information for a criminal activity. Without having the user name and passwords, this forensically important information can still be gathered by the investigators. Although there are a few VoIP forensic investigative applications available in the literature, most of them are particularly designed to collect evidence from the Skype product. Therefore, in order to assist law enforcement with collecting forensically important information from variety of Betamax VoIP tools, CVOIP-FRU framework is developed. CVOIP-FRU provides a data gathering solution that retrieves usernames, contact lists, as well as call and SMS logs from Betamax VoIP products. It is a scripting utility that searches for data within the registry, logs and the user roaming profiles in Windows and Mac OSX operating systems. Subsequently, it parses the output into readable text and html formats. One superior way of CVOIP-FRU compared to the other applications that due to intelligent data filtering capabilities and cross platform scripting back end of CVOIP-FRU, it is expandable to include other VoIP solutions as well. Overall, this paper reveals the exploratory analysis performed in order to find the key data paths and locations, the development stages of the framework, and the empirical testing and quality assurance of CVOIP-FRU.
Abstract: In an emergency, combining Wireless Sensor Network's data with the knowledge gathered from various other information sources and navigation algorithms, could help safely guide people to a building exit while avoiding the risky areas. This paper presents an emergency response and navigation support architecture for data gathering, knowledge manipulation, and navigational support in an emergency situation. At normal state, the system monitors the environment. When an emergency event detects, the system sends messages to first responders and immediately identifies the risky areas from safe areas to establishing escape paths. The main functionalities of the system include, gathering data from a wireless sensor network which is deployed in a multi-story indoor environment, processing it with information available in a knowledge base, and sharing the decisions made, with first responders and people in the building. The proposed architecture will act to reduce risk of losing human lives by evacuating people much faster with least congestion in an emergency environment.
Abstract: One of the important applications of gas turbines is
their utilization for heat recovery steam generator in combine-cycle technology. Exhaust flow and energy are two key parameters for
determining heat recovery steam generator performance which are mainly determined by the main gas turbine components performance
data. For this reason a method was developed for determining the
exhaust energy in the new edition of ASME PTC22. The result of this investigation shows that the method of standard has considerable
error. Therefore in this paper a new method is presented for modifying of the performance calculation. The modified method is
based on exhaust gas constituent analysis and combustion calculations. The case study presented here by two kind of General
Electric gas turbine design data for validation of methodologies. The
result shows that the modified method is more precise than the ASME PTC22 method. The exhaust flow calculation deviation from
design data is 1.5-2 % by ASME PTC22 method so that the deviation regarding with modified method is 0.3-0.5%. Based on precision of
analyzer instruments, the method can be suitable alternative for gas
turbine standard performance test. In advance two methods are
proposed based on known and unknown fuel in modified method procedure. The result of this paper shows that the difference between
the two methods is below than %0.02. In according to reasonable esult of the second procedure (unknown fuel composition), the
method can be applied to performance evaluation of gas turbine, so that the measuring cost and data gathering should be reduced.
Abstract: In this paper, we investigate multihop polling and data gathering schemes in layered sensor networks in order to extend the life time of the networks. A network consists of three layers. The lowest layer contains sensors. The middle layer contains so called super nodes with higher computational power, energy supply and longer transmission range than sensor nodes. The top layer contains a sink node. A node in each layer controls a number of nodes in lower layer by polling mechanism to gather data. We will present four types of data gathering schemes: intermediate nodes do not queue data packet, queue single packet, queue multiple packets and aggregate data, to see which data gathering scheme is more energy efficient for multihop polling in layered sensor networks.
Abstract: Data gathering is an essential operation in wireless
sensor network applications. So it requires energy efficiency
techniques to increase the lifetime of the network. Similarly,
clustering is also an effective technique to improve the energy
efficiency and network lifetime of wireless sensor networks. In this
paper, an energy efficient cluster formation protocol is proposed with
the objective of achieving low energy dissipation and latency without
sacrificing application specific quality. The objective is achieved by
applying randomized, adaptive, self-configuring cluster formation
and localized control for data transfers. It involves application -
specific data processing, such as data aggregation or compression.
The cluster formation algorithm allows each node to make
independent decisions, so as to generate good clusters as the end.
Simulation results show that the proposed protocol utilizes minimum
energy and latency for cluster formation, there by reducing the
overhead of the protocol.
Abstract: A Decision Support System/Expert System for stock
portfolio selection presented where at first step, both technical and
fundamental data used to estimate technical and fundamental return
and risk (1st phase); Then, the estimated values are aggregated with
the investor preferences (2nd phase) to produce convenient stock
portfolio.
In the 1st phase, there are two expert systems, each of which is
responsible for technical or fundamental estimation. In the technical
expert system, for each stock, twenty seven candidates are identified
and with using rough sets-based clustering method (RC) the effective
variables have been selected. Next, for each stock two fuzzy rulebases
are developed with fuzzy C-Mean method and Takai-Sugeno-
Kang (TSK) approach; one for return estimation and the other for
risk. Thereafter, the parameters of the rule-bases are tuned with backpropagation
method. In parallel, for fundamental expert systems,
fuzzy rule-bases have been identified in the form of “IF-THEN" rules
through brainstorming with the stock market experts and the input
data have been derived from financial statements; as a result two
fuzzy rule-bases have been generated for all the stocks, one for return
and the other for risk.
In the 2nd phase, user preferences represented by four criteria and
are obtained by questionnaire. Using an expert system, four estimated
values of return and risk have been aggregated with the respective
values of user preference. At last, a fuzzy rule base having four rules,
treats these values and produce a ranking score for each stock which
will lead to a satisfactory portfolio for the user.
The stocks of six manufacturing companies and the period of
2003-2006 selected for data gathering.
Abstract: In a complex project environment, project teams face
multi-dimensional communication problems that can ultimately lead
to project breakdown. Team Performance varies in Face-to-Face
(FTF) environment versus groups working remotely in a computermediated
communication (CMC) environment. A brief review of the
Input_Process_Output model suggested by James E. Driskell, Paul H.
Radtke and Eduardo Salas in “Virtual Teams: Effects of
Technological Mediation on Team Performance (2003)", has been
done to develop the basis of this research. This model theoretically
analyzes the effects of technological mediation on team processes,
such as, cohesiveness, status and authority relations, counternormative
behavior and communication. An empirical study
described in this paper has been undertaken to test the
“cohesiveness" of diverse project teams in a multi-national
organization. This study uses both quantitative and qualitative
techniques for data gathering and analysis. These techniques include
interviews, questionnaires for data collection and graphical data
representation for analyzing the collected data. Computer-mediated
technology may impact team performance because of difference in
cohesiveness among teams and this difference may be moderated by
factors, such as, the type of communication environment, the type of
task and the temporal context of the team. Based on the reviewed
model, sets of hypotheses are devised and tested. This research,
reports on a study that compared team cohesiveness among virtual
teams using CMC and non-CMC communication mediums. The
findings suggest that CMC can help virtual teams increase team
cohesiveness among their members, making CMC an effective
medium for increasing productivity and team performance.
Abstract: Since primary school trips usually start from home,
attention by many scholars have been focused on the home end for
data gathering. Thereafter category analysis has often been relied
upon when predicting school travel demands. In this paper, school
end was relied on for data gathering and multivariate regression for
future travel demand prediction. 9859 pupils were surveyed by way
of questionnaires at 21 primary schools. The town was divided into 5
zones. The study was carried out in Skudai Town, Malaysia. Based
on the hypothesis that the number of primary school trip ends are
expected to be the same because school trips are fixed, the choice of
trip end would have inconsequential effect on the outcome. The
study compared empirical data for home and school trip end
productions and attractions. Variance from both data results was
insignificant, although some claims from home based family survey
were found to be grossly exaggerated. Data from the school trip ends
was relied on for travel demand prediction because of its
completeness. Accessibility, trip attraction and trip production were
then related to school trip rates under daylight and dry weather
conditions. The paper concluded that, accessibility is an important
parameter when predicting demand for future school trip rates.
Abstract: Many agricultural and especially greenhouse
applications like plant inspection, data gathering, spraying and
selective harvesting could be performed by robots. In this paper
multiple nonholonomic robots are used in order to create a desired
formation scheme for screening solar energy in a greenhouse through
data gathering. The formation consists from a leader and a team
member equipped with appropriate sensors. Each robot is dedicated
to its mission in the greenhouse that is predefined by the
requirements of the application. The feasibility of the proposed
application includes experimental results with three unmanned
ground vehicles (UGV).
Abstract: The aim of this paper is to investigate the effect of
mean size of industry on survival of new firms in East-Azarbaijan
province through 1981-2006 using hazard function. So the effect of
two variables including mean employment of industry and mean
capital of industry are investigated on firm's survival. The Industry &
Mine Ministry database has used for data gathering and the data are
analyzed using the semi-parametric cox regression model. The results
of this study shows that there is a meaningful negative relationship
between mean capital of industry and firm's survival, but the mean
employment of industry has no meaningful effect on survival of new
firms.
Abstract: The necessity of accurate and timely field data is
shared among organizations engaged in fundamentally different
activities, public services or commercial operations. Basically, there
are three major components in the process of the qualitative research:
data collection, interpretation and organization of data, and analytic
process. Representative technological advancements in terms of
innovation have been made in mobile devices (mobile phone, PDA-s,
tablets, laptops, etc). Resources that can be potentially applied on the
data collection activity for field researches in order to improve this
process.
This paper presents and discuss the main features of a mobile
phone based solution for field data collection, composed of basically
three modules: a survey editor, a server web application and a client
mobile application. The data gathering process begins with the
survey creation module, which enables the production of tailored
questionnaires. The field workforce receives the questionnaire(s) on
their mobile phones to collect the interviews responses and sending
them back to a server for immediate analysis.
Abstract: Sensor network applications are often data centric and
involve collecting data from a set of sensor nodes to be delivered
to various consumers. Typically, nodes in a sensor network are
resource-constrained, and hence the algorithms operating in these
networks must be efficient. There may be several algorithms available
implementing the same service, and efficient considerations may
require a sensor application to choose the best suited algorithm. In
this paper, we present a systematic evaluation of a set of algorithms
implementing the data gathering service. We propose a modular
infrastructure for implementing such algorithms in TOSSIM with
separate configurable modules for various tasks such as interest
propagation, data propagation, aggregation, and path maintenance.
By appropriately configuring these modules, we propose a number
of data gathering algorithms, each of which incorporates a different
set of heuristics for optimizing performance. We have performed
comprehensive experiments to evaluate the effectiveness of these
heuristics, and we present results from our experimentation efforts.