1,373 research outputs found
PORElicit A Pair Oriented Approach For Improving Multi-Lingual Requirements Elicitation For Requirements Engineers
The rise of software project offshoring has resulted in the increased of usage of multi- lingual in software development activities including requirements elicitation. In Malaysia, code switching between Malay and English language has become a common practice in multi-lingual requirements elicitation which leads to miscommunication and misinterpretation between both requirements engineers and stakeholders, due to the language barriers. Motivated from this problem, this research proposes a Pair Oriented Requirements Elicitation approach (PORElicit), to improve the performance and the accuracy of multi-lingual requirements. We adopt the concept of pair which involves two requirements engineers, namely the elicitor and the reviewer in eliciting the multi-lingual requirements. Three studies were carried out in this research: 1) controlled experiments 2) surveys and 3) observations. The focus of these studies was to evaluate PORElicit approach in comparison to solo approach in term of its performance (time spent) and the accuracy of the multi-lingual requirements (requirements correctness). The other focus is on its usability which is based on user perception on its usability and satisfaction. Based on these studies, we found that PORElicit approach is able to provide better multi-lingual requirements with lesser effort in comparison to solo approach. Further, PORElicit approach is also able to improve the communication between the requirements engineers and the stakeholders. The collaboration between the requirements engineers in PORElicit approach also helps in validating the multi-lingual requirements at the early stage of requirements engineering activities
Forex prediction engine: framework, modelling techniques and implementations
Having accurate prediction in foreign exchange (Forex) market is useful because it
provides intelligent information for investment strategy. This paper studies extracted repeating
patterns of historical Forex time series, so to predict future trend direction by matching the
forming trend with a repeating pattern. In the proposed Forex prediction engine, global pattern
movements over a period of time are extracted using a linear regression line (LRL) enhanced
technique, and then further segmented into what we called up and down curves. Subsequently,
the artificial neural network (ANN) is applied to classify or group the uptrend and downtrend
patterns. Finally, the dynamic time warping (DTW) is used through brute force to identify a trend
pattern similar to the current trend at least for the beginning part. The remaining part of the
matched pattern can provide predictive clues about next day trend movement. The experimental
results generated on the dataset of AUD–USD and EUR–USD currencies between 2012 and 2013
demonstrate reliable accuracy performance of 72%
Prediction of forex trend movement using linear regression line, two-stage of multi-layer perceptron and dynamic time warping algorithms
Foreign Exchange Currency prediction has become a challenging task since the late 1970s due to uncertainty movement of exchange rates. However, most researchers in this area were neglecting to analyse trend patterns from historical Forex data as input features. Thus, this motivates us to investigate possibility of repeated trend patterns from historical Forex data. This paper aims to investigate the repeated trend patterns as features from historical Forex data, which proposes new combination techniques - Linear Regression Line, two-stage of Multi-Layer Perceptron and Dynamic Time Warping algorithms in order to improve the performance of prediction significantly, thus achieving greater accuracy
Accurate Tree Roots Positioning and Sizing over Undulated Ground Surfaces by Common Offset GPR Measurements
Tree roots detection is a popular application of the Ground-penetrating radar
(GPR). Normally, the ground surface above the tree roots is assumed to be flat,
and standard processing methods based on hyperbolic fitting are applied to the
hyperbolae reflection patterns of tree roots for detection purposes. When the
surface of the land is undulating (not flat), these typical hyperbolic fitting
methods becomes inaccurate. This is because, the reflection patterns change
with the uneven ground surfaces. When the soil surface is not flat, it is
inaccurate to use the peak point of an asymmetric reflection pattern to
identify the depth and horizontal position of the underground target. The
reflection patterns of the complex shapes due to extreme surface variations
results in analysis difficulties. Furthermore, when multiple objects are buried
under an undulating ground, it is hard to judge their relative positions based
on a B-scan that assumes a flat ground. In this paper, a roots fitting method
based on electromagnetic waves (EM) travel time analysis is proposed to take
into consideration the realistic undulating ground surface. A wheel-based (WB)
GPR and an antenna-height-fixed (AHF) GPR System are presented, and their
corresponding fitting models are proposed. The effectiveness of the proposed
method is demonstrated and validated through numerical examples and field
experiments.Comment: 11 pages, 6 figures, accepted by IEEE TI
Examining the Informational Role of Analysts’ Forecasts and its Impact on the Relation between Earnings Surprises and Investors’ Responses
DMRF-UNet: A Two-Stage Deep Learning Scheme for GPR Data Inversion under Heterogeneous Soil Conditions
Traditional ground-penetrating radar (GPR) data inversion leverages iterative
algorithms which suffer from high computation costs and low accuracy when
applied to complex subsurface scenarios. Existing deep learning-based methods
focus on the ideal homogeneous subsurface environments and ignore the
interference due to clutters and noise in real-world heterogeneous
environments. To address these issues, a two-stage deep neural network (DNN),
called DMRF-UNet, is proposed to reconstruct the permittivity distributions of
subsurface objects from GPR B-scans under heterogeneous soil conditions. In the
first stage, a U-shape DNN with multi-receptive-field convolutions (MRF-UNet1)
is built to remove the clutters due to inhomogeneity of the heterogeneous soil.
Then the denoised B-scan from the MRF-UNet1 is combined with the noisy B-scan
to be inputted to the DNN in the second stage (MRF-UNet2). The MRF-UNet2 learns
the inverse mapping relationship and reconstructs the permittivity distribution
of subsurface objects. To avoid information loss, an end-to-end training method
combining the loss functions of two stages is introduced. A wide range of
subsurface heterogeneous scenarios and B-scans are generated to evaluate the
inversion performance. The test results in the numerical experiment and the
real measurement show that the proposed network reconstructs the
permittivities, shapes, sizes, and locations of subsurface objects with high
accuracy. The comparison with existing methods demonstrates the superiority of
the proposed methodology for the inversion under heterogeneous soil conditions
3DInvNet: A Deep Learning-Based 3D Ground-Penetrating Radar Data Inversion
The reconstruction of the 3D permittivity map from ground-penetrating radar
(GPR) data is of great importance for mapping subsurface environments and
inspecting underground structural integrity. Traditional iterative 3D
reconstruction algorithms suffer from strong non-linearity, ill-posedness, and
high computational cost. To tackle these issues, a 3D deep learning scheme,
called 3DInvNet, is proposed to reconstruct 3D permittivity maps from GPR
C-scans. The proposed scheme leverages a prior 3D convolutional neural network
with a feature attention mechanism to suppress the noise in the C-scans due to
subsurface heterogeneous soil environments. Then a 3D U-shaped encoder-decoder
network with multi-scale feature aggregation modules is designed to establish
the optimal inverse mapping from the denoised C-scans to 3D permittivity maps.
Furthermore, a three-step separate learning strategy is employed to pre-train
and fine-tune the networks. The proposed scheme is applied to numerical
simulation as well as real measurement data. The quantitative and qualitative
results show the network capability, generalizability, and robustness in
denoising GPR C-scans and reconstructing 3D permittivity maps of subsurface
objects
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