The Journal of Modern Project Management, Sep 10, 2018
Controlling software cost is crucial to complete projects within the planned budgets. Unfortunate... more Controlling software cost is crucial to complete projects within the planned budgets. Unfortunately, the existing software cost control models; especially the earned value management system (EVM) experience some limitations. These models, mainly focus on factors such as time and cost while ignoring the related human factors. Since software is delivered by people and affected by their competencies, it is expected that their associated attributes would be quantified and incorporated in such models. Although many papers discussed the human factor issues, however, very few suggested quantified indices to model such factors. This paper aims at introducing the Developer Competency Index (DCI) and Team Competency Index (TCI) as a quantification of the human skills needed by the software engineers. Function Point Analysis (FPA) is extensively used in the calculation process. Moreover, using data drawn from five actual projects, the study shows how these indices are related to the cost of software reworks and how they can be used to monitor and control the team performance throughout the project lifecycle.
ABSTRACT The rapid proliferation of computing processing power has facilitated a rise in the adop... more ABSTRACT The rapid proliferation of computing processing power has facilitated a rise in the adoption of computers in various aspects of human lives. From education to shopping and other everyday activities to critical applications in finance, banking and, recently, degree awarding online education. Several approaches for user authentication based on Behavioral Biometrics (BB) were suggested in order to identify unique signature/footprint for improved matching accuracy for genuine users and flagging for abnormal behaviors from intruders. In this paper we present a comparison between two classification algorithms for identifying users' behavior using mouse dynamics. The algorithms are based on support vector machines (SVM) classifier allowing for direct comparison between different authentication-based metrics. The voting technique shows low False Acceptance Rate(FAR) and noticeably small learning time; making it more suitable for incorporation within different authentication applications.
2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon), 2017
Deep Convolutional Neural Networks (CNNs) have recently demonstrated the state-of-the-art classif... more Deep Convolutional Neural Networks (CNNs) have recently demonstrated the state-of-the-art classification performance on ImageNet Large Scale Visual Recognition Challenge (ILSVRC) since 2012, yet there is relatively no clear understanding of the reasons behind their outstanding performance, or how they might be improved, In this paper we present a novel benchmarking of multiple state-of-the-art deep CNN architectures by providing an analysis of important performance metrics: speed, memory consumption, and network parameters utilization. Key findings are: (1) fully connected layers are of a high cost on speed and memory consumption compared to the sparsely connected layers; (2) the depth and the performance of an architecture are in a nonlinear relationship and constrained by layers transformation types; (3) 1×1 convolutions are an efficient way to reduce dimensionality and pooling features; (4) addition units as in residual and densely connected networks accelerate the backpropagation time by distributing the gradients through the graph, we believe our set of benchmarks are a step towards better realization of the best architectural design choices of Deep CNNs.
2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon), Aug 1, 2017
Deep Convolutional Neural Networks (CNNs) have recently demonstrated the state-of-the-art classif... more Deep Convolutional Neural Networks (CNNs) have recently demonstrated the state-of-the-art classification performance on ImageNet Large Scale Visual Recognition Challenge (ILSVRC) since 2012, yet there is relatively no clear understanding of the reasons behind their outstanding performance, or how they might be improved, In this paper we present a novel benchmarking of multiple state-of-the-art deep CNN architectures by providing an analysis of important performance metrics: speed, memory consumption, and network parameters utilization. Key findings are: (1) fully connected layers are of a high cost on speed and memory consumption compared to the sparsely connected layers; (2) the depth and the performance of an architecture are in a nonlinear relationship and constrained by layers transformation types; (3) 1×1 convolutions are an efficient way to reduce dimensionality and pooling features; (4) addition units as in residual and densely connected networks accelerate the backpropagation time by distributing the gradients through the graph, we believe our set of benchmarks are a step towards better realization of the best architectural design choices of Deep CNNs.
2017 International Conference on Communication, Control, Computing and Electronics Engineering (ICCCCEE), 2017
Defects uncovered during software testing usually consume a considerable amount of the overall pr... more Defects uncovered during software testing usually consume a considerable amount of the overall project's budget. Unfortunately, project managers are not well-equipped with techniques to estimate such cost overrun. Moreover, incorporating defects removal process as part of the software testing activities has made the project managers overlook this important cost component during their planning processes. This paper suggested a fuzzy model to estimate the cost of defects removal at the early stages of project planning in terms of the project's functional size and the experience of the development team. The suggested model is supposed to enhance the accuracy of the software project cost estimation.
Deep Convolutional Neural Networks (CNNs) have recently demonstrated the state-of-the-art classif... more Deep Convolutional Neural Networks (CNNs) have recently demonstrated the state-of-the-art classification performance on ImageNet Large Scale Visual Recognition Challenge (ILSVRC) since 2012, yet there is relatively no clear understanding of the reasons behind their outstanding performance, or how they might be improved, In this paper we present a novel benchmarking of multiple state-of-the-art deep CNN architectures by providing an analysis of important performance metrics: speed, memory consumption, and network parameters utilization. Key findings are: (1) fully connected layers are of a high cost on speed and memory consumption compared to the sparsely connected layers; (2) the depth and the performance of an architecture are in a nonlinear relationship and constrained by layers transformation types; (3) 1×1 convolutions are an efficient way to reduce dimensionality and pooling features; (4) addition units as in residual and densely connected networks accelerate the backpropagatio...
Indonesian Journal of Electrical Engineering and Computer Science, 2021
Industrial reports show massive cost overruns associated with software projects. The cost of soft... more Industrial reports show massive cost overruns associated with software projects. The cost of software reworks constitutes a large portion of the overall cost, reflecting a substantial challenge in cost control. Earned value management (EVM) is the most recognized model for project cost control. However, it shows many limitations in forecasting the software project cost, leading to a considerable challenge in cost control. Nevertheless, the major EVM limitation found is its inability to forecast the cost of software rework. This research investigated the factors affecting this limitation and suggests an enhanced EVM model. The significant contribution of this research is its incorporation of software-related factors into the EVM model. We introduced the software rework index (SRI), which is incorporated into the traditional EVM model to enhance its predictability of the software project cost at completion, including the rework cost. We defined the SRI in terms of two factors: product...
Indonesian Journal of Electrical Engineering and Computer Science, 2021
Industrial reports show massive cost overruns associated with software projects. The cost of soft... more Industrial reports show massive cost overruns associated with software projects. The cost of software reworks constitutes a large portion of the overall cost, reflecting a substantial challenge in cost control. Earned value management (EVM) is the most recognized model for project cost control. However, it shows many limitations in forecasting the software project cost, leading to a considerable challenge in cost control. Nevertheless, the major EVM limitation found is its inability to forecast the cost of software rework. This research investigated the factors affecting this limitation and suggests an enhanced EVM model. The significant contribution of this research is its incorporation of software-related factors into the EVM model. We introduced the software rework index (SRI), which is incorporated into the traditional EVM model to enhance its predictability of the software project cost at completion, including the rework cost. We defined the SRI in terms of two factors: product functional complexity and the team competency. Finally, we evaluated the proposed model using a dataset drawn from five actual projects. The results showed a significant enhancement in forecasting accuracy.
Controlling software cost is crucial to complete projects within the planned budgets. Unfortunate... more Controlling software cost is crucial to complete projects within the planned budgets. Unfortunately, the existing software cost control models; especially the earned value management system (EVM) experience some limitations. These models, mainly focus on factors such as time and cost while ignoring the related human factors. Since software is delivered by people and affected by their competencies, it is expected that their associated attributes would be quantified and incorporated in such models. Although many papers discussed the human factor issues, however, very few suggested quantified indices to model such factors. This paper aims at introducing the Developer Competency Index (DCI) and Team Competency Index (TCI) as a quantification of the human skills needed by the software engineers. Function Point Analysis (FPA) is extensively used in the calculation process. Moreover, using data drawn from five actual projects, the study shows how these indices are related to the cost of so...
Deep Convolutional Neural Networks (CNNs) have recently demonstrated the state-of-the-art classif... more Deep Convolutional Neural Networks (CNNs) have recently demonstrated the state-of-the-art classification performance on ImageNet Large Scale Visual Recognition Challenge (ILSVRC) since 2012, yet there is relatively no clear understanding of the reasons behind their outstanding performance, or how they might be improved, In this paper we present a novel benchmarking of multiple state-of-the-art deep CNN architectures by providing an analysis of important performance metrics: speed, memory consumption, and network parameters utilization. Key findings are: (1) fully connected layers are of a high cost on speed and memory consumption compared to the sparsely connected layers; (2) the depth and the performance of an architecture are in a nonlinear relationship and constrained by layers transformation types; (3) 1×1 convolutions are an efficient way to reduce dimensionality and pooling features; (4) addition units as in residual and densely connected networks accelerate the backpropagation time by distributing the gradients through the graph, we believe our set of benchmarks are a step towards better realization of the best architectural design choices of Deep CNNs.
Indonesian Journal of Electrical Engineering and Computer Science, 2021
Industrial reports show massive cost overruns associated with software projects. The cost of soft... more Industrial reports show massive cost overruns associated with software projects. The cost of software reworks constitutes a large portion of the overall cost, reflecting a substantial challenge in cost control. Earned value management (EVM) is the most recognized model for project cost control. However, it shows many limitations in forecasting the software project cost, leading to a considerable challenge in cost control. Nevertheless, the major EVM limitation found is its inability to forecast the cost of software rework. This research investigated the factors affecting this limitation and suggests an enhanced EVM model. The significant contribution of this research is its incorporation of software-related factors into the EVM model. We introduced the software rework index (SRI), which is incorporated into the traditional EVM model to enhance its predictability of the software project cost at completion, including the rework cost. We defined the SRI in terms of two factors: product...
The rapid proliferation of computing processing power has facilitated a rise in the adoption of c... more The rapid proliferation of computing processing power has facilitated a rise in the adoption of computers in various aspects of human lives. From education to shopping and other everyday activities to critical applications in finance, banking and, recently, degree awarding online education. Several approaches for user authentication based on Behavioral Biometrics (BB) were suggested in order to identify unique signature/footprint for improved matching accuracy for genuine users and flagging for abnormal behaviors from intruders. In this paper we present a comparison between two classification algorithms for identifying users' behavior using mouse dynamics. The algorithms are based on support vector machines (SVM) classifier allowing for direct comparison between different authentication-based metrics. The voting technique shows low False Acceptance Rate(FAR) and noticeably small learning time; making it more suitable for incorporation within different authentication applications.
The Journal of Modern Project Management, Sep 10, 2018
Controlling software cost is crucial to complete projects within the planned budgets. Unfortunate... more Controlling software cost is crucial to complete projects within the planned budgets. Unfortunately, the existing software cost control models; especially the earned value management system (EVM) experience some limitations. These models, mainly focus on factors such as time and cost while ignoring the related human factors. Since software is delivered by people and affected by their competencies, it is expected that their associated attributes would be quantified and incorporated in such models. Although many papers discussed the human factor issues, however, very few suggested quantified indices to model such factors. This paper aims at introducing the Developer Competency Index (DCI) and Team Competency Index (TCI) as a quantification of the human skills needed by the software engineers. Function Point Analysis (FPA) is extensively used in the calculation process. Moreover, using data drawn from five actual projects, the study shows how these indices are related to the cost of software reworks and how they can be used to monitor and control the team performance throughout the project lifecycle.
ABSTRACT The rapid proliferation of computing processing power has facilitated a rise in the adop... more ABSTRACT The rapid proliferation of computing processing power has facilitated a rise in the adoption of computers in various aspects of human lives. From education to shopping and other everyday activities to critical applications in finance, banking and, recently, degree awarding online education. Several approaches for user authentication based on Behavioral Biometrics (BB) were suggested in order to identify unique signature/footprint for improved matching accuracy for genuine users and flagging for abnormal behaviors from intruders. In this paper we present a comparison between two classification algorithms for identifying users' behavior using mouse dynamics. The algorithms are based on support vector machines (SVM) classifier allowing for direct comparison between different authentication-based metrics. The voting technique shows low False Acceptance Rate(FAR) and noticeably small learning time; making it more suitable for incorporation within different authentication applications.
2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon), 2017
Deep Convolutional Neural Networks (CNNs) have recently demonstrated the state-of-the-art classif... more Deep Convolutional Neural Networks (CNNs) have recently demonstrated the state-of-the-art classification performance on ImageNet Large Scale Visual Recognition Challenge (ILSVRC) since 2012, yet there is relatively no clear understanding of the reasons behind their outstanding performance, or how they might be improved, In this paper we present a novel benchmarking of multiple state-of-the-art deep CNN architectures by providing an analysis of important performance metrics: speed, memory consumption, and network parameters utilization. Key findings are: (1) fully connected layers are of a high cost on speed and memory consumption compared to the sparsely connected layers; (2) the depth and the performance of an architecture are in a nonlinear relationship and constrained by layers transformation types; (3) 1×1 convolutions are an efficient way to reduce dimensionality and pooling features; (4) addition units as in residual and densely connected networks accelerate the backpropagation time by distributing the gradients through the graph, we believe our set of benchmarks are a step towards better realization of the best architectural design choices of Deep CNNs.
2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon), Aug 1, 2017
Deep Convolutional Neural Networks (CNNs) have recently demonstrated the state-of-the-art classif... more Deep Convolutional Neural Networks (CNNs) have recently demonstrated the state-of-the-art classification performance on ImageNet Large Scale Visual Recognition Challenge (ILSVRC) since 2012, yet there is relatively no clear understanding of the reasons behind their outstanding performance, or how they might be improved, In this paper we present a novel benchmarking of multiple state-of-the-art deep CNN architectures by providing an analysis of important performance metrics: speed, memory consumption, and network parameters utilization. Key findings are: (1) fully connected layers are of a high cost on speed and memory consumption compared to the sparsely connected layers; (2) the depth and the performance of an architecture are in a nonlinear relationship and constrained by layers transformation types; (3) 1×1 convolutions are an efficient way to reduce dimensionality and pooling features; (4) addition units as in residual and densely connected networks accelerate the backpropagation time by distributing the gradients through the graph, we believe our set of benchmarks are a step towards better realization of the best architectural design choices of Deep CNNs.
2017 International Conference on Communication, Control, Computing and Electronics Engineering (ICCCCEE), 2017
Defects uncovered during software testing usually consume a considerable amount of the overall pr... more Defects uncovered during software testing usually consume a considerable amount of the overall project's budget. Unfortunately, project managers are not well-equipped with techniques to estimate such cost overrun. Moreover, incorporating defects removal process as part of the software testing activities has made the project managers overlook this important cost component during their planning processes. This paper suggested a fuzzy model to estimate the cost of defects removal at the early stages of project planning in terms of the project's functional size and the experience of the development team. The suggested model is supposed to enhance the accuracy of the software project cost estimation.
Deep Convolutional Neural Networks (CNNs) have recently demonstrated the state-of-the-art classif... more Deep Convolutional Neural Networks (CNNs) have recently demonstrated the state-of-the-art classification performance on ImageNet Large Scale Visual Recognition Challenge (ILSVRC) since 2012, yet there is relatively no clear understanding of the reasons behind their outstanding performance, or how they might be improved, In this paper we present a novel benchmarking of multiple state-of-the-art deep CNN architectures by providing an analysis of important performance metrics: speed, memory consumption, and network parameters utilization. Key findings are: (1) fully connected layers are of a high cost on speed and memory consumption compared to the sparsely connected layers; (2) the depth and the performance of an architecture are in a nonlinear relationship and constrained by layers transformation types; (3) 1×1 convolutions are an efficient way to reduce dimensionality and pooling features; (4) addition units as in residual and densely connected networks accelerate the backpropagatio...
Indonesian Journal of Electrical Engineering and Computer Science, 2021
Industrial reports show massive cost overruns associated with software projects. The cost of soft... more Industrial reports show massive cost overruns associated with software projects. The cost of software reworks constitutes a large portion of the overall cost, reflecting a substantial challenge in cost control. Earned value management (EVM) is the most recognized model for project cost control. However, it shows many limitations in forecasting the software project cost, leading to a considerable challenge in cost control. Nevertheless, the major EVM limitation found is its inability to forecast the cost of software rework. This research investigated the factors affecting this limitation and suggests an enhanced EVM model. The significant contribution of this research is its incorporation of software-related factors into the EVM model. We introduced the software rework index (SRI), which is incorporated into the traditional EVM model to enhance its predictability of the software project cost at completion, including the rework cost. We defined the SRI in terms of two factors: product...
Indonesian Journal of Electrical Engineering and Computer Science, 2021
Industrial reports show massive cost overruns associated with software projects. The cost of soft... more Industrial reports show massive cost overruns associated with software projects. The cost of software reworks constitutes a large portion of the overall cost, reflecting a substantial challenge in cost control. Earned value management (EVM) is the most recognized model for project cost control. However, it shows many limitations in forecasting the software project cost, leading to a considerable challenge in cost control. Nevertheless, the major EVM limitation found is its inability to forecast the cost of software rework. This research investigated the factors affecting this limitation and suggests an enhanced EVM model. The significant contribution of this research is its incorporation of software-related factors into the EVM model. We introduced the software rework index (SRI), which is incorporated into the traditional EVM model to enhance its predictability of the software project cost at completion, including the rework cost. We defined the SRI in terms of two factors: product functional complexity and the team competency. Finally, we evaluated the proposed model using a dataset drawn from five actual projects. The results showed a significant enhancement in forecasting accuracy.
Controlling software cost is crucial to complete projects within the planned budgets. Unfortunate... more Controlling software cost is crucial to complete projects within the planned budgets. Unfortunately, the existing software cost control models; especially the earned value management system (EVM) experience some limitations. These models, mainly focus on factors such as time and cost while ignoring the related human factors. Since software is delivered by people and affected by their competencies, it is expected that their associated attributes would be quantified and incorporated in such models. Although many papers discussed the human factor issues, however, very few suggested quantified indices to model such factors. This paper aims at introducing the Developer Competency Index (DCI) and Team Competency Index (TCI) as a quantification of the human skills needed by the software engineers. Function Point Analysis (FPA) is extensively used in the calculation process. Moreover, using data drawn from five actual projects, the study shows how these indices are related to the cost of so...
Deep Convolutional Neural Networks (CNNs) have recently demonstrated the state-of-the-art classif... more Deep Convolutional Neural Networks (CNNs) have recently demonstrated the state-of-the-art classification performance on ImageNet Large Scale Visual Recognition Challenge (ILSVRC) since 2012, yet there is relatively no clear understanding of the reasons behind their outstanding performance, or how they might be improved, In this paper we present a novel benchmarking of multiple state-of-the-art deep CNN architectures by providing an analysis of important performance metrics: speed, memory consumption, and network parameters utilization. Key findings are: (1) fully connected layers are of a high cost on speed and memory consumption compared to the sparsely connected layers; (2) the depth and the performance of an architecture are in a nonlinear relationship and constrained by layers transformation types; (3) 1×1 convolutions are an efficient way to reduce dimensionality and pooling features; (4) addition units as in residual and densely connected networks accelerate the backpropagation time by distributing the gradients through the graph, we believe our set of benchmarks are a step towards better realization of the best architectural design choices of Deep CNNs.
Indonesian Journal of Electrical Engineering and Computer Science, 2021
Industrial reports show massive cost overruns associated with software projects. The cost of soft... more Industrial reports show massive cost overruns associated with software projects. The cost of software reworks constitutes a large portion of the overall cost, reflecting a substantial challenge in cost control. Earned value management (EVM) is the most recognized model for project cost control. However, it shows many limitations in forecasting the software project cost, leading to a considerable challenge in cost control. Nevertheless, the major EVM limitation found is its inability to forecast the cost of software rework. This research investigated the factors affecting this limitation and suggests an enhanced EVM model. The significant contribution of this research is its incorporation of software-related factors into the EVM model. We introduced the software rework index (SRI), which is incorporated into the traditional EVM model to enhance its predictability of the software project cost at completion, including the rework cost. We defined the SRI in terms of two factors: product...
The rapid proliferation of computing processing power has facilitated a rise in the adoption of c... more The rapid proliferation of computing processing power has facilitated a rise in the adoption of computers in various aspects of human lives. From education to shopping and other everyday activities to critical applications in finance, banking and, recently, degree awarding online education. Several approaches for user authentication based on Behavioral Biometrics (BB) were suggested in order to identify unique signature/footprint for improved matching accuracy for genuine users and flagging for abnormal behaviors from intruders. In this paper we present a comparison between two classification algorithms for identifying users' behavior using mouse dynamics. The algorithms are based on support vector machines (SVM) classifier allowing for direct comparison between different authentication-based metrics. The voting technique shows low False Acceptance Rate(FAR) and noticeably small learning time; making it more suitable for incorporation within different authentication applications.
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