Requirements Prioritization Based on Benefit and Cost Prediction: A Method Classification Framework

<p>Increase in globalization of the industry of software requires an exploration of requirements engineering (RE) in software development institutes at multiple locations. Requirements engineering task is very complicated when it is performed at single site, but it becomes too much complex when stakeholder groups define well-designed requirements under language, time zone and cultural limits. Requirements prioritization (RP) is considered as an imperative part of software requirements engineering in which requirements are ranked to develop best-quality software. In this research, a comparative study of the requirements prioritization techniques was done to overcome the challenges initiated by the corporal distribution of stakeholders within the organization at multiple locations. The objective of this study was to make a comparison between five techniques for prioritizing software requirements and to discuss the results for global software engineering. The selected techniques were Analytic Hierarchy Process (AHP), Cumulative Voting (CV), Value Oriented Prioritization (VOP), Binary Search Tree (BST), and Numerical Assignment Technique (NAT). At the end of the research a framework for Global Software Engineering (GSE) was proposed to prioritize the requirements for stakeholders at distributed locations.<strong></strong></p>

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PUBLISHING PATTERNS IN SPECIALIST ACCOUNTING HISTORY JOURNALS IN THE ENGLISH LANGUAGE, 1996–1999

Accounting Historians Journal ◽

10.2308/0148-4184.27.2.177 ◽

2000 ◽

Vol 27 (2) ◽

pp. 177-198 ◽

Cited By ~ 60

Author(s):

Garry D. Carnegie ◽

Brad N. Potter

Keyword(s):

Empirical Evidence ◽

English Language ◽

Time Span ◽

International Accounting ◽

Accounting History ◽

Classification Framework ◽

International Publishing ◽

The Subject

While accounting researchers have explored international publishing patterns in the accounting literature generally, little is known about recent contributions to the specialist international accounting history journals. Specifically, this study surveys publishing patterns in the three specialist, internationally refereed, accounting history journals in the English language during the period 1996 to 1999. The survey covers 149 contributions in total and provides empirical evidence on the location of their authors, the subject country or region in each investigation, and the time span of each study. It also classifies the literature examined based on the literature classification framework provided by Carnegie and Napier [1996].

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A Classification Framework for IoT Scenarios

Business Process Management Workshops - Lecture Notes in Business Information Processing ◽

10.1007/978-3-030-11641-5_36 ◽

2019 ◽

pp. 458-469 ◽

Cited By ~ 3

Author(s):

Sankalita Mandal ◽

Marcin Hewelt ◽

Maarten Oestreich ◽

Mathias Weske

Keyword(s):

Classification Framework

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Automated Extraction and Time-Cost Prediction of Contractual Reporting Requirements in Construction Using Natural Language Processing and Simulation

Applied Sciences ◽

10.3390/app11136188 ◽

2021 ◽

Vol 11 (13) ◽

pp. 6188

Author(s):

Parinaz Jafari ◽

Malak Al Hattab ◽

Emad Mohamed ◽

Simaan AbouRizk

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Stochastic Simulations ◽

Clear Understanding ◽

Time Cost ◽

Cost Prediction ◽

Construction Contracts ◽

Reporting Requirements ◽

Reporting Requirement

Due to a lack of suitable methods, extraction of reporting requirements from lengthy construction contracts is often completed manually. Because of this, the time and costs associated with completing reporting requirements are often informally approximated, resulting in underestimations. Without a clear understanding of requirements, contractors are prevented from implementing improvements to reporting workflows prior to project execution. This study developed an automated reporting requirement identification and time–cost prediction framework to overcome this challenge. Reporting requirements are extracted using Natural Language Processing (NLP) and Machine Learning (ML), and stochastic simulations are used to predict overhead costs and durations associated with report preparation. Functionality and validity of the framework were demonstrated using real contracts, and an accuracy of over 95% was observed. This framework provides a tool to rapidly and efficiently retrieve requirements and quantify the time and costs associated with reporting, in turn providing necessary insights to streamline reporting workflows.

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Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing

Sensors ◽

10.3390/s21041065 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1065

Author(s):

Moshe Bensimon ◽

Shlomo Greenberg ◽

Moshe Haiut

Keyword(s):

Neural Network ◽

Network Structure ◽

Analog Circuits ◽

Continuous Time ◽

Spiking Neurons ◽

Spiking Neural Network ◽

Sound Classification ◽

Classification Framework ◽

Neural Network Structure ◽

Simple Analog

This work presents a new approach based on a spiking neural network for sound preprocessing and classification. The proposed approach is biologically inspired by the biological neuron’s characteristic using spiking neurons, and Spike-Timing-Dependent Plasticity (STDP)-based learning rule. We propose a biologically plausible sound classification framework that uses a Spiking Neural Network (SNN) for detecting the embedded frequencies contained within an acoustic signal. This work also demonstrates an efficient hardware implementation of the SNN network based on the low-power Spike Continuous Time Neuron (SCTN). The proposed sound classification framework suggests direct Pulse Density Modulation (PDM) interfacing of the acoustic sensor with the SCTN-based network avoiding the usage of costly digital-to-analog conversions. This paper presents a new connectivity approach applied to Spiking Neuron (SN)-based neural networks. We suggest considering the SCTN neuron as a basic building block in the design of programmable analog electronics circuits. Usually, a neuron is used as a repeated modular element in any neural network structure, and the connectivity between the neurons located at different layers is well defined. Thus, generating a modular Neural Network structure composed of several layers with full or partial connectivity. The proposed approach suggests controlling the behavior of the spiking neurons, and applying smart connectivity to enable the design of simple analog circuits based on SNN. Unlike existing NN-based solutions for which the preprocessing phase is carried out using analog circuits and analog-to-digital conversion, we suggest integrating the preprocessing phase into the network. This approach allows referring to the basic SCTN as an analog module enabling the design of simple analog circuits based on SNN with unique inter-connections between the neurons. The efficiency of the proposed approach is demonstrated by implementing SCTN-based resonators for sound feature extraction and classification. The proposed SCTN-based sound classification approach demonstrates a classification accuracy of 98.73% using the Real-World Computing Partnership (RWCP) database.

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A comparative unit production cost prediction on isomerization by AlCl3 in comparison with HY(30) zeolite for the synthesis of tetrahydro tricyclopentadiene through a two-step process

Korean Journal of Chemical Engineering ◽

10.1007/s11814-020-0652-9 ◽

2020 ◽

Vol 37 (12) ◽

pp. 2161-2170

Author(s):

Chul Hae Ryu ◽

Young-Hoon Cho ◽

Byunghun Jeong ◽

Donguk Seo ◽

Minjun Seong ◽

...

Keyword(s):

Production Cost ◽

Cost Prediction ◽

Unit Production Cost ◽

Step Process

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