Prioritization Of Features Research Articles

Modelling of household electricity consumption with the aid of computational intelligence methods

ABSTRACTThe installation of smart meters for electricity consumption monitoring is common practice in many countries. Such meters usually provide information for the temporal variation of electricity consumption-related parameters, at an aggregated (household) level. In some cases, such meters may monitor individual appliances, or appliance groups installed in household departments. In the current study, a Computational Intelligence approach is used to analyse and model appliance group electricity consumption and to investigate the best possible computational approaches for improving consumption model performance. For this purpose, meta-features are used, a new feature prioritization method is introduced and a set of selected algorithms is employed. Results indicate an improvement in modelling capacity and an ability to construct models that effectively perform partial electricity consumption disaggregation. Overall, such methods may be used for the support of household electricity consumption modelling and for related demand management.

MethylDMV: SIMULTANEOUS DETECTION OF DIFFERENTIAL DNA METHYLATION AND VARIABILITY WITH CONFOUNDER ADJUSTMENT.

DNA methylation has emerged as promising epigenetic markers for disease diagnosis. Both the differential mean (DM) and differential variability (DV) in methylation have been shown to contribute to transcriptional aberration and disease pathogenesis. The presence of confounding factors in large scale EWAS may affect the methylation values and hamper accurate marker discovery. In this paper, we propose a exible framework called methylDMV which allows for confounding factors adjustment and enables simultaneous characterization and identification of CpGs exhibiting DM only, DV only and both DM and DV. The proposed framework also allows for prioritization and selection of candidate features to be included in the prediction algorithm. We illustrate the utility of methylDMV in several TCGA datasets. An R package methylDMV implementing our proposed method is available at http://www.ams.sunysb.edu/~pfkuan/softwares.html#methylDMV.

Linking advanced biofuels policies with stakeholder interests: A method building on Quality Function Deployment

The field of renewable energy policy is inherently complex due to the long-term impacts of its policies, the broad range of potential stakeholders, the intricacy of scientific, engineering and technological developments, and the interplay of complex policy mixes that may result in unintended consequences. Quality Function Deployment (QFD) provides a systematic consideration of all relevant stakeholders, a rigorous analysis of the needs of stakeholders, and a prioritization of design features based on stakeholders needs. We build on QFD combined with Analytical Hierarchy Process (AHP) to develop a novel method applied to the area of advanced biofuel policies. This Multi-Stakeholder Policy QFD (MSP QFD) provides a systematic approach to capture the voice of the stakeholders and align it with the broad range of potential advanced biofuels policies. To account for the policy environment, the MSP QFD utilizes a novel approach to stakeholder importance weights. This MSP QFD adds to the literature as it permits the analysis of the broad range of relevant national policies with regards to the development of advanced biofuels, as compared to more narrowly focused typical QFD applications. It also allows policy developers to gain additional insights into the perceived impacts of policies, as well as international comparisons.

Hierarchical Method for Prioritization of Sustainable Products' Characteristics

The conception of the manufacturing process to be initially applied in the parts rapid production targeting a first embodiment of an idea (prototypes) and without many demands on strength and accuracy is called rapid prototyping. New rapid prototyping technologies are more accessible under the aspects of cost, accuracy, usability, environmental and prototypes started to be increasingly explored in the sustainable product development process. This paper proposes the application of hierarchical analysis method to support the decision-making in Rapid Prototyping processes based on the analysis of product and RP technology and prototype characteristics. The application of the developed method consists of assigning numeric values to the product characteristics and to the important characteristics for users in order to build the comparison matrix to perform the assessment and the prioritization steps of the relevant features. Performing the prioritization of features considered most important along with the characteristics of the chosen prototyping technology make possible the analysis of the effect of the characteristics prioritization. The concepts of product development, rapid prototyping and analytic hierarchy process for the sustainable product were used in a structure (lightweight dual) to evaluate the proposed strategy. The lightweight dual product from the 3D printing technology has been successful to show the product’s purpose and functionality due to its lower cost, short execution time, good surface finishing and dimensional accuracy. The results show that the analysis of the sustainable product for rapid prototyping technology based on characteristics prioritization strategy improves the product design.

Systems-Wide High-Dimensional Data Acquisition and Informatics Using Structural Mass Spectrometry Strategies.

Untargeted multiomics data sets are obtained for samples in systems, synthetic, and chemical biology by integrating chromatographic separations with ion mobility-mass spectrometry (IM-MS) analysis. The data sets are interrogated using bioinformatics strategies to organize the data for identification prioritization. The use of big data approaches for data mining of massive data sets in systems-wide analyses is presented. Untargeted biological data across multiomics dimensions are obtained using a variety of chromatography strategies with structural MS. Separation timescales for different techniques and the resulting data deluge when combined with IM-MS are presented. Data mining self-organizing map strategies are used to rapidly filter the data, highlighting those features describing uniqueness to the query. Examples are provided in longitudinal analyses in synthetic biology and human liver exposure to acetaminophen, and in chemical biology for natural product discovery from bacterial biomes. Matching the separation timescales of different forms of chromatography with IM-MS provides sufficient multiomics selectivity to perform untargeted systems-wide analyses. New data mining strategies provide a means for rapidly interrogating these data sets for feature prioritization and discovery in a range of applications in systems, synthetic, and chemical biology.

Injecting Value-Thinking into Prioritization Decisions

A proposed approach injects value-thinking into feature prioritization, using story mapping. The Web extra at http://youtu.be/Xm5VqODvVZE is an audio podcast in which author Jane Cleland-Huang provides an audio recording of the Requirements column, in which she discusses an approach that injects value-thinking into feature prioritization by using story mapping.

Dissolved gas analysis method based on novel feature prioritisation and support vector machine

Dissolved gas analysis (DGA) has been widely used for the detection of incipient faults in oil-filled transformers. This research presents a novel approach to DGA feature prioritisation and classification, which considers not only the relations between a fault type and specific gas ratios but also their statistical characteristics based on data derived from onsite inspections. Firstly, new gas features are acquired based on the analysis of current international gas interpretation standards. Combined with conventional gas ratios, all features are then prioritised by using the Kolmogorov–Smirnov test. The rankings are obtained by using their values of maximum statistic distance. The first three features in ranking are employed as input vectors to a multi-layer support vector machine, whose tuning parameters are acquired by particle swarm optimisation. In the experiment, a bootstrap technique is implemented to approximately equalise sample numbers of different fault cases. A common 10-fold cross-validation technique is employed for performance assessment. Typical artificial intelligence classifiers with gas features extracted from genetic programming are evaluated for comparison purposes.

Identifying duplicate functionality in textual use cases by aligning semantic actions

Developing high-quality requirements specifications often demands a thoughtful analysis and an adequate level of expertise from analysts. Although requirements modeling techniques provide mechanisms for abstraction and clarity, fostering the reuse of shared functionality (e.g., via UML relationships for use cases), they are seldom employed in practice. A particular quality problem of textual requirements, such as use cases, is that of having duplicate pieces of functionality scattered across the specifications. Duplicate functionality can sometimes improve readability for end users, but hinders development-related tasks such as effort estimation, feature prioritization, and maintenance, among others. Unfortunately, inspecting textual requirements by hand in order to deal with redundant functionality can be an arduous, time-consuming, and error-prone activity for analysts. In this context, we introduce a novel approach called ReqAligner that aids analysts to spot signs of duplication in use cases in an automated fashion. To do so, ReqAligner combines several text processing techniques, such as a use case-aware classifier and a customized algorithm for sequence alignment. Essentially, the classifier converts the use cases into an abstract representation that consists of sequences of semantic actions, and then these sequences are compared pairwise in order to identify action matches, which become possible duplications. We have applied our technique to five real-world specifications, achieving promising results and identifying many sources of duplication in the use cases.

User-driven prioritization of features for a prospective InterPersonal Health Record: Perceptions from the Italian context

In this paper we present two large user studies in which we gather evidence about the adoption and satisfaction level of users in regard to electronic records that manage health related information from two distinct but complementary perspectives: that of General Practitioners (GPs) about their Electronic Medical Records (EMRs); and that of citizens/patients about their Personal Health Records (PHRs). In these user studies we also probe the user attitudes towards innovative functionalities from these two perspectives and, on the basis of the collected perceptions, we apply an original ranking method to infer what features are valued most and hence could inspire design to make PHRs more situated into the users׳ lives and drive a higher adoption of these tools. On the basis of the perceived shortcomings of current records, we envision an InterPersonal Health Record (IPHR) that is a sort of hybrid electronic record that merges together typical EMR- and PHR-related features and is endowed with specific functionalities aimed at enhancing interpersonal relationships, communication and collaboration between citizens/patients and their GPs through the record and about its contents. This study is then a contribution in understanding the current attitudes and expectations of potential users towards full-fledged prospective PHRs, as well as a first step in identifying those requirements and priority areas on which to focus further for the design and deployment of more community- and communication-oriented tools in the primary health care domain.

Lead users’ ideas on core features to support physical activity in rheumatoid arthritis: a first step in the development of an internet service using participatory design

BackgroundDespite the growing evidence of the benefits of physical activity (PA) in individuals with rheumatoid arthritis (RA), the majority is not physically active enough. An innovative strategy is to engage lead users in the development of PA interventions provided over the internet. The aim was to explore lead users’ ideas and prioritization of core features in a future internet service targeting adoption and maintenance of healthy PA in people with RA.MethodsSix focus group interviews were performed with a purposively selected sample of 26 individuals with RA. Data were analyzed with qualitative content analysis and quantification of participants’ prioritization of most important content.ResultsSix categories were identified as core features for a future internet service: up-to-date and evidence-based information and instructions, self-regulation tools, social interaction, personalized set-up, attractive design and content, and access to the internet service. The categories represented four themes, or core aspects, important to consider in the design of the future service: (1) content, (2) customized options, (3) user interface and (4) access and implementation.ConclusionsThis is, to the best of our knowledge, the first study involving people with RA in the development of an internet service to support the adoption and maintenance of PA.Participants helped identifying core features and aspects important to consider and further explore during the next phase of development. We hypothesize that involvement of lead users will make transfer from theory to service more adequate and user-friendly and therefore will be an effective mean to facilitate PA behavior change.

Analysis of Agile Testing Attributes for Faster Time to Market: Context of Manufacturing Sector Related IT Projects

Agile methodologies are helpful to develop and implement systems to meet the market demands in a faster way, by helping various organizations to evaluate projects.Agile testing strategy is one of the methodologies that is suitable for implementation of IT projects. This paper focuses on IT projects related to manufacturing sector by deriving the significance of various agile project testing attributes across every phase of information systems development life cycle (SDLC) for faster time to market the products. The attributes include prioritization of features according to customer requirement, early customer involvement, incremental and iterative sprints, flexible development environment with less documentation and complexity management. The results highlight the varying significance of these attributes and identifying the key attributes in each project phase; thereby helping software professionals and project managers by providing a handy framework for focusing on phase-wise core attributes that might be essential for tacklingrelated project issues and fullfilling the managerial priorities.

Roadmapping problems in practice: value creation from the perspective of the customers

Roadmapping has been recognized as a critical activity for adding business value to a software product. The marketing literature has placed increasing emphasis on customer value and that value is created in customers' everyday activities. This paper presents an action research study on the roadmapping problems of two software product companies. The key findings were that customer knowledge was fragmented across different functions of the companies and that roadmapping neglected service development. To solve these problems, the analysis of customer value and customers' processes was integrated into roadmapping. Facilitated workshops with cross-functional teams were used to analyze the customers' processes in their entirety and to create a holistic view to roadmapping. The lessons learned offer practical means for software companies to shift their focus from the prioritization of software features to the analysis of customers' processes and the prioritization of customers' activities. That shift can promote value for customers and uncover new service business opportunities. Accordingly, this paper provides a six-step approach to adopt the value-creation logic from the customers' perspective into the existing roadmapping process of a company.

BRIDGING GAP OF USER GOAL REQUIREMENTS AND IMPLEMENTED APPLICATIONS BASED ON FEATURE MODEL

Feature modeling is a conceptual thinking for identifying and classification feature in order for support software product lines. However, there are lack of the user goal requirements. It related with a technique for managing of features commonalities and variability. It has a hierarchy of features with variability and the purpose is to organize features. In practice of implemented applications, the feature model development lack of goal user requirement. The goal of user requirement in Indonesian government has described in document regulations. It should be a fundamental concern to develop e-government applications. However, In order to capture degree of software feature importance, some of features compared with implemented e-government applications. We have extracted some of features which can be compared with the implemented e-government applications. Our technique is extracted are derived from document regulations to business process model and feature model also. We Choose SIPKD and SIMDA applications which has implemented in Indonesian local government which has variation from one and another. We use extended AHP and S-AHP to find the prioritization of software features. The results are 80 features in SIPKD and 90 features in SIMDA. There are 65 features common and 25 variant features .This make un-optimization usage applications.

Supervised learning approaches and feature selection - a case study in diabetes

Data description and classification are important tasks in supervised learning. In this study, three supervised learning methods such as k-nearest neighbour k-NN, support vector data description SVDD and support vector machine SVM are considered because they do not suffer from the problem of introducing a new class. The data sample chosen is Pima Indians diabetes. The results show that feature selection based on mean information gain and a standard deviation threshold can be considered as a substitute for forward selection. This indicates that data variation using information gain is an important factor that must be considered in selecting feature subset. Finally, among eight candidate features, glucose level is the most prominent feature for diabetes detection in all classifiers and feature selection methods under consideration. Relevancy measurement in information gain can sort out the most important feature to the least significant one. It can be very useful in medical applications such as defining feature prioritisation for symptom recognition.

Improving software testing process: feature prioritization to make winners of success‐critical stakeholders

SUMMARYFor a successful software project, acceptable quality must be achieved within an acceptable cost, demonstrating business value to customers and satisfactorily meeting delivery timeliness. Testing serves as the most widely used approaches to determine that the intended functionalities are performed correctly and achieve the desired level of services; however, it is also a labor‐intensive and expensive process during the whole software life cycle. Most current testing processes are often technique‐centered, rather than organized to maximize business value. In this article, we extend and elaborate the ‘4+1’ theoretical lenses of Value‐based Software Engineering (VBSE) framework in the software testing process; propose a multi‐objective feature prioritization strategy for testing planning and controlling, which aligns the internal testing process with value objectives coming from customers and markets. Our case study in a real‐life business project shows that this method allows reasoning about the software testing process in different dimensions: it helps to manage the testing process effectively and efficiently, provides information for continuous internal software process improvement, and increases customer satisfaction, which makes winners of all success‐critical stakeholders (SCSs) in the software testing process. Copyright © 2010 John Wiley & Sons, Ltd.

Meta-analysis of untargeted metabolomic data from multiple profiling experiments

metaXCMS is a software program for the analysis of liquid chromatography/mass spectrometry-based untargeted metabolomic data. It is designed to identify the differences between metabolic profiles across multiple sample groups (e.g., 'healthy' versus 'active disease' versus 'inactive disease'). Although performing pairwise comparisons alone can provide physiologically relevant data, these experiments often result in hundreds of differences, and comparison with additional biologically meaningful sample groups can allow for substantial data reduction. By performing second-order (meta-) analysis, metaXCMS facilitates the prioritization of interesting metabolite features from large untargeted metabolomic data sets before the rate-limiting step of structural identification. Here we provide a detailed step-by-step protocol for going from raw mass spectrometry data to metaXCMS results, visualized as Venn diagrams and exported Microsoft Excel spreadsheets. There is no upper limit to the number of sample groups or individual samples that can be compared with the software, and data from most commercial mass spectrometers are supported. The speed of the analysis depends on computational resources and data volume, but will generally be less than 1 d for most users. metaXCMS is freely available at http://metlin.scripps.edu/metaxcms/.

Simulation-based Feature Selection for Software Requirements Baseline

Requirements baseline is the set of features intended to be delivered in a specific version of a software application under development. During this decade the constant growth of software products along with the evident pressure on time to market has made the selection of features a crucial step for a software project success. It is both a challenging and time consuming process that requires a substantial expertise from project managers. Prioritization of features is one of the means that help in making the choice. It is typically performed by grouping features into three priority levels: critical, important, and useful. Critical and important features are seen as “must have”, while useful features are qualified as “nice-to-have”. Paradoxically, the latter plays an important role in customer satisfaction and achieving the “wow” factor. A good selection of useful features identifies efficiently those features that can be delivered by the end of the project without any additional delay. So far, managers have little support in this process increasing the chances of making a poor selection. To answer this need, we propose a new modeling and simulation approach that takes into account feature priorities and calculates the probabilities of having useful features implemented within the timeframe of the project. It also incorporates uncertainties related to human resources availability providing a more realistic schedule and estimation.

Improved Radiometric Identification of Wireless Devices Using MIMO Transmission

Authenticating wireless devices based on features of their transmitted waveform has become a topic of considerable interest. Recent work in this arena has shown that examination of features in a modulated waveform can lead to highly precise identification of 802.11 devices. This paper experimentally demonstrates improved identification accuracy and reduced sensitivity to identification parameters when such techniques are applied to the multiple transmitters within multiple-input multiple-output devices. It further studies an information theoretic technique for determining which radiometric features are most effective in discriminating devices and analyzes the identification performance as a function of the number of features used. Finally, because some radiometric features are sensitive to environmental conditions and, therefore, drift with time, the analysis explores the impact of radiometric feature drift both on feature prioritization and on identification performance.

Discourse Prosody and Teachers' Stated Beliefs and Practices

Over the past few decades, research has demonstrated the important role that prosody (i.e., stress, rhythm, intonation) plays in the intelligibility of speakers of English as a second language (ESL). Yet the impact of this research on teacher cognition—the beliefs and knowledge that teachers possess in relation to their classroom practices—has received limited attention. This article explores how research into English prosody has influenced pronunciation instruction in the ESL classroom, teachers' knowledge and beliefs about this instruction, and what teachers believe to be the most effective way to improve their knowledge base in this area. In particular, the opinions of teachers of beginning, intermediate, and advanced classes are investigated. Through semistructured interviews with five instructors and the pronunciation journal of the author, these issues are examined. Results show that research into English pronunciation has clearly influenced the teaching and prioritization of features of pronunciation for instructors with graduate education that has included a course dedicated to pronunciation or pronunciation pedagogy. However, even with this education, teachers may lack confidence in teaching different aspects of pronunciation.

Specificity of Transporter Associated with Antigen Processing Protein as Revealed by Feature Selection Method

Peptide fragments that serve as the cytotoxic T lymphocyte (CTL) epitopes are processed from antigens by the proteasome and then are transported to the endoplasmic reticulum through transporter associated with antigen processing (TAP) before being loaded onto the MHC class I molecule. Here, we studied TAP specificity by a neighborhood rough set (NRS) model based feature selection and prioritization method. By means of binary, amino acid properties, and binary plus properties of amino acids encoding, respectively, we adopted NRS based feature selection method to select multiple optimal feature sets for TAP binding peptides binary classification. The features in these optimal sets were ranked according to their occurrence frequency. Results show that the NRS is effective for prediction improvement and analysis of the specificity of TAP transporter. The proposed method can be used as a tool for predicting TAP binding peptides and be useful for subunit vaccine rational design and related bioinformatics cases.