Development and Evaluation of a Software Metrics Markup Language

Software measurement is the dimension and/or decision criteria as to what a piece of software can provide. The output of software measurement is in the form of metric data. Metric data are important because it can be used as the input for software analysis in the software engineering field. Software engineering relies on these data to investigate many factors in software development such as cost, scheduling, affordability, quality, etc., in order to gain better control of the engineering processes. These days, people store data in different data formats, media and database technologies. These heterogeneous formats have posed many problems in data analysis, especially in terms of the integration and reusability of historical data. These problems have prompted efforts to find a data format that is compliant with the concepts of software measurement and which is applicable to any metric data. extensible Markup Language (XML) is the latest platform independent and self-explanatory data model that is widely used in the world, especially significant in the heterogeneous computing environment, such as World Wide Web. Hence, XML has been chosen to be the markup language for software metrics data in order to produce Software Metrics Markup Language (SMML), which is the major output of this research. There are shortcomings of the existing data models and XML can be used to overcome these shortcomings, and further enhances the portability, extensibility and appendability of software metrics data. The build and evaluate framework is used to ascertain that the design goals of the SMML have been archived accordingly. The SMML Toolkit and the SMML API have been built as the instruments to evaluate the viability of SMML. The SMML vocabulary and grammar, which is synonymous to the XML elements and the elementary structure of SMML respectively are defined and implemented physically in XML schema for SMML. It determines and controls how SMML should be constructed to hold informative software metrics data. The experimental evaluation shows that SMML is viable to be the data model for software metrics. Data can be easily stored and manipulated, either in the existing SMML model or transformed into the structured relational databases, provided that the SMML API is used. Future research can be extended to enhancing the structure and enriching the vocabulary of SMML, and introduce ontology studies on this model, besides conducting performance tuning on the SMML API

Omega-3 polyunsaturated fatty acid supplements and cognitive decline: Singapore Longitudinal Aging Studies

10.1007/s12603-011-0010-zJournal of Nutrition, Health and Aging15132-3

Automated Printed Circuit Board Assembly Verification and Validation System

With the fast-paced evolution in the engineering field especially in electronics, the design of circuitry is becoming more and more complex. Hence, to make sure the Printed Circuit Board Assembly (PCBA) is designed correctly, the prototypes of the PCBA have to be tested and validated before moving on to manufacturing and production process. The InCircuit Test (ICT) and flying probes are too expensive to be applied for a prototype stage. Hence, the verification and validation (V&V) test for the prototype of PCBA is done manually by the V&V engineers. However, it is a complex and time-consuming process. Therefore, there is a requirement to improve the current PCBA prototype verification and validation. This project is proposed to assist V&V engineers to perform a V&V test for PCBA prototype. This project basically consists of a CNC machine, which has total five degrees of freedom with measuring probe at the end effector. Three stepper motors were used to move the x, y and z coordinate of the probe. The stepper motors were controlled by controller myRIO with stepper motor driver A4988. Besides that, another two smaller stepper motors were used for the probing mechanism. The probing mechanism was designed and simulated by using SolidWorks software. For software, the data extraction from the PCB file was done by the algorithm built using LabVIEW. In addition, a graphical user interface (GUI) was also designed and built using LabVIEW. The system was tested in terms of accuracy and consistency by using samples of PCB. The results from the evaluation showed about 70.83% of accuracy in average. Overall, the performance of the system is acceptable and the accuracy of the system can be improved by the implementation of closed-loop control into the system

Serum albumin and hemoglobin are associated with physical function in community-living older persons in Singapore

10.1007/s12603-011-0120-7Journal of Nutrition, Health and Aging1510877-88

Exploring sources of satisfaction and dissatisfaction in Airbnb accommodation using unsupervised and supervised topic modeling

This study aims to examine key attributes affecting Airbnb users' satisfaction and dissatisfaction through the analysis of online reviews. A corpus that comprises 59,766 Airbnb reviews form 27,980 listings located in 12 different cities is analyzed by using both Latent Dirichlet Allocation (LDA) and supervised LDA (sLDA) approach. Unlike previous LDA based Airbnb studies, this study examines positive and negative Airbnb reviews separately, and results reveal the heterogeneity of satisfaction and dissatisfaction attributes in Airbnb accommodation. In particular, the emergence of the topic “guest conflicts” in this study leads to a new direction in future sharing economy accommodation research, which is to study the interactions of different guests in a highly shared environment. The results of topic distribution analysis show that in different types of Airbnb properties, Airbnb users attach different importance to the same service attributes. The topic correlation analysis reveals that home like experience and help from the host are associated with Airbnb users' revisit intention. We determine attributes that have the strongest predictive power to Airbnb users' satisfaction and dissatisfaction through the sLDA analysis, which provides valuable managerial insights into priority setting when developing strategies to increase Airbnb users' satisfaction. Methodologically, this study contributes by illustrating how to employ novel approaches to transform social media data into useful knowledge about customer satisfaction, and the findings can provide valuable managerial implications for Airbnb practitioners

Systemic inflammation, depression and obstructive pulmonary function: a population-based study

10.1186/1465-9921-14-53Respiratory Research145

A requirement engineering model for big data software

Most prevailing software engineering methodologies assume that software systems are developed from scratch to capture business data and subsequently generate reports. Nowadays, massive data may exist even before software systems are developed. These data may also be freely available on Internet or may present in silos in organizations. The advancement in artificial intelligence and computing power has also prompted the need for big data analytics to unleash more business values to support evidence-based decisions. Some business values are less evident than others, especially when data are analyzed in silos. These values could be potentially unleashed and augmented from the insights discovered by data scientists through data mining process. Data mining may involve overlaying and merging data from different sources to extract data patterns. Ideally, these values should be eventually incorporated into the information systems to be. To realize this, we propose that software engineers ought to elicit software requirements together with data scientists. However, in the traditional software engineering process, such collaboration and business values are usually neglected. In this paper, we present a new requirement engineering model that allows software engineers and data scientists to discover these values hand in hand as part of software requirement process. We also demonstrate how the proposed requirement model captures and expresses business values that unleashed through big data analytics using an adapted use case diagram

Identification and analysis of core topics in educational artificial intelligence research: a bibliometric analysis

Educational artificial intelligence (EAI) means an integration of artificial intelligence (AI) and educational science that is going to serve as an authoritative element of the education system in the future. However, limited bibliographic analysis study have been carried out with the purpose of conceptualising the advancements in this field of educational billiography This study aim is to identify and analysis the core topics using keywords. The method of study is using the Keywords and cluster analysis by conducting a bibliometric review of 8,660 articles that have been published from 2000 to 2020 with the help of CiteSpace software. The results reveal that EAI research primarily encompasses three controversial topics. There is controversy about the AI application to students, AI does not replace teachers and AI algorithms have great contribution in the development of education sector. Study concluded that, AI applications can improve the effectiveness of students’ learning, AI can replace part of the teachers’ work, the relationship between the teacher and the machine should be cooperation, not the relationship between replacement and being replaced, under the premise that teachers give full play to their initiative and innovation

Ontology reuse for multiagent systems development through pattern classification

Ontologies play a crucial role in multiagent systems (MASs) development, especially for domain knowledge modeling, interaction specifications, and behavioral aspect representation. Domain‐specific ontologies can be developed in an ad hoc or systematic manner through the incorporation of ontology development steps on the basis of agent‐oriented methodologies. Developing such ontologies, however, is challenging because of the extensive amounts of knowledge and experience required. Moreover, since many ontologies cater for very specific domains, the question arises of whether some can be reused for faster systems development. This paper attempts to answer this question by proposing an ontology pattern classification scheme to allow the reuse of existing ontology knowledge for MAS development. Specifically, ontology patterns relevant to the design problem at hand are identified through the pattern classification scheme. These patterns are then reused and shared among agent software communities during the system development phase. The effectiveness of the proposed approach is validated using a restaurant‐finder MAS case study. Our findings suggest that utilization of the classified ontology patterns reduces development time and complexity when dealing with domain‐specific applications. The scheme also seems useful for software practitioners, where searching and reusing the patterns can easily be done during the analysis, design, and implementation of MAS development