Can Citizen Science Seriously Contribute to Policy Development?

This chapter considers the potential for citizen science to contribute to policy development. A background to evidence-based policy making is given, and the requirement for data to be robust, reliable and, increasingly, cost-effective is noted. The potential for the use of ‘co-design' strategies with stakeholders, to add value to their engagement as well as provide more meaningful data that can contribute to policy development, is presented and discussed. Barriers to uptake can be institutional and the quality of data used in evidence-based policy making will always need to be fully assured. Data must be appropriate to the decision making process at hand and there is potential for citizen science to fill important, existing data-gaps.

Research Strategy for Studying User's Acceptance of Tourism-Related ITs

This paper aims to guide future researchers on research strategy for studying user's acceptance of tourism-related Information Technologies (ITs). In a study on user's acceptance of the technological-combination of virtual reality and augmented reality application in the UNESCO World Heritage Site of Melaka, the author proposes 11 steps of research strategy which begin with developing the research framework (Step 1), proposing the research hypotheses (Step 2), determining research design (Step 3), designing sampling processes (Step 4), designing questionnaire (Step 5), conducting face validity (Step 6), developing the prototypes (Step 7), conducting pilot testing (Step 8), collecting data (Step 9), analyzing data (Step 10), as well as providing conclusion (Step 11).

Social Media in Accelerating Mobile Apps

Mobile application stores have become very popular, and the two most popular, Google Play and Apple App Store, both have over a million applications (apps) available. Social media is extensively used for marketing products and services; but, its true potential, in service and product acceleration, has not been researched much. This paper studies the differences in actions between successful and less successful app developers and especially their social media use in accelerating applications and its impact on success. In this study, a longitudinal analysis is performed on 682 applications, from four Google Play categories, at three data points. This study concludes that almost 50% of the analyzed applications use some form of social media to promote their app, and that successful apps use social media more actively than less successful ones. The qualitative analysis of the apps sheds some light as to why some apps succeed without social media use, and why some fail while using it.

Machine Learning Approach for Multi-Layered Detection of Chemical Named Entities in Text

Identification of chemical named entities in text and subsequent linkage of information to biological events is of immense value to fulfill the knowledge needs of pharmaceutical and chemical R&D. A significant amount of investigation has been carried out since a decade for identifying chemical named entities at morphological level. However, a barrier still remains in terms of value proposition to scientists at chemistry level. Therefore, the work described here aims to circumvent the information barrier by adaptation of a Conditional Random Fields-based approach for identifying chemical named entities at various levels namely generic chemical level, morphological level, and chemistry level. Substantial effort has been invested on generation of suitable multi-level annotated corpora. Recommended machine learning practices such as active learning-based training corpus generation and feature optimization have been systematically performed. Evaluation of system performance and benchmarking against the other state-of-the-approaches showed improved results.

Explore the Use of Handwriting Information and Machine Learning Techniques in Evaluating Mental Workload

Mental workload is considered one of the most important factors in interaction design and how to detect a user's mental workload during tasks is still an open research question. Psychological evidence has already attributed a certain amount of variability and “drift” in an individual's handwriting pattern to mental stress, but this phenomenon has not been explored adequately. The intention of this paper is to explore the possibility of evaluating mental workload with handwriting information by machine learning techniques. Machine learning techniques such as decision trees, support vector machine (SVM), and artificial neural network were used to predict mental workload levels in the authors' research. Results showed that it was possible to make prediction of mental workload levels automatically based on handwriting patterns with relatively high accuracy, especially on patterns of children. In addition, the proposed approach is attractive because it requires no additional hardware, is unobtrusive, is adaptable to individual users, and is of very low cost.

Supervised Machine Learning for Plants Identification Based on Images of Their Leaves

Botanists study in general the characteristics of leaves to give to each plant a scientific name; such as shape, margin...etc. This paper proposes a comparison of supervised plant identification using different approaches. The identification is done according to three different features extracted from images of leaves: a fine-scale margin feature histogram, a Centroid Contour Distance Curve shape signature and an interior texture feature histogram. First represent each leaf by one feature at a time in, then represent leaves by two features, and each leaf was represented by the three features. After that, the authors classified the obtained vectors using different supervised machine learning techniques; the used techniques are Decision tree, Naïve Bayes, K-nearest neighbour, and neural network. Finally, they evaluated the classification using cross validation. The main goal of this work is studying the influence of representation of leaves' images on the identification of plants, and also studying the use of supervised machine learning algorithm for plant leaves classification.

Data Mining for Multicriteria Single Facility Location Problems

This chapter focuses on available data analysis and data mining techniques to find the optimal location of the Multicriteria Single Facility Location Problem (MSFLP) at diverse business settings. Solving for the optimal of an MSFLP, there exists numerous multicriteria decision analysis techniques. Mainstream models are mentioned in this chapter, while presenting a general classification of the MSFLP and its framework. Besides, topics from machine learning with respect to decision analysis are covered: Unsupervised Principal Components Analysis ranking (PCA-rank) and supervised Support Vector Machines ranking (SVM-rank). This chapter proposes a data mining perspective for the multicriteria single facility location problem and proposes a new approach to the facility location problem with the combination of the PCA-rank and ranking SVMs.

Feedback-Driven Refinement of Mandarin Speech Recognition Result Based on Lattice Modification and Rescoring

In real world applications of speech recognition, recognition errors are inevitable, and manual correction is necessary. This paper presents an approach for the refinement of Mandarin speech recognition result by exploiting user feedback. An interface incorporating character-based candidate lists and feedback-driven updating of the candidate lists is introduced. For dynamic updating of candidate lists, a novel method based on lattice modification and rescoring is proposed. By adding words with similar pronunciations to the candidates next to the corrected character into the lattice and then performing rescoring on the modified lattice, the proposed method can improve the accuracy of the candidate lists even if the correct characters are not in the original lattice, with much lower computational cost than that of the speech re-recognition methods. Experimental results show that the proposed method can reduce 24.03% of user inputs and improve average candidate rank by 25.31%.

Applications of Longitudinal Research to Advance Organizational Theory

The purpose of this article is to inform researchers about and encourage the use of longitudinal designs to further understanding of human resource development and organizational theory. This article presents information about a variety of longitudinal research designs, related statistical procedures, and an overview of general data collecting approaches, covering their strengths and weaknesses. The article also serves as a reminder of the importance of alignment between the research purpose, questions and methodology while highlighting the benefits of using multiple methods. Finally, importance of research replicability in the context of longitudinal inquiry is stressed.

A Comparative Study of Machine Learning Techniques for Gesture Recognition Using Kinect

Progress and technological innovation achieved in recent years, particularly in the area of entertainment and games, have promoted the creation of more natural and intuitive human-computer interfaces. For example, natural interaction devices such as Microsoft Kinect allow users to explore a more expressive way of human-computer communication by recognizing body gestures. In this context, several Supervised Machine Learning techniques have been proposed to recognize gestures. However, scarce research works have focused on a comparative study of the behavior of these techniques. Therefore, this chapter presents an evaluation of 4 Machine Learning techniques by using the Microsoft Research Cambridge (MSRC-12) Kinect gesture dataset, which involves 30 people performing 12 different gestures. Accuracy was evaluated with different techniques obtaining correct-recognition rates close to 100% in some results. Briefly, the experiments performed in this chapter are likely to provide new insights into the application of Machine Learning technique to facilitate the task of gesture recognition.