Maffee Peng-Hui Wan

Interactive experiences designed for agricultural communities

With the rapid development of technology, smartphone and Internet adoption in the rural areas will continue to increase in the coming years. These technologies can easily support multimedia elements and are strong platforms to deliver added value. However, constraints faced by the rural population, including low literacy level and lack of exposure to electronic devices must be considered in order to develop suitable solutions. This paper introduces VillageTree, a unique suite of intelligent pest management solutions with the integration of analytical capabilities with simplicity in usability, to meet the needs of agricultural communities in developed and developing countries.

Location Privacy Apprehensions in Location-Based Services Among Literate and Semi-literate Users

Personalized services provided by Location Based Services (LBS) are becoming increasingly prevalent to the large population of semi-literate users living in emerging economies due to the low costs and ubiquity. However, usage of LBS is still threatened by location privacy threats as it keeps track of the individuals’ location. Studies typically only addressed how to mitigate location privacy apprehensions for the literate users and not the semi-literate users. To fill that gap and better understand location privacy apprehensions among different communities, this study draws upon theories of Restrict Access/Limited Control and Familiarity to identify the antecedents of location privacy apprehensions related to personalized services provided by LBS and user literacy. The proposed research model is empirically tested in a laboratory experiment. The findings show that the different types of LBS do affect the degree of location privacy apprehensions between the literate and semi-literate users. Implications for enhancing usage intentions and mitigating location privacy apprehensions for different types of mobile applications are discussed.

Multiple Regression and Artificial Neural Network for the Prediction of Crop Pest Risks

The reduction of crop yield losses caused by pests is a major challenge to productive and sustainable food production for preventing food insecurity and emergencies, and for alleviating world food crisis. Multiple regression (MR) and artificial neural network (ANN) are two widely adopted modelling approaches for the prediction of crop pest risks, which are based on empirical statistics and artificial intelligence, respectively. Each of the two alternative approaches has its advantages and disadvantages. This study evaluates the two models from two aspects: their performances on pest risk prediction, and their methodological advantages and disadvantages. Two pest species are modelled using the two approaches as case studies, which are the melon thrip Thrips palmi Karny (T. palmi) and the diamondback moth Plutella xylostella (L.) (P. xylostella). Results show that ANN has higher prediction accuracy for both species. However, ANN has some methodological demerits compared to MR modelling.

Representation Strategies Adopted by Participants in a Population Stereotype Hunt: A Case Study for Icon Design

Population Stereotype tells interaction designers just one-half of the complete story. It informs them only about the level of general consensus regarding each representation generated by different participants. It does not provide answers to those questions, which ask how the representation is to be achieved. Identification of different representation strategies adopted by different participants can reveal the rest of the story. In the presence of more than one or no strong contenders (population stereotype), adoption of the right representation strategy can be really beneficial. As most of the representational strategies are complementary to each other, the combination of different representational strategies can lead towards a more representative icon development.

Mobile and Sensor Integration for Increased Interactivity and Expandability in Mobile Gaming and Virtual Instruments

AerialBeats is an interactive system that brings air drumming into reality. It comprises a set of drumsticks and feet bands equipped with motion sensors and a mobile application. AerialBeats brings a higher level of interactivity over standalone virtual drum mobile apps and digital drumsticks, yet retains the high ubiquity that is missing from physical drum sets. This creates a platform for learning, games and delivering entertaining performances. The integration of sensory hardware with mobile platform provides infinite possibilities for expansion interaction and customization surpassing physical instruments and mobile apps. In the larger context, this project seeks to 1) explore the benefits of bringing mobile interfaces into current standalone sensor equipment/toys and 2) utilize the addition of portable sensory hardware (e.g. wearables) to bring about a dimension to popular mobile gaming.