Building a needs-based curriculum in data science and artificial intelligence: case studies in Indonesia, Sri Lanka, and Thailand

Indonesia and Thailand are middle-income countries within the South-East Asia region. They have well-established and growing higher education systems, increasingly focused on quality improvement. However, they fall behind regional leaders in educating people who design, develop, deploy and train data science and artificial intelligence (DS&AI) based technology, as evident from the technological market, regionally dominated by Singapore and Malaysia, while the region as a whole is far behind China. A similar situation holds also for Sri Lanka, in the South Asia region technologically dominated by India. In this paper, we describe the design of a master's level curriculum in data science and artificial intelligence using European experience on building such curricula. The design of such a curriculum is a nontrivial exercise because there is a constant trade-off between having a sufficiently broad academic curriculum and adequately meeting regional needs, including those of industrial stakeholders. In fact, findings from a gap analysis and assessment of needs from three case studies in Indonesia, Sri Lanka, and Thailand comprise the most significant component of our curriculum development process.The authors would like to thank the European Union Erasmus+ programme which provided funding through the Capacity Building Higher Education project on Curriculum Development in Data Science and Artificial Intelligence, registered under the reference number 599600-EPP-1-2018-1-TH-EPPKA2-CBHE-JP

A Design and Comparative Analysis of a Home Energy Disaggregation System Based on a Multi-Target Learning Framework

Insightful information on energy use encourages home residents to conduct home energy conservation. This paper proposes an experimental design for an energy disaggregation system based on the low-computational-cost approaches of multi-target classification and multi-target regression, which are under the multi-target learning framework. The experiments are set up to determine the optimal learning algorithm and model parameters. In addition, the designated system can provide inference of the appliance power state and the estimated power consumption from both approaches. The kernel density estimation technique is utilized to formulate the appliance power state as a finite-state machine for the multi-target classification approach. Multi-target regression can directly provide the estimation of appliance power demand from the aggregate data, and this work unifies the system’s design together with multi-target classification. The predictive performances obtained through the F-score (micro-averaged) and power estimation accuracy index for the power state inference and the estimated power demand, respectively, are shown to outperform a deep-learning-based denoising autoencoder network under the same data settings from both approaches. The results lead to a recommendation to apply the approach in home energy monitoring, which is mainly based on the characteristics of appliance power and the information that the residents wish to perceive

Cleaner Potential for Natural Rubber Drying Process Using Microwave Technology Powered by Solar Energy

To reduce carbon dioxide emissions from traditional drying methods, this research investigated the use of microwave technology for drying Standard Thai Rubber (STR) in Thailand. Commercial microwave ovens were modified and integrated with the microwave emitting power control system to maintain the appropriate temperature levels to evaporate the moisture from rubber. Throughout the drying process, the temperature of the rubber was measured both internally and outside. The results revealed that STR5L and STR20 could be dried satisfactorily and met the requirements for standard Thai rubber properties by utilizing 500 W for 140 and 120 min, respectively. By keeping the temperatures less than 150 °C, rubbers’ molecular structure is not destroyed from internal heat stress. Although utilizing less power for a longer period of time is possible, more energy was used, which is unfavorable. Compared to traditional hot air drying technologies, which take approximately 4–6 h for the drying process, microwave technology potentially reduces the drying time by half or more. If solar energy is used to supply electrical energy, 300,000 tons of Carbon dioxide can potentially be eliminated annually in the STR drying industry in Thailand by promoting approximately 1115 MW of Photovoltaic technology installations with the solar resources in southern Thailand