CoAcT: A framework for context-aware trip planning using active transport

Policy makers and urban planners around the world are encouraging people to use active transport by providing more easily accessible facilities for active transport users. However, trip planning using active transport is not straight forward and requires consideration of various trip contexts such as congestion, accessibility, attractiveness, safety as well as the physical ability of the traveller. The existing approaches do not provide a unified solution to integrate and represent these diverse set of contexts in active transport trip planning. In this paper, we propose a new framework called CoAcT which is able to integrate and represent various trip contexts for context aware trip planning using active transport. We also present two real world deployments of our proposed framework

Spatially aggregated photovoltaic power prediction using wavelet and convolutional neural networks

Forecasting the power generation from intermittent renewable energy sources, such as Photovoltaic (PV) systems, is crucial for the reliable operations of power systems. In this paper, we consider the task of spatially aggregated PV power generation from large-scale, grid-connected and geographically dispersed PV sites. PV power generation data is highly uncertain, non-linear and non-stationary, making accurate forecasting very challenging. We present a new approach, Wavelet Convolutional Neural Networks (WCNNs), by combining Wavelet Transformation (WT) with Convolutional Neural Networks (CNNs). The WCNNs approach first applies time-invariant WT to decompose the highly fluctuating PV power time series into multiple components. It then predicts the approximation (i.e., low frequency smoothed time series) and details (i.e., high frequency random noise) using CNNs and linear regression, respectively. Extensive evaluation using a real dataset from the Australian Energy Market Operator (AEMO) shows that WCNNs is an effective approach and outperforms the state-of-the-art machine learning models both with and without WT

A blockchain-based architecture for integrated smart parking systems

In this paper, we introduce an integrated smart parking system. The proposed integrated smart parking system brings multiple parking service providers together under a unified platform aiming to provide one-stop parking information services to the commuters in a smart city. However, the adaptation of such a system is prone to tempering while a massive amount of data is shared among different parties which raise concerns related to trust and performance. To address this challenge, we propose a blockchain-based architecture specific to the integrated smart parking systems. Finally, we present a set of design principles which shows the applicability of our proposed blockchain-based integrated parking system

Investigating the reliability of self-report data in the wild: The quest for ground truth

Inferring human mental state (e.g., emotion, depression, engagement) with sensing technology is one of the most valuable challenges in the affective computing area, which has a profound impact in all industries interacting with humans. Self-report is the most common way to quantify how people think, but prone to subjectivity and various responses bias. It is usually used as the ground truth for human mental state prediction. In recent years, many data-driven machine learning models are built based on self-report annotations as the target value. In this research, we investigate the reliability of self-report data in the wild by studying the confidence level of responses and survey completion time. We conduct a case study (i.e., student engagement inference) by recruiting 23 students in a high school setting over a period of 4 weeks. Overall, our participants volunteered 488 self-reported responses and sensing data from smart wristbands. We find that the physiologically measured student engagement and perceived student engagement are not always consistent. The findings from this research have great potential to benefit future studies in predicting engagement, depression, stress, and other emotion-related states in the field of affective computing and sensing technologies

Solar power time series forecasting utilising wavelet coefficients

Accurate and reliable prediction of power output is critical to electricity grid stability and power dispatching capabilities. However, photovoltaic (PV) power generation is highly volatile and unstable due to different reasons. The wavelet transform (WT) has been utilised in time series applications, such as PV power prediction, to model the stochastic volatility and reduce prediction errors. Yet the existing WT approach has a limitation in terms of time complexity. It requires reconstructing the decomposed components and modelling them separately and thus needs more time for reconstruction, model configuration and training. The aim of this study is to improve the efficiency of applying WT by proposing a new method that uses a single simplified model. Given a time series and its WT coefficients, it trains one model with the coefficients as features and the original time series as labels. This eliminates the need for component reconstruction and training numerous models. This work contributes to the day-ahead aggregated solar PV power time series prediction problem by proposing and comprehensively evaluating a new approach of employing WT. The proposed approach is evaluated using 17 months of aggregated solar PV power data from two real-world datasets. The evaluation includes the use of a variety of prediction models, including Linear Regression, Random Forest, Support Vector Regression, and Convolutional Neural Networks. The results indicate that using a coefficients-based strategy can give predictions that are comparable to those obtained using the components-based approach while requiring fewer models and less computational time

Forecasting regional level solar power generation using advanced deep learning approach

Reliable integration of solar photovoltaic (PV) power into the electricity grid requires accurate forecasting at the regional level. While previous research has been primarily concerned with forecasting PV power output from a single plant, this research focuses on regional level forecasting which is more beneficial for economic operations of power systems. This paper presents an advanced deep learning-based approach, called CNNs-LSTM Encoder-Decoder (CLED), to predict the regional level aggregated PV power generation for the next day at half-hourly intervals. The proposed approach utilizes the ability of Convolutional Neural Networks (CNNs) to capture and learn the internal representation of intermittent time-series data. It also uses Long Short-Term Memory (LSTM) network for recognizing temporal dependencies in the data. The performance of the CLED model is evaluated using a large data set from the Australian Energy Market Operator (AEMO). Results demonstrate that CLED provides accurate predictions, outperforming baselines and state-of-the-art models in the literature

n-Gage: Predicting in-class emotional, behavioural and cognitive engagement in the wild

The study of student engagement has attracted growing interests to address problems such as low academic performance, disaffection, and high dropout rates. Existing approaches to measuring student engagement typically rely on survey-based instruments. While effective, those approaches are time-consuming and labour-intensive. Meanwhile, both the response rate and quality of the survey are usually poor. As an alternative, in this paper, we investigate whether we can infer and predict engagement at multiple dimensions, just using sensors. We hypothesize that multidimensional student engagement level can be translated into physiological responses and activity changes during the class, and also be affected by the environmental changes. Therefore, we aim to explore the following questions: Can we measure the multiple dimensions of high school student's learning engagement including emotional, behavioural and cognitive engagement with sensing data in the wild? Can we derive the activity, physiological, and environmental factors contributing to the different dimensions of student learning engagement? If yes, which sensors are the most useful in differentiating each dimension of the engagement? Then, we conduct an in-situ study in a high school from 23 students and 6 teachers in 144 classes over 11 courses for 4 weeks. We present the n-Gage, a student engagement sensing system using a combination of sensors from wearables and environments to automatically detect student in-class multidimensional learning engagement. Extensive experiment results show that n-Gage can accurately predict multidimensional student engagement in real-world scenarios with an average mean absolute error (MAE) of 0.788 and root mean square error (RMSE) of 0.975 using all the sensors. We also show a set of interesting findings of how different factors (e.g., combinations of sensors, school subjects, CO2 level) affect each dimension of the student learning engagement

CoSEM: Contextual and semantic embedding for App usage prediction

App usage prediction is important for smartphone system optimization to enhance user experience. Existing modeling approaches utilize historical app usage logs along with a wide range of semantic information to predict the app usage; however, they are only effective in certain scenarios and cannot be generalized across different situations. This paper address this problem by developing a model called Contextual and Semantic Embedding model for App Usage Prediction (CoSEM) for app usage prediction that leverages integration of 1) semantic information embedding and 2) contextual information embedding based on historical app usage of individuals. Extensive experiments show that the combination of semantic information and history app usage information enables our model to outperform the baselines on three real-world datasets, achieving an MRR score over 0.55,0.57,0.86 and Hit rate scores of more than 0.71, 0.75, and 0.95, respectively

Individual and group-wise classroom seating experience: Effects on student engagement in different courses

Seating location in the classroom can affect student engagement, attention and academic performance by providing better visibility, improved movement, and participation in discussions. Existing studies typically explore how traditional seating arrangements (e.g. grouped tables or traditional rows) influence students' perceived engagement, without considering group seating behaviours under more flexible seating arrangements. Furthermore, survey-based measures of student engagement are prone to subjectivity and various response bias. Therefore, in this research, we investigate how individual and group-wise classroom seating experiences affect student engagement using wearable physiological sensors. We conducted a field study at a high school and collected survey and wearable data from 23 students in 10 courses over four weeks. We aim to answer the following research questions: 1. How does the seating proximity between students relate to their perceived learning engagement? 2. How do students' group seating behaviours relate to their physiologically-based measures of engagement (i.e. physiological arousal and physiological synchrony)? Experiment results indicate that the individual and group-wise classroom seating experience is associated with perceived student engagement and physiologically-based engagement measured from electrodermal activity. We also find that students who sit close together are more likely to have similar learning engagement and tend to have high physiological synchrony. This research opens up opportunities to explore the implications of flexible seating arrangements and has great potential to maximize student engagement by suggesting intelligent seating choices in the future

App usage on-the-move: Context- and commute-aware next app prediction

The proliferation of digital devices and connectivity enables people to work anywhere, anytime, even while they are on the move. While mobile applications have become pervasive, an excessive amount of mobile applications have been installed on mobile devices. Nowadays, commuting takes a large proportion of daily human life, but studies show that searching for the desired apps while commuting can decrease productivity significantly and sometimes even cause safety issues. Although app usage behaviour has been studied for general situations, little to no study considers the commuting context as vital information. Existing models for app usage prediction cannot be easily generalised across all commuting contexts due to: (1) continuous change in user locations; and (2) limitation of necessary contextual information (i.e., lack of knowledge to identify which contextual information is necessary for different commuting situations. We aim to address these challenges by extracting essential contextual information for on-commute app usage prediction. Using the extracted features, we propose AppUsageOTM, a practical statistical machine learning framework to predict both destination amenity and utilise the inferred destination to contextualise the app usage prediction with travelling purposes as crucial information. We evaluate our framework in terms of accuracy, which shows the feasibility of our work. Using a real-world mobile and app usage behaviour dataset with more than 12,495 trajectory records and more than 1046 mobile applications logged, AppUsageOTM significantly outperformed all baseline models, achieving Accuracy@k 46.4%@1, 66.4%@5, and 75.9%@10