An adaptive quasi harmonic broadcasting scheme with optimal bandwidth requirement

The aim of Harmonic Broadcasting protocol is to reduce the bandwidth usage in video-on-demand service where a video is divided into some equal sized segments and every segment is repeatedly transmitted over a number of channels that follows harmonic series for channel bandwidth assignment. As the bandwidth of channels differs from each other and users can join at any time to these multicast channels, they may experience a synchronization problem between download and playback. To deal with this issue, some schemes have been proposed, however, at the cost of additional or wastage of bandwidth or sudden extreme bandwidth requirement. In this paper we present an adaptive quasi harmonic broadcasting scheme (AQHB) which delivers all data segment on time that is the download and playback synchronization problem is eliminated while keeping the bandwidth consumption as same as traditional harmonic broadcasting scheme without cost of any additional or wastage of bandwidth. It also ensures the video server not to increase the channel bandwidth suddenly that is, also eliminates the sudden buffer requirement at the client side. We present several analytical results to exhibit the efficiency of our proposed broadcasting scheme over the existing ones.Comment: IEEE International Conference on Informatics, Electronics & Vision (ICIEV), 2013, 6pages, 8 figure

Transfer Learning for Thermal Comfort Prediction in Multiple Cities

HVAC (Heating, Ventilation and Air Conditioning) system is an important part of a building, which constitutes up to 40% of building energy usage. The main purpose of HVAC, maintaining appropriate thermal comfort, is crucial for the best utilisation of energy usage. Besides, thermal comfort is also crucial for well-being, health, and work productivity. Recently, data-driven thermal comfort models have got better performance than traditional knowledge-based methods (e.g. Predicted Mean Vote Model). An accurate thermal comfort model requires a large amount of self-reported thermal comfort data from indoor occupants which undoubtedly remains a challenge for researchers. In this research, we aim to tackle this data-shortage problem and boost the performance of thermal comfort prediction. We utilise sensor data from multiple cities in the same climate zone to learn thermal comfort patterns. We present a transfer learning based multilayer perceptron model from the same climate zone (TL-MLP-C*) for accurate thermal comfort prediction. Extensive experimental results on ASHRAE RP-884, the Scales Project and Medium US Office datasets show that the performance of the proposed TL-MLP-C* exceeds the state-of-the-art methods in accuracy, precision and F1-score

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 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 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. Experiment results show that n-Gage can accurately predict multidimensional student engagement in real-world scenarios with an average MAE of 0.788 and 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.Comment: This paper has been accepted by the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) volume 4 issue 3, 202

Inferring Transportation Mode and Human Activity from Mobile Sensing in Daily Life

In this paper, we focus on simultaneous inference of transportation modes and human activities in daily life via modelling and inference from multivariate time series data, which are streamed from off-the- shelf mobile sensors (e.g. embedded in smartphones) in real-world dynamic environments. The transportation mode will be inferred from the structured hierarchical contexts associated with human activities. Through our mobile context recognition system, an ac- curate and robust solution can be obtained to infer transportation mode, human activity and their associated contexts (e.g. whether the user is in moving or stationary environment) simultaneously. There are many challenges in analysing and modelling human mobility patterns within urban areas due to the ever-changing en- vironments of the mobile users. For instance, a user could stay at a particular location and then travel to various destinations depend- ing on the tasks they carry within a day. Consequently, there is a need to reduce the reliance on location-based sensors (e.g. GPS), since they consume a significant amount of energy on smart de- vices, for the purpose of intelligent mobile sensing (i.e. automatic inference of transportation mode, human activity and associated contexts). Nevertheless, our system is capable of outperforming the simplistic approach that only considers independent classifications of multiple context label sets on data streamed from low energy sensors

An Ambient-Physical System to Infer Concentration in Open-plan Workplace

One of the core challenges in open-plan workspaces is to ensure a good level of concentration for the workers while performing their tasks. Hence, being able to infer concentration levels of workers will allow building designers, managers, and workers to estimate what effect different open-plan layouts will have and to find an optimal one. In this research, we present an ambient-physical system to investigate the concentration inference problem. Specifically, we deploy a series of pervasive sensors to capture various ambient and physical signals related to perceived concentration at work. The practicality of our system has been tested on two large open-plan workplaces with different designs and layouts. The empirical results highlight promising applications of pervasive sensing in occupational concentration inference, which can be adopted to enhance the capabilities of modern workplaces.Comment: 12 pages, 14 figure

MoParkeR : Multi-objective Parking Recommendation

Existing parking recommendation solutions mainly focus on finding and suggesting parking spaces based on the unoccupied options only. However, there are other factors associated with parking spaces that can influence someone's choice of parking such as fare, parking rule, walking distance to destination, travel time, likelihood to be unoccupied at a given time. More importantly, these factors may change over time and conflict with each other which makes the recommendations produced by current parking recommender systems ineffective. In this paper, we propose a novel problem called multi-objective parking recommendation. We present a solution by designing a multi-objective parking recommendation engine called MoParkeR that considers various conflicting factors together. Specifically, we utilise a non-dominated sorting technique to calculate a set of Pareto-optimal solutions, consisting of recommended trade-off parking spots. We conduct extensive experiments using two real-world datasets to show the applicability of our multi-objective recommendation methodology.Comment: 6 pages, 5 figure

Generative Adversarial Networks for Spatio-temporal Data: A Survey

Generative Adversarial Networks (GANs) have shown remarkable success in the computer vision area for producing realistic-looking images. Recently, GAN-based techniques are shown to be promising for spatiotemporal-based applications such as trajectory prediction, events generation and time-series data imputation. While several reviews for GANs in computer vision been presented, nobody has considered addressing the practical applications and challenges relevant to spatio-temporal data. In this paper, we conduct a comprehensive review of the recent developments of GANs in spatio-temporal data. we summarise the popular GAN architectures in spatio-temporal data and common practices for evaluating the performance of spatio-temporal applications with GANs. In the end, we point out the future directions with the hope of benefiting researchers interested in this area