Bridging the Gap Between Training and Inference for Spatio-Temporal Forecasting

Spatio-temporal sequence forecasting is one of the fundamental tasks in spatio-temporal data mining. It facilitates many real world applications such as precipitation nowcasting, citywide crowd flow prediction and air pollution forecasting. Recently, a few Seq2Seq based approaches have been proposed, but one of the drawbacks of Seq2Seq models is that, small errors can accumulate quickly along the generated sequence at the inference stage due to the different distributions of training and inference phase. That is because Seq2Seq models minimise single step errors only during training, however the entire sequence has to be generated during the inference phase which generates a discrepancy between training and inference. In this work, we propose a novel curriculum learning based strategy named Temporal Progressive Growing Sampling to effectively bridge the gap between training and inference for spatio-temporal sequence forecasting, by transforming the training process from a fully-supervised manner which utilises all available previous ground-truth values to a less-supervised manner which replaces some of the ground-truth context with generated predictions. To do that we sample the target sequence from midway outputs from intermediate models trained with bigger timescales through a carefully designed decaying strategy. Experimental results demonstrate that our proposed method better models long term dependencies and outperforms baseline approaches on two competitive datasets.Comment: ECAI 2020 Accepted, preprin

Bridging the gap between training and inference for spatio-temporal forecasting

Spatio-temporal sequence forecasting is one of the fundamental tasks in spatio-temporal data mining. It facilitates many real world applications such as precipitation now casting, city wide crowd flow prediction and air pollution forecasting. Recently, a few Seq2Seq based approaches have been proposed, but one of the drawbacks of Seq2Seq models is that, small errors can accumulate quickly along the generated sequence at the inference stage due to the different distributions of training and inference phase. That is because Seq2Seq models minimise single step errors only during training, however the entire sequence has to be generated during the inference phase which generates a discrepancy between training and inference. In this work, we propose a novel curriculum learning based strategy named Temporal Progressive Growing Sampling to effectively bridge the gap between training and inference for spatio-temporal sequence forecasting, by transformin the training process from a fully-supervised manner which utilises all available previous groundtruth values to a less-supervised manner which replaces some of theground-truth context with generated predictions. To do that we sam-ple the target sequence from midway outputs from intermediate models trained with bigger timescales through a carefully designed decaying strategy. Experimental results demonstrate that our proposed method better models long term dependencies and outperforms baseline approaches on two competitive datasets

Visual Analytics of Higher Order Information for Trajectory Datasets

Due to the widespread of mobile sensing, there is a strong need to handle trails of moving objects, trajectories. This paper proposes three visual analytic approaches for higher order information of trajectory data sets based on the higher order Voronoi diagram data structure. Proposed approaches reveal geometrical information, topological, and directional information. Experimental results demonstrate the applicability and usefulness of proposed three approaches

Using mobile-based augmented reality and object detection for real-time Abalone growth monitoring

Abalone are becoming increasingly popular for human consumption. Whilst their popularity has risen, measuring the number and size distribution of Abalone at various stages of growth in existing farms remains a significant challenge. Current Abalone stock management techniques rely on manual inspection which is time consuming, causes stress to the animal, and results in mediocre data quality. To rectify this, we propose a novel mobile-based tool which combines object detection and augmented reality for the real-time counting and measuring of Abalone, that is both network and location independent. We applied our portable handset tool to both measure and count Abalone at various growth stages, and performed extended measuring evaluation to assess the robustness of our proposed approach. Our experimental results revealed that the proposed tool greatly outperforms traditional approaches and was able to successfully count up to 15 Abalone at various life stages with above 95% accuracy, as well as significantly decrease the time taken to measure Abalone while still maintaining an accuracy within a maximum error range of 2.5% of the Abalone’s actual size

The impact of dynamic locking on collaborative programming

The parallel strategy of collaborative writing is commonly used by academia and industry. However, the nature of this approach may not scale to other genres of collaboration including collaborative programming. In this paper, we present a web-based prototype that supports parallel programming with the use of dynamic locking, and evaluate its scalability and system usability. Our results suggest there are benefits to our approach, and we discuss the implications and future directions for research

Towards realistic meteorological predictive learning using conditional GAN

Meteorological imagery prediction is an important and challenging problem for weather forecasting. It can also be seen as a video frame prediction problem that estimates future frames based on observed meteorological imageries. Despite it is a widely-investigated problem, it is still far from being solved. Current state-of-the-art deep learning based approaches mainly optimise the mean square error loss resulting in blurry predictions. We address this problem by introducing a Meteorological Predictive Learning GAN model (in short MPL-GAN) that utilises the conditional GAN along with the predictive learning module in order to handle the uncertainty in future frame prediction. Experiments on a real-world dataset demonstrate the superior performance of our proposed model. Our proposed model is able to map the blurry predictions produced by traditional mean square error loss based predictive learning methods back to their original data distributions, hence it is able to improve and sharpen the prediction. In particular, our MPL-GAN achieves an average sharpness of 52.82, which is 14% better than the baseline method. Furthermore, our model correctly detects the meteorological movement patterns that traditional unconditional GANs fail to do

Voronoi image segmentation and its applications to geoinformatics

As various geospatial images are available for analysis, there is a strong need for an intelligent geospatial image processing method. Segmenting and districting digital images is a core process and is of great importance in many geo-related applications. We propose a flexible image segmentation framework based on generalized Voronoi diagrams through Euclidean distance transforms. We introduce a three-scan algorithm that segments images in O(N) time when N is the number of pixels. The algorithm is capable of handling generators of complex types (point, line and area), Minkowski metrics and different weights. This paper also provides applications of the proposed method in various geoinformation datasets. Illustrated examples demonstrate the usefulness and robustness of our proposed method

Fast OBJ file importing and parsing in CUDA

Alias-Wavefront OBJ meshes are a common text file type for transferring 3D mesh data between applications made by different vendors. However, as the mesh complexity gets higher and denser, the files become larger and slower to import. This paper explores the use of GPUs to accelerate the importing and parsing of OBJ files by studying file read-time, runtime, and load resistance. We propose a new method of reading and parsing that circumvents GPU architecture limitations and improves performance, seeing the new GPU method outperforms CPU methods with a 6× - 8× speedup. When running on a heavily loaded system, the new method only received an 80% performance hit, compared to the 160% that the CPU methods received. The loaded GPU speedup compared to unloaded CPU methods was 3.5×, and, when compared to loaded CPU methods, 8×. These results demonstrate that the time is right for further research into the use of data-parallel GPU acceleration beyond that of computer graphics and high performance computing

Multi-synchronous collaboration between desktop and mobile users: A case study of report writing for emergency management

The development of multi-synchronous decision support systems to facilitate collaboration between diverse users is an emerging field in emergency management. Traditionally, information management for emergency response has been a centralised effort. However, modern devices such as smartphones provide new methods for gaining real-time information about a disaster from users in the field. In this paper, we present a framework for multi-synchronous collaborative report writing in the scope of emergency management. This framework supports desktop-based users as information providers and consumers, alongside mobile users as information providers to facilitate multi-synchronous collaboration. We consider the benefits of our framework for writing collaborative Situation Reports and discuss future directions for research

A hybrid data gathering and agent based cognitive architecture for realistic crowd simulations

This paper proposes a realistic agent-based framework for crowd simulations that can encompass the input phase, the simulation process phase, and the output evaluation phase. In order to achieve this gathering, the three types of real-world data (physical, mental and visual) need to be considered. However, existing research has not used all the three data types to develop an agent-based framework since current data gathering methods are unable to collect all the three types. This paper introduces anew hybrid data gathering approach using a combination of virtual reality and questionnaires to gather all three data types. The data collected are incorporated into the simulation model to provide realism and flexibility. The performance of the framework is evaluated and benchmarked to prove the robustness and effectiveness of our framework. Various types of settings (self-set parameters and random parameters) are simulated to demonstrate that the framework can produce real-world like simulation