Towards Managing Uncertain Geo-Information for Drilling Disasters Using Event Tracking Sensitivity Analysis

In sub-surface drilling rigs, one key critical crisis is unwanted influx into the borehole as a result of increasing the influx rate while drilling deeper into a high-pressure gas formation. Although established risk assessments in drilling rigs provide a high degree of protection, uncertainty arises due to the behavior of the formation being drilled into, which may cause crucial situations at the rig. To overcome such uncertainties, real-time sensor measurements are used to predict, and thus prevent, such crises. In addition, new understandings of the effective events were derived from raw data. In order to avoid the computational overhead of input feature analysis that hinders time-critical prediction, EventTracker sensitivity analysis, an incremental method that can support dimensionality reduction, was applied to real-world data from 1600 features per each of the 4 wells as input and 6 time series per each of the 4 wells as output. The resulting significant input series were then introduced to two classification methods: Random Forest Classifier and Neural Networks. Performance of the EventTracker method was understood correlated with a conventional manual method that incorporated expert knowledge. More importantly, the outcome of a Neural Network Classifier was improved by reducing the number of inputs according to the results of the EventTracker feature selection. Most important of all, the generation of results of the EventTracker method took fractions of milliseconds that left plenty of time before the next bunch of data samples.

Regression-Based Camera Pose Estimation through Multi-Level Local Features and Global Features

Accurate and robust camera pose estimation is essential for high-level applications such as augmented reality and autonomous driving. Despite the development of global feature-based camera pose regression methods and local feature-based matching guided pose estimation methods, challenging conditions, such as illumination changes and viewpoint changes, as well as inaccurate keypoint localization, continue to affect the performance of camera pose estimation. In this paper, we propose a novel relative camera pose regression framework that uses global features with rotation consistency and local features with rotation invariance. First, we apply a multi-level deformable network to detect and describe local features, which can learn appearances and gradient information sensitive to rotation variants. Second, we process the detection and description processes using the results from pixel correspondences of the input image pairs. Finally, we propose a novel loss that combines relative regression loss and absolute regression loss, incorporating global features with geometric constraints to optimize the pose estimation model. Our extensive experiments report satisfactory accuracy on the 7Scenes dataset with an average mean translation error of 0.18 m and a rotation error of 7.44° using image pairs as input. Ablation studies were also conducted to verify the effectiveness of the proposed method in the tasks of pose estimation and image matching using the 7Scenes and HPatches datasets.

How IoT-Driven Citizen Science Coupled with Data Satisficing Can Promote Deep Citizen Science

To study and understand the importance of Internet of Things-driven citizen science (IoT-CS) combined with data satisficing, we set up and undertook a citizen science experiment for air quality (AQ) in four Pakistan cities using twenty-one volunteers. We used quantitative methods to analyse the AQ data. Three research questions (RQ) were posed as follows: Which factors affect CS IoT-CS AQ data quality (RQ1)? How can we make science more inclusive by dealing with the lack of scientists, training and high-quality equipment (RQ2)? Can a lack of calibrated data readings be overcome to yield otherwise useful results for IoT-CS AQ data analysis (RQ3)? To address RQ1, an analysis of related work revealed that multiple causal factors exist. Good practice guidelines were adopted to promote higher data quality in CS studies. Additionally, we also proposed a classification of CS instruments to help better understand the data quality challenges. To answer RQ2, user engagement workshops were undertaken as an effective method to make CS more inclusive and also to train users to operate IoT-CS AQ devices more understandably. To address RQ3, it was proposed that a more feasible objective is that citizens leverage data satisficing such that AQ measurements can detect relevant local variations. Additionally, we proposed several recommendations. Our top recommendations are that: a deep (citizen) science approach should be fostered to support a more inclusive, knowledgeable application of science en masse for the greater good; It may not be useful or feasible to cross-check measurements from cheaper versus more expensive calibrated instrument sensors in situ. Hence, data satisficing may be more feasible; additional cross-checks that go beyond checking if co-located low-cost and calibrated AQ measurements correlate under equivalent conditions should be leveraged.

Using an Internet of Behaviours to Study How Air Pollution Can Affect People's Activities of Daily Living: A Case Study of Beijing, China

This study aims to quantitatively model rather than to presuppose whether or not air pollution in Beijing (China) affects people's activities of daily living (ADLs) based on an Internet of Behaviours (IoB), in which IoT sensor data can signal environmental events that can change human behaviour on mass. Peoples' density distribution computed by call detail records (CDRs) and air quality data are used to build a fixed effect model (FEM) to analyse the influence of air pollution on four types of ADLs. The following four effects are discovered: Air pollution negatively impacts people going sightseeing in the afternoon; has a positive impact on people staying-in, in the morning and the middle of the day. Air pollution lowers people's desire to go to restaurants for lunch, but far less so in the evening. As air quality worsens, people tend to decrease their walking and cycling and tend to travel more by bus or subway. We also find a monotonically decreasing nonlinear relationship between air quality index and the average CDR-based distance for each person of two citizen groups that go walking or cycling. Our key and novel contributions are that we first define IoB as a ubiquitous concept. Based on this, we propose a methodology to better understand the link between bad air pollution events and citizens' activities of daily life. We applied this methodology in the first comprehensive study that provides quantitative evidence of the actual effect, not the presumed effect, that air pollution can significantly affect a wide range of citizens' activities of daily living.

Automatic Fracture Characterization Using Tactile and Proximity Optical Sensing

This paper demonstrates how tactile and proximity sensing can be used to perform automatic mechanical fractures detection (surface cracks). For this purpose, a custom-designed integrated tactile and proximity sensor has been implemented. With the help of fiber optics, the sensor measures the deformation of its body, when interacting with the physical environment, and the distance to the environment's objects. This sensor slides across different surfaces and records data which are then analyzed to detect and classify fractures and other mechanical features. The proposed method implements machine learning techniques (handcrafted features, and state of the art classification algorithms). An average crack detection accuracy of ~94% and width classification accuracy of ~80% is achieved. Kruskal-Wallis results (p < 0.001) indicate statistically significant differences among results obtained when analysing only integrated deformation measurements, only proximity measurements and both deformation and proximity data. A real-time classification method has been implemented for online classification of explored surfaces. In contrast to previous techniques, which mainly rely on visual modality, the proposed approach based on optical fibers might be more suitable for operation in extreme environments (such as nuclear facilities) where radiation may damage electronic components of commonly employed sensing devices, such as standard force sensors based on strain gauges and video cameras.

A Challenge-Response Assisted Authorisation Scheme for Data Access in Permissioned Blockchains

Permissioned blockchains can be applied for sharing data among permitted users to authorise the data access requests in a permissioned blockchain. A consensus network constructed using pre-selected nodes should verify a data requester's credentials to determine if he or she have the correct permissions to access the queried data. However, current studies do not consider how to protect users' privacy for data authorisation if the pre-selected nodes become untrusted, e.g., the pre-selected nodes are manipulated by attackers. When a user's credentials are exposed to pre-selected nodes in the consensus network during authorisation, the untrusted (or even malicious) pre-selected nodes may collect a user's credentials and other private information without the user's right to know. Therefore, the private data exposed to the consensus network should be tightly restricted. In this paper, we propose a challenge-response based authorisation scheme for permissioned blockchain networks named Challenge-Response Assisted Access Authorisation (CRA³) to protect users' credentials during authorisation. In CRA³, the pre-selected nodes in the consensus network do not require users' credentials to authorise data access requests to prevent privacy leakage when these nodes are compromised or manipulated by attackers. Furthermore, the computational burden on the consensus network for authorisation is reduced because the major computing work of the authorisation is executed by the data requester and provider in CRA³.

Large-Scale, Fine-Grained, Spatial, and Temporal Analysis, and Prediction of Mobile Phone Users' Distributions Based upon a Convolution Long Short-Term Model

Accurate and timely estimations of large-scale population distributions are a valuable input for social geography and economic research and for policy-making. The most popular large-scale method to calculate such estimations uses mobile phone data. We propose a novel method, firstly based upon using a kernel density estimation (KDE) to estimate dynamic mobile phone users' distributions at a two-hourly scale temporal resolution. Secondly, a convolutional long short-term memory (ConvLSTM) model was used in our study to predict mobile phone users' spatial and temporal distributions for the first time at such a fine-grained temporal resolution. The evaluation results show that the predicted people's mobility derived from the mobile phone users' density correlates much better with the actual density, both temporally and spatially, as compared to traditional methods such as time-series prediction, autoregressive moving average model (ARMA), and LSTM.

A Game-Theory Based Incentive Framework for an Intelligent Traffic System as Part of a Smart City Initiative

Intelligent Transportation Systems (ITSs) can be applied to inform and incentivize travellers to help them make cognizant choices concerning their trip routes and transport modality use for their daily travel whilst achieving more sustainable societal and transport authority goals. However, in practice, it is challenging for an ITS to enable incentive generation that is context-driven and personalized, whilst supporting multi-dimensional travel goals. This is because an ITS has to address the situation where different travellers have different travel preferences and constraints for route and modality, in the face of dynamically-varying traffic conditions. Furthermore, personalized incentive generation also needs to dynamically achieve different travel goals from multiple travellers, in the face of their conducts being a mix of both competitive and cooperative behaviours. To address this challenge, a Rule-based Incentive Framework (RIF) is proposed in this paper that utilizes both decision tree and evolutionary game theory to process travel information and intelligently generate personalized incentives for travellers. The travel information processed includes travellers’ mobile patterns, travellers’ modality preferences and route traffic volume information. A series of MATLAB simulations of RIF was undertaken to validate RIF to show that it is potentially an effective way to incentivize travellers to change travel routes and modalities as an essential smart city service.

Piecing together the puzzle: Improving event content coverage for real-time sub-event detection using adaptive microblog crawling

In an age when people are predisposed to report real-world events through their social media accounts, many researchers value the benefits of mining user generated content from social media. Compared with the traditional news media, social media services, such as Twitter, can provide more complete and timely information about the real-world events. However events are often like a puzzle and in order to solve the puzzle/understand the event, we must identify all the sub-events or pieces. Existing Twitter event monitoring systems for sub-event detection and summarization currently typically analyse events based on partial data as conventional data collection methodologies are unable to collect comprehensive event data. This results in existing systems often being unable to report sub-events in real-time and often in completely missing sub-events or pieces in the broader event puzzle. This paper proposes a Sub-event detection by real-TIme Microblog monitoring (STRIM) framework that leverages the temporal feature of an expanded set of news-worthy event content. In order to more comprehensively and accurately identify sub-events this framework first proposes the use of adaptive microblog crawling. Our adaptive microblog crawler is capable of increasing the coverage of events while minimizing the amount of non-relevant content. We then propose a stream division methodology that can be accomplished in real time so that the temporal features of the expanded event streams can be analysed by a burst detection algorithm. In the final steps of the framework, the content features are extracted from each divided stream and recombined to provide a final summarization of the sub-events. The proposed framework is evaluated against traditional event detection using event recall and event precision metrics. Results show that improving the quality and coverage of event contents contribute to better event detection by identifying additional valid sub-events. The novel combination of our proposed adaptive crawler and our stream division/recombination technique provides significant gains in event recall (44.44%) and event precision (9.57%). The addition of these sub-events or pieces, allows us to get closer to solving the event puzzle.