A rubbish idea : how blockchains could tackle the world’s waste problem

Waste litters our oceans, beaches and wider environment, making it one of the pressing issues of our times. Blockchains are virtual ledgers on which data can be permanently stored. They are a public record, so they are very transparent and accountable. This post aims to set out how blockchains may be used as part of the waste management toolkit

Selection of compressible signals from telemetry data

Sensors are deployed in all aspects of modern city infrastructure and generate vast amounts of data. Only subsets of this data, however, are relevant to individual organisations. For example, a local council may collect suspension movement from vehicles to detect pot-holes, but this data is not relevant when assessing traffic flow. Supervised feature selection aims to find the set of signals that best predict a target variable. Typical approaches use either measures of correlation or similarity, as in filter methods, or predictive power in a learned model, as in wrapper methods. In both approaches selected features often have high entropies and are not suitable for compression. This is of particular issue in the automotive domain where fast communication and archival of vehicle telemetry data is likely to be prevalent in the near future, especially with technologies such as V2V and V2X. In this paper, we adapt a popular feature selection filter method to consider the compressibility of signals being selected for use in a predictive model. In particular, we add a compression term to the Minimal Redundancy Maximal Relevance (MRMR) filter and introduce Minimal Redundancy Maximal Relevance And Compression (MRMRAC). Using MRMRAC, we then select features from the Controller Area Network (CAN) and predict each of current instantaneous fuel consumption, engine torque, vehicle speed, and gear position, using a Support Vector Machine (SVM). We show that while performance is slightly lower when compression is considered, the compressibility of the selected features is significantly improved

Redundant feature selection using permutation methods

Automatic feature selection aims to select the features with highest performance when used in a classifier. One popular measure for estimating feature relevancy and redundancy is Mutual Information (MI), although it is biased toward features with multiple values. Permutation methods have been successfully applied in normalizing for numerous biases including that of MI; however they are computationally expensive and complete redundancy computation is infeasible. In this paper, we introduce a measure that can be used to approximate all m2 redundancies between m features, while performing only m permutation methods for their relevancies. We then show using simulated data that this permutation redundancy measure holds similar properties to normalized MI and apply it in selecting features from example datasets using minimal Redundancy Maximal Relevancy (mRMR)

Data mining of vehicle telemetry data

Driving a safety critical task that requires a high level of attention and workload from the driver. Despite this, people often perform secondary tasks such as eating or using a mobile phone, which increase workload levels and divert cognitive and physical attention from the primary task of driving. As well as these distractions, the driver may also be overloaded for other reasons, such as dealing with an incident on the road or holding conversations in the car. One solution to this distraction problem is to limit the functionality of in-car devices while the driver is overloaded. This can take the form of withholding an incoming phone call or delaying the display of a non-urgent piece of information about the vehicle. In order to design and build these adaptions in the car, we must first have an understanding of the driver's current level of workload. Traditionally, driver workload has been monitored using physiological sensors or camera systems in the vehicle. However, physiological systems are often intrusive and camera systems can be expensive and are unreliable in poor light conditions. It is important, therefore, to use methods that are non-intrusive, inexpensive and robust, such as sensors already installed on the car and accessible via the Controller Area Network (CAN)-bus. This thesis presents a data mining methodology for this problem, as well as for others in domains with similar types of data, such as human activity monitoring. It focuses on the variable selection stage of the data mining process, where inputs are chosen for models to learn from and make inferences. Selecting inputs from vehicle telemetry data is challenging because there are many irrelevant variables with a high level of redundancy. Furthermore, data in this domain often contains biases because only relatively small amounts can be collected and processed, leading to some variables appearing more relevant to the classification task than they are really. Over the course of this thesis, a detailed variable selection framework that addresses these issues for telemetry data is developed. A novel blocked permutation method is developed and applied to mitigate biases when selecting variables from potentially biased temporal data. This approach is infeasible computationally when variable redundancies are also considered, and so a novel permutation redundancy measure with similar properties is proposed. Finally, a known redundancy structure between features in telemetry data is used to enhance the feature selection process in two ways. First the benefits of performing raw signal selection, feature extraction, and feature selection in different orders are investigated. Second, a two-stage variable selection framework is proposed and the two permutation based methods are combined. Throughout the thesis, it is shown through classification evaluations and inspection of the features that these permutation based selection methods are appropriate for use in selecting features from CAN-bus data

Stereotype reputation with limited observability

Assessing trust and reputation is essential in multi-agent systems where agents must decide who to interact with. Assessment typically relies on the direct experience of a trustor with a trustee agent, or on information from witnesses. Where direct or witness information is unavailable, such as when agent turnover is high, stereotypes learned from common traits and behaviour can provide this information. Such traits may be only partially or subjectively observed, with witnesses not observing traits of some trustees or interpreting their observations differently. Existing stereotype-based techniques are unable to account for such partial observability and subjectivity. In this paper we propose a method for extracting information from witness observations that enables stereotypes to be applied in partially and subjectively observable dynamic environments. Specifically, we present a mechanism for learning translations between observations made by trustor and witness agents with subjective interpretations of traits. We show through simulations that such translation is necessary for reliable reputation assessments in dynamic environments with partial and subjective observability

Reputation-based provider incentivisation for provenance provision

Knowledge of circumstances under which past service provisions have occurred enables clients to make more informed selection decisions regarding their future interaction partners. Service providers, however, may often be reluctant to release such circumstances due to the cost and effort required, or to protect their interests. In response, we introduce a reputation-based incentivisation framework, which motivates providers towards the desired behaviour of reporting circumstances via influencing two reputation-related factors: the weights of past provider interactions, which directly impact the provider’s reputation estimate, and the overall confidence in such estimate

Westerville Jaycee Pool

Due to low attendance and poor community support, the owner of the Westerville Jaycee (WJC) Swimming Pool considered permanently closing this facility two years ago. The goal of this project was to increase awareness for WJC and draw attention to the property itself. By setting objectives to improve the landscaping conditions, and creating a social media presence for WJC, we hoped to achieve these goals. To meet these objectives, the team created a WJC Twitter account, and asked that friends and family like and share their preexisting Facebook page. Our team also contacted several companies to inquire about landscaping donations. We were successful in setting a date to mulch and plant flowers with The Grounds Guys of Westerville at WJC. An evaluation of our success was determined by an increase in Twitter followers, and also by management’s satisfaction with our work at the property. In regards to our evaluation standards, our results were successful. The legacy we hope to leave behind is a property that WJC can be proud of, as they are an important asset to the Westerville Community. We hope that our creation of a social media presence will help them reach out to new age groups. By the end of our project, WJC was able to strengthen its ties within the community, by forming a relationship The Grounds Guys of Westerville. Our recommendations for the continuation of this project would be to obtain a boulder that WJC can use as a landscaping piece for the front lawn

Bootstrapping trust with partial and subjective observability

Assessment of trust and reputation typically relies on prior experiences of a trustee agent, which may not exist, e.g. especially in highly dynamic environments. In these cases stereotypes can be used, where traits of trustees can be used as an indicator of their behaviour during interactions. Communicating observations of traits to witnesses who are unable to observe them is difficult, however, when the traits are interpreted subjectively. In this paper we propose a mechanism for learning translations between such subjective observations, evaluating it in a simulated marketplace

Investigating the feasibility of vehicle telemetry data as a means of predicting driver workload

Driving is a safety critical task that requires a high level of attention and workload from the driver. Despite this, people often also perform secondary tasks such as eating or using a mobile phone, which increase workload levels and divert cognitive and physical attention from the primary task of driving. If a vehicle is aware that the driver is currently under high workload, the vehicle functionality can be changed in order to minimize any further demand. Traditionally, workload measurements have been performed using intrusive means such as physiological sensors. Another approach may be to use vehicle telemetry data as a performance measure for workload. In this paper, we present the Warwick-JLR Driver Monitoring Dataset (DMD) and analyse it to investigate the feasibility of using vehicle telemetry data for determining the driver workload. We perform a statistical analysis of subjective ratings, physiological data, and vehicle telemetry data collected during a track study. A data mining methodology is then presented to build predictive models using this data, for the driver workload monitoring problem