Learning Behavioural Context

The original publication is available at www.springerlink.co

Unsupervised learning of generative topic saliency for person re-identification

(c) 2014. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.© 2014. The copyright of this document resides with its authors. Existing approaches to person re-identification (re-id) are dominated by supervised learning based methods which focus on learning optimal similarity distance metrics. However, supervised learning based models require a large number of manually labelled pairs of person images across every pair of camera views. This thus limits their ability to scale to large camera networks. To overcome this problem, this paper proposes a novel unsupervised re-id modelling approach by exploring generative probabilistic topic modelling. Given abundant unlabelled data, our topic model learns to simultaneously both (1) discover localised person foreground appearance saliency (salient image patches) that are more informative for re-id matching, and (2) remove busy background clutters surrounding a person. Extensive experiments are carried out to demonstrate that the proposed model outperforms existing unsupervised learning re-id methods with significantly simplified model complexity. In the meantime, it still retains comparable re-id accuracy when compared to the state-of-the-art supervised re-id methods but without any need for pair-wise labelled training data

Guidelines for assessing pedestrian evacuation software applications

This paper serves to clearly identify and explain criteria to consider when evaluating the suitability of a pedestrian evacuation software application to assess the evacuation process of a building. Guidelines in the form of nine topic areas identify different modelling approaches adopted, as well as features / functionality provided by applications designed specifically for simulating the egress of pedestrians from inside a building. The paper concludes with a synopsis of these guidelines, identifying key questions (by topic area) to found an evaluation

Transport Impacts on Land Use: Towards A Practical Understanding for Urban Policy Making – Introduction and Research Plan.

INTRODUCTION This working paper forms a general introduction to an EPSRC CASE research project, presenting the objectives of the research, the rationale behind the study, a summary of some of the results obtained so far, and a plan for the remainder of the research work. The project is due for completion in November 1996. In other words, the project is examining: 1. The current understanding of the nature of the influence that transport has upon activity patterns and land use. Specifically, this is making use of empirical studies of transport impacts on land use, plus behavioural studies of the factors in location choice. 2. Whether this relationship can be adequately represented in a predictive context. This consists of two elements. How the relationship of transport on land use can be studied and 'formalised', and secondly, the ability to use this relationship for estimation of land use response to transport impacts. Use will be made of published modelling studies, plus some original modelling work, using a model constructed for this research. 3. The benefits of predicting transport impacts upon land use to planners involved in strategic land use and transport planning. This is the main objective of the research, and addresses why transport impacts on land use appear to have a minor role in structure planning, why model representations are seldom used, and given a model's predictions, what use will be made of the model results. Initial results from the first round of interviews are given in this paper. There are several themes that underpin this research: The nature of the 'transport on land use' relationship. How far it can he formalised, what we know about it, and how it is best to study it. Strategic planning processes in the UK, how the planning system handles the transport on land use relationship, under what circumstances the relationship is important, and the role of model predictions in the planning process. Whether the remit of 'planning' should examine transport impacts on land use, plus anticipation of the impacts of local government reorganisation. The issue of whether predictive modellmg in this context is an appropriate tool beyond the scope of academic research

A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users

Spatial item recommendation has become an important means to help people discover interesting locations, especially when people pay a visit to unfamiliar regions. Some current researches are focusing on modelling individual and collective geographical preferences for spatial item recommendation based on users' check-in records, but they fail to explore the phenomenon of user interest drift across geographical regions, i.e., users would show different interests when they travel to different regions. Besides, they ignore the influence of public comments for subsequent users' check-in behaviors. Specifically, it is intuitive that users would refuse to check in to a spatial item whose historical reviews seem negative overall, even though it might fit their interests. Therefore, it is necessary to recommend the right item to the right user at the right location. In this paper, we propose a latent probabilistic generative model called LSARS to mimic the decision-making process of users' check-in activities both in home-town and out-of-town scenarios by adapting to user interest drift and crowd sentiments, which can learn location-aware and sentiment-aware individual interests from the contents of spatial items and user reviews. Due to the sparsity of user activities in out-of-town regions, LSARS is further designed to incorporate the public preferences learned from local users' check-in behaviors. Finally, we deploy LSARS into two practical application scenes: spatial item recommendation and target user discovery. Extensive experiments on two large-scale location-based social networks (LBSNs) datasets show that LSARS achieves better performance than existing state-of-the-art methods.Comment: Accepted by KDD 201

Secure forecasting of user activities for distributed urban applications

Modelling human mobility is an interesting yet challenging research topic. Such mobility models can give valuable insight into user behavior. Such models can be used to forecast movement of people. Even though an interesting problem, it was not studied as widely due to lack of available mobility data. But modern communication and digital infrastructure has solved this problem. Thus, as a result, over the past decade and a half, this topic has attracted a lot of attraction. The modelling and forecasting of human mobility has widespread applications from transportation to advertisement. Such models can be used to in a collaborative manner to segment people or used in isolation to bring better services to an individual. Previous researches have presented different approaches for modelling human mobility. These range from neural networks to Markov chains. Some researchers have focused on location data while others have worked with accelerometer data. There are also recommendations to add more information to the data to understand the motive of mobility. This thesis approaches the problem of forecasting human mobility in the form of activities. GPS data is analyzed to mine information and find patterns. The forecasting is done in a twostep process. The first step is to analyze the data to identify and label activities, that are done on a routine basis. This is achieved by using an Adaptive Neuro-Fuzzy Inference System. This additional information helps understand the motive of moving from one place to another. In the second and final step the Markov Chain model is built for the movement among visited locations. The forecasting is done with respect to current time and location, keeping in view the motive of movement. The proposed system is implemented in JAVA and deployed as a combination of RESTful web services. Finally, accuracy tests are made on different datasets which show promising results

Flexible modelling in statistics: past, present and future

In times where more and more data become available and where the data exhibit rather complex structures (significant departure from symmetry, heavy or light tails), flexible modelling has become an essential task for statisticians as well as researchers and practitioners from domains such as economics, finance or environmental sciences. This is reflected by the wealth of existing proposals for flexible distributions; well-known examples are Azzalini's skew-normal, Tukey's $g$-and-$h$, mixture and two-piece distributions, to cite but these. My aim in the present paper is to provide an introduction to this research field, intended to be useful both for novices and professionals of the domain. After a description of the research stream itself, I will narrate the gripping history of flexible modelling, starring emblematic heroes from the past such as Edgeworth and Pearson, then depict three of the most used flexible families of distributions, and finally provide an outlook on future flexible modelling research by posing challenging open questions.Comment: 27 pages, 4 figure