Ubiquitous intelligence for smart cities: a public safety approach

Citizen-centered safety enhancement is an integral component of public safety and a top priority for decision makers in a smart city development. However, public safety agencies are constantly faced with the challenge of deterring crime. While most smart city initiatives have placed emphasis on the use of modern technology for fighting crime, this may not be sufficient to achieve a sustainable safe and smart city in a resource constrained environment, such as in Africa. In particular, crime series which is a set of crimes considered to have been committed by the same offender is currently less explored in developing nations and has great potential in helping to fight against crime and promoting safety in smart cities. This research focuses on detecting the situation of crime through data mining approaches that can be used to promote citizens' safety, and assist security agencies in knowledge-driven decision support, such as crime series identification. While much research has been conducted on crime hotspots, not enough has been done in the area of identifying crime series. This thesis presents a novel crime clustering model, CriClust, for crime series pattern (CSP) detection and mapping to derive useful knowledge from a crime dataset, drawing on sound scientific and mathematical principles, as well as assumptions from theories of environmental criminology. The analysis is augmented using a dual-threshold model, and pattern prevalence information is encoded in similarity graphs. Clusters are identified by finding highly-connected subgraphs using adaptive graph size and Monte-Carlo heuristics in the Karger-Stein mincut algorithm. We introduce two new interest measures: (i) Proportion Difference Evaluation (PDE), which reveals the propagation effect of a series and dominant series; and (ii) Pattern Space Enumeration (PSE), which reveals underlying strong correlations and defining features for a series. Our findings on experimental quasi-real data set, generated based on expert knowledge recommendation, reveal that identifying CSP and statistically interpretable patterns could contribute significantly to strengthening public safety service delivery in a smart city development. Evaluation was conducted to investigate: (i) the reliability of the model in identifying all inherent series in a crime dataset; (ii) the scalability of the model with varying crime records volume; and (iii) unique features of the model compared to competing baseline algorithms and related research. It was found that Monte Carlo technique and adaptive graph size mechanism for crime similarity clustering yield substantial improvement. The study also found that proportion estimation (PDE) and PSE of series clusters can provide valuable insight into crime deterrence strategies. Furthermore, visual enhancement of clusters using graphical approaches to organising information and presenting a unified viable view promotes a prompt identification of important areas demanding attention. Our model particularly attempts to preserve desirable and robust statistical properties. This research presents considerable empirical evidence that the proposed crime cluster (CriClust) model is promising and can assist in deriving useful crime pattern knowledge, contributing knowledge services for public safety authorities and intelligence gathering organisations in developing nations, thereby promoting a sustainable "safe and smart" city

Unsupervised Graph-based Rank Aggregation for Improved Retrieval

This paper presents a robust and comprehensive graph-based rank aggregation approach, used to combine results of isolated ranker models in retrieval tasks. The method follows an unsupervised scheme, which is independent of how the isolated ranks are formulated. Our approach is able to combine arbitrary models, defined in terms of different ranking criteria, such as those based on textual, image or hybrid content representations. We reformulate the ad-hoc retrieval problem as a document retrieval based on fusion graphs, which we propose as a new unified representation model capable of merging multiple ranks and expressing inter-relationships of retrieval results automatically. By doing so, we claim that the retrieval system can benefit from learning the manifold structure of datasets, thus leading to more effective results. Another contribution is that our graph-based aggregation formulation, unlike existing approaches, allows for encapsulating contextual information encoded from multiple ranks, which can be directly used for ranking, without further computations and post-processing steps over the graphs. Based on the graphs, a novel similarity retrieval score is formulated using an efficient computation of minimum common subgraphs. Finally, another benefit over existing approaches is the absence of hyperparameters. A comprehensive experimental evaluation was conducted considering diverse well-known public datasets, composed of textual, image, and multimodal documents. Performed experiments demonstrate that our method reaches top performance, yielding better effectiveness scores than state-of-the-art baseline methods and promoting large gains over the rankers being fused, thus demonstrating the successful capability of the proposal in representing queries based on a unified graph-based model of rank fusions

PROTEIN FUNCTION, DIVERISTY AND FUNCTIONAL INTERPLAY

Functional annotations of novel or unknown proteins is one of the central problems in post-genomics bioinformatics research. With the vast expansion of genomic and proteomic data and technologies over the last decade, development of automated function prediction (AFP) methods for large-scale identification of protein function has be-come imperative in many aspects. In this research, we address two important divergences from the “one protein – one function” concept on which all existing AFP methods are developed

A New Feature Selection Method Based on Class Association Rule

Feature selection is a key process for supervised learning algorithms. It involves discarding irrelevant attributes from the training dataset from which the models are derived. One of the vital feature selection approaches is Filtering, which often uses mathematical models to compute the relevance for each feature in the training dataset and then sorts the features into descending order based on their computed scores. However, most Filtering methods face several challenges including, but not limited to, merely considering feature-class correlation when defining a feature’s relevance; additionally, not recommending which subset of features to retain. Leaving this decision to the end-user may be impractical for multiple reasons such as the experience required in the application domain, care, accuracy, and time. In this research, we propose a new hybrid Filtering method called Class Association Rule Filter (CARF) that deals with the aforementioned issues by identifying relevant features through the Class Association Rule Mining approach and then using these rules to define weights for the available features in the training dataset. More crucially, we propose a new procedure based on mutual information within the CARF method which suggests the subset of features to be retained by the end-user, hence reducing time and effort. Empirical evaluation using small, medium, and large datasets that belong to various dissimilar domains reveals that CARF was able to reduce the dimensionality of the search space when contrasted with other common Filtering methods. More importantly, the classification models devised by the different machine learning algorithms against the subsets of features selected by CARF were highly competitive in terms of various performance measures. These results indeed reflect the quality of the subsets of features selected by CARF and show the impact of the new cut-off procedure proposed

Enhancing Stratified Graph Sampling Algorithms based on Approximate Degree Distribution

Sampling technique has become one of the recent research focuses in the graph-related fields. Most of the existing graph sampling algorithms tend to sample the high degree or low degree nodes in the complex networks because of the characteristic of scale-free. Scale-free means that degrees of different nodes are subject to a power law distribution. So, there is a significant difference in the degrees between the overall sampling nodes. In this paper, we propose an idea of approximate degree distribution and devise a stratified strategy using it in the complex networks. We also develop two graph sampling algorithms combining the node selection method with the stratified strategy. The experimental results show that our sampling algorithms preserve several properties of different graphs and behave more accurately than other algorithms. Further, we prove the proposed algorithms are superior to the off-the-shelf algorithms in terms of the unbiasedness of the degrees and more efficient than state-of-the-art FFS and ES-i algorithms.Comment: 10 pages, 23 figures, the concept of approximate degree distribution, scale-free networks, graph sampling methods, stratified technolog

Representation learning on heterogeneous spatiotemporal networks

“The problem of learning latent representations of heterogeneous networks with spatial and temporal attributes has been gaining traction in recent years, given its myriad of real-world applications. Most systems with applications in the field of transportation, urban economics, medical information, online e-commerce, etc., handle big data that can be structured into Spatiotemporal Heterogeneous Networks (SHNs), thereby making efficient analysis of these networks extremely vital. In recent years, representation learning models have proven to be quite efficient in capturing effective lower-dimensional representations of data. But, capturing efficient representations of SHNs continues to pose a challenge for the following reasons: (i) Spatiotemporal data that is structured as SHN encapsulate complex spatial and temporal relationships that exist among real-world objects, rendering traditional feature engineering approaches inefficient and compute-intensive; (ii) Due to the unique nature of the SHNs, existing representation learning techniques cannot be directly adopted to capture their representations. To address the problem of learning representations of SHNs, four novel frameworks that focus on their unique spatial and temporal characteristics are introduced: (i) collective representation learning, which focuses on quantifying the importance of each latent feature using Laplacian scores; (ii) modality aware representation learning, which learns from the complex user mobility pattern; (iii) distributed representation learning, which focuses on learning human mobility patterns by leveraging Natural Language Processing algorithms; and (iv) representation learning with node sense disambiguation, which learns contrastive senses of nodes in SHNs. The developed frameworks can help us capture higher-order spatial and temporal interactions of real-world SHNs. Through data-driven simulations, machine learning and deep learning models trained on the representations learned from the developed frameworks are proven to be much more efficient and effective”--Abstract, page iii