Analysis of crime patterns through the integration of an agent-based model and a population microsimulation

In recent years, criminologists have become interested in understanding crime variations at progressively finer spatial scales, right down to individual streets or even houses. To model at these fine spatial scales, and to better account for the dynamics of the crime system, agent-based models of crime are emerging. Generally, these have been more successful in representing the behaviour of criminals than their victims. In this paper it is suggested that individual representations of criminal behaviour can be enhanced by combining them with models of the criminal environment which are specified at a similar scale. In the case of burglary this means the identification of individual households as targets. We will show how this can be achieved using the complementary technique of microsimulation. The work is significant because it allows agent-based models of crime to be refined geographically (to allow, for example, individual households with varying wealth or occupancy measures) and leads to the identification of the characteristics of individual victims.     

A NON-PARAMETRIC APPROACH TO SIMPLICITY CLUSTERING

The simplicity principle—an updating of Ockham's razor to take into account modern information theory—states that the preferred theory for a set of data is the one that allows for the most efficient encoding of the data. We consider this in the context of classification, or clustering, as a data reduction technique that helps describe a set of objects by dividing the objects into groups. The simplicity model we present favors clusters such that the similarity of the items in the clusters is maximal, while the similarity of items between clusters is minimal. Several novel features of our clustering criterion make it especially appropriate for clustering of data derived from, psychological procedures (e.g., similarity ratings): It is non-parametric, and may be applied in situations where the metric axioms are violated without requiring (information-forgetting) transformation procedures. We illustrate the use of the criterion with a selection of data sets. A distinctive aspect of this research is that it motivates a clustering algorithm from psychological principles.     

DLEJena: A practical forward-chaining OWL 2 RL reasoner combining Jena and Pellet

This paper describes DLEJena, a practical reasoner for the OWL 2 RL profile that combines the forward-chaining rule engine of Jena and the Pellet DL reasoner. This combination is based on rule templates, instantiating at run-time a set of ABox OWL 2 RL/RDF Jena rules dedicated to a particular TBox that is handled by Pellet. The goal of DLEJena is to handle efficiently, through instantiated rules, the OWL 2 RL ontologies under direct semantics, where classes and properties cannot be at the same time individuals. The TBox semantics are treated by Pellet, reusing in that way efficient and sophisticated TBox DL reasoning algorithms. The experimental evaluation shows that DLEJena achieves more scalable ABox reasoning than the direct implementation of the OWL 2 RL/RDF rule set in the Jena’s production rule engine, which is the main target of the system. DLEJena can be also used as a generic framework for applying an arbitrary number of entailments beyond the OWL 2 RL profile.     

Dense subgraph maintenance under streaming edge weight updates for real-time story identification

Recent years have witnessed an unprecedented proliferation of social media. People around the globe author, everyday, millions of blog posts, social network status updates, etc. This rich stream of information can be used to identify, on an ongoing basis, emerging stories, and events that capture popular attention. Stories can be identified via groups of tightly coupled real-world entities, namely the people, locations, products, etc, that are involved in the story. The sheer scale and rapid evolution of the data involved necessitate highly efficient techniques for identifying important stories at every point of time. The main challenge in real-time story identification is the maintenance of dense subgraphs (corresponding to groups of tightly coupled entities) under streaming edge weight updates (resulting from a stream of user-generated content). This is the first work to study the efficient maintenance of dense subgraphs under such streaming edge weight updates. For a wide range of definitions of density, we derive theoretical results regarding the magnitude of change that a single edge weight update can cause. Based on these, we propose a novel algorithm, DynDens, which outperforms adaptations of existing techniques to this setting and yields meaningful, intuitive results. Our approach is validated by a thorough experimental evaluation on large-scale real and synthetic datasets.     

PAC-learnability of probabilistic deterministic finite state automata in terms of variation distance

We consider the problem of PAC-learning distributions over strings, represented by probabilistic deterministic finite automata (PDFAs). PDFAs are a probabilistic model for the generation of strings of symbols, that have been used in the context of speech and handwriting recognition, and bioinformatics. Recent work on learning PDFAs from random examples has used the KL-divergence as the error measure; here we use the variation distance. We build on recent work by Clark and Thollard, and show that the use of the variation distance allows simplifications to be made to the algorithms, and also a strengthening of the results; in particular that using the variation distance, we obtain polynomial sample size bounds that are independent of the expected length of strings.     

End-user adoption of animated interface agentsin everyday work applications

Recognizing the potential contribution that interactive software agents bring to everyday work applications, this paper reports on end-user adoption of animated interface agents in one particular work application environment: Microsoft® Office. The paper develops and empirically tests a theoretical model of the factors affecting an end-user's choice to adopt and utilize such interface agents. From this theoretical model, a survey instrument was adapted and administered to 261 participants, familiar with animated interface agents. Results from a partial least squares (PLS) analysis indicates that a variety of factors are at play, which inhibit or foster a person's choice to utilize and adopt animated interface agents. Of significance is that: (a) both perceived usefulness and perceived enjoyment are important influencing factors; (b) users with high scores in innovativeness toward information technology are less likely to find animated interface agents enjoyable; (c) individuals with high animation predisposition scores perceive animated interface agents to be more enjoyable; and (d) users who perceive animated interface agents to be more enjoyable also perceive them to be more useful. Such insights can be used to leverage the introduction and rollout of animated interface agents in everyday work applications in ways that promote their avid adoption and use.     

User acceptance of hedonic digital artifacts: A theory of consumption values perspective

Hedonic digital artifacts have become prevalent in today's society. Their users typically pay for them, and in exchange are generally provided with benefits involving enjoyment. Today's research on technology adoption and use, though, has focused mostly on organizational or personal aids that provide efficiency and effectiveness and are free of charge for users. To bridge this gap, we identified several value drivers of hedonic digital artifacts and measured them in the context of mobile phone ringtones using the theory of consumption values. Hypothesis testing was performed using PLS on data collected from 422 ringtone users. Results confirmed that the overall value of hedonic digital artifacts is a third-order composite assessment, which successfully predicted behavioral usage and positive word-of-mouth intentions. Theoretical and practical implications were discussed.     

Problematic Internet use and psychosocial well-being among MMO players

The current study examined problematic Internet use (PIU) among people who play MMO games and sought to determine whether aspects of the MMO experience are useful predictors of PIU. The study sought to determine whether game-related variables could predict PIU scores after accounting for their relationships with psychosocial well-being. Novel methods allowed us, for the first time, to connect in-game behaviors with survey results of over 4000 MMO players. The results revealed that MMO gaming variables contributed a substantively small, but statistically significant amount of explained variance to PIU scores.     

The wake up and report problem is time-equivalent to the firing squad synchronization problem

We consider several problems relating to strongly-connected directed networks of identical finite-state processors that work synchronously in discrete time steps. The conceptually simplest of these problems is the Wake Up and Report Problem; this is the problem of having a unique root processor send a signal to all other processors in the network and then enter a special done state only when all other processors have received the signal. The most difficult of the problems we consider is the classic Firing Squad Synchronization Problem; this is the much-studied problem of achieving macro-synchronization in a network given micro-synchronization. We show via a complex algorithmic application of the snake data structure first introduced in Even, Litman, and Winkler [6] that these two problems in particular are asymptotically time-equivalent up to a constant factor. This result leads immediately to the inclusion of several other related problems into this new asymptotic time-class.Published online: 6 February 2004     

Intelligent planning and the design of a new risk-based,intelligent flight plan

A safe flight starts with effective performance of the pre-flight flight planning and briefing task. However, several problems related to the execution of this task can be identified. Potentially, the introduction of an improved flight plan provides an opportunity to improve the quality and availability of information provided to Flight Crew, thereby enhancing the quality of crew briefings. The proposed risk-based, intelligent flight plan is designed from the perspective of the current operational concept (e.g. fixed routes and ATC managerial role for separation), and associated airline Flight Planning and Dispatch functions. In this case, the focus is sharing information across specific airline stakeholders (e.g. Flight Operations Management and Safety functions) and Maintenance, to support a safe and efficient flight operation. Overall, the introduction of this new flight plan will result in the definition of new operational and organisational processes, along with a new way of performing the pre-flight, planning and briefing task. It is anticipated that this will impact positively on the operational and safety outcome of the flight.