The structural properties of sexual fantasies for sexual offenders : a preliminary model

While the phenomenon of sexual fantasy has been researched extensively, little contemporary inquiry has investigated the structural properties of sexual fantasy within the context of sexual offending. In this study, a qualitative analysis was used to develop a descriptive model of the phenomena of sexual fantasy during the offence process. Twenty-four adult males convicted of sexual offences provided detailed retrospective descriptions of their thoughts, emotions and behaviours—before, during and after their offences. A data-driven approach to model development, known as Grounded Theory, was undertaken to analyse the interview transcripts. A model was developed to elucidate the structural properties of sexual fantasy in the process of sexual offending, as well as the physiological and psychological variables associated with it. The Sexual Fantasy Structural Properties Model (SFSPM) comprises eight categories that describe various properties of sexual fantasy across the offence process. These categories are: origin, context, trigger, perceptual modality, clarity, motion, intensity and emotion. The strengths of the SFSPM are discussed and its clinical implications are reviewed. Finally, the limitations of the study are presented and future research directions discussed.

Geographical Information Systems (GIS) and their role in sustainable planning : a case study from a Local Government Area (LGA) in Australia

A case study is used to demonstrate the application of Geographical Information Systems (GIS) to inform sustainable development. The suitability of the landscape to support tourism accommodation in a Local Government Area (LGA) is modelled by integrating existing datasets, including conservation areas, residential zones, major roads and known locations of tourism operators into a logistic regression framework. By using a data-driven approach an indication of the relative importance of each explanatory variable can be accounted for, therefore informing planners of the importance of different assets. In a region where tourism is reliant upon natural features, this use of information systems in conjunction with quantitative statistical modelling can value-add to existing datasets. The provision of this kind of knowledge is important as it would otherwise not factor into the decision-making process had the datasets been considered independently of each other – a concept that applies to both the public and private sectors.

Eventscapes : visualizing events over time with emotive facets

The scale and dynamicity of social media, and interaction between traditional news sources and online communities, has created challenges to information retrieval approaches. Users may have no clear information need or be unable to express it in the appropriate idiom, requiring instead to be oriented in an unfamiliar domain, to explore and learn. We present a novel data-driven visualization, termed Eventscape, that combines time, visual media, mood, and controversy. Formative evaluation highlights the value of emotive facets for rapid evaluation of mixed news and social media topics, and a role for such visualizations as pre-cursors to deeper search. Copyright 2011 ACM.

Semi-supervised classification based on subspace sparse representation

Graph plays an important role in graph-based semi-supervised classification. However, due to noisy and redundant features in high-dimensional data, it is not a trivial job to construct a well-structured graph on high-dimensional samples. In this paper, we take advantage of sparse representation in random subspaces for graph construction and propose a method called Semi-Supervised Classification based on Subspace Sparse Representation, SSC-SSR in short. SSC-SSR first generates several random subspaces from the original space and then seeks sparse representation coefficients in these subspaces. Next, it trains semi-supervised linear classifiers on graphs that are constructed by these coefficients. Finally, it combines these classifiers into an ensemble classifier by minimizing a linear regression problem. Unlike traditional graph-based semi-supervised classification methods, the graphs of SSC-SSR are data-driven instead of man-made in advance. Empirical study on face images classification tasks demonstrates that SSC-SSR not only has superior recognition performance with respect to competitive methods, but also has wide ranges of effective input parameters.

Bootstrapping mobile app development

Modern IDEs provide limited support for developers when starting a new data-driven mobile app. App developers are currently required to write copious amounts of boilerplate code, scripts, organise complex directories, and author actual functionality. Although this scenario is ripe for automation, current tools are yet to address it adequately. In this paper we present RAPPT, a tool that generates the scaffolding of a mobile app based on a high level description specified in a Domain Specific Language (DSL). We demonstrate the feasibility of our approach by an example case study and feedback from a professional development team. Demo at: https://www.youtube.com/watch?v=ffquVgBYpLM.

Review on the impact of urban geometry and pedestrian level greening on outdoor thermal comfort

The number of hot days is increasing in many parts of the world because of the heat island phenomenon and global climate change. High air temperature greatly affects human thermal comfort and public health, particularly in urban areas. Therefore, the challenging task of, urban designers and urban planners in accommodating the increasing population is to make cities with the least level of vulnerability to future climate change. Interest in transferring urban climatic knowledge into urban planning practices, and developing mitigation strategies to adapt to climate change, has been increased in recent years. The use of vegetation and appropriate urban geometry are shown very promising in mitigating the adverse effects of heat island and providing a better pedestrian thermal comfort. This article reviews studies on pedestrian level urban greening and geometry in improving thermal comfort in cities. Such strategies can be applied at the preliminary stages of urban planning and thus directly affect the microclimate. The analyzed data include simulation and field measurement studies. The discussion of this research clearly reflects how urban design guidelines can be applied to enhance outdoor thermal comfort and minimize the heat island effect. This study is helpful in controlling the consequences of city design from the early design stage.

Small cluster in cyber physical systems: network topology, interdependence and cascading failures

In cyber physical system (CPS), computational resources and physical resources are strongly correlated and mutually dependent. Cascading failures occur between coupled networks, cause the system more fragile than single network. Besides widely used metric giant component, we study small cluster (small component) in interdependent networks after cascading failures occur. We first introduce an overview on how small clusters distribute in various single networks. Then we propose a percolation theory based mathematical method to study how small clusters be affected by the interdependence between two coupled networks. We prove that the upper bounds exist for both the fraction and the number of operating small clusters. Without loss of generality, we apply both synthetic network and real network data in simulation to study small clusters under different interdependence models and network topologies. The extensive simulations highlight our findings: except the giant component, considerable proportion of small clusters exists, with the remaining part fragmenting to very tiny pieces or even massive isolated single vertex; no matter how the two networks are tightly coupled, an upper bound exists for the size of small clusters. We also discover that the interdependent small-world networks generally have the highest fractions of operating small clusters. Three attack strategies are compared: Inter Degree Priority Attack, Intra Degree Priority Attack and Random Attack. We observe that the fraction of functioning small clusters keeps stable and is independent from the attack strategies.

A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting

Accurate and timely traffic flow prediction is crucial to proactive traffic management and control in data-driven intelligent transportation systems (D2ITS), which has attracted great research interest in the last few years. In this paper, we propose a Spatial-Temporal Weighted K-Nearest Neighbor model, named STW-KNN, in a general MapReduce framework of distributed modeling on a Hadoop platform, to enhance the accuracy and efficiency of short-term traffic flow forecasting. More specifically, STW-KNN considers the spatial-temporal correlation and weight of traffic flow with trend adjustment features, to optimize the search mechanisms containing state vector, proximity measure, prediction function, and K selection. urthermore, STW-KNN is implemented on a widely adopted Hadoop distributed computing platform with the MapReduce parallel processing paradigm, for parallel prediction of traffic flow in real time. inally, with extensive experiments on real-world big taxi trajectory data, STW-KNN is compared with the state-of-the-art prediction models including conventional K-Nearest Neighbor (KNN), Artificial Neural Networks (ANNs), Naïve Bayes (NB), Random orest (R), and C4.. The results demonstrate that the proposed model is superior to existing models on accuracy by decreasing the mean absolute percentage error (MAPE) value more than 11.9% only in time domain and even achieves 89.71% accuracy improvement with the MAPEs of between 4% and 6.% in both space and time domains, and also significantly improves the efficiency and scalability of short-term traffic flow forecasting over existing approaches.

New modalities of state power: neoliberal responsibilisation and the work of academy chains

This paper draws on interview data gathered as part of a broader study around issues of equity and schooling. It features the voices of the Executive Director and four Head Teachers from one of England's top performing academy chains, ‘CONNECT’. The notion of neoliberal responsibilisation is drawn on to examine, first, the ways in which Head Teachers describe their work and, second, the chain's expectations of them as CONNECT leaders. Responsibilisation of the self was apparent in Head Teachers' construction of themselves as ideal neoliberal workers – performing and enterprising subjects who readily accept the business principles and results-orientation of their ‘data-driven’ environment. Responsibilising of Head Teachers by the organisation was evident in the rigorous ‘non-negotiable’ standards and accountabilities at CONNECT that they were expected to comply with. These non-negotiables cultivated and rewarded Head Teachers’ entrepreneurial identity of achievement motivation. The paper illustrates how such neoliberal responsibilisation is both a crucial and highly troubling element in the work of academy chains as new modalities of state power.

Validation of the use of Australian input-output data for building embodied energy simulation

Traditional!y, the simulation of buildings has focused 011 operational energy consumption in an attempt to determine the potential for energy savings. Whilst operational energy of Australian buildings accounts for around 20% of total energy consumption nationally, embodied energy represents 20 to 50 times the annual operational energy of 1110st Australian buildings. Lower values have been shown through a number of studies that have analysed the embodied energy of buildings and their products, however these have now shown to be incomplete in system boundary. Many of these studies have used traditional embodied energy analysis methods, such as process analysis and input-output analysis, Hybrid embodied energy analysis methods have been developed, but these need to be compared and validated. This paper reports on preliminary work on this topic. The findings so far suggest that current best-practice methods are sufficiently accurate for most typical applications, but this is heavily dependant upon data quality and availability.

Dynamic request-driven scheme for data collectors in sensor networks

Wireless sensor networks lifetime is prolonged through a dynamic scheme for collecting sensory information using intelligent mobile elements. The data collection routes are optimised for fast and reliable delivery. The scheme minimises high levels of energy consumption to extend the network operational time.

Evaluation of thermal and visual comfort in offices considering realistic input data and user behaviour in building simulation

Thermal and visual comfort play a very important role regarding the satisfaction of occupants with their working environments. The most effective method to achieve thermal comfort in offices is to reduce cooling loads in order to avoid additional energy-consuming devices for cooling. Building simulation software can be a helpful tool for optimisation, and typically standard values for the influencing parameters are used in order to ensure compliance to norms and regulations.

In practice many of those parameters turn out to be different compared to the simulation assumptions and the reasons may be the chosen room or building related properties as well as the user behaviour influenced by the task and the corporate culture of the company.

This paper investigates exemplary for the climate of Hamburg, Germany and a naturally ventilated typical office room, the optimisation potential of the building- and user-related parameters for thermal comfort, daylighting and view when using realistic input data for building simulation. The study has been conducted with the EnergyPlus based simulation software “Primero-Komfort” [1].

A generalised data analysis approach for baggage handling systems simulation

Airport baggage handling systems are a critical infrastructure component within major airports, and essential to ensure smooth luggage transfer while preventing dangerous material being loaded onto aircraft. This paper proposes a standard set of measures to assess the expected performance of a baggage handling system through discrete event simulation. These evaluation methods also have application in the study of general network systems. Results from the application of these methods reveal operational characteristics of the studied BHS, in terms of metrics such as peak throughput, in-system time and system recovery time.

Video driven traffic modelling in ParamicsTM

With urbanization and vehicle availability, there exist many traffic problems including congestion, environmental impact and safety. In order to address these problems, we propose a video driven traffic modelling system in this paper. The system can simulate real-world traffic activities in a computer, based on traffic data recorded in videos. Video processing is employed to estimate metrics such as traffic volumes. These metrics are used to update the traffic system model, which is then simulated using the Paramics™ traffic simulation platform. Video driven traffic modelling has widespread potential application in traffic systems, due to the convenience and reduced costs of model development and maintenance. Experiments are conducted in this paper to demonstrate the effectiveness of the proposed system.