Aggregate distance based clustering using Fibonacci Series -FIBCLUS

This paper proposes an innovative instance similarity based evaluation metric that reduces the search map for clustering to be performed. An aggregate global score is calculated for each instance using the novel idea of Fibonacci series. The use of Fibonacci numbers is able to separate the instances effectively and, in hence, the intra-cluster similarity is increased and the inter-cluster similarity is decreased during clustering. The proposed FIBCLUS algorithm is able to handle datasets with numerical, categorical and a mix of both types of attributes. Results obtained with FIBCLUS are compared with the results of existing algorithms such as k-means, x-means expected maximization and hierarchical algorithms that are widely used to cluster numeric, categorical and mix data types. Empirical analysis shows that FIBCLUS is able to produce better clustering solutions in terms of entropy, purity and F-score in comparison to the above described existing algorithms.

Effective hybrid recommendation combining users-searches correlations using tensors

Most recommendation methods employ item-item similarity measures or use ratings data to generate recommendations. These methods use traditional two dimensional models to find inter relationships between alike users and products. This paper proposes a novel recommendation method using the multi-dimensional model, tensor, to group similar users based on common search behaviour, and then finding associations within such groups for making effective inter group recommendations. Web log data is multi-dimensional data. Unlike vector based methods, tensors have the ability to highly correlate and find latent relationships between such similar instances, consisting of users and searches. Non redundant rules from such associations of user-searches are then used for making recommendations to the users.

Hydrogen gas sensing performance of a Pt/graphene/SiC device

In this work, we present the development of a Pt/graphene/SiC device for hydrogen gas sensing. A single layer of graphene was deposited on 6H-SiC via chemical vapor deposition. The presence of graphene C-C bonds was observed via X-ray photoelectron spectroscopy analysis. Current-voltage characteristics of the device were measured at the presence of hydrogen at different temperatures, from 25°C to 170°C. The dynamic response of the device was recorded towards hydrogen gas at an optimum temperature of 130°C. A voltage shift of 191 mV was recorded towards 1% hydrogen at −1 mA constant current.

Chongqing International Circus City

The design grows out of the rich culture of circus and the rugged dynamic topography of Chongqing. The site for this project is nestled on the banks of the mighty Yangzte, China's longest river: a vast sweeping watery ribbon carving its way through the mountainous terrain. This swirling sinuous environmental thread replicates in nature the tweisting ribbons circling the gyrating circus gymnast. The project grows from intertwining these swirling parallel conceptions of 'ribbon'. A multi-layered envelope of glass and steel ribbons creates a dome like enclosure that wraps itself around the dynamic performing heart of the circus. The main auditorium and stage area are accommodated in this space. Key public elements and facilities are located adjacent to the new riverfront boulevard maximising the positive relationship with this attractive landscape zone. Service and support areas are located along the southern boundary. Key Statistics; Client: Chongqing Broadcast Bureau Developer: Chongqing Real Estate Site: 3.3 Ha Development: Total G.F.A.: 36,800m2 Project Cost: Total Investment: RMB 300 Million (A$48 million) Other competition participants were BIG-Bjarke Ingels Group (Denmark)/arquitectonica (USA)/Beijing Architectural Design Institute/East China Architectural Design Institute/China Architectural Design Academy.

me as al, you as bobby, me as bobby, you as al

In ‘me as al, you as bobby, me as bobby, you as al’, appropriated footage is looped and supplemented with superimposed text, creating a scenario where Robert De Niro and Al Pacino endlessly stalk each other, with their readied-guns chased by hovering words. These titans of Hollywood screen acting represent opposing approaches to the construction of filmic identity, and as the text labels loosely adhere to one weapon and the next, the action on screen becomes an investigation of the subjective and objective potential within screen surrogate constructions of personalized identity. The work was included in the group show 'Vernacular Terrain' (curated by Lubi Thomas and Steven Danzig) for the Songzhuang Art Museum, Beijing.

Exploring the opportunities and challenges of using mobile sensing for gamification and achievements

Gamified services delivered on smart phones, such as Foursquare, are able to utilise the sensors on the phone to capture user contexts as a means of triggering game elements. This paper identifies and discusses opportunities and challenges that exist when using mobile sensors as input for game elements. We present initial findings from a field study of a gamified mobile application made to support the university orientation event for new students using game achievements. The study showed that overall the use of context was well received by participants when compared to game elements that required no context to complete. It was also found that using context could help validate that an activity was completed however there were still technical challenges when using sensors that led to exploits in the game elements, or cheating.

Optimal sitting and sizing of distributed generators considering plug-in electric vehicles

Some uncertainties such as the stochastic input/output power of a plug-in electric vehicle due to its stochastic charging and discharging schedule, that of a wind unit and that of a photovoltaic generation source, volatile fuel prices and future uncertain load growth, all together could lead to some risks in determining the optimal siting and sizing of distributed generators (DGs) in distributed systems. Given this background, under the chance constrained programming (CCP) framework, a new method is presented to handle these uncertainties in the optimal sitting and sizing problem of DGs. First, a mathematical model of CCP is developed with the minimization of DGs investment cost, operational cost and maintenance cost as well as the network loss cost as the objective, security limitations as constraints, the sitting and sizing of DGs as optimization variables. Then, a Monte Carolo simulation embedded genetic algorithm approach is developed to solve the developed CCP model. Finally, the IEEE 37-node test feeder is employed to verify the feasibility and effectiveness of the developed model and method. This work is supported by an Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO) Project on Intelligent Grids Under the Energy Transformed Flagship, and Project from Jiangxi Power Company.

Globalization and suburbanization : can creative industries develop in outer urban zones? the Australian case

The creative cities literature gives an emphasis to developing cultural amenity and creative clusters in inner city areas, in order to attract both international visitors and what Richard Florida termed the “creative class”. But many creative workers live in outer urban zones (suburbs). How do creative industries policies meet their needs? This paper reports on a three-year study supported by the Australian Research Council into creative workforce in Australian suburbs in the cities of Melbourne and Brisbane.

Big-five personality factors affecting driving behaviours

The factors affecting driving behaviors are various and interact simultaneously. Therefore, study of their correlations affecting on driving behaviors is of interest. This paper reports a questionnaire survey in China, focusing on the effect of Big-Five factors on speeding, drink driving, and distracted driving while Akers' social learning theory and Homel's deterrence theory were applied. The results showed that personalities had significant effect on speeding and drink driving; social factors had significant effect on speeding and distracted driving; deterrence had significant effect on speeding and drink driving; however, social learning theory did not contribute to drink driving; deterrence did not affect distracted driving. The results were discussed along with the limitation of this study.

Spectrum sensing using two-stage detection to compensate for reduced primary user duty cycle

Spectrum sensing is considered to be one of the most important tasks in cognitive radio. One of the common assumption among current spectrum sensing detectors is the full presence or complete absence of the primary user within the sensing period. In reality, there are many situations where the primary user signal only occupies a portion of the observed signal and the assumption of primary user duty cycle not necessarily fulfilled. In this paper we show that the true detection performance can degrade from the assumed achievable values when the observed primary user exhibits a certain duty cycle. Therefore, a two-stage detection method incorporating primary user duty cycle that enhances the detection performance is proposed. The proposed detector can improve the probability of detection under low duty cycle at the expense of a small decrease in performance at high duty cycle.

Activity analysis in complicated scenes using DFT coefficients of particle trajectories

Modelling activities in crowded scenes is very challenging as object tracking is not robust in complicated scenes and optical flow does not capture long range motion. We propose a novel approach to analyse activities in crowded scenes using a “bag of particle trajectories”. Particle trajectories are extracted from foreground regions within short video clips using particle video, which estimates long range motion in contrast to optical flow which is only concerned with inter-frame motion. Our applications include temporal video segmentation and anomaly detection, and we perform our evaluation on several real-world datasets containing complicated scenes. We show that our approaches achieve state-of-the-art performance for both tasks.

Unusual scene detection using distributed behaviour model and sparse representation

The ability to detect unusual events in surviellance footage as they happen is a highly desireable feature for a surveillance system. However, this problem remains challenging in crowded scenes due to occlusions and the clustering of people. In this paper, we propose using the Distributed Behavior Model (DBM), which has been widely used in computer graphics, for video event detection. Our approach does not rely on object tracking, and is robust to camera movements. We use sparse coding for classification, and test our approach on various datasets. Our proposed approach outperforms a state-of-the-art work which uses the social force model and Latent Dirichlet Allocation.