Op-Objects

The work relates to Australian history in that it reconfigures the found objects - furniture, paintings and narratives - to move implicate the viewer in unexpected ways.

Functional system maps as boundary objects in complex system development

A trend towards the provision of product-service packaging and the proliferation of service businesses introduces both tangible and intangible elements into system design. In this paper, we consider the utility of hierarchical system models as a way of flexibly combining such elements by focusing on requisite functionality. Four cases illustrate how the same approach may be used to clarify the requirements of business or socio-technical systems during system development, operation or reengineering stages. It is suggested that a suitable loosely coupled model has significant utility as a 'boundary object' - a term first coined in the study of museum artefacts. Discussion of such objects requires the use of imagination, which may support innovative system design and development. It is suggested that a well-crafted model has multiple uses - as a foundation for system development, in combining traditional and agile project management strategies and in providing a framework to facilitate the capture and organisation of project knowledge.

Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM)

Electronic medical record (EMR) offers promises for novel analytics. However, manual feature engineering from EMR is labor intensive because EMR is complex - it contains temporal, mixed-type and multimodal data packed in irregular episodes. We present a computational framework to harness EMR with minimal human supervision via restricted Boltzmann machine (RBM). The framework derives a new representation of medical objects by embedding them in a low-dimensional vector space. This new representation facilitates algebraic and statistical manipulations such as projection onto 2D plane (thereby offering intuitive visualization), object grouping (hence enabling automated phenotyping), and risk stratification. To enhance model interpretability, we introduced two constraints into model parameters: (a) nonnegative coefficients, and (b) structural smoothness. These result in a novel model called eNRBM (EMR-driven nonnegative RBM). We demonstrate the capability of the eNRBM on a cohort of 7578 mental health patients under suicide risk assessment. The derived representation not only shows clinically meaningful feature grouping but also facilitates short-term risk stratification. The F-scores, 0.21 for moderate-risk and 0.36 for high-risk, are significantly higher than those obtained by clinicians and competitive with the results obtained by support vector machines.

Discovery of stop regions for understanding repeat travel behaviors of moving objects

GPS trajectory dataset with high sampling-rates is usually in large volume that challenges the processing efficiency. Most of the data points on trajectories are useless. This paper summarizes trajectories using stop points. We define a new concept of stay stability (i.e., time dividing distance or reciprocal of speed) between any two GPS points to detect stop points on individual trajectories. We propose a novel Mining Repeat Travel Behaviors Using Stop Regions (MRTBUSR) method. In MRTBUSR, a stop region is a popular region containing a certain number of close stop points that can be grouped into a cluster. We then retrieve common sequences of stop regions to denote repeat route patterns and further analyze the stop durations on a stop region to find repeat travel behaviors. The experiments on 20 labeled trajectories selected from GeoLife demonstrated the semantic effect, accuracy and near linear efficiency of our proposed method.

DoSTra: Discovering common behaviors of objects using the duration of staying on each location of trajectories

Since semantic trajectories can discover more semantic meanings of a user's interests without geographic restrictions, research on semantic trajectories has attracted a lot of attentions in recent years. Most existing work discover the similar behavior of moving objects through analysis of their semantic trajectory pattern, that is, sequences of locations. However, this kind of trajectories without considering the duration of staying on a location limits wild applications. For example, Tom and Anne have a common pattern of Home→Restaurant → Company → Restaurant, but they are not similar, since Tom works at Restaurant, sends snack to someone at Company and return to Restaurant while Anne has breakfast at Restaurant, works at Company and has lunch at Restaurant. If we consider duration of staying on each location we can easily to differentiate their behaviors. In this paper, we propose a novel approach for discovering common behaviors by considering the duration of staying on each location of trajectories (DoSTra). Our approach can be used to detect the group that has similar lifestyle, habit or behavior patterns and predict the future locations of moving objects. We evaluate the experiment based on synthetic dataset, which demonstrates the high effectiveness and efficiency of the proposed method.

Husserl and the problem of abstract objects

One major difficulty confronting attempts to clarify the epistemological and ontological status of abstract objects is determining the sense, if any, in which such entities may be characterised as mind and language independent. Our contention is that the tolerant reductionist position of Michael Dummett can be strengthened by drawing on Husserl's mature account of the constitution of ideal objects and mathematical objectivity. According to the Husserlian position we advocate, abstract singular terms pick out weakly mind-independent sedimented meaning-contents. These meaning-contents serve as the 'thin' referents of abstract singular terms, but are ultimately founded in prior acts of meaning-constitution.

Efficiently retrieving longest common route patterns of moving objects by summarizing turning regions

The popularity of online location services provides opportunities to discover useful knowledge from trajectories of moving objects. This paper addresses the problem of mining longest common route (LCR) patterns. As a trajectory of a moving object is generally represented by a sequence of discrete locations sampled with an interval, the different trajectory instances along the same route may be denoted by different sequences of points (location, timestamp). Thus, the most challenging task in the mining process is to abstract trajectories by the right points. We propose a novel mining algorithm for LCR patterns based on turning regions (LCRTurning), which discovers a sequence of turning regions to abstract a trajectory and then maps the problem into the traditional problem of mining longest common subsequences (LCS). Effectiveness of LCRTurning algorithm is validated by an experimental study based on various sizes of simulated moving objects datasets. © 2011 Springer-Verlag.

Inter-Frame Change Directing online clustering of multiple moving objects for video-based sensor networks

Recognition of multiple moving objects is a very important task for achieving user-cared knowledge to send to the base station in wireless video-based sensor networks. However, video based sensor nodes, which have constrained resources and produce huge amount of video streams continuously, bring a challenge to segment multiple moving objects from the video stream online. Traditional efficient clustering algorithms such as DBSCAN cannot run time-efficiently and even fail to run on limited memory space on sensor nodes, because the number of pixel points is too huge. This paper provides a novel algorithm named Inter-Frame Change Directing Online clustering (IFCDO clustering) for segmenting multiple moving objects from video stream on sensor nodes. IFCDO clustering only needs to group inter-frame different pixels, thus it reduces both space and time complexity while achieves robust clusters the same as DBSCAN. Experiment results show IFCDO clustering excels DBSCAN in terms of both time and space efficiency. © 2008 IEEE.

Treacherous objects : a Jacobite compass in Australia?

What does a Jacobite compass in Australia tell us about 'treacherous objects', nationalism, material culture, and diaspora today?

The origin of unknown source DNA from touched objects

The presence of DNA in a criminal investigation often requires scrutiny in relation to how it came to be where it was found. There is a paucity of data with respect to the extent to which one can assume that the last person handling an object, which has previously been touched by others, will contribute to the DNA profile generated from it. There are limited data in detailing the extent to which any foreign DNA is picked-up from a previously touched object and transferred to subsequently touched objects. This study focuses on DNA transfer and persistence on a knife handle after multiple handlings with the knife by different individuals soon after each other, as well as handprints left on flat DNA-free surfaces immediately after touching a knife handle with a known history of prior handling. The profiles of later handlers of a knife are more prominent than earlier handlers; however, the last handler is not always the major contributor to the profile. Proportional contributions to the profiles retrieved from knife handles vary depending on the individuals touching the knife handle. They can also vary when knife handles have been handled in the same manner by the same individuals in the same sequence on different occasions. Hands readily pickup DNA left on objects by others and transfer it to subsequently touched objects. The quantity of foreign DNA picked up by a hand and deposited on subsequently touched objects diminishes as more DNA-free objects are handled soon after each other. Caution is advised when considering how DNA from different individuals may have been transferred to the object from which it was collected.