The Data Airlock

One of the challenges in the AiLECS lab is finding ways to deeply collaborate with our AFP colleagues in machine […]

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A meandering journey: introducing Greg Rolan

As is sometimes customary, I’ll open with a joke.  Well, actually, my wife’s joke. She and I used to watch […]

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My Pictures Matter

Monash University experts are calling for people to contribute to a world first ethically-sourced and managed image bank for research […]

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My Pictures Matter

Machine learning does all sorts of things for us, from the mundane to the extraordinary. But where does the underlying […]

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Briefing Note
BN22/02: Metior Telum – Measure the Weapon

This briefing note provides a broad introduction to the Metior Telum project for a general audience.

Writing the future

When I was little and learning how to write, I asked my mother why the words had to go from […]

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Screening of “The Children in the Pictures” and Panel Discussion

The AiLECS Lab is proud to present a screening of the documentary film The Children in the Pictures at Monash University […]

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Report
TR22/03: The Data Airlock: infrastructure for restricted data informatics

Access to operational data from outside an organisation may be prohibited for a variety of reasons. There are significant challenges when performing collaborative data science work against such restricted data.

This report describes a range of causes and risks associated with restricted data along with the social, environmental, data, and cryptographic measures that may be used to mitigate such issues. These are generally inadequate for restricted data contexts. We introduce the ’Data Airlock’, secure infrastructure that facilitates eyes-off data-science workloads. After describing our use-case, we detail the architecture and implementation of a first, single-organisation version of this infrastructure. We conclude with learnings from this implementation, and outline requirements for a second, federated version.

Watch, Listen and Learn: How a Partnership is Building AI to Tackle Crime

“Watch, listen and learn!” This was the advice given to me on my first shift with the Australian Federal Police […]

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Technical and Socio-Technical Responses to Deepfakes

Generously funded by the Monash Data Futures Institute, this is a joint research program between the Faculties of Arts, Law […]

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Tech note
TN22/03: Law Enforcement Data Interoperability (Student thesis paper)

In law enforcement (LE), interoperability, i.e., the ability to exchange information between databases and systems, enhances the ability of agencies to detect and investigate crime. A fundamental way of improving interoperability is data integration, but integrating LE databases is often difficult due to heterogeneity of database types and the semantics of the data. In this study, an ontology-based and Linked Data approach for integrating heterogeneous LE databases is proposed.

The approach is evaluated for use in an operational setting by LE data domain experts. The evaluation feedback indicates that the approach has the potential to address some of the common challenges faced when integrating heterogeneous LE databases, and could provide benefit if used in an LE agency’s operational systems.

Tech note
TN22/05: Geolocation of Images (Student thesis paper)

In this paper, I propose and explore a method for image location classification. Most existing works concentrate on outdoor scenes as scenery or an iconic landmark make it easier to point out the location. Few researchers have addressed the issue of indoor scenes. Although indoor images increase the difficulty of tracking geolocation, it is necessary to respond to this shortcoming as many crimes happen indoors.

 

To address this problem, I propose a method for indoor image location classification by segmenting patterns of extracted objects from images. Specifically, I extract objects from images. Then, based on the accuracy levels of the bounding boxes of specific kinds of objects in the image, I only crop that kind of objects from original images. Moreover, I segment patterns from the extracted objects and crop those patterns by thresholding techniques. To classify images by these segmented patterns, I employ convolutional neural networks. Experimental results in the dataset of hotel rooms across the globe show promising accuracies, which witnesses that my method contributes to ultimately identifying the hotel chain which the image belongs to from the hotel dataset.