A review and recommendations for the integration of forensic expertise within police cold case reviews
- Authors: Chapman, Brendan , Keatley, David , Oatley, Giles , Coumbaros, John , Maker, Garth
- Date: 2020
- Type: Text , Journal article , Review
- Relation: Journal of Criminal Psychology Vol. 10, no. 2 (2019), p. 79-91
- Full Text: false
- Reviewed:
- Description: Purpose: Cold case review teams and the processes that they adopt in their endeavour to solve historic crimes are varied and largely underreported. Of the limited literature surrounding the topic of cold case reviews, the focus is on clearance rates and the selection of cases for review. While multiple reports and reviews have been undertaken and recommend that the interface between investigators and forensic scientists be improved, there is little evidence of cold case teams comprised of a mixture of investigators and scientists or experts. With the growing reliance on forensic science as an aide to solvability, the authors propose that the inclusion of forensic scientists to the central cold case investigation may be a critical factor in future success. The paper aims to discuss this issue. Design/methodology/approach: To support the proposed approach, the authors conducted a review of the current literature seeking insight into the reported make-up of cold case teams. In conjunction with this, the authors reviewed a number of commissioned reports intended to improve cold case reviews and forensic services. Findings: While many of the reviewed reports and recommendations suggested better integration with scientists and external expertise, little evidence of this in practice was reported within published literature. Open dialogue and cross pollination between police investigators and forensic scientists are likely to mitigate biases, inform case file triage and better equip investigations with contemporary and cutting-edge scientific solutions to the evidence analysis for cold cases. Furthermore, with respect to scientists within academia, large pools of resources by way of student interns or researchers may be available to assist resource-sparse policing jurisdictions. Originality/value: To the authors’ knowledge, this is the first peer-reviewed recommendation for the consideration of integrated forensic scientists within a cold case review team. Multiple reports suggest the need for closer ties, but it is the anecdotal experience of the authors that the benefits of a blended task force approach may yield greater success. © 2020, Emerald Publishing Limited.
Adaptive low-power wireless sensor network architecture for smart street furniture-based crowd and environmental measurements
- Authors: Nassar, Mohammed , Luxford, Len , Cole, Peter , Oatley, Giles , Koutsakis, Polychronis , IEEE
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 2019 IEEE 20th International Symposium on a World of Wireless, Mobile and Multimedia Networks
- Full Text: false
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- Description: Street furniture such as bins, seats and bus shelters can become "smart" with the inclusion of wireless sensor nodes, which consist of environmental sensors, wireless modules, processors and microcontrollers. One of the most crucial challenges for smart street furniture is how to manage power consumption efficiently without affecting data freshness. In this work, we propose a novel Wireless Sensor Network (WSN) architecture for smart street furniture. Unlike existing WSNs which are based on a one-way communication model between wireless sensor nodes and the server, the proposed architecture employs a two-way communication model and a dynamic adaptation of the time interval of measurements to balance between power consumption and data updates. Our approach also provides a real-time low-power design for wireless sensor nodes which efficiently communicate the updated data instead of sending the same data on a regular basis. To the best of our knowledge, this is the first work in the relevant literature which extends the functionality of the wireless module in wireless sensor nodes to act not only as a station sending environmental data but also as soft Access Point (AP), sensing MAC addresses and WiFi signal strengths from surrounding WiFi-enabled devices. We have conducted experiments on the Murdoch University campus and our results show that our proposal improves lifetime of wireless sensor nodes up to 293% compared to static architectures similar to the ones that have been proposed in the literature. Moreover, network bandwidth is improved up to 38% without affecting data freshness. Finally, storage space for the database at the server is reduced up to 99%.
- Description: E1
Behavioral modeling and cognitive assessment in smart textiles
- Authors: Oatley, Giles , Choudhury, Tanveer , Buckman, Paul
- Date: 2022
- Type: Text , Conference paper
- Relation: 2022 Australasian Computer Science Week, ACSW 2022, Virtual, Online, 14-17 February 2022, ACM International Conference Proceeding Series p. 229-231
- Full Text: false
- Reviewed:
- Description: Smart textiles can be used as innovative solutions to amuse, meaningfully engage, comfort, entertain, stimulate, and to overall improve the quality of life for people living in care homes with dementia or its precursor mild cognitive impairment (MCI). We have developed a prototype smart textile system that uses capacitive sensing to loosely couple the textile overlay from the underlying technology layer. This inclusion of technology adds to the user experience and quality of life is increased. Additionally, by using microelectronics, light-emitting diodes (LEDs) and capacitive touch sensors we can represent and design a range of sophisticated memory and reasoning diagnostic/ assessment tools, which are detailed in this paper. © 2022 ACM.
Behaviour tracking : using geospatial and behaviour sequence analysis to map crime
- Authors: Keatley, David , Arntfield, Michael , Gill, Paul , Clare, Joe , Oatley, Giles , Bouhana, Noemie , Clarke, David
- Date: 2021
- Type: Text , Journal article
- Relation: Security Journal Vol. 34, no. 1 (2021), p. 184-201
- Full Text: false
- Reviewed:
- Description: Crime is a complex phenomenon. To understand the commission of crime, researchers must map both the temporal and the spatial processes involved. The current research combines a temporal method of analysis, Behaviour Sequence Analysis, with geospatial mapping, to outline a new method of integrating temporal and spatial movements of criminals. To show how the new method can be applied, a burglary scenario was used, and the movements and behaviours of a criminal tracked around the property. Results showed that combining temporal and spatial analyses allows for a clearer account of the process of a crime scene. The current method has application to a large range of other crimes and terrorist movements, for instance between cities and movements within each city. Therefore, the current research provides the foundation framework for a novel method of spatio-temporal analyses of crime. © 2019, Springer Nature Limited.
CenGCN : centralized convolutional networks with vertex imbalance for scale-free graphs
- Authors: Xia, Feng , Wang, Lei , Tang, Tao , Chen, Xin , Kong, Xiangjie , Oatley, Giles , King, Irwin
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Transactions on Knowledge and Data Engineering Vol. 35, no. 5 (2023), p. 4555-4569
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- Description: Graph Convolutional Networks (GCNs) have achieved impressive performance in a wide variety of areas, attracting considerable attention. The core step of GCNs is the information-passing framework that considers all information from neighbors to the central vertex to be equally important. Such equal importance, however, is inadequate for scale-free networks, where hub vertices propagate more dominant information due to vertex imbalance. In this paper, we propose a novel centrality-based framework named CenGCN to address the inequality of information. This framework first quantifies the similarity between hub vertices and their neighbors by label propagation with hub vertices. Based on this similarity and centrality indices, the framework transforms the graph by increasing or decreasing the weights of edges connecting hub vertices and adding self-connections to vertices. In each non-output layer of the GCN, this framework uses a hub attention mechanism to assign new weights to connected non-hub vertices based on their common information with hub vertices. We present two variants CenGCN_D and CenGCN_E, based on degree centrality and eigenvector centrality, respectively. We also conduct comprehensive experiments, including vertex classification, link prediction, vertex clustering, and network visualization. The results demonstrate that the two variants significantly outperform state-of-the-art baselines. © 1989-2012 IEEE.
Crime concentration in Perth CBD : A comparison of officer predicted hot spots, data derived hot spots and officer GPS patrol data
- Authors: Oatley, Giles , Williams, Stephen , Barnes, Geoffrey , Clare, Joseph , Chapman, Brendan
- Date: 2019
- Type: Text , Journal article
- Relation: Australian Journal of Forensic Sciences Vol. 51, no. sup1 (2019), p. S136-S140
- Full Text: false
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- Description: In an applied criminology context, recent meta-analyses and randomized control trials have demonstrated the benefits of targeting police patrols at hot spots or concentrations of street level crime and disorder. This study asked a group of 79 police officers from Perth to make a prediction, based on their experience, of where hot spots of crime would occur in the near future. Officer-defined hot spots were then compared with hot spots derived from police crime data over the preceding 24 month period. Finally, officer patrol time was tracked using a GPS-enabled smart phone and overlayed against both types of hot spot. This analysis indicates that police officers should be supported with hot spot mapping tools which identify data derived micro-places with persistent issues. Analysis also reveals officers patrol both their own and data-derived hot spots regularly; however, they only stay for a matter of a few minutes. These short stays are contrary to best evidence, which dictates officer patrols in hot spots should last for approximately 15 minutes in order to create both initial and residual deterrence.
Data-driven decision-making in COVID-19 response : a survey
- Authors: Yu, Shuo , Qing, Qing , Zhang, Chen , Shehzad, Ahsan , Oatley, Giles , Xia, Feng
- Date: 2021
- Type: Text , Journal article , Review
- Relation: IEEE Transactions on Computational Social Systems Vol. 8, no. 4 (2021), p. 989-1002
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- Description: COVID-19 has spread all over the world, having an enormous effect on our daily life and work. In response to the epidemic, a lot of important decisions need to be taken to save communities and economies worldwide. Data clearly play a vital role in effective decision-making. Data-driven decision-making uses data-related evidence and insights to guide the decision-making process and verify the plan of action before it is committed. To better handle the epidemic, governments and policy-making institutes have investigated abundant data originating from COVID-19. These data include those related to medicine, knowledge, media, and so on. Based on these data, many prevention and control policies are made. In this survey article, we summarize the progress of data-driven decision-making in the response to COVID-19, including COVID-19 prevention and control, psychological counseling, financial aid, work resumption, and school reopening. We also propose some current challenges and open issues in data-driven decision-making, including data collection and quality, complex data analysis, and fairness in decision-making. This survey article sheds light on current policy-making driven by data, which also provides a feasible direction for further scientific research. © 2014 IEEE.
Exploring the relationship between testosterone and diabetes within the UK Biobank data
- Authors: Oatley, Giles
- Date: 2023
- Type: Text , Conference paper
- Relation: 2023 Australasian Computer Science Week, ACSW 2023, Melbourne Australia, 31 January-3 February 2023, ACSW '23: Proceedings of the 2023 Australasian Computer Science Week p. 244-247
- Full Text: false
- Reviewed:
- Description: The UK Biobank (UKB) cohort data aims to improve the prevention, diagnosis, and treatment of a wide range of serious diseases, including diabetes. Presented is a population-based retrospective cohort study to explore the relationship between steroid hormones and the prevalence of diabetes. In particular, free testosterone is calculated from available serum biochemical markers in the UKB data, prevalent diabetes is calculated across a range of UKB data fields and ICD10 codes are generalized to their top-level classifications. It is then possible to explore relationships between testosterone levels, diabetes presence, and associated morbidities. © 2023 ACM.
Forensic intelligence and the analytical process
- Authors: Oatley, Giles , Chapman, Brendan , Speers, James
- Date: 2020
- Type: Text , Journal article
- Relation: Wiley interdisciplinary reviews. Data mining and knowledge discovery Vol. 10, no. 3 (2020), p.
- Full Text: false
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- Description: A review was undertaken of the developments made with integrating forensicevidence into the analytical process to support police investigations. Evidencesuch as DNA, fingerprints, fibers, accelerants, tyre marks, and so forth, cansupport to differing degrees the various working theories or hypotheses aboutthe nature of the alleged crime, the persons of interest and the modusoperandi. Investigators however, either forensic or detective, bring variousbiases to evidence capture and analysis, biases which are better understood inthe intelligence community. Structured analytical techniques have a long his-tory in intelligence analysis, for example analysis of competing hypotheses,which serves several purposes: information sharing, clarity of communication,and to highlight the common forms of bias brought to bear in an investigation.We illustrate the representation of links based on traces and intelligence, andhow these can be stored in databases permitting better“reasoning”with evi-dence. We also present some recommendations for integration of forensic intelligence into the investigative analytic process and review information sys-tems in this area
Incorporating emotion and personality-based analysis in user-centered modelling
- Authors: Mostafa, Mohamed , Crick, Tom , Calderon, Ana C. , Oatley, Giles
- Date: 2016
- Type: Text , Conference proceedings
- Relation: 36th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence AI 2016; SGAI 2016. 2016-11-05; p. 383-389
- Full Text: false
- Reviewed:
- Description: Understanding complex user behaviour under various conditions, scenarios and journeys is fundamental to improving the user-experience for a given system. Predictive models of user reactions, responses—and in particular, emotions—can aid in the design of more intuitive and usable systems. Building on this theme, the preliminary research presented in this paper correlates events and interactions in an online social network against user behaviour, focusing on personality traits. Emotional context and tone is analysed and modelled based on varying types of sentiments that users express in their language using the IBM Watson Developer Cloud tools. The data collected in this study thus provides further evidence towards supporting the hypothesis that analysing and modelling emotions, sentiments and personality traits provides valuable insight into improving the user experience of complex social computer systems.
Non-invasive smartphone use monitoring to assess cognitive impairment
- Authors: Thang, Nguyen , Oatley, Giles , Stranieri, Andrew , Walker, Darren
- Date: 2021
- Type: Text , Conference paper
- Relation: 13th International Conference on Computer and Automation Engineering, ICCAE 2021 p. 64-67
- Full Text: false
- Reviewed:
- Description: Background: There are many tests for the early detection of Mild Cognitive Impairment (MCI) to prevent or delay the development of dementia, particularly amongst the elderly. However, many tests are complex and are required to be performed repeatedly. Cognitive assessment apps for a smartphone have emerged, but like other tests, they require the user to perform complex tasks like drawing time on a clock. Few studies have explored non-invasive ways of tracking and assessing MCI without having the user perform specific tests. Objective: This research ultimately aims to develop an app that runs in the background and collects smartphone usage data that correlates well with MCI test results. The focus of this preliminary study was to develop an app that collects usage data and common MCI questionnaires to see if usage data between people varied, and to establish associations between phone usage and cognitive tests results. Method: An android application was developed to gather data over three weeks by three volunteers (authors). Usage data collected included: SMS and call log, accelerometer, location, app usage, self-report. Cognitive tests implemented were Stroop, Go/No Go tests and absent-mindedness questionnaires. Due to the small sample size and Covid-19 restrictions (October 2020), location data was not reliable. SMS text was not collected for privacy reasons. Results: App categories can differentiate people, but the app usage cannot be used to distinguish people. © 2021 IEEE.
On average, a professional rugby union player is more likely than not to sustain a concussion after 25 matches
- Authors: Rafferty, James , Ranson, Craig , Oatley, Giles , Mostafa, Mohamed , Mathema, Prabhat , Crick, Tom , Moore, Isabel
- Date: 2019
- Type: Text , Journal article
- Relation: British Journal of Sports Medicine Vol. 53, no. 15 (2019), p. 969-973
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- Description: To investigate concussion injury rates, the likelihood of sustaining concussion relative to the number of rugby union matches and the risk of subsequent injury following concussion. A four-season (2012/2013-2015/2016) prospective cohort study of injuries in professional level (club and international) rugby union. Incidence (injuries/1000 player-match-hours), severity (days lost per injury) and number of professional matches conferring a large risk of concussion were determined. The risk of injury following concussion was assessed using a survival model. Concussion incidence increased from 7.9 (95% CI 5.1 to 11.7) to 21.5 injuries/1000 player-match-hours (95% CI 16.4 to 27.6) over the four seasons for combined club and international rugby union. Concussion severity was unchanged over time (median: 9 days). Players were at a greater risk of sustaining a concussion than not after an exposure of 25 matches (95% CI 19 to 32). Injury risk (any injury) was 38% greater (HR 1.38 95% CI 1.21 to 1.56) following concussion than after a non-concussive injury. Injuries to the head and neck (HR 1.34 95% CI 1.06 to 1.70), upper limb (HR 1.59 95% CI 1.19 to 2.12), pelvic region (HR 2.07 95% CI 1.18 to 3.65) and the lower limb (HR 1.60 95% CI 1.21 to 2.10) were more likely following concussion than after a non-concussive injury. Concussion incidence increased, while severity remained unchanged, during the 4 years of this study. Playing more than 25 matches in the 2015/2016 season meant that sustaining concussion was more likely than not sustaining concussion. The 38% greater injury risk after concussive injury (compared with non-concussive injury) suggests return to play protocols warrant investigation.
Positive design of smart interactive fabric artifacts for people with dementia
- Authors: Mann, Graham , Oatley, Giles
- Date: 2017
- Type: Text , Conference proceedings
- Relation: 2017 IEEE 5th International Conference on Serious Games and Applications for Health (SeGAH); Perth, WA, Australia; 2-4 April 2017 p. 1-8
- Full Text: false
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- Description: Confronting the expected rise of dementia as a major health care problem raises many questions about the best ways to adapt the health system to deal with it. To the extent that intelligent assistive technologies can help, there seems to be value in comforting fabric artifacts enhanced by electronic games and activities designed to support, engage and entertain people with dementia. Local cottage industries which now support the creation of textile crafts should be empowered to scale up to meet the growing demand for such products. New design concepts are required to accomplish this in the face of rising costs and limited resources. This paper proposes a four-step design process that meets this need, and provides practical suggestions about how it could be applied in this context. A number of examples are included.
Real-time localisation system for GPS-denied open areas using smart street furniture
- Authors: Nassar, Mohamed , Luxford, Len , Cole, Peter , Oatley, Giles , Koutsakis, Polychronis
- Date: 2021
- Type: Text , Journal article
- Relation: Simulation Modelling Practice and Theory Vol. 112, no. (2021), p.
- Full Text: false
- Reviewed:
- Description: Wifi-based localisation systems have gained significant interest with many researchers proposing different localisation techniques using publicly available datasets. However, these datasets are limited because they only contain Wifi fingerprints collected and labelled by users, and they are restricted to indoor locations. We have generated the first Wifi-based localisation datasets for a GPS-denied open area. We selected a busy open area at Murdoch University to generate the datasets using so-called “smart bins”, which are rubbish bins that we enabled to work as access points. The data gathered consists of two different datasets. In the first, four users generated labelled WiFi fingerprints for all available Reference Points using four different smartphones. The second dataset includes 2450865 auto-generated rows received from more than 1000 devices. We have developed a light-weight algorithm to label the second dataset from the first and we proposed a localisation approach that converts the second dataset from asynchronous format to synchronous, applies feature engineering and a deep learning classifier. Finally, we have demonstrated via simulations that by using this approach we achieve higher prediction accuracy, with up to 19% average improvement, compared with using only the fingerprint dataset. © 2021 Elsevier B.V.
Smart textiles for improved quality of life and cognitive assessment
- Authors: Oatley, Giles , Choudhury, Tanveer , Buckman, Paul
- Date: 2021
- Type: Text , Journal article
- Relation: Sensors Vol. 21, no. 23 (2021), p.
- Full Text:
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- Description: Smart textiles can be used as innovative solutions to amuse, meaningfully engage, comfort, entertain, stimulate, and to overall improve the quality of life for people living in care homes with dementia or its precursor mild cognitive impairment (MCI). This concept paper presents a smart textile prototype to both entertain and monitor/assess the behavior of the relevant clients. The prototype includes physical computing components for music playing and simple interaction, but additionally games and data logging systems, to determine baselines of activity and interaction. Using microelectronics, light-emitting diodes (LEDs) and capacitive touch sensors woven into a fabric, the study demonstrates the kinds of augmentations possible over the normal manipulation of the traditional non-smart activity apron by incorporating light and sound effects as feedback when patients interact with different regions of the textile. A data logging system will record the patient’s behavioral patterns. This would include the location, frequency, and time of the patient’s activities within the different textile areas. The textile will be placed across the laps of the resident, which they then play with, permitting the development of a behavioral profile through the gamification of cognitive tests. This concept paper outlines the development of a prototype sensor system and highlights the challenges related to its use in a care home setting. The research implements a wide range of functionality through a novel architecture involving loosely coupling and concentrating artifacts on the top layer and technology on the bottom layer. Components in a loosely coupled system can be replaced with alternative implementations that provide the same services, and so this gives the solution the best flexibility. The literature shows that existing architectures that are strongly coupled result in difficulties modeling different individuals without incurring significant costs. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
The current and future role of smart street furniture in smart cities
- Authors: Nassar, Mohamed , Luxford, Len , Cole, Peter , Oatley, Giles , Koutsakis, Polychronis
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Communications Magazine Vol. 57, no. 6 (2019), p. 68-73
- Full Text: false
- Reviewed:
- Description: Recently, street furniture, including bins, seats, and bus shelters, has become smart as it has been equipped with environmental sensors, wireless modules, processors, and microcontrollers. Accordingly, smart furniture is expected to become a vital part of the IoT infrastructure and one of the drivers of future smart cities. This work focuses on how smart street furniture can be exploited within the IoT architecture as a basis of recommender systems, toward achieving smart cities' different components. We present and discuss recent relevant work as well as the key challenges and opportunities for future research. We explain that much work is still required when it comes to combining scalability, real-time processing, smart furniture, and recommender systems.
Wifi-based localisation datasets for No-GPS open areas using smart bins
- Authors: Nassar, Mohamed , Hasan, Mahmud , Khan, Md , Sultana, Mirza , Hasan, Md , Luxford, Len , Cole, Peter , Oatley, Giles , Koutsakis, Polychronis
- Date: 2020
- Type: Text , Journal article , Data article
- Relation: Computer Networks Vol. 180, no. (2020), p. 1-5
- Full Text: false
- Reviewed:
- Description: In recent years, Wifi-based localisation systems have gained significant interest because of the lack of Global Positioning System (GPS) signal in indoor and certain open areas. Over the past decade, many datasets have been introduced to enable researchers to compare different localisation techniques. Existing datasets, however, have failed to cover open areas such as parks in cases where GPS is still unavailable, and there is a lack of Wifi access points. Also, the existing datasets only focus on getting Wifi fingerprint collected and labelled by users. To the best of our knowledge, no dataset provides Received Signal Strengths (RSS) collected by Wireless Access Points (APs). In this work, we offer two datasets publicly. The first is the Fingerprint dataset in which four users generated 16,032 accurate and consistently labelled WiFi fingerprints for all available Reference Points (RPs) in a central and busy area of Murdoch University, known as Bush Court. The second is the APs dataset that includes 2,450,865 auto-generated records received from 1000 users' devices, including the four users, associated with Wifi signal strengths. To overcome the Wifi coverage problem for the Bush Court, we attached our previously designed Wireless Sensor Nodes (WSNs) to existing garbage bins, enabling them to provide real-time environmental sensing and act as soft APs that sense MAC addresses and Wifi signals from surrounding devices.