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Critical perspectives on datafication and artificial intelligence.
No full textConference Contribution.Panel presentation
The bold hyperbolic claims associated with emerging technologies, datafication and Artificial Intelligence(AI) have saturated the media in recent months, causing key actors in almost every profession and discipline–artists, authors, the military, police and social work professionals–to adopt a position, make a statement, align themselves for or against the apparent transformational changes in the social order that are sweeping over us. There seems little doubt that, in relation to Generative AI such as ChatGPT, we are
at the top of the Gartner hype cycle associated with inflated expectations generating speculative hopes and fears that AI will bring mass unemployment, solutions to major health and social problems or existential threats to the future of humanity. In this panel, we want to take a carefully considered and
evidence-informed look at the actual and emerging impacts of datafication, machine learning and AI.
Avoiding speculative positions, we will describe the nature of these technological innovations and–from the perspective of social work education, research and practice–consider their potential benefits and harms. We will also discuss the wider impacts of emerging technologies on social work service users and society and reflect on a range of possible responses from adoption to abolition
Children at the centre of collaborative inquiry: A perspective from Aotearoa, New Zealand.
No full textOral Presentation
Ka ora te wairua, ka ora te tangata: A kaupapa Māori praxis exploration of wairua maori and its role in Māori healing, mental health, wellbeing, and development: An application to The Royal Society Te Apārangi Tūāpapa Future Leader Fellowship 2024.
No full text
Ensemble feature selection based lightweight IDS tailored for DDoS attacks detection over IoT devices.
No full textThe attack surface has grown due to the widespread use of weak Internet of Things (IoT) devices, and distributed denial of service (DDoS) attacks are becoming more common. The necessity for adaptive intrusion detection systems (IDS) that are effective at detecting threats and need less resources is highlighted by the increase in attacks, particularly for IoT devices with limited resources. Although feature selection (FS) techniques are widely used for creating lightweight intrusion detection systems (IDS), depending solely on one approach can be dangerous and lead to bias in the dataset. Therefore, a flexible and varied FS approach is required. ELIDS (Ensemble Feature Selection for Lightweight IDS), an Ensemble FS strategy that utilizes the advantages of seven different filter-based techniques, is presented in this work. The primary goal of ELIDS is to choose the most important features found by each FS approach. This leads to build strong classification models, which are then thoroughly assessed for both performance and resource efficiency using both in-domain and cross-domain testing. The evaluation findings demonstrate that the suggested model reaches a peak accuracy of 100% for in-domain testing in addition to being lightweight. Cross-domain testing, however, shows that classifiers constructed using individual FS methods suffer a notable reduction in accuracy, while ELIDS-based classifiers exhibit robustness and significantly exceed current methods. Specifically, ELIDS-based classifiers outperform other models by 24%, particularly when Random Forest (RF) is used as the learning technique
