Swarm-Based Machine Learning Algorithm for Building Interpretable Classifiers

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RACER: A Lightweight Distributed Consensus Algorithm for the IoT with Peer-Assisted Latency-Aware Traffic Optimisation

Internet-of-Things (IoT) devices are interconnected objects embedded with sensors and software, enabling data collection and exchange. These devices encompass a wide range of applications, from household appliances to industrial systems, designed to enhance connectivity and automation. In distributed IoT networks, achieving reliable decision-making necessitates robust consensus mechanisms that allow devices to agree on a shared state of truth without reliance on central authorities. Such mechanisms are critical for ensuring system resilience under diverse operational conditions. Recent research has identified three common limitations in existing consensus mechanisms for IoT environments: dependence on synchronised networks and clocks, reliance on centralised coordinators, and suboptimal performance. To address these challenges, this paper introduces a novel consensus mechanism called Randomised Asynchronous Consensus with Efficient Real-time Sampling (RACER). The RACER framework eliminates the need for synchronised networks and clocks by implementing the Sequenced Probabilistic Double Echo (SPDE) algorithm, which operates asynchronously without timing assumptions. Furthermore, to mitigate the reliance on centralised coordinators, RACER leverages the SPDE gossip protocol, which inherently requires no leaders, combined with a lightweight transaction ordering mechanism optimised for IoT sensor networks. Rather than using a blockchain for transaction ordering, we opted for an eventually consistent transaction ordering mechanism to specifically deal with high churn, asynchronous networks and to allow devices to independently and deterministically order transactions. To enhance the throughput of IoT networks, this paper also proposes a complementary algorithm, Peer-assisted Latency-Aware Traffic Optimisation (PLATO), designed to maximise efficiency within RACER-based systems. The combination of RACER and PLATO is able to maintain a throughput of above 600 mb/s on a 100-node network, significantly outperforming the compared consensus mechanisms in terms of network node size and performance.</p

Emotions of COVID-19: Content analysis of self-reported information using artificial intelligence

Background: The COVID-19 pandemic has disrupted human societies around the world. This public health emergency was followed by a significant loss of human life; the ensuing social restrictions led to loss of employment, lack of interactions, and burgeoning psychological distress. As physical distancing regulations were introduced to manage outbreaks, individuals, groups, and communities engaged extensively on social media to express their thoughts and emotions. This internet-mediated communication of self-reported information encapsulates the emotional health and mental well-being of all individuals impacted by the pandemic. Objective: This research aims to investigate the human emotions related to the COVID-19 pandemic expressed on social media over time, using an artificial intelligence (AI) framework. Methods: Our study explores emotion classifications, intensities, transitions, and profiles, as well as alignment to key themes and topics, across the four stages of the pandemic: declaration of a global health crisis (ie, prepandemic), the first lockdown, easing of restrictions, and the second lockdown. This study employs an AI framework comprised of natural language processing, word embeddings, Markov models, and the growing self-organizing map algorithm, which are collectively used to investigate social media conversations. The investigation was carried out using 73,000 public Twitter conversations posted by users in Australia from January to September 2020. Results: The outcomes of this study enabled us to analyze and visualize different emotions and related concerns that were expressed and reflected on social media during the COVID-19 pandemic, which could be used to gain insights into citizens' mental health. First, the topic analysis showed the diverse as well as common concerns people had expressed during the four stages of the pandemic. It was noted that personal-level concerns expressed on social media had escalated to broader concerns over time. Second, the emotion intensity and emotion state transitions showed that fear and sadness emotions were more prominently expressed at first; however, emotions transitioned into anger and disgust over time. Negative emotions, except for sadness, were significantly higher (P</p

A Comprehensive Review of Digital Twin Technology for Grid-Connected Microgrid Systems: State of the Art, Potential and Challenges Faced

The concept of the digital twin has been adopted as an important aspect in digital transformation of power systems. Although the notion of the digital twin is not new, its adoption into the energy sector has been recent and has targeted increased operational efficiency. This paper is focused on addressing an important gap in the research literature reviewing the state of the art in utilization of digital twin technology in microgrids, an important component of power systems. A microgrid is a local power network that acts as a dependable island within bigger regional and national electricity networks, providing power without interruption even when the main grid is down. Microgrids are essential components of smart cities that are both resilient and sustainable, providing smart cities the opportunity to develop sustainable energy delivery systems. Due to the complexity of design, development and maintenance of a microgrid, an efficient simulation model with ability to handle the complexity and spatio-temporal nature is important. The digital twin technologies have the potential to address the above-mentioned requirements, providing an exact virtual model of the physical entity of the power system. The paper reviews the application of digital twins in a microgrid at electrical points where the microgrid connects or disconnects from the main distribution grid, that is, points of common coupling. Furthermore, potential applications of the digital twin in microgrids for better control, security and resilient operation and challenges faced are also discussed

Emotions of COVID-19: Content analysis of self-reported information using artificial intelligence

Background: The COVID-19 pandemic has disrupted human societies around the world. This public health emergency was followed by a significant loss of human life; the ensuing social restrictions led to loss of employment, lack of interactions, and burgeoning psychological distress. As physical distancing regulations were introduced to manage outbreaks, individuals, groups, and communities engaged extensively on social media to express their thoughts and emotions. This internet-mediated communication of self-reported information encapsulates the emotional health and mental well-being of all individuals impacted by the pandemic. Objective: This research aims to investigate the human emotions related to the COVID-19 pandemic expressed on social media over time, using an artificial intelligence (AI) framework. Methods: Our study explores emotion classifications, intensities, transitions, and profiles, as well as alignment to key themes and topics, across the four stages of the pandemic: declaration of a global health crisis (ie, prepandemic), the first lockdown, easing of restrictions, and the second lockdown. This study employs an AI framework comprised of natural language processing, word embeddings, Markov models, and the growing self-organizing map algorithm, which are collectively used to investigate social media conversations. The investigation was carried out using 73,000 public Twitter conversations posted by users in Australia from January to September 2020. Results: The outcomes of this study enabled us to analyze and visualize different emotions and related concerns that were expressed and reflected on social media during the COVID-19 pandemic, which could be used to gain insights into citizens' mental health. First, the topic analysis showed the diverse as well as common concerns people had expressed during the four stages of the pandemic. It was noted that personal-level concerns expressed on social media had escalated to broader concerns over time. Second, the emotion intensity and emotion state transitions showed that fear and sadness emotions were more prominently expressed at first; however, emotions transitioned into anger and disgust over time. Negative emotions, except for sadness, were significantly higher (P</p

Optimizing Speech Emotion Recognition with Machine Learning Based Advanced Audio Cue Analysis

In today’s fast-paced and interconnected world, where human–computer interaction is an integral component of daily life, the ability to recognize and understand human emotions has emerged as a crucial facet of technological advancement. However, human emotion, a complex interplay of physiological, psychological, and social factors, poses a formidable challenge even for other humans to comprehend accurately. With the emergence of voice assistants and other speech-based applications, it has become essential to improve audio-based emotion expression. However, there is a lack of specificity and agreement in current emotion annotation practice, as evidenced by conflicting labels in many human-annotated emotional datasets for the same speech segments. Previous studies have had to filter out these conflicts and, therefore, a large portion of the collected data has been considered unusable. In this study, we aimed to improve the accuracy of computational prediction of uncertain emotion labels by utilizing high-confidence emotion labelled speech segments from the IEMOCAP emotion dataset. We implemented an audio-based emotion recognition model using bag of audio word encoding (BoAW) to obtain a representation of audio aspects of emotion in speech with state-of-the-art recurrent neural network models. Our approach improved the state-of-the-art audio-based emotion recognition with a 61.09% accuracy rate, an improvement of 1.02% over the BiDialogueRNN model and 1.72% over the EmoCaps multi-modal emotion recognition models. In comparison to human annotation, our approach achieved similar results in identifying positive and negative emotions. Furthermore, it has proven effective in accurately recognizing the sentiment of uncertain emotion segments that were previously considered unusable in other studies. Improvements in audio emotion recognition could have implications in voice-based assistants, healthcare, and other industrial applications that benefit from automated communication.</p

Leveraging explainable AI for enhanced decision making in humanitarian logistics: An adversarial coevolution (ACTION) framework

Abstract: This study examines the potential of AI-enabled wargames to enhance strategic decisionmaking in humanitarian assistance and disaster relief (HADR). We introduce an Adversarial CoevoluTION (ACTION) framework, which showcases AI’s capacity to evolve adaptable policies capable of responding to dynamic changes and adversarial actions in HADR wargame scenarios. The framework presented employs a grammar-based genetic programming algorithm to evolve intelligent and interpretable player policies. We apply the ACTION framework to a HADR wargame case study, commonly used by the Australian Defence Science and Technology Group for research purposes. The case study centres on a hypothetical disaster relief scenario in the fictional Joadia Islands, struck by a tsunami, necessitating the evacuation of dispersed civilians. Experimental results illustrate that the ACTION framework can evolve policies that adapt to environmental uncertainties and respond effectively to adversarial actions. This study offers evidence of the potential and practical application of AI-enabled technology in real-life humanitarian situations. Our findings suggest practical guidelines for humanitarian practitioners to enhance the efficiency and effectiveness of logistics planning for humanitarian aid, ultimately leading to improved outcomes in HADR scenarios.</p

Value co-creation for open innovation: An evidence-based study of the data driven paradigm of social media using machine learning.

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Profiling Somatosensory Impairment after Stroke: Characterizing Common “Fingerprints” of Impairment Using Unsupervised Machine Learning-Based Cluster Analysis of Quantitative Measures of the Upper Limb

Altered somatosensory function is common among stroke survivors, yet is often poorly characterized. Methods of profiling somatosensation that illustrate the variability in impairment within and across different modalities remain limited. We aimed to characterize post-stroke somatosensation profiles (“fingerprints”) of the upper limb using an unsupervised machine learning cluster analysis to capture hidden relationships between measures of touch, proprioception, and haptic object recognition. Raw data were pooled from six studies where multiple quantitative measures of upper limb somatosensation were collected from stroke survivors (n = 207) using the Tactile Discrimination Test (TDT), Wrist Position Sense Test (WPST) and functional Tactile Object Recognition Test (fTORT) on the contralesional and ipsilesional upper limbs. The Growing Self Organizing Map (GSOM) unsupervised machine learning algorithm was used to generate a topology-preserving two-dimensional mapping of the pooled data and then separate it into clusters. Signature profiles of somatosensory impairment across two modalities (TDT and WPST; n = 203) and three modalities (TDT, WPST, and fTORT; n = 141) were characterized for both hands. Distinct impairment subgroups were identified. The influence of background and clinical variables was also modelled. The study provided evidence of the utility of unsupervised cluster analysis that can profile stroke survivor signatures of somatosensory impairment, which may inform improved diagnosis and characterization of impairment patterns.</p

Applications of Automatic Writing Evaluation to Guide the Understanding of Learning and Teaching

This paper provides an overview of tools and approaches to Automated Writing Evaluation (AWE). It provides a summary of the two emerging disciplines in learning analytics then outlines two approaches used in text analytics. A number of tools currently available for AWE are discussed and the issues of validity and reliability of AWE tools examined. We then provide details of three areas where the future direction for AWE look promising and have been identified in the literature. These areas include opportunities for large-scale marking, their use in MOOCs and in formative feedback for students. We introduce a fourth opportunity previously not widely canvased; where learning analytics can be used to guide teachers’ insights to provide assistance to students based on an analysis of the assignment corpus and to support moderation between markers. We conclude with brief details of a project exploring these insights being undertaken at an Australian institution