The rapid advancement of generative Artificial Intelligence (AI) technologies, particularly Generative Pre-trained Transformer (GPT) models such as ChatGPT, has the potential to significantly impact cybersecurity. In this study, we investigated the impact of GPTs, specifically ChatGPT, on tertiary education in cybersecurity, and provided recommendations for universities to adapt their curricula to meet the evolving needs of the industry. Our research highlighted the importance of understanding the alignment between GPT's ``mental model'' and human cognition, as well as the enhancement of GPT capabilities to human skills based on Bloom's taxonomy. By analyzing current educational practices and the alignment of curricula with industry requirements, we concluded that universities providing practical degrees like cybersecurity should align closely with industry demand and embrace the inevitable generative AI revolution, while applying stringent ethics oversight to safeguard responsible GPT usage. We proposed a set of recommendations focused on updating university curricula, promoting agility within universities, fostering collaboration between academia, industry, and policymakers, and evaluating and assessing educational outcomes.

Motivated by agility, 3D mobility, and low-risk operation compared to human-operated management systems of autonomous unmanned aerial vehicles (UAVs), this work studies UAV-based active wildfire monitoring where a UAV detects fire incidents in remote areas and tracks the fire frontline. A UAV path planning solution is proposed considering realistic wildfire management missions, where a single low-altitude drone with limited power and flight time is available. Noting the limited field of view of commercial low-altitude UAVs, the problem formulates as a partially observable Markov decision process (POMDP), in which wildfire progression outside the field of view causes inaccurate state representation that prevents the UAV from finding the optimal path to track the fire front in limited time. Common deep reinforcement learning (DRL)-based trajectory planning solutions require diverse drone-recorded wildfire data to generalize pre-trained models to real-time systems, which is not currently available at a diverse and standard scale. To narrow down the gap caused by partial observability in the space of possible policies, a belief-based state representation with broad, extensive simulated data is proposed where the beliefs (i.e., ignition probabilities of different grid areas) are updated using a Bayesian framework for the cells within the field of view. The performance of the proposed solution in terms of the ratio of detected fire cells and monitored ignited area (MIA) is evaluated in a complex fire scenario with multiple rapidly growing fire batches, indicating that the belief state representation outperforms the observation state representation both in fire coverage and the distance to fire frontline.

Generative AI has ushered in the ability to generate content that closely mimics human contributions, introducing an unprecedented threat: Deployed en masse, these models can be used to manipulate public opinion and distort perceptions, resulting in a decline in trust towards digital platforms. This study contributes to marketing literature and practice in three ways. First, it demonstrates the proficiency of AI in fabricating disinformative user-generated content (UGC) that mimics the form of authentic content. Second, it quantifies the disruptive impact of such UGC on marketing research, highlighting the susceptibility of analytics frameworks to even minimal levels of disinformation. Third, it proposes and evaluates advanced detection frameworks, revealing that standard techniques are insufficient for filtering out AI-generated disinformation. We advocate for a comprehensive approach to safeguarding marketing research that integrates advanced algorithmic solutions, enhanced human oversight, and a reevaluation of regulatory and ethical frameworks. Our study seeks to serve as a catalyst, providing a foundation for future research and policy-making aimed at navigating the intricate challenges at the nexus of technology, ethics, and marketing.

Artificial neural network (NN) architecture design is a nontrivial and time-consuming task that often requires a high level of human expertise. Neural architecture search (NAS) serves to automate the design of NN architectures and has proven to be successful in automatically finding NN architectures that outperform those manually designed by human experts. NN architecture performance can be quantified based on multiple objectives, which include model accuracy and some NN architecture complexity objectives, among others. The majority of modern NAS methods that consider multiple objectives for NN architecture performance evaluation are concerned with automated feed forward NN architecture design, which leaves multi-objective automated recurrent neural network (RNN) architecture design unexplored. RNNs are important for modeling sequential datasets, and prominent within the natural language processing domain. It is often the case in real world implementations of machine learning and NNs that a reasonable trade-off is accepted for marginally reduced model accuracy in favour of lower computational resources demanded by the model. This paper proposes a multi-objective evolutionary algorithm-based RNN architecture search method. The proposed method relies on approximate network morphisms for RNN architecture complexity optimisation during evolution. The results show that the proposed method is capable of finding novel RNN architectures with comparable performance to state-of-the-art manually designed RNN architectures, but with reduced computational demand.

Simulation can and should play a critical role in the development and testing of algorithms for autonomous agents. What might reduce its impact is the ``sim2real'' gap -- the algorithm response differs between operation in simulated versus real-world environments. This paper introduces an approach to evaluate this gap, focusing on the accuracy of sensor simulation -- specifically IMU and GPS -- in velocity estimation tasks for autonomous agents. Using a scaled autonomous vehicle, we conduct 40 real-world experiments across diverse environments then replicate the experiments in simulation with five distinct sensor noise models. We note that direct comparison of raw simulation and real sensor data fails to quantify the sim2real gap for robotics applications. We demonstrate that by using a state of the art state-estimation package as a ``judge'', and by evaluating the performance of this state-estimator in both real and simulated scenarios, we can isolate the sim2real discrepancies stemming from sensor simulations alone. The dataset generated is open-source and publicly available for unfettered use.

This paper presents a novel fleet management strategy for battery-powered robot fleets tasked with intra-factory logistics in an autonomous manufacturing facility. In this environment, repetitive material handling operations are subject to real-world uncertainties such as blocked passages, and equipment or robot malfunctions. In such cases, centralized approaches enhance resilience by immediately adjusting the task allocation between the robots. To overcome the computational expense, a two-step methodology is proposed where the nominal problem is solved a priori using a Monte Carlo Tree Search algorithm for task allocation, resulting in a nominal search tree. When a disruption occurs, the nominal search tree is rapidly updated a posteriori with costs to the new problem while simultaneously generating feasible solutions. Computational experiments prove the real-time capability of the proposed algorithm for various scenarios and compare it with the case where the search tree is not used and the decentralized approach that does not attempt task reassignment.

The rapid proliferation of the Internet of Things (IoT) has ushered in transformative connectivity between physical devices and the digital realm. Nonetheless, the escalating threat of Distributed Denial of Service (DDoS) attacks jeopardizes the integrity and reliability of IoT networks. Conventional DDoS mitigation approaches are ill-equipped to handle the intricacies of IoT ecosystems, potentially compromising data privacy. This paper introduces an innovative strategy to bolster the security of IoT networks against DDoS attacks by harnessing the power of Federated Learning that allows multiple IoT devices or edge nodes to collaboratively build a global model while preserving data privacy and minimizing communication overhead. The research aims to investigate Federated Learning's effectiveness in detecting and mitigating DDoS attacks in IoT. Our proposed framework leverages IoT devices' collective intelligence for real-time attack detection without compromising sensitive data. This study proposes innovative deep autoencoder approaches for data dimensionality reduction, retraining, and partial selection to enhance the performance and stability of the proposed model. Additionally, two renowned aggregation algorithms, FedAvg and FedAvgM, are employed in this research. Various metrics, including true positive rate, false positive rate, and F1-score, are employed to evaluate the model. The dataset utilized in this research, N-BaIoT, exhibits non-IID data distribution, where data categories are distributed quite differently. The negative impact of these distribution disparities is managed by employing retraining and partial selection techniques, enhancing the final model's stability. Furthermore, evaluation results demonstrate that the FedAvgM aggregation algorithm outperforms FedAvg, indicating that in non-IID datasets, FedAvgM provides better stability and performance.

Energy-Based Models (EBMs) are an important class of probabilistic models, also known as random fields and undirected graphical models. EBMs are un-normalized and thus radically different from other popular self-normalized probabilistic models such as hidden Markov models (HMMs), autoregressive models, generative adversarial nets (GANs) and variational auto-encoders (VAEs). Over the past years, EBMs have attracted increasing interest not only from the core machine learning community, but also from application domains such as speech, vision, natural language processing (NLP) and so on, due to significant theoretical and algorithmic progress. The sequential nature of speech and language also presents special challenges and needs a different treatment from processing fix-dimensional data (e.g., images). Therefore, the purpose of this monograph is to present a systematic introduction to energy-based models, including both algorithmic progress and applications in speech and language processing. First, the basics of EBMs are introduced, including classic models, recent models parameterized by neural networks, sampling methods, and various learning methods from the classic learning algorithms to the most advanced ones. Then, the application of EBMs in three different scenarios is presented, i.e., for modeling marginal, conditional and joint distributions, respectively. 1) EBMs for sequential data with applications in language modeling, where the main focus is on the marginal distribution of a sequence itself; 2) EBMs for modeling conditional distributions of target sequences given observation sequences, with applications in speech recognition, sequence labeling and text generation; 3) EBMs for modeling joint distributions of both sequences of observations and targets, and their applications in semi-supervised learning and calibrated natural language understanding.

Estimating 3D pose from monocular images is a highly ambiguous problem. Physical constraints can be exploited to restrict the space of feasible configurations. In this paper we propose an approach to constraining the prediction of a discriminative predictor. We first show that the mean prediction of a Gaussian process implicitly satisfies linear constraints if those constraints are satisfied by the training examples. We then show how, by performing a change of variables, a GP can be forced to satisfy quadratic constraints. As evidenced by the experiments, our method outperforms state-of-the-art approaches on the tasks of rigid and non-rigid pose estimation.

While loopy Belief Propagation (LBP) has been utilized in a wide variety of applications with empirical success, it comes with few theoretical guarantees. Especially, if the interactions of random variables in a graphical model are strong, the behaviors of the algorithm can be difficult to analyze due to underlying phase transitions. In this paper, we develop a novel approach to the uniqueness problem of the LBP fixed point; our new "necessary and sufficient" condition is stated in terms of graphs and signs, where the sign denotes the types (attractive/repulsive) of the interaction (i.e., compatibility function) on the edge. In all previous works, uniqueness is guaranteed only in the situations where the strength of the interactions are "sufficiently" small in certain senses. In contrast, our condition covers arbitrary strong interactions on the specified class of signed graphs. The result of this paper is based on the recent theoretical advance in the LBP algorithm; the connection with the graph zeta function.