We present a machine learning framework for predicting the structural dimensionality of hybrid metal halides (HMHs), including organic-inorganic perovskites, using a combination of chemically-informed feature engineering and advanced class-imbalance handling techniques. The dataset, consisting of 494 HMH structures, is highly imbalanced across dimensionality classes (0D, 1D, 2D, 3D), posing significant challenges to predictive modeling. This dataset was later augmented to 1336 via the Synthetic Minority Oversampling Technique (SMOTE) to mitigate the effects of the class imbalance. We developed interaction-based descriptors and integrated them into a multi-stage workflow that combines feature selection, model stacking, and performance optimization to improve dimensionality prediction accuracy. Our approach significantly improves F1-scores for underrepresented classes, achieving robust cross-validation performance across all dimensionalities.

Mis- and disinformation, commonly collectively called fake news, continue to menace society. Perhaps, the impact of this age-old problem is presently most plain in politics and healthcare. However, fake news is affecting an increasing number of domains. It takes many different forms and continues to shapeshift as technology advances. Though it arguably most widely spreads in textual form, e.g., through social media posts and blog articles. Thus, it is imperative to thwart the spread of textual misinformation, which necessitates its initial detection. This thesis contributes to the creation of representations that are useful for detecting misinformation. Firstly, it develops a novel method for extracting textual features from news articles for misinformation detection. These features harness the disparity between the thematic coherence of authentic and false news stories. In other words, the composition of themes discussed in both groups significantly differs as the story progresses. Secondly, it demonstrates the effectiveness of topic features for fake news detection, using classification and clustering. Clustering is particularly useful because it alleviates the need for a labelled dataset, which can be labour-intensive and time-consuming to amass. More generally, it contributes towards a better understanding of misinformation and ways of detecting it using Machine Learning and Natural Language Processing.

In this paper, we explore the challenges of ensuring security and privacy for users from diverse demographic backgrounds. We propose a threat modeling approach to identify potential risks and countermeasures for product inclusion in security and privacy. We discuss various factors that can affect a user's ability to achieve a high level of security and privacy, including low-income demographics, poor connectivity, shared device usage, ML fairness, etc. We present results from a global security and privacy user experience survey and discuss the implications for product developers. Our work highlights the need for a more inclusive approach to security and privacy and provides a framework for researchers and practitioners to consider when designing products and services for a diverse range of users.

In the analysis of natural images, Gaussian scale mixtures (GSM) have been used to account for the statistics of (cid:2)lter responses, and to inspire hi- erarchical cortical representational learning schemes. GSMs pose a crit- ical assignment problem, working out which (cid:2)lter responses were gen- erated by a common multiplicative factor. We present a new approach to solving this assignment problem through a probabilistic extension to the basic GSM, and show how to perform inference in the model using Gibbs sampling. We demonstrate the ef(cid:2)cacy of the approach on both synthetic and image data. Understanding the statistical structure of natural images is an important goal for visual neuroscience.

My research investigates the use of cutting-edge hybrid In the era of information technology, the distinction between deep learning models to accurately di erentiate between text generated by arti cial intelligence (AI) and that authored AI-generated text and human writing. I applied a robust by humans has become increasingly blurred. This convergence methodology, utilising a carefully selected dataset comprising has profound implications, not only for the eld of AI and human texts from various sources, each tagged natural language processing (NLP) but also for broader societal with instructions.

Effective collaboration requires groups to strategically regulate themselves to overcome challenges. Research has shown that groups may fail to regulate due to differences in members' perceptions of challenges which may benefit from external support. In this study, we investigated the potential of leveraging three distinct natural language processing models: an expert knowledge rule-based model, a supervised machine learning (ML) model and a Large Language model (LLM), in challenge detection and challenge dimension identification (cognitive, metacognitive, emotional and technical/other challenges) from student discourse, was investigated. The results show that the supervised ML and the LLM approaches performed considerably well in both tasks, in contrast to the rule-based approach, whose efficacy heavily relies on the engineered features by experts. The paper provides an extensive discussion of the three approaches' performance for automated detection and support of students' challenge moments in collaborative learning activities. It argues that, although LLMs provide many advantages, they are unlikely to be the panacea to issues of the detection and feedback provision of socially shared regulation of learning due to their lack of reliability, as well as issues of validity evaluation, privacy and confabulation. We conclude the paper with a discussion on additional considerations, including model transparency to explore feasible and meaningful analytical feedback for students and educators using LLMs.

We introduce a novel learning algorithm for binary classi(cid:12)cation with hyperplane discriminants based on pairs of training points from opposite classes (dyadic hypercuts). This algorithm is further extended to nonlinear discriminants using kernel functions satisfy- ing Mercer's conditions. An ensemble of simple dyadic hypercuts is learned incrementally by means of a con(cid:12)dence-rated version of Ad- aBoost, which provides a sound strategy for searching through the (cid:12)nite set of hypercut hypotheses. In experiments with real-world datasets from the UCI repository, the generalization performance of the hypercut classi(cid:12)ers was found to be comparable to that of SVMs and k-NN classi(cid:12)ers. Furthermore, the computational cost of classi(cid:12)cation (at run time) was found to be similar to, or bet- ter than, that of SVM.

Materials discovery is frequently driven by historical data sets that lack characteristics of the data sets specifically constructed to meet the needs of particular discovery efforts. They carry imprints of the ever-changing historical context of the research and development. Shifting priorities of the external funding, pressure for momentous technological breakthroughs, community perception of high-profile topics, and evolution of experimental capabilities render historical data a patchwork of findings with poorly understood internal structure. Statistical learning methods are typically concerned with statistical characteristics of the data. In the materials discovery, there is an additional pressure to understand the shape of the data in terms of what is known and what is missing and inform laborious and expensive data acquisition associated with material preparation, processing, and characterization. In this contribution, we are investigating the interplay between the shape of the historical data expressed as the structure of lacunae, such as gaps, loops, and voids, and the hypothesis generation that informs subsequent data acquisition. We describe an approach that explicitly identifies lacunae via topological data analysis (TDA) and fills them in using constrained generative modeling. TDA is concerned with capturing the shape of the data - the characteristics that are preserved under continuous deformations. The simplest widely accepted form of TDA is clustering.

A drawback of these methods is that they consider only global image context, which may contain information Accurate and e cient product classi cation is signi cant for E-irrelevant to the question. To overcome this, some methods commerce applications, as it enables various downstream tasks have proposed visual attention models that attend to local spatial such as recommendation, retrieval, and pricing. Items often contain regions pertaining to a given question, and then perform multimodal textual and visual information, and utilizing both modalities usually fusion to classify answers accurately [4, 19, 21, 22]. More outperforms classi cation utilizing either mode alone. In this recently, dual attention models have been proposed.

Physics-inspired Artificial Intelligence (AI) is at the forefront of methods development in molecular modeling and computational chemistry. In particular, interatomic potentials derived with Machine Learning algorithms such as Deep Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations. The applicability domain of DNN potentials is usually limited by the type of training data. As such, transferable models are aimed to be extensible in the description of chemical and conformational diversity of organic molecules. However, most DNN potentials, such as the AIMNet model we proposed previously, were parametrized for neutral molecules or closed-shell ions due to architectural limitations.