Facial expressions convey human emotions and can be categorized into macro-expressions (MaEs) and micro-expressions (MiEs) based on duration and intensity. While MaEs are voluntary and easily recognized, MiEs are involuntary, rapid, and can reveal concealed emotions. The integration of facial expression analysis with Internet-of-Thing (IoT) systems has significant potential across diverse scenarios. IoT-enhanced MaE analysis enables real-time monitoring of patient emotions, facilitating improved mental health care in smart healthcare. Similarly, IoT-based MiE detection enhances surveillance accuracy and threat detection in smart security. This work aims at providing a comprehensive overview of research progress in facial expression analysis and explores its integration with IoT systems. We discuss the distinctions between our work and existing surveys, elaborate on advancements in MaE and MiE techniques across various learning paradigms, and examine their potential applications in IoT. We highlight challenges and future directions for the convergence of facial expression-based technologies and IoT systems, aiming to foster innovation in this domain. By presenting recent developments and practical applications, this study offers a systematic understanding of how facial expression analysis can enhance IoT systems in healthcare, security, and beyond.

Few inventions and innovations have genuinely transformed education at large, particularly by enhancing access to knowledge. Notable among these are the advent of writing around 3300 BCE, which facilitated the transmission of knowledge across generations and cultures; the Gutenberg printing press in approximately 1440 CE, which greatly simplified the duplication and dissemination of ideas and knowledge, thereby encouraging wider literacy and education; the large-scale deployment of the World Wide Web in the late 1990s and early 2000s, which allowed for rapid, affordable, and accessible information sharing via the Internet, especially through online encyclopedias such as Wikipedia; and, more recently, the public emergence of Large Language Models (LLMs) [1] in 2022, such as ChatGPT (Chat Generative Pre-Trained Transformer) [2], which have made information access even more straightforward. However, LLMs differ from previous inventions that facilitated the spread of information and knowledge in several key ways [3, 4]. While writing, the printing press, and the Internet primarily made information more accessible, LLMs provide an array of additional functions, such as multi-language translation, summarisation, simplification of complex information, and advanced writing capabilities to structure and organise content. In other words, LLMs assist people not only with accessing information but also with tasks traditionally considered cognitive.

Recent advancements in Artificial Intelligence (AI) have transformed decision-making in aeronautics and aerospace. These advancements in AI have brought with them the need to understand the reasons behind the predictions generated by AI systems and models, particularly by professionals in these sectors. In this context, the emergence of eXplainable Artificial Intelligence (XAI) has helped bridge the gap between professionals in the aeronautical and aerospace sectors and the AI systems and models they work with. For this reason, this paper provides a review of the concept of XAI is carried out defining the term and the objectives it aims to achieve. Additionally, the paper discusses the types of models defined within it and the properties these models must fulfill to be considered transparent, as well as the post-hoc techniques used to understand AI systems and models after their training. Finally, various application areas within the aeronautical and aerospace sectors will be presented, highlighting how XAI is used in these fields to help professionals understand the functioning of AI systems and models.

Large Language Models (LLMs) have advanced rapidly in recent years, with their applications in software engineering expanding to more complex repository-level tasks. GitHub issue resolving is a key challenge among these tasks. While recent approaches have made progress on this task, they focus on textual data within issues, neglecting visual data. However, this visual data is crucial for resolving issues as it conveys additional knowledge that text alone cannot. We propose CodeV, the first approach to leveraging visual data to enhance the issue-resolving capabilities of LLMs. CodeV resolves each issue by following a two-phase process: data processing and patch generation. To evaluate CodeV, we construct a benchmark for visual issue resolving, namely Visual SWE-bench. Through extensive experiments, we demonstrate the effectiveness of CodeV, as well as provide valuable insights into leveraging visual data to resolve GitHub issues.

The increasing demands of sustainable energy, electronics, and biomedical applications call for next-generation functional materials with unprecedented properties. Of particular interest are emerging materials that display exceptional physical properties, making them promising candidates in energy-efficient microelectronic devices. As the conventional Edisonian approach becomes significantly outpaced by growing societal needs, emerging computational modeling and machine learning (ML) methods are employed for the rational design of materials. However, the complex physical mechanisms, cost of first-principles calculations, and the dispersity and scarcity of data pose challenges to both physics-based and data-driven materials modeling. Moreover, the combinatorial composition-structure design space is high-dimensional and often disjoint, making design optimization nontrivial. In this Account, we review a team effort toward establishing a framework that integrates data-driven and physics-based methods to address these challenges and accelerate materials design. We begin by presenting our integrated materials design framework and its three components in a general context. We then provide an example of applying this materials design framework to metal-insulator transition (MIT) materials, a specific type of emerging materials with practical importance in next-generation memory technologies. We identify multiple new materials which may display this property and propose pathways for their synthesis. Finally, we identify some outstanding challenges in data-driven materials design, such as materials data quality issues and property-performance mismatch. We seek to raise awareness of these overlooked issues hindering materials design, thus stimulating efforts toward developing methods to mitigate the gaps.

Clustering is a key task in machine learning, with $k$-means being widely used for its simplicity and effectiveness. While 1D clustering is common, existing methods often fail to exploit the structure of 1D data, leading to inefficiencies. This thesis introduces optimized algorithms for $k$-means++ initialization and Lloyd's algorithm, leveraging sorted data, prefix sums, and binary search for improved computational performance. The main contributions are: (1) an optimized $k$-cluster algorithm achieving $O(l \cdot k^2 \cdot \log n)$ complexity for greedy $k$-means++ initialization and $O(i \cdot k \cdot \log n)$ for Lloyd's algorithm, where $l$ is the number of greedy $k$-means++ local trials, and $i$ is the number of Lloyd's algorithm iterations, and (2) a binary search-based two-cluster algorithm, achieving $O(\log n)$ runtime with deterministic convergence to a Lloyd's algorithm local minimum. Benchmarks demonstrate over a 4500x speedup compared to scikit-learn for large datasets while maintaining clustering quality measured by within-cluster sum of squares (WCSS). Additionally, the algorithms achieve a 300x speedup in an LLM quantization task, highlighting their utility in emerging applications. This thesis bridges theory and practice for 1D $k$-means clustering, delivering efficient and sound algorithms implemented in a JIT-optimized open-source Python library.

Carbon emissions significantly contribute to climate change, and carbon credits have emerged as a key tool for mitigating environmental damage and helping organizations manage their carbon footprint. Despite their growing importance across sectors, fully leveraging carbon credits remains challenging. This study explores engineering practices and fintech solutions to enhance carbon emission management. We first review the negative impacts of carbon emission non-disclosure, revealing its adverse effects on financial stability and market value. Organizations are encouraged to actively manage emissions and disclose relevant data to mitigate risks. Next, we analyze factors influencing carbon prices and review advanced prediction algorithms that optimize carbon credit purchasing strategies, reducing costs and improving efficiency. Additionally, we examine corporate carbon emission prediction models, which offer accurate performance assessments and aid in planning future carbon credit needs. By integrating carbon price and emission predictions, we propose research directions, including corporate carbon management cost forecasting. This study provides a foundation for future quantitative research on the financial and market impacts of carbon management practices and is the first systematic review focusing on computing solutions and engineering practices for carbon credits.

Large Language Models (LLMs) can generate content that is as persuasive as human-written text and appear capable of selectively producing deceptive outputs. These capabilities raise concerns about potential misuse and unintended consequences as these systems become more widely deployed. This review synthesizes recent empirical work examining LLMs' capacity and proclivity for persuasion and deception, analyzes theoretical risks that could arise from these capabilities, and evaluates proposed mitigations. While current persuasive effects are relatively small, various mechanisms could increase their impact, including fine-tuning, multimodality, and social factors. We outline key open questions for future research, including how persuasive AI systems might become, whether truth enjoys an inherent advantage over falsehoods, and how effective different mitigation strategies may be in practice.

Automatic detection and severity assessment of dysarthria are crucial for delivering targeted therapeutic interventions to patients. While most existing research focuses primarily on speech modality, this study introduces a novel approach that leverages both speech and text modalities. By employing cross-attention mechanism, our method learns the acoustic and linguistic similarities between speech and text representations. This approach assesses specifically the pronunciation deviations across different severity levels, thereby enhancing the accuracy of dysarthric detection and severity assessment. All the experiments have been performed using UA-Speech dysarthric database. Improved accuracies of 99.53% and 93.20% in detection, and 98.12% and 51.97% for severity assessment have been achieved when speaker-dependent and speaker-independent, unseen and seen words settings are used. These findings suggest that by integrating text information, which provides a reference linguistic knowledge, a more robust framework has been developed for dysarthric detection and assessment, thereby potentially leading to more effective diagnoses.

Solving algebra problems (APs) continues to attract significant research interest as evidenced by the large number of algorithms and theories proposed over the past decade. Despite these important research contributions, however, the body of work remains incomplete in terms of theoretical justification and scope. The current contribution intends to fill the gap by developing a review framework that aims to lay a theoretical base, create an evaluation scheme, and extend the scope of the investigation. This paper first develops the State Transform Theory (STT), which emphasizes that the problem-solving algorithms are structured according to states and transforms unlike the understanding that underlies traditional surveys which merely emphasize the progress of transforms. The STT, thus, lays the theoretical basis for a new framework for reviewing algorithms. This new construct accommodates the relation-centric algorithms for solving both word and diagrammatic algebra problems. The latter not only highlights the necessity of introducing new states but also allows revelation of contributions of individual algorithms obscured in prior reviews without this approach.