Semantic text classification requires the understanding of the contextual significance of specific tokens rather than surface-level patterns or keywords (as in rule-based or statistical text classification), making large language models (LLMs) well-suited for this task. However, semantic classification applications in industry, like customer intent detection or semantic role labeling, tend to be highly specialized. They require annotation by domain experts in contrast to general-purpose corpora for pretraining. Further, they typically require high inference throughputs which limits the model size from latency and cost perspectives. Thus, for a range of specialized classification tasks, the preferred solution is to develop customized classifiers by finetuning smaller language models (e.g., mini-encoders, small language models). In this work, we develop a token-driven sparse finetuning strategy to adapt small language models to specialized classification tasks. We identify and finetune a small sensitive subset of model parameters by leveraging task-specific token constructs in the finetuning dataset, while leaving most of the pretrained weights unchanged. Unlike adapter approaches such as low rank adaptation (LoRA), we do not introduce additional parameters to the model. Our approach identifies highly relevant semantic tokens (case study in the Appendix) and outperforms end-to-end finetuning, LoRA, layer selection, and prefix tuning on five diverse semantic classification tasks. We achieve greater stability and half the training costs vs. end-to-end finetuning.

Personalized conversational information retrieval (CIR) systems aim to satisfy users' complex information needs through multi-turn interactions by considering user profiles. However, not all search queries require personalization. The challenge lies in appropriately incorporating personalization elements into search when needed. Most existing studies implicitly incorporate users' personal information and conversational context using large language models without distinguishing the specific requirements for each query turn. Such a ``one-size-fits-all'' personalization strategy might lead to sub-optimal results. In this paper, we propose an adaptive personalization method, in which we first identify the required personalization level for a query and integrate personalized queries with other query reformulations to produce various enhanced queries. Then, we design a personalization-aware ranking fusion approach to assign fusion weights dynamically to different reformulated queries, depending on the required personalization level. The proposed adaptive personalized conversational information retrieval framework APCIR is evaluated on two TREC iKAT datasets. The results confirm the effectiveness of adaptive personalization of APCIR by outperforming state-of-the-art methods.

Due to perceptions of efficiency and significant productivity gains, various organisations, including in education, are adopting Large Language Models (LLMs) into their workflows. Educator-facing, learner-facing, and institution-facing LLMs, collectively, Educational Large Language Models (eLLMs), complement and enhance the effectiveness of teaching, learning, and academic operations. However, their integration into an educational setting raises significant cybersecurity concerns. A comprehensive landscape of contemporary attacks on LLMs and their impact on the educational environment is missing. This study presents a generalised taxonomy of fifty attacks on LLMs, which are categorized as attacks targeting either models or their infrastructure. The severity of these attacks is evaluated in the educational sector using the DREAD risk assessment framework. Our risk assessment indicates that token smuggling, adversarial prompts, direct injection, and multi-step jailbreak are critical attacks on eLLMs. The proposed taxonomy, its application in the educational environment, and our risk assessment will help academic and industrial practitioners to build resilient solutions that protect learners and institutions.

Neural artistic style transfers and blends the content and style representation of one image with the style of another. This enables artists to create unique innovative visuals and enhances artistic expression in various fields including art, design, and film. Color transfer algorithms are an important in digital image processing by adjusting the color information in a target image based on the colors in the source image. Color transfer enhances images and videos in film and photography, and can aid in image correction. We introduce a methodology that combines neural artistic style with color transfer. The method uses the Kullback-Leibler (KL) divergence to quantitatively evaluate color and luminance histogram matching algorithms including Reinhard global color transfer, iteration distribution transfer (IDT), IDT with regrain, Cholesky, and PCA between the original and neural artistic style transferred image using deep learning. We estimate the color channel kernel densities. Various experiments are performed to evaluate the KL of these algorithms and their color histograms for style to content transfer.

In digital substations, security events pose significant challenges to the sustained operation of power systems. To mitigate these challenges, the implementation of robust defense strategies is critically important. A thorough process of anomaly identification and detection in information and communication technology (ICT) frameworks is crucial to ensure secure and reliable communication and coordination between interconnected devices within digital substations. Hence, this paper addresses the critical cybersecurity challenges confronting IEC61850-based digital substations within modern smart grids, where the integration of advanced communication protocols, e.g., generic object-oriented substation event (GOOSE), has enhanced energy management and introduced significant vulnerabilities to cyberattacks. Focusing on the limitations of traditional anomaly detection systems (ADSs) in detecting threats, this research proposes a transformative approach by leveraging generative AI (GenAI) to develop robust ADSs. The primary contributions include the suggested advanced adversarial traffic mutation (AATM) technique to generate synthesized and balanced datasets for GOOSE messages, ensuring protocol compliance and enabling realistic zero-day attack pattern creation to address data scarcity. Then, the implementation of GenAI-based ADSs incorporating the task-oriented dialogue (ToD) processes has been explored for improved detection of attack patterns. Finally, a comparison of the GenAI-based ADS with machine learning (ML)-based ADSs has been implemented to showcase the outperformance of the GenAI-based frameworks considering the AATM-generated GOOSE datasets and standard/advanced performance evaluation metrics.

Advances in large language models (LLMs) have enabled a wide range of applications. However, depression prediction is hindered by the lack of large-scale, high-quality, and rigorously annotated datasets. This study introduces DepressLLM, trained and evaluated on a novel corpus of 3,699 autobiographical narratives reflecting both happiness and distress. DepressLLM provides interpretable depression predictions and, via its Score-guided Token Probability Summation (SToPS) module, delivers both improved classification performance and reliable confidence estimates, achieving an AUC of 0.789, which rises to 0.904 on samples with confidence $\geq$ 0.95. To validate its robustness to heterogeneous data, we evaluated DepressLLM on in-house datasets, including an Ecological Momentary Assessment (EMA) corpus of daily stress and mood recordings, and on public clinical interview data. Finally, a psychiatric review of high-confidence misclassifications highlighted key model and data limitations that suggest directions for future refinements. These findings demonstrate that interpretable AI can enable earlier diagnosis of depression and underscore the promise of medical AI in psychiatry.

Video dubbing aims to translate original speech in visual media programs from the source language to the target language, relying on neural machine translation and text-to-speech technologies. Due to varying information densities across languages, target speech often mismatches the source speech duration, causing audio-video synchronization issues that significantly impact viewer experience. In this study, we approach duration alignment in LLM-based video dubbing machine translation as a preference optimization problem. We propose the Segment Supervised Preference Optimization (SSPO) method, which employs a segment-wise sampling strategy and fine-grained loss to mitigate duration mismatches between source and target lines. Experimental results demonstrate that SSPO achieves superior performance in duration alignment tasks.

As AI becomes more "agentic," it faces technical and socio-legal issues it must address if it is to fulfill its promise of increased economic productivity and efficiency. This paper uses technical and legal perspectives to explain how things change when AI systems start being able to directly execute tasks on behalf of a user. We show how technical conceptions of agents track some, but not all, socio-legal conceptions of agency. That is, both computer science and the law recognize the problems of under-specification for an agent, and both disciplines have robust conceptions of how to address ensuring an agent does what the programmer, or in the law, the principal desires and no more. However, to date, computer science has under-theorized issues related to questions of loyalty and to third parties that interact with an agent, both of which are central parts of the law of agency. First, we examine the correlations between implied authority in agency law and the principle of value-alignment in AI, wherein AI systems must operate under imperfect objective specification. Second, we reveal gaps in the current computer science view of agents pertaining to the legal concepts of disclosure and loyalty, and how failure to account for them can result in unintended effects in AI ecommerce agents. In surfacing these gaps, we show a path forward for responsible AI agent development and deployment.

Most large-scale neural network training methods assume homogeneous parallel computing resources. For example, synchronous SGD with data parallelism, the most widely used parallel training strategy, incurs significant synchronization overhead when workers process their assigned data at different speeds. Consequently, in systems with heterogeneous compute resources, users often rely solely on the fastest components, such as GPUs, for training. In this work, we explore how to effectively use heterogeneous resources for neural network training. We propose a system-aware local stochastic gradient descent (local SGD) method that allocates workloads to each compute resource in proportion to its compute capacity. To make better use of slower resources such as CPUs, we intentionally introduce bias into data sampling and model aggregation. Our study shows that well-controlled bias can significantly accelerate local SGD in heterogeneous environments, achieving comparable or even higher accuracy than synchronous SGD with data-parallelism within the same time budget. This fundamental parallelization strategy can be readily extended to diverse heterogeneous environments, including cloud platforms and multi-node high-performance computing clusters.

--Access to real-world medical data is often restricted due to privacy regulations, posing a significant barrier to the advancement of healthcare research. Synthetic data offers a promising alternative; however, generating realistic, clinically valid, and privacy-conscious records remains a major challenge. Recent advancements in Large Language Models (LLMs) offer new opportunities for structured data generation; however, existing approaches frequently lack systematic prompting strategies and comprehensive, multi-dimensional evaluation frameworks. In this paper, we present SynLLM, a modular framework for generating high-quality synthetic medical tabular data using 20 state-of-the-art open-source LLMs, including LLaMA, Mistral, and GPT variants, guided by structured prompts. We propose four distinct prompt types, ranging from example-driven to rule-based constraints, that encode schema, metadata, and domain knowledge to control generation without model fine-tuning. Our framework features a comprehensive evaluation pipeline that rigorously assesses generated data across statistical fidelity, clinical consistency, and privacy preservation. We evaluate SynLLM across three public medical datasets, including Diabetes, Cirrhosis, and Stroke, using 20 open-source LLMs. Our results show that prompt engineering significantly impacts data quality and privacy risk, with rule-based prompts achieving the best privacy-quality balance. SynLLM establishes that, when guided by well-designed prompts and evaluated with robust, multi-metric criteria, LLMs can generate synthetic medical data that is both clinically plausible and privacy-aware, paving the way for safer and more effective data sharing in healthcare research. Access to real-world medical data is frequently restricted due to privacy regulations, ethical constraints, and institutional barriers, posing a significant challenge for the development of AI-driven healthcare solutions. While data protection laws such as the Health Insurance Portability and Accountability Act (HIP AA) [11] and the General Data Protection Regulation (GDPR) [37] are essential for safeguarding patient confidentiality, they often hinder the availability of data for clinical model development and research.