2026 June - 33 articles
Although many researchers use pre-trained models to better solve downstream tasks, further exploration of more effective pre-training methods remains necessary, especially for multi-modal pre-training where high-quality training data is more difficul...
As the environmental sustainability of blockchain technology becomes a focal point of public and academic debate, understanding how technically engaged communities frame this issue is increasingly important. This study examines 3000 long-form comment...
Most existing dialogue understanding methods rely on flat classification paradigms, failing to capture hierarchical semantic structures and cross-level dependencies. To address this limitation, we reformulate dialogue understanding as a hierarchical...
Purpose-aware AI systems are increasingly deployed in safety-critical, multi-agent, and human-facing environments, where they must transform heterogeneous data into timely, explainable, and goal-aligned decisions under uncertainty. Existing architect...
Accurate obesity risk classification is often hindered by the asymmetric and heteroscedastic nature of health data, where traditional mean-based machine learning models fail to capture critical distribution tails. This study addresses this gap by pro...
The integration of domain-specific conversational assistants into administrative and payroll information systems has the potential to improve operational efficiency and decision support in regulated enterprise environments. However, empirical evidenc...
Recent advances in multi-modal large language models have enabled systems to jointly process text, images, and audio. However, these developments have primarily benefited high-resource languages, leaving many low-resource communities underserved. In...
Foreign-exchange decisions rest on hierarchically organized evidence whose latent structure is inadequately captured by Euclidean representations. Reinforcement-learning agents trained on flat embeddings inherit stability guarantees that do not trans...
High-frequency monitoring of manufacturing agility and Environmental, Social, and Governance (ESG) responsiveness is increasingly required in data-rich operations, yet many practical indices remain low-frequency, weakly decomposable, or difficult to...
Discharge notes contain complex clinical language that patients often struggle to understand. We investigate the impact of automatic highlighting and multi-step prompting on discharge note simplification using in-context learning with large language...
Upstream oil and gas operations generate large volumes of multivariate data from seismic surveys, well logs, production sensor networks, and reservoir simulation models. Advances in machine learning, artificial intelligence, and other Industry 4.0 te...
This study examines the evolution of container throughput and competitive market-share dynamics in the three principal Baltic container ports—Klaipeda, Riga, and Tallinn—during the period 2005–2024 and provides baseline forecasts to...
Artificial Intelligence is increasingly used to support decision-making across many domains. However, concerns related to transparency, reliability and human oversight indicate the need for improved human-centered AI (HCAI) approaches in decision sup...
Named entity recognition in natural disaster emergencies is a critical foundational task for emergency management. However, existing methods face challenges including complex entity types, frequent emergence of new terminology, model knowledge obsole...
Forecasting urban traffic requires consideration of both general temporal patterns and local dynamics, which depend on specific sensors. In this paper, we develop and evaluate a cluster-based forecasting architecture for the PEMS04 and PEMS08 traffic...
This paper addresses the lack of bilingual annotated resources for automatic fake news detection in the Kazakh–Russian media space, as well as the limitations of binary annotation, which does not always allow disinformation to be represented as...
Intrusion Detection Systems (IDSs) remain essential for securing modern network infrastructures, where traffic data are often high-dimensional and contain redundant or weakly informative attributes. This study proposes a hybrid feature selection appr...
Disambiguation resolution in speech synthesis is one of the main challenges in text-to-speech conversion. Machine learning methods and artificial neural networks have been successfully applied to this problem in synthesis systems for English, Spanish...
Chronic stress and depression-adjacent emotional states affect over 970 million people worldwide, yet their continuous, objective detection from physiological signals remains unsolved, particularly when cues are subtle and conventional approaches fai...
The manual annotation of images remains a major challenge in building robust classifiers, especially in the Big Data era where labeled data are limited. Graph-based semi-supervised learning (SSL) provides an effective solution by leveraging both labe...
Recurrence-Aware Long-Term Cognitive Network (rLTCN) classifiers have reported comparable performance to mainstream black-box models, including tree ensembles and support vector machines, in tabular pattern classification tasks. These classifiers use...
Dataset-Based Damage Detection (DBDD) methods are effective for structures with geometric complexity, strong interaction effects, and operational variability, such as liquid-containing tanks with changing fluid levels. In many DBDD applications, accu...
Task planning for robots in real-life settings is inherently complex due to challenges such as identifying grounded sequences of actions to achieve a goal, bridging the gap between high-level planning and low-level execution, and addressing the compu...
Neonatal hypoxic–ischemic encephalopathy (HIE) remains a critical neurological emergency resulting from perinatal asphyxia, often leading to lifelong neurodevelopmental disabilities or mortality. The accurate and timely grading of HIE severity...
The recent emergence of conversational Artificial Intelligence (AI) agents has profoundly transformed learning and teaching practices in higher education. These tools offer multiple advantages, ranging from cognitive assistance to enhanced student au...
Open fiscal data in Ecuador remains largely unexplored beyond basic descriptive reporting, despite its potential for territorial intelligence and fiscal planning. This study examines how taxpayers are distributed across Ecuador’s provinces and...
This paper addresses the challenge of providing operational access to current metadata in complex, ever-changing relational data warehouses. Traditional catalogs struggle to keep up with changes in schemas, code, and processes. The paper presents a m...
Background: Mechanistic reporting from large-scale biomedical knowledge graphs remains challenging, particularly when integrating structured graph evidence with large language model (LLM)–based explanation in a reproducible and auditable manner...
The increasing prevalence of digital communication has made students a primary target for various cyber threats, including identity deception and impersonation techniques that can lead to data breaches and financial loss. In Saudi Arabia, where the y...
Aspect-Based Sentiment Analysis explores sentiment trends related to specific opinion aspects and holds significant commercial potential for monitoring brand reputation, understanding customer satisfaction, and personalizing recommendations. However,...
The idea of disentangled representations is to reduce the data to a set of generative factors that produce it. Typically, such representations are vectors in latent space, where each coordinate corresponds to one of the generative factors. The object...
The growing prevalence of encrypted network traffic has rendered traditional payload-based inspection ineffective, shifting attention toward flow-level statistical analysis combined with machine learning. At the same time, privacy regulations and dis...
Large Language Models (LLMs) offer opportunities for processing heterogeneous information streams relevant to energy-market decision-making, but their practical role in forecasting-oriented analytical workflows remains uncertain. This paper presents...
