The dataset consists of airport specific ground crew and allocation data for four major airports - Kempegowda International Airport (BLR), Rajiv Gandhi International Airport (HYD), Indira Gandhi International Airport (DEL), and Chhatrapati Shivaji Maharaj International Airport (BOM). The tasks, floors and gates, i.e, the tasks and their locations are factual data where as the allocation data is approximately close to realistic demand. The crew demand is synthetically generated.
The accelerated development of Machine Learning (ML) tools, combined with broader access to frameworks and infrastructures, has driven the rapid adoption of ML-based solutions in industry. However, their integration into software systems introduces unique challenges, particularly for managing technical debt (TD). While existing frameworks/standards such as Cross-Industry Standard Process for Data Mining (CRISP-DM) and ISO/IEC 5338 provide guidance for ML development, they fail to address the complex interplay of technical and nontechnical factors contributing to TD.
This opinion explores the integration of Artificial Intelligence in foreign language education, examining both its potential benefits and inherent risks. AI tools offer personalized learning experiences, interactive practice, and access to authentic resources, potentially reducing learning-related stress. However, over-reliance on AI may hinder critical thinking and raise concerns about accuracy, originality, and ethical considerations like algorithmic bias. A balanced approach is crucial, emphasizing the importance of academic integrity, ethical conduct, and responsible technology use.
With the advent of 6G Open-RAN architecture, multiple operational services can be simultaneously executed in RAN, leveraging the near-Real-Time Radio Intelligent Controller (near-RT-RIC) and real-time (RT) nodes. The architecture provides an ideal platform for Federated Learning (FL): The xAPP is hosted in the near-RT-RIC to perform global aggregation, whereas the Open Radio Unit (ORU) allocates power to users to participate in FL in a RT manner. This paper identifies power and latency optimization as critical factors for enhancing FL in a stochastic environment.
This dataset comprises a collection of CSV files containing paired time-series measurements essential for nonlinear compensation research in electrochemical seismometers (MET). Each CSV file, named according to specific magnitude-frequency combinations (magX_freqY.csv), contains two columns: 'origin' representing the original system response and 'target' representing the desired compensated output.
Māori enterprises are pivotal to the economic and cultural prosperity of Aotearoa, yet predictive analysis of business outcomes tailored to these enterprises remains underexplored. This research examines the application of recurrent neural networks (RNNs) and transformer architectures to forecast key performance indicators (KPIs) for Māori small and medium-sized enterprises (SMEs).
The Fuyong dataset records 134 stroke patients who received treatment in the Shenzhen Fuyong People's Hospital between March 1, 2022, and September 31, 2024. Besides, medical records of 435 stroke patients treated in the Affiliated Taizhou People's Hospital of Nanjing Medical University between January 1, 2020, and December 31, 2023, are included in the Taizhou dataset. These two datasets use the pre- and post-thrombolysis of the NIHSS scores as a metric for evaluating the immediate efficacy of the thrombolytic intervention.
The recent developments in the field of the Internet of Things (IoT) bring alongside them quite a few advantages. Examples include real-time condition monitoring, remote control and operation and sometimes even remote fault remediation. Still, despite bringing invaluable benefits, IoT-enriched entities inherently suffer from security and privacy issues. This is partially due to the utilization of insecure communication protocols such as the Open Charge Point Protocol (OCPP) 1.6. OCPP 1.6 is an application-layer communication protocol used for managing electric vehicle chargers.
The process of allocating the intestate inheritance among the statutory heirs is sophisticated yet occurs regularly. Many scholars have attempted to develop automated allocation systems to tackle this High task. However, most amply existing systems rely on conventional form-based input, which may overwhelm the general users. Furthermore, no existing system concerning intestate inheritance allocation according to the Civil and Commercial Code of Thailand is publicly available.
This dataset extends the standard Myers-Briggs Type Indicator (MBTI) dataset, widely available on Kaggle, by incorporating advanced data augmentation techniques leveraging GPT-based Transformers. The augmentation addresses inherent class imbalance and data sparsity issues in the original dataset, significantly enriching the volume and diversity of textual samples while maintaining linguistic and contextual fidelity to the MBTI personality types.
