Artificial Intelligence
In the captured image, a drone is seen in flight, displaying its advanced technological features and capabilities. The image highlights the drone's robust design and aerodynamic structure, which are essential for its diverse applications in research and development. Drones, also known as Unmanned Aerial Vehicles (UAVs), are increasingly being utilized in various fields due to their ability to collect data from hard-to-reach or hazardous areas.
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Multimodal large language models (MLLMs) have shown remarkable progress in high-level semantic tasks such as visual question answering, image captioning, and emotion recognition. However, despite advancements, there remains a lack of standardized benchmarks for evaluating MLLMs performance in multi-object sentiment analysis, a key task in semantic understanding. To address this gap, we introduce MOSABench, a novel evaluation dataset designed specifically for multi-object sentiment analysis.
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These datasets originate from two separate power transformers, identified as Transformer 1 and Transformer 2. Each dataset is further categorized into two distinct time intervals for data collection. The first time interval, labeled as "m," represents a short-term sampling period with data recorded every 15 minutes, capturing detailed temporal fluctuations over shorter durations. The second time interval, labeled as "h," signifies a longer-term sampling period with data recorded at hourly intervals, providing an overview of broader trends and patterns.
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The Human voice Natural Language from On-demand media (HENLO) dataset is a high-quality emotional speech dataset created to address the need for representative and realistic data in speech emotion recognition research. Unlike many existing datasets, which rely on simulated emotions performed by untrained speakers or directed participants, HENLO sources its data from professionally produced films and podcasts available on Media On-Demand (MOD).
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In this paper we use Natural Language Processing techniques to improve different machine learning approaches (Support Vector Machines (SVM), Local SVM, Random Forests) to the problem of automatic keyphrases extraction from scientific papers. For the evaluation we propose a large and high-quality dataset: 2000 ACM papers from the Computer Science domain. We evaluate by comparison with expert-assigned keyphrases.
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Backlog refinement is a critical process within Agile practices which often faces challenges like ambiguous user stories, prioritization difficulties, and cognitive overload among team members. Teams spend a lot of time in grooming user stories and refining them based on the client or business requirements and customer feedback. In this paper, we present an empirical study, exploring the integration of Generative AI (GenAI), specifically Large Language Models into backlog refinement workflows to address these challenges.
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Vision-language (VL) datasets are essential for advancing the capabilities of VL models, particularly in specialized domains like medical imaging. However, existing medical VL datasets are relatively small and predominantly focus on chest X-rays, limiting their applicability to other areas. To address this gap, we introduce the Skin-Path dataset, a comprehensive VL dataset specifically curated for histopathology.
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Hyperspectral images are represented by numerous
narrow wavelength bands in the visible and near-infrared parts
of the electromagnetic spectrum. As hyperspectral imagery gains
traction for general computer vision tasks, there is an increased
need for large and comprehensive datasets for use as training
data.
Recent advancements in sensor technology allow us to capture
hyperspectral data cubes at higher spatial and temporal reso-
lution. However, there are few publicly available multi-purpose
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