We conducted a study utilizing a lightweight generative network to create flying motions. The existing datasets used for training did not include any data on flying motions. Therefore, we selected certain classes from the existing motion datasets and ...

This paper presents an overview of OpenPack, a comprehensive dataset developed for recognizing packaging work activities, and discusses an activity recognition competition using this dataset. The availability of sensor datasets for recognizing work ...

Fashion image captioning is a critical task in the fashion industry that aims to automatically generate product descriptions for fashion items. However, existing fashion image captioning models predict a fixed caption for a particular fashion item once ...

Abstractive text summarization techniques for Malayalam language is still in its infancy. The lack of benchmarked datasets for this task is one of the constraints in developing and testing good models. Malayalam has seven nominal case forms, two nominal ...

The remarkable capability of large language models (LLMs) in generating high-quality code has drawn increasing attention in the software testing community. However, existing code LLMs often demonstrate unsatisfactory capabilities in generating accurate, ...

In this work, we introduce the NavProc 1.0 Corpus – a medium-scale, annotated corpus of procedural texts within the naval domain – for use as a first step in modeling procedural structures derived from real-world data sources. In particular, we ...

Thermal comfort inside buildings is a well-studied field where human judgment for thermal comfort is collected and may be used for automatic thermal comfort estimation. However, indoor scenarios are rather static in terms of thermal state changes and, ...

This work introduces the BRESSAY dataset, a novel contribution to the field of offline Handwritten Text Recognition (HTR), specifically targeting Brazilian Portuguese. Despite significant advancements in HTR, challenges remain due to the ...

Image similarity has been extensively studied in computer vision. In recent years, machine-learned models have shown their ability to encode more semantics than traditional multivariate metrics. However, in labelling semantic similarity, assigning a ...

Real-world last-mile express datasets are crucial for research in logistics, supply chain management, and spatio-temporal data mining. Despite a plethora of algorithms developed to date, no widely accepted, publicly available last-mile express dataset ...

In recent years, machine learning-based anomaly detection (AD) has become an important measure against security threats from Internet of Things (IoT) networks. Machine learning (ML) models for network traffic AD require datasets to be trained, evaluated ...

Action recognition refers to the process of categorizing a video by identifying and classifying the specific actions it encompasses. Videos originate from several domains, and within each domain of video analysis, comprehending actions holds paramount ...

5G in mid-bands has become the dominant deployment of choice in the world. We present - to the best of our knowledge - the first comprehensive and comparative cross-country measurement study of commercial mid-band 5G deployments in Europe and the U.S., ...

This paper deals with the problem of creating a keyword spotting (KWS) system with real-world audio data. The paper describes the different methods used to build KWS systems, deep learning models such as convolutional neural networks (CNN), ...

Emoji prediction is an important research task that focuses on finding the most appropriate emoji(s) quickly and effortlessly for a specific text. Now that Turkish is on the list of the top 20 most spoken languages in the world and there are a ...

Advancements in large language models (LLMs) have changed information retrieval, offering users a more personalised and natural search experience with technologies like OpenAI ChatGPT, Google Bard (Gemini), or Microsoft Copilot. Despite these ...

The diversification of product search aims to offer diverse products to satisfy different user intents. Existing diversified product search approaches mainly relied on datasets sourced from online platforms. However, these datasets often present ...

As a representative information retrieval task, site recommendation, which aims at predicting the optimal sites for a brand or an institution to open new branches in an automatic data-driven way, is beneficial and crucial for brand development in modern ...

Click-through rate (CTR) prediction plays a crucial role in recommendation systems, with significant impact on user experience and platform revenue generation. Despite the various public CTR datasets available due to increasing interest from both ...

Extracting relevant information from legal documents is a challenging task due to the technical complexity and volume of their content. These factors also increase the costs of annotating large datasets, which are required to train state-of-the-art ...