Artificial Intelligence
Due to the lack of publicly available injection-molded product defect datasets and the diversity of defects in terms of shapes, sizes, and textures, we collects defect samples from injection molding factories to ensure the model performs well in real industrial scenarios. To ensure the quality and usability of the data, after analyzing the sample data, data cleaning is performed to remove the irregular images.
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The Unified Multimodal Network Intrusion Detection System (UM-NIDS) dataset is a comprehensive, standardized dataset that integrates network flow data, packet payload information, and contextual features, making it highly suitable for machine learning-based intrusion detection models. This dataset addresses key limitations in existing NIDS datasets, such as inconsistent feature sets and the lack of payload or time-window-based contextual features.
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We collected programming problems and their solutions from previous studies. After applying some pre-processing steps, we queried advanced LLMs, such as GPT4, with the collected problems to produce machine-generated codes, while the original solutions were labeled as human-written codes. Finally, the entire collected dataset was divided into training, validation, and test sets, ensuring that there is no overlap among these sets, meaning no solutions in two different sets that solve the same programming problem.
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This dataset was developed in the context of the NANCY project and it is the output of the experiments involving cyberattacks against services that are running in a 5G coverage expansion scenario. The coverage expansion scenario involves a main operator and a micro-operator which extends the main operator’s coverage and can also provide additional services, such as Artificial Intelligence-based cyberattack detection.
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We have created a dataset named FDA, which consists of diverse images of flexible objects collected from real-world scenarios or online sources. To the best of our knowledge, FDA is the first extensive, multi-category dataset specifically designed for the recognition of flexible objects. This dataset establishes a benchmark for evaluating the performance of models in flexible objects recognition tasks.
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This dataset includes generated demand series, forecasts, forecast errors, and simulation results for the manuscript "Assessing the Effect of One-Step and Multi-Step Forecasting on Bullwhip Effect and Supply Chain Performance." Autoregressive demand series were generated using varying parameters, specifically based on an AR(1) demand process. Additionally, the M5 series, derived from Walmart, one of the world’s largest retailers, captures actual sales data from Walmart stores across the United States and is included as real-world data.
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Brushless DC (BLDC) motors depend on accurate rotor position detection via Hall sensors for optimal performance. Faults, such as sensor displacement, can disrupt commutation and lead to efficiency losses. This study utilizes deep learning to detect Hall sensor faults, focusing on a meticulously prepared dataset designed for this purpose. The dataset study consists of phase current measurements under various Hall sensor displacement conditions, categorized as No Delay, 0.0001 Delay, 0.005 Delay, and 0.01 Delay.
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The Universal Networking Language (UNL) UDE Dictionary for Indian Cooking is a pioneering framework aimed at facilitating seamless communication and knowledge sharing across diverse languages and cultures, with a special focus on the rich culinary traditions of India. This dictionary provides a comprehensive and structured representation of essential culinary terms, ingredients, cooking techniques, and descriptors in Hindi, paired with their corresponding UNL equivalents. Each entry includes a UNL term, a definition and examples.
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