Artificial Intelligence
The Army Cyber Institute (ACI) Internet of Things (IoT) Network Traffic Dataset 2023 (ACI-IoT-2023) is a novel dataset tailored for machine learning (ML) applications in the realm of IoT network security. This effort focuses on delivering a distinctive and realistic dataset designed to train and evaluate ML models for IoT network environments. By addressing a gap in existing resources, this dataset aims to propel advancements in ML-based solutions, ultimately fortifying the security of IoT operations.
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4114 Views
In the domain of Natural Language Processing (NLP), the English Writing Fluency Improvement for non-native speakers, particularly in academic contexts, poses significant challenges. While Sentence-level Revision (SentRev) endeavors to address this concern, the existing evaluation corpus, SMITH, falls short in offering a robust and comprehensive assessment of the task. To bridge this gap, our research offers a novel evaluation corpus generation scheme, leading to the creation of Ten-Country Non-native Academic English Corpus (TCNAEC).
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54 Views
Simulated dataset for deriving parametric constraints for Bayesian Knowedge Tracing. The classical Expectation-Maximization method results in degenerate parameters (i.e., parameters that violate the conceptual interpretation of the model, such as by saying that a learner with no knowledge of a skill is more likely to get an answer correct than a learner with knowledge). A novel approach based on Newton's method rescues these paramters using mathematically derived constraints on the parameter space.
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83 Views
We introduce a novel dataset consisting of approximately 5,700 video files, specifically designed to enhance the development of real-time traffic accident detection systems in smart city environments. It encompasses a diverse range of traffic scenarios, captured through Traffic/Surveillance Cameras (Trafficam) and Dash Cameras (Dashcam), along with additional external data sources. The dataset is meticulously organized into three segments: Training, Validation, and Testing, with each segment offering a unique blend of traffic and dashcam footage across different scenarios.
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5348 Views
The D-Faust dataset [26] consists of 41k scanned human body models represented as 3D point clouds and triangular meshes. These models are obtained through scanning human bodies in various poses, allowing for the inclusion of various defects such as noise, holes, missing body parts, and occasional artifacts caused by reflections. This dataset is essential for research on human body reconstruction and provides a rich resource for studying variations in human body shape and pose.
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71 Views
Fingerprint recognition is crucial for device and data
security, especially with the widespread use of capacitive sensors
in mobile devices. However, denoising wet fingerprints from
these sensors poses challenges due to small fingerprint areas,
limited features, and significant moisture-induced dark regions.
Our ”DRB-FD” method combines a Featured Discriminator (FD)
and a Deformed Residual Block (DRB) with attention mechanisms,
drop-out layers, and pre-activation. In experiments using the Nasic9395
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56 Views
The data was constructed using the publicly available C-MAPSS dataset, from which 14 effective degradation indicators were selected, and the health state of the engine was described by the knowledge graph, and the health state knowledge graph was constructed.
1：The first column in the data represents the engine ID, and the second column indicates the current health status label of the engine.
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58 Views
Graph neural networks (GNNs) are widely applied in graph data modeling. However, existing GNNs are often trained in a task-driven manner that fails to fully capture the intrinsic nature of the graph structure, resulting in sub-optimal node and graph representations. To address this limitation, we propose a novel \textbf{G}raph structure \textbf{P}rompt \textbf{L}earning method (GPL) to enhance the training of GNNs, which is inspired by prompt mechanisms in natural language processing.
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5 Views
This dataset contains product reviews and metadata from Amazon, including 142.8 million reviews spanning May 1996 - July 2014.
This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs).
These data have been reduced to extract the k-core, such that each of the remaining users and items have k reviews each.
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60 Views
We propose a more challenging dataset known as Weibo23. By amalgamating all available fake news from the Weibo Management Community until March 2023 with existing samples from public datasets [1], we formed a comprehensive collection of fake news for Weibo23. Fabricated news articles were thoroughly examined and authenticated by certified experts.
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1323 Views