Machine Learning
ARImulti-mic: real-world speech recordings on a humanoid robot (ARI)
This dataset includes “real-world” experiments. A recording campaign was held in the acoustic laboratory at Bar-Ilan University. This lab is a [6×6×2.4]m room with a reverberation time controlled by 60 interchangeable panels covering the room facets.
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Contains the benchmark Bayesian network dataset, which uses the seed of Bayesian networks from https://www.bnlearn.com. Some of the data comes from https://pages.mtu.edu/~lebrown/supplements/mmhc_paper/mmhc_index.html. And other datasets from the UCI that contain mixed data.
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The dataset, titled "SensorNetGuard: A Dataset for Identifying Malicious Sensor Nodes," comprises 10,000 samples with 21 features. It is designed to facilitate the identification of malicious sensor nodes in a network environment, specifically focusing on IoT-based sensor networks.
General Metrics
§ Node ID: The unique identifier for each node.
§ Timestamp: The time at which data or a packet is sent or received.
§ IP Address: Internet Protocol address of the node.
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RITA (Resource for Italian Tests Assessment), is a new NLP dataset of academic exam texts written in Italian by second-language learners for obtaining the CEFR certification of proficiency level.
RITA dataset is available for automatic processing in CSV and XML format, under an agreement of citation.
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SYPHAXAR dataset is a dataset for Arabic text detection in the wild. It was collected from Tunisia in “Sfax” city, the second largest Tunisian city after the capital. A total of 3078 images were gathered through manual collection one by one, with each image energizing text detection challenges in nature according to real existing complexity of 15 different routes along with ring roads, intersections and roundabouts. These annotated images consist of more than 31000 objects, each of which is enclosed within a bounding box.
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Overview
The dataset under consideration is a comprehensive compilation of code snippets, function descriptions, and their respective binary representations aimed at fostering research in software engineering. It contains a variety of code functionalities and serves as a valuable resource for understanding the behavior and characteristics of C programs. This data is sourced from the AnghaBench repository, a well-documented collection of C programs available on GitHub.
Columns and Data Types
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Physically unclonable functions (PUFs) are foundational components that offer a cost-efficient and promising solution for diverse security applications, including countering integrated circuit (IC) counterfeiting, generating secret keys, and enabling lightweight authentication. PUFs exploit semiconductor variations in ICs to derive inherent responses from imposed challenges, creating unique challenge-response pairs (CRPs) for individual devices. Analyzing PUF security is pivotal for identifying device vulnerabilities and ensuring response credibility.
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Dataset description:
This contains ten categories of gas data, each category contains 5 concentrations, 10, 20, 30, 40, 50ppm.
There are 160 groups of 10, 20, 30, 40, each group contains 6000 sampled voltage signals, and the sampling frequency is 10HZ.
There are only 80 groups for 50ppm concentration, and each group also contains 6000 sampled voltage signals.
The label corresponding to each gas includes category and concentration, which can be split by gas category and concentration.
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