Artificial Intelligence
Drafting is a game mode in collectible card games where players build their decks from a restricted pool of cards. Throughout one draft, players are offered a series of selections, from which they must build their deck. Although drafting is a popular game variant in \textit{Magic: The Gathering}, few machine learning models have been developed to learn card selection strategies. We model drafts with a Siamese neural network that is trained on real-world data and predicts human expert selection. Our model learns an embedding space of preferences by comparing cards in the context of a deck.
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<p>A dataset to detect knowledge conflict.</p>
The dataset contains 90 groups of natural language sentences with contradictions and 10 groups without contradictions, each group containing 5 sentences, usually 3 identical questions and 2 declarative sentences. The Agent should be able to accurately detect the contradictory statements.
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10 Views
Dataset for "SynEL: A Synthetic Benchmark for Entity Linking" paper. The dataset integrates structured information from two primary sources: DBpedia for English, representing a high-resource language environment, and the Russian Public Company Register, a challenging low-resource dataset. Each dataset includes extensive annotations and structured entity links, ensuring high relevance for real-world applications in diverse industries.
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239 Views
With multiple large open source datasets, the development of action recognition is rapid. However, we noticed the lack of annotated data of cilvil aircraft pilots, while distribution of whose action can be very different from daily casual activities. After discussion with experienced pilots and experts and close look into standard operation procedure, we present Airline-Pilot-Action (APA) benchmark, containing 5090 RGB and depth images together with corresponding flight computer data.
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149 Views
M. Kacmajor and J.D. Kelleher, "ExTra: Evaluation of Automatically Generated Source Code Using Execution Traces" (submitted to IEEE TSE)
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16 Views
M. Kacmajor and J.D. Kelleher, "ExTra: Evaluation of Automatically Generated Source Code Using Execution Traces" (submitted to IEEE TSE)
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35 Views
The Ogbn-Arxiv dataset (Arxiv for short) represents an academic citation network. In this network structure, papers serve as nodes, citations between papers form edges, and paper abstracts constitute the textual attributes. The primary task involves subject prediction for papers. We utilize the publicly available partitions, ground truth labels, and textual data from OGB
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<p>This dataset represents a user interaction network from Reddit, where individual users are represented as nodes. The network connections (edges) are established when users interact through replies. Each node contains features derived from the user's subreddit posting history. The classification goal is to identify users within the top 50% popularity bracket, based on their subreddit score averages. </p>
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15 Views
This dataset comprises a social media network structure where user accounts function as nodes and follower relationships constitute edges. The initial dataset is from [1], and the work adds the graph structure information through Instagram's public API. The main objective is distinguish between commercial and regular user accounts.
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53 Views