Computational Intelligence

This data set comprises 4223 videos from a laser surface heat treatment process (also called laser heat treatment) applied to cylindrical workpieces made of steel. The purpose of the dataset is to detect anomalies in the laser heat treatment learning a model from a set of non-anomalous videos.

In the laser heat treatment, the laser beam is following a pattern similar to an "eight" with a frequency of 100 Hz. This pattern is sometimes modified to avoid obstacles in the workpieces.

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This is the dataset for the manuscript entitled "Physics-prior Bayesian neural networks in semiconductor processing", IEEE Access

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This contains data for ISFET based pH sensor drift compensation using machine learning techniques

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347 Views

Database for FMCW THz radars (HR workspace) and sample code for federated learning 

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1368 Views

Reinforcement Learning (RL) agents can learn to control a nonlinear system without using a model of the system. However, having a model brings benefits, mainly in terms of a reduced number of unsuccessful trials before achieving acceptable control performance. Several modelling approaches have been used in the RL domain, such as neural networks, local linear regression, or Gaussian processes. In this article, we focus on a technique that has not been used much so far:\ symbolic regression, based on genetic programming.

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Real life business processes change over time, in both planned and unexpected ways. These changes over time are called concept drifts and its detection is a big challenge in process mining since the inherent complexity of the data makes difficult distinguishing between a change and an anomalous execution. The following logs were generated synthetically in order to prove the quality of different concept drift detection algorithms.

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Code duplicates in large code corpora have adverse effects on the evaluation and use of machine learning models that rely on them. Most existing corpora suffer from this problem to some extent. This dataset contains a "duplication" index for some of the existing corpora in Big Code research. The method for collecting this dataset is described in "The Adverse Effects of Code Duplication in Machine Learning Models of Code" by Allamanis [ArXiV, to appear in SPLASH 2019].

 

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This dataset contains a sequence of network events extracted from a commercial network monitoring platform, Spectrum, by CA. These events, which are categorized by their severity, cover a wide range of events, from a link state change up to critical usages of CPU by certain devices. Regarding the layers they cover, they are focused on the physical, network and application layer. As such, the whole set gives a complete overview of the network’s general state.

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The compressed file contains:

  • Data files in spreadsheet format from three different networks (friendship, companionship and acquaintances).
  • Analysis files from UCINET, Pajek, Cytoscape and Gephi.

It is thus possible to corroborate the results mentioned in different studies that refer to these data.

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352 Views

OntoSNAQA is the name that combines Social Network Analysis (SNA), People and Questionnaires (Question and Answers - QA).

This ontology will be updated in this project of github and in the url http://www.jabenitez.com/ontologies/OntoSNAQA.owl.

It's an ontology that combines three different domains:
- People
- Questionnaires
- Social Network Analysis terms

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