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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/

    Topics API Analysis This repository provides the experimental results of the paper The Privacy-Utility Trade-off in the Topics API. Usage The notebooks were run using: Python v3.11.8 bvmlib v1.0.0 matplotlib 3.8.0 numpy 1.24.3 pandas 2.0.1 qif 1.2.3 requests 2.31.0 scipy 1.11.3 tldextract 5.1.2 tqdm 4.66.1 urllib3 1.26.16 The datasets produced for the experiments can be found on Zenodo: AOL Dataset for Browsing History and Topics of Interest (DOI: 10.5281/zenodo.11029572). Notebooks Data treatment: AOL-data-treatment.ipynb: Converts the original AOL dataset. Treats inconsistencies; Randomly remaps AnonID to RandID; Defines domains from URLs; and Filters domains by eTLD using tldextract and Mozilla's Public Suffix List, as of commit 5e6ac3a, extended by the discontinued TLDs: .bg.ac.yu, .ac.yu, .cg.yu, .co.yu, .edu.yu, .gov.yu, .net.yu, .org.yu, .yu, .or.tp, .tp, and .an. Generates the datasets AOL-treated.csv and AOL-treated-unique-domains.csv. The dataset AOL-treated.csv can be used for analyses of browsing history vulnerability and utility, as enabled by third-party cookies. This dataset contains singletons (individuals with only one domain in their browsing histories) and one outlier (one user with 150.802 domain visits in three months) that are dropped in some analyses. Citizen-Lab-Classification-data-treatment.ipynb: Converts the Citizen Lab Classification data, as of commit ebd0ee8. Treats inconsistencies; Defines domains from URLs; Filters domains by eTLD using tldextract and Mozilla's Public Suffix List, as of commit 5e6ac3a, extended by the discontinued TLDs: .bg.ac.yu, .ac.yu, .cg.yu, .co.yu, .edu.yu, .gov.yu, .net.yu, .org.yu, .yu, .or.tp, .tp, and .an; and Merges classifications by domain. Generates the dataset Citizen-Lab-Classification.csv. AOL-treated-Citizen-Lab-Classification-domain-matching.ipynb: Matches domains from AOL-treated-unique-domains.csv with domains and respective topics from Citizen-Lab-Classification.csv. Generates the dataset AOL-treated-Citizen-Lab-Classification-domain-match.csv. AOL-treated-Google-Topics-Classification-v1-domain-matching.ipynb: Matches domains from AOL-treated-unique-domains.csv with domains and respective topics from Google-Topics-Classification-v1.txt, as provided by Google with the Chrome browser. Generates the dataset AOL-treated-Google-Topics-Classification-v1-domain-match.csv. AOL-reduced-Citizen-Lab-Classification.ipynb: Converts the dataset AOL-treated.csv. Reduces the dataset AOL-treated.csv according to the dataset AOL-treated-Citizen-Lab-Classification-domain-match.csv. Generates the dataset AOL-reduced-Citizen-Lab-Classification.csv. The dataset AOL-reduced-Citizen-Lab-Classification.csv can be used for analyses of browsing history vulnerability and utility, as enabled by third-party cookies, and for analyses of topics of interest vulnerability and utility, as enabled by the Topics API. This dataset contains singletons and the outlier that are dropped in some analyses. This dataset can be used for analyses including the (data-dependent) randomness of trimming-down or filling-up the top-s sets of topics for each individual so each set has s topics. Privacy results for Generalization and utility results for Generalization, Bounded Noise, and Differential Privacy are expected to slightly vary with each run of the analyses over this dataset. AOL-reduced-Google-Topics-Classification-v1.ipynb: Converts the dataset AOL-treated.csv. Reduces the dataset AOL-treated.csv according to the dataset AOL-treated-Google-Topics-Classification-v1-domain-match.csv. Generates the dataset AOL-reduced-Google-Topics-Classification-v1.csv. The dataset AOL-reduced-Google-Topics-Classification-v1.csv can be used for analyses of browsing history vulnerability and utility, as enabled by third-party cookies, and for analyses of topics of interest vulnerability and utility, as enabled by the Topics API. This dataset contains singletons and the outlier that are dropped in some analyses. This dataset can be used for analyses including the (data-dependent) randomness of trimming-down or filling-up the top-s sets of topics for each individual so each set has s topics. Privacy results for Generalization and utility results for Generalization, Bounded Noise, and Differential Privacy are expected to slightly vary with each run of the analyses over this dataset. AOL-experimental.ipynb: Converts the dataset AOL-treated.csv. Drops singletons (individuals with only one domain in their browsing histories) and one outlier (one user with 150.802 domain visits in three months); and Defines browsing histories. Generates the dataset AOL-experimental.csv. The dataset AOL-experimental.csv can be used to empirically verify code correctness. All privacy and utility results are expected to remain the same with each run of the analyses over this dataset. AOL-experimental-Citizen-Lab-Classification.ipynb: Converts the dataset AOL-reduced-Citizen-Lab-Classification.csv. Generates the dataset AOL-experimental-Citizen-Lab-Classification.csv. The dataset AOL-experimental-Citizen-Lab-Classification.csv can be used to empirically verify code correctness. All privacy and utility results are expected to remain the same with each run of the analyses over this dataset. AOL-experimental-Google-Topics-Classification-v1.ipynb: Converts the dataset AOL-reduced-Google-Topics-Classification-v1.csv. Generates the dataset AOL-experimental-Google-Topics-Classification-v1.csv. The dataset AOL-experimental-Google-Topics-Classification-v1.csv can be used to empirically verify code correctness. All privacy and utility results are expected to remain the same with each run of the analyses over this dataset. Analyses: QIF-analyses-AOL-treated.ipynb: QIF analyses based on the dataset AOL-treated.csv. All privacy and utility results are expected to remain the same with each run of the analyses over this dataset. QIF-analyses-AOL-reduced-Citizen-Lab.ipynb: QIF analyses based on the dataset AOL-reduced-Citizen-Lab-Classification.csv. Privacy results for Generalization and utility results for Generalization, Bounded Noise, and Differential Privacy are expected to slightly vary with each run of the analyses over this dataset. QIF-analyses-AOL-reduced-Google-Topics-v1.ipynb: QIF analyses based on the dataset AOL-reduced-Google-Topics-Classification-v1.csv. Privacy results for Generalization and utility results for Generalization, Bounded Noise, and Differential Privacy are expected to slightly vary with each run of the analyses over this dataset. QIF-analyses-counting-experiment.ipynb: QIF analysis for counting topics popularity using the binomial distribution. QIF-analyses-AOL-experimental.ipynb: QIF analyses based on the dataset AOL-experimental.csv. All privacy and utility results are expected to remain the same with each run of the analyses over this dataset. QIF-analyses-AOL-experimental-Citizen-Lab.ipynb: QIF analyses based on the dataset AOL-experimental-Citizen-Lab-Classification.csv. All privacy and utility results are expected to remain the same with each run of the analyses over this dataset. QIF-analyses-AOL-experimental-Google-Topics-v1.ipynb: QIF analyses based on the dataset AOL-experimental-Google-Topics-Classification-v1.csv. All privacy and utility results are expected to remain the same with each run of the analyses over this dataset. License GNU GPLv3. To understand how the various GNU licenses are compatible with each other, please refer to the GNU licenses FAQ.

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  • AOL Dataset for Browsing History and Topics of Interest This record provides the datasets of the paper The Privacy-Utility Trade-off in the Topics API. The datasets generating code and the experimental results can be found in 10.5281/zenodo.11032231 (github.com/nunesgh/topics-api-analysis). Files AOL-treated.csv: This dataset can be used for analyses of browsing history vulnerability and utility, as enabled by third-party cookies. It contains singletons (individuals with only one domain in their browsing histories) and one outlier (one user with 150.802 domain visits in three months) that are dropped in some analyses. AOL-treated-unique-domains.csv: Auxiliary dataset containing all the unique domains from AOL-treated.csv. Citizen-Lab-Classification.csv: Auxiliary dataset containing the Citizen Lab Classification data, as of commit ebd0ee8, treated for inconsistencies and filtered according to Mozilla's Public Suffix List, as of commit 5e6ac3a, extended by the discontinued TLDs: .bg.ac.yu, .ac.yu, .cg.yu, .co.yu, .edu.yu, .gov.yu, .net.yu, .org.yu, .yu, .or.tp, .tp, and .an. AOL-treated-Citizen-Lab-Classification-domain-match.csv: Auxiliary dataset containing domains matched from AOL-treated-unique-domains.csv with domains and respective topics from Citizen-Lab-Classification.csv. Google-Topics-Classification-v1.txt: Auxiliary dataset containing the Google Topics API taxonomy v1 data as provided by Google with the Chrome browser. AOL-treated-Google-Topics-Classification-v1-domain-match.csv: Auxiliary dataset containing domains matched from AOL-treated-unique-domains.csv with domains and respective topics from Google-Topics-Classification-v1.txt. AOL-reduced-Citizen-Lab-Classification.csv: This dataset can be used for analyses of browsing history vulnerability and utility, as enabled by third-party cookies, and for analyses of topics of interest vulnerability and utility, as enabled by the Topics API. It contains singletons and the outlier that are dropped in some analyses.This dataset can be used for analyses including the (data-dependent) randomness of trimming-down or filling-up the top-s sets of topics for each individual so each set has s topics. Privacy results for Generalization and utility results for Generalization, Bounded Noise, and Differential Privacy are expected to slightly vary with each run of the analyses over this dataset. AOL-reduced-Google-Topics-Classification-v1.csv: This dataset can be used for analyses of browsing history vulnerability and utility, as enabled by third-party cookies, and for analyses of topics of interest vulnerability and utility, as enabled by the Topics API. It contains singletons and the outlier that are dropped in some analyses.This dataset can be used for analyses including the (data-dependent) randomness of trimming-down or filling-up the top-s sets of topics for each individual so each set has s topics. Privacy results for Generalization and utility results for Generalization, Bounded Noise, and Differential Privacy are expected to slightly vary with each run of the analyses over this dataset. AOL-experimental.csv: This dataset can be used to empirically verify code correctness for 10.5281/zenodo.11032231. All privacy and utility results are expected to remain the same with each run of the analyses over this dataset. AOL-experimental-Citizen-Lab-Classification.csv: This dataset can be used to empirically verify code correctness for 10.5281/zenodo.11032231. All privacy and utility results are expected to remain the same with each run of the analyses over this dataset. AOL-experimental-Google-Topics-Classification-v1.csv: This dataset can be used to empirically verify code correctness for 10.5281/zenodo.11032231. All privacy and utility results are expected to remain the same with each run of the analyses over this dataset. License Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International.

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    Em situação de desastre, mapear o território é imprescindível na identificação das principais áreas afetadas. E para realizar este tipo de mapeamento de forma segura e eficaz, a utilização de aeronave remotamente pilotada se torna a opção mais viável, essa, capaz de fornecer dados precisos, alta resolução dos produtos e celeridade na geração da geoinformação. Diante disso, objetivou-se, utilizar a aerofotogrametria para mapear as áreas afetadas por fortes chuvas, que foram o estopim para o desastre que aconteceu na cidade de São Sebastião, Litoral norte do Estado de São Paulo, e identificar feições de movimentos de massas no terreno. O bairro de Juquehy foi usado como estudo de caso por ser uma área que possuí relevo acentuado, com moradias localizadas no meio da encosta, com taludes instáveis a montante e a jusante. Por meio da aerofotogrametria e uso de técnicas de geoprocessamento, foi possível identificar 17 (dezessete) áreas de movimentos de massas e 7 (sete) áreas possivelmente afetadas. Com essas informações em mãos, auxiliando no gerenciamento da área afetada.

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    This is the data set for our paper published at IEEE WCCI/CEC 2024, https://2024.ieeewcci.org. Title: "Towards Adaptation in Multiobjective Evolutionary Algorithms for Integer Problems"Abstract: Parameter control refers to the techniques that dynamically adapt the parameter values of the evolutionary algorithm during the optimization process, such as population size, crossover rate, or operator selection. Adaptation can improve the performance and robustness of the algorithm, however, parameter control mechanisms themselves need to be designed and configured carefully. With this article, we contribute a systematic investigation of an adaptive, multi-objective algorithm that is designed for the optimisation of integer decision spaces. We find that (1) adaptation outperforms the best static configurations, and (2) performance of the multi-objective algorithm is often independent of the adaptation scheme's initial configuration.

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  • This is the original data collected for the paper titled:"A machine learning approach for rapid early detection of Campylobacter spp. using absorbance spectra collected from enrichment cultures"

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    Authors: Chang, Jia Jin Marc; Raupach, Michael J; Cheng, Lanna; Damgaard, Jakob; +15 Authors

    Datasets, codes, and results.

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  • Describes the taxonomy used in the paper "PRICER: Leveraging Few-Shot Learning with Fine-Tuned Large Language Models for Unstructured Economic Data", presented at the Second Workshop on Semantic Technologies and Deep Learning Models for Scientific, Technical and Legal Data at the Extended Semantic Web Conference (ESWC) 2024.

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  • Data including UMATs, Abaqus input files and experimental measurements related to the paper 'Computational micromechanics of bioabsorbable magnesium stents' https://doi.org/10.1016/j.jmbbm.2014.01.007

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  • Context: Code smells are symptoms of bad design choices implemented on the source code. To manage and enhance software quality, it is important to be aware of code smells and refactor them whenever possible. As a result, several code smell detection tools and techniques have been proposed over the years. These tools and techniques present different strategies to detect code smells. More recently, machine learning algorithms have also been proposed to support code smell detection. However, we lack empirical evidence on how expert feedback could improve detection of these machine learning based techniques. Objective: This paper aims to propose and evaluate a machine-learning based strategy to improve detection of code smells by means of continuous feedback provided by the system expert. Method: To evaluate the strategy, we follow an experimental design to compare results of the detection before and after the feedback, both when feedback is provided at once and continuously. We focus on four code smells - God Class, Long Method, Feature Envy, and Refused Bequest - detected in twenty Java systems by using five code smell detection tools. We also extracted class- and method-level metrics from the systems for training the machine learning algorithms. Results: We observed that continuous feedback improves the performance of code smell detection. For the detection of God Class, a code smell with a detection performance initially good, we achieved an average improvement of 0.13 in terms of F1. For Refused Bequest, another class-level code smell, we achieved an average improvement of 0.58 in terms of F1 after all interactions of the strategy. For the method-level code smells, Long Method and Feature Envy, we achieved an average improvement of 0.66 and 0.72 in terms of F1, respectively. Conclusions: Our promising results are a stepping stone towards the development of tools relying on continuous feedback for machine learning detection of code smells.

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    The materials include plots, texts, and calibrator image models (in fits format).

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/

    Topics API Analysis This repository provides the experimental results of the paper The Privacy-Utility Trade-off in the Topics API. Usage The notebooks were run using: Python v3.11.8 bvmlib v1.0.0 matplotlib 3.8.0 numpy 1.24.3 pandas 2.0.1 qif 1.2.3 requests 2.31.0 scipy 1.11.3 tldextract 5.1.2 tqdm 4.66.1 urllib3 1.26.16 The datasets produced for the experiments can be found on Zenodo: AOL Dataset for Browsing History and Topics of Interest (DOI: 10.5281/zenodo.11029572). Notebooks Data treatment: AOL-data-treatment.ipynb: Converts the original AOL dataset. Treats inconsistencies; Randomly remaps AnonID to RandID; Defines domains from URLs; and Filters domains by eTLD using tldextract and Mozilla's Public Suffix List, as of commit 5e6ac3a, extended by the discontinued TLDs: .bg.ac.yu, .ac.yu, .cg.yu, .co.yu, .edu.yu, .gov.yu, .net.yu, .org.yu, .yu, .or.tp, .tp, and .an. Generates the datasets AOL-treated.csv and AOL-treated-unique-domains.csv. The dataset AOL-treated.csv can be used for analyses of browsing history vulnerability and utility, as enabled by third-party cookies. This dataset contains singletons (individuals with only one domain in their browsing histories) and one outlier (one user with 150.802 domain visits in three months) that are dropped in some analyses. Citizen-Lab-Classification-data-treatment.ipynb: Converts the Citizen Lab Classification data, as of commit ebd0ee8. Treats inconsistencies; Defines domains from URLs; Filters domains by eTLD using tldextract and Mozilla's Public Suffix List, as of commit 5e6ac3a, extended by the discontinued TLDs: .bg.ac.yu, .ac.yu, .cg.yu, .co.yu, .edu.yu, .gov.yu, .net.yu, .org.yu, .yu, .or.tp, .tp, and .an; and Merges classifications by domain. Generates the dataset Citizen-Lab-Classification.csv. AOL-treated-Citizen-Lab-Classification-domain-matching.ipynb: Matches domains from AOL-treated-unique-domains.csv with domains and respective topics from Citizen-Lab-Classification.csv. Generates the dataset AOL-treated-Citizen-Lab-Classification-domain-match.csv. AOL-treated-Google-Topics-Classification-v1-domain-matching.ipynb: Matches domains from AOL-treated-unique-domains.csv with domains and respective topics from Google-Topics-Classification-v1.txt, as provided by Google with the Chrome browser. Generates the dataset AOL-treated-Google-Topics-Classification-v1-domain-match.csv. AOL-reduced-Citizen-Lab-Classification.ipynb: Converts the dataset AOL-treated.csv. Reduces the dataset AOL-treated.csv according to the dataset AOL-treated-Citizen-Lab-Classification-domain-match.csv. Generates the dataset AOL-reduced-Citizen-Lab-Classification.csv. The dataset AOL-reduced-Citizen-Lab-Classification.csv can be used for analyses of browsing history vulnerability and utility, as enabled by third-party cookies, and for analyses of topics of interest vulnerability and utility, as enabled by the Topics API. This dataset contains singletons and the outlier that are dropped in some analyses. This dataset can be used for analyses including the (data-dependent) randomness of trimming-down or filling-up the top-s sets of topics for each individual so each set has s topics. Privacy results for Generalization and utility results for Generalization, Bounded Noise, and Differential Privacy are expected to slightly vary with each run of the analyses over this dataset. AOL-reduced-Google-Topics-Classification-v1.ipynb: Converts the dataset AOL-treated.csv. Reduces the dataset AOL-treated.csv according to the dataset AOL-treated-Google-Topics-Classification-v1-domain-match.csv. Generates the dataset AOL-reduced-Google-Topics-Classification-v1.csv. The dataset AOL-reduced-Google-Topics-Classification-v1.csv can be used for analyses of browsing history vulnerability and utility, as enabled by third-party cookies, and for analyses of topics of interest vulnerability and utility, as enabled by the Topics API. This dataset contains singletons and the outlier that are dropped in some analyses. This dataset can be used for analyses including the (data-dependent) randomness of trimming-down or filling-up the top-s sets of topics for each individual so each set has s topics. Privacy results for Generalization and utility results for Generalization, Bounded Noise, and Differential Privacy are expected to slightly vary with each run of the analyses over this dataset. AOL-experimental.ipynb: Converts the dataset AOL-treated.csv. Drops singletons (individuals with only one domain in their browsing histories) and one outlier (one user with 150.802 domain visits in three months); and Defines browsing histories. Generates the dataset AOL-experimental.csv. The dataset AOL-experimental.csv can be used to empirically verify code correctness. All privacy and utility results are expected to remain the same with each run of the analyses over this dataset. AOL-experimental-Citizen-Lab-Classification.ipynb: Converts the dataset AOL-reduced-Citizen-Lab-Classification.csv. Generates the dataset AOL-experimental-Citizen-Lab-Classification.csv. The dataset AOL-experimental-Citizen-Lab-Classification.csv can be used to empirically verify code correctness. All privacy and utility results are expected to remain the same with each run of the analyses over this dataset. AOL-experimental-Google-Topics-Classification-v1.ipynb: Converts the dataset AOL-reduced-Google-Topics-Classification-v1.csv. Generates the dataset AOL-experimental-Google-Topics-Classification-v1.csv. The dataset AOL-experimental-Google-Topics-Classification-v1.csv can be used to empirically verify code correctness. All privacy and utility results are expected to remain the same with each run of the analyses over this dataset. Analyses: QIF-analyses-AOL-treated.ipynb: QIF analyses based on the dataset AOL-treated.csv. All privacy and utility results are expected to remain the same with each run of the analyses over this dataset. QIF-analyses-AOL-reduced-Citizen-Lab.ipynb: QIF analyses based on the dataset AOL-reduced-Citizen-Lab-Classification.csv. Privacy results for Generalization and utility results for Generalization, Bounded Noise, and Differential Privacy are expected to slightly vary with each run of the analyses over this dataset. QIF-analyses-AOL-reduced-Google-Topics-v1.ipynb: QIF analyses based on the dataset AOL-reduced-Google-Topics-Classification-v1.csv. Privacy results for Generalization and utility results for Generalization, Bounded Noise, and Differential Privacy are expected to slightly vary with each run of the analyses over this dataset. QIF-analyses-counting-experiment.ipynb: QIF analysis for counting topics popularity using the binomial distribution. QIF-analyses-AOL-experimental.ipynb: QIF analyses based on the dataset AOL-experimental.csv. All privacy and utility results are expected to remain the same with each run of the analyses over this dataset. QIF-analyses-AOL-experimental-Citizen-Lab.ipynb: QIF analyses based on the dataset AOL-experimental-Citizen-Lab-Classification.csv. All privacy and utility results are expected to remain the same with each run of the analyses over this dataset. QIF-analyses-AOL-experimental-Google-Topics-v1.ipynb: QIF analyses based on the dataset AOL-experimental-Google-Topics-Classification-v1.csv. All privacy and utility results are expected to remain the same with each run of the analyses over this dataset. License GNU GPLv3. To understand how the various GNU licenses are compatible with each other, please refer to the GNU licenses FAQ.

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  • AOL Dataset for Browsing History and Topics of Interest This record provides the datasets of the paper The Privacy-Utility Trade-off in the Topics API. The datasets generating code and the experimental results can be found in 10.5281/zenodo.11032231 (github.com/nunesgh/topics-api-analysis). Files AOL-treated.csv: This dataset can be used for analyses of browsing history vulnerability and utility, as enabled by third-party cookies. It contains singletons (individuals with only one domain in their browsing histories) and one outlier (one user with 150.802 domain visits in three months) that are dropped in some analyses. AOL-treated-unique-domains.csv: Auxiliary dataset containing all the unique domains from AOL-treated.csv. Citizen-Lab-Classification.csv: Auxiliary dataset containing the Citizen Lab Classification data, as of commit ebd0ee8, treated for inconsistencies and filtered according to Mozilla's Public Suffix List, as of commit 5e6ac3a, extended by the discontinued TLDs: .bg.ac.yu, .ac.yu, .cg.yu, .co.yu, .edu.yu, .gov.yu, .net.yu, .org.yu, .yu, .or.tp, .tp, and .an. AOL-treated-Citizen-Lab-Classification-domain-match.csv: Auxiliary dataset containing domains matched from AOL-treated-unique-domains.csv with domains and respective topics from Citizen-Lab-Classification.csv. Google-Topics-Classification-v1.txt: Auxiliary dataset containing the Google Topics API taxonomy v1 data as provided by Google with the Chrome browser. AOL-treated-Google-Topics-Classification-v1-domain-match.csv: Auxiliary dataset containing domains matched from AOL-treated-unique-domains.csv with domains and respective topics from Google-Topics-Classification-v1.txt. AOL-reduced-Citizen-Lab-Classification.csv: This dataset can be used for analyses of browsing history vulnerability and utility, as enabled by third-party cookies, and for analyses of topics of interest vulnerability and utility, as enabled by the Topics API. It contains singletons and the outlier that are dropped in some analyses.This dataset can be used for analyses including the (data-dependent) randomness of trimming-down or filling-up the top-s sets of topics for each individual so each set has s topics. Privacy results for Generalization and utility results for Generalization, Bounded Noise, and Differential Privacy are expected to slightly vary with each run of the analyses over this dataset. AOL-reduced-Google-Topics-Classification-v1.csv: This dataset can be used for analyses of browsing history vulnerability and utility, as enabled by third-party cookies, and for analyses of topics of interest vulnerability and utility, as enabled by the Topics API. It contains singletons and the outlier that are dropped in some analyses.This dataset can be used for analyses including the (data-dependent) randomness of trimming-down or filling-up the top-s sets of topics for each individual so each set has s topics. Privacy results for Generalization and utility results for Generalization, Bounded Noise, and Differential Privacy are expected to slightly vary with each run of the analyses over this dataset. AOL-experimental.csv: This dataset can be used to empirically verify code correctness for 10.5281/zenodo.11032231. All privacy and utility results are expected to remain the same with each run of the analyses over this dataset. AOL-experimental-Citizen-Lab-Classification.csv: This dataset can be used to empirically verify code correctness for 10.5281/zenodo.11032231. All privacy and utility results are expected to remain the same with each run of the analyses over this dataset. AOL-experimental-Google-Topics-Classification-v1.csv: This dataset can be used to empirically verify code correctness for 10.5281/zenodo.11032231. All privacy and utility results are expected to remain the same with each run of the analyses over this dataset. License Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International.

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    Em situação de desastre, mapear o território é imprescindível na identificação das principais áreas afetadas. E para realizar este tipo de mapeamento de forma segura e eficaz, a utilização de aeronave remotamente pilotada se torna a opção mais viável, essa, capaz de fornecer dados precisos, alta resolução dos produtos e celeridade na geração da geoinformação. Diante disso, objetivou-se, utilizar a aerofotogrametria para mapear as áreas afetadas por fortes chuvas, que foram o estopim para o desastre que aconteceu na cidade de São Sebastião, Litoral norte do Estado de São Paulo, e identificar feições de movimentos de massas no terreno. O bairro de Juquehy foi usado como estudo de caso por ser uma área que possuí relevo acentuado, com moradias localizadas no meio da encosta, com taludes instáveis a montante e a jusante. Por meio da aerofotogrametria e uso de técnicas de geoprocessamento, foi possível identificar 17 (dezessete) áreas de movimentos de massas e 7 (sete) áreas possivelmente afetadas. Com essas informações em mãos, auxiliando no gerenciamento da área afetada.

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    This is the data set for our paper published at IEEE WCCI/CEC 2024, https://2024.ieeewcci.org. Title: "Towards Adaptation in Multiobjective Evolutionary Algorithms for Integer Problems"Abstract: Parameter control refers to the techniques that dynamically adapt the parameter values of the evolutionary algorithm during the optimization process, such as population size, crossover rate, or operator selection. Adaptation can improve the performance and robustness of the algorithm, however, parameter control mechanisms themselves need to be designed and configured carefully. With this article, we contribute a systematic investigation of an adaptive, multi-objective algorithm that is designed for the optimisation of integer decision spaces. We find that (1) adaptation outperforms the best static configurations, and (2) performance of the multi-objective algorithm is often independent of the adaptation scheme's initial configuration.

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  • This is the original data collected for the paper titled:"A machine learning approach for rapid early detection of Campylobacter spp. using absorbance spectra collected from enrichment cultures"

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    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
      addClaim

      This Research product is the result of merged Research products in OpenAIRE.

      You have already added works in your ORCID record related to the merged Research product.
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