A Review on Anonymization Techniques for Privacy Protection in Data Mining

Saranya, U.; Logeswari, A.; Sujatha, U.

doi:10.33564/ijeast.2020.v04i10.057

Cited by 6 publications

(8 citation statements)

References 2 publications

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“…As different features of test scenarios are represented by a wide range of values, the feature values are first scaled between [0, 1] using Min-Max normalisation [70]. Next the diversity of the 𝑘 𝑡ℎ feature of two scenarios 𝑆 𝑖 , 𝑆 𝑗 is calculated as below:…”

Section: Euclidean Diversitymentioning

confidence: 99%

“…Standard deviation, 𝑆𝑇 𝐷, measures the variability or dispersion of data around the mean value and is widely recognised as a robust statistical measure to quantify the variability or spread of a test suite [5,16,63]. For calculating 𝑆𝑇 𝐷 for this work, the feature values are first scaled between [0, 1] using Min-Max normalisation [70], and 𝑆𝑇 𝐷 is then computed as the norm of the 𝑆𝑇 𝐷 of each feature.…”

Section: Standard Deviationmentioning

confidence: 99%

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Towards Reliable AI: Adequacy Metrics for Ensuring the Quality of System-level Testing of Autonomous Vehicles

Neelofar,

Aleti

2024

Proceedings of the IEEE/ACM 46th International Conference on Software Engineering

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AI-powered systems have gained widespread popularity in various domains, including Autonomous Vehicles (AVs). However, ensuring their reliability and safety is challenging due to their complex nature. Conventional test adequacy metrics, designed to evaluate the effectiveness of traditional software testing, are often insufficient or impractical for these systems. White-box metrics, which are specifically designed for these systems, leverage neuron coverage information. These coverage metrics necessitate access to the underlying AI model and training data, which may not always be available. Furthermore, the existing adequacy metrics exhibit weak correlations with the ability to detect faults in the generated test suite, creating a gap that we aim to bridge in this study.In this paper, we introduce a set of black-box test adequacy metrics called "Test suite Instance Space Adequacy" (TISA) metrics, which can be used to gauge the effectiveness of a test suite. The TISA metrics offer a way to assess both the diversity and coverage of the test suite and the range of bugs detected during testing. Additionally, we introduce a framework that permits testers to visualise the diversity and coverage of the test suite in a two-dimensional space, facilitating the identification of areas that require improvement.We evaluate the efficacy of the TISA metrics by examining their correlation with the number of bugs detected in system-level simulation testing of AVs. A strong correlation, coupled with the short computation time, indicates their effectiveness and efficiency in estimating the adequacy of testing AVs.

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Section: Euclidean Diversitymentioning

confidence: 99%

Section: Standard Deviationmentioning

confidence: 99%

Towards Reliable AI: Adequacy Metrics for Ensuring the Quality of System-level Testing of Autonomous Vehicles

Neelofar,

Aleti

2024

Proceedings of the IEEE/ACM 46th International Conference on Software Engineering

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“…Considering that the dataset is small-scale and only a small percentage of data is missing, we used the KNN method to fill in this missing data. Additionally, in order to deal with the significant data differences among different indicators, the Z-Score normalization operations were applied to each water quality indicator separately [17], as shown in (4).…”

Section: A Data Normalisationmentioning

confidence: 99%

Design of a Variable-Length Sequential Prediction Framework GTV-STP Based on Spatial and Temporal Water Quality Information of Taihu Lake

Lei,

Hu,

Huang

et al. 2024

IEEE Access

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Water quality predictions have a great importance in water resource managements and water pollution protections. Most the currently used water quality models can only predict time sequence of fixed length and ignore the real hydrological flow information. In this paper, we propose a new water quality prediction framework GTV-STP that adopts an embedding-Encoder-Decoder structure, in which the timestamps are introduced into the water quality information. The spatial embedding method is developed to introduce the spatial hydrological features into the water quality information, in order to achieve multisite parallel predictions. In addition, a variablelength decoder is put forward for the variable-length sequencial predictions. The practical water quality predictions of WT, pH, DO, COD Mn , NH 3 -N, P and N for Taihu Lake with GTV-STP framework are performed on two datasets of 6to12 in smaller-scale and 12to24 in larger-scale. Using the Huber Loss to balance the MAE and the RMSE, the val-Huber Loss of the GTV-STP framework is used to compare with that of the baseline models such as LSTM-CNN-ATT, LSTMNet and MLP in predicting accuracy. Results show that the GTV-STP framework has the highest accuracy with the val-Huber Loss of 0.079183 on 6to12 and 0.033561 on 12to24. It is shown that the GTV-STP framework has highly accurate not only for smaller-scale water quality predictions but also suitable for larger-scale predictions. In future, water quality predictions using other more precise frameworks containing spatial and time series information under deep learning will be one of the research directions.INDEX TERMS GTV-STP, spatial and temporal water quality information, Taihu Lake, variable length sequential predictions, water quality predictions.

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“…MMN reduces the un-normalized data to a specific lower and upper boundary, which is typically 0 to 1 or -1 to 1. The formula in Equation ( 7) was used to calculate MMN [43].…”

Section: Data Preprocessingmentioning

confidence: 99%

Vision-based biomechanical markerless motion classification

Liew¹,

Feng²

2023

MG&V

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This study used stick model augmentation on single-camera motion video to create a markerless motion classification model of manual operations. All videos were augmented with a stick model composed of keypoints and lines by using the programming model, which later incorporated the COCO dataset, OpenCV and OpenPose modules to estimate the coordinates and body joints. The stick model data included the initial velocity, cumulative velocity, and acceleration for each body joint. The extracted motion vector data were normalized using three different techniques, and the resulting datasets were subjected to eight classifiers. The experiment involved four distinct motion sequences performed by eight participants. The random forest classifier performed the best in terms of accuracy in recorded data classification in its min-max normalized dataset. This classifier also obtained a score of 81.80% for the dataset before random subsampling and a score of 92.37% for the resampled dataset. Meanwhile, the random subsampling method dramatically improved classification accuracy by removing noise data and replacing them with replicated instances to balance the class. This research advances methodological and applied knowledge on the capture and classification of human motion using a single camera view.

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A Review on Anonymization Techniques for Privacy Protection in Data Mining

Cited by 6 publications

References 2 publications

Towards Reliable AI: Adequacy Metrics for Ensuring the Quality of System-level Testing of Autonomous Vehicles

Towards Reliable AI: Adequacy Metrics for Ensuring the Quality of System-level Testing of Autonomous Vehicles

Design of a Variable-Length Sequential Prediction Framework GTV-STP Based on Spatial and Temporal Water Quality Information of Taihu Lake

Vision-based biomechanical markerless motion classification

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