correcting cqs

Author: Vamsi-Kommineni

Evaluation/CQ_answers/KG_cqs_pub_1.csv

@@ -1,14 +1,14 @@
 CQ_no,CQ,CQ_ans,Human_Evaluation
-2,"What data formats are used in the deep learning pipeline (e.g., image, audio, video, CSV)?","The deep learning pipeline uses audio and image data formats. The audio data is presented as spectrograms, while the image data is presented as image crops around the object of interest, with contrast enhancement and consecutive frame differences. ",TRUE
-5,"What are the datasets used in the deep learning pipeline (e.g., MNIST, CIFAR, ImageNet)? ","The paper does not explicitly mention the use of any specific datasets such as MNIST, CIFAR, or ImageNet. However, it does mention that the deep learning pipeline uses large amounts of labeled data produced by analysts using the UI. This data is used to classify dozens of species signals and event types across ongoing projects. The paper also mentions the use of deep convolutional neural networks (CNNs) and deep feed forward neural networks (DNNs) to audio spectrogram and image data. This suggests that the datasets used in the deep learning pipeline are likely audio and image data related to biodiversity monitoring. ",TRUE
-6,"What preprocessing steps are involved before training a deep learning model (e.g., normalization, scaling, cleaning)?","The context does not provide specific information on the preprocessing steps involved before training a deep learning model. However, common preprocessing steps for deep learning models include normalization, scaling, cleaning, and augmentation of the data. Normalization and scaling help to bring the data to a similar scale, cleaning helps to remove any noise or irrelevant data, and augmentation helps to increase the size of the dataset. ",TRUE
-4,"What are the data augmentation techniques applied in the deep learning pipeline (e.g., Fliping, Roating, Scaling)?","The provided context does not mention any specific data augmentation techniques applied in the deep learning pipeline. Therefore, I cannot provide a helpful answer to this query. ",TRUE
-12,"What type of deep learning model is used in the pipeline (e.g., CNN, RNN, Transformer)?",The pipeline uses deep convolutional neural networks (CNNs) and deep feed forward neural networks (DNNs) to audio spectrogram and image data.,TRUE
-15,"What optimization techniques are applied in the deep learning pipeline (e.g., SGD, Adam)?","The provided context does not mention any specific optimization techniques applied in the deep learning pipeline, such as SGD (Stochastic Gradient Descent) or Adam. ",TRUE
-13,"What are the hyperparameters used in the deep learning model (e.g., learning rate, optimizer)?","The paper does not provide information on the hyperparameters used in the deep learning model, such as learning rate or optimizer. ",TRUE
-22,"What metrics are used to evaluate the performance of the deep learning model (e.g., accuracy, precision, recall)?",The context does not provide information on the specific metrics used to evaluate the performance of the deep learning model.,TRUE
-17,"What are the regularization methods used to prevent overfitting in the deep learning pipeline (e.g., dropout, L2 regularization)?",The provided context does not mention any specific regularization methods used to prevent overfitting in the deep learning pipeline.,TRUE
-19,"Which frameworks are used to build the deep learning model (e.g., TensorFlow, PyTorch)?",The context does not provide information on the specific frameworks used to build the deep learning model.,TRUE
-20,"Which hardware resources are used for training the deep learning model (e.g., GPUs, TPUs)?",The context does not provide information on the specific hardware resources used for training the deep learning model.,TRUE
-8,"Where is the code repository of the deep learning pipeline available (e.g., GitHub, GitLab, BitBucket)?",The code repository of the deep learning pipeline is not available in the provided context.,TRUE
-25,"What is the purpose of the deep learning model (e.g., classification, segmentation, detection)?","The purpose of the deep learning model is to classify the presence or absence and activity rates of a number of different endangered species, or in some cases, the sounds of birds colliding with energy infrastructure. In total, the model has the ability to classify dozens of species signals and event types across ongoing projects, and aims to scale this up to encompass whole communities. ",TRUE
+2,"What data formats are used in the deep learning pipeline (e.g., image, audio, video, CSV)?","The deep learning pipeline uses audio and image data formats. The audio data is presented as spectrograms, while the image data is presented as image crops around the object of interest, with contrast enhancement and consecutive frame differences. ",True
+5,"What are the datasets used in the deep learning pipeline (e.g., MNIST, CIFAR, ImageNet)? ","The paper does not explicitly mention the use of any specific datasets such as MNIST, CIFAR, or ImageNet. However, it does mention that the deep learning pipeline uses large amounts of labeled data produced by analysts using the UI. This data is used to classify dozens of species signals and event types across ongoing projects. The paper also mentions the use of deep convolutional neural networks (CNNs) and deep feed forward neural networks (DNNs) to audio spectrogram and image data. This suggests that the datasets used in the deep learning pipeline are likely audio and image data related to biodiversity monitoring. ",True
+6,"What preprocessing steps are involved before training a deep learning model (e.g., normalization, scaling, cleaning)?","The context does not provide specific information on the preprocessing steps involved before training a deep learning model. However, common preprocessing steps for deep learning models include normalization, scaling, cleaning, and augmentation of the data. Normalization and scaling help to bring the data to a similar scale, cleaning helps to remove any noise or irrelevant data, and augmentation helps to increase the size of the dataset. ",True
+4,"What are the data augmentation techniques applied in the deep learning pipeline (e.g., Flipping, Rotating, Scaling)?","The provided context does not mention any specific data augmentation techniques applied in the deep learning pipeline. Therefore, I cannot provide a helpful answer to this query. ",True
+12,"What type of deep learning model is used in the pipeline (e.g., CNN, RNN, Transformer)?",The pipeline uses deep convolutional neural networks (CNNs) and deep feed forward neural networks (DNNs) to audio spectrogram and image data.,True
+15,"What optimization techniques are applied in the deep learning pipeline (e.g., SGD, Adam)?","The provided context does not mention any specific optimization techniques applied in the deep learning pipeline, such as SGD (Stochastic Gradient Descent) or Adam. ",True
+13,"What are the hyperparameters used in the deep learning model (e.g., learning rate, optimizer)?","The paper does not provide information on the hyperparameters used in the deep learning model, such as learning rate or optimizer. ",True
+22,"What metrics are used to evaluate the performance of the deep learning model (e.g., accuracy, precision, recall)?",The context does not provide information on the specific metrics used to evaluate the performance of the deep learning model.,True
+17,"What are the regularization methods used to prevent overfitting in the deep learning pipeline (e.g., dropout, L2 regularization)?",The provided context does not mention any specific regularization methods used to prevent overfitting in the deep learning pipeline.,True
+19,"Which frameworks are used to build the deep learning model (e.g., TensorFlow, PyTorch)?",The context does not provide information on the specific frameworks used to build the deep learning model.,True
+20,"Which hardware resources are used for training the deep learning model (e.g., GPUs, TPUs)?",The context does not provide information on the specific hardware resources used for training the deep learning model.,True
+8,"Where is the code repository of the deep learning pipeline available (e.g., GitHub, GitLab, BitBucket)?",The code repository of the deep learning pipeline is not available in the provided context.,True
+25,"What is the purpose of the deep learning model (e.g., classification, segmentation, detection)?","The purpose of the deep learning model is to classify the presence or absence and activity rates of a number of different endangered species, or in some cases, the sounds of birds colliding with energy infrastructure. In total, the model has the ability to classify dozens of species signals and event types across ongoing projects, and aims to scale this up to encompass whole communities. ",True

Evaluation/CQ_answers/KG_cqs_pub_10.csv

@@ -1,14 +1,14 @@
 CQ_no,CQ,CQ_ans,Human_Evaluation
-2,"What data formats are used in the deep learning pipeline (e.g., image, audio, video, CSV)?","The data formats used in the deep learning pipeline are grey-coloured 1-channel images, specifically audio spectrograms. These images are fed to the input-layer of the U-net. The image fed to the output layer during training is the segmentation of the spectrogram of the input image. ",TRUE
-5,"What are the datasets used in the deep learning pipeline (e.g., MNIST, CIFAR, ImageNet)? ","The context does not provide specific information about the datasets used in the deep learning pipeline. However, it mentions that the training set is composed of spectrogram figures of bird recordings as well as recordings void of any bird activity and their corresponding binary masks. ",FALSE
-6,"What preprocessing steps are involved before training a deep learning model (e.g., normalization, scaling, cleaning)?","The context does not provide specific information on the preprocessing steps involved before training the deep learning model. However, common preprocessing steps in deep learning include normalization, scaling, cleaning, and data augmentation. These steps help to improve the model's performance and convergence during training. ",FALSE
-4,"What are the data augmentation techniques applied in the deep learning pipeline (e.g., Fliping, Roating, Scaling)?","The data augmentation techniques applied in the deep learning pipeline are small random rotations between -5 and 5 degrees as well as random displacements at the maximum of the 10% of the image. Although this is a large displacement to apply in order to accommodate within-species vocal variations, it was decided to use it and proved beneficial probably because a global bird detector was made and not one dedicated to a specific species. ",TRUE
-12,"What type of deep learning model is used in the pipeline (e.g., CNN, RNN, Transformer)?","The pipeline uses a U-net deep autoencoder, which is a type of convolutional neural network (CNN). ",TRUE
-15,"What optimization techniques are applied in the deep learning pipeline (e.g., SGD, Adam)?",The context does not provide information on the optimization techniques applied in the deep learning pipeline.,TRUE
-13,"What are the hyperparameters used in the deep learning model (e.g., learning rate, optimizer)?","The paper does not provide information on the hyperparameters used in the deep learning model, such as the learning rate or the optimizer. ",TRUE
-22,"What metrics are used to evaluate the performance of the deep learning model (e.g., accuracy, precision, recall)?","The performance of the deep learning model is evaluated using the mean Dice coefficient loss function. The Dice coefficient is used to compare the pixel-wise agreement between a predicted segmentation and its corresponding ground truth. The formula is given by: YXYX*2 Where, X is the predicted set of pixels and Y is the ground truth. The Dice coefficient is the quotient of similarity and ranges between 0 and 1. It can be viewed as a similarity measure over sets. The loss function is just the minus of the Dice coefficient with the additions of a smoothing factor inserted in the denominator. The score in Table I is the mean of the Dice coefficients of images in the evaluation set. ",TRUE
-17,"What are the regularization methods used to prevent overfitting in the deep learning pipeline (e.g., dropout, L2 regularization)?","The context does not provide information on the regularization methods used to prevent overfitting in the deep learning pipeline. Therefore, I don't know the answer to this query based on the provided context. ",TRUE
-19,"Which frameworks are used to build the deep learning model (e.g., TensorFlow, PyTorch)?",The Keras Deep learning library is used to build the deep learning model. It is used in CUDA-CuDNN GPU mode. Python code in Anaconda Python 2. 7. 11 running in Ubuntu Linux environment and the Linux version of Matlab 2014b are also used.,TRUE
-20,"Which hardware resources are used for training the deep learning model (e.g., GPUs, TPUs)?",The context mentions that the network is trained using a single TITAN-X GPU.,TRUE
-8,"Where is the code repository of the deep learning pipeline available (e.g., GitHub, GitLab, BitBucket)?",The context does not provide information on the code repository of the deep learning pipeline.,TRUE
-25,"What is the purpose of the deep learning model (e.g., classification, segmentation, detection)?","The purpose of the deep learning model is segmentation. It is used to segment bird vocalizations from audio spectrograms without requiring manual, laborious tagging of thousands of audio spectrograms. The model is a modified version of [6] and is trained on spectrogram figures of bird recordings as well as recordings void of any bird activity and their corresponding binary masks. During training, the spectrogram is presented as input, and the mask is presented as output, whereas the network learns the mapping in-between them. The network is forced to learn, by an iterative procedure, the mapping of vocalizations to binary blobs and to suppress everything else. ",TRUE
+2,"What data formats are used in the deep learning pipeline (e.g., image, audio, video, CSV)?","The data formats used in the deep learning pipeline are grey-coloured 1-channel images, specifically audio spectrograms. These images are fed to the input-layer of the U-net. The image fed to the output layer during training is the segmentation of the spectrogram of the input image. ",True
+5,"What are the datasets used in the deep learning pipeline (e.g., MNIST, CIFAR, ImageNet)? ","The context does not provide specific information about the datasets used in the deep learning pipeline. However, it mentions that the training set is composed of spectrogram figures of bird recordings as well as recordings void of any bird activity and their corresponding binary masks. ",False
+6,"What preprocessing steps are involved before training a deep learning model (e.g., normalization, scaling, cleaning)?","The context does not provide specific information on the preprocessing steps involved before training the deep learning model. However, common preprocessing steps in deep learning include normalization, scaling, cleaning, and data augmentation. These steps help to improve the model's performance and convergence during training. ",False
+4,"What are the data augmentation techniques applied in the deep learning pipeline (e.g., Flipping, Rotating, Scaling)?","The data augmentation techniques applied in the deep learning pipeline are small random rotations between -5 and 5 degrees as well as random displacements at the maximum of the 10% of the image. Although this is a large displacement to apply in order to accommodate within-species vocal variations, it was decided to use it and proved beneficial probably because a global bird detector was made and not one dedicated to a specific species. ",True
+12,"What type of deep learning model is used in the pipeline (e.g., CNN, RNN, Transformer)?","The pipeline uses a U-net deep autoencoder, which is a type of convolutional neural network (CNN). ",True
+15,"What optimization techniques are applied in the deep learning pipeline (e.g., SGD, Adam)?",The context does not provide information on the optimization techniques applied in the deep learning pipeline.,True
+13,"What are the hyperparameters used in the deep learning model (e.g., learning rate, optimizer)?","The paper does not provide information on the hyperparameters used in the deep learning model, such as the learning rate or the optimizer. ",True
+22,"What metrics are used to evaluate the performance of the deep learning model (e.g., accuracy, precision, recall)?","The performance of the deep learning model is evaluated using the mean Dice coefficient loss function. The Dice coefficient is used to compare the pixel-wise agreement between a predicted segmentation and its corresponding ground truth. The formula is given by: YXYX*2 Where, X is the predicted set of pixels and Y is the ground truth. The Dice coefficient is the quotient of similarity and ranges between 0 and 1. It can be viewed as a similarity measure over sets. The loss function is just the minus of the Dice coefficient with the additions of a smoothing factor inserted in the denominator. The score in Table I is the mean of the Dice coefficients of images in the evaluation set. ",True
+17,"What are the regularization methods used to prevent overfitting in the deep learning pipeline (e.g., dropout, L2 regularization)?","The context does not provide information on the regularization methods used to prevent overfitting in the deep learning pipeline. Therefore, I don't know the answer to this query based on the provided context. ",True
+19,"Which frameworks are used to build the deep learning model (e.g., TensorFlow, PyTorch)?",The Keras Deep learning library is used to build the deep learning model. It is used in CUDA-CuDNN GPU mode. Python code in Anaconda Python 2. 7. 11 running in Ubuntu Linux environment and the Linux version of Matlab 2014b are also used.,True
+20,"Which hardware resources are used for training the deep learning model (e.g., GPUs, TPUs)?",The context mentions that the network is trained using a single TITAN-X GPU.,True
+8,"Where is the code repository of the deep learning pipeline available (e.g., GitHub, GitLab, BitBucket)?",The context does not provide information on the code repository of the deep learning pipeline.,True
+25,"What is the purpose of the deep learning model (e.g., classification, segmentation, detection)?","The purpose of the deep learning model is segmentation. It is used to segment bird vocalizations from audio spectrograms without requiring manual, laborious tagging of thousands of audio spectrograms. The model is a modified version of [6] and is trained on spectrogram figures of bird recordings as well as recordings void of any bird activity and their corresponding binary masks. During training, the spectrogram is presented as input, and the mask is presented as output, whereas the network learns the mapping in-between them. The network is forced to learn, by an iterative procedure, the mapping of vocalizations to binary blobs and to suppress everything else. ",True