dataesr/scientific-paragraphs-categorization huggingface dataset:
Both the raw power data (Figure  10 ) and the CWT image (Figure  11b ) are distinguishable from the data of normal behaviors. This is because the frequency of amplitude change or drastic amplitude change leads to noticeable CWT image patterns. Including the Idle and Attack cases, we present CWT image samples of the six classes in Figure  12. The visualizer feature generates high-amplitude data but at different frequency levels (Figure  12d ). All the Infrared (Figure  12c ), the Light Switch (Figure  12b ), and the Dimmable Light (Figure  12f ) generate spreading amplitude patterns in CWT images. The CWT images not only include time-domain features but also have frequency-domain features in each instance. Consequently, our experiment results demonstrate that training with the CWT dataset outperforms using the raw dataset.
Furthermore, we add random noise to the CWT input images during training to make our classifier more robust. Small-size datasets often struggle with overfitting problems as the learning process more easily fits the small given inputs  [51]. For each CWT image, we add Gaussian white noise with a mean (𝜇) of 0. 01 and variance (𝜎) of 0. 01. This means that for each pixel, we add a noise value generated by the Gaussian distribution. Figure  13  illustrates the original sample (Figure  13a ) and the augmented sample with 1% Gaussian noise (Figure  13b ). By adding 1% Gaussian noise to each instance, we doubly augment the size of the original CWT dataset. Table  2  summarizes the collected dataset of power traces. In the performance evaluation of the CNN classifier, the augmented images are only used in the training dataset but not in the testing set.
2D-CNN Design LightAuditor uses a 10-layer CNN as the classifier. The CNN takes a CWT image and passes the image through multiple layers. The layers assign parameters including weights and biases to the input and feed the output to the next layer. These parameters are "learnable" in that during training the neural network adjusts the values in order to predict inference results better. During each training round, the neural network also calculates a cost function that represents how far the predicted values are away from the actual value. Thus, the neural network aims to minimize this cost function  [2]. Figure  14  shows the overview of the proposed CNN design. The input layer takes data input of size 227 × 227 × 3 and passes it to the convolutional layer. This input size corresponds to processed CWT images of 227 × 227 pixels with RGB value.
Transfer Learning Classifiers. We first consider a transfer learning method, which allows using pre-trained weights and biases for solving other classification problems. This approach is especially beneficial when the source dataset does not have enough instances to train  [50]. With the pre-trained models, users can easily test their datasets and also avoid the problem of not having enough training sets. Thus, we choose two well-known image classifiers: AlexNet  [33]  and GoogLeNet  [52]  for performance comparisons. To transfer the existing classifiers to our problem, we replaced the last layers of those classifiers, and the final fully-connected layer is set to the number of our classes. Then, we retrained the inherited parameters of all layers in the modified network on our dataset. By doing this, we do not need to train the parameters from scratch.
1D-CNN baseline model. Another model we compared is a 1D-CNN  [30]  that takes into account raw power consumption data as its input. In order to match the same size as our power trace dataset, we adjusted the input and output layers of the 1D-CNN. Our input instance is 4 seconds with 220 data points under the sample rate 55Hz, and the output layer has six labels. For other layers, we kept the original design principles of the 1D-CNN model. The kernel size is 1 × 16 and the stride size is 1 × 8 with 10 kernels, accordingly. Since this classifier receives raw power consumption data like in Figure  10 , it only predicts results from time-domain features.
Implementation of the CNNs. In order to compare the classifier design, we used a department server that has an Nvidia RTX 3090 GPU with 24GB of memory. On this server, we first implemented and tested our proposed 2D-CNN classifier (Section 6) in Matlab. For the performance comparison, we transferred the two well-known image classifiers: AlexNet  [33]  and GoogLeNet  [52] , adjusted the input and the last layers to fit our classification problem, and retrained them. Regarding the input size, AlexNet has the same as our CWT images (227 × 227 × 3). GoogLeNet requires a slightly different input size (224 × 224 × 3), so we rescaled the CWT images to fit the input layer of GoogLeNet. Lastly, we downloaded an open-source of the 1D-CNN classifier  [47]  and adjusted its input and output layers in Matlab, as described in Section 7.
Cross-validation Tests. To measure the performance of each model, we ran five-fold cross-validation tests. In the cross-validation tests, we first divided the dataset into five subsets. Four of them are used together as the training dataset, and another one is held as the testing dataset. Then, we ran the data augmentation specified in Section 5. 3 on the training datasets and trained the model with the augmented training sets. Next, we tested the trained model with the remaining test set, which is not augmented. We conducted the same procedure five times with different combinations, each time recording the accuracy, precision, and F1-score. For each metric, we took the average of the five runs. Table  5  shows the results of cross-validation tests on three CNNs. The LightAuditor CNN achieves an average of 87.
7. 2. 2 Impact of Data Pre-processing. We ran another cross-validation tests with the original dataset as a training set and obtained the results in Table  6. Tables  5  and 6  demonstrate the effect of data augmentation on classification performance. As illustrated in Section 5. 3, our data augmentation doubles the size of the original dataset in training. Together with the added noise, the data augmentation improves our CNN model's validation accuracy and makes the model more robust. Interestingly, we observe that the improvement on CNN performance is the largest in our 2D-CNN. The accuracy difference is almost 10%, while the transfer learning models have an accuracy difference of less than 5%. An explanation is that the transfer learning models have already many parameters pre-trained while our CNN trains from scratch.
Thus, our model benefits more significantly from the data augmentation. Furthermore, we measure the performance metrics of the 1D-CNN model  [30]  with our raw power consumption dataset. The average accuracy of the classification is 80. 9%, which is about 7% lower than the results from our 2D-CNN model. It is explainable that the 1D-CNN does not take into account frequency-domain features from the power consumption data, while our 2D-CNN and the other transfer learning models utilize the CWT images as input.
Performance against Unseen Patterns In the previous subsection, we tested and classified within the collected dataset; the power consumption data used for training was collected when the smart bulb was transmitting an image. However, there are various scenarios in which adversaries can change their behavior. For example, data exfiltration attacks may leak any format of information including images, texts, and audio. In addition, the power traces when the smart bulb leaks private data under different bitrates or encoding schemes would be different in that the amplitude changes in the smart bulb's infrared signal may differ. In order to cover those potential cases, we also evaluate the classifier with attack data unseen during training. While it is nearly impossible to consider all possible anomalies, the adversaries must leak data through the covert channel in this type of attack.
Unseen Data File. To validate whether our classifier is versatile to identify data leakage of other data files, we used different image files unseen during training. We first recorded power consumption data when other image files were being transmitted. Then, we replaced the original testing instances with the newly-collected power consumption instances. By doing so, we can see whether our classifier is robust against unseen data leakage behavior. Table  7  shows the performance of two CNNs in the tests. AlexNet gives a high precision of 95%. This is reasonable since AlexNet scores highest in the cross-validation tests. Nevertheless, all models still have an accuracy greater than 90%, which is similar to the crossvalidation tests. In addition, our model's classification results are as good as the results from the cross-validation tests.
Unseen File Type. In addition to new image files, attacks leaking different file types may also generate unseen power patterns. For example, a text or audio file that includes users' private data can also be a target of adversaries. In this subsection, we also tested with power consumption data when the adversary program leaks text and audio files. That way, we can see if the classifier is able to detect malicious behavior when exfiltrating different file types. Table  8  illustrates the classification performance when the unseen file types were exfiltrated. Still, the transfer learning classifiers' sophisticated structure gives them higher performance against unseen file types. Our CNN's performance against unseen file types also provides consistent classification results. Figure  16  shows the confusion matrices of LightAuditor and AlexNet when different file type was the attack target.
We recorded the power consumption when the bulb emitted infrared at the fixed bitrate and trained the model with this data. However, if the attacker program transmits the data at a different rate, the power consumption pattern might be different. This also leads to different patterns in CWT input images in both time and frequency domains. As such, we generated new testing sets under two different bitrates: one is 80% of the original bitrate, and another is 120% of the original bitrate. This test can provide how tolerant each model is against new data patterns as well. Table  10  shows the classification results against unseen data under different bitrates. While AlexNet still achieves better results at about 93%, our proposed CNN also classifies new anomalies with an accuracy of more than 85%.
System Performance Evaluation In this subsection, we also measure system-level performance metrics. Processing or inference time matters when it comes to online inference scenarios. We used a Linux server with 32 cores and 64 GB RAM. It is a common setting for an edge server in IoT environments. Even in IoT environments, network bandwidth should be minimized so that online inference can be done in real time. Each CWT image is a 227 × 227 pixel RGB image. It is then approximately What makes LightAuditor a comparable option to transfer learning CNNs is its lightweightness. As shown in Table  4 , the huge number of parameters in AlexNet and GoogLeNet makes them more power-and time-consuming options. Meanwhile, the classification performance of LightAuditor is similarly accurate as other CNNs.
Edge Device Deployment Another enhancement we plan to do is to deploy our detection system into edge devices. For proof-of-concept, we tested with the collected dataset on an offline server. Although we did not conduct an online study, we provide several system metrics in Section 7. 4 to demonstrate the potential online system. Those values suggest that the proposed system does not require many resources, and thus it can be executed on an edge device with limited computing resources. Furthermore, we plan to deploy our system in a real-world setting where over-the-shelf IoT devices are involved in attacks for realtime detection. Doing this will validate the system performance results presented in our experimental environments.
Introduction Computing solutions, such as three-dimensional (3D) imaging and visualization, virtual/ augmented reality (VR/AR), in silico simulations, and visual analytics, among others, have seen an outstanding progress in recent years. Advances and improved access to big and high-resolution data, hardware infrastructure (e. g. , high-performance computing clusters and graphical processing units), affordable devices (e. g. , VR/AR headsets), and open-source codes thanks to the commitment to open and reproducible science by researchers, have made it possible. Biomedical applications are not an exception, with an unprecedented availability to medical datasets at different multi-scale and resolution levels. Therefore, some visual computing technologies are already disrupting traditional concepts of medical image exploration.
Complex medical interventions are usually planned by exploring pre-operative medical images, where advanced visual computing solutions, in combination with technologies such as 3D printing, could contribute to reduce potential complications. Until recently, the visualization and analysis of medical imaging data were always performed with visualization tools, usually as two-dimensional (2D) images and standard multiplanar reconstruction (MPR) visualization viewed in 2D monitors. Commonly, these are the only tools available during the intervention since they are fully integrated with the acquisition systems in the operating room, with clinicians mentally integrating the 3D structure and functional information provided by multiple sources. Offline image analysis can be performed with numerous tools, including open-source software such as 3D Slicer  [1]  or commercial solutions, which are usually used as the stand-alone software tools tailored to specific imaging modalities and type of pathologies.
However, there is still a debate on which is the most adequate imaging technique since there are discrepancies on the measurements from different modalities  [9]. At present, commercial solutions are available with standard MPR visualization of the 3D imaging modalities (e. g. , CT images) such as the 3mensio Structural Heart software (Pie Medical Imaging, Bilthoven, the Netherlands)  [10]  , which also includes advanced 3D photorealistic rendering of the heart for an advanced exploration of the LAA, or Mimics (Materialise NV, Leuven, Belgium). Karagodin et al. [11]  recently demonstrated an improved delineation of cardiac structures, including the LAA, with a new tissue transparency transillumination tool when visualizing 3D echocardiographic images, compared to standard 3D rendering in a system developed by Philips (Andover, MA).
Most of these imaging tools to explore 3D LAA anatomies are based on stand-alone software but web-based frameworks with cloud-based engines are becoming modern and more flexible alternatives for medical image visualization and analysis. The Virtual Implantation and Device selection in the left Atrial Appendages (VIDAA) platform was recently developed, providing a clinician-friendly web-based tool to support the pre-operative planning of LAAO interventions  [12]. It allows clinicians to interactively explore the LA geometry as a 3D mesh with different computer-aided design (CAD) LAAO models, together with some morphological indices and the original CT images in MPR format. However, these software tools are still limited in visualizing and analyzing imaging data with the standard MPR, volume rendering, and surface mesh views in 2D monitors; therefore, these tools have limited degrees of freedom interaction and could prevent a correct perception of the 3D nature of the studied anatomy (e.
However, Conti et al. recently compared 3D printing recommendations and implanted devices, with an agreement of only 35%  [19]. Moreover, computational costs and time required for models to be printed with realistic materials are not negligible in the clinical workflow. Although in an earlier phase of development and application in the biomedical field, there already exist proof-of-concept studies of using VR technologies for cardiac devices such as the pre-operative planning of transcatheter closure of cardiac deficiencies, such as ventricular septal  [20]  or sinus venous defects  [21, 22]. Nam et al. [23]  used new functionalities of the 3D-Slicer open-source software (i. e. , link with VR headsets) to develop a tool for the virtual testing, selection, and placement of transcatheter device closures of atrial and ventricular septal defects.
In silico simulations from virtual physiological models of the heart, also known as digital twins, are emerging as a valuable technology in cardiology to support clinical decisions, such as interventional planning, diagnosis, and device optimization  [28, 29]. For instance, the commercial software HEARTguideTM (FEops NV, Gent, Belgium) provides patient-specific structural simulations of LAAO deployments with different device configurations  [30]  , but without user interaction and lacking functional information. Computational fluid dynamics (CFD) solvers compute flow and pressure throughout a welldefined geometrical domain and can provide useful functional information about blood flow patterns with high spatial resolution, currently unattainable with in vivo imaging techniques. Different therapeutic scenarios can be in silico tested with CFD before the intervention, thus reducing operation costs with enhanced efficiency  [31]  and accelerating research and development understanding of fluid mechanics within device testing  [2].
Regarding LAA applications, several researchers have run CFD simulations to study blood flow in the left atria  [32] [33] [34] [35]  , including after the implantation of LAAO to find the optimal configuration of device  [12, 36, 37]. However, comprehensive pre-operative simulations may take between hours and days depending on the complexity of the anatomy and potential interactions between cardiac tissue and the blood flow to be modeled  [2]  , thus limiting its application in a clinical environment. In this paper, we present the evaluation of several advanced visual computing solutions for the planning of LAAO interventions (i. e. , web-based platform with 3D imaging visualization, VR, and in silico fluid simulations), together with 3D printing with standard and affordable materials. 3D imaging data from CT scans of five patients, who were the candidates for LAAO implantation, were visualized with different visual computing and 3D printing technologies by six domain knowledge experts (three interventional and three imaging cardiologists).
Clinical data The clinical data used in this work were provided by Hospital Haut-Lévêque (Bordeaux, France), including AF patients that underwent a LAAO intervention and with available pre-procedural high-quality CT scans. Five of them were randomly selected. The study was approved by the Institutional Ethics Committee; patients gave the informed consent for having their data used for research purposes, including tasks such as the ones presented in this study. Cardiac CT studies were performed on a 64-slice dual source CT system (Siemens Definition, Siemens Medical Systems, Forchheim, Germany). Tube current was set to 120 kV in patients with body mass index (BMI) > 27 and 100 kV in those with BMI < 27. Acquisition was set on end systole using prospective ECG triggering, the delay being set in percentage of the RR interval in patients in sinus rhythm, and in ms in those with arrhythmia.
VIDAA: Web-based exploration of 3D imaging Both CT scans and the LA meshes were introduced to the VIDAA platform for visualization (as MPR and 3D, respectively) and morphological analysis (Figure  2 ). The 3D surface mesh of the LA can be visualized with different levels of transparency, both in solid and wireframe formats. Some landmarks relevant to LAA interventions such as the circumflex artery can be manually selected by the user. The centerline of the LAA was computed using the Python's VMTK library (https://www. vmtk. com) from the center of the LA mesh to a point on the LAA tip, the latter interactively selected by the user. Perpendicular 2D contours along the resulting centerline were then obtained to estimate morphological measurements on the LAA (i. e. , maximum and minimum LAA diameters).
3D printing The 3D model generated from the CT scan of each patient was printed at Hospital de la Santa Creu i Sant Pau (Barcelona, Spain) with a Fused Deposition Modeling (FDM) 3D printer, the Ultimaker S5 (Ultimaker BV, Geldermalsen, Netherlands), which costs 6000 euros approximately. The 3D LA model was prepared for printing with the Ultimaker Cura 3 software from the printer provider. Typical rigid polylactic acid (PLA) was the material used for the LA models (Figure  3 ), with an associated cost of 1. 5 euros for each model. Twenty hours were needed to print all LA cases (i. e. , 4 h/case). Moreover, CAD models of Amplatzer and Watchman FLX LAAO devices corresponding to the different commercially available sizes were printed with thermoplastic polyurethane (TPU) to add more flexibility.
Its cost was of 1239 euros. Image processing outside the platform was performed using Python libraries. Once uploaded into VRIDAA, the user can interact with the 3D meshes using the HTC Vive Pro controller to freely move it (6 degrees of freedom, i. e. , rotations and translations) or zoom it to navigate inside the patient's LA. In its current implementation, the VRIDAA platform is intended to be used with the possibility of moving within the virtual environment. Thus, a clear space of around 2 m × 1. 5 m is required. All meshes and images were uploaded to VRIDAA before the practical session with participants. In VRIDAA, the centerline was not selected by the user; each case had a centerline previously generated in the web-based VIDAA platform, as well as the measurements and the proposed devices, which were transferred into the VR platform.
In silico flow simulations The participants were presented with the visualization of in silico fluid simulations in the studied LA anatomies and any possible LAAO device to be implanted, using the Ansys Discovery Live (ANSYS Inc. , USA) software, under the Academic License (i. e. , free of charge). It includes a GPU-based Lattice Boltzmann method that allows almost real-time simulations, once each case was set-up. Therefore, the participants could manually choose a given device (i. e. , type and size) and place it in each position to analyze resulting blood flow patterns, which can be iterated as many times as desired. The volumetric meshes required for the fluid simulations were generated from the 3D surface models using the Gmsh 4. 5. 4 software (https://gmsh. info/). The final meshes were around 800 thousand elements for all cases.
Initially, we offered the possibility of visualizing the raw CT medical images of each patient. Afterward, the web-based VIDAA platform followed, since it is similar to some of the most advanced software solutions commercially available in the market (e. g. , 3mensio). Next, 3D printing and VRIDAA were presented to incorporate data visualization with enhanced depth perception, starting with 3D printing since most physicians are more familiar with it. Finally, the in silico simulations were shown in the Ansys Discovery Live software to include functional information complementary to the structural knowledge provided by the other technologies.
CT imaging The visualization of CT medical images was optional since some of the physicians do not work on their daily basis with CT measurements. Although it was not assessed in SUS questionnaire, CT visualization was offered since it is the gold standard technique to explore LA anatomy, thus useful to plan LAAO interventions. Moreover, there were questions related to CT use in the general questionnaire. Physicians could explore the CT medical images with the Open-Source ITK-Snap software (http://www. itksnap. org/) to: (1) browse through the CT slices for inspecting the LAA shape and other anatomical landmarks such as the circumflex or the ostium and (2) take measurements of these anatomical landmarks.