Abstract: A computer implemented data augmentation method comprising receiving a dataset to be processed and, upon the received dataset being unclassified into classes, performing a clustering algorithm to partition the dataset whereby clusters formed are interpreted as the signal classes. The method further includes forming a sample dataset by gathering, for each class of a plurality of classes, at least two sample signals then applying a discrete Fourier transform (DFT) to each sample signal of the sample dataset. The method includes computing frequency parameters of each sample signal to determine, based on a spectral coherence threshold, frequency bands: relevant bands that characterizes a class. The method further includes injecting random noise in a phase spectrum of the non-relevant frequency bands of each sample signal of the sample dataset, to generate a set of augmented sample signals, and applying an inverse DFT, in each of the generated augmented sample signals.

Abstract: A method of managing filesystem elements between a user device and a network connected to the user device comprising: receiving a request related to a filesystem element operation over the network; performing the filesystem element operation; updating a local metadata file based on the operation; downloading an encrypted network metadata file including data of the filesystem on the network from the network to the user device. The method comprises decrypting the encrypted network metadata file on the user device with a network encryption key; and provided the network metadata stored in the network is newer than the updated local metadata file, merging the network metadata file stored in the network with the updated local metadata file to update the local metadata file. The method comprises encrypting the updated local metadata file on the user device with the network encryption key; and uploading the encrypted updated metadata file to the network.

Abstract: A method to compute a variable number of image planes, which are selected to better represent the scene while reducing the artifacts on produced novel views. This method analyses the structure of the scene by means of a depth map and selects the position in the Z-axis to split the original image into individual layers. The method also determines the number of layers in an adaptive way.

Abstract: A computer implemented pre-processing method for facilitating machine learning signal classes separability by evaluating candidate time-frequency transformations to be applied to a set of input signals to generate less redundant features. The computer implemented pre-processing method includes obtaining M candidates of time-frequency transformation rules for the set of input signals, for the M candidates, employing the obtained transformations to calculate time-frequency matrices for at least one signal of each class within the set of input signals and assessing class separability in feature space through the minimization of a multi-parameter objective function that considers the value of the overlapping between the areas of intra-class and the inter-class distributions of distances between the time-frequency matrices. A system and a non-transitory computer readable storage medium for performing the method for evaluating candidate time-frequency transformations and data-augmentation method effects.

Abstract: Android has been a constant target of cybercriminals that try to attack one of the most used operating systems, commonly using malicious applications (denominated malwares). The present invention uses a lightweight and non-invasive approach to detect malware in the Android system. The method employs a set of specific-type detectors in which each one performs a multi-stage analysis, based on rules and machine learning techniques, in different phases of the application cycle. The invention includes a process to obtain application's characteristics that does not infringe licenses and terms of use of applications; a more efficient process to classify applications on Android devices that requires less processing power; and, finally, by using different detection phases that employ distinct sets of characteristics obtained from the application, the present invention can outperform the detection performance of techniques of the prior-art and reduce the number of misclassified samples.

Abstract: Accurate peak detection in physiological signals is fundamental for several tasks related to health monitoring. A method for fine-tuning candidate peak positions and detecting peaks of interest in signals is provided. The fine-tuning method addresses the problem of low signal resolution and reduces the error with respect to the gold-standard reference signal usually collected at higher sampling frequencies. Obtaining accurate peak positions without modifying the sampling frequency is essential in the context of wearable devices, which often present limited computational resources and storage. Furthermore, the method enables selection of the peaks of interest by classifying their tuned positions according to a set of features extracted from morphological characteristics of the signal. The present pipeline is illustrated through inter-beat interval (IBI) estimation from wrist-PPG signals collected from smartwatches.

Abstract: A method for predicting heart rate (HR) of a user during a physical activity, comprising: monitoring the user during the physical activity with an wearable electronic device comprising a photoplethysmography (PPG) sensor, an accelerometer, a memory and a processor; wherein the memory is configured to store user demographic data, PPG data and accelerometer data detected by the PPG sensor and the accelerometer, respectively. The method includes computing a PPG heart rate estimation and a PPG quality flag using the PPG data and accelerometer data, wherein the PPG quality flag indicates whether the PPG data is reliable or not; computing an exponential heart rate estimation by applying the PPG heart rate estimation, the PPG quality flag and the user's demographic data on an exponential approximation model; if the PPG quality flag is reliable, outputting the PPG heart rate estimation; otherwise, outputting the exponential heart rate estimation.

Abstract: A computer implemented method for predicting the oxygen uptake (% VO2Max) during an exercise of a user in proportion to the user's maximum oxygen uptake through a wearable device. The method comprising obtaining information from a user profile, capturing at least three temporal input data from a user during an exercise, using the information from a user profile and the at least three temporal input data to extract features to compose features vectors, identifying the exercise being performed by the user based on the feature vectors, selecting a predictor model to compute predictions for the % VO2Max from the user based on the exercise being performed, and predicting the % VO2Max from the user based on the selected predictor model.

Abstract: The present invention relates to a prediction-based technique for encoding light field data by removing redundant information of light field data, reducing a number of bits by employing a prediction of a pixel value in all four dimensions of the light field. Using this technique to represent light field data, allows it to be transferred through a limited-bandwidth medium and/or to significantly reduce the required storage capacity for this purpose.

Abstract: A hardware-friendly transform method in codecs for plenoptic point clouds. Given that existing video-based point cloud compression codec (V-PCC) is based on multimedia processor video codecs embedded in System-on-Chip (SoC) mobile devices, the remaining V-PCC steps should be as efficient as possible to ensure fair power consumption. In this sense, the method seeks to reduce the complexity of the transform, using integer transforms and imposing limits on the number of distinct transform dimensions, in which these limits are designed in order to minimize the losses of coding efficiency.

Abstract: The present invention relates to a prediction-based technique for encoding light field data by removing redundant information of light field data, reducing a number of bits by employing a prediction of a pixel value in all four dimensions of the light field. Using this technique to represent light field data, allows it to be transferred through a limited-bandwidth medium and/or to significantly reduce the required storage capacity for this purpose.

Abstract: The present invention concerns a hardware-friendly transform method in codecs for plenoptic point clouds. Given that existing video-based point cloud compression codec (V-PCC) is based on multimedia processor video codecs embedded in System-on-Chip (SoC) mobile devices, the remaining V-PCC steps should be as efficient as possible to ensure fair power consumption. In this sense, the method seeks to reduce the complexity of the transform, using integer transforms and imposing limits on the number of distinct transform dimensions, in which these limits are designed in order to minimize the losses of coding efficiency.

Abstract: An improved sleep onset/offset detection method based on a compact neural network that runs in a wearable device processing sensor data in near real-time, which means accumulating data from a few minutes instead of seconds before starting predictions.

Abstract: A method for creating a non-symmetric codec architecture where a single decoder is able to decode the latent representations produced by different neural encoders. Being a single general decoder, the codec generated does not require multiple symmetric decoders, which saves a large amount of disk space. Therefore, beyond reducing the complexity in execution runtime, the embodiments presented herein significantly reduces the space complexity of learning-based codecs and saves huge amounts of disk space, enabling real applications specially in mobile devices.

Abstract: A method for HyperText Transfer Protocol (HTTP) based fingerprint and classification. The method includes training a HTTP-based machine-learning model, using machine-learning training techniques and a historical dataset of labelled Access Point HTTP service response features collected. The method is useful to detect benign or malicious classes, to assess the potential trustworthiness, to detect any type of bad behavior of an HTTP server, and any other threats that modify or implement an AP HTTP server or webpage. The method takes advantage of the captive portal detection packet exchange between a station and an Access Point (AP) to passively classify the AP.

Abstract: A system to enable a full Desktop Experience based on a mobile device. The system includes a Linux kernel and a Linux distribution, wherein a mobile operating system, packaged on a virtual container, is running on top of a standard Linux distribution, or wherein desktop and mobile operating systems run on their own Virtual container isolated from each other.

Abstract: A method to compute a variable number of image planes, which are selected to better represent the scene while reducing the artifacts on produced novel views. This method analyses the structure of the scene by means of a depth map and selects the position in the Z-axis to split the original image into individual layers. The method also determines the number of layers in an adaptive way.

Abstract: A method for predicting maximal oxygen uptake (VO2Max) in wearable devices with memory restriction using ensemble of machine learning algorithms is described. The method is related to the fields of well-being, healthcare and artificial intelligence, and includes a technique that predicts maximal oxygen uptake (VO2Max) in running sessions at different paces using wearable devices with memory restriction. The proposed method requires less than 5 KB of memory to run on a wearable device. Specifically, equipped of user's profile data (age, gender, height and weight), a set of heart rate (HR) and speed readings of a running session, the method is able to estimate VO2Max.

Abstract: The present invention refers to removal of redundant information from plenoptic point cloud data, reducing the number of bits needed to represent them and thus making the plenoptic point cloud data more suitable to be transferred through a medium of limited bandwidth. The proposed solution is based on predictive differential coding, using the standard color channel of a point clouds as a reference for plenoptic data, and on the application of transforms for greater data compression.

Abstract: The present disclosure is related to the network communication technology field and relates to a method for the classification and recognition of the Domain Name System (DNS) server, using machine-learning techniques. The classification process assigns a given DNS server as belonging to a preset of classes. For example, it enables to label a DNS server as either benign or malicious. On the other hand, the recognition process seeks the identification of the DNS server behavioral profile, which, consequently, can be used to assess the DNS server trustworthiness before DNS responses can be reliably used, e.g. identification of well-known and trusted DNS servers. Hence, the present patent, by the means of detecting the DNS server RFC adherence improves user security through the classification and recognition of DNS characteristics. Therefore, security solutions can use the DNS server characteristics to assess its trustworthiness before DNS responses can be reliably used.