In this paper, we introduce a real-time face recognition (and announcement) system targeted at aiding the blind and low-vision people. The system uses a Microsoft Kinect sensor as a wearable device, performs face detection, and uses temporal coherence along with a simple biometric procedure to generate a sound associated with the identified person, virtualized at his/her estimated 3-D location. Our approach uses a variation of the K-nearest neighbors algorithm over histogram of oriented gradient descriptors dimensionally reduced by principal component analysis. The results show that our approach, on average, outperforms traditional face recognition methods while requiring much less computational resources (memory, processing power, and battery life) when compared with existing techniques in the literature, deeming it suitable for the wearable hardware constraints. We also show the performance of the system in the dark, using depth-only information acquired with Kinect's infrared camera. The validation uses a new dataset available for download, with 600 videos of 30 people, containing variation of illumination, background, and movement patterns. Experiments with existing datasets in the literature are also considered. Finally, we conducted user experience evaluations on both blindfolded and visually impaired users, showing encouraging results.

We present a new anthropometry-based method to personalize head-related transfer functions (HRTFs) using manifold learning in both azimuth and elevation angles with a single nonlinear regression model. The core element of our approach is a domain-specific nonlinear dimensionality reduction technique, denominated Isomap, over the intraconic component of HRTFs resulting from a spectral decomposition. HRTF intraconic components encode the most important cues for HRTF individualization, leaving out subject-independent cues. First, we modify the graph construction procedure of Isomap to integrate relevant prior knowledge of spatial audio into a single manifold for all subjects by exploiting the existing correlations among HRTFs across individuals, directions, and ears. Then, with the aim of preserving the multifactor nature of HRTFs (i.e. subject, direction and frequency), we train a single artificial neural network to predict low-dimensional HRTFs from anthropometric features. Finally, we reconstruct the HRTF from its estimated low-dimensional version using a neighborhood-based reconstruction approach. Our findings show that introducing prior knowledge in Isomap's manifold is a powerful way to capture the underlying factors of spatial hearing. Our experiments show, with p-values less than 0.05, that our approach outperforms using, either a PCA linear reduction, or the full HTRF, in its intermediate stages.

Malware targeting user privacy has seen a surge in recent times, attributed to evolving global regulations and the boost of electronic commerce and online services. Moreover, recognizing privacy malware that employs obfuscation as evasion mechanism presents a major challenge due to its dynamics, resilience, and polymorphism at runtime, necessitating the application of forensic techniques such as memory dumping analysis in order to reveal suitable patterns and behaviors that enable its subsequent detection and classification. In this paper, we present three obfuscated privacy malware classifiers trained on the CIC-MalMem-2022 dataset. These solutions include a binary classifier to distinguish benign from malicious samples using logistic regression (LR), a multiclass classifier that further categorizes benign, spyware, ransomware, and trojan horse obfuscated privacy malware; and a more detailed multiclass classifier capable of discriminating benign samples from fifteen specific obfuscated privacy malware families. Multiclass classifiers were built using several traditional machine learning algorithms and a novel Deep Neural Network (DNN). We applied the Synthetic Minority Oversampling Technique (SMOTE) to address data imbalances. In particular, our results demonstrate that DNN outperforms traditional machine learning algorithms, yielding statistically significant improvements in key metrics. These achievements reach practical thresholds, suggesting the potential for enhanced malware protection systems. The dataset and all the coding files required for experiments reproducibility are publicly available at https://github.com/dcevallossalas/PrivacyMalwareClassifiers.

The auditory brain circuits are biologically constructed to recognize and localize sounds by encoding a combination of cues that help individuals interpret sounds. The development of computational methods inspired by human capacities has established opportunities for improving machine hearing. Recent studies based on deep learning show that using convolutional recurrent neural networks (CRNNs) is a promising approach for sound event detection and localization in spatial sound. Nevertheless, depending on the sound environment, the performance of these systems is still far from reaching perfect metrics. Therefore, this work intends to boost the performance of state-of-the-art (SOTA) systems by using bio-inspired gammatone auditory filters and intensity vectors (IVs) for the acoustic feature extraction stage, along with the implementation of a temporal convolutional network (TCN) block into a CRNN model, to capture long term dependencies. Three data augmentation techniques are applied to increase the small number of samples in spatial audio datasets. The mentioned stages constitute our proposed Gammatone-based Sound Events Localization and Detection (G-SELD) system, which exceeded the SOTA results on four spatial audio datasets with different levels of acoustical complexity and with up to three sound sources overlapping in time.

Unmanned aerial vehicles (UAVs) have seen remarkable improvements in their flight capabilities and computational power. Their applications span various domains, such as agriculture, military, and communications. To improve communication quality and coverage in UAV-enabled systems, understanding effective placement techniques is crucial. While research has explored optimal UAV placement in two dimensions, there is a need for a comprehensive three-dimensional (3D) analysis, particularly for resource optimization. This article presents a systematic mapping study on 3D placement in UAV-enabled communication systems, incorporating a threat analysis for the reliability of the results. It provides valuable insights for future research on 3D placement optimization techniques, specifically examining optimization objectives, model system features, problem types, and solution strategies. Our research reveals, on one hand, an emphasis on optimizing data rate throughput, power transmission, and ground coverage, and a predominant use of large-scale fading in the air-to-ground channel model; on the other hand, heuristic algorithms prevail as the solution strategy. Moreover, we identify gaps in achieving critical objectives such as the probability of outage, deployment costs, quality of experience, and spectrum optimization. This highlights the need for further exploration in the formulation aspects of the system, which includes small-scale fading, access techniques, and interference management.