Abstract
Metaplasia detection in upper gastrointestinal endoscopy is crucial to identify patients at higher risk of gastric cancer. Deep learning algorithms can be useful for detecting and localising these lesions during an endoscopy exam. However, to train these types of models, a lot of annotated data is needed, which can be a problem in the medical field. To overcome this, data augmentation techniques are commonly applied to increase the dataset's variability but need to be adapted to the specificities of the application scenario. In this study, we discuss the potential benefits and identify four key research challenges of a promising data augmentation approach, namely image combination methodologies, such as CutMix, for metaplasia detection and localisation in gastric endoscopy imaging modalities.

Abstract
Location Privacy-Preserving Mechanisms (LPPMs) have been proposed to mitigate the risks of privacy disclosure yielded from location sharing. However, due to the nature of this type of data, spatio-temporal correlations can be leveraged by an adversary to extenuate the protections. Moreover, the application of LPPMs at collection time has been limited due to the difficulty in configuring the parameters and in understanding their impact on the privacy level by the end-user. In this work we adopt the velocity of the user and the frequency of reports as a metric for the correlation between location reports. Based on such metric we propose a generalization of Geo-Indistinguishability denoted Velocity-Aware Geo-Indistinguishability (VA-GI). We define a VA-GI LPPM that provides an automatic and dynamic trade-off between privacy and utility according to the velocity of the user and the frequency of reports. This adaptability can be tuned for general use, by using city or country-wide data, or for specific user profiles, thus warranting fine-grained tuning for users or environments. Our results using vehicular trajectory data show that VA-GI achieves a dynamic trade-off between privacy and utility that outperforms previous works. Additionally, by using a Gaussian distribution as estimation for the distribution of the velocities, we provide a methodology for configuring our proposed LPPM without the need for mobility data. This approach provides the required privacy-utility adaptability while also simplifying its configuration and general application in different contexts.

Abstract

Abstract
Systems for wireless energy transmission (WET) are gaining prominence nowadays. This work presents a WET system based on the photo-thermoelectric effect. With an incident laser beam at lambda = 1450 nm, a temperature gradient is generated in the radial flexible thermoelectric (TE) device, with a carbon-based light collector in its center to enhance the photoheating. The three-part prototype presents a unique approach by using a radial TE device with one simple manufacturing process - screen-printing. A TE ink with a polymeric matrix of poly(3,4-ethylenedioxythiophene) polystyrene sulfonate and doped-Poly(vinyl alcohol) with Sb-Bi-Te microparticles is developed (S similar to 33 mu VK-1 and s similar to 10.31 Sm-1), presenting mechanical and electrical stability. Regarding the device, a full electrical analysis is performed, and the influence of the light collector is investigated using thermal tests, spectrophotometry, and numerical simulations. A maximum output voltage (Vout) of similar to 16 mV and maximum power density of similar to 25 mu Wm(-2) are achieved with Plaser = 2 W. Moreover, the device's viability under extreme conditions is explored. At T similar to 180 K, a 25% increase in Vout compared to room-temperature conditions is achieved, and at low pressures (similar to 10(-6) Torr), an increase of 230% is obtained. Overall, this prototype allows the supply of energy at long distances and remote places, especially for space exploration.

Abstract
Purpose: Visual perception enables robots to perceive the environment. Visual data is processed using computer vision algorithms that are usually time-expensive and require powerful devices to process the visual data in real-time, which is unfeasible for open-field robots with limited energy. This work benchmarks the performance of different heterogeneous platforms for object detection in real-time. This research benchmarks three architectures: embedded GPU-Graphical Processing Units (such as NVIDIA Jetson Nano 2 GB and 4 GB, and NVIDIA Jetson TX2), TPU-Tensor Processing Unit (such as Coral Dev Board TPU), and DPU-Deep Learning Processor Unit (such as in AMD-Xilinx ZCU104 Development Board, and AMD-Xilinx Kria KV260 Starter Kit). Methods: The authors used the RetinaNet ResNet-50 fine-tuned using the natural VineSet dataset. After the trained model was converted and compiled for target-specific hardware formats to improve the execution efficiency.Conclusions and Results: The platforms were assessed in terms of performance of the evaluation metrics and efficiency (time of inference). Graphical Processing Units (GPUs) were the slowest devices, running at 3 FPS to 5 FPS, and Field Programmable Gate Arrays (FPGAs) were the fastest devices, running at 14 FPS to 25 FPS. The efficiency of the Tensor Processing Unit (TPU) is irrelevant and similar to NVIDIA Jetson TX2. TPU and GPU are the most power-efficient, consuming about 5 W. The performance differences, in the evaluation metrics, across devices are irrelevant and have an F1 of about 70 % and mean Average Precision (mAP) of about 60 %.

Abstract
In an automatic music playlist generator, such as an automated online radio channel, how should the system react when a user hits the skip button? Can we use this type of negative feedback to improve the list of songs we will playback for the user next? We propose SkipAwareRec, a next-item recommendation system based on reinforcement learning. SkipAwareRec recommends the best next music categories, considering positive feedback consisting of normal listening behaviour, and negative feedback in the form of song skips. Since SkipAwareRec recommends broad categories, it needs to be coupled with a model able to choose the best individual items. To do this, we propose Hybrid SkipAwareRec. This hybrid model combines the SkipAwareRec with an incremental Matrix Factorisation (MF) algorithm that selects specific songs within the recommended categories. Our experiments with Spotify's Sequential Skip Prediction Challenge dataset show that Hybrid SkipAwareRec has the potential to improve recommendations by a considerable amount with respect to the skip-agnostic MF algorithm. This strongly suggests that reformulating the next recommendations based on skips improves the quality of automatic playlists. Although in this work we focus on sequential music recommendation, our proposal can be applied to other sequential content recommendation domains, such as health for user engagement.