Abstract
Object identification, such as tree trunk detection, is fundamental for forest robotics. Intelligent vision systems are of paramount importance in order to improve robotic perception, thus enhancing the autonomy of forest robots. To that purpose, this paper presents three contributions: an open dataset of 5325 annotated forest images; a tree trunk detection Edge AI benchmark between 13 deep learning models evaluated on four edge-devices (CPU, TPU, GPU and VPU); and a tree trunk mapping experiment using an OAK-D as a sensing device. The results showed that YOLOR was the most reliable trunk detector, achieving a maximum F1 score around 90% while maintaining high scores for different confidence levels; in terms of inference time, YOLOv4 Tiny was the fastest model, attaining 1.93 ms on the GPU. YOLOv7 Tiny presented the best trade-off between detection accuracy and speed, with average inference times under 4 ms on the GPU considering different input resolutions and at the same time achieving an F1 score similar to YOLOR. This work will enable the development of advanced artificial vision systems for robotics in forestry monitoring operations.

Abstract
Physical activity is movement of the body or part of the body to make muscles more active and to lose the energy from the body. Regular physical activity in the daily routine is very important to maintain good physical and mental health. It can be performed at home, a rehabilitation center, gym, etc., with a regular monitoring system. How long and which physical activity is essential for specific people is very important to know because it depends on age, sex, time, people that have specific diseases, etc. Therefore, it is essential to monitor physical activity either at a physical activity center or even at home. Physiological parameter monitoring using contact sensor technology has been practiced for a long time, however, it has a lot of limitations. In the last decades, a lot of inexpensive and accurate non-contact sensors became available on the market that can be used for vital sign monitoring. In this study, the existing research studies related to the non-contact and video-based technologies for various physiological parameters during exercise are reviewed. It covers mainly Heart Rate, Respiratory Rate, Heart Rate Variability, Blood Pressure, etc., using various technologies including PPG, Video analysis using deep learning, etc. This article covers all the technologies using non-contact methods to detect any of the physiological parameters and discusses how technology has been extended over the years. The paper presents some introductory parts of the corresponding topic and state of art review in that area.

Abstract
The development of foot ulcers is associated with the Diabetic Foot (DF), which is a problem detected in patientswith Diabetes Mellitus (DM). Several studies demonstrate that thermography is a technique that can be used to identify and monitor the DF problems, thus helping to analyze the possibility of ulcers arising, as tissue inflammation causes temperature variation. There is great interest in developing methods to detect abnormal plantar temperature changes, since healthy individuals generally show characteristic patterns of plantar temperature variation and that the plantar temperature distribution of DF tissues does not followa specific pattern, so temperature variations are difficult to measure. In this sequel, a methodology, that uses thermograms to analyze the diversity of thermal changes that exist in the plant of a foot and classifies it as being from an individual with possibility of ulcer arising or not, is presented in this paper. Therefore, the concept of clustering is used to propose binary classifiers with different descriptors, obtained using two clustering algorithms, to predict the risk of ulceration in a foot. Moreover, for each descriptor, a numerical indicator and a classification thresholder are presented. In addition, using a combination of two different descriptors, a hybrid quantitative indicator is presented. A public dataset (containing 90 thermograms of the sole of the foot healthy people and 244 of DM patients) was used to evaluate the performance of the classifiers; using the hybrid quantitative indicator and the k-means clustering, the following metrics were obtained: Accuracy = 80%, AUC = 87% and F-measure = 86%.

Abstract
Melanoma is considered the deadliest type of skin cancer and in the last decade, the incidence rate has increased substantially. However, automatic melanoma classification has been widely used to aid the detection of lesions as well as prevent eventual death. Therefore, in this paper we decided to investigate how an attention mechanism combined with a classical backbone network would affect the classification of melanomas. This mechanism is known as triplet attention, a lightweight method that allows to capture cross-domain interactions. This characteristic helps to acquire rich discriminative feature representations. The different experiments demonstrate the effectiveness of the model in five different datasets. The model was evaluated based on sensitivity, specificity, accuracy, and F1-Score. Even though it is a simple method, this attention mechanism shows that its application could be beneficial in classification tasks.

Abstract
To tackle wildfires and improve forest biomass management, cost effective and reliable mowing and pruning robots are required. However, the development of visual perception systems for forestry robotics needs to be researched and explored to achieve safe solutions. This paper presents two main contributions: an annotated dataset and a benchmark between edge-computing hardware and deep learning models. The dataset is composed by nearly 5,400 annotated images. This dataset enabled to train nine object detectors: four SSD MobileNets, one EfficientDet, three YOLO-based detectors and YOLOR. These detectors were deployed and tested on three edge-computing hardware (TPU, CPU and GPU), and evaluated in terms of detection precision and inference time. The results showed that YOLOR was the best trunk detector achieving nearly 90% F1 score and an inference average time of 13.7ms on GPU. This work will favour the development of advanced vision perception systems for robotics in forestry operations.

Abstract
In the current technological scenario of artificial intelligence growth, especially using machine learning, large datasets are necessary. Recommender systems appear with increasing frequency with different techniques for information filtering. Few large wine datasets are available for use with wine recommender systems. This work presents X-Wines, a new and consistent wine dataset containing 100,000 instances and 21 million real evaluations carried out by users. Data were collected on the open Web in 2022 and pre-processed for wider free use. They refer to the scale 1-5 ratings carried out over a period of 10 years (2012-2021) for wines produced in 62 different countries. A demonstration of some applications using X-Wines in the scope of recommender systems with deep learning algorithms is also presented.