Abstract

Abstract
When compared with traditional local shops where the customer has a personalised service, in large retail departments, the client has to make his purchase decisions independently, mostly supported by the information available in the package. Additionally, people are becoming more aware of the importance of the food ingredients and demanding about the type of products they buy and the information provided in the package, despite it often being hard to interpret. Big shops such as supermarkets have also introduced important challenges for the retailer due to the large number of different products in the store, heterogeneous affluence and the daily needs of item repositioning. In this scenario, the automatic detection and recognition of products on the shelves or off the shelves has gained increased interest as the application of these technologies may improve the shopping experience through self-assisted shopping apps and autonomous shopping, or even benefit stock management with real-time inventory, automatic shelf monitoring and product tracking. These solutions can also have an important impact on customers with visual impairments. Despite recent developments in computer vision, automatic grocery product recognition is still very challenging, with most works focusing on the detection or recognition of a small number of products, often under controlled conditions. This paper discusses the challenges related to this problem and presents a review of proposed methods for retail product label processing, with a special focus on assisted analysis for customer support, including for the visually impaired. Moreover, it details the public datasets used in this topic and identifies their limitations, and discusses future research directions of related fields.

Abstract
The widespread use of smartphones and other low-cost equipment as recording devices, the massive growth in bandwidth, and the ever-growing demand for new applications with enhanced capabilities, made visual data a must in several scenarios, including surveillance, sports, retail, entertainment, and intelligent vehicles. Despite significant advances in analyzing and extracting data from images and video, there is a lack of solutions able to analyze and semantically describe the information in the visual scene so that it can be efficiently used and repurposed. Scientific contributions have focused on individual aspects or addressing specific problems and application areas, and no cross-domain solution is available to implement a complete system that enables information passing between cross-cutting algorithms. This paper analyses the problem from an end-to-end perspective, i.e., from the visual scene analysis to the representation of information in a virtual environment, including how the extracted data can be described and stored. A simple processing pipeline is introduced to set up a structure for discussing challenges and opportunities in different steps of the entire process, allowing to identify current gaps in the literature. The work reviews various technologies specifically from the perspective of their applicability to an end-to-end pipeline for scene analysis and synthesis, along with an extensive analysis of datasets for relevant tasks.

Abstract
Liquid-based cytology (LBC) plays a crucial role in the effective early detection of cervical cancer, contributing to substantially decreasing mortality rates. However, the visual examination of microscopic slides is a challenging, time-consuming, and ambiguous task. Shortages of specialized staff and equipment are increasing the interest in developing artificial intelligence (AI)-powered portable solutions to support screening programs. This paper presents a novel approach based on a RetinaNet model with a ResNet50 backbone to detect the nuclei of cervical lesions on mobile-acquired microscopic images of cytology samples, stratifying the lesions according to The Bethesda System (TBS) guidelines. This work was supported by a new dataset of images from LBC samples digitalized with a portable smartphone-based microscope, encompassing nucleus annotations of 31,698 normal squamous cells and 1395 lesions. Several experiments were conducted to optimize the model's detection performance, namely hyperparameter tuning, transfer learning, detected class adjustments, and per-class score threshold optimization. The proposed nucleus-based methodology improved the best baseline reported in the literature for detecting cervical lesions on microscopic images exclusively acquired with mobile devices coupled to the & mu;SmartScope prototype, with per-class average precision, recall, and F1 scores up to 17.6%, 22.9%, and 16.0%, respectively. Performance improvements were obtained by transferring knowledge from networks pre-trained on a smaller dataset closer to the target application domain, as well as including normal squamous nuclei as a class detected by the model. Per-class tuning of the score threshold also allowed us to obtain a model more suitable to support screening procedures, achieving F1 score improvements in most TBS classes. While further improvements are still required to use the proposed approach in a clinical context, this work reinforces the potential of using AI-powered mobile-based solutions to support cervical cancer screening. Such solutions can significantly impact screening programs worldwide, particularly in areas with limited access and restricted healthcare resources.

Abstract
We present a new large-scale emotion-labeled symbolic music dataset consisting of 12 k MIDI songs. To create this dataset, we first trained emotion classification models on the GoEmotions dataset, achieving state-of-the-art results with a model half the size of the baseline. We then applied these models to lyrics from two large-scale MIDI datasets. Our dataset covers a wide range of fine-grained emotions, providing a valuable resource to explore the connection between music and emotions and, especially, to develop models that can generate music based on specific emotions. Our code for inference, trained models, and datasets are available online.

Abstract
The increased demand for and use of autonomous driving and advanced driver assistance systems has highlighted the issue of abnormalities occurring within the perception layers, some of which may result in accidents. Recent publications have noted the lack of standardized independent testing formats and insufficient methods with which to analyze, verify, and qualify LiDAR (Light Detection and Ranging)-acquired data and their subsequent labeling. While camera-based approaches benefit from a significant amount of long-term research, images captured through the visible spectrum can be unreliable in situations with impaired visibility, such as dim lighting, fog, and heavy rain. A redoubled focus upon LiDAR usage would combat these shortcomings; however, research involving the detection of anomalies and the validation of gathered data is few and far between when compared to its counterparts. This paper aims to contribute to expand the knowledge on how to evaluate LiDAR data by introducing a novel method with the ability to detect these patterns and complement other performance evaluators while using a statistical approach. Although it is preliminary, the proposed methodology shows promising results in the evaluation of an algorithm's confidence score, the impact that weather and road conditions may have on data, and fringe cases in which the data may be insufficient or otherwise unusable.