Abstract Food plays an important role in our lives that goes beyond mere sustenance. Food affects behavior, mood, and social life. It has recently become an important focus of multimedia and social media applications. The rapid increase of available image data and the fast evolution of artificial intelligence, paired with a raised awareness of people’s nutritional habits, have recently led to an emerging field attracting significant attention, called food computing, aimed at performing automatic food analysis. Food computing benefits from technologies based on modern machine learning techniques, including deep learning, deep convolutional neural networks, and transfer learning. These technologies are broadly used to address emerging problems and challenges in food-related topics, such as food recognition, classification, detection, estimation of calories and food quality, dietary assessment, food recommendation, etc. However, the specific characteristics of food image data, like visual heterogeneity, make the food classification task particularly challenging. To give an overview of the state of the art in the field, we surveyed the most recent machine learning and deep learning technologies used for food classification with a particular focus on data aspects. We collected and reviewed more than 100 papers related to the usage of machine learning and deep learning for food computing tasks. We analyze their performance on publicly available state-of-art food data sets and their potential for usage in multimedia food-related applications for various needs (communication, leisure, tourism, blogging, reverse engineering, etc.). In this paper, we perform an extensive review and categorization of available data sets: to this end, we developed and released an open web resource in which the most recent existing food data sets are collected and mapped to the corresponding geographical regions. Although artificial intelligence methods can be considered mature enough to be used in basic food classification tasks, our analysis of the state-of-the-art reveals that challenges related to the application of this technology need to be addressed. These challenges include, among others: poor representation of regional gastronomy, incorporation of adaptive learning schemes, and reverse engineering for automatic food creation and replication.

We present a novel automatic few-shot selection (FSS) approach with a noisy web-collected dataset for underwater tropical fishes. Underwater image segmentation is of utmost importance in marine biology and underwater robotics. However, obtaining high-quality annotated underwater images is a challenging task due to the scarcity of underwater imaging systems and the high cost of data acquisition. Alternatively, web data is a readily available source of images, but it frequently contains web-corrupted noisy labels, making it challenging to curate a clean dataset for underwater tropical fish segmentation. Generally, the manual selection of high-quality images from web data requires human supervision and expert proofreading, which is both expensive and time-consuming. To address this issue, we propose Noise2Seg, an automatic processing framework composed by: (i) an automated web scrapping tool, (ii) a robust and automatic FSS process using a loss ranking scheme; (iii) a manual annotation component using “Roboflow”, (iv) the latest You Only Look Once version 8 (YOLOv8) for underwater fish detection and segmentation. Additionally, we curated and annotated a novel dataset for the segmentation of tropical fishes from Qatar marine ecosystem (Qatar Tropical Fishes-10 QTF-10). We compared the performance of manual and automatic FSS using mean average precision (mAP) score; manual FSS achieved all mAP (91.5%, 93.4%), and minimum mAP (19.5%, 54.8%), while automatic FSS achieved all mAP (92%, 99.5%), and minimum mAP (24.9%, 99.5%) with 5 and 10 images per class, respectively. The code and dataset related to this paper can be found on GitHub11https://github.com/GilalNauman/Automatic-Few-shot-Selction-ISNCC/tree/main, accessed on 1st of March 2023.

We present a shape processing framework for visual exploration of cellular nuclear envelopes extracted from microscopic images arising in histology and neuroscience. The framework is based on a novel shape descriptor of closed contours in 2D and 3D. In 2D, it relies on a geodesically uniform resampling of discrete curves to compute unsigned curvatures at vertices and edges based on discrete differential geometry. Our descriptor is, by design, invariant under translation, rotation, and parameterization. We achieve the latter invariance under parameterization shifts by using elliptic Fourier analysis on the resulting curvature vectors. Uniform scale-invariance is optional and is a result of scaling curvature features to z-scores. We further augment the proposed descriptor with feature coefficients obtained through sparse coding of the extracted cellular structures using K-sparse autoencoders. For the analysis of 3D shapes, we compute mean curvatures based on the Laplace-Beltrami operator on triangular meshes, followed by computing a spherical parameterization through mean curvature flow. Finally, we compute the Spherical Harmonics decomposition to obtain invariant energy coefficients. Our invariant descriptors provide an embedding into a fixed-dimensional feature space that can be used for various applications, e.g., as input features for deep and shallow learning techniques or as input for dimension reduction schemes to provide a visual reference for clustering shape collections. We demonstrate the capabilities of our framework in the context of visual analysis and unsupervised classification of 2D histology images and 3D nuclear envelopes extracted from serial section electron microscopy stacks.

Internet of Things (IoT) is an innovative paradigm envisioned to provide massive applications that are now part of our daily lives. Millions of smart devices are deployed within complex networks to provide vibrant functionalities, including communications, monitoring, and controlling of critical infrastructures. However, this massive growth of IoT devices and the corresponding huge data traffic generated at the edge of the network created additional burdens on the state-of-the-art centralized cloud computing paradigm due to the bandwidth and resource scarcity. Hence, edge computing (EC) is emerging as an innovative strategy that brings data processing and storage near to the end users, leading to what is called the EC-assisted IoT. Although this paradigm provides unique features and enhanced Quality of Service (QoS), it also introduces huge risks in data security and privacy aspects. This article conducts a comprehensive survey on security and privacy issues in the context of EC-assisted IoT. In particular, we first present an overview of EC-assisted IoT, including definitions, applications, architecture, advantages, and challenges. Second, we define security and privacy in the context of EC-assisted IoT. Then, we extensively discuss the major classifications of attacks in EC-assisted IoT and provide possible solutions and countermeasures along with the related research efforts. After that, we further classify some security and privacy issues as discussed in the literature based on security services and based on security objectives and functions. Finally, several open challenges and future research directions for secure EC-assisted IoT paradigm are also extensively provided.

In this paper, we present a novel data structure, called the Mixture Graph. This data structure allows us to compress, render, and query segmentation histograms. Such histograms arise when building a mipmap of a volume containing segmentation IDs. Each voxel in the histogram mipmap contains a convex combination (mixture) of segmentation IDs. Each mixture represents the distribution of IDs in the respective voxel's children. Our method factorizes these mixtures into a series of linear interpolations between exactly two segmentation IDs. The result is represented as a directed acyclic graph (DAG) whose nodes are topologically ordered. Pruning replicate nodes in the tree followed by compression allows us to store the resulting data structure efficiently. During rendering, transfer functions are propagated from sources (leafs) through the DAG to allow for efficient, pre-filtered rendering at interactive frame rates. Assembly of histogram contributions across the footprint of a given volume allows us to efficiently query partial histograms, achieving up to 178 x speed-up over naive parallelized range queries. Additionally, we apply the Mixture Graph to compute correctly pre-filtered volume lighting and to interactively explore segments based on shape, geometry, and orientation using multi-dimensional transfer functions.