Abstract The aim of this work is to provide a systematic literature review of techniques for taxonomy generation across the cybersecurity domain. Cybersecurity taxonomies can be classified into manual and dynamic, each one of which focuses on different characteristics and tails different goals. Under this premise, we investigate the current state of the art in both categories with respect to their characteristics, applications and methods. To this end, we perform a systematic literature review in accordance with an extensive analysis of the tremendous need for dynamic taxonomies in the cybersecurity landscape. This analysis provides key insights into the advantages and limitations of both techniques, and it discusses the datasets which are most commonly used to generate cybersecurity taxonomies.

This tutorial provides an overview of multimedia retrieval in the context of eXtended Reality (XR), including using virtual and augmented/mixed reality as a user interface for multimedia retrieval, as well as multimedia search tasks addressing content needs for the creation of XR experiences.It will discuss the opportunities and limitations of XR-based search, the evaluation of XR-based multimedia retrieval systems, the demonstration of selected research systems, and open research challenges.

We present the 3D Media Search Engine (3DMSE), which is designed to facilitate the exploration and retrieval of 3D models and images. 3DMSE incorporates unimodal, cross-modal and multimodal retrieval, using any combinations of mesh, point-cloud and multi-image representations. The 3DMSE system is built on the recently proposed MuseHash approach for multimodal representation, and offers a user-friendly web interface that enables formulating queries, presenting search results, and visualising 3D information in an accessible manner.

We face up to the challenge of explainability in Multimodal Artificial Intelligence (MMAI). At the nexus of neuroscience-inspired and quantum computing, interpretable and transparent spin-geometrical neural architectures for early fusion of large-scale, heterogeneous, graph-structured data are envisioned, harnessing recent evidence for relativistic quantum neural coding of (co-)behavioral states in the self-organizing brain, under competitive, multidimensional dynamics. The designs draw on a self-dual classical description – via special Clifford-Lipschitz operations – of spinorial quantum states within registers of at most 16 qubits for efficient encoding of exponentially large neural structures. Formally ‘trained’, Lorentz neural architectures with precisely one lateral layer of exclusively inhibitory interneurons accounting for anti-modalities, as well as their co-architectures with intra-layer connections are highlighted. The approach accommodates the fusion of up to 16 time-invariant interconnected (anti-)modalities and the crystallization of latent multidimensional patterns. Comprehensive insights are expected to be gained through applications to Multimodal Big Data, under diverse real-world scenarios.

Object detection appears to be omnipresent nowadays with detectors being available for every problem available, covering solutions from extra-light to ultra resource demanding models. Yet, the vast majority of these approaches are based on large datasets to provide the required feature diversity. This work focuses on object detection solutions which do not rely heavily on abundant training datasets but rather on medium-sized data collections. It uses Efficientdet object detector as base for the application of novel modifications which achieve better performance both in efficiency as well in effectiveness. The focus on medium-sized datasets aim at representing more commonplace datasets which can be accumulated and compiled with relative ease.

This paper presents a novel method for supporting multiple modalities in the field of image retrieval, called Multimodal Bayesian Supervised Hashing (MuseHash). The method takes into consideration the semantic information of the training data through the use of Bayesian regression to estimate the semantic probabilities and statistical properties in the retrieval process. MuseHash is an extension of the previously proposed Bayesian ridge-based Semantic Preserving Hashing (BiasHash) method. Experimentation on various domain-specific and benchmark datasets demonstrates that MuseHash outperforms seven existing state-of-the-art methods in image retrieval performance, regardless of the feature extractor type, code length, and visual or textual descriptors used. This highlights the robustness and adaptability of MuseHash, making it a promising solution for multimodal image retrieval.

The identification of key actors in complex networks has gathered significant interest by virtue of their importance in modern applications. Several of the existing methods employ standard centrality measures to achieve their goal and as a result, one of the main challenges is identifying key actor nodes with high relevance across all such measures. In this work, we propose a model based on the use of graph convolutional networks (GeNs) that retrieves the key actors in a network based on a centrality measure ranking aggregation scheme. We experimentally demonstrate the effectiveness of our solution compared to baseline and state-of-the-art approaches in terms of: i) accuracy, ii) performance compared to standard machine learning approaches, and iii) influence propagation capabilities.

Unexploded Ordnance (UXO) classification is a challenging task which is currently tackled using electromagnetic induction devices that are expensive and may require physical presence in potentially hazardous environments. The limited availability of open UXO data has, until now, impeded the progress of image-based UXO classification, which may offer a safe alternative at a reduced cost. In addition, the existing sporadic efforts focus mainly on small scale experiments using only a subset of common UXO categories. Our work aims to stimulate research interest in image-based UXO classification, with the curation of a novel dataset that consists of over 10000 annotated images from eight major UXO categories. Through extensive experimentation with supervised deep learning we uncover key insights into the challenging aspects of this task. Finally, we set the baseline on our novel benchmark by training state-of-the-art Convolutional Neural Networks and a Vision Transformer that are able to discriminate between highly overlapping UXO categories with 84.33% accuracy.

This work presents a spatio-temporal activity detection and recognition framework for untrimmed surveillance videos consisting of a three-step pipeline: object detection, tracking, and activity recognition. The framework relies on the YOLO v4 architecture for object detection, Euclidean distance for tracking, while the activity recognizer uses a 3D Convolutional Deep learning architecture employing spatio-temporal boundaries and addressing it as multi-label classification. The evaluation experiments on the VIRAT dataset achieve accurate detections of the temporal boundaries and recognitions of activities in untrimmed videos, with better performance for the multi-label compared to the multi-class activity recognition.

This work examines violence detection in video scenes of crowds and proposes a crowd violence detection framework based on a 3D convolutional deep learning architecture, the 3D-ResNet model with 50 layers. The proposed framework is evaluated on the Violent Flows dataset against several state-of-the-art approaches and achieves higher accuracy values in almost all cases, while also performing the violence detection activities in (near) real-time.

The deliberate misuse of technical infrastructure (including the Web and social media) for cyber deviant and cybercriminal behaviour, ranging from the spreading of extremist and terrorism-related material to online fraud and cyber security attacks, is on the rise. This workshop aims to better understand such phenomena and develop methods for tackling them in an effective and efficient manner. The workshop brings together interdisciplinary researchers and experts in Web search, security informatics, social media analysis, machine learning, and digital forensics, with particular interests in cyber security. The workshop programme includes refereed papers, invited talks and a panel discussion for better understanding the current landscape, as well as the future of data mining for detecting cyber deviance.

Effective multimedia retrieval requires the combination of the heterogeneous media contained within multimedia objects and the features that can be extracted from them. To this end, we extend a unifying framework that integrates all well-known weighted, graph-based, and diffusion-based fusion techniques that combine two modalities (textual and visual similarities) to model the fusion of multiple modalities. We also provide a theoretical formula for the optimal number of documents that need to be initially selected, so that the memory cost in the case of multiple modalities remains the same as in the case of two modalities. Experiments using two test collections and three modalities (similarities based on visual descriptors, visual concepts, and textual concepts) indicate improvements in the effectiveness over bimodal fusion under the same memory complexity.

Ontology alignment is the process where two different ontologies that usually describe similar domains are ’aligned’, i.e. a set of correspondences between their entities, regarding semantic equivalence, is determined. In order to identify these correspondences several methods and metrics that measure semantic equivalence have been proposed in literature. The most common features that these metrics employ are string-, lexical-, structure- and semantic-based similarities for which several approaches have been developed. However, what hasn’t been investigated is the usage of visual-based features for determining entity similarity in cases where images are associated with concepts. Nowadays the existence of several resources (e.g. ImageNet) that map lexical concepts onto images allows for exploiting visual similarities for this purpose. In this paper, a novel approach for ontology matching based on visual similarity is presented. Each ontological entity is associated with sets of images, retrieved through ImageNet or web-based search, and state of the art visual feature extraction, clustering and indexing for computing the similarity between entities is employed. An adaptation of a popular Wordnet-based matching algorithm to exploit the visual similarity is also proposed. Our method is compared with traditional metrics against a standard ontology alignment benchmark dataset and demonstrates promising results.

This paper investigates the role of gaze movements as implicit user feedback during interactive video retrieval tasks. In this context, we use a content-based video search engine to perform an interactive video retrieval experiment, during which, we record the user gaze movements with the aid of an eye-tracking device and generate features for each video shot based on aggregated past user eye fixation and pupil dilation data. Then, we employ support vector machines, in order to train a classifier that could identify shots marked as relevant to a new query topic submitted by new users. The positive results provided by the classifier are used as recommendations for future users, who search for similar topics. The evaluation shows that important information can be extracted from aggregated gaze movements during video retrieval tasks, while the involvement of pupil dilation data improves the performance of the system and facilitates interactive video search.