Secure communication makes the widespread use of telecommunication networks and services possible. With the constant progress of computing and mathematics, new cryptographic methods are being diligently developed. Quantum Key Distribution (QKD) is a promising technology that provides an Information-Theoretically Secure (ITS) solution to the secret-key agreement problem between two remote parties. QKD

In recent years, deep learning has made significant strides, especially in computer vision applications and, more specifically, in information forensics. On the other hand, data-driven approaches have shown much promise in identifying manipulations in images and videos. However, most forensic tools ignore deep learning in favour of more traditional methodologies. This article thoroughly analyses the

The rising interest in creating versatile robots to handle multiple tasks in various environments, with humans interacting through immersive interfaces. This survey provides a comprehensive review of extended reality (XR) applications in remote human-robot interaction (HRI). We developed a systematic search strategy based on the PRISMA methodology, focusing on peer-reviewed publications that demonstrate

Text summarization research has undergone several significant transformations with the advent of deep neural networks, pre-trained language models (PLMs), and recent large language models (LLMs). This survey thus provides a comprehensive review of the research progress and evolution in text summarization through the lens of these paradigm shifts. It is organized into two main parts: (1) a detailed

Programming Language Virtual Machines (VMs) are composed of several components that together execute and manage languages efficiently. They are deployed in virtually all computing systems through modern web browsers. However, vulnerabilities in any VM component pose a significant threat to security and privacy. In this paper, we present a survey of software attacks on Just-In-Time (JIT) compilers,

In geo-replicated distributed systems, data is redundantly stored across nodes at different geographical sites, increasing fault tolerance and ensuring low access latency by placing data closer to the end user. With data being concurrently updated across sites, replicas should converge to a consistent view of the data, which leads toward adopting fine-tuned consistency models, namely causal consistency

Emerging from the software crisis of the 1960s, conventional software systems have vastly improved through Software Engineering (SE) practices. Simultaneously, Artificial Intelligence (AI) endeavors to augment or replace human decision-making. In the contemporary landscape, Machine Learning (ML), a subset of AI, leverages extensive data from diverse sources, fostering the development of ML-enabled

The Metaverse is a hybrid environment that integrates both physical and virtual realms. The Metaverse has been accessible due to many facilitating technologies. One of the essential technologies that contribute to the Metaverse is AIGC. It is crucial in creating artificial assets and presenting natural interactions efficiently and effectively. Nevertheless, AIGC models encounter external and internal

Artificial Intelligence (AI) models have reached a very significant level of accuracy. While their superior performance offers considerable benefits, their inherent complexity often decreases human trust, which slows their application in high-risk decision-making domains, such as finance. The field of eXplainable AI (XAI) seeks to bridge this gap, aiming to make AI models more understandable. This

Autonomous driving represents a significant advancement in the transportation industry, enhancing vehicle intelligence, optimizing traffic management, and improving user experiences. Central to these innovations is deep learning, which enables systems to handle complex data and make informed decisions. Our survey explores critical applications of deep learning in autonomous driving, such as perception

Our behavior —the way we talk, walk, act, or think— is unique and can be used as a biometric trait. It also correlates with sensitive attributes like emotions and health conditions. With more and more behavior tracking techniques (e.g. fitness trackers, mixed reality) entering our everyday lives more of our behavior is captured and processed. Hence, techniques to protect individuals’ privacy against

Pre-trained language models (PLMs) have demonstrated significant proficiency in solving a wide range of general natural language processing (NLP) tasks. Researchers have observed a direct correlation between the performance of these models and their sizes. As a result, the sizes of these models have notably expanded in recent years, persuading researchers to adopt the term large language models (LLMs)

Emerging technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) have transformed intelligent transportation systems, providing novel solutions to the increasing complexity of managing traffic on roads as cities grow and traffic density rises, particularly in developing countries. Smart road traffic management systems seek to alleviate traffic-related issues, benefiting citizens

Recent trends in deep learning (DL) have made hardware accelerators essential for various high-performance computing (HPC) applications, including image classification, computer vision, and speech recognition. This survey summarizes and classifies the most recent developments in DL accelerators, focusing on their role in meeting the performance demands of HPC applications. We explore cutting-edge approaches

The evolution of computing paradigms, such as Fog and Edge, has led to the emergence of new applications that require a placement of services close to the end users. Optimal placement of such applications over network nodes is therefore an important issue in integrated Cloud-Fog / Edge computing environments. The service placement problem is challenging due to the complexity of such distributed systems

Text classification stands as a cornerstone within the realm of Natural Language Processing (NLP), particularly when viewed through computer science and engineering. The past decade has seen deep learning revolutionize text classification, propelling advancements in text retrieval, categorization, information extraction, and summarization. The scholarly literature includes datasets, models, and evaluation

Dialog systems (e.g., chatbots) have been widely studied, yet related research that leverages artificial intelligence (AI) and natural language processing (NLP) is constantly evolving. These systems have typically been developed to interact with humans in the form of speech, visual, or text conversation. As humans continue to adopt dialog systems for various objectives, there is a need to involve humans

The increasing utilization of surveillance cameras in smart cities, coupled with the surge of online video applications, has heightened concerns regarding public security and privacy protection, which propelled automated Video Anomaly Detection (VAD) into a fundamental research task within the Artificial Intelligence (AI) community. With the advancements in deep learning and edge computing, VAD has

Deep learning models have exhibited exceptional effectiveness in Computational Pathology (CPath) for various tasks on multi-gigapixel histology images. Nevertheless, the presence of out-of-distribution data (stemming from different sources such as disparate imaging devices) can cause domain shift (DS). DS decreases the generalization of trained models to unseen datasets with slightly different data

Managing complex disaster risks requires interdisciplinary efforts. Breaking down silos between law, social sciences, and natural sciences is critical for all processes of disaster risk reduction. It is essential to explore how AI enhances understanding of legal frameworks and environmental management, while also examining how legal and environmental factors may limit AI's role in society. From a co‐production

Computational Linguistics is an interdisciplinary field of computer science and linguistics that focuses on designing computational models and algorithms for processing, analyzing, and generating human language. Over recent years, this field has made substantial progress. While its primary emphasis tends to center around widely spoken languages, there is equal importance in investigating languages

Learners increasingly use digital devices such as laptops or tablets for studying. While these devices offer advantages, they also pose challenges. They present attractive alternative opportunities for learners, such as communication or entertainment opportunities that may distract from learning. Such alternatives increase demand for self-control, particularly over time. We investigated the potential

Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation. It serves as a fundamental methodology in the field of Artificial General Intelligence (AGI). With the ongoing development of foundation models, there is a growing interest in exploring their abilities in reasoning tasks. In

The aortic vessel tree, composed of the aorta and its branches, is crucial for blood supply to the body. Aortic diseases, such as aneurysms and dissections, can lead to life-threatening ruptures, often requiring open surgery. Therefore, patients commonly undergo treatment under constant monitoring, which requires regular inspections of the vessels through medical imaging techniques. Overlapping and

Many European countries are facing plasma shortages, with lack of awareness and knowledge about plasma donation being a potential explanation for low donor numbers. One approach to increasing knowledge and awareness about plasma is through informal educational methods, such as serious games. We developed a serious game focused on plasma and plasma donation for children and adolescents (8–17 years)

This paper reviews the emerging literature on blockchain, structuring key insights in a conceptual framework and setting out a research agenda from a socio-technical and information systems (IS) perspective. The review covers 234 blockchain-focused papers across IS, management, computer science and related fields. It categorises blockchain use into three main types and reframes blockchain use within

The growing use of algorithmic and biometric technologies in border control is part of a larger trend in global security governance that has significant legal and ethical implications for their effect on individual rights and procedural justice. As central features in the EU's shifting security regime, ETIAS and facial recognition technologies deploy algorithmic profiling and biometric risk assessment

Recommendation systems (RSs) have become indispensable features in nearly all web applications. Sifting through data and alleviating information overload, these systems offer more streamlined and personalized recommendations. E‐commerce giants such as Amazon, Netflix, and YouTube are offering recommendations to users based on their interests, past experiences, demographic information, etc. hence, increasing

Stereophotogrammetry [62] is an established technique for scene understanding. Its origins go back to at least the 1800s when people first started to investigate using photographs to measure the physical properties of the world. Since then, thousands of approaches have been explored. The classic geometric technique of Shape from Stereo is built on using geometry to define constraints on scene and camera

The emergence of Vision Transformers (ViTs) has marked a significant advancement in machine learning, particularly in applications requiring robust visual recognition capabilities, such as traffic sign detection for autonomous driving systems. But, deploying these models in adversarial environments where robustness is critical remains a challenge. This survey provides a comprehensive review of the

A unified understanding of terms is essential for every scientific discipline: steganography is no exception. Being divided into several domains (e.g., network and text steganography), it is crucial to provide a unified terminology as well as a taxonomy that is not limited to few applications or areas. A prime attempt towards a unified understanding of terms was conducted in 2015 with the introduction

Proof scores can be regarded as outlines of the formal verification of system properties. They have been historically used by the OBJ family of specification languages. The main advantage of proof scores is that they follow the same syntax as the specification language they are used in, so specifiers can easily adopt them and use as many features as the particular language provides. In this way, proof

This paper reviews benchmarking methods for evaluating large language models (LLMs) in healthcare settings. It highlights the importance of rigorous benchmarking to ensure LLMs' safety, accuracy, and effectiveness in clinical applications. The review also discusses the challenges of developing standardized benchmarks and metrics tailored to healthcare‐specific tasks such as medical text generation

Efficient compression and tuning techniques have become indispensable in addressing the increasing computational and memory demands of large language models (LLMs). While these models have demonstrated exceptional performance across a wide range of natural language processing tasks, their growing size and resource requirements pose significant challenges to accessibility and sustainability. This survey

The ICT sector, responsible for 2% of global carbon emissions, is under scrutiny calling for methodologies and tools to design and develop software in an environmentally sustainable-by-design manner. However, the software engineering solutions for designing and developing carbon-efficient software are currently scattered over multiple different pieces of literature, which makes it difficult to consult

The increasing use of black-box networks in high-risk contexts has led researchers to propose explainable methods to make these networks transparent. Most methods that allow us to understand the behavior of Deep Neural Networks (DNNs) are post-hoc approaches, implying that the explainability is questionable, as these methods do not clarify the internal behavior of a model. Thus, this demonstrates the

Many sequential decision-making problems in finance like trading, portfolio optimisation, etc. have been modelled using reinforcement learning (RL) and evolutionary computation (EC). Recent studies on problems from various domains have shown that EC can be used to improve the performance of RL and vice versa. Over the years, researchers have proposed different ways of hybridising RL and EC for trading

Visual question answering (VQA) is a dynamic field of research that aims to generate textual answers from given visual and question information. It is a multimodal field that has garnered significant interest from the computer vision and natural language processing communities. Furthermore, recent advances in these fields have yielded numerous achievements in VQA research. In VQA research, achieving

Measuring similarity of neural networks to understand and improve their behavior has become an issue of great importance and research interest. In this survey, we provide a comprehensive overview of two complementary perspectives of measuring neural network similarity: (i) representational similarity, which considers how activations of intermediate layers differ, and (ii) functional similarity, which

AI-generated content (AIGC) methods aim to produce text, images, videos, 3D assets, and other media using AI algorithms. Due to its wide range of applications and the potential of recent works, AIGC developments – especially in Machine Learning (ML) and Deep Learning (DL) – have been attracting significant attention, and this survey focuses on comprehensively reviewing such advancements in ML/DL. AIGC

This study examines how the effects of school socioeducational advantage and rurality upon mathematics learning outcomes, are impacted by students’ usage of the ITS platform AdaptiveMath. Activity log data from the AdaptiveMath platform was merged with school sociodemographic data from the public MySchool database. The final analytic sample comprised of 66,451 Australian high school students across

Digital transformation (DT) has emerged as a central and strategically relevant research phenomenon in information systems (IS) and related disciplines. As a result, a significant body of research on the DT phenomenon has accumulated in recent years. However, while existing research highlights the processual nature of the phenomenon, our theoretical understanding of DT processes remains fragmented

Lithium‐ion batteries are widely used in moving devices due to their many advantages compared to other battery types. The prevalence of Lithium‐ion batteries is evident, playing its clear role in the operation of small devices as well as large systems such as electric vehicles, flying devices, mobile devices, and more. Monitoring lithium‐ion battery health is crucial for assessing, minimizing degradation

Graph Convolutional Networks (GCNs) have become an essential tool for handling graph‐structured data, enhancing the functionality of conventional convolutional neural networks (CNNs) in non‐Euclidean contexts. GCNs are particularly proficient in tasks such as node classification, link prediction, and graph clustering by collecting information from neighboring nodes. These models are utilized in a range

The integration of generative AI in visual art has revolutionized not only how visual content is created but also how AI interacts with and reflects the underlying domain knowledge. This survey explores the emerging realm of diffusion-based visual art creation, examining its development from both artistic and technical perspectives. We structure the survey into three phases, data feature and framework