Recommender systems (RS) play an integral role in many online platforms. Exponential growth and potential commercial interests are raising significant concerns around privacy, security, fairness, and overall responsibility. The existing literature around responsible recommendation services is diverse and multi-disciplinary. Most literature reviews cover a specific aspect or a single technology for

The entire Image Signal Processor (ISP) of a camera relies on several processes to transform the data from the Color Filter Array (CFA) sensor, such as demosaicing, denoising, and enhancement. These processes can be executed either by some hardware or via software. In recent years, Deep Learning(DL) has emerged as one solution for some of them or even to replace the entire ISP using a single neural

Multi-Agent Reinforcement Learning (MARL) is susceptible to Adversarial Machine Learning (AML) attacks. Execution-time AML attacks against MARL are complex due to effects that propagate across time and between agents. To understand the interaction between AML and MARL, this survey covers attacks and defences for MARL, Multi-Agent Learning (MAL), and Deep Reinforcement Learning (DRL). This survey proposes

Procedural content generation (PCG) can be applied to a wide variety of tasks in games, from narratives, levels and sounds, to trees and weapons. A large amount of game content is comprised of graphical assets , such as clouds, buildings or vegetation, that do not require gameplay function considerations. There is also a breadth of literature examining the procedural generation of such elements for

Artificial intelligence (AI), and especially its sub-field of Machine Learning (ML), are impacting the daily lives of everyone with their ubiquitous applications. In recent years, AI researchers and practitioners have introduced principles and guidelines to build systems that make reliable and trustworthy decisions. From a practical perspective, conventional ML systems process historical data to extract

The open and broadcast nature of wireless mediums introduces significant security vulnerabilities, making authentication a critical concern in wireless networks. In recent years, Physical-Layer Authentication (PLA) techniques have garnered considerable research interest due to their advantages over Upper-Layer Authentication (ULA) methods, such as lower complexity, enhanced security, and greater compatibility

Vision is the most important sense for people, and it is also one of the main ways of cognition. As a result, people tend to utilize visual content to capture and share their life experiences, which greatly facilitates the transfer of information. Meanwhile, it also increases the risk of privacy violations, e.g., an image or video can reveal different kinds of privacy-sensitive information. Scholars

Distributed machine learning on edges is widely used in intelligent transportation, smart home, industrial manufacturing, and underground pipe network monitoring to achieve low latency and real time data processing and prediction. However, the presence of a large number of sensing and edge devices with limited computing, storage, and communication capabilities prevents the deployment of huge machine

Caching content at the edge network is a popular and effective technique widely deployed to alleviate the burden of network backhaul, shorten service delay and improve service quality. However, there has been some controversy over privacy violations in caching content at the edge network. On the one hand, the multi-access open edge network provides an ideal entrance or interface for external attackers

Recently, foundation language models (LMs) have marked significant achievements in the domains of natural language processing (NLP) and computer vision (CV). Unlike traditional neural network models, foundation LMs obtain a great ability for transfer learning by acquiring rich commonsense knowledge through pre-training on extensive unsupervised datasets with a vast number of parameters. Despite these

Recently, Artificial Intelligence Generated Content (AIGC) has gained significant attention from society, especially with the rise of Generative AI (GAI) techniques such as ChatGPT, GPT-4 [165], DALL-E-3 [184], and Sora [137]. AIGC involves using AI models to create digital content, such as images, music, and natural language, with the goal of making the content creation process more efficient and

The rapid development of deep learning has driven significant progress in image semantic segmentation—a fundamental task in computer vision. Semantic segmentation algorithms often depend on the availability of pixel-level labels (i.e., masks of objects), which are expensive, time-consuming, and labor-intensive. Weakly-supervised semantic segmentation (WSSS) is an effective solution to avoid such labeling

Adversarial binaries are executable files that have been altered without loss of function by an AI agent in order to deceive malware detection systems. Progress in this emergent vein of research has been constrained by the complex and rigid structure of executable files. Although prior work has demonstrated that these binaries deceive a variety of malware classification models which rely on disparate

LiDAR has gained popularity in autonomous driving due to advantages like long measurement distance, rich 3D information, and stability in harsh environments. Place Recognition (PR) enables vehicles to identify previously visited locations despite variations in appearance, weather, and viewpoints, even determining their global location within prior maps. This capability is crucial for accurate localization

The Internet of Bio-Nano Things (IoBNT) is collaborative cell biology and nanodevice technology interacting through Molecular Communication (MC). The IoBNT can be accomplished by using the Information and Communication Theory (ICT) study of biological networks. Various technologies such as the Internet of Nano Things (IoNT), the Internet of Bio-degradable Things (IoBDT) and the Internet of Ingestible

Wireless Sensor Networks (WSNs) represent an innovative technology that integrates compact, energy-efficient sensors with wireless communication functionalities, facilitating instantaneous surveillance and data gathering from the surrounding environment. WSNs are utilized across diverse domains, such as environmental monitoring, industrial automation, healthcare, smart agriculture, home automation

In the ever-evolving information technology landscape, the Internet of Things (IoT) is a groundbreaking concept that bridges the physical and digital worlds. It is the backbone of an increasingly sophisticated interactive environment, yet it is a subject of intricate security challenges spawned by its multifaceted manifestations. Central to securing IoT infrastructures is the crucial aspect of authentication

Low-rate threats are a class of attack vectors that are disruptive and stealthy, typically crafted for security vulnerabilities. They have been the significant concern for cyber security, impacting both conventional IP-based networks and emerging Software-Defined Networking (SDN). SDN is a revolutionary architecture that separates the control and data planes, offering advantages such as enhanced manageability

Enterprise Architecture (EA) is a systematic and holistic approach to designing and managing an organization's information systems components, aiding in optimizing resources, managing risk, and facilitating change. It weighs different architectural quality attributes against each other to achieve the most advantageous architecture. However, the evaluation of EA lacks a systematic approach. This study

Object selection and manipulation are the foundation of VR interactions. With the rapid development of VR technology and the field of virtual object selection and manipulation, the literature demands a structured understanding of the core research challenges and a critical reflection of the current practices. To provide such understanding and reflections, we systematically reviewed 106 papers. We identified

Large foundation models, including large language models, vision transformers, diffusion, and LLM-based multimodal models, are revolutionizing the entire machine learning lifecycle, from training to deployment. However, the substantial advancements in versatility and performance these models offer come at a significant cost in terms of hardware resources. To support the growth of these large models

Administrator-centered access control failures can cause data breaches, putting organizations at risk of financial loss and reputation damage. Existing graphical policy configuration tools and automated policy generation frameworks attempt to help administrators configure and generate access control policies by avoiding such failures. However, graphical policy configuration tools are prone to human

Recent advancements in wireless communication technologies have made Wi-Fi signals indispensable in both personal and professional settings. The utilization of these signals for Human Activity Recognition (HAR) has emerged as a cutting-edge technology. By leveraging the fluctuations in Wi-Fi signals for HAR, this approach offers enhanced privacy compared to traditional visual surveillance methods.

Decentralized finance (DeFi) represents a novel financial system but faces significant fraud challenges, leading to substantial losses. Recent advancements in artificial intelligence (AI) show potential for complex fraud detection. Despite growing interest, a systematic review of these methods is lacking. This survey correlates fraud types with DeFi project stages, presenting a taxonomy based on the

There is an urgent need in many application areas for eXplainable ArtificiaI Intelligence (XAI) approaches to boost people’s confidence and trust in Artificial Intelligence methods. Current works concentrate on specific aspects of XAI and avoid a comprehensive perspective. This study undertakes a systematic survey of importance, approaches, methods, and application domains to address this gap and provide

Temporal data, representing chronological observations of complex systems, has always been a typical data structure that can be widely generated by many domains, such as industry, finance, healthcare and climatology. Analyzing the underlying structures, i.e. , the causal relations, could be extremely valuable for various applications. Recently, causal discovery from temporal data has been considered

Facial recognition is one of the most academically studied and industrially developed areas within computer vision where we readily find associated applications deployed globally. This widespread adoption has uncovered significant performance variation across subjects of different racial profiles leading to focused research attention on racial bias within face recognition spanning both current causation

Developing smart cities is vital for ensuring sustainable development and improving human well-being. One critical aspect of building smart cities is designing intelligent methods to address various decision-making problems that arise in urban areas. As machine learning techniques continue to advance rapidly, a growing body of research has been focused on utilizing these methods to achieve intelligent

Humans possess an extraordinary ability to create and utilize tools. With the advent of foundation models, artificial intelligence systems have the potential to be equally adept in tool use as humans. This paradigm, which is dubbed as tool learning with foundation models , combines the strengths of tools and foundation models to achieve enhanced accuracy, efficiency, and automation in problem-solving

Various collaborative distributed machine learning (CDML) systems, including federated learning systems and swarm learning systems, with different key traits were developed to leverage resources for the development and use of machine learning (ML) models in a confidentiality-preserving way. To meet use case requirements, suitable CDML systems need to be selected. However, comparison between CDML systems

Bots are software systems designed to support users by automating specific processes, tasks, or activities. When these systems implement a conversational component to interact with users, they are also known as conversational agents or chatbots . Bots—particularly in their conversation-oriented version and AI-powered—have seen increased adoption over time for software development and engineering purposes

Traditionally, deep learning practitioners would bring data into a central repository for model training and inference. Recent developments in distributed learning, such as federated learning and deep learning as a service (DLaaS) do not require centralized data and instead push computing to where the distributed datasets reside. These decentralized training schemes, however, introduce additional security

Since the emergence of security concerns in artificial intelligence (AI), there has been significant attention devoted to the examination of backdoor attacks. Attackers can utilize backdoor attacks to manipulate model predictions, leading to significant potential harm. However, current research on backdoor attacks and defenses in both theoretical and practical fields still has many shortcomings. To

Sharing data with third parties is essential for advancing science, but it is becoming more and more difficult with the rise of data protection regulations, ethical restrictions, and growing fear of misuse. Fully synthetic data, which transcends anonymisation, may be the key to unlocking valuable untapped insights stored away in secured data vaults. This review examines current synthetic data generation

Deep reinforcement learning has led to dramatic breakthroughs in the field of artificial intelligence for the past few years. As the amount of rollout experience data and the size of neural networks for deep reinforcement learning have grown continuously, handling the training process and reducing the time consumption using parallel and distributed computing is becoming an urgent and essential desire

Emerging cloud-centric networks span from edge clouds to large-scale datacenters with shared infrastructure among multiple tenants and applications with high availability, isolation, fault tolerance, security, and energy efficiency demands. Live migration (LiMi) plays an increasingly critical role in these environments by enabling seamless application mobility covering the edge-to-cloud continuum and

Federated learning is a decentralized machine learning approach where clients train models locally and share model updates to develop a global model. This enables low-resource devices to collaboratively build a high-quality model without requiring direct access to the raw training data. However, despite only sharing model updates, federated learning still faces several privacy vulnerabilities. One

The increasing popularity of Unmanned Aerial Vehicle (UAV) swarms is attributed to their ability to generate substantial returns for various industries at a low cost. Additionally, in the future landscape of wireless networks, UAV swarms can serve as airborne base stations, alleviating the scarcity of communication resources. However, UAV swarm networks are vulnerable to various security threats that

The advent of artificial intelligence-generated content (AIGC) represents a pivotal moment in the evolution of information technology. With AIGC, it can be effortless to generate high-quality data that is challenging for the public to distinguish. Nevertheless, the proliferation of generative data across cyberspace brings security and privacy issues, including privacy leakages of individuals and media

This survey summarizes the most recent methods for building and assessing helpful, honest, and harmless neural language models, considering small, medium, and large-size models. Pointers to open-source resources that help to align pre-trained models are given, including methods that use parameter-efficient techniques, specialized prompting frameworks, adapter modules, case-specific knowledge injection

A delay, interruption, or failure in the wireless connection has a significant impact on the performance of wirelessly connected medical equipment. Researchers presented the fastest technological innovations and industrial changes to address these problems and improve the applications of information and communication technology. The development of the 6G communication infrastructure was greatly aided

Researchers’ interest in Fog Computing and its application in different sectors has been increasing since the last decade. To discover the emerging trends inherent to this architecture, we analyzed the scientific literature indexed in Scopus through a bibliometric study. Exposing trends in areas of development will allow researchers to understand the changes and evolution over time. For analysis purposes

Man-at-the-end (MATE) attackers have full control over the system on which the attacked software runs, and try to break the confidentiality or integrity of assets embedded in the software. Both companies and malware authors want to prevent such attacks. This has driven an arms race between attackers and defenders, resulting in a plethora of different protection and analysis methods. However, it remains

Taking an interdisciplinary approach to surveying issues around gender bias in textual and visual AI, we present literature on gender bias detection and mitigation in NLP, CV, as well as combined visual-linguistic models. We identify conceptual parallels between these strands of research as well as how methodologies were adapted cross-disciplinary from NLP to CV. We also find that there is a growing

Deep learning techniques have achieved superior performance in computer-aided medical image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in potential misdiagnosis in clinical practice. Oppositely, recent years have also witnessed remarkable progress in defense against these tailored adversarial examples in deep medical diagnosis systems. In this exposition

Voice Recognition Systems (VRSs) employ deep learning for speech recognition and speaker recognition. They have been widely deployed in various real-world applications, from intelligent voice assistance to telephony surveillance and biometric authentication. However, prior research has revealed the vulnerability of VRSs to backdoor attacks, which pose a significant threat to the security and privacy

This review paper looks at recent research on Federated Learning (FL) for Collaborative Intrusion Detection Systems (CIDS) to establish a taxonomy and survey. The motivation behind this review comes from the difficulty of detecting coordinated cyberattacks in large-scale distributed networks. Collaborative anomalies are one of the network anomalies that need to be detected through robust collaborative

Blockchain provides several advantages, including decentralization, data integrity, traceability, and immutability. However, despite its advantages, blockchain suffers from significant limitations, including scalability, resource greediness, governance complexity, and some security related issues. These limitations prevent its adoption in mainstream applications. Artificial Intelligence (AI) can help

Fake news on social networks is a challenging problem due to the rapid dissemination and volume of information, as well as the ease of creating and sharing content anonymously. Fake news stories are problematic not only for the credibility of online journalism, but also due to their detrimental real-world consequences. The primary research objective of this study is: What are the recent state-of-the-art

ive text summarization is a task in natural language processing that automatically generates the summary from the source document in a human-written form with minimal loss of information. Research in text summarization has shifted towards abstractive text summarization due to its challenging aspects. This study provides a broad systematic literature review of abstractive text summarization on single-document

Quantum and quantum-inspired algorithms have not yet been systematically classified in the context of potential Operations Research (OR) applications. Our systematic mapping is designed for quick consultation and shows which algorithms have been significantly explored in the context of OR, as well as which algorithms have been vaguely addressed in the same context. The study provides rapid access to

The Internet of Underwater Things (IoUT) pertains to a system that utilizes technology of Internet of Things (IoT) for data collection, communication, and control in the underwater environment. The monitoring and management of various parameters in the underwater domain are gathered through the deployment of underwater sensors, communication devices, and controllers. It is crucial in emerging ocean

Recently, academics and the corporate sector have paid attention to serverless computing, which enables dynamic scalability and an economic model. In serverless computing, users only pay for the time they actually use resources, enabling zero scaling to optimise cost and resource utilisation. However, this approach also introduces the serverless cold start problem. Researchers have developed various

In recent years, frequent Advanced Persistent Threat (APT) attacks have caused disastrous damage to critical facilities, leading to severe information leakages, economic losses, and even social disruptions. Via sophisticated, long-term, and stealthy network intrusions, APT attacks are often beyond the capabilities of traditional intrusion detection methods. Existing methods employ various techniques

Clustering is a fundamental machine learning task which aims at assigning instances into groups so that similar samples belong to the same cluster while dissimilar samples belong to different clusters. Shallow clustering methods usually assume that data are collected and expressed as feature vectors within which clustering is performed. However, clustering high-dimensional data, such as images, texts

As social media platforms evolve from text-based forums into multi-modal environments, the nature of misinformation in social media is also transforming accordingly. Taking advantage of the fact that visual modalities such as images and videos are more favorable and attractive to users, and textual content is sometimes skimmed carelessly, misinformation spreaders have recently targeted contextual connections

This work presents a structured view of the state-of-the-art research on Artificial Intelligence (AI), from the point of view of efficiency and reduction of the energy consumption of AI Software. We analysed the current research on energy consumption of AI algorithms and its improvements, which gave us a starting literature corpus of 2688 papers that we identified as Green AI with a software perspective

With the development of the theory and technology of computer science, machine or computer painting is increasingly being explored in the creation of art. Machine-made works are referred to as artificial intelligence (AI) artworks. Early methods of AI artwork generation have been classified as non-photorealistic rendering (NPR) and, latterly, neural-style transfer methods have also been investigated

Muti-Robot system is gaining attention and is one of the critical areas of research when it comes to robotics. Coordination among multiple robots and how different tasks are allocated to different system agents are being studied. The objective of this Systematic Literature Review (SLR) is to provide insights on the recent advancement in Multi Robot Task Allocation(MRTA) problems emphasizing promising

Rare event prediction involves identifying and forecasting events with a low probability using machine learning (ML) and data analysis. Due to the imbalanced data distributions, where the frequency of common events vastly outweighs that of rare events, it requires using specialized methods within each step of the ML pipeline, i.e., from data processing to algorithms to evaluation protocols. Predicting