Video analysis tasks such as action recognition have received increasing research interest with growing applications in fields such as smart healthcare, thanks to the introduction of large-scale datasets and deep learning-based representations. However, ...

Relation extraction (RE) involves identifying the relations between entities from underlying content. RE serves as the foundation for many natural language processing (NLP) and information retrieval applications, such as knowledge graph completion and ...

Capsule networks emerged as a promising alternative to convolutional neural networks for learning object-centric representations. The idea is to explicitly model part-whole hierarchies by using groups of neurons called capsules to encode visual entities, ...

In recent years, the notion of “the right to be forgotten” (RTBF) has become a crucial aspect of data privacy for digital trust and AI safety, requiring the provision of mechanisms that support the removal of personal data of individuals upon their ...

Feature attribution methods (AMs) are a simple means to provide explanations for the predictions of black-box models like neural networks. Due to their conceptual differences, the numerous different methods, however, yield ambiguous explanations. While ...

Music is a powerful medium for altering the emotional state of the listener. In recent years, with significant advancements in computing capabilities, artificial intelligence-based (AI-based) approaches have become popular for creating affective music ...

Estimating depth from single RGB images and videos is of widespread interest due to its applications in many areas, including autonomous driving, 3D reconstruction, digital entertainment, and robotics. More than 500 deep learning-based papers have been ...

In the past decade, the number of malware variants has increased rapidly. Many researchers have proposed to detect malware using intelligent techniques, such as Machine Learning (ML) and Deep Learning (DL), which have high accuracy and precision. These ...

UAVs have found their applications in numerous applications from recreational activities to business in addition to military and strategic fields. However, research on UAVs is not going on as quickly as the technology. Especially, when it comes to the ...

Responsible AI must be able to make or support decisions that consider human values and can be justified by human morals. Accommodating values and morals in responsible decision making is supported by adopting a perspective of macro ethics, which views ...

Emotion recognition based on electroencephalography (EEG) signals has emerged as a prominent research field, facilitating objective evaluation of diseases like depression and motion detection for heathy people. Starting from the basic concepts of temporal-...

Lexical Semantic Change (LSC) is the task of identifying, interpreting, and assessing the possible change over time in the meanings of a target word. Traditionally, LSC has been addressed by linguists and social scientists through manual and time-...

Privacy and security challenges in Machine Learning (ML) have become increasingly severe, along with ML’s pervasive development and the recent demonstration of large attack surfaces. As a mature system-oriented approach, Confidential Computing has been ...

Malware detection has become a major concern due to the increasing number and complexity of malware. Traditional detection methods based on signatures and heuristics are used for malware detection, but unfortunately, they suffer from poor generalization ...

Image and video synthesis has become a blooming topic in computer vision and machine learning communities along with the developments of deep generative models, due to its great academic and application value. Many researchers have been devoted to ...

In the past few years, Differentiable Neural Architecture Search (DNAS) rapidly imposed itself as the trending approach to automate the discovery of deep neural network architectures. This rise is mainly due to the popularity of DARTS (Differentiable ...

The paper reviews methods used in the fashion compatibility recommendation domain. We select methods based on reproducibility, explainability, and novelty aspects and then organize them chronologically and thematically. We presented general ...

There is a growing interest in the area of machine learning and creativity. This survey presents an overview of the history and the state of the art of computational creativity theories, key machine learning techniques (including generative deep learning),...

Deep-learning (DL) performs well in computer-vision and medical-imaging automated decision-making applications. A bottleneck of DL stems from the large amount of labelled data required to train accurate models that generalise well. Data scarcity and ...

In the field of Sequential Decision Making (SDM), two paradigms have historically vied for supremacy: Automated Planning (AP) and Reinforcement Learning (RL). In the spirit of reconciliation, this article reviews AP, RL and hybrid methods (e.g., novel ...