ACM Computing Surveys

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, ...

Nowadays, with the development of artificial intelligence (AI), privacy issues attract wide attention from society and individuals. It is desirable to make the data available but invisible, i.e., to realize data analysis and calculation without disclosing ...

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 ...

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 ...

Location information has initiated a multitude of applications such as location-based services, health care, emergency response and rescue operations, and assets tracking. A plethora of techniques and technologies have been presented to ensure enhanced ...

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 ...

Building and maintaining a silicon foundry is a costly endeavor that requires substantial financial investment. From this scenario, the semiconductor business has largely shifted to a fabless model where the Integrated Circuit (IC) supply chain is ...

Machine learning plays a role in many deployed decision systems, often in ways that are difficult or impossible to understand by human stakeholders. Explaining, in a human-understandable way, the relationship between the input and output of machine ...

Fully Homomorphic Encryption (FHE) is a key technology enabling privacy-preserving computing. However, the fundamental challenge of FHE is its inefficiency, due primarily to the underlying polynomial computations with high computation complexity and ...

Blockchain is a decentralized distributed ledger that combines multiple technologies, including chain data structures, P2P networks, consensus algorithms, cryptographic principles, and smart contracts. This gives the blockchain the characteristics of ...

From the innovation, Artificial Intelligence (AI) materialized as one of the noticeable research areas in various technologies and has almost expanded into every aspect of modern human life. However, nowadays, the development of AI is unpredictable with ...

Collaborative Filtering (CF) is achieving a plateau of high popularity. Still, recommendation success is challenged by the diversity of user preferences, structural sparsity of user-item ratings, and inherent subjectivity of rating scales. The increasing ...

Due to their agility, cost-effectiveness, and high maneuverability, Unmanned Aerial Vehicles (UAVs) have attracted considerable attention from researchers and investors alike. Path planning is one of the practical subsets of motion planning for UAVs. It ...

Abstract Recent advancements in face recognition (FR) technology in surveillance systems make it possible to monitor a person as they move around. FR gathers a lot of information depending on the quantity and data sources. The most severe privacy concern ...

The past years have been characterized by an upsurge in opaque automatic decision support systems, such as Deep Neural Networks (DNNs). Although DNNs have great generalization and prediction abilities, it is difficult to obtain detailed explanations for ...

Hardware has been constantly augmented for security considerations since the advent of computers. There is also a common perception among computer users that hardware does a relatively better job on security assurance compared to software. Yet, the ...

This study investigates the impact of machine learning models on the generation of counterfactual explanations by conducting a benchmark evaluation over three different types of models: a decision tree (fully transparent, interpretable, white-box model), ...

Federated learning (FL) is a promising technique for resolving the rising privacy and security concerns. Its main ingredient is to cooperatively learn the model among the distributed clients without uploading any sensitive data. In this paper, we ...

Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness remains elusive and constitutes a key issue that impedes large-scale adoption. Besides, robustness is interpreted differently across domains and contexts of AI. ...

Causal inference is the idea of cause-and-effect; this fundamental area of sciences can be applied to problem space associated with Newton’s laws or the devastating COVID-19 pandemic. The cause explains the “why” whereas the effect describes the “what”. ...

Code intelligence leverages machine learning techniques to extract knowledge from extensive code corpora, with the aim of developing intelligent tools to improve the quality and productivity of computer programming. Currently, there is already a thriving ...

Automated medical coding, an essential task for healthcare operation and delivery, makes unstructured data manageable by predicting medical codes from clinical documents. Recent advances in deep learning and natural language processing have been widely ...

Privacy-enhancing technologies are technologies that implement fundamental data protection principles. With respect to biometric recognition, different types of privacy-enhancing technologies have been introduced for protecting stored biometric data which ...

Aerial object detection, as object detection in aerial images captured from an overhead perspective, has been widely applied in urban management, industrial inspection, and other aspects. However, the performance of existing aerial object detection ...

Deep learning approaches have achieved great success in the field of Natural Language Processing (NLP). However, directly training deep neural models often suffer from overfitting and data scarcity problems that are pervasive in NLP tasks. In recent years,...

This survey paper proposes a clearer view of natural language reasoning in the field of Natural Language Processing (NLP), both conceptually and practically. Conceptually, we provide a distinct definition for natural language reasoning in NLP, based on ...

One hurdle to accurate indoor localization using time-based networks is the presence of Non-Line-Of-Sight (NLOS) and multipath signals, affecting the accuracy of ranging in indoor environments. NLOS identification and mitigation have been studied over the ...

Despite its astounding success in learning deeper multi-dimensional data, the performance of deep learning declines on new unseen tasks mainly due to its focus on same-distribution prediction. Moreover, deep learning is notorious for poor generalization ...

Every software system undergoes changes, for example, to add new features, fix bugs, or refactor code. The importance of understanding software changes has been widely recognized, resulting in various techniques and studies, for instance, on change-impact ...

Intelligent Transportation Systems (ITS) promise significant increases in throughput and reductions in trip delay. ITS makes extensive use of Connected and Autonomous Vehicles (CAV) frequently broadcasting location, speed, and intention information. ...

Voice is a natural mode of expression offered by modern computer-based systems. Qualitative perspectives on voice-based user experiences (voice UX) offer rich descriptions of complex interactions that numbers alone cannot fully represent. We conducted a ...

Information sharing is vital in any communication network environment to enable network operating services take decisions based on the information collected by several deployed computing devices. The various networks that compose cyberspace, as Internet-...

Fine-tuning visual models has been widely shown promising performance on many downstream visual tasks. With the surprising development of pre-trained visual foundation models, visual tuning jumped out of the standard modus operandi that fine-tunes the ...

Tables are everywhere, from scientific journals, papers, websites, and newspapers all the way to items we buy at the supermarket. Detecting them is thus of utmost importance to automatically understanding the content of a document. The performance of ...

Complex system simulation has been playing an irreplaceable role in understanding, predicting, and controlling diverse complex systems. In the past few decades, the multi-scale simulation technique has drawn increasing attention for its remarkable ability ...

Artificial Intelligence (AI) is becoming more ubiquitous throughout our lives. As our reliance on this technology increases, ensuring human operators maintain an adequate level of trust is integral to their safe and effective operations. To facilitate the ...

We survey the adversarial robustness of neural networks from the perspective of Lipschitz calculus in a unifying fashion by expressing models, attacks and safety guarantees, that is, a notion of measurable trustworthiness, in a mathematical language. ...

With the rapid development of deep learning, AI systems are being used more in complex and important domains and necessitates the simultaneous fulfillment of multiple constraints: accurate, robust, and fair. Accuracy measures how well a DNN can generalize ...

Emerging Distributed AI systems are revolutionizing big data computing and data processing capabilities with growing economic and societal impact. However, recent studies have identified new attack surfaces and risks caused by security, privacy, and ...

Despite being responsible for state-of-the-art results in several computer vision and natural language processing tasks, neural networks have faced harsh criticism due to some of their current shortcomings. One of them is that neural networks are ...

We live in an information society that strongly relies on information retrieval systems, such as search engines and conversational assistants. Consequently, the trustworthiness of these systems is of critical importance, and has attracted a significant ...

The impact of automated decision-making systems on human lives is growing, emphasizing the need for these systems to be not only accurate but also fair. The field of algorithmic fairness has expanded significantly in the past decade, with most approaches ...

With the power of parallel processing, large datasets,and fast computational resources, deep neural networks (DNNs) have outperformed highly trained and experienced human experts in medical applications. However, the large global community of healthcare ...