78 hasil
Menampilkan 1–20 · 1,73 detik · RSS
ACM Computing Surveys
Scopus
ISSN 0360-0300 · Association for Computing Machinery · 78 artikel di indeks
-
Artikel Jurnal Teks lengkap
Deep Learning for Time Series Anomaly Detection: A Survey
Time series anomaly detection is important for a wide range of research fields and applications, including financial markets, economics, earth sciences, manufacturing, and healthcare. The presence of anomalies can indicate novel or unexpected events, such as production faults, system defects, and…
-
Artikel Jurnal Teks lengkap
Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey
Time Series Classification and Extrinsic Regression are important and challenging machine learning tasks. Deep learning has revolutionized natural language processing and computer vision and holds great promise in other fields such as time series analysis where the relevant features must often be…
-
Artikel Jurnal Teks lengkap
A Survey of Graph Neural Networks for Social Recommender Systems
Social recommender systems (SocialRS) simultaneously leverage the user-to-item interactions as well as the user-to-user social relations for the task of generating item recommendations to users. Additionally exploiting social relations is clearly effective in understanding users’ tastes due to the…
-
Artikel Jurnal Teks lengkap
Fairness in Machine Learning: A Survey
When Machine Learning technologies are used in contexts that affect citizens, companies as well as researchers need to be confident that there will not be any unexpected social implications, such as bias towards gender, ethnicity, and/or people with disabilities. There is significant literature on…
-
Artikel Jurnal Teks lengkap
A Survey on Measuring Cognitive Workload in Human-Computer Interaction
The ever-increasing number of computing devices around us results in more and more systems competing for our attention, making cognitive workload a crucial factor for the user experience of human-computer interfaces. Research in Human-Computer Interaction (HCI) has used various metrics to determine…
-
Artikel Jurnal Teks lengkap
A Comprehensive Survey on Automatic Knowledge Graph Construction
Automatic knowledge graph construction aims at manufacturing structured human knowledge. To this end, much effort has historically been spent extracting informative fact patterns from different data sources. However, more recently, research interest has shifted to acquiring conceptualized…
-
Artikel Jurnal Teks lengkap
Multimodal Sentiment Analysis: A Survey of Methods, Trends, and Challenges
Sentiment analysis has come long way since it was introduced as a natural language processing task nearly 20 years ago. Sentiment analysis aims to extract the underlying attitudes and opinions toward an entity. It has become a powerful tool used by governments, businesses, medicine, marketing, and…
-
Artikel Jurnal Teks lengkap
A Survey of Privacy Attacks in Machine Learning
As machine learning becomes more widely used, the need to study its implications in security and privacy becomes more urgent. Although the body of work in privacy has been steadily growing over the past few years, research on the privacy aspects of machine learning has received less focus than the…
-
Artikel Jurnal Teks lengkap
A Survey of Blockchain Consensus Protocols
Blockchain consensus protocols have been a focus of attention since the advent of Bitcoin. Although classic distributed consensus algorithms made significant contributions to the development of blockchain consensus protocols, there are still many issues to be resolved due to the complexity and…
-
Artikel Jurnal Teks lengkap
Diffusion Models: A Comprehensive Survey of Methods and Applications
Diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications, including image synthesis, video generation, and molecule design. In this survey, we provide an overview of the rapidly expanding body of work on diffusion models…
-
Artikel Jurnal Teks lengkap
Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey
Large, pre-trained language models (PLMs) such as BERT and GPT have drastically changed the Natural Language Processing (NLP) field. For numerous NLP tasks, approaches leveraging PLMs have achieved state-of-the-art performance. The key idea is to learn a generic, latent representation of language…
-
Artikel Jurnal Teks lengkap
Deep Learning-based Human Pose Estimation: A Survey
Human pose estimation aims to locate the human body parts and build human body representation (e.g., body skeleton) from input data such as images and videos. It has drawn increasing attention during the past decade and has been utilized in a wide range of applications including human-computer…
-
Artikel Jurnal Teks lengkap
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities
Few-shot learning (FSL) has emerged as an effective learning method and shows great potential. Despite the recent creative works in tackling FSL tasks, learning valid information rapidly from just a few or even zero samples remains a serious challenge. In this context, we extensively investigated…
-
Artikel Jurnal Teks lengkap
Heterogeneous Federated Learning: State-of-the-art and Research Challenges
Federated learning (FL) has drawn increasing attention owing to its potential use in large-scale industrial applications. Existing FL works mainly focus on model homogeneous settings. However, practical FL typically faces the heterogeneity of data distributions, model architectures, network…
-
Artikel Jurnal Teks lengkap
Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better
Deep learning has revolutionized the fields of computer vision, natural language understanding, speech recognition, information retrieval, and more. However, with the progressive improvements in deep learning models, their number of parameters, latency, and resources required to train, among…
-
Artikel Jurnal Teks lengkap
A Review of the F-Measure: Its History, Properties, Criticism, and Alternatives
Methods to classify objects into two or more classes are at the core of various disciplines. When a set of objects with their true classes is available, a supervised classifier can be trained and employed to decide if, for example, a new patient has cancer or not. The choice of performance measure…
-
Artikel Jurnal Teks lengkap
EEG Based Emotion Recognition: A Tutorial and Review
Emotion recognition technology through analyzing the EEG signal is currently an essential concept in Artificial Intelligence and holds great potential in emotional health care, human-computer interaction, multimedia content recommendation, etc. Though there have been several works devoted to…
-
Artikel Jurnal Teks lengkap
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep neural networks (DNNs) to execute complex inference tasks such as image classification and speech recognition, among others. However, continuously executing the entire DNN on mobile devices can quickly deplete…
-
Artikel Jurnal
Blockchain-Based Federated Learning for Securing Internet of Things: A Comprehensive Survey
The Internet of Things (IoT) ecosystem connects physical devices to the internet, offering significant advantages in agility, responsiveness, and potential environmental benefits. The number and variety of IoT devices are sharply increasing, and as they do, they generate significant data sources…
-
Artikel Jurnal Teks lengkap
AUC Maximization in the Era of Big Data and AI: A Survey
Area under the ROC curve, a.k.a. AUC, is a measure of choice for assessing the performance of a classifier for imbalanced data. AUC maximization refers to a learning paradigm that learns a predictive model by directly maximizing its AUC score. It has been studied for more than two decades dating…