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Article
Adaptive moving average Q-learning
A variety of algorithms have been proposed to address the long-standing overestimation bias problem of Q-learning. Reducing this overestimation bias may lead to an underestimation bias, such as double Q-learni...
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Article
Open AccessA Transfer-Learning-Like Neural Dynamics Algorithm for Arctic Sea Ice Extraction
Sea ice plays a pivotal role in ocean-related research, necessitating the development of highly accurate and robust techniques for its extraction from diverse satellite remote sensing imagery. However, convent...
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Chapter and Conference Paper
Self-supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection
Time Series Anomaly Detection (TSAD) finds widespread applications across various domains such as financial markets, industrial production, and healthcare. Its primary objective is to learn the normal patterns...
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Article
Open AccessKAGN:knowledge-powered attention and graph convolutional networks for social media rumor detection
Rumor posts have received substantial attention with the rapid development of online and social media platforms. The automatic detection of rumor from posts has emerged as a major concern for the general publi...
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Article
Open AccessRTiSR: a review-driven time interval-aware sequential recommendation method
The emerging topic of sequential recommender (SR) has attracted increasing attention in recent years, which focuses on understanding and learning the sequential dependencies of user behaviors hidden in the use...
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Chapter and Conference Paper
A Predictive Coding Approach to Multivariate Time Series Anomaly Detection
This paper proposes LPC-AD, a fast and accurate multivariate time series (MTS) anomaly detection method. LPC-AD is motivated by the ever-increasing needs for fast and accurate MTS anomaly detection methods to sup...
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Chapter and Conference Paper
A Self-decoupled Interpretable Prediction Framework for Highly-Variable Cloud Workloads
Cloud workloads prediction plays a crucial role in the various tasks of cloud computing, such as resource scheduling, performance optimization, cost management, etc. However, current time series prediction met...
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Article
MCFL: multi-label contrastive focal loss for deep imbalanced pedestrian attribute recognition
Pedestrian Attribute Recognition (PAR) can provide valuable clues for several innovative surveillance applications. It is also a difficult task because inference of the multiple attributes at a far distance is...
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Article
Open AccessLower limb movement intention recognition for rehabilitation robot aided with projected recurrent neural network
For the lower limb rehabilitation robot, how to better realize intention recognition is the key issue in the practical application. Recognition of the patient’s movement intention is a challenging research wor...
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Article
Open AccessAn efficient annealing-assisted differential evolution for multi-parameter adaptive latent factor analysis
A high-dimensional and incomplete (HDI) matrix is a typical representation of big data. However, advanced HDI data analysis models tend to have many extra parameters. Manual tuning of these parameters, general...
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Chapter and Conference Paper
Toward Auto-Learning Hyperparameters for Deep Learning-Based Recommender Systems
Deep learning (DL)-based recommendation system (RS) has drawn extensive attention during the past years. Its performance heavily relies on hyperparameter tuning. However, the most common approach of hyperparam...
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Chapter and Conference Paper
Performance and Cost-Aware Task Scheduling via Deep Reinforcement Learning in Cloud Environment
In the cloud computing environment, task scheduling with multiple objectives optimization becomes a highly challenging problem in such a dynamic and bursty environment. Previous studies have mostly emphasized ...
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Chapter and Conference Paper
Heterogeneous-Aware Online Cloud Task Scheduler Based on Clustering and Deep Reinforcement Learning Ensemble
Taking advantage of the powerful computing and flexibility charge abilities of cloud computing, an increasing number of applications moved to the cloud. As a result, how to assign the large-scale and dynamical...
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Chapter and Conference Paper
A Robust Classification Framework for Medical Patents Based on Deep Learning
With the repaid development of bioinformatics and pharmaceutical engineering, pharmaceutical company and institutes increasingly pay attention to intellectual property protection via medical patents. As a resu...
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Chapter and Conference Paper
Diversity-Aware Top-N Recommendation: A Deep Reinforcement Learning Way
The increasing popularity of the recommender system deeply influences our decisions on the Internet, which is a typical continuous interaction process between the system and its users. Most previous recommende...
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Chapter and Conference Paper
Random Forest-Based Ensemble Estimator for Concrete Compressive Strength Prediction via AdaBoost Method
As one of the most important building materials, the quality of concrete directly affects the safety of buildings. Hence, it is an important and hot issue to predict the compressive strength of concrete with h...
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Chapter and Conference Paper
A Clustering-Based Collaborative Filtering Recommendation Algorithm via Deep Learning User Side Information
Collaborative filtering (CF) is a widely used recommendation approach that relies on user-item ratings. However, the natural sparsity of user-item ratings can be problematic in many domains, limiting the abili...
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Chapter and Conference Paper
A Deep Self-learning Classification Framework for Incomplete Medical Patents with Multi-label
The classification of medical patents play an important role for pharmaceutical company, since medical patens with well labeled can significantly accelerate the process of new drug research. The previous studi...
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Chapter and Conference Paper
A Data-Aware Latent Factor Model for Web Service QoS Prediction
Accurately predicting unknown quality-of-service (QoS) data based on historical QoS records is vital in web service recommendation or selection. Recently, latent factor (LF) model has been widely and successfu...
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Article
Open AccessPopularity and Novelty Dynamics in Evolving Networks
Network science plays a big role in the representation of real-world phenomena such as user-item bipartite networks presented in e-commerce or social media platforms. It provides researchers with tools and tec...
