Article
Computer Science, Artificial Intelligence
Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, Philip S. Yu
Summary: This survey provides a comprehensive review of knowledge graphs, covering topics such as knowledge graph representation learning, knowledge acquisition and completion, temporal knowledge graphs, and knowledge-aware applications. The study proposes a categorization and taxonomies on these topics, as well as explores emerging themes like metarelational learning, commonsense reasoning, and temporal knowledge graphs. Additionally, the research offers curated data sets and open-source libraries to facilitate future research in the field of knowledge graphs.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Zewen Li, Fan Liu, Wenjie Yang, Shouheng Peng, Jun Zhou
Summary: This review provides insights into the development history of CNN, a overview of various convolutions, introduction to classic and advanced CNN models, conclusions drawn from experimental analysis, rules of thumb for function and hyperparameter selection, and applications of 1-D, 2-D, and multidimensional convolutions. Moreover, it also discusses open issues and promising directions for CNN as guidelines for future work.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yongming Li, Yanjun Liu, Shaocheng Tong
Summary: This article presents an adaptive neural network output feedback optimized control design for strict-feedback nonlinear systems with unknown internal dynamics. By constructing optimal cost functions for subsystems and using the actor-critic architecture, virtual and actual optimal controllers are developed to ensure the boundedness of all closed-loop signals. The proposed strategy also guarantees that system states are always confined within some preselected compact sets.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng
Summary: This method achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. The algorithm represents a scene using a fully connected deep network and synthesizes views by querying 5D coordinates and using volume rendering techniques.
COMMUNICATIONS OF THE ACM
(2022)
Article
Computer Science, Artificial Intelligence
Zhifei Li, Hai Liu, Zhaoli Zhang, Tingting Liu, Neal N. Xiong
Summary: This article proposes a novel heterogeneous GNNs framework based on attention mechanism to address the aggregation of complex graph data containing various types of entities and relations in KGs. By learning weight values to aggregate features from different relation-paths for embedding representation, it captures various types of semantic information and selectively aggregates informative features.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Hao Shen, Xiaohui Hu, Jing Wang, Jinde Cao, Wenhua Qian
Summary: This work explores the $H_{infinity }$ synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints and gain uncertainties. A novel double-layer switching regulation containing Markov chain and persistent dwell-time switching regulation (PDTSR) is proposed to design a mode-dependent synchronization controller for the network. New sufficient conditions are established to ensure the mean-square exponential stability of the synchronization error systems with the specified level of the $H_{infinity }$ performance.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Xin Liao, Jiaojiao Yin, Mingliang Chen, Zheng Qin
Summary: This article introduces the application of multiple image steganography in the era of cloud storage, discusses payload distribution strategies for enhancing security performance, and provides supporting evidence through experiments.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2022)
Article
Computer Science, Hardware & Architecture
Xiaorui Zhang, Wenfang Zhang, Wei Sun, Xingming Sun, Sunil Kumar Jha
Summary: In this paper, a 3D medical watermarking algorithm based on wavelet transform is proposed, which utilizes PCA transform to reduce data dimension and a BF-PSO model to find the optimal embedding parameters, achieving the optimal balance between embedding capacity and imperceptibility. Experimental results based on a standard MRI brain volume dataset in MATLAB software show that the proposed algorithm has strong robustness and minimal deformation of the 3D model after watermark embedding.
COMPUTER SYSTEMS SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Hwanjun Song, Minseok Kim, Dongmin Park, Yooju Shin, Jae-Gil Lee
Summary: The lack of high-quality labels in real-world scenarios is a concern in deep learning. This survey provides a comprehensive review of robust training methods and compares their superiority. It also analyzes noise rate estimation and evaluation methodology.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yan-Jun Liu, Wei Zhao, Lei Liu, Dapeng Li, Shaocheng Tong, C. L. Philip Chen
Summary: This article investigates the problem of tracking control for a class of nonlinear time-varying full state constrained systems. The intelligent controller and adaptive law are developed by constructing the time-varying asymmetric barrier Lyapunov function (BLF) and combining it with the backstepping algorithm. Neural networks (NNs) are used to approximate the uncertain function. This article considers constraint boundaries that are both related to state and time, making the design of the control algorithm more complex and difficult. The effectiveness of the control algorithm is verified through numerical simulation.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Review
Computer Science, Artificial Intelligence
Sicheng Zhao, Xiangyu Yue, Shanghang Zhang, Bo Li, Han Zhao, Bichen Wu, Ravi Krishna, Joseph E. Gonzalez, Alberto L. Sangiovanni-Vincentelli, Sanjit A. Seshia, Kurt Keutzer
Summary: This article reviews the latest single-source deep unsupervised domain adaptation (DA) methods for visual tasks and discusses new perspectives for future research. The article starts with the definitions of different DA strategies and descriptions of existing benchmark datasets, then summarizes and compares different categories of methods, and finally discusses future research directions.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Zhen Li, Deqing Zou, Shouhuai Xu, Hai Jin, Yawei Zhu, Zhaoxuan Chen
Summary: This article introduces a systematic framework for using deep learning to detect vulnerabilities in C/C++ programs. Through experiments, the practicality of the framework is demonstrated, and several previously unreported vulnerabilities are successfully detected.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Hongyi Li, Ying Wu, Mou Chen, Renquan Lu
Summary: This article proposes a fault-tolerant adaptive multigradient recursive reinforcement learning event-triggered tracking control scheme for strict-feedback discrete-time multiagent systems. The scheme improves system stability and tracking accuracy by introducing a new event-triggered control strategy, adaptive compensation technique, and multigradient recursive RL algorithm.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yuqiao Liu, Yanan Sun, Bing Xue, Mengjie Zhang, Gary G. Yen, Kay Chen Tan
Summary: Deep neural networks have achieved great success in many applications, but their architectures require labor-intensive and expert-designed processes. Neural architecture search (NAS) technology enables automatic design of architectures, with evolutionary computation (EC) methods gaining attention and success. However, there is currently no comprehensive summary of EC-based NAS algorithms.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Shuming Qiu, Ding Wang, Guoai Xu, Saru Kumari
Summary: This article proposes a provably secure three-factor AKA protocol based on extended chaotic-maps for mobile lightweight devices. By utilizing Fuzzy-Verifiers and Honeywords techniques, the protocol achieves a good balance between security and usability.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Mei Liu, Liangming Chen, Xiaohao Du, Long Jin, Mingsheng Shang
Summary: This article proposes a novel method of using gradient activation function to address issues in deep neural networks. Theoretical and experimental evidence demonstrate the effectiveness of this method, which can be applied to various neural networks.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Qiang Lai, Zhiqiang Wan, Hui Zhang, Guanrong Chen
Summary: This article presents a design of a new Hopfield neural network that can generate multiscroll attractors by utilizing a new memristor as a synapse in the network. The memristor is constructed with hyperbolic tangent functions and its parameters can effectively control the number of double scrolls in an attractor. Numerical analysis reveals amplitude control effects and quantitatively controllable multistability. Furthermore, a novel image encryption scheme based on the proposed memristive neural network is designed and evaluated, demonstrating good encryption performances.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Huimin Zhao, Jie Liu, Huayue Chen, Jie Chen, Yang Li, Junjie Xu, Wu Deng
Summary: This paper proposes a novel method for extracting vibration amplitude spectrum imaging features using continuous wavelet transform and image conversion, as well as a new CDBN for bearing fault classification. The proposed method outperforms traditional methods in performance, as shown in experiments on motor bearing datasets.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Computer Science, Artificial Intelligence
Alysa Ziying Tan, Han Yu, Lizhen Cui, Qiang Yang
Summary: With the rapid advancement of artificial intelligence, there is increasing concern about data privacy. Federated Learning (FL) has become popular as a privacy-preserving training method for machine learning models. In this survey, personalized FL is explored to address the challenges of FL on heterogeneous data.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yong-Sheng Ma, Wei-Wei Che, Chao Deng, Zheng-Guang Wu
Summary: This article addresses the problem of distributed model-free adaptive control for learning nonlinear multiagent systems subjected to denial-of-service attacks. An improved dynamic linearization method is proposed to obtain an equivalent linear data model, and an attack compensation mechanism is developed to alleviate the influence of DoS attacks. A novel learning-based DMFAC algorithm is developed based on the equivalent linear data model and the attack compensation mechanism to resist DoS attacks, providing a unified framework to solve various control problems.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
