Article
Computer Science, Artificial Intelligence
Jing Li, Aixin Sun, Jianglei Han, Chenliang Li
Summary: This paper provides a comprehensive review of existing deep learning techniques for named entity recognition (NER), including NER resources, categorization methods, and recent applied techniques. The paper introduces the basics of NER systems and outlines future research directions and challenges in the field.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Xiao Liu, Fanjin Zhang, Zhenyu Hou, Li Mian, Zhaoyu Wang, Jing Zhang, Jie Tang
Summary: Deep supervised learning has been successful, but it is limited by manual labels and vulnerable to attacks. In contrast, self-supervised learning utilizes input data as supervision, showing promising performance on representation learning. This survey comprehensively reviews self-supervised learning methods in computer vision, natural language processing, and graph learning.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Yu Zhang, Qiang Yang
Summary: This paper provides a survey of Multi-Task Learning (MTL) from the perspective of algorithmic modeling, applications, and theoretical analyses. It discusses different MTL algorithms and their characteristics, as well as the combination of MTL with other learning paradigms. The paper also reviews MTL models for large-scale tasks or high-dimensional data, as well as dimensionality reduction and feature hashing. Real-world applications of MTL are examined, and theoretical analyses and future directions are discussed.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Chemistry, Medicinal
Jiahui Chen, Rui Wang, Nancy Benovich Gilby, Guo-Wei Wei
Summary: The Omicron variant of the SARS-CoV-2 virus has caused global panic due to its high infectivity and ability to escape vaccines. A comprehensive analysis using an artificial intelligence model and antibody structure analysis reveals that Omicron may be over 10 times more contagious than the original virus and has an 88% likelihood of vaccine escape. This study highlights the importance of developing mutation-proof vaccines and antibodies.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2022)
Article
Computer Science, Artificial Intelligence
Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, Qing He
Summary: Knowledge graph-based recommender systems have attracted considerable interest in recent years as a way to solve the challenges faced by traditional recommender systems. In this paper, a systematical survey of knowledge graph-based recommender systems is conducted, categorizing them into embedding-based, connection-based, and propagation-based methods. The paper also explores how these approaches utilize the knowledge graph for accurate and explainable recommendation, and proposes potential research directions in this field.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Review
Computer Science, Information Systems
Achilles D. Boursianis, Maria S. Papadopoulou, Panagiotis Diamantoulakis, Aglaia Liopa-Tsakalidi, Pantelis Barouchas, George Salahas, George Karagiannidis, Shaohua Wan, Sotirios K. Goudos
Summary: This paper investigates the applications of IoT and UAV technology in agriculture. The main principles of IoT technology, including intelligent sensors, IoT sensor types, networks, and protocols used in agriculture, as well as IoT applications and solutions in smart farming are described. The role of UAV technology in smart agriculture is also presented, analyzing its applications in various scenarios such as irrigation, fertilization, use of pesticides, weed management, plant growth monitoring, crop disease management, and field-level phenotyping. IoT and UAV technology are identified as two important technologies that transform traditional cultivation practices into a new perspective of intelligence in precision agriculture.
INTERNET OF THINGS
(2022)
Article
Computer Science, Artificial Intelligence
Wei Wang, Xiaoyang Suo, Xiangyu Wei, Bin Wang, Hao Wang, Hong-Ning Dai, Xiangliang Zhang
Summary: Graph Auto-Encoder is a framework for unsupervised learning on graph-structured data. However, it is not applicable for heterogeneous graphs that contain more abundant semantic information. Therefore, this work proposes a novel HGATE method for unsupervised representation learning on heterogeneous graph-structured data.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Information Systems
Sang-Min Park, Young-Gab Kim
Summary: This paper discusses the Metaverse based on the social value of Generation Z, analyzing its technological development, conceptual definition, and implementation methods in detail. A new definition of Metaverse is proposed, dividing the key concepts and technologies necessary for realizing Metaverse, as well as the impacts and challenges.
Proceedings Paper
Computer Science, Information Systems
Peiyuan Jiang, Daji Ergu, Fangyao Liu, Ying Cai, Bo Ma
Summary: This paper provides a brief overview of the YOLO algorithm and its subsequent advanced versions, highlighting the ongoing improvement of the algorithm. The analysis reveals the differences and similarities among different YOLO versions and between YOLO and CNNs.
8TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND QUANTITATIVE MANAGEMENT (ITQM 2020 & 2021): DEVELOPING GLOBAL DIGITAL ECONOMY AFTER COVID-19
(2022)
Article
Computer Science, Information Systems
Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, Dusit Niyato, Octavia Dobre, H. Vincent Poor
Summary: This article explores the emerging opportunities brought by 6G technologies in IoT networks and applications. It sheds light on fundamental 6G technologies and discusses their roles in various prospective IoT applications. The article highlights research challenges and potential directions for further research in this promising area.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Wu Deng, Xiaoxiao Zhang, Yongquan Zhou, Yi Liu, Xiangbing Zhou, Huiling Chen, Huimin Zhao
Summary: This paper proposes an enhanced fast NSGA-II algorithm (ASDNSGA-II) for solving multi-modal multi-objective optimization problems. By using a special congestion strategy and adaptive crossover strategy, ASDNSGA-II improves the distribution and convergence of solutions. Experimental results show that ASDNSGA-II can effectively find the global Pareto solution set and improve the distribution and convergence of solutions.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Yongrui Chen, Huiying Li, Guilin Qi, Tianxing Wu, Tenggou Wang
Summary: Query graph construction aims to generate a correct SPARQL query to answer natural language questions on a knowledge graph. Existing methods face challenges in handling complex questions including complicated SPARQL syntax, huge search space, and locally ambiguous query graphs. This paper proposes a novel end-to-end approach that leverages a unified graph grammar called AQG and hierarchical autoregressive decoding to construct query graphs effectively. Experimental results demonstrate that the proposed method significantly improves the state-of-the-art performance on complex KGQA benchmarks.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Review
Computer Science, Information Systems
Utkarsh Mahadeo Khaire, R. Dhanalakshmi
Summary: Feature selection technique is a tool for understanding problems by analyzing relevant features, which can improve classifier performance and reduce computational load. However, the high correlation between features often leads to instability in traditional feature selection algorithms, resulting in reduced confidence in the selected features. Therefore, achieving high stability in feature selection algorithms is crucial.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Olaide Nathaniel Oyelade, Absalom El-Shamir Ezugwu, Tehnan I. A. Mohamed, Laith Abualigah
Summary: This study proposes a novel bio-inspired and population-based optimization algorithm named Ebola Optimization Search Algorithm (EOSA) based on the propagation mechanism of the Ebola virus disease. The algorithm outperforms popular metaheuristic algorithms such as Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), and Artificial Bee Colony Algorithm (ABC) in terms of scalability, convergence, and sensitivity analyses. The algorithm is also successfully applied to the problem of selecting the best combination of convolutional neural network (CNN) hyperparameters in the image classification of digital mammography.
Article
Computer Science, Information Systems
Jiechao Gao, Haoyu Wang, Haiying Shen
Summary: Large-scale cloud data centers often face high failure rates due to hardware and software failures, which can greatly reduce service reliability and require significant resources for recovery. Predicting task and job failures with high accuracy is crucial to avoid wastage. This article proposes a failure prediction algorithm based on multi-layer Bi-LSTM, which outperforms other methods with 93% accuracy for task failure and 87% accuracy for job failures.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Wenqi Wang, Run Wang, Lina Wang, Zhibo Wang, Aoshuang Ye
Summary: Deep neural networks have achieved remarkable success in various tasks, but they are vulnerable to adversarial examples in both image and text domains. Adversarial examples in the text domain can evade DNN-based text analyzers and pose threats to the spread of disinformation. This paper comprehensively surveys the existing studies on adversarial techniques for generating adversarial texts and the corresponding defense methods, aiming to inspire future research in developing robust DNN-based text analyzers against known and unknown adversarial techniques.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Kai Zhang, Qi Liu, Hao Qian, Biao Xiang, Qing Cui, Jun Zhou, Enhong Chen
Summary: This paper proposes a novel model called EATN for accurately classifying sentiment polarities towards aspects in multiple domains in sentiment analysis tasks. The model incorporates a Domain Adaptation Module (DAM) to learn common features and uses multiple-kernel selection method to reduce feature discrepancy among domains. Additionally, EATN includes an aspect-oriented multi-head attention mechanism to capture the direct associations between aspects and contextual sentiment words. Extensive experiments on six public datasets demonstrate the effectiveness and universality of the proposed method compared to current state-of-the-art methods.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(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, Information Systems
Peng An, Zhiyuan Wang, Chunjiong Zhang
Summary: Previous studies on cyberattack detection have overlooked data skewness. This paper proposes an approach that combines ensemble autoencoders with Gaussian mixture models to adapt to multiple domains and effectively detect network attack anomalies.
INFORMATION PROCESSING & MANAGEMENT
(2022)
Article
Computer Science, Information Systems
N. N. Misra, Yash Dixit, Ahmad Al-Mallahi, Manreet Singh Bhullar, Rohit Upadhyay, Alex Martynenko
Summary: The Internet of Things (IoT), big data, and artificial intelligence (AI) have significantly impacted the agricultural and food industry, providing opportunities for monitoring, supply chain modernization, social media applications, food quality assessment, and food safety.
IEEE INTERNET OF THINGS JOURNAL
(2022)
