Article
Computer Science, Artificial Intelligence
Jing Yang, Laurence T. Yang, Hao Wang, Yuan Gao, Yaliang Zhao, Xia Xie, Yan Lu
Summary: This paper investigates the progress and function of representation learning models adopted in knowledge fusion and reasoning, providing new perspectives and ideas for scholars. The paper comprehensively reviews classic methods and investigates advanced and emerging works. Additionally, an integrated knowledge representation learning framework and tensor-based knowledge fusion and reasoning models are proposed.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Yuqi Wang, Wei Wang, Qi Chen, Kaizhu Huang, Anh Nguyen, Suparna De, Amir Hussain
Summary: This paper provides a focused review of the emerging topic of fusing external knowledge resources to improve the performance of natural language processing tasks. Three main categories of methods, based on when, how and where external knowledge is fused into learning models, are summarized. The solutions to address knowledge inclusion and inconsistency between language and knowledge are discussed, along with the design, strengths, and limitations of each representative method. Potential future research directions based on the latest trends in natural language processing are also identified.
INFORMATION FUSION
(2023)
Article
Computer Science, Hardware & Architecture
Bilal Abu-Salih
Summary: Knowledge Graphs have revolutionized knowledge representation, offering better understanding and interpretation for both human and machine. However, there is no consensus on a definition for domain-specific KGs, and current construction approaches have limitations and deficiencies.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2021)
Article
Mathematics
Yong Chen, Xinkai Ge, Shengli Yang, Linmei Hu, Jie Li, Jinwen Zhang
Summary: This survey comprehensively reviews the related advances of multimodal knowledge graphs, including their construction, completion, and typical applications. The methods of named entity recognition, relation extraction, and event extraction are outlined for construction, while multimodal knowledge graph representation learning and entity linking are discussed for completion. The mainstream applications of multimodal knowledge graphs in various domains are summarized.
Article
Computer Science, Artificial Intelligence
Ghulam Muhammad, Fatima Alshehri, Fakhri Karray, Abdulmotaleb El Saddik, Mansour Alsulaiman, Tiago H. Falk
Summary: Smart healthcare integrates technologies like wearables, IoMT, machine learning, and wireless communication to access health records and link resources. The fusion of multimodal medical signals is a key research focus in this field, with recent developments and challenges being explored in the survey of research works from 2014-2020.
INFORMATION FUSION
(2021)
Article
Multidisciplinary Sciences
Meihong Wang, Linling Qiu, Xiaoli Wang
Summary: Knowledge graphs are widely used in artificial intelligence, but their open nature often results in incompleteness, requiring the construction of a more comprehensive knowledge graph. Link prediction is a fundamental task in knowledge graph completion, utilizing existing relations to infer new ones. KG-embedding models have significantly advanced the state of the art in recent years.
Article
Computer Science, Artificial Intelligence
Saiping Guan, Xueqi Cheng, Long Bai, Fujun Zhang, Zixuan Li, Yutao Zeng, Xiaolong Jin, Jiafeng Guo
Summary: In addition to entity-centric knowledge organized as Knowledge Graph (KG), events are also an important form of knowledge that led to the emergence of event-centric knowledge representation forms like Event KG (EKG). EKG plays a crucial role in various downstream applications such as search, question-answering, recommendation, financial quantitative investments, and text generation. This paper provides a comprehensive survey of EKG from history, ontology, instance, and application perspectives, including its development processes, trends, and prospective directions for future research.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Yancong Li, Xiaoming Zhang, Fang Wang, Bo Zhang, Feiran Huang
Summary: In this work, a dual-track model DuMF is proposed for enhancing knowledge graph embedding. The model fuses multi-modal content and network structure information through two tracks, improving the expressiveness of joint features and learning task-specific important features. Experimental results demonstrate that the model outperforms baselines in link prediction and exhibits promising flexibility for further improvement.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Yong Dai, Linjun Shou, Ming Gong, Xiaolin Xia, Zhao Kang, Zenglin Xu, Daxin Jiang
Summary: Text classification is an important problem in natural language processing, and Graph Neural Networks (GNNs) have shown outstanding performance in this area. However, current methods still face limitations in adapting to new documents and considering the quality of text graphs. To address these issues, a Graph Fusion Network (GFN) is proposed to overcome these limitations and improve text classification performance.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xingwang Shen, Xinyu Li, Bin Zhou, Yanan Jiang, Jinsong Bao
Summary: Compared with ordinary mass-produced apparel products, custom apparel products generate more data and have complex relationships between the data at each stage of their life cycle. However, the traditional relational data storage method has drawbacks, such as high redundancy, weak correlation, and limited storage capacity. Therefore, a knowledge graph-based dynamic knowledge modeling and fusion method is proposed for the production process of custom apparel. This method includes ontology-based knowledge modeling and a bi-directional fusion approach to construct a knowledge graph, enabling dynamic knowledge fusion of the custom apparel production process. The effectiveness of the proposed method is verified using the suit production process of a custom apparel factory.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Computer Science, Information Systems
Tewodros Alemu Ayall, Huawen Liu, Changjun Zhou, Abegaz Mohammed Seid, Fantahun Bogale Gereme, Hayla Nahom Abishu, Yasin Habtamu Yacob
Summary: This paper provides an overview, classification, and investigation of popular graph partitioning and computing systems, discussing their methods, approaches, challenges, and future research directions.
Review
Computer Science, Artificial Intelligence
Bilin Shao, Xiaojun Li, Genqing Bian
Summary: The paper analyzes the theme of recommendation system using literature data, identifies research directions and hotspots through statistics and analysis, and explores potential future research directions and solutions.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Ruixin Ma, Fangqing Guo, Zeyang Li, Liang Zhao
Summary: This study proposes a Knowledge Graph Random Neural Networks (KRNN) for recommender systems, which addresses the issues of over-smoothing and data sparsity in existing graph neural network methods on knowledge graph. By utilizing a random dropout strategy and feature propagation method, the proposed KRNN achieves superior performance in predicting user preferences, especially in data sparse scenarios.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Theory & Methods
Jiaru Bai, Kok Foong Lee, Markus Hofmeister, Sebastian Mosbach, Jethro Akroyd, Markus Kraft
Summary: This work develops a framework to annotate how information can be derived from others in a dynamic knowledge graph. It encodes this using the notion of a derivation and captures its metadata with a lightweight ontology. The framework provides an agent template for monitoring and standardizing the process, and implements synchronous and asynchronous communication modes for agents interacting with the knowledge graph. It is applied in the context of smart cities and demonstrates the ability to handle sequential events across different timescales.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
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)
Article
Computer Science, Software Engineering
Geon Ju Lee, Jason J. Jung, David Camacho
Summary: This study aims to make decisions on player selection and tactical formation based on opponent level using data instead of manager's intuition. By analyzing a clustering dataset and game appearance dataset, and utilizing association rule mining algorithm and weighted association rule mining algorithm, player selection and tactical formation are established. The synergy between positions, tactical formation, and player characteristics depending on the opponent level are visualized using the obtained results.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2023)
Article
Computer Science, Software Engineering
Luong Vuong Nguyen, Tri-Hai Nguyen, Jason J. Jung, David Camacho
Summary: This paper proposes a hybrid recommendation approach that combines collaborative filtering methods with word embedding-based content analysis, and achieves good results in experiments in the movie domain.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2023)
Article
Computer Science, Information Systems
Hyeon-Ju Jeon, Jason J. Jung
Summary: In this study, a role model for authors to improve research abilities was discovered based on bibliographic networks, with an emphasis on high research performance and a similar research history. The effectiveness of research history embeddings and the accuracy of recommended role models were verified using real data.
JOURNAL OF INFORMATION SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Geon Ju Lee, Jason J. Jung
Summary: This study aims to predict soccer tactics, including formations, game styles, and game outcome, using deep neural networks and feature engineering. The proposed model outperforms previous simple machine learning techniques in predicting tactics.
PEERJ COMPUTER SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Minsung Hong, Jason J. Jung
Summary: This paper proposes a sentiment aware tensor model-based multi-criteria recommender system (SATM) that maps user feedback and sentiment information to handle partial preferences and improve the system's performance.
APPLIED INTELLIGENCE
(2022)
Article
Social Sciences, Interdisciplinary
Giang T. C. Tran, Luong Vuong Nguyen, Jason J. Jung, Jeonghun Han
Summary: This study proposes a novel approach for measuring political polarization using a user-activity-based model. By analyzing YouTube comments, the study reveals strong polarization among users, with a small percentage of neutral users. The model is implemented across different channels, identifying 30 fully polarized YouTube channels.
Article
Computer Science, Artificial Intelligence
Gen Li, Jason J. Jung
Summary: This study presents a novel approach for anomaly detection in IoT time series data and achieves better performance compared to other models on industrial IoT datasets.
Article
Computer Science, Artificial Intelligence
Tri-Hai Nguyen, Jason J. Jung
Summary: This paper proposes a decentralized traffic routing system based on a new pheromone model and automated negotiation technique. Connected vehicles use a new inverted pheromone model and perform collective learning-based negotiation process to distribute traffic and reduce congestion.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Chemistry, Multidisciplinary
Yuxuan Gu, Jiakai Gu, Gen Li, Heeseung Yun, Jason J. Jung, Sojung An, David Camacho
Summary: This paper presents a system called the abnormal-weather monitoring and curation service (AWMC), which analyzes weather datasets to show abnormal conditions in specific cities on certain dates. The system uses a dynamic graph-embedding-based anomaly detection method to measure anomaly scores, and evaluations show high precision, recall, and F1 score for all cities monitored by AWMC.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Gen Li, Jason J. Jung
Summary: This study reviews the state-of-the-art deep learning techniques for anomaly detection in different types, including abnormal time points, time intervals, and time series. Long short-term memory and autoencoders are commonly used methods, and dynamic graphs have been implemented to detect abnormal time intervals. However, anomaly detection still faces challenges in explaining the anomalies.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
JiaKai Gu, Gen Li, Nam D. Vo, Jason J. Jung
Summary: This chapter proposes the use of a contextual Word2Vec model for understanding OOV. The authors extract the OOV using left-right entropy and point information entropy. They construct a word vector space using Word2Vec and obtain contextual information using CBOW. The results show that the proposed model achieves a higher accuracy rate than Skip-Gram.
INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Tri-Hai Nguyen, Gen Li, Hyoenseong Jo, Jason J. Jung, David Camacho
Summary: This paper proposes a distributed cooperative negotiation method for optimizing traffic flow by utilizing collective learning algorithm and exchanging routing information among connected vehicles. Simulation results show that the proposed method performs better in high traffic demand scenarios.
INTELLIGENT DISTRIBUTED COMPUTING XIV
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Israel Edem Agbehadji, Abdultaofeek Abayomi, Richard C. Millham, Samuel Ofori Frimpong, Jason J. Jung
Summary: Solar irradiation and wind speed are popular renewable energy sources that can be combined to minimize energy production costs and meet the demand of consumers in rural areas where national grid infrastructure is not economically viable. The proposed nature-inspired/meta-heuristic optimization framework aims to optimize the operational costs of hybrid renewable energy from solar and wind power while meeting consumer power load demand. Experimentation with empirical data in Ghana shows that using the KSA algorithm for hybridizing solar and wind energy can effectively minimize electricity costs and meet consumer demand.
PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND PATTERN RECOGNITION (SOCPAR 2021)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Samuel Ofori Frimpong, Richard C. Millham, Israel Edem Agbehadji, Jason J. Jung
Summary: This paper proposes an enhancement to the SSP algorithm, which mimics the foraging behavior of social spiders, to improve the algorithm's searching strategy. Experimental results demonstrate that SSP performs outstandingly in dealing with complex optimization problems, indicating its prospects for solving such problems.
INFORMATICS AND INTELLIGENT APPLICATIONS
(2022)
Proceedings Paper
Computer Science, Information Systems
Riski Nur Azizah, Yea Som Lee, Jason J. Jung, Bong-Soo Sohn
Summary: This paper presents a gesture recognition algorithm for note generation in VR rhythm games. Traditional manual note generation is time-consuming, so a new method of generating notes from hand taps dance is proposed.
35TH INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING (ICOIN 2021)
(2021)
Article
Computer Science, Artificial Intelligence
C. Lopez-Molina, S. Iglesias-Rey, B. De Baets
Summary: Quantitative image comparison is a critical topic in image processing literature, with diverse applications. Existing measures of comparison often overlook the context in which the comparison takes place. This paper presents a context-aware comparison method for binary images, tested on the BSDS500 benchmark.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Lorenz Linhardt, Klaus-Robert Mueller, Gregoire Montavon
Summary: This paper investigates the issue of mismatches between the decision strategy of the explainable model and the user's domain knowledge, and proposes a new method EGEM to mitigate hidden flaws in the model. Experimental results demonstrate that the approach can significantly reduce reliance on Clever Hans strategies and improve the accuracy of the model on new data.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Zhimin Shao, Weibei Dou, Yu Pan
Summary: This paper proposes a novel algorithm, Dual-level Deep Evidential Fusion (DDEF), to integrate multimodal information at both the BBA level and multimodal level, aiming to enhance accuracy, robustness, and reliability. The DDEF approach utilizes the Dirichlet framework and BBA methods for effective uncertainty estimation and employs the Dempster-Shafer Theory for dual-level fusion. The experimental results show that the proposed DDEF outperforms existing methods in synthetic digit classification and real-world medical prognosis after BCI treatment.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Abhishek K. Ghosh, Danilo S. Catelli, Samuel Wilson, Niamh C. Nowlan, Ravi Vaidyanathan
Summary: The inability of current FM monitoring methods to be used outside clinical environments has made it challenging to understand the nature and evolution of FM. This investigation introduces a novel wearable FM monitor with a heterogeneous sensor suite and a data fusion architecture to efficiently capture and separate FM from interfering artifacts. The performance of the device and architecture were validated through at-home use, demonstrating high accuracy in detecting and recognizing FM events. This research is a major milestone in the development of low-cost wearable FM monitors for pervasive monitoring of FM in unsupervised environments.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Jianlei Kong, Xiaomeng Fan, Min Zuo, Muhammet Deveci, Xuebo Jin, Kaiyang Zhong
Summary: In this study, we propose an intelligent traffic flow prediction framework based on the adaptive dual-graphic transformer with a cross-fusion strategy, aiming to uncover latent graphic feature representations that transcend temporal and spatial limitations. By establishing a traffic spatiotemporal prediction model using a cross-fusion attention mechanism, our proposed model achieves superior prediction performance on practical urban traffic flow datasets, particularly for long-term predictions.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Huilai Zhi, Jinhai Li
Summary: This article addresses the issue that conflict analysis based on single-valued information systems is no longer valid. It proposes a conflict analysis method based on component similarity, which uses three-way n-valued concept lattices to handle set-valued formal contexts and realizes fast conflict analysis from an information fusion viewpoint. Experimental results verify the effectiveness of this method in reducing time consumption.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Huchang Liao, Jiaxin Qi, Jiawei Zhang, Chonghui Zhang, Fan Liu, Weiping Ding
Summary: In this paper, a hospital selection approach based on a fuzzy multi-criterion decision-making method is proposed. This approach considers sentiment evaluation values of unstructured data from online reviews and structured data of public indexes simultaneously. The methodology involves collecting and processing online reviews, classifying topics and sentiments, quantifying sentiment analysis results using fuzzy numbers, and obtaining final preference scores of hospitals based on patients' preferences. A case study and robustness analysis are conducted to validate the effectiveness of the method.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Faramarz Farhangian, Rafael M. O. Cruz, George D. C. Cavalcanti
Summary: The proliferation of social networks has posed challenges in combating fake news, but automatic fake news detection using artificial intelligence has become more feasible. This paper revisits the definitions and perspectives of fake news and proposes an updated taxonomy, based on multiple criteria, for the field. The study finds that optimal feature extraction techniques vary depending on the dataset, and context-dependent models based on transformer models consistently exhibit superior performance.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Mariana A. Souza, Robert Sabourin, George D. C. Cavalcanti, Rafael M. O. Cruz
Summary: In this study, a dynamic selection technique is proposed to handle sparse and overlapped data. The technique leverages the relationships between instances and classifiers to learn a dynamic classifier combination rule. Experimental results show that the proposed method outperforms static selection and other dynamic selection techniques.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Bin Yu, Ruihui Xu, Mingjie Cai, Weiping Ding
Summary: This paper introduces a clustering method based on non-Euclidean metric and multi-granularity staged clustering to address the challenges posed by complex spatial structure data to traditional clustering methods. The method improves the similarity measure and employs an attenuation-diffusion pattern for local to global clustering, achieving good clustering results.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Jian Zhu, Pengbo Hu, Bingqian Li, Yi Zhou
Summary: The acquisition of multi-view hash representation for heterogeneous data is highly important for multimedia retrieval. Current approaches suffer from limited retrieval precision due to insufficient integration of multi-view features and failure to effectively utilize metric information from diverse samples. In this paper, we propose an innovative method called Fast Metric Multi-View Hashing (FMMVH), which demonstrates the superiority of gate-based fusion over traditional methods. We also introduce a novel deep metric loss function to leverage metric information from dissimilar samples. By optimizing and employing model compression techniques, our FMMVH method significantly outperforms existing state-of-the-art methods on benchmark datasets, with up to 7.47% improvement in mean Average Precision (mAP).
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Fayaz Ali Dharejo, Iyyakutti Iyappan Ganapathi, Muhammad Zawish, Basit Alawode, Moath Alathbah, Naoufel Werghi, Sajid Javed
Summary: The resource-limited nature of underwater vision equipment affects underwater robotics and ocean engineering tasks. Super-resolution methods, particularly using Vision Transformers (ViTs), have emerged to enhance low-resolution underwater images. However, ViTs face challenges in handling severe degradation in underwater imaging. In contrast, Multi-scale ViTs (MViTs) overcome these challenges by preserving long-range dependencies through evolving channel capacity. This study proposes a novel algorithm, SwinWave-SR, for efficient and accurate multi-scale super-resolution for underwater images.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Weiwei Jiang, Haoyu Han, Yang Zhang, Jianbin Mu
Summary: This study incorporates federated learning and split learning paradigms with satellite-terrestrial integrated networks and introduces a split-then-federated learning framework and federated split learning with long short-term memory to handle sequential data in STINs. The proposed solution is demonstrated to be effective through a case study of electricity theft detection based on a real-world dataset.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Najah Abuali, Mohammad Bilal Khan, Farman Ullah, Mohammad Hayajneh, Hikmat Ullah, Shahid Mumtaz
Summary: The demand for innovative solutions in biomedical systems for precise diagnosis and management of critical diseases is increasing. A promising technology, non-invasive and intelligent Internet of Medical Things (IoMT) system, emerges to assess patients with reduced health risks. This research introduces a comprehensive framework for early diagnosis of respiratory abnormalities through RF sensing and SDR technology. The results highlight the superior performance of deep learning frameworks in classifying respiratory anomalies.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Shichen Huang, Weina Fu, Zhaoyue Zhang, Shuai Liu
Summary: In the era of adversarial machine learning (AML), developing robust and generalized algorithms has become a key research topic. This study proposes a global similarity matching module and a global-local cognition fusion training mechanism based on relationship adversarial sample generation to improve image-text matching algorithm. Experimental results show significant improvements in accuracy and robustness, performing well in facing security challenges and promoting the fusion of visual and linguistic modalities.
INFORMATION FUSION
(2024)
