Article
Computer Science, Artificial Intelligence
Heyan Huang, Ming Lei, Chong Feng
Summary: This paper proposes using corpus subgraphs and sentence subgraphs to obtain linguistic knowledge and classification knowledge, building a relation knowledge graph to extract relations from sentences, and treating multiple relation extraction as a reasoning process for knowledge completion.
Article
Computer Science, Artificial Intelligence
Gustavo Isaza, Fabian Munoz, Luis Castillo, Felipe Buitrago
Summary: This paper presents a computational model for classifying Cybergrooming attacks in child online protection using Natural Language Processing (NLP) and Convolutional Neural Networks (CNN). The model achieves high accuracy but low precision and F-score due to a high number of false positives. Training machine learning algorithms with neural networks, semantic analysis, and NLP helps to discover pseudo-intelligent information and reduce human intervention in characterizing abnormal behavior.
Article
Computer Science, Information Systems
Salha Alzahrani, Hanan Aljuaid
Summary: This study aims to achieve cross-lingual semantic text similarity and plagiarism detection and judgement tasks using deep neural networks and rich semantic features. Experimental results show encouraging results compared to baseline methods.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Changsen Yuan, Heyan Huang, Chong Feng, Qianwen Cao
Summary: The PGCN-EA model addresses the issues of redundant syntactic information and wrong dependency parsing results in GCN by utilizing piecewise adjacency matrix based on entity pair and Edge-level Attention. The model achieves the best PR curve on a benchmark dataset compared to seven baseline models.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Mathematics
Jun Long, Lei Liu, Hongxiao Fei, Yiping Xiang, Haoran Li, Wenti Huang, Liu Yang
Summary: Relation extraction tasks often suffer from low accuracy due to the lack of semantic guidance from contextual information. To address this issue, we propose a contextual semantic-guided entity-centric graph convolutional network (CEGCN) model, which enhances relation extraction accuracy by effectively utilizing semantic-guided contextual information.
Article
Computer Science, Artificial Intelligence
Changsen Yuan, Heyan Huang, Chong Feng
Summary: The Graph Convolutional Network (GCN) is a universal method for relation extraction by capturing sentences' syntactic features, but the quality of dependency parsing affects its performance. The Multi-Graph Cooperative Learning model (MGCL) proposed in this article focuses on extracting reliable syntactic features from different graphs to improve sentence representation, achieving state-of-the-art performance in relation extraction.
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Qian Wan, Shangheng Du, Yaqi Liu, Jing Fang, Luona Wei, Sannyuya Liu
Summary: The paper proposes a document-level relation extraction model based on the Hierarchical Dependency Tree and Bridge Path (HDT-BP), which improves the performance of relation extraction by independently extracting fine-grained features for each hierarchy and modeling the bridge path feature.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yangning Li, Yinghui Li, Xi Chen, Hai-Tao Zheng, Ying Shen
Summary: This article addresses two major problems in Open Relation Extraction (OpenRE): insufficient capacity to discriminate between known and novel relations, and the inability to label human-readable and meaningful types for novel relations. To solve these issues, the Active Relation Discovery (ARD) framework, which includes relational outlier detection and active learning, is proposed. Extensive experiments demonstrate the superior performance of ARD in both conventional and general OpenRE settings.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Information Systems
Xuechao Du, Xiang Pan, Yinzhi Cao, Boyuan He, Gan Fan, Yan Chen, Daigang Xu
Summary: The legitimacy of Android apps accessing private information depends on whether the app provides sufficient semantics to justify the access. Existing analysis methods are limited to coarse-grained app-level analysis and cannot address the correctness of specific app behaviors under different runtime contexts. To address this, we propose FlowCog, an automated system that extracts semantics related to information flows and correlates them with given flows.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Review
Computer Science, Theory & Methods
Yohan Bonescki Gumiel, Lucas Emanuel Silva e Oliveira, Vincent Claveau, Natalia Grabar, Emerson Cabrera Paraiso, Claudia Moro, Deborah Ribeiro Carvalho
Summary: Temporal relation extraction in clinical texts is a major research focus, with attention-based models being the current state-of-the-art. However, further experiments and research advancements are still needed. Additionally, dataset imbalance and task complexity directly impact system performance, requiring new annotation projects to provide datasets for different medical specialties and languages.
ACM COMPUTING SURVEYS
(2021)
Article
Chemistry, Multidisciplinary
Jun Long, Ye Wang, Xiangxiang Wei, Zhen Ding, Qianqian Qi, Fang Xie, Zheman Qian, Wenti Huang
Summary: Relation classification is an important task in natural language processing, and the proposed GCN-based relation classifier in this paper effectively utilizes the dependencies between nodes to improve the performance of relation classification. The model outperforms all compared baselines on two widely used datasets, showing its ability to simplify the model complexity and generate expressive relation representations.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Ming Lei, Heyan Huang, Chong Feng
Summary: The proposed MGSR model successfully bridges the semantic gap between low-level and high-level semantic abstraction by learning multi-granularity semantic representations at word, phrase, and sentence levels. By segmenting sentences into entity chunks and context chunks, and utilizing different self-attention mechanisms for learning semantic representations, the model achieves superior performance in relation extraction tasks.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Klim Zaporojets, Giannis Bekoulis, Johannes Deleu, Thomas Demeester, Chris Develder
Summary: This study proposes a method for solving arithmetic word problems in natural language processing systems, using Tree-RNN to score candidate solution equations. Experimental results show that this method outperforms previous algorithms in terms of accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Lei Sha, Oana-Maria Camburu, Thomas Lukasiewicz
Summary: Explaining the predictions of AI models is crucial in safety-critical applications. This paper proposes a method of extracting rationales by calibrating the information extracted from the predictor, using a two-model joint training approach. An adversarial technique is used to adjust the difference between the two models' predictions, and a language-model-based regularizer is introduced for natural language tasks. Experimental results demonstrate the effectiveness of the proposed approach in various tasks.
ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Deem Alsaleh, Souad Larabi-Marie-Sainte
Summary: The number of Arabic documents is increasing significantly, and there is a need for better classification methods. A new model using Genetic Algorithms to optimize CNN for Arabic Text Classification has shown a 4 to 5% improvement in classification accuracy.
Article
Fisheries
Xiaoli Yin, Xueqi Zhuang, Meiqiu Liao, Qiqian Cui, Chunxia Yan, Jiayi Huang, Zixiang Jiang, Lin Huang, Weitao Luo, Yuan Liu, Weina Wang
Summary: Andrographis paniculata, a traditional medicinal plant, shows various pharmacological activities and can enhance the immune system and growth performance of Litopenaeus vannamei, while also providing protection against Vibrio alginolyticus.
DEVELOPMENTAL AND COMPARATIVE IMMUNOLOGY
(2023)
Review
Immunology
Weifeng Shang, Bo Zhang, Yali Ren, Weina Wang, Dengfeng Zhou, Yuanyuan Li
Summary: This systematic review and meta-analysis aimed to evaluate the effect of Thymosin alpha1 (Ta1) as a therapy for COVID-19. The results indicate that Ta1 therapy has no significant effect on hospitalized adult COVID-19 patients.
INTERNATIONAL IMMUNOPHARMACOLOGY
(2023)
Article
Engineering, Biomedical
Xiaojing Zhang, Weina Wang, Jia Chen, Min Lai
Summary: In this study, a micro/nanostructured titanium surface was created by dual-acid treatment and anodic oxidation, and then coated with GelMA hydrogel loaded with GL13K to develop anti-bacterial titanium implants. The results showed that the modified titanium surface had good biocompatibility, promoted osteoblast differentiation, and prevented the growth of gram-positive and gram-negative bacteria by the release of GL13K.
JOURNAL OF BIOMATERIALS SCIENCE-POLYMER EDITION
(2023)
Article
Chemistry, Physical
Miao Pan, Chenlong Duan, Long Huang, Weinan Wang, Haishen Jiang, Jinpeng Qiao, Yuemin Zhao
Summary: This study investigated the flocculation processes of coal sludge and kaolinite solutions, and analyzed the surface properties of the settling materials. The results showed that the flocculation effect increased and then decreased as the concentration of polyacrylamide (PAM) increased. The kaolinite particles covered the coal surface, reducing the exposed area and the amount of PAM molecules adsorbed. Understanding the effect of mineral hydration on the adsorption of flocculant molecules is important for improving solid-liquid separation.
JOURNAL OF MOLECULAR LIQUIDS
(2023)
Article
Medicine, Research & Experimental
Kaige Yang, Yufang Xie, Lele Xue, Fanping Li, Chenghua Luo, Weihua Liang, Haijun Zhang, Ya Li, Yilin Ren, Mengmeng Zhao, Weinan Wang, Jia Liu, Xihua Shen, Wenhu Zhou, Jing Fei, Weigang Chen, Wenyi Gu, Lianghai Wang, Feng Li, Jianming Hu
Summary: This study found that M2 tumor-associated macrophages (M2-TAMs) promote stemness characteristics and induce cisplatin resistance in esophageal squamous cell carcinoma (ESCC) cells by secreting TGF-beta 1. This finding helps to understand the mechanism of chemotherapy resistance in ESCC.
JOURNAL OF TRANSLATIONAL MEDICINE
(2023)
Article
Immunology
Wenfeng Gao, Xiaoru Chen, Liang Yao, Jing Li, Yuan Gao, Ting Li, Yunkui Li, Weina Wang, Shu Zhang, Jinge He
Summary: This study aimed to investigate drug-resistant tuberculosis (DR-TB) in the Yi ethnic group and its relationship with HIV/AIDS. Whole-genome sequencing was performed on Mycobacterium tuberculosis samples collected from the Yi ethnic group. The study found that re-treatment patients, women, and referred patients were at high risk of MDR/RR-TB or pre-XDR-TB. HIV and lineage 2 had negligible association with drug resistance. Whole-genome sequencing should be used to guide treatment regimens and interventions.
JOURNAL OF IMMUNOLOGY RESEARCH
(2023)
Article
Oncology
Xiying Yuan, Wenjing Yong, Lei Dai, Weinan Wang, Lingqian Wu
Summary: This study analyzes the complementary roles of non-invasive prenatal testing (NIPT) and ultrasound phenotypes in prenatal screening of fetal chromosome abnormalities. The results show that ultrasound phenotypes are helpful in identifying false positive results, especially for T18 and T13. NIPT-Plus has important clinical significance in reducing missed diagnosis of SCA, MMS, and RAT, but its screening performance needs further improvement.
ANNALS OF TRANSLATIONAL MEDICINE
(2023)
Article
Biochemistry & Molecular Biology
Jishuang Liu, Yu Xin, Zhidong Qiu, Qi Zhang, Tianzhu He, Ye Qiu, Weinan Wang
Summary: A biocatalytic system mediated by Cordyceps Sinensis transformed ginsenoside Rf specifically into 25-OH-(20S)-Rf with an 88.03% conversion rate. The time, structure, and bioactivity of the target compound were studied, indicating significant anti-inflammatory effects after saturation of the C24-C25 double bond. This biocatalytic system could be utilized for macrophage-mediated inflammation under specific conditions.
CHEMISTRY & BIODIVERSITY
(2023)
Article
Immunology
Xi Cheng, Mu Zhang, Yuanyuan Xie, Yanhua Xu, Rong Du, Bowen Wu, Zongyu Guan, Weina Wang, Weiwei Sun, Ting Xu, Shunxing Zhu, Liucheng Wu, Xu Wang, Hongyun Shi, Baolan Sun, Yuquan Zhang
Summary: This study investigated the effect of bone marrow-derived mesenchymal stem cells (BMMSCs) on angiogenesis and pregnancy outcomes in rats with obstetric deep venous thrombosis (DVT). The results showed that injecting BMMSCs into pregnant rats with DVT significantly reduced thrombus length and weight, promoted angiogenesis, and improved embryo absorption rate. In vitro experiments also demonstrated that BMMSC-derived conditioned medium (BM-CM) enhanced the abilities of impaired endothelial cells. Transcriptome sequencing revealed that BMMSCs upregulated the expression of pro-angiogenic genes, including secretogranin II (SCG2). Knocking down SCG2 expression attenuated the pro-angiogenic effects of BMMSCs. Overall, BMMSCs enhance angiogenesis via upregulation of SCG2, providing a potential therapy for obstetric DVT.
INTERNATIONAL IMMUNOPHARMACOLOGY
(2023)
Article
Oncology
Yifan Wang, Shanshan Liu, Feifei Lv, Wenjing Zhai, Weina Wang, Yanhao Duan, Yongle Qiu
Summary: Oral cancer is a leading cause of death worldwide and requires more effective therapies. MiRNAs have shown therapeutic potential in various cancers. This study analyzed the miR-216a-3p/beta-catenin expression in oral cancer patients and found that miR-216a-3p was upregulated and positively correlated with tumor stage. Inhibition of miR-216a-3p suppressed cell viability and induced apoptosis in oral cancer cells. The effects of miR-216a-3p were mediated through the Wnt3a and beta-catenin signaling pathways.
MOLECULAR MEDICINE REPORTS
(2023)
Article
Biochemistry & Molecular Biology
Ling Guo, Weinan Wang, Qian Wang, Dan Hao, Misa Ito, Bin Huang, Chieko Mineo, Philip W. Shaul, Jaebok Choi, L. Frank Huang, Xiang-An Li
Summary: Cytokine release syndrome (CRS) is a harmful systemic inflammatory syndrome that can lead to organ failure and death. Researchers have discovered that relative adrenal insufficiency (RAI) is a risk factor for severe CRS. By inducing glucocorticoids (GC) production through the scavenger receptor B1 (SR-BI), protection against lethal CRS can be achieved. Preemptive treatment with low doses of GC can effectively prevent CRS and improve survival in RAI patients without compromising T cell function.
Article
Obstetrics & Gynecology
Xiying Yuan, Weinan Wang, Lei Dai, Wenjing Yong, Chenlin Pei, Jingzhi Li, Lingqian Wu
Summary: This study aimed to evaluate the screening performance of NIPT for fetal chromosomal abnormalities in twin pregnancies and the PDR in the second and third trimesters. The results showed that the sensitivity, specificity, and positive predictive value of NIPT for trisomy 21 were 100%, 99.9%, and 75%, respectively. The PDR for high-risk NIPT results was 78.6%, and for low-risk NIPT results, it was 50%.
BMC PREGNANCY AND CHILDBIRTH
(2023)
Article
Environmental Sciences
Zhuolin Shi, Qiangling Yao, Weinan Wang, Fengsheng Su, Xuehua Li, Liu Zhu, Chengle Wu
Summary: Percolation experiments were conducted on coal samples to study the effects of fracture length and inclination angle on seepage. A three-dimensional seepage model was constructed to investigate the seepage pattern. The results showed that permeability increases non-linearly with fracture length for small fractures, while it decreases and then increases for large fractures. The study provides insights into the mechanism of fracture seepage under different stress conditions.
Article
Chemistry, Analytical
Dong An, Ji Li, Songhua Li, Meng Shao, Weinan Wang, Chuan Wang, Yixiao Yang
Summary: This paper analyzes the causes of starting errors from both the physical properties of materials and voltages. It adopts an image-only model based on the classical Prandtl-Ishlinskii model to improve the positioning accuracy of the nanopositioning platform. The experimental results show that the DSPI model can solve the nonlinear start-up error problem existing under open-loop control and has better performance in terms of compensation results compared to other models.
Review
Chemistry, Multidisciplinary
Weina Wang, Qingxia Yue
Summary: In this paper, the time variation law of the compressive strength of concrete was reviewed from three aspects: single, multiple and material internal factors. The mathematical models of compressive strength relative to time under single factors and the coupling effects of multiple factors were summarized. The differences in time variation models of compressive strength in existing buildings were discussed, and the influences of concrete internal factors on compressive strength were outlined.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)
