Article
Chemistry, Multidisciplinary
Xiaoyan Meng, Tonghai Jiang, Xi Zhou, Bo Ma, Yi Wang, Fan Zhao
Summary: This paper introduces a noise detection-based relation extraction approach (NDRE) to automatically detect noisy labels and dynamically correct them, alleviating both instance-level and bag-level noisy problems in distant supervised relation extraction. Experimental results show that the proposed approach achieves significant improvements over existing baselines on a public benchmark dataset.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Jing Zhang, Meilin Cao
Summary: This paper proposes hierarchical attention-based networks that can de-noise at both sentence and bag levels. In the calculation of bag representation, we provide weights to sentence representations using sentence-level attention that considers correlations among sentences in each bag. The proposed method has shown significant advantages in relation extraction tasks according to experimental results.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
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
Yong Shi, Yang Xiao, Pei Quan, MingLong Lei, Lingfeng Niu
Summary: This paper proposes a new DSRE framework A2DSRE, which addresses the issues in distant supervision relation extraction by introducing dependency trees and knowledge graph supervision, effectively reducing data noise and improving the accuracy of relation paths.
Article
Computer Science, Artificial Intelligence
Yanru Zhou, Limin Pan, Chongyou Bai, Senlin Luo, Zhouting Wu
Summary: A distant supervision relation extraction method with self-selective attention is proposed in this study, which uses convolution and self-attention mechanism to encode instances, making full use of the correlation information between instances and achieving better results.
Article
Computer Science, Information Systems
Yu-Ming Shang, Heyan Huang, Xin Sun, Wei Wei, Xian-Ling Mao
Summary: This paper introduces a novel distant supervised relation extraction model, which utilizes a pattern-aware self-attention network to discover relational patterns and process them in pre-trained Transformers. By applying the probability distribution as a constraint in the first Transformer layer, fine-grained pattern information in the pre-trained Transformer is enhanced without sacrificing global dependencies.
INFORMATION SCIENCES
(2022)
Article
Medical Informatics
Qi Ye, Tingting Cai, Xiang Ji, Tong Ruan, Hong Zheng
Summary: This paper proposes a method of subsequence and distant supervision based active learning for relation extraction in medical texts. The method improves efficiency and cost-effectiveness by annotating information-rich subsequences instead of full sentences. It saves labeled subsequence texts and their labels in a continuously updated dictionary, and pre-labels the unlabeled set through text matching using distant supervision. The method combines a Chinese-RoBERTa-CRF model and achieves the best performance on the CMeIE dataset with a best F1 value of 55.96% among different sampling strategies.
BMC MEDICAL INFORMATICS AND DECISION MAKING
(2023)
Article
Chemistry, Multidisciplinary
Xuxin Chen, Xinli Huang
Summary: Distant supervision for relation extraction (DSRE) automatically acquires annotated data by aligning corpus with knowledge base, reducing manual annotation cost, but it is affected by noisy data. This paper introduces negative training to filter out noisy data and improves model performance by using entity attributes.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Yu-Ming Shang, Heyan Huang, Xin Sun, Wei Wei, Xian-Ling Mao
Summary: Relation ties are critical for distant supervised relation extraction. This study proposes a novel force-directed graph method to comprehensively learn relation ties. By constructing a global co-occurrence graph and introducing the concept of attractive force and repulsive force, the method accurately models the correlation and mutual exclusion between relations. Experimental results show that it outperforms existing baselines and can be used to augment relation extraction systems.
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yan Xiao, Yaochu Jin, Ran Cheng, Kuangrong Hao
Summary: This study introduces a new framework for relation extraction using Transformer block and multi-instance learning, which effectively addresses the challenges in relation extraction. Experimental results demonstrate that the proposed approach outperforms state-of-the-art algorithms on the selected dataset.
Article
Computer Science, Artificial Intelligence
Jiasheng Wang, Qiongxin Liu
Summary: Distant supervision reduces manual labor by automatically labeling data. Existing relation extraction methods under distant supervision ignore the repetition of entity pairs in sentences and the varying noise between sentence bags. A novel method with position feature attention and selective bag attention is proposed to address these issues, demonstrating effectiveness in experimental results.
Article
Computer Science, Artificial Intelligence
Chong Chen, Tao Wang, Yu Zheng, Ying Liu, Haojia Xie, Jianfeng Deng, Lianglun Cheng
Summary: Fault diagnosis is crucial in operating and maintaining industrial assets. A fault diagnosis knowledge graph can assist engineers in conducting maintenance tasks. However, manually labeling the corpus from multiple sources is time-consuming, and the presence of noisy sentences hampers the performance of relation extraction modeling.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Computer Science, Information Systems
Xiang Ying, Zechen Meng, Mankun Zhao, Mei Yu, Shirui Pan, Xuewei Li
Summary: This paper proposes an enhanced representation method that addresses the issue of wrong labeling in distant supervised relation extraction. By incorporating enhanced representations into a gated graph convolutional network, the proposed method achieves significant improvement on two popular datasets.
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Haixu Wen, Xinhua Zhu, Lanfang Zhang
Summary: Selective attention in distant supervision relation extraction is effective for handling incorrectly labeled sentences, but not for cases with one-sentence bags. To address this, we propose an entity-guided enhancement neural network that captures relation features and enhances sentence representations through entity guidance and semantic fusion.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Qing Zhao, Dezhong Xu, Jianqiang Li, Linna Zhao, Faheem Akhtar Rajput
Summary: This paper proposes a knowledge-guided distant supervision model for biomedical relation extraction from Chinese electronic medical records. By employing entity-type alignment and knowledge-enhanced bootstrapping learning process, the model achieves the best performance on real-world dataset.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Transportation
Hossein Nasr Esfahani, Ziqi Song, Keith Christensen
Summary: In this study, a novel LSTM-based deep neural network was designed to simulate the different walking behaviors of individuals with and without disabilities, which is important for pedestrian trajectory prediction.
TRANSPORTMETRICA A-TRANSPORT SCIENCE
(2023)
Article
Geography
Aziz Laleg, Ahmed Bousmaha
Summary: The aim of this article is to analyze and characterize daily mobility in mountainous regions through the study of the Ath Irathen's territory in Algeria. The study reveals the significant dependence of this overpopulated territory on the regional capital and the challenges faced during travel. Despite the difficulties, there is a growing trend of pendulum movements between the mountains and the plain, highlighting widening socio-economic disparities between these territories.
Article
Geography
Romit Chowdhury
Summary: This article investigates sexual assault on commuter trains in Tokyo and explores how passengers perceive and cope with this issue in their daily lives. It also highlights the importance of geographical analysis in understanding how stranger violence is manifested in the intersections of urban forms and national expressions. By integrating feminist studies with sociology of urban crowds and critical geographies, a more comprehensive understanding of sexual assault on public transportation can be achieved.
SOCIAL & CULTURAL GEOGRAPHY
(2023)
Article
Criminology & Penology
Alasdair Booth, Lee Bosher, Ksenia Chmutina
Summary: This research investigates whether current protective security advice and training meet the needs of security managers, and finds that some advice hinders the enhancement of protective security, highlighting the need for further efforts to promote counter-terrorism security.
Article
Medicine, Research & Experimental
Candace A. Flagg, John P. Marinelli, Matthew L. Carlson, Eric J. Kezirian, Gregory R. Dion, Kathryn M. Van Abel, Garret Choby, Grant S. Hamilton, Sarah N. Bowe
Summary: This study examined how social media is used in the dissemination of new information within otolaryngology, and highlighted the need for standardizing Twitter hashtag use. Results showed considerable variation in hashtag use among key stakeholders in the otolaryngology social media space, and a standardized hashtag ontology covering all subspecialties within otolaryngology was proposed.
Article
Engineering, Civil
Konstantinos Gkiotsalitis, Tao Liu
Summary: This study proposes a model for optimizing bus dispatching time in response to travel time and passenger demand variations. By adjusting the schedules periodically, public transport service providers can avoid overcrowding beyond the COVID-19 capacity restrictions. Case study results demonstrate the potential gains of rescheduling the trip dispatching times and vehicle schedules.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Computer Science, Interdisciplinary Applications
Saeedeh Sadeghi, Ricardo Daziano, So-Yeon Yoon, Adam K. Anderson
Summary: This study examined the influence of objective number of items, subjective affect, and heart rate on the experience of time in the context of a virtual subway ride. The results showed that increased crowding decreased pleasantness and increased trip duration, and heart rate changes were related to experienced trip time. This study demonstrates the socioemotional regulation of time experience and the effects of social crowding on perception and affect in a solitary virtual setting.
Article
Transportation
Fernando Feres, Franco Basso, Raul Pezoa, Mauricio Varas, Eusebio Vargas-Estrada
Summary: This paper proposes a public transport users' scheduling model that takes into account vehicle crowding, waiting time, and punctuality as measures of reliability. The study analyzes the users' equilibrium, social optimum, first-best pricing, and second-best pricing. Numerical analysis shows that punctuality plays a crucial role in commuters' strategy and the system's reliability, and second-best pricing is only efficient for limited cases.
TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH
(2023)
Article
Engineering, Civil
Zhihong Li, Jing Zhang, Yanjie Wen, Yang Dong, Wangtu Xu
Summary: Urban public security incidents are prone to occur, and better understanding of pedestrian abnormal behavior and trajectory in crowded places is crucial for crowd management and safety monitoring. A novel pedestrian abnormal behavior detection model (PABDM) is proposed, which shows notable advantages in prediction accuracy and detection efficiency compared to other models. This model effectively solves the problem of missing detection caused by various factors and has great significance for real-time crowd monitoring in complex scenes.
JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS
(2023)
Article
Cultural Studies
Leilah Vevaina
Summary: This article explores the controversy surrounding the construction of a metro line in Mumbai, with opposition from environmental groups and the Parsis (Indian Zoroastrians) who argue that the metro line will disrupt their temples.
Article
Engineering, Civil
Yanda Meng, Joshua Bridge, Yitian Zhao, Martha Joddrell, Yihong Qiao, Xiaoyun Yang, Xiaowei Huang, Yalin Zheng
Summary: This paper proposes an adaptive auxiliary task learning-based approach for transport object counting problems. The approach combines a standard Convolution Neural Network (CNN) and a Graph Convolution Network (GCN) for feature extraction and reasoning, and fuses features across different task branches of the adaptive CNN backbone. Experimental results show superior performance compared to state-of-the-art counting methods. The code is publicly available.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Robotics
Panpan Cai, David Hsu
Summary: Real-time planning under uncertainty is crucial for robots operating in complex dynamic environments. This study introduces a new algorithm called LeTS-Drive, which integrates planning and learning to achieve real-time performance for autonomous driving in crowded urban traffic. The algorithm learns on its own in a self-supervised manner, without the need for explicit data labeling.
IEEE TRANSACTIONS ON ROBOTICS
(2023)
Article
Computer Science, Information Systems
Ayesha, Muhammad Javed Iqbal, Iftikhar Ahmad, Madini O. Alassafi, Ahmed S. Alfakeeh, Ahmed Alhomoud
Summary: This research focuses on comprehensive methodology of tiny vehicle detection using Deep Neural Networks (DNN) and achieves better performance compared to other SOTA techniques on KITTI benchmark dataset.
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
Article
Classics
Amber Taylor, Arlene Holmes-Henderson, Sharon Jones
Summary: This study examines the perspectives of teachers and pupils on the advantages and challenges of teaching Classics in primary classrooms in Northern Ireland. The study, conducted in 2020, involved interviews with six teachers from three schools and a focus group with eight children. The findings highlight the positive impacts of teaching Classics on various subjects, including Modern Foreign Languages, and the main challenge identified is the crowded curriculum. Both teachers and pupils suggest providing training and support to educators to enhance the integration of Latin, English literacy, and MFL understanding. The study concludes with recommendations for future research on Classics in Northern Ireland.
JOURNAL OF CLASSICS TEACHING
(2023)
Article
Biodiversity Conservation
Sofia Eleni Spatharioti, Eliza Boetsch, Scott Eustis, Kutub Gandhi, Matt Rota, Archana Apte, Seth Cooper, Sara Wylie
Summary: This study demonstrates that crowdsourcing image analysis of wetland morphology can supplement and accelerate academic and government studies, while also engaging and educating the public. It shows that volunteers can easily be trained to identify characteristic wetland morphologies and that their assessments align with expert classifications.
CONSERVATION SCIENCE AND PRACTICE
(2023)
Article
Computer Science, Artificial Intelligence
Qianghua Liu, Yu Tian, Tianshu Zhou, Kewei Lyu, Ran Xin, Yong Shang, Ying Liu, Jingjing Ren, Jingsong Li
Summary: This study proposes a few-shot disease diagnosis decision making model based on a model-agnostic meta-learning algorithm (FSDD-MAML). It significantly improves the diagnostic process in primary health care and helps general practitioners diagnose few-shot diseases more accurately.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2024)
Article
Computer Science, Artificial Intelligence
Balazs Borsos, Corinne G. Allaart, Aart van Halteren
Summary: The study demonstrates the feasibility of predicting functional outcomes for ischemic stroke patients and the usability of multimodal deep learning architectures for this purpose.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2024)
Article
Computer Science, Artificial Intelligence
Abdelmoniem Helmy, Radwa Nassar, Nagy Ramdan
Summary: This study utilizes machine learning models to detect depression symptoms in Arabic and English texts, and provides manually and automatically annotated tweet corpora. The study also develops an application that can detect tweets with depression symptoms and predict depression trends.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2024)