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2022 Conference on Empirical methods in Natural Language Processing, EMNLP 2022

作者： Wang, Xiaozhi Chen, Yulin Ding, Ning Peng, Hao Wang, Zimu Lin, Yankai Han, Xu Hou, Lei Li, Juanzi Liu, Zhiyuan Li, Peng Zhou, Jie Department of Computer Science and Technology BNRist Tsinghua University Beijing China Shenzhen International Graduate School Tsinghua University Beijing China THU-Siemens Ltd. China Joint Research Center for Industrial Intelligence and IoT Tsinghua University Beijing China Tsinghua University Beijing China Xi'an Jiaotong-Liverpool University Suzhou China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Pattern Recognition Center WeChat AI Tencent Inc China

The diverse relationships among real-world events, including coreference, temporal, causal, and subevent relations, are fundamental to understanding natural languages. However, two drawbacks of existing datasets limit event relation extraction (ERE) tasks: (1) Small scale. Due to the annotation complexity, the data scale of existing datasets is limited, which cannot well train and evaluate data-hungry models. (2) Absence of unified annotation. Different types of event relations naturally interact with each other, but existing datasets only cover limited relation types at once, which prevents models from taking full advantage of relation interactions. To address these issues, we construct a unified large-scale human-annotated ERE dataset MAVEN-ERE with improved annotation schemes. It contains 103, 193 event coreference chains, 1, 216, 217 temporal relations, 57, 992 causal relations, and 15, 841 subevent relations, which is larger than existing datasets of all the ERE tasks by at least an order of magnitude. Experiments show that ERE on MAVEN-ERE is quite challenging, and considering relation interactions with joint learning can improve performances. The dataset and source codes can be obtained from https://***/THU-KEG/MAVEN-ERE. © 2022 Association for Computational Linguistics.

关键词： Extraction

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Curriculum pre-training heterogeneous subgraph transformer for top-N recommendation

arXiv

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arXiv 2021年

作者： Wang, Hui Zhou, Kun Zhao, Wayne X.I.N. Wang, Jingyuan Wen, Ji-Rong School of Information Renmin University of China Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods School of Computer Science and Engineering Beihang University

Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in top-N recommender systems, called HIN-based recommendation. HIN characterizes complex, heterogeneous data relations, containing a variety of information that may not be related to the recommendation task. Therefore, it is challenging to effectively leverage useful information from HINs for improving the recommendation performance. To address the above issue, we propose a Curriculum pre-training based HEterogeneous Subgraph Transformer (called CHEST) with new data characterization, representation model and learning algorithm. Specifically, we consider extracting useful information from HIN to compose the interaction-specific heterogeneous subgraph, containing both sufficient and relevant context information for recommendation. Then we capture the rich semantics (e.g., graph structure and path semantics) within the subgraph via a heterogeneous subgraph Transformer, where we encode the subgraph with multi-slot sequence representations. Besides, we design a curriculum pre-training strategy to provide an elementary-to-advanced learning process, by which we smoothly transfer basic semantics in HIN for modeling user-item interaction relation. Extensive experiments conducted on three real-world datasets demonstrate the superiority of our proposed method over a number of competitive baselines, especially when only limited training data is available. Copyright © 2021, The Authors. All rights reserved.

关键词： Information services

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Comparison of the Effectiveness of Machine Learning Techniques for the Number of Road Traffic Injuries

Comparison of the Effectiveness of Machine Learning Techniqu...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Wirawan Puttamat Wikanda Phaphan Anuchit Jitpattanakul Chainarong Pianpailoon Sakorn Mekruksavanich Department of Mathematics Faculty of Education Chaiyaphum Rajabhat University Chaiyaphum Thailand Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Research Group in Statistical Learning and Inference Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Curriculum and Teaching Program (Mathematics) Faculty of Education Sakon Nakhon Rajabhat University Sakon Nakhon Thailand Department of Computer Engineering School of Information and Communication University of Phayao Phayao Thailand

This article aims to compare forecasting techniques with machine learning techniques for predicting the number of people injured in road accidents using data from the Injury Information Collaboration Center, Department of Disease Control, Ministry of Public Health Outpatient Files (OPD) spanning the years 2018 to 2022. The four machine-learning techniques examined in this study include Decision Tree Regression, Random Forest Regression, Support Vector Regression, and Multiple Linear Regression. The data analysis was performed using the R programming language. The results revealed that the Decision Tree Regression technique yielded the most accurate predictions for the number of road traffic injuries, as evidenced by its lowest values of MAE, MSE, and RMSE.

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An experimental study of vehicle detection on aerial imagery using deep learning-based detection approaches 4

An experimental study of vehicle detection on aerial imagery...

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2020 4th International Workshop on Advanced Algorithms and Control Engineering, IWAACE 2020

作者： Liao, Xuanzhi Sahran, Shahnorbanun Abdul Shukor, Syaimak Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia UKM Bangi 43600 Malaysia Computer Teaching and Research Office Youjiang Medical University for Nationalities Baise Guangxi533000 China

Deep convolutional neural network technology is widely used to deal with general object detection in computer vision, and it achieved remarkable progress. Unmanned aerial vehicles provide large numbers of aerial imagery that significantly facilitate several applications including traffic monitoring, surveillance, tracking, rescue, and safe military tasks. This paper presents an experimental study to evaluate the performances of several state-of-the-art deep learning-based detection approaches on vehicle detection from aerial imagery. The pre-trained models, including Faster R-CNN, R-FCN, and SSD, are adopted from the TensorFlow model zoo, and the VEDAI dataset is used as the benchmark. The results show that Faster R-CNN combined with Resnet101 backbone achieved the highest mAP, which is 39.73% on the COCO metric. This experimental study expects to be a guideline to choose suitable approaches for particular applications. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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Evolving an efficient and effective off-the-shelf computing infrastructure for rural communities of South Africa 11th

Evolving an efficient and effective off-the-shelf computing ...

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11th International Development Informatics Association Conference, IDIA 2020

作者： Siebörger, Ingrid Terzoli, Alfredo Hodgkinson-Williams, Cheryl Department of Information Systems Rhodes University Makhanda South Africa Department of Computer Science Rhodes University Makhanda South Africa Centre for Innovation in Learning and Teaching University of Cape Town Cape Town South Africa

ISBN: (纸本)9783030520137

Information Communication Technologies (ICTs) have diffused into almost every area of life for citizens living in the Global North. Data is seen as a key element in the fourth industrial revolution and is the foundation of Information, Knowledge and Wisdom. Knowledge has become the new "make or break" asset and an inability to access the world’s data and technologies that facilitate access, synthesis and interrogation of data places one at a disadvantage. In 2013, the Department of Communications in South Africa announced a new national broadband policy to promote the reduction of a digital divide and sup-port citizens and the economy in digital interactions. Implementation has thus far not been successful. Furthermore, the Broadband policy does not address how South Africans are to access the Internet once available. The Siyakhula Living Lab (SLL) provides an example of a computing infrastructure model for the introduction of ICTs into rural communities. Through the deployment of "broadband islands" and low-cost computing infrastructure to promote both knowledge creation and consumption, access to the proposed ubiquitous Internet connectivity can be unlocked. However, the initial computing infrastructure deployed to the SLL suffers from a single point of failure. In this paper, we discuss alternative computing infrastructure configurations that were tested and deployed within the SLL in order to determine a more appropriate computing infrastructure model for the SLL and potentially other rural South African communities;such that learners, teachers and community members can be active contributors and consumers of data, information, knowledge and wisdom. © Springer Nature Switzerland AG 2020.

关键词： Costs

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Coordinative hyper-heuristic resource scheduling in mobile cellular networks 1

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1st International Conference on Neural Computing for Advanced Applications, NCAA 2020

作者： Dong, Bei Su, Yuping Zhou, Yun Wu, Xiaojun Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an710062 China School of Computer Science Shaanxi Normal University Xi’an710119 China School of Education Shaanxi Normal University Xi’an710119 China

ISBN: (数字)9789811576706

ISBN: (纸本)9789811576690

In this work, a novel parallel-spacing coordinative hyper-heuristic algorithm is proposed for solving radio resource scheduling problem in mobile cellular networks. The task of this problem is to minimize the required bandwidth to satisfy diverse channel demand from all micro cellular, while without interference violation. Based on the undirected weighted graph generated by each network topology, six problem-related low-level heuristics are constructed. In the high-level heuristic space, a group of evolutionary strategies are implemented to manage the searching process in the low-level solution space. In classical hyper-heuristic framework, exploration ability might be partially decreased by non-single mapping from heuristic space to solution space. To that end, a group of problem distinctive local search mechanisms are developed and executed on elite population in the solution space parallelly and periodically. Effectiveness of parallel space coordinative searching technique is verified on a set of real-world problems, and the comparison results show that the proposed parallel-spacing coordinative hyper-heuristic algorithm works effectively on most problems. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Network topology

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Micro-Doppler Analysis of Target Based on the Clustering Prior to Solve the Block Sparse Forward and Backward TVAR Model

Micro-Doppler Analysis of Target Based on the Clustering Pri...

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IEEE International Conference on Radar

作者： Shuojing Jin Ling Hong Fengzhou Dai National Laboratory of Radar Signal Processing Xidian University Xi'an China Key Laboratory of Modern Teaching Technology Ministry of Education School of Computer Science Shaanxi Normal University Xi'an China

ISBN: (纸本)9781665468893

Micro-Doppler features generated by target micromotion are usually unique and can be used as an important basis for target recognition, which reflects the fine features of target. In this paper, we propose an algorithm based on cluster-structured prior to solve block sparse forward and backward time-varying autoregressive (TVAR) model to perform Micro-Doppler analysis of the target. Firstly, convert the time-varying coefficients into the form of time-invariant coefficients. Then, an improved algorithm based on extended block sparse Bayesian learning (EBSBL) is used to solve the time-invariant block sparse coefficients by adopting a cluster-structured prior. At the same time, the priori information with known boundary is applied. Finally, the electromagnetic simulation results show that the proposed algorithm has advantages in Micro-Doppler analysis, which can not only give a clearer TF diagram than traditional methods, but also has a strong anti-noise performance.

关键词： Analytical models Target recognition Simulation Clustering algorithms Radar Bayes methods Electromagnetics

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Modeling and Analysis of Medical Resource Sharing and Scheduling for Public Health Emergencies based on Petri Nets

Modeling and Analysis of Medical Resource Sharing and Schedu...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： WangYang Yu MengHan Jia Bo Yuan Ministry of Education Key Laboratory for Modern Teaching Technology School of Computer Science Shaanxi Normal University Xi’an China School of Computing and Engineering University of Derby Derby U.K.

ISBN: (纸本)9781665406697

Medical information systems (MIS) play a vital role in managing and scheduling medical resources to underpin healthcare services, which has become more critically important during major public health emergencies. During the Covid-19 pandemic, MIS is facing significant challenges to cope with the surge in demands of medical resources, resulting in more deaths and wider spreading of the disease. Our research examines how to allocate and utilize the medical resources across hospitals in a more accurate, and effective way to mitigate medical resource shortages and sustain the resource provisions. This paper mainly investigated the hospital’s supply-and-demand problems for medical resources under major public health emergencies by analyzing the allocation of medical staff resources. Furthermore, a formal method based on the Colored Petri Nets (CPN) has been proposed to model and characterize the medical business process and resource scheduling tasks. The experiments demonstrate that our approach can correctly and efficiently complete the dynamical scheduling process for surging requests.

关键词： COVID-19 Analytical models Hospitals Petri nets Sensors Resource management Public healthcare

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Crystal Structure Prediction by Joint Equivariant Diffusion

arXiv

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arXiv 2023年

作者： Jiao, Rui Huang, Wenbing Lin, Peijia Han, Jiaqi Chen, Pin Lu, Yutong Liu, Yang Dept. of Comp. Sci. & Tech. Institute for AI Tsinghua University China Institute for AIR Tsinghua University China Gaoling School of Artificial Intelligence Renmin University China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China National Supercomputer Center in Guangzhou School of Computer Science and Engineering Sun Yat-sen University China Stanford University United States

Crystal Structure Prediction (CSP) is crucial in various scientific disciplines. While CSP can be addressed by employing currently-prevailing generative models (e.g. diffusion models), this task encounters unique challenges owing to the symmetric geometry of crystal structures—the invariance of translation, rotation, and periodicity. To incorporate the above symmetries, this paper proposes DiffCSP, a novel diffusion model to learn the structure distribution from stable crystals. To be specific, DiffCSP jointly generates the lattice and atom coordinates for each crystal by employing a periodic-E(3)-equivariant denoising model, to better model the crystal geometry. Notably, different from related equivariant generative approaches, DiffCSP leverages fractional coordinates other than Cartesian coordinates to represent crystals, remarkably promoting the diffusion and the generation process of atom positions. Extensive experiments verify that our DiffCSP significantly outperforms existing CSP methods, with a much lower computation cost in contrast to DFT-based methods. Moreover, the superiority of DiffCSP is also observed when it is extended for ab initio crystal generation. Code is available at https://***/jiaor17/DiffCSP. © 2023, CC BY-SA.

关键词： Geometry

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Impacts of oxytactic microorganisms and viscous dissipation in Carreau nanoliquid featuring ferromagnetic nanoparticles

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Partial Differential Equations in Applied Mathematics 2025年 13卷

作者： Tabrez, Muhammad Hussain, I. Khan, W.A. Khan, S.U. Waqas, M. Rashid, Rzgar Farooq Anwar, M.I. Ammar, Mohamed Ben Zamri, Nurnadiah Abiyev, Ruzimurod Garalleh, Hakim AL Department of Mathematics Mohi-ud-Din Islamic University Nerian Sharif Azad Jammu and Kashmir 12010 Pakistan Department of Mathematics Namal University Mianwali 42250 Pakistan NUTECH School of Applied Sciences and Humanities National University of Technology Islamabad 44000 Pakistan Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Besut Campus Terengganu Besut 22200 Malaysia Department of Medical Laboratory Science College of Science Knowledge University Erbil 44001 Iraq Department of Medical Laboratory Technique College of Medical Technology AL-Kitab University Kirkuk Iraq Department of Mathematics School of Sciences and Humanities Nazarbayev University Astana Kazakhstan Center for Scientific Research and Entrepreneurship Northern Border University Arar 73213 Saudi Arabia Department of Mathematics and its Teaching Methods in Primary Education Tashkent State Pedagogical University Tashkent Uzbekistan Department of Mathematical Science College of Engineering University of Business and Technology Jeddah 21361 Saudi Arabia

Owing to rapid progress in field of nanotechnology, various mathematical model has been proposed regarding the flow of nanofluids. Based on boosted thermal properties, multidisciplinary applications of such materials has been claimed in various engineering and industrial processes. In current work, a theoretical analysis is performed for bioconvective flow of Carreau fluid with applications of magnetic dipole. The significance of magnetic dipole are presented with interaction of ferrofluid. The applications of viscous dissipation have also been entertained. The flow is subject to applications of stretched surface. Similarity variables are used for making transformation to obtain set of nonlinear ODEs from set of PDEs. The numerical computation is also made by using of well-known numerical method termed as bvp4c. Influences of various non dimensional fluid parameters like Brownian motion, viscous dissipation, Curie temperature, Weissenberg number, motile difference parameter and bioconvection Paclet number are examined and expressed graphically. It is perceived that ferrofluid's concentration declines when Brownian diffusive variable is augmented while opposite characteristics are reported for Schmidt number. Current results conveying applications in the biofuels, better heat transport medium, fertilizers, catalysts, electronics, paints, semiconductors, speakers, drug delivery, fertilizers etc. © 2024 The Author(s)

关键词： Bioconvection Carreau fluid Ferromagnetic flow Magnetic dipole Viscous dissipation

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