Tsinghua Science and Technology最新文献_第2页

A Survey of the Application of Neural Networks to Event Extraction 神经网络在事件提取中的应用概览

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-12-09 DOI: 10.26599/TST.2023.9010139

Jianye Xie;Yulan Zhang;Huaizhen Kou;Xiaoran Zhao;Zhikang Feng;Lekang Song;Weiyi Zhong

{"title":"A Survey of the Application of Neural Networks to Event Extraction","authors":"Jianye Xie;Yulan Zhang;Huaizhen Kou;Xiaoran Zhao;Zhikang Feng;Lekang Song;Weiyi Zhong","doi":"10.26599/TST.2023.9010139","DOIUrl":"https://doi.org/10.26599/TST.2023.9010139","url":null,"abstract":"Event extraction is an important part of natural language information extraction, and it's widely employed in other natural language processing tasks including question answering and machine reading comprehension. However, there is a lack of recent comprehensive survey papers on event extraction. In the past few years, numerous high-quality and innovative event extraction methods have been proposed, making it necessary to consolidate these new developments with previous work in order to provide a clear overview for researchers and serve as a reference for future studies. In addition, event detection is a fundamental sub-task in event extraction, previous survey papers have often overlooked the related work on event detection. Therefore, this paper aims to bridge these gaps by presenting a comprehensive survey of event extraction, including recent advancements and an analysis of previous research on event detection. The resources for event extraction are first introduced in this research, and then the numerous neural network models currently employed in event extraction tasks are divided into four types: word sequence-based methods, graph-based neural network methods, external knowledge-based approaches, and prompt-based approaches. We compare and contrast them in depth, pointing out the flaws and difficulties with existing research. Finally, we discuss the future of event extraction development.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"748-768"},"PeriodicalIF":6.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786941","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Study of Driver's Perception in Driving Tasks Based on Naturalistic Driving Experiments and fNIRS Measurement 基于自然驾驶实验和 fNIRS 测量的驾驶员驾驶任务感知研究

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-12-09 DOI: 10.26599/TST.2024.9010002

Bilu Li;Xin Pei;Dan Zhang;Xinmiao Zhang;Zhuoran Li;Duanrui Yu;Shifei Shen

引用次数: 0

Maximization of k-Submodular Function with d-Knapsack Constraints Over Sliding Window 具有 d-Knapsack 约束条件的 k 次模态函数在滑动窗口上的最大化

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-12-09 DOI: 10.26599/TST.2023.9010121

Wenqi Wang;Yuefang Sun;Zhiren Sun;Donglei Du;Xiaoyan Zhang

引用次数: 0

Diversity-Based Recruitment in Crowdsensing by Combinatorial Multi-Armed Bandits 通过组合多臂强盗在人群感应中进行基于多样性的招募

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-12-09 DOI: 10.26599/TST.2024.9010053

Abdalaziz Sawwan;Jie Wu

{"title":"Diversity-Based Recruitment in Crowdsensing by Combinatorial Multi-Armed Bandits","authors":"Abdalaziz Sawwan;Jie Wu","doi":"10.26599/TST.2024.9010053","DOIUrl":"https://doi.org/10.26599/TST.2024.9010053","url":null,"abstract":"Mobile Crowdsensing (MCS) represents a transformative approach to collecting data from the environment as it utilizes the ubiquity and sensory capabilities of mobile devices with human participants. This paradigm enables scales of data collection critical for applications ranging from environmental monitoring to urban planning. However, the effective harnessing of this distributed data collection capability faces significant challenges. One of the most significant challenges is the variability in the sensing qualities of the participating devices while they are initially unknown and must be learned over time to optimize task assignments. This paper tackles the dual challenges of managing task diversity to mitigate data redundancy and optimizing task assignment amidst the inherent variability of worker performance. We introduce a novel model that dynamically adjusts task weights based on assignment frequency to promote diversity and incorporates a flexible approach to account for the different qualities of task completion, especially in scenarios with overlapping task assignments. Our strategy aims to maximize the overall weighted quality of data collected within the constraints of a predefined budget. Our strategy leverages a combinatorial multi-armed bandit framework with an upper confidence bound approach to guide decision-making. We demonstrate the efficacy of our approach through a combination of regret analysis and simulations grounded in realistic scenarios.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"732-747"},"PeriodicalIF":6.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786946","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Key Mechanisms on Resource Optimization Allocation in Minority Game Based on Reinforcement Learning

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-12-09 DOI: 10.26599/TST.2023.9010155

Changyan Di;Tianyi Wang;Qingguo Zhou;Jinqiang Wang

{"title":"Key Mechanisms on Resource Optimization Allocation in Minority Game Based on Reinforcement Learning","authors":"Changyan Di;Tianyi Wang;Qingguo Zhou;Jinqiang Wang","doi":"10.26599/TST.2023.9010155","DOIUrl":"https://doi.org/10.26599/TST.2023.9010155","url":null,"abstract":"The emergence of coordinated and consistent macro behavior among self-interested individuals competing for limited resources represents a central inquiry in comprehending market mechanisms and collective behavior. Traditional economics tackles this challenge through a mathematical and theoretical lens, assuming individuals are entirely rational and markets tend to stabilize through the price mechanism. Our paper addresses this issue from an econophysics standpoint, employing reinforcement learning to construct a multi-agent system modeled on minority games. Our study has undertaken a comparative analysis from both collective and individual perspectives, affirming the pivotal roles of reward feedback and individual memory in addressing the aforementioned challenge. Reward feedback serves as the guiding force for the evolution of collective behavior, propelling it towards an overall increase in rewards. Individuals, drawing insights from their own rewards through accumulated learning, gain information about the collective state and adjust their behavior accordingly. Furthermore, we apply information theory to present a formalized equation for the evolution of collective behavior. Our research supplements existing conclusions regarding the mechanisms of a free market and, at a micro level, unveils the dynamic evolution of individual behavior in synchronization with the collective.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"721-731"},"PeriodicalIF":6.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786935","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Extraction of Uygur Medicine Knowledge with Edge Computing 利用边缘计算联合提取维吾尔医药知识

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-12-09 DOI: 10.26599/TST.2024.9010006

Fan Lu;Quan Qi;Huaibin Qin

{"title":"Joint Extraction of Uygur Medicine Knowledge with Edge Computing","authors":"Fan Lu;Quan Qi;Huaibin Qin","doi":"10.26599/TST.2024.9010006","DOIUrl":"https://doi.org/10.26599/TST.2024.9010006","url":null,"abstract":"Edge computing, a novel paradigm for performing computations at the network edge, holds significant relevance in the healthcare domain for extracting medical knowledge from traditional Uygur medical texts. Medical knowledge extraction methods based on edge computing deploy deep learning models on edge devices to achieve localized entity and relation extraction. This approach avoids transferring substantial sensitive data to cloud data centers, effectively safeguarding the privacy of healthcare services. However, existing relation extraction methods mainly employ a sequential pipeline approach, which classifies relations between determined entities after entity recognition. This mode faces challenges such as error propagation between tasks, insufficient consideration of dependencies between the two subtasks, and the neglect of interrelations between different relations within a sentence. To address these challenges, a joint extraction model with parameter sharing in edge computing is proposed, named CoEx-Bert. This model leverages shared parameterization between two models to jointly extract entities and relations. Specifically, CoEx-Bert employs two models, each separately sharing hidden layer parameters, and combines these two loss functions for joint backpropagation to optimize the model parameters. Additionally, it effectively resolves the issue of entity overlapping when extracting knowledge from unstructured Uygur medical texts by considering contextual relations. Finally, this model is deployed on edge devices for real-time extraction and inference of Uygur medical knowledge. Experimental results demonstrate that CoEx-Bert outperforms existing state-of-the-art methods, achieving accuracy, recall, and F1-score of 90.65%, 92.45%, and 91.54%, respectively, in the Uygur traditional medical literature dataset. These improvements represent a 6.45% increase in accuracy, a 9.45% increase in recall, and a 7.95% increase in F1-score compared to the baseline.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"782-795"},"PeriodicalIF":6.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786944","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Traceability-Based Operation with Retailer's Altruistic Preference Under Blockchain Technology 区块链技术下基于零售商利他主义偏好的溯源运营

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-12-09 DOI: 10.26599/TST.2023.9010120

Chunming Xu;Yi Zhao;Chenchen Wu

{"title":"A Traceability-Based Operation with Retailer's Altruistic Preference Under Blockchain Technology","authors":"Chunming Xu;Yi Zhao;Chenchen Wu","doi":"10.26599/TST.2023.9010120","DOIUrl":"https://doi.org/10.26599/TST.2023.9010120","url":null,"abstract":"The application of Blockchain Technology (BT) makes consumers trace more product information and enhances their trust in the product brand, but it also brings cost pressure to some participants who adopt the technology. Thus, it is particularly important how to encourage and support more enterprises to participate in BT. This study incorporates the altruistic preference into a BT-enabled three-echelon supply chain consisting of a powerful retailer, a supplier, and a manufacturer. We investigate the optimal strategies of the supply chain in different scenarios: without and with the BT, and without and with the altruistic preference. Then, we explore the effects of the BT and the retailer's altruistic preference on the profit performance of the supply chain system. The results show that the retailer, the manufacturer, and the supplier do not always benefit from the traceability of the BT, and while the powerful retailer's altruistic preference decreases his or her own profit but increases the profits of the manufacturer, the supplier, and the entire supply chain. Finally, a profit redistribution mechanism is designed to coordinate the supply chain with the retailer's altruistic preference under the BT.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"499-518"},"PeriodicalIF":6.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786951","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Attention-Enhanced and Knowledge-Fused Dual Item Representations Network for Recommendation

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-12-09 DOI: 10.26599/TST.2023.9010143

Qiang Hua;Jiachao Zhou;Feng Zhang;Chunru Dong;Dachuan Xu

{"title":"Attention-Enhanced and Knowledge-Fused Dual Item Representations Network for Recommendation","authors":"Qiang Hua;Jiachao Zhou;Feng Zhang;Chunru Dong;Dachuan Xu","doi":"10.26599/TST.2023.9010143","DOIUrl":"https://doi.org/10.26599/TST.2023.9010143","url":null,"abstract":"Integrating Knowledge Graphs (KGs) into recommendation systems as supplementary information has become a prevalent strategy. By leveraging the semantic relationships between entities in KGs, recommendation systems can better comprehend user preferences. Due to the unique structure of KGs, methods based on Graph Neural Networks (GNNs) have emerged as the current technical trend. However, existing GNN-based methods struggle to (1) filter out noisy information in real-world KGs, and (2) differentiate the item representations obtained from the knowledge graph and bipartite graph. In this paper, we introduce a novel model called Attention-enhanced and Knowledge-fused Dual item representations Network for recommendation (namely AKDN) that employs attention and gated mechanisms to guide aggregation on both knowledge graphs and bipartite graphs. In particular, we firstly design an attention mechanism to determine the weight of each edge in the information aggregation on KGs, which reduces the influence of noisy information on the items and enables us to obtain more accurate and robust representations of the items. Furthermore, we exploit a gated aggregation mechanism to differentiate collaborative signals and knowledge information, and leverage dual item representations to fuse them together for better capturing user behavior patterns. We conduct extensive experiments on two public datasets which demonstrate the superior performance of our AKDN over state-of-the-art methods, like Knowledge Graph Attention Network (KGAT) and Knowledge Graph- based Intent Network (KGIN).","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"585-599"},"PeriodicalIF":6.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786928","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ILP-Based Heuristics for the Multi-Modal Stable Matching Problem

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-12-09 DOI: 10.26599/TST.2023.9010135

Yang Yang;Rolf H. Möhring;Junteng Song;Yicheng Xu;Yong Zhang

{"title":"ILP-Based Heuristics for the Multi-Modal Stable Matching Problem","authors":"Yang Yang;Rolf H. Möhring;Junteng Song;Yicheng Xu;Yong Zhang","doi":"10.26599/TST.2023.9010135","DOIUrl":"https://doi.org/10.26599/TST.2023.9010135","url":null,"abstract":"In this paper, we investigate the stable matching problem with multiple preferences in bipartite graphs, where each agent has various preference lists for all available partners with respect to different criteria. The problem requires that each matched agent must have exactly one partner and the obtained matching should be stable for all criteria. As our main contribution, we present an integer linear programming (ILP) model for determining whether there exists a globally stable matching in bipartite graphs, which has been proved to be NP-hard. Since the time consumed for solving ILPs might dramatically increase as the size of instances grows, we develop a preprocessing technique that helps to eliminate pairs that will never be a member of any globally stable matching and thus accelerates the computing process. We perform experiments on randomly generated preference lists and observe a significant speedup when we preprocess the instance before solving the ILPs. As there does not need to exist a perfect matching that is stable for all given criteria, we extend our ILP to the optimized version of the aforementioned problem, which asks to find a matching with maximum cardinality that is stable among all matched agents.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"479-487"},"PeriodicalIF":6.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786943","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Fall Detection Device Based on Single Sensor Combined with Joint Features 基于单传感器和关节特征的跌倒检测设备

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-12-09 DOI: 10.26599/TST.2023.9010129

Li Zhang;Yu-An Liu;Qiuyu Wang;Huilin Chen;Jingao Xu;Danyang Li

{"title":"A Fall Detection Device Based on Single Sensor Combined with Joint Features","authors":"Li Zhang;Yu-An Liu;Qiuyu Wang;Huilin Chen;Jingao Xu;Danyang Li","doi":"10.26599/TST.2023.9010129","DOIUrl":"https://doi.org/10.26599/TST.2023.9010129","url":null,"abstract":"Accidental falls pose a significant threat to the well-being of the elderly, thus facilitating a quantum leap in the field of fall detection technology. For fall detection, accurate identification of fall behavior is a key priority. Our study proposes an innovative methodology to detect falls during activities of daily living (ADL), with the objective of preventing further harm. Our design aims to achieve precise identification of falls by extracting a variety of features obtained from the simultaneous acquisition of acceleration and angular velocity data using a single sensor. To enhance detection accuracy and reduce false alarms, we establish a classifier based on the joint acceleration and Euler angle feature (JAEF) analysis. With the aid of a support vector machine (SVM) classifier, human activities are classified into eight categories: going upstairs, going downstairs, running, walking, falling forward, falling backward, falling left, and falling right. In particular, we introduce a novel approach to enhance the accuracy of fall detection algorithms by introducing the Equal Signal Amplitude Difference method. Through experimental demonstration, the proposed method exhibits a remarkable sensitivity of 99.25%, precision of 98.75%, and excels in classification accuracy. It is noteworthy that the utilization of multiple features proves more effective than relying solely on a single aspect. The preliminary findings highlight the promising applications of our study in the field of fall injury systems.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"695-707"},"PeriodicalIF":6.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786934","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0