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Methodology, progress and challenges of geoscience knowledge graph in International Big science Program of Deep-Time Digital Earth

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Journal of Geographical sciences 2025年第5期35卷 1132-1156页

作者： ZHU Yunqiang WANG Qiang WANG Shu SUN Kai WANG Xinbing LV Hairong HU Xiumian ZHANG Jie WANG Bin QIU Qinjun YANG Jie ZHOU Chenghu State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchCAS Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application University of Chinese Academy of Sciences School of Electronic Information and Electrical EngineeringShanghai Jiao Tong University Department of Automation Tsinghua University State Key Laboratory for Mineral Deposits Research School of Earth Sciences and EngineeringNanjing University State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Command Center of Natural Resources Comprehensive Survey School of Computer Science China University of Geosciences

Deep-time Earth research plays a pivotal role in deciphering the rates, patterns, and mechanisms of Earth's evolutionary processes throughout geological history, providing essential scientific foundations for climate prediction, natural resource exploration, and sustainable planetary stewardship. To advance Deep-time Earth research in the era of big data and artificial intelligence, the International Union of Geological sciences initiated the “Deeptime Digital Earth International Big science Program”(DDE) in 2019. At the core of this ambitious program lies the development of geoscience knowledge graphs, serving as a transformative knowledge infrastructure that enables the integration, sharing, mining, and analysis of heterogeneous geoscience big data. The DDE knowledge graph initiative has made significant strides in three critical dimensions:(1) establishing a unified knowledge structure across geoscience disciplines that ensures consistent representation of geological entities and their interrelationships through standardized ontologies and semantic frameworks;(2) developing a robust and scalable software infrastructure capable of supporting both expert-driven and machine-assisted knowledge engineering for large-scale graph construction and management;(3) implementing a comprehensive three-tiered architecture encompassing basic, discipline-specific, and application-oriented knowledge graphs, spanning approximately 20 geoscience disciplines. Through its open knowledge framework and international collaborative network, this initiative has fostered multinational research collaborations, establishing a robust foundation for next-generation geoscience research while propelling the discipline toward FAIR(Findable, Accessible, Interoperable, Reusable) data practices in deep-time Earth systems research.

关键词： Deep-time Digital Earth International Big science Program deep-time Earth geoscience knowledge graph

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A third-party platoon coordination service: Pricing under government subsidies

A third-party platoon coordination service: Pricing under go...

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作者： Bai, Ting Johansson, Alexander Li, Shaoyuan Johansson, Karl Henrik Mårtensson, Jonas School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden KTH Royal Institute of Technology Stockholm Sweden Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Digital Futures Stockholm Sweden

This paper models a platooning system consisting of trucks and a third-party service provider (TPSP), which performs platoon coordination, distributes the platooning profit in platoons, and charges trucks in exchange for the services. Government subsidies used to incentivize platooning are also considered. We propose a pricing rule for the TPSP, which keeps part of the platooning profit including the subsidy each time a platoon is formed. In addition, a platoon coordination solution based on the distributed model predictive control (MPC) is proposed, in which the pricing rule under government subsidies is integrated. We perform a realistic simulation over the Swedish road network to evaluate the impact of the pricing rule and subsidies on the achieved profits and fuel savings. Our results show that subsidies are an effective mean to boost fuel savings from platooning. Moreover, the simulation study indicates that high pricing corresponds to a low platooning rate of the system, as trucks' incentives for platooning decrease. © 2023 The Authors. Asian Journal of Control published by John Wiley & Sons Australia, Ltd on behalf of Chinese Automatic Control Society.

关键词： Trucks

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Enhancing Parkinson’s Disease Prediction Using Machine Learning and Feature Selection Methods

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computers, Materials & Continua 2022年第6期71卷 5639-5657页

作者： Faisal Saeed Mohammad Al-Sarem Muhannad Al-Mohaimeed Abdelhamid Emara Wadii Boulila Mohammed Alasli Fahad Ghabban College of Computer Science and Engineering Taibah UniversityMedina41477Saudi Arabia School of Computing and Digital Technology Birmingham City UniversityBirminghamB47XGUnited Kingdom Information System Department Saba’a Region UniversityMareebYemen Computers and Systems Engineering Department Al-Azhar UniversityCairo11884Egypt RIADI Laboratory National School of Computer SciencesUniversity of ManoubaManouba2010Tunisia

Several millions of people suffer from Parkinson’s disease ***’s affects about 1%of people over 60 and its symptoms increase with *** voice may be affected and patients experience abnormalities in speech that might not be noticed by listeners,but which could be analyzed using recorded speech *** the huge advancements of technology,the medical data has increased dramatically,and therefore,there is a need to apply data mining and machine learning methods to extract new knowledge from this *** classification methods were used to analyze medical data sets and diagnostic problems,such as Parkinson’s Disease(PD).In addition,to improve the performance of classification,feature selection methods have been extensively used in many *** paper aims to propose a comprehensive approach to enhance the prediction of PD using several machine learning methods with different feature selection methods such as filter-based and *** dataset includes 240 recodes with 46 acoustic features extracted from3 voice recording replications for 80 *** experimental results showed improvements when wrapper-based features selection method was used with K-NN classifier with accuracy of 88.33%.The best obtained results were compared with other studies and it was found that this study provides comparable and superior results.

关键词： Filter-based feature selection methods machine learning parkinson’s disease wrapper-based feature selection methods

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Improving Vision Transformers with Nested Multi-head Attentions

Improving Vision Transformers with Nested Multi-head Attenti...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Jiquan Peng Chaozhuo Li Yi Zhao Yuting Lin Xiaohan Fang Jibing Gong School of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Microsoft Research Asia Beijing China

Vision transformers have significantly advanced the field of computer vision in recent years. The cornerstone of these transformers is the multi-head attention mechanism, which models interactions between visual elements within a feature map. However, the vanilla multi-head attention paradigm independently learns parameters for each head, which ignores crucial interactions across different attention heads and may result in redundancy and under-utilization of the model’s capacity. To enhance model expressiveness, we propose a novel nested attention mechanism, Ne-Att, that explicitly models cross-head interactions via a hierarchical variational distribution. We conducted extensive experiments on image classification, and the results demonstrate the superiority of Ne-Att.

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Learning to Cluster Person in the Video via Graph Convolution Network

Learning to Cluster Person in the Video via Graph Convolutio...

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IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Wei Li Sixin Liang Ang Bian Guodong Du College of Computer Science Sichuan University Chengdu China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province School of Information Science and Engineering Yanshan University Qinhuangdao P.R. China

Video person re-identification is receiving academic interest. However, the practical application of the algorithm is hardly supported because of prohibitive annotated data. Hence, the study for unlabeled data will lead to an attractive alternative. This work explores an innovative strategy, namely, learning to cluster unlabeled person in the video through graph convolutional networks. In this paper, we find that the possibility of inter-frame linkage can be inferred from context. Therefore, a pose-guided topology linkage clustering framework is proposed. Our framework consists of three modules: (i) a pose-guided representation module; (ii) a pose-guided embedding module; (iii) a link prediction module. Firstly, the representation coding alone is performed at the level of relational induction bias, embedding the implicit pose structure information in image features. Then, based on the consideration of the topology relationship between adjacent and cross-frame, graph convolutional network is introduced to infer the likelihood of linkage between frame nodes. Experiments show that the method demonstrates excellent scalability in addition to being an effective response to person clustering in case of changes, and does not need the number of clusters as a prior.

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An Effective CU Depth Decision Method for HEVC Using Machine Learning

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computer systems science & engineering 2021年第11期39卷 275-286页

作者： Xuan Sun Pengyu Liu Xiaowei Jia Kebin Jia Shanji Chen Yueying Wu Beijing University of Technology Beijing100124China Beijing Laboratory of Advanced Information Networks Beijing100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124China Department of Computer Science University of PittsburghPittsburgh15260USA School of Physics and Electronic Information Engineering Qinghai Nationalities University810007QinghaiChina

This paper presents an effective machine learning-based depth selection algorithm for CTU(Coding Tree Unit)in HEVC(High Efficiency Video Coding).Existing machine learning methods are limited in their ability in handling the initial depth decision of CU(Coding Unit)and selecting the proper set of input features for the depth selection *** this paper,we first propose a new classification approach for the initial division depth *** particular,we study the correlation of the texture complexity,QPs(quantization parameters)and the depth decision of the CUs to forecast the original partition depth of the current ***,we further aim to determine the input features of the classifier by analysing the correlation between depth decision of the CUs,picture distortion and the *** the found relationships,we also study a decision method for the end partition depth of the current CUs using bit-rate and picture distortion as ***,we formulate the depth division of the CUs as a binary classification problem and use the nearest neighbor classifier to conduct *** proposed method can significantly improve the efficiency of interframe coding by circumventing the traversing cost of the division *** shows that the mentioned method can reduce the time spent by 34.56%compared to HM-16.9 while keeping the partition depth of the CUs correct.

关键词： HEVC inter-frame coding CU depth

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Graph-Based Proximal Policy Optimization Empowered Adaptive Task Scheduling Leveraging Cloud-Edge Collaboration for Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年

作者： Li, Peisong Yi, Meng Iqbal, Muddesar Wang, Xinheng Xiao, Ziren Xi'an Jiaotong-Liverpool University School of Advanced Technology Suzhou215123 China Southeast University School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing211189 China Prince Sultan University Renewable Energy Laboratory Communications and Networks Engineering Department College of Engineering Riyadh11586 Saudi Arabia CIP Technology ltd United Kingdom

The rapid advancement of IoT, AI, and edge computing has led to a significant increase in consumer devices and computation tasks, with new electronics incorporating these technologies to enhance services like VR/AR and autonomous driving, requiring real-time processing for safety and efficiency. However, recent research has focused on optimizing IoT task scheduling and resource allocation through various methods, yet overlooks the dynamic nature of IoT environments, fails to adapt to changing device counts and movements, and often ignores task completion rate in favor of minimizing latency and energy cost. In this context, an adaptive task scheduling and resource allocation strategy is proposed for Edge IoT systems, based on the designed Graph-based Proximal Policy Optimization (GPPO) algorithm. Firstly, the GPPO algorithm enhances PPO for adaptive task scheduling in the fluctuating MEC scenarios, adjusting for the varying number of nearby edge servers. Secondly, it accounts for consumer mobility by opting for local task execution if the consumer risks moving outside the edge server's range, ensuring result reception. Thirdly, it prioritizes task completion rate to increase the number of tasks finished within their acceptable duration. Experimental results demonstrated that the proposed method outperforms traditional methods. © 1975-2011 IEEE.

关键词： Scheduling algorithms

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Performance Analysis of RouteSegmentation Algorithm in Identifying Perimeter Area Around Travel Route

Performance Analysis of RouteSegmentation Algorithm in Ident...

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International Conference on information Technology and Electrical engineering (ICITEE)

作者： Muhammad Avied Bachmid Muhammad Yasir Anshari Haq Muhammad Rafly Mumtaz Aryo Pinandito Media Game and Mobile Laboratory Department of Informatics Engineering Faculty of Computer Science Universitas Brawijaya Malang Indonesia Faculty of Computer Science Universitas Brawijaya Malang Indonesia Media Game and Mobile Laboratory Department of Information System Faculty of Computer Science Universitas Brawijaya Malang Indonesia

ISBN: (数字)9798350375817

ISBN: (纸本)9798350375824

Prior study has developed the RouteSegmentation algorithm to identify the perimeter area surrounding a route. In this study, a comparative experiment was carried out to investigate the performance of the RouteSegmentation algorithm implementation in contrast to the RouteBoxer algorithm. Both algorithms play a crucial role in geographical information systems (GIS), particularly in enhancing navigational tools by identifying points of interest along specified routes. Through systematic experimentation using various perimeter distance values and types of route, the trade-offs between processing efficiency and the granularity of the area identified by these algorithms were evaluated. The results demonstrate that RouteSegmentation maintained relatively consistent performance across different perimeter distance values and statistically significantly faster processing time than RouteBoxer. Furthermore, while the RouteBoxer algorithm benefits from larger perimeter distance values by reducing processing time, it compromises the coverage results of the algorithm and potentially undermines its usefulness in practical scenarios. This study not only provides insights into the optimal use of perimeter distance values for each algorithm but also guides users in selecting the appropriate algorithm based on their specific application needs, balancing detailed geographical analysis and runtime efficiency.

关键词： Meters Visualization systematics Runtime Navigation Stability analysis Performance analysis Reliability Thermal stability Geographic information systems

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Reinforced Heterogeneous Graphlet Design for Knowledge Graph Representation Learning

SSRN

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SSRN 2023年

作者： Gong, Jibing Lin, Yuting Zhao, Yi Lin, Tianyu Fang, Xiaohan Feng, Xinchao Peng, Jiquan School of Information Science and Engineering Yanshan University Qinhuangdao066004 China The Key Laboratory for Computer Virtual Technology System Integration of Hebei Province Yanshan University Qinhuangdao066004 China

Knowledge Graphs have been practical tools to represent and integrate plentiful structural and semantic information in mainstream industrial scenarios. While promising, the heterogeneity and complexity of KGs pose a formidable obstacle, especially for graph representation learning. Most existing KG embedding models omit the dynamic high-order connectivity patterns, and heavily rely on the handcrafted patterns to handle the complex semantic relationships, thereby limiting their ability to adaptively capture nuanced and intricate relationships of knowledge graphs in different tasks. To fill this gap, we present ReHGD — a model designed for KGs that focuses on adaptively designing typed graphlets through a cooperative Multi-agent Reinforcement Learning algorithm. This approach can learn more discriminative graph representations tailored to specific downstream tasks. Specifically, ReHGD engages in the creation of typed graphlets through a two-stage process: it (1) establishes a reinforced chain design module to generate chains without predefined rules, and (2) employs a buffer-aware sampling technique to derive episodic chains from prior experiences. Subsequently, motifs are deduced by applying commute count and Hadamard product operations to the episodic chain-based subgraphs. Finally, ReHGD utilize chain and motif aggregations to learn graph representations. Experimental results on three real-world KGs show that ReHGD outperforms baselines on entity classification. © 2023, The Authors. All rights reserved.

关键词： Knowledge graph

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The Side-Channel Vulnerability in Network Protocol 11

The Side-Channel Vulnerability in Network Protocol

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11th International Conference on Communication and Network Security, ICCNS 2021

作者： Ru, Kaiqi Zheng, Yaning Feng, Xuewei Wang, Dongxia National Key Laboratory of Science and Technology on Information System Security Institute of System Engineering AMS PLA China Department of Computer Science and Technology Tsinghua University China National Key Laboratory of Science and Technology on Information System Security China

ISBN: (纸本)9781450386425

Some recent studies have found that there are some side-channel vulnerabilities in the operating system. Attackers would exploit the side-channel vulnerability for malicious purpose, such as hijack connections, denial of service attacks, etc. Currently, most attacks are detected manually. In this paper, we found that the reason for the existence of network protocol side-channel vulnerability is the use of shared resources. Since the state of shared resource affects all connections, when a connection uses a shared resource, information about that connection can be inferred by observing the usage of the shared resource. In order to find the shared resources, we implemented a tool called TASR which is a method of static analysis. The first is to find out what shared resources are available by the definition of shared resources in static analysis. Then, the data packet is used as the taint source to search the tainted shared resources. The second step is to analyze the taint-transmission-path according to the acquired tainted shared variable. Then it can find the side-channel vulnerability. By using this method on TCP, UDP and ICMP protocols, we find the following four shared variables: challenge_count, tcp_memory_allocated, tcp_memory_pressure, sysctl_icmp_msg_per_sec. It is difficult for tcp_memory allocated and tcp_memory pressure to exploit, because they will go through multiple strict checks. Using challenge_count can hijack the connection and inject malicious packets. Using sysctl_icmp_msg_per_sec can assist in DNS cache poisoning attack. © 2021 ACM.

关键词： Static analysis

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