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3rd IEEE International Conference on Computer Science and Educational Informatization, CSEI 2021

作者： Wang, Junchao Pang, Jianmin Shan, Zheng Wei, Jin Yao, Jinyang Liu, Fudong State Key Laboratory of Mathematical Engineering and Advanced Computing Zheng Zhou China School of Computer Science Data Arena Institute Fudan University Zheng Zhou China

ISBN: (纸本)9781728184739

The course of operating system's labs usually fall behind the state of art technology. In this paper, we propose a Software Diversity-Assisted Defense (SDAD) lab based on software diversity, mainly targeting for students majoring in cyber security and computer science. This lab is consisted of multiple modules and covers most of the important concepts and principles in operating systems. Thus, the knowledge learned from the theoretical course will be deepened with the lab. For students majoring in cyber security, they can learn this new software diversity-based defense technology and understand how an exploit works from the attacker's side. The experiment is also quite stretchable, which can fit all level students. © 2021 IEEE.

关键词： Students

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Inductive Relation Prediction by Disentangled Subgraph Structure

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Tsinghua Science and Technology 2024年第5期29卷 1566-1579页

作者： Guiduo Duan Rui Guo Wenlong Luo Guangchun Luo Tianxi Huang Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of ChinaChengdu 611731Chinaand also with Trusted Cloud Computing and Big Data Key Laboratory of Sichuan ProvinceChengdu 611731China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Hangzhou NetEase Cloud Music Technology Co. Ltd.Hangzhou 310052China Department of Fundamental Courses Chengdu Textile CollegeChengdu 611700China

Currently,most existing inductive relation prediction approaches are based on subgraph structures,with subgraph features extracted using graph neural networks to predict ***,subgraphs may contain disconnected regions,which usually represent different semantic *** not all semantic information about the regions is helpful in relation prediction,we propose a relation prediction model based on a disentangled subgraph structure and implement a feature updating approach based on relevant semantic *** indirectly achieve the disentangled subgraph structure from a semantic perspective,the mapping of entity features into different semantic spaces and the aggregation of related semantics on each semantic space are *** disentangled model can focus on features having higher semantic relevance in the prediction,thus addressing a problem with existing approaches,which ignore the semantic differences in different subgraph ***,using a gated recurrent neural network,this model enhances the features of entities by sorting them by distance and extracting the path information in the ***,it is shown that when there are numerous disconnected regions in the subgraph,our model outperforms existing mainstream models in terms of both Area Under the Curve-Precision-Recall(AUC-PR)and Hits@*** prove that semantic differences in the knowledge graph can be effectively distinguished and verify the effectiveness of this method.

关键词： disentangled subgraph structure knowledge graph completion Gated Recurrent Unit(GRU) feature updating

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ProgMate: An Intelligent Programming Assistant Based on LLM 54

ProgMate: An Intelligent Programming Assistant Based on LLM

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Li, Ying Yang, Runze Zhang, Xiantao Shi, Peng Yang, Da Huang, Xuefei School of Computer Science and Engineering Beihang University Beijing China International Innovation Institute Beihang University Key Laboratory of Data Science and Intelligent Computing Hangzhou China

ISBN: (纸本)9798350351507

This study addresses the challenges faced in personalized tutoring within large-scale programming courses, such as significant ability gaps among students, limited available resources, among others. For these reasons, we proposed an intelligent programming assistant, ProgMate, based on large language models (LLMs). Benefitting from the robust understanding and learning capabilities of LLM, ProgMate not only comprehensively monitors the learning process, but can also surpass human teaching in aspects such as intelligent assignment grading, identification of knowledge gaps, and assessment of learning abilities. ProgMate embodies a new 4-A digital teaching paradigm, characterized by its ability to provide precise guidance with anything, to anyone, anywhere, at any time, with features like 'omnipresence', 'adaptive guidance' and 'customization'. It facilitates the organic integration of collective teaching and individualized guidance, continuous learning, and long-term development, as well as resource efficiency and precision nurturing, offering a viable path and empirical support for the digital transformation of future education. © 2024 IEEE.

关键词： Knowledge graph

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visualizing Program Behavior: A Study of Enhanced Program Diagrams Using LLM 54

Visualizing Program Behavior: A Study of Enhanced Program Di...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Li, Ying Yang, Runze Gui, ShiJie Shi, Peng Huang, Xuefei Yang, Da Zhang, Xiaozhou Gai, Yiming School of Computer Science and Engineering Beihang University Beijing China International Innovation Institute Beihang University Key Laboratory of Data Science and Intelligent Computing Hangzhou China

ISBN: (纸本)9798350351507

This paper aims to address the difficulties faced by novice programmers in grasping code structure and execution flow, improving programming thinking, and pinpointing code errors with accuracy. It proposes providing students with program behavior diagrams based on large language models (LLMs) and visualization techniques to achieve personalized guidance. Specifically, these program behavior diagrams include programming thinking visualization diagrams and code vulnerability visualization diagrams. A programming thinking visualization diagram employs static code analysis to gather code structure information, combined with the structured chain-of-thought method to collectively optimize the LLM. This enables the LLM to explain each interpretable part of the code from top to bottom, detailing the programming concepts, and displaying them on a modularized code structure diagram. The code vulnerability visualization diagram primarily utilizes the fine-tuned LLM, optimizing it based on program analysis and clustering analysis methods to accurately identify vulnerabilities in student code and display them on a code flow diagram. Its feature is to visually display to students the error location, error information, and the impact of errors on program flow, rather than providing the programming answers. Lastly, through experiments and statistical analysis of actual teaching data, this paper serves a demonstration that the enhanced models used in the visualization diagram generation process have a noticeable effect on mainstream LLMs, and that visualization diagrams hold significant value for students at different stages of learning. © 2024 IEEE.

关键词： Students

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Research on water quality prediction based on SARIMA-LSTM: A case study of beilun estuary 21

Research on water quality prediction based on SARIMA-LSTM: A...

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21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Xu, Rui Xiong, Qingyu Yi, Hualing Wu, Chao Ye, Jianxin Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education School of Big Data AND Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9781728120584

As water environment is an important part of mangrove ecosystem, an efficient prediction of water quality is the foundation for judging the health of wetland ecosystem. And it also contributes a lot to the smooth development of environmental protection work. Based on the data of water quality and weather in Beilun Estuary, this paper chooses permanganate index and the content of ammonia nitrogen, which can reflect the water quality, as forecasting targets. We propose a multi-feature prediction method called SARIMA-LSTM on the basis of seasonal autoregressive integrated moving average model and long short-term memory. Through the combination of linear and non-linear model, this method can possess a better prediction effect considering the influence of weather on water quality. And the experimental results of four models show that this method has higher accuracy, stability and reliability. © 2019 IEEE.

关键词： Long short-term memory

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HGeoHashBase: an optimized storage model of spatial objects for location-based services

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Frontiers of Computer Science 2020年第1期14卷 208-218页

作者： Jingwei ZHANG Chao YANG Qing YANG Yuming LIN Yanchun ZHANG Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic TechnologyGuilin 541004China Guangxi Key Laboratory of Automatic Measurement Technology and Instrument Guilin University of Electronic TechnologyGuilin 541004China Centre for Applied Informatics College of Engineering and ScienceVictoria UniversityMelbourne 8001Australia

Many location-based services need to query objects existing in a specific space,such as location-based tourism resource *** a large number of spatial objects and the real-time object access requirements of location-based services pose a big challenge for spatial object storage and query *** this paper,we propose HGeoHashBase,an improved storage model by integrating GeoHash with key-value structure,to organize spatial objects for efficient range *** is responsible for spatial encoding and key-value structure as underlying data *** the similarity of the encodings for objects in the close geographical locations and the multi-version data mechanism are blended into the proposed model *** the tradeoff between encoding precision and query performance,a theoretical proof is *** experiments are designed and conducted,whose results show that the proposed model can gain significant performance improvement.

关键词： location-based services distributed storage model storage and access optimization

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Network Embedding with Topology-Aware Textual Representations 18th

Network Embedding with Topology-Aware Textual Representation...

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Chen, Jiaxing Xu, Zenan Su, Qinliang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (纸本)9783030891879

Textual network embedding aims to learn meaningful low-dimensional representations for vertices with the consideration of the associated texts. When learning the representations for texts in network embedding, existing methods mostly only exploit information from neighboring texts (i.e., contexts), while rarely taking advantages of the valuable network topological (structural) information. To bridge the gap, in this paper, a model based on adaptive-filter convolutional neural networks (CNN) is developed, in which the filters are adapted to local network topologies, rather than clamped to fixed values as in traditional CNNs. The dependency enables the learned text representations to be aware of local network topologies. It is shown that the proposed topology-aware representations can be viewed as a complement to existing context-aware ones. When the two are used together, experimental results on three real-world benchmarks demonstrate that significant performance improvements on the tasks of link prediction and vertex classification. © 2021, Springer Nature Switzerland AG.

关键词： Network topology

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A comparison of four global land cover maps on a provincial scale based on China’s 30 m GlobeLand30 4th

A comparison of four global land cover maps on a provincial ...

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4th International Conference on Geo-Informatics in Resource Management and Sustainable Ecosystem, GRMSE 2016

作者： Ye, Xiaohui Zhao, Jinling Huang, Linsheng Zhang, Dongyan Hong, Qi Anhui Engineering Laboratory of Agro-Ecological Big Data Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China

ISBN: (纸本)9789811039652

To monitor the entire earth system, several global land cover mapping products have been produced based on different remote sensing imagery (e.g., AVHRR, MODIS, SPOT, HJ-1), including UMD Land Cover, Global Land Cover 2000, GlobCover 2009 and GlobeLand30. However, the application potential of those products has not been fully explored at a provincial scale. The primary objective of this study is to compare and investigate the potential of these products in Anhui Province, China. The area and spatial consistency were used to evaluate the four datasets based on China’s GlobeLand 30 due to 30 m spatial resolution and the total accuracy of 83.51%. The Pearson’s correlation coefficient (R) and the percentage disagreement (PD) were used to evaluate the area consistency. The spatial similarity coefficient (O) was here adopted in order to verify the accuracy of spatial positions. A total of eight cover classes including cropland, woodland, grassland, shrubland, wetland, water bodies, artificial surfaces and bareland were reclassified and mapped to perform the intercomparison. The analysis results show that the PD of GlobCover 2009 is respectively 15.36% and −8.43% for "cropland" and "woodland", while they are −20.20% and 2.10% for UMD. The "woodland" has better agreement percentage in comparison with "cropland". The O of UMD is the worst of 69.25%, indicating that the spatial consistency is weak for "cropland". Conversely, the O of GlobCover 2009 reaches up to 83.50%. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Remote sensing

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Collaborative wi-fi fingerprinting indoor positioning using near relation 24

Collaborative wi-fi fingerprinting indoor positioning using ...

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2020 24th ISPRS Congress - Technical Commission IV on Spatial Information Science

作者： Wang, Y. Wang, W. Li, X. Zhang, W. Guo, R. National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Urban Informatics Shenzhen Key Laboratory of Spatial Smart Sensing and Services Research Institute for Smart Cities School of Architecture and Urban Planning Shenzhen University Shenzhen China

Indoor positioning is of great importance to the era of mobile computing. Currently, much attention has been paid to RSS-based location for that it can provide position information without additional equipment. However, this method suffers from many challenges: (1) fingerprint ambiguity;(2) labor-intensive of fingerprint collection;(3) low efficiency of fingerprint matching. To get over these drawbacks, we provide a collaborative WiFi fingerprinting indoor positioning method using near relation. The base idea of this method is that interpolation method is used to enrich sparse Wi-Fi fingerprint. Near relation boundary is provided and Wi-Fi fingerprints is constrained to this region to reduce fingerprint ambiguity, which also can improve the efficiency of fingerprint matching. Extensive experiments show that a positioning accuracy of 3.8m can be achieved with the near relation under 1m interpolation density. © 2020 Authors.

关键词： Efficiency

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Exploiting Multiple Features for Hash Codes Learning with Semantic-Alignment-Promoting Variational Auto-encoder 12th

Exploiting Multiple Features for Hash Codes Learning with Se...

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12th National CCF Conference on Natural Language Processing and Chinese computing, NLPCC 2023

作者： Chen, Jiayang Su, Qinliang School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Beijing China

ISBN: (纸本)9783031446924

Semantic hashing is an effective technique to empower information retrieval. Currently, considerable efforts have been dedicated to generating high-quality hash codes by modeling document features using generative models and other approaches. However, most of these methods rely solely on a single type of feature, such as TFIDF features, BERT embeddings, etc. As different types of features have distinct but complementary information of documents, e.g. TFIDF mainly contains the keywords information and BERT focuses on the semantics, hash codes generated solely from either may not capture the full essence of the documents. To overcome this challenge, we propose a semantic-alignment-promoting variational auto-encoder to generate hash codes from multiple document features. Specifically, a VAE-based generative model is first developed to model the multiple features. Then, we propose a semantic-alignment-promoting inference network to estimate the parameters of the variational posterior from multiple features. Additionally, the quality of hash codes is further improved by promoting the semantic alignment between the hash codes of connected documents in a constructed connection graph. The results of extensive experiments on three public datasets demonstrate that our proposed model significantly outperforms current state-of-the-art models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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