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2024 International Symposium on Networks, Computers and Communications, ISNCC 2024

作者： Inaba, Terunobu Ishikawa, Yutaka Igarashi, Atsushi Sekiyama, Taro Graduate School of Informatics Kyoto University Kyoto Japan National Institute of Informatics Information Systems Architecture Science Research Division Tokyo Japan

ISBN: (纸本)9798350364910

Threat modeling is an effective approach to fortifying security, where system designers model a target system and investigate potential security weaknesses during the design phase. To investigate security flaws in the system rigorously, the model has to be detailed to the extent that the flow of each asset is minutely tracked. For this purpose, we propose Rabbit, a language to model a networked system with the ability to describe data flows within the system. Rabbit also aims to formally verify security properties under the specified system model and attacker model. In this paper, we model a simple client-server system and demonstrate the possibility of automatic verification using Rabbit. In an experiment we conducted, we have found a non-trivial execution path that violates a desired security property, demonstrating the potential of automatic security verification using Rabbit. © 2024 IEEE.

关键词： Modeling languages

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A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries

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Frontiers of Computer Science 2024年第5期18卷 1-10页

作者： Lingling ZHAO Shitao SONG Pengyan WANG Chunyu WANG Junjie WANG Maozu GUO Faculty of Computing Harbin Institute of TechnologyHarbin 150001China School of Electrical Engineering Liaoning University of TechnologyJinzhou 121004China School of Computer Science Northeast Electric Power UniversityJilin 132000China Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.

关键词： lithium-ion battery remaining useful life deep learning MLP-Mixer mixture-of-experts

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Deep Learning-driven Mobile Traffic Measurement Collection and Analysis

arXiv

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arXiv 2024年

作者： Fang, Yini Institute of Computing Systems Architecture School of Informatics University of Edinburgh United Kingdom

The global mobile traffic consumption has been growing sharply, driven by the surge of smart IoT devices and the adoption of 5G technology. Further, the COVID-19 pandemic has accelerated the digital transformation of work and study, leading to tremendous traffic demands. Due to these reasons, mobile operators are facing pressing resource management problems related to storage and overhead of transferring large amounts of metadata that serve for analysing user demands and planning the infrastructure needed to accommodate these. In this context, intelligent measurement collection, precise geo-spatial traffic analysis, and forecasting are becoming essential to assuring high performance and cost-efficiency of cellular network deployments. Modelling dynamic traffic patterns and especially the continuously changing dependencies between different base stations, which previous studies overlook, is challenging. Traditional algorithms struggle to process large volumes of data and to extract deep insights that help elucidate mobile traffic demands with fine granularity, as well as how these demands will evolve in the future. Therefore, in this thesis we harness the powerful hierarchical feature learning abilities of Deep Learning (DL) techniques in both spatial and temporal domains and develop solutions for precise city-scale mobile traffic analysis and forecasting. Firstly, we design Spider, a mobile traffic measurement collection and reconstruction framework with a view to reducing the cost of measurement collection and inferring traffic consumption with high accuracy, despite working with sparse information. In particular, we train a reinforcement learning agent to selectively sample subsets of target mobile coverage areas and tackle the large action space problem specific to this setting. We then introduce a lightweight neural network model to reconstruct the traffic consumption based on historical sparse measurements. Our proposed framework outperforms existing solutions

关键词： Volume measurement

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A Cross-Domain Ontology Semantic Representation Based on NCBI-BlueBERT Embedding

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Chinese Journal of Electronics 2022年第5期31卷 860-869页

作者： ZHAO Lingling WANG Junjie WANG Chunyu GUO Maozu Faculty of Computing Harbin Institute of Technology Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical University Beijing Key Laboratory of Intelligent Processing for Building Big Data School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

A common but critical task in biological ontologies data analysis is to compare the difference between ontologies. There have been numerous ontologybased semantic-similarity measures proposed in specific ontology domain, but it still remains a challenge for crossdomain ontologies comparison. An ontology contains the scientific natural language description for the corresponding biological aspect. Therefore, we develop a new method based on natural language processing(NLP) representation model bidirectional encoder representations from transformers(BERT) for cross-domain semantic representation of biological ontologies. This article uses the BERT model to represent the word-level of the ontologies as a set of vectors, facilitating the semantic analysis or comparing the biomedical entities named in an ontology or associated with ontology terms. We evaluated the ability of our method in two experiments: calculating similarities of pair-wise disease ontology and human phenotype ontology terms and predicting the pair-wise of proteins interaction. The experimental results demonstrated the comparative performance. This gives promise to the development of NLP methods in biological data analysis.

关键词： human phenotype ontology cross-domain semantic representation natural language description pair-wise disease ontology data analysis bidirectional encoder representations NLP protein interaction diseases proteins natural language processing ontologies (artificial intelligence) natural language processing representation neural nets bioinformatics semantic-similarity measures cross-domain ontology semantic representation semantic analysis NCBI-BlueBERT embedding biological ontology data analysis

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A Report on the 40th JSSST Annual Conference.

A Report on the 40th JSSST Annual Conference.

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作者： Suenaga, Kohei Sekiyama, Taro Igarashi, Yuki Graduate School of Informatics Kyoto University Japan Information Systems Architecture Science Research Division National Institute of Informatics Japan Department of Information Sciences Faculty of Sciences Ochanomizu University Japan

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Build-It-Here: Utilizing 2D Inpainting Models for On-Site 3D Object Generation 9

Build-It-Here: Utilizing 2D Inpainting Models for On-Site 3D...

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9th IEEE International Conference on Computational Intelligence and Applications, ICCIA 2024

作者： Wu, Zhennan Sato, Hiroyuki Graduate School of Engineering The University of Tokyo Tokyo Japan National Institute of Informatics Japan Information Systems Architecture Science Research Division Tokyo Japan

ISBN: (纸本)9798350352214

Significant progress has been made in both 2D and 3D content creation recently. In order to integrate the advantages of both, we present Build-it-here, a novel 3D generation method for open vocabulary furniture creation and placing inside empty rooms. By modulating textual input, we achieve few-shot prompting large language models for layout planning. The output room layout is represented by bounding boxes in the empty house. Upon sampling camera view around bounding-box for rendering, we perform text-guided inpainting on masked 2D images. The inpainted object is then reconstructed in 3D and back projected to the room. Qualitative experiments have demonstrated that our method not only achieved open vocabulary diversity but also ensuring the rationality of placements. The scenes generated through this method is photo-realistic and ready for applications like game industry asset usage. © 2024 IEEE.

关键词： 3D modeling

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Research on rapid measurement technology of railway line based on 3D coordinate acquisition trolley 6

Research on rapid measurement technology of railway line bas...

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6th International Conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Zhang, Maolin Cui, Mengzhen Li, Congxu Chen, Yuan Beijing University of Civil Engineering and Architecture School of Geomatics and Urban Spatial Informatics Beijing100044 China Institute of Computing Technologies China Academy of Railway Sciences Corporation Limited Beijing100015 China Beijing University of Civil Engineering and Architecture Beijing100044 China

ISBN: (纸本)9781510688018

The traditional method of remeasurement of operational railroad has many processes, complicated operations and low efficiency, and the time of railroad maintenance days is decreasing, so new demands are made on the remeasurement method of operational railroad. In response to this problem, this paper researches the rapid measurement technology of operational railroad to achieve the rapid and high-precision acquisition of spatial measurement data in a wide range of operational railroad and the rapid and high-precision measurement of railroad track line information. This paper mainly studies the railroad line 3D coordinate acquisition trolley and its acquisition system, mainly including the key technology research of data synchronization technology, data fusion Kalman filter technology, combined navigation solution technology and track flat and longitudinal section fitting optimization research. Through this research, the three-dimensional coordinate acquisition trolley of railroad line can obtain high-precision railroad track alignment information without relying on CPⅢ control network, reduce the number of times on the track, reduce the operation safety hazards, save cost and human resources. © 2025 SPIE.

关键词： Data fusion

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Research on railroad line spatial information management technology

Research on railroad line spatial information management tec...

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2023 International Conference on Remote Sensing, Mapping, and Geographic systems, RSMG 2023

作者： Li, Congxu Xu, Xiaolei Feng, Boqing Zhang, Maolin Institute of Computing Technologies China Academy of Railway Sciences Corporation Limited China Beijing University of Civil Engineering and Architecture School of Geomatics and Urban Spatial Informatics Beijing100044 China

ISBN: (纸本)9781510668898

With the completion of railroad line resurvey work, the measurement data are scattered in various units and lack unified storage and management, which brings many challenges and problems to railroad operation and maintenance. To solve this problem, this study proposes a multi-version, multi-temporal spatial data storage technology, as well as a distributed fast storage technology and a spatial service engine by studying the data management work, aiming to realize unified and fast storage, management and sharing of railroad survey data to improve the reliability, consistency and validity of data. First, this study focuses on the multi-version and multi-temporal spatial data storage technology. By adopting this technology, a large amount of spatial data generated by railroad line resurvey can be effectively handled, and the storage and management of different versions and temporal phases of data can be realized. Meanwhile, distributed fast storage technology is introduced to provide high-speed data access and processing capability to cope with the storage and processing demand of massive data. In addition, using the spatial service engine, fast query and analysis of measurement data can be realized. Second, this study also conducts an in-depth research on the railroad line measurement data management system. First, the overall architecture of the system is designed to ensure the scalability and flexibility of the system. Then, the data structure of the system was optimized to accommodate the storage and query requirements of multi-version and multi-temporal data. In terms of technical architecture, distributed storage technology is adopted to realize fast storage and retrieval of remote sensing, 2D vector and 3D model data. In addition, the sharing and exchange of measurement data is realized through information sharing and information interface. The innovation of this study is to propose a multi-version and multi-temporal spatial data storage technology, and to reali

关键词： Information management

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Modeling technology for fusion of massive strip 3D terrain and feature models

Modeling technology for fusion of massive strip 3D terrain a...

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2023 International Conference on Remote Sensing, Mapping, and Geographic systems, RSMG 2023

作者： Li, Congxu Feng, Boqing Xu, Xiaolei Zhang, Miao Institute of Computing Technologies China Academy of Railway Sciences Corporation Limited China School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and Architecture Beijing100044 China

ISBN: (纸本)9781510668898

In order to solve the problem that traditional GIS methods are difficult to handle massive data, complex spatial relationships and accurate geographic phenomenon modeling. By fusing terrain and feature models, geographic phenomena can be efficiently and accurately represented and analyzed to provide a reliable basis for decision making. This paper proposes a fusion modeling technique for massive strip 3D terrain and feature models, aiming to solve the problem of effective fusion and modeling of 3D terrain and feature models for strip data in large-scale spatial data processing. Based on open-source geographic information technology, the traditional method is designed to unify and model 2D vector data, raster data, BIM data and 3D data from different sources, formats and scales to form a standardized spatial data set, in response to the problems of high computational complexity, large storage requirements and data redundancy in processing massive data. For the characteristics of strip data and 3D terrain and feature models, geometric matching and semantic matching methods are used to fuse and process vector data representing the same entity with different attribute information and spatial location information to solve the problem of data redundancy and duplication;it is used to describe the different levels and precision of spatial data, and the appropriate data precision can be flexibly selected in the fusion modeling process, so as to achieve a balance between data quality and calculation The purpose of balancing data quality and computational efficiency. Based on LOD hierarchical detail technology fusion processing of terrain-independent BIM models, terrain-dependent fusion of linear 3D feature models, to achieve the surface fitting of multi-source 3D model data in a specific scene, to improve the 3D scene display effect. In terms of data processing efficiency: traditional GIS methods may face bottlenecks in computation and storage when dealing with massive data.

关键词： 3D modeling

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Understanding the Student Experience Better: Analyzing Remote Learning Tools Through Engagement, Participation, and Liveness Concepts 17

Understanding the Student Experience Better: Analyzing Remot...

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17th International Conference on Computer Supported Education, CSEDU 2025

作者： Gomes, Genildo Silva, Paulo Magalhães, Sâmila Galvão, Leandro Lauschner, Tanara Steinmacher, Igor Conte, Tayana Gadelha, Bruno Institute of Computing Federal University of Amazonas AM Manaus Brazil Department of Design and Graphic Expression Federal University of Amazonas AM Manaus Brazil School of Informatics Computing and Cyber Systems Nothern Arizona University FlagstaffAZ United States

ISBN: (纸本)9789897587467

Technologies have changed how students engage and participate during remote classes, whether by encouraging interaction via chat on platforms such as Google Meet or by using voting features such as Poll. Our research aims to better understand the role of these tools in the educational context and how their interactive features influence student experience through the lens of engagement, participation, and liveness. In this sense, we address the following question: How can educational technologies be classified by the Engagement, Participation, and Liveness lens? To explore the relationship between the lens and the interactive features of Mentimeter, Google Meet, and Google Presentations, we conducted a study with three independent evaluations, measuring agreement using Cohen's Kappa coefficient. Our results suggest that these interactive features can be used as a lens to understand different types of experiences and how they can be applied to promote various dynamics, such as competitive and collaborative activities. Our results expand the professor's perspective on the student experience, providing ways for professors to understand the emotional and student behavioral reactions from using interactive tools in remote learning environments. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Students

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