检索结果-内蒙古大学图书馆

30th International Conference on MultiMedia Modeling, MMM 2024

作者： Lokoč, Jakub Vopálková, Zuzana Stroh, Michael Buchmueller, Raphael Schlegel, Udo SIRET Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Visual Computing Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany Data Analysis and Visualization Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany

ISBN: (纸本)9783031533013

This paper presents a tool relying on data service architecture, where technical details of all VBS datasets are completely hidden behind an abstract stateless data layer. The data services allow independent development of interactive search interfaces and refinement techniques, which is demonstrated by a smart front-end component. The component supports common search features and allows users to exploit content-based statistics for effective filtering. We believe that video data services might be a valuable addition to the open-source VBS toolkit, especially when available for the competition on a shared server with all VBS datasets, extracted features, and meta-data behind. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Video recording

来源：评论

学校读者我要写书评

暂无评论

HaWANet: Road Scene Understanding with Multi-modal Sensor data Using Height-Width-Driven Attention Network 23rd

HaWANet: Road Scene Understanding with Multi-modal Sensor D...

引用

23rd International Conference of the Italian Association for Artificial Intelligence, AIxIA 2024

作者： Chatterjee, Soumick Xu, Jiahua Kuzhipathalil, Adarsh Nürnberger, Andreas Faculty of Computer Science Otto von Guericke University Magdeburg Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy Hertie Institute for Clinical Brain Research Tuebingen Germany XenomatiX NV Leuven Belgium

ISBN: (纸本)9783031806063

In recent years, the field of autonomous vehicles and driverless technology has seen remarkable advancements, driven by contributions from mainstream automotive manufacturers and open-source projects. This research aims to develop a pipeline for road scene understanding through semantic segmentation. The proposed pipeline utilises a multi-modal segmentation model, incorporating greyscale images and point cloud data from Xenolidar, specifically designed to capture the structural priors of highway road scenes. The fusion of input modalities and the design of an encoder-decoder architecture with a novel attention scheme called HaWANet is introduced, which focuses on the height and width contextual information to improve the accuracy of road segmentation, are the primary aspects explored for the proposed model. The output of the encoder is a two-dimensional point cloud, which effectively represents the road’s planar nature, and is crucial for improving the accuracy of road segmentation, particularly in edge cases, addressing current challenges in autonomous driving research. This research, aimed at addressing the segmentation problem for multimodal sensor data, has presented significant performance improvement over single-modal approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Intrusion Detection in the Internet of Things Using Fusion of GRU-LSTM Deep Learning Model

引用

Intelligent Automation & Soft Computing 2023年第8期37卷 2279-2290页

作者： Mohammad S.Al-kahtani Zahid Mehmood Tariq Sadad Islam Zada Gauhar Ali Mohammed ElAffendi Department of Computer Engineering Prince Sattam Bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Computer Engineering University of Engineering and TechnologyTaxila47050Pakistan The FAMLIR Group The University of LahoreLahore54000Pakistan Department of Computer Science University of Engineering&TechnologyMardan23200Pakistan Faculty of Computing International Islamic UniversityIslamabad44000Pakistan EIAS Data Science and Blockchain Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

Cybersecurity threats are increasing rapidly as hackers use advanced *** a result,cybersecurity has now a significant factor in protecting organizational *** detection systems(IDSs)are used in networks to flag serious issues during network management,including identifying malicious traffic,which is a *** remains an open contest over how to learn features in IDS since current approaches use deep learning *** learning,which combines swarm intelligence and evolution,is gaining attention for further improvement against cyber *** this study,we employed a PSO-GA(fusion of particle swarm optimization(PSO)and genetic algorithm(GA))for feature selection on the CICIDS-2017 *** achieve better accuracy,we proposed a hybrid model called LSTM-GRU of deep learning that fused the GRU(gated recurrent unit)and LSTM(long short-term memory).The results show considerable improvement,detecting several network attacks with 98.86%accuracy.A comparative study with other current methods confirms the efficacy of our proposed IDS scheme.

关键词： Cyber security deep learning intrusion detection PSO-GA CICIDS-2017 intelligent system security and privacy IoT

来源：评论

学校读者我要写书评

暂无评论

Decreasing the Design Complexity of Cyber-Physical Systems through Conceptual Modeling: The Robot Abstraction Method

Decreasing the Design Complexity of Cyber-Physical Systems t...

引用

IEEE World Forum on Internet of Things (WF-IoT)

作者： Alexander Voelz Daniel Kiesenhofer Dimitris Karagiannis Faculty of Computer Science Doctoral School Computer Science University of Vienna Vienna Austria Faculty of Computer Science Research Group Knowledge Engineering University of Vienna Vienna Austria

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

The increasing complexity of designing, deploying, and maintaining Cyber-Physical Systems (CPS), particularly those incorporating multiple interacting robots, presents significant challenges regarding programming and system integration. Existing methods and tools that aim to decrease the complexity of setting up such systems through modeling-based approaches often focus on single-robot interaction and rely on manual data entries, thus limiting their scalability and applicability to more intricate multi-robot environments. This contribution introduces the Robot ion Method (RobAM), a conceptual modeling method developed to decrease the design complexity of CPS through additional layers of abstraction. By utilizing conceptual modeling, capabilities of humanoid robots can be leveraged for the design of multi-robot interactions. Thereby, our approach enhances the modeling of complex CPS as well as the subsequent generation of code to enable efficient deployments. Through an experimental scenario, we demonstrate how RobAM simplifies the development process of CPS, paving the way for advancements regarding the integration of AI-supported technologies, such as Large Language Models, to ultimately generate contextually relevant semantics for future system design.

关键词： Adaptation models Limiting Scalability Large language models Semantics System integration Manuals Cyber-physical systems Programming Complexity theory

来源：评论

学校读者我要写书评

暂无评论

A Recognition Service for Haptic Modelling in Scene2Model Neural Networks for Design Thinking

A Recognition Service for Haptic Modelling in Scene2Model Ne...

引用

AAAI 2023 Spring Symposium on Challenges Requiring the Combination of Machine Learning and knowledge engineering, AAAI-MAKE 2023

作者： Muck, Christian Utz, Wilfrid University of Vienna Faculty of Computer Science Research Group Knowledge Engineering Währingerstraße 29 Vienna1090 Austria OMiLAB NPO Lützowufer 1 Berlin10785 Germany

Design thinking has gained popularity in recent years as a toolbox to support the development of innovative business models, product offerings and services. Applying a user-centric paradigm during the co-creation phase of stakeholders with various backgrounds, where ideas are created, prototyped, and iteratively evaluated. A challenge observed in such settings relates to how knowledge about novel ideas can be communicated and developed in collaborative settings, specifically from the perspective of processing artefacts realised. We will introduce the Scene2Model tool, developed in the OMiLAB@University of Vienna as a way to tackle this challenge. Scene2Model enables the digitalisation of design thinking artefacts, developed using haptic elements as conceptual models. The tool supports a dynamic metamodel representation and builds on training results to a) recognise the haptic elements as model concepts, b) elevate the Scene2Model metamodel continuously with domain-specific vocabulary and c) align these concepts with external definitions on type level. The digital models in Scene2Model become therefore digital representations that can be annotated and refined, queried or transformed as model value capabilities. This paper provides a demonstration of this idea. © 2023 CEUR-WS. All rights reserved.

关键词： Image recognition

来源：评论

学校读者我要写书评

暂无评论

Evaluating the Impact of dataset Size on Univariate Prediction Techniques for Moroccan Agriculture

Evaluating the Impact of Dataset Size on Univariate Predicti...

引用

International Conference on Artificial Intelligence and Smart Environment, ICAISE 2022

作者： Ed-daoudi, Rachid Alaoui, Altaf Zerouaoui, Jad Ettaki, Badia Zerouaoui, Jamal Laboratory of Engineering Sciences and Modeling Faculty of Sciences Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco LyRICA: Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering School of Information Sciences Rabat Rabat Morocco

ISBN: (纸本)9783031262531

Learning models used for prediction are mostly developed without taking into account the size of datasets that can produce models of high accuracy and better performance. Although, the general believe is that, large dataset is needed to construct a predictive learning model. To describe a data set as large in size depends on the circumstances and context of prediction. This means that what makes a dataset to be considered as being big or small is controversial. In this paper, the ability of the predictive model to adapt to a particular size of data in training is examined. The study experiments on three different sizes of Moroccan agricultural data using a variety of statistical and Machine Learning techniques, to create predictive models with a view to establishing if the size of data has any effect on the accuracy of a model. The output of each model is measured using the Mean Absolute Error (MAE) and r-squared, and comparisons are made. The results of training the models through the three partitioned dataset show that, the models trained with the smallest and largest size of training data appear to be less accurate, while the models trained with a medium sized dataset delivers a much better results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Assessing the Improvements Brought by Artificial Intelligence on the Prediction of Aerodynamic Coefficients

Assessing the Improvements Brought by Artificial Intelligenc...

引用

International Conference on Artificial Intelligence and Smart Environment, ICAISE 2022

作者： Zerouaoui, Jad Alaoui, Altaf Ettaki, Badia Chakir, ElMahjoub Laboratory of Material and Subatomic Physics Faculty of Sciences Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco LyRICA: Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering School of Information Sciences Rabat Rabat Morocco

ISBN: (纸本)9783031262531

The implementation of Artificial Intelligence in the aerospace field is fairly new and various domains of the aerospace discipline are to be explored. This paper provides a general review on the upbringing and improvements of Artificial Intelligence as a pilar of the next generation of scientific research. Applications and developments brought by Artificial Intelligence and Deep Learning to the aerospace industry generally, mainly the prediction of aerodynamic coefficients and design optimization using intricate neural networks replicating the process of the human brain. Scientific developments participated in improving major areas in the aerospace field. Due to the expensive and time-consuming process related to the complexity of the geometry and the precision of the results required, researchers integrated surrogate models based on Deep Learning in order to improve the calculation process of the aerodynamic coefficients. On one hand, this paper introduces and discusses the general architecture of several networks used to enhance the calculation ability of the machine, while presenting the process of each work along with recounting the different methods employed. On the other hand, a discussion regarding the challenges presented with utilizing Computational Fluid Dynamics while assessing and comparing the results brought by surrogate models based on efficiency and time consumption is also tackled. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Using Machine Learning with Pyspark for Solving a Big data Problem: Searching for Particles in High Energy Physics with Big Mass (HEPMASS) 7th

Using Machine Learning with Pyspark for Solving a Big Dat...

引用

7th International Conference on Smart City Applications, SCA 2022

作者： Azhari, Mourad Ahaji, Khalid Abarda, Abdallah Ettaki, Badia Zerouaoui, Jamal Center of Guidance and Planning Rabat Morocco Laboratoire de Modélisation Mathématiques et de Calculs Economiques FSJES Université Hassan 1er Settat Morocco Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering Department of Data Content and Knowledge Engineering School of Information Sciences Rabat Morocco Laboratory of Engineering Sciences and Modeling Faculty of Sciences Ibn Tofail University Kenitra Morocco

ISBN: (纸本)9783031268519

The search for exotic particles is one of the most challenging topics for physicists. This work aims to resolve the Big data problem in the exotic particle area using the Apache Spark environment with the MLlib library. For this purpose, we analyze and discuss the performance of the following methods: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Tree (GBT). The results are very encouraging and prove that these proposed methods run efficiently with an Accuracy exceeding (83%). While the Gradient Boosted Tree (GBT) reaches a high AUC score 85%, better than Decision Tree (DT) and Random Forest. In this manuscript, we attempt to solve a big data problem using the "HEPMASS" dataset. Our data includes 10.5 million examples collected from the UCI website for the experimental phase. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Iterative Intraoperative Digital Tomosynthesis Image Reconstruction using a Prior as Initial Image 7

Iterative Intraoperative Digital Tomosynthesis Image Reconst...

引用

7th International Conference on Image Formation in X-Ray Computed Tomography

作者： Saad, Fatima Frysch, Robert Pfeiffer, Tim Saalfeld, Sylvia Schulz, Jessica Georgi, Jens-Christoph Nürnberger, Andreas Lauritsch, Guenter Rose, Georg Institute for Medical Engineering Otto-von-Guericke University Magdeburg Germany Forschungscampus STIMULATE Otto-von-Guericke University Magdeburg Germany Faculty of Computer Science Otto-von-Guericke University Magdeburg Germany Siemens Healthcare GmbH Forchheim Germany Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-Guericke University Magdeburg Germany

ISBN: (纸本)9781510656697

The efficacy of interventional treatments highly relies on an accurate identification of the target lesions and the interventional tools in the guidance images. Whereas X-ray radiography poses low doses to the patient, its weakness is in the superposition of the different image structures in a 2D image. Cone-beam computed tomography (CBCT) might look ideal providing exact 3D information, however this is at the cost of a higher radiation dose, longer imaging time, and more space requirements in the operating room. Introducing some depth information with relatively low dose, and requiring less space, digital tomosynthesis (DTS) is a potential candidate for guiding interventions. However, due to the few number of projections and to the limited angle acquisition, DTS has poor depth resolution. Since high quality patient-specific prior CT scans are usually performed prior to the intervention for diagnosis or to plan the intervention, and given that such images share a fair amount of information with the intraoperative DTS images, we propose in this work a prior-based iterative reconstruction framework to improve the intraoperative DTS image quality. The framework is based on registering the prior CT image to an intermediate low-quality intraoperative DTS image, then iteratively re-reconstructing the intraoperative DTS image using the coregistered prior CT as the starting image. We acquired prior CT and intraoperative CBCT data of a liver phantom and simulated some intraoperative DTS projection images using a spherical ellipse scan geometry. Our results show a great improvement in the DTS image quality with the proposed method and prove the importance of choosing a good starting point for the iterative DTS reconstruction. © 2022 SPIE.

关键词： computerized tomography

来源：评论

学校读者我要写书评

暂无评论

FERI: Feature Enhancement and Relational Interaction for Image-text Matching 30

FERI: Feature Enhancement and Relational Interaction for Ima...

引用

30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Yu Zhang, Jianqiang Song, Gongpeng Lu, Qin Zhao, Shuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9798331515966

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： cross-modal retrieval feature enhancement image-text matching relationship interaction

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：