检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Günther, Wiebke Popescu, Oana-Iuliana Rabel, Martin Ninad, Urmi Gerhardus, Andreas Runge, Jakob Institute of Data Science Jena Germany Technische Universität Berlin Institute of Computer Engineering and Microelectronics Berlin Germany Dresden Germany Technische Universität Dresden Faculty of Computer Science Dresden Germany Leipzig Germany

Causal systems often exhibit variations of the underlying causal mechanisms between the variables of the system. Often, these changes are driven by different environments or internal states in which the system operates, and we refer to context variables as those variables that indicate this change in causal mechanisms. An example are the causal relations in soil moisture-temperature interactions and their dependence on soil moisture regimes: Dry soil triggers a dependence of soil moisture on latent heat, while environments with wet soil do not feature such a feedback, making it a context-specific property. Crucially, a regime or context variable such as soil moisture need not be exogenous and can be influenced by the dynamical system variables – precipitation can make a dry soil wet – leading to joint systems with endogenous context variables. In this work we investigate the assumptions for constraint-based causal discovery of context-specific information in systems with endogenous context variables. We show that naive approaches such as learning different regime graphs on masked data, or pooling all data, can lead to uninformative results. We propose an adaptive constraint-based discovery algorithm and give a detailed discussion on the connection to structural causal models, including sufficiency assumptions, which allow to prove the soundness of our algorithm and to interpret the results causally. Numerical experiments demonstrate the performance of the proposed method over alternative baselines, but they also unveil current limitations of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： Soil moisture

来源：评论

学校读者我要写书评

暂无评论

Cancer Prediction Using Clinical and Genomic data Fusion: A Systematic Review

Cancer Prediction Using Clinical and Genomic Data Fusion: A ...

引用

International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Kelvin Julian Kuncahyo Setyo Nugroho Rudi Nirwantono Bens Pardamean Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

This systematic review provides a comprehensive overview of the methods used to integrate genomic and clinical data in cancer prediction. The review includes 19 studies across various cancers, including breast, colorectal, melanoma, lung, pancreatic, and thyroid. The studies employed different methods to combine genomic and clinical data, including weighted polygenic risk scores, genetic and non-genetic risk scores, and different machine learning algorithms. The results show significant improvements in model prediction performance accuracy across multiple studies. The review highlights the potential benefits of integrating genetic and phenotypic information to improve disease risk prediction models and inform personalized healthcare strategies.

关键词： Accuracy Machine learning algorithms Genomics data integration Medical services Predictive models data models Bioinformatics Cancer Systematic literature review

来源：评论

学校读者我要写书评

暂无评论

Power Allocation Strategy for Joint Communications and Sensing in NOMA-assisted Low-Latency Networks

Power Allocation Strategy for Joint Communications and Sensi...

引用

2022 European Wireless Conference, EW 2022

作者： Zhu, Yao Hu, Yulin Tong, Wuwan Caire, Giuseppe Schmeink, Anke Information Theory and Data Analytics RWTH Aachen University AachenD-52074 Germany Faculty of Electrical Engineering and Computer Science Technical University of Berlin Berlin10587 Germany

ISBN: (纸本)9781713865698

Joint communication and sensing (JCAS) is envisioned as the enabler to many advanced applications in future 6G networks. This paper investigates a power allocation strategy for JCAS networks with multiple transmitters and receivers. To take advantage of spatial diversity, the dual-functional radio signal is constructed via superimposed NOMA. Moreover, to fulfill the stringent latency requirement, transmissions are carried out with finite blocklength (FBL) codes. We formulate the optimization problem aimed at maximizing the minimal transmission rate among NOMA users while guaranteeing the target estimation requirement, which is represented by the Cramer-Rao lower bound (CRLB). An efficient approach is proposed to obtain the optimal solutions based on the characterization of communication and sensing metrics. Via simulation, the analytical finding are validated and insights into the trade-off between communication and radar sensing in JCAS networks are provided. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Economic and social effects

来源：评论

学校读者我要写书评

暂无评论

Causal Modeling in Multi-Context Systems: Distinguishing Multiple Context-Specific Causal Graphs which Account for Observational Support

arXiv

引用

arXiv 2024年

作者： Rabel, Martin Günther, Wiebke Runge, Jakob Gerhardus, Andreas Institute of Data Science Jena Germany Dresden Germany Leipzig Germany Technische Universität Dresden Faculty of Computer Science Dresden Germany Technische Universität Berlin Institute of Computer Engineering and Microelectronics Berlin Germany

Causal structure learning with data from multiple contexts carries both opportunities and challenges. Opportunities arise from considering shared and context-specific causal graphs enabling to generalize and transfer causal knowledge across contexts. However, a challenge that is currently understudied in the literature is the impact of differing observational support between contexts on the identifiability of causal graphs. Here we study in detail recently introduced [6] causal graph objects that capture both causal mechanisms and data support, allowing for the analysis of a larger class of context-specific changes, characterizing distribution shifts more precisely. We thereby extend results on the identifiability of context-specific causal structures and propose a framework to model context-specific independence (CSI) within structural causal models (SCMs) in a refined way that allows to explore scenarios where these graph objects differ. We demonstrate how this framework can help explaining phenomena like anomalies or extreme events, where causal mechanisms change or appear to change under different conditions. Our results contribute to the theoretical foundations for understanding causal relations in multi-context systems, with implications for generalization, transfer learning, and anomaly detection. Future work may extend this approach to more complex data types, such as time-series. © 2024, CC BY-SA.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Homophily Within and Across Groups

arXiv

引用

arXiv 2024年

作者： Rizi, Abbas K. Michielan, Riccardo Stegehuis, Clara Kivelä, Mikko DTU Compute Technical University of Denmark Kongens Lyngby2800 Denmark Copenhagen Center for Social Data Science University of Copenhagen Denmark Department of Computer Science School of Science Aalto University FI-00076 Finland Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

Traditional social network analysis often models homophily—the tendency of similar individuals to form connections—using a single parameter, overlooking finer biases within and across groups. We present an exponential family model that integrates both local and global homophily, distinguishing between strong homophily within tightly knit cliques and weak homophily spanning broader community interactions. By modeling these forms of homophily through a maximum entropy approach and deriving the network behavior under percolation, we show how higher-order assortative mixing influences network dynamics. Our framework is useful for decomposing homophily into finer levels and studying the spread of information and diseases, influence dynamics, and innovation diffusion. We demonstrate that the interaction between different levels of homophily results in complex percolation thresholds. We tested our model on various datasets with distinct homophily patterns, showcasing its applicability. These homophilic connections significantly affect the effectiveness of intervention and mitigation strategies. Hence, our findings have important implications for improving public health measures, understanding information dissemination on social media, and optimizing intervention strategies. © 2024, CC BY.

关键词： Percolation (computer storage)

来源：评论

学校读者我要写书评

暂无评论

A Novel Image Encryption and Decoding Approach Based on DNA Coding and Arnold's Transformation for IoT Applications

A Novel Image Encryption and Decoding Approach Based on DNA ...

引用

Telecommunications Conference (ITC-Egypt), International

作者： Nawal Shaltout Ahmed A. Abd El-Latif Waleed Al-Adrousy Mohamed Hammad Abdelhamied A. Ateya Samir Elmougy Department of Computer Science Faculty of Computers and Information Mansoura University Mansoura Egypt EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Egypt Department of Information Technology Faculty of Computers and Information Menoufia University Shibin El Kom Egypt Department of Electronics and Communications Engineering Zagazig University Zagazig Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

Nowadays, the Internet of Things (IoT) is significantly utilized by an extensive range of applications that lead to the enhancement of different aspects of defense and military capabilities. In IoT, a wide range of smart devices that can communicate with each other are connected through it. Therefore, it is essential to protect multimedia data, especially images with an encryption approach that offers a sufficient degree of security in military applications. Hence, this paper introduces an effective scheme for image encryption and decoding relying on Arnold's transformation DNA coding. It proposes a new pseudo-random number generation PRNG using the Sine-Logistic map and Duffing map called SLDM. The encryption method involves three phases: 1- Key generation and PRNG. 2- confusion phase using the Arnold transformation method. 3- diffusion using DNA rules and operations. The results based on the USC-SIPI dataset, show that the suggested scheme is sufficiently resistant to every kind of attack. Furthermore, the suggested method yields NPCR scores of around 99.62% and UACI scores of approximately 33.48%. Due to its low complexity, it has a great deal of potential for safe and real-time picture applications.

关键词： Resistance Image coding Chaotic communication DNA Encryption Decoding Telecommunications

来源：评论

学校读者我要写书评

暂无评论

Analysis and Comparison of Image-Based UAV Detection and Identification 9

Analysis and Comparison of Image-Based UAV Detection and Ide...

引用

9th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer Engineering, UPCON 2022

作者： Dubey, Nidhish Sai Nithin, Nanduri Mahathi Tripathi, Shrivishal Data Science & Artificial Intelligence Dr. SPM IIIT Naya Raipur India Faculty of Electrical and Computer Engineering Dr. SPM IIIT Naya Raipur India

ISBN: (数字)9798350332506

ISBN: (纸本)9798350332506

This paper presents Unmanned Aerial Vehicles (UAVs) detection and classification with the help of different image-based machine learning modalities. The field of UAVs attracted researchers in recent years in response to the exponential rise in the number of UAVs available in the market with applications ranging from entertainment to defense operations and the risk associated risk by the same. Presently, visual, radar, radio frequency, and acoustic sensing systems are the prevailing technologies in the field of detection and identification of UAVs. The general results of this study show that UAV machine learning-based classifications are propitious and that there are many successful individual contributions. In this research, UAVs were detected and classified using classification methods like Support Vector Machines (SVM), k-nearest neighbor (KNN), and Convolutional Neural Networks (CNN). The results demonstrated that CNN, SVM, and KNN had an accuracy of 91%, 87%, and 78%, respectively. The classifier CNN outperformed other classifiers under the same empirical circumstances. © 2022 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Exploring the Potential of Large Vision-Language Models for Unsupervised Text-Based Person Retrieval 39

Exploring the Potential of Large Vision-Language Models for ...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Zongyi Jianbo, Li Shi, Yuxuan Chen, Jiazhong Huang, Shijuan Tu, Linnan Shen, Fei Ling, Hefei Department of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center of Educational Big Data The Faculty of Artificial Intelligence in Education Central China Normal University China Nanjing University of Science and Technology China

ISBN: (纸本)157735897X

The aim of text-based person retrieval is to identify pedestrians using natural language descriptions within a large-scale image gallery. Traditional methods rely heavily on manually annotated image-text pairs, which are resource-intensive to obtain. With the emergence of Large Vision-Language Models (LVLMs), the advanced capabilities of contemporary models in image understanding have led to the generation of highly accurate captions. Therefore, this paper explores the potential of employing Large Vision-Language Models for unsupervised text-based pedestrian image retrieval and proposes a Multi-grained Uncertainty Modeling and Alignment framework (MUMA). Initially, multiple Large Vision-Language Models are employed to generate diverse and hierarchically structured pedestrian descriptions across different styles and granularities. However, the generated captions inevitably introduce noise. To address this issue, an uncertainty-guided sample filtration module is proposed to estimate and filter out unreliable image-text pairs. Additionally, to simulate the diversity of styles and granularities in captions, a multi-grained uncertainty modeling approach is applied to model the distributions of captions, with each caption represented as a multivariate Gaussian distribution. Finally, a multi-level consistency distillation loss is employed to integrate and align the multi-grained captions, aiming to transfer knowledge across different granularities. Experimental evaluations conducted on three widely-used datasets demonstrate the significant advancements achieved by our approach. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Gaussian distribution

来源：评论

学校读者我要写书评

暂无评论

Analytical Study for Cyber Threat Intelligence (CTI) 7

Analytical Study for Cyber Threat Intelligence (CTI)

引用

7th IET Smart Cities Symposium, SCS 2023

作者： Alkhateeb, Ibrahim R. Al-Haija, Qasem Abu Abu-Soud, Saleh Dept. of Cybersecurity/ Data Science King Hussein School of Computing Sciences Prince Sumaya University for Technology P.O. Box 1438 Amman11941 Jordan Dept. of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology P.O. Box 3030 Irbid22110 Jordan

ISBN: (纸本)9781839539831

Cyber Threat Intelligence (CTI) involves collecting and analyzing cyber security data using advanced algorithms and rigorous techniques to identify and address potential threats, harmful incidents, and existing vulnerabilities that cybercriminals exploit. Threat intelligence facilitates faster and well-informed security decisions while understanding threat actors' motives, targets, and attack patterns. Defenders employ diverse strategies to prevent, detect, and disrupt incoming attacks. However, traditional security solutions are inadequate for today's complex security landscape, often proving ineffective against the rapid proliferation of cyber threats. Automating information sharing related to cyber threats presents new challenges for researchers and analysts, requiring them to detect deceptive practices and produce accurate, timely, and pertinent intelligence. This research explores the profound impact of Artificial Intelligence (AI) and Machine Learning (ML) in cyber security, including their various types, applications, challenges, and promising future. Leveraging AI knowledge, particularly ML technology, organizations can strengthen their defenses against malicious threats, reduce cyber risks, and establish a dynamic, automated, and up-to-date security system by analyzing security data. © The Institution of Engineering & Technology 2023.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

A GoogLeNet Performance Approach for COVID-19 Detection using Chest X-ray Images

A GoogLeNet Performance Approach for COVID-19 Detection usin...

引用

International Conference on Knowledge and Smart Technology (KST)

作者： Patipan Rattanawin Tidatep Pakinsee Pokpong Songmuang Data Science and Innovation College of Interdisciplinary Studies Thammasat University Pathum Thani Thailand Department of Computer Science Faculty of Science and Technology Thammasat University Pathum Thani Thailand

Coronavirus disease (COVID-19) is a major pandemic disease that has already infected millions of people worldwide and affects many aspects, especially public health. There are many clinical techniques for the diagnosis of this disease, such as RT-PCR and CT-Scan. X-ray image is one of the important techniques for medical diagnosis and easily accessible in classifying suspected cases of COVID-19 infection. In this study, we classified COVID-19 images with four classes: COVID-19, Normal, Lung opacity and Viral pneumonia by compared three models: EfficientNetB0, MobileNet and GoogLeNet for the performance of classification using 1,000 chest X-ray images from Kaggle dataset within scenario of resource limitations. The experiment reveals that GoogLeNet shows superiority over other models that produced the highest accuracy results of 88% and F1 score of 0.88 with a total time of 1 hour and 15 minutes. Along with its confusion matrix that shows model can better classify images than other models.

关键词： COVID-19 Knowledge engineering Pandemics Pulmonary diseases Lung Medical diagnosis Public healthcare

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：