检索结果-内蒙古大学图书馆

International Conference on Fuzzy Theory and it's Applications (iFUZZY)

作者： M. Kowsalya In-Ho Ra K. S. Naveenraj S. Thruvenkadam Seok-Woo Park School of Electrical Engineering Vellore Institute of Technology Vellore India School of Computer & Software Kunsan National University Gunsan South Korea Internet Protocol Division Jio Platform Limited Mumbai India

ISBN: (数字)9798350352788

ISBN: (纸本)9798350352795

The need for flexible and effective 5G communication networks has prompted studies into novel approaches for network slicing, a fundamental paradigm that permits network service personalization for a range of applications. In this research, we provide a unique technique that combines Graph Convolution Networks (GCN) and Grey Wolf Optimization (GWO) strategies to improve the adaptive configuration of 5G network slices. GCN, a software capable of recording detailed network interactions, and GWO, well-known for its metaheuristic optimization, combine to offer a strong foundation for tackling dynamic network slicing challenges. We optimize GWO settings via rigorous testing and demonstrate the effectiveness of the integrated strategy by achieving gains in important performance measures including latency, throughput, and resource use. The investigation not only advances the continuing development of 5G networks but also highlights how cutting-edge machine learning and optimization techniques can completely transform network slicing approaches.

关键词： Adaptive systems 5G mobile communication Convolution Network slicing Transforms Throughput software

来源：评论

学校读者我要写书评

暂无评论

Learning Activation Functions for Sparse Neural Networks

arXiv

引用

arXiv 2023年

作者： Loni, Mohammad Mohan, Aditya Asadi, Mehdi Lindauer, Marius Division of Computer Science and Software Engineering Mälardalen University Sweden Institute of Artificial Intelligence Leibniz University Hannover Germany Department of Electrical Engineering Tarbiat Modares University Tehran Iran

Sparse Neural Networks (SNNs) can potentially demonstrate similar performance to their dense counterparts while saving significant energy and memory at inference. However, the accuracy drop incurred by SNNs, especially at high pruning ratios, can be an issue in critical deployment conditions. While recent works mitigate this issue through sophisticated pruning techniques, we shift our focus to an overlooked factor: hyperparameters and activation functions. Our analyses have shown that the accuracy drop can additionally be attributed to (i) Using ReLU as the default choice for activation functions unanimously, and (ii) Fine-tuning SNNs with the same hyperparameters as dense counterparts. Thus, we focus on learning a novel way to tune activation functions for sparse networks and combining these with a separate hyperparameter optimization (HPO) regime for sparse networks. By conducting experiments on popular DNN models (LeNet-5, VGG-16, ResNet-18, and EfficientNet-B0) trained on MNIST, CIFAR-10, and ImageNet-16 datasets, we show that the novel combination of these two approaches, dubbed Sparse Activation Function Search, short: SAFS, results in up to 15.53%, 8.88%, and 6.33% absolute improvement in the accuracy for LeNet-5, VGG-16, and ResNet-18 over the default training protocols, especially at high pruning ratios.1 © 2023, CC BY.

关键词： Drops

来源：评论

学校读者我要写书评

暂无评论

SCS: A Secure Cloud Storage Framework with Enhanced Integrity and Auditability Using Consortium Blockchain System

SCS: A Secure Cloud Storage Framework with Enhanced Integrit...

引用

IEEE International Conference on Smart Internet of Things (SmartIoT)

作者： Aguru Aswani Devi Erukala Suresh Babu Mekala Srinivasa Rao Rajesh Kaluri Thippa Reddy Gadekallu Department of Computer Science & Engineering SRM University AP Amaravati Andhra Pradesh India Department of Computer Science & Engineering National Institute of Technology Warangal India Department of Computer Science & Engineering Lakireddy Bali Reddy College of Engineering India Department of Software and Systems Engineering Vellore Institute of Technology Vellore India The College of Mathematics and Computer Science Zhejiang A&F University Hangzhou China Division of Research and Development Lovely Professional University Phagwara India

ISBN: (数字)9798350366440

ISBN: (纸本)9798350366457

The widespread adoption of cloud storage enables users to remotely access resources through a self-service model. Utilizing pay-per-use storage services provided by cloud service providers (CSPs) requires users to commit financially to their resources. This paper introduces a Secure Cloud Storage (SCS) framework, offering a secure architecture for cloud storage using a consortium blockchain network to address trust issues. This framework substitutes the third-party auditor with peers of a consortium blockchain network, which handles the role of data storage and verification. Storage space is divided into uncommitted and committed segments. Uncommitted storage is used for storing unverified documents, while committed storage is reserved for documents that have been validated through a consensus mechanism. In contrast, committed storage is des-ignated for the storage of committed documents. Documents validated by a consensus threshold of peer nodes are moved from uncommitted to committed storage. The implementation of the SCS framework is conducted using Hyperledger Fabric, a modular blockchain platform optimized for permissioned networks. The security analysis demonstrates that SCS effectively protects cloud storage against attacks, including unauthorized access attacks, data integrity attacks, and malicious server attacks, while maintaining data integrity and auditability. The performance evaluation shows that document upload and retrieval times, block acceptance, execution times, and latency are all improved compared to state-of-the-art cloud storage techniques.

关键词： Performance evaluation Cloud computing Data integrity Memory Peer-to-peer computing Servers Security Internet of Things Secure storage Self-service

来源：评论

学校读者我要写书评

暂无评论

Poisoning Actuation Attacks Against the Learning of an Optimal Controller

Poisoning Actuation Attacks Against the Learning of an Optim...

引用

American Control Conference (ACC)

作者： Filippos Fotiadis Aris Kanellopoulos Kyriakos G. Vamvoudakis Jérôme Hugues Daniel Guggenheim School of Aerospace Engineering Georgia Institute of Technology Atlanta GA USA Division of Information Science and Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Carnegie Mellon University/Software Engineering Institute Pittsburgh PA USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

In this paper, we study the problem of poisoning the learning of an optimal controller by means of an actuation attack. We specifically consider a user who is gathering data from a linear system in the form of input and state measurements, and who uses these data to learn an optimal controller. Nevertheless, these measurements are corrupted by an attacker who has access to the system's actuators, and who is using them to launch an actuation attack during the learning process. We design this actuation attack so that it optimally corrupts the data used by the user: it forces the user to learn as closely as possible a gain that the attacker has selected, and which is unrelated to the actual optimal control gain. We prove that this poisoning actuation attack design boils down to the solution of certain coupled matrix equations, which we solve using the block successive over-relaxation (SOR) iterative procedure. Simulations on an aircraft model demonstrate theoretical findings, showing how the poisoning attack is effective in misleading the user towards learning an incorrect gain for the system.

关键词： Linear systems Actuators Atmospheric modeling Optimal control Mathematical models Iterative methods Aircraft

来源：评论

学校读者我要写书评

暂无评论

Towards Fully-Automated Materials Discovery via Large-Scale Synthesis Dataset and Expert-Level LLM-as-a-Judge

arXiv

引用

arXiv 2025年

作者： Kim, Heegyu Jeon, Taeyang Choi, Seungtaek Hong, Jihoon Jeon, Dongwon Baek, Ga-Yeon Kwak, Kyung-Won Lee, Dong-Hee Bae, Jisu Lee, Chihoon Kim, Yunseo Choi, Sun-Jin Park, Jinsung Cho, Sungbum Cho, Hyunsouk Department of Artificial Intelligence Korea Republic of Department of Software and Computer Engineering Korea Republic of Department of Materials Science and Engineering Department of Energy Systems Research Ajou University Suwon16499 Korea Republic of Division of Materials Science and Engineering Hanyang University Seoul04763 Korea Republic of

Materials synthesis is vital for innovations such as energy storage, catalysis, electronics, and biomedical devices. Yet, the process relies heavily on empirical, trial-and-error methods guided by expert intuition. Our work aims to support the materials science community by providing a practical, data-driven resource. We have curated a comprehensive dataset of 17K expert-verified synthesis recipes from open-access literature, which forms the basis of our newly developed benchmark, AlchemyBench. AlchemyBench offers an end-to-end framework that supports research in large language models applied to synthesis prediction. It encompasses key tasks, including raw materials and equipment prediction, synthesis procedure generation, and characterization outcome forecasting. We propose an LLM-as-a-Judge framework that leverages large language models for automated evaluation, demonstrating strong statistical agreement with expert assessments. Overall, our contributions offer a supportive foundation for exploring the capabilities of LLMs in predicting and guiding materials synthesis, ultimately paving the way for more efficient experimental design and accelerated innovation in materials science. © 2025, CC BY-SA.

关键词： Characterization (materials science)

来源：评论

学校读者我要写书评

暂无评论

The Future of Differentiated Thyroid Cancer Recurrence Prediction Using a Machine Learning Framework Advancements, Challenges, and Prospects

The Future of Differentiated Thyroid Cancer Recurrence Predi...

引用

International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Irsa Imtiaz Attique Ur Rehman Sabeen Javaid Tahir Mohammad Ali Azka Mir Mehedi Masud Yadaiah Nirsanametla Department of Software Engineering University of Sialkot Sialkot Pakistan Department of Software Engineering GULF University for Sciences and Technology Mubarak Al-Abdullah Kuwait Department of Computer Science Taif University Taif Saudia Arabia Division of Research and Development Lovely Professional University Phagwara KKR and KSR institute of technology and sciences Guntur Andhra Pradesh

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Differentiated thyroid cancer originates in the thyroid gland, which is positioned in the front of the neck. The thyroid gland generates hormones that control metabolism, heart rate, and other bodily functions. Differentiated thyroid carcinoma (DTC) recurrence is a major challenge in clinical therapy. Early detection and treatment play an important role in reducing the impact of thyroid cancer recurrence. The development of precise prediction algorithms is demanding. The prognosis, diagnosis, and treatment of differentiated thyroid carcinoma have been the subject of extensive investigation. Scholars have investigated diverse methodologies to forecast the likelihood of getting thyroid cancer, refine early identification techniques, and augment therapeutic results. Machine learning (ML) frameworks have emerged as useful tools in this setting, with the potential to improve prediction accuracy and patient outcomes. This paper provides a detailed evaluation of the current state and future directions of DTC recurrence prediction using machine learning. We examine current advances in machine learning techniques, data sources, and feature selection approaches used in DTC recurrence prediction models. Several machine-learning algorithms have been applied. We have suggested the model containing the classifier with the highest accuracy after comparing the accuracy percentage of the various classifiers that were produced. Our suggested model has a 98.17% accuracy rate with Bagging. Finally, we propose solutions to these difficulties and emphasize ML's potential to revolutionize the landscape of DTC recurrence prediction. We hope that this analysis will provide insights into the developing role of machine learning in DTC management and motivate more research in this crucial area.

关键词： Accuracy Machine learning algorithms Thyroid cancer Soft sensors Machine learning Predictive models Prediction algorithms Neck Prognostics and health management Thyroid

来源：评论

学校读者我要写书评

暂无评论

Predict and Interpret Health Risk Using Ehr Through Typical Patients

Predict and Interpret Health Risk Using Ehr Through Typical ...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhihao Yu Chaohe Zhang Yasha Wang Wen Tang Jiangtao Wang Liantao Ma School of Computer Science Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China National Engineering Research Center of Software Engineering Peking University Beijing China Division of Nephrology Peking University Third Hospital Beijing China The Centre for Intelligent Healthcare Coventry University UK

Predicting health risks from electronic health records (EHR) is a topic of recent interest. Deep learning models have achieved success by modeling temporal and feature interaction. However, these methods learn insufficient representations and lead to poor performance when it comes to patients with few visits or sparse records. Inspired by the fact that doctors may compare the patient with typical patients and make decisions from similar cases, we propose a Progressive Prototypical Network (PPN) to select typical patients as prototypes and utilize their information to enhance the representation of the given patient. In particular, a progressive prototype memory and two prototype separation losses are proposed to update prototypes. Besides, a novel integration is introduced for better fusing information from patients and prototypes. Experiments on three real-world datasets demonstrate that our model brings improvement on all metrics. To make our results better understood by physicians, we developed an application at http://***. Our code is released at https://***/yzhHoward/PPN.

关键词：

来源：评论

学校读者我要写书评

暂无评论

AircraftVerse: a large-scale multimodal dataset of aerial vehicle designs 23

AircraftVerse: a large-scale multimodal dataset of aerial ve...

引用

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Adam D. Cobb Anirban Roy Daniel Elenius F. Michael Heim Brian Swenson Sydney Whittington James D. Walker Theodore Bapty Joseph Hite Karthik Ramani Christopher McComb Susmit Jha Computer Science Laboratory SRI International Mechanical Engineering Division Southwest Research Institute Intelligent Systems Division Southwest Research Institute Institute of Software Integrated Systems Vanderbilt University School of Mechanical Engineering Purdue University Department of Mechanical Engineering Carnegie Mellon University

We present AircraftVerse, a publicly available aerial vehicle design dataset. Aircraft design encompasses different physics domains and, hence, multiple modalities of representation. The evaluation of these cyber-physical system (CPS) designs requires the use of scientific analytical and simulation models ranging from computer- aided design tools for structural and manufacturing analysis, computational fluid dynamics tools for drag and lift computation, battery models for energy estimation, and simulation models for flight control and dynamics. AircraftVerse contains 27,714 diverse air vehicle designs - the largest corpus of engineering designs with this level of complexity. Each design comprises the following artifacts: a symbolic design tree describing topology, propulsion subsystem, battery subsystem, and other design details; a STandard for the Exchange of Product (STEP) model data; a 3D CAD design using a stereolithography (STL) file format; a 3D point cloud for the shape of the design; and evaluation results from high fidelity state-of-the-art physics models that characterize performance metrics such as maximum flight distance and hover-time. We also present baseline surrogate models that use different modalities of design representation to predict design performance metrics, which we provide as part of our dataset release. Finally, we discuss the potential impact of this dataset on the use of learning in aircraft design and, more generally, in CPS. AircraftVerse is accompanied by a data card, and it is released under Creative Commons Attribution-ShareAlike (CC BY-SA) license. The dataset is hosted at https://***/record/6525446 baseline models and code at https://***/SRI-CSL/AircraftVerse and the dataset description at https://***/.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Efficient Privacy-Preserving ML for IoT: Cluster-Based Split Federated Learning Scheme for Non-IID Data 7

Efficient Privacy-Preserving ML for IoT: Cluster-Based Split...

引用

7th Cyber Security in Networking Conference, CSNet 2023

作者： Arafeh, Mohamad Wazzeh, Mohamad Ould-Slimane, Hakima Talhi, Chamseddine Mourad, Azzam Otrok, Hadi Department of Software and It Engineering Montreal Canada Department of Mathematics and Computer Science Canada New York University Division of Science Abu Dhabi United Arab Emirates Cyber Security Systems and Applied Ai Research Center Lebanese American University Department of Csm Beirut Lebanon Khalifa University Department of Eecs Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350342871

This paper proposes a scheme addressing the challenges of integrating privacy-preserving distributed machine learning in the Internet of Things (IoT) context while improving the efficiency of the learning process and accelerating the convergence of the model. Specifically, it addresses the presence of non-independent and identically distributed (Non-IID) data and device heterogeneity, which occurs during parallel training and are common characteristics of IoT environments. In this context, SplitFed is a privacy-preserving technique that combines Federated Learning (FL) and Split Learning (SL) to enable fast and privacy-preserving model training on IoT devices. Through parallelism, FL allows fast and parallel model training. Furthermore, SL enables weak devices to train models through model splitting. However, SplitFed suffers from the increasing convergence time due to the FL parallelism introducing Non-IID and stragglers issues. In this paper, we aim to address the aforementioned problems by proposing a clustering parallelization scheme. As such, similar clients are combined based on their weight divergence and considered a single FL unit addressing the Non-IID problem. Moreover, to improve time efficiency and overcome the stragglers' presence, we offer a second clustering stage that separates clients by efficiency and uses FedAsync for model aggregation. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Incremental Instance Segmentation for Cluttered Baggage Threat Detection

Incremental Instance Segmentation for Cluttered Baggage Thre...

引用

IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications

作者： Ammara Nasim Divya Velayudhan Abdelfatah Hassan Ahmed Taimur Hassan Samet Akcay Muhammad Usman Akram Naoufel Werghi Department of Computer and Software Engineering National University of Sciences and Technology Pakistan Department of Electrical Engineering and Computer Science KUCARS and C2PS Khalifa University UAE Department of Electrical and Computer Engineering Abu Dhabi University Abu Dhabi UAE R&D Division Intel Corporation UK

Identification of contraband items from highly oc-cluded baggage of air travelers is a challenging task even for human experts with very high experience. Many researchers have been working rigorously to develop computer vision-based techniques for baggage screening through X-ray images. Nu-merous machine learning and deep learning-based frameworks have been proposed by researchers in the last two decades. However, all of these techniques face limitations in segmenting prohibited items from highly occluded and cluttered baggage. In this paper, we propose a novel framework based on semantic segmentation to automatically detect concealed prohibited items from X-ray baggage scans. Furthermore, to detect different overlapping instances of the same contraband item, we propose an instance-aware segmentation model that enables the semantic segmentation model to identify multiple instances of the same threat category through incremental learning without requiring additional overhead. The proposed framework is computationally lighter compared to other similar approaches as it requires min-imal training examples and leverages previous knowledge. The proposed model has outperformed state-of-the-art instance seg-mentation techniques when tested on publicly available GDXray and SIXray datasets, giving mean average precision scores of 0.50 and 0.47, respectively. In addition, the proposed framework leads other instance segmentation baseline models in terms of mean inference time.

关键词：

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：