检索结果-内蒙古大学图书馆

Smart Electronics and Communication (ICOSEC), International Conference on

作者： B. Md. Irfan V. Poornima S Mohana Kumar Upendra Singh Aswal N. Krishnamoorthy Ramya Maranan Department of Information Technology Nalsar University of Law Hyderabad Telengana India Department of Computer Science VISTASPallavaram Chennai Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru Karnataka India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Software Systems and Engineering Vellore Institute of Technology Vellore Campus Vellore Tamilnadu Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

The security of computer networks is increasingly difficult to maintain due to the rising complexity and frequency of cyber-attacks. Important tools for finding and neutralizing these dangers are intrusion detection systems. This study sets out to do a thorough examination and comparison of the efficacy of several machine learning algorithms for use in intrusion detection. This research study evaluates the efficacy of several machine learning algorithms in correctly categorizing instances of network traffic as normal or invasive via extensive experiments performed on representative datasets. Algorithms like random forests, decision trees, SVMs, DL models and NNs are all being tested and rated. Effectiveness is measured and compared using a variety of performance indicators including accuracy, recall, precision, false positive rate, and F1-score. The results of this study emphasize the potential of deep learning models and Random Forests for use in intrusion detection and add to the body of knowledge around machine learning methods for this task. Professionals in the field of network security might use the results to their advantage when building intrusion detection systems. Future research areas are also mentioned, which will hopefully lead to even greater improvements in the field and safer, more reliable intrusion detection systems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

SHORTCUTSBENCH: A LARGE-SCALE REAL-WORLD BENCHMARK FOR API-BASED AGENTS

arXiv

引用

arXiv 2024年

作者： Shen, Haiyang Li, Yue Meng, Desong Cai, Dongqi Qi, Sheng Zhang, Li Xu, Mengwei Ma, Yun Institute for Artificial Intelligence Peking University China School of Computer Science Peking University China School of Software & Microelectronics Peking University China School of Electronics Engineering and Computer Science Peking University China Beijing University of Posts and Telecommunications China

Recent advancements in integrating large language models (LLMs) with application programming interfaces (APIs) have gained significant interest in both academia and industry. Recent work demonstrates that these API-based agents exhibit relatively strong autonomy and planning capabilities. However, their ability to handle multi-dimensional difficulty levels, diverse task types, and real-world demands remains unknown. In this paper, we introduce SHORTCUTSBENCH, a large-scale benchmark for the comprehensive evaluation of API-based agents in solving real-world complex tasks. SHORTCUTSBENCH includes a wealth of real APIs from Apple Inc., refined user queries, human-annotated high-quality action sequences, detailed parameter filling values, and parameters requesting necessary input from the system or user. We revealed how existing benchmarks / datasets struggle to accommodate the advanced reasoning capabilities of existing more intelligent LLMs. Moreover, our extensive evaluation of agents built with 5 leading open-source (size ≥ 57B) and 5 closed-source LLMs (e.g. Gemini-1.5-Pro and GPT-4o-mini) with varying intelligence level reveals significant limitations of existing API-based agents in the whole process of handling complex queries related to API selection, parameter filling, and requesting necessary input from the system and the user. These findings highlight the great challenges that API-based agents face in effectively fulfilling real and complex user queries. All datasets, code, experimental logs, and results are available at https://***/EachSheep/ShortcutsBench . Copyright © 2024, The Authors. All rights reserved.

关键词： Application programming interfaces (API)

来源：评论

学校读者我要写书评

暂无评论

Survey of Hidden Markov Models (HMMs) for Sign Language Recognition (SLR)

Survey of Hidden Markov Models (HMMs) for Sign Language Reco...

引用

Industrial Cyber-Physical systems (ICPS)

作者： Iwan Sandjaja Ahmad Alsharoa Donald Wunsch Jian Liu Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Kummer Institute Center for Artificial Intelligence and Autonomous Systems Missouri University of Science and Technology Rolla USA

ISBN: (数字)9798350363012

ISBN: (纸本)9798350363029

This paper surveyed several significant papers on specific topics applying the Hidden Markov Models (HMMs) for Sign Language Recognition (SLR), divided into five main episodes: Classical HMMs, Extended HMMs, HMMs and Machine Learning, HMMs and Sensor Fusion, and HMMs and Big Data. This stringent survey would contribute significantly to advanced research on unification brain models such as neural networks, adaptive resonance theory, and confabulation theory. First, the HMM was introduced as one of the popular methods of performing SLR, and each episode of its development was expounded. In each episode, a main paper and several supporting papers were summarized. Next, HMM’s general methodology and the remarkable feature extraction methods from each episode should be discussed. The unique feature extraction methods and the Markov decision processes were highlighted. Third, each episode simulation or numerical results were presented, compared, and commented on. Finally, the paper concludes with the author’s takeaway and insight on each paper, especially the relation of HMMs with Recurrent Neural Networks, and provides a road map for future research. One innovation point of this paper is the relic of the suitable topology for both HMMs and recurrent neural networks for the SLR system.

关键词： Surveys Sign language Recurrent neural networks Hidden Markov models Machine learning Sensor fusion Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Hyper Spectral Spatial Image Segmentation Using Machine Learning Algorithms

Hyper Spectral Spatial Image Segmentation Using Machine Lear...

引用

Intelligent Technologies for Sustainable Electric and Communications systems (iTech SECOM), International Conference on

作者： Saravanan G Suresh A Jagannathan J Thanga Mariappan L Najeem Dheen Abdul Majeeth Nithish Kumar Department of Computer Science and Engineering Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamilnadu India School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamilnadu India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamilnadu India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Tamilnadu India

In recent years, there has been a notable surge in the development of methods dedicated to classifying hyper spectral image samples. However, the inherent high dimensionality of hyper spectral data presents significant challenges for traditional supervised classification algorithms. Prior studies have attempted to address these challenges by incorporating active learning methods for hyper spectral data segmentation. Nevertheless, a crucial drawback of active learning lies in its dependence on labeled data samples, leading to less improved classification results for unlabeled data samples. In response to this limitation, our work introduces Supervised Active Learning (SAL) techniques that harness both spectral and spatial information. The key innovation of our research lies in the introduction of a novel hyper spectral image segmentation approach based on the k-dependence Bayesian (KDB) model. We estimate the maximizer of the posterior marginal using Symmetric Iterative Proportional Fitting (SIPF) within this KDB model. By implementing the KDB model for hyper spectral image segmentation, our goal is to enhance the classification accuracy of our SAL classifiers. The SAL framework integrates shape prior probability estimation, class density results from Improved Multi-Level Logistic (IMLL) methods, and the maximization of the posterior marginal estimated through Symmetric Iterative Proportional Fitting (SIPF). We evaluate the accuracy of our proposed MPM-SIPF-SAL system using the real ROSIS Hyper Spectral Dataset, demonstrating that our system outperforms existing methods in terms of classification accuracy.

关键词：

来源：评论

学校读者我要写书评

暂无评论

FedKD-hybrid: Federated Hybrid Knowledge Distillation for Lithography Hotspot Detection

arXiv

引用

arXiv 2025年

作者： Li, Yuqi Lin, Xingyou Zhang, Kai Yang, Chuanguang Guo, Zhongliang Gou, Jianping Li, Yanli Institute of Computing Technology Chinese Academy of Sciences China College of Information Engineering Sichuan Agricultural University China Shenzhen University of Advanced Technology China School of Computer Science University of St Andrews United Kingdom College of Computer and Information Science College of Software Southwest University China School of Artificial Intelligence and Computer Science Nantong University China The School of Electrical and Computer Engineering The University of Sydney Australia

Federated Learning (FL) provides novel solutions for machine learning (ML)-based lithography hotspot detection (LHD) under distributed privacy-preserving settings. Currently, two research pipelines have been investigated to aggregate local models and achieve global consensus, including parameter/non-parameter based (also known as knowledge distillation, namely KD). While these two kinds of methods show effectiveness in specific scenarios, we note they have not fully utilized and transferred the information learned, leaving the potential of FL-based LDH remains unexplored. Thus, we propose FedKD-hybrid in this study to mitigate the research gap. Specifically, FedKD-hybrid clients agree on several identical layers across all participants and a public dataset for achieving global consensus. During training, the trained local model will be evaluated on the public dataset, and the generated logits will be uploaded along with the identical layer parameters. The aggregated information is consequently used to update local models via the public dataset as a medium. We compare our proposed FedKD-hybrid with several state-of-the-art (SOTA) FL methods under ICCAD-2012 and FAB (real-world collected) datasets with different settings;the experimental results demonstrate the superior performance of the FedKD-hybrid algorithm. Our code is available at https://***/itsnotacie/NN-FedKD-hybrid. © 2025, CC BY-NC-ND.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Pancreatic Cancer Diagnosis using Swarm Optimization Combined with Machine Learning Techniques

Pancreatic Cancer Diagnosis using Swarm Optimization Combine...

引用

Smart Technologies, Communication and Robotics (STCR)

作者： N Bharanidharan Pratham Aggarwal Aryan Batra Deep Shah Sannasi Chakravarthy S R Harikumar Rajaguru School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India

When compared to different types of malignant tumors, pancreatic malignancy is the 12 th widespread tumor disease among humans globally. Pancreatic cancer can be identified at the earlier stages using microarray gene analysis. The objective of this work is to classify the gene samples as normal and tumoral through the usage of Particle Swarm Optimization technique along with the supervised machine learning classifiers. A variance-based feature selection is employed to select the most appropriate features and the selected features are transformed using particle swarm optimization to improve the classification performance. Four different machine learning supervised algorithms are realized as classification techniques. When these four vanilla classifiers are considered, the random forest algorithm performs relatively well with balanced accuracy score of 87.5%. This score is improved to 94.4% through the usage of the proposed technique. In addition, the proposed technique enhances the prediction of the other two classifiers as well.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Probabilistic Framework for Pruning Transformers Via a Finite Admixture of Keys 48

A Probabilistic Framework for Pruning Transformers Via a Fin...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Nguyen, Tan M. Nguyen, Tam Bui, Long Do, Hai Nguyen, Duy Khuong Le, Dung D. Tran-The, Hung Ho, Nhat Osher, Stan J. Baraniuk, Richard G. University of California Department of Mathematics Los Angeles United States Rice University Department of Electrical and Computer Engineering Houston United States Fpt Software Ai Center Ha Noi Viet Nam Vin University College of Engineering and Computer Science Viet Nam Deakin University Applied Artificial Intelligence Institute Geelong Australia The University of Texas Department of Statistics and Data Sciences Austin United States

ISBN: (纸本)9781728163277

Pairwise dot product-based self-attention is key to the success of transformers which achieve state-of-the-art performance across a variety of applications in language and vision, but are costly to compute. It has been shown that most attention scores and keys in transformers are redundant and can be removed without loss of accuracy. In this paper, we develop a novel probabilistic framework for pruning attention scores and keys in transformers. We first formulate an admixture model of attention keys whose input data to be clustered are attention queries. We show that attention scores in self-attention correspond to the posterior distribution of this model when attention keys admit a uniform prior distribution. We then relax this uniform prior constraint and let the model learn these priors from data, resulting in a new Finite Admixture of Keys (FiAK). The learned priors are used for pruning away redundant attention scores and keys in the baseline transformers, improving the diversity of attention patterns that the models capture. We corroborate the efficiency of transformers pruned with FiAK on the ImageNet object classification and WikiText-103 language modeling tasks. Our experiments demonstrate that transformers pruned with FiAK yield similar or better accuracy than the baseline dense transformers while being much more efficient in terms of memory and computational cost. © 2023 IEEE.

关键词： admixture models pruning Transformers

来源：评论

学校读者我要写书评

暂无评论

Semantic Change Driven Generative Semantic Communication Framework

Semantic Change Driven Generative Semantic Communication Fra...

引用

IEEE Conference on Wireless Communications and Networking

作者： Wanting Yang Zehui Xiong Hongyang Du Yanli Yuan Tony Q.S. Quek Information Systems Technology and Design Pillar Singapore University of Technology and Design School of Computer Science and Engineering Nanyang Technological University School of Cyberspace Science and Technology Beijing Institute of Technology

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

The burgeoning generative artificial intelligence technology offers novel insights into the development of semantic communication (SemCom) frameworks. These frameworks hold the potential to address the challenges associated with the black-box nature inherent in existing end-to-end training manner for the existing SemCom framework, as well as deterioration of the user experience caused by the inevitable error floor in deep learning-based SemCom. In this paper, we focus on the widespread remote monitoring scenario, and propose a semantic change driven generative SemCom framework. Therein, the semantic encoder and semantic decoder can be optimized independently. Specifically, we develop a modular semantic encoder with value of information based semantic sampling function. In addition, we propose a conditional denoising diffusion probabilistic mode-assisted semantic decoder that relies on received semantic information from the source, namely, the semantic map, and the local static scene information to remotely regenerate scenes. Moreover, we demonstrate the effectiveness of the proposed semantic encoder and decoder as well as the considerable potential in reducing energy consumption through simulation based on the realistic F composite channel fading model. The code is available at https://***/wty2011jl/***.

关键词： Training Energy consumption Generative AI Simulation Semantics Closed box User experience

来源：评论

学校读者我要写书评

暂无评论

Volt-Var Control for Utility-scale Solar PV Plants to Replace SVCs

Volt-Var Control for Utility-scale Solar PV Plants to Replac...

引用

International Conference on High Voltage engineering and Power systems (ICHVEPS)

作者： Ken Shimomukai Haruka Maeda Goro Fujita Functional Control Systems Graduate School of Engineering and Science Shibaura Institute of Technology Tokyo Japan Department of Electrical Engineering and Computer Science Graduate School of Engineering and Science Shibaura Institute of Technology Tokyo Japan Department of Electrical Engineering Shibaura Institute of Technology Tokyo Japan

One way to increase solar photovoltaic penetration in the grid is management of voltage fluctuations. This is because a photovoltaic plant cannot be interconnected to the grid if it causes voltage violations. Voltage violation is where voltage exceeds the acceptable range. Often, grid operators request photovoltaic plant owners to regulate voltage sufficiently with expensive and space-consuming static Var compensators. Unfortunately, this sometimes makes the project less feasible. This paper argues that there are better ways to regulate voltage. We ran a simulation with a 70 MWp photovoltaic plant as an addition to the grid. Without voltage regulation, voltage violations were found to be significant. This paper found that oversizing the inverter sufficiently would remove all voltage violations without deploying a static Var compensator. This is often a cheaper and space-saving solution for voltage management. This paper argues that economics and spatial efficiency of reactive power compensation devices is key to increasing photovoltaic penetration.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Interactive 3D Medical Image Segmentation with SAM 2

arXiv

引用

arXiv 2024年

作者： Shen, Chuyun Li, Wenhao Shi, Yuhang Wang, Xiangfeng Huang, Yifei School of Computer Science and Technology East China Normal University Shanghai200062 China School of Software Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai200092 China Shanghai United Imaging Healthcare Advanced Technology Research Institute Co. Ltd Shanghai201807 China School of Software Engineering East China Normal University Shanghai200062 China

Interactive medical image segmentation (IMIS) has shown significant potential in enhancing segmentation accuracy by integrating iterative feedback from medical professionals. However, the limited availability of enough 3D medical data restricts the generalization and robustness of most IMIS methods. The Segment Anything Model (SAM), though effective for 2D images, requires expensive semi-auto slice-by-slice annotations for 3D medical images. In this paper, we explore the zero-shot capabilities of SAM 2, the next-generation Meta SAM model trained on videos, for 3D medical image segmentation. By treating sequential 2D slices of 3D images as video frames, SAM 2 can fully automatically propagate annotations from a single frame to the entire 3D volume. We propose a practical pipeline for using SAM 2 in 3D medical image segmentation and present key findings highlighting its efficiency and potential for further optimization. Concretely, numerical experiments on the BraTS2020 and the medical segmentation decathlon datasets demonstrate that SAM 2 still has a gap with supervised methods but can narrow the gap in specific settings and organ types, significantly reducing the annotation burden on medical professionals. Our code will be open-sourced and available at https://***/Chuyun-Shen/SAM_2_Medical_3D. Copyright © 2024, The Authors. All rights reserved.

关键词： Medical imaging

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：