检索结果-内蒙古大学图书馆

17th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2024

作者： Lu, Wei Yu, Jiaqi Ding, Jie Chen, Jilong Liu, Yulong Lv, Jiahui Huang, Xiaolin Wang, Jun Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing China

ISBN: (纸本)9798331507398

The brain is a typical nonlinear complex system influenced by various factors. This study aims to explore the changes in conditional entropy during memory tasks to quantify cognitive Electroencephalogram(EEG) activity under different memory loads and to evaluate the effectiveness of conditional entropy in revealing the working memory process. A total of 18 graduate students from Nanjing University participated in the memory task experiment, during which EEG signals were collected synchronously and analyzed using conditional entropy. Two methods were employed for calculating conditional entropy: K-nearest neighbor estimation and kernel estimation. The kernel estimation method utilized Sigmoid, Gaussian, and Heaviside kernel functions. Particularly in the kernel estimation method employing Gaussian and Heaviside functions, better results and stability were obtained, demonstrating optimal performance. The study findings indicate that as working memory load increases, the complexity of cognitive processing in the brain also rises, especially in brain regions associated with working memory, attention, and task execution, such as the frontal pole (FP1), middle frontal gyrus (F8), and central region (Cz). © 2024 IEEE.

关键词： Nearest neighbor search

来源：评论

学校读者我要写书评

暂无评论

Self-Transriber: Few-Shot Lyrics Transcription With Self-Training 48

Self-Transriber: Few-Shot Lyrics Transcription With Self-Tra...

引用

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

作者： Gao, Xiaoxue Yue, Xianghu Li, Haizhou National University of Singapore Department of Electrical and Computer Engineering Singapore Shenzhen Research Institute of Big Data Shenzhen China The Chinese University of Hong Kong School of Data Science Shenzhen China

ISBN: (纸本)9781728163277

The current lyrics transcription approaches heavily rely on supervised learning with labeled data, but such data are scarce and manual labeling of singing is expensive. How to benefit from unlabeled data and alleviate limited data problem have not been explored for lyrics transcription. We propose the first semi-supervised lyrics transcription paradigm, Self-Transcriber, by leveraging on unlabeled data using selftraining with noisy student augmentation. We attempt to demonstrate the possibility of lyrics transcription with a few amount of labeled data. Self-Transcriber generates pseudo labels of the unlabeled singing using teacher model, and augments pseudo-labels to the labeled data for student model update with both self-training and supervised training losses. This work closes the gap between supervised and semi- supervised learning as well as opens doors for few-shot learning of lyrics transcription. Our experiments show that our approach using only 12.7 hours of labeled data achieves competitive performance compared with the supervised approaches trained on 149.1 hours of labeled data for lyrics transcription. © 2023 IEEE.

关键词： Lyrics transcription music information retrieval singing processing and self-training

来源：评论

学校读者我要写书评

暂无评论

A GNN-GUIDED PREDICT-AND-SEARCH FRAMEWORK FOR MIXED-INTEGER LINEAR PROGRAMMING 11

A GNN-GUIDED PREDICT-AND-SEARCH FRAMEWORK FOR MIXED-INTEGER ...

引用

11th International Conference on Learning Representations, ICLR 2023

作者： Han, Qingyu Yang, Linxin Chen, Qian Zhou, Xiang Zhang, Dong Wang, Akang Sun, Ruoyu Luo, Xiaodong Shenzhen Research Institute of Big Data China Shandong University China School of Data Science The Chinese University of Hong Kong Shenzhen China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Huawei China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data China

Mixed-integer linear programming (MILP) is widely employed for modeling combinatorial optimization problems. In practice, similar MILP instances with only coefficient variations are routinely solved, and machine learning (ML) algorithms are capable of capturing common patterns across these MILP instances. In this work, we combine ML with optimization and propose a novel predict-and-search framework for efficiently identifying high-quality feasible solutions. Specifically, we first utilize graph neural networks to predict the marginal probability of each variable, and then search for the best feasible solution within a properly defined ball around the predicted solution. We conduct extensive experiments on public datasets, and computational results demonstrate that our proposed framework achieves 51.1% and 9.9% performance improvements to MILP solvers SCIP and Gurobi on primal gaps, respectively. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Noise-Filtering Enhanced Deep Cognitive Diagnosis Model for LatentSkill Discovering

引用

Intelligent Automation & Soft Computing 2023年第8期37卷 1311-1324页

作者： Jing Geng Huali Yang Shengze Hu National Engineering Research Center of Educational Big Data Central China Normal UniversityWuhan430079China Faculty of Artificial Intelligence in Education Central China Normal UniversityWuhan430079China School of Computer Science and Artificial Intelligence Wuhan Textile UniversityWuhan430079China

Educational data mining based on student cognitive diagnosis analysis can provide an important decision basis for personalized learning tutoring of students,which has attracted extensive attention from scholars at home and abroad and has made a series of important research *** this end,we propose a noise-filtering enhanced deep cognitive diagno-sis method to improve the fitting ability of traditional models and obtain students’skill mastery status by mining the interaction between students and problems nonlinearly through neural ***,modeling complex interactions between students and problems with multidimensional features based on cognitive processing theory can enhance the interpretability of the proposed model;second,the neural network is used to predict students’learning performance,diagnose students’skill mastery and provide immediate feedback;finally,by comparing the proposed model with several baseline models,extensive experimental results on real data sets demonstrate that the proposed Finally,by comparing the proposed model with several baseline models,the extensive experimental results on the actual data set demon-strate that the proposed model not only improves the accuracy of predicting students’learning performance but also enhances the interpretability of the neurocognitive diagnostic model.

关键词： Cognitive diagnosis nonlinear interaction interpretability intelligent education system skill diagnosis

来源：评论

学校读者我要写书评

暂无评论

Automated Testing for Service-Oriented Architecture: Leveraging Large Language Models for Enhanced Service Composition

引用

IEEE Access 2025年 13卷 89627-89640页

作者： Altin, Mahsun Mutlu, Behcet Kilinc, Deniz Cakir, Altan Istanbul Technical University Department of Computer Engineering Istanbul34467 Turkey Wingie Enuygun Group Istanbul34750 Turkey University of Bakirçay Department of Computer Engineering Izmir35665 Turkey Istanbul Technical University Artificial Intelligence Data Science Research and Application Center Istanbul34467 Turkey ITU Ari Teknokent Parton Big Data Analytics and Consulting Istanbul34467 Turkey Adin.Ai WilmingtonDE19806 United States

This article explores the application of Large Language Models (LLMs), including proprietary models such as OpenAI's ChatGPT 4o and ChatGPT 4o-mini, Anthropic's Claude 3.5 Sonnet and Claude 3.7 Sonnet, and Google's Gemini 1.5 Pro, Gemini 2.0 Flash, and Gemini 2.0 Flash-Lite, as well as open-source alternatives including Qwen2.5-14B-Instruct-1M, and commercially accessed models such as DeepSeek R1 and DeepSeek V3, which were tested via APIs despite having open-source variants, to automate validation and verification in Application Programming Interface (API) testing within a Service-Oriented Architecture (SOA). Our system compares internal responses from the Enuygun Web Server against third-party API outputs in both JSON and XML formats, validating critical parameters such as flight prices, baggage allowances, and seat availability. We generated 100 diverse test scenarios across varying complexities (1-4 flight results) by randomly altering request and response parameters. Experimental results show that Google Gemini 2.0 Flash achieved high accuracy (up to 99.98%) with the lowest completion time (85.34 seconds), while Qwen2.5-14B-Instruct-1M exhibited limited capability in processing complex formats. Models such as OpenAI's ChatGPT and Anthropic's Claude Sonnet models also demonstrated strong performance in single-flight validation scenarios, making them suitable for low-latency, high-precision tasks. Our findings indicate that some open-source models can offer promising cost-effective alternatives, though performance significantly varies. This integration of LLMs reduced manual workload, improved test scalability, and enabled real-time validation across large-scale datasets. As LLM technologies mature, we anticipate further advances in automation, accuracy, and efficiency in software validation systems. © 2013 IEEE.

关键词： Service oriented architecture (SOA)

来源：评论

学校读者我要写书评

暂无评论

Imitation Learning from Purified Demonstrations 41

Imitation Learning from Purified Demonstrations

引用

41st International Conference on Machine Learning, ICML 2024

作者： Wang, Yunke Dong, Minjing Zhao, Yukun Du, Bo Xu, Chang School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan Institute of Data Intelligence Wuhan University China Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science Faculty of Engineering The University of Sydney Australia

Imitation learning has emerged as a promising approach for addressing sequential decision-making problems, with the assumption that expert demonstrations are optimal. However, in real-world scenarios, most demonstrations are often imperfect, leading to challenges in the effectiveness of imitation learning. While existing research has focused on optimizing with imperfect demonstrations, the training typically requires a certain proportion of optimal demonstrations to guarantee performance. To tackle these problems, we propose to purify the potential noises in imperfect demonstrations first, and subsequently conduct imitation learning from these purified demonstrations. Motivated by the success of diffusion model, we introduce a two-step purification via diffusion process. In the first step, we apply a forward diffusion process to smooth potential noises in imperfect demonstrations by introducing additional noise. Subsequently, a reverse generative process is utilized to recover the optimal demonstration from the diffused ones. We provide theoretical evidence supporting our approach, demonstrating that the distance between the purified and optimal demonstration can be bounded. Empirical results on MuJoCo and RoboSuite demonstrate the effectiveness of our method from different aspects. Copyright 2024 by the author(s)

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion 10

Multi-Label Text Classification for Judicial Texts via Dual ...

引用

10th IEEE Smart World Congress, SWC 2024

作者： Gu, Qiliang Lu, Qin Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331520861

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional Network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to 2. 2 8 % and a minimum of 1. 2 9 % across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model. © 2024 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

MeHyper: Accelerating Hypergraph Neural Networks by Exploring Implicit dataflows 31

MeHyper: Accelerating Hypergraph Neural Networks by Explorin...

引用

31st IEEE International Symposium on High Performance computer Architecture, HPCA 2025

作者： Zhao, Wenju Yao, Pengcheng Chen, Dan Zheng, Long Liao, Xiaofei Wang, Qinggang Ma, Shaobo Li, Yu Liu, Haifeng Xiao, Wenjing Sun, Yufei Zhu, Bing Jin, Hai Xue, Jingling Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China National University of Singapore School of Computing 119077 Singapore Guangxi University School of Computer Electronics and Information NanNing530004 China University of New South Wales School of Computer Science and Engineering SydneyNSW2052 Australia

ISBN: (纸本)9798331506476

Hypergraph Neural Networks (HGNNs) are increasingly utilized to analyze complex inter-entity relationships. Traditional HGNN systems, based on a hyperedge-centric dataflow model, independently process aggregation tasks for hyperedges and vertices, leading to significant computational redundancy. This redundancy arises from recalculating shared information across different tasks. For the first time, we identify and harness implicit dataflows (i.e., dependencies) within HGNNs, introducing the microedge concept to effectively capture and reuse intricate shared information among aggregation tasks, thereby minimizing redundant computations. We have developed a new microedge-centric dataflow model that processes shared information as fine-grained microedge aggregation tasks. This dataflow model is supported by the Read-Process-Activate-Generate execution model, which aims to optimize parallelism among these tasks. Furthermore, our newly developed MeHyper, a microedge-centric HGNN accelerator, incorporates a decoupled pipeline for improved computational parallelism and a hierarchical feature management strategy to reduce off-chip memory accesses for large volumes of intermediate feature vectors generated. Our evaluation demonstrates that MeHyper substantially outperforms the leading CPUbased system PyG-CPU and the GPU-based system HyperGef, delivering performance improvements of 1,032.23 × and 10.51 ×, and energy efficiencies of 1,169.03 × and 9.96 ×, respectively. © 2025 IEEE.

关键词： Redundancy

来源：评论

学校读者我要写书评

暂无评论

Hash Function Based on Quantum Walks with Two-Step Memory

Hash Function Based on Quantum Walks with Two-Step Memory

引用

2024 International Conference on Computing, Machine Learning and data science, CMLDS 2024

作者： Zhou, Qing Lu, Songfeng Yang, Hao Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Guangdong Shenzhen China

ISBN: (纸本)9798400716393

We propose a new quantum-walk-based hash function QHF2M by combining two types of quantum walks with two-step memory and numerically test its statistical performance. The test result shows that QHF2M is on a par with the state-of-the-art hash functions based on discrete quantum walks in terms of sensitivity of hash value to message, diffusion and confusion properties, uniform distribution property, and collision resistance property. Moreover, both unequal and equivalent coin angles used by the underlying quantum walks can lead to near-ideal statistical performance. This result indicates that neither using quantum walks with different memory length nor adopting distinct coin angles is a necessary condition for constructing good quantum-walk-with-memory-based hash functions. © 2024 ACM.

关键词： Hash functions

来源：评论

学校读者我要写书评

暂无评论

Layered Structure Aware Dependent Microservice Placement Toward Cost Efficient Edge Clouds 42

Layered Structure Aware Dependent Microservice Placement Tow...

引用

42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Zeng, Deze Geng, Hongmin Gu, Lin Li, Zhexiong China University of Geosciences School of Computer Science Wuhan China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350334142

Although the containers are featured by light-weightness, it is still resource-consuming to pull and startup a large container image, especially in relatively resource-constrained edge cloud. Fortunately, Docker, as the most widely used container, provides a unique layered architecture that allows the same layer to be shared between microservices so as to lower the deployment cost. Meanwhile, it is highly desirable to deploy dependent microservices of an application together to lower the operation cost. Therefore, the balancing of microservice deployment cost and the operation cost should be considered comprehensively to achieve minimal overall cost of an on-demand application. In this paper, we first formulate this problem into a Quadratic Integer Programming form (QIP) and prove it as a NP-hard problem. We further propose a Randomized Rounding-based Microservice Deployment and Layer Pulling (RR-MDLP) algorithm with low computation complexity and guaranteed approximation ratio. Through extensive experiments, we verify the high efficiency of our algorithm by the fact that it significantly outperforms existing state-of-the-art microservice deployment strategies. © 2023 IEEE.

关键词： Containers

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：