检索结果-内蒙古大学图书馆

International Conference on computer and Communications (ICCC)

作者： Abebe Tamrat Tegene Qiao Liu Yanglei Gan Elias Jimale Melak Ayenew Habte Leka School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Center of Excellence of AI and Robotics Addis Ababa Science and Technology University Addis Ababa Ethiopia School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

In many e-commerce recommendation systems, user profiles are mostly not available. In such cases, the recommendation task considers user behaviors like click sessions or browsing history. Traditional recommender methods often rely on users’ past interactions for recommending user interests, which is difficult to apply to session-based recommender systems. However, these methods perform very poorly on this task. Recently, deep learning techniques such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs) showed outstanding performance in session-based recommendation problems. In this paper, we propose a hybrid CNN and gated recurrent unit (GRU) model that exploits the advantages of both CNN and GRU in modeling the rigid order requirement for interactions inside sessions and modeling the sequential long-term dependencies in session data, respectively. Our proposed hybrid model has two architectures: the first part consists of the CNN layers that take input from Item ID to learn local features and relationships in sessions, and the second part is a GRU layer that takes the output from the CNN layer to learn the long-term dependencies in that session. This hybrid technique enables the architecture for modeling prominent features in the session to provide accurate recommendations. Extensive experiments over a real-world dataset demonstrate that the proposed hybrid model performs well and outperforms those GRU and CNN-based models in the state-of-the-art recommendation task on both metrics used.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A New Class of Multiple Optical ZCZ Sequence Sets with Good Cross-Correlation

A New Class of Multiple Optical ZCZ Sequence Sets with Good ...

引用

Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Yang Yan Rize Jin Qiuyan Wang Shimin Sun School of Information Technology and Engineering Tianjin University of Technology and Education Tianjin China School of Software Tiangong University Tianjin Chian School of Computer Science and Technology Tiangong University Tianjin China

Zero correlation zone (ZCZ) sequences have been widely applied to quasi-synchronous code division multiple access system (QS-CDMA) and therefore have been extensively studied in recent years. In this paper, ZCZ sequence sets are constructed. The presented ZCZ sequence sets have the following properties: 1) each sequence in the constructed sequence set is perfect; 2) each set is an optimal ZCZ sequence set with parameters (N 2 ,N,N); 3) the maximum inter-set cross-correlation of the given multiple sequence sets meets the Sarwate bound.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Learning dynamics of kernel-based deep neural networks in manifolds

引用

science China(Information sciences) 2021年第11期64卷 105-119页

作者： Wei WU Xiaoyuan JING Wencai DU Guoliang CHEN School of Computer Science Wuhan University Institute of Deep-sea Science and Engineering Chinese Academy of Sciences School of Computer Guangdong University of Petrochemical Technology Institute of Data Science City University of Macau College of Computer Science and Software Engineering Shenzhen University

Convolutional neural networks(CNNs) obtain promising results via layered kernel convolution and pooling operations, yet the learning dynamics of the kernel remain obscure. We propose a continuous form to describe kernel-based convolutions through integration in neural manifolds. The status of spatial expression is proposed to analyze the stability of kernel-based CNNs. We divide CNN dynamics into the three stages of unstable vibration, collaborative adjusting, and stabilized fluctuation. According to the system control matrix of the kernel, the kernel-based CNN training proceeds via the unstable and stable status and is verified by numerical experiments.

关键词： learning dynamics kernel-based convolution manifolds control model network stability

来源：评论

学校读者我要写书评

暂无评论

Assessing Team-Based Capstone Projects: Challenges and Recommendations 2025

Assessing Team-Based Capstone Projects: Challenges and Recom...

引用

Proceedings of the 30th ACM Conference on Innovation and Technology in computer science Education V. 2

作者： Sara Hooshangi Asma Shakil Steve Riddle Ilknur Aydin Nayla Nasir Tejasvi Parupudi Attiqa Rehman Michael James Scott Jan Vahrenhold Amali Weerasinghe Xi Wu Department of Computer Science Virginia Tech Blacksburg VA USA School of Computer Science University of Auckland Auckland New Zealand School of Computing Newcastle University Newcastle upon Tyne United Kingdom Computer Systems Farmingdale State College Farmingdale NY USA Software Engineering Research Lab Blekinge Institute of Technology Karlskrona Blekinge Sweden School of Computing and Augmented Intelligence Arizona State University Tempe AZ USA Department of Computer Science Norwegian University of Science and Technology Trondheim Norway Faculty of Screen Technology and Performance Falmouth University Penryn Cornwall United Kingdom Department of Computer Science University of Munster Munster Germany School of Computer and Mathematical Sciences University of Adelaide Adelaide Australia School of Computer Science University of Sydney Sydney NSW Australia

ISBN: (纸本)9798400715693

Team-based capstone projects are vital in preparing computer science students for real-world challenges by fostering teamwork, communication, and industry-relevant technical skills. However, their assessment presents challenges, such as aligning academic criteria with other stakeholders' expectations, evaluating individual contributions within teams, fairly addressing the diverse skills required, and determining the appropriate level of external partners' involvement in the evaluation process. Moreover, the high stakes of these projects necessitate transparent and equitable assessment methods that all stakeholders perceive as fair. Our working group (WG) aims to address the challenges of assessing capstone projects by examining the perspectives of instructors, students, and other stakeholders to ensure fair and effective evaluation. Building on insights from our previous WG and a comprehensive review of the literature, we will employ a mixed-methods approach to explore the issues faced by various stakeholders in assessing capstone projects and to capture both common challenges experienced (quantitative), and delve into nuanced individual experiences (qualitative). By conducting this research in a multi-national, multi-institutional context, we aim to capture a diverse range of global perspectives while accounting for the variation in capstone courses. Our goal is to provide actionable recommendations that enhance assessment practices, improve learning outcomes, and foster effective team collaboration in team-based capstone courses, ultimately preparing students for real-world challenges.

关键词： capstone

来源：评论

学校读者我要写书评

暂无评论

Complementing Event Streams and RGB Frames for Hand Mesh Reconstruction

arXiv

引用

arXiv 2024年

作者： Jiang, Jianping Zhou, Xinyu Wang, Bingxuan Deng, Xiaoming Xu, Chao Shi, Boxin National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China National Engineering Research Center of Visual Technology School of Computer Science Peking University China AI Innovation Center School of Computer Science Peking University China National Key Lab of General AI School of Intelligence Science and Technology Peking University China Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Reliable hand mesh reconstruction (HMR) from commonly-used color and depth sensors is challenging especially under scenarios with varied illuminations and fast motions. Event camera is a highly promising alternative for its high dynamic range and dense temporal resolution properties, but it lacks salient texture appearance for hand mesh reconstruction. In this paper, we propose EvRGBHand – the first approach for 3D hand mesh reconstruction with an event camera and an RGB camera compensating for each other. By fusing two modalities of data across time, space, and information dimensions, EvRGBHand can tackle overexposure and motion blur issues in RGB-based HMR and foreground scarcity as well as background overflow issues in event-based HMR. We further propose EvRGBDegrader, which allows our model to generalize effectively in challenging scenes, even when trained solely on standard scenes, thus reducing data acquisition costs. Experiments on real-world data demonstrate that EvRGBHand can effectively solve the challenging issues when using either type of camera alone via retaining the merits of both, and shows the potential of generalization to outdoor scenes and another type of event camera. For code, models, and dataset, please refer to https://***/***/. Copyright © 2024, The Authors. All rights reserved.

关键词： Cameras

来源：评论

学校读者我要写书评

暂无评论

Travel Intention Prediction Method Based on User Needs 2

Travel Intention Prediction Method Based on User Needs

引用

2022 2nd International Conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2022

作者： Zhang, Lingyu Bian, Wenjie Li, Jianxing Zhang, Ying Liang, Jian Wu, Guobin Zhang, Liang School of Computer Science and Technology Shandong University Qingdao China Didi Chuxing Beijing China School of Software Engineering Xi'an Jiaotong University Xi'an China Didi Chuxing Beijing China School of Electronics and Information Xi'an Jiaotong University Xi'an China Didi Chuxing Beijing China

ISBN: (纸本)9783800760268

Taxi travel is playing an increasingly important role in daily travel. Accurately predicting the user's travel intention can not only cut off the time spending on inputting a place, but also can recommend travel and entertainment venues for the user according to his/her travel intention, thereby improving the user experience. In this paper, we first marked the label of travel intention on the historical taxi orders of 100 users in a city. By analyzing the user's intention to take a taxi, we found that the user's travel intention can be influenced by various factors. In addition, we propose an ensemble learning method based on Embedding DNN working on user travel data characteristics to predict the user's travel intention. When users have recommendation needs and authorize prediction, we provide travel intention prediction and make subsequent destination recommendation according to the intention. By taking comparing experiments on real-world data sets, the experimental results show that our model is better than other models in both accuracy and stability. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Augmented V2V Radio Fingerprinting With Heterogeneous Federated Scattering Network for Autonomous Vehicle Road Cooperation

引用

IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Tiantian Xu, Dongyang Ma, Jing Alharbi, Abdullah Yu, Keping Guizani, Mohsen Leung, Victor C.M Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an710049 China Xi'an Jiaotong University School of Information and Communication Engineering Xi'an710049 China Xidian University State Key Laboratory of Integrated Services Networks China King Saud University Computer Science Department Community College Riyadh11437 Saudi Arabia Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan 999041 United Arab Emirates Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen528060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Augmented Internet of vehicle (IoV) network, providing natural infrastructure support for autonomous vehicle road cooperation (VRC), has been deemed as one of key enablers for autonomous VRC while simultaneously facing the challenge of impersonation, tampering or replay attack towards the high layer digital certificate for critical information. In this paper, we propose a heterogeneous scattering fingerprinting network (HSFNet) which amalgamates wavelet scattering and deep learning to achieve lightweight, rapid and reliable physical layer authentication by radio frequency fingerprinting (RFF) which employs the unique inherent hardware impairments within for vehicle-to everything (V2X) communication. Particularly, we first exploit a wavelet scattering network to extract RFF coefficients and eliminate redundancies within heterogeneous datasets. The establishment of a high-information-density datasets facilitates the efficient processing of extensive heterogeneous data, especially for resource-limited vehicles nodes. To improve the interpretability, we combine the multi-scale coefficients of input waveform and shapley values, formulating a complete interpretable framework and completing parameters tuning. Finally, considering heterogeneous applications in practical scenarios, we design a distributed federated learning framework which not only completes distributed training but also protects privacy with the resources-constrained intelligent IoV nodes. Experimental results demonstrate that the proposed scheme maintains excellent performance while utilizing only about 25.7% of original samples, significantly improving identification accuracy. Furthermore, the identification accuracy of federated learning ultimately converges to 98.1%. © 1967-2012 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Research on Human Resource Management and Prediction Model Based on Neural Network Optimized by Genetic Algorithm

Research on Human Resource Management and Prediction Model B...

引用

Image Processing and computer Applications (ICIPCA), IEEE International Conference on

作者： Zexuan Qi Sihan Yang Xinjian He Desheng Shi Xinjiang University of Science & Technology Korla Xinjiang China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Engineering Guangzhou Huali College Guangzhou China Modern Agricultural College Honghe Vocational and Technical College Mengzi Yunnan China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

This paper investigates the application of artificial neural networks optimized based on genetic algorithms in human resource management in hospital enterprises and constructs a human resource management and predictive analysis model. The model aims to support enterprises in achieving the optimal allocation of human resources by mining their internal human resource data and revealing the intrinsic correlation between personnel structure, business type and future development strategy. The study adopts multiple regression analysis method and combines the artificial neural network optimized by genetic algorithm to construct a human resource demand prediction model. Taking the operational data of Hospital X as an example, the model predicts the number of human resources in the hospital and analyzes in depth the demand for staff types in each department. The results show that the model has high accuracy and reliability in predicting the variables of hospital revenue, wage level, R&D investment and number of personnel, with a prediction error range between 0.09% and 1.27%. The research in this paper provides strong data support for enterprises, which helps them to realize the rational allocation of human resources and strategic transformation, and then maintain their competitive position in the market.

关键词： Analytical models Hospitals Artificial neural networks Predictive models Data models Regression analysis Resource management Personnel Predictive analytics Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning

arXiv

引用

arXiv 2024年

作者： Xu, Yongxin Zhang, Ruizhe Jiang, Xinke Feng, Yujie Xiao, Yuzhen Ma, Xinyu Zhu, Runchuan Chu, Xu Zhao, Junfeng Wang, Yasha School of Computer Science School of Software & Microelectronics Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China Center on Frontiers of Computing Studies Peking University Beijing China National Engineering Research Center For Software Engineering Peking University Beijing China Department of Computing The Hong Kong Polytechnic University Hong Kong

Retrieval-Augmented Generation (RAG) offers an effective solution to the issues faced by Large Language Models (LLMs) in hallucination generation and knowledge obsolescence by incorporating externally retrieved knowledge. However, due to potential conflicts between internal and external knowledge, as well as retrieval noise, LLMs often struggle to effectively integrate external evidence, leading to a decline in performance. Although existing methods attempt to tackle these challenges, they often struggle to strike a balance between model adherence and robustness, resulting in significant learning variance. Inspired by human cognitive processes, we propose Parenting, a novel framework that decouples adherence and robustness within the parameter space of LLMs. Specifically, Parenting utilizes a key parameter mining method based on forward activation gain to identify and isolate the crucial parameter units that are strongly linked to adherence and robustness. Then, Parenting employs a type-guided tailored tuning strategy, applying specific and appropriate fine-tuning methods to parameter units representing different capabilities, aiming to achieve a balanced enhancement of adherence and robustness. Extensive experiments on various datasets and models validate the effectiveness and generalizability of our methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Obsolescence

来源：评论

学校读者我要写书评

暂无评论

Dual-Channel Early Rumor Detection Based on Factual Evidence

SSRN

引用

SSRN 2022年

作者： Wu, Yue Sun, Jiehu Zhang, Yi School of Computer and Software Engineering Xihua University Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Economics and Management Yibin University Yibin China

Early rumor detection requires the balance between accuracy and timeliness. However, most existing methods focused on accuracy but neglected efficiency. How to improve accuracy and timeliness at the same time is not easy. By analyzing a large number of online rumors, we find that a part of rumors spread repeatedly with only a few changes, such old rumors can be quickly identified through factual evidence, while other comparatively new rumors need more clues to detect. Based on this finding, we propose a dual-channel early rumor detection model according to factual evidence, called ERD-DC. ERD-DC is constructed by two channels, a fast channel called channel-E and a slow channel called channel-D. channel-E takes charge in picking up the old rumors that transmitted repeatedly with searchable factual evidence, which ensures the timeliness. Meanwhile, channel-D is used for detecting the new rumors with richer characteristics, and raising up overall accuracy. Experimental results on two real datasets show that ERD-DC significantly outperforms other state-of-the-art models on early rumors detection. © 2022, The Authors. All rights reserved.

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：