检索结果-内蒙古大学图书馆

SSRN 2025年

作者： Yu, Zhihao Jin, Yujie Xu, Yongxin Zhang, Xiaoyun Wang, Yasha School of Computer Science Peking University Beijing100091 China National Research and Engineering Center of Software Engineering Beijing100091 China School and Hospital of Stomatology National Center for Stomatology National Clinical Research Center for Oral Diseases National Engineering Research Center of Oral Biomaterials and Digital Medical Devices Beijing100081 China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing100091 China Peking University Information Technology Institute Tianjin300450 China

While pioneering deep learning methods have made great strides in analyzing electronic health record (EHR) data, they often struggle to fully capture the semantics of diverse medical codes from limited data. The integration of external knowledge from Large Language Models (LLMs) presents a promising avenue for improving healthcare predictions. However, LLM analyses may exhibit significant variance due to ambiguity problems and inconsistency issues, hindering their effective utilization. To address these challenges, we propose IntelliCare, a novel framework that leverages LLMs to provide high-quality patient-level external knowledge and enhance existing EHR models. Concretely, IntelliCare identifies patient cohorts and employs task-relevant statistical information to augment LLM understanding and generation, effectively mitigating the ambiguity problem. Additionally, it refines LLM-derived knowledge through a hybrid approach, generating multiple analyses and calibrating them using both the EHR model and perplexity measures. Experimental evaluations on three clinical prediction tasks across two large-scale EHR datasets demonstrate that IntelliCare delivers significant performance improvements to existing methods, highlighting its potential in advancing personalized healthcare predictions and decision support systems. Our code is available at https://***/yzhHoward/IntelliCare. © 2025, The Authors. All rights reserved.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

How to respond to emerging threats of cyberspace security via social engineering? A novel paradigm to solve cyberspace security issues

TechRxiv

引用

TechRxiv 2022年

作者： Lin, Hao Wang, Chun-Dong Gao, Hao-Yu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing Novel Software Technology Ministry of Education Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China College of Data Science and Application Inner Mongolia University of Technology Hohhot China

Cybersecurity researchers always ignore the "people" behind the issues when dealing with cyberspace security issues. The human and technological aspects of cyberspace security must be simultaneously addressed to enable a secure cyber environment. Social engineering is a discipline whose research object is people, and the combination of social engineering and cyberspace security can provide new solution ideas for lots of cyberspace security issues. This paper attempts to answer the question —— "How to solve cyberspace security issues via social engineering?" We briefly review existing frameworks for social engineering attacks and propose a novel, recursive, and iterative framework for social engineering attacks in cyberspace. The existing research achievements of combination of social engineering and cyberspace security were reviewed in the order of cyber personality, hacker psychology, human flesh search, and social engineering defense. To answer this question, we summarized four "guidelines" for using social engineering. Finally, we look ahead, give our thoughts, and discuss answers of the question and future research. It is aimed at getting more researchers of cyberspace security and social science to pay attention to this field. © 2022, CC BY.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

PPPR: Portable Plug-in Prompt Refiner for Text to Audio Generation

arXiv

引用

arXiv 2024年

作者： Shi, Shuchen Fu, Ruibo Wen, Zhengqi Tao, Jianhua Wang, Tao Qiang, Chunyu Lu, Yi Qi, Xin Liu, Xuefei Liu, Yukun Li, Yongwei Wang, Zhiyong Wang, Xiaopeng School of Computer and Information Engineering Institute for Artificial Intelligence Shanghai Polytechnic University China Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence Chinese Academy of Sciences China Department of Automation Beijing National Research Center for Information Science and Technology Tsinghua University China Tianjin University China University of Chinese Academy of Sciences China

Text-to-Audio (TTA) aims to generate audio that corresponds to the given text description, playing a crucial role in media production. The text descriptions in TTA datasets lack rich variations and diversity, resulting in a drop in TTA model performance when faced with complex text. To address this issue, we propose a method called Portable Plug-in Prompt Refiner, which utilizes rich knowledge about textual descriptions inherent in large language models to effectively enhance the robustness of TTA acoustic models without altering the acoustic training set. Furthermore, a Chain-of-Thought that mimics human verification is introduced to enhance the accuracy of audio descriptions, thereby improving the accuracy of generated content in practical applications. The experiments show that our method achieves a state-of-the-art Inception Score (IS) of 8.72, surpassing AudioGen, AudioLDM and Tango. © 2024, CC BY.

关键词： Refining

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Modern Networks: Advances in AI, Machine Learning, and Blockchain for Quantum Satellites and UAV-based Communication

arXiv

引用

arXiv 2023年

作者： Kumar, Adarsh Awad, Ali Ismail Sharma, Gaurav Krishnamurthi, Rajalakshmi Jain, Saurabh Srikanth, P. Sharma, Kriti Hedabou, Mustapha Sood, Surinder School of Computer Science University of Petroleum and Energy Studies Uttrakhand Dehradun India College of Information Technology United Arab Emirates University United Arab Emirates Department of Computer Science and Engineering Jaypee Institute of Information Technology Uttar Pradesh Noida India School of Engineering and Technology Sushant University Haryana Gurugram India DRDO Banglore India College of Computing Mohammed VI Polytechnic University Benguerir Morocco Department of Electrical Computer and Software Engineering University of Auckland New Zealand

Quantum communication is the most secure technique of transmitting data available today. Fiber communication lines and satellite-to-ground links have served as the basis for the most successful quantum networks that have been developed so far. Using a UAV, satellite or both for free-space quantum communication reduces the need for permanent ground connections and takes advantage of the lower loss limit in space, which makes it more efficient. This work surveys the recent development in Quantum Satellites and Quantum UAVs-based networks. Here, the importance of the latest technologies, including quantum artificial intelligence, blockchain quantum machine learning, quantum satellites and quantum UAVs, are explored from network perspectives. Further, this work discussed the role of satellite-based images and artificial intelligence. © 2023, CC BY.

关键词： Satellites

来源：评论

学校读者我要写书评

暂无评论

Attention-Weighted Federated Deep Reinforcement Learning for Device-to-Device Assisted Heterogeneous Collaborative Edge Caching

Attention-Weighted Federated Deep Reinforcement Learning for...

引用

作者： Wang, Xiaofei Li, Ruibin Wang, Chenyang Li, Xiuhua Taleb, Tarik Leung, Victor C. M. College of Intelligence and Computing Tianjin University Tianjin China Ministry of Education China Department of Communications and Networking School of Electrical Engineering Aalto University Espoo Finland College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Big Data and Software Engineering Chongqing University Chongqing401331 China Department of Information Technology and Electrical Engineering Oulu University Oulu90570 Finland Department of Computer and Information Security Sejong University Seoul05006 Korea Republic of Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

In order to meet the growing demands for multimedia service access and release the pressure of the core network, edge caching and device-to-device (D2D) communication have been regarded as two promising techniques in next generation mobile networks and beyond. However, most existing related studies lack consideration of effective cooperation and adaptability to the dynamic network environments. In this article, based on the flexible trilateral cooperation among user equipment, edge base stations and a cloud server, we propose a D2D-assisted heterogeneous collaborative edge caching framework by jointly optimizing the node selection and cache replacement in mobile networks. We formulate the joint optimization problem as a Markov decision process, and use a deep Q-learning network to solve the long-term mixed integer linear programming problem. We further design an attention-weighted federated deep reinforcement learning (AWFDRL) model that uses federated learning to improve the training efficiency of the Q-learning network by considering the limited computing and storage capacity, and incorporates an attention mechanism to optimize the aggregation weights to avoid the imbalance of local model quality. We prove the convergence of the corresponding algorithm, and present simulation results to show the effectiveness of the proposed AWFDRL framework in reducing average delay of content access, improving hit rate and offloading traffic. © 1983-2012 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

MrTF: Model Refinery for Transductive Federated Learning

arXiv

引用

arXiv 2023年

作者： Li, Xin-Chun Yang, Yang Zhan, De-Chuan State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing210023 China School of Artificial Intelligence Nanjing University Jiangsu Nanjing210023 China School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing210094 China Department of Computing The Hong Kong Polytechnic University 100872 Hong Kong

We consider a real-world scenario in which a newly-established pilot project needs to make inferences for newly-collected data with the help of other parties under privacy protection policies. Current federated learning (FL) paradigms are devoted to solving the data heterogeneity problem without considering the to-be-inferred data. We propose a novel learning paradigm named transductive federated learning (TFL) to simultaneously consider the structural information of the to-be-inferred data. On the one hand, the server could use the pre-available test samples to refine the aggregated models for robust model fusion, which tackles the data heterogeneity problem in FL. On the other hand, the refinery process incorporates test samples into training and could generate better predictions in a transductive manner. We propose several techniques including stabilized teachers, rectified distillation, and clustered label refinery to facilitate the model refinery process. Abundant experimental studies verify the superiorities of the proposed Model refinery framework for Transductive Federated learning (MrTF). The source code is available at https://***/lxcnju/MrTF. © 2023, CC BY-NC-ND.

关键词： Distillation

来源：评论

学校读者我要写书评

暂无评论

Ranking-based fuzzy min-max classification neural network 1

引用

17th International Conference on Web Information Systems and Applications, WISA 2020

作者： Xue, Lingli Huang, Wei Wang, Jinsong Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin300384 China School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China

ISBN: (数字)9783030600297

ISBN: (纸本)9783030600280

The performance of fuzzy min-max classification neural network (FMM) is affected by the input sequence of training set patterns. This paper proposes a ranking-based fuzzy min-max Classification Neural Network (RFMM) to overcome this shortcoming. RFMM improves FMM through the following three aspects. First, RFMM ranks the input order of the training set patterns according to their membership degree to the center point of same class, so that the finally constructed network is fixed and does not depend on the input order of the training set. Second, a new membership function based on Manhattan distance is constructed, which overcomes the problem that the membership degree obtained by the membership function in the FMM will not decrease steadily with the increase of the distance between the input pattern and the hyperbox. At last, RFMM uses the method based on individual contour coefficient to classify the patterns in overlapping regions, which overcomes the problem that when the FMM eliminates the overlapping region by shrinking hyperboxes, the membership degree of the patterns in the contracted region to the class they belong is changed. Experimental results show that RFMM has better learning ability, and compared with other FMM methods, RFMM shows higher classification accuracy and lower network complexity. © Springer Nature Switzerland AG 2020.

关键词： Membership functions

来源：评论

学校读者我要写书评

暂无评论

PointCG: Self-supervised Point Cloud Learning via Joint Completion and Generation

arXiv

引用

arXiv 2024年

作者： Liu, Yun Li, Peng Yan, Xuefeng Nan, Liangliang Wang, Bing Chen, Honghua Gong, Lina Zhao, Wei Wei, Mingqiang The School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The Shenzhen Institute of Research Nanjing University of Aeronautics and Astronautics Shenzhen China The Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Urban Data Science section Delft University of Technology Delft Netherlands The Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic University Hong Kong

The core of self-supervised point cloud learning lies in setting up appropriate pretext tasks, to construct a pre-training framework that enables the encoder to perceive 3D objects effectively. In this paper, we integrate two prevalent methods, masked point modeling (MPM) and 3D-to-2D generation, as pretext tasks within a pre-training framework. We leverage the spatial awareness and precise supervision offered by these two methods to address their respective limitations: ambiguous supervision signals and insensitivity to geometric information. Specifically, the proposed framework, abbreviated as PointCG, consists of a Hidden Point Completion (HPC) module and an Arbitrary-view Image Generation (AIG) module. We first capture visible points from arbitrary views as inputs by removing hidden points. Then, HPC extracts representations of the inputs with an encoder and completes the entire shape with a decoder, while AIG is used to generate rendered images based on the visible points' representations. Extensive experiments demonstrate the superiority of the proposed method over the baselines in various downstream tasks. Our code will be made available upon acceptance. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

A novel channel model and optimal power control schemes for mobile mmWave two-tier networks

TechRxiv

引用

TechRxiv 2022年

作者： Ghosh, Joydev Haci, Huseyin Kumar, Neeraj Al-Utaibi, Khaled A. Sait, Sadiq M. So-In, Chakchai School of Computer Science and Robotics Tomsk Polytechnic University Tomsk Russia Department of Electrical and Electronics Engineering Near East University Mersin 10 Turkey Department of Computer Science & Engineering Thapar Institute of Engineering & Technology Punjab India Computer Science and Software Engineering Department University of Ha’il Saudi Arabia Department of Computer Engineering King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Department of Computer Science College of Computing Khon Kaen University Thailand

We present a unified system model and framework for the analytical performance study of two heterogeneous and physically-distinct, but coexisting, networks that work harmoniously at the same time, space, and frequency domains. The two-tier network model considered in this paper is an overlaying of femtocells on a macrocell. Overlaying femtocells improves the performance by offloading traffic from macrocells and providing spatial diversity. The mmWave channel model employed considers the number of clusters and rays within each cluster to vary due to the end-user mobility. This is a new and different model compared to the widely used channel models for mmWave two-tier networks. Optimal power control is formulated as a sum-rate maximization problem for downlink and uplink transmissions at two-tier networks and a power allocation scheme is proposed by following Shannon-Hartley theorem. A comprehensive and interesting performance investigation is provided, where it is shown that the upper bound on the number of admitted secondary users has a linear relationship with the outage probability threshold, logarithmic relationship with SINR and exponential relationship with channel gain factors. Simulation results show that the proposed scheme with sub-channel iterative Lagrange multipliers search algorithm is very effective at managing the cross-tier interference and can outperform a competitive scheme from literature that is based on cognitive radio technology. The computational complexity analysis of proposed algorithms are also given, since the complexity of second algorithm can be a performance-complexity trade-off issue for systems with limited computation power and time requirements. © 2022, CC BY.

关键词： Femtocell

来源：评论

学校读者我要写书评

暂无评论

GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning

arXiv

引用

arXiv 2024年

作者： Zhang, Yaning Yu, Zitong Wang, Tianyi Huang, Xiaobin Shen, Linlin Gao, Zan Ren, Jianfeng Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China Jinan250014 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China School of Computer Science University of Nottingham Ningbo China

The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Thus, benchmarking and advancing techniques detecting digital manipulation become an urgent issue. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology, which does not involve the most recent technologies like diffusion. The diversity and quality of images generated by diffusion models have been significantly improved and thus a much more challenging face forgery dataset shall be used to evaluate SOTA forgery detection literature. In this paper, we propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection, which contains a large number of forgery faces generated by advanced generators such as the diffusion-based model and more detailed labels about the manipulation approaches and adopted generators. In addition to evaluating SOTA approaches on our benchmark, we design an innovative Cross Appearance-Edge Learning (CAEL) detector to capture multi-grained appearance and edge global representations, and detect discriminative and general forgery traces. Moreover, we devise an Appearance-Edge Cross-Attention (AECA) module to explore the various integrations across two domains. Extensive experiment results and visualizations show that our detection model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Code and datasets will be available at https://***/Jenine-321/GenFace. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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：