检索结果-内蒙古大学图书馆

11th IEEE International conference on Cyber Security and Cloud Computing, CSCloud 2024

作者： Fang, Zhengkang Yu, Jing Fang, Shufen Wang, Shuo Chan, Weilin Gao, Zexin Gai, Keke School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Muguo Technology Co. Ltd. Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy Beijing Institute of Technology Zhejiang Jiaxing China

ISBN: (纸本)9798350376982

Along with the development of the biological industry, the system of biological assets governance has a higher-level requirement in asset identity verification due to the demands of digitization and Financial Technology (FinTech). In order to achieve broad scalability and adaptability in the governance of biological assets' identities, this work proposes a Biological Assets Decentralized Identity Management (BA-DID) system that develops a Decentralized Identity (DID)-based solution to addressing the verification issues in biological assets while considering multi-dimensional requirements. Blockchain technology is the fundamental infrastructure of the system. Authenticated certificates of the asset owners verify identity attributes. Financial Service Institutions (FSI) and governance agencies act as issuers, providing Verifiable Credentials (VC) for biological assets by using a group of attributes over a consensus. The asset owners hold VCs and can be available to other organizations as proof of the corresponding asset attributes. Our evaluations have demonstrated that the proposed approach has superior performance in biological assets verification, security, and maintenance. © 2024 IEEE.

关键词： Decentralized finance

来源：评论

学校读者我要写书评

暂无评论

MetaCo-Distillation: A Robust Collaborative Distillation Framework for Multivariate Knowledge 22

MetaCo-Distillation: A Robust Collaborative Distillation Fra...

引用

22nd IEEE International conference on Smart City, SmartCity 2024

作者： Wang, Jianing Liu, Jiqiang Zhang, Tao Wang, Jian Tang, Xiangyun Sun, Geng Yang, Zhaohui Han, Zhu Niyato, Dusit School of Cyberspace Science and Technology Beijing Jiaotong University Beijing China School of Information Engineering Minzu University of China Beijing China College of Computer Science and Technology Jilin University Changchun China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Electrical and Computer Engineering University of Houston HoustonTX United States School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798331540487

Federated Learning (FL), as a rapidly evolving privacy-preserving collaborative machine learning paradigm, has shown great potential in the emerging Metaverse areas. Although numerous studies have theoretically demonstrated the feasibility of FL, practical applications in the Metaverse face challenges such as learning forgetting due to non-independent and identically distributed (non-i.i.d.) data, high communication overhead from model parameter transmission, and the inability to accommodate heterogeneous models. To address these challenges, this paper proposes a collaborative distillation framework called Co-distillation. In this distributed environment, each device independently trains its local model using private data, communicates predictions on an unlabeled public dataset, and generates surrogate labels for the public dataset based on received predictions. These surrogate labels, along with the received predictions, serve as teacher logits for local model distillation. This approach is more adaptable to heterogeneous streaming data continuously generated by industrial devices, offers higher communication efficiency than traditional methods, allow for heterogeneous models. Experiments have been conducted, demonstrating the superior performance of the proposed framework. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Dynamic Insights: Well-being Activity Infused Fine-Tuning of Large Language Models

Dynamic Insights: Well-being Activity Infused Fine-Tuning of...

引用

2024 IEEE International conference on Big Data, BigData 2024

作者： Mohammed, Hamza Haruna Kiss, Gabriel Serrano, J. Artur Lindseth, Frank Norwegian University of Science and Technology Faculty of Information Technology and Electrical Engineering Department of Computer Science Trondheim Norway Norwegian University of Science and Technology Faculty of Medicine and Health Sciences Department of Neuromedicine and Movement Science Trondheim Norway

ISBN: (纸本)9798350362480

The fast development of Large Language Models (LLMs) has made transformative applications in several fields attainable or possible. However, language models must often be more effective in specialized areas, especially health and prevention. This paper presents a novel method for fine-tuning LLMs using activity-related data to optimize them for the applicability of such models in health and wellbeing applications. We thus empirically evaluate this approach on the Cardiac Exercise Research corpus for fine-tuning the LLMs. The fine-tuning utilizes Quantized Low-Rank Adaptation (QLoRA) to ensure the models' size remains small while maintaining high performance and accuracy to keep the semantic understanding and relevance with health-related queries. Our results in answering domain-related prompts showed an improved user satisfaction and sentiment scores, providing strong confidence in the method's effectiveness. This study highlights the potential of domain-specific LLMs in advancing personalized healthcare. It instills a sense of optimism about the future of healthcare and the seamless integration of AI within health prevention and well-being domains. © 2024 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Robot control with multitasking of brain-computer interface 17

Robot control with multitasking of brain-computer interface

引用

17th International conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Zhou, Yajun Lu, Zilin Li, Yuanqing South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Peoples R China Res Ctr Brain Comp Interface Pazhou Lab Guangzhou 510330 Peoples R China

ISBN: (纸本)9781665476874

Brain-computer interfaces (BCI) have been extensively researched to assist people with motor paralysis in controlling external devices such as a robotic limb. However, most BCI systems required participants to focus on a single task, limiting their ability to generate other mental or physical activities. Therefore, people's performance of the BCI-based robotic control in multitasking was discussed, as eight healthy subjects performed motor-related tasks of motor imagery and two-handed balancing ball movement, while simultaneously performing visuospatial attention to asynchronously trigger "drinking" actions of a humanoid robot arm with accuracies of 90% and 87.5%, respectively. The online results indicate that the BCI-based robot control system developed for multi-task conditions has a high potential for human augmentation.

关键词： Anthropomorphic robots

来源：评论

学校读者我要写书评

暂无评论

CCMFRNet: A Real-Time Semantic Segmentation Network with Context Cascade and Multi-scale Feature Refinement 7th

CCMFRNet: A Real-Time Semantic Segmentation Network with Con...

引用

7th International conference on Information Science, Communication and Computing, ISCC 2023

作者： Hua, Shuai Cheng, Jieren Han, Wenbao Xu, Wenhang Sheng, Victor S. School of Cyberspace Security Hainan University Haikou570228 China Hainan Blockchain Technology Engineering Research Center Haikou570228 China School of Computer Science and Technology Hainan University Haikou570228 China Department of Computer Science Texas Tech University LubbockTX79409 United States

ISBN: (纸本)9789819971602

At present, many of the popular semantic segmentation networks focus on accuracy and require a lot of computational overhead, which results in a very slow inference speed and is difficult to deploy in practical application scenarios. On the other hand, many works sacrifice the performance of segmentation networks in pursuit of real-time inference speed. Therefore, in semantic segmentation, balancing accuracy and real-time performance becomes a formidable challenge. For this challenge, we propose a lightweight semantic segmentation network that takes into account both accuracy and real-time performance, named CCMFRNet. The core components of CCMFRNet are the Context Cascade Module (CCM) and the Multi-scale Feature Refinement Module (MFRM). CCM consists of three Dense Cascade Dilated Convolution Modules (DCDM), which are cascaded in a short-term dense cascade, aiming to obtain rich multi-scale context information to enhance information representation. MFRM adopts the attention mechanism to realize deep features to guide the captured shallow multi-scale spatial features. It aims to capture high-quality and multi-scale shallow features to enrich the feature space and more effectively refine the spatial details information. The proposed method achieves an accuracy of 72.6% MIoU at speed of 32 fps on Cityscapes test datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Cloudformer: Contrastive Learning Based Cloud Workload Prediction

Cloudformer: Contrastive Learning Based Cloud Workload Predi...

引用

7th International conference on Electronics Technology, ICET 2024

作者： Lu, Hui Zhang, Yi Wu, Zebin Wei, Zhihui Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350363951

During the last 3 years, researchers have endeavored to extend the efficacy of contrastive learning (CL) towards addressing the challenges inherent in cloud workload prediction, which has demonstrated considerable success in the domains of computer Vision (CV) and Natural Language Processing(NLP). However, due to the distinctive temporal characteristics of serialized workload information, relying solely on empirical guidance from other domains may prove insufficient for cloud workload prediction. Therefore, we systematically investigated three key components of CL, including: 1) designing backbone encoder for feature extraction, 2) designing methods for extracting negative samples for contrast, and 3) designing the CL loss. We found that inappropriate construction of negative samples may introduce excessive pseudo-negative samples, which neither preserve temporal characteristics nor provide sufficient discriminative features. Additionally, focusing solely on the discrimination between positive and negative samples in the CL loss may not be adequate for enabling the model to learn the temporal patterns of serialized workload information. To address these problems, we propose a novel self-supervised model named Cloudformer. Specifically, we first constructed a CL network based on the Transformer architecture using a pre-train-finetune framework. Following this, we proposed a method to filter pseudo-negative samples based on differential cosine similarity, aimed at assisting the model in more effectively distinguishing between positive and negative samples. Additionally, we introduced the BatchNCE loss, combining MSE loss with InfoNCE loss as a joint optimization objective for CL, effectively enhancing the predictive capability of the model. The experimental results indicate that Cloudformer is capable of learning high-quality patterns in serialized workload sequences and achieves more advanced performance in short sequence prediction tasks. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Exploiting Multi-Model Collaborative Inference for Privacy Enhancement in Text Classification 10

Exploiting Multi-Model Collaborative Inference for Privacy E...

引用

10th IEEE conference on Big Data Security on Cloud, BigDataSecurity 2024

作者： Lin, Yong Jiang, Peng Gai, Keke Zhu, Liehuang Beijing Institute of Technology School of Computer Science and Technology Beijing China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing China

ISBN: (纸本)9798350389524

Text classification is a foundational task in natural language processing that involves categorizing raw text into pre-defined classes. This task holds significant importance in various applications, including but not limited to sentiment analysis and intent detection. With collaborative inference of multiple models, text classification may achieve an improved performance compared to the single model. However, if multiple models have access to the input text directly, it may create challenges on the privacy of sensitive data or model information. It is not easy to realize collaborative inference while preserving the privacy. This paper presents PPJP, a privacy-preserving joint system that helps achieve private collaborative inference in text classification with machine learning. Our method to instantiate it, is based on secure multiparty computation (MPC) and differential privacy (DP). We fulfill the privacy and scalability of text classification under multiple models inference. Secret-sharing-based MPC is used to protect the input and model parameters, while DP is used to protect against membership inference attack. We implement and evaluate prototype of our PPJP system based on the Twitter dataset. Experimental results show that text classification can guarantee privacy for model owners and clients with 54% inference accuracy. It achieves a balance between privacy and accuracy in case of collaborative inference. © 2024 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Editors' Notes

引用

computer MUSIC JOURNAL 2022年第1-2期46卷 5-7页

作者： Cadiz, Rodrigo F. Schumacher, Federico

Despite computer music being commonly identified as having North American or European origins, Latin American composers and performers have had a great interest in it since its inception, with a long and prolific history comparable to these other traditions. Research and music from this region have had limited visibility, however, resulting in a significant gap in academic knowledge, partially due to the language barrier and the lack of technological means and international exposure for many artists. Nevertheless, in recent times Latin America has experienced an interesting increase in activity in the field, as some of the most important international academic conferences have taken place in the region, such as the International conference on New Interfaces for Musical Expression (NIME) in Brazil in 2019, and both the International computer Music conference (ICMC) and the International conference on Live Coding (ICLC) in Chile in 2021. These conferences have showcased a significant amount of artistic work and research that is being accomplished in the region. This special issue ofcomputer Music Journalaims to bridge some of this still-existing gap by publishing new technical and historical research on computer music practice in Latin *** special issue comprises eight articles written by artists and researchers with ties to Argentina, Brazil, Chile, Mexico, and Uruguay, offering some fresh perspectives on the computer music that Latin America is currently producing and addressing some of the most important challenges that it faces *** begin with the article of Ricardo Dal Farra, who is an important Argentinian composer and researcher of the history of electroacoustic and computer music in Latin America. He argues that the progression of this genre in the region has been astounding despite political and economic instability that has affected the lives of inhabitants for decades. One example of this development is the Latin American Electroacoustic Music

关键词：

来源：评论

学校读者我要写书评

暂无评论

How to Leverage Diverse Demonstrations in Offline Imitation Learning 41

How to Leverage Diverse Demonstrations in Offline Imitation ...

引用

41st International conference on Machine Learning, ICML 2024

作者： Yue, Sheng Liu, Jiani Hua, Xingyuan Ren, Ju Lin, Sen Zhang, Junshan Zhang, Yaoxue Department of Computer Science and Technology Tsinghua University Beijing China Zhongguancun Laboratory Beijing China Department of Computer Science University of Houston Texas United States Department of Electrical and Computer Engineering University of California Davis United States

Offline Imitation Learning (IL) with imperfect demonstrations has garnered increasing attention owing to the scarcity of expert data in many real-world domains. A fundamental problem in this scenario is how to extract positive behaviors from noisy data. In general, current approaches to the problem select data building on state-action similarity to given expert demonstrations, neglecting precious information in (potentially abundant) diverse state-actions that deviate from expert ones. In this paper, we introduce a simple yet effective data selection method that identifies positive behaviors based on their resultant states - a more informative criterion enabling explicit utilization of dynamics information and effective extraction of both expert and beneficial diverse behaviors. Further, we devise a lightweight behavior cloning algorithm capable of leveraging the expert and selected data correctly. In the experiments, we evaluate our method on a suite of complex and high-dimensional offline IL benchmarks, including continuous-control and vision-based tasks. The results demonstrate that our method achieves state-of-the-art performance, outperforming existing methods on 20/21 benchmarks, typically by 2-5x, while maintaining a comparable runtime to Behavior Cloning (BC). 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

On the impact of pre-training datasets for matching dendritic identifiers using residual nets

On the impact of pre-training datasets for matching dendriti...

引用

2024 International Workshop on Artificial Intelligence for Signal, Image Processing and Multimedia, AI-SIPM 2024

作者： Jhaj, Baaz Shukla, Ankita Turaga, Pavan Kozicki, Michael Geometric Media Lab Arizona State University TempeAZ United States Computer Science and Engineering University of Nevada RenoNV United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ United States

ISBN: (纸本)9798400705489

Dendrites are easy to synthesize branching structures that exhibit randomness;yet they are unique, non-repeatable, and identifiable with the right algorithmic innovations. This has created a novel application area where manufactured dendritic structures are being used as product identifiers - essentially "fingerprints for things". Unlike barcodes, which are linear structures, dendrites exhibit spatial randomness. This, coupled with a unique optical signal generated by light scattering from material inhomogeneities, ensures that each dendrite is unique and unclonable. While there have not yet been any established methods on reading dendritic patterns for verification using image data, identifying dendrites using computer vision techniques could have high potential. Due to limited data and low variance, dendrite identification can be considered to be a fine-grained classification task. In this paper, we examine how the selection of pre-trained models influences dendrite classification. The dendrites we work with share similarity to human fingerprints, thus we begin with a model trained for matching fingerprint data to extract features relevant to dendrites. Additionally, we explore broader pre-training approaches, using ImageNet-1K for our second model and ImageNet-21K for our third model. Surprisingly, our results indicate that even with the visual similarity with human fingerprints, more general pre-training with common image datasets achieves better performance on dendrite classification. © 2024 ACM.

关键词： Classification (of information)

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：