检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Wang, Xiao Wang, Yutong Yang, Jing Jia, Xiaofeng Li, Lijun Ding, Weiping Wang, Fei-Yue School of Artificial Intelligence Anhui University Hefei266114 China Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China National Engineering Laboratory for Big Data Collaborative Security Technology Beijing100015 China School of Automation Southeast University Nanjing210018 China School of Information Science and Technology Nantong University Nantong226019 China Faculty of Innovation Engineering Macau University of Science and Technology 999078 China

As the concept of Industries 5.0 develops, industrial metaverses are expected to operate in parallel with the actual industrial processes to offer "Human-Centric" Safe, Secure, Sustainable, Sensitive, Service, and Smartness "6S" manufacturing solutions. Industrial metaverses not only visualize the process of productivity in a dynamic and evolutional way, but also provide an immersive laboratory experimental environment for optimizing and remodeling the process. Besides, the customized user needs that are hidden in social media data can be discovered by social computing technologies, which introduces an input channel for building the whole social manufacturing process including industrial metaverses. This makes the fusion of multi-source data cross Cyber-Physical-Social Systems (CPSS) the foundational and key challenge. This work firstly proposes a multi-source-data-fusion-driven operational architecture for industrial metaverses on the basis of conducting a comprehensive literature review on the state-of-the-art multi-source data fusion methods. The advantages and disadvantages of each type of method are analyzed by considering the fusion mechanisms and application scenarios. Especially, we combine the strengths of deep learning and knowledge graphs in scalability and parallel computation to enable our proposed framework the ability of prescriptive optimization and evolution. This integration can address the shortcomings of deep learning in terms of explainability and fact fabrication, as well as overcoming the incompleteness and the challenges of construction and maintenance inherent in knowledge graphs. The effectiveness of the proposed architecture is validated through a parallel weaving case study. In the end, we discuss the challenges and future directions of multi-source data fusion cross CPSS for industrial metaverses and social manufacturing in Industries 5.0. Copyright © 2024, The Authors. All rights reserved.

关键词： data fusion

来源：评论

学校读者我要写书评

暂无评论

Evidence-aware inferential text generation with vector quantised variational AutoEncoder 58

Evidence-aware inferential text generation with vector quant...

引用

58th Annual Meeting of the Association for Computational Linguistics, ACL 2020

作者： Guo, Daya Tang, Duyu Duan, Nan Yin, Jian Jiang, Daxin Zhou, Ming The School of Data and Computer Science Sun Yat-Sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Microsoft Research Asia Beijing China Microsoft Search Technology Center Asia Beijing China

ISBN: (纸本)9781952148255

Generating inferential texts about an event in different perspectives requires reasoning over different contexts that the event occurs. Existing works usually ignore the context that is not explicitly provided, resulting in a context-independent semantic representation that struggles to support the generation. To address this, we propose an approach that automatically finds evidence for an event from a large text corpus, and leverages the evidence to guide the generation of inferential texts. Our approach works in an encoder-decoder manner and is equipped with a Vector Quantised-Variational Autoencoder, where the encoder outputs representations from a distribution over discrete variables. Such discrete representations enable automatically selecting relevant evidence, which not only facilitates evidence-aware generation, but also provides a natural way to uncover rationales behind the generation. Our approach provides state-ofthe-art performance on both Event2Mind and ATOMIC datasets. More importantly, we find that with discrete representations, our model selectively uses evidence to generate different inferential texts. © 2020 Association for Computational Linguistics

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

A Cost-Effective Smart Helmet for Human Safety and Road Accident Detection Using loT

A Cost-Effective Smart Helmet for Human Safety and Road Acci...

引用

Recent Progresses in science, Engineering and technology (ICRPSET), International Conference on

作者： Sharmin Akter Md Abu Yousuf K. M. Rafiqul Alam Md. Sahidullah Md. Monirul Islam Jia Uddin Computer Science and Engineering Atish Dipankar University of Science and Technology Dhaka Bangladesh Department of Electrical Engineering Wright State University Dayton Ohio USA Department of Statistics Jahangirnagar University Dhaka bangladesh Computer Science and Engineering Asian University of Bangladesh (AUB) Dhaka Bangladesh Information and Communications Engineering Hankuk University of Foreign Studies Gyeonggi-do South Korea AI and Big Data Department Woosong University Daejeon South Korea

ISBN: (数字)9798331509477

ISBN: (纸本)9798331509484

A smart helmet is a type of protective headgear used by the rider, which makes bike driving safer than before. Nowadays road accidents occur very frequently in our daily lives because motorcyclists are not always wearing helmets. To avoid road accidents, motorcyclists have to wear helmets. By wearing it, riders can save their life up to some instant. In this paper, we proposed a smart helmet system based on Arduino and loT devices named Ultrasonic sensor, Vibration sensor, GPS module, MQ-3 Alcohol sensor, Wi-Fi module, IR sensor, and Relay module. This system aims to improve the safety of riders. The design of this smart helmet is to detect whether the user wears a helmet or not with the help of an IR sensor. Apart from this, this smart system can detect whether the user is an alcoholic or not. If the user is alcoholic, the ignition of the bike gets OFF. Only the ignition gets ON while the users wear a helmet and do not drink alcohol. It will benefit riders and medical practitioners when an accident occurs with immediate treatment. This will bring more safety to bike riding and will save many lives as well.

关键词： Vibrations Head Road accidents Microcontrollers Motorcycles Ignition Smart systems Safety Relays Wireless fidelity

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence-enabled discovery of a RIPK3 inhibitor with neuroprotective effects in an acute glaucoma mouse model

引用

Chinese Medical Journal 2025年第2期138卷 172-184页

作者： Xing Tu Zixing Zou Jiahui Li Simiao Zeng Zhengchao Luo Gen Li Yuanxu Gao Kang Zhang Guangzhou Institutes of Biomedicine and Health Chinese Academy of SciencesGuangzhouGuangdong 510530China Guangzhou National Laboratory Guangzhou International Bio IslandGuangzhouGuangdong 510530China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangzhouGuangdong 510623China University of Chinese Academy of Sciences Beijing 100049China Guangzhou Women and Children’s Medical Center Guangzhou Medical UniversityGuangzhouGuangdong 510623China Department of Big Data and Biomedical AI College of Future TechnologyPeking UniversityBeijing 100871China Institute for Artificial Intelligence in Medicine and Faculty of Medicine Macao University of Science and TechnologyMacao Special Administrative Region 999078China

Background:Retinal ganglion cell(RGC)death caused by acute ocular hypertension is an important characteristic of acute ***-interacting protein kinase 3(RIPK3)that mediates necroptosis is a potential therapeutic target for RGC ***,the current understanding of the targeting agents and mechanisms of RIPK3 in the treatment of glaucoma remains ***,artificial intelligence(AI)technologies have significantly advanced drug *** study aimed to discover RIPK3 inhibitor with AI ***:An acute ocular hypertension model was used to simulate pathological ocular hypertension in *** employed a series of AI methods,including large language and graph neural network models,to identify the target compounds of ***,these target candidates were validated using molecular simulations(molecular docking,absorption,distribution,metabolism,excretion,and toxicity[ADMET]prediction,and molecular dynamics simulations)and biological experiments(Western blotting and fluorescence staining)in vitro and in ***:AI-driven drug screening techniques have the potential to greatly accelerate drug development.A compound called HG9-91-01,identified using AI methods,exerted neuroprotective effects in acute *** research indicates that all five candidates recommended by AI were able to protect the morphological integrity of RGC cells when exposed to hypoxia and glucose deficiency,and HG9-91-01 showed a higher cell survival rate compared to the other ***,HG9-91-01 was found to protect the retinal structure and reduce the loss of retinal layers in an acute glaucoma *** was also observed that the neuroprotective effects of HG9-91-01 were highly correlated with the inhibition of PANoptosis(apoptosis,pyroptosis,and necroptosis).Finally,we found that HG9-91-01 can regulate key proteins related to PANoptosis,indicating that this compound exerts neuroprotective effects in the retina by inhibiting the expr

关键词： Virtual screening AI model PANoptosis acute ocular hypertension HG9-91-01 Glucoma

来源：评论

学校读者我要写书评

暂无评论

An Efficient data Prefetch Strategy for Deep Learning Based on Non-volatile Memory 15th

An Efficient Data Prefetch Strategy for Deep Learning Based ...

引用

15th International Conference on Green, Pervasive, and Cloud Computing, GPC 2020

作者： Jiang, Wenbin Liu, Pai Jin, Hai Peng, Jing 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 Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783030642426

Deep learning (DL) systems usually utilize asynchronous prefetch to improve data reading performance. However, the efficiency of the data transfer path from hard disk to DRAM is still limited by disk performance. The emerging non-volatile memory (NVRAM) provides a novel solution for this problem, while few existing researches have considered it. We propose a novel efficient data prefetch strategy for DL based on a heterogeneous memory system combining NVRAM with DRAM. Benefitting from the large capacity and fast reading speed of NVRAM, the strategy uses an asynchronous reading method named sliding NVRAM cache (SNC) to improve the performance of the data transfer paths. A sliding window is applied to map the data from disk to NVRAM and continuously update the data, while non-ideal writing performance of NVRAM can be remitted to a large extent in this strategy. Experiments show that SNC can improve the time performance of diverse deep neural networks training by more than 30%. © 2020, Springer Nature Switzerland AG.

关键词： Dynamic random access storage

来源：评论

学校读者我要写书评

暂无评论

Wasserstein Adversarial Learning based Temporal Knowledge Graph Embedding

arXiv

引用

arXiv 2022年

作者： Dai, Yuanfei Guo, Wenzhong Eickhoff, Carsten The Department of Computer Science and Technology Nanjing Tech University Nanjing211816 China The Department of Computer and Big Data Fuzhou University Fuzhou350108 China The Center for Biomedical Informatics Brown University Providence02912 United States

Research on knowledge graph embedding (KGE) has emerged as an active field in which most existing KGE approaches mainly focus on static structural data and ignore the influence of temporal variation involved in time-aware triples. In order to deal with this issue, several temporal knowledge graph embedding (TKGE) approaches have been proposed to integrate temporal and structural information in recent years. However, these methods only employ a uniformly random sampling to construct negative facts. As a consequence, the corrupted samples are often too simplistic for training an effective model. In this paper, we propose a new temporal knowledge graph embedding framework by introducing adversarial learning to further refine the performance of traditional TKGE models. In our framework, a generator is utilized to construct high-quality plausible quadruples and a discriminator learns to obtain the embeddings of entities and relations based on both positive and negative samples. Meanwhile, we also apply a Gumbel-Softmax relaxation and the Wasserstein distance to prevent vanishing gradient problems on discrete data;an inherent flaw in traditional generative adversarial networks. Through comprehensive experimentation on temporal datasets, the results indicate that our proposed framework can attain significant improvements based on benchmark models and also demonstrate the effectiveness and applicability of our framework. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

A BCS-GDE Multi-objective Optimization Algorithm for Combined Cooling, Heating and Power Model with Decision Strategies

arXiv

引用

arXiv 2022年

作者： Sun, Jiaze Deng, Jiahui Li, Yang Han, Nan School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121 China Xi 'an Key Laboratory of Big Data and Intelligent Computing Xi’an710127 China School of Information Science and Technology Northwest University Xi’an710127 China School of Electrical Engineering Northeast Electric Power University Jilin132012 China

District energy systems can not only reduce energy consumption but also set energy supply dispatching schemes according to demand. In addition to economic cost, energy consumption and pollutant are more worthy of attention when evaluating combined cooling, heating and power (CCHP) models. In this paper, the CCHP model is established with the objective of economic cost, primary energy consumption, and pollutant emissions. The mathematical expression of the CCHP system is proposed, and a multi-objective optimization model with constraints is established. According to different usage requirements, two decision-making strategies are designed, which can adapt to different scenarios. Besides, a generalized differential evolution with the best compromise solution processing mechanism (BCS-GDE) algorithm is proposed to optimize the CCHP model for the first time. The algorithm provides the optimal energy scheduling scheme by optimizing the production capacity of different capacity equipment. The simulation is conducted in three application scenarios: hotels, offices, and residential buildings. The simulation results show that the model established in this paper can reduce economic cost by 72%, primary energy consumption by 73%, and pollutant emission by 88%. Concurrently, the Wilcoxon signed-rank test shows that BCS-GDE is significantly better than OMOPSO, NSGA-II, and SPEA2 with greater than 95% confidence. Copyright © 2022, The Authors. All rights reserved.

关键词： Evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

Towards Feature Distribution Alignment and Diversity Enhancement for data-Free Quantization

arXiv

引用

arXiv 2022年

作者： Gao, Yangcheng Zhang, Zhao Hong, Richang Zhang, Haijun Fan, Jicong Yan, Shuicheng School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei230009 China Xili University Town Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China National University of Singapore Singapore117583 Singapore

To obtain lower inference latency and less memory footprint of deep neural networks, model quantization has been widely employed in deep model deployment, by converting the floating points to low-precision integers. However, previous methods (such as quantization aware training and post training quantization) require original data for the fine-tuning or calibration of quantized model, which makes them inapplicable to the cases that original data are not accessed due to privacy or security. This gives birth to the data-free quantization method with synthetic data generation. While current data-free quantization methods still suffer from severe performance degradation when quantizing a model into lower bit, caused by the low inter-class separability of semantic features. To this end, we propose a new and effective data-free quantization method termed ClusterQ, which utilizes the feature distribution alignment for synthetic data generation. To obtain high inter-class separability of semantic features, we cluster and align the feature distribution statistics to imitate the distribution of real data, so that the performance degradation is alleviated. Moreover, we incorporate the diversity enhancement to solve class-wise mode collapse. We also employ the exponential moving average to update the centroid of each cluster for further feature distribution improvement. Extensive experiments based on different deep models (e.g., ResNet-18 and MobileNet-V2) over the ImageNet dataset demonstrate that our proposed ClusterQ model obtains state-of-the-art performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Alignment

来源：评论

学校读者我要写书评

暂无评论

MLAE: MASKED LORA EXPERTS FOR VISUAL PARAMETER-EFFICIENT FINE-TUNING

arXiv

引用

arXiv 2024年

作者： Wang, Junjie Yang, Guangjing Chen, Wentao Yi, Huahui Wu, Xiaohu Lin, Zhouchen Lao, Qicheng Beijing University of Posts and Telecommunications China National Key Lab of General AI School of Intelligence Science and Technology Peking University China Shanghai Jiao Tong University China West China Biomedical Big Data Center West China Hospital Sichuan University China Institute for Artificial Intelligence Peking University China Shanghai AI Laboratory China

In response to the challenges posed by the extensive parameter updates required for full fine-tuning of large-scale pre-trained models, parameter-efficient fine-tuning (PEFT) methods, exemplified by Low-Rank Adaptation (LoRA), have emerged. LoRA simplifies the fine-tuning process but may still struggle with a certain level of redundancy in low-rank matrices and limited effectiveness from merely increasing their rank. To address these issues, a natural idea is to enhance the independence and diversity of the learning process for the low-rank matrices. Therefore, we propose Masked LoRA Experts (MLAE), an innovative approach that applies the concept of masking to visual PEFT. Our method incorporates a cellular decomposition strategy that transforms a low-rank matrix into independent rank-1 submatrices, or "experts", thus enhancing independence. Additionally, we introduce a binary mask matrix that selectively activates these experts during training to promote more diverse and anisotropic learning, based on expert-level dropout strategies. Our investigations reveal that this selective activation not only enhances performance but also fosters a more diverse acquisition of knowledge with a marked decrease in parameter similarity among MLAE, significantly boosting the quality of the model. Remarkably, MLAE achieves new state-of-the-art (SOTA) performance with an average accuracy score of 78.8% on the VTAB-1k benchmark and 90.9% on the FGVC benchmark, surpassing the previous SOTA result by an average of 0.8% on both benchmarks with approximately half parameters. Our code is available at https://***/jie040109/MLAE. Copyright © 2024, The Authors. All rights reserved.

关键词： Matrix algebra

来源：评论

学校读者我要写书评

暂无评论

Predicting the next stage of China’s household sustainable energy consumption research based on a comparative knowledge graph analysis between China and foreign countries

Predicting the next stage of China’s household sustainable ...

引用

IEEE International Conference on big data

作者： Li Li Yiming Yuan Fang Liu Jianjun Wang Laboratory of Big Data Decision Making for Green Development Beijing International Science and Technology Cooperation Base of Intelligent Decision Making and Big Data Application Beijing Knowledge Management Research Base Beijing Information Science and Technology University Beijing China Beijing Information Science and Technology University Beijing China North China Electric Power University Beijing China

ISBN: (纸本)9781665445993

By using the knowledge graph tool, this paper compared the development course and content evolution trend of the scientific research on household sustainable energy consumption in China and abroad, and then based on Shneider's theory of scientific evolution, the current evolution stage was judged and the future innovative direction of relevant scientific research in China was pointed out. It was found that the overall scientific research of household sustainable energy consumption has evolved to its third stage, that is, the stage of rapid accumulation of knowledge and sustainability enhancement. Despite the late start, the gap between China's research progress and that of other advanced regions is narrowing. And the main path of sustainable transition of household energy consumption is becoming clear, namely, to develop sustainable buildings, use renewable energy and change residents’ energy consumption behavior. The current research of Chinese scholars should return to the basic point of how to guide the change of household energy consumption behavior according to China’s national conditions.

关键词： Energy consumption Renewable energy sources Conferences Buildings big data Market research Sustainable development

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：