检索结果-内蒙古大学图书馆

7th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2022

作者： Wang, Hongwei Hao, Xiaohan Yin, Lian Gong, Peisong Xiong, Feng Ren, Wei Wuhan Municipal Road and Bridge Co. Ltd Wuhan430050 China School of Software University of Technology Sydney UltimoNSW2007 Australia School of Computer Science China University of Geosciences Wuhan430074 China School of Economics and Management China University of Geosciences Wuhan430074 China School of Business Administration Zhongnan University of Economics and Law Wuhan430073 China Hubei Key Laboratory of Intelligent Geo-Information Processing Wuhan430074 China

ISBN: (纸本)9781665487115

Currently, cloud computing and big data are largely applied in smart construction, or so-called industrial Internet of Things 4.0. Building Information Model (BIM) performs as an elemental information and data unit, which is extensively employed in entire construction processes (lifespan) and inter-Acted with various entities including investors, employers, design institutes, construction companies, and monitors. They thus can achieve quantitative computing, measurement, and managements by sharing BIM data. The accumulation of BIM data will finally approach to a big data for construction intelligence. However, sharing BIM big data may be subtle in terms of privacy leakage, since BIM data may enclose the critical infrastructure information, homeland security data, structure parameters, and design intellectual property. It thus is of urgent importance to protect privacy in the BIM data sharing. In this paper, we propose a blockchain based method to protect data privacy in BIM data sharing, which can be applied for guaranteeing the information security in next generation industrial Internet of Things for smart construction. © 2022 IEEE.

关键词： Data privacy Cloud computing Computational modeling Information security Big Data Data models Blockchains

来源：评论

学校读者我要写书评

暂无评论

Prompting Disentangled Embeddings for Knowledge Graph Completion with Pre-trained Language Model

arXiv

引用

arXiv 2023年

作者： Geng, Yuxia Chen, Jiaoyan Zeng, Yuhang Chen, Zhuo Zhang, Wen Pan, Jeff Z. Wang, Yuxiang Xu, Xiaoliang Powerchina Huadong Engineering Corporation Limited Hangzhou311112 China School of Computer Science Hangzhou Dianzi University Hangzhou310018 China Department of Computer Science The University of Manchester ManchesterM13 9PL United Kingdom HDU-ITMO Joint Institute Hangzhou Dianzi University Hangzhou310018 China College of Computer Science and Technology Zhejiang University Hangzhou310028 China School of Software Technology Zhejiang University Ningbo315048 China School of Informatics The University of Edinburgh EdinburghEH8 9AB United Kingdom

Both graph structures and textual information play a critical role in Knowledge Graph Completion (KGC). With the success of Pre-trained Language Models (PLMs) such as BERT, they have been applied for text encoding for KGC. However, the current methods mostly prefer to fine-tune PLMs, leading to huge training costs and limited scalability to larger PLMs. In contrast, we propose to utilize prompts and perform KGC on a frozen PLM with only the prompts trained. Accordingly, we propose a new KGC method named PDKGC with two prompts — a hard task prompt which is to adapt the KGC task to the PLM pre-training task of token prediction, and a disentangled structure prompt which learns disentangled graph representation so as to enable the PLM to combine more relevant structure knowledge with the text information. With the two prompts, PDKGC builds a textual predictor and a structural predictor, respectively, and their combination leads to more comprehensive entity prediction. Solid evaluation on three widely used KGC datasets has shown that PDKGC often outperforms the baselines including the state-of-the-art, and its components are all effective. Our codes and data are available at https://***/genggengcss/PDKGC. © 2023, CC BY.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Pyscribe - Learning to Describe Python Code

SSRN

引用

SSRN 2022年

作者： Guo, Juncai Liu, Jin Wan, Yao Zhao, Yanjie Li, Li Liu, Kui Klein, Jacques Bissyandé, Tegawendé F. School of Computer Science Wuhan University Wuhan China Key Laboratory of Network Assessment Technology Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Faculty of Information Technology Monash University VIC Australia Huawei Software Engineering Application Technology Lab Hangzhou China SnT Centre University of Luxembourg 1855 Luxembourg

Code comment generation, which aims to describe the functionality of source code in natural language, plays an important role in automatic software development. Currently, several structural neural networks have been exploited to preserve the syntax structure of source code based on Abstract Syntax Trees (ASTs). However, they can not well capture both the long-distance and local relations between nodes while retaining the overall structural information of AST. To mitigate this problem, we present to the community a prototype tool called PYSCRIBE, which expands the Transformer model to a new encoder-decoder architecture to incorporate triplet positions into the node-level and edge-level features of AST towards automatically generating comments for Python code. To the best of our knowledge, this paper makes the first attempt to consider the edges of AST as an explicit component for better code representation, as to generate higher qualified comments. By specifying triplet positions for each node and edge, the overall structural information can be well preserved in the learning process. In the decoder, we perform a two-stage decoding process over the extracted node and edge features to generate comments. To evaluate the effectiveness of PYSCRIBE, we resort to a large dataset of code-comment pairs by mining Jupyter notebooks from GitHub, for which we have made it publicly available to support further studies. The evaluation results show that PYSCRIBE is indeed effective, outperforming the state-of-the-art by achieving an average BLEU score (i.e., av-BLEU) of ≈0.28. © 2022, The Authors. All rights reserved.

关键词： Large dataset

来源：评论

学校读者我要写书评

暂无评论

A simulated annealing method for minimum initial marking estimation in labeled Petri nets: Applications and comparative studies

引用

Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering 2025年

作者： Yue, Hao Wang, Can Hu, Hesuan Chen, Junjie Wang, Min Pang, Shanchen College of Computer Science and Technology China University of Petroleum (East China) Qingdao China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao China School of Electro-Mechanical Engineering Xidian University Xian China School of Computer Science and Engineering College of Engineering Nanyang Technological University Singapore Singapore School of Information Engineering Yangzhou University Jiangsu Yangzhou China State Key Laboratory of Chemical Safety Qingdao China

State estimation plays an important role in the field of system supervisory control. With the increase in the scale and complexity of practical systems, problems of state/event estimation for discrete event systems in Petri nets have attracted significant attention. One of the well-studied problems is how to estimate the minimum initial markings (MIMs) in a known labeled Petri net based on the observation of its event sequence. The existing approaches have disadvantages such as (1) much higher computational complexity in order to increase the MIM quantity;and (2) much fewer MIMs in order to decrease the computational complexity. In this paper, a variant of simulated annealing (SA) algorithm is developed to determine the set of MIMs. We take advantage of SA for global search in the problem’s search space to find initial markings that are minimal in their token counts, and then use the selection strategy of SA to determine MIMs from them. Finally, the effectiveness of the proposed method is illustrated through two simulation experiments of industrial applications. Comparisons with existing work are conducted to demonstrate the merits of our method. © IMechE 2025.

关键词： Discrete event systems initial marking estimation labeled Petri nets Petri nets simulated annealing supervisory control

来源：评论

学校读者我要写书评

暂无评论

Machine Learning in E-health: A Comprehensive Survey of Anxiety

Machine Learning in E-health: A Comprehensive Survey of Anxi...

引用

2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021

作者： Teelhawod, Bibi Nushrina Akhtar, Faijan Heyat, Md Belal Bin Tripathi, Pragati Mehrotra, Rajat Asfaw, Ashamo Betelihem Shorman, Omar Al Masadeh, Mahmoud University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu Sichuan China Shenzhen University IoT Research Center College of Computer Science and Software Engineering Guangdong Shenzhen China Gautam Buddha University Department of Electrical Engineering Greater Noida India Gl Bajaj Institute of Technology and Management Aktu UP India Northwestern Polytechnical University School of Software Shaanxi Xian China Najran University College of Engineering and A1 Shrouk Trading Company Najran Saudi Arabia Yarmouk University Computer Engineering Department Irbid Jordan

ISBN: (纸本)9781665416566

Anxiety is the sixth major disorder that arises due to sleeplessness, inspiration, energy, hunger loss and desperate ideas. This study aims to define the extent of the research carried on anxiety with machine learning (ML) and to generate a database. We highlight serious problems for additional research obligations and communications. This paper designs a perception-based image of ML We have investigated deep insights of material to re-claim the current understanding of anxiety with ML from engineering detection to clinical treatment. We found 18 research articles and 28 closest keywords in the Web of science (WoS) database. In addition, we extracted the information including objective, subject, major, scoring technique, classifier, and performance measure from previously published 18 articles. This study has discovered the research gap and future direction, so far focused on anxiety with ML. In addition, it also delivers objectives for the upcoming work to be competent in a significant and scientific manner. © 2021 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Time Series Classification via Enhanced Temporal Representation Learning

Time Series Classification via Enhanced Temporal Representat...

引用

IEEE International Conference on Big Data Analysis (ICBDA)

作者： Kun Wang Chun Wang Yunxiao Wang Wei Luo Peng Zhan Yupeng Hu Xueqing Li School of Computer Science & Technology Shandong University Qingdao China School of Software Shandong University Jinan China

Due to the booming of time series (a temporal data sequence, including continuous recorded values and timestamps), time series classification has been considered as one of the most challenging studies in time series data mining, attracting great interest from industry and academia over the last decades. However, the current time series classification methods have their own flaws. On the one hand, most existing methods focus on using traditional machine learning models for achieving classification accuracy, while ignoring the advantages of deep representation learning; on the other hand, the current deep neural network models only rely on one single deep learning model, and hence fail to improve performance effectively. In this paper, we propose an end-to-end representation learning model for time series classification. Concretely, we first utilize 1-D temporal convolution to obtain the feature representations. Secondly, we separately adopt the residual network and bidirectional long short-term memory network to achieve temporal representation reinforcement. Finally, we adopt the multi-layer perception network for final classification. Experimental results on open source benchmark datasets have justified the superiority of our model.

关键词： Industries Deep learning Convolution Conferences Time series analysis Neural networks Tools

来源：评论

学校读者我要写书评

暂无评论

A Novel Tree-based Neural Network for Android Code Smells Detection

A Novel Tree-based Neural Network for Android Code Smells De...

引用

IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Jing Yu Chenguang Mao Xiaojun Ye School of Software Tsinghua University Beijing China School of Computer Science and Technology Soochow University Suzhou China

ISBN: (纸本)9781665458146

The quality of mobile applications has received broad attention due to the booming development of mobile devices. Detection of Android code smells to refactor codes is an effective way to eliminate potential risks of applications. Besides traditional methods based on manually selected features, deep learning models provide an additional perspective of detection solutions. Though deep learning models are dispensed with artificial decisions, the paucity of dedicated datasets in Android code smells. The inadequate embedded syntax and semantics information acquired from source codes by token-based neural networks become new challenges. These obstacles will seriously affect the prediction performance of models. This paper put forward ACS-TNN, a novel Tree-based Neural Network approach for Android Code Smells detection. ACS-TNN took advantage of RvNN and LSTM while introducing ASTs for the first time in order to retain maximum semantic and structural information for extracting features of source codes. We constructed a dataset from practical open-source Android projects and labeled Java files data with popular Android code smells. The dataset would help researchers to train and test their deep learning models. Numerous experiments were conducted to investigate the prediction performance of ACS-TNN. The conclusions were that compared with two state-of-the-art token-based deep neural networks and another natural language tree-based model, ACS-TNN gained the best prediction performance. Further, ACS-TNN and our dataset are conducive to the maintenance of mobile applications.

关键词： Deep learning Codes Neural networks Semantics software quality Syntactics Predictive models

来源：评论

学校读者我要写书评

暂无评论

Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction

arXiv

引用

arXiv 2021年

作者： Wei, Wei Liu, Jiayi Mao, Xianling Guo, Guibing Zhu, Feida Zhou, Pan Hu, Yuchong Laboratory School of Computer Science and Technology Huazhong University of Science and Technology China School of Computer Science and Technology Beijing Institute of Technology China Software College Northeastern University United States School of Information Systems Singapore Management University Singapore School of Electronic Information and Communications Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions. However, this challenge is not well addressed in the literature, since most of the approaches neglect the emotional information conveyed by a post while generating responses. This article addresses this problem by proposing a unified end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post for generating more intelligent responses with appropriately expressed emotions. Extensive experiments on real-world data demonstrate that the proposed method outperforms the state-of-the-art methods in terms of both content coherence and emotion appropriateness. Copyright © 2021, The Authors. All rights reserved.

关键词： Speech processing

来源：评论

学校读者我要写书评

暂无评论

Road Surface Damage Detection Based on Knowledge Distillation

Road Surface Damage Detection Based on Knowledge Distillatio...

引用

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

作者： Qiao Sun Yi Yang Jingjun Gu Zhi Yu Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China School of Electronic Engineering Naval University of Engineering Wuhan China Zhejiang Provincial Key Laboratory of Service Robot School of Software Technology Zhejiang University Ningbo China

As an important public infrastructure, road contributes to economic development and growth, brings significant social benefits. However, due to a variety of factors, the road surfaces will wear and deteriorate over time. When there is a slight crack in the pavement, most will deteriorate rapidly if not repaired promptly, which can greatly affect the life of the road. Therefore, developing a road surface damage detection algorithm is an urgent demand. We use the knowledge distillation technology to solve this problem. First, the similarity-preserving knowledge distillation was improved by adding a mask block to make the model transmit only the key feature information of the local region. Secondly, by making the boundary box of the teacher output and ground truth do non-maximum suppression, the image area of the distillation in the output layer is restricted, and the transmission of background information by the teacher is reduced. the performance of the student is improved by 1.2% in mAP@0.5 on average and 2.2% in mAP@0.5:0.95 on average compared with the state-of-the-art method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

LanCOPE: Language-Guided Category-Level Object Pose Estimation from a Single RGB Image

引用

IEEE Robotics and Automation Letters 2025年第7期10卷 7555-7562页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Dai, Zhenqi Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha 410082 China Central South University School of Architecture and Art Changsha 410082 China The University of Western Australia Department of Computer Science and Software Engineering 6009 WA Australia

Monocular RGB-based category-level object pose estimation is more practical and cost-effective for robotics. However, existing methods do not fully exploit the rich semantic and contextual information in multimodal data (e.g. language) that provides additional object attributes to guide the model in extracting category features more reliably. We propose a language-guided category-level object pose estimation method (LanCOPE), taking a single RGB image as input. Our method uses DINOv2 to recover depth from a single RGB image and converts it into point cloud to perceive the object's geometry. We then introduce language descriptions for the RGB image, estimated point cloud and overall scene to better guide the point cloud encoder and image encoder in learning category features. We develop a cross-modal differential perception feature fusion network to fuse multimodal features. This network employs a differential perception module to eliminate redundant information across different modalities, highlighting signifcant semantic differences and similarities. Furthermore, it uses a cross-attention mechanism to fuse the semantic information of the language and vision features, improving the overall perception. Finally, we design a denoising network based on the skip fusion transformer to recover the object pose accurately. Extensive experiments on REAL275 and Wild6D datasets show that LanCOPE achieves state-of-the-art performance. Our code is available at LanCOPE. © 2016 IEEE.

关键词： category-level object pose estimation languag RGB image

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：