检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Zhao, Ying Chen, Zhi Wang, Chengnan Hu, Xudong Liu, Zhaofei He, Hongyi Observatory for Geodynamic of the East Yangtze Block in Jiujiang Jiujiang332006 China Jiangxi Earthquake Agency Nanchang330000 China CEA Key Laboratory of Earthquake Forecasting Institute of Earthquake Science China Earthquake Administration Beijing100036 China Geological Survey of Jiangxi Province Nanchang330000 China Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology East China University of Technology Nanchang330013 China Hubei Subsurface Multi-scale Imaging Key Laboratory School of Geophysics and Geomatics China University of Geosciences Wuhan430074 China

As an emerging method for monitoring and prediction of seismic underground fluid, the reliability and stability of in-situ online monitoring devices for soil radon are crucial performance indicators that are directly related to the successful implementation of continuous monitoring of soil radon in seismic areas. This study conducted laboratory testing and field applications of a new in-situ online monitoring instrument for seismic soil radon, utilizing the experimental testing conditions provided by the radon monitoring instrument detection platform of the China Earthquake Administration and the natural experimental site at Xianshuihe Fault Zone in the China Seismic Experimental Site. The results indicate that laboratory measurement repeatability of the instrument is 5.53%, the relative intrinsic error of radon volume response activity is 1.53%, and the response capability of hourly sampling at high radon volume response activity (greater than 1.0×105 Bq/m3) lags behind the standard instrument by 2 hours, essentially meeting the requirements for seismic observation. Under field conditions, the instruments exhibit good synchronization in response at different depths at the same location. However, further improvements are needed to enhance the consistency of long-term operation in the field, as well as the adaptability to outdoor self-powering, data transmission, and environmental conditions. © 2024, The Authors. All rights reserved.

关键词： Radon

来源：评论

学校读者我要写书评

暂无评论

Categorizing npm Packages by Analyzing the Text Information in Software Repositories

Categorizing npm Packages by Analyzing the Text Information ...

引用

Asia-Pacific Conference on Software engineering

作者： Yu Wang Huaxiao Liu Shanquan Gao Shujia Li College of computer science and technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China

ISBN: (纸本)9781665437851

To prevent JavaScript developers from reinventing wheels, npm ecosystem provides numerous third-party libraries for developers to realize relevant functionalities. Npm displays the tags provided by the creators for these packages to help developers find suitable ones. However, not all creators have the habit of tagging their packages, and thus npm cannot provide tag information of a lot of packages for developers to help them understand the package functionalities effectively. Considering that many tags are unrelated to the functionality of packages, we propose a method to find out the tags that are important to distinguish the functionality categories of packages and assign them to untagged packages for assisting developers in the process of retrieving the packages. Firstly, we analyze the attribute of existing tags in npm to establish category tags (functionality categories). Then, we further mine the readme of tagged packages to generate keywords for each category tag. Finally, our method identifies category tags for untagged packages by measuring the similarity between their readme and the keywords of category tags. The evaluation demonstrates that our approach has a good performance in assigning category tags to untagged packages.

关键词： Correlation Codes Ecosystems Wheels Tagging Software Libraries

来源：评论

学校读者我要写书评

暂无评论

arXiv

引用

arXiv 2022年

作者： Liu, Bin Papadopoulos, Dimitrios Malliaros, Fragkiskos D. Tsoumakas, Grigorios Papadopoulos, Apostolos N. Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and Telecommunications Chongqing400065 China School of Informatics Aristotle University of Thessaloniki Thessaloniki54124 Greece Gif-sur-Yvette91190 France

The discovery of drug-target interactions (DTIs) is a very promising area of research with great potential. The accurate identification of reliable interactions among drugs and proteins via computational methods, which typically leverage heterogeneous information retrieved from diverse data sources, can boost the development of effective pharmaceuticals. Although random walk and matrix factorization techniques are widely used in DTI prediction, they have several limitations. Random walk-based embedding generation is usually conducted in an unsupervised manner, while the linear similarity combination in matrix factorization distorts individual insights offered by different views. To tackle these issues, we take a multi-layered network approach to handle diverse drug and target similarities, and propose a novel optimization framework, called Multiple similarity DeepWalk-based Matrix Factorization (MDMF), for DTI prediction. The framework unifies embedding generation and interaction prediction, learning vector representations of drugs and targets that not only retain higher-order proximity across all hyper-layers and layer-specific local invariance, but also approximate the interactions with their inner product. Furthermore, we develop an ensemble method (MDMF2A) that integrates two instantiations of the MDMF model, optimizing the area under the precision-recall curve (AUPR) and the area under the receiver operating characteristic curve (AUC) respectively. The empirical study on real-world DTI datasets shows that our method achieves statistically significant improvement over current state-of-the-art approaches in four different settings. Moreover, the validation of highly ranked non-interacting pairs also demonstrates the potential of MDMF2A to discover novel DTIs. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Tensor and Minimum Connected Dominating Set based Confident Information Coverage Reliability Evaluation for IoT

IEEE Transactions on Sustainable Computing

引用

IEEE Transactions on Sustainable Computing 2024年

作者： Xiao, Ziheng Zhu, Chenlu Feng, Wei Liu, Shenghao Deng, Xianjun Lu, Hongwei Yang, Laurence T. Park, Jong Hyuk Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China Artificial Intelligence and Intelligent Transportation Joint Technical Center of HUST and Hubei Chutian Intelligent Transportation Company Ltd United States Network and Industrial Control Information Security Technology Department China Nuclear Power Operation Technology Company Ltd. 1011 Xiongchu Avenue Hubei Wuhan430070 China The Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of

Internet of Things (IoT) reliability evaluation contributes to the sustainable computing and enhanced stability of the network. Previous algorithms usually evaluate the reliability of IoT by enumenating the states of nodes and networks, which are difficult to handle IoT with hundreds of nodes because the computational cost. In this paper, a novel algorithm, TMCRA, is proposed to evaluate the reliability of IoT in complex network environment, which consider both coverage and connectivity. For coverage, TMCRA employs the Confident Information Coverage (CIC) model to divide the target area into independent grids and calculates the coverage rate. In terms of connectivity, TMCRA forming the Virtual Backbone Network (VBN) based on two proposed methods: TMA and MGIN, and evaluate connectivity by analyzing the VBN rather than the whole network. The TMA and MGIN are two algorithms for constructing Minimum Connected Dominant Sets (MCDS), which are suitable for different scale networks. Finally, based on the data of coverage and connectivity, TMCRA utilizes tensors for the unified modeling and representation of network structure, and calculates IoT reliability based on the tensors. Simulations are carried out for various sizes of IoT to show the advantages and effectiveness of the proposed approach in reliability evaluation. © 2016 IEEE.

关键词： data reliability

来源：评论

学校读者我要写书评

暂无评论

Range Nearest Neighbor Query with the Direction Constraint 20th

Range Nearest Neighbor Query with the Direction Constraint

引用

20th International Conference on Web Information Systems engineering, WISE 2019 and on the International Workshop on Web Information Systems in the Era of AI, 2019

作者： Miao, Xue Guo, Xi Yang, Xiaochun Wang, Zhaoshun Lv, Peng Beijing Key Laboratory of Knowledge Engineering for Materials University of Science and Technology Beijing Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9789811532801

In this paper, we study a direction-aware spatial data query method, i.e., range nearest neighbor query with the direction constraint (Range-DCNN query). Traditional DCNN query retrieves the top-k nearest neighbors within an angular range. Our Range-DCNN query finds all nearest neighbors within an angular range for all points in a rectangle. Dissimilar to the traditional DCNN query, the user’s location in the Range-DCNN query is abstracted to a rectangle rather than a point and the user’s location can be anywhere in the rectangle. In doing so, the user’s precise location will not be leaked, which ensures an effective privacy protection of user’s location. In Range-DCNN query, an observation is made that splitting points can be utilized to obtain all query results without having to search for all points. We propose some properties of locating splitting points. According to these properties, efficient algorithms are designed with the assistance of the R-tree. Extensive experiments have been conducted on both real and synthetic datasets. The experimental results demonstrate that our algorithms are capable of locating all results precisely and efficiently. © Springer Nature Singapore Pte Ltd 2020.

关键词： Location based services

来源：评论

学校读者我要写书评

暂无评论

Siamese Delta Network with Multimodality Fusion for Systemic Treatment Response Prediction in Pancreatic Cancer*

Siamese Delta Network with Multimodality Fusion for Systemic...

引用

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Peijun Hu Xiang Li Na Lu Xueli Bai Tingbo Liang Jingsong Li Research Center for Healthcare Data Science Zhejiang Laboratory Hangzhou China Department of Hepatobiliary and Pancreatic Surgery First Affiliated Hospital Zhejiang University School of Medicine Hangzhou China Zhejiang Provincial Key Laboratory of Pancreatic Disease Hangzhou China Zhejiang University Cancer Center Hangzhou China Engineering Research Center of EMR and Intelligent Expert System Ministry of Education College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou China

Systemic treatment is a main way for pancreas cancer patients that are ineligible for surgery. A subgroup of patients showed good response to systemic treatment and the rest received limited benefits. CT images provide a non-invasive way to assess the treatment response. Alternative non-image methods include radiology analysis, tumor marker analysis and combination analysis. To combine the image and non-image data, we propose the Siamese Delta Network with Multimodality Fusion (SDN-MF) to predict systemic treatment response in an end-to-end way. First, a Siamese Delta Network (SDN) is designed to process pre-treatment and pre-surgery CT images and get the image feature changes to predict response. Then, patients’ characteristics from EMR and alternative analysis results forms non-image data, which is incorporated into SDN with a multimodality fusion (MF) module. The proposed SDN-MF is evaluated on a private dataset and achieves average AUC value of 0.883 with five cross-validation. Comparison among image-only, non-image-only, and fusion models verifies the superior of multimodality model in predicting systemic treatment response of pancreas cancer patients.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Low Rank Reduced Biquaternion Tensor Ring Decomposition and Tensor Completion

SSRN

引用

SSRN 2024年

作者： Luo, Hui Liu, Xin Liu, Wei Zhang, Yang School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology Avenida Wai Long TaiPa 999078 China Macau Institute of Systems Engineering Faculty of Innovation Engineering Macau University of Science and Technology Avenida Wai Long TaiPa 999078 China School of Artificial Intelligence Sun Yat-sen University The Guangdong Key Laboratory of Big Data Analysis and Processing No.135 West Xingang Road Guangdong Provience Guangzhou510275 China Department of Mathematics University of Manitoba No.135 West Xingang Road WinnipegMBR3T 2N2 Canada

Reduced biquaternions (RBs), as an extension of complex numbers, have demonstrated exceptional capabilities in digital signal and image processing. They not only excel particularly in encoding color information but also feature multiplicative commutativity, which simplify the theoretical analysis of related algorithms. Concurrently, tensor completion methods based on tensor ring decomposition in the real domain have been proven effective in exploiting low-rank structures. Building upon these insights, this paper first introduces the Reduced Biquaternion Tensor Ring (RBTR) decomposition, along with a detailed exposition of the corresponding algorithm RBTR-SVD. Leveraging RBTR decomposition, we propose a novel low-rank tensor completion algorithm RBTR-TV integrating RBTR rank with Total Variation (TV) regularization to optimize the process. In color image completion tasks, our method exhibits remarkable stability and robustness, outperforming traditional methods, especially if there are a lot of missing data in the image. © 2024, The Authors. All rights reserved.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

Adaptive Pruning for Large Language Models with Structural Importance Awareness

arXiv

引用

arXiv 2024年

作者： Zheng, Haotian Ren, Jinke Sun, Yushan Zhang, Ruichen Zhang, Wenbo Li, Zhen Niyato, Dusit Cui, Shuguang Han, Yatong National Key Laboratory of Autonomous Marine Vehicle Technology Harbin Engineering University Harbin150001 China The Chinese University of Hong Kong Shenzhen518172 China The Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Shenzhen518172 China College of Computing and Data Science Nanyang Technological University Singapore Co. Ltd Shenzhen518048 China SSE The FNii-Shenzhen The Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Shenzhen518172 China FNii-Shenzhen The Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Shenzhen518172 China Infused Synapse AI Shenzhen518048 China

The recent advancements in large language models (LLMs) have significantly improved language understanding and generation capabilities. However, it is difficult to deploy LLMs on resource-constrained edge devices due to their high computational and storage resource demands. To address this issue, we propose a novel LLM model pruning method, namely structurally-aware adaptive pruning (SAAP), to significantly reduce the computational and memory costs while maintaining model performance. We first define an adaptive importance fusion metric to evaluate the importance of all coupled structures in LLMs by considering their homoscedastic uncertainty. Then, we rank the importance of all modules to determine the specific layers that should be pruned to meet particular performance requirements. Furthermore, we develop a new group fine-tuning strategy to improve the inference efficiency of LLMs. Finally, we evaluate the proposed SAAP method on multiple LLMs across two common tasks, i.e., zero-shot classification and text generation. Experimental results show that our SAAP method outperforms several state-of-the-art baseline methods, achieving 2.17%, 2.37%, and 2.39% accuracy gains on LLaMA-7B, Vicuna- 7B, and LLaMA-13B. Additionally, SAAP improves the token generation speed by 5%, showcasing its practical advantages in resource-constrained scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Joint Power and 3D Trajectory Optimization for UAV-enabled Wireless Powered Communication Networks with Obstacles

arXiv

引用

arXiv 2023年

作者： Pan, Hongyang Liu, Yanheng Sun, Geng Fan, Junsong Liang, Shuang Yuen, Chau The College of Computer Science and Technology Jilin University Changchun130012 China Pillar Singapore University of Technology and Design 487372 Singapore The Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China The School of Information Science and Technology Northeast Normal University Changchun130024 China

Unmanned aerial vehicle (UAV)-enabled wireless powered communication networks (WPCNs) are promising technologies in 5G/6G wireless communications, while there are several challenges about UAV power allocation and scheduling to enhance the energy utilization efficiency, considering the existence of obstacles. In this work, we consider a UAV-enabled WPCN scenario that a UAV needs to cover the ground wireless devices (WDs). During the coverage process, the UAV needs to collect data from the WDs and charge them simultaneously. To this end, we formulate a joint-UAV power and three-dimensional (3D) trajectory optimization problem (JUPTTOP) to simultaneously increase the total number of the covered WDs, increase the time efficiency, and reduce the total flying distance of UAV so as to improve the energy utilization efficiency in the network. Due to the difficulties and complexities, we decompose it into two sub optimization problems, which are the UAV power allocation optimization problem (UPAOP) and UAV 3D trajectory optimization problem (UTTOP), respectively. Then, we propose an improved non-dominated sorting genetic algorithm-II with K-means initialization operator and Variable dimension mechanism (NSGA-II-KV) for solving the UPAOP. For UTTOP, we first introduce a pretreatment method, and then use an improved particle swarm optimization with Normal distribution initialization, Genetic mechanism, Differential mechanism and Pursuit operator (PSO-NGDP) to deal with this sub optimization problem. Simulation results verify the effectiveness of the proposed strategies under different scales and settings of the networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

Simultaneous Speech Extraction for Multiple Target Speakers under the Meeting Scenarios

arXiv

引用

arXiv 2022年

作者： Zeng, Bang Suo, Hongbing Wan, Yulong Li, Ming School of Computer Science Wuhan University Wuhan China Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Duke Kunshan University Kunshan China Data & AI Engineering System OPPO Beijing China

The common target speech separation directly estimate the target source, ignoring the interrelationship between different speakers at each frame. We propose a multiple-target speech separation model (MTSS) to simultaneously extract each speaker's voice from the mixed speech rather than just optimally estimating the target source. Moreover, we propose a speaker diarization (SD) aware MTSS system (SD-MTSS), which consists of a SD module and MTSS module. By exploiting the TSVAD decision and the estimated mask, our SD-MTSS model can extract the speech signal of each speaker concurrently in a conversational recording without additional enrollment audio in advance. Experimental results show that our MTSS model achieves 1.38dB SDR, 1.34dB SI-SDR, and 0.13 PESQ improvements over the baseline on the WSJ0- 2mix-extr dataset, respectively. The SD-MTSS system makes 19.2% relative speaker dependent character error rate (CER) reduction on the Alimeeting dataset. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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：