检索结果-内蒙古大学图书馆

HUSS:A Heuristic Method for Understanding the Semantic Structure of Spreadsheets

Data intelligence 2023年第3期5卷 537-559页

作者： Xindong Wu Hao Chen Chenyang Bu Shengwei Ji Zan Zhang Victor S.Sheng Key Laboratory of Knowledge Engineering with Big Data(the Ministry of Education of China) Hefei University of TechnologyChinaSchool of Computer Science and Information EngineeringHefei University of TechnologyHefeiChina Research Institute of Artificial Intelligence Zhejiang LabHangzhouChina Department of Computer Science Texas Tech UniversityLubbockTX 79409USA

Spreadsheets contain a lot of valuable data and have many practical *** key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets,e.g.,identifying cell function types and discovering relationships between cell *** existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells.A few studies do,but they ignore the layout structure information of spreadsheets,which affects the performance of cell function classification and the discovery of different relationship types of cell *** this paper,we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets(HUSS).Specifically,for improving the cell function classification,we propose an error correction mechanism(ECM)based on an existing cell function classification model[11]and the layout features of *** improving the table structure analysis,we propose five types of heuristic rules to extract four different types of cell pairs,based on the cell style and spatial location *** experimental results on five real-world datasets demonstrate that HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.

关键词： Spreadsheet semantic structure Information extraction Heuristics Cell function analysis Table structure analysis

来源：评论

学校读者我要写书评

暂无评论

NABP: Social Network Alignment Algorithm based on Pre-trained Language Models 3

NABP: Social Network Alignment Algorithm based on Pre-traine...

引用

3rd International Conference on artificial intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Ding, Linjun Chen, Jing School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computing Intelligence Jiangnan University Jiangsu Wuxi214122 China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

The existing social network matching algorithms have problems in processing text attribute information, as they cannot handle polysemy issues of word meanings well and cannot effectively extract deep semantic information from the text. To address the aforementioned issues, this paper introduces pretrained models to extract information from attribute text and proposes a social network alignment algorithm called NABP (Network Alignment based on Pre-trained Language Models). NABP first preprocesses the attribute text data to extract the text feature vectors;then, it uses the DeepWalk algorithm to perform graph representation learning on the two networks to extract their structural features. Next, the attribute feature vectors and structural feature vectors are concatenated to form the final feature vector. Then, a projection function is learned using supervised information to further improve the algorithm's performance. To verify the effectiveness of NABP algorithm, this study conducted experiments on two real social network datasets. The results show that the NABP algorithm performs better than existing algorithms in both the Accuracy and Precision@k metrics, demonstrating the effectiveness of the algorithm and pre-trained language models. © 2023 SPIE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Design of a Virtual Simulation Experimental Teaching Platform Based on the Internet of Things 13

Design of a Virtual Simulation Experimental Teaching Platfor...

引用

13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Xiehua, Yu Shengjie, You Rentao, Lin School of Computer and Information MinNan Science and Technology University Quanzhou China University of Fujian Province Key Laboratory of Big Data and Artificial Intelligence Quanzhou China

ISBN: (纸本)9798350319156

The virtual simulation experiment teaching system based on the Internet of Things simulates the Internet of Things sensing device in the form of software, and integrates relatively new teaching methods and theoretical knowledge, designs and completes a characteristic, can adapt to various needs of the Internet of Things experiment teaching system, and strives to provide more efficient protection and help for teachers' teaching tasks and students' learning tasks. To a large extent, it can solve the problems in the design and implementation of the Internet of Things application system, provide students with practical operation opportunities in the way of virtual simulation, combine theory and practice, and cultivate application-oriented talents urgently needed by the industry. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Review and Prospect: Applications of Exponential Signals with Machine Learning in Nuclear Magnetic Resonance

引用

Spectroscopy (Santa Monica) 2023年第9期38卷 22-32页

作者： Guo, Di Chen, Xianjing Lu, Mengli He, Wangfeng Luo, Sihui Lin, Yanqin Huang, Yuqing Xiao, Lizhi Qu, Xiaobo School of Computer and Information Engineering Xiamen University of Technology Xiamen China School of Electronic Science Xiamen University Xiamen China College of Artificial Intelligence State Key Laboratory of Petroleum Resources and Prospecting China University of Petroleum Beijing China

Nuclear magnetic resonance (NMR) spectroscopy presents an important analytical tool for composition analysis, molecular structure elucidation, and dynamic study in the fields of chemistry, biomedicine, food science, energy and more. As a basic function, exponential functions can be applied to model NMR signals of free induction decay, relaxation, and diffusion. In this paper, we will review Fourier and Laplace NMR exponential signals separately, as well as the performance of state-of-the-art machine learning on NMR applications. © 2023 Advanstar Communications Inc.. All rights reserved.

关键词： Nuclear magnetic resonance spectroscopy

来源：评论

学校读者我要写书评

暂无评论

3D Reconstruction and Texture Optimization Based on Differentiable Rendering 8

3D Reconstruction and Texture Optimization Based on Differen...

引用

8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Liu, Yongxin Zhao, Haifeng Zhang, Shaojie Fu, Yanping Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9798350302455

Due to noise and quantization errors inherent in depth cameras, RGB-D based 3D reconstruction algorithms inevitably suffer from defects such as texture blurring and ghosting. However, current texture optimization methods tend to only focus on optimizing one aspect, such as camera pose, geometry, or texture, without considering the highly coupled relationship between them. As a result, it is difficult to achieve global optimality. To address this issue, we propose a joint optimization framework based on differentiable rendering that simultaneously optimizes camera pose, geometry, and texture. By fully exploiting the complementary relationship between these aspects, we are able to reconstruct textures with high fidelity while recovering fine geometric details. First, we propose an optimized geometry model combined with adaptive subdivision, where we selectively subdivide the model only in texture-rich regions to increase the number of vertex facets. Then, we perform geometry optimization to recover high-frequency geometric details evenly in the presence of large geometric errors without incurring computational costs. After the pose correction and geometric model optimization, we use pixel regeneration pipelines to generate clear texture atlases. Comparative evaluations with state-of-the-art texture optimization methods on a public dataset demonstrate the effectiveness of our proposed approach in reducing errors in the reconstruction of 3D models, generating clear textures, and recovering high-frequency geometric details. © 2023 IEEE.

关键词： Textures

来源：评论

学校读者我要写书评

暂无评论

A detection method for image content aware tampering based on ULBP

A detection method for image content aware tampering based o...

引用

2023 International Conference on computer Application and Information Security, ICCAIS 2023

作者： Lin, Yiran Lu, Ming Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province Wenzhou University Zhejiang Wenzhou325035 China College of Computer Science and Artificial Intelligence Wenzhou University Zhejiang Wenzhou325035 China

ISBN: (纸本)9781510675032

A digital image tampering detection method based on a combination of unified local binary mode features and energy deviation features is proposed for digital image tampering achieved using content aware scaling technology. This method describes the changes in pixel correlation within local neighborhoods caused by image tampering by unifying local binary mode features, and achieves the same detection effect as traditional local binary modes using fewer dimensional features. Considering the changes in energy deviation of the image before and after tampering, a unified local binary pattern feature and energy deviation feature are combined to train the classifier using joint features, thereby achieving more accurate and efficient detection results. The experimental results show that this method can effectively detect content aware tampering in images and accurately locate tampered areas in Seam Insertion. © 2024 SPIE.

关键词： Local binary pattern

来源：评论

学校读者我要写书评

暂无评论

Controllable Music Loops Generation with MIDI and Text via Multi-Stage Cross Attention and Instrument-Aware Reinforcement Learning 24

Controllable Music Loops Generation with MIDI and Text via M...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Chen, Guan-Yuan Soo, Von-Wun Chunghwa Telecom Laboratories Advanced Technology Laboratory Taoyuan Taiwan National Tsing Hua University Department of Computer Science Hsinchu Taiwan Department of Artificial Intelligence Chang Gung University Taoyuan Taiwan

ISBN: (纸本)9798400706868

The burgeoning field of text-to-music generation models has shown great promise in their ability to generate high-quality music aligned with users' textual descriptions. These models effectively capture abstract/global musical features such as style and mood. However, they often inadequately produce the precise rendering of critical music loop attributes, including melody, rhythms, and instrumentation, which are essential for modern music loop production. To overcome this limitation, this paper proposed a Loops Transformer and a Multi-Stage Cross Attention mechanism that enable a cohesive integration of textual and MIDI input specifications. Additionally, a novel Instrument-Aware Reinforcement Learning technique was introduced to ensure the correct adoption of instrumentation. We demonstrated that the proposed model can generate music loops that simultaneously satisfy the conditions specified by both natural language texts and MIDI input, ensuring coherence between the two modalities. We also showed that our model outperformed the state-of-the-art baseline model, MusicGen, in both objective metrics (by lowering the FAD score by 1.3, indicating superior quality with lower scores, and by improving the Normalized Dynamic Time Warping Distance with given melodies by 12%) and subjective metrics (by +2.56% in OVL, +5.42% in REL, and +7.74% in Loop Consistency). These improvements highlight our model's capability to produce musically coherent loops that satisfy the complex requirements of contemporary music production, representing a notable advancement in the field. Generated music loop samples can be explored at: https://***/. © 2024 ACM.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Multilingual Brain Surgeon: Large Language Models Can be Compressed Leaving No Language Behind 30

Multilingual Brain Surgeon: Large Language Models Can be Com...

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zeng, Hongchuan Xu, Hongshen Chen, Lu Yu, Kai X-LANCE Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence SJTU AI Institute Shanghai Jiao Tong University Shanghai China Suzhou Laboratory Suzhou China

ISBN: (纸本)9782493814104

Large Language Models (LLMs) have ushered in a new era in Natural Language Processing, but their massive size demands effective compression techniques for practicality. Although numerous model compression techniques have been investigated, they typically rely on a calibration set that overlooks the multilingual context and results in significant accuracy degradation for low-resource languages. This paper introduces Multilingual Brain Surgeon (MBS), a novel calibration data sampling method for multilingual LLMs compression. MBS overcomes the English-centric limitations of existing methods by sampling calibration data from various languages proportionally to the language distribution of the model training datasets. Our experiments, conducted on the BLOOM multilingual LLM, demonstrate that MBS improves the performance of existing English-centric compression methods, especially for low-resource languages. We also uncover the dynamics of language interaction during compression, revealing that the larger the proportion of a language in the training set and the more similar the language is to the calibration language, the better performance the language retains after compression. In conclusion, MBS presents an innovative approach to compressing multilingual LLMs, addressing the performance disparities and improving the language inclusivity of existing compression techniques. The codes are available at: https://***/X-LANCE/MBS. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Calibration

来源：评论

学校读者我要写书评

暂无评论

Multi-branch Attention Fusion Network for Aspect Sentiment Classification 8

Multi-branch Attention Fusion Network for Aspect Sentiment C...

引用

8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Zhao, HaiFeng Yan, Kun Cao, MingWei Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9798350302455

For specific aspects (aspect words) of the object described in a given sentence, the research goal of aspect sentiment classification is to predict the sentiment polarity expressed by different aspect words. At present, sentiment classification methods based on attention mechanism and graph neural networks use attention models to capture contextual information related to aspect words, but ignore the global information of the sentence where aspect words are located. The classification accuracy in dealing with complex sentences still needs to be further improved. To solve the above problems, we propose a novel syntax-aware method - Multi-branch Attention Fusion Network (MAFNet) for aspect sentiment classification. Firstly, we use graph attention networks to capture contextual information related to aspect words, while using the mean-pooling layer to capture global information of sentences. Secondly, we propose a novel attention fusion mechanism to integrate contextual information related to aspect words and global information of sentences, which can enrich the semantic relationship between aspect words and opinion words in the sentence. Then, we use orthogonal and differential regularization to constrain the attention weight of contextual information, which can accurately capture the semantic correlation between aspect words and opinion words in the sentence. Finally, we verify the effectiveness of the algorithm on three public datasets. The experimental results prove that our method can correctly establish the semantic relationship between aspect words and opinion words, which can achieve better classification accuracy than traditional methods, attention-based methods, and graph neural network-based methods. © 2023 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

A Fabrication and Simulation Recipe for Untethering Soft-Rigid Robots with Cable-Driven Stiffness Modulation

A Fabrication and Simulation Recipe for Untethering Soft-Rig...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： James M. Bern Zach J. Patterson Leonardo Zamora Yañez Kristoff K. Misquitta Daniela Rus Computer Science Williams College Computer Science and Artificial Intelligence Laboratory MIT

We explore the idea of robotic mechanisms that can shift between soft and rigid states, with the long-term goal of creating robots that marry the flexibility and robustness of soft robots with the strength and precision of rigid robots. We present a simple yet effective method to achieve large and rapid stiffness variations by compressing and relaxing a flexure using cables. Next, we provide a differentiable modeling framework that can be used for motion planning, which simultaneously reasons about the modulated stiffness joints, tendons, rigid joints, and basic hydrodynamics. We apply this stiffness tuning and simulation recipe to create SoRiTu, an untethered soft-rigid robotic sea turtle capable of various swimming maneuvers.

关键词：

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：