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data Catalogs with Artificial Intelligence and Active Metadata: A Case Study of China Mobile

Data Catalogs with Artificial Intelligence and Active Metada...

引用

Next Generation data-driven Networks (NGDN), International Conference on

作者： Jibin Wang Yanmei Guo Yong Jiang Jing Shang Zhuo Chen Yu Liu Qingyuan Hu Zheng Yin China Mobile Information Technology Center Beijing China Cloud Computing and Big Data Research Institute China Academy of Information and Communications Technology Beijing China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

In the context of the rapid development of artificial intelligence, telecommunications operators have accumulated an increasing amount of data. China Mobile has developed a set of practical methods of enhanced data catalog in response to current issues such as difficulties in scheduling heterogeneous data from multiple sources, lack of macroscopic views of data usage, unintelligent data operation methods, and high risk of data leakage. By leveraging data virtualization, active metadata management, machine learning, along with other large-scale model technologies, China Mobile has crafted an enhanced data catalog, improving the data management efficiency and business decision-making process, further strengthening its competitiveness in the telecommunication industry.

关键词： Industries Technological innovation Decision making Machine learning Metadata User experience Telecommunications Virtualization Next generation networking Business

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TimesKAN: A Generic Backbone Network for Time Series Prediction Based on Kolmogorov-Arnold Networks

TimesKAN: A Generic Backbone Network for Time Series Predict...

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International Conference on Computer and Communications (ICCC)

作者： Bihui Yu Zong Wang Lijun Fu Shenyang Institute of Computing Technology University of Chinese Academy of Sciences Shenyang China Laboratory of Big Data and Artificial Intelligence Technology Shandong University Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

The field of time series analysis has significant applications in various domains, including physiological signals, weather prediction, and power systems. However, the intricate data patterns inherent in time series often lead to suboptimal performance of conventional models. To address this challenge, this paper proposes a novel multivariate time series prediction model called TimesKAN based on the Kolmogorov-Arnold network, drawing inspiration from the Kolmogorov-Arnold representation theorem. This model consists of two key components: (i) KAN layers constructed using B-splines as learnable activation function feature extractors instead of commonly used convolution and transformer structures in traditional time series models. B-splines are smooth piecewise polynomial functions capable of approximating complex shapes and patterns effectively, enabling the network to capture subtle changes in the data without being affected by abrupt changes or discontinuities; (ii) We introduce embedding multivariate channel independence for KAN layers where each channel contains a single univariate time series that utilizes independent KAN weights for each KAN input while sharing independent channel weights across all embeddings. This extension allows KAN layers to effectively handle complex data models. Experimental results demonstrate that TimesKAN outperforms many state-of-the-art frameworks for long-term predictions on 85% of datasets and short-term predictions on all datasets. The feature extraction method based on the Kolmogorov-Arnold representation theorem significantly enhances both long-term and short-term prediction accuracy. Furthermore, TimesKAN exhibits excellent performance when dealing with periodicity and trend within time series data while also adapting well to unstable changes within such sequences. The Kolmogorov-Arnold representation theorem provides a promising alternative, presenting opportunities for further enhancements to contemporary deep learning models

关键词： Deep learning Accuracy Convolution Time series analysis Computer architecture Feature extraction Transformers data models Splines (mathematics) Forecasting

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Meta-Path Based Social Relation Reasoning in a Deep and Robust Way 28th

Meta-Path Based Social Relation Reasoning in a Deep and R...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Jiang, Xuhui Shen, Yinghan Wang, Yuanzhuo Shen, Huawei Xu, Chengjin Ma, Shengjie Data Intelligent System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology UCAS Gloucester United Kingdom Big Data Academy Zhongke Zhengzhou China International Digital Economy Academy Shenzhen China Gaoling School of Artificial Intelligence Renmin University of China Beijing China

ISBN: (纸本)9783031306747

Social relation reasoning in heterogeneous social networks (HSNs) should not only infer whether people are connected, but also why they know each other and what their relationship is. This task is challenging because the heterogeneous social network is non-trivial to leverage and the collected data is always noisy and incomplete, which leads that the existing rule-based and representation methods cannot efficiently and accurately reason social relations. To reason social relations in noisy and incomplete HSNs, we shift from simply inferring meta-path based social relations by rule-based methods to reasoning by social relation learning. Then, we present an inductive model named Heterogeneous Graph Variational AutoEncoders (HGVAE) towards robust social relation learning on noisy and incomplete HSN data. In HGVAE, the well-designed heterogeneous graph encoder, multi-signal decoder, and variational inference mechanism bring prominent robustness and significant performance improvement. Extensive experiments are conducted to compare our methods against a set of the most representative baseline methods. The efficiency analysis, robust analysis and ablation studies confirm the value of our proposed approach for modelling meta-path based social relations buried in various HSNs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning systems

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A Survey of Research Progress and Theory Foundation in Large Model 8

A Survey of Research Progress and Theory Foundation in Large...

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8th IEEE International Conference on Cloud computing and Intelligence Systems, CCIS 2022

作者： Xiaofei, Dong Xueqiang, Zhang Dan, Zhang Feng, Cao Bingfeng, Bai Institute of Cloud Computing and Big Data China Academy of Information and Communications Technology Beijing100191 China Nanjing Research Institute of Next-generation Artificial Intelligence Ai Standardization Service Department Nanjing Jiangsu210046 China

ISBN: (纸本)9781665477352

In recent years, with the rapid development of key elements and core technologies of artificial intelligence, large-scale pre-training model (large model) has achieved remarkable effects. As specific practice progresses of large model, it is useful to realize the universality and generalizability of artificial intelligence, and respond to the strategic goal of building a strong model framework. From the perspective of theory, this article explores the support points of large model in the theories of intrinsic subspace, effective model complexity, and low rank decomposition. We discuss the research findings, implications and limitations of model development, and puts forward relevant suggestions for the future trend. © 2022 IEEE.

关键词： Artificial intelligence

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Research on named entity recognition method for high-speed railway technology transformation project text 4

Research on named entity recognition method for high-speed r...

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2024 4th International Conference on Artificial Intelligence and Industrial Technology Applications, AIITA 2024

作者： Lian, Xiaoqin Bao, Kexin Gao, Chao Gong, Yonggang Wu, Yanhua Cheng, Zhibo School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing100048 China Key Laboratory of Industrial Internet and Big Data China National Light Industry Beijing Technology and Business University Beijing100048 China Institute of Computing Technologies China Academy of Railway Sciences Corporation Limited Beijing100081 China

High-speed railway technology transformation projects are crucial to the layout of high-speed rail transportation production, high-speed rail safety, and high-speed rail operation and management. However, the data of high-speed railway technology transformation projects is mainly stored in the form of unstructured text, and the relevant personnel of high-speed railroad often need to manually go through the form to extract the key information of the project data and enter it into the system, with a low degree of automation and intelligence. Therefore, to improve the efficiency and quality of processing the data of high-speed railway technology transformation projects, and help the intelligent development of high-speed railways, this paper constructs the RoBERTa-BiLSTM-CRF fusion model to automate the identification of key information entities in the data of high-speed railway technology transformation project. The experimental data were selected as technology transformation project documents of a railway bureau from 2013 to 2022, and six defined entity types were identified. Experimental results show that the constructed model can effectively identify six types of defined entities, and the F1 value of each type of entity is above 90%. In addition, compared with other commonly used models, the F1 value has been significantly improved. The fusion model proposed in this paper can be used as a method to automatically extract key information for high-speed rail technology transformation projects. © Published under licence by IOP Publishing Ltd.

关键词： Railroads

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Analysis of Labanotation generation based on motion capture data 5

Analysis of Labanotation generation based on motion capture ...

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5th International Conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Chen, Boran Wang, Jiaji Miao, Zhenjiang Li, Jianpo Yang, Shengchun Jilin Electric Power Big Data Intelligent Processing Engineering Technology Research Center School of Computing Northeast Electric Power University Jilin Jilin132012 China Institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781510668621

Labanotation is an approach to record human movement with different distributions in space that have different representations of symbols. Motion capture is a technique that records the trajectory of an object in the time domain by tracking the motion of key points, observed using different sensors, and recorded in files. This paper is to manually mark the Labanotation based on 3D human motion capture data, to divide the 3D human motion into gaits and poses, and to record and represent different human movements and positions in space in the notation. This is an exploration of motion capture data to generate Labanotation, while also contributing to the preservation of folk-art forms. © 2023 SPIE.

关键词： Time domain analysis

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Pedestrian Behavior Recognition on Roads Based on Deep Learning

Pedestrian Behavior Recognition on Roads Based on Deep Learn...

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International Conference on Computer and Communications (ICCC)

作者： Xiangdong He Wei Wang Hongwei Zeng Lijun Fu Shenyang Institute of Computing Technology University of Chinese Academy of Sciences Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China Laboratory of Big Data and Artificial Intelligence Technolgy Shandong University Jinan China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

With the exploration and development of autonomous driving technology, road pedestrian video analysis functionality is gaining increasing attention. Through the use of artificial intelligence for safety level analysis of road pedestrian behavior, traffic accidents can be reduced. Traditional in-vehicle pedestrian safety alert algorithms use computer vision to detect pedestrian positions, and radar to calculate pedestrian distance and assess collision risk. These recognition algorithms follow simple logic, which can easily overlook hidden dangers during driving. For example, when pedestrians engage in activities like pushing and shoving in what the vehicle system deems a safe area, they may suddenly move to the center of the roadway, significantly increasing the likelihood of a traffic accident. Many other risky pedestrian behaviors exist, such as looking down at their phones, making phone calls, Walk side by side (SBS), carrying large items, running, etc. Considering the variety of pedestrian behaviors and the current limitations of video behavior recognition (BR) algorithms, which cannot be directly applied to driving scenarios, and the lack of publicly available datasets, several high-risk road behavior video datasets were created. A BR method based on deep learning, called C3dTe, was proposed. The C3dTe algorithm was trained and tested using publicly available datasets like UCF101 and HMDB51. Experimental results show that the C3dTe algorithm achieved a maximum recognition rate of 98.33% on the UCF101 dataset and 68.82% on the HMDB51 dataset. On the custom road scene dataset, the recognition rate exceeded 82%, demonstrating its effectiveness in identifying road pedestrian behaviors.

关键词： Deep learning Adaptation models Computer vision Pedestrians Roads Computational modeling Inference algorithms Safety Autonomous vehicles Accidents

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Long Text Semantic Span Extraction Algorithm for Medical Electronic Medical Record 6

Long Text Semantic Span Extraction Algorithm for Medical Ele...

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6th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2022

作者： Chen, Xueliang Liu, Tianming Tang, Gongzheng Liu, Yujiang Shenyang Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Laboratory of Big Data and Artificial Intelligence Technology Shandong University Beijing China

ISBN: (纸本)9781450397148

Named entity recognition (NER) is one of the hotspots in the field of Natural Language Processing (NLP). NER, as one of the most important sub tasks in the field of information extraction, aims to find some key phrases in massive texts, such as names of people, places, diseases and drugs. At present, researchers have proposed some models that are easy to implement and have good effects. However, because entity span usually contains little semantics and is not easy to understand, and in the medical field, there are high requirements for the semantics of the extracted span, so the semantic span extraction task appears. Semantic span may be a word or phrase, or it may be composed of one or more sentences. Due to the limitation of coding length, the extraction of semantic span from long text is subject to many restrictions. Because of the particularity of this task, so far, there is little research on the direction of semantic span extraction. We tried a variety of models on our medical electronic medical record dataset. The experimental results show that the proposed model is simple and effective for extracting semantic span from long text. © 2022 Association for computing Machinery.

关键词： Semantics

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HairDiffusion: vivid multi-colored hair editing via latent diffusion 24

HairDiffusion: vivid multi-colored hair editing via latent d...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yu Zeng Yang Zhang Jiachen Liu Linlin Shen Kaijun Deng Weizhao He Jinbao Wang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University and Shenzhen Institute of Artificial Intelligence and Robotics for Society and National Engineering Laboratory for Big Data System Computing Technology Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University and Guangdong Provincial Key Laboratory of Intelligent Information Processing

ISBN: (纸本)9798331314385

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images.

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Over-the-Air Federated Edge Learning with Error-Feedback One-Bit Quantization and Power Control

Over-the-Air Federated Edge Learning with Error-Feedback One...

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International Conference on Computer and Communications (ICCC)

作者： Yuding Liu Dongzhu Liu Richeng Jin Guangxu Zhu Qingjiang Shi College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Computing Science University of Glasgow Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Over-the-air federated edge learning (Air-FEEL) is a communication-efficient framework for distributed machine learning using training data distributed at edge devices. This framework enables all edge devices to transmit model updates simultaneously over the entire available bandwidth, allowing for over-the-air aggregation. The existing one-bit digital over-the-air aggregation scheme features one-bit gradient quantization at edge devices and majority voting-based decoding at edge server, and therefore offers enhanced communication efficiency. However, the low-resolution one-bit gradient quantization slows down the model convergence and leads to performance degradation, while the aggregation errors caused by channel fading in Air-FEEL further impair the performance. To address these issues, we propose the error-feedback one-bit broadband digital aggregation (EFOBDA) with an optimized power control policy. To this end, we first derive the impact of aggregation errors on the convergence of FEEL with EFOBDA. Then, we further introduce a power control policy by maximizing the convergence rate under instantaneous power constraints. The analytical results and optimized power control policy are validated by the experiments, which show that the proposed scheme achieves significantly faster convergence and higher test accuracy in image classification tasks compared with the vanilla one-bit quantization scheme without error feedback or optimized power control policy.

关键词： Performance evaluation Fading channels Training Quantization (signal) Wireless networks Atmospheric modeling Power control Channel estimation Training data Convergence

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