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arXiv 2024年

作者： Zhou, Shu Wang, Xin Zhou, Zhengda Yi, Haohan Zheng, Xuhui Wang, Hao School of Information Management Nanjing University China Key Laboratory of Data Engineering and Knowledge Services in Jiangsu Provincial Universities Nanjing University China Baidu Inc. Beijing China

In order to solve the problem of insufficient generation quality caused by traditional patent text abstract generation models only originating from patent specifications, the problem of new terminology OOV caused by rapid patent updates, and the problem of information redundancy caused by insufficient consideration of the high professionalism, accuracy, and uniqueness of patent texts, we proposes a patent text abstract generation model (MSEA) based on a master-slave encoder architecture;Firstly, the MSEA model designs a master-slave encoder, which combines the instructions in the patent text with the claims as input, and fully explores the characteristics and details between the two through the master-slave encoder;Then, the model enhances the consideration of new technical terms in the input sequence based on the pointer network, and further enhances the correlation with the input text by re weighing the "remembered" and "for-gotten" parts of the input sequence from the encoder;Finally, an enhanced repetition suppression mechanism for patent text was introduced to ensure accurate and non redundant abstracts generated. On a publicly available patent text dataset, compared to the state-of-the-art model, Improved Multi-Head Attention Mechanism (IMHAM), the MSEA model achieves an improvement of 0.006, 0.005, and 0.005 in Rouge-1, Rouge-2, and Rouge-L scores, respectively. MSEA leverages the characteristics of patent texts to effectively enhance the quality of patent text generation, demonstrating its advancement and effectiveness in the experiments. Copyright © 2024, The Authors. All rights reserved.

关键词： Specifications

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SALI: A Scalable Adaptive Learned Index Framework based on Probability Models

arXiv

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arXiv 2023年

作者： Ge, Jiake Zhang, Huanchen Shi, Boyu Luo, Yuanhui Guo, Yunda Chai, Yunpeng Chen, Yuxing Pan, Anqun Key Laboratory of Data Engineering and Knowledge Engineering MOE China Engineering Research Center of Database and Business Intelligence MOE China School of Information Renmin University of China China China and Shanghai Qi Zhi Institute China Tsinghua University China Tencent Inc China

The growth in data storage capacity and the increasing demands for high performance have created several challenges for concurrent indexing structures. One promising solution is learned indexes, which use a learning-based approach to fit the distribution of stored data and predictively locate target keys, significantly improving lookup performance. Despite their advantages, prevailing learned indexes exhibit constraints and encounter issues of scalability on multi-core data storage. This paper introduces SALI, the Scalable Adaptive Learned Index framework, which incorporates two strategies aimed at achieving high scalability, improving efficiency, and enhancing the robustness of the learned index. Firstly, a set of node-evolving strategies is defined to enable the learned index to adapt to various workload skews and enhance its concurrency performance in such scenarios. Secondly, a lightweight strategy is proposed to maintain statistical information within the learned index, with the goal of further improving the scalability of the index. Furthermore, to validate their effectiveness, SALI applied the two strategies mentioned above to the learned index structure that utilizes fine-grained write locks, known as LIPP. The experimental results have demonstrated that SALI significantly enhances the insertion throughput with 64 threads by an average of 2.04× compared to the second-best learned index. Furthermore, SALI accomplishes a lookup throughput similar to that of LIPP+. © 2023, CC BY-NC-ND.

关键词： Scalability

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Deep Reinforcement Learning with Transformers for Text Adventure Games

Deep Reinforcement Learning with Transformers for Text Adven...

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IEEE Symposium on Computational Intelligence and Games, CIG

作者： Yunqiu Xu Ling Chen Meng Fang Yang Wang Chengqi Zhang Centre for Artificial Intelligence University of Technology Sydney Sydney Australia Tencent Robotics X Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) Hefei University of Technology China

ISBN: (数字)9781728145334

ISBN: (纸本)9781728145341

In this paper, we study transformers for text-based games. As a promising replacement of recurrent modules in Natural Language Processing (NLP) tasks, the transformer architecture could be treated as a powerful state representation generator for reinforcement learning. However, the vanilla transformer is neither effective nor efficient to learn with a huge amount of weight parameters. Unlike existing research that encodes states using LSTMs or GRUs, we develop a novel lightweight transformer-based representation generator featured with reordered layer normalization, weight sharing and block-wise aggregation. The experimental results show that our proposed model not only solves single games with much fewer interactions, but also achieves better generalization on a set of unseen games. Furthermore, our model outperforms state-of-the-art agents in a variety of man-made games.

关键词： Games Generators Task analysis Reinforcement learning Logic gates Buildings Computer architecture

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Kernelized multiview subspace analysis by self-weighted learning

arXiv

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arXiv 2019年

作者： Wang, Huibing Wang, Yang Zhang, Zhao Fu, Xianping Wang, Meng College of Information and Science Technology Dalian Maritime University Dalian Liaoning116021 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology China

—With the popularity of multimedia technology, information is always represented or transmitted from multiple views. Most of the existing algorithms are graph-based ones to learn the complex structures within multiview data but overlooked the information within data representations. Furthermore, many existing works treat multiple views discriminatively by introducing some hyperparameters, which is undesirable in practice. To this end, abundant multiview based methods have been proposed for dimension reduction. However, there are still no research to leverage the existing work into a unified framework. To address this issue, in this paper, we propose a general framework for multiview data dimension reduction, named Kernelized Multiview Subspace Analysis (KMSA). It directly handles the multi-view feature representation in the kernel space, which provides a feasible channel for direct manipulations on multiview data with different dimensions. Meanwhile, compared with those graph-based methods, KMSA can fully exploit information from multiview data with nothing to lose. Furthermore, since different views have different influences on KMSA, we propose a self-weighted strategy to treat different views discriminatively according to their contributions. A co-regularized term is proposed to promote the mutual learning from multi-views. KMSA combines self-weighted learning with the co-regularized term to learn appropriate weights for all views. We also discuss the influence of the parameters in KMSA regarding the weights of multi-views. We evaluate our proposed framework on 6 multiview datasets for classification and image retrieval. The experimental results validate the advantages of our proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Multimedia systems

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Autonomous Human-Robot Collaborative Assembly Method Driven by the Fusion of Large Language Model and Digital Twin

Autonomous Human-Robot Collaborative Assembly Method Driven ...

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2024 International Conference on Intelligent Systems and Robotics, CISR 2024

作者： Chen, Jianpeng Luo, Haiwei Huang, Sihan Zhang, Meidi Wang, Guoxin Yan, Yan Jing, Shikai School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China Capital Aerospace Machinery Corporation Limited Beijing100076 China Key Laboratory of Industry Knowledge & Data Fusion Technology and Application Ministry of Industry and Information Technology Beijing Institute of Technology Beijing100081 China China North Artificial Intelligence & Innovation Research Institute Beijing100071 China

Human-robot collaboration (HRC) plays an important role in human-centric manufacturing, which requires cooperative robots to have the ability of collaborate with human autonomously. It is very complex to understand the intention of human during the assembly process, therefore, we proposed a method of autonomous HRC assembly driven by the fusion of large language model (LLM) and digital twin in this paper. The assembly state is recognized from two perspectives, including the perception of key parts based on transfer learning and YOLO, and perceive operator actions based on LSTM and attention mechanism. In order to improve the autonomy of HRC, a collaborative task decision method driven by fine-tuning LLM based on assembly domain knowledge is proposed. A case study of reducer assembly is presented to verify the effectiveness of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Collaborative robots

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Multi-source Multi-label Feature Selection

Multi-source Multi-label Feature Selection

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiulan Yuan Xuegang Hu Peipei Li Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei City China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei City China

Feature selection for multi-source multi-label data has attracted much attention, because there are a lot of scenarios that produce multiple source data with multi-labels in the real-world applications, which aggravates the problems of dimensional disaster and the label skewness. However, most of existing multi-source feature selection methods miss the multi-label issue while existing multi-label feature selection methods can not select the optimal feature set in the multi-source environment. Motivated by this, we propose a novel feature selection method, called MSMLFS. To be specific, the Inf-FS algorithm is firstly introduced to handle multi-label feature selection for each data source, which considers the label weight in the feature selection. Secondly, the over-sampling mechanism and the inter-source feature fusion method are used to handle the label skewness of multi-label data and the feature selection in multiple sources respectively. Finally, extensive experiments conducted on synthetic and real-world multi-source multi-label data sets demonstrate that the proposed method outperforms several state-of-the-art multi-source or multi-label feature selection methods.

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Forecasting Chinese Stock Market Prices using Baidu Search Index with a Learning-Based data Collection Method

Forecasting Chinese Stock Market Prices using Baidu Search I...

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作者： Dong, Jichang Dai, Wei Liu, Ying Yu, Lean Wang, Jie School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Economics and Management Beijing University of Chemical Technology Beijing100029 China Department of Civil and Environmental Engineering Stanford University StanfordCA94305 United States

In this study, to address search index selection and volatility problems, we propose a learning-based search index collection method that collects the search data fraction for modeling by learning the best criteria from robust statistics. Based on the fraction of collected search index from internet search engine (***) data sources, a novel model is formulated for Chinese stock market price forecasting. We empirically test our method on the two main Chinese stock market price indexes and discover that the prediction accuracy is equivalent or superior to the benchmarks from previous studies that used alternative search index collection methods or lagged data prediction models. All prediction results outstand the importance of an effective data collection method for the robustness of forecast models and demonstrate the utility of a learning-based collection method for addressing search index collection problem, leading to a significant improvement in Chinese stock market price prediction accuracy. © 2019 World Scientific Publishing Company.

关键词： Search engines

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An improved elastic net with heuristic cloning strategy for traveling salesman problem

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ICIC Express Letters 2011年第11期5卷 3995-4000页

作者： Yang, Gang Yang, Nan Yi, Junyan Key Laboratory of Data Engineering and Knowledge Engineering MOE School of Information Renmin University of China No. 59 Zhongguancun Street Haidian District Beijing 100872 China Department of Computer Science and Technology Zhejiang University of Technology No. 18 Chaowang Road Hangzhou 310014 China

Based on analysis of relationships between solution qualities and number of initial dynamic points in elastic net algorithm, we propose an improved elastic network algorithm (IENA) introduced in a heuristic cloning strategy for traveling salesman problem (TSP). The improved elastic net consists of four evolution processes with less dynamic points and gradually reducing cities, and it accelerates the convergent process without compromising the equality of solutions. IENA is conducted on some random TSP instances to verify its performance by comparison with other algorithms. © 2011 ICIC International.

关键词： Traveling salesman problem

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AI-Enhanced Predictive Maintenance in Manufacturing Processes

AI-Enhanced Predictive Maintenance in Manufacturing Processe...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Ponrudee Netisopakul Nawarat Phumee Shool of Information Technology Knowledge Management and Knowledge Engineering Laboratory King Mongkut’s Institute of Technology Ladkrabang Bangkok Thailand School of Information Technology Data Technology and Innovation King Mongkut’s Institute of Technology Ladkrabang Bangkok Thailand

This research aims to apply artificial intelligence technology to a manufacturing industry, specifically, to forecast temperature and insulation values of motors from the CNC machine. dataset from motor sensors are collected and forecasting models are trained using four deep learning models, namely, multilayer perceptron (MLP), long-short term memory (LSTM), LSTM autoencoder, and bidirectional LSTM (Bi-LSTM). Models are evaluated by measuring the deviation of forecasting values from the real values. Two measures, root mean square error (RMSE) and mean absolute error (MAE), are used to assess model’s performance. Experiments are conducted and found that the Bi-LSTM yielded the lowest RMSE and MAE numbers, hence, the best model to be selected. Further development has been implemented by integrating Bi-LSTM and genetic algorithm (GA) in order to optimize the model performance. Instead of searching the huge hyperparameter space of the neural network, the integrate GA-LSTM model using RMSE as a fitness function to reduce the search space and obtain the optimal or near optimal hyperparameters. The empirically best model is found which yields a lower RMSE value of 0.041 comparing to 0.18 when not optimized.

关键词： Deep learning Temperature measurement Temperature sensors Manufacturing processes Sociology Predictive models Forecasting

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Unveiling Funded Research: Impact and Dual Innovation

SSRN

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SSRN 2023年

作者： Yang, Alex Jie School of Information Management Nanjing University Nanjing210023 China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing210023 China International Joint Informatics Laboratory Nanjing University Nanjing210023 China

In the relentless pursuit of scientific advancement, comprehending the profound impact and innovation nature inherent in funded research projects assumes paramount significance. To illuminate this matter, I delve into the realm of research supported by the esteemed National Institutes of Health (NIH) and the distinguished National Science Foundation (NSF). Our evaluative framework encompasses a spectrum of metrics, including the 5-year citation, normalized citation, and citation to patents, as markers of research impact. Moreover, I embrace ex-ante innovation (Novelty) and ex-post innovation (Disruption) as dual indispensable yardsticks for evaluating the innovative nature of research projects. Novelty denotes the manifestation of atypical combinations of existing knowledge, while Disruption signifies the extent of paradigm-shifting potential and the ability to exert a disruptive influence on future research endeavors. First, the analysis reveals that funded research projects manifest a conspicuously heightened impact in comparison to their non-funded counterparts. Second, I uncover a noteworthy finding, namely that funded research demonstrates significantly higher levels of ex-ante innovation (Novelty). However, in a surprising twist, the investigation also uncovers that funded research exhibits significantly lower levels of ex-post innovation (Disruption). Additionally, I undertake a meticulous scrutiny of the robustness of the research findings by scrutinizing patterns across disparate years and fields. This comprehensive analysis further unveils inconspicuous heterogeneity, thus substantiating the multifaceted nature of funded research and the vast potential for its innovation across various disciplines and temporal dimensions. © 2023, The Authors. All rights reserved.

关键词： Patents and inventions

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