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8th IEEE International Conference on Big Data Security on Cloud, 8th IEEE International Conference on High Performance and Smart Computing, and 8th IEEE International Conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2022

作者： Liang, Chenjun Deng, Li Zhu, Jincan Cao, Zhen Li, Chao College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Hubei University Department of Information Development and Management Wuhan China

ISBN: (纸本)9781665480697

With the rapid development of cloud storage, the number of disks has increased dramatically, and the absolute number of failed disks has also continued to increase. Since the impact of these failures on the quality of cloud services cannot be ignored, the demand for disk failure prediction is also increasing. In addition, the accuracy of the disk failure prediction algorithm largely depends on the input data set. Therefore, according to the large difference in the feature morphology in the disk tracking dataset, we analyze the change trend of features in different dimensions, and propose a new method. The SW-Disk feature engineering method for disk failure prediction constructs features from both horizontal and vertical dimensions. Including feature crossover between indicators and indicators to construct higher-order features. In the time dimension, the time series features under the multi-scale sliding window are constructed. We use the random forest prediction model to predict whether the disk will fail in the next 30 days, so as to reserve more time to take measures on the disk to achieve the purpose of improving the reliability of the cloud storage system. We validated our SW-Disk feature engineering method and prediction model on the Backblaze2019 disk dataset, and the experimental results demonstrate the effectiveness and superiority of our proposed method. © 2022 IEEE.

关键词： Decision trees

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Distributed online bandit tracking for Nash equilibrium under partial-decision information setting

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Science China(Technological Sciences) 2023年第11期66卷 3129-3138页

作者： FENG ZhangCheng XU WenYing CAO JinDe YANG ShaoFu RUTKOWSKI Leszek Jiangsu Provincial Key Laboratory of Networked Collective Intelligence School of MathematicsSoutheast UniversityNanjing 211189China Frontiers Science Center for Mobile Information Communication and Security Nanjing 210096China School of Computer Science and Engineering Southeast UniversityNanjing 211189China The Systems Research Institute of the Polish Academy of Sciences Warsaw 01-447Poland AGH University of Science and Technology Krakow 30-059Poland

This paper is concerned with a Nash equilibrium(NE)tracking issue in online games with bandit feedback,where cost functions vary with time and agents only have access to the values of these functions at two points during each round.A partial-decision information setting is considered,in which agents have only access to the decisions of their *** primary objective of this paper is to develop a distributed online NE tracking algorithm that ensures sublinear growth of regret with respect to the total round T,under both the bandit feedback and partial-decision information *** utilizing a two-point estimator together with the leader-following consensus method,a new distributed online NE tracking algorithm is established with the estimated gradient and local estimated decisions based on the projection gradient-descent ***,sufficient conditions are derived to guarantee an improved upper bound of dynamic regret compared to existing bandit ***,a simulation example is presented to demonstrate the effectiveness of the proposed algorithm.

关键词： online game bandit feedback partial-decision two-point gradient estimator

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GuideNER: Annotation Guidelines Are Better than Examples for In-Context Named Entity Recognition 39

GuideNER: Annotation Guidelines Are Better than Examples for...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Huang, Shizhou Xu, Bo Yu, Yang Li, Changqun Lin, Xin Alex School of Computer Science and Technology East China Normal University Shanghai China School of Computer Science and Technology Donghua University Shanghai China Shanghai Key Laboratory of Multidimensional Information Processing China

ISBN: (纸本)157735897X

Large language models (LLMs) demonstrate impressive performance on downstream tasks through in-context learning (ICL). However, there is a significant gap between their performance in Named Entity Recognition (NER) and in fine-tuning methods. We believe this discrepancy is due to inconsistencies in labeling definitions in NER. In addition, recent research indicates that LLMs do not learn the specific input-label mappings from the demonstrations. Therefore, we argue that using examples to implicitly capture the mapping between inputs and labels in in-context learning is not suitable for NER. Instead, it requires explicitly informing the model of the range of entities contained in the labels, such as annotation guidelines. In this paper, we propose GuideNER, which uses LLMs to summarize concise annotation guidelines as contextual information in ICL. We have conducted experiments on widely used NER datasets, and the experimental results indicate that our method can consistently and significantly outperform state-of-the-art methods, while using shorter prompts. Especially on the GENIA dataset, our model outperforms the previous state-of-the-art model by 12.63 F1 scores. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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computer Science and Blockchain (CCSB), International Conference on

作者： Guimin Huang Canjun Shi Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350356502

ISBN: (纸本)9798350356519

Short textual similarity has been more and more widely used in the field of natural language processing. However, people tend to pay more attention to the semantic similarity of sentences and ignore the word order similarity in sentences. For this reason, we propose a short text similarity computation model that fuses semantics and word order. In terms of semantics, we propose a adjacency-aware similarity model based on deep convolutional neural networks, which effectively exploits the adjacency relationship in sentences and extracts text features from multiple perspectives for similarity prediction. In terms of word order, a word order model incorporating an external knowledge base is proposed, which uses an external knowledge base with pointwise mutual information to construct word order dense vectors to assess word order similarity. Ultimately, we propose a comprehensive assessment model for short text similarity that incorporates semantic and word order information. We conducted experiments on the public dataset MRPC, and the experimental results show that our model outperforms existing models in terms of performance.

关键词： Computational modeling Semantics Knowledge based systems Syntactics Predictive models Feature extraction Vectors Natural language processing Convolutional neural networks Mutual information

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A Tibetan Ancient Uchen Text Line Dataset for OCR

A Tibetan Ancient Uchen Text Line Dataset for OCR

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Image processing, computer Vision and Machine Learning (ICICML), International Conference on

作者： Zhuome Gongqu Peng Luo Dongzhou Jiayang Jia Cairang Jiacuo Cizhen Dongzhu Renqing The College of Computer Qinghai Normal University Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province Xining China The College of Information Science and Technology Tibet University Lhasa China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Characters written by hand that represent distinct writing styles are referred to as handwritten text; these are especially common in ancient texts. In the field of digitizing ancient literature, Tibetan ancient document image recognition is a focus of both domestic and international research. However, technological advancement in this field has been hampered by the absence of a specific recognition dataset for image recognition systems of Tibetan ancient documents. This paper addresses this by proposing a dataset for Uchen style text line recognition in Tibetan ancient documents. This dataset includes basic synthetic images of multi-style Uchen text lines as well as real-world images of multi-format Tibetan Uchen text lines that have been manually annotated. Cross-Validation and several rounds of annotation by various people guarantee the quality of the data. Additionally, three popular text line recognition models are used to assess the dataset and validate its usability.

关键词： Hands computer vision Image recognition Text recognition Annotations Computational modeling Optical character recognition Machine learning Writing Usability

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VisionEmbedder: Bit-Level-Compact key-Value Storage with Constant Lookup, Rapid Updates, and Rare Failure

VisionEmbedder: Bit-Level-Compact Key-Value Storage with Con...

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International Conference on Data Engineering

作者： Yuhan Wu Feiyu Wang Yifan Zhu Zhuochen Fan Zhiting Xiong Tong Yang Bin Cui National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China College of Computer National University of Defense Technology Changsha China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

In key-value storage scenarios where storage space is at a premium, our focus is on a class of solutions that only store the value, which is highly space-efficient. While these solutions have proven their worth in distributed storage, networking, and bioinformatics, they still face two significant issues: one is that their space cost could be further reduced; the other is their are vulnerable to update failures, which can necessitate a complete table reconstruction. To address these issues, we introduce VisionEmbedder, a compact key-value embedder with constant-time lookup, fast dynamic updates, and a near-zero risk of reconstruction. VisionEmbedder cuts down the storage requirement from 2.2L bits to just 1.6L bits per key-value pair with an L-bit value, and it significantly reduces the chance of update failures by a factor of n, where $n$ is the number of keys (for instance, 1 million or more). The compromise with VisionEmbedder comes with a minor reduction in query throughput on certain data sizes. The enhancements offered by VisionEmbedder have been theoretically validated and are effective across any dataset. Additionally, we have implemented VisionEmbedder on both FPGA and CPU platforms, with all codes made available as open-source.

关键词： Costs Codes Mathematical analysis Throughput Data engineering Bioinformatics Field programmable gate arrays

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NL2SQL Enhancement through Semantic Representation Aggregation 5

NL2SQL Enhancement through Semantic Representation Aggregati...

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5th International Conference on computer information Science and Application technology, CISAT 2022

作者： Long, Lingli Zhu, Yongjin Shao, Jun Kong, Zheng Li, Jian Liu, Zejie Zhang, Xu Jiangsu Frontier Electric Technology Co. Ltd. China School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

ISBN: (纸本)9781510660076

Natural Language to SQL (NL2SQL) is a technology that converts natural language into executable SQL statements. For single table tasks, the dominant approach is a slot-filling-based model which actually performs multi-objective prediction. However, in existing multi-objective models, it is difficult to optimize multiple objectives simultaneously. Low correlation or conflict between various objectives can result in the model not being able to learn the parameters effectively. To effectively address this problem, this paper considers redesigning the sharing mechanism between multiple tasks and the specific network structure of individual tasks through a semantic aggregation approach in order to alleviate the above phenomenon. © 2022 SPIE.

关键词： Semantics

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A Corpus Preprocessing Method for Syllable-Level Tibetan Text Classification 3

A Corpus Preprocessing Method for Syllable-Level Tibetan Tex...

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3rd International Conference on Natural Language processing, ICNLP 2021

作者： Dao, Ji-Zhaxi Cai, Zhi-Jie Cai, Rang-Zhuoma San, Maocuo Ban, Mabao College of Computer Science and Technology Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province Key Laboratory of Tibetan Information Processing Ministry of Education Qinghai Normal University Xining Qinghai China College of Computer Science and Technology School of Computer Science and Technology Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province Key Laboratory of Tibetan Information Processing Ministry of Education Qinghai Normal University Southwest Minzu University Sichuan Chengdu Qinghai Xining China

ISBN: (纸本)9781665414111

Text classification is one of the most common and important tasks in the application field of natural language processing. With the rapid development of machine learning landscape, deep learning has become the mainstream approach for implementing text classification applications. However, deep learning has high requirements on the scale and quality of corpus, therefore, it is particularly important to build large-scale and high-quality corpus. In order to improve the quality of Tibetan text classification corpus, based on the analysis of the research status of corpus preprocessing, this paper proposes a syllable level Tibetan text classification corpus preprocessing model, and presents the core module of a text normalization algorithm which we refer as TC_ TCCNL. The proposed method lays a foundation for the construction of Tibetan text classification corpus. © 2021 IEEE.

关键词： Natural language processing systems

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An Enhancing Stability Control Strategy of VSG-Based Power Source Under PWM Rectifier Load

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Gaoxiang Liu, Bowen Song, Wenhao Jining, Chen Liu, Xiao Guangxi University Guangxi Key Laboratory of Power System Optimization and Energy Technology School of Electrical Engineering Nanning530004 China Guangxi Zhuang Autonomous Region Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning530200 China Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing School of Computer Electronics and Information Nanning530004 China

Under PWM rectifier load, the power source based on virtual synchronous generator (VSG) is prone to cause mid-frequency oscillation. Firstly, the system oscillation mechanism and characteristic of the VSG-based power source are analyzed based on impedance method. Due to the negative resistance and capacitive characteristic in the middle frequency range, the PWM rectifier load is prone to interact with the VSG-based power source, which can lead to impedance resonance. Secondly, to improve the stability of the VSG-based power source under the PWM rectifier load, a voltage differentiation feedforward control is proposed. In addition, since the mid-frequency impedance magnitude decrease obviously, the improved VSG-based power source is closer to the ideal voltage source, which can automatically suppress the middle harmonics induced by uncontrollable rectifier load. Finally, the proposed control and the corresponding theoretical analysis are verified by simulations and experiments. © 1975-2011 IEEE.

关键词： Synchronous generators

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Spatio-Temporal Transformer-Based Reinforcement Learning for Robot Crowd Navigation

Spatio-Temporal Transformer-Based Reinforcement Learning for...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： He, Haodong Fu, Hao Wang, Qiang Zhou, Shuai Liu, Wei Chen, Yang Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan430081 China Eng. Res. Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan430081 China

ISBN: (纸本)9798350325706

Ensuring robots can move safely and adhere to social norms in dynamic human environments is a crucial step towards robot autonomous decision-making. In existing work, double serial separate modules are generally used to capture spatial and temporal interactions, respectively. However, such methods lead to extra difficulties in improving the utilization of spatio-temporal features and reducing the conservatism of navigation policy. In light of this, this paper proposes a spatiotemporal transformer-based policy optimization algorithm to more effectively preserve the human-robot interactions. Specifically, a gated embedding mechanism is introduced to effectively fuses the spatial and temporal representations by integrating both modalities at the feature level. Then Transformer is leveraged to encode the spatio-temporal semantic information, with the hope of finding the optimal navigation policy. Finally, a combination of spatio-temporal Transformer and self-adjusting policy entropy significantly reduce the conservatism of navigation policies. Experimental results demonstrate the priority of the proposed algorithm over the state-of-the-art methods. © 2023 IEEE.

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