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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 popularization of cloud computing and the deepening of its application, more and more cloud block storage systems have been put into use. The performance optimization of cloud block storage systems has become an important challenge facing today, which is manifested in the reduction of system performance caused by the unbalanced resource load of cloud block storage systems. Accurately predicting the I/O load status of the cloud block storage system can effectively avoid the load imbalance problem. However, the cloud block storage system has the characteristics of frequent random reads and writes, and a large amount of I/O requests, which makes prediction difficult. Therefore, we propose a novel I/O load prediction method for XB-IOPS feature engineering. The feature engineering is designed according to the I/O request pattern, I/O size and I/O interference, and realizes the prediction of the actual load value at a certain moment in the future and the average load value in the continuous time interval in the future. Validated on a real dataset of Alibaba Cloud block storage system, the results show that the XB-IOPS feature engineering prediction model in this paper has better performance in Alibaba Cloud block storage devices where random I/O and small I/O dominate. The prediction performance is better, and the prediction time is shorter than other prediction models. © 2022 IEEE.

关键词： Continuous time systems

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An Improved Method for Long-term Drift Compensation of Electronic Nose with Batch Control 12

An Improved Method for Long-term Drift Compensation of Elect...

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12th IEEE International Conference on Electronics information and Emergency Communication, ICEIEC 2022

作者： Cui, Hao Dong, Xiaorui Shang, Kai Shandong Institute of Petroleum and Chemical Technology Key Laboratory of Intelligent Information Processing Shandong Province Dongying China College of Computer Science and Technology China University of Petroleum Shandong Province Qingdao China

ISBN: (数字)9781665407540

ISBN: (纸本)9781665407540

Sensor long-time drift compensation has always been a huge and important research challenge. To address this problem, this paper proposes a simple and efficient long-time drift compensation method based on a short-time memory-forgetting mechanism. The proposed improved method controls the update frequency of memory and forgetting by introducing a batch control coefficient to achieve a balance between the update frequency of memory queue and model iteration. In this paper, we use the gas sensor array drift dataset as the research object and design two experimental strategies to verify the processing time compression degree and the accuracy change of the classification model by adjusting the batch control coefficient. The experiments prove that the proposed method in this paper has an excellent effect on compressing data processing time and relieving hardware pressure, which has a certain reference value for related research. © 2022 IEEE.

关键词： Support vector machines

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A Dynamic Short-range Animal Migration Forecast Model Based on Weather Radar Network

A Dynamic Short-range Animal Migration Forecast Model Based ...

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IET International Radar Conference 2023, IRC 2023

作者： Liu, Xuan Cui, Kai Hu, Cheng Wang, Rui Mao, Huafeng Wu, Dongli Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing100081 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China School of Computer Sciences Beijing Institute of Technology Beijing100081 China Meteorological Observation Centre of China Meteorological Administration Beijing100081 China

ISBN: (纸本)9781839539954

Predicting aerial animal migration has great implications for biological research and conservation, and agricultural production. Animal migration is deeply coupled with spatio-temporal and meteorological factors, however, the existing forecast model is only based on the spatio-temporal features of animal migration and the impact of meteorological factors on migration is not considered. In this paper, we propose a dynamic forecast model to forecast airborne migration from the perspective of the influence of wind field data on migration and makes prediction with the help of neural network. Specifically, we calculate the transition probability matrix using the wind data and the migration spatial field to represent the migration dynamic model, and integrated this model into deep neural network. We use the data of China's weather radar network to verify that our model outperforms the competing methods. In addition, the cause of prediction error at peak value is analysed by visualization results. In future applications, more meteorological factors should be considered to improve the representation of dynamic models. © The Institution of Engineering & technology 2023.

关键词： Wind forecasting

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BeFA: A General Behavior-driven Feature Adapter for Multimedia Recommendation

arXiv

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arXiv 2024年

作者： Fan, Qile Yu, Penghang Tan, Zhiyi Bao, Bing-Kun Lu, Guanming School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

Multimedia recommender systems focus on utilizing behavioral information and content information to model user preferences. Typically, it employs pre-trained feature encoders to extract content features, then fuses them with behavioral features. However, pre-trained feature encoders often extract features from the entire content simultaneously, including excessive preference-irrelevant details. We speculate that it may result in the extracted features not containing sufficient features to accurately reflect user preferences. To verify our hypothesis, we introduce an attribution analysis method for visually and intuitively analyzing the content features. The results indicate that certain items’ content features exhibit the issues of information drift and information omission, reducing the expressive ability of features. Building upon this finding, we propose an effective and efficient general Behavior-driven Feature Adapter (BeFA) to tackle these issues. This adapter reconstructs the content feature with the guidance of behavioral information, enabling content features accurately reflecting user preferences. Extensive experiments demonstrate the effectiveness of the adapter across all multimedia recommendation methods. Our code is made publicly available on https://***/fqldom/BeFA. Copyright © 2024, The Authors. All rights reserved.

关键词： Recommender systems

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Anomize: Better Open Vocabulary Video Anomaly Detection

arXiv

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arXiv 2025年

作者： Li, Fei Liu, Wenxuan Chen, Jingjing Zhang, Ruixu Wang, Yuran Zhong, Xian Wang, Zheng National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China State Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology China

Open Vocabulary Video Anomaly Detection (OVVAD) seeks to detect and classify both base and novel anomalies. However, existing methods face two specific challenges related to novel anomalies. The first challenge is detection ambiguity, where the model struggles to assign accurate anomaly scores to unfamiliar anomalies. The second challenge is categorization confusion, where novel anomalies are often misclassified as visually similar base instances. To address these challenges, we explore supplementary information from multiple sources to mitigate detection ambiguity by leveraging multiple levels of visual data alongside matching textual information. Furthermore, we propose incorporating label relations to guide the encoding of new labels, thereby improving alignment between novel videos and their corresponding labels, which helps reduce categorization confusion. The resulting Anomize framework effectively tackles these issues, achieving superior performance on UCF-CRIME and XD-VIOLENCE datasets, demonstrating its effectiveness in OVVAD. Copyright © 2025, The Authors. All rights reserved.

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Disk Failure Prediction Based on SW-Disk Feature Engineering 8

Disk Failure Prediction Based on SW-Disk Feature Engineering

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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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