检索结果-内蒙古大学图书馆

13th International Conference of Information and Communication technology, ICTech 2024

作者： Xiu, Pengfei Fu, Lijun Sun, Yukun Wang, Hongliang Li, Xuerao Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shandong University Big Data Technology and Cognitive Intelligence Laboratory University of Chinese Academy of Sciences Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (纸本)9798350376258

Anaerobic fermentation is the most important link to gas production, this paper solves the current problem with the help of the current complete machine learning technology, combining perception and intelligent processing, intelligent processing of information, and derive a decision-making basis for its control or management [1]. Aiming at the current problems of the anaerobic fermentation process in biogas engineering, the system design was carried out, and the anaerobic fermentation process of biogas and its main influencing factors were investigated, and the monitoring parameters of the system were selected to be temperature, PH value, liquid level and redox potential [2]. Previous studies are mainly based on a single prediction model, which leads to poor generalization ability of the model. In this study, unbalanced samples are handled by Borderline-SMOTE algorithm. Based on the Stacking integrated learning approach, a common single prediction model is used to construct a combined prediction model. The accuracy, precision, recall, F1 score and area under the ROC curve are used to evaluate the advantages and disadvantages of the combined prediction model over the single prediction model. ©2024 IEEE.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

A Clustering-Based Color Reordering Method for Reversible data Hiding in Palette Images

引用

IET Image Processing 2025年第1期19卷

作者： Deng, Jianxuan Chen, Yi Wang, Hongxia Guo, Chun Cui, Yunhe Shen, Guowei State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Guizhou University Guiyang China School of Cyber Science and Engineering Sichuan University Chengdu China

A recent research work pointed out that the reversible data hiding algorithms proposed for gray-scale images can be implemented on the reconstructed palette images to improve embedding capacity and visual quality by reordering the color table. However, the reordering effect has a significant impact on performance improvement. Therefore, we propose a clustering-based color reordering method for reversible data hiding in palette images to improve the reordering effect and further enhance the performance. In this method, we first design a centroid initialization method to select the initial centroids and then exploit the K-means algorithm to generate (Formula presented.) clusters for the colors in the original color table. In the following, our proposed method, respectively, reorders the colors of these clusters by a greedy strategy and concatenates them into the reordered color table. Based on the relationship between the original and the reordered color tables, a novel index matrix can be reconstructed. Finally, state-of-the-art reversible data hiding algorithms can be implemented on the reconstructed index matrix for performance improvement. Since our proposed method improves the reordering effect, enhances the correlation of the reconstructed index matrix, and reduces the length of the encoded location map, the maximal embedding capacities and the visual quality under the fixed embedding capacities are improved. We conducted experiments on two image datasets and six standard images to verify that the performance improvement of our proposed reordering method is better than that of the state-of-the-art methods. © 2025 The Author(s). IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and technology.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Characteristic-Aware Time-Series Representation Learning for Unsupervised Anomaly Detection

Characteristic-Aware Time-Series Representation Learning for...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Yaming Yang Zhuo Li Pingping Lin Juanyong Duan Hao Zhang Tianmeng Yang Congrui Huang Zhengjie Lin Yunhai Tong Yujing Wang School of Intelligence Science and Technology Peking University Big Data Center of State Grid Corporation of China Microsoft National Key Laboratory of General Artificial Intelligence Peking University

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Time-series anomaly detection is an important research topic in data mining, popular in both academia and industry. Recently, unsupervised anomaly detection draws considerable attention, since it can detect anomalies without parameter tuning on labels and meets the demands of industrial applications. Time-series representation learning plays a vital role in addressing unsupervised anomaly detection. However, it remains challenging to learn a unified representation model with diverse distributions and handle multivariate times-series with various *** alleviate these challenges, we propose a novel representation strategy, termed CAT-AD, for unsupervised time-series anomaly detection. It learns characteristic-aware priors for representations of time-series by incorporating embeddings of broad characteristics and is capable of handling diverse anomaly detection tasks, regardless of their lengths and *** proposed strategy is simple yet effective, which has been verified on two univariate datasets and five multivariate datasets from public sources.

关键词： Representation learning Industries Training Neural networks Feature extraction Anomaly detection Tuning

来源：评论

学校读者我要写书评

暂无评论

Dynamic Prompt Optimizing for Text-to-Image Generation

Dynamic Prompt Optimizing for Text-to-Image Generation

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wenyi Mo Tianyu Zhang Yalong Bai Bing Su Ji–Rong Wen Qing Yang Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Du Xiaoman Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Text-to-image generative models, specifically those based on diffusion models like Imagen and Stable Diffusion, have made substantial advancements. Recently, there has been a surge of interest in the delicate refinement of text prompts. Users assign weights or alter the injection time steps of certain words in the text prompts to improve the quality of generated images. However, the success of fine-control prompts depends on the accuracy of the text prompts and the careful selection of weights and time steps, which requires significant manual intervention. To address this, we introduce the Prompt Auto-Editing (PAE) method. Besides refining the original prompts for image generation, we further employ an online reinforcement learning strategy to explore the weights and injection time steps of each word, leading to the dynamic fine-control prompts. The re-wardfunction during training encourages the model to consider aesthetic score, semantic consistency, and user prefer-ences. Experimental results demonstrate that our proposed method effectively improves the original prompts, generating visually more appealing images while maintaining semantic alignment. Code is available at this https URL.

关键词： Uniform resource locators Training Image synthesis Semantics Refining Text to image Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Winning the CVPR'2022 AQTC Challenge: A Two-stage Function-centric Approach

arXiv

引用

arXiv 2022年

作者： Wu, Shiwei He, Weidong Xu, Tong Wang, Hao Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application State Key Laboratory of Cognitive Intelligence University of Science and Technology of China China

Affordance-centric Question-driven Task Completion for Egocentric Assistant (AQTC) is a novel task which helps AI assistant learn from instructional videos and scripts and guide the user step-by-step. In this paper, we deal with the AQTC via a two-stage Function-centric approach, which consists of Question2Function Module to ground the question with the related function and Function2Answer Module to predict the action based on the historical steps. We evaluated several possible solutions in each module and obtained significant gains compared to the given baselines. Our code is available at https://***/starsholic/LOVEU-CVPR22-AQTC. © 2022, CC BY-NC-ND.

关键词：

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Generating Enlightened Suggestions Based on Mental State Evolution for Emotional Support Conversation 9th

Generating Enlightened Suggestions Based on Mental State Ev...

引用

19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Gan, Mengjiao Yu, Jianxing Dong, Xiao Qiu, Shuang Liu, Wei Yin, Jian School of Artificial Intelligence Sun Yat-sen University Zhuhai China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Pazhou Lab Guangzhou510330 China Key Laboratory of Sustainable Tourism Smart Assessment Technology Ministry of Culture and Tourism Zhuhai China

ISBN: (纸本)9783031466601

Emotional support conversation aims to provide comfort and suggestions to users and gradually reduce their negative emotions such as anxiety. It is a valuable topic for many applications, including mental health support and customer service chats. However, due to the lack of enough expert knowledge, existing methods fail to provide enlightened suggestions to reverse users’ worries. Additionally, these methods neglect to grasp the mental state evolution of users. To address these problems, we propose a novel method that considers Mental State Evolution to provide Knowledge-grounded Suggestions (MEKS). In detail, we first create a suggestion corpus called MentalQA to grasp the psychological knowledge by resorting to the mental health forum. The relevant passages are selected based on both the context and the original response. Then we leverage graph structure to enrich the context with the inferred user’s mental state evolution. Furthermore, we introduce a gate to combine textual expert knowledge with the mental state evolution graph, so as to facilitate the generation of supportive responses. Experimental results show that this method can provide reasonable solutions to help the users. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Quantum partial least squares regression algorithm for multiple correlation problem

引用

Chinese Physics B 2022年第3期31卷 177-186页

作者： Yan-Yan Hou Jian Li Xiu-Bo Chen Yuan Tian School of Artificial Intelligence Beijing University of Post and TelecommunicationsBeijing 100876China College of Information Science and Engineering Zaozhuang UniversityZaozhuang 277160China Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Post and TelecommunicationsBeijing 100876China GuiZhou University Guizhou Provincial Key Laboratory of Public Big DataGuiyang 550025China

Partial least squares(PLS) regression is an important linear regression method that efficiently addresses the multiple correlation problem by combining principal component analysis and multiple regression. In this paper, we present a quantum partial least squares(QPLS) regression algorithm. To solve the high time complexity of the PLS regression, we design a quantum eigenvector search method to speed up principal components and regression parameters construction. Meanwhile, we give a density matrix product method to avoid multiple access to quantum random access memory(QRAM)during building residual matrices. The time and space complexities of the QPLS regression are logarithmic in the independent variable dimension n, the dependent variable dimension w, and the number of variables m. This algorithm achieves exponential speed-ups over the PLS regression on n, m, and w. In addition, the QPLS regression inspires us to explore more potential quantum machine learning applications in future works.

关键词： quantum machine learning partial least squares regression eigenvalue decomposition

来源：评论

学校读者我要写书评

暂无评论

Cooperative Retriever and Ranker in Deep Recommenders 23

Cooperative Retriever and Ranker in Deep Recommenders

引用

2023 World Wide Web Conference, WWW 2023

作者： Huang, Xu Lian, Defu Chen, Jin Zheng, Liu Xie, Xing Chen, Enhong School of Computer Science University of Science and Technology of China Hefei China School of Data Science University of Science and Technology of China Hefei China State Key Laboratory of Cognitive Intelligence Hefei China University of Electronic Science and Technology of China Chengdu China Microsoft Research Asia Beijing China

ISBN: (纸本)9781450394161

Deep recommender systems (DRS) are intensively applied in modern web services. To deal with the massive web contents, DRS employs a two-stage workflow: retrieval and ranking, to generate its recommendation results. The retriever aims to select a small set of relevant candidates from the entire items with high efficiency;while the ranker, usually more precise but time-consuming, is supposed to further refine the best items from the retrieved candidates. Traditionally, the two components are trained either independently or within a simple cascading pipeline, which is prone to poor collaboration effect. Though some latest works suggested to train retriever and ranker jointly, there still exist many severe limitations: item distribution shift between training and inference, false negative, and misalignment of ranking order. As such, it remains to explore effective collaborations between retriever and ranker. In this work, we present a novel framework for the joint training of retriever and ranker, named CoRR (Cooperative Retriever and Ranker). With CoRR, the retriever is improved by deriving high-quality training signals from the ranker, while the ranker is improved by learning to discriminate hard negatives sampled by the retriever. We introduce two critical techniques. Firstly, we develop an adaptive and scalable sampler based on the retriever, to generate hard negative samples for the ranker's training. Compared with the widely-used exact top-k sampling, our method effectively alleviates the issues of false negative and item distribution shift, and thus improves the ranker's discriminability. Secondly, we propose a novel asymptotic-unbiased estimation of KL divergence, which serves as the objective for knowledge distillation. The new objective can be efficiently optimized with commonly-used optimizers. More importantly, it leads to better alignment of ranking order between retriever and ranker, which helps to improve the retrieval quality. We conduct comprehensive ex

关键词： Distillation

来源：评论

学校读者我要写书评

暂无评论

Discovery of Emotion Implicit Causes in Products Based on Commonsense Reasoning 9th

Discovery of Emotion Implicit Causes in Products Based on...

引用

19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Guo, Qiutong Yu, Jianxing Zhang, Yufeng Jiang, Haowei Liu, Wei Yin, Jian School of Artificial Intelligence Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Pazhou Lab Guangzhou510330 China Key Laboratory of Sustainable Tourism Smart Assessment Technology Ministry of Culture and Tourism Guangzhou China

ISBN: (纸本)9783031466601

This paper focuses on the task of product emotion cause analysis which aims to find essential causes of certain emotions from product reviews. Current works only study the explicit causes which are some spans in the given text. However, some crucial and useful causes may be expressed vaguely. They may not mention but can be inferred from the text semantics. They can capture the deeper reasons to explain some unknown phenomena, which can well support the applications like market research and product optimization. To address this problem, we in this paper propose a new task of Emotion Implicit Cause Discovery (EICD). We develop a novel method that can deduce the implicit causes based on the contexts and commonsense knowledge. Our method first retrieves related knowledge from the large language model to construct reasoning graphs for the emotions and potential causes. We then encode the structural knowledge in the graph and infer the implicit cause by deductive reasoning. To evaluate our method, we construct a large dataset called EICDset, based on Amazon product reviews. Experiments on it demonstrate the effectiveness of our model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Large dataset

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：