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检索条件"任意字段=Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition"

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patternGPT :A pattern-Driven Framework for Large Language Model Text Generation 12

PatternGPT :A Pattern-Driven Framework for Large Language Mo...

引用

12th international conference on computing and pattern recognition, ICCPR 2023

作者： Xiao, Le Shan, Xin Chen, Xiaolin College of Information Science and Engineering Henan University of Technology Zhengzhou China

ISBN: (纸本)9798400707988

Large language models(LLMS) have shown excellent text generation capabilities, capable of generating fluent human-like responses for many downstream tasks. However, applying large language models to real-world critical tasks remains challenging due to their susceptibility to hallucinations and inability to directly use external knowledge. To cope with the above challenges, this paper proposes patternGPT, a pattern-driven text generation framework for Large Language Models. Firstly, the framework utilizes the extraction capability of Large Language Models to generate rich and diversified structured and formalized patterns, which facilitates the introduction of external knowledge to do the computation, and then draws on the idea of federated learning to use multiple agents to achieve the sharing in order to obtain more diversified patterns, and finally uses judgment criteria and optimization algorithm to search for high-quality patterns to guide the generation of models. Finally, external knowledge such as judgment criteria and optimization algorithms are used to search for high-quality patterns, and the searched patterns are used to guide model generation. this framework has the advantages of generating diversified patterns, protecting data privacy, combining external knowledge, and improving the quality of generation, which provides an effective method to optimize the text generation capability of large language models, and make it better applied to the field of intelligent dialogue and content generation. © 2023 ACM.

关键词： Data privacy

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TALCLIP: CLIP for Temporal Action Localization 12

TALCLIP: CLIP for Temporal Action Localization

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12th international conference of Information and Communication Technology, ICTech 2023

作者： Wang, Ke Yu, Bihui Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350332919

For regulators of online live streaming and short video platforms, the problem of producing and disseminating soft pornographic videos is becoming serious. For online cultural market supervision, it is a challenging and high-value task to detect and locate soft pornographic temporal regions in videos of live streaming and short video platforms. We present a dataset about Video Frame-Danmaku Dataset containing soft pornographic information based on the Bilibili Danmaku Website. On the task of temporal action localization for soft pornographic video clips, we use the multi-modal model (CLIP) to extract video features and text features. Meanwhile we use the prompt mechanism to convert the danmaku which is splicing similar danmaku in the same release time into labels. Finally, we use a boundary-matching network to get the start and end positions containing soft pornographic information on Bilibili Video-Danmaku Dataset and containing specified action on ActivityNet-1.3. © 2023 IEEE.

关键词： Video streaming

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Rough Sets, Fuzzy Sets, Data Mining and Granular computing - 12th international conference, RSFDGrC 2009, proceedings

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12th international conference on Rough Sets, Fuzzy Sets, Data Mining and Granular computing, RSFDGrC 2009

ISBN: (纸本)3642106455

the proceedings contain 63 papers. the topics discussed include: discovering concurrent process models in data: a rough set approach;intelligent science;pattern structures for analyzing complex data;fuzzy sets and rough sets for scenario modeling and analysis;innovation game as a tool of chance discovery;an algebraic semantics for the logic of multiple-source approximation systems;towards an algebraic approach for cover based rough semantics and combinations of approximation spaces;new approach in defining rough approximations;rough set approximations based on granular labels;on a criterion of similarity between partitions based on rough set theory;a formal concept analysis approach to rough data tables;covering based approaches to rough sets and implication lattices;some proof theoretic results depending on context from the perspective of graded consequence;and dynamic reduct from partially uncertain data using rough sets.

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Elastic Net for Paralinguistic Speech recognition 12

Elastic Net for Paralinguistic Speech Recognition

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14th ACM international conference on Multimodal Interaction (ICMI)

作者： Fewzee, Pouria Karray, Fakhri Univ Waterloo Ctr Pattern Anal & Machine Intelligence Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781450314671

Given the fact that the length of the feature vector that is being used for the paralinguistic recognition of speech has exceeded some thousands, the importance of a sparse representation of a model becomes notable. the importance of a sparse representation is mainly due to the more interpretability, higher generalization capability, and numerically more efficiency of such a model. In this work, as an endeavor to search for a sparse representation of speech features used for paralinguistic speech modeling, we make use of the elastic net. As for the benchmark, we use the frameworks of the second audio/visual emotion challenge and the Interspeech 2012 speaker trait challenge. Also proposed in this work is the use of part-of-speech tags as syntactic features of speech for emotional speech recognition. Results of this work show that despite the relatively small number of features that is used for the modeling tasks, generalization capability of the suggested models is comparable to those of other models that use thousands of features and more elaborate learning algorithms.

关键词： Affective computing Emotion recognition Trait recognition Speech Dimensionality Reduction

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proceedings of 12th Latin-American Symposium on Dependable and Secure computing, LADC 2023

Proceedings of 12th Latin-American Symposium on Dependable a...

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12th Latin-American Symposium on Dependable and Secure computing, LADC 2023

ISBN: (纸本)9798400708442

the proceedings contain 32 papers. the topics discussed include: ALR-LoRaWAN — an application-level retransmission management algorithm for LoRaWAN networks;using formal methods for on-the-fly time series verification;loom: a closed-box disaggregated database system;efficient synchronization of CRDTs using VCube-PS;generating realistic attack data for microservices: framework and case study;leveraging time series autocorrelation through numerical differentiation for improving failure prediction;third-party trackers in COVID-19 mobile applications can enable privacy leaks;joining parallel and partitioned state machine replication models for enhanced shared logging performance;a time-phased partitioned checkpoint approach to reduce state snapshot overhead;and parallel execution of transactions based on dynamic and self-verifiable conflict analysis.

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Database Knowledge Discovery Combining Cluster Analysis and Classification Using Data Mining 12

Database Knowledge Discovery Combining Cluster Analysis and ...

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12th international conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Singh, Mandeep Kaur, Tanmeet Shukla, Anand Kumar University Institute of Computing Chandigarh University Mohali Punjab Cgc Jhangeri Punjab Mohali India Noida International University Electronics and Communication Engineering Greater Noida India

ISBN: (纸本)9798350369861

Two key data mining methods are clustering and classification. the problem of classification involves categorizing object into one-to-many pre-established categories according to the characteristics of the object. While clustering groups objects based on their proximity or similarity and is a clear learning problem. Every group is referred to as a cluster. In the study, we perform integration to data mining by the clustering and classification techniques using a big database called the 'Fisher's Iris Dataset' with the attribute and some examples. Using the Data Mining application Waikato Environment for Knowledge Analysis, We contrasted the outcomes of integrating a clustering and classification technique based on numerous criteria with the outcomes of a straightforward classification strategy (using the J48 classifier). the experiment's findings demonstrate that combining clustering and classification produces effective outcomes with a high robustness and accuracy rate, and the data set contains some values that are missing. © 2023 IEEE.

关键词： Data mining

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Scabies Classification in Animal Using Uniform Local Binary patterns 12

Scabies Classification in Animal Using Uniform Local Binary ...

引用

12th international conference on Information Technology and Electrical Engineering, ICITEE 2020

作者： Salsabila, Annisa Suciati Sthevanie, Febryanti Ramadhani, Kurniawan Nur Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9781728110974

Scabies is a disease caused by the Sarcoptes Scabiei mite and can affect humans and animals. In animals, this disease is recognized by crusted skin on the ears, nose, and feet, but if the disease is not treated immediately, the mites will spread throughout the animal's body and can even cause *** processing has been done a lot to classify diseases in humans and plants, but there are still a few that involve scabies in animals, so in this research the authors build a system that can classify animal skin images into two classes, namely animals with scabies disease, and animals with other skin *** Uniform Local Binary pattern feature extraction method has been proven to optimize the feature extraction results and minimize the processing time for the feature extraction process, so the system is built by processing the dataset using the Uniform Local Binary pattern method and the Random Forest classification method so that the system performance reaches 52% © 2020 IEEE.

关键词： Animals

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Optimizing assignment of students to courses based on learning activity analytics 12

Optimizing assignment of students to courses based on learni...

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12th international conference on Educational Data Mining, EDM 2019

作者： Shimada, Atsushi Mouri, Kousuke Taniguchi, Yuta Ogata, Hiroaki Taniguchi, Rin-Ichiro Konomi, Shin'ichi Department of Advanced Information Technology Kyushu University Japan Institute of Engineering Tokyo University of Agriculture and Technology Japan Academic Center for Computing and Media Studies Kyoto University Japan Faculty of Arts and Science Kyushu University Japan

ISBN: (纸本)9781733673600

In this paper, we focus on optimizing the assignment of students to courses. the target courses are conducted by different teachers using the same syllabus, course design, and lecture materials. More than 1,300 students are mechanically assigned to one of ten courses taught by different teachers. therefore, mismatches often occur between students' learning behavior patterns and teachers' approach to teaching. As a result, students may be less satisfied, have a lower level of understanding of the material, and achieve less. To solve these problems, we propose a strategy to optimize the assignment of students to courses based on learning activity analytics. the contributions of this study are 1) clarifying the relationship between learning behavior pattern and teaching based on learning activity analytics using large-scale educational data, 2) optimizing the assignment of students to courses based on learning behavior pattern analytics, and 3) demonstrating the effectiveness of assignment optimization via simulation experiments. © EDM 2019 - proceedings of the 12th international conference on Educational Data Mining. All rights reserved.

关键词： Students

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Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification

Outlier Detection in Logistic Regression: A Quest for Reliab...

引用

12th IEEE international conference on Data Mining (ICDM)

作者： Nurunnabi, Abdul West, Geoff Curtin Univ Dept Spatial Sci Perth WA 6845 Australia

ISBN: (纸本)9780769549255;9781467351645

Logistic regression is well known to the data mining research community as a tool for modeling and classification. the presence of outliers is an unavoidable phenomenon in data analysis. Detection of outliers is important to increase the accuracy of the required estimates and for reliable knowledge discovery from the underlying databases. Most of the existing outlier detection methods in regression analysis are based on the single case deletion approach that is inefficient in the presence of multiple outliers because of the well known masking and swamping effects. To avoid these effects the multiple case deletion approach has been introduced. We propose a group deletion approach based diagnostic measure for identifying multiple influential observations in logistic regression. At the same time we introduce a plotting technique that can classify data into outliers, high leverage points, as well as influential and regular observations. this paper has two objectives. First, it investigates the problems of outlier detection in logistic regression, proposes a new method that can find multiple influential observations, and classifies the types of outlier. Secondly, it shows the necessity for proper identification of outliers and influential observations as a prelude for reliable knowledge discovery from modeling and classification via logistic regression. We demonstrate the efficiency of our method, compare the performance with the existing popular diagnostic methods, and explore the necessity of outlier detection for reliability and robustness in modeling and classification by using real datasets.

关键词： data mining high leverge point influential observation knowledge discovery outlier pattern recognition regression reliability statistical computing

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Fault Detection and Diagnosis Technique for a SRM Drive Based on a Multilevel Converter Using a Machine Learning Approach 12

Fault Detection and Diagnosis Technique for a SRM Drive Base...

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12th international conference on Renewable Energy Research and Applications (ICRERA)

作者： Amaral, Tito G. Pires, V. Fernao Foito, Daniel Pires, A. J. Martins, J. F. Inst Politecn Setubal ESTSetubal Setubal Portugal INESC ID Lisbon Portugal CTS UNINOVA Lisbon Portugal Univ Nova Lisboa CTS UNINOVA DEEC FCT Lisbon Portugal

ISBN: (纸本)9798350337938

One of today's well-accepted solutions for the SRM drives is based on multilevel converters. In fact, they present interesting features like an extended voltage range and the capability of fault tolerance. the guarantee of fault tolerance is fundamental in the context of preventive maintenance. However, regarding the power electronic converter, this requires a fault detection and diagnosis algorithm for failures in power semiconductors. thus, this paper proposes a novel detection and diagnostic approach for the failure of those semiconductors. In this case, it will focus on one of the most commonly used topologies, namely the asymmetric neutral point clamped converter. this approach was developed with the purpose to develop specific patterns that are associated with each semiconductor and fault type. In this way, through the image identification of the multilevel converter current patterns, it will be possible to identify a distinct semiconductor and fault type. Several tests obtained from a simulation tool allowed to show the capability of the proposed approach.

关键词： SRM drive Multilevel ANPC topology Fault Diagnosis Detection pattern recognition

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