One main goal of paleographers is to identify the different writers who wrote a given manuscript. Recently, paleographers are starting to use digital tools which provide new and more objective ways to analyze ancient ...
详细信息
The acceleration of smart grid construction imposes higher demands on the intelligent planning of power transmission and transformation projects. To address the inefficiencies and high costs in existing planning metho...
详细信息
ISBN:
(纸本)9798400707032
The acceleration of smart grid construction imposes higher demands on the intelligent planning of power transmission and transformation projects. To address the inefficiencies and high costs in existing planning methods, this paper proposes an intelligent planning approach based on multi-source heterogeneous data integration and artificial intelligence. By integrating multi-source heterogeneous data to analyze on-site information, and utilizing Bayesian networks and Convolutional Neural Networks with Attention Mechanisms (CNNAM), the planning and construction processes of power transmission and transformation projects are optimized. This approach significantly improves the accuracy and reliability of cost estimation, providing strong data support and technical assurance for the intelligent planning of power transmission and transformation projects, thereby making the planning process more effective and practical.
The proceedings contain 38 papers. The special focus in this conference is on Multiple Classifier Systems. The topics include: Ensemble methods in machinelearning;experiments with classifier combining rules;a survey ...
ISBN:
(纸本)3540677046
The proceedings contain 38 papers. The special focus in this conference is on Multiple Classifier Systems. The topics include: Ensemble methods in machinelearning;experiments with classifier combining rules;a survey of sequential combination of word recognizers in handwritten phrase recognition at CEDAR;multiple classifier combination methodologies for different output levels;a mathematically rigorous foundation for supervised learning;implementations and theoretical issues;some results on weakly accurate base learners for boosting regression and classification;complexity of classification problems and comparative advantages of combined classifiers;effectiveness of error correcting output codes in multiclass learning problems;combining fisher linear discriminants for dissimilarity representations;a learning method of feature selection for rough classification;analysis of a fusion method for combining marginal classifiers;a hybrid projection based and radial basis function architecture;combining multiple classifiers in probabilistic neural networks;supervised classifier combination through generalized additive multi-model;dynamic classifier selection;boosting in linear discriminant analysis;different ways of weakening decision trees and their impact on classification accuracy of DT combination;applying boosting to similarity literals for time series classification;boosting of tree-based classifiers for predictive risk modeling in GIS;a new evaluation method for expert combination in multi-expert system designing;diversity between neural networks and decision trees for building multiple classifier systems;self-organizing decomposition of functions;classifier instability and partitioning;a hierarchical multiclassifier system for hyperspectral data analysis and consensus based classification of multisource remote sensing data.
In this paper, Anomaly Detection by Resource Monitoring (Ayaka), a novel lightweight anomaly and fault detection infrastructure, is presented for Information Appliances. Ayaka provides a general monitoring method for ...
详细信息
ISBN:
(纸本)9781424435654
In this paper, Anomaly Detection by Resource Monitoring (Ayaka), a novel lightweight anomaly and fault detection infrastructure, is presented for Information Appliances. Ayaka provides a general monitoring method for detecting anomalies using only resource usage information on systems independent of its domain, target application and programming languages. Ayaka modifies the kernel to detect faults and uses a completely application black-box approach based on machinelearning methods. It uses the clustering method to quantize the resource usage vector data and learn the normal patterns with Hidden Markov Model. In the running phase, Ayaka finds anomalies by comparing the application resource usage with learned model. The evaluation experiment indicates that our prototype system is able to detect anomalies, such as SQL injection and buffer overrun, without significant overheads.
In the post-pandemic era, online courses have been adopted universally. Manually assessing online course teaching quality requires significant time and professional pedagogy experience. To address this problem, we des...
详细信息
ISBN:
(数字)9781665490627
ISBN:
(纸本)9781665490627
In the post-pandemic era, online courses have been adopted universally. Manually assessing online course teaching quality requires significant time and professional pedagogy experience. To address this problem, we design an evaluation protocol and propose a multimodal machinelearning framework1 for automated teaching quality assessment of one-to-many online instruction videos. Our framework evaluates online teaching quality from five aspects, namely Clarity, Classroom interaction, Technical management of online teaching, Empathy, and Time management. Our method includes mid-level behavior feature extraction, high-level interpretable feature extraction, and supervised learning prediction. Our automated multimodal teaching quality assessment system achieves comparable performance to human annotators on our one-to-many online instruction videos. For binary classification, the best average accuracy of five aspects is 0.898. For regression, the best average means square error is 0.527 on a 0-10 scale.
In this position paper, we present efficient and practical integrity verification techniques that check whether the un-trusted cloud has returned correct result of outsourced data analytics computations. We consider t...
详细信息
ISBN:
(纸本)9781450315968
In this position paper, we present efficient and practical integrity verification techniques that check whether the un-trusted cloud has returned correct result of outsourced data analytics computations. We consider the computation of summation form that is used in a large class of machinelearning and datamining problems. We discuss our verification techniques for both non-collusive and collusive malicious workers in MapReduce. Copyright 2012 ACM.
The proceedings contain 14 papers. The topics discussed include: an anonymization tool for open data publication of legal documents;building and analyzing the Brazilian legal knowledge graph;introduction of artificial...
The proceedings contain 14 papers. The topics discussed include: an anonymization tool for open data publication of legal documents;building and analyzing the Brazilian legal knowledge graph;introduction of artificial intelligence in Belgian court: failures, challenges and opportunities;LawSampo portal and data service for publishing and using legislation and case law as linked open data on the semantic web;finding case law: leveraging machinelearning research to enhance public access to UK judgments;evaluation of data augmentation for named entity recognition in the German legal domain;towards building a legal virtual assistant based on knowledge graphs;an Indian court decision annotated corpus and knowledge graph;and summaries of knowledge graph entities: firststeps to measure the relevance of symptoms to infer diseases.
A novel approach for obtaining labeled training data is presented to directly estimate the model parameters in a supervised learning algorithm for automatic chord recognition from the raw audio. To this end, harmonic ...
详细信息
In this paper, a partially supervised machinelearning approach is proposed for the recognition of emotional user states in HCI from bio-physiological data. To do so, an unsupervised learning preprocessing step is int...
详细信息
As the amount of multimodal meetings data being recorded increases, so does the need for sophisticated mechanisms for accessing this data. This process is complicated by the different informational needs of users, as ...
详细信息
ISBN:
(纸本)354024509X
As the amount of multimodal meetings data being recorded increases, so does the need for sophisticated mechanisms for accessing this data. This process is complicated by the different informational needs of users, as well as the range of data collected from meetings. This paper examines the current state of the art in meeting browsers. We examine both systems specifically designed for browsing multimodal meetings data and those designed to browse data collected from different environments, for example broadcast news and lectures. As a result of this analysis, we highlight potential directions for future research - semantic access, filtered presentation, limited display environments, browser evaluation and user requirements capture.
暂无评论