检索结果-内蒙古大学图书馆

IFIP Advances in Information and Communication Technology 2014年 437卷 146-154页

作者： Politi, Olga Mporas, Iosif Megalooikonomou, Vasileios Multidimensional Data Analysis and Knowledge Management Laboratory Dept. of Computer Engineering and Informatics University of Patras Rion-Patras26500 Greece

In this article we conduct an evaluation of feature extraction methods for the problem of human motion detection based on 3-dimensional inertial sensor data. For the purpose of this study, different preprocessing methods are used, and statistical as well as physical features are extracted from the motion signals. At each step, state-of-the-art methods are applied, and the produced results are finally compared in order to evaluate the importance of the applied feature extraction and preprocessing combinations, for the human activity recognition task. © IFIP International Federation for Information Processing 2014.

关键词： Motion estimation

来源：评论

学校读者我要写书评

暂无评论

Eye blink artifact removal in eeg using tensor decomposition

IFIP Advances in Information and Communication Technology

引用

IFIP Advances in Information and Communication Technology 2014年 437卷 155-164页

作者： Triantafyllopoulos, Dimitrios Megalooikonomou, Vasileios Multidimensional Data Analysis and Knowledge Management Laboratory Dept. of Computer Engineering and Informatics University of Patras Rion-Patras26500 Greece

EEG data are usually contaminated with signals related to subject's activities, the so called artifacts, which degrade the information contained in recordings. The removal of this additional information is essential to the improvement of EEG signals' interpretation. The proposed method is based on the analysis, using Tucker decomposition, of a tensor constructed using continuous wavelet transform. Our contribution is an automatic method which processes simultaneously spatial, temporal and frequency information contained in EEG recordings in order to remove eye blink related information. The proposed method is compared with a matrix based removal method and shows promising results regarding reconstruction error and retaining the texture of the artifact free signal. © IFIP International Federation for Information Processing 2014.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

Search result diversification based on hierarchical intents 15

Search result diversification based on hierarchical intents

引用

24th ACM International Conference on Information and knowledge management, CIKM 2015

作者： Hu, Sha Dou, Zhicheng Wang, Xiaojie Sakai, Tetsuya Wen, Ji-Rong Beijing Key Laboratory of Big Data Management and Analysis Methods China Key Laboratory of Data Engineering and Knowledge Engineering MOE China School of Information Renmin University of China China Waseda University Japan

ISBN: (纸本)9781450337946

A large percentage of queries issued to search engines are broad or ambiguous. Search result diversification aims to solve this problem, by returning diverse results that can fulfill as many different information needs as possible. Most existing intent-aware search result diversification algorithms formulate user intents for a query as a flat list of subtopics. In this paper, we introduce a new hierarchical structure to represent user intents and propose two general hierarchical diversification models to leverage hierarchical intents. Experimental results show that our hierarchical diversification models outperform state-of-the-art diversification methods that use traditional flat subtopics. © 2015 ACM.

关键词： Search engines

来源：评论

学校读者我要写书评

暂无评论

On the weight sparsity of multilayer perceptrons

On the weight sparsity of multilayer perceptrons

引用

International Conference on Information, Intelligence, Systems and Applications

作者： Georgios Drakopoulos Vasileios Megalooikonomou Multidimensional Data Analysis and Knowledge Management Lab Computer Engineering and Informatics Department University of Patras Patras 26500 Hellas

ISBN: (纸本)9781467393126

Approximating and representing a process, a function, or a system with an adaptive parametric model constitutes a major part of current machine learning research. An important characteristic of these models is parameter sparsity, an indicator of how succintly a model can codify fundamental properties of the approximated function. This paper investigates the sparsity patterns of a multilayer perceptron netwrok trained to mount a man-on-the-middle attack on the DES symmetric cryptosystem. The notions of absolute and effective synaptic weight sparsity are introduced and their importance to network learning procedure is explained. Finally, the results from the training of the actual multilayer perceptron are outlined and discussed. In order to promote reproducible research, the MatLab network implementation has been posted in GitHub.

关键词： Absolute synaptic weight sparsity Effective synaptic weight sparsity Higher order computation Private key cryptography Man-in-the-middle attack

来源：评论

学校读者我要写书评

暂无评论

Classification of epileptic and non-epileptic events using tensor decomposition 15

Classification of epileptic and non-epileptic events using t...

引用

15th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2015

作者： Kanas, Vasileios G. Zacharaki, Evangelia I. Pippa, Evangelia Tsirka, Vasiliki Koutroumanidis, Michael Megalooikonomou, Vasileios Multidimensional Data Analysis and Knowledge Management Laboratory Dept. of Computer Engineering and Informatics University of Patras Greece Dept. of Electrical and Computer Engineering University of Patras Greece Dept. of Clinical Neurophysiology and Epilepsies Guy's and St. Thomas' and Evelina Hospital for Children NHS Foundation Trust/King's College London United Kingdom

ISBN: (纸本)9781467379830

Misdiagnosis of epilepsy, even by experienced clinicians, can cause exposure of patients to medical procedures and treatments with potential complications. Moreover, diagnostic delays (for 7 to 10 years on average) impose economic burden at individual and population levels. In this paper, a seizure classification framework of epileptic and non-epileptic events from multi-channel EEG data is proposed. In contrast to relevant studies found in the literature, in this study, the non-epileptic class consists of two types of paroxysmal episodes of loss of consciousness, namely the psychogenic non-epileptic seizure (PNES) and the vasovagal syncope (VVS). EEG signals are represented in the spectral-spatial-temporal domain. A tensor-based approach is employed to extract signature features to feed the classification models. TUCKER decomposition is applied to learn the essence of original, high-dimensional domain of feature space and extract a multilinear discriminative subspace. The classification models were evaluated on EEG epochs from 11 subjects in an inter-subject cross-validation setting and achieved an accuracy of 96%. © 2015 IEEE.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

Few-Shot Charge Prediction with Multi-grained Features and Mutual Information 20

Few-Shot Charge Prediction with Multi-grained Features and M...

引用

20th China National Conference on Computational Linguistics, CCL 2021

作者： Zhang, Han Dou, Zhicheng Zhu, Yutao Wen, Jirong School of Information Renmin University of China Beijing China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Université de Montréal Montreal Canada Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China

ISBN: (纸本)9783030841850

Charge prediction aims to predict the final charge for a case according to its fact description and plays an important role in legal assistance systems. With deep learning based methods, prediction on high-frequency charges has achieved promising results but that on few-shot charges is still challenging. In this work, we propose a framework with multi-grained features and mutual information for few-shot charge prediction. Specifically, we extract coarse- and fine-grained features to enhance the model’s capability on representation, based on which the few-shot charges can be better distinguished. Furthermore, we propose a loss function based on mutual information. This loss function leverages the prior distribution of the charges to tune their weights, so the few-shot charges can contribute more on model optimization. Experimental results on several datasets demonstrate the effectiveness and robustness of our method. Besides, our method can work well on tiny datasets and has better efficiency in the training, which provides better applicability in real scenarios. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Classification of Epileptic and Non-epileptic Events using Tensor Decomposition

Classification of Epileptic and Non-epileptic Events using T...

引用

IEEE International Conference on Bioinformatics and Bioengineering

作者： Vasileios G. Kanas Evangelia I. Zacharaki Evangelia Pippa Vasiliki Tsirka Michael Koutroumanidis Vasileios Megalooikonomou Multidimensional Data Analysis and Knowledge Management Laboratory Dept. of Computer Engineering and Informatics University of Patras Dept. of Clinical Neurophysiology and Epilepsies Guy's & St. Thomas' and Evelina Hospital for Children

ISBN: (纸本)9781467379847

关键词： Electroencephalography Feature extraction Tensile stress Epilepsy Brain models Training

来源：评论

学校读者我要写书评

暂无评论

USER: A unified information search and recommendation model based on integrated behavior sequence

arXiv

引用

arXiv 2021年

作者： Yao, Jing Dou, Zhicheng Xie, Ruobing Lu, Yanxiong Wang, Zhiping Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China WeChat Search Application Department Tencent China Beijing Key Laboratory of Big Data Management and Analysis Methods Key Laboratory of Data Engineering and Knowledge Engineering MOE

Search and recommendation are the two most common approaches used by people to obtain information. They share the same goal - satisfying the user's information need at the right time. There are already a lot of Internet platforms and Apps providing both search and recommendation services, showing us the demand and opportunity to simultaneously handle both tasks. However, most platforms consider these two tasks independently - they tend to train separate search model and recommendation model, without exploiting the relatedness and dependency between them. In this paper, we argue that jointly modeling these two tasks will benefit both of them and finally improve overall user satisfaction. We investigate the interactions between these two tasks in the specific information content service domain. We propose first integrating the user's behaviors in search and recommendation into a heterogeneous behavior sequence, then utilizing a joint model for handling both tasks based on the unified sequence. More specifically, we design the Unified Information SEarch and Recommendation model (USER), which mines user interests from the integrated sequence and accomplish the two tasks in a unified way. Experiments on a dataset from a real-world information content service platform verify that our model outperforms separate search and recommendation baselines. Copyright © 2021, The Authors. All rights reserved.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Graph Neural Networks with Learnable and Optimal Polynomial Bases

arXiv

引用

arXiv 2023年

作者： Guo, Yuhe Wei, Zhewei Gaoling School of Articial Intelligence Renmin University of China China Peng Cheng Laboratory China Beijing Key Laboratory of Big Data Management and Analysis Methods China MOE Key Lab of Data Engineering and Knowledge Engineering China

Polynomial filters, a kind of Graph Neural Networks, typically use a predetermined polynomial basis and learn the coefficients from the training data. It has been observed that the effectiveness of the model is highly dependent on the property of the polynomial basis. Consequently, two natural and fundamental questions arise: Can we learn a suitable polynomial basis from the training data? Can we determine the optimal polynomial basis for a given graph and node features? In this paper, we propose two spectral GNN models that provide positive answers to the questions posed above. First, inspired by Favard’s Theorem, we propose the FavardGNN model, which learns a polynomial basis from the space of all possible orthonormal bases. Second, we examine the supposedly unsolvable definition of optimal polynomial basis from Wang & Zhang (2022) and propose a simple model, OptBasisGNN, which computes the optimal basis for a given graph structure and graph signal. Extensive experiments are conducted to demonstrate the effectiveness of our proposed models. Our code is available at https://***/yuziGuo/FarOptBasis. © 2023, CC BY.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Personalizing search results using hierarchical RNN with query-aware attention

arXiv

引用

arXiv 2019年

作者： Ge, Songwei Dou, Zhicheng Jiang, Zhengbao Nie, Jian-Yun Wen, Ji-Rong School of Information Renmin University of China DIRO Université de Montréal Beijing Key Laboratory of Big Data Management and Analysis Methods National Engineering Laboratory of Big Data System Software Beijing Institute of Technology Key Laboratory of Data Engineering and Knowledge Engineering MOE

Search results personalization has become an effective way to improve the quality of search engines. Previous studies extracted information such as past clicks, user topical interests, query click entropy and so on to tailor the original ranking. However, few studies have taken into account the sequential information underlying previous queries and sessions. Intuitively, the order of issued queries is important in inferring the real user interests. And more recent sessions should provide more reliable personal signals than older sessions. In addition, the previous search history and user behaviors should influence the personalization of the current query depending on their relatedness. To implement these intuitions, in this paper we employ a hierarchical recurrent neural network to exploit such sequential information and automatically generate user profile from historical data. We propose a query-aware attention model to generate a dynamic user profile based on the input query. Significant improvement is observed in the experiment with data from a commercial search engine when compared with several traditional personalization models. Our analysis reveals that the attention model is able to attribute higher weights to more related past sessions after fine training. Copyright © 2019, The Authors. All rights reserved.

关键词： Search engines

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：