2019 27th Signal Processing and Communications Applications Conference (SIU)最新文献_第3页

The Impact of Sentence Embeddings in Turkish Paraphrase Detection 句子嵌入对土耳其语释义检测的影响

2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-24 DOI: 10.1109/SIU.2019.8806506

B. Karaoglan, Hakki Engin Yorgancioglu, T. Kışla, S. K. Metin

{"title":"The Impact of Sentence Embeddings in Turkish Paraphrase Detection","authors":"B. Karaoglan, Hakki Engin Yorgancioglu, T. Kışla, S. K. Metin","doi":"10.1109/SIU.2019.8806506","DOIUrl":"https://doi.org/10.1109/SIU.2019.8806506","url":null,"abstract":"In recent studies, it is shown that word embeddings achieve in several natural language processing (NLP) tasks. Though paraphrase identification in Turkish is well-studied by traditional statistical NLP methods, to the best of our knowledge there exists no study where word and/or sentence embeddings are employed. In this paper, three methods, which are well-known as “using average vector for word embeddings” (AWE), “concatenated vectors for word embeddings” (CWE) and “word mover's distance word embeddings” (WMDWE) to build sentence embeddings from word embeddings are examined and their effect in performance of paraphrase identification is measured. The results are presented comparatively for English (MSRP) and Turkish (PARDER and TuPC) paraphrase corpora. The study doesn't cover the optimization of parameters used in training of word embeddings and also the features specific to Turkish langauge are not considered. Despite this naive approach, the test results obtained from PARDER corpus are inspiring that a more detailed study that involves such improvements may result with more convincing performance values.","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127392046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Model-Free Reinforcement Learning Algorithms: A Survey 无模型强化学习算法:综述

2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-24 DOI: 10.1109/SIU.2019.8806389

Sinan Çalisir, Meltem Kurt PehlIvanoõlu

引用次数: 22

An Experimental Analysis of Minimum Cut Size in Wireless Sensor Networks 无线传感器网络中最小切割尺寸的实验分析

2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-24 DOI: 10.1109/SIU.2019.8806304

Umut Can Çabuk, V. Akram, O. Dagdeviren

{"title":"An Experimental Analysis of Minimum Cut Size in Wireless Sensor Networks","authors":"Umut Can Çabuk, V. Akram, O. Dagdeviren","doi":"10.1109/SIU.2019.8806304","DOIUrl":"https://doi.org/10.1109/SIU.2019.8806304","url":null,"abstract":"Wireless sensor networks (WSN) and their applications become more and more common as the technology advances. The emerging concepts of Internet of Things and the Industry 4.0 boost their popularity, too. But there are still dominant bottlenecks including battery lifetime, transmission range, routing issues and others. These limitations may cause permanent faults on nodes, and thus, cause exclusion of the nodes from the network, which eventually may disconnect the network. Minimum cut size, a metric regarding the edge-connectivity of a graph, can be used to measure the reliability of a WSN. This work discusses that role of the minimum cut size; and more importantly, presents the results of a fundamental simulation showing the correlations between the total node count, the transmission range, and the achieved minimum cut size as an indicator of the maximum flow, in a randomly generated WSN topology. Our simulation results showed that at least 100 nodes with a transmission range of 140 m are required to create a random connected network on a field of $1000times 1000 mathbf{m}$ area. If the ranges of the nodes be 80 m, then at least 200 nodes should be distributed on the field to expect a reliable connected network.","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"103 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116272837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy Efficient Base Station Deployment in Ultra Dense Heterogeneous Networks via Poisson Hole Process 基于泊松洞过程的超密集异构网络节能基站部署

2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-24 DOI: 10.1109/SIU.2019.8806459

Mine Ardanuc, M. Başaran, L. D. Ata

引用次数: 3

Modeling Human Activities via Long Short Term Memory Networks 通过长短期记忆网络模拟人类活动

2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-24 DOI: 10.1109/SIU.2019.8806573

Berkan Solmaz, Kaan Karaman

引用次数: 0

Error Analysis of Threshold Based Three-hop Device to Device (D2D) Communication Systems 基于阈值的三跳设备到设备(D2D)通信系统误差分析

2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-24 DOI: 10.1109/SIU.2019.8806529

Emre Çakar, F. Kara, Hakan Kaya

引用次数: 0

Pneumonia Detection from Radiography Images using Convolutional Neural Networks 基于卷积神经网络的影像学肺炎检测

2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-24 DOI: 10.1109/SIU.2019.8806614

Muhammed Talo

引用次数: 8

Antenna Selection on Spatial Modulation: A Machine Learning Approach 空间调制天线选择:一种机器学习方法

2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-24 DOI: 10.1109/SIU.2019.8806300

Selen Gecgel, Caner Goztepe, Günes Karabulut-Kurt

引用次数: 0

Classification of Extracranial and Intracranial EEG Signals by using Finite Impulse Response Filter through Ensemble Learning 基于集成学习的有限脉冲响应滤波器对颅外和颅内脑电信号的分类

2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-24 DOI: 10.1109/SIU.2019.8806334

S. Bayrak, Eylem Yücel, Hidayet Takçi

{"title":"Classification of Extracranial and Intracranial EEG Signals by using Finite Impulse Response Filter through Ensemble Learning","authors":"S. Bayrak, Eylem Yücel, Hidayet Takçi","doi":"10.1109/SIU.2019.8806334","DOIUrl":"https://doi.org/10.1109/SIU.2019.8806334","url":null,"abstract":"Electroencephalogram (EEG) is the main diagnostic tool for the monitoring, diagnosis and treatment of epilepsy which is a neurological disorder. EEG signals can disrupt easily by involuntary movements that are called artifact contaminants such as blinking, coughing. In this study, the artifacts in the extrac- and intracranial EEG signals have been cancelled out from the EEG with the use of Kaiser window based Finite Impulse Response (FIR) filter. The most important features in the EEG signals have been selected by the Principle Component Analysis (PCA) method. The selected features have been classified by applying ensemble learning methods that are Boosting, Bagging and Random Subspace. The aim of this study is to increase the extrac- and intracranial EEG signal classification by calculating window spectral parameters. The algorithms' classification performances have been compared in terms of accuracy rates, sensitivities, specificities, prediction rates and training times according to the 5 × 5 cross validation. Subspace KNN algorithm, as revealed by results, is higher than the other algorithms' classification performances.","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115574972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Automatic Standardization System for Free Text Addresses 自由文本地址自动标准化系统

2019 27th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2019-04-24 DOI: 10.1109/SIU.2019.8806349

Salih Cebeci, Merve Özyilmaz, G. Ince

引用次数: 1