2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)最新文献_第10页

Optimizing the parameters of spiking neural networks for mobile robot implementation 峰值神经网络参数优化在移动机器人中的实现

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303660

V. Azimirad, Saleh Valizadeh Sotubadi, F. Janabi Sharifi

引用次数: 2

An Adaptive Yao-based topology control algorithm for wireless ad-hoc networks 基于自适应yao的无线自组织网络拓扑控制算法

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303711

M. Kadivar

引用次数: 0

ParsiPayesh: Persian Plagiarism Detection based on Semantic and Structural Analysis ParsiPayesh:基于语义和结构分析的波斯语剽窃检测

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303672

S. Lazemi, H. Ebrahimpour-Komleh

{"title":"ParsiPayesh: Persian Plagiarism Detection based on Semantic and Structural Analysis","authors":"S. Lazemi, H. Ebrahimpour-Komleh","doi":"10.1109/ICCKE50421.2020.9303672","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303672","url":null,"abstract":"In recent years, the rapid increase of Persian electronic resources and facility of access to them has seriously triggered the plagiarism problem of the Iranian scientific community. Despite the automatic systems of plagiarism detection, like Turnitin, Eve2, this problem has strongly remained due to lack of support from Persian. The main purpose of this article is to detect exact plagiarisms and re-writings in Persian science texts. In our proposed method, after the candidate retrieval based on the statistical characteristics, in the text alignment step, structural analysis and semantic analysis of expression has been performed to detect re-writing plagiarisms. Firstly, data-driven dependency parser has been improved with the help of a deep learning model for Persian language to analyze the structure of the expression, and then the degree of structural similarity of the expression is evaluated through the analysis of the dependency tree. In this paper, our suggestion to examine the semantic similarity of expression is to use the semantic role labeling obtained from the deep learning model presented. The experiments have been performed on the corpus prepared in the AAIC2015 and corpus of the PAN2015 competitions. The results indicate that structural and semantic information improves the performance of the proposed method. ParsiPayesh is available on http://www.parsipayesh.ir.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124113876","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: 2

Asymmetric Semi-Nonnegative Matrix Factorization for Directed Graph Clustering 有向图聚类的非对称半非负矩阵分解

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303649

Reyhaneh Abdollahi, Seyed Amjad Seyedi, Mohamad Reza Noorimehr

引用次数: 1

Opinion Spam Detection based on Supervised Sentiment Analysis Approach 基于监督情感分析方法的意见垃圾检测

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303677

Sepideh Jamshidi Nejad, Fatemeh Ahmadi-Abkenari, P. Bayat

{"title":"Opinion Spam Detection based on Supervised Sentiment Analysis Approach","authors":"Sepideh Jamshidi Nejad, Fatemeh Ahmadi-Abkenari, P. Bayat","doi":"10.1109/ICCKE50421.2020.9303677","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303677","url":null,"abstract":"Reading other user's experience on different products and services becomes part of customer's behavior for purchase decision making process nowadays. For this reason, such online resources grow into a target for review spammers with the aim of either boosting their desired products or destroying the reputation of their competitors. Distinguishing between spam and true expressed sentiments is highly challenging due to the fact that this process demands linguistic and grammatical knowledge. Because of the language dependency nature of opinion analysis, natural language processing and opinion mining fields are utilized to overcome the challenges in each language. In this paper, we focus on building up a feature set to be employed with different classifiers as a trustworthy input for opinion spam detection in Persian language. Research on review spam detection in English uncovered some meaningful features so far that we utilized some of them, modify the meaning and usage of some of them to be adapted on Persian language and also add some innovative features accordingly. Our experiments reveal that first Decision Tree and then AdaBoost with the accuracy percentage of 98.67 and 98.00 are the best classifiers for Persian opinion spam detection. Also, the robustness of our extended feature set has been checked in comparison to other feature sets in the task of opinion spam detection.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"288 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134220348","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

A proactive caching approach in 5G networks 5G网络中的主动缓存方法

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303693

Saideh Ahangary, Hamid Chitsaz, M. J. Sobouti, A. Mohajerzadeh, M. Yaghmaee, H. Ahmadi

引用次数: 0

Optimum Feature Selection Using Hybrid Grey Wolf Differential Evolution for Motor Imagery Brain Computer Interface 基于混合灰狼差分进化的运动图像脑机接口最优特征选择

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303629

Marzieh Hajizamani, M. Helfroush, K. Kazemi

{"title":"Optimum Feature Selection Using Hybrid Grey Wolf Differential Evolution for Motor Imagery Brain Computer Interface","authors":"Marzieh Hajizamani, M. Helfroush, K. Kazemi","doi":"10.1109/ICCKE50421.2020.9303629","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303629","url":null,"abstract":"One of the challenges in improving the performance of brain computer interface systems is to overcome the large number of extracted features from EEG signals. Feature selection can reduce noisy data, overtraining effects, necessary storage, computational complexity, and can improve the performance of the classifier. Different feature selection methods have been used to achieve these goals. In this study, a new hybrid feature selection method is proposed. It employs a filter bank common spatial pattern for feature extraction and a grey wolf optimization algorithm to search and generate optimal feature subset with performance evaluated by support vector machine classifier. Also, In order to increase the search performance of the proposed feature selection algorithm, a new parallel combined grey wolf and differential evolution optimization algorithm is proposed. Experimental results show that the proposed methods improve the performance of motor imagery brain computer interface system in comparison to the state-of-the-art methods, even with small training data.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134491792","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: 2

Persian Emoji Prediction Using Deep Learning and Emoji Embedding 波斯语表情符号预测使用深度学习和表情符号嵌入

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303639

Ehsan Tavan, A. Rahmati, Mohammad Ali Keyvanrad

{"title":"Persian Emoji Prediction Using Deep Learning and Emoji Embedding","authors":"Ehsan Tavan, A. Rahmati, Mohammad Ali Keyvanrad","doi":"10.1109/ICCKE50421.2020.9303639","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303639","url":null,"abstract":"The appearance of social networks and the increasing expansion of these networks has created many challenges, especially in the field of natural language processing. One of these social networks that has been welcomed by many researchers is Twitter. Twitter’s users have the opportunity to consider one or more emojis for a tweet depending on the feeling and meaning of the tweet. Emojis contain information and concepts that the author of each tweet has in mind, the semantic and emotional range of each emoji is very wide and each emoji can be used in many different types of sentences. Therefore, by analyzing the content and emotion of each tweet, we can achieve the appropriate emoji of that tweet. For such reasons, predicting an emoji for a textual data is one of the challenges that has attracted the attention of researchers. In this article, using deep neural networks an attempt for the first time has been made to predict the emoji for Persian text data extracted from Twitter. And we were able to achieve F-score of 33% in 10 most frequent emojis which is 5% higher than the result of the SVM model and also 11% better than the result of the Naïve Bayes model, and F- score of 46% in 5 most frequent emojis which is 5% higher than the result of the SVM model and also 5% better than the result of the Naïve Bayes model.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133554845","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: 7

DASH: Dynamic Scheduling Algorithm for Single-ISA Heterogeneous Nano-scale Many-Cores DASH:单isa异构纳米多核动态调度算法

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303673

Keihaneh Kia, Amir Rajabzadeh

引用次数: 1

Microsatellite Finder algorithm with High Memory Efficiency for Even Super Long Sequences 偶超长序列高存储效率的微卫星查找算法

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303640

Hossein Savari, Nazanin Hadiniya, Abdorreza Savadi, Mahmoud Naghibzadeh

{"title":"Microsatellite Finder algorithm with High Memory Efficiency for Even Super Long Sequences","authors":"Hossein Savari, Nazanin Hadiniya, Abdorreza Savadi, Mahmoud Naghibzadeh","doi":"10.1109/ICCKE50421.2020.9303640","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303640","url":null,"abstract":"An important issue in bioinformatics is to identify microsatellites that are a type of tandem repeats in genomic sequences. Changes in the number of repetitions of microsatellites can cause many diseases including Huntington’s and cancer. Therefore, identifying microsatellites in organisms’ genome in order to diagnose diseases and advise a treatment method is of the utmost importance. Many algorithms and tools have been developed to identify these sequences. Considering the importance of the application any improvement in accuracy, speed, and memory utilization can have a positive effect on the quality of human lives. The study proposes an algorithm that has a constant memory consumption that is independent of the input-size. Thus, its main memory consumption is significantly lower than the existing methods, while having a very high processing speed. In the end, this algorithm is implemented and the resulting tool which is named Memory Efficient Microsatellite Finder (MEMF) is compared to the state of the art tools available in terms of memory consumption and execution time. Its superiority is clear from the developed method and forms the comparison results.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124867179","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: 1