2017 International Conference on Computer Science and Engineering (UBMK)最新文献_第3页

Examining self-efficacy perception and attitudes of introduction to programming course students with respect to gender and course language 程序设计导论学生自我效能感与态度之性别与课程语言研究

2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093519

Ezgi Deveci, D. Aydin, Kristin S. Benli, F. Tek

{"title":"Examining self-efficacy perception and attitudes of introduction to programming course students with respect to gender and course language","authors":"Ezgi Deveci, D. Aydin, Kristin S. Benli, F. Tek","doi":"10.1109/UBMK.2017.8093519","DOIUrl":"https://doi.org/10.1109/UBMK.2017.8093519","url":null,"abstract":"The purpose of this study is to examine generalized self-efficacy and attitudes of the F.M.V. Işık University Engineering Faculty students of Introduction to Programming Course (CSE101) with respect to student gender and course language (Turkish-English). A total of 114 university students, 40 female and 74 male, participated in the research. In order to measure students' self-efficacy perceptions, General Self-Efficacy scale was used and open-ended questions were asked for evaluation of course outcomes (success and failure) and basic demographic information such as age and gender were collected. The open-ended questions were examined by qualitative analysis method. The quantitative analysis revealed a significant relation between the self-efficacy scores of the students with the General Grade Point Average (GPA); and a non-significant relation with the CSE101 end of term grade average. In addition, it was seen that students' self-efficacy scores did not significantly differ with respect to the gender and language (Turkish-English). The motivation scores of the students do not differ according to the course language. In qualitative analysis, we observed that the frequency percentages of the answers given by the students changed according to their genders. By revealing the gender difference in attitudes towards programming course, it is expected that this study will contribute to the process of determining the variables that predict student success in engineering programming education.","PeriodicalId":201903,"journal":{"name":"2017 International Conference on Computer Science and Engineering (UBMK)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128602144","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

Detecting phishing attacks from URL by using NLP techniques 利用NLP技术检测来自URL的网络钓鱼攻击

2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093406

Ebubekir Buber, B. Diri, O. K. Sahingoz

{"title":"Detecting phishing attacks from URL by using NLP techniques","authors":"Ebubekir Buber, B. Diri, O. K. Sahingoz","doi":"10.1109/UBMK.2017.8093406","DOIUrl":"https://doi.org/10.1109/UBMK.2017.8093406","url":null,"abstract":"Nowadays, cyber attacks affect many institutions and individuals, and they result in a serious financial loss for them. Phishing Attack is one of the most common types of cyber attacks which is aimed at exploiting people's weaknesses to obtain confidential information about them. This type of cyber attack threats almost all internet users and institutions. To reduce the financial loss caused by this type of attacks, there is a need for awareness of the users as well as applications with the ability to detect them. In the last quarter of 2016, Turkey appears to be second behind China with an impact rate of approximately 43% in the Phishing Attack Analysis report between 45 countries. In this study, firstly, the characteristics of this type of attack are explained, and then a machine learning based system is proposed to detect them. In the proposed system, some features were extracted by using Natural Language Processing (NLP) techniques. The system was implemented by examining URLs used in Phishing Attacks before opening them with using some extracted features. Many tests have been applied to the created system, and it is seen that the best algorithm among the tested ones is the Random Forest algorithm with a success rate of 89.9%.","PeriodicalId":201903,"journal":{"name":"2017 International Conference on Computer Science and Engineering (UBMK)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129783680","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: 32

Implemention of Benford analysis for electronic invoice audit 对电子发票审计实施本福德分析

2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093447

İlknur Gür Nalçacı

引用次数: 0

Automation of FMEA for computer servers using log data with grey relational analysis 利用日志数据和灰色关联分析实现计算机服务器FMEA自动化

2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093480

Halil Ibrahim Ayaz, M. Testik

引用次数: 2

Car indoor gas detection system 汽车室内气体检测系统

2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093579

P. Panahi, C. Bayilmis

引用次数: 2

Preprocessing framework for Twitter bot detection 用于Twitter机器人检测的预处理框架

2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093483

Mücahit Kantepe, M. Ganiz

{"title":"Preprocessing framework for Twitter bot detection","authors":"Mücahit Kantepe, M. Ganiz","doi":"10.1109/UBMK.2017.8093483","DOIUrl":"https://doi.org/10.1109/UBMK.2017.8093483","url":null,"abstract":"One of the important problems in social media platforms like Twitter is the large number of social bots or sybil accounts which are controlled by automated agents, generally used for malicious activities. These include directing more visitors to certain websites which can be considered as spam, influence a community on a specific topic, spread misinformation, recruit people to illegal organizations, manipulating people for stock market actions, and blackmailing people to spread their private information by the power of these accounts. Consequently, social bot detection is of great importance to keep people safe from these harmful effects. In this study, we approach the social bot detection on Twitter as a supervised classification problem and use machine learning algorithms after extensive data preprocessing and feature extraction operations. Large number of features are extracted by analysis of Twitter user accounts for posted tweets, profile information and temporal behaviors. In order to obtain labeled data, we use accounts that are suspended by Twitter with the assumption that majority of these are social bot accounts. Our results demonstrate that our framework can distinguish between bot and normal accounts with reasonable accuracy.","PeriodicalId":201903,"journal":{"name":"2017 International Conference on Computer Science and Engineering (UBMK)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128853846","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: 40

2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093399

Mümine Kaya Keleş, S. A. Özel

{"title":"Similarity detection between Turkish text documents with distance metrics","authors":"Mümine Kaya Keleş, S. A. Özel","doi":"10.1109/UBMK.2017.8093399","DOIUrl":"https://doi.org/10.1109/UBMK.2017.8093399","url":null,"abstract":"The aim of this study is to compare the successes of various distance metrics and to determine the most appropriate methods in order to detect similarities among textual documents written in Turkish. Computing similarities between text documents is the basic step of plagiarism detection, and text mining methods like author detection, text classification and clustering. Therefore, plagiarism detection and text mining applications will be more successful by using the distance metrics that are determined according to the results obtained in this study. For this purpose, chunks of texts in different lengths are selected as the experimental dataset in this study. After that, preprocessing methods are applied to the dataset that is used; therefore new and different experimental scenarios are created by removing stopwords and Turkish characters, and stemming words with Zemberek. According to the experimental results, it is observed that the preprocessing phase increases the accuracy of similarity detection. Especially, stemming using Zemberek increases the success rate. In all cases, the Cosine Similarity method has been observed as more successful than other distance metrics, because of producing more realistic results.","PeriodicalId":201903,"journal":{"name":"2017 International Conference on Computer Science and Engineering (UBMK)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126735693","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: 5

Gövde-Türk: A Turkish stemming method Gövde-Türk:土耳其词干法

2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093407

S. Yücebaş, Rabia Tintin

引用次数: 1

Link prediction methods in bipartite networks 二部网络中的链路预测方法

2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093495

Serpil Aslan, Mehmet Kaya

引用次数: 7

An image-based recommender system based on feature extraction techniques 基于特征提取技术的图像推荐系统

2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093527

Zuhal Kurt, Kemal Özkan

引用次数: 5