2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)最新文献

Pre-training of an artificial neural network for software fault prediction 用于软件故障预测的人工神经网络预训练

2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2017-10-26 DOI: 10.1109/ICCKE.2017.8167880

Moein Owhadi-Kareshk, Yasser Sedaghat, M. Akbarzadeh-T.

{"title":"Pre-training of an artificial neural network for software fault prediction","authors":"Moein Owhadi-Kareshk, Yasser Sedaghat, M. Akbarzadeh-T.","doi":"10.1109/ICCKE.2017.8167880","DOIUrl":"https://doi.org/10.1109/ICCKE.2017.8167880","url":null,"abstract":"Software fault prediction is one of the significant stages in the software testing process. At this stage, the probability of fault occurrence is predicted based on the documented information of the software systems that are already tested. Using this prior knowledge, developers and testing teams can better manage the testing process. There are many efforts in the field of machine learning to solve this classification problem. We propose to use a pre-training technique for a shallow, i.e. with fewer hidden layers, Artificial Neural Network (ANN). While this method is usually employed to prevent over-fitting in deep ANNs, our results indicate that even in a shallow network, it improves the accuracy by escaping from local minima. We compare the proposed method with four SVM-based classifiers and a regular ANN without pre-training on seven datasets from NASA codes in the PROMISE repository. Results confirm that the pre-training improves accuracy by achieving the best overall ranking of 1.43. Among seven datasets, our method has higher accuracy in four of them, while ANN and support vector machine are the best for two and one datasets, respectively.","PeriodicalId":151934,"journal":{"name":"2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125479235","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: 11

A survey on digital evidence collection and analysis 数字证据收集与分析研究综述

2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2017-10-26 DOI: 10.1109/ICCKE.2017.8167885

S. Soltani, Seyed Amin Hosseeini Seno

引用次数: 23

A reinforcement learning approach to score goals in RoboCup 3D soccer simulation for nao humanoid robot 机器人世界杯三维足球仿真中的强化学习方法

2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2017-10-01 DOI: 10.1109/ICCKE.2017.8167920

Mohammad Amin Fahami, M. Roshanzamir, N. H. Izadi

引用次数: 3

Android malware detection based on overlapping of static features 基于静态特征重叠的Android恶意软件检测

2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2017-10-01 DOI: 10.1109/ICCKE.2017.8167899

Maryam Nezhadkamali, S. Soltani, Seyed Amin Hosseeini Seno

引用次数: 6

Sentiment analysis on Twitter using McDiarmid tree algorithm 利用McDiarmid树算法对Twitter进行情感分析

2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2017-10-01 DOI: 10.1109/ICCKE.2017.8167924

Z. Rezaei, Mehrdad Jalali

{"title":"Sentiment analysis on Twitter using McDiarmid tree algorithm","authors":"Z. Rezaei, Mehrdad Jalali","doi":"10.1109/ICCKE.2017.8167924","DOIUrl":"https://doi.org/10.1109/ICCKE.2017.8167924","url":null,"abstract":"In recent years advent of social networking services has created large amounts of data. Microblogging website is a kind of social network in which users share short messages with others. One of the most popular microblogging services is Twitter. Every day millions of people post their opinions and sentiments in this microblog. Due to the large numbers of tweets, finding new approaches to discover and summarize the general overview of a specific topic has become a new challenge. Twitter messages are generated constantly and arrive at high speed and follow data stream model; hence, to predict the sentiment on Twitter we must apply algorithms which can do this in real time and under limited time. Hoeffding tree algorithm is the most popular tool in mining data streams. For this tree algorithm the Hoeffding's bound is utilized to find the smallest amount of instances required in a node to choose a splitting attribute. Replacing the MacDiarmid's bound in Hoeffding tree algorithm, we obtain McDiarmid tree algorithm which is employed in this paper. The accuracy from the McDiarmid tree for sentiment analysis on Twitter is very close to that from the Hoeffding tree; however, the process time of the former has considerably decreased.","PeriodicalId":151934,"journal":{"name":"2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"28 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":"121568652","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: 11

Patchwise object tracking via structural local sparse appearance model 基于结构局部稀疏外观模型的斑块目标跟踪

2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2017-10-01 DOI: 10.1109/ICCKE.2017.8167940

Hossein Kashiyani, S. B. Shokouhi

引用次数: 2

MDMP: A new algorithm to create inverted index files in BigData, using MapReduce MDMP:使用MapReduce在大数据中创建倒排索引文件的新算法

2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2017-10-01 DOI: 10.1109/ICCKE.2017.8167907

Ahmad Arab, S. Abrishami

引用次数: 3

Segmentation of skin lesions using an improved FLICM method 使用改进的FLICM方法分割皮肤损伤

2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2017-10-01 DOI: 10.1109/ICCKE.2017.8167919

S. Kanaani, M. Helfroush, H. Danyali, M. A. Kazemi

引用次数: 2

Learning multi-objective binary features for image representation 学习用于图像表示的多目标二值特征

2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2017-10-01 DOI: 10.1109/ICCKE.2017.8167927

N. Saeidi, Hossein Karshenas, H. Mohammadi

{"title":"Learning multi-objective binary features for image representation","authors":"N. Saeidi, Hossein Karshenas, H. Mohammadi","doi":"10.1109/ICCKE.2017.8167927","DOIUrl":"https://doi.org/10.1109/ICCKE.2017.8167927","url":null,"abstract":"Image representation is proven as a long-standing activity in computer vision. The rich context and large amount of information in images makes image recognition hard. So the image features must be extracted and learned correctly. Obtaining good image descriptors is greatly challenging. In recent years Learning Binary Features has been applied for many representation tasks of images, but it is shown to be efficient and effective just on face images. Therefore, designing a method that can be simultaneously successful in representing both texture and face images as well as other type of images is very important. Moreover, advanced binary feature methods need strong prior knowledge as they are hand-crafted. In order to address these problems, here a method is proposed that applies a pattern called Multi Cross Pattern (MCP) to extract the image features, which calculates the difference between all the pattern neighbor pixels and the pattern center pixel in a local square. In addition, a Multi-Objective Binary Feature method, named MOBF for short, is presented to address the aforementioned problems by the following four objectives: (1) maximize the variance of learned codes, (2) increase the information capacity of the binary codes, (3) prevent overfitting and (4) decrease the difference between binary codes of neighboring pixels. Experimental result on standard datasets like FERET, CMU-PIE, and KTH-TIPS show the superiority of MOBF descriptor on texture images as well as face images compared with other descriptors developed in literature for image representation.","PeriodicalId":151934,"journal":{"name":"2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"4 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":"123995442","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

Semi-supervised approach for Persian word sense disambiguation 波斯语词义消歧的半监督方法

2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2017-10-01 DOI: 10.1109/ICCKE.2017.8167937

Mohamadreza Mahmoodvand, Maryam Hourali

{"title":"Semi-supervised approach for Persian word sense disambiguation","authors":"Mohamadreza Mahmoodvand, Maryam Hourali","doi":"10.1109/ICCKE.2017.8167937","DOIUrl":"https://doi.org/10.1109/ICCKE.2017.8167937","url":null,"abstract":"Word-sense disambiguation is one of the key concepts in natural language processing. The main goal of a language is to present a specific concept to the audience. This concept is extracted from the meaning of words in that language. System should be able to identify role and meaning of words in order to identify the concepts in texts properly. This issue becomes more problematic if there are words that take different meanings because of their surrounding words. Regarding that different practical programs have been developed in Persian language, it is vital now to find a solution for word-sense disambiguation in Persian language. Lack of training data is the biggest challenge in the course of word-sense disambiguation in Persian language. In order to face this problem, machine learning approach with minimal supervision is employed in this research. The applied method tries to disambiguate word senses by considering defined features of target words and applying collaborative learning method. Extracted corpus from published news by news agencies is used as the reference corpus. Evaluating the program by the available corpus on three considered ambiguous words, the implemented method has been able to properly identify the meaning of 5368 documents with 88% recall, 95% precision and 93% accuracy rate.","PeriodicalId":151934,"journal":{"name":"2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)","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":"126156923","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