2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS)最新文献_第8页

A Chinese Question Answering System based on GPT 基于GPT的中文问答系统

2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2019-10-01 DOI: 10.1109/ICSESS47205.2019.9040807

Shuai Liu, Xiaojun Huang

引用次数: 2

Sequential Multi-Kernel Convolutional Recurrent Network for Sentiment Classification 面向情感分类的顺序多核卷积循环网络

2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2019-10-01 DOI: 10.1109/ICSESS47205.2019.9040746

Ariyo Oluwasanmi, Shokanbi Akeem, Jackson Jehoaida, Muhammad Umar Aftab, N. Hundera, Bulbula Kumeda, Edward Baagere, Zhiguang Qin

{"title":"Sequential Multi-Kernel Convolutional Recurrent Network for Sentiment Classification","authors":"Ariyo Oluwasanmi, Shokanbi Akeem, Jackson Jehoaida, Muhammad Umar Aftab, N. Hundera, Bulbula Kumeda, Edward Baagere, Zhiguang Qin","doi":"10.1109/ICSESS47205.2019.9040746","DOIUrl":"https://doi.org/10.1109/ICSESS47205.2019.9040746","url":null,"abstract":"The emergence of deep learning as a commanding technique for learning heterogeneous layers of feature representations have consequently substituted traditional machine learning algorithms which are generally poor in analyzing compound sentences. Additionally, convolutional and recurrent neural networks have auspiciously yielded state-of-the-art results in sentiment classification and Natural Language Processing (NLP). In this paper, a deep sentiment representation model through the combination of multiple Convolutional Neural Networks (CNN) kernels with Long Short-Term Memory (LSTM) is proposed for sentiment classification. Our model gains word vector representation using pre-trained Global Vectors for Word Representation (GloVe) embeddings, thereafter used as input to the CNN layer which extracts higher local text representations. Finally, Bidirectional LSTM (biLSTM) generates sentiment classification of sentence representation based on context dependent features. Our combined approach of CNN and biLSTM was experimented using the Stanford Large Movie Review Dataset (IMDB) and Stanford Sentiment Treebank Dataset (SSTB) for binary classification. The evaluation achieves outstanding results in outperforming several existing approaches with 90.4% accuracy on the Stanford Sentiment Treebank dataset and 94.8% accuracy on the Stanford Large Movie Review dataset. These results are achieved with a drastic reduction of model parameters and without a pooling layer in the CNN architecture, helping to retain local and structural information in comparison to other existing deep neural network frameworks. (Abstract)","PeriodicalId":203944,"journal":{"name":"2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116609650","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

Implementation of Behavior Recognition Based on Machine Vision 基于机器视觉的行为识别实现

2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2019-10-01 DOI: 10.1109/ICSESS47205.2019.9040773

Yuanjun Ding, Haibo Pu, Jiaxin Zhang, J. Yang

引用次数: 0

Scheduling Algorithm Comparative Study on Mixed-Flow Blending Production System with JSSP 基于JSSP的混流混合生产系统调度算法比较研究

2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2019-10-01 DOI: 10.1109/ICSESS47205.2019.9040758

Hanlin Yu, Yabo Luo

引用次数: 0

A Segmentation-driven Handwritten Uighur Word Recognition Algorithm Based on Feedback Structure 基于反馈结构的分词驱动手写维吾尔语词识别算法

2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2019-10-01 DOI: 10.1109/ICSESS47205.2019.9040846

Yamei Xu, Jili Xue

引用次数: 0

The Smart Shopping Basket Based on IoT Applications 基于物联网应用的智能购物篮

2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2019-10-01 DOI: 10.1109/ICSESS47205.2019.9040750

S. Mekruksavanich

{"title":"The Smart Shopping Basket Based on IoT Applications","authors":"S. Mekruksavanich","doi":"10.1109/ICSESS47205.2019.9040750","DOIUrl":"https://doi.org/10.1109/ICSESS47205.2019.9040750","url":null,"abstract":"Every day, huge numbers of customers come to buy numerous goods at supermarkets around the world. These days, shoppers use a shopping cart or basket when buying the groceries at a supermarket. In addition, the procurement of products involves a complicated process in which the customers must bring the items they want to purchase to the check-out area, then stand and wait in a long line so that the products can be scanned, the total amount calculated and the bill paid. As a result of this problem, this research study presents the development of a smart basket for shopping. A barcode tag is found on every item in a supermarket, and the smart basket will include a barcode reader on a mobile device. While shopping, customers can scan the products and then place them in the basket, and the mobile device will record and display the price and name of each item. Also, the basket will have a weight sensor system that can confirm the accurate pricing of produce during the shopping process. Calculation of the total cost of the customer’s groceries will be performed and stored in the memory of the smart basket’s microcontroller. This data will be sent from the basket to the main computer’s server via a transmitter. Therefore, the proposed smart basket will allow shoppers to avoid waiting in line and having to constantly think about the amount of money they will need to spend.","PeriodicalId":203944,"journal":{"name":"2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123901552","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 of SSD Lifecycle Prediction 固态硬盘生命周期预测研究综述

2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2019-10-01 DOI: 10.1109/ICSESS47205.2019.9040759

Qiang Li, Hui Li, Kaiqing Zhang

引用次数: 2

An Empirical Evaluation of Machine Learning Algorithms for Identifying Software Requirements on Stack Overflow: Initial Results 用于识别堆栈溢出软件需求的机器学习算法的经验评估:初步结果

2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2019-10-01 DOI: 10.1109/ICSESS47205.2019.9040720

Arshad Ahmad, Chong Feng, Adnan Tahir, Asif Khan, M. Waqas, Sadique Ahmad, A. Ullah

{"title":"An Empirical Evaluation of Machine Learning Algorithms for Identifying Software Requirements on Stack Overflow: Initial Results","authors":"Arshad Ahmad, Chong Feng, Adnan Tahir, Asif Khan, M. Waqas, Sadique Ahmad, A. Ullah","doi":"10.1109/ICSESS47205.2019.9040720","DOIUrl":"https://doi.org/10.1109/ICSESS47205.2019.9040720","url":null,"abstract":"Context: The recent developments made during the last decade or two in requirements engineering (RE) methods have seen a rise in using different machine-learning (ML) algorithms to solve some complex RE problems. One such problem is identifying and classifying software requirements on Stack Overflow (SO). The suitability of ML-based techniques to this tackle problem has shown convincing results, much better than those generated by some traditional natural language processing (NLP) techniques. Nevertheless, a comprehensive and systematic comprehension of these ML based techniques is still deficient. Objective: To identify and classify the type of ML algorithms used for identifying software requirements on SO. Method: This article reports systematic literature review (SLR) gathering evidence published up to August, 2019. Results: This study identified 1073 published papers related to RE and SO. Only 12 primary papers were selected. The data extraction process revealed that; 1) Latent Dirichlet Allocation (LDA) topic modeling is the most widely used ML algorithm in the selected studies, and 2) Precision and recall are the most commonly used evaluation method to measure the performance of these ML algorithms. Conclusion: The SLR finds that while ML algorithms have great potential in the identification of RE on SO, they face some open issues that will ultimately affect their performance and practical application. The SLR calls for the collaboration between RE and ML researchers, to tackle the open issues facing the development of real-world ML systems.","PeriodicalId":203944,"journal":{"name":"2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127527865","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

DeepDroid: Feature Selection approach to detect Android malware using Deep Learning DeepDroid:使用深度学习检测Android恶意软件的特征选择方法

2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2019-10-01 DOI: 10.1109/ICSESS47205.2019.9040821

Arvind Mahindru, A. L. Sangal

{"title":"DeepDroid: Feature Selection approach to detect Android malware using Deep Learning","authors":"Arvind Mahindru, A. L. Sangal","doi":"10.1109/ICSESS47205.2019.9040821","DOIUrl":"https://doi.org/10.1109/ICSESS47205.2019.9040821","url":null,"abstract":"Smartphones are now able to use for various purposes such as online banking, social networking, web browsing, ubiquitous services, MMS, and more daily essential needs through various apps. However, these apps are highly vulnerable to various types of malware attacks attributed to their open nature and high popularity in the market. The fault lies in the underneath permission model of Android apps. These apps need several sensitive permissions during their installation and runtime, which enables possible security breaches by malware. Hence, there is a requirement to develop a malware detection that can provide an effective solution to defense the mobile user from any malicious threat. In this paper, we proposed a framework which works on the principals of feature selection methods and Deep Neural Network (DNN) as a classifier. In this study, we empirically evaluate 1,20,000 Android apps and applied five different feature selection techniques. Out of which by using a set of features formed by Principal component analysis (PCA)can able to detect 94% Android malware from real-world apps.","PeriodicalId":203944,"journal":{"name":"2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114245295","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: 19

HDCNN-CRF for Biomedical Text Named Entity Recognition 生物医学文本命名实体识别的HDCNN-CRF

2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2019-10-01 DOI: 10.1109/ICSESS47205.2019.9040749

Mingyuan Gao, Hao Wei, Fei Chen, Wenqiang Qu, Mingyu Lu

引用次数: 1