Proceedings of the 1st International Conference on Advanced Information Science and System最新文献_第2页

Improve the performance of neural network training with accurate information: take Connect6 for example 利用准确的信息提高神经网络训练的性能:以Connect6为例

Proceedings of the 1st International Conference on Advanced Information Science and System Pub Date : 2019-11-15 DOI: 10.1145/3373477.3373703

Shih‐Hao Huang, Chih-Hung Chen, Shun-Shii Lin

引用次数: 0

Continuous integration for Laravel applications with GitLab 持续集成的Laravel应用与GitLab

Proceedings of the 1st International Conference on Advanced Information Science and System Pub Date : 2019-11-15 DOI: 10.1145/3373477.3373479

Phyu Phyu Than, Myat Pwint Phyu

引用次数: 0

Identification of hate speech and abusive language on indonesian Twitter using the Word2vec, part of speech and emoji features 使用Word2vec，部分语言和表情符号功能识别印度尼西亚推特上的仇恨言论和辱骂语言

Proceedings of the 1st International Conference on Advanced Information Science and System Pub Date : 2019-11-15 DOI: 10.1145/3373477.3373495

Muhammad Okky Ibrohim, Muhammad Akbar Setiadi, I. Budi

{"title":"Identification of hate speech and abusive language on indonesian Twitter using the Word2vec, part of speech and emoji features","authors":"Muhammad Okky Ibrohim, Muhammad Akbar Setiadi, I. Budi","doi":"10.1145/3373477.3373495","DOIUrl":"https://doi.org/10.1145/3373477.3373495","url":null,"abstract":"Freedom of speech for the people of Indonesia on social media makes the spread of hate speech and abusive language inevitable. If there is no proper handling, this will lead to social disharmony between individuals and communities. The identification of hate speech and abusive language on Twitter in the Indonesian language is quite challenging. Because of its ability to understand the meaning of a sentence, semantic features such as word embedding can be relied on to understand tweets that contain hateful and abusive words. In this study, word embedding (word2vec) feature and its combinations with part of speech and/or emoji were used to identify hate speech and abusive language on Twitter in the Indonesian language. Furthermore, some combinations of unigram with part of speech and/or emojis were also utilized during the experiment and the results were studied. The classification algorithms used in this study were Support Vector Machine, Random Forest Decision Tree, and Logistic Regression. The combination of unigram features, part of speech and emoji obtained the highest accuracy value of 79.85% with F-Measure of 87.51%.","PeriodicalId":300431,"journal":{"name":"Proceedings of the 1st International Conference on Advanced Information Science and System","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122984917","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: 17

A task-oriented dialogue bot using long short-term memory with attention for Thai language 一个以任务为导向的对话机器人，使用长短期记忆和注意力学习泰语

Proceedings of the 1st International Conference on Advanced Information Science and System Pub Date : 2019-11-15 DOI: 10.1145/3373477.3373704

Ramon Robloke, B. Kijsirikul

引用次数: 1

A blockchain internet of things data integrity detection model 区块链物联网数据完整性检测模型

Proceedings of the 1st International Conference on Advanced Information Science and System Pub Date : 2019-11-15 DOI: 10.1145/3373477.3373498

Xia Wu, Fan-xin Kong, Jupeng Shi, L. Bao, Feng Gao, Jing Li

{"title":"A blockchain internet of things data integrity detection model","authors":"Xia Wu, Fan-xin Kong, Jupeng Shi, L. Bao, Feng Gao, Jing Li","doi":"10.1145/3373477.3373498","DOIUrl":"https://doi.org/10.1145/3373477.3373498","url":null,"abstract":"The Industrial Internet of Things (IIoT) plays an indispensable role in Industry 4.0. People are committed to a universal, scalable, and secure IIoT system for use in a variety of industries. However, existing IIoT systems are not prone to provide stable services due to single points of failure and malicious attacks. In the era of big data, countless machines and equipment are generating data all the time, which can be used for scientific research, commodity production, and improving the efficiency of industrial development. Since the Internet of Things (IoT) connects all the smart machines together, ensuring data integrity is of great significance. How to ensure and detect the data integrity of the IoT is a topic of concern. This paper designs and implements a distributed blockchain data simulation system for data integrity assurance and detection in the context of IIoT applications. A method for assuring and detecting the integrity of IoT data is presented. A complete simulation system of IoT data based on blockchain is designed and implemented. The time consumed in the block mining process under different parameters is measured, and the correlation between the block mining time and different parameters is found, and the law of the influence of different parameters on the block mining time is obtained.","PeriodicalId":300431,"journal":{"name":"Proceedings of the 1st International Conference on Advanced Information Science and System","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121112532","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: 3

Predictive human emotion recognition system using deep functional affective state modeling 基于深度功能情感状态建模的预测人类情感识别系统

Proceedings of the 1st International Conference on Advanced Information Science and System Pub Date : 2019-11-15 DOI: 10.1145/3373477.3373706

Raja Majid Mehmood, Hyung-Jeong Yang, Sun-Hee Kim

{"title":"Predictive human emotion recognition system using deep functional affective state modeling","authors":"Raja Majid Mehmood, Hyung-Jeong Yang, Sun-Hee Kim","doi":"10.1145/3373477.3373706","DOIUrl":"https://doi.org/10.1145/3373477.3373706","url":null,"abstract":"Emotions and humans are closely related to each other as emotion alleviate adaptive response to environmental changes and can act as a manner of communication about what is important to us. Emotions can be expressed through facial expressions, words, voice or speech articulation thus allowing us to conceive the emotional state of other individual and communicate with them in the best of our behavior. Emotion recognition is a process to classify different affective states of a human brain. It is a method through which we can analyze the emotive response to certain stimuli and develop human-computer interaction applications. Deep Learning algorithms recently gained attention for their accuracy, precision, speed and real time implementation. Emotion recognition has proved to be quite challenging because of its spectral-temporal pattern problems. In this study we propose a Deep Functional Affective State Model (DFASM) predictive model based on convolutional long short-term memory (ConvLSTM) using margin-based loss function. We evaluate the influence of eight emotional responses. The loss function used in this method observes more specific feelings during the training phase and allows the model to be more confident. The model is tested on public dataset (DEAP) and we recorded an increase up to 79% in the accuracy. Our proposed model is capable of capturing spatial-temporal data while learning, which helps in better emotional recognition. The proposed model was tested by using a public dataset (DEAP) and it outperformed other state-of-the-art methods.","PeriodicalId":300431,"journal":{"name":"Proceedings of the 1st International Conference on Advanced Information Science and System","volume":"384 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121716252","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

E-mail spoofing based on the datalink layers and its application to e-mail aggregation systems: is it possible to make good use of e-mail spoofing? 基于数据链层的电子邮件欺骗及其在电子邮件聚合系统中的应用:是否有可能利用好电子邮件欺骗?

Proceedings of the 1st International Conference on Advanced Information Science and System Pub Date : 2019-11-15 DOI: 10.1145/3373477.3373503

Yao Tong, Shigeo Akashi

引用次数: 1

Fuzzy linear regression for tuberculosis case notification rate prediction in Surabaya 泗水市肺结核病例通报率预测的模糊线性回归

Proceedings of the 1st International Conference on Advanced Information Science and System Pub Date : 2019-11-15 DOI: 10.1145/3373477.3373492

S. Halim, R. Intan, L. P. Dewi

引用次数: 1

Deep URL: design of adult URL classifier using deep neural network 深度URL:基于深度神经网络的成人URL分类器设计

Proceedings of the 1st International Conference on Advanced Information Science and System Pub Date : 2019-11-15 DOI: 10.1145/3373477.3373497

R. Rajalakshmi, Joel Raymann, A. Jerwin Prabu, C. Aravindan

{"title":"Deep URL: design of adult URL classifier using deep neural network","authors":"R. Rajalakshmi, Joel Raymann, A. Jerwin Prabu, C. Aravindan","doi":"10.1145/3373477.3373497","DOIUrl":"https://doi.org/10.1145/3373477.3373497","url":null,"abstract":"Nowadays, many people rely on internet for various information needs, due to the development of advanced technologies. The internet has unlimited web resources, but some contents are not appropriate for all the age groups, especially children under 18. The number of adult websites increases every day thereby posing challenge for existing content-based / black listing approaches, which require entire web page contents for classification purpose / frequent database updates. To overcome the above issues, we propose an URL based deep learning model that not only avoids the unnecessary content downloads, but also handles the dynamic nature of web. As the URL is a sequence of characters, a novel embedding method is proposed for effective URL representation. A Recurrent Convolutional Neural Network based approach is also proposed that can classify the Adult websites by learning the significant features derived only from URLs. By conducting various experiments on the benchmark ODP dataset, we have analyzed the performance of the proposed approach. From the experimental results, it is shown that an accuracy of 87.6% has been achieved which is a significant improvement over the existing approaches.","PeriodicalId":300431,"journal":{"name":"Proceedings of the 1st International Conference on Advanced Information Science and System","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127102620","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

Fuzzy decision tree for breast cancer prediction 模糊决策树用于乳腺癌预测

Proceedings of the 1st International Conference on Advanced Information Science and System Pub Date : 2019-11-15 DOI: 10.1145/3373477.3373489

Mylene J. Domingo, B. Gerardo, Ruji P. Medina

{"title":"Fuzzy decision tree for breast cancer prediction","authors":"Mylene J. Domingo, B. Gerardo, Ruji P. Medina","doi":"10.1145/3373477.3373489","DOIUrl":"https://doi.org/10.1145/3373477.3373489","url":null,"abstract":"Medical errors are considered as the leading cause of death and injury. Breast cancer becomes one of the leading causes of death among women, not only in the Philippines but worldwide. In this paper, data mining was used to predict the stage of breast cancer using a hybrid of fuzzy logic and decision tree. This aims to help experts to make decisions rather than replacing them. The result will only give an expert a recommendation, but the final decision is still on the hands of the experts. Feature selection was used to determine the best attribute in the dataset from Surveillance Epidemiology and End Results (SEER). The data set consists of incidence from 1975 to 2016, but the study limits the analysis from 2010 to 2016. Different cleaning and preprocessing of data are conducted. After thorough preprocessing of data, six (6) attributes are selected, and one (1) target class. Performance comparison shows that the fuzzy decision tree achieved a higher accuracy of 99.96%, sensitivity of 99.26% and specificity of 99.98% than the decision tree classification technique. The simulation result shows a correctly classified instance of 165,124, which is equivalent to 99.97% and only 351 incorrect classified instances or 0.21%. Thus, a fuzzy decision tree is more robust than the traditional decision tree classifier for predicting the stage of breast cancer.","PeriodicalId":300431,"journal":{"name":"Proceedings of the 1st International Conference on Advanced Information Science and System","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131067088","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: 6