International Conference on Artificial Intelligence and Virtual Reality最新文献

{"title":"Cross-Language Automatic Plagiarism Detector Using Latent Semantic Analysis and Self-Organizing Map","authors":"A. A. P. Ratna, Paskalis Nandana Yestha Nabhastala, Ihsan Ibrahim, F. A. Ekadiyanto, Muhammad Salman, Muhammad Yusuf Irfan Herusaktiawan, Prima Dewi Purnamasari","doi":"10.1145/3293663.3293681","DOIUrl":"https://doi.org/10.1145/3293663.3293681","url":null,"abstract":"Computer assisted detection or automatic detection for plagiarism could help human to check whether an author of a paper do plagiarism or not. Department of Electrical Engineering, Universitas Indonesia had been developing cross-language automatic plagiarism detection which test paper is written on Indonesian and reference paper written on English. More accurate automatic detection system is needed to prevent plagiarism act, especially on academic paper. The system is based on Latent Semantic Analysis (LSA) algorithm with addition of Self-Organizing Map (SOM) to do classification of the output from LSA. Some features for SOM are extracted from singular value matrix from LSA, they are Frobenius Norm and Cosine Similarity. Together with percentage of technical term, all of the features are used as the input for SOM to classify into 10, 5, 3, and 2 classes. The use of 5 classes in LSA could give equal accuracy for all classes, with the highest accuracy reach 83.09%. While in LSA-SOM, the best accuracy is 83.53% for training data and 80.47% for testing data, in 2-classes configuration with 3 features, they were percentage of technical term, frobenius norm, and pad.","PeriodicalId":420290,"journal":{"name":"International Conference on Artificial Intelligence and Virtual Reality","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121542199","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}

{"title":"Anomaly based Detection Analysis for Intrusion Detection System using Big Data Technique with Learning Vector Quantization (LVQ) and Principal Component Analysis (PCA)","authors":"Muhammad Salman, Diyanatul Husna, Stella Gabriella Apriliani, Josua Geovani Pinem","doi":"10.1145/3293663.3293683","DOIUrl":"https://doi.org/10.1145/3293663.3293683","url":null,"abstract":"Data security has become a very serious parf of any organizational information system. More and more threats across the Internet has evolved and capable to deceive firewall as well as antivirus software. In addition, the number of attacks become larger and become more dificult to be processed by the firewall or antivirus software. To improve the security of the system is usually done by adding Intrusion Detection System(IDS), which divided into anomaly-based detection and signature-based detection. In this research to process a huge amount of data, Big Data technique is used. Anomaly-based detection is proposed using Learning Vector Quantization Algorithm to detect the attacks. Learning Vector Quantization is a neural network technique that learn the input itself and then give the appropriate output according to the input. Modifications were made to improve test accuracy by varying the test parameters that present in LVQ. Varying the learning rate, epoch and k-fold cross validation resulted in a more efficient output. The output is obtained by calculating the value of information retrieval from the confusion matrix table from each attack classes. Principal Component Analysis technique is used along with Learning Vector Quantization to improve system performance by reducing the data dimensionality. By using 18-Principal Component, dataset successfully reduced by 47.3%, with the best Recognition Rate of 96.52% and time efficiency improvement up to 43.16%.","PeriodicalId":420290,"journal":{"name":"International Conference on Artificial Intelligence and Virtual Reality","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130171360","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}

{"title":"Artificial Intelligent Drone-Based Encrypted Machine Learning of Image Extraction Using Pretrained Convolutional Neural Network (CNN)","authors":"M. Shibli, Pascual Marqués, E. Spiridon","doi":"10.1145/3293663.3297155","DOIUrl":"https://doi.org/10.1145/3293663.3297155","url":null,"abstract":"Recently Pretrained Convolutional Neural Networks (CNNs) have proven its effectiveness in image extraction and classification. This powerful feature of CNNs in image processing is facilitated by machine learning to train and classify big data. Image capturing and security transformation are considered as a central necessity of remote sensing imagery of unmanned aerial vehicles (UAVs) and drones. This paper presents a novel artificial intelligent drone-based encrypted machine learning of image classification using a pertained CNN and image encryption-decryption by utilizing singular value decomposition (SVD) and XOR-Secret-Key block cipher cryptology. Initially, pretrained convolutional neural networks (CNN) are extensively used to extract and classify image features making advantage of machine learning training tools features. Training of partial set of image data can be performed to test, classify and label the untrained image data. Pretrained CNN can classify images into object categories. Afterward, the CNN the classified image output is transformed into a digital matrix using SVD and identifies its associated eigenvalues. These eigenvalues are then converted into a binary code. The image data encryption is implemented according to suggested keys. The first part applies the exclusive OR (XOR) operation of the eigenvalues with a selected cipher key. Meanwhile, the second part implements the XOR operation of the output of part one with a randomly generated key using Poisson distribution. The last step in the encryption will be obtained by generating a non-real SVD decomposition matrix; according to which a non-readable image will be resulted. The original image-matrix can be constructed by reversing the process using the security key-cipher block (Poisson Distribution Key and Stand-alone Cipher Code). Finally, SVD image processing results are demonstrated to verify the effectiveness and security of the applied approach that can be implemented for different images.","PeriodicalId":420290,"journal":{"name":"International Conference on Artificial Intelligence and Virtual Reality","volume":"107 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114101416","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}

{"title":"Recommendation Algorithm for Federated User Reviews and Item Reviews","authors":"XingJie Feng, Yunze Zeng, Yixiong Xu","doi":"10.1145/3293663.3293667","DOIUrl":"https://doi.org/10.1145/3293663.3293667","url":null,"abstract":"The recommendation model based on scoring matrix is widely used. Although it has achieved certain recommendation accuracy, it ignores the large amount of semantic information available in the reviews that reflects the user's interests, and the data sparsity problem still exists. In response to the above problems, a two-channel CNN recommendation algorithm (C-DCNN, Combine-Double CNN) that combines user reviews and item reviews is proposed. First, the user and item review texts are vectorized into word vectors, and then the features of users and the items are extracted by using two CNN networks respectively. Finally, the abstract features are mapped to the same feature space through the dot product in the shared layer which aims at predicting the user's rating for a particular item. Experiments on the public datasets of Amazon, Yelp, and Beer show that the C-DCNN model makes full use of reviews to characterize the deep features of users and items. The MSE of the model on different datasets is smaller than other benchmark algorithms. And C-DCNN effectively alleviates the problem of data sparsity.","PeriodicalId":420290,"journal":{"name":"International Conference on Artificial Intelligence and Virtual Reality","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130877550","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}

{"title":"Bayesian Belief Network Model of Indirect Speech Act Theory","authors":"Xianbo Li, Huanrun Qiao, Zhicheng Ma, Diaodiao Yang, Yongtai Pan, Zhixin Ma","doi":"10.1145/3293663.3293685","DOIUrl":"https://doi.org/10.1145/3293663.3293685","url":null,"abstract":"Naive Bayesian probability model has been employed in rational speech act and uncertain rational speech act. The requirement, all attributes must be conditionally independent of each other, is too strict for indirect speech act because the attributes do not necessarily satisfy the assumption of independent class conditions in discourse. Therefore, a Bayesian belief network model of indirect speech act theory is proposed to cancel the independent conditions. Cognitive model theory and non-parametric estimation methods are used to construct the cognitive attributes for the real world and the judgment attributes of the speaker's behavior to achieve the illocutionary act and perlocutionary act in the indirect speech act. The conditional probability table is constructed to make the model more objective and practical.","PeriodicalId":420290,"journal":{"name":"International Conference on Artificial Intelligence and Virtual Reality","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134413920","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}

{"title":"Haiku Generation Using Gap Techniques","authors":"Takuya Ito, Jumpei Ono, Takashi Ogata","doi":"10.1145/3293663.3293666","DOIUrl":"https://doi.org/10.1145/3293663.3293666","url":null,"abstract":"Haiku is the shortest type of formal poem in the world. Haiku includes a set of fragmentary elements; the selection of individual elements plays an important role in their creation. A haiku in which the connections between elements are easily understood tends to be a boring haiku. However, it is difficult to select an element whose connection to other elements is hard to understand, as there is a possibility that the haiku will be interpreted as a combination of simple elements without context. In this thesis, pay attention to the elements of the story that exists in the background of a haiku, and then generate an event from a haiku and a \"surprise\" for that event. From the event with the \"surprise,\" the authors aim to generate a \"poetic haiku,\" that seems at first glance to be disconnected, but has a connection in its background stories.","PeriodicalId":420290,"journal":{"name":"International Conference on Artificial Intelligence and Virtual Reality","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114292142","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}

{"title":"Wiener Loss: A Strong Correlative Loss Applied to Conditional GAN for Color Prediction","authors":"Jingbei Li, Yu Liu, Huaxin Xiao, Hanlin Tan, Maojun Zhang","doi":"10.1145/3293663.3293674","DOIUrl":"https://doi.org/10.1145/3293663.3293674","url":null,"abstract":"Colorization is a task to generate plausible color for a given grayscale image, where the target in input always have variable color styles. Due to the ill-posed inverse problem in colorization, the generated color image easily suffers from the phenomenon of color cast, where unexpected particular color affects the generated image. To cope with such problem, this paper proposes a novel loss function, called Wiener Loss, to constrain the training of colorization network. Concretely, we adopt conditional generative network for training. This paper uses the colorized image from generator and ground truth corporately to calculate their relevance defined as Wiener Loss and feeds this loss back into generator network for training. The experiments demonstrate that our method generates better results compared with its counterpart.","PeriodicalId":420290,"journal":{"name":"International Conference on Artificial Intelligence and Virtual Reality","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134056045","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}

{"title":"The Indonesian Mixed Emotion Dataset (IMED): A Facial Expression Dataset for Mixed Emotion Recognition","authors":"D. Liliana, T. Basaruddin, I. Oriza","doi":"10.1145/3293663.3293671","DOIUrl":"https://doi.org/10.1145/3293663.3293671","url":null,"abstract":"In Social Signal Processing (SSP) and affective computing area, the publicly available facial expression dataset for emotion recognition task is still limited for basic emotion categories. Whereas in everyday life, various types of emotions are being used by humans more than basic emotion, such as mixed emotion. To enrich the diversity of the existing dataset, we developed the Indonesian Mixed Emotion Dataset (IMED). The objective of creating this dataset is to provide the annotated data for mixed emotion recognition as a ground-truth for benchmarking. Mixed emotion is constructed by combining basic emotion categories to resulting new ones. This dataset can be used to facilitate mixed emotion recognition experiments. Our dataset displays 19 categories of emotions performed by 15 subjects, all are Indonesians with various ethnicities: Javanese, Sundanese, Malay, Bataknese, Minang, and Manadonian. Subjects are 60% female and 40% male with age ranging from 17 to 32 demonstrated basic and mixed emotion classes in videos. We then used a computational model to show that mixed emotion categories were discriminable to be recognized by machine classifiers. We believe that IMED dataset is useful for researchers on the same field to test their novel method by using our dataset.","PeriodicalId":420290,"journal":{"name":"International Conference on Artificial Intelligence and Virtual Reality","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126022956","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}

{"title":"Virtual and Augmented Reality to Historical Site Reconstruction: A Pilot Study of East Taiwan Old Railway Station","authors":"Chun-Chun Wei, F. Chen, Chien-Hsu Chen, Yang-Cheng Lin","doi":"10.1145/3293663.3293675","DOIUrl":"https://doi.org/10.1145/3293663.3293675","url":null,"abstract":"The cultural assets of Taiwan's railway heritage are rich and Taiwanese railway museums have gradually appeared each showcasing somewhat different characteristics through a variety of static or animated presentations. The Taitung old railway station is a historical site and in this pilot study we choose it as a good example to illustrate how to pave the way for an interdisciplinary and cutting-edge cultural creation by applying new technologies, such as virtual reality (VR) and augmented reality (AR), to a historical site. With their powerful advantages, we can reconstruct the historical site to develop the VR and AR simulation systems that provide an interactive environment for people to experience and appreciate the east Taiwan old railway station and railway culture.","PeriodicalId":420290,"journal":{"name":"International Conference on Artificial Intelligence and Virtual Reality","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124777851","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}

{"title":"Automatic Essay Grading System Based on Latent Semantic Analysis with Learning Vector Quantization and Word Similarity Enhancement","authors":"A. A. P. Ratna, Adam Arsy Arbani, Ihsan Ibrahim, F. A. Ekadiyanto, Kristofer Jehezkiel Bangun, Prima Dewi Purnamasari","doi":"10.1145/3293663.3293684","DOIUrl":"https://doi.org/10.1145/3293663.3293684","url":null,"abstract":"Department of Electrical Engineering Universitas Indonesia has developed an automatic essay grading system called Simple-O since 2007. Simple-O uses the Latent Semantic Analysis (LSA) method to compare two essays by extracting the essay into matrix. The previous development of Simple-O is the addition of Learning Vector Quantization (LVQ) which is a method of artificial neural network. This research will discuss and provide analysis related to the effect of adding word similarity function to the automatic essay grading system (Simple-O) to the accuracy of the system itself. The experiment will be conducted with five different scenarios by varying the number of keywords in the student's answer essay to 100%, 80%, 60%, 40%, and 20% of the reference essay keywords. According to the result, there are scenarios that has decreased and increased in accuracy. The average accuracy of the Simple-O system after the addition of word similarity function has increased, though not significant. The average increase in accuracy after the addition of word similarity function is 5.4% from 90.9% to 96.3%.","PeriodicalId":420290,"journal":{"name":"International Conference on Artificial Intelligence and Virtual Reality","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130454313","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}