The 12th International Conference on Advances in Information Technology最新文献_第3页

Deep Learning for Bibliographic Catalogue Assisting System 书目目录辅助系统的深度学习

The 12th International Conference on Advances in Information Technology Pub Date : 2021-06-29 DOI: 10.1145/3468784.3470657

S. Maneewongvatana, A. Suntornacane

{"title":"Deep Learning for Bibliographic Catalogue Assisting System","authors":"S. Maneewongvatana, A. Suntornacane","doi":"10.1145/3468784.3470657","DOIUrl":"https://doi.org/10.1145/3468784.3470657","url":null,"abstract":"Academic libraries play a major role in providing the information and resources to support formal and informal learning. In order to provide the circulation service, librarians have to deal with the cataloguing process after acquisition. Cataloguing has been a major workload process that requires the intellectuals of librarians. With different experiences of the librarians and the complexity of the content, the quality of cataloguing information and the time spending is out of control. This study developed a catalogue assisting model to reduce the bottleneck of assigning subject access fields in bibliographic records which presumed as the most difficult task in the cataloguing process. The Neural Network models were built by applying the words appearing in the title and table of contents of bibliographic records as the input and predict the list of suggested subjects. The performance of the models was evaluated through the value of precision, recall, and the percentage of bibliographic records that correctly assigned at least 1 subject. The experimental results suggested that combining the suggested subject list obtained from the title word and table of content word models provides better results than using only an individual model.","PeriodicalId":341589,"journal":{"name":"The 12th International Conference on Advances in Information Technology","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115920525","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

Color blind: Can you sight? 色盲:你能看见吗?

The 12th International Conference on Advances in Information Technology Pub Date : 2021-06-29 DOI: 10.1145/3468784.3471602

Ngan V. T. Nguyen, V. Nguyen, Tommy Dang

引用次数: 1

An Application of Evaluation of Human Sketches using Deep Learning Technique 深度学习技术在人体素描评价中的应用

The 12th International Conference on Advances in Information Technology Pub Date : 2021-06-29 DOI: 10.1145/3468784.3469852

Sarayut Thibhodee, Waraporn Viyanon

引用次数: 0

Robust Adaptive Beamforming using Desired Signal Steering Vector Estimation and Variable Loading 基于期望信号导向矢量估计和可变负载的鲁棒自适应波束形成

The 12th International Conference on Advances in Information Technology Pub Date : 2021-06-29 DOI: 10.1145/3468784.3468790

R. Suleesathira

{"title":"Robust Adaptive Beamforming using Desired Signal Steering Vector Estimation and Variable Loading","authors":"R. Suleesathira","doi":"10.1145/3468784.3468790","DOIUrl":"https://doi.org/10.1145/3468784.3468790","url":null,"abstract":"It is known that the desired signal steering vector error, the number of signal samples and the input signal to noise ratio (SNR) are crucial factors to the adaptive beamforming performance. In the presence of steering vector mismatch or lack of samples or strong desired signal, the minimum variance distortionless response (MVDR) beamformer can generate the distorted mainbeam with high sidelobe levels. Diagonal loading of the sample covariance matrix is a widespread technique to provide robustness against such cases. However, there is a tradeoff between the robustness improvement and interference and noise cancellation capability to determine a proper value of loading. Rather than a fixed loading as the diagonal loading, variable loading can provide more robust and protect the rising sidelobe levels in the presence of mismatch. To remedy the effect of mismatch, the presumed desired signal steering vector is utilized to estimate its actual one. The estimation is done by the max/min optimization of the array output power. Then, an algorithm to create the robust MVDR beamformer against the desired signal steering vector mismatch is presented by using the estimated desired signal steering vector and variable loading. Simulation results show that the proposed method has significantly beampattern improvement when the error due to the steering vector mismatch, small number of signal samples and high input SNR exist.","PeriodicalId":341589,"journal":{"name":"The 12th International Conference on Advances in Information Technology","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132442809","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

Afghanistan Vehicle Number Plate Detection and Recognition Using Image Processing and Convolutional Neural Networks 基于图像处理和卷积神经网络的阿富汗车牌检测与识别

The 12th International Conference on Advances in Information Technology Pub Date : 2021-06-29 DOI: 10.1145/3468784.3469948

Javid Hamdard, Worarat Krathu

{"title":"Afghanistan Vehicle Number Plate Detection and Recognition Using Image Processing and Convolutional Neural Networks","authors":"Javid Hamdard, Worarat Krathu","doi":"10.1145/3468784.3469948","DOIUrl":"https://doi.org/10.1145/3468784.3469948","url":null,"abstract":"Although numerous research studies have been conducted concerning automatic vehicle number plate detection and recognition, various presented automated number plate recognition systems are devised for specific countries where number plates follow standard patterns. However, such systems cannot be applied in Afghanistan because of the different designs and the language. Moreover, due to the cursive nature, writing direction, and shape variation of the Pashto characters, the segmentation of words into isolated characters is a more complicated task. Hence, the Pashto optical character recognition is a less developed area. To date, no research study has been conducted for Afghanistan number plate detection and recognition. The details on the Afghanistan number plate include character, numbers, and each province's name. The paper presents the study of its type attempting to detect the number plate from the vehicle image and then recognize the province's name, characters, and numbers on the number plate. In particular, the new method incorporating four core steps. The first step is number plate detection applying canny edge detection based on user-defined thresholding and extracts the number plate involving several image processing techniques. The second phase is number plate adjustment using Randon transform-based techniques. The third stage is number plate segmentation isolating each character, number, and province name on the number plate using a scanning approach. The final step employs a convolutional neural network to classify the number plate's alphanumeric characters and provinces' names. In addition, two datasets have been created: the dataset for alphanumeric characters contains 2800 images of 14 classes, and the dataset for provinces' names contains 6800 images of 34 classes. The proposed models present 99.93 percent accuracy for provinces' names classification and 98.93 percent for alphanumeric characters' classification.","PeriodicalId":341589,"journal":{"name":"The 12th International Conference on Advances in Information Technology","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133216572","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

Assuring long-term operational resilience in a pandemic: Lessons learned from COVID-19 确保大流行期间的长期业务复原力:从COVID-19吸取的经验教训

The 12th International Conference on Advances in Information Technology Pub Date : 2021-06-29 DOI: 10.1145/3468784.3470466

S. Hofbauer, G. Quirchmayr

引用次数: 3

Link Prediction for Biomedical Network 生物医学网络的链路预测

The 12th International Conference on Advances in Information Technology Pub Date : 2021-06-29 DOI: 10.1145/3468784.3471608

Chau Pham, Tommy Dang

引用次数: 4

Urban Flood Management: Bangkok Survey 城市洪水管理:曼谷调查

The 12th International Conference on Advances in Information Technology Pub Date : 2021-06-29 DOI: 10.1145/3468784.3468888

Narongrit Waraporn, V. Vanijja, Montri Supattatham, Olarn Rojanapornpun, Nannapat Termsak, Piraporn Sirisawatvatana

引用次数: 0

Computing Resource Estimation by using Machine Learning Techniques for ALICE O2 Logging System 基于机器学习技术的ALICE O2测井系统计算资源估计

The 12th International Conference on Advances in Information Technology Pub Date : 2021-06-29 DOI: 10.1145/3468784.3468786

Juthaporn Vipatpakpaiboon, V. C. Barroso, K. Akkarajitsakul

{"title":"Computing Resource Estimation by using Machine Learning Techniques for ALICE O2 Logging System","authors":"Juthaporn Vipatpakpaiboon, V. C. Barroso, K. Akkarajitsakul","doi":"10.1145/3468784.3468786","DOIUrl":"https://doi.org/10.1145/3468784.3468786","url":null,"abstract":"Resource estimation is a technique used to estimate computing resources of a system based on historical data and make the system more efficient. There are many researchers who apply machine learning to estimate the computing resources and fulfill their problems. The European Organization for Nuclear Research (CERN) is currently developing a new logging system for A Large Ion Collider Experiment detector (ALICE) based on the Elastic Logstash Kibana (ELK) software stack. Beat which is a data shipper installed on the First Level Processor (FLP) nodes will receive the log data and transfer these to Logstash, a data preprocessing pipeline. It ingests the data and sends the ingested data to Elasticsearch which is a search and analytics engine. The difficulty of this work is about how to handle the large cluster which in future, the number of nodes may increase or decrease, and the number of services in the machine likewise. To make the system more reliable and adaptable to change, a regression model can be used to estimate and plan the number of resources for Logstash. In this paper, we use Metricbeat to get the historical computing metrics of machines from Logstash. In order to find an appropriate regression model, we applied different machine learning algorithms including random forest regression, multiple linear regression, and multi-layer perceptron. The efficiency of these models is measured and compared using coefficient of determination, mean absolute error (MAE)and mean squared error (MSE). The experimental results show that our random forest regression model can outperform the others in both the tuned and not tuned models for estimating CPU, memory and disk space. However, in terms of the training time, the multiple linear regression model spends less time due to the lower number of parameters and lower complexity of the model.","PeriodicalId":341589,"journal":{"name":"The 12th International Conference on Advances in Information Technology","volume":"443 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115280842","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

Sentiment Polarity Classification using Minimal Feature Vectors and Machine Learning Algorithms 基于最小特征向量和机器学习算法的情感极性分类

The 12th International Conference on Advances in Information Technology Pub Date : 2021-06-29 DOI: 10.1145/3468784.3469947

N. Wattanakitrungroj, Nichapat Pinpo, Sasiporn Tongman

{"title":"Sentiment Polarity Classification using Minimal Feature Vectors and Machine Learning Algorithms","authors":"N. Wattanakitrungroj, Nichapat Pinpo, Sasiporn Tongman","doi":"10.1145/3468784.3469947","DOIUrl":"https://doi.org/10.1145/3468784.3469947","url":null,"abstract":"Recently, social media users can comment as texts to describe their opinions. These texts can be analyzed to classify them into either positive or negative attitude. Feature vectors for representing the texts must be designed and prepared before building a classifier. Generally, texts are represented by vectors of weights or frequencies of terms that appear in the text. The length of the feature vector is equal to the number of terms in the dictionary derived from the possible words in all texts. The large amount of words in dictionary leads to the high dimensional vector for representing text and bring about the long processing time to training and testing the text classification models. This paper, the low-dimensional vectors, V8D, were proposed for representing the texts. The set of positive and negative words including the words of negation which have the significant meanings were considered as information to create these vectors. Four machine learning algorithms to solve the classification problem, i.e., k-Nearest Neighbors, Naïve Bayes classifier, Artificial Neural Networks and Support Vector Machine, were applied to classify the opinion texts. By experimenting on eight data sets with various domains, the proposed V8D vectors were compared with the traditional TF-IDF vector in term of the predictive correctness. The experimental results show that representing text as our V8D vector for opinion text classification can provide the best efficiency in both of space usage and processing time.","PeriodicalId":341589,"journal":{"name":"The 12th International Conference on Advances in Information Technology","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127980305","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