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International Symposium on computer Architecture and High Performance Computing (SBAC-PAD)

作者： Bruno Da Silva Alves Lucas Mello Schnorr Graduate Program in Computer Science (PPGC) Institute of Informatics – UFRGS Porto Alegre Brazil

Adaptive Mesh Refinement (AMR) is a widely known technique to adapt the accuracy of a solution in critical areas of the problem domain instead of using regular or irregular but static meshes. The MARE2DEM is a parallel application that employs the AMR technique to model 2D electromagnetics in oil and gas exploration. The modeling consists in iteratively applying a data inversion based on a set of measurements collected and registered by a survey on an area of interest. The parallelism of the MARE2DEM works by dividing the workload into a set of refinement groups that represent overlapping areas of the problem domain. Each refinement group can be computed independently of the others by a set of workers, carrying out the AMR in the meshes when necessary. The shape and compute performance of the refinement group depend directly of a set of user-defined parameters. In this article, we provide a method to estimate the MARE2DEM performance for all possible values that can be used in the influencing parameters of the application for a given case study. Our relatively cheap method enables the geologist to configure MARE2DEM correctly and extract the best performance for a given cluster configuration. We detail how the method works and evaluate its effectiveness with success, pinpointing the best values for the creating refinement groups using a real case study from the Marlim field on the coast of Rio de Janeiro, Brazil. Although we demonstrate our evaluation with this scenario, our method works for any input of MARE2DEM.

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Social media data crowdsourcing as a new stream for environmental planning & monitoring: A review 2

Social media data crowdsourcing as a new stream for environm...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Lawu, B.L. Lim, F. Susilo, A. Surantha, N. Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

This research focused on social media applications that had been used by large-scale users. Data might be in the form of text, image, video, each with its own data processing complexity. In this study, the researchers had performed a systematic review of literature on the development and usage of crowdsourcing data collection in recent days, with a range of techniques/methods/approaches to present meaningful data results/conclusions for environmental management. From the collected references, it is found that most papers discuss the monitoring and planning the environment through the use of social media data. An in-depth review of the literature was performed on the state-of-the-art environmental monitoring and planning and the general architecture of the data crowdsourcing system. Finally, based on the literature review, the challenge of data crowdsourcing for environmental monitoring and planning is identified and addressed. © Published under licence by IOP Publishing Ltd.

关键词： Crowdsourcing

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Analyzing Company’s Share Purchase Value Based on Financial Reports Using Artificial Neural Network (ANN)

Analyzing Company’s Share Purchase Value Based on Financial...

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Jeremy Shawn Jonathan Gunawan Chen Zi Hao Maria Susan Anggreainy Afdhal Kurniawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia

Because financial time series forecasting is sensitive to political, economic, and social factors, it is not a simple task. As a result, those who make investments in currency exchange and financial markets typically search for reliable models that can guarantee they will maximize their profile and minimize their losses. Fortunately, many studies have used a method from Artificial Neural Networks (ANNs) called Backpropagation, could improve the predictive accuracy of the behavior of the financial data over time. This paper aims to forecast stock share prediction from closing value of PT. Bank Central Asia Tbk, and PT. Bank Maybank Indonesia Tbk. The results show that the using Backpropagation gives the closest result. And for the rating of judgement for cast accuracy, it exceeded 10% accuracy, which means high accurate from the prediction. For further checking, comparing the results of research from Victor’s results, it almost hits the same accuracy percentage. Which means, these prediction are accurate enough to do time series forecasting.

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Explainable Tree-Based Boosting Algorithms for Smoker Detection Using Bio-Signals

Explainable Tree-Based Boosting Algorithms for Smoker Detect...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Jason Sebastian Sulistyawan Kuncahyo Setyo Nugroho Bens Pardamean Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

Many smoking-related diseases are difficult to treat and often fatal. Rather than treating diseased smokers, preventing the diseased is more achievable, though, many of them deny to being smokers, leading to another problem. Thus, this study aims to detect important aspects that can detect that the person is a smoker or not through their bio-signals through using SHAP, along with a comprehensive analysis of the used methods, gradient-boosting algorithms XGBoost, LightGBM, and CatBoost, known for their efficiency in handling complex datasets and non-linear relationships. The study then found that triglyceride, Gtp, hemoglobins significantly affect the body's responses to smoking, based on the CatBoosts’ results, having an AUC score of up to 0.8612 and an accuracy score of up to 0.7776 with the selected features.

关键词： Seminars Measurement Accuracy Predictive models Feature extraction Boosting Physiology Classification algorithms Reliability Diseases

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Six Sigma for Evaluating Electronic Signature in eProcurement System: A Case Study 4

Six Sigma for Evaluating Electronic Signature in eProcuremen...

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4th Asia Pacific Conference on Research in Industrial and Systems Engineering: Building Business Resilience to Face the Challenge in Pandemic Era, APCORISE 2021

作者： Nugroho, Eko Cahyo Saputra, Herry Gunarso, Davy Yeria Wibowo, Antoni Utama, Ditdit Nugeraha Computer Science Department Binus Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781450390385

Six-sigma is an approach to appraise a company's prospect in generating a number of piece with homogenized processes without any production defects or zero faults. It is operated not only for declining defect numbers, but also for reducing the company's imperfection itself. It means, company is able to decrease the cost (in term of money or time), also to increase the efficiency by evaluating current process and planning to improve company management process. In this study, six-sigma benefited to determine the success level of implementation of e-signature application in e-procurement system based on user experience rate. Conclusively, the implementation of "change management"has enhanced the business process in detail, while it was still in the equivalent sigma level 2. © 2021 Owner/Author.

关键词： Six sigma

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Linear Regression Model to Predict the Spread of COVID-19 in Tangerang City 9

Linear Regression Model to Predict the Spread of COVID-19 in...

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9th International Conference on Information and Communication Technology, ICoICT 2021

作者： Sudiyono, Yusuf Trisetyarso, Agung Prabowo, Harjanto Meyliana, Meyliana Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781665404471

The outbreak of acute respiratory syndrome virus disease in China at the end of 2019 has caused a global epidemic as well as high mortality rates in affected countries. This research aimed at examining the extent of the spread of confirmed Covid-19 cases in Tangerang City. The data used included the data of confirmed Covid-19 patients. Such data was integrated with geospatial data found in 13 sub-districts in Tangerang City. The prediction of the spread of confirmed Covid-19 cases was made by using Linear Regression model. The results of the MAPE calculation with a value below 10% in 13 districts resulted in a very good predictive value. This prediction resulted in a graph and was connected to each other in a thematic map coordinate point system. The results of the Covid-19 spread prediction were divided into several districts and indicated with different color variations. Therefore, the darker the resulting color on the thematic map visualization, indicates an increase in Covid-19 cases that have occurred. © 2021 IEEE.

关键词： COVID-19

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Street View Object Detection for Autonomous Car Steering Angle Prediction Using Convolutional Neural Network 1

Street View Object Detection for Autonomous Car Steering Ang...

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1st International Conference on computer science and Artificial Intelligence, ICCSAI 2021

作者： Sonata, Ilvico Heryadi, Yaya Budiharto, Widodo Wibowo, Antoni Bina Nusantara University BINUS Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Autonomous car research is currently developing rapidly to find optimal and accurate steering angle and speed control. Various sensors such as cameras, LIDAR, and RADAR are used to recognize the surrounding environment to determine the correct steering angle prediction in avoiding obstacles. In addition to being expensive, LIDAR and RADAR have several drawbacks such as the level of accuracy that depends on the weather and the ability to detect adjacent objects. This paper will propose the prediction of steering angle and speed control in autonomous cars based on the detection of street view objects such as cars in front, traffic signs, pedestrians, and lane lines. The process of object detection and prediction of steering angle, as well as prediction of speed control using a convolutional neural network (CNN) through video captured using a single camera. In this method, other sensors such as LIDAR and RADAR are no longer needed so that the costs required are lower and the weaknesses found in LIDAR and RADAR can be eliminated. The results obtained are very good with 92% accuracy for steering angle prediction and 85% for speed control prediction. The autonomous car can run well in the simulator environment through the video taken on the real road. © 2021 IEEE.

关键词： Object recognition

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Recommendation System Journalist for Getting Top News Based on Twitter Data

Recommendation System Journalist for Getting Top News Based ...

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2019 International Conference of science and Information Technology in Smart Administration, ICSINTeSA 2019

作者： Girsang, Abba Suganda Isa, Sani Muhamad Harvy, Ikrar Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

One application that can be utilized in finding the latest news is by utilizing the development of information and communication technology such as seeing the delivery of public information through social media such as twitter. This study implements the system which is able to get the trending or top news based on twitter data. This information will connect to the journalist via mobile application. It helps journalists to get the potential news to be reported in their media. The application will group each tweets into various themes based on the cosine similarity. The tweets which are more the certain threshold, can be indicated as the top news. The top news is then classified into one category news by using SVM algorithm. The training data becomes from the source data of one media news. The specific topic of this result will ease to order the journalist cover this top news. The system can be implemented well. © Published under licence by IOP Publishing Ltd.

关键词： Social networking (online)

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Decomposition and Reorganization of Phonetic Information for Speaker Embedding Learning

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IEEE/ACM Transactions on Audio Speech and Language Processing 2023年 31卷 1745-1757页

作者： Hong, Qian-Bei Wu, Chung-Hsien Wang, Hsin-Min National Cheng Kung University and Academia Sinica Graduate Program of Multimedia Systems and Intelligent Computing Tainan701 Taiwan Academia Sinica Taipei115 Taiwan National Cheng Kung University Department of Computer Science and Information Engineering Tainan701401 Taiwan Academia Sinica Institute of Information Science Taipei115 Taiwan

Speech content is closely related to the stability of speaker embeddings in speaker verification tasks. In this paper, we propose a novel architecture based on self-constraint learning (SCL) and reconstruction task (RT) to remove the influence of phonetic information on speaker embedding generation. First, SCL is used to reduce the divergence of frame-level features, which can avoid ambiguity between the resulting embeddings of the two utterances being compared. Second, RT is used to further remove phonetic information in frame-level layers, focusing on speaker-discriminative feature transformation. In our experiments, the speaker embedding models were trained on the VoxCeleb2 dataset and evaluated on the VoxCeleb1, Librispeech, SITW and VoxMovies datasets. Experimental results on VoxCeleb1 show that the proposed DROP-TDNN system reduced the EER by 7.5%, compared to the state-of-the-art ECAPA-TDNN system. Furthermore, the proposed DROP-TDNN system also outperformed the ECAPA-TDNN system in the experiments on SITW, Librispeech and VoxMovies under cross-dataset conditions. In the experiments on SITW, the proposed system reduced the EER by 3.4% compared to the ECAPA-TDNN system. In the experiments on Librispeech, the proposed system demonstrated the advantage of removing phonetic information under the clean speech condition, with a significant reduction of 25.5% in EER compared to the ECAPA-TDNN system. In the experiments on VoxMovies, the proposed system reduced the EER by up to 7.9% compared to the ECAPA-TDNN system under different pronunciation and background conditions. © 2014 IEEE.

关键词： Speech recognition

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Smart Insurance System Model Concept for Marine Cargo Business

Smart Insurance System Model Concept for Marine Cargo Busine...

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2021 International Conference on Data science and Its Applications, ICoDSA 2021

作者： Santoso, Cahyono Budy Prabowo, Harjanto Warnars, Harco Leslie Hendric Spits Fajar, Ahmad Nurul Bina Nusantara University BINUS Graduate Program-Doctor of Computer Science Computer Science Departement Jakarta11480 Indonesia

ISBN: (纸本)9781665443036

In this digitalization era, insurance companies have to face many new challenges, namely a very competitive market that has resulted in decreased margins and revenues and increased expectations from customers for insurance company services. Insurance companies are required to be fast-paced in providing accurate services and pricing. In addition, insurance companies must carry out marketing strategies that make customers feel valued so that strong engagement will arise. The focus of this research is on the field of marine cargo insurance, which is an insurance product that guarantees the loss of transportation of goods by sea. The purpose of this research is to analyze the underwriting and marketing processes in marine cargo business loss insurance and identify insurance models that can be smart and comprehensive solutions. The research method is to conduct a literature review to obtain previous problems and solutions related to this research topic. Then the researchers conducted observations and interviews with experts at an international insurance company in Jakarta. From this method, the main problems in this research are the underwriting process, product information services, and system recommendations that are less effective and efficient. This study proposes a smart insurance model concept by combining technological features, namely expert systems, chatbot, and recommend systems. It is hoped that this research will contribute to increasing underwriting and marketing productivity, as well as customer satisfaction in getting fast and appropriate product services. © 2021 IEEE.

关键词： Expert systems

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