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computer science, Information Technology and engineering (ICCoSITE), International Conference on

作者： Muhammad Fikri Hasani Yaya Heryadi Yulyani Arifin Lukas Wayan Suparta Computer Science Department BINUS Graduate Program Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classification is a task of text classification. As in general text classification, the required dataset requires a label to carry out the classification process. To speed up and help the utterance analysis process, there is already a method, namely clustering, and Density-based clustering is a part of clustering that can determine cluster patterns based on arbitrary data, with DBScan as one of its algorithms. This research used 10000 client utterance data of awhatsapp based e-commerce conversation. SentenceBert also used as a state of art sentence embedding. This research yield silhouette score of 0.327 as the best result from eps of 0.1 and MinPts of 95. However, based on the cluster result, sentences labelled as noise can be further clustered. Text Preprocessing, text augmentation and sentence embedding techniques can be explored to increase the cluster performance.

关键词： computer science Art Text categorization Clustering algorithms Oral communication Chatbots Classification algorithms

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Moving target detection from the roadside by millimeter-wave radar

Moving target detection from the roadside by millimeter-wave...

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International Symposium on Antennas and Propagation (ISAP)

作者： Toshifumi Moriyama Sora Matsumoto Yang-Lang Chang Graduate School of Engineering Nagasaki University Nagasaki Japan College of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350364774

ISBN: (纸本)9798350364781

In this study, we investigate a traffic monitoring method to detect passing cars, bikes, and humans from roadsides using millimeter-wave radar during road work. Because a road worker is one of the most dangerous occupations, the number of people trying to get this type of job is decreasing. To give a safety work condition and reduce road accidents with respect to the road works, a millimeter-wave radar is one of the candidates for infrastructure sensors that can be expected to offer robust detection performance regardless of the surrounding environment. Then, we applied millimeter-wave radar to traffic monitoring during road work. We examine that the radar detects passing cars, bikes, and humans, and estimates their direction with two corner reflectors from the roadside. These experiments were carried out, and the results are presented in this paper.

关键词： Road accidents Roads Radar detection Object detection Millimeter wave radar Millimeter wave propagation Sensors Safety Automobiles Monitoring

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Neural Network-based Genetic Algorithm for Autonomous Boat Pathfinding

Neural Network-based Genetic Algorithm for Autonomous Boat P...

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IEEE International Conference on Knowledge Innovation and Invention (ICKII)

作者： Nur Hamid Willy Dharmawan Hidetaka Nambo Department of Electrical Engineering and Computer Science Graduate School of Natural Science and Technology Kanazawa University Kanazawa Japan

Genetic algorithms become widely used in various optimization as a nature-inspired algorithm. This biological-based algorithm includes three genetic operators: selection, crossover or recombination, and mutation. In this study, the genetic operator processed neural network parameters, including the weight and bias. Three-ray sensors with different directions input data into the neural network. We applied the method to autonomous boats for pathfinding responding to dynamic environment challenges. Generally, autonomous boats face difficulty in path-finding in dynamic environments. To evaluate the method, we experimented with the scenario of the static and dynamic environment (with wave surface) in a three-dimensional simulation platform. The hyperparameter of layer number and mutation rate variation were used. The results showed that the method was useful in pathfinding in static and dynamic environments.

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Development of Support Vector Machine Method to Forecast Dengue-Endemic Areas

Development of Support Vector Machine Method to Forecast Den...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Meileni, Hetty Ermatita Abdiansah Husni, Nyayu Latifah Desmawati Doctoral Program in Engineering Science Palembang30139 Indonesia Universitas Sriwijaya Faculty of Computer Science Palembang30139 Indonesia Palembang30139 Indonesia Politeknik Negeri Sriwijaya Palembang Department of Electrical Engineering Palembang30139 Indonesia Universitas Pembangunan Nasional Veteran Jakarta Faculty of Health Science Jakarta12450 Indonesia

ISBN: (纸本)9798350307306

The cases of dengue hemorrhagic fever (DHF) in Indonesia have increased significantly since 2020. Data shown by the Central Statistics Agency, for example, in South Sumatra Province, there were 6,348 cases of DHF during that period. To suppress the case number of DHF, forecasting areas that are susceptible to this disease is urgently needed. This research aims to develop a machine learning method Support Vector Machine (SVM). The data used include area data, population density data, weather data, and dengue fever case data. The development of the SVM model is carried out using a machine learning method that combines regional data, population density data, weather data, and dengue case data. In the end, using the SVM method can be used to forecast dengue-endemic areas, so that the government and society can take further action to reduce the negative impact of DHF. © 2023 IEEE.

关键词： Forecasting

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Blockchain and API Development to Improve Relational Database Integrity and System Interoperability

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Procedia computer science 2023年 216卷 151-160页

作者： Andhika Naafi Ramadhan Kharisma Naufal Pane Kun Rifki Wardhana Suharjito Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia

Technological developments have resulted in a trend of cryptocurrencies that use a technology called blockchain to create and record all transactions made into a digital ledger. Along with the emergence of the trend of cryptocurrencies, this development has also resulted in crimes that hit the digital world, such as data leakage and cyber espionage. This threat can be prevented by applying blockchain technology to the database that has been used. Therefore, we need a system that can facilitate the use of blockchain technology and can process data from an existing relational database into a blockchain-based database. The system developed in this study was built with the FastAPI framework that uses the Python programming language and the React framework that uses the Javascript programming language. This system was tested using Katalon and Wireshark software to perform throughput testing and man-in-the-middle attacks. Evaluation of this system is assessed based on the average throughput time and also the results of Wireshark packet capture. The system designed in this research is expected to help overcome interoperability problems when using blockchain and improve relational database integrity. The results of the test show that the system is safe from man-in-the-middle attacks while sending data through API and has a faster throughput time than BigchainDB system by 4.151 seconds.

关键词： Blockchai Cybercrim Database Interoperability

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DSPI - Dual Semantic Parsing Image: A Robust Person Representation for Person Re-Identification

DSPI - Dual Semantic Parsing Image: A Robust Person Represen...

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International Conference on Communications and Electronics, ICCE

作者： Dau Anh Dung Yasuhiro Nakamura Department of Information and Mathematical Sciences Graduate School of Science and Engineering National Defense Academy Yokosuka Japan Department of Computer Science School of Electrical and Computer Engineering National Defense Academy Yokosuka Japan

ISBN: (数字)9798350379792

ISBN: (纸本)9798350379808

One method to significantly enhance the performance of Person-ReID models is to leverage segmentation models to extract semantic parsing information, thus emphasizing the characteristics of human body parts. Studies employing this approach typically utilize CNN architectures as the backbone of Person-ReID, integrating human semantic parsing information into the output of the backbone model, resulting in substantial performance improvement. However, Transformer architectures, utilizing self-attention mechanisms and employing different image data processing techniques from CNN architectures, necessitate a direct integration method for human semantic parsing information into the model’s input. In this study, we propose a novel, simple, and highly adaptable method for integrating human semantic parsing information into input data, called Dual Semantic Parsing Image (DSPI). In this method, a pre-trained segmentation model is employed to extract human pixels from the background and create a new foreground image. The DSPI is formed by horizontally merging the foreground image with the original image, which serves as the input image for the Re-ID model. Our study demonstrates that the Person Re-ID Backbone model ViT+DSPI achieves 93.1% Rank-1 accuracy and 91.1% mAP in the same-domain test of DukeMTMC-ReID, thus attaining state-of-the-art performance. Additionally, significant performance enhancements were observed in cross-domain tests.

关键词： Degradation Adaptation models Image segmentation Electric potential Semantics Merging Transformers

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End-of-Sentence Token Modeling for Streaming Conformer-Based Korean Children’s Speech Recognition Applied to a Social Robot

End-of-Sentence Token Modeling for Streaming Conformer-Based...

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IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)

作者： Seunghun Jeong Yejin Park Hong Kook Kim AI Graduate School School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju Korea

This paper proposes an end-of-sentence (EOS) token modeling approach for streaming conformer-based Korean children’s speech recognition applied to a social robot. The baseline system for speech recognition is a streaming conformer-based speech recognition model with the joint connectionist temporal classification attention-based encoder-decoder architecture, and it is trained with the AIHub Child•Infant Free-Talk dataset. However, it is difficult to determine sentence boundaries in children’s speech because the speaking style of children is somewhat different from that of adults. Thus, the proposed approach is to insert EOS tokens into each sentence of a large children’s dataset to detect sentence boundaries. Finally, we evaluated the performance of the proposed approach on a dataset collected under scenarios of a voice interaction between children and a social robot. Consequently, the proposed EOS token modeling decreased character error rate by 1.7%, compared to the baseline.

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The effect of visual advanced driver assistance systems on a following human driver in a mixed-Traffic condition 7

The effect of visual advanced driver assistance systems on a...

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Ariansyah, Dedy Pardamean, Bens Caruso, Giandomenico Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Department of Mechanical Engineering Politecnico di Milano Milan 20156 Italy

Rapid development in vehicular technology has caused more automated vehicle control to increase on the roads. Studies showed that driving in mixed traffic with an autonomous vehicle (AV) had a negative impact on the time headway (THW) of conventional vehicles (CVs) (i.e., driven by humans). To address this issue, there is a need to equip CV with visual advanced driver assistance systems (ADASs) that helps the driver maintain safe headway when driving near AVs. This study examines the perception of drivers using visual ADAS and their associated risk while driving behind the AV at constant and varying speeds. The preliminary results showed that while visual ADAS could help drivers keep the safe THW, it could affect drivers' ability to react to emergencies. This implies that visual modality alone might not be sufficient and therefore requires some other feedback or intelligent transport systems to help drivers maintain safe driving in a mixed-Traffic condition. © 2022 Elsevier B.V.. All rights reserved.

关键词： Advanced driver assistance systems

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Smart Residential Parking 7

Smart Residential Parking

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7th International Conference on Informatics and Computational sciences, ICICoS 2024

作者： Suhartono Chiang, Jung-Lung Sakanti, Maria Mahardini Kuncoro, C. Bambang Dwi National Chin-Yi University of Technology Prospective Technology of Electrical Engineering and Computer Science Taichung Taiwan National Chin-Yi University of Technology Graduate Institute of Precision Manufacturing Taichung Taiwan National Chin-Yi University of Technology Department of Refrigeration Air Conditioning and Energy Engineering Taichung41170 Taiwan

ISBN: (纸本)9798350375886

This research aims to create a smart residential system that will improve parking management in residential areas using mini-server technology. Another aim is to provide a safe and effective smart parking solution for residential areas with limited capacity. This research method uses mini-server technology and control to create smart system ideas. Apart from that, this research also uses servo motor technology, proximity sensors, cameras, and IoT applications for smart parking management. The result is the creation of intelligent residential parking management, which uses a Raspberry Pi 5 Model B as a server and control to improve the parking management system in residential areas, and the use of number plate recognition technology to be applied to Smart City. This further research offers a new perspective on creating intelligent parking solutions using two input validation parameters simultaneously, namely facial and license plate recognition © 2024 IEEE.

关键词： IoT Parking Plate Number Residential Smart

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UC3M-PUCPR at SemEval-2022 Task 11: An Ensemble Method of Transformer-based Models for Complex Named Entity Recognition 16

UC3M-PUCPR at SemEval-2022 Task 11: An Ensemble Method of Tr...

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16th International Workshop on Semantic Evaluation, SemEval 2022

作者： Schneider, Elisa Terumi Rubel Rivera-Zavala, Renzo M. Martinez, Paloma Moro, Claudia Paraiso, Emerson Cabrera Graduate Program on Informatics Pontifícia Universidade Catolica do Paraná Brazil Computer Science and Engineering Department Universidad Carlos III de Madrid Spain

ISBN: (纸本)9781955917803

This study introduces the system submitted to the SemEval 2022 Task 11: MultiCoNER (Multilingual Complex Named Entity Recognition) by the UC3M-PUCPR team. We proposed an ensemble of transformer-based models for entity recognition in cross-domain texts. Our deep learning method benefits from the transformer architecture, which adopts the attention mechanism to handle the long-range dependencies of the input text. Also, the ensemble approach for named entity recognition (NER) improved the results over baselines based on individual models on two of the three tracks we participated in. The ensemble model for the code-mixed task achieves an overall performance of 76.36% F1-score, a 2.85 percentage point increase upon our individually best model for this task, XLM-RoBERTa-large (73.51%), outperforming the baseline provided for the shared task by 18.26 points. Our preliminary results suggest that contextualized language models ensembles can, even if modestly, improve the results in extracting information from unstructured data. © 2022 Association for Computational Linguistics.

关键词： Deep learning

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