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2024 International Conference on Information technology Research and Innovation, ICITRI 2024

作者： Irawan, Dedi Pardede, Hilman Ferdinandus Anton Putri, Sukmawati Anggraeni Mardiana, Tati Nusa Mandiri University Graduate School of Computer Science Jakarta Indonesia National Research and Innovation Agency Bandung Indonesia Nusa Mandiri University Faculty of Information Technology Jakarta Indonesia

ISBN: (纸本)9798350376210

Sentiment analysis is a technique that is able to analyze reviews, sentiment, people's behavior and emotions towards entities such as services, products, organizations, events and social media. Several sentiment analysis approaches have been carried out, one of which uses deep learning such as Convolutional Neural Network (CNN). This research implements the Attentive model using 3 models, namely Attention network, Multi-head Attention and Convolutional Block Attention Module (CBAM). Attentive mechanisms are used to capture important words so that the model can understand important information. Combining Attentive and CNN models to find optimal performance for each model can be done by hyperparameters tuning settings. From the results of selecting the best hyperparameters, it proves that the Attentive mechanism model is able to increase the performance of the baseline model, each accuracy value that increases is Attention network 0.37%, Multi-head Attention 0.43%, and CBAM 0.21%. The highest improvement value was obtained by Multi-head Attention. © 2024 IEEE.

关键词： Convolutional neural networks

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Implementation of computer Vision of Jakarta Weather Image Categorization Using ResNet

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Procedia computer science 2023年 227卷 813-822页

作者： Kartika Purwandari Join W.C. Sigalingging Alam Ahmad Hidayat Tjeng Wawan Cenggoro Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

At the moment, weather data is crucial for supporting neighborhood activities. The economy and trade are both centered in Jakarta, which is also Indonesia's capital. Therefore, it is crucial to have access to weather information so that these activities don't get disrupted, which would then hinder commercial and trade activity. Social media has been a very popular tool for spreading information recently. Particularly on Instagram, where users favor taking images and sharing the information they encounter. @jktinfo is the Instagram account that posts information about the situation in Jakarta and the area, including the current weather. The @jktinfo account is utilized in this project to gather data. Utilizing a variety of techniques, the collected photographs of sunny, cloudy, and wet situations were.

关键词： computer vision weather image categorization data augmentation deep transfer learning residual network

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Audio-guided implicit neural representation for local imagestylization

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Computational Visual Media 2024年第6期10卷 1185-1204页

作者： Seung Hyun Lee Sieun Kim Wonmin Byeon Gyeongrok Oh Sumin In Hyeongcheol Park Sang Ho Yoon Sung-Hee Hong Jinkyu Kim Sangpil Kim Department of Artificial Intelligence Korea UniversitySeoul 02841Republic of Korea NVIDIA Research Nvidia CorporationSanta ClaraCA 95051USA Graduate School of Culture Technology KAISTSeoul 34141Republic of Korea Hologram Research Center Korea Electronics Technology InstituteSeoul 03924Republic of Korea Department of Computer Science and Engineering Korea UniversitySeoul 02841Republic of Korea

We present a novel framework for audio-guided localized image *** often provides information about the specific context of a scene and is closely related to a certain part of the scene or ***,existing image stylization works have focused on stylizing the entire image using an image or text *** a particular part of the image based on audio input is natural but *** work proposes a framework in which a user provides an audio input to localize the target in the input image and another to locally stylize the target object or *** first produce a fine localization map using an audio-visual localization network leveraging CLIP embedding *** then utilize an implicit neural representation(INR)along with the predicted localization map to stylize the target based on sound *** INR manipulates local pixel values to be semantically consistent with the provided audio *** experiments show that the proposed framework outperforms other audio-guided stylization ***,we observe that our method constructs concise localization maps and naturally manipulates the target object or scene in accordance with the given audio input.

关键词： audio guidance image style transfer implicit neural representations(INR)

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Fully Explicit Dynamic Gaussian Splatting

arXiv

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arXiv 2024年

作者： Lee, Junoh Won, Changyeon Jung, Hyunjun Bae, Inhwan Jeon, Hae-Gon School of Electrical Engineering and Computer Science AI Graduate School Gwangju Institute of Science and Technology Korea Republic of

3D Gaussian Splatting has shown fast and high-quality rendering results in static scenes by leveraging dense 3D prior and explicit representations. Unfortunately, the benefits of the prior and representation do not involve novel view synthesis for dynamic motions. Ironically, this is because the main barrier is the reliance on them, which requires increasing training and rendering times to account for dynamic motions. In this paper, we design a Explicit 4D Gaussian Splatting(Ex4DGS). Our key idea is to firstly separate static and dynamic Gaussians during training, and to explicitly sample positions and rotations of the dynamic Gaussians at sparse timestamps. The sampled positions and rotations are then interpolated to represent both spatially and temporally continuous motions of objects in dynamic scenes as well as reducing computational cost. Additionally, we introduce a progressive training scheme and a point-backtracking technique that improves Ex4DGS’s convergence. We initially train Ex4DGS using short timestamps and progressively extend timestamps, which makes it work well with a few point clouds. The point-backtracking is used to quantify the cumulative error of each Gaussian over time, enabling the detection and removal of erroneous Gaussians in dynamic scenes. Comprehensive experiments on various scenes demonstrate the state-of-the-art rendering quality from our method, achieving fast rendering of 62 fps on a single 2080Ti GPU. © 2024, CC BY-NC-ND.

关键词： Gaussian distribution

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Deep neural networks and weighted word embeddings for sentiment analysis of drug product reviews

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Procedia computer science 2023年 216卷 664-671页

作者： Derwin Suhartono Kartika Purwandari Nicholaus Hendrik Jeremy Samuel Philip Panji Arisaputra Ivan Halim Parmonangan Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

One of the major tasks of natural language processing is sentiment analysis. The web is a source of unstructured and rich informa-tion with thousands of opinions and reviews. Individuals, businesses, and governments can all benefit from recognizing sentiment. As part of this study, we propose a deep learning-based approach for sentiment analysis on drug product review data obtained from the UCI machine learning repository. As an alternative to deep learning models, this architecture integrates glove word embedding with convolutional neural networks (CNN). Word2vec and GloVe word embedding schemes have been evaluated empirically for their predictive performance in CNN architectures. Based on a comparison of the deep learning architecture with RoBERTa, itcan be seen that BERT architecture outperforms both of them in training and validation. However, CNN models using Glove word embedding provided superior results in testing.

关键词： transformers machine learning sentiment analysis weighted word embeddings

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A Clustering-Aided Optimization Algorithm for Antenna Beamforming in Multi-Cell HAPS Systems

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IEEE Internet of Things Journal 2025年

作者： Dai, Wei Yang, Siyuan Yin, Yue Bouazizi, Mondher Ohtsuki, Tomoaki Keio University Graduate School of Science and Technology Kanagawa223-8522 Japan Keio University Faculty of Science and Technology Department of Information and Computer Science Kanagawa223-8522 Japan

High altitude platform station (HAPS) systems have emerged as a key solution to address the increasing networking demands of the Internet of Things (IoT), providing wide-area coverage, low latency, enhanced network resilience, and cost-effective service delivery, particularly in remote regions. Given that the continuous movement of HAPS and the inherent mobility of user equipments (UEs) often lead to low and unevenly distributed UE throughput, it is crucial for HAPS systems to dynamically control the antenna using beamforming techniques. However, the current reactive approaches to dynamic control fail to effectively minimize the number of low throughput UEs and achieve low time complexity. To overcome these challenges, we propose a clustering-aided particle swarm optimization (PSO) algorithm to determine the antenna parameters, enabling HAPS to configure multiple cells and dynamically control beams based on UE distribution. This algorithm leverages UE clustering information to redefine the search space, reducing the complexity while enhancing the ability to find the global optimum. Specifically, we propose a novel regulated K-means algorithm that groups UEs into appropriately balanced clusters, precisely reducing the search space for global optimization. Simulations using real-world UE distributions demonstrate that our proposed method outperforms conventional approaches in reducing low throughput UEs and providing balanced throughput distribution, while maintaining low computational complexity. © 2014 IEEE.

关键词： Particle swarm optimization (PSO)

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BERT and RoBERTa Models for Enhanced Detection of Depression in Social Media Text

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Procedia computer science 2024年 245卷 202-209页

作者： Felix Indra Kurniadi Ni Luh Putu Satyaning Pradnya Paramita Erna Fransisca Angela Sihotang Maria Susan Anggreainy Rongfang Zhang Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11530 Indonesia Statistic Department School of Computer Science Bina Nusantara University Jakarta 11530 Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta 11530 Indonesia Department of Rehabilitation and Health Service University of North Texas

Depression is a disease that affects everyone, both young and old. This mental illness not only affects the surrounding environment but everyone. Depression is characterized by deep sadness, behavioral changes and many other actions that are risky for people. In this research we try to solve the problem of detecting depression using Natural Language Processing (NLP) approaches, these two methods are Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT Approach (RoBERTa), where these two methods are used to detect posts made in reddit. The dataset is taken from Kaggle. The results obtained found that the average use of BERT and RoBERTa resulted in a high accuracy value of around 98% and with a well balanced precision, recall and F1-Score ratio. This research shows that there is a possibility of using BERT and RoBERTa in depression detection.

关键词： depression natural language processing BERT RoBERTa

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Implementation of Dynamic Image for Facial Expression Recognition on Indonesian Facial Expression Dataset

Implementation of Dynamic Image for Facial Expression Recogn...

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IEEE International Conference on Artificial Intelligence in Engineering and technology (IICAIET)

作者： Irene Anindaputri Iswanto Andry Chowanda Haryono Soeparno Widodo Budiharto Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

In recent times, there has been considerable attention directed towards Facial Expression Recognition (FER) due to its extensive utility across diverse domains. However, the universality of facial expressions has been challenged by studies suggesting that cultural backgrounds significantly influence the perception and recognition of emotions. This paper addresses the need for culturally specific datasets in FER tasks, particularly in underrepresented regions like Indonesia. The study introduces dynamic images as an alternative input representation for facial expression recognition tasks, aiming to assess their efficacy using the Indonesian Mixed Emotions Dataset (IMED). Through experimentation using EfficientNet model, the performance of dynamic images is compared with static image and video inputs. Results indicate that dynamic images exhibit promising performance, with an accuracy of 94.28%. These results outperform static image datasets and nearly match the performance of video-based models, which achieved an accuracy of 97.93 %, despite using fewer data. Nonetheless, challenges such as data imbalance and the quality of generated dynamic images persist, suggesting avenues for further research and model refinement. This study provides valuable insights into methodological advancements in FER, particularly in limited dataset conditions, laying the groundwork for future developments in dynamic image-based facial expression recognition algorithms.

关键词： Emotion recognition Image recognition Accuracy Face recognition Heuristic algorithms Data models Cultural differences Artificial intelligence

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Artificial Intelligence-Powered Intent Classification for Indonesian E-Wallet Customer Service Chatbots

Artificial Intelligence-Powered Intent Classification for In...

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Intelligent Cybernetics technology & Applications (ICICyTA), International Conference on

作者： Christopher Owen Derwin Suhartono Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

The rapid expansion of e-wallet services in Indonesia has significantly heightened the need for efficient customer service solutions, making chatbots an essential tool for user support. However, many providers continue to rely on rule-based chatbots, resulting in rigid interactions that struggle to meet diverse user needs or handle the linguistic complexities of the Indonesian language. This study addresses these limitations by developing an AI-powered intent classification model specifically tailored for Indonesian e-wallet customer service, aimed at delivering a more accurate, adaptive, and user-centered experience. A custom dataset was created from user comments on social media associated with Indonesia's top e-wallet providers, followed by data pre-processing and clustering using the BERTopic model. To improve interpretability, OpenAI's GPT-4 was employed for label refinement, resulting in enhanced clarity. Various models were tested, including IndoBERT, RoBERTa, and Convolutional Neural Network (CNN) architectures in both 2D and 3D configurations. The highest-performing model combined IndoBERT embeddings with a 3D CNN classifier, achieving an accuracy of 84.30%, precision of 84.33%, recall of 84.30%, and an F1-score of 84.24%. This study contributes a unique Indonesian-specific dataset and demonstrates the potential of AI to transform customer service interactions in Indonesia's e-wallet sector, offering a clear advancement over traditional rule-based approaches.

关键词： Adaptation models Solid modeling Three-dimensional displays Accuracy Online banking Intent recognition Customer services Transforms Chatbots Convolutional neural networks

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Data Analytics and Visualization in Bimanual Rehabilitation Monitoring Systems: A User-Centered Design Approach to Healthcare Professionals Decision Support

Data Analytics and Visualization in Bimanual Rehabilitation ...

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2024 International Conference on Electrical Engineering and Informatics, ICELTICs 2024

作者： Wisedsri, Parkpoom Anutariya, Chutiporn Sujarae, Aekavute Vachalathiti, Roongtiwa Bovonsunthonchai, Sunee School of Engineering and Technology Asian Institute of Technology Computer Science Program Pathum Thani Thailand Punsarn Asia Co. Ltd Thailand Mahidol University Faculty of Physical Therapy Nakhon Pathom Thailand

ISBN: (纸本)9798350366822

This study presents the design of a user-centered data visualization dashboard for stroke rehabilitation, which integrates the principles of visualization data analytics techniques in the field of healthcare. Addressing the complexities of visualizing for robotic bimanual rehabilitation, the design introduces novel methods to enhance data visualization, broadening monitoring capabilities, and improving the clarity of torque data. These enhancements lead to a clearer understanding of patient robot interactions. A prototype has been developed to visualize transparent torque data from a bimanual master slave robotic system, enabling healthcare professionals to monitor patient progress effectively. The dashboard features intuitive visual representations of master, slave, and assistive torques, simulating elbow joint movements through line and sunburst charts. Feedback from rehabilitation therapists during the design process ensures that the visualization approach is aligned with clinical needs. The findings suggest that the system can assist physiotherapists in devising personalized treatment strategies by providing actionable insights from the recorded data. However, additional research is needed to integrate real clinical data into the system for further enhancement and validation. This innovation has the potential to significantly improve stroke rehabilitation effectiveness and patient outcomes through a bimanual rehabilitation visualization and decision support system. © 2024 IEEE.

关键词： Clinical research

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