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2023 International Conference on computer science, information Technology and engineering, ICCoSITE 2023

作者： Heryadi, Yaya Wijanarko, Bambang Dwi Fitria Murad, Dina Tho, Cuk Hashimoto, Kiyota BINUS Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Information Systems Department Jakarta11480 Indonesia Prince of Songkla University Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Phuket83120 Thailand

ISBN: (纸本)9798350320954

In the past ten years, there has been a proliferation of unstructured textual data from a variety of fields, allowing for the analysis of the sentiment polarity of the text authors in each of those datasets. To analyze sentiment polarity from unstructured data, machine learning tasks such as aspect-based sentiment analysis are used. The public dataset of restaurant patron feedback was used as the input for this study's empirical results of aspect-based sentiment analysis utilizing the Aspect Embedding Long Short-Term Memory model. The primary experiment's results show that the aspect embedding long short-term memory model can predict aspect sentiment polarization from customer feedback to restaurant service and aspect text representation (aspect text embedding) as input with excellent performance. For example, the Aspect Embedding Long Short-term Memory model, which uses ReLU as its activation function in the Dense layer tends to achieve higher performance (0.97 average accuracies) than the same model equipped with Sigmoid, and Tanh functions which achieve 0.954 and 0.956 average training accuracy respectively. © 2023 IEEE.

关键词： Sentiment analysis

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Sharing Food with FoodLifeSavr Smartphone App 2

Sharing Food with FoodLifeSavr Smartphone App

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Andres, Arfandi Tanjaya, Edwin Setiawan Ghaniyardi Sandhi, Joshua Christian Warnars, Harco Leslie Hendric Spits Bina Nusantara University Information Systems Department School of Information Systems Jakarta11480 Indonesia Bina Nusantara University Computer Science Department Binus Graduate Program - Computer Science Jakarta11480 Indonesia

ISBN: (纸本)9781665400527

In this era, people often overeat, make parties or events, where there are excessive availability of food. As well as shops or restaurants that sell food, has ready-to-eat foods that only lasts one day and then treated as expired food. Often this precious food is thrown in the trash, and meanwhile, many destitute people cannot eat enough per day. Therefore, this research paper aims to mobilize people who overeat and donate their excess food that can still be consumed to those in need. This smartphone application is equipped with activities such as registering for new donors who want to join the mobile application and logging in for donors who are already registered in the mobile application. Donors can monitor couriers taking photos of recipients with food donated from donors. In addition, donors can select the food category for donation and input amount, track the donated food that the courier has picked up, and send it to the recipient. This mobile application is also equipped with donors to share experiences, ideas, and views based on topics created by staff and available threads. © 2022 IEEE.

关键词： Mobile computing

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Comparing the Effect of Attention Mechanism and Aspect Embedding on the Performance of Long Short-term Memory Model for Aspect-Based Sentiment Analysis

Comparing the Effect of Attention Mechanism and Aspect Embed...

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2023 International Conference on computer science, information Technology and engineering, ICCoSITE 2023

作者： Heryadi, Yaya Wijanarko, Bambang Dwi Murad, Dina Fitria Tho, Cuk Hashimoto, Kiyota BINUS Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Information Systems Department Jakarta11480 Indonesia Prince of Songkla University Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Phuket83120 Thailand

ISBN: (纸本)9798350320954

The attention mechanism is one of the key enablers which have positioned transformer models as the state-of-the-art models in Natural Language Processing. By having the attention mechanism, the first version (vanilla) transformer model can retrieve the hidden state but at the same time can maintain a low requirement on the context vector dimension by selectively choosing to which encoder hidden states to give priority attention. The objective of this study is to evaluate the effect of modified Long Short-term Memory models architecture on the model performance as a predictive model. The first model is modified by adding an attention mechanism to its architecture;whilst, the second model is modified by adding additional input to its architecture. The performance of both models is compared by assigning each model the same task: to address the aspect-based sentiment analysis task. The experiment results show that adding an attention mechanism is not significantly increase the performance of the Long Short-term Memory model in comparison to adding input to the same model. In particular, the average training accuracy of the former model is 0.885;whilst, the average training accuracy of the latter model is 0.942. © 2023 IEEE.

关键词： Sentiment analysis

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Learn from Balance: Rectifying Knowledge Transfer for Long-Tailed Scenarios

Learn from Balance: Rectifying Knowledge Transfer for Long-T...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xinlei Huang Jialiang Tang Xubin Zheng Jinjia Zhou Wenxin Yu Ning Jiang School of Computer Science and Technology Southwest University of Science and Technology School of Information Science and Technology Great Bay University School of Computer Science and Engineering Nanjing University of Science and Technology Graduate School of Science and Engineering Hosei University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Knowledge Distillation (KD) transfers knowledge from a large pre-trained teacher network to a compact and efficient student network, making it suitable for deployment on resource-limited media terminals. However, traditional KD methods require balanced data to ensure robust training, which is often unavailable in practical applications. In such scenarios, a few head categories occupy a substantial proportion of examples. This imbalance biases the trained teacher network towards the head categories, resulting in severe performance degradation on the less represented tail categories for both the teacher and student networks. In this paper, we propose a novel framework called Knowledge Rectification Distillation (KRDistill) to address the imbalanced knowledge inherited in the teacher network through the incorporation of the balanced category priors. Furthermore, we rectify the biased predictions produced by the teacher network, particularly focusing on the tail categories. Consequently, the teacher network can provide balanced and accurate knowledge to train a reliable student network. Intensive experiments conducted on various long-tailed datasets demonstrate that our KRDistill can effectively train reliable student networks in realistic scenarios of data imbalance.

关键词： Knowledge engineering Training Heavily-tailed distribution Focusing Tail Signal processing Media Reliability Speech processing Knowledge transfer

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Aspect-based Sentiment Analysis using Long Short-term Memory Model for Leveraging Restaurant Service Management

Aspect-based Sentiment Analysis using Long Short-term Memory...

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2023 International Conference on computer science, information Technology and engineering, ICCoSITE 2023

ISBN: (纸本)9798350320954

In general, the hospitality industry has been acknowledged as a major sector that gives a high contribution to economic development in many countries including Indonesia. For that reason, many initiatives have been implemented to help the growth of the hospitality industry in many countries including Indonesia to rebound from the harsh impact of the Covid-19 Pandemic. One such initiative is improving restaurant services as the main sector of the hospitality industry. This paper presents empirical results of sentiment analysis as a means to assess the quality of restaurant services as the first step to improving service quality. In particular, this study explores the aspect-based sentiment analysis method to identify some aspects of restaurant service which need improvement by learning the polarity of customers toward the restaurant services without having to meet the customers directly. By using the aspect-based sentiment analysis method, the customer sentiments comprising opinions, sentiments, evaluations, attitudes, and emotions from restaurant service can be analyzed using customers' online reviews as input. The main experiment findings showed that the Long Short-term Memory model can achieve high performance in predicting aspect polarization in restaurant service reviews. Other findings suggest that Sigmoid as an activation function achieved 0.97 average training accuracy and 0.69 average testing accuracy giving a better performance to the model in comparison to ReLU, Tanh, and ELU activation functions. © 2023 IEEE.

关键词： Sentiment analysis

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Detection of a Novel Object-Detection-Based Cheat Tool for First-Person Shooter Games Using Machine Learning 21

Detection of a Novel Object-Detection-Based Cheat Tool for F...

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21st IEEE/ACIS International Conference on Software engineering Research, Management and Applications, SERA 2023

作者： Xiao, Zhang Goto, Takaaki Ghosh, Partha Kirishima, Tadaaki Tsuchida, Kensei Toyo University Graduate School of Information Sciences and Arts Saitama Japan Toyo University Faculty of Information Sciences and Arts Saitama Japan Academy of Technology Department of Computer Science and Engineering West Bengal India

ISBN: (纸本)9798350345889

Detection of novel game cheating tools is critical for ensuring fair online play. Such cheating tools are visual-based and effectively avoid detection because they do not change the data of game software. With the development and popularity of artificial intelligence technology, it has become easier for individuals to develop cheating tools, such as a new cheating tool for first-person shooter games that searches for characters on the game screen and automatically targets them. Therefore, in this study, a new cheat detection method is proposed using machine learning. The proposed method can be used to detect new cheating tools based on object detection. © 2023 IEEE.

关键词： Machine learning

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Improved YOLOv5 based on CBAM and BiFPN for Rice Pest and Disease Detection

Improved YOLOv5 based on CBAM and BiFPN for Rice Pest and Di...

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2023 International Conference on computer, Internet of Things and Smart City, CIOTSC 2023

作者： Zhao, Yanqin Cui, Yichao Wang, Ziyi Heilongjiang University of Science and Technology Computer and Information Engineering Harbin150022 China Heilongjiang University of Science and Technology Graduate School Harbin150022 China Jiangxi Institute of Fashion Technology Fashion Design School 330201 China

ISBN: (纸本)9798350306569

Addressing the issue where traditional YOLOv5 algorithm struggles in accurately detecting pests and diseases of small to medium scale with dense leaf distribution in rice, this study introduces an enhanced YOLOv5-CW algorithm model, derived from the original YOLOv5, specifically for rice pest and disease detection. Firstly, the feature fusion is improved by introducing the improved three-layer BiFPN (Bidirectional Feature Pyramid Network) weighted bidirectional feature pyramid network BiFPN-W into the original YOLOv5 algorithm to replace the original Feature Pyramid Network (FPN) structure and Path Aggregation Network (PAN) structure in the Neck part, which can fuse more features without increasing the cost. Second, the introduction of Convolutional Block Attention Module (CBAM) attention mechanism will significantly improve the detection of small targets for the identification of small-scale pests and dis-eases in rice plants. Finally, experiments are conducted on the dataset. The experimental results showed that compared with the detection of rice pests using the traditional YOLOv5 algorithm, the precision (P) and recall (R) rates of the improved YOLOv5-CW algorithm model detection increased from 86.05% and 77.46% to 90.65% and 85.63%, respectively, and the mean Average Precision (mAP) also increased from 83.52% to 87.40%, while the detection speed reached 22.38ms, which is faster than the detection speed of the original YOLOv5 algorithm. Meanwhile, compared with target detection algorithms such as SSD, Faster R-CNN, and YOLOv4, this improved algorithm has certain superiority and is an ideal model in rice pests and diseases. © 2023 IEEE.

关键词： Attentional Mechanisms BiFPN CBAM component Rice pest and disease detection YOLOv5

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Fundamental Components of Microlearning for Sustainable Quality Education: A Systematic Literature Review 8

Fundamental Components of Microlearning for Sustainable Qual...

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8th International Conference on information Management and Technology, ICIMTech 2023

作者： Rahutomo, Reza Hiererra, Siti Elda Arifin, Yulyani Suprayogi, Muhamad Nanang Pardamean, Bens School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Humanities Psychology Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

E-learning was disrupted by a new concept of education named microlearning that carried various digital learning materials in convenient learning paths that were customizable according to users' necessity by using digital application. The competitive advantage of microlearning has been published but caused ubiquitous bias definition. This study aims to identify and analyze fundamental components of existing microlearning research and implementations in e-learning platforms. 42 research articles from SCOPUS database were collected from 2018 to 2022. They were reviewed to discover fundamental components of microlearning that must be considered in its implementations. The result of this study uncovered 'Content's variety' the most fundamental components. In contrast, this study finds that advance technology is the least discussed but required in supporting specific usage of microlearning. Limitation of this study lies on the determination of database and keyword usage and the methodology. This study recommends to consider combination of microlearning components to concept a specialized microlearning for sustainable quality education. © 2023 IEEE.

关键词： E-learning

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Potentials and Challenges in Combining the Internet of Things and Augmented Reality: a Systematic Literature Review 1

Potentials and Challenges in Combining the Internet of Thing...

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1st International Conference on Smart Technology, Applied Informatics, and engineering, APICS 2022

作者： Arin, Ikrar Adinata Meyliana Prabowo, Harjanto Hidayanto, Achmad Nizar Binus Graduate Program Computer Science Information System Department School of Information Systems Bina Nusantara University Computer Science Department Jakarta Indonesia School of Information Systems Bina Nusantara University Information System Department Jakarta Indonesia Binus Business School Undergraduate Program Bina Nusantara University Management Department Jakarta Indonesia Universitas Indonesia Faculty of Computer Science Jakarta Indonesia

ISBN: (数字)9781665489850

ISBN: (纸本)9781665489850

The line between the real world and the internet is blurred by augmented reality (AR). The internet of things (IoT) is transforming the way we communicate with the rest of the world. AR is a promising technology that is emerging as a key to unlocking the full potential of the IoT. The potential and challenges of combining the internet of things with augmented reality are investigated in this study. After a rigorous searching process, around fifty-one (51) publications were chosen and thoroughly examined to summarize the most recent developments in this field. In terms of research findings, four main clusters of potential benefits were identified: visualization sensor data, human-object interaction, application diversity, and IoT use case adoption. In contrast, the challenges posed by IoT and AR combinations were clustered around technical, organizational, and ergonomic considerations. The accumulation of knowledge and actionable insights will benefit both academics and practitioners interested in which a revolution will create by these emerging technology. © 2022 IEEE.

关键词： Augmented reality

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Overview of Versatile Video Coding (H.266/VVC) and Its Coding Performance Analysis

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IEIE Transactions on Smart Processing and Computing 2023年第2期12卷 122-154页

作者： Lee, Minhun Song, HyeonJu Park, Jeeyoon Jeon, Byeungwoo Kang, Jungwon Kim, Jae-Gon Lee, Yung-Lyul Kang, Je-Won Sim, Donggyu Department of Computer Engineering Kwangwoon University Seoul Korea Republic of Department of Computer Engineering Sejong University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Kyunggi-do Korea Republic of Communication and Media Research Laboratory Electronics and Telecommunications Research Institute Deajeon Korea Republic of School of Electronics and Information Engineering Korea Aerospace University Kyunggi-do Korea Republic of Department of Electronic and Electrical Engineering Graduate Program in Smart Factory Ewha Womans University Seoul Korea Republic of

Versatile Video Coding (H.266/VVC) is the newest video coding standard jointly developed by the Joint Video Experts Team (JVET), which is organized by the ITU-T Video Coding Experts Group (VCEG) and the ISO/IEC Moving Picture Experts Group (MPEG). H.266/VVC provides about 40% bitrate reduction compared with High Efficiency Video Coding (H.265/HEVC) for the same visual quality. This paper introduces in detail the core structure of H.266/VVC by highlighting its features within block partitioning structure, intra/inter prediction, transform, quantization, and in-loop filtering, compared to its predecessor (H.265/HEVC). H.266/VVC yields significantly improved the coding performance, but it increased the computational complexity, particularly for the encoder side, which remains a problem to be tackled for successful commercialization in the future. This paper examined the statistical performance of H.266/VVC coding tools from the bitstreams encoded by the VVC test model (VTM12.0) encoder through rate-distortion optimization under the JVET common test conditions. In addition, the complexity and performance analyses are conducted on the block partitioning structure and group of picture structure. It is expected that an optimized H.266/VVC encoder can be designed and developed by minimizing the coding loss based on the analysis data. © 2023 Institute of Electronics and information Engineers. All rights reserved.

关键词： Image compression

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