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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Gusti Pangestu Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program Computer Science. 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 Cognitive Engineering Research (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Sign language has importance rule to deal with communication process especially with impairments hearing people. Sign language detection also attract lot of researchers to join the challenge of research to detect and recognize the sign language in the field of computer science. Hence, there is still no any standard approach and method to recognize the meaning in every pose of sign language. This research proposed a mechanism to detect Alphabet American Sign Language by utilizing Convolutional Neural Network (CNN) process. The CNN approach was chosen based on the ability and capability to recognize image. In this research, MNIST dataset is used for traning and testing process. The proposed CNN architecture produced 97% of accuracy that outperform the previous research using the same dataset which made this architecture promising.

关键词： Deep learning computer science Image recognition Gesture recognition computer architecture Auditory system Assistive technologies

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Sentiment Analysis of User Review on Indonesian Food and Beverage Group using Machine Learning Techniques

Sentiment Analysis of User Review on Indonesian Food and Bev...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Yanfi Yanfi Yaya Heryadi Lukas Lukas Wayan Suparta Yulyani Arifin 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

A vast number of technologies based on Artificial Intelligence (AI) have proliferated into various application domains. As part of its objectives to develop agents which can behave and think like humans, some branches of AI have focused on developing various algorithms that can learn from input data without explicitly being programmed. In the food industry, notably in restaurants, smartphone technology is typically used to collect data from customers efficiently such as orders or feedback toward the services provided. The use of smartphones by some restaurants is motivated by their convenience and efficiency. Furthermore, various promotions, discounts, and membership benefits offered by cellular service providers have attracted the use of smartphones as devices for improving restaurant services to their customers. This study aimed to conduct a sentiment analysis related to multi-brand restaurants in Indonesia based on customer feedback. The data retrieved from the Google Play Store consists of 756 reviews from Boga, Champs, Hangry, Ismaya, Kulo, and Union groups. In this study, TF-IDF was used as a vectorizer to represent customer feedback as numeric vectors. The polarity sentiment of customer feedback was recognized using classifier models based on machine learning such as Logistic Regression, Naive Bayes (NB), Random Forest, and Support Vector Machine (SVM) as the classifier. The empirical results showed that SVM has the best accuracy of 93.10 % average accuracy followed by Logistic Regression with 92.41 %, Random Forest with 84.14 % average accuracy, and Naive Bayes with 76.55 % average accuracy.

关键词： Support vector machines Sentiment analysis Analytical models Machine learning algorithms Food industry Companies Classification algorithms

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Multidimensional clinical data denoising via Bayesian CP factorization

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science China(Technological sciences) 2020年第2期63卷 249-254页

作者： CUI GaoChao ZHU Li GUI LiHua ZHAO QiBin ZHANG JianHai CAO JianTing Graduate School of Engineering Saitama Institute of TechnologySaitama 3690293Japan School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Cognitive Science Department School of Information Science and EngineeringXiamen UniversityXiamen 361005China Tensor Learning Unit Center for Advanced Intelligence ProjectRIKENSaitama 1030027Japan School of Automation Guangdong University of TechnologyGuangzhou 510006China

CANDECOMP/PARAFAC(CP) tensor factorization is an efficient technique for incomplete tensor-data processing through capturing the multilinear latent factors. Based on the incorporate a sparsity-inducing prior over multiple latent factors and appropriate hyper-priors over all hyper-parameters, a Bayesian-based hierarchical probabilistic CP factorization model could be formed. By this way, the rank of the incomplete tensor can be determined automatically. In this paper, we explored the tensor completion method in processing incomplete multidimensional electroencephalogram(EEG) and magnetic resonance imaging(MRI) clinical data. The empirical results indicated that the Bayesian CP tensor factorization of incomplete data method can effectively recover EEG signal with missing data and denoised the noisy MRI data.

关键词： electroencephalogram(EEG) magnetic resonance imaging(MRI) Bayesian tensor factorization CANDECOMP/PARAFAC(CP)

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Social Media Analysis using Sentiment Analysis on COVID-19 from Twitter

Social Media Analysis using Sentiment Analysis on COVID-19 f...

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International Conference on Information Technology, Information Systems and Electrical engineering (ICITISEE)

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

The development of the internet is getting faster, participating in encouraging the emergence of new and innovative information. In filtering the various information that appears, we need a recommended system to perform well for users in today's internet era. A well-performing recommendation system in question is a reliable recommendation algorithm. This algorithm is fundamental to analyzing various information, such as responses on social media based on user behavior data related to the topic of COVID. This data is crawled from tweets on social media Twitter. The data analysis algorithm obtained uses Python, which is then visualized in the form of a diagram. The processed data is user comments on Twitter, and the text data is analyzed using Python, using more than 60000 data sets taken to form visualizations and conclusions. From sentiment analysis, polarity and subjectivity data are obtained to be analyzed, which are negative, neutral, or positive. The result is show positive tweets with 29.2%, negative tweets is 13%, and 57.8% neutral tweets. Lastly, sentiment analysis can help people effectively infer vast and complex data from social media like Twitter.

关键词： COVID-19 Sentiment analysis Analytical models Data analysis Social networking (online) Blogs Data visualization

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Tracing the emergence of gendered language in childhood 42

Tracing the emergence of gendered language in childhood

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42nd Annual Meeting of the cognitive science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020

作者： Prystawski, Ben Grant, Erin Nematzadeh, Aida Lee, Spike W.S. Stevenson, Suzanne Xu, Yang Computer Science and Cognitive Science Programs University of Toronto Canada Department of Electrical Engineering & Computer Sciences University of California Berkeley United States DeepMind Rotman School of Management Department of Psychology University of Toronto Canada Department of Computer Science University of Toronto Canada Department of Computer Science Cognitive Science Program University of Toronto Canada

Are gender associations in general language reflected in the words spoken to and by children? Previous work has suggested that language reveals gender differences in discourse, speech style, language use and acquisition. Work in artificial intelligence has shown that word embeddings trained on large corpora reflect human gender associations. We connect this work to developmental psychology by exploring whether gender associations in word embeddings are present in the linguistic input and output of children, and if so, how early gendered language emerges. We present a computational method that quantifies the gender associations of words and use a corpus of child-caretaker speech to show that these gender associations correlate significantly with those in word embeddings. We discover that gendered word use emerges in English-speaking children around age 2, and the gender associations cannot be explained solely by variables including word length, frequency, concreteness, and valence. © 2020 The Author(s)

关键词： Embeddings

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Unleashing the Power of CNN and Transformer for Balanced RGB-Event Video Recognition

arXiv

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arXiv 2023年

作者： Wang, Xiao Rong, Yao Wang, Shiao Chen, Yuan Wu, Zhe Jiang, Bo Tian, Yonghong Tang, Jin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei230601 China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University Hefei230601 China School of Computer Science and Technology Anhui University Hefei230601 China The School of Internet Anhui University Hefei230601 China The Peng Cheng Laboratory Shenzhen China The Peng Cheng Laboratory China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China

Pattern recognition based on RGB-Event data is a newly arising research topic and previous works usually learn their features using CNN or Transformer. As we know, CNN captures the local features well and the cascaded self-attention mechanisms are good at extracting the long-range global relations. It is intuitive to combine them for high-performance RGB-Event based video recognition, however, existing works fail to achieve a good balance between the accuracy and model parameters, as shown in Fig. 1. In this work, we propose a novel RGB-Event based recognition framework termed TSCFormer, which is a relatively lightweight CNN-Transformer model. Specifically, we mainly adopt the CNN as the backbone network to first encode both RGB and Event data. Meanwhile, we initialize global tokens as the input and fuse them with RGB and Event features using the BridgeFormer module. It captures the global long-range relations well between both modalities and maintains the simplicity of the whole model architecture at the same time. The enhanced features will be projected and fused into the RGB and Event CNN blocks, respectively, in an interactive manner using F2E and F2V modules. Similar operations are conducted for other CNN blocks to achieve adaptive fusion and local-global feature enhancement under different resolutions. Finally, we concatenate these three features and feed them into the classification head for pattern recognition. Extensive experiments on two large-scale RGB-Event benchmark datasets (PokerEvent and HARDVS) fully validated the effectiveness of our proposed TSCFormer. The source code and pre-trained models will be released at https://***/Event-AHU/TSCFormer Copyright © 2023, The Authors. All rights reserved.

关键词： Large dataset

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Relationship Between VPD, Transpiration Rate and Vapor Conductance for Plants of Different Growth Levels

Relationship Between VPD, Transpiration Rate and Vapor Condu...

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SICE Annual Conference

作者： Renta NOGUCHI Asahi SAKUMA Yuko KAKAZU Hiroshi IWATA Yosuke ASANO Yuichi ITOH Achyut Sapkota Atsushi SHIRAKI Ikusaburo KURIMOTO Dept. of Applied and Cognitive Infomatics Graduate School of Science and Engineering Chiba University Chiba Japan Dept. of Civil Engineering National Institute of Technology Kisarazu College Chiba Japan Dept. of Natural Science National Institute of Technology Kisarazu College Chiba Japan Dept. of Information Engineering National Institute of Technology Nara College Nara Japan Dept. of Electrical and Electronic Engineering National Institute of Technology Kisarazu College Chiba Japan Dept. of Mechanical Engineering National Institute of Technology Kisarazu College Chiba Japan Dept. of Information and Computer Engineering National Institute of Technology Kisarazu College Chiba Japan Graduate School of Engineering Chiba University Chiba Japan

ISBN: (数字)9784907764838

ISBN: (纸本)9798331544461

To obtain a stable supply of high-quality agricultural products, controlling the environment properly, such as temperature, humidity, and light, is necessary. Plant factories are attracting attention as a means to realize such an environment. In our laboratory, we have constructed and demonstrated the temperature and VPD control to control the spray pressure of fine mist cooling. However, the relationship between plants and water needs to be clarified to adapt to different regions, changing weather conditions, cultivars and plans, and quality objectives. Therefore, in this study, we developed an IoT system that can measure VPD and the amount of weight change due to transpiration in a plant canopy. Leaf area was measured using 3D model construction, and transpiration rate and vapor conductance were derived. As a result, the relationship between VPD and stomata was partially clarified.

关键词： Weight measurement Temperature measurement Transpiration Solid modeling Temperature Three-dimensional displays Area measurement Inspection Mathematical models Time measurement

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Indoor Smartphone SLAM with Learned Echoic Location Features

arXiv

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arXiv 2022年

作者： Luo, Wenjie Song, Qun Yan, Zhenyu Tan, Rui Lin, Guosheng Singtel Cognitive and AI Lab for Enterprises Nanyang Technological University Singapore ERI@N Interdisciplinary Graduate School Nanyang Technological University Singapore Department of Information Engineering The Chinese University of Hong Kong Hong Kong School of Computer Science and Engineering Nanyang Technological University Singapore

Indoor self-localization is a highly demanded system function for smartphones. The current solutions based on inertial, radio frequency, and geomagnetic sensing may have degraded performance when their limiting factors take effect. In this paper, we present a new indoor simultaneous localization and mapping (SLAM) system that utilizes the smartphone’s built-in audio hardware and inertial measurement unit (IMU). Our system uses a smartphone’s loudspeaker to emit near-inaudible chirps and then the microphone to record the acoustic echoes from the indoor environment. Our profiling measurements show that the echoes carry location information with sub-meter granularity. To enable SLAM, we apply contrastive learning to construct an echoic location feature (ELF) extractor, such that the loop closures on the smartphone’s trajectory can be accurately detected from the associated ELF trace. The detection results effectively regulate the IMU-based trajectory reconstruction. Extensive experiments show that our ELF-based SLAM achieves median localization errors of 0.1 m, 0.53 m, and 0.4 m on the reconstructed trajectories in a living room, an office, and a shopping mall, and outperforms the Wi-Fi and geomagnetic SLAM systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Smartphones

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High-Frequency Stereo Matching Network

High-Frequency Stereo Matching Network

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Haoliang Zhao Huizhou Zhou Yongjun Zhang Jie Chen Yitong Yang Yong Zhao Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry State Key Laboratory of Public Big Data College of Computer Science and Technology Institute of Artificial Intelligence Guizhou University Guiyang Guizhou China Ghost-Valley AI Technology Shenzhen Guangdong China School of Physics and Optoelectronic Engineering Guangdong University of Technology Guangzhou China The Key Laboratory of Integrated Microsystems Shenzhen Graduate School Peking University China

In the field of binocular stereo matching, remarkable progress has been made by iterative methods like RAFT-Stereo and CREStereo. However, most of these methods lose information during the iterative process, making it difficult to generate more detailed difference maps that take full advantage of high-frequency information. We propose the Decouple module to alleviate the problem of data coupling and allow features containing subtle details to transfer across the iterations which proves to alleviate the problem significantly in the ablations. To further capture high-frequency details, we propose a Normalization Refinement module that unifies the disparities as a proportion of the disparities over the width of the image, which address the problem of module failure in cross-domain scenarios. Further, with the above improvements, the ResNet-like feature extractor that has not been changed for years becomes a bottleneck. Towards this end, we proposed a multi-scale and multi-stage feature extractor that introduces the channel-wise self-attention mechanism which greatly addresses this bottleneck. Our method (DLNR) ranks 1st on the Middlebury leaderboard, significantly outperforming the next best method by 13.04%. Our method also achieves SOTA performance on the KITTI-2015 benchmark for D1-fg. Code and demos are available at: https://***/David-Zhao-1997/High-frequency-Stereo-Matching-Network.

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PhyAug: Physics-directed data augmentation for deep sensing model transfer in cyber-physical systems

arXiv

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arXiv 2021年

作者： Luo, Wenjie Song, Qun Yan, Zhenyu Tan, Rui Singtel Cognitive and AI Lab for Enterprises Nanyang Technological University Singapore ERI@N Interdisciplinary Graduate School Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

Run-time domain shifts from training-phase domains are common in sensing systems designed with deep learning. The shifts can be caused by sensor characteristic variations and/or discrepancies between the design-phase model and the actual model of the sensed physical process. To address these issues, existing transfer learning techniques require substantial target-domain data and thus incur high post-deployment overhead. This paper proposes to exploit the first principle governing the domain shift to reduce the demand on target-domain data. Specifically, our proposed approach called PhyAug uses the first principle fitted with few labeled or unlabeled source/target-domain data pairs to transform the existing source-domain training data into augmented data for updating the deep neural networks. In two case studies of keyword spotting and DeepSpeech2-based automatic speech recognition, with 5-second unlabeled data collected from the target microphones, PhyAug recovers the recognition accuracy losses due to microphone characteristic variations by 37% to 72%. In a case study of seismic source localization with TDoA fingerprints, by exploiting the first principle of signal propagation in uneven media, PhyAug only requires 3% to 8% of labeled TDoA measurements required by the vanilla fingerprinting approach in achieving the same localization accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Embedded systems

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