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10th IEEE Global Conference on Consumer Electronics, GCCE 2021

作者： Muchtar, Kahlil Saddami, Khairun Bintang, Akhyar Cenggoro, Tjeng Wawan Pardamean, Bens Lin, Chih-Yang Ernita, Tia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Bina Nusantara University School of Computer Science Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Yuan-Ze University Department of Electrical And Engineering Taoyuan Taiwan

ISBN: (纸本)9781665436762

In this paper, we propose a novel approach to locate and detect moving pedestrians in a video. Our proposed method first locates the region of interest (ROI) using a background subtraction algorithm based on guided filtering. This novel background subtraction algorithm allows our method to also filter unexpected noises at the same time, which could benefit the performance of our proposed method. Subsequently, the pedestrians are detected using YOLOv3 within the provided ROI. Our experiments showed that the proposed method has a competitive performance in the CDNET2014 dataset with a fast-processing time. © 2021 IEEE.

关键词： Convolutional neural networks

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Building A Face Database Of Arab Faces Toward Evaluating Bias In Facial Analysis Systems 17

Building A Face Database Of Arab Faces Toward Evaluating Bia...

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17th International Conference on Interfaces and Human computer Interaction 2023, IHCI 2023, the 17th International Conference on computer Graphics, Visualization, computer Vision and Image Processing 2023, CGVCVIP 2023 and 16th International Conference on Game and Entertainment Technologies 2023, GET 2023

作者： Khalil, Ashraf Glal, Suha Ahmed, Khider Khan, Sana Zeb Abdulgani, Aysha College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates Mechatronics Engineering Graduate Program American university of Sharjah Sharjah United Arab Emirates Department of Computer Science and Engineering American university of Sharjah Sharjah United Arab Emirates United Arab Emirates University Al Ain United Arab Emirates

ISBN: (纸本)9789898704498

Machine learning algorithms are fundamentally driven by the data provided by humans;consequently, the decisions made by those algorithms are not free from human bias. This is particularly evident in the case of facial analysis systems that employ machine learning algorithms. Recent studies have shown that the decisions made by many of the commercially available facial analysis systems are prejudiced against certain groups of race, ethnicity, age, gender and culture. Further studies have identified that the underlying reason for such biased decisions is that the open source material available for facial image databases which are used in commerce and academia to train the algorithms has meager diversity in these categories. To compound this issue, facial analysis technology is promoted by influential companies and artificial intelligence service providers without affirming the fairness and accuracy of the decisions given by these systems. To minimize bias and ensure representation of the Middle Eastern population in the imminent growth of this technology, we propose the development of two Arab face databases along with an algorithmic audit involving seven commercially available facial analysis systems. Of the databases, the first, Arab-LEANA, will include 300 Arab subjects' face images with variation in lighting, expression, accessory, nationality and age (LEANA). The second, Arab Public Figures Faces (APFF), will contain images and videos of 300 Arab public figures captured "in the wild". Faces for APFF will be selected manually from the internet since manual selection of faces will result in a high degree of variability in scale, pose, expression, illumination, age, occlusion and make-up. These databases will provide the worldwide community of face recognition researchers with a large-scale, diverse collection of Arab face images for training and evaluating algorithms toward developing a more representative, and therefore more robust, capacity for facial analysis. T

关键词： Machine learning

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DeepCat: A Deep Learning Approach to Understand Your Cat's Body Language

DeepCat: A Deep Learning Approach to Understand Your Cat's B...

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Electronics, Communications, and Computations (JAC-ECC), International Japan-Africa Conference on

作者： Arwa Fawzy Ahmed Hassan Ahmed Gad Ahmed B. Zaky Haitham El-Hussieny Computer Science and Information Technology (CSIT) program Egypt-Japan University of Science and Technology Alexandria Egypt Department of Computer Science Engineering (CSE) Egypt-Japan University of Science and Technology Alexandria Egypt Electrical Engineering Department Faculty of Engineering at Shoubra Benha University Egypt Department of Mechatronics and Robotics Engineering Egypt-Japan University of Science and Technology Alexandria Egypt

Pet owners experience difficulty in understanding their pets' body language and its implications for animal welfare, given that animals cannot utilize human speech to communicate their emotions and health conditions. However, previous experiments for analyzing cat behavior have demonstrated that cats are precisely expressive. DeepCat, a deep-learning approach developed in this study, translates cats' body language signals, enabling owners to discern their feline companions' intended messages and emotional states. Our DeepCat model was trained on a dataset comprising 10,000 cat images, implementing automatic labeling to track key features, including the tail, eyes, and mouth. Presented as a Flutter application, DeepCat can function everywhere, allowing owners to easily monitor their cats and make informed decisions in situations that require caution. This paper discusses the potential benefits and limitations of DeepCat and provides suggestions for future research in this domain.

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Multi-Lingual ESG Impact Duration Inference 7

Multi-Lingual ESG Impact Duration Inference

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Joint Workshop of the 7th Financial Technology and Natural Language Processing, 5th Knowledge Discovery from Unstructured Data in Financial Services and 4th Economics and Natural Language Processing, FinNLP-KDF-ECONLP 2024

作者： Chen, Chung-Chi Tseng, Yu-Min Kang, Juyeon Lhuissier, Anaïs Seki, Yohei Lee, Hanwool Day, Min-Yuh Tu, Teng-Tsai Chen, Hsin-Hsi AIST Japan Data Science Degree Program National Taiwan University Academia Sinica Taiwan 3DS Outscale France University of Tsukuba Japan NCSOFT Korea Republic of Graduate Institute of Information Management National Taipei University Taiwan Graduate Institute of International Business National Taipei University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan

ISBN: (纸本)9782493814197

To accurately assess the dynamic impact of a company’s activities on its Environmental, Social, and Governance (ESG) scores, we have initiated a series of shared tasks, named ML-ESG. These tasks adhere to the MSCI guidelines for annotating news articles across various languages. This paper details the third iteration of our series, ML-ESG-3, with a focus on impact duration inference—a task that poses significant challenges in estimating the enduring influence of events, even for human analysts. In ML-ESG-3, we provide datasets in five languages (Chinese, English, French, Korean, and Japanese) and share insights from our experience in compiling such subjective datasets. Additionally, this paper reviews the methodologies proposed by ML-ESG-3 participants and offers a comparative analysis of the models’ performances. Concluding the paper, we introduce the concept for the forthcoming series of shared tasks, namely multi-lingual ESG promise verification, and discuss its potential contributions to the field. © 2024 ELRA Language Resource Association.

关键词： Iterative methods

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A Numerical Method to Analyze Electromagnetic Fields on Semiconductor Nanostructures

A Numerical Method to Analyze Electromagnetic Fields on Semi...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio science Meeting, AP-S/URSI 2022

作者： Uulu, Doolos Aibek Chen, Rui Chen, Liang Li, Ping Bagci, Hakan Computer Electrical and Mathematical Science and Engineering Division Electrical and Computer Engineering Program Thuwal23955-6900 Saudi Arabia Shanghai Jiao Tong University Key Lab. of Min. of Educ. of Des. and Electromagnetic Compatibility of High-Speed Electronic Systems Shanghai200240 China

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

A coupled system of volume integral and two-fluid hydrodynamic equations is solved to analyze electromagnetic field interactions with non-local dispersion effects on semiconductor nanostructures. This coupled system, where the hydrodynamic currents and the electric flux are the unknowns, is discretized using Schaubert-Wilton-Glisson basis functions. Numerical results obtained using this solver show the presence of so-called acoustic modes at the frequencies lower than the resonance frequency of localized surface plasmon (LSP) mode. These modes cannot be identified if a Drude-like model or the single-fluid hydrodynamic equation is used to represent the electrical properties of the semiconductors. © 2022 IEEE.

关键词： Electromagnetic fields

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3D Geometric Shape Assembly via Efficient Point Cloud Matching

arXiv

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arXiv 2024年

作者： Lee, Nahyuk Min, Juhong Lee, Junha Kim, Seungwook Lee, Kanghee Park, Jaesik Cho, Minsu Department of Computer Science and Engineering POSTECH Pohang Korea Republic of Graduate School of Artificial Intelligence POSTECH Pohang Korea Republic of Department of Computer Science and Engineering Seoul National University Seoul Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of

Learning to assemble geometric shapes into a larger target structure is a pivotal task in various practical applications. In this work, we tackle this problem by establishing local correspondences between point clouds of part shapes in both coarse- and fine-levels. To this end, we introduce Proxy Match Transform (PMT), an approximate high-order feature transform layer that enables reliable matching between mating surfaces of parts while incurring low costs in memory and computation. Building upon PMT, we introduce a new framework, dubbed Proxy Match TransformeR (PMTR), for the geometric assembly task. We evaluate the proposed PMTR on the large-scale 3D geometric shape assembly benchmark dataset of Breaking Bad and demonstrate its superior performance and efficiency compared to state-of-the-art methods. Project page: https://***/pmtr. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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Interconnected Through Robotic System in English Foreign Language Education 5th

Interconnected Through Robotic System in English Foreign Lan...

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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Muthmainnah, M. de Barros Cardoso, Luís Miguel Oliveira Kaur, Dalwinder Burhamzah, Muftihaturrahmah Aeni, Nur Al Yakin, Ahmad Sharma, Aditi Teacher Training and Education Faculty Indonesian Language Study Program Universitas Al Asyariah Mandar Polewali Mandar Indonesia Portugal Department of Languages and Communication Sciences Polytechnic Institute of Portalegre Portalegre Portugal Manipal Globalnxt University Kuala Lumpur Malaysia English Study Program Universitas Negeri Makassar Makassar Indonesia Teacher Training and Education Faculty Civic Education Study Program Universitas Al Asyariah Mandar Polewali Mandar Indonesia Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International [Deemed University Pune India

ISBN: (纸本)9789819761050

Robots have become increasingly important in a variety of life domains as a result of the rapid advancement of technology. Additionally, robots are employed in education as teaching assistants in science, art, and language courses. This research aims to explore the potential of robotic systems as a multipurpose tool for increasing cultural awareness, language proficiency, and communicative competence in the GenAI era. Language games, conversation simulations, and real-time feedback mechanisms are just a few examples of interactive scenarios enabled by virtual robot systems or tutor robots that enable immersive language experiences that go beyond the limitations of conventional schooling. This research contributes knowledge that explores how AI can be used to personalize language teaching according to the needs and learning styles of each student in higher education. The total population of undergraduate students involved in this research was 350 for the 2023 academic year, with 24 students selected purposefully based on sample characteristics from this group who were exposed to using AI for one semester. The research method used is a mix method with quantitative analysis of the results of surveys and interviews with students who are known to have given positive responses and proposes a comprehensive strategy for using AI robot technology in EFL classrooms, which can significantly improve effective learning outcomes. The survey results also show that if educators have a solid understanding of the relationship between linguistic acquisition and technological advances, they will be better prepared to design engaging, individualized lessons that prepare their students to use the language effectively in real-world contexts. The robot-assisted language learning (RALL) approach used in this study, with the integration of robots in the classroom, has the potential to change the way people learn languages to make it more interesting, fun, and motivating, increase engagement

关键词： Students

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Uncertainty Quantification Using Subsampling for Assembly-Free Estimates of Genomic Distance and Phylogenetic Relationships 26th

Uncertainty Quantification Using Subsampling for Assembly-F...

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26th International Conference on Research in Computational Molecular Biology, RECOMB 2022

作者： Rachtman, Eleonora Sarmashghi, Shahab Bafna, Vineet Mirarab, Siavash Bioinformatics and Systems Biology Graduate Program UC San Diego La JollaCA United States Department of Electrical and Computer Engineering UC San Diego La JollaCA United States Department of Computer Science and Engineering UC San Diego La JollaCA United States

Computing the distance between two genomes, often without using alignments or even access to assembled sequences, is fundamental to many downstream analyses. © 2022, The Author(s), under exclusive license to Spri... 详细信息

ISBN: (纸本)9783031047480

关键词： Uncertainty analysis

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Modified Isolated SEPIC Converter for EV Charging and High Voltage Applications

Modified Isolated SEPIC Converter for EV Charging and High V...

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Power, Instrumentation, Energy and Control (PIECON), International Conference on

作者： Javed Ahmad Chang-Hua Lin Mohammad Suhail Khan Hwa-Dong Liu Mohammad Fahad Department of Electrical Engineering College of Electrical Engineering and Computer Science National Taiwan University of Science and Technology Taipei Taiwan Undergraduate Program of Vehicle and Energy Engineering National Taiwan Normal University Taipei Taiwan

This research article presents a modified isolated SEPIC converter suitable for high-power applications. It uses a transformer instead of coupled inductor for isolation between the input power source and the output load. The steady-state analysis is explained in detail. Thermal loss analysis is performed by using PLECS software. Further, a hardware prototype is developed to verify the performance aspects of the proposed converter.

关键词： Instruments Prototypes High-voltage techniques Transformers Software Hardware Electric vehicle charging

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Slice-Fusion: Reducing False Positives in Liver Tumor Detection for Mask R-CNN

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2023年第5期20卷 3267-3277页

作者： Tu, Deng-Yao Lin, Peng-Chan Chou, Hsin-Hung Shen, Meng-Ru Hsieh, Sun-Yuan National Cheng Kung University Master Degree Program on Artificial Intelligence Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Informatics Department of Oncology Department of Genomic Medicine National Cheng Kung University Hospital College of Medicine Department of Computer Science and Information Engineering Tainan City70101 Taiwan National Chi Nan University Department of Computer Science and Information Engineering Nantou County54561 Taiwan National Cheng Kung University Graduate Institute of Clinical Medicine Department of Obstetrics and Gynecology Department of Pharmacology National Cheng Kung University Hospital College of Medicine Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Information Institute of Manufacturing Information and Systems Center for Innovative FinTech Business Models International Center for the Scientific Development of Shrimp Aquaculture Department of Computer Science and Information Engineering Tainan City70101 Taiwan

Automatic liver tumor detection from computed tomography (CT) makes clinical examinations more accurate. However, deep learning-based detection algorithms are characterized by high sensitivity and low precision, which hinders diagnosis given that false-positive tumors must first be identified and excluded. These false positives arise because detection models incorrectly identify partial volume artifacts as lesions, which in turn stems from the inability to learn the perihepatic structure from a global perspective. To overcome this limitation, we propose a novel slice-fusion method in which mining the global structural relationship between the tissues in the target CT slices and fusing the features of adjacent slices according to the importance of the tissues. Furthermore, we design a new network based on our slice-fusion method and Mask R-CNN detection model, called Pinpoint-Net. We evaluated proposed model on the Liver Tumor Segmentation Challenge (LiTS) dataset and our liver metastases dataset. Experiments demonstrated that our slice-fusion method not only enhance tumor detection ability via reducing the number of false-positive tumors smaller than 10mm, but also improve segmentation performance. Without bells and whistles, a single Pinpoint-Net showed outstanding performance in liver tumor detection and segmentation on LiTS test dataset compared with other state-of-the-art models. © 2004-2012 IEEE.

关键词： Tumors

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