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arXiv 2023年

作者： Yang, Zuhao Xu, Yang Yuan, Yisheng Bai, Huajun Pang, Wei Hu, Yeqi Luo, Zhang Wang, Yue Yuan, Yingfang School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computer Science San Diego State University United States Graphic Design Department Rhode Island School of Design United States Seafog AI China School of Mathematical and Computer Sciences Heriot-Watt University United Kingdom College of Information Science and Engineering Ocean University of China China Graduate School of Advanced Integrated Studies in Human Survivability Kyoto University Japan

AI-Generated Content (AIGC) has recently gained a surge in popularity, powered by its high efficiency and consistency in production, and its capability of being customized and diversified. The cross-modality nature of the representation learning mechanism in most AIGC technology allows for more freedom and flexibility in exploring new types of art that would be impossible in the past. Inspired by the pictogram subset of Chinese characters, we proposed PaCaNet, a CycleGAN-based pipeline for producing novel artworks that fuse two different art types, traditional Chinese painting and calligraphy. In an effort to produce stable and diversified output, we adopted three main technical innovations: (1) Using one-shot learning to increase the creativity of pre-trained models and diversify the content of the fused images. (2) Controlling the preference over generated Chinese calligraphy by freezing randomly sampled parameters in pre-trained models. (3) Using a regularization method to encourage the models to produce images similar to Chinese paintings. Furthermore, we conducted a systematic study to explore the performance of PaCaNet in diversifying fused Chinese painting and calligraphy, which showed satisfying results. In conclusion, we provide a new direction of creating arts by fusing the visual information in paintings and the stroke features in Chinese calligraphy. Our approach creates a unique aesthetic experience rooted in the origination of Chinese hieroglyph characters. It is also a unique opportunity to delve deeper into traditional artwork and, in doing so, to create a meaningful impact on preserving and revitalizing traditional heritage. © 2023, CC BY-NC-SA.

关键词： Transfer learning

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Ensemble-learning error mitigation for variational quantum shallow-circuit classifiers

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Physical Review Research 2024年第1期6卷 013027-013027页

作者： Qingyu Li Yuhan Huang Xiaokai Hou Ying Li Xiaoting Wang Abolfazl Bayat Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu 611731 China The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology 999077 Hong Kong China Graduate School of China Academy of Engineering Physics Beijing 100193 China Key Laboratory of Quantum Physics and Photonic Quantum Information Ministry of Education University of Electronic Science and Technology of China Chengdu 611731 China

Classification is one of the main applications of supervised learning. Recent advancements in developing quantum computers have opened a new possibility for machine learning on such machines. Due to the noisy performance of near-term quantum computers, error mitigation techniques are essential for extracting meaningful data from noisy raw experimental measurements. Here, we propose two ensemble-learning error mitigation methods, namely, bootstrap aggregating and adaptive boosting, which can significantly enhance the performance of variational quantum classifiers for both classical and quantum datasets. The idea is to combine several weak classifiers, each implemented on a shallow noisy quantum circuit, to make a strong one with high accuracy. While both of our protocols substantially outperform error-mitigated primitive classifiers, the adaptive boosting shows better performance than the bootstrap aggregating. The protocols have been exemplified for classical handwriting digits as well as quantum phase discrimination of a symmetry-protected topological Hamiltonian, in which we observe a significant improvement in accuracy. Our ensemble-learning methods provide a systematic way of utilizing shallow circuits to solve complex classification problems.

关键词： Machine learning Quantum algorithms Quantum computation

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Color Aesthetics: Fuzzy based User-driven Method for Harmony and Preference Prediction

arXiv

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arXiv 2023年

作者： Shamoi, Pakizar Inoue, Atsushi Kawanaka, Hiroharu Department of Information Systems Management Kazakh-British Technical University Almaty Kazakhstan Department of Computer Science Eastern Washington University Washington United States Graduate School of Engineering Mie University Tsu Japan

Color is the most important intrinsic sensory feature that has a powerful impact on product sales. Color is even responsible for raising the aesthetic senses in our brains. Account for individual differences is crucial in color aesthetics. It requires user-driven mechanisms for various e-commerce applications. We propose a method for quantitative evaluation of all types of perceptual responses to color(s): distinct color preference, color harmony, and color combination preference. Preference for color schemes can be predicted combining preferences for the basic colors and ratings of color harmony. Harmonious pallets are extracted from big data set using comparison algorithms based on fuzzy similarity and *** proposed model results in useful predictions of harmony and preference of multicolored images. For example, in the context of apparel coordination, it allows predicting a preference for a look based on clothing colors. Our approach differs from standard aesthetic models, since in accounts for a personal variation. In addition, it can process not only lower-order color pairs, but also groups of several colors. © 2023, CC BY.

关键词： Color

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On the Security Properties of Combinatorial All-or-nothing Transforms

On the Security Properties of Combinatorial All-or-nothing T...

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IEEE International Symposium on information Theory

作者： Yujie Gu Sonata Akao Navid Nasr Esfahani Ying Miao Kouichi Sakurai Graduate School and Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan David R. Cheriton School of Computer Science University of Waterloo Waterloo Canada Faculty of Engineering Information and Systems University of Tsukuba Tsukuba Ibaraki Japan

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

All-or-nothing transforms (AONT) were proposed by Rivest as a message preprocessing technique for encrypting data to protect against brute-force attacks, and have many applications in cryptography and information security. Later the unconditionally secure AONT and their combinatorial characterization were introduced by Stinson. Informally, a combinatorial AONT is an array with the unbiased requirements and its security properties in general depend on the prior probability distribution on the inputs s-tuples. Recently, it was shown by Esfahani and Stinson that a combinatorial AONT has perfect security provided that all the inputs s-tuples are equiprobable, and has weak security provided that all the inputs s-tuples are with non-zero probability. This paper aims to explore on the gap between perfect security and weak security for combinatorial (t, s, v)-AONTs. Concretely, we consider the typical scenario that all the s inputs take values independently (but not necessarily identically) and quantify the amount of information $H(\mathcal{X}\mid \mathcal{Y})$ about any t inputs $\mathcal{X}$ that is not revealed by any s−t outputs $\mathcal{Y}$. In particular, we establish the general lower and upper bounds on $H(\mathcal{X}\mid \mathcal{Y})$ for combinatorial AONTs using information-theoretic techniques, and also show that the derived bounds can be attained in certain cases.

关键词： Upper bound information security Transforms Probability distribution Entropy Security Cryptography

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Mapping of Smart Economy Research Themes: A Nine-Year Review 8

Mapping of Smart Economy Research Themes: A Nine-Year Review

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8th International Conference on ICT for Smart Society, ICISS 2021

作者： Purnomo, Agung Dian Sano, Albert Verasius Nindito, Hendro Madyatmadja, Evaristus Didik Sianipar, Corinthias P. M. Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Graduate School of Global Environmental Studies Kyoto University Department of Global Ecology Kyoto606-8502 Japan

ISBN: (纸本)9781665416979

Research on the smart economy as a part of a smart city continues to develop but is limited to one country and/or one field. From a bibliometric review, this study aims to visually study mapping and research trends in the field of the smart economy on an international scale. This study used bibliometric techniques with secondary data from Scopus. Analyze and visualize data using the VOSViewer program and the analyze search results function on Scopus. This study analyzed 125 scientific documents published from 2011 to 2020. According to the research, the Peter the Great St. Petersburg Polytechnic University, Russia, and Mboup, G. had the most active individual scientists and in smart economy research. computer science and Advances in 21st Century Human Settlements were the most studied and disseminated outlet of smart economy research. There was one category map of collaborative researchers from around the world. Based on the identification of a collection of knowledge generated from nine years of publication, this research proposes a grouping of smart economy research themes: Economy, Application of smart cities, Smart technology, Smart cities character, Economy of urban, Education and Smart environment, abbreviated as EASSEES research themes. © 2021 IEEE.

关键词： Mapping

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A New Study of Integration Between Social Robotic Systems and the Metaverse for Dealing with Healthcare in the Post-COVID-19 Situations 14th

A New Study of Integration Between Social Robotic Systems an...

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14th International Conference on Social Robotics, ICSR 2022

作者： Kerdvibulvech, Chutisant Chang, Chin-Chen Graduate School of Communication Arts and Management Innovation National Institute of Development Administration 148 SeriThai Road Klong-Chan Bangkapi Bangkok10240 Thailand Department of Information Engineering and Computer Science Feng Chia University Xitun District No. 100 Wenhua Road Taichung City40712 Taiwan

ISBN: (纸本)9783031246692

With the popularity of the metaverse, researchers are turning to augmented reality and virtual reality to innovate their recent pain points, particularly healthcare issues during COVID-19. At the same time, social robots can be a great tool for alleviating many challenges during the pandemic. However, before the integrated technology’s possibilities for the metaverse and social robots can be suitably harnessed, certain recent developments for integration during the pandemic should be addressed. For this reason, this paper proposes a new systematic summary of pioneering social robotic systems using the metaverse through immersive experiences from an interdisciplinary healthcare perspective during the COVID-19 outbreak. We also highlight social robots to deal with medical healthcare issues during the virus outbreak both elderly adults and younger people. Moreover, we compare recent metaverse-driven social robotic works for dealing with assisted living and healthcare issues through telepresence and remote interaction during COVID-19. Ultimately, we provide a recommendation and forecast a future scenario for the integration between socially interactive robots and metaverse technology to improve and help the quality of life both in the current COVID-19 situation and in the post-COVID-19 society. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

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Improvement of Jakarta's air quality during large scale social restriction 2

Improvement of Jakarta's air quality during large scale soci...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Rahutomo, R. Purwandari, K. Sigalingging, J.W.C. Pardamean, B. Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta10720 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

After Large-Scale Social Restriction (PSBB) established in Jakarta, a change of air quality was indicated by the citizens. Representatives of Indonesia's Agency for Meteorology, Climatology, and Geophysics (BMKG) supported as the fact that mobility of Jakarta's citizen and commuters were reduced. With utilizing Weather Data Acquisition System managed by BMKG, this research compared fluctuations of Jakarta's air quality during two periods of time: Pre-PSBB and During-PSBB. This study proved that Jakarta's air quality has positively improved compared to the previous year, although in the lower range of unhealthy level. © Published under licence by IOP Publishing Ltd.

关键词： Air quality

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Learning Background Prompts to Discover Implicit Knowledge for Open Vocabulary Object Detection

Learning Background Prompts to Discover Implicit Knowledge f...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Jiaming Li Jiacheng Zhang Jichang Li Ge Li Si Liu Liang Lin Guanbin Li School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computer Science The University of Hong Kong Hong Kong SECE Shenzhen Graduate School Peking University Shenzhen China Institute of Artificial Intelligence Beihang University China GuangDong Province Key Laboratory of Information Security Technology Research Institute Sun Yat-sen University Shenzhen China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Open vocabulary object detection (OVD) aims at seeking an optimal object detector capable of recognizing objects from both base and novel categories. Recent advances leverage knowledge distillation to transfer insightful knowledge from pre-trained large-scale vision-language models to the task of object detection, significantly generalizing the powerful capabilities of the detector to identify more unknown object categories. However, these methods face significant challenges in background interpretation and model overfitting and thus often result in the loss of crucial back-ground knowledge, giving rise to sub-optimal inference performance of the detector. To mitigate these issues, we present a novel OVD framework termed LBP to propose learning background prompts to harness explored implicit background knowledge, thus enhancing the detection performance w.r.t. base and novel categories. Specifically, we devise three modules: Background Category-specific Prompt, Background Object Discovery, and Inference Probability Rectification, to empower the detector to discover, represent, and leverage implicit object knowledge explored from background proposals. Evaluation on two benchmark datasets, OV-COCO and OV-LVIS, demonstrates the superiority of our proposed method over existing state-of-the-art approaches in handling the OVD tasks.

关键词： Vocabulary computer vision Face recognition Detectors Object detection Benchmark testing Proposals

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Gallium Oxide as Photonic Integrated Platforms in the UV-Visible Spectra

Gallium Oxide as Photonic Integrated Platforms in the UV-Vis...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Zhou, Jingan Zhao, Xuan Xu, Rui Zhang, Xiang Fu, Kai Li, Tao Xu, Mingfei Luo, Shisong He, Ziyi Robinson, Jacob T. Zhu, Hanyu Ajayan, Pulickel M. Zhang, Xiaodong Zhao, Yuji Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Nanofabrication facility Suzhou Institute of Nano-Tech and Nano-Bionics Chinese Academy of Sciences Jiangsu Suzhou215123 China

We report the Ga2O3 as photonic integrated platforms and its nonlinear optical effects in the UV-visible spectra. A low propagation loss of 3.7 dB/cm and second-order susceptibility of x(2)=4.89xl03pm/V were obtained.... 详细信息

ISBN: (纸本)9781957171258

关键词： Gallium compounds

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Adaptive Curriculum Learning for Semi-supervised Segmentation of 3D CT-Scans 28th

Adaptive Curriculum Learning for Semi-supervised Segmentati...

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28th International Conference on Neural information Processing, ICONIP 2021

作者： Nartey, Obed Tettey Yang, Guowu Agyapong, Dorothy Araba Yakoba Wu, JinZhao Sarpong, Asare K. Frempong, Lady Nadia Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China The School of Computer Science and Electronic Information Guangxi University Nanning530004 China Biomedical Engineering Program Kwame Nkrumah University of Science and Technology Kumasi Ghana School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Medicine Department of Radiology and Imaging Sciences Emory University 1364 Clitton Rd. AtlantaGA30322 United States

ISBN: (纸本)9783030921842

Semi-supervised learning algorithms make use of both labelled training data and unlabelled data. However, the visual domain gap between these sets poses a challenge which prevents deep learning models from obtaining the results they have achieved most especially in the field of medical imaging. Recently, self-training with deep learning has become a powerful approach to leverage labelled training and unlabelled data. However, a challenge of generating noisy pseudo-labels and placing over-confident labelling belief on incorrect classes leads to deviation from the solution. To solve this challenge, the study investigates a curriculum-styled approach for deep semi-supervised segmentation which relaxes and treats pseudo-labels as continuous hidden variables by developing an adaptive pseudo-label generation strategy to jointly optimized the pseudo-label generation and selection process. A regularization scheme is further proposed to smoothen the probability outputs and sharpen the less represented pseudo-label regions. The proposed method was evaluated on three publicly available computer Tomography (CT) scan benchmarks and extensive experiments on all modules have demonstrated the efficacy of the proposed method. © 2021, Springer Nature Switzerland AG.

关键词： Curricula

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