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arXiv 2021年

作者： Gong, Shuhui Mo, Xiaopeng Cao, Rui Liu, Yu Tu, Wei Bai, Ruibin School of Information Engineering China University of Geosciences Beijing Beijing China School of Computer Science University of Nottingham Ningbo China Ningbo China Dept. of LSGI & SCRI The Hong Kong Polytechnic University Hong Kong Institute of Remote Sensing and Geographical Information Systems Peking University Beijing China GD Key Lab. of Urban Informatics Dept. of Urban Informatics Shenzhen University Shenzhen China

Parking demand forecasting and behaviour analysis have received increasing attention in recent years because of their critical role in mitigating traffic congestion and understanding travel behaviours. However, previous studies usually only consider temporal dependence but ignore the spatial correlations among parking lots for parking prediction. This is mainly due to the lack of direct physical connections or observable interactions between them. Thus, how to quantify the spatial correlation remains a significant challenge. To bridge the gap, in this study, we propose a spatial-aware parking prediction framework, which includes two steps, i.e. spatial connection graph construction and spatio-temporal forecasting. A case study in Ningbo, China is conducted using parking data of over one million records before and during COVID-19. The results show that the approach is superior on parking occupancy forecasting than baseline methods, especially for the cases with high temporal irregularity such as during COVID-19. Our work has revealed the impact of the pandemic on parking behaviour and also accentuated the importance of modelling spatial dependence in parking behaviour forecasting, which can benefit future studies on epidemiology and human travel behaviours. © 2021, CC BY-NC-ND.

关键词： Forecasting

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OTE: Optimal Trustworthy EdgeAI solutions for smart cities

OTE: Optimal Trustworthy EdgeAI solutions for smart cities

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IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID)

作者： Vasileios Mygdalis Lorenzo Carnevale Jose Ramiro Martí nez-De-Dios Dmitriy Shutin Giovanni Aiello Massimo Villari Ioannis Pitas Department of Informatics Aristotle University of Thessaloniki Thessaloniki Greece Department of Mathematical and Computer Science Physics and Hearth Sciences University of Messina Messina Italy Gruppo Nazionale per il Calcolo Scientifico (GNCS) Istituto Nazionale di Alta Matematica (INdAM) &#x201C F. Severi&#x201D Rome Italy Robotics Vision and Control Group University of Seville Seville Spain Institute of Communications and Navigation German Aerospace Center (DLR) Wessling Germany Research and Development Lab. Engineering Ingegneria Informatica S.p.A Rome Italy

This work studies and defines the problem of providing extensive and opportunistic Edge AI-based area coverage in smart city application scenarios, by researching and determining the optimal configuration of sensing and computational resources for minimizing the environmental/technology footprint of the solution. A typical smart city computing continuum consists of statically installed multimodal sensing Internet-of-Things (IoT) nodes at various city locations, accompanied by interconnected computational Cloud/Edge/IoT nodes. This paper presents Optimal Trustworthy EdgeAI (OTE), an entirely novel research pipeline, that complements existing smart city infrastructure with intelligent drone Edge/IoT nodes (in the form of modularly equipped unmanned aerial vehicles), capable of autonomous repositioning according to individual/collective sensing and coverage criteria. Thereby, we envisage the emerging cutting-edge technologies of trustworthy sensing, perceiving, modelling technologies for predicting the behavior of moving targets (e.g., citizens/vehicles/objects), understanding natural phenomena (e.g., sea wave motion, urban flora/fauna, biodiversity) in order to anticipate events (people's bad habits, environmental changes), by exploiting novel continuous data processing services across the whole span of the enhanced Cloud-Edge-IoT computing continuum.

关键词： Cloud computing Privacy Smart cities Pipelines Semantics Robot sensing systems Software

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Using the PerFECt Framework to Establish an Onlife Community for Theatre in Mathematics to Teach Principles of Computing

Using the PerFECt Framework to Establish an Onlife Community...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Nektarios Moumoutzis Dessislava Paneva-Marinova Chara Xanthaki Polyxeni Arapi Nikos Pappas Stavros Christodoulakis Institute of Mathematics & Informatics Bulgarian Academy of Sciences (IMI-BAS) Institute of Mathematics & Informatics Bulgarian Academy of Sciences (IMI-BAS) Sofia Bulgaria Computer Science Teacher in Secondary Education Chania Greece Lab. of Distr. Multimedia Inf. Systems & Applications School of Electrical and Computer Engineering Technical University of Crete (TUC/MUSIC) Chania Greece

ISBN: (数字)9781728173030

ISBN: (纸本)9781728173047

The new reality brought about by digital technologies is characterized by new qualities, new opportunities for action, new community affordances, that are captures by the term onlife. This term was initially proposed in the Onlife Manifesto and used in this paper to signify certain qualities that are important in understanding modern digital platforms and applications as well as guide the efforts for effectively extending to provide new capabilities to their users. Specific design principles are presented exemplified by the presentation of the design of a Community of Practice for teachers involved in the pilot activities of the Erasmus+ project TIM - Theatre in Mathematics that promote the adoption of Theatre in teaching Mathematics in Primary and Lower Secondary Education targeting 8-14 years old students.

关键词： Mathematics Education Electronic mail computers Software Conferences informatics

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Biometrics and quality of life of lymphoma patients: A longitudinal mixed-model approach

Biometrics and quality of life of lymphoma patients: A longi...

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作者： Oliveira, Alexandra Silva, Eliana Aguiar, Joyce Faria, Brígida Mónica Reis, Luís Paulo Cardoso, Henrique Gonçalves, Joaquim Oliveira e Sá, Jorge Carvalho, Victor Marques, Herlander LIACC - Artificial Intelligence and Computer Science Laboratory Porto Portugal ESS-IPP - Department of Biomathematics Biostatistics and Bioinformatics School of Health Polytechnic Institute of Porto Porto Portugal DEI-FEUP - Department of Informatics Engineering Faculty of Engineering University of Porto Porto Portugal Centro ALGORITMI – University of Minho Guimarães Portugal LITEC - Lab. de Inovação Tecnológica e Engenharia do Conhecimento Porto Portugal 2Ai - Applied Artificial Intelligence Laboratory School of Technology of Polytechnic Institute of Cávado and Ave Barcelos Portugal CINTESIS - Centro de Investigação em Tecnologias e Serviços de Saúde Porto Portugal Centro Hospitalar de Braga Braga Portugal

Knowledge Engineering has become essential in the fields of Medical and Health Care with emphasis for helping citizens to improve their health and quality of life. This includes individual methods and techniques in health-related knowledge acquisition and representation and their application in the construction of intelligent systems capable of using the acquired information to improve the patients' health and/or quality of life. Haemato-oncological diseases can provide significant disability and suffering, with severe symptoms and psychological distress. They can create difficulties in fulfilling professional, family and social roles, affecting an individual's quality of life. Health related quality of life (HRQoL) is a subjective concept but there is also an objective component related to physiological indicators. Some of these physiological indicators can be easily assessed by wearable technology such heart rate variability (HRV). This paper introduces an intelligent system to assess, in real-time, potential HRV indices, that can predict HRQoL in lymphoma patients throughout chemotherapy treatment and to account the individuals' variability. The system is based on wearable technology and intelligent processing of the patients' biometric information to assess some quality of life related parameters. A longitudinal study was conducted among 16 lymphoma patients using this intelligent system. Mixed-effect regression models were performed to investigate predictors for and time effects on HRQoL. There were no significant changes in all HRQoL domains over time. Some quality of life domains revealed similar time trends as HRV indices. These HRV indices also have a significant effect on the domains of quality of life. © 2020 John Wiley & Sons Ltd

关键词： Regression analysis

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Detection of Cell Nuclei using LadderNet 9

Detection of Cell Nuclei using LadderNet

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9th International Congress on Advanced Applied informatics, IIAI-AAI 2020

作者： Ando, Ren Iwahori, Yuji Fukui, Shinji Wang, Aili Bhuyan, M.K. Iwamoto, Takashi Ueda, Jun Chubu University Dept. of Computer Science Kasugai487-8501 Japan Aichi University of Education Informatics and Communication Tech. Kariya448-8542 Japan Harbin Univ. of Sci. and Tech. Higher Education Key Lab. Harbin China Indian Institute of Technology Guwahati Dept. of Electronics and Electrical Eng. Guwahati781039 India Chubu University Dept. of Biomedical Sciences Kasugai487-8501 Japan Asahikawa Medical University Center for Advanced Research and Education Asahikawa078-8510 Japan

ISBN: (纸本)9781728173979

Cytology which directly examines cells in the early detection of cancer plays an important role but this diagnosis depends on the experience and technology of a pathologist. The problem is that it takes time and the objectivity is poor. An automatic diagnosis system for pathologically diagnosing cancer is necessary to solve these problems. To classify cells into benign or malignant automatically, it is important to detect the cell nucleus from the cell image in advance. This paper proposes a new approach to detect cell nuclei for automatically using LadderNet which is a recent extension model of U-Net used in the Deep Learning Approach. © 2020 IEEE.

关键词： Cytology

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How Gender and Skin Tone Modifiers Affect Emoji Semantics in Twitter 7

How Gender and Skin Tone Modifiers Affect Emoji Semantics in...

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7th Joint Conference on Lexical and Computational Semantics, SEM 2018, co-located with NAACL HLT 2018

作者： Barbieri, Francesco Camacho-Collados, Jose LASTUS Lab. TALN Universitat Pompeu Fabra Barcelona Spain School of Computer Science and Informatics Cardiff University United Kingdom

ISBN: (纸本)9781948087223

In this paper we analyze the use of emojis in social media with respect to gender and skin tone. By gathering a dataset of over twenty two million tweets from United States some findings are clearly highlighted after performing a simple frequency-based analysis. Moreover, we carry out a semantic analysis on the usage of emojis and their modifiers (e.g. gender and skin tone) by embedding all words, emojis and modifiers into the same vector space. Our analyses reveal that some stereotypes related to the skin color and gender seem to be reflected on the use of these modifiers. For example, emojis representing hand gestures are more widely utilized with lighter skin tones, and the usage across skin tones differs significantly. At the same time, the vector corresponding to the male modifier tends to be semantically close to emojis related to business or technology, whereas their female counterparts appear closer to emojis about love or makeup. © 2018 Association for Computational Linguistics.

关键词： Vector spaces

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Nearest neighborhood-based deep clustering for source data-absent unsupervised domain adaptation

arXiv

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arXiv 2021年

作者： Tang, Song Yang, Yan Ma, Zhiyuan Hendrich, Norman Zeng, Fanyu Ge, Shuzhi Sam Zhang, Changshui Zhang, Jianwei The Institute of Machine Intelligence University of Shanghai for Science and Technology Shanghai China The State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu China Group Department of Informatics Universität Hamburg Hamburg Germany The Institute of Machine Intelligence University of Shanghai for Science and Technology Shanghai China The State Key Lab. for Novel Software Technology Nanjing University Nanjing China The Engineering Research Center of Wideband Wireless Communication Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China The Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore The Department of Automation Tsinghua University Beijing China

In the classic setting of unsupervised domain adaptation (UDA), the lab.led source data are availab.e in the training phase. However, in many real-world scenarios, owing to some reasons such as privacy protection and information security, the source data is inaccessible, and only a model trained on the source domain is availab.e. This paper proposes a novel deep clustering method for this challenging task. Aiming at the dynamical clustering at feature-level, we introduce extra constraints hidden in the geometric structure between data to assist the process. Concretely, we propose a geometry-based constraint, named semantic consistency on the nearest neighborhood (SCNNH), and use it to encourage robust clustering. To reach this goal, we construct the nearest neighborhood for every target data and take it as the fundamental clustering unit by building our objective on the geometry. Also, we develop a more SCNNH-compliant structure with an additional semantic credibility constraint, named semantic hyper-nearest neighborhood (SHNNH). After that, we extend our method to this new geometry. Extensive experiments on three challenging UDA datasets indicate that our method achieves state-of-the-art results. The proposed method has significant improvement on all datasets (as we adopt SHNNH, the average accuracy increases by over 3.0% on the large-scaled dataset). Code is availab.e at https://***/tntek/N2DCX. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

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Segmentation-guided Domain Adaptation and Data Harmonization of Multi-device Retinal Optical Coherence Tomography using Cycle-Consistent Generative Adversarial Networks

arXiv

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arXiv 2022年

作者： Chen, Shuo Ma, Da Lee, Sieun Yu, Timothy T.L. Xu, Gavin Lu, Donghuan Popuri, Karteek Ju, Myeong Jin Sarunic, Marinko V. Beg, Mirza Faisal School of Engineering Science Simon Fraser University BurnabyBC Canada Department of Internal Medicine Section of Gerontology and Geriatric Medicine Wake Forest University School of Medicine Winston-SalemNC United States Center for Biomedical Informatics Wake Forest School of Medicine Winston-SalemNC United States Alzheimer’s Disease Research Center Wake Forest School of Medicine Winston-SalemNC United States Mental Health & Clinical Neuroscience University of Nottingham Nottingham United Kingdom Precision Imaging Beacon University of Nottingham Nottingham United Kingdom Tencent Jarvis Lab. Shenzhen China Department of Computer Science Memorial University of Newfoundland St. John’sNL Canada School of Biomedical Engineering University of British Columbia BC Canada Department of Ophthalmology & Visual Sciences The University of British Columbia VancouverBC Canada Institute of Ophthalmology University College London London United Kingdom Department of Medical Physics and Biomedical Engineering University College London United Kingdom

Background: Medical images such as Optical Coherence Tomography (OCT) images acquired from different devices may show significantly different intensity profiles. An automatic segmentation model trained on images from one device may perform poorly when applied to images acquired using a different device, resulting in a lack of generalisability. This study aims to address this issue using domain adaptation methods improved by Cycle-Consistent Generative Adversarial Networks(CycleGAN), especially in cases when the ground-truth lab.ls are only availab.e in the source domain. Methods: A two-stage pipeline is proposed to generate segmentation in the target domain. The first stage involves the training of a state-of-the-art segmentation model in the source domain. The second stage aims to adapt the images from the target domain into the source domain. The adapted target domain images are segmented using the model in the first stage. Ablation tests were performed with a different integration of loss functions, and the statistical significance of these models is reported. Both the segmentation performance and the adapted image quality metrics are evaluated. Results: Regarding the segmentation Dice score, the proposed ssppg model achieves a significant improvement of 46.24% compared to no adaptation, and it reaches 87.4% of the upper limit of the segmentation performance. Moreover, the image quality metrics including the FID and KID score indicate that adapted images with better segmentation also have better image qualities. Conclusion: The proposed method demonstrates the effectiveness of segmentation-driven domain adaptation in retinal imaging processing. It reduces the lab.ur cost of manual lab.lling, incorporates prior anatomic information to regulate and guide the domain adaptation, and provides insights into improving segmentation qualities in image domains without lab.ls. © 2022, CC BY-NC-SA.

关键词： Optical tomography

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computer aided diagnosis system for liver cirrhosis based on ultrasound images 18

Computer aided diagnosis system for liver cirrhosis based on...

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7th International Conference on Software and Information Engineering, ICSIE 2018

作者： Rabie, Reham Eltoukhy, Mohamed Meselhy Al-Shatouri, Mohammad Rashed, Essam A. Image Science Lab. Dept. of Math. Faculty of Science Suez Canal University Ismailia Egypt Faculty of Computers and Informatics Suez Canal University Ismailia Egypt Dept. of Radiology Faculty of Medicine Suez Canal University Ismailia Egypt Faculty of Informatics and Computer Science British University in Egypt Cairo Egypt

ISBN: (纸本)9781450364690

This work introduces a computer-aided diagnosis (CAD) system for diagnosing liver cirrhosis in ultrasound (US) images. The proposed system uses a set of features obtained from different feature extraction methods. These features are the first order statistics (FOS), the fractal dimension (FD), the gray level co-occurrence matrix (GLCM), the Gabor filter (GF), the wavelet (WT) and the curvelet (CT) features. The measured features are presented in two different classifiers such as support vector machine (SVM) and k-nearest neighbors (K-NN). The proposed system is applied on dataset consists of 72 cirrhosis and 75 normal regions each of 128128 pixels. The classification accuracy rates are calculated using a 10-fold cross validation. A correlation-based feature selection (CFS) is used resulting in better accuracy predictions. The results showed that SVM and K-NN classifiers achieved higher performance with the combination of the wavelet and curvelet feature vectors than other feature extraction methods. © 2018 Association for Computing Machinery.

关键词： Support vector machines

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Experimental investigation of Markovian and non-Markovian channel addition

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Physical Review A 2020年第5期101卷 052107-052107页

作者： S. A. Uriri F. Wudarski I. Sinayskiy F. Petruccione M. S. Tame School of Chemistry and Physics University of KwaZulu-Natal Durban 4001 South Africa Quantum Artificial Intelligence Lab. (QuAIL) Exploration Technology Directorate NASA Ames Research Center Moffett Field California 94035 USA USRA Research Institute for Advanced Computer Science (RIACS) Mountain View California 94035 USA Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Grudziądzka 5/7 87-100 Toruń Poland National Institute for Theoretical Physics KwaZulu-Natal Durban 4001 South Africa Department of Physics Stellenbosch University Matieland 7602 South Africa

The study of memory effects in quantum channels helps in developing characterization methods for open quantum systems and strategies for quantum error correction. Two main sets of channels exist, corresponding to system dynamics with no memory (Markovian) and with memory (non-Markovian). Interestingly, these sets have a nonconvex geometry, allowing one to form a channel with memory from the addition of memoryless channels and vice versa. Here, we experimentally investigate this nonconvexity in a photonic setup by subjecting a single qubit to a convex combination of Markovian and non-Markovian channels. We use both divisibility and distinguishability as criteria for the classification of memory effects, with associated measures. Our results highlight some practical considerations that may need to be taken into account when using memory criteria to study system dynamics given by the addition of Markovian and non-Markovian channels in experiments.

关键词： Open quantum systems Quantum channels Quantum simulation

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