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IEEE International Conference on Internet of Things and Intelligence System (IOTAIS)

作者： Ramez Hosny Sherief Hashima Ehab Mahmoud Mohamed Kohei Hatano Rokaia M. Zaki Basem M. ElHalawany Electrical Engineering Department Faculty of Engineering at Shoubra Benha University Cairo Egypt Higher Technology Institute 10th of Ramadan Egypt Computational Learning Theory Team RIKEN-Advanced Intelligence Project (AIP) Fukuoka Japan Engineering Dept NRC Egyptian Atomic Energy Authority Cairo Egypt College of Engineering Prince Sattam Bin Abdulaziz University Wadi Aldwaser Saudi Arabia Informatics Dept Kyushu University Fukuoka Japan Kuwait College of Science and Technology Kuwait

Modern Non-Orthogonal Multiple Access (NOMA) and Unmanned Aerial Vehicles (UAVs) wireless technologies are valuable assets in emergency scenarios to enable quick and flexible infrastructure of wireless communication networks. Still, efficient trajectory planning of UAV-NOMA emergency networks is a big challenge where the UAV should assist the maximum number of survivors and optimize its limited battery energy. Hence, in this paper, we formulate this problem as Budgeted Thompson Sampling for UAV Trajectory Planning (BTS-UTP), which is a theoretically guaranteed Budgeted Multi-Armed Bandits (BMAB) derived from the original TS via considering the random cost for drawing an arm and the total cost is constrained by budget (UAV energy). Here the bandit player, arms, cost, and payoff are the UAV, disaster area grids, UAV battery expenditure, and the number of assisted survivors. Numerical results ensure the superior performance of the proposed BTS-UTP technique.

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Reconstructing suspended sediment concentrations in the Mekong River Basin via semi-supervised-based deep neural networks

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science of the Total Environment 2024年 955卷 176758页

作者： Nguyen, Thi Thu Ha Vu, Duc Quang Doan, Ngoc Phu Chi, Huynh Thi Khanh Li, Peixin Binh, Doan Van An, Yimeng Dung, Pham Tuan Hoang, Tuan A. Son, Mai Thai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat City Viet Nam

The Mekong River Basin (MRB) is crucial for the livelihoods of over 60 million people across six Southeast Asian countries. Understanding long-term sediment changes is crucial for management and contingency plans, but the sediment concentration data in the MRB are extremely sporadic, making analysis challenging. This study focuses on reconstructing long-term suspended sediment concentration (SSC) data using a novel semi-supervised machine learning (ML) model. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on daily hydroclimate and SSC data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the superior performance of the proposed method compared to the state-of-the-art supervised techniques (i.e., Random Forest, XGBoost, CatBoost, MLP, CNN, and LSTM), and surpasses existing semi-supervised methods (i.e., CoReg, ⊓ Model, ICT, and Mean Teacher). This approach is the first semi-supervised method to reconstruct sediment data in the field and has the potential for broader application in other river systems. © 2024 The Author(s)

关键词： Self-supervised learning

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A Bottom-Up, Multi-Scale Model for Transient Mass Transport of Redox-Active Species Through Porous Electrodes Beyond the Pseudo-Steady Limit

SSRN

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SSRN 2022年

作者： Hamid, Md Abdul Smith, Kyle C. Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Materials Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Computational Science and Engineering Program University of Illinois at Urbana-Champaign UrbanaIL61801 United States Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States

A transient, multi-scale model is introduced to capture the dynamic response of redox-active electrolyte flowing through porous electrodes under time-dependent current input. Unlike conventional models with time-invariant mass transfer coefficient, certain frequency-dependent transfer functions (TFs) are introduced to embed transient pore-scale transport physics into an up-scaled model. One of the introduced TFs is a spectral Sherwood number, that extends the film law of mass transfer to transient conditions. Another TF is introduced to capture the acceleration/suppression of solute advection resulting from inhomogeneity in local concentration distribution. The frequency dependence of these TFs is demonstrated for electrodes comprised of solid cylinders in crossflow. Three distinct spectral regions are observed, where with increasing frequency a transition from lagless response to semi-infinite Warburg response occurs. The results show orders of magnitude higher mass transfer rate for fast-changing applied current. These TFs are then embedded into an up-scaled model to obtain the time domain response of flow batteries. Analysis of concentration polarization and the average reactant concentration inside electrodes is used to construct non-dimensional maps of their operation space. From this analysis we find that fast current fluctuations can be sustained at conditions exceeding the limiting current that is expected for pseudo-steady operation. © 2022, The Authors. All rights reserved.

关键词： Flow batteries

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Spatial atlas of the mouse central nervous system at molecular resolution (vol 622, pg 552, 2023)

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NATURE 2024年第7993期625卷 E6-E6页

作者： Shi, Hailing He, Yichun Zhou, Yiming Huang, Jiahao Maher, Kamal Wang, Brandon Tang, Zefang Luo, Shuchen Tan, Peng Wu, Morgan Lin, Zuwan Ren, Jingyi Thapa, Yaman Tang, Xin Chan, Ken Y. Deverman, Benjamin E. Shen, Hao Liu, Albert Liu, Jia Wang, Xiao Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry Massachusetts Institute of Technology Cambridge MA USA John A. Paulson School of Engineering and Applied Sciences Harvard University Boston MA USA Computational and Systems Biology PhD Program Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Biology Massachusetts Institute of Technology Cambridge MA USA Klarman Cell Observatory Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry and Chemical Biology Harvard University Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA

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The bourque distances for mutation trees of cancers 20

The bourque distances for mutation trees of cancers

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20th International Workshop on Algorithms in Bioinformatics, WABI 2020

作者： Jahn, Katharina Beerenwinkel, Niko Zhang, Louxin Department of Biosystems Science and Engineering ETH Zurich Basel Switzerland SIB Swiss Institute of Bioinformatics Basel Switzerland Department of Mathematics and Computational Biology Program National University of Singapore Singapore

ISBN: (纸本)9783959771610

Mutation trees are rooted trees of arbitrary node degree in which each node is labeled with a mutation set. These trees, also referred to as clonal trees, are used in computational oncology to represent the mutational history of tumours. Classical tree metrics such as the popular RobinsonFoulds distance are of limited use for the comparison of mutation trees. One reason is that mutation trees inferred with different methods or for different patients often contain different sets of mutation labels. Here, we generalize the RobinsonFoulds distance into a set of distance metrics called Bourque distances for comparing mutation trees. A connection between the RobinsonFoulds distance and the nearest neighbor interchange distance is also presented. © Katharina Jahn, Niko Beerenwinkel, and Louxin Zhang;licensed under Creative Commons License CC-BY

关键词： Bourque distance Clonal trees Mutation trees Phylogenetic trees Robinson–Foulds distance Tree distance Tree metric

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Tensions in design and participation processes: An ethnographic approach to the design, building and evaluation of a collective intelligence model

Tensions in design and participation processes: An ethnograp...

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International Conference on Computer Supported Cooperative Work in Design

作者： Ramon Chaves Claudia Motta Antonio Correia Jano de Souza Daniel Schneider Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research (NCE) Federal University of Rio de Janeiro Brazil UTAD INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal College of Information Science & Technology University of Nebraska at Omaha Omaha NE USA

This paper explores the challenges and strategies used to design, build and evaluate a collective intelligence (CI) model to support discussions about cities. Through an autoethnography of ideas and discussion practices in both online and offline contexts, this work explores the tensions between the chosen methodological approaches, including design science research (DSR) and participatory action research (PAR). Moreover, we also examine the challenges and pitfalls observed during the practical conduction of this research when involving participants in the process of empirically evaluating the proposed model. Finally, aspects related to the autoethnographic process itself as a reflective method are discussed alongside the consequences of desiring and seeking participation in the research process.

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Mapping Tokenomics Arrangements to Expand the Digital Nomad Ecosystem

Mapping Tokenomics Arrangements to Expand the Digital Nomad ...

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International Conference on Computer Supported Cooperative Work in Design

作者： Marcos Antonio de Almeida António Correia Jano Moreira de Souza Daniel Schneider Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal College of Information Science & Technology University of Nebraska at Omaha Omaha NE USA Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research NCE/UFRJ Rio de Janeiro Brazil

The Massification of remote work, in response to the COVID-19 pandemic, has been causing significant changes in productive and working arrangements, both for individuals, organizations, and society. At the level of personal life philosophy, for example, this transformation can be evidenced in the dissemination of digital nomadism values such as work/leisure balance among corporate workers. On the other hand, at the level of gig/crowd work platforms, the emergence of tensions may indicate the exhaustion of sociotechnical design models adopted by big techs. We show how evidence collected from digital nomads in empirical ethnography studies can inform HCI/CSCWD researchers of design-oriented strands to explore emerging opportunities in new creative digital crypto-economic ecosystems. Finally, we present a proposed research agenda to explore DNs’ activities in the crypto-economic ecosystem.

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Unsupervised Behaviour Analysis of News Consumption in Turkish Media

arXiv

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arXiv 2022年

作者： Makaroğlu, Didem Cakir, Altan Töreyin, Behçet Uğur Demirören Teknoloji A.S. İstanbul Turkey Signal Processing for Computational Intelligence Group Informatics Institute Istanbul Technical University Istanbul Turkey Physics Engineering Faculty of Science and Letters Istanbul Technical University Istanbul Turkey Istanbul Technical University Artificial Intelligence Data Science Research and Application Center Istanbul Turkey

Clickstream data, which come with a massive volume generated by human activities on websites, have become a prominent feature for identifying readers’ characteristics by newsrooms after the digitization of news outlets. It is essential to have elastic architectures to process the streaming data, particularly for unprecedented traffic, enabling conducting more comprehensive analyses, such as recommending mostly related articles to readers. Although the nature of clickstream data has a similar logic within websites, it has inherent limitations to recognize human behaviours when looking from a broad perspective, which brings the need to limit the problem in niche areas. This study investigates the anonymized readers’ click activities on the organizations’ websites to identify news consumption patterns following referrals from Twitter, who incidentally reach but propensity is mainly routed news content. The investigation is widened to a broad perspective by linking the log data with news content to enrich the insights rather than sticking into the web journey. Methodologies for ensemble cluster analysis with mixed-type embedding strategies are applied and compared to find similar reader groups and interests independent of time. Various internal validation perspectives are used to determine the optimality of the quality of clusters, where the Calinski Harabasz Index (CHI) is found to give a generalizable result. Our findings demonstrate that clustering a mixed-type dataset approaches the optimal internal validation scores, which we define to discriminate the clusters and algorithms considering applied strategies, when embedded by Uniform Manifold Approximation and Projection (UMAP) and using a consensus function as a key to access the most applicable hyper parameter configurations in the given ensemble rather than using consensus function results directly. Evaluation of the resulting clusters highlights specific clusters repeatedly present in the separated monthly sample

关键词： Digital storage

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Deep-Motion-Net: GNN-based volumetric organ shape reconstruction from single-view 2D projections

arXiv

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arXiv 2024年

作者： Wijesinghe, Isuru Nix, Michael Zakeri, Arezoo Hokmabadi, Alireza Al-Qaisieh, Bashar Gooya, Ali Taylor, Zeike A. Centre for Computational Imaging and Simulation Technologies in Biomedicine School of Mechanical Engineering University of Leeds Leeds United Kingdom Institute of Medical and Biological Engineering School of Mechanical Engineering University of Leeds Leeds United Kingdom Department of Medical Physics and Engineering St James’s University Hospital Leeds Teaching Hospitals NHS Trust Leeds United Kingdom Division of Informatics Imaging and Data Sciences Faculty of Biology Medicine and Health University of Manchester Manchester United Kingdom School of Medicine and Population Health University of Sheffield Sheffield United Kingdom School of Computing Science University of Glasgow Glasgow United Kingdom

We propose Deep-Motion-Net: an end-to-end graph neural network (GNN) architecture that enables 3D (volumetric) organ shape reconstruction from a single in-treatment kV planar X-ray image acquired at any arbitrary projection angle. Estimating and compensating for true anatomical motion during radiotherapy is essential for improving the delivery of planned radiation dose to target volumes while sparing organs-at-risk, and thereby improving the therapeutic ratio. Achieving this using only limited imaging available during irradiation and without the use of surrogate signals or invasive fiducial markers is attractive. The proposed model learns the mesh regression from a patient-specific template and deep features extracted from kV images at arbitrary projection angles. A 2D-CNN encoder extracts image features, and four feature pooling networks fuse these features to the 3D template organ mesh. A ResNet-based graph attention network then deforms the feature-encoded mesh. The model is trained using synthetically generated organ motion instances and corresponding kV images. The latter is generated by deforming a reference CT volume aligned with the template mesh, creating digitally reconstructed radiographs (DRRs) at required projection angles, and DRR-to-kV style transferring with a conditional CycleGAN model. The overall framework was tested quantitatively on synthetic respiratory motion scenarios and qualitatively on in-treatment images acquired over full scan series for liver cancer patients. Overall mean prediction errors for synthetic motion test datasets were 0.16±0.13 mm, 0.18±0.19 mm, 0.22±0.34 mm, and 0.12±0.11 mm. Mean peak prediction errors were 1.39 mm, 1.99 mm, 3.29 mm, and 1.16 mm. © 2024, CC BY-SA.

关键词： Radiotherapy

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Improving Land Cover Segmentation Using Multispectral Dataset

Improving Land Cover Segmentation Using Multispectral Datase...

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International Conference on informatics and Computing (ICIC)

作者： Herlawati Herlawati Rahmadya Trias Handayanto Yaya Heryadi Edi Abdurachman Haryono Soeparno Edy Irwansyah Eka Miranda Informatics Universitas Bhayangkara Jakarta Raya Jakarta Indonesia Computer Engineering Universitas Islam 45 Bekasi Indonesia Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Trisakti Institute of Transportation and Logistic Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia

Computer vision has been used in many areas such as medical, transportation, military, geography, etc. The fast development of sensor devices inside camera and satellite provides not only red-greed-blue (RGB) images but also multispectral dataset with some channels including RGB, infrared, short-wave, and thermal wave. Most of the dataset is panchromatic (black and white) and RGB, for example Google Map and other satellite-based map applications. This study examines the effects of multispectral dataset for semantic segmentation of land cover. The comparison between RGB with band 2 to band 7 of Landsat 8 Satellite shows an improvement of accuracy from 90.283 to 94.473 for U-Net and from 91.76 to 95.183 for DeepLabV3+. In addition, this research also compares two well-known semantic segmentation methods, namely U-Net and DeepLabV3+, that shown that DeepLabV3+ outperformed U-Net regarding to speed and accuracy. Testing was conducted in the Karawang Regency area, West Java, Indonesia.

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