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arXiv 2022年

作者： Cao, Xiao-Kai Wang, Chang-Dong Lai, Jian-Huang Huang, Qiong Chen, C.L. Philip School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China

Multi-party learning is an indispensable technique for improving the learning performance via integrating data from multiple parties. Unfortunately, directly integrating multiparty data would not meet the privacy preserving requirements. Therefore, Privacy-Preserving Machine Learning (PPML) becomes a key research task in multi-party learning. In this paper, we present a new PPML method based on secure multi-party interactive protocol, namely Multi-party Secure Broad Learning System (MSBLS), and derive security analysis of the method. The existing PPML methods generally cannot simultaneously meet multiple requirements such as security, accuracy, efficiency and application scope, but MSBLS achieves satisfactory results in these aspects. It uses interactive protocol and random mapping to generate the mapped features of data, and then uses efficient broad learning to train neural network classifier. This is the first privacy computing method that combines secure multi-party computing and neural network. Theoretically, this method can ensure that the accuracy of the model will not be reduced due to encryption, and the calculation speed is very fast. We verify this conclusion on three classical datasets. © 2022, CC0.

关键词： Learning systems

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Synergizing Airborne Non-Terrestrial Networks and Reconfigurable Intelligent Surfaces-Aided 6G IoT

arXiv

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arXiv 2023年

作者： Jamshed, Muhammad Ali Kaushik, Aryan Toka, Mesut Shin, Wonjae Shakir, Muhammad Zeeshan Dash, Soumya P. Dardari, Davide The College of Science and Engineering University of Glasgow United Kingdom The School of Engineering & Informatics University of Sussex United Kingdom The Department of Electrical and Computer Engineering Ajou University Korea Republic of The School of Electrical Engineering Korea University Korea Republic of The Department of Electrical and Computer Engineering Princeton University United States The School of Computing Engineering and Physical Sciences University of the West of Scotland United Kingdom The School of Electrical Sciences Indian Institute of Technology Bhubaneswar India The Department of Electrical Electronic and Information Engineering University of Bologna Italy

On the one hand, Reconfigurable Intelligent Surfaces (RISs) emerge as a promising solution to meet the demand for higher data rates, improved coverage, and efficient spectrum utilization. On the other hand, Non-Terrestrial Networks (NTNs) offer unprecedented possibilities for global connectivity. Moreover, the NTN can also support the upsurge in the number of Internet of Things (IoT) devices by providing reliable and ubiquitous connectivity. Although NTNs have shown promising results, there are several challenges associated with their usage, such as signal propagation delays, interference, security, etc. In this article, we have discussed the possibilities of integrating RIS with an NTN platform to overcome the issues associated with NTN. Furthermore, through experimental validation, we have demonstrated that the RIS-assisted NTN can play a pivotal role in improving the performance of the entire communication system. © 2023, CC BY.

关键词： Internet of things

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Synergizing Airborne Non-Terrestrial Networks and Reconfigurable Intelligent Surfaces-Aided 6G IoT

TechRxiv

引用

TechRxiv 2023年

作者： Jamshed, Muhammad Ali Shin, Wonjae Kaushik, Aryan Toka, Mesut Shakir, Muhammad Zeeshan Dash, Soumya Prakash Dardari, Davide The College of Science and Engineering University of Glasgow United Kingdom The School of Engineering & Informatics University of Sussex United Kingdom The Department of Electrical and Computer Engineering Ajou University Korea Republic of The School of Electrical Engineering Korea University Korea Republic of The Department of Electrical and Computer Engineering Princeton University United States The School of Computing Engineering and Physical Sciences University of the West of Scotland United Kingdom The School of Electrical Sciences Indian Institute of Technology Bhubaneswar India The Department of Electrical Electronic and Information Engineering University of Bologna Italy

关键词： Internet of things

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A Brief Review of Network Embedding

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Big Data Mining and Analytics 2019年第1期2卷 35-47页

作者： Yaojing Wang Yuan Yao Hanghang Tong Feng Xu Jian Lu the State Key Laboratory for Novel Software Technology Nanjing University the School of Computing Informatics and Decision Systems Engineering Arizona State University

Learning the representations of nodes in a network can benefit various analysis tasks such as node classification, link prediction, clustering, and anomaly detection. Such a representation learning problem is referred to as network embedding, and it has attracted significant attention in recent years. In this article, we briefly review the existing network embedding methods by two taxonomies. The technical taxonomy focuses on the specific techniques used and divides the existing network embedding methods into two stages, i.e., context construction and objective design. The non-technical taxonomy focuses on the problem setting aspect and categorizes existing work based on whether to preserve special network properties, to consider special network types, or to incorporate additional inputs. Finally, we summarize the main findings based on the two taxonomies, analyze their usefulness,and discuss future directions in this area.

关键词： network embedding node representations context construction

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Single Image 3D Shape Retrieval via Cross-Modal Instance and Category Contrastive Learning

Single Image 3D Shape Retrieval via Cross-Modal Instance and...

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International Conference on Computer Vision (ICCV)

作者： Ming-Xian Lin Jie Yang He Wang Yu-Kun Lai Rongfei Jia Binqiang Zhao Lin Gao Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Center on Frontiers of Computing Studies Peking University School of Computer Science and Informatics Cardiff University Alibaba Group Zhejiang Lab

ISBN: (纸本)9781665428132

In this work, we tackle the problem of single image-based 3D shape retrieval (IBSR), where we seek to find the most matched shape of a given single 2D image from a shape repository. Most of the existing works learn to embed 2D images and 3D shapes into a common feature space and perform metric learning using a triplet loss. Inspired by the great success in recent contrastive learning works on self-supervised representation learning, we propose a novel IBSR pipeline leveraging contrastive learning. We note that adopting such cross-modal contrastive learning between 2D images and 3D shapes into IBSR tasks is non-trivial and challenging: contrastive learning requires very strong data augmentation in constructed positive pairs to learn the feature invariance, whereas traditional metric learning works do not have this requirement. Moreover, object shape and appearance are entangled in 2D query images, thus making the learning task more difficult than contrasting single-modal data. To mitigate the challenges, we propose to use multi-view grayscale rendered images from the 3D shapes as a shape representation. We then introduce a strong data augmentation technique based on color transfer, which can significantly but naturally change the appearance of the query image, effectively satisfying the need for contrastive learning. Finally, we propose to incorporate a novel category-level contrastive loss that helps distinguish similar objects from different categories, in addition to classic instance-level contrastive loss. Our experiments demonstrate that our approach achieves the best performance on all the three popular IBSR benchmarks, including Pix3D, Stanford Cars, and Comp Cars, outperforming the previous state-of-the-art from 4% - 15% on retrieval accuracy.

关键词： Representation learning Deep learning Three-dimensional displays Shape Image color analysis Pipelines Gray-scale

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ELECTRA-DTA: A New Compound-protein Binding Affinity Prediction Model based on the Contextualized Sequence Encoding

Research Square

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Research Square 2021年

作者： Junjie, Wang NaiFeng, Wen Chunyu, Wang Lingling, Zhao Liang, Cheng Department of Medical Informatics School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China School of Mechanical and Electrical Engineering Dalian Minzu University Dalian China Faculty of Computing Harbin Institute of Technology Harbin China College of Bioinformatics Science and Technology Harbin Medical University Harbin China

Motivation: Drug-target binding affinity (DTA) reflects the strength of the drug-target interaction;therefore, predicting the DTA can considerably benefit drug discovery by narrowing the search space and pruning drug-target (DT) pairs with low binding affinity scores. Representation learning using deep neural networks has achieved promising performance compared with traditional machine learning methods;hence, extensive research efforts have been made in learning the feature representation of proteins and compounds. However, such feature representation learning relies on a large-scale labelled dataset, which is not always available. Results: We present an end-to-end deep learning framework, ELECTRA-DTA, to predict the binding affinity of drug-target pairs. This framework incorporates an unsupervised learning mechanism to train two ELECTRA-based contextual embedding models, one for protein amino acids and the other for compound SMILES string encoding. In addition, ELECTRA-DTA leverages a squeeze-and-excitation (SE) convolutional neural network block stacked over three fully connected layers to further capture the sequential and spatial features of the protein sequence and SMILES for the DTA regression task. Experimental evaluations show that ELECTRA-DTA outperforms various state-of-the-art DTA prediction models, especially with the challenging, interaction-sparse BindingDB dataset. In target selection and drug repurposing for COVID-19, ELECTRA-DTA also offers competitive performance, suggesting its potential in speeding drug discovery and generalizability for other compound- or protein-related computational tasks. © , CC BY.

关键词： Forecasting

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Application of GIS in Public Health Practice: A Consortium’s Approach to Tackling Travel Delays in Obstetric Emergencies in Urban Areas 12

Application of GIS in Public Health Practice: A Consortium’...

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12th International Conference on Geographic Information Science, GIScience 2023

作者： Wang, Jia Osayande, Itohan Macharia, Peter M. Makanga, Prestige Tatenda Wong, Kerry L.M. Olubodun, Tope Gwacham-Anisiobi, Uchenna Ogunyemi, Olakunmi Olaniran, Abimbola Abejirinde, Ibukun-Oluwa O. Beňová, Lenka Afolabi, Bosede B. Banke-Thomas, Aduragbemi School of Computing & Mathematical Sciences University of Greenwich London United Kingdom School of Human Sciences University of Greenwich London United Kingdom Department of Public Health Institute of Tropical Medicine Antwerp Belgium Population & Health Impact Surveillance Group Kenya Medical Research Institute-Wellcome Trust Research Programme Nairobi Kenya Centre for Health Informatics Computing and Statistics Lancaster Medical School Lancaster University United Kingdom Surveying and Geomatics Department Faculty of Science and Technology Midlands State University Gweru Zimbabwe Climate and Health Division Centre for Sexual Health and HIV/AIDS Research Zimbabwe Faculty of Epidemiology and Population Health London School of Hygiene and Tropical Medicine United Kingdom Department of Community Medicine and Primary Care Federal Medical Centre Abeokuta Ogun Abeokuta Nigeria Nuffield Department of Population Health University of Oxford United Kingdom Lagos State Ministry of Health Nigeria Royal Tropical Institute Amsterdam Netherlands Dalla Lana School of Public Health University of Toronto Canada Women’s College Hospital Institute for Health System Solutions and Virtual Care Toronto Canada Department of Obstetrics and Gynaecology College of Medicine University of Lagos Nigeria Maternal and Reproductive Health Research Collective Lagos Nigeria

ISBN: (纸本)9783959772884

Geographic Information System (GIS) has become an effective and reliable tool for researchers, policymakers, and decision-makers to map health outcomes and inform targeted planning, evaluation, and monitoring. With the advent of big data-enabled GIS, researchers can now identify disparities and spatial inequalities in health at more granular levels, enabling them to provide more accurate and robust services and products for healthcare. This paper aims to showcase the progress of the On Tackling In-transit Delays for Mothers in Emergency (OnTIME) project, which is a unique collaborative effort between academia, policymakers, and industrial partners. The paper demonstrates how the limitations of traditional spatial accessibility models and data gaps have been overcome by combining GIS and big data to map the geographic accessibility and coverage of health facilities capable of providing emergency obstetric care (EmOC) in conurbations in Africa. The OnTIME project employs various GIS technologies and concepts, such as big spatial data, spatial databases, and public participation geographic information systems (PPGIS). We provide an overview of these concepts in relation to the OnTIME project to demonstrate the application of GIS in public health practice. © Jia Wang, Itohan Osayande, Peter M. Macharia, Prestige Tatenda Makanga, Kerry L. M. Wong, Tope Olubodun, Uchenna Gwacham-Anisiobi, Olakunmi Ogunyemi, Abimbola Olaniran, Ibukun-Oluwa O. Abejirinde, Lenka Beňová, Bosede B. Afolabi, and Aduragbemi Banke-Thomas.

关键词： Geographic information systems

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Directional Modulation Based on a Quantum Genetic Algorithm for a Multiple-Reflection Model

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Computers, Materials & Continua 2020年第9期64卷 1771-1783页

作者： Yuwei Huang Xiubo Chen Kaiguo Yuan Jianyi Zhang Biao Liu Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China Beijing Electronic Science and Technology Institute Beijing100070China Sate Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Computing and Informatics The University of Louisiana at LafayetteLafayetteLA 70503USA.

Directional modulation is one of the hot topics in data security *** fulfill the requirements of communication security in wireless environment with multiple paths,this study takes into account the factors of reflections and antenna radiation pattern for directional *** other previous works,a novel multiple-reflection model,which is more realistic and complex than simplified two-ray reflection models,is proposed based on two *** focus is a quantum genetic algorithm applied to optimize antenna excitation in a phased directional modulation antenna *** quantum approach has strengths in convergence speed and the globe searching ability for the complicated model with the large-size antenna array and multiple *** this,a phased directional modulation transmission system can be optimized as regards communication safety and improve performance based on the constraint of the pattern of the antenna *** work can spur applications of the quantum evolutionary algorithm in directional modulation technology,which is also studied.

关键词： Directional modulation quantum genetic algorithm phased antenna array multiple reflection

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Meta Architecture for Point Cloud Analysis

arXiv

引用

arXiv 2022年

作者： Lin, Haojia Zheng, Xiawu Li, Lijiang Chao, Fei Wang, Shanshan Wang, Yan Tian, Yonghong Ji, Rongrong Media Analytics and Computing Lab Department of Artificial Intelligence School of Informatics Xiamen University China Peng Cheng Laboratory China Chinese Academy of Sciences China Samsara Inc United States National Engineering Research Center for Visual Technology Peking University China

Recent advances in 3D point cloud analysis bring a diverse set of network architectures to the field. However, the lack of a unified framework to interpret those networks makes any systematic comparison, contrast, or analysis challenging, and practically limits healthy development of the field. In this paper, we take the initiative to explore and propose a unified framework called PointMeta, to which the popular 3D point cloud analysis approaches could fit. This brings three benefits. First, it allows us to compare different approaches in a fair manner, and use quick experiments to verify any empirical observations or assumptions summarized from the comparison. Second, the big picture brought by PointMeta enables us to think across different components, and revisit common beliefs and key design decisions made by the popular approaches. Third, based on the learnings from the previous two analyses, by doing simple tweaks on the existing approaches, we are able to derive a basic building block, termed PointMetaBase. It shows very strong performance in efficiency and effectiveness through extensive experiments on challenging benchmarks, and thus verifies the necessity and benefits of high-level interpretation, contrast, and comparison like PointMeta. In particular, PointMetaBase surpasses the previous state-of-the-art method by 0.7%/1.4/%2.1% mIoU with only 2%/11%/13% of the computation cost on the S3DIS datasets. The code and models are available at https://***/linhaojia13/PointMetaBase. Copyright © 2022, The Authors. All rights reserved.

关键词： Network architecture

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Utilization of the internet of things in traditional musical instrument Talempong learning

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AIP Conference Proceedings 2024年第1期2927卷

作者： Novian Anggis Suwastika Maslin Masrom Qori Qonita Muhammad Nur Fuadi Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Kuala Lumpur Malaysia School of Computing Telkom University Bandung Indonesia Public Vocational High School 10 Bandung Bandung Indonesia

Talempong is a traditional musical instrument from the Minangkabau tribe in West Sumatra, Indonesia. The challenges of learning Talempong musical instruments are the lack of availability of Talempong musical instruments, limited trainers or music teachers to train and evaluate, and limited time for students to learn. The Internet of Things (IoT) has the ability to automate activities, integrate with various sensors, and store data on the internet that can be accessed anytime and anywhere. In this study, we developed an IoT-based Talempong musical instrument learning system. The system was built with an IoT development model consisting of five phases. Hardware components consist of LED lights, vibration sensors, microcontrollers, and wiring, whereas the software component is a website-based application that displays the results of student learning activities and programs for the Talempong training level. The communication protocol used in the system is the MQTT protocol. The system implementation results show that the IoT-based Talempong system follows the functionality and performance designed to solve the problem of learning the Talempong musical instrument. The performance results show that hardware and software components have worked, the delay from the vibration sensor to the microcontroller is always under 1 second, and the system provides 100% feedback on Talempong learning activities at level three. This prototype can be a reference for developing an application based on IoT for music education.

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