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IEEE Conference on Wireless Communications and Networking

作者： Jiangtian Nie Jun Luo Zehui Xiong Dusit Niyato Ping Wang Yang Zhang ERI@N Interdisciplinary Graduate Programme Nanyang Technological University (NTU) Singapore School of Computer Science and Engineering Nanyang Technological University (NTU) Singapore York University Canada Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology Wuhan China

ISBN: (纸本)9781728195056;9781728195063

As the paradigm of crowdsensing involves the data collection from users, the issue of designing reward to incentivize the users is fundamentally important to be addressed, thereby effectively enhancing the participation. In this paper, we revisit this issue in the context of socially-aware crowdsensing which integrates crowdsensing into social networks. For example, in crowdsensing-based healthcare services, the accuracy of diet recommendation for a certain user can be promoted by exploiting the nutritional information contributed and shared by the socially-connected friends of him/her taking similar types of food. To be more general and practical, we study the incentive schemes in presence of multiple crowdsensing service providers and multiple users. Understanding the behaviors of users and service providers in socially-aware crowdsensing is of paramount importance for incentive schemes. Considering this, we propose a multi-leader and multi-follower Stackelberg game approach to model the strategic interactions among service providers and users, where the social influence of users and the strategic interconnections of service providers are jointly and formally integrated into the game modeling. Through backward induction methods, we theoretically validate the existence and uniqueness of the Stackelberg equilibrium. Simulations are conducted to evaluate game equilibrium properties, and the results are presented to assess and demonstrate the performance effectiveness of the proposed game model.

关键词： Crowdsensing Social networking (online) System performance Conferences Games Medical services Data collection

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Learning from Data: Cleft Lip and Palate Patients in the West Coast of Sabah 12

Learning from Data: Cleft Lip and Palate Patients in the Wes...

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12th Seminar on science and Technology 2018

作者： Omar, Z.A. Chin, S.N. Hamzah, Norhafiza Yassin, Fouziah Mathematics with Computer Graphics Programme Faculty of Science and Natural Resources Universiti Malaysia Sabah Kota Kinabalu Sabah88400 Malaysia Mathematics with Economy Programme Faculty of Science and Natural Resources Universiti Malaysia Sabah Kota Kinabalu Sabah88400 Malaysia Physic with Electronics Programme Faculty of Science and Natural Resources Universiti Malaysia Sabah Kota Kinabalu Sabah88400 Malaysia

Analysing data can be quite a challenge sometimes due to the nature of the data and the vast options of methods and techniques that can be used on the data. In this study, for example, a six years Cleft Lip and Palate dataset were gathered on these patients' conditions in the quest to identify the contributing factors for a successful pre-graft orthodontic treatment. The challenges faced was in the small number of datasets and imbalance sample class. Therefore, this study had taken a step back and tried to approach the dataset with a combination of unsupervised and supervised learning methods to tackle the challenges by incorporating clustering - for testing records creation and;resampling - for balancing sample class. We also observed if the auto-created testing records are replaceable with the manually selected testing records by looking at the performances of the classification models. Based on the feature that was selected, k-Means and PAM were implemented as the clustering algorithm using the Euclidean formula as the distance measure. Resampling was done using SMOTE and Random Forest as the classification model. When the comparison was done on the models, the ones that were fed by resampled training records showed an increase in the AUC values and decrease in the OOB error. Comparable results were also achieved between the training records produced by PAM and by manual selection as both models, based on the AUC values, was classified as excellent classification models. © Published under licence by IOP Publishing Ltd.

关键词： Statistical tests

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Phishing Detection Using Significant Feature Selection

Phishing Detection Using Significant Feature Selection

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： D. N. Goswami Manali Shukla Anshu Chaturvedi S.O.S.in Computer Science And Applications Jiwaji University Gwalior India Department Of CSE & IT Programme M.C.A Madhav Institute Of Technology &Science Gwalior India

ISBN: (数字)9781728149769

ISBN: (纸本)9781728149776

Growth of cyber attacks is rapidly increasing in the entire world. To provide prevention from these attacks is a great challenge for the experts. Intruders are keep on adapting new methods and techniques to carry out their malicious goals. Phishing plays a dominant role in the field of web attacks and it has been used as a weapon by the attackers. In this paper we have given two algorithmic approaches to the problem of Phishing identification with reduced number of attributes. It makes this approach simple yet efficient. The first algorithm assigns weight to all attributes with respect to uniform resource locators. We have employed various analysis mechanism to identify significant role of selected attributes for the purpose of Phishing identification. The second approach takes former's output as input and classifies the uniform resource locators labeling as phishing or non phishing. The experimental work verifies that the approach for phishing detection proposed in this paper can attain a high accuracy in comparison to existing algorithms.

关键词： Cybercrime Phishing Phishers subdomain url Weka phishing computer information security Uniform Resource Locator computer crime Gallirallus australis sub-domain Feature extraction

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Collaborative Learning of Gesture Recognition and 3D Hand Pose Estimation with Multi-order Feature Analysis 16th

Collaborative Learning of Gesture Recognition and 3D Hand Po...

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16th European Conference on computer Vision, ECCV 2020

作者： Yang, Siyuan Liu, Jun Lu, Shijian Er, Meng Hwa Kot, Alex C. Lab Interdisciplinary Graduate Programme Nanyang Technological University Singapore Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9783030585792

Gesture recognition and 3D hand pose estimation are two highly correlated tasks, yet they are often handled separately. In this paper, we present a novel collaborative learning network for joint gesture recognition and 3D hand pose estimation. The proposed network exploits joint-aware features that are crucial for both tasks, with which gesture recognition and 3D hand pose estimation boost each other to learn highly discriminative features. In addition, a novel multi-order multi-stream feature analysis method is introduced which learns posture and multi-order motion information from the intermediate feature maps of videos effectively and efficiently. Due to the exploitation of joint-aware features in common, the proposed technique is capable of learning gesture recognition and 3D hand pose estimation even when only gesture or pose labels are available, and this enables weakly supervised network learning with much reduced data labeling efforts. Extensive experiments show that our proposed method achieves superior gesture recognition and 3D hand pose estimation performance as compared with the state-of-the-art. © 2020, Springer Nature Switzerland AG.

关键词： Gesture recognition

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Coastalwql for Stitching, Pre-Processing and Visualization of Push Broom Hyperspectral Imagery for Uav-Based Coastal Water Quality Monitoring

SSRN

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SSRN 2023年

作者： Pak, Hui Ying Kieu, Hieu Trung Law, Adrian Wing-Keung Lin, Weisi Khoo, Eugene Environmental Process Modelling Centre Nanyang Environment and Water Research Institute Nanyang Technological University 1 CleanTech Loop 637141 Singapore Interdisciplinary Graduate Programme Graduate College Nanyang Technological University 61 Nanyang Drive 637335 Singapore School of Civil and Environmental Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore School of Computer Science and Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore Engineering and Project Management Division Maritime and Port Authority of Singapore Singapore119963 Singapore

In recent years, Unmanned-Aerial Vehicles (UAVs) and hyperspectral sensors are emerging as popular alternatives for monitoring water quality on-demand. However, image stitching for largely featureless scenes such as open seas has shown to be challenging, and few studies have delved into this domain for push broom hyperspectral images. This study introduces CoastalWQL, with a proposed workflow that entails a GPS-based image stitching method with time delay correction, on top of essential pre-processing procedures to improve the quality of the image mosaic such as sun glint correction and object classification using either an XGBoost and U-net model. CoastalWQL also provides the flexibility for users to specify a trained Deep Neural Network (DNN) or XGBoost model to produce a prediction map for water quality monitoring applications, and a visualization platform to efficiently generate production-ready maps. The results of CoastalWQL have been validated with field surveys at the coastal region of Singapore. © 2023, The Authors. All rights reserved.

关键词： Remote sensing

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Volatile organic compounds sensing based on Bennet doubler-inspired triboelectric nanogenerator and machine learning-assisted ion mobility analysis

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science Bulletin 2021年第12期66卷 1176-1185,M0003页

作者： Jianxiong Zhu Zhongda Sun Jikai Xu Rafal D.Walczak Jan ADziuban Chengkuo Lee School of Mechanical Engineering.Southeast University Nanjing 211189China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117576Singapore Center for Intelligent Sensors and MEMS(CISM) National University of SingaporeSingapore 117576Singapore NUS Suzhou Research Institute(NUSRI) Suzhou 215123China Department of Mircroengineering and Photovoltaics Wroclaw University of Science and TechnologyWroclaw 50-370Poland Integrative Sciences and Engineering Programme(ISEP) National University of Singapore.Singapore 119077Singapore

Ion mobility analysis is a well-known analytical technique for identifying gas-phase compounds in fastresponse gas-monitoring ***,the conventional plasma discharge system is bulky,operates at a high temperature,and inappropriate for volatile organic compounds(VOCs)concentration ***,we report a machine learning(ML)-enhanced ion mobility analyzer with a triboelectric-based ionizer,which offers good ion mobility selectivity and VOC recognition ability with a small-sized device and non-strict operating *** on the charge accumulation mechanism,a multi-switched manipulation triboelectric nanogenerator(SM-TENG)can provide a direct current(DC)bias at the order of a few hundred,which can be further leveraged as the power source to obtain a unique and repeatable discharge characteristic of different VOCs,and their mixtures,with a special tip-plate electrode *** to tackle the grand challenge in the detection of multiple VOCs,the ML-enhanced ion mobility analysis method was successfully demonstrated by extracting specific features automatically from ion mobility spectrometry data with ML algorithms,which significantly enhance the detection ability of the SM-TENG based VOC analyzer,showing a portable real-time VOC monitoring solution with rapid response and low power consumption for future internet of things based environmental monitoring applications.

关键词： Machine learning Volatile organic compounds Ion mobility Triboelectric nanogenerator Plasma discharge

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USEV: Universal Speaker Extraction with Visual Cue

arXiv

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arXiv 2021年

作者： Pan, Zexu Ge, Meng Li, Haizhou The Integrative Sciences and Engineering Programme The Institute of Data Science The Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore The Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore The Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin300072 China The School of Data Science The Chinese University of Hong Kong Shenzhen518172 China The University of Bremen 28359 Germany Kriston AI Xiamen China

A speaker extraction algorithm seeks to extract the target speaker's speech from a multi-talker speech mixture. The prior studies focus mostly on speaker extraction from a highly overlapped multi-talker speech mixture. However, the target-interference speaker overlapping ratios could vary over a wide range from 0% to 100% in natural speech communication, furthermore, the target speaker could be absent in the speech mixture, the speech mixtures in such universal multi-talker scenarios are described as general speech mixtures. The speaker extraction algorithm requires an auxiliary reference, such as a video recording or a pre-recorded speech, to form top-down auditory attention on the target speaker. We advocate that a visual cue, i.e., lip movement, is more informative than an audio cue, i.e., pre-recorded speech, to serve as the auxiliary reference for speaker extraction in disentangling the target speaker from a general speech mixture. In this paper, we propose a universal speaker extraction network with a visual cue, that works for all multi-talker scenarios. In addition, we propose a scenario-aware differentiated loss function for network training, to balance the network performance over different target-interference speaker pairing scenarios. The experimental results show that our proposed method outperforms various competitive baselines for general speech mixtures in terms of signal fidelity. Copyright © 2021, The Authors. All rights reserved.

关键词： Mixtures

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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Selective Listening by Synchronizing Speech with Lips

arXiv

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arXiv 2021年

作者： Pan, Zexu Tao, Ruijie Xu, Chenglin Li, Haizhou The Integrative Sciences and Engineering Programme The Institute of Data Science National University of Singapore 119077 Singapore The Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore Kuaishou Technology Shenzhen518063 China The Chinese University of Hong Kong Shenzhen China The University of Bremen Bremen28359 Germany

A speaker extraction algorithm seeks to extract the speech of a target speaker from a multi-talker speech mixture when given a cue that represents the target speaker, such as a pre-enrolled speech utterance, or an accompanying video track. Visual cues are particularly useful when a pre-enrolled speech is not available. In this work, we don’t rely on the target speaker’s pre-enrolled speech, but rather use the target speaker’s face track as the speaker cue, that is referred to as the auxiliary reference, to form an attractor towards the target speaker. We advocate that the temporal synchronization between the speech and its accompanying lip movements is a direct and dominant audio-visual cue. Therefore, we propose a self-supervised pre-training strategy, to exploit the speech-lip synchronization cue for target speaker extraction, which allows us to leverage abundant unlabeled in-domain data. We transfer the knowledge from the pre-trained model to the attractor encoder of the speaker extraction network. We show that the proposed speaker extraction network outperforms various competitive baselines in terms of signal quality, perceptual quality, and intelligibility, achieving state-of-the-art performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Extraction

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Spider Plot Model for Analysis of Individual Appraisal Performance Towards Career Planning in Organizations 1

Spider Plot Model for Analysis of Individual Appraisal Perfo...

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1st International Conference on computer, science, Engineering and Technology, ICComSET 2018

作者： Djufri, Muhamad A Tambi, Abdul Malek Bin Mamat, Mustafa Sukono Budiono Hadi, Ismanto Bandung Indonesia Faculty of Economics and Management Science Universiti Sultan Zainal Abidin Malaysia Faculty of Informatice and Computer Science Universiti Sultan Zainal Abidin Malaysia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Padjadjaran Malaysia Master Programme in Management Post Graduate Programme Universitas Narotama Surabaya Indonesia

Career planning at the organizational level must be translated into career planning at the individual employee level. Performance appraisal can be used more broadly so that it can be the main tool for companies in employee career planning. The purpose of this study is to measure how much influence the Performance Evaluation of Career Planning from employees who have occupied managerial levels goes to the next career level. In this paper, analysis of individual employee performance is carried out using the Spider Plot model. The results of analyzing individual performance from managers in different units can explain quickly and easily the appearance of the Spider Plot diagram. So, it is expected that individual career planning can be easily mapped through performance appraisal so that it can be used in career planning. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Managers

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