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Development,validation,and transportability of several machine-learned,non-exercise-based VO_(2max)prediction models for older adults

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Journal of Sport and Health Science 2024年第5期13卷 611-620页

作者： Benjamin T.Schumacher Michael J.LaMonte Andrea Z.LaCroix Eleanor M.Simonsick Steven P.Hooker Humberto Parada Jr. John Bellettiere Arun Kumar Herbert Wertheim School of Public Health and Human Longevity Science University of California San DiegoLa JollaCA 92093USA Department of Epidemiology and Environmental Health School of Public Health and Health ProfessionsUniversity at BuffaloState University of New YorkBuffaloNY 14214USA Translational Gerontology Branch Intramural Research ProgramNational Institute on AgingNational Institutes of HealthBaltimoreMD 21225USA College of Health and Human Services San Diego State UniversitySan DiegoCA 92182USA Division of Epidemiology and Biostatistics School of Public HealthSan Diego State UniversitySan DiegoCA 92182USA University of California San Diego Moores Cancer Center La JollaCA 92093USA Computer Science and Engineering and Halicioglu Data Science Institute University of California San DiegoLa JollaCA 92093USA

Background:There exist few maximal oxygen uptake(VO_(2max))non-exercise-based prediction equations,fewer using machine learning(ML),and none specifically for older *** direct measurement of VO_(2max)is infeasible in large epidemiologic cohort studies,we sought to develop,validate,compare,and assess the transportability of several ML VO_(2max)prediction ***:The Baltimore Longitudinal Study of Aging(BLSA)participants with valid VO2_(max)tests were included(n=1080).Least absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine(SVM)algorithms were trained to predict VO_(2max)*** developed these algorithms for:(a)the overall BLSA,(b)by sex,(c)using all BLSA variables,and(d)variables common in aging ***,we quantified the associations between measured and predicted VO_(2max)and ***:The age was 69.0±10.4 years(mean±SD)and the measured VO_(2max)was 21.6±5.9 mL/kg/*** absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine yielded root mean squared errors of 3.4 mL/kg/min,3.6 mL/kg/min,3.4 mL/kg/min,3.6 mL/kg/min,and 3.5 mL/kg/min,*** quartiles of measured VO_(2max)showed an inverse gradient in mortality *** VO_(2max)variables yielded similar effect estimates but were not robust to ***:Measured VO_(2max)is a strong predictor of *** ML can improve the accuracy of prediction as compared to simpler approaches but estimates of association with mortality remain sensitive to *** studies should seek to reproduce these results so that VO_(2max),an important vital sign,can be more broadly studied as a modifiable target for promoting functional resiliency and healthy aging.

关键词： Cardiorespiratory fitness prediction algorithms Epidemiology Mortality

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BAD-FM:Backdoor Attacks Against Factorization-Machine Based Neural Network for Tabular Data prediction

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Chinese Journal of Electronics 2024年第04期33卷 1077-1092页

作者： Lingshuo MENG Xueluan GONG Yanjiao CHEN College of Electrical Engineering Zhejiang University School of Computer Science Wuhan University

Backdoor attacks pose great threats to deep neural network models. All existing backdoor attacks are designed for unstructured data(image, voice, and text), but not structured tabular data, which has wide real-world applications, e.g., recommendation systems, fraud detection, and click-through rate prediction. To bridge this research gap, we make the first attempt to design a backdoor attack framework, named BAD-FM, for tabular data prediction models. Unlike images or voice samples composed of homogeneous pixels or signals with continuous values, tabular data samples contain well-defined heterogeneous fields that are usually sparse and discrete. Tabular data prediction models do not solely rely on deep networks but combine shallow components(e.g., factorization machine, FM) with deep components to capture sophisticated feature interactions among fields. To tailor the backdoor attack framework to tabular data models, we carefully design field selection and trigger formation algorithms to intensify the influence of the trigger on the backdoored model. We evaluate BAD-FM with extensive experiments on four datasets, i.e.,HUAWEI, Criteo, Avazu, and KDD. The results show that BAD-FM can achieve an attack success rate as high as 100%at a poisoning ratio of 0.001%, outperforming baselines adapted from existing backdoor attacks against unstructured data models. As tabular data prediction models are widely adopted in finance and commerce, our work may raise alarms on the potential risks of these models and spur future research on defenses.

关键词： Adaptation models Systematics Frequency modulation Finance Predictive models prediction algorithms Data models

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Friendship Inference Based on Interest Trajectory Similarity and Co-occurrence

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Chinese Journal of Electronics 2024年第3期33卷 708-720页

作者： Junfeng TIAN Zhengqi HOU School of Cyber Security and Computer Hebei University Hebei Key Laboratory of High Confidence Information Systems Hebei University

Most of the current research on user friendship speculation in location-based social networks is based on the co-occurrence characteristics of users,however,statistics find that co-occurrence is not common among all users;meanwhile,most of the existing work focuses on mining more features to improve the accuracy but ignoring the time complexity in practical *** this basis,a friendship inference model named ITSIC is proposed based on the similarity of user interest tracks and joint user location *** utilizing MeanShift clustering algorithm,ITSIC clustered and filtered user check-ins and divided the dataset into interesting,abnormal,and noise *** interest trajectories were constructed from user interest check-in data,which allows ITSIC to work efficiently even for users without *** the same time,by application of clustering,the single-moment multi-interest trajectory was further proposed,which increased the richness of the meaning of the trajectory *** extensive experiments on two real online social network datasets show that ITSIC outperforms existing methods in terms of AUC score and time efficiency compared to existing methods.

关键词： Data privacy Social networking (online) Noise Clustering algorithms prediction algorithms Inference algorithms Data models

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An Interactive Perception Method Based Collaborative Rating prediction Algorithm

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Chinese Journal of Electronics 2023年第1期32卷 97-110页

作者： YAN Wenjie ZHANG Jiahao LI Ziqi School of Artificial Intelligence Hebei University of Technology

To solve the rating prediction problems of low accuracy and data sparsity on different datasets,we propose an interactive perception method based collaborative rating prediction algorithm named DCAE-MF,by fusing dual convolutional autoencoder(DCAE) and probability matrix factorization(PMF). Deep latent representations of users and items are captured simultaneously by DCAE and are deeply integrated with PMF to collaboratively make rating predictions based on the known rating history of users. A global multi-angle collaborative optimization learning method is developed to effectively optimize all the parameters of DCAE-MF. Extensive experiments are performed on seven real-world datasets to demonstrate the superiority of DCAE-MF on key rating accuracy metrics of the root mean squared error(RMSE) and mean absolute error(MAE).

关键词： Measurement Learning systems Collaboration prediction algorithms History Optimization

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A Novel Method for Maximum Power Point Tracking of the Grid-Connected Three-Phase Solar Systems Based on the PV Current prediction

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Chinese Journal of Electronics 2023年第2期32卷 353-364页

作者： Saeid Bairami Mahdi Salimi Davar Mirabbasi Department of Electrical Engineering Ardabil Branch Islamic Azad University Faculty of Engineering and Science University of Greenwich

In this paper, it is first attempted to provide a small signal model of the photovoltaic(PV) system, DC-DC boost converter, and pulse width modulation(PWM) generator. Then, a technique is provided for maximum power point tracking(MPPT) in grid-connected solar systems based on variable and adaptive perturbation and observation with predictive control of the PV current. An innovative aspect of the proposed predictive current control method is to use the current controller to achieve the value of PV impedance, which has been used in DC-DC boost converter. The proposed method is to obtain the coming current value on the basis of the current predictive model. The goal of the proposed method is to make the DC-DC boost converter inductor current track the current reference. Voltage and current ripple minimization is added to the cost function simultaneously as a system constraint to optimize system *** reduces the amount of voltage and current fluctuations around the maximum power point. The proposed method is capable of detecting rapid changes in solar radiation. A sudden and simultaneous increase in voltage and current is detected by the algorithm and then the duty cycle becomes increasing instead of decreasing. The simulation is carried out in MATLAB Simulink environment in real-time for a 26.6 k W three-phase grid-connected solar system. The simulation results of current predictive control are compared with perturbation and observation techniques and linear voltage and current proportional integral derivative(PID) controller-based adaptive *** results show that the total harmonic distortion(THD%) of the inverter voltage with proposed method has been reduced by 0.16% compared to the PID *** addition, the THD% of the current in the proposed method is reduced by 0.1% compared to the PID *** solar system output voltage variation of the proposed method is less than 5 V.

关键词： Maximum power point trackers Fluctuations Voltage fluctuations Simulation prediction algorithms Inverters Real-time systems

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A Risk prediction Model Based on Crash History Data for Railway Trams

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Chinese Journal of Electronics 2023年第5期32卷 963-971页

作者： JI Wenjiang YANG Jiangcheng WANG Yichuan ZHU Lei QIU Yuan HEI Xinhong School of Computer Science and Engineering Xi’an University of Technology

Risk prediction is an important task to ensuring the driving safety of railway trams. Although data-driven intelligent methods are proved to be effective for driving risk prediction, accuracy is still a top concern for the challenges of data quality which mainly represent as the unbalanced datasets. This study focuses on applying feature extraction and data augmentation methods to achieve effective risk prediction for railway trams, and proposes an approach based on a self-adaptive K-means clustering algorithm and the least squares deep convolution generative adversarial network(LS-DCGAN). The data preprocessing methods are proposed, which include the K-means algorithm to cluster the locations of trams and the extreme gradient boosting recursive feature elimination based feature selection algorithm to retain the key features. The LS-DCGAN model is designed for sparse sample expansion, aiming to address the sample category distribution imbalance problem. The experiments implemented with the public and real datasets show that the proposed approach can reach a high accuracy of 90.69%,which can greatly enhances the tram driving safety.

关键词： Solid modeling Clustering algorithms Feature extraction prediction algorithms Rail transportation Safety History

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AttrLeaks on the Edge: Exploiting Information Leakage from Privacy-Preserving Co-inference

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Chinese Journal of Electronics 2023年第1期32卷 1-12页

作者： WANG Zhibo LIU Kaixin HU Jiahui REN Ju GUO Hengchang YUAN Wei School of Cyber Science and Engineering Wuhan University School of Cyber Science and Technology Zhejiang University Department of Computer Science and Technology Tsinghua University

Collaborative inference(co-inference) accelerates deep neural network inference via extracting representations at the device and making predictions at the edge server, which however might disclose the sensitive information about private attributes of users(e.g.,race). Although many privacy-preserving mechanisms on co-inference have been proposed to eliminate privacy concerns, privacy leakage of sensitive attributes might still happen during inference. In this paper, we explore privacy leakage against the privacy-preserving co-inference by decoding the uploaded representations into a vulnerable form. We propose a novel attack framework named AttrL eaks, which consists of the shadow model of feature extractor(FE), the susceptibility reconstruction decoder,and the private attribute classifier. Based on our observation that values in inner layers of FE(internal representation) are more sensitive to attack, the shadow model is proposed to simulate the FE of the victim in the blackbox scenario and generates the internal ***, the susceptibility reconstruction decoder is designed to transform the uploaded representations of the victim into the vulnerable form, which enables the malicious classifier to easily predict the private attributes. Extensive experimental results demonstrate that AttrLeaks outperforms the state of the art in terms of attack success rate.

关键词： Deep learning Privacy Collaboration Transforms Feature extraction prediction algorithms Iron

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Towards V2I Age-Aware Fairness Access: A DQN Based Intelligent Vehicular Node Training and Test Method

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Chinese Journal of Electronics 2023年第6期32卷 1230-1244页

作者： WU Qiong SHI Shuai WAN Ziyang FAN Qiang FAN Pingyi ZHANG Cui School of Internet of Things Engineering Jiangnan University State Key Laboratory of Integrated Services Networks Xidian University Qualcomm Beijing National Research Center for Information Science and Technology Tsinghua University Banma Network Technology Co. Ltd.

Vehicles on the road exchange data with base station frequently through vehicle to infrastructure(V2I) communications to ensure the normal use of vehicular applications, where the IEEE 802.11 distributed coordination function is employed to allocate a minimum contention window(MCW) for channel access. Each vehicle may change its MCW to achieve more access opportunities at the expense of others, which results in unfair communication performance. Moreover, the key access parameter MCW is privacy information and each vehicle is not willing to share it with other vehicles. In this uncertain setting, age of information(Ao I), which measures the freshness of data and is closely related with fairness, has become an important communication *** this basis, we design an intelligent vehicular node to learn the dynamic environment and predict the optimal MCW, which can make the intelligent node achieve age fairness. In order to allocate the optimal MCW for the vehicular node, we employ a learning algorithm to make a desirable decision by learning from replay history data. In particular, the algorithm is proposed by extending the traditional deep-Q-learning(DQN) training and testing method. Finally, by comparing with other methods, it is proved that the proposed DQN method can significantly improve the age fairness of the intelligent node.

关键词： Training Privacy Heuristic algorithms Vehicle-to-infrastructure Roads Measurement uncertainty prediction algorithms

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PDSMA:Pseudo-Deterministic Statistical Multiple Access for Voice Packets Uplink Scheduling

PDSMA:Pseudo-Deterministic Statistical Multiple Access for V...

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2008 IEEE International Conference on Communications(ICC 2008)

作者： Marwen Abdennebi Samir Tohmé PRiSM Laboratory CNRS-UMR 8144 University of Versailles-France

Statistical multiple access gives opportunity to decrease bandwidth wastes in radio cellular networks when sources activities are ***,2 states exponential ON-OFF models are used for voice sources and to study statistical multiplexing in *** this paper,we propose a distributed algorithm based on a 3-states model with downlink activity detection for a refined *** proposed algorithm makes the distinction between silent sources according to their downlink activities to enhance the statistical multiple access for ***,silent sources are pre-allocated available channels on the uplink according to their respective switching probabilities;hence the designation "pseudo-deterministic" for our proposed statistical multiple access *** evaluation （analytical and simulation model） is given to illustrate the benefits of our proposal.

关键词： Telephony Downlink Analytical models Bandwidth Land mobile radio cellular systems Distributed algorithms Predictive models prediction algorithms Probability Performance analysis

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