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Smartwatch User Authentication Based on the Arm-Raising Gesture

INTERACTING WITH COMPUTERS 2020年第5-6期32卷 569-580页

作者： Zhao, Yanchao Gao, Ran Tu, Huawei Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211100 Jiangsu Peoples R China La Trobe Univ Dept Comp Sci & Informat Technol Melbourne Vic 3086 Australia

Smartwatches have arguably become a popular wearable device nowadays. It is important to protect privacy data stored in smartwatches from being stolen. This study proposes a novel smartwatch user authentication technique based on the arm-raising gesture, which is the process of moving the arm from one side of the body to the chest height. We conducted two experiments to verify the effectiveness of the proposed technique. In Experiment 1, we investigated the performance of identifying users with the arm-raising gesture. We selected a set of features and applied them to five basic machine learning algorithms (i.e. random forest, simple logistic, naive Bayes, multilayer perceptron and linear classifier). Results with 32 participants show that with combined features, these classifiers generally achieved high authentication accuracy with high true accept rate (TAR) (92.1% for random forest, simple logistic and multilayer perceptron), low false accept rate (FAR) (0.6%) and large area under the curve (AUC) of receiver operating characteristics) (92.4%). In Experiment 2, we examined the performance of identifying the arm-raising gesture across different day-to-day gestures. Results show that the arm-raising gesture can be identified from other eight common gestures with high TAR (99.5%), low FAR (3.6%) and large AUC (99%). Overall, the results indicate that our technique could be a viable alternative for smartwatch user authentication. RESEARCH HIGHLIGHTS A first study was conducted to investigate smartwatch user authentication based on the arm-raising gesture. The arm-raising gesture is distinguishable among users with basic classifiers. The arm-raising can be identified from other common day-to-day gestures with basic classifiers. The study provides insights into applying the arm-raising gesture to user authentication for smartwatches.

关键词： authentication arm-raising gesture gesture identification smartwatches machine learning algorithms

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Anomaly Detection Based on Multidimensional Data Processing for Protecting Vital Devices in 6G-Enabled Massive IIoT

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IEEE INTERNET OF THINGS JOURNAL 2021年第7期8卷 5219-5229页

作者： Han, Guangjie Tu, Juntao Liu, Li Martinez-Garcia, Miguel Peng, Yan Hohai Univ Coll Internet Things Engn Changzhou 213022 Peoples R China Loughborough Univ Dept Aeronaut & Automot Engn Loughborough LE11 3TU Leics England Shanghai Univ Sch Artificial Intelligence Shanghai 200000 Peoples R China

As a result of the increasing deployment of Industrial-Internet-of-Things (IIoT) architectures, large volumes of multidimensional data are continuously generated. An important issue with these data is that higher dimensionality increases the degree of fragmentation. Furthermore, data sets collected by IIoT nodes often display outliers, which are usually caused by anomalous events or errors. These outliers contain considerable valuable information, which prevent the normal operation of the system. Thus, methodologies are able to quantify the obtained information to protect the high priority IIoT nodes, are crucial. This study aims at developing such a method driven by sixth-generation (6G) networks. The proposed algorithm uses a multidimensional data relationship diagram to characterize the spatiotemporal correlations among heterogeneous data. Then, an autoregressive exogenous model is used to eliminate the effects of noise on sensor data, and to help in detecting anomalies. Finally, the algorithm produces a Cumulative Coefficient of Value (CCoV), to identify high-value sensing devices and enable massive Internet of Things (IoT) with 6G-using the characteristic patterns hidden within the data. The experimental results demonstrate that the proposed method can effectively handle the effects of the ubiquitous interference noise in complex industrial environments. Moreover, the method yields effective anomaly detection and compensates for some of the shortcomings in traditional methods.

关键词： Industrial Internet of Things Anomaly detection 6G mobile communication Principal component analysis machine learning algorithms Spatiotemporal phenomena Data models Artificial intelligence Industrial Internet of Things (IIoT) multidimensional data processing (MDP) sixth-generation (6G) networks

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An Adaptive Clustering Algorithm Based on Local-Density Peaks for Imbalanced Data Without Parameters

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第4期35卷 3419-3432页

作者： Tong, Wuning Wang, Yuping Liu, Delong Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China Shaanxi Univ Chinese Med Dept Sci & Technol Xianyang 712046 Shaanxi Peoples R China

Imbalanced data clustering is a challenging problem in machine learning. The main difficulty is caused by the imbalance in both cluster size and data density distribution. To address this problem, we propose a novel clustering algorithm called LDPI based on local-density peaks in this study. First, an initial sub-cluster construction scheme is designed based on a 3-dimensional (3-D) decision graph that can easily detect the initial sub-cluster centers and identify the noise points. Second, a sub-cluster updating strategy is designed, which can automatically identify the false sub-cluster centers and update the initial sub-clusters. Third, a sub-cluster merging scheme is designed, which merges the updated initial sub-clusters into final clusters. Consequently, the proposed algorithm has three advantages: 1) It does not require any input parameters;2) It can automatically determine the cluster centers and number of clusters;3) It is suitable for imbalanced datasets and datasets with arbitrary shapes and distributions. The effectiveness of LDPI is demonstrated experimentally and the superiority of LDPI is identified by comparison with 5 state-of-the-art algorithms.

关键词： Clustering algorithms machine learning algorithms machine learning Computer science Clustering methods Task analysis Shape Data clustering density peaks imbalanced data multiple centers

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A novel Parkinson's Disease Diagnosis Index using higher-order spectra features in EEG signals

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NEURAL COMPUTING & APPLICATIONS 2018年第4期30卷 1225-1235页

作者： Yuvaraj, Rajamanickam Acharya, U. Rajendra Hagiwara, Yuki SSN Coll Engn Dept Biomed Engn Kalavakkam 603110 Tamil Nadu India Ngee Ann Polytech Dept Elect & Comp Engn Clementi 599489 Singapore Univ Kentucky Dept Biomed Engn Neural Syst Lab 143 Graham Ave Lexington KY 40506 USA

Higher-order spectra (HOS) is an efficient feature extraction method used in various biomedical applications such as stages of sleep, epilepsy detection, cardiac abnormalities, and affective computing. The motive of this work was to explore the application of HOS for an automated diagnosis of Parkinson's disease (PD) using electroencephalography (EEG) signals. Resting-state EEG signals collected from 20 PD patients with medication and 20 age-matched normal subjects were used in this study. HOS bispectrum features were extracted from the EEG signals. The obtained features were ranked using t value, and highly ranked features were used in order to develop the PD Diagnosis Index (PDDI). The PDDI is a single value, which can discriminate the two classes. Also, the ranked features were fed one by one to the various classifiers, namely decision tree (DT), fuzzy K-nearest neighbor (FKNN), K-nearest neighbor (KNN), naive bayes (NB), probabilistic neural network (PNN), and support vector machine (SVM), to choose the best classifier using minimum number of features. We have obtained an optimum mean classification accuracy of 99.62%, mean sensitivity and specificity of 100.00 and 99.25%, respectively, using the SVM classifier. The proposed PDDI can aid the clinicians in their diagnosis and help to test the efficacy of drugs.

关键词： Electroencephalogram Parkinson's disease Higher-order spectra machine learning algorithms Diagnosis index

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Scientometric Analysis of Quantum algorithms for VANET Optimization

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第10期54卷 6447-6457页

作者： Pooja Sood, Sandeep Kumar Natl Inst Technol Kurukshetra Dept Comp Applicat Kurukshetra 136119 Haryana India

The rapid proliferation of quantum information technologies, spanning theoretical investigations to practical experiments, has generated a number of research papers and documents in quantum algorithms. Consequently, the current research serves as a gateway for interested readers to comprehend the status quo of quantum algorithms, with a specific focus on vehicular network optimization. It aims to explore the research patterns and latest trends by analyzing the dataset sourced from the Scopus and Web of Science databases. The scientometric implications offer valuable insights into publication patterns, keyword co-occurrence, author co-citation, country collaboration, and burst reference. These analyses delineate the temporal progression, prominent research topics, emerging research areas, leading collaborative nations, prolific authors, and research trends within this knowledge domain. The results reveal that smart power grids, traveling salesman problem, electric vehicle charging, battery life estimation, and air traffic control are emerging research areas. Similarly, quantum approximate optimization algorithms, adiabatic quantum computing, quantum-inspired evolutionary algorithms, and quantum annealing emerge as prominent quantum algorithms employed for vehicular network optimization problems. In addition, systematic literature analysis is objectively conducted to discern key insights, research challenges and future research directions in the current knowledge domain.

关键词： Quantum computing Optimization Vehicular ad hoc networks machine learning algorithms Quantum mechanics Market research Bibliometrics CiteSpace quantum algorithm taxonomy quantum optimization vehicular ad-hoc network (VANET)

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On-Shelf Load Cell Calibration for Positioning and Weighing Assisted by Activity Detection: Smart Store Scenario

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IEEE SENSORS JOURNAL 2022年第4期22卷 3455-3463页

作者： Lin, Ming-Hong Sarwar, Muhammad Atif Daraghmi, Yousef-Awwad Ik, Tsi-Ui Natl Yang Ming Chiao Tung Univ Coll Comp Sci Dept Comp Sci Hsinchu 30010 Taiwan Palestine Tech Univ Kadoorie Dept Comp Syst Engn Kadoorie Tulkarem Palestine

Accurate tracking of shelves in markets is necessary for efficient and real-time management of stock and products. Smart technologies assist in determining the positions of items and the movement to or from the shelves. Recently, several shelf tracking systems have been proposed utilizing sensor fusion, image processing, deep learning, and the Internet of Things. However, these systems suffer from the imbalance calibration of load cell sensors, high cost of hardware installation, and low positioning accuracy. Therefore, we propose an accurate online Weighing Distribution Positional Model that enables accurate load cell calibration and tracks item position and weight on shelves utilizing the load cells sensor fusion and deep learning. The load cell sensors are used to weigh shelves, and cameras are used to record the item's movement which is analyzed by image processing. Four well-known machine learning algorithms are tested on the system to identify the best operative environment. The system and the algorithms are evaluated using a real-world dataset consisting of item weights, and the Artificial Neural Network (ANN) was found more accurate than the others with 97.61% accuracy and zero error tolerance.

关键词： Intelligent sensors Calibration machine learning algorithms Deep learning Support vector machines Radiofrequency identification Internet of Things Load cell sensors positional algorithm smart shelf smart store calibration

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The Sparse Principal Component of a Constant-Rank Matrix

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IEEE TRANSACTIONS ON INFORMATION THEORY 2014年第4期60卷 2281-2290页

作者： Asteris, Megasthenis Papailiopoulos, Dimitris S. Karystinos, George N. Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA Tech Univ Crete Dept Elect & Comp Engn Khania 73100 Greece

The computation of the sparse principal component of a matrix is equivalent to the identification of its principal submatrix with the largest maximum eigenvalue. Finding this optimal submatrix is what renders the problem NP-hard. In this paper, we prove that, if the matrix is positive semidefinite and its rank is constant, then its sparse principal component is polynomially computable. Our proof utilizes the auxiliary unit vector technique that has been recently developed to identify problems that are polynomially solvable. In addition, we use this technique to design an algorithm which, for any sparsity value, computes the sparse principal component with complexity O(ND+1), where N and D are the matrix size and rank, respectively. Our algorithm is fully parallelizable and memory efficient.

关键词： Eigenvalues and eigenfunctions feature extraction information processing machine learning algorithms principal component analysis signal processing algorithms

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Development of an Extreme Gradient Boosting Model Integrated With Evolutionary algorithms for Hourly Water Level Prediction

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IEEE ACCESS 2021年 9卷 125853-125867页

作者： Nguyen, Duc Hai Hien Le, Xuan Heo, Jae-Yeong Bae, Deg-Hyo Sejong Univ Dept Civil & Environm Engn Seoul 143747 South Korea Thuyloi Univ Fac Water Resources Engn Hanoi 116705 Vietnam

The establishment of reliable water level prediction models is vital for urban flood control and planning. In this paper, we develop hybrid models (GA-XGBoost and DE-XGBoost) that couple two evolutionary models, a genetic algorithm (GA) and a differential evolution (DE) algorithm, with the extreme gradient boosting (XGBoost) model for hourly water level prediction. The Jungrang urban basin located on the Han River, South Korea, was selected as a case study for the proposed models. Hourly rainfall and water level data were collected between 2003 and 2020 to construct and evaluate the performance of the selected models. To compare the prediction efficiency, two other tree-based models were chosen: classification and registration tree (CART) and random forest (RF) models. A comparison of the results showed that two hybrid models, GA-XGBoost and DE-XGBoost, outperformed RF and CART in the multistep-ahead prediction of water level, and the relative errors of the hybrid model ranged from [2.18%-9.21%], compared to [3.76%-10.41%] and [2.99%-11.88%] for the RF and CART, respectively. Reliable performance was also supported by other measures. In general, the GA-XGBoost and DE-XGBoost models displayed relatively similar performance despite their small differences. The CART model was not preferable for multistep-ahead water level predictions, even though it yielded the lowest Akaike information criterion (AIC) value. This study verifies that despite having some drawbacks when considering long step-ahead prediction and model complexity, hybrid XGBoost models might be superior to many existing models for hourly water level prediction.

关键词： Predictive models Radio frequency Floods machine learning algorithms Genetic algorithms Urban areas Prediction algorithms Extreme gradient boosting evolutionary algorithms water level prediction tree-based model urban floods

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machine Fault Detection for Intelligent Self-Driving Networks

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IEEE COMMUNICATIONS MAGAZINE 2020年第1期58卷 40-46页

作者： Huang, Huakun Zhao, Lingjun Huang, Huawei Guo, Song Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China Univ Aizu Sch Comp Sci & Engn Aizu Wakamatsu Fukushima Japan Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

To build the mechanism of a SelfDN is becoming an emerging direction for future IoT. The detection of device status, such as fault or normal, is a very fundamental module in SelfDN. In this article, we first review recent studies devoted to applying fault detection techniques in IoT networks. Taking the challenge of processing the real-valued IoT data into account, we propose a novel fault detection architecture for SelfDN. Under this architecture, we present an algorithm, named GBRBM-based deep neural network with auto-encoder (i.e., GBRBM-DAE) to transform the fault detection problem into a classification problem. The real-world trace-driven experimental results show that the proposed algorithm outperforms other popular machine learning algorithms, including linear discriminant analysis, support vector machine, pure deep neural network, and so on. Finally, we summarize some open issues of this study. We expect that this article will inspire successive studies on the related topics of SelfDN.

关键词： Fault detection Feature extraction machine learning algorithms Neural networks Support vector machines Real-time systems Sensors

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A Comparison Study About Parameter Optimization Using Swarm algorithms

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IEEE ACCESS 2022年 10卷 55488-55498页

作者： Pereira De Carvalho, Halcyon Davys Soares, Wedson L. Santos, Wylliams Barbosa Fagundes, Roberta Univ Pernambuco Dept Comp Engn BR-50720001 Recife PE Brazil

Adjusting the parameters of a machine learning algorithm can be difficult if the possible domain of expansion of these parameters is too high. In addition, if a sensible parameter is not adjusted correctly, the changes can be very impactful in the final results, making adjusting it manually not trivial. In order to adjust these features automatically, the current work proposes six models based on the use of optimization algorithms to adjust the models' parameters automatically. These models were built around two machine learning-based algorithms, an extreme learning machine neural network, and a support vector regression. The optimization algorithms used are Particle Swarm Optimization, the Artificial Bee Colony, and the genetic algorithm. The models were compared with each other based on predictive precision in the criterion of Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and statistical tests. The experimental results on ten datasets in different contexts indicated that optimized algorithms models perform better in convergence, precision, and robustness than the non-optimized algorithms models. Therefore, the automatic adjustment of the parameters of optimized algorithms is a powerful tool for analyzing different data contexts. Thus, this study shows that the optimized algorithm models (in particular the ELM PSO model) are more accurate than all experimental evaluations.

关键词： Optimization Training Particle swarm optimization machine learning algorithms Genetic algorithms Prediction algorithms Predictive models machine learning extreme learning machine support vector regression ensemble optimization algorithm

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