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autoencoder-enabled eye-tracking data analytics for objective assessment of user preference in humanoid robot appearance design

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartC期249卷

作者： Li, Fan Chen, Chun-Hsien Liu, Yisi Chang, Danni Cui, Jian Sourina, Olga Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore Singapore Fraunhofer Singapore Singapore Shanghai Jiao Tong Univ Sch Design Shanghai Peoples R China

Humanoid robots have been extensively utilized in service industries to provide information and product delivery through direct interactions with users. As the design of humanoid robot appearance significantly impacts human-robot interactions, it is crucial to assess user preference towards it. Traditional evaluation tools, such as surveys, field observations, and interviews, are often time-consuming and subjective. Therefore, this study aims to develop a novel eye-tracking-based assessment tool to investigate user preference towards humanoid robot appearance design. We analyze the critical factors influencing user preference from two perspectives: the attributes of robot appearance and users' selective attention distribution. Accordingly, we propose an integrated machine learning method, combining an autoencoder neural network with a support vector machine to handle the collected visual data. This method, named ASVM, extracts several novel indicators from the eye-tracking data via an unsupervised autoencoder neural network and manual entropy analysis. The proposed ASVM achieves an accuracy of 91%, outperforming other classical machine learning methods, including decision tree, naive Bayes, and support vector machine. ASVM can objectively assess user preference towards humanoid robot appearance design with high time resolution. Furthermore, it can enhance humanoid robot design by revealing the visual attention distribution in assessing robot appearance.

关键词： autoencoder Eye -tracking Robot appearance design User preference

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autoencoder Application for Anomaly Detection in Power Consumption of Lighting Systems

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IEEE ACCESS 2023年 11卷 124150-124162页

作者： Smialkowski, Tomasz Czyzewski, Andrzej Gdansk Univ Technol Fac Elect Telecommun & Informat PL-80233 Gdansk Poland

Detecting energy consumption anomalies is a popular topic of industrial research, but there is a noticeable lack of research reported in the literature on energy consumption anomalies for road lighting systems. However, there is a need for such research because the lighting system, a key element of the Smart City concept, creates new monitoring opportunities and challenges. This paper examines algorithms based on the deep learning method using the autoencoder model with LSTM and 1D Convolutional networks for various configurations and training periods. The evaluation of the algorithms was carried out based on real data from an extensive lighting control system. A practical approach was proposed using real-time, unsupervised algorithms employing limited computing resources that can be implemented in industrial devices designed to control intelligent city lighting. An anomaly detection algorithm based on classic LSTM networks, single-layer and multi-layer, was used for comparison purposes. Error matrix calculus was used to assess the quality of the models. It was shown that based on the autoencoder method, it is possible to construct an algorithm that correctly detects anomalies in power measurements of lighting systems, and it is possible to build a model so that the algorithm works correctly regardless of the season of the year.

关键词： Anomaly detection autoencoder machine learning road lighting systems smart city smartmeters

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autoencoder-based drug synergy framework for malignant diseases

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2024年 113卷 108273页

作者： Rani, Pooja Dutta, Kamlesh Kumar, Vijay Natl Inst Technol Comp Sci & Engn Dept Hamirpur 177005 HP India Dr BR Ambedkar Natl Inst Technol Jalandhar Informat Technol Dept Jalandhar 144027 Punjab India

Drug combination emerges as a viable option for the treatment of malignant diseases. Drug combination outperforms monotherapy by improving therapeutic efficacy, reducing toxicity, and overcoming drug resistance. To find viable drug combinations it is difficult to traverse empirically because of enormous combinational space. Machine learning and deep learning approaches are used to uncover novel synergistic drug combinations in enormous combinational space. Here, AESyn, a novel autoencoder-based drug synergy framework for malignant diseases using a bag of words encoding is proposed. The bag of word encoding technique is used to extract drug-targeted genes. The framework utilized screening data from NCI-ALMANAC, and O'Neil datasets. autoencoders take drug embeddings with drug-targeted genes as input for processing. The autoencoder in the proposed framework is used to extract drug features. The proposed framework is evaluated on classification and regression metrics. The performance of the proposed framework is compared with existing methods of drug synergy. According to the findings, the proposed framework achieved high performance with an accuracy of 95%, AUROC of 94.2%, and MAPE of 7.2. The autoencoder-based framework for malignant diseases using an encoding technique provides a stable, order-independent drug synergy prediction.

关键词： Embeddings Drug synergy autoencoder Deep learning Encoding Neural network Malignant diseases

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autoencoder-Based Feature Extraction for Identifying Hate Speech Spreaders in Social Media

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第4期11卷 4819-4827页

作者： Kumar, Gunjan Singh, Jyoti Prakash Singh, Amit Kumar Natl Inst Technol Patna Dept Comp Sci & Engn Patna 800005 India

Hate speech on social media has become a big problem, making regular users very upset and giving victims depression and suicidal thoughts. Early identification of the user spreading this type of hate speech may be a better solution, allowing hate speech to be stopped at source. In this article, we attempt to identify these hate speech spreaders by finding a representation for each user. Each user's comments are aggregated and fed to an auto-encoder to train it. The encoder part of the auto-encoder is used to get an encoded vector for each user. The encoded vector is used with different machine learning (ML) classifiers to determine if a user is spreading hate speech. The proposed model was tested using the dataset released by PAN 2021 (https://***/***) hate speech spreader profiling competition in English and Spanish. The experimental results show that support vector machine (SVM) with encoded vectors as features outperforms existing models with an accuracy of 92% for both English and Spanish dataset. The proposed features extraction technique is found to be equally effective at identifying fake news spreaders on fake news datasets provided by PAN 2020 yielding accuracy values of 95% and 83% for English and Spanish, respectively.

关键词： autoencoder hate speech hate speech spreader long short-term memory (LSTM)

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autoencoder-based nonlinear Bayesian locally weighted regression for soft sensor development

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ISA TRANSACTIONS 2020年 103卷 143-155页

作者： Liu, Kang Shao, Weiming Chen, Guoming China Univ Petr Ctr Offshore Engn & Safety Technol Qingdao 266580 Peoples R China China Univ Petr Coll New Energy Qingdao 266580 Peoples R China

The framework of locally weighted learning (LWL) has established itself as a popular tool for developing nonlinear soft sensors in process industries. For LWL-based soft sensors, the key factor for achieving high performance is to construct accurate localized models. To this end, in this paper a nonlinear local model training algorithm called nonlinear Bayesian weighted regression (NBWR) is proposed. In the NBWR, the nonlinear features of process data are first extracted by the autoencoder;then, given a query sample a local dataset is selected on the feature space and a fully Bayesian regression model with differentiated sample weights is developed. The benefits of this approach, which include better consistency of correlation, stronger abilities to deal with process nonlinearities and uncertainties, overfitting and numerical issues, lead to superior performance. The NBWR is used for developing a soft sensor under the LWL framework, and a real-world industrial process is used to evaluate the performance of the NBWR-based soft sensor. The experimental results demonstrate that the proposed method outperforms several benchmarking soft sensing approaches. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Soft sensor Locally weighted learning Bayesian weighted regression autoencoder Nonlinear feature extraction

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autoencoder Neural Network Based Intelligent Hybrid Beamforming Design for mmWave Massive MIMO Systems

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2020年第3期6卷 1019-1030页

作者： Tao, Jiyun Chen, Jienan Xing, Jing Fu, Shengli Xie, Junfei Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu 611731 Peoples R China Univ North Texas Dept Elect Engn Denton TX 76201 USA San Diego State Univ Dept Elect & Comp Engn San Diego CA 92182 USA

Hybrid beamforming (HB) is a promising technology for the millimeter-wave (mmWave) massive multiple-input-multiple-output (MIMO) system, which supplies high data capacity with low complexity for next-generation communication systems. However, the joint design of digital and analog beamformer is a non-convex optimization problem due to the hardware constraints of analog shifter arrays. To address this issue, we proposed an intelligent HB design method based on the autoencoder (AE) neural network in this paper. By mapping the HB system to an AE neural network, the solving of the original non-convex optimization problem is converted to the neural network training process. The beamformer and combiner can be automatically formulated by the training process of the neural network. We also discuss the chosen of hyper-parameter and provide a guideline for the AE neural network HB design. With the strong representation ability of the deep neural network, the proposed intelligent HB exhibits superior performance in terms of bit error rate (BER).

关键词： Millimeter-wave massive MIMO hybrid beamforming autoencoder deep neural networks

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autoencoder node saliency: Selecting relevant latent representations

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PATTERN RECOGNITION 2019年 88卷 643-653页

作者： Fan, Ya Ju Lawrence Livermore Natl Lab Ctr Appl Sci Comp Livermore CA 94551 USA

The autoencoder is an artificial neural network that performs nonlinear dimension reduction and learns hidden representations of unlabeled data. With a linear transfer function it is similar to the principal component analysis (PCA). While both methods use weight vectors for linear transformations, the autoencoder does not come with any indication similar to the eigenvalues in PCA that are paired with eigenvectors. We propose a novel autoencoder node saliency method that examines whether the features constructed by autoencoders exhibit properties related to known class labels. The supervised node saliency ranks the nodes based on their capability of performing a learning task. It is coupled with the normalized entropy difference (NED). We establish a property for NED values to verify classifying behaviors among the top ranked nodes. By applying our methods to real datasets, we demonstrate their ability to provide indications on the performing nodes and explain the learned tasks in autoencoders. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： autoencoder Latent representations Unsupervised learning Neural networks Node selection Model interpretation

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autoencoder with Fitting Network for Terahertz Wireless Communications:A Deep Learning Approach

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China Communications 2022年第3期19卷 172-180页

作者： Zhaohui Huang Dongxuan He Jiaxuan Chen Zhaocheng Wang Sheng Chen Beijing National Research Center for Information Science and Technology Department of Electronic EngineeringTsinghua UniversityBeijing 100084China Shenzhen International Graduate School Tsinghua UniveristyShenzhen 518055China School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJU.K.

Terahertz wireless communication has been regarded as an emerging technology to satisfy the ever-increasing demand of ultra-high-speed wireless ***,affected by the imperfections of cheap and energy-efficient Terahertz devices,Terahertz signals suffer from serve hybrid distortions,including in-phase/quadrature imbalance,phase noise and nonlinearity,which degrade the demodulation performance *** improve the robustness against these hybrid distortions,an improved autoencoder is proposed,which includes coding the transmitted symbols at the transmitter and decoding the corresponding signals at the ***,due to the lack of information of Terahertz channel during the training of the autoencoder,a fitting network is proposed to approximate the characteristics of Terahertz channel,which provides an approximation of the gradients of *** results show that our proposed autoencoder with fitting network can recover the transmitted symbols under serious hybrid distortions,and improves the demodulation performance significantly.

关键词： Terahertz wireless communication hybrid distortion signal demodulation autoencoder

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autoencoder-Based End-to-End OTFS System Design With Hardware Impairments

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第8期13卷 2285-2289页

作者： Singh, Amit Sharma, Sanjeev Sharma, Mohit Deka, Kuntal da Costa, Daniel B. Indian Inst Technol BHU Varanasi Varanasi 221005 India Technol Innovat Inst AI & Telecom Dept Abu Dhabi U Arab Emirates Indian Inst Technol Guwahati EEE Dept Gauhati 781039 India King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia

Orthogonal time-frequency space (OTFS) modulation is an innovative waveform which effectively multiplexes information symbols across a delay-Doppler (DD) plane, resulting in a superior performance, particularly in doubly dispersive channels. At higher carrier frequencies, the hardware impairments (HIs) at transceivers significantly degrade the performance of OTFS wireless systems. To mitigate the impact of HIs, conventionally an HI-aware channel equalization is performed, which is difficult to achieve in practice. In contrast to this, an autoencoder-based end-to-end design for OTFS (AE-OTFS) system is proposed, which does not require HI-aware channel equalization. Due to its end-to-end design approach, the proposed AE-OTFS significantly enhances the error performance of the OTFS system in the presence of HIs. In particular, it is found that the proposed HI-aware AE-OTFS achieves approximately 3 dB higher performance compared to existing autoencoder based OTFS design, which does not consider the impact of HIs. In addition, comparisons are performed against the conventional OTFS system with state-of-the-art signal detectors for HI-compensation, based on convolutional neural network (CNN), and it is found that due to its end-to-end design the proposed AE-OTFS results in signal-to-noise ratio improvement of up to 8 dB.

关键词： Symbols Vectors Receivers Channel estimation Wireless communication Time-domain analysis autoencoder deep learning HIs mapper and demapper OTFS signal detection signal detection

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autoencoder-Based Enhanced Orthogonal Time Frequency Space Modulation

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IEEE COMMUNICATIONS LETTERS 2023年第10期27卷 2628-2632页

作者： Tek, Yusuf Islam Dogukan, Ali Tugberk Basar, Ertugrul Koc Univ Dept Elect & Elect Engn Commun Res & Innovat Lab CoreLab TR-34450 Istanbul Turkiye

Orthogonal time frequency space (OTFS) is a novel waveform that provides a superior performance in doubly-dispersive channels. Since it spreads information symbols across the entire delay-Doppler plane, OTFS can achieve full diversity. However, reliability still needs to be improved in OTFS systems to meet the stringent demands of future communication systems. To address this issue, we propose an autoencoder (AE)-based enhanced OTFS (AEE-OTFS) modulation scheme. By training an AE under an additive white Gaussian noise (AWGN) channel, a feasible mapper and demapper are learned to improve the error performance and decrease the detection complexity of the OTFS system. The learned mapper is used to map incoming bits into high-dimensional symbols while the learned demapper recovers the information bits in the delay-Doppler domain. Additionally, we derive a theoretical upper bound for the frame error rate (FER). Simulation results confirm that AEE-OTFS outperforms conventional OTFS in terms of FER under perfect and imperfect channel conditions. AEE-OTFS also enjoys low decoding complexity in addition to its superior error performance.

关键词： Orthogonal time frequency space (OTFS) time-varying channel autoencoder minimum Euclidian distance AWGN diversity

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