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Role Discovery-Guided Network Embedding Based on autoencoder and Attention Mechanism

IEEE TRANSACTIONS ON CYBERNETICS 2023年第1期53卷 365-378页

作者： Jiao, Pengfei Tian, Qiang Zhang, Wang Guo, Xuan Jin, Di Wu, Huaming Tianjin Univ Law Sch Coll Intelligence & Comp Tianjin 300350 Peoples R China Tianjin Univ Law Sch Ctr Biosafety Res & Strategy Tianjin 300350 Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China Tianjin Univ Ctr Appl Math Tianjin 300072 Peoples R China

Recently, network embedding (NE) is an amazing research point in complex networks and devoted to a variety of tasks. Nearly, all the methods and models of NE are based on the local, high-order, or global similarity of the networks, and few studies have focused on the role discovery or structural similarity, which is of great significance in spreading dynamics and network theory. Meanwhile, existing NE models for role discovery suffer from two limitations, that is: 1) they fail to model the varying dependencies between each node and its neighbor nodes and 2) they cannot capture the effective node features which are helpful to role discovery, which makes these methods ineffective when applied to the role discovery task. To solve the above problems of NE for role discovery or structural similarity, we propose a unified deep learning framework, called RDAA, which can effectively represent features of nodes and benefit the Role Discovery-guided NE with a deep autoencoder, while modeling the local links with an Attention mechanism. In addition, we design an elaborately binding technique to combine both parts and optimize the framework in a unified way. We conduct different experiments, including visualization, role classification, role discovery, and running time compared to popular NE methods for both proximity and structural similarity. The RDAA has better performance on all the datasets and achieves good tradeoffs.

关键词： Feature extraction Task analysis Social networking (online) Deep learning Matrix decomposition Complex networks Neural networks Attention mechanism autoencoder network representation role discovery structural similarity

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Learning-Based autoencoder for Multiple Access and Interference Channels in Space Optical Communications

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IEEE COMMUNICATIONS LETTERS 2023年第10期27卷 2662-2666页

作者： Elfikky, Abdelrahman Soltani, Morteza Rezki, Zouheir Univ Calif Santa Cruz Dept Elect Engn & Comp Engn Santa Cruz CA 95064 USA Qualcomm Inc San Diego CA USA

This letter studies an autoencoder (AE) model for a multi-user space optical communications (SOC) system. In particular, a novel layered framework is proposed with batch normalization in the encoder and layer normalization in the decoders. This enhances the performance of the standard AE since the error cost function can be effectively minimized using gradient descent. Furthermore, to emulate a practical SOC channel model, a system tool kit simulator is used. The numerical results reveal that the proposed AE outperforms state-of-the-art learning frameworks by a 2 dB gain in multiple access channel and a 1.7 dB gain in interference channel, with a 20% complexity reduction.

关键词： Transmitters Receivers Decoding Testing Training Satellites Symbols Space optical communications autoencoder multiple access channel interference channel

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An adaptive serial cascaded autoencoder and LSTM with multivariate regression for ambient air quality prediction with improved flow direction algorithm

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2023年第4期45卷 10304-10329页

作者： Lakshmipathy, Munirathnam Jeevandharakumar, Shanthi Prasad Mysore Kodandaramaiah, Goddamachinnehalli Narayanappa Visvesvaraya Technol Univ Dept ECE Belagavi India Cambridge Inst Technol Dept ECE Bangalore India Kuppam Engn Coll Dept ECE Kuppam India

Forecasting of air quality is an emerging process to evade the entailment of several defects for human as well as environmental resources. Because of the poor quality of air substances, air pollution occurs. Living beings and other economic resources get affected by this polluted air very frequently. Therefore, air quality prediction is a trendsetting method to maintain a healthy life and infrastructure. Though multiple existing models are implemented, managing high-level data and deploying such standard models become cumbersome. Rather than indoor air quality, the ambient (outdoor) air quality should require the prediction process as it exists in the open environment. Thus, an intelligent ambient air quality prediction model is needed, which is designed in this paper by adopting a heuristic-aided deep learning model. The original air data is initially fetched from the three diverse data sources. It is followed by the data pre-processing stage with standard techniques. Subsequently, the resultant data is given to Adaptive Serial Cascaded autoencoder and Long Short-Term Memory (LSTM) with Multivariate Regression (ASCA-LSMR), in which some of the hyper-parameters are tuned by proposing the novel algorithm as Fitness-based Improved Flow Direction Algorithm (FIFDA) to produce the better prediction results. Finally, experimental results indicate that our method enables more accurate predictions than all the listed traditional models and performs better in predictive performance. The RMSE of the designed FIFDA-ASCA-LSMR method attains 31.9%, 33.5%, 6.66%, and 23.7% elevated than SSA-ASCA-LSMR, DHOA-ASCA-LSMR, EHO-ASCA-LSMR, and FDA-ASCA-LSMR, for dataset 2. Thus, the designed ambient air quality prediction model reveals better performance than the other baseline approaches.

关键词： Adaptive serial cascaded autoencoder and long short-term memory (LSTM) with multivariate regression (ASCA-LSMR) ambient air quality prediction autoencoder data pre-processing fitness-based improved flow direction algorithm long short-term memory multivariate regression

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A Discriminative Cross-Aligned Variational autoencoder for Zero-Shot Learning

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第6期53卷 3794-3805页

作者： Liu, Yang Gao, Xinbo Han, Jungong Shao, Ling Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Shanghai Jiao Tong Univ Key Lab Artificial Intelligence Minist Educ Shanghai 200240 Peoples R China Shandong Univ Key Lab Cryptol Technol & Informat Secur Minist Educ Jinan 250100 Peoples R China Southeast Univ Key Lab Comp Network & Informat Integrat Minist Educ Nanjing 211189 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing 400065 Peoples R China Aberystwyth Univ Dept Comp Sci Aberystwyth SY23 3DB Dyfed Wales Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates

Zero-shot learning (ZSL) aims to classify unseen samples based on the relationship between the learned visual features and semantic features. Traditional ZSL methods typically capture the underlying multimodal data structures by learning an embedding function between the visual space and the semantic space with the Euclidean metric. However, these models suffer from the hubness problem and domain bias problem, which leads to unsatisfactory performance, especially in the generalized ZSL (GZSL) task. To tackle such a problem, we formulate a discriminative cross-aligned variational autoencoder (DCA-VAE) for ZSL. The proposed model effectively utilizes a modified cross-modal-alignment variational autoencoder (VAE) to transform both visual features and semantic features obtained by the discriminative cosine metric into latent features. The key to our method is that we collect principal discriminative information from visual and semantic features to construct latent features which contain the discriminative multimodal information associated with unseen samples. Finally, the proposed model DCA-VAE is validated on six benchmarks including the large dataset ImageNet, and several experimental results demonstrate the superiority of DCA-VAE over most existing embedding or generative ZSL models on the standard ZSL and the more realistic GZSL tasks.

关键词： Visualization Semantics Task analysis Training Solid modeling Prototypes Generative adversarial networks autoencoder cosine discriminative latent zero-shot learning (ZSL)

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Parameterized Gompertz-Guided Morphological autoencoder for Predicting Pulmonary Nodule Growth

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2023年第12期42卷 3602-3613页

作者： Fang, Jiansheng Wang, Jingwen Li, Anwei Yan, Yuguang Liu, Hongbo Li, Jiajian Yang, Huifang Hou, Yonghe Yang, Xuening Yang, Ming Liu, Jiang CVTE Res Guangzhou 510530 Peoples R China Guangdong Univ Technol Sch Comp Guangzhou 510006 Peoples R China CVTE Yibicom Hlth Management Ctr Guangzhou 510530 Peoples R China Guangdong Acad Med Sci Guangdong Lung Canc Inst Guangdong Prov Peoples Hosp Guangzhou 510080 Peoples R China Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

The growth rate of pulmonary nodules is a critical clue to the cancerous diagnosis. It is essential to monitor their dynamic progressions during pulmonary nodule management. To facilitate the prosperity of research on nodule growth prediction, we organized and published a temporal dataset called NLSTt with consecutive computed tomography (CT) scans. Based on the self-built dataset, we develop a visual learner to predict the growth for the following CT scan qualitatively and further propose a model to predict the growth rate of pulmonary nodules quantitatively, so that better diagnosis can be achieved with the help of our predicted results. To this end, in this work, we propose a parameterized Gempertz-guided morphological autoencoder (GM-AE) to generate any future-time-span high-quality visual appearances of pulmonary nodules from the baseline CT scan. Specifically, we parameterize a popular mathematical model for tumor growth kinetics, Gompertz, to predict future masses and volumes of pulmonary nodules. Then, we exploit the expected growth rate on the mass and volume to guide decoders generating future shape and texture of pulmonary nodules. We introduce two branches in an autoencoder to encourage shape-aware and textural-aware representation learning and integrate the generated shape into the textural-aware branch to simulate the future morphology of pulmonary nodules. We conduct extensive experiments on the self-built NLSTt dataset to demonstrate the superiority of our GM-AE to its competitive counterparts. Experiment results also reveal the learnable Gompertz function enjoys promising descriptive power in accounting for inter-subject variability of the growth rate for pulmonary nodules. Besides, we evaluate our GM-AE model on an in-house dataset to validate its generalizability and practicality. We make its code publicly available along with the published NLSTt dataset.

关键词： Pulmonary nodule Gompertz function autoencoder computed tomography growth prediction

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Feature Extraction of fMRI Signals Based on autoencoder for Constructing a Two-Stage Multi-class Emotion Classification Model 6th

Feature Extraction of fMRI Signals Based on Autoencoder for ...

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6th International Conference on Biomedical and Health Informatics-ICBHI

作者： Chang, Chi-Sheng Lu, Shao-Pu Lee, Chia-Yen Natl United Univ Dept Elect Engn Miaoli Taiwan Natl Yang Ming Chiao Tung Univ Inst Biomed Engn Hsinchu Taiwan

ISBN: (纸本)9783031863226;9783031863233

This study utilizes the pre-processed fMRI data provided by the 2024 ICBHI challenge and excludes two other signal data types with missing information to construct a CNN model that can distinguish three emotion classes and their corresponding levels. However, due to the high variability and noise in the pre-processed fMRI data, a simple CNN model alone cannot achieve good classification performance. This study addresses the issue of noisy data by proposing a two-stage deep learning model training framework. In the first stage, an autoencoder method is adopted, leveraging its ability to effectively encode and decode data to extract useful signal features from the noisy data for use in the subsequent second stage. In the second stage, the effective features obtained by the encoder are transferred, and the weights of the encoding layers are combined with a fully connected layer for model retraining. This study also analyzes different methods of transferring the weights of the encoding layers. The best model for this study achieved an error rate of only 0.3383 on the official evaluation metric.

关键词： fMRI autoencoder Two-stage training

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Photovoltaic power forecasting with a long short-term memory autoencoder networks

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SOFT COMPUTING 2023年第15期27卷 10533-10553页

作者： Sabri, Mohammed El Hassouni, Mohammed Mohammed V Univ Rabat LRIT Rabat 1014 Morocco Mohammed V Univ Rabat FLSH Rabat 1014 Morocco

In many developed countries, photovoltaic solar power, which is considered the most cost-effective renewable energy source, accounts for a major portion of electricity production. The photovoltaic (PV) power generation is unpredictable and imprecise due to its high variation that can be caused of meteorological elements, to reduce the negative influence of the use of PV power, accurate PV power prediction is of crucial significance for the secure and efficient operation of photovoltaic power system operation. In light of this, we propose a long short-term memory (LSTM) autoencoder (AE) for photovoltaic power forecasting. Initially, to generate encoded sequences the LSTM-encoder extracts the characteristics from the input data. Then the LSTM-decoder decoded the encoded sequences to advance them to the last dense layer for photovoltaic power prediction. Furthermore, we conducted experiments using a 23.40 kW PV power plants dataset from DKASC in Australia. The results have confirmed that the LSTM-AE model can achieve better prediction accuracy than the benchmark deep learning methods, in terms of mean absolute error (MAE), mean square error (MSE), mean bias error (MBE), root mean square error (RMSE) and coefficient of determination (R-2) performance measures. When the results are analyzed, the LSTM-AE model gives the best results with average RMSE, MAE, and R-2 to 0.0762 kW, 0.0389 kW, and 99.93%, respectively. The experimental results confirm that proposed method with the highest R-2 values and minimum forecasting errors compared to the benchmark models can effectively improve stable performance and achieve better accurate photovoltaic power forecasting.

关键词： autoencoder Deep learning Long short-term memory Photovoltaic power forecasting

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Compressed Feedback Using autoencoder Based on Deep Learning for D2D Communication Networks

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IEEE WIRELESS COMMUNICATIONS LETTERS 2023年第4期12卷 590-594页

作者： Ban, Tae-Won Gyeongsang Natl Univ Dept Intelligent Commun Engn Tongyeong 53064 South Korea

In this letter, we propose a feedback reduction scheme based on autoencoder and deep reinforcement learning for frequency division duplex (FDD) overlay device-to-device (D2D) communication networks. All D2D receivers and transmitters are equipped with an Encoder and Decoder, respectively, of the trained autoencoder. The D2D receivers compress the feedback information using the Encoder before transmitting while the transmitters decompress the received feedback information. We also employ a dueling deep Q network (DQN) to allow each D2D transmitter to autonomously determine whether to transmit data based on the decompressed feedback information. The performance of the proposed feedback reduction scheme is analyzed in terms of the average MSE of the autoencoder and the average sum-rate of a D2D communication network. Our numerical results show that the proposed feedback reduction scheme using the autoencoder can achieve 100%, 89%, and 86% of the average sum-rate of the perfect feedback scheme with no compression when the signal-to-noise ratio is 10dB, -5dB, and -20dB, respectively, while reducing the feedback by 50%.

关键词： Device-to-device communication Transmitters Receivers Decoding Deep learning Communication networks Signal to noise ratio Device-to-device (D2D) dueling deep Q network (DQN) feedback reduction autoencoder

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Patch-Wise autoencoder Based on Transformer for Radar High-Resolution Range Profile Target Recognition

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IEEE SENSORS JOURNAL 2023年第23期23卷 29406-29414页

作者： Zhang, Xianwen Wei, Yao Wang, Wenying Nanjing Res Inst Elect Technol Nanjing 210039 Peoples R China

High-resolution range profile (HRRP) is indispensable for modern radar automatic target recognition (RATR) systems and commonly has noisy background and misalignment along the range dimension. In this article, we propose a novel algorithm for HRPP recognition with the improvement of recognition accuracy, stability against range translation, and robustness to noise background. This method divides the HRRP into patches and encodes the patches to latent feature vectors via a multihead attention mechanism, modeling the spatial dependence, eliminating the influence of range translation, and focusing on the target area. A decoder is adopted to reconstruct the HRRP and improve the robustness against the noise. Experiments on measured data prove that the proposed algorithm outperforms other existing methods with recognition accuracy increased significantly, sensitivity to the range translation eliminated, and noise robustness improved. This study provides a promising and effective approach for HRRP target recognition.

关键词： autoencoder high-resolution range profile (HRRP) multihead self-attention (MSA) radar automatic target recognition (RART) transformer

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autoencoder-based Data Augmentation for Deepfake Detection 23

Autoencoder-based Data Augmentation for Deepfake Detection

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2nd ACM International Workshop on Multimedia AI against Discrimination (MAD)

作者： Stanciu, Dan-Cristian Ionescu, Bogdan Univ Politehn Bucuresti AI Multimedia Lab Bucharest Romania

ISBN: (纸本)9798400701870

Image generation has seen huge leaps in the last few years. Less than 10 years ago we could not generate accurate images using deep learning at all, and now it is almost impossible for the average person to distinguish a real image from a generated one. In spite of the fact that image generation has some amazing use cases, it can also be used with ill intent. As an example, deepfakes have become more and more indistinguishable from real pictures and that poses a real threat to society. It is important for us to be vigilant and active against deepfakes, to ensure that the false information spread is kept under control. In this context, the need for good deepfake detectors feels more and more urgent. There is a constant battle between deepfake generators and deepfake detection algorithms, each one evolving at a rapid pace. But, there is a big problem with deepfake detectors: they can only be trained on so many data points and images generated by specific architectures. Therefore, while we can detect deepfakes on certain datasets with near 100% accuracy, it is sometimes very hard to generalize and catch all real-world instances. Our proposed solution is a way to augment deepfake detection datasets using deep learning architectures, such as autoencoders or U-Net. We show that augmenting deepfake detection datasets using deep learning improves generalization to other datasets. We test our algorithm using multiple architectures, with experimental validation being carried out on state-of-the-art datasets like CelebDF and DFDC Preview. The framework we propose can give flexibility to any model, helping to generalize to unseen datasets and manipulations.

关键词： deepfake deep learning digital video forensics face manipulation data augmentation generalization autoencoder

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