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15th RIVF International Conference on Computing and Communication Technologies (RIVF)

作者： Bao Ngoc Vi Nguyen Ngoc Tran Trung Giap Vu The Le Quy Don Tech Univ Hanoi Vietnam

ISBN: (纸本)9781665404358

Password is the most widely used authenticate method. Individuals ordinarily have numerous passwords for their documents or devices, and, in some cases, they need to recover them with password guessing tools. Most popular guessing tools require a dictionary of common passwords to check with password hashes. Thus, generative adversarial networks (GANs) are suitable choices to automatically create a high-quality dictionary without any additional information from experts or password structures. One of the successful GAN-based models is the PassGAN. However, existing GAN-based models suffer from the discrete nature of passwords. Therefore, we proposed and evaluated two improvement of the PassGAN model to tackle this problem: the GS-PassGAN model using Gumbel-Softmax relaxation and the S-PassGAN using a smooth representation of a real password obtained by an additional auto-encoder. Experiment results on three different popular datasets show that the proposed method is better than the PassGAN both in the standalone and combining cases. Moreover, the matching rate of the proposed method can be increased by more than 5%.

关键词： generative adversarial network password guessing Gumbel-Softmax auto-encoder

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Training auto-encoders effectively via eliminating task-irrelevant input variables

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2019年第4期18卷 332-339页

作者： Shen, Hui Li, Dehua Wu, Hong Zang, Zhaoxiang Huazhong Univ Sci & Technol Sch Automat 1037 Luoyu Rd Wuhan 430074 Hubei Peoples R China China Three Gorges Univ Hubei Key Lab Intelligent Vis Based Monitoring Hy Yichang 443002 Hubei Peoples R China

auto-encoders are often used as building blocks of deep network classifiers to learn feature extractors, but task-irrelevant information in the input data may lead to bad extractors and result in poor generalisation performance of the network. In this paper, via dropping the task-irrelevant input variables, the performance of auto-encoders can be obviously improved. Specifically, an importance-based variable selection method is proposed to aim at finding the task-irrelevant input variables and dropping them. It firstly estimates importance of each variable, and then drops the variables with importance value lower than a threshold. In order to obtain better performance, the method can be employed for each layer of stacked auto-encoders. Experimental results show that when combined with our method the stacked denoising auto-encoders achieve significantly improved performance on three challenging datasets.

关键词： feature learning deep learning neural network auto-encoder variable selection stacked auto-encoders feature selection unsupervised training

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Development of Honey Badger-Cat Swarm Optimisation-Based Parallel Cascaded Deep Network for Software Bug Prediction Framework

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JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2023年第6期22卷

作者： Gupta, Anurag Sharma, Mayank Srivastava, Amit Amity Univ Amity Inst Informat Technol Noida Uttar Pradesh India Jaypee Inst Informat Technol Dept Math Noida Uttar Pradesh India

Software bug prediction is mainly used for testing and code inspection. So, software bug prediction is carried out by network measures over the decades. But, the classical fault prediction method failed to obtain the semantic difference among various programs. Thus, it degrades the working of the prediction model, which is designed using these aspects. It is necessary to obtain the semantic difference for designing the prediction model accurately and effectively. In a software defect prediction system, it faces many difficulties in identifying the defect modules like correlation, irrelevance aspects, data redundancy, and missing samples or values. Consequently, many researchers are designed to detect software bug prediction that categorises faulty as well as non-faulty modules with software matrices. But, there are only a few works focussed to mitigate the class imbalance problem in bug prediction. In order to overcome the problem, it is required to develop an efficient software bug prediction method with the enhanced classifier. For this experimentation, the input data are taken from the standard online data sources. Initially, the input data undergo pre-processing phase and then, the pre-processed data are provided as input to the feature extraction by utilising the auto-encoder. These obtained features are utilised in getting the optimal fused features with the help of a new Hybrid Honey Badger Cat Swarm Algorithm (HHBCSA). Finally, these features are fed as input to the Optimised Parallel Cascaded Deep Network (OPCDP), where the "Extreme Learning Machine (ELM) and Deep Belief Network (DBN)" are used for the prediction of software bugs, in which the parameters from both classifiers are optimised by proposed HHBCSA algorithm. From the investigations, the recommended software bug prediction method offers a quicker bug prediction result, which helps to detect and remove the software bug easily and accurately.

关键词： Software bug prediction auto-encoder optimal fused features hybrid honey badger cat swarm optimised parallel cascaded deep network extreme learning machine deep belief network

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Adaptive neighborhood size and effective geometric features selection for 3D scattered point cloud classification

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APPLIED SOFT COMPUTING 2022年第0期115卷 108196-108196页

作者： Gunen, Mehmet Akif Erciyes Univ Engn Fac Dept Geomat Engn TR-38039 Kayseri Turkey

Classification of 3D scatter and unorganized point cloud (PC) is an ongoing hard problem due to high redundancy, unbalanced sampling density, and large data structure of PC. Geometric and spectral features derived from the PC are generally used for classification. In this paper, an Omnivariance based adaptive neighborhood size selection method and a new feature set composed of 14 features are proposed for extraction of geometric features for each individual point within the local neighborhood. Performance of 8 modern classifiers with different strategies (i.e., boosting, ensemble, and deep learning etc.) were evaluated on the Oakland, Vaihingen, and ISPRS datasets. These 3 datasets are identified by 5, 9, and 2 distinct object classes, respectively. The results were compared with different neighborhood size selection methods (i.e., eigenentropy based, fixed number of the k-nearest neighbors) and feature set (i.e., 21 features). Only 3D local features were employed to classify datasets with varying characteristics and properties. The proposed optimum neighbor selection method and feature set provided the best statistical results with auto-encoder classifier (the overall accuracies are over 85%, 60% and, 90% the Oakland, Vaihingen, and ISPRS datasets, respectively). Especially for the ISPRS dataset, the auto-encoder obtained over 94%, 90%, and 93% precision, recall, and f-score, respectively. (C) 2021 Published by Elsevier B.V.

关键词： Point cloud classification Neighborhood size selection Geometric feature vector auto-encoder

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CONTRASTIVE auto-encoder FOR PHONEME RECOGNITION

CONTRASTIVE AUTO-ENCODER FOR PHONEME RECOGNITION

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Zheng, Xin Wu, Zhiyong Meng, Helen Cai, Lianhong Tsinghua Univ Grad Sch Shenzhen Tsinghua CUHK Joint Res Ctr Media Sci Technol & S Shenzhen 518055 Peoples R China

ISBN: (纸本)9781479928934

Speech data typically contains task irrelevant information lying within features. Specifically, phonetic information, speaker characteristic information, emotional information and noise are always mixed together and tend to impair one another for certain task. We propose a new type of auto-encoder for feature learning called contrastive auto-encoder. Unlike other variants of auto-encoders, contrastive auto-encoder is able to leverage class labels in constructing its representation layer. We achieve this by modeling two auto-encoders together and making their differences contribute to the total loss function. The transformation built with contrastive auto-encoder can be seen as a task-specific and invariant feature learner. Our experiments on TIMIT clearly show the superiority of the feature extracted from contrastive auto-encoder over original acoustic feature, feature extracted from deep auto-encoder, and feature extracted from a model that contrastive auto-encoder originates from.

关键词： auto-encoder contrastive auto-encoder phoneme recognition deep neural network (DNN) restricted Boltzmann machine (RBM)

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Skeleton Silhouette Based Disentangled Feature Extraction Network for Invariant Gait Recognition 35

Skeleton Silhouette Based Disentangled Feature Extraction Ne...

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35th International Conference on Information Networking (ICOIN)

作者： Yoo, Jae-Seok Park, Kwang-Hyun Kwangwoon Univ Sch Robot Seoul South Korea

ISBN: (纸本)9781728191010

Recently, the demand for biometric recognition systems through deep-learning algorithms has increased, and related researches have been actively underway. Gait has its unique characteristics like fingerprints and is receiving much attention due to its capability to obtain from a long distance without contact. Gait recognition means to identify an individual by analyzing a person's gait patterns, and can be applied to various fields such as criminal investigations. However, the accuracy of gait recognition suffers from external factors such as variation of view angles and clothes. To address this problem, we propose a skeleton silhouette-based disentangled gait recognition network, which learns view-invariant features. Experimental results on CASIA-B dataset are also shown comparing with previous methods.

关键词： Gait recognition skeleton silhouette image deep convolutional neural networks disentangled feature extraction auto-encoder

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Dynamic Gesture Recognition Based on RF Sensor and AE-LSTM Neural Network 53

Dynamic Gesture Recognition Based on RF Sensor and AE-LSTM N...

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IEEE International Symposium on Circuits and Systems (IEEE ISCAS)

作者： Zhang, Bo Zhang, Lei Wu, Mojun Wang, Yan Tsinghua Univ Inst Microelect Beijing 100084 Peoples R China Tsinghua Univ Beijing Innovat Ctr Future Chips Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781728192017

In this paper, we present a novel deep neural network (DNN) dedicated to dynamic gesture control based on a radio-frequency (RF) type sensors. The proposed network is based on auto-encoder (AE) and long short-term memory recurrent neural network (LSTM-RNN). The encoder for reduction and effective feature extraction is obtained by unsupervised training an AE with a large untagged RF database. Further a light LSTM network is trained use a small size of tagged datasets for dynamic gesture classification. Training the network with semi-supervised learning methods greatly reduces the requirement on the size of the tagged gestures datasets. Experimental results demonstrate that our AE-LSTM method outperforms existing networks in terms of accuracy and realizability. The proposed application is used for the human-computer interaction (HCI) of intelligent home, automatic driving and robot scenes.

关键词： Gesture recognition Human-Computer Interaction auto-encoder LSTM RF sensors

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Wind Turbine Failure Prediction Model using SCADA-based Condition Monitoring System

Wind Turbine Failure Prediction Model using SCADA-based Cond...

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14th IEEE Madrid PowerTech Conference (IEEE POWERTECH)

作者： Alzawaideh, Bara Baboli, Payam Teimourzadeh Babazadeh, Davood Horodyvskyy, Susanne Koprek, Isabel Lehnhoff, Sebastian Carl von Ossietzky Univ Oldenburg Postgrad Programme Renewable Energy Oldenburg Germany OFFIS Inst IT Energy Div Oldenburg Germany Hamburg Univ Technol Inst Elec Power & Energy Tech Hamburg Germany EWE Offshore Serv & Solut GmbH Performance Management Offshore Wind Oldenburg Germany

ISBN: (纸本)9781665435970

The ultimate goal of a condition monitoring system for wind turbines (WT) is to predict the upcoming failures;this could be achieved using artificial intelligence techniques. In this paper, a model for detecting excessive temperature anomalies in key components of WT i.e. gearbox, generator and transformer is proposed. This model consists of integrated modules continuously interact following the never-ending learning paradigm based on artificial neural networks addressing the challenge of the limited pre-classified data and lacking of the concept to be learned for a system with continuous change of its operating conditions: (i) the Normal Behavior (NB) module estimates the temperature of the WT key components, (ii) the Expected Time To Failure (ETTF) module calculates the deviation between the estimated normal temperature and the real-time measurement data to predict the upcoming failure of WT key components a few hours before occurring a failure, (iii) in the Anomaly Detection (AD) module, the temperature deviation time series signal is divided into normal or abnormal clusters. The proposed methodology has been applied on a real wind farm data in Germany. The results show that the system could correctly detect a large number of WT upcoming failures, this implies the effectiveness and generalization of the proposed model in terms of classification accuracy.

关键词： Artificial neural network auto-encoder condition-based maintenance Time series wind turbine

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Deep Attributed Network Embedding Based on the PPMI 1

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26th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Dong, Kunjie Huang, Tong Zhou, Lihua Wang, Lizhen Chen, Hongmei Yunnan Univ Sch Informat Kunming 650091 Yunnan Peoples R China

ISBN: (数字)9783030732165

ISBN: (纸本)9783030732158;9783030732165

The attributed network embedding aims to learn the latent low-dimensional representations of nodes, while preserving the neighborhood relationship of nodes in the network topology as well as the similarities of attribute features. In this paper, we propose a deep model based on the positive point-wise mutual information (PPMI) for attributed network embedding. In our model, attribute features are transformed into an attribute graph, such that attribute features and network topology can be handled in the same way. Then, we perform the random surfing and calculate the PPMI on the attribute/topology graph to effectively maintain the structural characteristics and the high-order proximity information. The node representations are learned by a shared auto-encoder. Besides, the local pairwise constraint is used in the shared auto-encoder to improve the quality of node representations. Extensive experimental results on four real-world networks show the superior performance of the proposed model over the 10 baselines.

关键词： Attributed network embedding Random surfing Positive point-wise mutual information auto-encoder

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Using Neural-Network-Driven Image Recognition Software to Detect Emotional Reactions in the Face of a Player While Playing a Horror Video Game 3rd

Using Neural-Network-Driven Image Recognition Software to De...

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3rd International Conference of HCI in Games (HCI-Games) held as Part of the 23rd International Conference on Human-Computer Interaction (HCII)

作者： Prossinger, Hermann Binter, Jakub Hladky, Tomas Riha, Daniel Univ Vienna Fac Life Sci Dept Evolutionary Biol Vienna Austria Charles Univ Prague Fac Humanities Prague Czech Republic Charles Univ Prague Fac Sci Prague Czech Republic

ISBN: (纸本)9783030772765;9783030772772

Image analysis of the webcam images of the player playing horror video games is used to infer his/her emotional state. We initially relied on the received wisdom that there are three distinguishable emotional states: joy, agitation (a combination of fear and surprise) and neutral. We applied modern image analysis techniques to extract the player's face in three sets of 50 consecutive frames by first generating 150 feature vectors and then using an auto-encoder neural network to dimension-reduce these to a mapping of 150 points on a 2D manifold. We find that these mapped points are not uniformly distributed in a plane and an overlap between neutral and each of the two affective states is highly improbable. We derive convex hulls using a k-medoids clustering algorithm;they seem to indicate a difference between the mouth region expression and that around the eyes. We use kernel density estimation and Monte Carlo methods to determine that the affective states joy and agitation have a small probability of overlap (6.6% and 10.6%, respectively), which can be interpreted as a confusion probability. Our advanced statistical analysis thus confirms that modern image analysis can indeed be used to monitor the player's emotional state while playing.

关键词： Image analysis using neural networks auto-encoder k-medoids clustering Face alignment algorithm Horror videos Facial expression of emotion Monte Carlo methods of probability estimation

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