Due to the increasing use of communication technologies for data transmission, security threats have increased over the past decade. One of the essential solutions to detect threats is NIDSs. Over the past few years, ...
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Due to the increasing use of communication technologies for data transmission, security threats have increased over the past decade. One of the essential solutions to detect threats is NIDSs. Over the past few years, much research considered unsupervised feature extraction for NIDS like sparse auto-encoders;however, there is no research on the supervised auto-encoder methods. In this work, we propose a novel supervised sparse auto-encoder, which aims to extract more useful information for classification models than unsupervised methods. The proposed approach validated using NSL-KDD, KDDCUP'99, CICIDS2017 data-sets, in the term of detection rate, and also response time. Experimental results in detection rate and test time are promising. In the case of binary classification, the accuracy of 90. 11% on NSL-KDDTest(+) and 91.21 on CICIDS2017 were achieved, which is a drastic improvement compared to state of the art with the more complex models and unsupervised representation learning models. Also, the result on 5class classification was considered.
Single-cell RNA-sequencing (scRNA-seq) techniques can measure gene expression at single-cell resolution but lack spatial information. Spatial transcriptomics (ST) techniques simultaneously provide gene expression data...
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Single-cell RNA-sequencing (scRNA-seq) techniques can measure gene expression at single-cell resolution but lack spatial information. Spatial transcriptomics (ST) techniques simultaneously provide gene expression data and spatial information. However, the data quality of the spatial resolution or gene coverage is still much lower than the quality of the single-cell transcriptomics data. To this end, we develop a ST-Aided Locator for single-cell transcriptomics (STALocator) to localize single cells to corresponding ST data. Applications on simulated data showed that STALocator performed better than other localization methods. When applied to the human brain and squamous cell carcinoma data, STALocator could robustly reconstruct the relative spatial organization of critical cell populations. Moreover, STALocator could enhance gene expression patterns for Slide-seqV2 data and predict genome-wide gene expression data for fluorescence in situ hybridization (FISH) and Xenium data, leading to the identification of more spatially variable genes and more biologically relevant Gene Ontology (GO) terms compared with the raw data. A record of this paper's transparent peer review process is included in the supplemental information.
Fault detection in heating, ventilation, and air conditioning (HVAC) systems is essential because faults lead to energy wastage, shortened lifespan of equipment, and uncomfortable indoor environments. In this study, w...
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Fault detection in heating, ventilation, and air conditioning (HVAC) systems is essential because faults lead to energy wastage, shortened lifespan of equipment, and uncomfortable indoor environments. In this study, we proposed a data-driven fault detection and diagnosis (FDD) scheme for air handling units (AHUs) in building HVAC systems to enable reliable maintenance by considering undefined states. We aimed to determine whether a neural-network-based FDD model can provide significant inferences for input variables using the supervised auto-encoder (SAE). We evaluated the fitness of the proposed FDD model based on the reconstruction error of the SAE. In addition, fault diagnosis is only performed by the FDD model if it can provide significant inferences for input variables;otherwise, feedback regarding the FDD model is provided. The experimental data of ASHRAE RP1312 were used to evaluate the performance of the proposed scheme. Furthermore, we compared the performance of the proposed model with those of well-known data-driven approaches for fault diagnosis. Our results showed that the scheme can distinguish between undefined and defined data with high performance. Furthermore, the proposed scheme has a higher FDD performance for the defined states than that of the control models. Therefore, the proposed scheme can facilitate the maintenance of the AHU systems in building HVAC systems.
Implementing computer vision applications on energy efficient and powerful single board computer devices is a hot topic of research. ODROID-XU4 is one such latest single board computing device which is extremely energ...
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Implementing computer vision applications on energy efficient and powerful single board computer devices is a hot topic of research. ODROID-XU4 is one such latest single board computing device which is extremely energy efficient and powerful, having a small form factor when compared to any other ARM based embedded devices. It supports open source operations systems and runs a variety of Linux flavors including Ubuntu and various Android versions including Lollipop. Moreover, it supports USB 3.0, eMMC 5.0 and Gigabit Ethernet interfaces thus, making the device feasible to transfer data at a very high speed. The key contribution of this paper is we have developed a novel technique to match computer generated sketches with face photos and implemented it on ODROID XU4 single board computer which makes it feasible to be used in real-time. Human face is detected on the face photos using Viola Jones method. On the detected faces and computer generated sketches, feature extraction is performed using supervised auto-encoder to build deep architecture and matching is performed between computer generated sketches and face photos using Parallel Convolutional Neural Network (PCNN). Finally decision level fusion is performed to find the optimal matching result. In this study, the authors have performed pilot testing of their technique and results of their analysis are presented to the readers. (C) 2016 Elsevier B.V. All rights reserved.
This paper targets learning robust image representation for single training sample per person face recognition. Motivated by the success of deep learning in image representation, we propose a supervisedautoencoder, w...
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This paper targets learning robust image representation for single training sample per person face recognition. Motivated by the success of deep learning in image representation, we propose a supervisedautoencoder, which is a new type of building block for deep architectures. There are two features distinct our supervisedautoencoder from standard autoencoder. First, we enforce the faces with variants to be mapped with the canonical face of the person, for example, frontal face with neutral expression and normal illumination;Second, we enforce features corresponding to the same person to be similar. As a result, our supervisedautoencoder extracts the features which are robust to variances in illumination, expression, occlusion, and pose, and facilitates the face recognition. We stack such supervisedautoencoders to get the deep architecture and use it for extracting features in image representation. Experimental results on the AR, Extended Yale B, CMU-PIE, and Multi-PIE data sets demonstrate that by coupling with the commonly used sparse representation-based classification, our stacked supervisedautoencoders-based face representation significantly outperforms the commonly used image representations in single sample per person face recognition, and it achieves higher recognition accuracy compared with other deep learning models, including the deep Lambertian network, in spite of much less training data and without any domain information. Moreover, supervisedautoencoder can also be used for face verification, which further demonstrates its effectiveness for face representation.
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