Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of DINCAE to Reconstruct the Gaps in Chlorophyll-a Satellite Observations in the South China Sea and West Philippine Sea

The Data Interpolating Empirical Orthogonal Functions (DINEOF) method has demonstrated usability and accuracy for filling spatial gaps in remote sensing datasets. In this study, we conducted the reconstruction of the chlorophyll-a concentration (Chl-a) data using a convolutional neural networks model called Data-Interpolating Convolutional Auto-Encoder (DINCAE), and we compared its performance with that of DINEOF. Furthermore, the cloud-free sea surface temperature (SST) was used as a phytoplankton dynamics predictor for the Chl-a reconstruction. Finally, four reconstruction schemes were implemented: DINCAE (Chl-a only), DINCAE (Chl-a and SST), DINEOF (Chl-a only), and DINEOF (Chl-a and SST), denoted rec1, rec2, rec3, and rec4 respectively. To quantitatively evaluate the accuracy of these reconstruction schemes, both the cross-validation and in situ data were used. The study domain was chosen to be the Northern South China Sea (SCS) and West Philippine Sea (WPS), bounded by 115–125°E and 16–24°N to test the model performance for the reconstruction of Chl-a under different Chl-a controlling mechanisms. The in situ validation showed that rec1 performs best among the four reconstruction schemes, and that adding SST into the Chl-a reconstruction cannot improve the reconstruction results. However, for cross validation, adding SST can slightly improve spatial distributions of the root mean square error (RMSE) between the reconstructed data and the original data, especially over the SCS continental shelf. Furthermore, the potential of DINCAE prediction is confirmed in this paper; thus, the trained DINCAE model can be re-applied to reconstruct other missing data, and more importantly, it can also be re-trained using the reconstructed data, thereby further improving reconstruction results. Another consideration is efficiency; with similar reconstruction conditions, DINCAE is 5–10 times faster than DINEOF.
Self-Organization of Spiking Neural Networks for Visual Object Recognition

On one hand, the visual system has the ability to differentiate between very similar objects. On the other hand, we can also recognize the same object in images that vary drastically, due to different viewing angle, distance, or illumination. The ability to recognize the same object under different viewing conditions is called invariant object recognition. Such object recognition capabilities are not immediately available after birth, but are acquired through learning by experience in the visual world. In many viewing situations different views of the same object are seen in a temporal sequence, e.g. when we are moving an object in our hands while watching it. This creates temporal correlations between successive retinal projections that can be used to associate different views of the same object. Theorists have therefore proposed a synaptic plasticity rule with a built-in memory trace (trace rule). In this dissertation I present spiking neural network models that offer possible explanations for learning of invariant object representations. These models are based on the following hypotheses: 1. Instead of a synaptic trace rule, persistent firing of recurrently connected groups of neurons can serve as a memory trace for invariance learning. 2. Short-range excitatory lateral connections enable learning of self-organizing topographic maps that represent temporal as well as spatial correlations. 3. When trained with sequences of object views, such a network can learn representations that enable invariant object recognition by clustering different views of the same object within a local neighborhood. 4. Learning of representations for very similar stimuli can be enabled by adaptive inhibitory feedback connections. The study presented in chapter 3.1 details an implementation of a spiking neural network to test the first three hypotheses. This network was tested with stimulus sets that were designed in two feature dimensions to separate the impact of temporal and spatial correlations on learned topographic maps. The emerging topographic maps showed patterns that were dependent on the temporal order of object views during training. Our results show that pooling over local neighborhoods of the topographic map enables invariant recognition. Chapter 3.2 focuses on the fourth hypothesis. There we examine how the adaptive feedback inhibition (AFI) can improve the ability of a network to discriminate between very similar patterns. The results show that with AFI learning is faster, and the network learns selective representations for stimuli with higher levels of overlap than without AFI. Results of chapter 3.1 suggest a functional role for topographic object representations that are known to exist in the inferotemporal cortex, and suggests a mechanism for the development of such representations. The AFI model implements one aspect of predictive coding: subtraction of a prediction from the actual input of a system. The successful implementation in a biologically plausible network of spiking neurons shows that predictive coding can play a role in cortical circuits.
An Assessment of the Impact that Service Quality and Customer Satisfaction Possess on Customer Loyalty in Internet Banking

The﻿purpose﻿of﻿this﻿study﻿was﻿to﻿explore﻿the﻿effect﻿of﻿service﻿quality﻿(SQ)﻿and﻿customer﻿satisfaction﻿ (CS)﻿on﻿consumer﻿loyalty﻿(CL)﻿in﻿internet﻿banking﻿in﻿the﻿case﻿of﻿the﻿KSA. ﻿The﻿basis﻿of﻿this﻿study﻿ was﻿set﻿upon﻿the﻿e-SERVQUAL﻿model﻿and﻿its﻿dimensions:﻿include﻿efficacy,﻿consistency,﻿privacy,﻿ fulfilment,﻿and﻿website﻿design. ﻿The﻿study﻿was﻿aimed﻿to﻿recognize﻿the﻿elements﻿influencing﻿service﻿ quality﻿ (SQ)﻿along﻿with﻿ the﻿concept﻿and﻿significance﻿of﻿SQ﻿in﻿online﻿banking﻿ in﻿particular. ﻿The﻿ study﻿was﻿based﻿on﻿the﻿quantitative﻿research﻿design﻿and﻿the﻿participants﻿for﻿this﻿study﻿were﻿the﻿bank﻿ customers﻿who﻿are﻿using﻿internet﻿services. ﻿The﻿sample﻿size﻿of﻿this﻿study﻿was﻿kept﻿to﻿150﻿participants. ﻿ A﻿close-ended﻿5﻿point﻿Likert﻿scale﻿questionnaire﻿was﻿formulated﻿in﻿order﻿to﻿assess﻿the﻿phenomenon﻿of﻿ the﻿research. ﻿In﻿addition,﻿the﻿SPSS﻿software﻿along﻿with﻿Smart﻿PLS﻿was﻿utilized﻿to﻿examine﻿the﻿data﻿ collected﻿from﻿150﻿customers﻿of﻿the﻿banks. ﻿The﻿SEM﻿and﻿correlation﻿test﻿were﻿applied﻿for﻿assessing﻿ the﻿association﻿between﻿SQ﻿and﻿CL﻿along﻿with﻿CS﻿and﻿CL. ﻿The﻿findings﻿of﻿this﻿study﻿indicated﻿that﻿ there﻿is﻿a﻿significant﻿positive﻿impact﻿of﻿service﻿quality﻿and﻿customer﻿satisfaction﻿on﻿the﻿customer﻿ loyalty﻿of﻿internet﻿banking﻿users﻿in﻿KSA. ﻿Hence,﻿the﻿banking﻿industry﻿of﻿KSA﻿has﻿been﻿recommended﻿ to﻿pay﻿specific﻿heed﻿to﻿all﻿the﻿dimensions﻿of﻿e-SQ﻿and﻿customer﻿satisfaction. KeywORdS Consistency, Customer Loyalty, Customer Satisfaction, Efficiency, e-SERVQUAL Model, Fulfilment, Privacy, Service Quality, Website Design
Facing Erosion Identification in Railway Lines Using Pixel-Wise Deep-Based Approaches

Soil erosion is considered one of the most expensive natural hazards with a high impact on several infrastructure assets. Among them, railway lines are one of the most likely constructions for the appearance of erosion and, consequently, one of the most troublesome due to the maintenance costs, risks of derailments, and so on. Therefore, it is fundamental to identify and monitor erosion in railway lines to prevent major consequences. Currently, erosion identification is manually performed by humans using huge image sets, a time-consuming and slow task. Hence, automatic machine learning methods appear as an appealing alternative. A crucial step for automatic erosion identification is to create a good feature representation. Towards such objective, deep learning can learn data-driven features and classifiers. In this paper, we propose a novel deep learning-based framework capable of performing erosion identification in railway lines. Six techniques were evaluated and the best one, Dynamic Dilated ConvNet, was integrated into this framework that was then encapsulated into a new ArcGIS plugin to facilitate its use by non-programmer users. To analyze such techniques, we also propose a new dataset, composed of almost 2000 high-resolution images.
A Fast Deep Perception Network for Remote Sensing Scene Classification

Current scene classification for high-resolution remote sensing images usually uses deep convolutional neural networks (DCNN) to extract extensive features and adopts support vector machine (SVM) as classifier. DCNN can well exploit deep features but ignore valuable shallow features like texture and directional information; and SVM can hardly train a large amount of samples in an efficient way. This paper proposes a fast deep perception network (FDPResnet) that integrates DCNN and Broad Learning System (BLS), a novel effective learning system, to extract both deep and shallow features and encapsulates a designed DPModel to fuse the two kinds of features. FDPResnet first extracts the shallow and the deep scene features of a remote sensing image through a pre-trained model on residual neural network-101 (Resnet101). Then, it inputs the two kinds of features into a designed deep perception module (DPModel) to obtain a new set of feature vectors that can describe both higher-level semantic and lower-level space information of the image. The DPModel is the key module responsible for dimension reduction and feature fusion. Finally, the obtained new feature vector is input into BLS for training and classification, and we can obtain a satisfactory classification result. A series of experiments are conducted on the challenging NWPU-RESISC45 remote sensing image dataset, and the results demonstrate that our approach outperforms some popular state-of-the-art deep learning methods, and present high-accurate scene classification within a shorter running time.
Combining Lexical Resources for Text Analysis of Game Walkthroughs

—One approach to text analysis is motivated by a desire to understand the actions that are most frequent within a body of text. By analyzing words in the text, primarily verbs, connections can be drawn to the actions that are described by the words. The trouble is that single words can have many meanings and evoke many different situations. For that reason, word sense disambiguation software is a vital part of this project. Lexical resources are also needed because they contain two major types of information; the meanings behind words and the relationships between meanings. Another resource needed by this project is a part of speech tagger, which is used for extracting important parts of speech to work with.
Evaluation of Landsat 8 OLI/TIRS Level-2 and Sentinel 2 Level-1C Fusion Techniques Intended for Image Segmentation of Archaeological Landscapes and Proxies

The use of medium resolution, open access, and freely distributed satellite images, such as those of Landsat, is still understudied in the domain of archaeological research, mainly due to restrictions of spatial resolution. This investigation aims to showcase how the synergistic use of Landsat and Sentinel optical sensors can efficiently support archaeological research through object-based image analysis (OBIA), a relatively new scientific trend, as highlighted in the relevant literature, in the domain of remote sensing archaeology. Initially, the fusion of a 30 m spatial resolution Landsat 8 OLI/TIRS Level-2 and a 10 m spatial resolution Sentinel 2 Level-1C optical images, over the archaeological site of “Nea Paphos” in Cyprus, are evaluated in order to improve the spatial resolution of the Landsat image. At this step, various known fusion models are implemented and evaluated, namely Gram–Schmidt, Brovey, principal component analysis (PCA), and hue-saturation-value (HSV) algorithms. In addition, all four 10 m available spectral bands of the Sentinel 2 sensor, namely the blue, green, red, and near-infrared bands (Bands 2 to 4 and Band 8, respectively) were assessed for each of the different fusion models. On the basis of these findings, the next step of the study, focused on the image segmentation process, through the evaluation of different scale factors. The segmentation process is an important step moving from pixel-based to object-based image analysis. The overall results show that the Gram–Schmidt fusion method based on the near-infrared band of the Sentinel 2 (Band 8) at a range of scale factor segmentation to 70 are the optimum parameters for the detection of standing visible monuments, monitoring excavated areas, and detecting buried archaeological remains, without any significant spectral distortion of the original Landsat image. The new 10 m fused Landsat 8 image provides further spatial details of the archaeological site and depicts, through the segmentation process, important details within the landscape under examination.
Using High Resolution Optical Imagery to Detect Earthquake-Induced Liquefaction: The 2011 Christchurch Earthquake

Using automated supervised methods with satellite and aerial imageries for liquefaction mapping is a promising step in providing detailed and region-scale maps of liquefaction extent immediately after an earthquake. The accuracy of these methods depends on the quantity and quality of training samples and the number of available spectral bands. Digitizing a large number of high-quality training samples from an event may not be feasible in the desired timeframe for rapid response as the training pixels for each class should be typical and accurately represent the spectral diversity of that specific class. To perform automated classification for liquefaction detection, we need to understand how to build the optimal and accurate training dataset. Using multispectral optical imagery from the 22 February, 2011 Christchurch earthquake, we investigate the effects of quantity of high-quality training pixel samples as well as the number of spectral bands on the performance of a pixel-based parametric supervised maximum likelihood classifier for liquefaction detection. We find that the liquefaction surface effects are bimodal in terms of spectral signature and therefore, should be classified as either wet liquefaction or dry liquefaction. This is due to the difference in water content between these two modes. Using 5-fold cross-validation method, we evaluate performance of the classifier on datasets with different pixel sizes of 50, 100, 500, 2000, and 4000. Also, the effect of adding spectral information was investigated by adding once only the near infrared (NIR) band to the visible red, green, and blue (RGB) bands and the other time using all available 8 spectral bands of the World-View 2 satellite imagery. We find that the classifier has high accuracies (75%–95%) when using the 2000 pixels-size dataset that includes the RGB+NIR spectral bands and therefore, increasing to 4000 pixels-size dataset and/or eight spectral bands may not be worth the required time and cost. We also investigate accuracies of the classifier when using aerial imagery with same number of training pixels and either RGB or RGB+NIR bands and find that the classifier accuracies are higher when using satellite imagery with same number of training pixels and spectral information. The classifier identifies dry liquefaction with higher user accuracy than wet liquefaction across all evaluated scenarios. To improve classification performance for wet liquefaction detection, we also investigate adding geospatial information of building footprints to improve classification performance. We find that using a building footprint mask to remove them from the classification process, increases wet liquefaction user accuracy by roughly 10%.
High-Resolution Reconstruction of the Maximum Snow Water Equivalent Based on Remote Sensing Data in a Mountainous Area

Currently, the accurate estimation of the maximum snow water equivalent (SWE) in mountainous areas is an important topic. In this study, in order to improve the accuracy and spatial resolution of SWE reconstruction in alpine regions, the Sentinel-2(MSI) and Landsat 8(OLI) satellite data with the spatial resolution of tens of meters are used instead of the Moderate-resolution Imaging Spectroradiometer (MODIS) data so that the pixel mixing problem is avoided. Meanwhile, geostationary satellite-based and topographic-corrected incoming shortwave radiation is used in the restricted degree-day model to improve the accuracy of radiation inputs. The seasonal maximum SWE accumulation of a river basin in the winter season of 2017–2018 is estimated. The spatial and temporal characteristics of SWE at a fine spatial and temporal resolution are then analyzed. And the results of reconstruction model with different input parameters are compared. The results showed that the average maximum SWE of the study area in 2017–2018 was 377.83mm and the accuracy of snow cover, air temperature and the radiation parameters all affects the maximum SWE distribution on magnitude, elevation and aspect. Although the accuracy of other forcing parameters still needs to be improved, the estimation of the local maximum snow water equivalent in mountainous areas benefits from the application of high-resolution Sentinel-2 and Landsat 8 data. The joint usage of high-resolution remote sensing data from different satellites can greatly improve the temporal and spatial resolution of snow cover and the spatial resolution of SWE estimation. This method can provide more accurate and detailed SWE for hydrological models, which is of great significance to hydrology and water resources research.
Angle Distance-Based Hierarchical Background Separation Method for Hyperspectral Imagery Target Detection

Traditional detectors for hyperspectral imagery (HSI) target detection (TD) output the result after processing the HSI only once. However, using the prior target information only once is not sufficient, as it causes the inaccuracy of target extraction or the unclean separation of the background. In this paper, the target pixels are located by a hierarchical background separation method, which explores the relationship between the target and the background for making better use of the prior target information more than one time. In each layer, there is an angle distance (AD) between each pixel spectrum in HSI and the given prior target spectrum. The AD between the prior target spectrum and candidate target ones is smaller than that of the background pixels. The AD metric is utilized to adjust the values of pixels in each layer to gradually increase the separability of the background and the target. For making better discrimination, the AD is calculated through the whitened data rather than the original data. Besides, an elegant and ingenious smoothing processing operation is employed to mitigate the influence of spectral variability, which is beneficial for the detection accuracy. The experimental results of three real hyperspectral images show that the proposed method outperforms other classical and recently proposed HSI target detection algorithms.
A New Deep Learning Network for Automatic Bridge Detection from SAR Images Based on Balanced and Attention Mechanism

Bridge detection from Synthetic Aperture Radar (SAR) images has very important strategic significance and practical value, but there are still many challenges in end-to-end bridge detection. In this paper, a new deep learning-based network is proposed to identify bridges from SAR images, namely, multi-resolution attention and balance network (MABN). It mainly includes three parts, the attention and balanced feature pyramid (ABFP) network, the region proposal network (RPN), and the classification and regression. First, the ABFP network extracts various features from SAR images, which integrates the ResNeXt backbone network, balanced feature pyramid, and the attention mechanism. Second, extracted features are used by RPN to generate candidate boxes of different resolutions and fused. Furthermore, the candidate boxes are combined with the features extracted by the ABFP network through the region of interest (ROI) pooling strategy. Finally, the detection results of the bridges are produced by the classification and regression module. In addition, intersection over union (IOU) balanced sampling and balanced L1 loss functions are introduced for optimal training of the classification and regression network. In the experiment, TerraSAR data with 3-m resolution and Gaofen-3 data with 1-m resolution are used, and the results are compared with faster R-CNN and SSD. The proposed network has achieved the highest detection precision (P) and average precision (AP) among the three networks, as 0.877 and 0.896, respectively, with the recall rate (RR) as 0.917. Compared with the other two networks, the false alarm targets and missed targets of the proposed network in this paper are greatly reduced, so the precision is greatly improved.
Defending Against Targeted Attacks with Pattern Recognition

The speed at which everything and everyone is being connected considerably outstrips the rate at which effective security mechanisms are introduced to protect them. This has created an opportunity for resourceful threat actors which have specialized in conducting low-volume persistent attacks through sophisticated techniques that are tailored to specific valuable targets. Consequently, traditional approaches are rendered ineffective against targeted attacks, creating an acute need for innovative defense mechanisms. This thesis aims at supporting the security practitioner in bridging this gap by introducing a holistic strategy against targeted attacks that addresses key challenges encountered during the phases of detection, analysis and response. The structure of this thesis is therefore aligned to these three phases, with each one of its central chapters taking on a particular problem and proposing a solution built on a strong foundation on pattern recognition and machine learning. In particular, we propose a detection approach that, in the absence of additional authentication mechanisms, allows to identify spear-phishing emails without relying on their content. Next, we introduce an analysis approach for malware triage based on the structural characterization of malicious code. Finally, we introduce MANTIS, an open-source platform for authoring, sharing and collecting threat intelligence, whose data model is based on an innovative unified representation for threat intelligence standards based on attributed graphs. As a whole, these ideas open new avenues for research on defense mechanisms and represent an attempt to counteract the imbalance between resourceful actors and society at large.