Reviewerly/exHarmony huggingface dataset:
Banking Digitalization: (Re)Thinking Strategies and Trends Using Problem Structuring Methods

Digital transformation is becoming more common in an increasingly digital world, which has contributed to significant changes in business processes around the globe. In the banking industry, this approach allows banking institutions to provide improved customer experiences. However, the determinants of successful banking digitalization are still unclear, as are their cause-and-effect relationships. This article sought to apply problem structuring methods, namely, fuzzy cognitive mapping and system dynamics, to develop a conceptual model that can be applied to analyses of banking industry digitalization. The results are based on two group sessions that were held with a panel of decision makers who deal with banking digitalization processes on a daily basis. The findings were validated by a senior staff member responsible for banking project assessment and development at Caixa Geral de Depósitos S.A.—a Portuguese state-owned banking corporation. The approach adopted in this article is unique because digitalization determinants were collected and analyzed based on the participants’ know-how and experience. Static and dynamic analyses produced a deeper, broader understanding of the dynamic cause-and-effect relationships between the identified determinants of banking digitalization, giving rise to a realistic model that can be used to support relevant decision-making processes.
Underwater Image Enhancement based on Histogram Manipulation and Multiscale Fusion

The underwater images are a good source of information which explores idea about sea creatures, to study about seafloor hydrother-mal vents. Low contrast, color distortion and poor visual appearance are the major issues that an underwater image has to undergo. Such problems were caused by dispersion and refraction of light as they penetrate from rarer to denser media. The scattering of light reduces color contrast. The influence of water in underwater images is not only due to scattering but also due to the presence of underwater organisms. Here we introduce an improved method for underwater image enhancement based on the fusion method that is capable to restore accurately underwater images. The proposed work takes a single image as the input and a sequence of operations such as white balancing, gamma correction, sharpening, manipulating weight maps are performed on the input image. Finally multiscale image fusion of the inputs is done to obtain the resultant output. In the initial stage, color distorted input image is white balanced to remove the color casts maintaining a realistic subsea image. In the second stage, CLAHE is performed on the gamma corrected image. CLAHE plays a significant role in luminance enhancement of underwater images. At the same time, histogram equalization is performed on the sharpened image. The weight maps analyze image characteristics that properly specify the spatial pixel relationship. Finally in the last stage, multiscale pyramidal fusion of the inputs and weight maps are performed. The fusion performed here is the Pyramidal fusion. Result analysis depict the improvement of the underwater images using proposed method.
Electric circuits for universal quantum gates and quantum Fourier transformation

Universal quantum computation may be realized based on quantum walk, by formulating it as a scattering problem on a graph. In this paper, we simulate quantum gates through electric circuits, following a recent report that a one-dimensional $LC$ electric circuit can simulate a Schr\"{o}dinger equation and hence a quantum walk. Especially, we propose a physical realization of a set of universal quantum gates consisting of the CNOT, Hadamard and $\pi /4$ phase-shift gates with the use of telegrapher wires and mixing bridges. Furthermore, we construct the $\pi /2^{n}$ phase-shift gate for an arbitrary integer $n$, which is an essential element to perform the quantum Fourier transformation and prime factorization based on the Shor algorithm. Our results will open a way to universal quantum computation based on electric circuits.
Positional head-eye tracking outside the lab: an open-source solution

Simultaneous head and eye tracking has traditionally been confined to a laboratory setting and real-world motion tracking limited to measuring linear acceleration and angular velocity. Recently available mobile devices such as the Pupil Core eye tracker and the Intel RealSense T265 motion tracker promise to deliver accurate measurements outside the lab. Here, the researchers propose a hard- and software framework that combines both devices into a robust, usable, low-cost head and eye tracking system. The developed software is open source and the required hardware modifications can be 3D printed. The researchers demonstrate the system’s ability to measure head and eye movements in two tasks: an eyes-fixed head rotation task eliciting the vestibulo-ocular reflex inside the laboratory, and a natural locomotion task where a subject walks around a building outside of the laboratory. The resultant head and eye movements are discussed, as well as future implementations of this system.
An attention recurrent model for human cooperation detection

User cooperative behaviour is mandatory and valuable to warranty data acquisition quality in forensic biometrics. In the present paper, we consider human cooperative behaviour in front of wearable security cameras. Moreover, we propose a human cooperation detection pipeline based on deep learning. Recently, recurrent neural networks (RNN) have shown remarkable performance on several tasks such as image captioning, video analysis, or natural language processing. Our proposal describes an RNN architecture with the aim at detecting whether a human is exhibiting an adversarial behaviour by trying to avoid the camera. This data is obtained by analysing the noise patterns of human movement. More specifically, we are not only providing an extensive analysis on the proposed pipeline considering different configurations and a wide variety of RNN types, but also an ensemble of the generated models to outperform each single model. The experiment has been carried out using videos captured from a mobile device camera (GOTCHA Dataset) and the obtained results have demonstrated the robustness of the proposed method.
Sampling of Bayesian posteriors with a non-Gaussian probabilistic learning on manifolds from a small dataset

This paper tackles the challenge presented by small-data to the task of Bayesian inference. A novel methodology, based on manifold learning and manifold sampling, is proposed for solving this computational statistics problem under the following assumptions: (1) neither the prior model nor the likelihood function are Gaussian and neither can be approximated by a Gaussian measure; (2) the number of functional input (system parameters) and functional output (quantity of interest) can be large; (3) the number of available realizations of the prior model is small, leading to the small-data challenge typically associated with expensive numerical simulations; the number of experimental realizations is also small; (4) the number of the posterior realizations required for decision is much larger than the available initial dataset. The method and its mathematical aspects are detailed. Three applications are presented for validation: The first two involve mathematical constructions aimed to develop intuition around the method and to explore its performance. The third example aims to demonstrate the operational value of the method using a more complex application related to the statistical inverse identification of the non-Gaussian matrix-valued random elasticity field of a damaged biological tissue (osteoporosis in a cortical bone) using ultrasonic waves.
Dynamic Hybrid-copy Live Virtual Machine Migration: Analysis and Comparison

Live VM migration has significantly gained attention as one of the most key strategies of improving data center efficiency through better resources allocation that leads to efficient services. The data center efficiency includes less power consumption, less unutilized resources, and less service interruption time in cases of maintenance or failures. The key point of the live VM migration is to copy the VM's state, memory, and disk with the minimal downtime to guarantee less interruption in the services the VM provides. Pre-copy, Post-copy, and Hybrid-copy are examples of Live VM migration methods that copy the VM's memory states to the destination host, while the VM still runs at the source host, as in the Pre-copy, or at the destination host, as in Post-copy. Each of these classical live migration methods has different characteristics and performance. Dynamic Hybrid-copy combines Pre-copy iterations with the Post-copy to have the best of both methods. In this paper, we present the mathematical model for the Dynamic Hybrid-copy live migration method for the total migration time, downtime, and total transferred data during the migration process. We compare the Dynamic Hybrid-copy with other classical live migration methods. We implement the models on JAVA and evaluate the performance of the Dynamic Hybrid-copy and compare it with other methods for different experimental parameters. Based on the performance results, we show how choosing the effective number of Pre-copy iterations leads to better migration performance. Mathematical models were also implemented in CloudSim and a simulation scenario was tested for the four migration methods.
VRLE: Lifelog Interaction Prototype in Virtual Reality

The Lifelog Search Challenge (LSC) invites researchers to share their prototypes for interactive lifelog retrieval and encourages competition to develop and evaluate effective methodologies to achieve this. With this paper we present a novel approach to visual lifelog exploration based on our research to date utilising virtual reality as a medium for interactive information retrieval. The prototype discussed is an iteration on a previous system which participated in the first LSC which occurred at ACM ICMR in 2018 where it ranked first place.
Certificateless Provable Data Possession Protocol for the Multiple Copies and Clouds Case

For user's extremely important data, storing multiple copies on cloud(s) may be a good option because even if the integrity of one or more copies is broken, it can still recover data from other intact ones, which increases the availability and durability of the outsourced data. Some provable data possession (PDP) protocols guaranteeing the integrity of multi-copies had been proposed in the past years. But almost all of them considered storing multi-copies to single cloud, and the necessary management of certificates as well as the dependence on PKI greatly decrease their efficiencies. Therefore, in recent work, Li et al. proposed an identity-based PDP protocol, which not only avoids the tedious certificates and PKI, but also supports muiti-copies stored on multi-clouds. However, it is well-known that identity-based protocols suffer from the key-escrow attack. In this paper, we consider the certificateless multi-copy-multi-cloud protocol. Specifically, we first present its security model and then construct a concrete protocol, whose security can be proven under the classical CDH assumption. Finally, the performance analysis demonstrates that our protocol yields better efficiency and hence is practical.
Chest X-Ray Image Denoising Using Nesterov Optimization Method with Total Variation Regularization

We propose a chest X-Ray image denoising method based on Total variation regularization with implementation on the Nesterov optimization method. The denoising problem is formulated in the form of the second-order cone programming problem and then it is transformed to a saddle point problem under the min-max form. The chest X-Ray images are also processed by the Anscombe transform to be appropriate for the formulated denoising problem. In the experiments, we test on chest X-Ray images of the Radiopaedia dataset. Denoising results are evaluated by using the Peak signal-to-noise ratio and the Structure similarity metrics. Based on the image quality assessment metrics, we compared images quality after denoising of the proposed method with ones of other similar denoising methods. The results confirmed that the proposed method outperforms other compared denoising methods.
Recognition of Ancient Tamil Palm Leaf Vowel Characters in Historical Documents using B-spline Curve Recognition

Tamil is one of the classic languages in the world. The ancient Tamil documents on medicines, literatures, drama, astrology and many other ancient contents were written in stones and palmleaf. The character recognition of palmleaf manuscript is one of the open problems to be solved. In this paper, the recognition of B-spline curves is used to recognize the twelve vowels in the palm leaf manuscripts. The advantage of B-spline curve are uniqueness and robustness. Each vowel in Tamil language has one or more curves of different angle. Recognition of the combination of curves is used to recognize the vowels. The results show that the proposed method can recognize characters written by different narrators and the proposed method performs better than other state - of - the art methods in terms of recognition accuracy.
Image Retrieval using Multi-scale CNN Features Pooling

In this paper, we address the problem of image retrieval by learning images representation based on the activations of a Convolutional Neural Network. We present an end-to-end trainable network architecture that exploits a novel multi-scale local pooling based on NetVLAD and a triplet mining procedure based on samples difficulty to obtain an effective image representation. Extensive experiments show that our approach is able to reach state-of-the-art results on three standard datasets.
Joint Demosaicing and Super-Resolution (JDSR): Network Design and Perceptual Optimization

Image demosaicing and super-resolution are two important tasks in color imaging pipeline. So far they have been mostly independently studied in the open literature of deep learning; little is known about the potential benefit of formulating a joint demosaicing and super-resolution (JDSR) problem. In this article, we propose an end-to-end optimization solution to the JDSR problem and demonstrate its practical significance in computational imaging. Our technical contributions are mainly two-fold. On network design, we have developed a Residual-Dense Squeeze-and-Excitation Networks (RDSEN) supported by a pre-demosaicing network (PDNet) as the pre-processing step. We address the issue of spatio-spectral attention for color-filter-array (CFA) data and discuss how to achieve better information flow by concatenating Residue-Dense Squeeze-and-Excitation Blocks (RDSEBs) for JDSR. Experimental results have shown that significant PSNR/SSIM gain can be achieved by RDSEN over previous network architectures including state-of-the-art RCAN. On perceptual optimization, we propose to leverage the latest ideas including relativistic discriminator and pre-excitation perceptual loss function to further improve the visual quality of textured regions in reconstructed images. Our extensive experiment results have shown that Texture-enhanced Relativistic average Generative Adversarial Network (TRaGAN) can produce both subjectively more pleasant images and objectively lower perceptual distortion scores than standard GAN for JDSR. Finally, we have verified the benefit of JDSR to high-quality image reconstruction from real-world Bayer pattern data collected by NASA Mars Curiosity.
Evaluation of Machine Translation Quality through the Metrics of Error Rate and Accuracy

The aim of the paper is to find out whether it is necessary to use all automatic measures of error rate and accuracy when evaluating the quality of machine translation output from the synthetic Slovak language into the analytical English language. We used multiple comparisons for the analysis and visualized the results for each sentence through the icon graphs. Based on the results, we can state that all examined metrics, which are based on textual similarity, except the f-measure, are needed to be included in the MT quality evaluation when analyzing, sentence by sentence, the machine translation output.
Network Steganography using the Overflow Field of Timestamp Option in an IPv4 Packet

Steganography is a technique of hiding secret data inside a cover. The most popularly used cover media include images, videos, audios, documents and network protocols. Network Steganography is a technique that uses common network protocols (the header field, the payload field or both) to hide a secret message. TCP/IP protocol suite has been a potential target for network steganography from the very beginning. It has a lot of possibilities for creation of hidden channels that can be used to communicate covertly. In this paper, we propose a technique that creates a covert channel using the Overflow field of Timestamp option of Internet Protocol, version 4 over a Local Area Network. This technique implements a storage based network steganography that uses the timestamp option which is used for debugging and measurement over the networks. In this technique, we use legitimate values in the Overflow field which makes it difficult to detect the possibility of covert communication.
Visible-infrared Person Re-identification via Colorization-based Siamese Generative Adversarial Network

With explosive surveillance data during day and night, visible-infrared person re-identification (VI-ReID) is an emerging challenge due to the apparent cross-modality discrepancy between visible and infrared images. Existing VI-ReID work mainly focuses on learning a robust feature to represent a person in both modalities despite the modality gap cannot be effectively eliminated. Recent research works have proposed various generative adversarial network (GAN) models to transfer the visible modality to another unified modality, aiming to bridge the cross-modality gap. However, they neglect the information loss caused by transferring the domain of visible images which is significant for identification. To effectively address the problems, we observe that key information such as textures and semantics in an infrared image can help to color the image itself and the colored infrared image maintains rich information from infrared image while reducing the discrepancy with the visible image. We therefore propose a colorization-based Siamese generative adversarial network (CoSiGAN) for VI-ReID to bridge the cross-modality gap, by retaining the identity of the colored infrared image. Furthermore, we also propose a feature-level fusion model to supplement the transfer loss of colorization. The experiments conducted on two cross-modality person re-identification datasets demonstrate the superiority of the proposed method compared with the state-of-the-arts.
Rician K-Factor Estimation Using Deep Learning

Wireless communications systems design and its performance depend on the wireless fading channels, which are often characterized using a Rician probability function. A Rician K-factor is used to describe the fading severity in a Rician fading channel and is used in the system design and performance evaluation. Therefore, the estimation of the Rician K-factor is important in wireless communications research and development. Traditionally, a Rician K-factor equation, the statistics of the instantaneous frequency of the received signal with a lookup table, or the James-Stein estimator with the maximum likelihood estimation is used for the K-factor estimation. In this paper, we explore the use of deep learning for K-factor estimation. Specifically, we use the convolutional neural network (CNN) to estimate the Rician K-factor from a waveform signal in a Rician channel. Numerical results demonstrate its good performance in estimating the K-factor of the Rician channel.
Face Recognition Using Light-Convolutional Neural Networks Based On Modified Vgg16 Model

Identification and verification systems nowadays utilize biometric technology such as face recognition, retina scan, and fingerprint mapping to recognize a person identity. For the face recognition technology, the traditional methods usually consist of four stages: face detection, face alignment, feature extraction, and classification. Recently, deep learning gains popularity in the face recognition task due to its performance that outperform the traditional methods and its simplicity that combines features extraction and classification in a single architecture. Deep learning especially Convolutional Neural Networks (CNN) has been successfully implemented in the face recognition applications. There are many variations of CNN based models that successfully improved the face recognition performance. However, the majority of the models have very deep layers and trained with large scale face image dataset that need a lot of computational resources. In this research, a light-CNN based on modified VGG16 model is proposed to recognize face with a limited dataset. The proposed light-CNN is compact yet produces good performances with 94.4% accuracy.
Adversarial Multimodal Network for Movie Story Question Answering

Visual question answering by using information from multiple modalities has attracted more and more attention in recent years. However, it is a very challenging task, as the visual content and natural language have quite different statistical properties. In this work, we present a method called Adversarial Multimodal Network (AMN) to better understand video stories for question answering. In AMN, we propose to learn multimodal feature representations by finding a more coherent subspace for video clips and the corresponding texts (e.g., subtitles and questions) based on generative adversarial networks. Moreover, a self-attention mechanism is developed to enforce our newly introduced consistency constraint in order to preserve the self-correlation between the visual cues of the original video clips in the learned multimodal representations. Extensive experiments on the benchmark MovieQA and TVQA datasets show the effectiveness of our proposed AMN over other published state-of-the-art methods.
Mesh-Guided Multi-View Stereo With Pyramid Architecture

Multi-view stereo (MVS) aims to reconstruct 3D geometry of the target scene by using only information from 2D images. Although much progress has been made, it still suffers from textureless regions. To overcome this difficulty, we propose a mesh-guided MVS method with pyramid architecture, which makes use of the surface mesh obtained from coarse-scale images to guide the reconstruction process. Specifically, a PatchMatch-based MVS algorithm is first used to generate depth maps for coarse-scale images and the corresponding surface mesh is obtained by a surface reconstruction algorithm. Next we project the mesh onto each of depth maps to replace unreliable depth values and the corrected depth maps are fed to fine-scale reconstruction for initialization. To alleviate the influence of possible erroneous faces on the mesh, we further design and train a convolutional neural network to remove incorrect depths. In addition, it is often hard for the correct depth values for low-textured regions to survive at the fine-scale, thus we also develop an efficient method to seek out these regions and further enforce the geometric consistency in these regions. Experimental results on the ETH3D high-resolution dataset demonstrate that our method achieves state-of-the-art performance, especially in completeness.