Reviewerly/exHarmony huggingface dataset:
High dynamic range image reconstruction using multi-exposure Wavelet HDRCNN

Despite the advances in technology in the world of mobile and photography, the images taken from such devices do not resemble the scene in terms of brightness or details that users see in the scene cannot be seen in the images. Standard devices capture images with limit dynamic range, hence these images will have high-exposure and low-exposure areas. To tackle this problem, High Dynamic Range (HDR) imaging algorithms are used in which these algorithms pay more attention to the detail reconstruction however in this research, a method will be proposed that in addition to the reconstruction of the details, also it will be focused on image generation time. For this purpose, merging images and image Wavelet coefficients are used to reconstruct more details and make data reduction, respectively. Eventually, the proposed algorithm will be evaluated using PSNR and SSIM evaluation metrics which the results will be shown that the proposed method has appropriate results.
Accuracy of Unit Under Test Identification Using Latent Semantic Analysis and Latent Dirichlet Allocation

Identification of unit under test (UUT) from a test is often difficult and requires wider source code comprehension. By automating this process it would be possible to support the program comprehension and reduce software maintenance process. In this paper the Latent Semantic Analysis (LSA) and the Latent Dirichlet Allocation (LDA) were used which proved to be inaccurate in the UUT identification. The experiment was conducted on 5 popular projects where 1,093,730 similarity results were obtained. It was found out that the best topic number for the LSA model is from 7 to 10, the LDA model had big differences in this value, so it was not possible to define a stable value. The best UUT identification accuracy compared to manual testing has been obtained with the LSA model with result of 7.63% success, where documents were preprocessed using words splitting based on naming conventions and Java keywords removal. The accuracy of the LDA model was almost zero. Further 8 manual identification errors were discovered during the experiment.
Volcanic Seismic Event Classification based on CNN Architectures

This paper explores the use of convolutional neural networks architectures in the context of volcano seismic events classification through the use of grayscale spectrogram images of long-period and volcano-tectonic seismic events. We combined three different architectures with a set of hyper-parameter configurations that produced 720 classification models, which were able to learn the morphological pattern described by the grayscale spectrogram images of seismic events. Downscaling of all grayscale spectrogram images was used to to reduce the computation time for each model without losing performance and avoidingreduce the computation time for each model without losing performance and avoiding any overfitting. The explored architectures provided good results in terms of the area under the receiver operating characteristic curve scores. However, when considering the accuracy scores in the selection process, the best model to classify grayscale spectrogram images of both types of seismic events was the CNN3 architecture with a (3 × 3) convolutional and pool kernel size, same padding type and 1×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> as learning rate, which achieved an area under the receiver operating characteristic curve and accuracy values of 0.94 and 94.13%, respectively.
Understanding the requirements Of the Indian IT industry using web scrapping

India produces over two million engineering graduates a year as well as another million graduates in related fields such as computer science, biotechnology etc. Employers cite new graduates lack communication skills, problem solving skills and technical skills. The Indian Information Technology (IT) sector has fast changing technical requirements and understanding its requirement is very important for academics and professional trainers so that they can better prepare students with relevant skills. In this paper, we have developed a simple methodology to analyse the requirements of the Indian IT industry by parsing their job portals and extracting information from their job descriptions. We selected the top 10 IT companies by market cap to conduct our analysis. We extracted the number of jobs that contained each keywords like Java, CCNA, SAP, Agile and computed the popularity of each skill and ranked them accordingly. We find that IT industry requires software and skills like Java, Python, SQL are covered by most universities but paid and in demand commercial software tools like SAP, Oracle are not part of the curriculum. This methodology can also track the evolution of skills and predict new skills that can become popular in Industries in the future.
Finite Automata for Fake Profile Identification in Online Social Networks

In this modern era, fraudsters discover various approaches to defame the popularity of high-profile social accounts associated with various social networking sites. In this paper, a finite automata-based fake profile identifier is proposed to curb fake profiles and to solve security issues. The proposed method is compared with the other general validation methods and found that our model gives better results than the existing models.
Fingerprinting of URL Logs: Continuous User Authentication from Behavioural Patterns

Security of computer systems is now a critical and evolving issue. Current trends try to use behavioural biometrics for continuous authorization. Our work is intended to strengthen network user authentication by a software interaction analysis. In our research, we use HTTP request (URLs) logs that network administrators collect. We use a set of full-convolutional autoencoders and one authentication (one-class) convolutional neural network. The proposed method copes with extensive data from many users and allows to add new users in the future. Moreover, the system works in a real-time manner, and the proposed deep learning framework can use other user behaviour- and software interaction-related features.
Voice Assisted and Gesture Controlled Companion Robot

This paper implements the design for a robot that can be controlled simply by using interactive inputs from the operator such as voice and gesture along with object tracking. The system aims to create a prototype of a futuristic automated personal assistant for domestic as well as industrial purposes. Google text to speech API and Grassfire algorithm is used to control the basic locomotion of the system. The robot consists of a gripper arm which is used to pick and hold objects as desired by the operator.
Towards Exploring the Potential of Alternative Quantum Computing Architectures

The recent advances in the physical realization of Noisy Intermediate Scale Quantum (NISQ) computers have motivated research on design automation that allows users to execute quantum algorithms on them. Certain physical constraints in the architectures restrict how logical qubits used to describe the algorithm can be mapped to physical qubits used to realize the corresponding functionality. Thus far, this has been addressed by inserting additional operations in order to overcome the physical constrains. However, all these approaches have taken the existing architectures as invariant and did not explore the potential of changing the quantum architecture itself-a valid option as long as the underlying physical constrains remain satisfied. In this work, we propose initial ideas to explore this potential. More precisely, we introduce several schemes for the generation of alternative coupling graphs (and, by this, quantum computing architectures) that still might be able to satisfy physical constraints but, at the same time, allow for a more efficient realization of the desired quantum functionality.
Solving Nonlinear Filtering Problems in Real Time by Legendre Galerkin Spectral Method

It is well known that the nonlinear filtering (NLF) problem has important applications in both military and civil industries. The central question is to solve the posterior conditional density function of the states, which satisfies the Kushner or the Duncan-Mortensen-Zakai (DMZ) equation after suitable change of probability measure. In this article, we shall follow the so-called Yau-Yau's algorithm to split the solution of the DMZ equation into on- and off-line part, where the off-line part is to solve the forward Kolmogorov equation (FKE) with the initial conditions to be the orthonormal bases in some suitable function space. Instead of the generalized Hermite function investigated by the second and the third author of this article, we shall explore the generalized Legendre polynomials. The Legendre spectral method (LSM) is used to numerically solve the FKE. Under certain conditions, the convergence rate of LSM is twice faster than that of the Hermite spectral method. Two two-dimensional numerical experiments of NLF problems (time-invariant and time-varying cases) have been numerically solved to illustrate the feasibility of our algorithm. Our algorithm outperforms the extended Kalman Filter and particle filter in both real-time manner and accuracy.
Leveled Adaptively Strong-Unforgeable Identity-Based Fully Homomorphic Signatures

The identity-based homomorphic signature (IBHS) enables an untrusted server to run some computation over the outsourced data and derive a short signature, vouching for the correctness of the output of the computation, while greatly simplifying key management.To our knowledge, constructions of IBHS have been few and far between.However, the existing IBHS schemes, which either handle only linear functions or has a large public key parameter and satisfies only the artificial notion of selective security.In this work, we construct the first leveled adaptively secure identity-based fully homomorphic signature (IBFHS) schemes without additional public parameters, which can be used to sign many different datasets.Thereby positively answering the open question of constructing a leveled IBFHS scheme with short public parameters, proposed by Wang et al., (ISC, 2015, Springer).We achieve the stronger security and better parameters by using the trapdoor vanishing and vector encoding technique.In our scheme, the size of every evaluated signature depends only logarithmically on the size of the input dataset, and the complexity of verifying a signature for a computation can be amortized when verifying the same computation on many different datasets.Furthermore, we prove that our construction is strongly-unforgeable against adaptively chosen identity and message attacks under the small integer solution (SIS) assumption in standard lattices.
A Systematic Review Of Bring Your Own Device (BYOD) Authentication Technique

Abstract This article provides a systematic review of Bring Your Own Device (BYOD) authentication technique. The review main goal is to identify the existing BYOD authentication technique and to categorize the technique according to BYOD security threat as well as to analyze the technique limitation. The review result shows that there are 25 authentication techniques proposed across both industry and academic areas to fit into BYOD implementation to enhance the security in preventing and detecting data leakage in the organization. It is hoped that the proposed BYOD authentication solution can help organizations to minimize the number of cases in data leakage while allowing the BYOD concept.
The augmented reality application for simulating electromotive force concept

Abstract The discussion of the concept of electromotive force often leads to misconceptions. Unity program can produce 3-dimensional media from 2-dimensional media. In this article, we will describe the form of electromotive force simulation that is displayed with augmented reality technology. Development of electromotive force simulation using 4D development model (define, design, develop, and disseminate). The application of augmented reality technology can visualize the three-dimensional form of abstract electromotive force processes into reality. The design process is based on pedagogical content knowledge. The electromotive force display can simulate the impact of changes in the number of turns, magnetic fields, and time of playback of the generator against the value of electromotive force. The simulation has succeeded in providing an interesting description of the ratio of variables to the magnitude of the force of electricity produced in the generator. Display simulation electromotive force with augmented reality technology has met the principle of technological pedagogical content knowledge (TPACK).
Online Data Dimensionality Reduction and Reconstruction Using Graph Filtering

Graphs have become a popular toolbox for capturing similarity relationships among data vectors in a variety of networks. Diffusion processes are used to model the data using the graph topology. A novel approach is put forth that exploits data similarity and diffusion models, in a graph, to improve upon the reconstruction mean-square error (MSE) performance of principal component analysis. The tasks of data dimensionality reduction and reconstruction are formulated as graph matrix filtering operations, that are utilized in a dimensionality reduction framework optimal in the MSE sense. The novel formulation is seeking MSE-optimal filter matrices that minimize the reconstruction MSE of all the graph data. Block coordinate descent techniques are employed in the graph spectral domain MSE cost to recursively determine the MSE-optimal dimensionality reducing and reconstruction filters. Online implementations are put forth to work with continuous streams of graph data, while almost sure convergence to a stationary point of the ensemble reconstruction MSE cost is established. Analysis of the reconstruction MSE reveals that the lowest MSE achieved by the novel approach is not larger than the standard PCA MSE, while there is a bound for the filter orders after which no more MSE reduction occurs. Extensive numerical tests using synthetic data, as well as real image datasets demonstrate the advantage of the novel graph-based framework over standard PCA and related methods.
An Android Case Study on Technical Anti-Forensic Challenges of WhatsApp Application

Cyberbullying has become a serious concern in this Internet era with the availability of various communication technologies. While no one wishes to be a victim of cyberbullying, some of the electronic platforms, specifically Instant Messaging Applications (IMAs) such as WhatsApp, have created a safe haven for cyberbullying. As the use of WhatsApp application is globally increasing, it is extremely important to consider it being misused as a tool of cybercrime. This paper investigates some of the technical issues with WhatsApp application that could open the door to cybercrimes and pose anti-forensic challenges. In addition, we also highlight possible anti-forensic techniques for WhatsApp application through two hypothetical case scenarios. Moreover, we propose a validated solution to the ‘delete for everyone’ feature of WhatsApp by presenting a demo mobile application that recovers any deleted messages even after block status.
Fuzzy based Regional Thresholding for Cyst Segmentation in Dental Radiographs

Dental cysts are the major problem caused due to infection in the tooth. Cysts cause any symptoms rarely, if not treated they become infected secondarily. The image processing helps to improve the diagnostic outcomes and provide effective early diagnostic yield, To achieve the highest possible degrees of automatization for the computer-aided diagnosis or detection of diseases, the proposed methodology utilizes both fuzzy membership function of each pixel and local spatial information of the neighbourhood pixels. There are five steps in implementing the fuzzy-based multi-region thresholding. From the comparative analysis of the results obtained by different segmentation methods like histogram-based multilevel thresholding methods. The proposed hybrid multi-region segmentation performs well and the outputs show that it is automated, accurate and it shows the well-connected boundary of the cystic region and there are no isolated cyst pixels.
Unsupervised Batch Normalization

Batch Normalization is a widely used tool in neural networks to improve the generalization and convergence of training. However, on small datasets due to the difficulty of obtaining unbiased batch statistics it cannot be applied effectively. In some cases, even if there is only a small labeled dataset available, there are larger unlabeled datasets from the same distribution. We propose using such unlabeled examples to calculate batch normalization statistics, which we call Unsupervised Batch Normalization (UBN). We show that using unlabeled examples for batch statistic calculations results in a reduction of the bias of the statistics, as well as regularization leveraging the data manifold. UBN is easy to implement, computationally inexpensive and can be applied to a variety problems. We report results on monocular depth estimation, where obtaining dense labeled examples is difficult and expensive. Using unlabeled samples, and UBN, we obtain an increase in accuracy of more than 6% on the KITTI dataset, compared to using traditional batch normalization only on the labeled samples.
MMED: A multi-domain and Multi-modality event dataset

In this work, we release a multi-domain and multi-modality event dataset (MMED), containing 25,052 textual news articles collected from hundreds of news media sites (e.g., Yahoo News, BBC News, etc.) and 75,884 image posts shared on Flickr by thousands of social media users. The articles contributed by professional journalists and the images shared by amateur users are annotated according to 410 real-world events, covering emergencies, natural disasters, sports, ceremonies, elections, protests, military intervention, economic crises, etc. The MMED dataset is collected by the following the principles of high relevance in supporting the application needs, a wide range of event types, non-ambiguity of the event labels, imbalanced event clusters, and difficulty discriminating the event labels. The dataset can stimulate innovative research on related challenging problems, such as (weakly aligned) cross-modal retrieval and cross-domain event discovery, inspire visual relation mining and reasoning, etc. For comparisons, 15 baselines for two scenarios have been quantitatively and qualitatively evaluated using the dataset.
Correspondence between multiwavelet shrinkage and nonlinear diffusion

Wavelet/frame shrinkage and nonlinear diffusion filtering are two popular methods for signal and image denoising. The relationship between these two methods has been studied recently. This relationship leads to new types of diffusion equations and helps to design the wavelet/frame-inspired diffusivity functions, and on the other hand it helps to design the diffusion-inspired shrinkage functions with a better performance in signal and image denoising. Multiwavelets have important properties such as orthogonality, short support, and symmetry, etc. that scalar orthogonal wavelets cannot possess simultaneously. There is rich literature on the theoretical study, construction and applications of multiwavelets. In particular, it has been shown that multiwavelets perform better than the scalar wavelets in signal and image denoising. Recently multiwavelet denoising has been applied in different applications including rolling bearing fault detection and study of load spectrum of computer numerical control lathe. Therefore it is worth to further study multiwavelet denoising. In this paper we investigate the correspondence between multiwavelet denoising and nonlinear diffusion. We show that the multiwavelet shrinkages of the commonly used CL(2) and DGHM multiwavelets are associated with a second-order nonlinear diffusion equation. We also derive high-order nonlinear diffusion equations associated with general multiwavelet shrinkages. The experimental results carried out in this paper show that the diffusion-inspired multiwavelet shrinkage performs better than the traditional multiwavelet hard- and soft-thresholding shrinkages.
Performance Comparison of Cloud Computing/IoT Virtualization Software, Hyper-V vs vSphere

Hyper-V and vSphere are amongst the top virtualization software's available in the market currently. Both of these products are from different vendors, Hyper-V is developed by Microsoft and vSphere is devolved by VMware. The intent of this paper is to compare the network performance of two different software's using IPv4 and IPv6 that will allow us to determine which perform better at different stages of the test. Virtualization is used in Cloud data centers that store most of IoT (Internet of Things) data. Results show VMware (vSphere) is able to achieve much higher throughput that Hyper-V. Also VMWare showed lower delay, but the differences is in milliseconds that is not noticeable.
GAT–GMM: Generative Adversarial Training for Gaussian Mixture Models

Generative adversarial networks (GANs) learn the distribution of observed samples through a zero-sum game between two machine players, a generator and a discriminator. While GANs achieve great success in learning the complex distribution of image, sound, and text data, they perform suboptimally in learning multimodal distribution-learning benchmarks such as Gaussian mixture models (GMMs). In this paper, we propose Generative Adversarial Training for Gaussian Mixture Models (GAT-GMM), a minimax GAN framework for learning GMMs. Motivated by optimal transport theory, we design the zero-sum game in GAT-GMM using a random linear generator and a softmax-based quadratic discriminator architecture, which leads to a nonconvex concave minimax optimization problem. We show that a gradient descent ascent (GDA) method converges to an approximate stationary minimax point of the GAT-GMM optimization problem. In the benchmark case of a mixture of two symmetric, well-separated Gaussians, we further show that this stationary point recovers the true parameters of the underlying GMM. We discuss the application of the proposed GAT-GMM framework for learning GMMs in the distributed federated learning setting, where the widely used expectation-maximization (EM) algorithm can incur great computational and communication costs. On the other hand, we show that GAT-GMM provides a scalable learning approach and a distributed GDA algorithm can still solve the GAT-GMM minimax problem without incurring extra computation costs. We numerically support our theoretical results by performing experiments which show that our minimax framework is successful in centralized learning tasks and can outperform standard EM-type algorithms in the federated setting.
DC-CNN: Computational Flow Redefinition for Efficient CNN through Structural Decoupling

Recently Convolutional Neural Networks (CNNs) are widely applied into novel intelligent applications and systems. However, the CNN computation performance is significantly hindered by its computation flow, which computes the model structure sequentially by layers with massive convolution operations. Such a layer-wise sequential computation flow can cause certain performance issues, such as resource under-utilization, huge memory overhead, etc. To solve these problems, we propose a novel CNN structural decoupling method, which could decouple CNN models into "critical paths" and eliminate the inter-layer data dependency. Based on this method, we redefine the CNN computation flow into parallel and cascade computing paradigms, which can significantly enhance the CNN computation performance with both multi-core and single-core CPU processors. Experiments show that, our DC-CNN framework could reduce 24% to 33% latency on multi-core CPUs for CIFAR and ImageNet. On small-capacity mobile platforms, cascade computing could reduce the latency by average 24% on ImageNet and 42% on CIFAR10. Meanwhile, the memory reduction could also reach average 21% and 64%, respectively.