Reviewerly/exHarmony huggingface dataset:
Blowfish Algorithm Implementation on Electronic Data in a Communication Network

Data, information, and knowledge are valuable assets of most business organizations. Protecting and securing these assets is crucial to their success. Computer security systems have increasingly become an integral part of the digital business. These systems have been used to safeguard key assets of enterprises from computer security threats. Secured storage and transmission of electronic data over a communication network can only be achieved through the use of cryptography techniques that offer strong data protection. In this paper, the researchers illustrate the design and implementation of a newly developed security system based on the Blowfish algorithm. The algorithm was tested with different types of electronic data to prove its time efficiency and accuracy. The results were gathered from small data and the next direction of this research is to simulate the system with big data.
Resource Allocation in Cloud Computing Via Optimal Control to Queuing Systems

We consider resource allocation problem in the cloud computing.We use queuing model to model the process of entering into the cloud and to schedule and to serve incoming jobs.In this paper, the main problem is to allocate resources in the queuing systems as a general optimization problem for controlled Markov process with finite state space.For this purpose, we study a model of cloud computing where the arrival jobs follow a stochastic process.We reduce this problem to a routing problem.In the case of minimizing, cost is given as a mixture of an average queue length and number of lost jobs.We use dynamic programming approach.Finally, we obtain the explicit form of the optimal control by the Bellman equation.
Extending class activation mapping using Gaussian receptive field

Understanding the inner behaviors of deep neural networks is an important issue, and various visualization methods have been suggested. However, recent studies showed that most existing saliency methods have low visualization accuracy, even compared to a random importance assignment. In this study, we seek an improved saliency algorithm. Focusing on class activation mapping (CAM)-based saliency methods, we discuss two problems with the existing studies. First, we introduce conservativeness, a property that prevents redundancy and deficiency in saliency map and ensures that the saliency map is on the same scale as the prediction score. We identify that existing CAM studies do not satisfy the conservativeness and derive a new CAM equation with the improved theoretical property. Second, we discuss the common practice of using bilinear upsampling as problematic. We propose Gaussian upsampling, an improved upsampling method that reflects deep neural networks’ properties. Based on these two options, we propose Extended-CAM, an advanced CAM-based visualization method. In various visualization benchmarks, datasets, and architectures, our Extended-CAM presents a more accurate visualization than is obtainable with other existing methods.
Directional attention weaving for text-grounded conversational question answering

Compared to the traditional single-turn question answering (QA) based on text, also known as machine reading comprehension (MRC), conversational MRC tasks aim to enable models to understand the question according to not only the information in the passage but also the previous question-answer pairs. In this paper, we introduce a directional attention weaving (DAW) mechanism to represent dialog context more comprehensively in two aspects. First, we use self-attention on each level of the question-aware passage hidden representation to collect global information for each question as conversation history input, which is referred to multi-round self-alignment. Second, different from existing methods using forward recurrent neural networks (RNNs) to pass the limited dialog context information, which is inefficient to deal with long conversation history and topic return, a unidirectional attention mechanism is proposed to build the history-aware passage representations by computing the attentions along the dialog progression. The DAW mechanism is used to build our model named CANet. We evaluate the proposed model on the CoQA dataset and achieve competitive results in published models, outperforming the published baselines on the hidden test dataset by a substantial margin. The further analysis shows that our DAW mechanism can be flexibly applied to powerful single-turn MRC models, and effective to augment the representations of dialog history to cater to long conversation and topic return settings.
News Recommendation Based on Collaborative Semantic Topic Models and Recommendation Adjustment

Providing news recommendations is an important trend for online news websites to attract more users and create more benefits. In this research, we propose a novel recommendation approach for recommending news articles. We propose A Collaborative Semantic Topic Model and an ensemble model to predict user preferences based on combining Matrix Factorization with articles' semantic latent topics derived from word embedding and Latent Dirichlet Allocation. The proposed ensemble model is further integrated with a recommendation adjustment mechanism to adjust users' online recommendation lists. We evaluate the proposed approach via offline experiments and online evaluation on a real news website. The experimental result demonstrates that our proposed approach can improve the recommendation quality of recommending news articles.
Towards Modeling Framework for DevOps: Requirements Derived from Industry Use Case

To succeed with the development, deployment, and operation of the new generation of complex systems, organizations need the agility to adapt to constantly evolving environments. In this context, DevOps has emerged as an evolution of the agile approaches. It focuses on optimizing the flow of activities involved in the creation of end-user value, from idea to deployed functionality and operating systems. However, in spite of its popularity, DevOps still lacks proper engineering frameworks to support continuous improvement. One of our key objectives is to contribute to the development of a DevOps engineering framework composed of process, methods, and tools. A core part of this framework relates to the modeling of the different aspects of the DevOps system. To better understand the requirements of modeling in a DevOps context, we focus on a Product Build use case provided by an industry partner.
ABMC-DH: An Adaptive Bit-Plane Data Hiding Method Based on Matrix Coding

This paper proposes a novel adaptive image steganography method combining matrix coding, which can achieve better visual quality according to the given embedding rate compared with other existing schemes. The embedding processes are divided into two phases: preparation and implementation. In the preparation phase, all 128 combinations of a 7-bit binary number are classified into eight groups by a parity check matrix based on matrix coding. Thus, each group corresponds to an octal number, and contains 16 candidates. Correspondingly, all pixels of a cover image are classified into three categories by a predefined embedding rate level: low, middle, and high. In the implementation phase, each pixel in the high level can be embedded with 3 bits by substituting its 7 LSBs (Least Significant Bit) using a close candidate from the group determined by the to-be-embedded 3 bits. For the middle level, pixel pairs are used to hide 3-bit data by substituting its (3, 4) LSBs combination using a close candidate form the group determined by the to-be-embedded 3 bits. For pixels in the low level, a triple pixel combination is used to embed 3-bit data by substituting its (2, 2, 3) LSBs combination using a close candidate form the group determined by the to-be-embedded 3 bits. The experimental results indicate that the proposed scheme is optimal compared to other existing matrix-coding data hiding schemes in terms of visual quality, while providing an identical embedding capacity.
Development of GUI Applications for Groundwater Modeling Using Python

GroundwaterVolume 58, Issue 4 p. 496-497 Technology Spotlight/ Development of GUI Applications for Groundwater Modeling Using Python Gengxin Ou, Corresponding Author Gengxin Ou gou@unl.edu School of Natural Resources, University of Nebraska-Lincoln, 101 Hardin Hall, 3310 Holdrege Street, Lincoln, NE, 68583; gou@unl.eduSearch for more papers by this author Gengxin Ou, Corresponding Author Gengxin Ou gou@unl.edu School of Natural Resources, University of Nebraska-Lincoln, 101 Hardin Hall, 3310 Holdrege Street, Lincoln, NE, 68583; gou@unl.eduSearch for more papers by this author First published: 08 January 2020 https://doi.org/10.1111/gwat.12979Citations: 3Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume58, Issue4July/August 2020Pages 496-497 RelatedInformation
Human Motion Capture Using a Multi-2D Pose Estimation Model

In this paper, we raise a method to find certain motion segments in the video using a Multi-2d pose estimation model. Several types of angles are extracted. We draw a continuous curve based on the entire video for all required angle values and smooth the angle curve to reduce the influence of human detection errors on motion recognition. After the smoothing process, the curve is again processed into a change curve as analysis and then search for two kinds of movements with a reasonable threshold. We also handle the noise in the recognition result to get more accurate motion fragments. By comparing and analyzing different parameters, the optimal parameters to achieve lower error is found. We coded the system and carried out a large number of experimental analysis mainly through the analysis of the characters in the video to turn around and get up, and the following results are achieved: 1)Be able to use effectively optimized data for effective analysis. 2)In a multi-person situation, the classification of each person's data based on time series is achieved. 3)Fragments of two motion states are detected more accurately.
Selective Attention Network for Image Dehazing and Deraining

Image dehazing and deraining are import low-level compute vision tasks. In this paper, we propose a novel method named Selective Attention Network (SAN) to solve these two problems. Due to the density of haze and directions of rain streaks are complex and non-uniform, SAN adopts the channel-wise attention and spatial-channel attention to remove rain streaks and haze both in globally and locally. To better capture various of rain and hazy details, we propose a Selective Attention Module(SAM) to re-scale the channel-wise attention and spatial-channel attention instead of simple element-wise summation. In addition, we conduct ablation studies to validate the effectiveness of the each module of SAN. Extensive experimental results on synthetic and real-world datasets show that SAN performs favorably against state-of-the-art methods.
The Quantum Illumination Story : (Invited Paper)

Superposition and entanglement, the quintessential characteristics of quantum physics, have been shown to provide communication, computation, and sensing capabilities that go beyond what classical physics will permit. It is natural, therefore, to explore their application to radar, despite the fact that decoherence-caused by the loss and noise encountered in radar sensing-destroys these fragile quantum properties. This paper tells the story of “quantum illumination”, an entanglement-based approach to quantum radar, from its inception to its current understanding. Remarkably, despite loss and noise that destroys its initial entanglement, quantum illumination does offer a target-detection performance improvement over a classical radar of the same transmitted energy. A realistic assessment of that improvement's utility, however, shows that its value is severely limited. Nevertheless, the fact that entanglement can be of value on an entanglement-breaking channel-the meta-lesson of the quantum illumination story-should spur continued research on quantum radar.
Hierarchical Adversarial Training for Multi-domain Adaptive Sentiment Analysis

Extracting useful insights with sentiment analysis is of increasing importance due to the growing availability of user-generated content. Sentiment analysis usually involves multiple different domains, and the labeled data is often difficult to obtain. In this paper we propose a hierarchical adversarial neural network (HANN) for adaptiveXu, Zhao sentiment analysis. Unlike most existing deep learning based methods, the proposed method HANN is able to share information between multiple domains bidirectionally, not justVon Ritter, Lorenzo transfers information from source domain to target domain in one direction only. In particular, the HANN method is inspired by the ideas of hierarchical Bayesian modeling and generative adversarial networks. We introduce each domain a distinct encoder to model the domain-specific distribution of the latent features. The learning procedures onSerra, Giuseppe different domains are coupled by a discriminator network to propagate the information, which can be viewed as adversarial networks in a supervised context by forcing the discriminator to identify domain labels. The proposed method HANN not only captures the distinct properties of each domain, but also shares common information across multiple domains. We demonstrate the superior performance of our method on real data including the Amazon review dataset and the Sanders Twitter sentiment dataset.
An improved user authentication scheme on smartphone using dominating attribute of touch data

In the era of science and technology, the user of Smartphone is omnipresent, as the data of research shows the number of Smartphone users is rising exponentially. Earlier Mobile phones used for calling and sending messages. Technology has evolved, and presently Smartphone is used as multipurpose device. The smartphone is used for online shopping, reading books, listing music, clicking photo, recording video, internet access, digital banking, and many more sophisticated services. Access of Smartphone data must be by secure and user-friendly system of authentication. Authentication methods of traditional computer systems cannot fit as it is into Smartphone authentication due to small size screen for input. Presently fingerprint authentication, pattern authentications systems and many more are used for Smartphone authentication, but they are just providing the security at entry-level authentication. Once a user does entry-level authentication device is open for use, and unattended phone can be unauthorized access. There is research going on in the area where the unauthorized person must not have access even the device is in unlock state. This dimension of authentication is called continuous Smartphone authentication. Our focus in this paper is on continuous mobile authentication. In this paper dataset of 41 users is used. Feature selection methods for continuous Smartphone authenticationuses dominating features. For the better tradeoff OptAT performance parameter is proposed. The improvement of 16% has been achieved using this parameter. The learning curve also has been drowned for the features.
Optimization of Lucy-Richardson Algorithm Using Modified Tikhonov Regularization for Image Deblurring

Abstract In this paper, we propose the method to optimize the iteration number of the Lucy-Richardson algorithm for image deblurring. This technique based on the modified Tikhonov regularization which composed of 2 parts which are designed for measuring the image similarity and noise enhancement due to the deblurring process. The regularization parameter will be used to control the desired deblurred image. Several sizes of the Gaussian blur kernel are applied for generating the degraded image in the simulation experiment. The Peak Signal to Noise Ratio (PSNR) metric is used to measure the deblurring performance. The results show that this method can be used to estimate the optimal iteration number and it also gives the PSNR value higher than the default Lucy-Richardson method and regularized filter all sizes of the experimental blur kernel. Moreover, it also tolerance to the deviated blur kernel especially it smaller than exact blur kernel.
Semi Few-Shot Attribute Translation

Recent studies have shown remarkable success in image-to-image translation for attribute transfer applications. However, most of existing approaches are based on deep learning and require an abundant amount of labeled data to produce good results, therefore limiting their applicability. In the same vein, recent advances in meta-learning have led to successful implementations with limited available data, allowing so-called few-shot learning.In this paper, we address this limitation of supervised methods, by proposing a novel approach based on GANs. These are trained in a meta-training manner, which allows them to perform image-to-image translations using just a few labeled samples from a new target class. This work empirically demonstrates the potential of training a GAN for few shot image-to-image translation on hair color attribute synthesis tasks, opening the door to further research on generative transfer learning.
Evaluating the classification of images from geoscience papers using small data

Image classification becomes a very challenging task when it involves classes that have shared characteristics and few data are available for training the classifier. Considering this problem, in this work we adopt a case study based on images from geoscience papers and investigate how different features can be combined in order to improve image classification results. In our investigation, we present a tool for evaluating class separability based on the position of the samples in a two-dimensional map according to different features. Moreover, we investigate the usefulness of classifiers’ membership probabilities for our scenario, validating if they can be used as reliable measures of the confidence in the predicted labels. Our experimental results show that it is possible to take advantage of deep learning models’ ability to learn discriminating features from data and combine them with hand-crafted features to improve classification. With this feature combination, we trained a Support Vector Machine (SVM) classifier whose results are better than the ones achieved using only deep learning.
Multimodality medical image retrieval using convolutional neural network

Healthcare industries and hospitals are on the rise and hence large archiving databases are used efficiently to store user data from diagnostic tests for treatment and future research. The scale of data that physicians must handle every single day, as well as an increasing number of patients, makes the data unmanageable. The process of annotating medical images with useful information is tiring and subject to change according to the diagnostician's language variability. A powerful system for image retrieval is necessary to manage these complex and large image databases to assist physicians in the accurate retrieval of images for faster diagnosis. Content-based image retrieval (CBIR) works by analyzing the content features like color and texture of an image rather than textual descriptors. Machine learning techniques are on the rise to perform complex computations in a large database. This chapter discusses in brief the basics of convolutional neural network (CNN) and its mathematics and introduces the state-of-the-art architectures LeNet and AlexNet. Analysis of the performance of the content-based multimodality medical image retrieval system (CBMMIR) by adapting the networks for medical images to the deep learning frameworks and their retrieval efficiency is measured and compared. The multimodal dataset containing 15 classes from 7 imaging modalities is given to the training model for classification, and the extracted features from the model are used for retrieval. The dataset of approximately 50,000 2D images are compiled and gathered from widely available open access medical databases to differentiate among multimodalities like X-ray, computed tomography (CT), magnetic resonance imaging (MRI), and fundus and dermatoscope images for different anatomies with variable data size. Features from the trained images as well as the query image was extracted from the CNN. The similarity between the trained features and the query features was determined by Euclidean distance. Evaluation parameters such as training and testing time duration, the percentage of accuracy derived from the trained model for different number of iterations, retrieval time, precision, recall, F score, and mean average precision (MAP) are determined from this experimentation. The trained system retrieves images of the relevant modality and anatomy similar to that of the query image with good precision. The MAP for LeNet and AlexNet are 83.9% and 86.9%, respectively. The chapter will conclude by stating the differences between the two architectures with respect to their advantages and drawbacks.
Bayesian network Fisher kernel for categorical feature spaces

Abstract We address the problem of defining similarity between vectors of possibly dependent categorical variables by deriving formulas for the Fisher kernel for Bayesian networks. While both Bayesian networks and Fisher kernels are established techniques, this result does not seem to appear in the literature. Such a kernel naturally opens up the possibility to conduct kernel-based analyses in completely categorical feature spaces with dependent features. We show experimentally how this kernel can be used to find subsets of observations that we see as representative for the underlying Bayesian network model.
Non-parametric learning of lifted Restricted Boltzmann Machines

We consider the problem of discriminatively learning Restricted Boltzmann Machines in the presence of relational data. Unlike previous approaches that employ a rule learner (for structure learning) and a weight learner (for parameter learning) sequentially, we develop a gradient-boosted approach that performs both simultaneously. Our approach learns a set of weak relational regression trees, whose paths from root to leaf are conjunctive clauses and represent the structure, and whose leaf values represent the parameters. When the learned relational regression trees are transformed into a lifted RBM, its hidden nodes are precisely the conjunctive clauses derived from the relational regression trees. This leads to a more interpretable and explainable model. Our empirical evaluations clearly demonstrate this aspect, while displaying no loss in effectiveness of the learned models.