Reviewerly/exHarmony huggingface dataset:
Underwater Image Enhancement using Scene-Specific Red Channel Prior and Fusion

Quality of underwater images are reduced with the depth since light is absorbed and scattered by the suspended particles in water. Although several optical model-based and model-free underwater image enhancement methods have been developed in the past, their performances are considerably poor since they fail to consider the scene-specific cues and depth information. In this study, a novel enhancement approach is proposed by combining optical model-based and model-free algorithms. In the model-based technique, a tentative depth map estimation technique is proposed and then used to compute more accurate red channel prior, background light, and transmission. White balancing and Gamma correction are used in the model-free technique. Outputs of both techniques have been combined by a fusion strategy to recover the details and color. Experimental results demonstrate the proposed enhancement method outperforms state-of-the-art approaches on UIEB, Ocean Dark, and U45 benchmark datasets.
Joint Dilated Convolution and Self-attention for Cross-domain Sentiment Analysis

Abstract Cross-domain sentiment analysis aims to use source domain resources or models to serve the sentiment analysis tasks in the target domain, and it can effectively alleviate the problem of insufficient tagged data in the target domain. In this paper, we investigate deep transfer learning for text sentiment analysis, and focus on customer reviews for different domains. Given the shortcomings of the conventional deep neural network, such as convolutional neural network (CNN) cannot obtain the long-term dependence between features, recurrent neural network (RNN) cannot achieve parallel computing, we propose a novel neural network which joint dilated convolution and self-attention (ADC). The ADC takes residual learning as its basic framework, which can capture more distant information by introducing a dilation rate in the convolution. Self-attention is used to make the ADC pay more attention to important sentiment information. First, the ADC is pre-trained with the data of the source domain, and the weight of the pre-training is frozen. Then, the model is fine-tuned by the method of gradual unfreezing, and the weight is updated through the data of the target domain. Finally, the updated model can be utilized for the target domain sentiment analysis. By using customer review datasets, we carry out extensive experiments to demonstrate that this method has superior performance and can better achieve cross-domain sentiment analysis.
Single Shot Corrective CNN for Anatomically Correct 3D Hand Pose Estimation

Hand pose estimation in 3D from depth images is a highly complex task. Current state-of-the-art 3D hand pose estimators focus only on the accuracy of the model as measured by how closely it matches the ground truth hand pose but overlook the resulting hand pose's anatomical correctness. In this paper, we present the Single Shot Corrective CNN (SSC-CNN) to tackle the problem of enforcing anatomical correctness at the architecture level. In contrast to previous works which use post-facto pose filters, SSC-CNN predicts the hand pose that conforms to the human hand's biomechanical bounds and rules in a single forward pass. The model was trained and tested on the HANDS2017 and MSRA datasets. Experiments show that our proposed model shows comparable accuracy to the state-of-the-art models as measured by the ground truth pose. However, the previous methods have high anatomical errors, whereas our model is free from such errors. Experiments show that our proposed model shows zero anatomical errors along with comparable accuracy to the state-of-the-art models as measured by the ground truth pose. The previous methods have high anatomical errors, whereas our model is free from such errors. Surprisingly even the ground truth provided in the existing datasets suffers from anatomical errors, and therefore Anatomical Error Free (AEF) versions of the datasets, namely AEF-HANDS2017 and AEF-MSRA, were created.
RISSNet: Retain low‐light image details and improve the structural similarity net

Images taken under low light conditions cause people's sensory vision to fall into blind areas, and the essential properties of objects are hidden in the dark. There are many excellent algorithms that can adjust the brightness of high and low light region, but they still have room for improvement in realizing the function of denoising and similarity restoration of internal attributes of objects. Inspired by the work principle of human retina, the image is decomposed into two modules, firstly, the enhancement network which is guided by the Attention Mask module completed the enhancement by the U-net network structure. In this structural network, an RISS module is designed here to effectively reduce the loss of image feature information, so as to improve the structural similarity of reconstructed images. Full convolutional network is used to maintain the position relation of illumination feature information. This network is trained on LOL-dataset, and the experimental results prove that the network can recover images with different illuminations. In the later stage, the effectiveness of this method is verified by the subjective and objective comparison with other methods on different datasets. This method can not only realize the function of low-light image enhancement, but also the function of image similarity restoration and image denoising.
3D object reconstruction algorithm based on dynamic threshold dithering technique

Aiming at the problem of insufficient similarity between the binary defocus fringe generated by the traditional dithering algorithm in the binary surface structured light coding technology and the standard sinusoidal fringe, a dynamic threshold dithering algorithm based on the traditional Floyd-Steinberg dithering algorithm is proposed, which can generate the binary fringe pattern with high sinusoidal similarity, improving the accuracy of structured light three-dimensional reconstruction. Considering the influence of the pixel gray value in the vicinity of the pixel point on the threshold of the dithering algorithm, the parameters &alpha; and edge sensitivity coefficient 𝓂 are introduced to dynamically adjust the dithering threshold to improve the sinusoidal similarity of the dithering stripes. In addition, a total error calculation and evaluation method that takes into account the effects of gray error and phase error is used to improve the robustness of the algorithm. The simulation shows that when the parameters α and 𝓂 in the algorithm are set to 0.68 and 0.20 respectively, the total error is reduced by 82.97% at the fringe period T = 20 pixel. Corresponding experiments have been established to verify that the dynamic threshold can be displayed more clearly in the small change area of the model, which improves the image quality of the three-dimensional reconstructed object.
An Interpretable Bi-Branch Neural Network for Matrix Completion

The task of recovering a low-rank matrix given an incomplete matrix, also termed as matrix completion, arises in various applications. The numerous methods for matrix completion can be classified into linear and nonlinear categories. In this paper, a novel and interpretable neural network is devised for matrix completion. Different from existing neural networks whose structure is created by empirical design, the proposed version is devised via unfolding the matrix factorization formulation. Specifically, the two factors decomposed by matrix factorization construct the two branches of the developed neural network, named as bi-branch neural network (BiBNN). The row and column indices of each entry are considered as the input of the BiBNN, while its output is the estimated value of the entry. The training procedure aims to minimize the fitting error between all observed entries and their predicted values, and then the unknown entries are estimated by inputting their coordinates into the trained network. The proposed BiBNN is compared with state-of-the-art methods, including linear and nonlinear models, in processing synthetic data, image inpainting, and recommender system. Experimental results demonstrate that the BiBNN is superior to the existing approaches in terms of restoration accuracy.
A Prototype of Raspberry Pi Camera Morphometric Measurements on Sheep Using Digital Image Processing

Abstract Quantitative data are needed to identify and predict opportunities for increasing livestock productivity. The increase in livestock productivity can be assessed from the dimensions of the body of the livestock by measuring directly using morphometric. Morphometric data is an important parameter used to study livestock anatomy, productivity, growth rate, and performance quality of livestock. This research is focused on how to make a prototype development of a camera tool that can measure livestock morphometrics at a distance. The Camera use in mini camera with a raspberry pi as a microcontroller. And method to process the capture of data is Artificial Neural Network that downloaded from tensor flow. The result of this experiment show that the accuracy of the system is 50%. However, the camera has not been able to identify the object of farm animals correctly. There are several livestock objects detected as dog or cat. For the future work the library must be modified so it can only detect the sheep or goat so the morphometrics can be done.
Quantification Procedures

In Chap. 3, we have expressed the data in terms of unknown numbers, $$y_i$$ and $$x_j$$ . Our task is to determine those unknown numbers under the constrains on them as we have discussed. To carry out this quantification task, we need some strategies so that the quantified data have certain optimal properties. Considering that quantification has many names, we should know that there are indeed many strategies. In this chapter, we will look at a few of them.
Discovery of Popular Languages from GitHub Repository: A Data Mining Approach

Usage of Open Source Software (OSS) has been increased over the past fifteen years among programmers and computer users. OSS communities work as a “Bazaar” where the project constructors and end-users meet together and search for suitable matches to their skills and requirements. OSS is emerging as a strong competitor to commercial or closed software. GitHub is an OSS forge started in 2008 in order to simplify code sharing. It is a Web site and cloud-based service that aids software developers to store, manage, track, and control changes to their code. When a GitHub project fails, it results in the loss of time, effort, and resources of this large community. The current need is to build models that find interesting factors that contributes to the success of these projects. The massive repositories make this domain a good candidate for exploratory research using the data mining approach. In this work, the FP-Growth method is used to find the popular two programming language combinations and is validated using the SPSS tool. The outcome of this work benefits the OSS community in terms of time and resources.
Visualizing Crossover Rate Influence in Differential Evolution with Expected Fitness Improvement

The area of heuristic optimization methods, which includes evolutionary algorithms and biology-inspired methods, is currently under rapid development of due to the high efficiency of such approaches in various domains and availability of large computational resources.
The Art of Point Clouds

Point cloud datasets, the pertinent product of remote sensing technologies, such as LiDAR and photogrammetry, are increasingly being used as a new digital medium in art practice. This paper presents an overview of purposely selected and prominent artworks, emerging from traditional representational onto-epistemologies, to identify currently prevailing parameters and their limitations when working with remote sensing technologies. Although these works differ markedly in aesthetic qualities and conceptual foundations, they all treat the scanner as an objective, passive and impartial means of recording and transcribing reality, rather than an active agent in its creation. By placing an emphasis on merely aesthetic and technical characteristics of point clouds and accompanying technology, they overlook the expressive, open-ended potential of the technology, as well as the multiplicity of the point cloud. Building upon entanglement theories, such as agential realism and new materialism, and drawing inspiration from Pearce's existing design-research projects, such as ‘The Masks of Fleet Street’ (2014), the paper exposes the common features of this contemporary approach, showcases its advantages and points to the potential for further practice-based investigation.
An access control protocol for IoT‐based critical infrastructure in smart grid environment

Summary Information and communication technology (ICT) can add appealing attributes such as transparency, energy conservation, cost reduction, reliability, and efficiency to the traditional power grid. The addition of these attributes is possible due to the concept of a smart grid (SG). However, ICT applications engender new access control challenges. Therefore, many studies have been conducted in recent years to investigate that the most significant challenge of the smart grid is privacy. In the light of these studies, two significant problems have been identified for SG access control protocols that must be addressed. First, we have all essential security requirements such as integrity, confidentiality, and authentication. Moreover, the cryptographic algorithms impose significant computational and communication costs on the system, however, in most of the contemporary infrastructure's devices usually have limited‐resource. Therefore, second, the lightweight design of access control protocols is also a significant challenge. To address these challenges, we introduce a provably secure and lightweight access control protocol for SGs that eliminates key exchange issues while increasing privacy and security. We evaluate the proposed protocol to determine its security strength against well‐known security attacks. Finally, we present a performance evaluation of the proposed protocol to demonstrate its efficiency in terms of computation and communication costs.
Analysis of Hindi-English Poetry Translation through Machine Translation Systems

Poetry is an expression of feelings. A poem is a thing which could be either sing or sometimes at least hummable. Over the years, specific rules have been defined for the structures and categories of the poems which can help in making them lyrical and rhythmic, although, all languages might have their own rules for the poems. Poetry translations through machines (computer systems) are very crucial and considered to be impossible tasks. Most of the times, Poetry translation of English language to other native languages may become quite easy but vice-versa is the toughest. The reasons are varieties in structures, categories and own emotions of the native languages. Numerous efforts have been applied around the world towards the poetries machine translations from native languages into English. This research presents rigorous survey for such kinds of poetry machine translation systems and their quality and accuracy measurement techniques. It also gives an idea of Hindi (our national and main native) language poetry machine translations into English Language. Further, a deep analysis and sources (corpuses) have been discussed for existing Hindi to English Machine Translations systems. At last, the research also discusses the importance of the Hindi to English Machine Translations systems [1] [2].
Seconder of the Vote of Thanks to Dong<i>et al.</i>and Contribution to the Discussion of ‘Gaussian Differential Privacy’

Journal of the Royal Statistical Society: Series B (Statistical Methodology)Volume 84, Issue 1 p. 39-41 DISCUSSION CONTRIBUTION Seconder of the vote of thanks to Dong et al. and contribution to the Discussion of ‘Gaussian Differential Privacy’ Marco Avella-Medina, Corresponding Author Marco Avella-Medina marco.avella@columbia.edu Department of Statistics, Columbia University, New York, NY, USA Correspondence Marco Avella-Medina, Columbia University, Department of Statistics, New York, NY, USA. Email: marco.avella@columbia.eduSearch for more papers by this author Marco Avella-Medina, Corresponding Author Marco Avella-Medina marco.avella@columbia.edu Department of Statistics, Columbia University, New York, NY, USA Correspondence Marco Avella-Medina, Columbia University, Department of Statistics, New York, NY, USA. Email: marco.avella@columbia.eduSearch for more papers by this author First published: 21 February 2022 https://doi.org/10.1111/rssb.12456Read 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 onFacebookTwitterLinked InRedditWechat Volume84, Issue1February 2022Pages 39-41 RelatedInformation
Paraphrase Quality Assessment Metric and Russian Paraphrase Dataset Collection

The task of generating paraphrases is becoming increasingly popular across many languages. It is not only useful for creating various content for websites and social media but also is frequently used to improve the performance of machine learning methods based on natural language processing (NLP) via data augmentation and enrichment. Because existing metrics for NLP are not really suitable for paraphrase quality assessment, there is always a challenge of quality assessment of a generated paraphrase. In this work, we propose a new metric for paraphrase quality assessment based on the topic modeling for long texts; we call this metric Intersecting Topic Similarity (ITS). We have collected the first of the kind dataset of strong paraphrases for the Russian language, which is now publicly available for future research. We conducted a comparison with existing metric families like BLEU and ROUGE, and METEOR metric on the collected dataset. Our metric ITS showed a better ability to distinguish paraphrases and is much more reliable in assessing paraphrase quality.
Similarities in African Ethnic Faces from the Biometric Recognition Viewpoint

Face pose, illumination, and facial expressions are known factors that affect face recognition performance and have been studied at length in the literature. The impacts of demographic factors such as gender, race, and age on performance have also been studied, with increasing interest recently in the context of algorithmic bias concerns. This work is a study of face recognition performance using a database of faces of Nigerian subjects drawn from 28 different ethnicities. There are documented differences in facial anthropometric characteristics between some Nigerian ethnicities, and this study was intended to establish initial results regarding the impact of these inter-ethnic differences on face recognition performance. A comparison to performance on a database of Caucasian/Asian face images is made. Our study analyzes how 28 African ethnicities affect face identification performance metrics by focusing on the genuine and impostor scores’ distributions. Our analysis shows that face identification performance is not remarkably influenced by varying ethnicities within the African race though there are significant differences in relation to the Caucasian/Asian set.
From Application Security Verification Standard (ASVS) to Regulation Compliance: A Case Study in Financial Services Sector

The OWASP Application Security Verification Standard (ASVS) is a widely used web application development guideline regarding the technical security controls and secure development requirements. While software development teams refer to ASVS to secure their applications and development process, they also need to ensure the compliance of various security related regulations, including sector-specific ones. In this work, we study the synergy of these two activities, i.e., by following ASVS, how does a development team position their developed applications in meeting those regulation requirements. We take the highly regulated financial services sector as a case study. In particular, we look at two recent guidelines published by Monetary Authority of Singapore (MAS) - the Technology Risk Management (TRM) guidelines and Notice 655 Cyber Hygiene. We developed a systematic approach to map ASVS to those two sector-specific regulations. Our results show that by adopting ASVS, a development team can achieve a high degree of regulatory compliance (38.6 % for the MAS TRM guidelines and 47.6% for the MAS Notice 655, respectively). That demonstrates the viability of using international standards (like ASVS) to support compliance with the two sector-specific regulations. In addition, our mapping approach can be useful for organizations to support their compliance efforts.
Peer-to-Peer Grading as a Way to Improve Educational Process Efficiency for Certain Industrial Sectors under Digitalization of Education at Universities

The purpose of the study is to examine the situation with online learning, especially under the pandemic crisis, in the context of knowledge assessment systems. Given these conditions, the search for modern forms of assessment is becoming increasingly important at all levels of education. In higher education, in order to comply with safety requirements, the so-called blended learning mode is widely used, which involves conducting part of the classes in person, and part of it remotely. In the current circumstances of a sudden and stressful transition to altogether unusual forms of interaction within the framework of education, one can observe tremendous progress in the academic environment in terms of mastering the methods of organizing and monitoring the educational process, which becomes relevant in the current conditions. One of these techniques, having significant advantages, is peer-to-peer grading, the practice of which this paper presents. Research methods include the analysis of teaching experience in terms of approaches to the organization of the educational process and assessment procedures. The research results characterize the method of peer-to-peer grading as potentially effective in conditions of time and information lack. In practice, these results are significant as a rationale for the use of such methods for a general increase in the educational process efficiency.
De keunst fan it oersetten

This article considers the art of translating from Dutch into Frisian by Jarich Hoekstra and Harke Bremer in De Grutte Barribal, a translation of one of the adventures of Ollie B. Bommel, a fictive gentleman created by Maarten Toonder and well-known in the Netherlands. I make an in-depth comparison and analysis of a sample of Dutch sentences and their Frisian translations. The success of the translation resides in the following factors: (1) the translation is more specific, more sensorial or more subjective than what is conveyed by the somewhat more abstract Dutch language; (2) in specifying things, the translators have a perfect feeling of contextual information, based on a correct estimation of the plot and the characters. My observations link up with the view of translation as the rendering of spirit and style (Brogan 1993; for theory construction, see also Nida & Taber 1969; Venuti 2012, 2013; Verstegen 1993).