Reviewerly/exHarmony huggingface dataset:
Optimal Compression of Medical Images

In today's healthcare system, medical images are playing a vital role in the diagnosis.The challenges arise to the hospital management systems (HMS) are to store and communicate the large volume of medical images generated by various imaging modalities.Efficient compression of medical images is required to reduce the bit rate to increase the storage capacity and speed-up the transmission without affecting its quality.Over the past few decades, several compression standards have been proposed.In this paper, an intelligent JPEG2000 compression scheme is presented to compress the medical images efficiently.Unlike the traditional compression techniques, genetic programming (GP)-based quantization matrices are used to quantize the wavelet coefficients of the input image.Experimental results validate the usefulness of the proposed intelligent compression scheme.
Cross-Sectorial Semantic Model for Support of Data Analytics in Process Industries

The process industries rely on various software systems and use a wide range of technologies. Predictive modeling techniques are often applied to data obtained from these systems to build the predictive functions used to optimize the production processes. Therefore, there is a need to provide a proper representation of knowledge and data and to improve the communication between the data scientists who develop the predictive functions and domain experts who possess the expert knowledge of the domain. This can be achieved by developing a semantic model that focuses on cross-sectorial aspects rather than concepts for specific industries, and that specifies the meta-classes for the formal description of these specific concepts. This model should cover the most important areas including modeling the production processes, data analysis methods, and evaluation using the performance indicators. In this paper, our primary objective was to introduce the specifications of the Cross-sectorial domain model and to present a set of tools that support data analysts and domain experts in the creation of process models and predictive functions. The model and the tools were used to design a knowledge base that could support the development of predictive functions in the green anode production in the aluminum production domain.
An Evaluation for the Use of Blockchain Technology in Logistics

The following article sums up the current problems of the logistics and explains how the Blockchain technology can help to solve some of them.A utility value analysis is used to quantify the scenarios the Blockchain is most likely to be used in.This offers a result that, in comparison to current research, does not only show the opportunities but also how likely they are to be used in these sections.In the next step these scenarios are compared with each other.These results lead to a outlook to potential applications of the Blockchain technology in the logistics.
Robust Factorization Machine: A Doubly Capped Norms Minimization

Factorization Machine (FM) is a general supervised learning framework for many AI applications due to its powerful capability of feature engineering. Despite being extensively studied, existing FM methods have several limitations in common. First of all, most existing FM methods often adopt the squared loss in the modeling process, which can be very sensitive when the data for learning contains noises and outliers. Second, some recent FM variants often explore the low-rank structure of the feature interactions matrix by relaxing the low-rank minimization problem as a trace norm minimization, which cannot always achieve a tight approximation to the original one. To address the aforementioned issues, this paper proposes a new scheme of Robust Factorization Machine (RFM) by exploring a doubly capped norms minimization approach, which employs both a capped squared trace norm in achieving a tighter approximation of the rank minimization and a capped ℓ1-norm loss to enhance the robustness of the empirical loss minimization from noisy data. We develop an efficient algorithm with a rigorous convergence proof of RFM. Experiments on public real-world datasets show that our method outperforms the state-of-the-art FM methods significantly.
High Quality Wavelets Features Extraction for Handwritten Arabic Numerals Recognition

Arabic handwritten digit recognition is the science of recognition and classification of handwritten Arabic digits. It has been a subject of research for many years with rich literature available on the subject. Handwritten digits written by different people are not of the same size, thickness, style, position or orientation. Hence, many different challenges have to overcome for resolving the problem of handwritten digit recognition. The variation in the digits is due to the writing styles of different people which can differ significantly. Automatic handwritten digit recognition has wide application such as automatic processing of bank cheques, postal addresses, and tax forms. A typical handwritten digit recognition application consists of three main stages namely features extraction, features selection, and classification. One of the most important problems is feature extraction. In this paper, a novel feature extraction approach for off-line handwritten digit recognition is presented. Wavelets-based analysis of image data is carried out for feature extraction, and then classification is performed using various classifiers. To further reduce the size of training data-set, high entropy subbands are selected. To increase the recognition rate, individual subbands providing high classification accuracies are selected from the over-complete tree. The features extracted are also normalized to standardize the range of independent variables before providing them to the classifier. Classification is carried out using k-NN and SVMs. The results show that the quality of extracted features is high as almost equivalently high classification accuracies are acquired for both classifiers, i.e. k-NNs and SVMs.
Simulation of Face Pose Tracking System using Adaptive Vision Switching

In this paper, a face pose tracking method using adaptive vision switching with a networked camera system is proposed and verified through simulation with a facial computer graphics model. The proposed method improves the pose estimation accuracy of the conventional techniques that use monocular camera. Additionally, adaptive vision switching provides a new pose tracking experience, pose lock-on, which has the potential for object recognition tasks including face recognition under dynamic conditions. Quantitative analysis and tracking demonstration with simulations of the proposed system are conducted and described.
Paving the Way to Open Data

It is easy to argue that open data are critical to enabling faster and more effective research discovery. In this article, we describe the approach we have taken at Wiley to support open data and to start enabling more data to be FAIR data (Findable, Accessible, Interoperable and Reusable) with the implementation of four data policies: “Encourages”, “Expects”, “Mandates” and “Mandates and Peer Reviews Data”. We describe the rationale for these policies and levels of adoption so far. In the coming months we plan to measure and monitor the implementation of these policies via the publication of data availability statements and data citations. With this information, we'll be able to celebrate adoption of data-sharing practices by the research communities we work with and serve, and we hope to showcase researchers from those communities leading in open research.
VISION BASED AUTOMATED ANTHROPOLOGICAL MEASUREMENTS AND ANALYSIS

Abstract. Modern techniques of optical 3D measurements such as photogrammetry, computer tomography, laser 3D scanning provide new possibilities for acquiring accurate 2D and 3D data of high resolution, thus creating new conditions for anthropological data analysis. Traditional anthropological manual point measurements can be substituted by analysis of accurate textured 3D models, which allow to retrieve more information about studied object and easily to share data for independent analysis. The paper presents the vision-based techniques for anthropological measurement and investigation needed in various applications, such as morphometric analysis, craniofacial identification, face approximation and other. Photogrammetric methods and their practical implementation in the automatic system for accurate digital 3D reconstruction of anthropological objects are described.
Short-time behavior of continuous-time quantum walks on graphs

Dynamical evolution of systems with sparse Hamiltonians can always be recognized as continuous-time quantum walks (CTQWs) on graphs. In this paper, we analyze the short-time asymptotics of CTQWs. In recent studies, it was shown that for the classical diffusion process the short-time asymptotics of the transition probabilities follows power laws whose exponents are given by the usual combinatorial distances of the nodes. Inspired by this result, we perform a similar analysis for CTQWs in both closed and open systems, including time-dependent couplings. For time-reversal symmetric coherent quantum evolutions, the short-time asymptotics of the transition probabilities is completely determined by the topology of the underlying graph analogously to the classical case, but with a doubled power-law exponent. Moreover, this result is robust against the introduction of on-site potential terms. However, we show that time-reversal symmetry-breaking terms and noncoherent effects can significantly alter the short-time asymptotics. The analytical formulas are checked against numerics, and excellent agreement is found. Furthermore, we discuss in detail the relevance of our results for quantum evolutions on particular network topologies.
Attention-Based Syllable Level Neural Machine Translation System for Myanmar to English Language Pair

Neural machine translation is a new approach to machine translation that has shown the effective results for high-resource languages.Recently, the attention-based neural machine translation with the large scale parallel corpus plays an important role to achieve high performance for translation results.In this research, a parallel corpus for Myanmar-English language pair is prepared and attention-based neural machine translation models are introduced based on word to word level, character to word level, and syllable to word level.We do the experiments of the proposed model to translate the long sentences and to address morphological problems.To decrease the low resource problem, source side monolingual data are also used.So, this work investigates to improve Myanmar to English neural machine translation system.The experimental results show that syllable to word level neural mahine translation model obtains an improvement over the baseline systems.
The Use of Naive Bayes for Broiler Digestive Tract Disease Detection

Broiler chicken is a species of chicken that have high productivity. In order to get a good quality of chicken, good treatments of the breeding factors is needed, so the chicken will not easily infected by diseases. Gastrointestinal diseases are common disease that infects chickens. The mortality level caused by gastrointestinal diseases is considered high. This study is designed to address the problem by developing a system using the Naive Bayes algorithm. 60 chicken data samples were used, and the result shows that Naive Bayes might be used to detect gastrointestinal diseases among chickens with accuracy level of 93.3%. The number was confirmed by using confusion matrix evaluation method, and gave same level of accuracy compared to the expert judgments.
An Assessment of HIV Counselling and Testing (HCT) Service Utilization in Nigeria: A Binary Logistic Regression Approach

HIV infection remains the most challenging health and development crisis in the last two decades as it continues to create health and socio-economic challenges in many parts of Nigeria and the world at large.HIV counselling and testing (HCT) can identify infection in early stages as it involves analysis of body fluids for the presence of antigens or antibodies produced in response to HIV and these are key to controlling the HIV epidemics.As a result, certain factors are considered in this study as deterrents to HCT service utilization in Nigeria applying the k-order binary logistic regression model using a structured questionnaire developed by the research team in some selected states in South-West of Nigeria.The sociodemographic details of respondents reveals that out of 788 people (out of 800 administered questionnaires) that were interviewed, 452 (57.4%) have had HCT while 336 (42.6%) never had HCT.Age 15-19 (the adolescent) with 68.1% are the groups with the highest respondents with HCT uptake.The chi-square test of independence also reveals that age, gender, religion, and marital status are not related with HCT uptake while ethnic, educational status, place of residence are associated with decision on HCT uptake.The binary logistic regression of HCT uptake on the investigated socio-demographic details of respondents reveals the age group (50 and above) has the highest odds of up-taking HCT while those in the group 25-29 years has the least.Male respondents have a slightly higher odds compared to female and the Yoruba ethnic group are the most likely to utilize HCT.Results also revealed that traditionalist are the most likely to utilize HCT (1.635) with the Christianity (with 1.000) being the least.Those who are single/never married has a non-significant highest odds (1.092) among the marital status considered while those who are separated/divorced has the least (0.712).The odds of utilizing HCT is least among those with no formal education.People with Primary education are about three times more likely to take HCT compared to those with no formal education.Rural settlers are about three times (2.818) more likely to uptake HCT in comparison with those who reside in urban centres.Finally, employment status of respondents is insignificant to HCT uptake, although the odds in favour of those that are schooling is highest (1.175), followed by those that are self-employed (1.013).
BLOCKCHAIN: SOCIAL INNOVATION IN FINANCE & ACCOUNTING

At present times, there has been a surge in cryptopians community.Since wealth maximization is the aim of many, the virtual currency has become one of the important investment avenues.Virtual currency like bitcoins have become grill for the gossip mill.Consumer enjoys sovereignty.The present paper provides insight of the behind the scene technology in order to attain better understanding of the securitization prospects pertaining to the same.Mining allures cryptopians to solve proof of work because then only an increment in rewarded in blockchain.Without a doubt as per statistics, the currency has seen an upsurge but vital determinants which may pose a threat needs to be taken care of
A Novel Color Image Watermarking Method based on Digital Wavelet Transform and Hungarian Algorithms

In the last few years, high data growth rate has funded the new outlook of ecommerce like electronically marketing, publishing, transmission and distribution of real time multimedia data in the form of video, audio, image and so on.Unauthorized access and copyright protection become a vital concern for almost all of the digital data service providers.In the past ten years, many researchers proposed various methods such as steganography, cryptography, watermarking and so on for different purposes [1].Watermarking is proposed to protect digital data against unauthorized activities and help to maintain copyright ownership by embedding watermark data into host signal using some modification in its content [2], [3].The effectiveness of a digital watermarking depends on the few characteristics such as imperceptibility, robustness, security, and real-time processing [4].Many researches have been done one gray scale image compare to color image because color image redundant data cannot be used sufficiently and cause poor ability to resist attacks which means the method has low robustness against image processing attacks [5].This paper proposed a new DWT-Hungarian method for color image watermarking to overcome this problem and the experimental results show that the proposed method has high robustness against JPEG (30) compression, cropping (50%), scaling, rotation and blurring attacks than other related works.
Visualizing undirected graphs and symmetric square matrices as overlapping sets

Undirected graphs and symmetric square matrices are frequently found in various domains. An example is character co-occurrence matrices in digital humanities. However, the visualization of these datasets is difficult, especially if the graph is highly connected. In this article, we propose a method for visualizing undirected graphs and symmetric square matrices, by transforming them into overlapping sets, and then by visualizing these overlapping sets using set visualization techniques such as Euler diagram or rainbow boxes. We also propose a clustering approach to simplify the visualization. We apply this method to the visualization of various character co-occurrence matrices extracted from novels or DBpedia, ranging from 21 to 114 characters. We show that this visualization allows the finding of several interesting insights. Finally, we discuss the advantages and drawbacks of this method, and we compare it to other approaches in the literature.
Designing a Skill Tree Model for Learning Media

In a modern era, digital media has been created and used for educational purposes. In the educational programs, teachers have many objectives which can be converted into some important points that can be mapped on a skill tree. A skill tree is actually commonly known for building gaming media as a decision making system but it has not been widely applied for educational purposes. This paper discusses how to design a skill tree model for effective learning media. The learning objective can be mapped in the skill tree and used for decision-making to decide whether the subject matter being taught by a teacher can proceed or not. The method used in this study was the development method. The findings indicated that the skill tree can be applied as learning media. The results of product testing reveal insignificant defects and smooth operation. Therefore the developed software can be considered as an eligible product to be applied. This software is aimed to assist teachers in understanding the weaknesses of the students and improving the quality of education.
ANTIVIRUS SOFTWARE AND INDUSTRIAL CYBER SECURITY SYSTEM CERTIFICATION IN RUSSIA

The article is dedicated to issues in certification of antivirus software and industrial cyber security systems. It was shown that certification time in Russia is much longer than in the USA, European Union and Germany. The life time and the development time of products of this field were analyzed in the article. Each variable was specified for new products and for new versions of existing products. Some statistical methods were used in the article: Cronbach’s alfa, t-statistics, and median value similarity that are typical for the articles in quality management. As a result, it was found that certification time in Russia for industrial cyber security systems is significantly longer than in other analyzed countries, up to three-fold. Product development and life time are also longer. However, the most important result is that certification in Russia adds from 32.1 to 40 percent of time to the development of a new version or a new product, correspondingly, whereas in other investigated countries these numbers are about 17 percent. Reduction of certification time will increase new product development efficiency in the field of cyber security, which will improve positions of Russian products at the international mark et.
On the Second-Order Asymptotics of the Partially Smoothed Conditional Min-Entropy &amp; Application to Quantum Compression

Recently, Anshu et al. introduced “partially” smoothed information measures and used them to derive tighter bounds for several information-processing tasks, including quantum state merging and privacy amplification against quantum adversaries [IEEE Trans. Inf. Theory 66, 5022 (2020)]. Yet, a tight second-order asymptotic expansion of the partially smoothed conditional min-entropy in the i.i.d. setting remains an open question. Here we establish the second-order term in the expansion for pure states, and find that it differs from that of the original “globally” smoothed conditional min-entropy. Remarkably, this reveals that the second-order term is not uniform across states, since for other classes of states the second-order term for partially and globally smoothed quantities coincides. In view of the tight bounds on the entanglement cost of state merging in terms of the partially-smoothed conditional min-entropy by Anshu et al., this indicates that the second-order asymptotic rate for that task will not separate so neatly into a term depending on the state (the variance) and a term depending on the error (the quantile). Finally, by relating the task of quantum compression to that of quantum state merging, our derived expansion allows us to determine the second-order asymptotic expansion of the optimal rate of quantum data compression. This closes a gap in the bounds determined by Datta and Leditzky [IEEE Trans. Inf. Theory 61, 582 (2015)], and shows that the straightforward compression protocol of cutting off the eigenspace of least weight is indeed asymptotically optimal at second order.
Face recognition in low-resolution surveillance video streams

Face recognition technology has been widely adopted in many mission-critical applications as a means of human identification, controlled admission, E-bank authentication, and mobile device access. Security surveillance is also a growing application for face recognition techniques; however, challenges exist from low resolution (LR) and high noise, multi-angle and multi-distance changes, and different light conditions. In comparison, algorithms applied to cell phone imagery or other specific camera devices mainly function on high resolution images with fixed angles and small changes of illumination. As face recognition in security surveillance becomes more important in the era of dense urbanization, it is essential to develop algorithms that are able to provide satisfactory performance in processing the video frames generated by low resolution surveillance cameras. In this paper, we propose a novel face recognition method that is suitable for low resolution surveillance cameras. The technique is demonstrated on a face dataset generated from real-world surveillance scenarios, from which an end-to-end approach is taken to match high resolution (HR) images with low resolution (LR) images from the surveillance video. The experimental results validate the effectiveness of the novel method that improves the accuracy of face recognition in surveillance security scenarios.
How to Compartment Secrets

Secret sharing splits a secret s into $$\ell $$ shares in such a way that $$k\le \ell $$ shares suffice to reconstruct s. Let $$\rho _{i,j}$$ be the probability that shareholder i disclose their share to shareholder j, with $$0 \le i,j < n$$ . Given $$k \le \ell \le n$$ , to whom $$\ell $$ individuals should we hand shares, if we wish to minimize the probability that one of them reconstitutes s?