Reviewerly/exHarmony huggingface dataset:
Speckle Reduction in Ultrasound Images Using Hybridization of Wavelet-Based Novel Thresholding Approach with Guided Filter

The research work is aimed to explore and develop some new techniques in the area of speckle reduction. The algorithms are devised and implemented in transform domain for speckle reduction in ultrasound images. The high-quality ultrasound images can improve the accuracy and speed of subsequent image processing tasks and can yield better diagnosis results. In this work, we have proposed a hybrid method to reduce speckle from ultrasound images while maintaining a trade-off between speckle reduction and edge preservation. Extensive work is carried out by taking four different types of images for research which contains synthetic images, simulated images, noise-free ultrasound images, and real ultrasound images. The performance of proposed methods is measured by using different image quality metrics such as PSNR, MSE, COC, SSIM, FSIM, and EPI for all given sets of images except real ultrasound images. The real ultrasound images are compared by using visual comparison and metric SSI because it does not use reference images.
Contact Tracing Apps in the COVID-19 Pandemic

Contact tracing apps for the COVID-19 pandemic, from a personal data protection point of view, are discussed in this chapter. More precisely, having the relevant European legal framework as a basis, the authors investigate the underlying personal data processing that occurs from these apps in relation with the relevant privacy risks that are inherent in the smart mobile ecosystem. The analysis indicates that there exist several different approaches across the world, whereas even in countries with unified legal framework there exist some discrepancies in the design philosophy and the operation of these apps. Since user trust is essential for the effectiveness of such types of apps, they discuss the importance of respecting the fundamental right of privacy when developing and operating such applications. The ultimate goal is to illustrate the need to strike the proper balance between substantial public interests and the right to the protection of personal data.
An Ensemble Framework for Dynamic Character Relationship Sentiment in Fiction

Fictional characters in a narrative text can experience various events in the narrative timeline as the progress of character development. Relationships between characters can also dynamically change over time. Summarizing the relationship dynamics in fiction through manual annotation can be very tedious even at a small scale, but highly impractical or even impossible in a large corpus. With the recent development of machine learning models in Natural Language Processing, many tasks have been introduced to help humans extract information from text automatically. Motivated by this development, we propose a conceptual model and an information extraction framework that combines two state-of-the-art machine learning algorithms to extract character relationships directly from an event sentence in a fictional narrative. For our use case, as we consider sequence in a story line, we also infer the dynamic sentiment relationships among characters over time. Since this approach is by nature unsupervised, we also preserve the provenance of each relation extracted in order to prepare a dataset to use in training a supervised model. We hope this approach can be a step toward more robust automatic character relation and event extraction from fictional texts.
Online Political Hate Speech in Europe: The Rise of New Extremisms, by G.Zaccardi (Cheltenham: Edward Elgar, 2020, ISBN 9781788113656); v+236pp., £90.00 hb.

JCMS: Journal of Common Market StudiesVolume 60, Issue 2 p. 497-498 Book Review Online Political Hate Speech in Europe: The Rise of New Extremisms, by G. Zaccardi ( Cheltenham: Edward Elgar, 2020, ISBN 9781788113656); v+236pp., £90.00 hb. Natalie Alkiviadou, Natalie Alkiviadou Justitia, Fruebjergvej 3, Copenhagen, 2100 DenmarkSearch for more papers by this author Natalie Alkiviadou, Natalie Alkiviadou Justitia, Fruebjergvej 3, Copenhagen, 2100 DenmarkSearch for more papers by this author First published: 16 February 2022 https://doi.org/10.1111/jcms.13319Read 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 No abstract is available for this article. Volume60, Issue2March 2022Pages 497-498 RelatedInformation
Hybrid Search based Enhanced Named Entity Annotation Tool

Identifying named entities is a crucial step in extracting information from text. Training NER models usually require annotated data. Human annotators spend a lot of time and effort annotating large datasets. In this paper, we introduce a novel hybrid search-based enhanced annotation tool. The annotation tool provides an easy-to-use GUI and several search modes to accelerate the annotation exercise. Users can look for similar text and terms and annotate the information in the results. We demonstrate the utility of our tool and evaluate our tool in comparison with other tools. We show that it provides faster annotation than typical annotators and comparable performance with state-of-the-art tools.
Kalman Filter Partial Innovation Sequence Monitor

Timely detection and exclusion of slow faults in an integrated positioning sensor systems are crucial to deliver safe navigation. This paper describes a computationally cheap monitor that utilizes a sequence of a subset (partial) Kalman filter innovation vectors only associated with fault hypotheses. The paper also establishes an analytical recursive formulas to evaluate the partial innovations monitor. Both analytical and Monte Carlo results showed that the partial sequencing is superior to conventional methods using full innovation vector at each time epoch.
Payroll Accounting System Evaluation

This study aims to determine the accounting system for the study that is applied in the process of running PR. Putra Maju Jaya, located in Tanggulangin, Sidoarjo. In this study, using a qualitative and descriptive approach to analyze and research the payroll accounting system in PR. Son Forward Jaya. Payroll accounting system is an accounting system that regulates the payroll process in a company or agency. The payroll accounting system that is running already meets the existing standards in the running process and is easier and simpler because it uses a computer-based system.
Producing H5P Web Content and Building Wordpress LMS Platform

COVID-19 transformed past offline-oriented education into an online-based one. At the same time, it was an opportunity to highlight the importance of educational content.<BR> Online content should be shared on a web-based basis and interactive with students. It can be said that the effect is high by satisfying these two points. In other words, the content must satisfy web affinity, ease of access, user convenience, and three-dimensional information expression.<BR> H5P can be said to be a representative web-based content production tool. because H5P is a free plug-in, it is possible to produce and serve various contents at a lower cost than other installable content authoring tools. H5P can be said to be the most competitive content authoring tool for educators to exert maximum effectiveness at a minimum cost.<BR> Wordpress accounts for 42.7% of the world
EMG-CapsNet: Elu Multiplication Gate Capsule Network for Complex Images Classification

Capsule Network presents a new generation of neural networks in the deep learning field. It shows its potential in image classification by grouping features into capsules and using parts to make the whole, which is accomplished through the use of dynamic routing algorithm to route between capsules. Nonetheless, the original Capsule Network is inefficient for complex images due to its limited ability to extract features and its tendency to explain everything in the image. To address the aforementioned concerns, we propose EMG-CapsNet an improved Capsule Network that uses two parallel convolutional layers, one of them with the Exponential Linear Unit activation function. Then we use a gate layer to control the information passed to the next layer. EMG-CapsNet shows better performance approximately 5% compared with the original Capsule Network on the CIFAR-10 dataset. Moreover, the Exponential Linear Unit function allows faster convergence.
Deploying a Data Controller in Indirect Mode

In the previous chapter, we’ve deployed a Kubernetes Cluster. Now it’s time to use this Cluster and deploy our first Azure Arc-enabled Data Controller to it, which we will deploy using indirectly connected – or in short, indirect – mode.
How Knowledge Graphs are Changing Data Analytics

Abstract Knowledge graphs are a big trend in the advanced data science world, but aren’t as widely known as they could be. Graph Database Expert Maya Natarajan says that needs to change.
Panoramic imaging with depth information from miniaturized aspherical catadioptric endoscopes

Omnidirectional side-view images from miniaturized catadioptric endoscopes may be used to generate mosaics of the epithelium of tubular organs, enabling the longitudinal monitoring of surface pathologies. Here, previous results are extended to create three-dimensional sub-mm resolution 3.5 cm × 360&deg; reconstructions of pediatric cardiac phantoms. From image stacks with a single annulus of best focus captured via parabolic mirrors, adjacent rings within the focused region may be used to infer depth via parallax while rings of best focus are used to color the inferred geometry. Potential applications include digital reconstruction of pediatric and small-animal organs for diagnostics and surgical guidance at near-cellular resolution.
How Important are Investment Indivisibilities for Development? Experimental Evidence from Uganda

Theoretically, indivisible investments together with financial frictions can lower development, generate poverty traps, and lead agents to become risk-loving. Using experimental cash grants involving a choice between a safer, low payoff and a riskier, large payoff lottery, we find that 27 percent choose the riskier, larger lottery. Small grant winners invest in livestock and business inventory, while large grant winners invest in land, which exhibits high capital gains. Our quantitative model shows that the aggregate effects of financial deepening are sizable if the indivisible investment can be accumulated (e.g., capital) but not if it is in fixed supply (e.g., land).Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Maternal metabolic and inflammatory state predict offspring peripheral and central inflammatory outcomes

Many organizations today store their critical business information permanently in XML format. XML data can be managed using: XML-Enabled Database (XED) systems which convert and store XML files in traditional database systems; Native XML Database (NXD) systems which store XML data natively using three main storage technologies – text-based, model-based, and schema-based techniques; and Hybrid Database systems which are comprised of both XML-Enabled and Native XML database systems. NXDs are faster than other database technologies because there is no need to convert the format of the data prior to storage. No performance evaluation has been carried out to compare all three storage strategies, hence, this paper reports on the first attempt to evaluate all three storage strategies by using open source products to measure the response time taken for each of the database basic tasks such as database creation, dataset insertion, and data manipulation. The results of the evaluation show that the schema-based storage strategy: performs 3.5 times faster than the other two storage techniques in data insertion; shows very good performance in query processing on small and large datasets; performs 10.33 times faster than text-based, and 7.5 times faster than model-based storage techniques in query processing of large datasets.
Learning a Fair Distance Function for Situation Testing

Situation testing is a method used in life sciences to prove discrimination. The idea is to put similar testers, who only differ in their membership to a protected-by-law group, in the same situation such as applying for a job. If the instances of the protected-by-law group are consistently treated less favorably than their non-protected counterparts, we assume discrimination occurred. Recently, data-driven equivalents of this practice were proposed, based on finding similar instances with significant differences in treatment between the protected and unprotected ones. A crucial and highly non-trivial component in these approaches, however, is finding a suitable distance function to define similarity in the dataset. This distance function should disregard attributes irrelevant for the classification, and weigh the other attributes according to their relevance for the label. Ideally, such a distance function should not be provided by the analyst but should be learned from the data without depending on external resources like Causal Bayesian Networks. In this paper, we show how to solve this problem based on learning a Weighted Euclidean distance function. We demonstrate how this new way of defining distances improves the performance of current situation testing algorithms, especially in the presence of irrelevant attributes. (Source code: https://github.com/calathea22/learning-fair-dist-func)
A Privacy Knowledge Transfer Method for Clinical Concept Extraction

Recent works have revealed that there is a training data leakage hazard in deep learning models, which is catastrophic for tasks like clinical concept extraction with high privacy requirements. Therefore, to alleviate privacy leakage during the model release phase, this paper propose a knowledge distillation based privacy protection method. The proposed method follows the teacher-student framework and utilizes a novel distillation method for sequence labeling so that the final model is trained without direct contact with sensitive source data. This paper mainly focuses on the scenario where the training data is multi-source and heterogeneous, and correspondingly proposes a re-normalization operation and weight adjustment strategy for knowledge aggregation. Experiment results on the public dataset show that the proposed privacy protection method could achieve comparable performance to the fully supervised method, demonstrating the effectiveness of the proposed method.
Availability and Utilization of ICT Facilities for Enhancing University Campus Security in South-South, Nigeria: A Recent Study

This research work determined the availability and utilization of ICT facilities for the purpose of enhancing university campus security in South-south, Nigeria. Two research questions guided the study and one null hypothesis was tested at .05 level of significance. Descriptive survey research design was adopted for the study. The study population comprised 1,520 security personnel from the 12 government-owned universities in South-South, Nigeria. The proportionate stratified sampling technique was used to sample 456 security personnel. The data was collected using a "Checklist on Availability of ICT Facilities for Enhancing University Campus Security (CAICTFEUCS)" and a "Utilization of ICT Facilities for Enhancing University Campus Security (UICTFEUCS)" five-point rating scale questionnaire. Three specialists independently validated the instruments. The reliability of the instruments was determined using the Cronbach Alpha method using SPSS, and reliability values of .75 and .78 were found for the two instruments, with an overall reliability coefficient of .77. Out of the 456 copies of the instrument administered on the respondents, 433 copies representing about 96 percent were successfully retrieved and used for data analysis. Frequency counts and percentages were used to analyze data related to research question 1 while mean and standard deviation were used to answer research question 2. At the .05 level of significance, the Z-test statistic was used to test the null hypothesis. The study's findings revealed that ICT facilities are insufficiently available for improving university campus security in South-South, Nigeria. The study also revealed that there is no significant difference in the mean ratings of respondents from federal and state universities on the extent ICT facilities are being utilized by university campus security personnel in South-South, Nigeria. According to the study, the level of availability and utilization of ICT facilities for enhancing university campus security in South-South, Nigeria was insufficient for combating security challenges in the university system. Based on the findings and conclusions, it was suggested that the Nigerian government and university administrators train campus security personnel on how to use modern ICT facilities. This is to ensure that they are fully competent and equipped to use ICT facilities in combating various campus-related crimes, thereby protecting lives and property within the university community.
Diversified Quality Aware Ensemble Resource Scheduling (DQAERS) for IAAS with Massive Load of Tasks and Resources in Cloud Computing

Identifying a deterministic approach to perform resource scheduling in cloud computing is crucial requirement, which is since, the volume of the anomalies and the high dimensionality of the values projected to these anomalies observed during resource scheduling. The volume of tasks that evinces flash-crowd state at resource broker of the IAAS, and high dimensionality of the anomalies projected for resource quality factors are out of scope in regard to contemporary resource scheduling strategies contributed in recent past. Hence’ the resource scheduling by contemporary methods in such conditions are insignificant as the resource scheduling optimality observed as probabilistic. In order to optimize the resource scheduling in the context of aforesaid properties high volume of tasks (flash-crowd state at resource broker) and high dimensional projection of anomalies, this manuscript derived an ensemble resource scheduling strategy, which fall in to the category of batch scheduling. The experimental study outlined that the proposal is most prominent and robust to deliver optimal resource scheduling in the context of anomalies of high volume and dimensionality that compared to the contemporary method.
Topological Analysis of Graphs

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A Novel Design of Adaptive and Hierarchical Convolutional Neural  Networks using Partial Reconfiguration on FPGA

Nowadays most research in visual recognition using Convolutional Neural Networks (CNNs) follows the "deeper model with deeper confidence" belief to gain a higher recognition accuracy. At the same time, deeper model brings heavier computation. On the other hand, for a large chunk of recognition challenges, a system can classify images correctly using simple models or so-called shallow networks. Moreover, the implementation of CNNs faces with the size, weight, and energy constraints on the embedded devices. In this paper, we implement the adaptive switching between shallow and deep networks to reach the highest throughput on a resource-constrained MPSoC with CPU and FPGA. To this end, we develop and present a novel architecture for the CNNs where a gate makes the decision whether using the deeper model is beneficial or not. Due to resource limitation on FPGA, the idea of partial reconfiguration has been used to accommodate deep CNNs on the FPGA resources. We report experimental results on CIFAR-10, CIFAR-100, and SVHN datasets to validate our approach. Using confidence metric as the decision making factor, only 69.8%, 71.8%, and 43.8% of the computation in the deepest network is done for CIFAR-10, CIFAR-100, and SVHN while it can maintain the desired accuracy with the throughput of around 400 images per second for SVHN dataset.
Mining the manifolds of deep generative models for multiple  data-consistent solutions of ill-posed tomographic imaging problems

Tomographic imaging is in general an ill-posed inverse problem. Typically, a single regularized image estimate of the sought-after object is obtained from tomographic measurements. However, there may be multiple objects that are all consistent with the same measurement data. The ability to generate such alternate solutions is important because it may enable new assessments of imaging systems. In principle, this can be achieved by means of posterior sampling methods. In recent years, deep neural networks have been employed for posterior sampling with promising results. However, such methods are not yet for use with large-scale tomographic imaging applications. On the other hand, empirical sampling methods may be computationally feasible for large-scale imaging systems and enable uncertainty quantification for practical applications. Empirical sampling involves solving a regularized inverse problem within a stochastic optimization framework in order to obtain alternate data-consistent solutions. In this work, we propose a new empirical sampling method that computes multiple solutions of a tomographic inverse problem that are consistent with the same acquired measurement data. The method operates by repeatedly solving an optimization problem in the latent space of a style-based generative adversarial network (StyleGAN), and was inspired by the Photo Upsampling via Latent Space Exploration (PULSE) method that was developed for super-resolution tasks. The proposed method is demonstrated and analyzed via numerical studies that involve two stylized tomographic imaging modalities. These studies establish the ability of the method to perform efficient empirical sampling and uncertainty quantification.
Optimizing voice conversion network with cycle consistency loss of  speaker identity

We propose a novel training scheme to optimize voice conversion network with a speaker identity loss function. The training scheme not only minimizes frame-level spectral loss, but also speaker identity loss. We introduce a cycle consistency loss that constrains the converted speech to maintain the same speaker identity as reference speech at utterance level. While the proposed training scheme is applicable to any voice conversion networks, we formulate the study under the average model voice conversion framework in this paper. Experiments conducted on CMU-ARCTIC and CSTR-VCTK corpus confirm that the proposed method outperforms baseline methods in terms of speaker similarity.