Reviewerly/exHarmony huggingface dataset:
Performance Analysis of Gaussian, Median, Mean and Weiner Filters on Biomedical Image De-Noising

Noise reduction in medical images is a perplexing undertaking for the researchers in digital image processing. Noise generates maximum critical disturbances as well as touches the medical images quality, ultrasound images in the field of biomedical imaging. The image is normally considered as gathering of data and existence of noises degradation the image quality. It ought to be vital to reestablish the original image noises for accomplishing maximum data from images. Medical images are debased through noise through its transmission and procurement. Image with noise reduce the image contrast and resolution, thereby decreasing the diagnostic values of the medical image. This paper mainly focuses on Gaussian noise, Pepper noise, Uniform noise, Salt and Speckle noise. Different filtering techniques can be adapted for noise declining to improve the visual quality as well as reorganization of images. Here four types of noises have been undertaken and applied on medical images. Besides numerous filtering methods like Gaussian, median, mean and Weiner applied for noise reduction as well as estimate the performance of filter through the parameters like mean square error (MSE), peak signal to noise ratio (PSNR), Average difference value (AD) and Maximum difference value (MD) to diminish the noises without corrupting the medical image data.
Cost-Sensitive and Sparse Ladder Network for Software Defect Prediction

Software defect prediction (SDP) plays a vital role in allocating testing resources reasonably and ensuring software quality. When there are not enough labeled historical modules, considerable semi-supervised SDP methods have been proposed, and these methods utilize limited labeled modules and abundant unlabeled modules simultaneously. Nevertheless, most of them make use of traditional features rather than the powerful deep feature representations. Besides, the cost of the misclassification of the defective modules is higher than that of defect-free ones, and the number of the defective modules for training is small. Taking the above issues into account, we propose a cost-sensitive and sparse ladder network (CSLN) for SDP. We firstly introduce the semi-supervised ladder network to extract the deep feature representations. Besides, we introduce the cost-sensitive learning to set different misclassification costs for defective-prone and defect-free-prone instances to alleviate the class imbalance problem. A sparse constraint is added on the hidden nodes in ladder network when the number of hidden nodes is large, which enables the model to find robust structures of the data. Extensive experiments on the AEEEM dataset show that the CSLN outperforms several state-of-the-art semi-supervised SDP methods.
Comparison of Bio-inspired Algorithms Applied to the Hospital Mortality Risk Stratification

The construction of patient classification (or risk adjustment) systems allows comparison of the effectiveness and quality of hospitals and hospital services, providing useful information for management decision making and management of hospitals. Risk adjustment systems to stratify patients’ severity in a clinical outcome are generally constructed from care variables and using statistical techniques based on logistic regression (RL). The objective of this investigation is to compare the hospital mortality prediction capacity of an artificial neural network (RNA) with other methods already known.
Periodisation of Primary and Secondary Technical and Vocational Education Development in the Republic of Bashkortostan

Introduction . Vocational education has always been and still is one of the main factors of successful sustainable development of society as a whole and of the individual in particular. Therefore, in the state policy of different countries of the world, including Russia, special attention is being given to the institutions, which train specialists for the leading sectors of the economy and production. For developing effective policy measures and predicting its effectiveness, it is essential to identify the potential of national vocational education system, to study its phase changes and evolution vectors, to analyse and summarise existing experience in the organisation of the system and to support its functioning at the national and regional levels. The aim of the present research is to identify and characterise the main periods of development of primary and secondary technical and vocational education in one of the major regions of Russia – the Republic of Bashkortostan. Methodology and research methods . The research methodology is based on the system and civilisational approaches. The retrospective analysis was conducted through historical, typological and comparative methods, as well as two conceptual models. In the first model, the pedagogical process is presented as a complete system determined by specific historical conditions. The second model, designed by one of the authors of this article, is based on the logic and structure of this process in its contemporary understanding. These concepts provide adequate analysis and extrapolation of historical and pedagogical experience into modern educational theory and practice. Results and scientific novelty . The authors identified socio-economic, pedagogical, organisational and managerial factors of periodisation of the system of primary and secondary technical and vocational education development of Bashkortostan. For the first time, at the local republican level, the main phases of the process have been identified and described, starting with the pre-institutional stage to the present day. It is demonstrated that the trajectory of gradual formation and improvement of the investigated directions and stages of vocational training in the territory of the Republic of Bashkortostan is regulated by the general laws on the development of social structures. Despite some specificity, the trends in regional education found are quite related to all-Russian processes. Practical significance . The research results can be useful and are already being used when developing and designing vocational development programmes, in particular, they enrich the content of such disciplines as “History of Pedagogy and Education”, “History”, various special courses and curriculum units, concerning the issues on the state and prospects of technical and vocational education. In addition, the research materials can be used for developing regional educational policy.
Neural Differential Equations for Single Image Super-resolution

Although Neural Differential Equations have shown promise on toy problems such as MNIST, they have yet to be successfully applied to more challenging tasks. Inspired by variational methods for image restoration relying on partial differential equations, we choose to benchmark several forms of Neural DEs and backpropagation methods on single image super-resolution. The adjoint method previously proposed for gradient estimation has no theoretical stability guarantees; we find a practical case where this makes it unusable, and show that discrete sensitivity analysis has better stability. In our experiments, differential models match the performance of a state-of-the art super-resolution model.
Fusing Appearance and Spatio-Temporal Models for Person Re-Identification and Tracking

Knowing who is where is a common task for many computer vision applications. Most of the literature focuses on one of two approaches: determining who a detected person is (appearance-based re-identification) and collating positions into a list, or determining the motion of a person (spatio-temporal-based tracking) and assigning identity labels based on tracks formed. This paper presents a model fusion approach, aiming towards combining both sources of information together in order to increase the accuracy of determining identity classes for detected people using re-ranking. First, a Sequential k-Means re-identification approach is presented, followed by a Kalman filter-based spatio-temporal tracking approach. A linear weighting approach is used to fuse the outputs from these models together, with modification of the weights using a decay function and a rule-based system to reflect the strengths and weaknesses of the models under different conditions. Preliminary experimental results with two different person detection algorithms on an indoor person tracking dataset show that fusing the appearance and spatio-temporal models significantly increases the overall accuracy of the classification operation.
Preparing worms for the COPAS (wormsorter) v1

This protocol is a guide of how to prepare the worms to be used in the COPAS (wormsorter)
Proactive Defense for Internet-of-Things: Integrating Moving Target Defense with Cyberdeception

Resource constrained Internet-of-Things (IoT) devices are highly likely to be compromised by attackers because strong security protections may not be suitable to be deployed. This requires an alternative approach to protect vulnerable components in IoT networks. In this paper, we propose an integrated defense technique to achieve intrusion prevention by leveraging cyberdeception (i.e., a decoy system) and moving target defense (i.e., network topology shuffling). We verify the effectiveness and efficiency of our proposed technique analytically based on a graphical security model in a software defined networking (SDN)-based IoT network. We develop four strategies (i.e., fixed/random and adaptive/hybrid) to address "when" to perform network topology shuffling and three strategies (i.e., genetic algorithm/decoy attack path-based optimization/random) to address "how" to perform network topology shuffling on a decoy-populated IoT network, and analyze which strategy can best achieve a system goal such as prolonging the system lifetime, maximizing deception effectiveness, maximizing service availability, or minimizing defense cost. Our results demonstrate that a software defined IoT network running our intrusion prevention technique at the optimal parameter setting prolongs system lifetime, increases attack complexity of compromising critical nodes, and maintains superior service availability compared with a counterpart IoT network without running our intrusion prevention technique. Further, when given a single goal or a multi-objective goal (e.g., maximizing the system lifetime and service availability while minimizing the defense cost) as input, the best combination of "how" and "how" strategies is identified for executing our proposed technique under which the specified goal can be best achieved.
Learning from Noisy Labels with Noise Modeling Network

Multi-label image classification has generated significant interest in recent years and the performance of such systems often suffers from the not so infrequent occurrence of incorrect or missing labels in the training data. In this paper, we extend the state-of the-art of training classifiers to jointly deal with both forms of errorful data. We accomplish this by modeling noisy and missing labels in multi-label images with a new Noise Modeling Network (NMN) that follows our convolutional neural network (CNN), integrates with it, forming an end-to-end deep learning system, which can jointly learn the noise distribution and CNN parameters. The NMN learns the distribution of noise patterns directly from the noisy data without the need for any clean training data. The NMN can model label noise that depends only on the true label or is also dependent on the image features. We show that the integrated NMN/CNN learning system consistently improves the classification performance, for different levels of label noise, on the MSR-COCO dataset and MSR-VTT dataset. We also show that noise performance improvements are obtained when multiple instance learning methods are used.
Data Annealing for Informal Language Understanding Tasks

There is a huge performance gap between formal and informal language understanding tasks. The recent pre-trained models that improved the performance of formal language understanding tasks did not achieve a comparable result on informal language. We pro-pose a data annealing transfer learning procedure to bridge the performance gap on informal natural language understanding tasks. It successfully utilizes a pre-trained model such as BERT in informal language. In our data annealing procedure, the training set contains mainly formal text data at first; then, the proportion of the informal text data is gradually increased during the training process. Our data annealing procedure is model-independent and can be applied to various tasks. We validate its effectiveness in exhaustive experiments. When BERT is implemented with our learning procedure, it outperforms all the state-of-the-art models on the three common informal language tasks.
Logic2Text: High-Fidelity Natural Language Generation from Logical Forms

Previous works on Natural Language Generation (NLG) from structured data have primarily focused on surface-level descriptions of record sequences. However, for complex structured data, e.g., multi-row tables, it is often desirable for an NLG system to describe interesting facts from logical inferences across records. If only provided with the table, it is hard for existing models to produce controllable and high-fidelity logical generations. In this work, we formulate logical level NLG as generation from logical forms in order to obtain controllable, high-fidelity, and faithful generations. We present a new large-scale dataset, \textsc{Logic2Text}, with 10,753 descriptions involving common logic types paired with the underlying logical forms. The logical forms show diversified graph structure of free schema, which poses great challenges on the model's ability to understand the semantics. We experiment on (1) Fully-supervised training with the full datasets, and (2) Few-shot setting, provided with hundreds of paired examples; We compare several popular generation models and analyze their performances. We hope our dataset can encourage research towards building an advanced NLG system capable of natural, faithful, and human-like generation. The dataset and code are available at https://github.com/czyssrs/Logic2Text.
Edge-Aware Patch Grouping for Image Denoising in the Complex Wavelet Domain

This paper develops a new image denoising framework based on the Dual Tree Complex Wavelet Transform and an edge based patch grouping. The proposed patch grouping mechanism considers the photometric features along with gradient features to cluster the image patches into different groups with similar properties. Furthermore, the K-means algorithm was accomplished for patch grouping instead of Euclidean distance metric. An adaptive thresholding mechanism is also developed here to remove the noise with less information loss at edge features. Extensive simulation is carried out through MATLAB software over different grayscale images at different noise levels and noise types and the performance is measured with the performance metrics such as PSNR and SSIM for varying noise levels. The obtained simulation revealed the outstanding performance of proposed approach both in the preservation of edge features and also in the quality improvisation by efficient noise removal.
E-Marketplace Printing Services Using Mobile-Based Application with Customer Relationship Management Approach

CetakCetak.id is an E-Marketplace application of mobile-based printing services that implements upselling and cross-selling strategies with the purpose of increasing printing sales.This research used descriptive qualitative method that described the level of relationship between Customer Relationship Management approach and the loyalty of printing service users in Palembang.After conducting survey to the customers about their satisfaction regarding E-Marketplace service, the results obtained are 54.5 percent of 55 respondents agree that CetakCetak.id provides ease of getting information about products and 41.8 percent stated that they extremely agree with the statement and the rest of them stated that they somewhat agree.Moreover, CetakCetak.id can increase the number of transactions of printing service providers using this application.They stated that there is an increase in the number of transactions that the highest percentage is 30.6 percent which means there is an increase in the number of transactions in the range of 21-40 percent.
Literary blogs and podcasts as formats of book media events

Using the example of literary-specialized blogs the article shows the gradual virtualization of readers' practices, the changes in book perceptions in the media environment, and the overall mediatization of Russian book culture. The main problem raised in the article: how digital technologies transform book culture, what are the specifics of these changes, and is it possible to preserve the key meanings of the Russian book culture in the context of convergence of traditionalism with the new cultural reality. The purpose of the article is to consider literary blogs and podcasts as book media formats in the context of the global cultural agenda and changes in the field of Russian book consumption. The author treats different aspects of book's phygitalization and digitalization, its rootedness in the media space and the need for media support; compares popular online readers services and media projects on reading popularization. The main conclusions are: it is impossible to imagine modern readership outside the media and blogosphere, primarily due to their high communicative capabilities. Book services provide a virtual platform for discussing what is read, initiating a dialogue; blogs are one of most popular and effective tools for orientation in the literary process and the development of book communication; blogosphere changes the perspective of the perception of book culture, offers non-standard approaches to its understanding.
Facilitating Access to Multilingual COVID-19 Information via Neural Machine Translation

Every day, more people are becoming infected and dying from exposure to COVID-19. Some countries in Europe like Spain, France, the UK and Italy have suffered particularly badly from the virus. Others such as Germany appear to have coped extremely well. Both health professionals and the general public are keen to receive up-to-date information on the effects of the virus, as well as treatments that have proven to be effective. In cases where language is a barrier to access of pertinent information, machine translation (MT) may help people assimilate information published in different languages. Our MT systems trained on COVID-19 data are freely available for anyone to use to help translate information published in German, French, Italian, Spanish into English, as well as the reverse direction.
Recognizing Magnification Levels in Microscopic Snapshots

Recent advances in digital imaging has transformed computer vision and machine learning to new tools for analyzing pathology images. This trend could automate some of the tasks in the diagnostic pathology and elevate the pathologist workload. The final step of any cancer diagnosis procedure is performed by the expert pathologist. These experts use microscopes with high level of optical magnification to observe minute characteristics of the tissue acquired through biopsy and fixed on glass slides. Switching between different magnifications, and finding the magnification level at which they identify the presence or absence of malignant tissues is important. As the majority of pathologists still use light microscopy, compared to digital scanners, in many instance a mounted camera on the microscope is used to capture snapshots from significant field-of-views. Repositories of such snapshots usually do not contain the magnification information. In this paper, we extract deep features of the images available on TCGA dataset with known magnification to train a classifier for magnification recognition. We compared the results with LBP, a well-known handcrafted feature extraction method. The proposed approach achieved a mean accuracy of 96% when a multi-layer perceptron was trained as a classifier.
Photons coalescence effect

Can two photons, define by several physical variables, that have never met know something about each other? The answer is yes, provided if the variables of two photons along same line are identical. The derivation for same on the ground of proposed complex coefficients with suitable conditions are done here.
QuickSync: A Quickly Synchronizing PoS-Based Blockchain Protocol

Proof-of-Stake(PoS) based blockchain protocols have gained popularity due to their higher throughput and low carbon footprint when compared with Proof-of-Work blockchain protocols. The two major parts of blockchain protocols are the selection of the next block proposer and the selection of the longest chain. In PoS the block publishers are selected based on their relative stake. However, PoS-based blockchain protocols may face vulnerability against Fully Adaptive Corruptions. This paper proposes a novel PoS-based blockchain protocol, QuickSync, to achieve security against Fully Adaptive Corruptions while improving performance. Towards this, we propose a metric for each block: block power. We compute the chain power of a chain as the sum of block powers of all the blocks comprising the chain. The chain selection rule selects the chain with the highest chain power as the valid chain. Since the block proposer is not selected upfront, this scheme is resilient to fully adaptive corruptions, which we also show formally. We also ensure that our block power mechanism is resistant to Sybil attacks. We prove the security of QuickSync by showing that it satisfies the common prefix, chain growth, and chain quality properties. Our analysis demonstrates that QuickSync performs better than Bitcoin by order of magnitude on both transactions per second and time to finality.
A failure mode and effects analysis of pragmatic errors in learner e-mails

E-mailing in the English is an important mode of communication for Japanese learners of computer science both during and after their studies. A key element of effective e-mail communication relates to pragmatic competence—understanding the relationship between social context and language choices and accordingly adapting one’s language in an appropriate manner. Despite this importance, there have been few learner e-mail corpora annotated for pragmatic errors. As part of a larger ongoing study developing online tools for assessing and developing learners’ e-mail writing ability, a failure mode and effects analysis was conducted on a learner corpus of e-mails, manually tagged for pragmatic errors. Tagged errors were ascribed a value between 1-5 in terms of severity, detectability and frequency, with most sever errors assigned a score of 5. Weighted priority scores were calculated, allowing for errors to be prioritized in terms of importance. Preliminary results indicate severe errors are associated with failing to address the face needs of the e-mail recipient, violating pragmatic norms, with potential negative consequences in terms of relationship maintenance. Results allow for the creation of a job queue for the software developer, and usefully inform teaching priorities in the language classroom.