Reviewerly/exHarmony huggingface dataset:
An Augmented Bio-Inspired Algorithm (JA-ABC5a) to Design Optimal Digital IIR Filter

Among swarm-intelligence based (SI) algorithms, artificial bee colony (ABC) algorithm that is inspired by the intelligent behavior of honeybees has recently captured much interest from optimization researchers. They have come out with the proposal of ABC variants with the aims to cater the limitations’ of ABC; slow convergence speed on unimodal functions and premature convergence tendency on multimodal functions. This research has recently become the main topic among optimization researchers. Nonetheless, the variants also have their limitations as they cannot solve the problem simultaneously. With the motivation from those inefficiencies, this work presents a new modified ABC variant, referred to as JA-ABC5a as a problem solver. JA-ABC5a is simulated on 27 commonly used benchmark functions prior to evaluating its optimization performance. Upon justifying its robustness, it is being applied to design an optimal digital IIR filter.
Attribute-guided image generation from layout

Recent approaches have achieved great success in image generation from structured inputs, e.g., semantic segmentation, scene graph or layout. Although these methods allow specification of objects and their locations at image-level, they lack the fidelity and semantic control to specify visual appearance of these objects at an instance-level. To address this limitation, we propose a new image generation method that enables instance-level attribute control. Specifically, the input to our attribute-guided generative model is a tuple that contains: (1) object bounding boxes, (2) object categories and (3) an (optional) set of attributes for each object. The output is a generated image where the requested objects are in the desired locations and have prescribed attributes. Several losses work collaboratively to encourage accurate, consistent and diverse image generation. Experiments on Visual Genome dataset demonstrate our model's capacity to control object-level attributes in generated images, and validate plausibility of disentangled object-attribute representation in the image generation from layout task. Also, the generated images from our model have higher resolution, object classification accuracy and consistency, as compared to the previous state-of-the-art.
The Impact of Blockchain Technology on Stock Price: An Emprical Study

With the rapid development of blockchain technology, more and more attention has shifted from the theoretical research of blockchain technology to the application of specific business of the company. However, there are few literatures on the quantitative research on the impact of blockchain technology on the company's market value after it is applied to the company's actual business. In this paper, 73 listed companies published blockchain application announcements from 2016 to 2019 are selected as the research objects, and the short-term event analysis method is used to quantitatively analyze the impact of the application about blockchain technology on the market performance of listed companies. The results show that: after the announcement of blockchain application in their own business, listed companies have a more significant abnormal income, which shows that the capital market has a significant positive response to the application of blockchain. Finally, this paper puts forward the theoretical and practical significance, limitations and future research.
Bosonic data hiding: power of linear vs non-linear optics

We show that the positivity of the Wigner function of Gaussian states and measurements provides an elegant way to bound the discriminating power of "linear optics", which we formalise as Gaussian measurement operations augmented by classical (feed-forward) communication (GOCC). This allows us to reproduce and generalise the result of Takeoka and Sasaki [PRA 78:022320, 2008], which tightly characterises the GOCC norm distance of coherent states, separating it from the optimal distinguishability according to Helstrom's theorem. Furthermore, invoking ideas from classical and quantum Shannon theory we show that there are states, each a probabilistic mixture of multi-mode coherent states, which are exponentially reliably discriminated in principle, but appear exponentially close judging from the output of GOCC measurements. In analogy to LOCC data hiding, which shows an irreversibility in the preparation and discrimination of states by the restricted class of local operations and classical communication (LOCC), we call the present effect GOCC data hiding. We also present general bounds in the opposite direction, guaranteeing a minimum of distinguishability under measurements with positive Wigner function, for any bounded-energy states that are Helstrom distinguishable. We conjecture that a similar bound holds for GOCC measurements.
Migrating a complex classification scheme to the semantic web: expressing the Integrative Levels Classification using SKOS RDF

Purpose The Integrative Levels Classification (ILC) is a comprehensive “freely faceted” knowledge organization system not previously expressed as SKOS (Simple Knowledge Organization System). This paper reports and reflects on work converting the ILC to SKOS representation. Design/methodology/approach The design of the ILC representation and the various steps in the conversion to SKOS are described and located within the context of previous work considering the representation of complex classification schemes in SKOS. Various issues and trade-offs emerging from the conversion are discussed. The conversion implementation employed the STELETO transformation tool. Findings The ILC conversion captures some of the ILC facet structure by a limited extension beyond the SKOS standard. SPARQL examples illustrate how this extension could be used to create faceted, compound descriptors when indexing or cataloguing. Basic query patterns are provided that might underpin search systems. Possible routes for reducing complexity are discussed. Originality/value Complex classification schemes, such as the ILC, have features which are not straight forward to represent in SKOS and which extend beyond the functionality of the SKOS standard. The ILC's facet indicators are modelled as rdf:Property sub-hierarchies that accompany the SKOS RDF statements. The ILC's top-level fundamental facet relationships are modelled by extensions of the associative relationship – specialised sub-properties of skos:related . An approach for representing faceted compound descriptions in ILC and other faceted classification schemes is proposed.
Extreme Memorization via Scale of Initialization

We construct an experimental setup in which changing the scale of initialization strongly impacts the implicit regularization induced by SGD, interpolating from good generalization performance to completely memorizing the training set while making little progress on the test set. Moreover, we find that the extent and manner in which generalization ability is affected depends on the activation and loss function used, with sin activation being the most extreme. In the case of the homogeneous ReLU activation, we show that this behavior can be attributed to the loss function. Our empirical investigation reveals that increasing the scale of initialization correlates with misalignment of representations and gradients across examples in the same class. This insight allows us to device an alignment measure over gradients and representations which can capture this phenomenon. We demonstrate that our alignment measure correlates with generalization of deep models trained on image classification tasks.
Günlük Bitcoin Değerini Tahmin Etmek İçin İstatistiksel ve Makine Öğrenimi Algoritmalarının Karşılaştırılması

Increasing fluctuations in pricing and having great profit potential, utilization in advanced machine learning technologies to make robust predictions of cryptocurrencies especially bitcoin have attracted great attention in recent years.In this study, various statistical techniques; Moving Average Analysis and Autoregressive Integrated Moving Average and machine learning (ML) techniques; Artificial Neural Network, Recurrent Neural Network (RNN) and Convolutional Neural Network have been conducted and compared to predict the future value of Bitcoin cryptocurrency price.They have been applied for the univariate time series analysis with a window size of 32.To prove the usefulness of ML algorithms, and to show that the results of RNN is a better, mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) indicators have been applied.The study revealed that recurrent neural network yields better results than other methods in predicting daily Bitcoin price in terms of MSE, MAE and MAPE metrics.Besides, Wilcoxon-Mann-Whitney nonparametric statistic test is applied to test the performance between ARIMA and machine learning algorithms.
Robust deep learning-based multi-image super-resolution using inpainting

Traditional super-resolution techniques are generally presented as optimization problems with variations in the choice of optimization methods and cost functions. Even for the overdetermined cases, the problem is ill-conditioned. The situation is worsened when considering underdetermined cases with unknown regions due to occlusions or lack of data. Deep learning-based methods have shown promise in solving a similar problem. One recent advancement has come in the form of partial convolutions, which were developed to perform infilling of holes in images. When used in an appropriate deep neural network, this particular variant of the convolutional filter has shown great promise in approximating missing spatial information. The method described is formulated as a two-stage process. Lower resolution images are first registered and placed on a high-resolution grid. The problem is then treated as an in-painting task where the missing regions are reconstructed using a deep neural network with partial convolutional filters. We compare our method against deep learning-based single image super-resolution methods and classical multi-image super-resolution techniques using two similarity metrics and show that our method is more robust to occlusions and errors in registration while also producing higher quality outputs.
Federated Mixture of Experts

Federated learning (FL) has emerged as the predominant approach for collaborative training of neural network models across multiple users, without the need to gather the data at a central location. One of the important challenges in this setting is data heterogeneity, i.e. different users have different data characteristics. For this reason, training and using a single global model might be suboptimal when considering the performance of each of the individual user's data. In this work, we tackle this problem via Federated Mixture of Experts, FedMix, a framework that allows us to train an ensemble of specialized models. FedMix adaptively selects and trains a user-specific selection of the ensemble members. We show that users with similar data characteristics select the same members and therefore share statistical strength while mitigating the effect of non-i.i.d data. Empirically, we show through an extensive experimental evaluation that FedMix improves performance compared to using a single global model across a variety of different sources of non-i.i.d.-ness.
Fast and Effective Dynamic Optimization for Chemical Processes with Catalyst Deactivation Based on Incremental Encoding and Random Search

Dynamic is widely encountered in the chemical industry, which may come from equipment aging or catalyst deactivation. Meanwhile, the drift of these variables generally has a certain trend of change, such as worse equipment status or lower catalyst activity. Most of the existing optimization methods focus on steady-state optimization problems, and the optimization schemes proposed for such dynamic optimization problems are generally time-consuming. Therefore, to achieve optimal economic benefits, drifting operating conditions are challenges that must be overcome. In this work, to use the prior knowledge of the research object and guide the algorithm to converge fast, incremental encoding (IE) combined with the population is introduced, which searches for a feasible and better control trajectory. Next, the coarse random search (RS) method often used in iterative dynamic programming is introduced to improve the performance of the algorithm. The proposed two-step IE-RS optimization algorithm based on the control vector parameterization (CVP) combines the advantages of a heuristic algorithm and iterative dynamic optimization, which not only ensures fast convergence but also ensures the effectiveness of the algorithm, and is finally demonstrated in the acetylene hydrogenation process.
Spanish Biomedical and Clinical Language Embeddings

We computed both Word and Sub-word Embeddings using FastText. For Sub-word embeddings we selected Byte Pair Encoding (BPE) algorithm to represent the sub-words. We evaluated the Biomedical Word Embeddings obtaining better results than previous versions showing the implication that with more data, we obtain better representations.
Overview of methods for automatic natural language text processing

This paper provides a brief overview of modern methods and approaches used for automatic processing of text information. In English-language literature, this area of science is called NLP-Natural Language Processing. The very name suggests that the subject of analysis (and for many tasks – and synthesis) are materials presented in one of the natural languages (and for a number of tasks – in several languages simultaneously), i.e. national languages of communication between people. Programming languages are not included in this group. In Russian-language literature, this area is called Computer (or mathematical) linguistics. NLP (computational linguistics) usually includes speech analysis along with text analysis, but in this review speech analysis does not consider. The review used materials from original works, monographs, and a number of articles published the «Open Systems.DBMS» journal.
Michigan-Style Fuzzy Genetics-Based Machine Learning for Class Imbalance Data

Recently, interpretability of classifiers has been actively discussed in some real-world applications like medical and financial fields. Michigan-type Fuzzy Genetics-based Machine Learning (FGBML) is one of the most-well known methods for generating fuzzy classifiers with high interpretability. However, many real-world classification problems require high classification performance of a specific class which is less frequent than the others. For those problems, fuzzy classifiers generated by FGBML often have low classification performance for minority classes. Therefore, in this study, we extend FGBML for minority classes by applying the following four operations to the conventional Michigan-type FGBML: i) weighting the rule evaluation function by the number of patterns for each class, ii) changing the rule set evaluation function to the harmonic mean of the classification accuracy of each class, iii) selecting the base pattern in the heuristic rule generation based on the classification accuracy of each class, and iv) weighting the consequent part of each rule by the ratio of the number of the patterns between classes. Through computational experiment, we examine the effect of each change on the classification performance and show that the proposed method generates a fuzzy classifier with better classification performance than the conventional FGBML with SMOTE.
Application of improved CNN in SAR image noise reduction

Abstract Aiming at the problem of coherent speckle noise in SAR image processing, this paper improves the network based on the convolutional neural network method, adjusts the hierarchical relationship in the network hierarchy, and introduces the principles of batch normalization and residual network to eliminate. The gradient disappears when the network level is too deep. The loss function is improved by using multiple convolution kernels of different sizes which are used to extract a variety of different levels of feature information. Finally, based on the peak signal-to-noise ratio (PSNR) and edge index two parameters, the experimental comparison with PPB, SAR-BM3D and NL-SAR three types of denoising methods, The experimental results show that the SAR image generated by the improved CNN network can better achieve despeckle and denoising, and the edges of the image are kept intact.
Localization of Blockchain and E-Currency Model for E-Government Services

Blockchain can reduce bureaucracy and increase the efficiency and performance of administrative processes through a platform possessing features and attributes such as storing and exchanging electronic messages in a decentralized environment and executing high level of security transactions and transparency, if used in government public service delivery.Many scholars believe that this distributed technology can bring new utilizations to a variety of industries and fields, including finance and banking, economics, supply chain, and authentication and increase economic productivity and efficiency dramatically by transforming many industries in the context of today's economy.The present study, presents the characteristics of the localized blockchain and e-currency conceptual model for the evolution of e-government services.It also examines the impact of the blockchain and e-currency model on the economy and electronic financial transactions as a viable, practical and constructive solution (rather than blocking and filtering of e-currency and blockchain).Ultimately designing a localized block chain and e-currency model, has played an effective role in exploit its high potential to speed up the administrative processes and reduce costs related to electronic transactions and payments in e-government and increase e-government revenues and ultimately it can speed up the customer service delivery and increase their satisfaction with the government.
Professional self-development of the educator in pedagogical university in the conditions of transformation of educational environment

The article examines the problems of self-development of teachers in the conditions of transformation of the modern educational environment, which are associated with the need to identify conditions that promote or hinder professional self-realization. The transformation of the educational environment of the university actualizes the processes of goal-setting and the possibility of planning the formation of the teacher's personality. The object of the research is the process of professional self-development of a teacher of a pedagogical university in the conditions of transformation of the educational environment. The subject of the study is the main characteristics of goal-setting in the structure of planning for the professional future of a teacher in the conditions of transformation of the educational environment. The evaluation levels (low, medium, high) of goal-setting for the professional and pedagogical self-development of a teacher of a pedagogical university are determined, taking into account the importance of its structural components, that is, indicators (awareness of the need for professional and pedagogical self-development, strategic planning, concreteness of goal-setting, etc.). The assessment of levels was carried out taking into account indicators in its structure (dyads), which they are logically interrelated and reflect the professional and pedagogical self-development of a university teacher. The overall self-development potential of teachers of a pedagogical university is enhanced by such semantic modalities as flexibility in achieving goals and creativity in planning their professional future. Within the framework of the study, the most significant determinants of goal-setting have been identified, which predetermine the need for professional and pedagogical self-development of a teacher of a pedagogical university, focused on assessing the prospects for planning the development of personnel of educational organizations. The novelty of the research lies in the need to identify the main characteristics of goal-setting in the structure of planning a professional future teacher, taking into account the need for his professional and pedagogical self-development and transformation of the modern educational environment. It was found that among teachers of pedagogical universities, 65% of respondents noted that they pay attention to professional and pedagogical self-development only if the requirements of the university management are met; 35% of respondents expressed a desire and willingness to develop themselves both in the field of the pedagogical profession and beyond.
The Impact of Blockchain on the Value Chain

Today‚ businesses are operating in an increasingly complex and competitive environment. They are continuously challenged by the need to increase value chain efficiency‚ reduce operating costs‚ improve organizational processes‚ and consolidate business relationships with supply chain partners. To attain these multiple goals the integration of new technologies into the business value chain is a critical factor for ensuring competitiveness and strengthening the market position. In this regard‚ block-chain technology gains special attention from both industry and academia. Beyond its first use in cryptocurrencies‚ block-chain technology proves to be a workable solution for a multiplicity of supply chain application areas and responds to several emerging business needs. In this paper‚ we set out to provide comprehensive insight into the potentialities of block-chain technology by unifying its value-creating abilities in the well-known Porter’s value chain framework. Through the lens of the framework it is possible to gain a deeper understanding of how this technological phenomenon disrupts the value chain and reshapes existing business practices.
Adapted Fringe Projection Sequences for Changing Illumination Conditions on the Example of Measuring a Wrought-Hot Object Influenced by Forced Cooling

Optical 3D geometry reconstruction, or more specific, fringe projection profilometry, is a state-of-the-art technique for the measurement of the shape of objects in confined spaces or under rough environmental conditions, e.g., while inspecting a wrought-hot specimen after a forging operation. While the contact-less method enables the measurement of such an object, the results are influenced by the light deflection effect occurring due to the inhomogeneous refractive index field induced by the hot air around the measurand. However, the developed active compensation methods to fight this issue exhibits a major drawback, namely an additional cooling of the object and a subsequent transient illumination component. In this paper, we investigate the cooling and its effect on temporal phase reconstruction algorithms and take a theoretical approach to its compensation. The simulated compensation measures are transferred to a fringe projection profilometry setup and are evaluated using established and newly developed methods. The results show a significant improvement when measuring specimens under a transient illumination and are easily transferable to any kind of multi-frequency phase-shift measurement.