Reviewerly/exHarmony huggingface dataset:
Image Artistic Style Transfer Based on Color Distribution Preprocessing

Style transfer is an increasingly popular field that can capture the styles of a particular artwork and use them to synthesize a new image with specific content. Previous NST algorithms have the limitation to transfer styles to correct regions in the output image. Therefore, some regions in the output image have deformed structures of the source image. In this paper, we propose a color preprocessing-based neural style transfer method to overcome the limitation. To reduce impacts caused by color differences between source image and style, we propose three models based on a color iterative distribution transform algorithm (IDT). The first one is named original color-preprocessed (OCp) model, which uses IDT to transform the color probability density function (PDF) of source image into that of style image. The second one is named exposure-corrected original color-preprocessed (EC-OCp) model, which adds an automatic detail-enhanced exposure correction module before OCp model. When source image is underexposed, EC-OCp model can achieve better results than OCp model. The third one is style color-preprocessed (SCp) model. It uses IDT to transform the color PDF of style image into that of source image. The original structures are well protected in the output image. According to experiments, the proposed models are robust to the source images with more conditions. Therefore, they have more usage values than the original method.
Blind coding classification in the presence of interference in MIMO systems using ML algorithm

Summary This paper presents a framework for coding classification in multiple‐input multiple‐output (MIMO) systems in the presence of inter‐user interference (IUI). This framework is performed at the receiver beginning with a signal separation step. The signal separation is implemented with a multi‐user kurtosis (MUK) algorithm. The classification step estimates the code parameter (CP) using the maximum‐likelihood (ML) method applied to the covariance matrix of the received signal without a priori knowledge about the transmitted signal. Experimental results show that the proposed coding classifier is easy to implement and efficient for the classification of the CP over Rayleigh fading channels in the presence of time and frequency offsets. Furthermore, the success rate of code classification is high at low signal‐to‐noise ratios (SNRs). The signal separation increases the probability of true classification.
The informational and methodical support of the aerospace monitoring of a sea shelf for the detection of anomalies over hydrocarbon deposits

The informational and methodical support of the aerospace monitoring of a sea shelf for the detection of anomalies over hydrocarbon deposits Presented by Corresponding Member of the NAS of Ukraine O.D. FedorovskyThe article describes the informational and methodological support of the aerospace monitoring of a sea shelf for revealing the temperature and optical anomalies over the deposits of hydrocarbons.The geology-geophysical and hydrophysical factors, as the most important ones, affecting the efficiency of the searching for hydrocarbon deposits are considered.It is established that, between the hydrophysical and hydrodynamic processes in near-surface water layers on the boundary between water and atmosphere, dynamical processes in a fluid, geological structure of the lithosphere, and underwater landscapes, there is an interconnection that should be taken into account, when detecting the hydrocarbon deposit, by using the remote sensing.
A Comparison of Warnsdorff’s Rule and Backtracking for Knight’s Tour on Square Boards

Knight’s tour has been an intriguing chess puzzle for many computer scientists and mathematicians. In this paper, we describe and analyze two of the most commonly used algorithms to solve the Knight’s tour puzzle in computer science—Warnsdorff’s rule and backtracking. Various case studies are performed to showcase the differences in time complexity of each algorithm. We also point out that the starting position and dimensions of a board play a key role in determining whether a tour is possible. By doing this, we prove that a tour does not exist when the knight starts from odd squares on boards of odd dimensions.
A Modern Method for Constructing the S-Box of Advanced Encryption Standard

The substitution table (S-Box) of Advanced Encryption Standard (AES) and its properties are key elements in cryptanalysis ciphering.We aim here to propose a straightforward method for the non-linear transformation of AES S-Box construction.The method reduces the steps needed to compute the multiplicative inverse, and computes the matrices multiplication used in this transformation, without a need to use the characteristic matrix, and the result is a modern method constructing the S-Box.
Small Dataset Modeling and Application of Plant Medicine Extraction

Intelligent modeling is an effective method to build prediction model of the plant medicine with ultrasonic extraction. However, there are obstacles when obtaining lots of data by the plant medicine extraction experiments, and small dataset will result in a model with low accuracy and poor generalization ability, which has a great influence on it. This paper proposes a novel virtual sample generation (VSG) approach based on Response Surface Methodology (RSM) and Extreme Learning Machine (ELM) algorithm, selecting through Cuckoo Search (CS) algorithm. The new prediction model is constructed based on ELM with the virtual sample dataset generated by this method and the original small sample dataset. The performance of the model is verified via the case of extracting the active ingredients, liquiritin, from liquorice by dual-frequency ultrasound. The experiment results show that the model established by the method proposed in this paper can significantly reduce the prediction error and improve the accuracy of the model, which provides a certain theoretical basis and reference for the industrialization of the active ingredients extraction of plant medicine.
Development of Multi-person Pose Estimation Method Based on PAFs

Human keypoints are effective human pose descriptions. Human behavior can be recognized by the motion of keypoints of human bodies. In this paper, we propose a method, which is based on a PAFs approach, for human keypoints detection. The proposed method makes improvements in two aspects: (1) It perfects the joint points matching algorithm by re-matching. (2) It uses multi-branch PAFs to correct the keypoint connections, thus improving the wrong connection problem of upper and lower limbs for multi-person keypoint detection. The improved PAFs method, whose mAP reaches 53.6% on HKD dataset, improved the score of the original method by 2%.
About the formation of the knowledge base for fuzzy control system ofpricing in the pharmacy network

This article offers the approach to the formation of a knowledge base in the form of a set of fuzzy production rules. It is assumed that the parameters characterizing a complex system are divided into basic and additional, which depend on the basic ones. To describe the trends of the basic parameters, a linguistic scale is formed. Using a special clustering method, the typical states of a complex system are determined, as well as the critical values of additional parameters. Each production rule establishes a correspondence between the typical state and the critical values of additional parameters. The proposed approach was tested in the task of managing pricing in the pharmacy network by determining the optimal mark-up value for pharmaceutical products using an expert system.
Verification for Everyone? An Overview of Dynamic Logic

This note, reporting the homonym keynote presented in the International Symposium on Molecular Logic and Computational Synthetic Biology 2018, traces an informal roadmap on Dynamic Logic (DL) field, focusing on its versatility and resilience to be adjusted and adopted in a wide class of application domains and computational paradigms. The exposition argues the room for developments on tagging DL to the analysis of synthetic biologic domain.
An Automatic Data Service Generation Approach for Cross-origin Datasets

As a unified data access model, data service has become a promising technique to integrate and share heterogeneous datasets. In order to publish overwhelming data on the web, it is a key to automatically extract and encapsulate data services from various datasets in cloud environment. In this paper, a novel data service generation approach for cross-origin datasets is proposed. An attribute dependency graph (ADG) is constructed by using inherent data dependency. Based on the ADG, an automatic data service extraction algorithm is implemented. The extracted atomic data services are further organized into another representation named data service dependency graph (DSDG). Then, a data service encapsulation framework, which includes an entity layer, a data access object layer and a service layer, is designed. Via a flexible RESTful service template, this framework can automatically encapsulate the extracted data services into the RESTful services which can be accessed by the exposed interfaces. In addition, a data service generation system has been developed. Experimental results show that the system has high efficiency and good quality for data service generation.
Discourse Marker Dico in the 16th Century Italian Language

The article is devoted to the functional and semantic analysis of the form dico in the 16 th-century Italian language, the main aim being to show how it gradually begins to function as a discourse marker. The study is performed on the basis of texts belonging to different genres (mainly comedies and treatises in the dialogue form) where certain peculiarities of spoken language are imitated. We come to the conclusion that the invariant pragmatic meaning of this unit is "second attempt to establish successful communication". We also claim that the promotion of dico to the role of a discourse marker is the result of the secondary metaphorization of dire. We point out to the parallelism between the metaphoric uses of dire, namely, the epistemic one ("to say" = "to claim, to consider") and the volitional one ("to say" = "to order, to command"), on the one hand, and the use of dico in its two main functions, on the other hand. The functions in question are a) adducing a comment to the previous statement, and b) urging the addressee to act in a certain way. In both cases one can speak of a partial desemantization of dico and its metaphorization that allows it to function as a pragmatic signal rather than a regular verbal form. On the formal level it is manifested by the absence of complements, syntactic freedom of dico and its tendency to occupy a certain position within a sentence depending exclusively on its pragmatic (and not grammatical-syntactic) function.
Methods for robot localization on a map

In many cases, to solve applied problems, the robot needs to know its real location, which is most often different from the data stored in the on-board system. For unmanned robotic devices, as well as for ground-based robots, it is most efficient to use local navigation algorithms, which consist in determining the coordinates of the device with respect to a certain starting point. The paper discusses localization algorithms provided that the map of the area is known in advance. Particular attention is paid to the Monte Carlo localization method because of several advantages. The paper presents an example of modeling the algorithm operation.
Understanding, Explaining, and Deriving Refinement

Much of what drove us in over twenty years of research in refinement, starting with Z in particular, was the desire to understand where refinement rules came from. The relational model of refinement provided a solid starting point which allowed the derivation of Z refinement rules. Not only did this explain and verify the existing rules—more importantly, it also allowed alternative derivations for different and generalised notions of refinement. In this chapter, we briefly describe the context of our early efforts in this area and Susan Stepney’s role in this, before moving on to the motivation and exploration of a recently developed primitive model of refinement: concrete state machines with anonymous transitions.
The Development of Teaching Materials Base on Inquiry Oriented Discovery

Textbooks are guidelines for training and improving students' skills in understanding material and solving problems. Inquiry-oriented discovery refers to academic situations, namely small groups of students trying to find answers to the topics of inquiry. In these situations, students can find concepts or detailed information. This study aims to produce textbooks. Type of research, namely development research through define, design, and develop stages. The results of the study showed that after the textbooks were validated, they were read and read and met the criteria for good (valid).
An Improved Real-Time Recommendation for Microblogs Based on Topic

With the rapid development of the Internet, people are confronted with information overload. Many recommendation methods are designed to solve this problem. The main contributions of recommendation methods proposed in this paper are as follows: (1) An improved collaborative filtering recommendation algorithm based on user clustering is proposed. Clustering is performed according to user similarity based on the user-item rating matrix. So the search space of recommendation algorithm is reduced. (2) Considering the factor that user’s interests may dynamically change over time, a time decay function is introduced. (3) A method of real-time recommendation based on topic for microblogs is designed to realize real-time recommendation effectively by preserving intermediate variables of user similarity. Experiments show that the proposed algorithms have been improved in terms of running time and accuracy.
Blockchain Technology in International Trade in Goods

No other technology has been mentioned as frequently as blockchain technology. No less than a technical revolution should go along with it. In addition to a brief introduction to the functionality of the blockchain technology, this chapter will also highlight various fields of application and the related legal challenges in international trade. The transport industry may be mentioned as an example. Especially in the transportation of goods, a large number of documentation obligations must be adhered to and permits must be obtained. Packing lists, export licenses, and product certificates are examples of this. All mentioned documents are based on the same data set, namely the details of the type, origin, and quantity of the goods. Large parts of these documents must be carried along with the goods and presented on instructions, which causes considerable bureaucracy and makes transportation more difficult and expensive. These documents could be displayed on the blockchain in the future.
Secondary Filtering Update Algorithm for Nonlinear Systems—Based on Extended Kalman Filter

Based on the fixed point theory, in this paper, a novel extended Kalman filtering algorithm with secondary filtering update is proposed for nonlinear systems. As we all known, the traditional extended Kalman filter which based on the Taylor series expansion of nonlinear system function, only retains the first-order approximation term, and omits the second-order and higher order terms. It is inevitable to reduce the estimation accuracy. This paper compensates and updates it by constructing a secondary filtering update algorithm. The secondary filtering algorithm constructs the filtering state estimation equation into a fixed point equation and updates the state estimate by solving the fixed point equation. The final simulation results verify the effectiveness and feasibility of the proposed method.
Development of a machine tool auxiliary machining system based on android phone

This paper presents an Android-based system for assisting manual machine tool machining. We developed some programs used on the PC for reading data from DXF (Drawing Exchange Format) files with C# language. These programs extracted the data from DXF files, formed custom EXF files and uploaded these files to the file server. The EXF format files were used as data source for an Android application. This application got files from the file server and displayed two dimensional engineering drawings by using OpenGL ES. Meanwhile, it was connected with the digital display device of the grating scale through Bluetooth in order to realize the real-time tracking of tool path and improve machining efficiency. Experiments proved this system can accurately display coordinates of the tool in real time and improve machining efficiency.
Interferometric precision measurement of highly reflective thin film using wavelength tuning Fizeau interferometry

Indium tin oxide (ITO) thin films have been widely used in displays such as liquid crystal displays and touch panels because of their favorable electrical conductivity and optical transparency. The surface shape and thickness of ITO thin films must be precisely measured to improve their reliability and performance. Conventional measurement techniques take single point measurements and require expensive systems. In this paper, we measure the surface shape of an ITO thin film on top of a transparent plate using wavelength-tuning Fizeau interferometry. The surface shape was determined by compensating for the phase error introduced by optical interference from the thin film, which was calculated using the phase and amplitude distributions measured by wavelength-tuning. The proposed measurement method achieved noncontact, large-aperture, and precise measurements of transparent thin films. The surface shape of the sample was experimentally measured to an accuracy of 40 nm, mainly limited by the accuracy of the reference surface of 30 nm.
Leadership Competences: Implications for the Higher Education Quality Management Curriculum at the Regional Level

Bologna process made the National Qualifications Frameworks compatible with the European Qualifications Framework three-cycle education and learning outcomes predominant to curriculum development (approval and reviewing) for new academic programmes in higher education in Russia. One of the previous initiatives on expressing learning outcomes was the TUNING Educational Structures in Europe project, which recommended writing learning outcomes in terms of competences. Since then Europe has been using learning outcomes to describe the educational programmes, while this notion is sometimes misunderstood and misinterpreted by Russian higher educational institutions. However, the more recent draft of National (Russian) Qualifications Framework employs a triangular set of descriptors, categorized as competences, skills and knowledge. This paper scrutinizes and discusses the changing definition of the concept of leadership competences and changes in categorizing learning outcomes in Russia and their probable impact on curriculum development in quality management at the regional level. The authors assume that any alterations in the definition of leadership competences and in categorizing the learning outcomes should be considered in the process of developing (reviewing and approving) of new curricula so that they may better associate with the appropriate levels of the National (Russian) Qualifications Framework.
Instagram image to match students with a librarian for research (Valentine's Day Theme)

Valentine's Day Themed Infographic to make students aware of the research resources available to them at Mugar Library.
An Intelligent Tool for Detection of Phishing Messages

Phishing messages are a common attack on the web that results in the theft of user information. Finding a solution for this problem is a difficult task because phishers are very creative, and often it is hard even for a human to differentiate between legitimate and malign content. The goal of this project was to develop an intelligent tool for phishing detection that integrates only local information (the full content of the message) and does not rely on external (usually commercial) sources or black lists. The major focus of this paper is the selection of appropriate features to discriminate between ordinary and phishing messages and the choice of an efficient classifier. The system can dynamically update the feature list and quickly adapt to new trends of phishing attacks. The proposed tool is suitable for implementation in email accounts or any other social network or communication channel. It is intended to reduce the workload on human experts that otherwise need to go through hundreds of messages everyday to verify their authenticity.
Powerful Data Analysis and Composition with the UNIX-Shell

In addition to a wide range of commercially available data processing tools for data analysis and knowledge discovery, there are a bundle of Unix-shell scripting and text processing tools practically available on every computer. This paper reports on some of these data processing tools and presents how they can be used together to manipulate and transform data and also to perform some sort of analysis like aggregation, etc. Beside the free availability, these tools have the advantage that they can be used immediately, without prior transformation and loading the data into the target system. Another important point is, that they are typically stream-based and thus, huge amounts of data can be processed without running out of main-memory.
ISRGAN: Improved Super-Resolution Using Generative Adversarial Networks

In this paper, we propose an approach for single image super-resolution (SISR) using generative adversarial network (GAN). The SISR has been an attractive research topic over the last two decades and it refers to the reconstruction of a high resolution (HR) image from a single low resolution (LR) observation. Recently, SISR using convolutional neural networks (CNNs) obtained remarkable performance in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics. Despite this, these methods suffer with a serious drawback in terms of visualization quality of the SR images; the results look overly-smoothed. This is due to the loss function in those methods has a pixel level difference which increases the values of PSNR and SSIM metrics; however the visualization quality is degraded. The GAN has a capability to generate visually appealable solutions. It can also recover the high-frequency texture details due to the discrimination process involved in GAN. Here, we propose improved single image super-resolution using GAN (ISRGAN) with the concept of densely connected deep convolutional networks for image super-resolution. Our proposed method consists two networks: ISRNet and ISRGAN. The ISRNet is trained using MSE based loss function to achieve higher PSNR and SSIM values and ISRGAN is trained by using a combination of VGG based perceptual loss and adversarial loss in order to improve the perceptual quality of the SR images. This training step forces the SR results more towards the natural image manifold. The efficiency of the proposed method is verified by conducting experiments on the different benchmark testing datasets and it shows that the proposed method of ISRGAN outperforms in terms of perception when compared to the other state-of-the-art GAN based SISR techniques.