Reviewerly/exHarmony huggingface dataset:
Mining Frequent Sequential Patterns

In recent times, the urge to collect data and analyze it has grown. Time stamping a data set is an important part of the analysis and data mining as it can give information that is more useful. Different mining techniques have been designed for mining time-series data, sequential patterns for example seeks relationships between occurrences of sequential events and finds if there exist any specific order of the occurrences. Many Algorithms has been proposed to study this data type based on the apriori approach. In this paper we compare two basic sequential algorithms which are General Sequential algorithm (GSP) and Sequential PAttern Discovery using Equivalence classes (SPADE). These two algorithms are based on the Apriori algorithms. Experimental results have shown that SPADE consumes less time than GSP algorithm.
Neutral Color Correction Algorithm for Color Transfer Between Multicolor Images

This paper designs a post-processing algorithm of neutral color correction to deal with its deviation due to color transfer between multicolor images. The algorithm, based on \({L}^{*}{a}^{*}{b}^{*}\) color space, uses saturation of each pixel in the original image as the weight of color transfer degree, and adjusts the weight of image transfer results. Meanwhile, as saturation weighting may cause insufficient transfer of some neutral colors and no transfer of hues accordingly, a global hue correction strategy is designed in the proposed algorithm of this paper to solve this problem. The method used in this paper preserves the neutral color of the original image while keeping the effect of color transfer between multicolor images, avoids neutral color deviation caused by color transfer and ensures the quality and natural authenticity of the new image.
Design Bioethics and Digital Research Creep

This article refers to:Design Bioethics: A Theoretical Framework and Argument for Innovation in Bioethics Research
OVERVIEW OF INTERACTIVE LEARNING APPLICATIONS

Several examples of training applications for smartphones are considered as possible ways to gain knowledge and skills. The key features of their design are highlighted and their strengths and weaknesses are analyzed
On Classification Hidden Concepts Language in Specialized Texts Based on Methods of the Intellectual Data Processing

In this article discussed and solved the problems of comparing language concepts in specialized texts, in particular, scientific texts in the Ukrainian language.A corpus of scientific texts and dictionaries as well as stop words and affixes has been formed for processing specialized texts.The resulting texts were analyzed and converted into text frequency-inverse document frequency (TF-IDF) feature representation.To transform the original vector of features, it is proposed to use an algorithm for the synthesis of linear systems, in combination with the T-stochastic neighbor embedding (T-SNE).A series of experiments were performed on test examples for the determination of informational density in the text and classification by keywords in specialized texts using the method of random samples consensus (RANSAC).A method of classification of hidden language concepts was proposed, use of clustering methods (K-means).As a result of the experiment, the structure of the classifier of hidden language concepts was obtained in structured texts.The stability of the proposed method is investigated by using the perturbation of the original data by a variational autoencoder.The obtained structure allowed to achieve a relatively high recognition accuracy (97%-99%) using decision trees and machines of extreme gradient amplification.
An study on the application of blockchain based 5G Technology in the Power IoT

Based on some key characteristics of 5G and blockchain, we analyze the technical feasibility and necessity of combining 5G and blockchain in IoT construction. We prove that the combination of 5G and blockchain can effectively improve network and data security of 5G by discussing the practical problems that can be solved by this method(including distributed deployment of network platforms, slicing management, network security authentication, spectrum resource management, data sharing security), while moderately reducing network complexity. Finally, we discuss the application of 5G and blockchain combined technical architecture in the construction of the Internet of Things (IoT), focusing on the requirements of power transmission, substation, distribution and consumption. We analyze the 5G network slicing management based on blockchain technology, which can realize the dynamic selection of the best resources among multiple resources to create end-to-end network slices that meet business needs and security isolation requirements, and enhance the security of the network and the data in multiple scenarios and applications, it will provide a strong network infrastructure support for the Energy IoT service system.
From Simple to Structural Clones: Tapping the Benefits of Non-redundancy

Similarities are common in software. We observe them at all levels, from software requirements, to design, to program code and to other software artifacts such as models or test cases. Similar program parts are termed code clones. Detection of clones in code, as well as methods to achieve non-redundancy in programs have been an active area of research for last decades. In this chapter, I discuss sources of redundancies, variety of forms of their manifestation, software productivity benefits that can be gained by avoiding clones, and difficulties to realize these benefits with conventional programming languages and design techniques. I point to generative techniques as a promising approach to tap the benefits of non-redundancy. Research so far has mainly focused on detection of similar code fragments, so-called simple clones. The knowledge and possible unification of simple clones can help in maintenance. Still, further gains can be obtained by elevating the level of software similarity analysis to the design-level, larger granularity similar program structures, such as recurring architecture-level patterns of collaborating components. Recurring patterns of simple clones often indicate the presence of interesting higher-level similarities that we call structural clones. Detection of structural clones can help in understanding the design of a system for quality assessment, better maintenance and re-engineering, opening new options for design recovery (reverse engineering) which has been an active area of research for last 25 years, with only limited impact on software practice. In this paper, I broadly discuss the landscape of clone research, with particular emphasis on structural clones (This study was supported by the grant of WZ/WI-IIT/3/2020 from Bialystok University of Technology and founded from the resources for research by Ministry of Science and Higher Education, Poland.)
Partially Disentangled Latent Relations for Multi-label Deep Learning

Identified the specific features from the instances belong to a certain class label is meaningful, the "purified" feature representation contains such label information can be shared with other feature learning. Besides, it is essential to distinguish the sample association relationship behind the multi-label da-tasets, which is conducive to improve the performance of the algorithm. However, most algorithms aim to capture the mapping between instances and labels, while ignoring the information about instance relations and label cor-relation hidden in the data structure. Motivated by these issues, we leverage the deep network to learn the special feature representations without aban-doning overlapped features. Meanwhile, the Euclidean metric matrices are leveraged to construct the diagonal matrix for the diffusion function, it en-sures that the results of model training by similar instance features are con-sistent. Further, considering the contributions of these feature representation are different and have influences on the final prediction results, thus the self-attention mechanism is introduced to fusion the other label specific in-stance features to build the new joint feature representation, which derive dynamic weights for multi-label prediction. Finally, experimental results of the real data sets show promising availabilities of our approach.
RESEARCH ABILITY OF STUDENTS OF PEDAGOGICAL SCHOOL OF MONGOL STATE PEDAGOGICAL UNIVERSITY IN SOLVING THEIR PROBLEMS IN DIFFERENT WAYS

In this article, we tried to show the results of mastering by students of a pedagogical school Mongolian State Pedagogical University with the skills of solving word problems in different ways and methodological knowledge of teaching. Since 2012, students of the pedagogical school have been taking the elective «Mathematical Science». In the previous school, students did not solve math word problems in different ways, and therefore they faced difficulties and interesting cases, but after each training there were positive results.
Computational Complexity of Compliance and Conformance: Drawing a Line Between Theory and Practice

In the present chapter we focus our attention on the computational complexity of proving regulatory compliance of business process models. While the topic has never received the deserved attention, we argue that the theoretical results, both existing and yet to find, are far reaching for many areas related to the problem of proving compliance of process models. Therefore, we provide here and discuss the existing results concerning the theoretical computational complexity of the problem, as well as discussing some further areas that can potentially advance the knowledge about the issue, and other closely related disciplines that can either bring or take insights to this area.
A Fault-Tolerant Automated Flight Path Planning System for an Ultralight Aircraft

The development and integration of fault-tolerant systems has considerably increased flight safety over the years. One of the research areas that has made this improvement possible is the development of more advanced flight guidance systems, that are able to compute feasible flight trajectories in an automated manner, even under non-nominal conditions. However, such highly automated systems are normally not available for low-cost ultralight aircraft, which are usually piloted by non-professional pilots, who may not react properly under adverse circumstances. In this paper, we propose a model-based flight path planning system that uses an automated AI planner. By leveraging the flexibility of the AI planner to adapt to different planning problem models, we integrate “fault-tolerant” capabilities into the planning system. Therefore, optimal control parameters learned for various non-nominal flight conditions can be considered too. Finally, extension tests were performed under a selected number of scenarios to validate the feasibility of the plans.
An Efficient Algorithm for Semi-stable Extensions

In this paper we introduce \(\textsf {AASExts}\), an algorithm for computing semi–stable extensions. We improve techniques developed for other semantics, notably preferred semantics, as well as leverage recent advances in All-SAT community. We prove our proposed algorithm is sound and complete, we describe the experiments to select the most appropriate encoding to adopt, and we show empirically that our implementation significantly outperforms even sophisticated ASP-based and SAT-based reduction approaches on existing benchmarks.
On Induction Principles for Diamond-Free Partial Orders

We propose a formulation of an induction principle for diamond-free partial orders, which can be considered as a generalization of one of the variants of the real induction principle. This principle may be useful for specification and verification of non-discrete systems using interactive proof assistant software. As an example, a formalization in Isabelle proof assistant is presented.
Deep Clustering for Improved Inter-Cluster Separability and Intra-Cluster Homogeneity with Cohesive Loss

To date, many studies have employed clustering for the classification of unlabeled data. Deep separate clustering applies several deep learning models to conventional clustering algorithms to more clearly separate the distribution of the clusters. In this paper, we employ a convolutional autoencoder to learn the features of input images. Following this, k-means clustering is conducted using the encoded layer features learned by the convolutional autoencoder. A center loss function is then added to aggregate the data points into clusters to increase the intra-cluster homogeneity. Finally, we calculate and increase the inter-cluster separability. We combine all loss functions into a single global objective function. Our new deep clustering method surpasses the performance of existing clustering approaches when compared in experiments under the same conditions.
Estimating Nonplanar Flow From 2D Motion-Blurred Widefield Microscopy Images Via Deep Learning

Optical flow is a method aimed at predicting the movement velocity of any pixel in the image and is used in medicine and biology to estimate flow of particles in organs or organelles. However, a precise optical flow measurement requires images taken at high speed and low exposure time, which induces phototoxicity due to the increase in illumination power. We are looking here to estimate the three-dimensional movement vector field of moving out-of-plane particles using normal light conditions and a standard microscope camera. We present a method to predict, from a single textured wide-field microscopy image, the movement of out-of-plane particles using the local characteristics of the motion blur. We estimated the velocity vector field from the local estimation of the blur model parameters using an deep neural network and achieved a prediction with a regression coefficient of 0.92 between the ground truth simulated vector field and the output of the network. This method could enable microscopists to gain insights about the dynamic properties of samples without the need for high-speed cameras or high-intensity light exposure.
Practices of Kindergarten Teachers in Achieving the nutritional knowledge of Kindergarten Children During the meal time

The study aimed to identify the practice of kindergarten teachers in providing، nutritional knowledge of the kindergarten child during the ، meal time. To achieve this، a descriptive method was used. The questionnaire was used as data collection tool. The sample consisted of (148) In the analysis of the data، the results of the study showed that the tool obtained an average of (2. 96 from 4. 00) and at the field level Axis (concept of nutritional knowledge (2. 89 of 4. 00)، followed by the difficulty of kindergarten teachers during the meal time، at an average (3. 49 of 4. 00)، both of which are rated (high)، while very high on (kindergarten teacher practices during the meal time at an average of (2. 50 of 4. 00) The teachers also showed that the meals attended by the children are healthy and that there is awareness among the families about the type of food provided to the children. .
Complexity Estimation for Distributed Software Development Using SRS

Abstract In recent years distributed software development has gathered pace. Many development programmes now require asynchronous coordination several time zones apart between geographically dispersed teams. Owing to considerations such as effort spent on team development and technology sharing, software complexity calculation for such projects is difficult to establish a software system infrastructure that can be efficiently spread and minimizes cross-site connectivity, enabling communication between remote teams working on interrelated sections of the architecture and their day-to-day interactions. A globally distributed software project faces, in addition to the technological complexity, an additional complexity contributed by the different supporting parameters. This paper defines these parameters and attempts to estimate all the complexities described above and incorporate these two measures to obtain the corresponding overall measure of complexity during the requirement analysis phase.
Theoretical and methodological principles of training of specialists in physical culture and sports using technologies of distance education»

In connection with the reform of education in higher education institutions, the concept of distance education is being actively developed, which provides for the development of educational courses aimed at ensuring the accessibility, quality and efficiency of education. There are various ways to solve them, one of which is the informatization of education. In turn, informatization allows to effectively develop various modern pedagogical technologies. The article is devoted to the theoretical and methodological principles of the organization of the educational process in the conditions of higher education with the use of distance learning technologies. The article reveals the essence of the concept of "distance learning", describes the benefits of distance technology, analyzes the effectiveness of distance learning for university students.&#x0D; The concept of "blended learning", "distance learning", methodological approaches, a number of general didactic and methodological principles on which the process of learning in higher education is based when applying the distance learning model are analyzed and specified. The methods, forms of organization and tools used in blended learning are described, as well as the advantages of such a learning model are highlighted. A schematic representation of the structure of the methodical system of blended learning in the conditions of higher education is developed. The use of blended learning in higher education solves the problems of individualization, intensification and optimization of learning, is the most logical and natural evolution of the traditional model of learning.
Graph Neural Networks for the Cross-Domain Histopathological Image Classification

Annotating histopathological images is generally a time-consuming and labor-intensive process, which requires broad-certificated pathologists carefully examining large-scale and multi-modal images pixel by pixel. Recent frontiers of transfer learning techniques have been widely investigated for image understanding tasks with limited annotations. However, when applied for the grading and recognition of histopathological images, few of them can effectively avoid the performance degradation caused by the domain discrepancy with diverse tissues/organs appearance and imaging devices. To this end, we present a novel method for the unsupervised domain adaptation(UDA) in histopathological image analysis, based on a backbone neural network with graph neural layers for propagating the supervision signals of images with labels. The graph model is first set up by connecting every image with its close neighbors in the embedded feature space. Then graph neural network is employed to synthesize new feature representation from each image. During the training stage, target samples with confident inferences are dynamically allocated with pseudo labels. Accordingly, the cross-entropy loss is used to constrain the predictions of source samples with manually marked labels and target samples with pseudo labels. Furthermore, the maximum mean diversity is adopted to facilitate the extraction of domain-invariant feature representations, where contrastive learning is exploited to enhance the category discrimination for learned features. Our proposed method is evaluated on multiple histopathological image datasets, showing promising classification performance in comparison with other domain adaptation state-of-the-arts.
A Robust and Secure Way for Sharing Confidential Images

With advent of increased Internet consumption, most of our exchanges occur through a digital medium. We share a lot of information with our family, friends, colleagues & people with whom we deal businesses & services. All this information we usually share via Smartphone's using popular messaging apps like WhatsApp or Messenger and similar social media apps. Most of these apps are secure in terms of security when it comes to informal discussions of information, but not very secure when it comes to sharing of sensitive information in form of images. Sensitive images of debit card, bank passbooks, identity cards or confidential documents usually gets stored in devices of recipient and thus can get misused knowingly or unknowingly. In this paper we discuss a robust and secure method of sharing the images (color) as well as important confidential codes in the form of images. As cases of malwares and other spywares are increasing in mobile platforms, it becomes important to address the issue where potent attackers can steal sensitive data like images stored in the device without the user's consent.
Polytope Conditioning and Linear Convergence of the Frank–Wolfe Algorithm

It is well known that the gradient descent algorithm converges linearly when applied to a strongly convex function with Lipschitz gradient. In this case, the algorithm’s rate of convergence is determined by the condition number of the function. In a similar vein, it has been shown that a variant of the Frank–Wolfe algorithm with away steps converges linearly when applied to a strongly convex function with Lipschitz gradient over a polytope. In a nice extension of the unconstrained case, the algorithm’s rate of convergence is determined by the product of the condition number of the function and a certain condition number of the polytope. We shed new light on the latter type of polytope conditioning. In particular, we show that previous and seemingly different approaches to define a suitable condition measure for the polytope are essentially equivalent to each other. Perhaps more interesting, they can all be unified via a parameter of the polytope that formalizes a key premise linked to the algorithm’s linear convergence. We also give new insight into the linear convergence property. For a convex quadratic objective, we show that the rate of convergence is determined by a condition number of a suitably scaled polytope.
Research on Map Knowledge Visualization Based on Augmented Reality

There are three key links in the process of map knowledge visualization: the data of map knowledge, the user, and the method of map knowledge visualization. Among the three links, the proper method of map knowledge visualization can not only enrich the presentation form of map data, but also reduce the cognitive barriers and understanding deviation of map knowledge. Based on the analysis of various data types involved in map information, this paper applies text, audio and video, 3D model and other data types to map knowledge visualization by augmented reality technology. The results show that the application of augmented reality technology to map knowledge visualization can enrich the representation of map knowledge visualization, realize real-time interaction between users and map knowledge, and further narrow the differences between map knowledge expression and user spatial cognitive.