Image Representation Structure of the Regional Government in Social Net Narrative based on the Accounts of S. E. Tsivilev, Governor of Kemerovo Region

The present research featured the way social net users perceive the image of the head of a region. The study involved comments to posts made by the governor of Kuzbass S. E. Tsivilev in VKontakte, Instagram, and Facebook. The analysis employed the method of narrative semiotics. The feedback between the population and the government appeared to depend on the specifics of the political regime in the region. Social nets became both a form of feedback and an indicator of the perception of the imposed image. Different social networks have different audiences, hence the different perception of the governor. Moreover, each social net has its own functionality, which sets the format of feedback. The initial hypothesis was that different social networks produce different attitudes towards the main figure of local executive power. The research results contradicted with this hypothesis and revealed a difference in the positioning of the executive branch and modeling of the governor’s image perception. Based on the data about different perception models, the local authorities can build new media strategies in order to improve the image of the governor in the eyes of the population.

On Machine-Learning Morphological Image Operators

Morphological operators are nonlinear transformations commonly used in image processing. Their theoretical foundation is based on lattice theory, and it is a well-known result that a large class of image operators can be expressed in terms of two basic ones, the erosions and the dilations. In practice, useful operators can be built by combining these two operators, and the new operators can be further combined to implement more complex transformations. The possibility of implementing a compact combination that performs a complex transformation of images is particularly appealing in resource-constrained hardware scenarios. However, finding a proper combination may require a considerable trial-and-error effort. This difficulty has motivated the development of machine-learning-based approaches for designing morphological image operators. In this work, we present an overview of this topic, divided in three parts. First, we review and discuss the representation structure of morphological image operators. Then we address the problem of learning morphological image operators from data, and how representation manifests in the formulation of this problem as well as in the learned operators. In the last part we focus on recent morphological image operator learning methods that take advantage of deep-learning frameworks. We close with discussions and a list of prospective future research directions.

WHITE-BOX CARTOONIZATION USING AN EXTENDED GAN FRAMEWORK

In the present study, we propose to implement a new framework for estimating generative models via an adversarial process to extend an existing GAN framework and develop a white-box controllable image cartoonization, which can generate high-quality cartooned images/videos from real-world photos and videos. The learning purposes of our system are based on three distinct representations: surface representation, structure representation, and texture representation. The surface representation refers to the smooth surface of the images. The structure representation relates to the sparse colour blocks and compresses generic content. The texture representation shows the texture, curves, and features in cartoon images. Generative Adversarial Network (GAN) framework decomposes the images into different representations and learns from them to generate cartoon images. This decomposition makes the framework more controllable and flexible which allows users to make changes based on the required output. This approach overcomes any previous system in terms of maintaining clarity, colours, textures, shapes of images yet showing the characteristics of cartoon images.

What Student Positions can be Created on the LBUSD BOE to Increase the Effectiveness of Student Representation?

The Long Beach Unified School District Board of Education and superintendent make efforts to have student representation in the decision making process. However, there are many shortcomings in the student representation structure of the LBUSD BOE. The systems set in place do not encourage consistent and active student participation, and evaluations of recent student board member involvement show minimal to no participation. By comparing LBUSD to other CORE school districts, there are clear differences in the way students are represented on the BOE. Notably, many CORE school districts elect student BOE members and include a Student Advisory Council to advise the Board. Other CORE school districts have experienced valuable student participation with their models. LBUSD can draw from other CORE school districts to create a system of student representation on the BOE that fosters student involvement and values student voice.

The applicability of COBIT processes representation structure for quality improvement in healthcare: a Delphi study

Objectives In healthcare, a variety of quality management practices are used. Although they are important sources for quality improvement initiatives, they do not focus on each particular process. On the other hand, ‘Control Objectives for Information and Related Technologies’ (COBIT) offers a well-defined process representation structure for representing potential process improvements. The objective of this study was to adopt the COBIT structure for healthcare processes and assess the applicability of such process representations. Design A two-round Delphi technique was applied: in round 1, open-ended interviews were performed with the participants; in round 2, the participants responded to the web questionnaire. Settings The participants provided their opinion between 11 September 2018 and 26 June 2019. Participants It included 37 members of an expert panel from 8 European countries. Intervention N/A Main Outcome Measures In round 1, strengths, weaknesses, opportunities and threats indicators of using the proposed structure in healthcare were identified. These were evaluated on a 9-point Likert scale in round 2. Results All participants noted that elements of the COBIT process representation structure were suitable for representing healthcare processes. The consensus was reached only for strengths and opportunities indicators. Conclusions A set of processes represented with the suggested structure has the potential to become a valid reference in healthcare quality improvements initiatives, as COBIT in IT domain. Despite the fact that the expert panel members confirmed the applicability of the COBIT process representation structure for healthcare processes, the identified weaknesses and threats cannot be ignored.

Deep Stock Prediction

The ongoing development of profound learning has empowered exchanging calculations to anticipate stock value developments all the more precisely. Tragically, there is a noteworthy hole in reality sending of this achievement. For instance, proficient brokers in their long haul professions have collected various exchanging rules, the legend of which they can see great. Then again, profound learning models have been not really interpretable. This paper presents DeepClue, a framework worked to connect content based profound learning models and end clients through outwardly deciphering the key components learned in the stock value forecast model. We make three commitments in DeepClue. To start with, by structuring the profound neural system engineering for translation and applying a calculation to separate important prescient variables, we give a valuable case on what can be deciphered out of the expectation model for end clients. Second, by investigating chains of command over the extricated factors and showing these variables in an intuitive, progressive representation interface, we shed light on the best way to successfully convey the translated model to end clients. Uncommonly, the elucidation isolates the anticipated from the eccentric for stock forecast using block model parameters and a hazard representation structure. Third, we assess the coordinated perception framework through two contextual analyses in anticipating the stock cost with online budgetary news and friends related tweets from web based life. Quantitative tests contrasting the proposed neural system design and cutting edge models and the human gauge are led and detailed. Criticisms from a casual client contemplate with area specialists are abridged and examined in detail. All the examination results show the viability of DeepClue in finishing securities exchange speculation and investigation assignments.