Reviewerly/exHarmony huggingface dataset:
Improving The Assessment System Of Professional Competencies Of Future Teachers

The urgency of an issue is stipulated by the necessity to assure compliance of the professional training quality of pedagogical science graduates with the requirements of the occupational pedagogical standard. Do this requires the improvement of a content and assessing procedure of the professional competencies, ensuring effective implementation of the labour functions established by the occupational standard by future teachers.The research objective consists of conceptual baseline and technology of improving the assessment system of professional competencies of future teachers in the context of requirements of the occupational pedagogical standard. The key research methods were: retrospective analysis, pedagogical modeling, study of regulatory legal acts in the field of education, benchmarking methods; expert methods.The article substantiates the methodological aspects of a research outlined by the competency-based and system approaches to the assessment of professional competencies of future teachers in high school. There is a proposed technology for improving the assessment of professional competencies of future teachers at a higher educational institution. On the basis of the best practices of educational organizations of higher education – the participants of the Pedagogical education modernization project, the features of assessment of professional competencies of future teachers are established and presented in the educational programs. Based on the experts survey, there are the following recommendations for improving the assessment system of professional competencies of future teachers.The article matter can be useful for upgrading the content and quality assessment procedures for mastering basic professional educational programs related to the enlarged group "Education and Pedagogics".
Generating Natural Adversarial Hyperspectral examples with a modified Wasserstein GAN

Adversarial examples are a hot topic due to their abilities to fool a classifier's prediction. There are two strategies to create such examples, one uses the attacked classifier's gradients, while the other only requires access to the clas-sifier's prediction. This is particularly appealing when the classifier is not full known (black box model). In this paper, we present a new method which is able to generate natural adversarial examples from the true data following the second paradigm. Based on Generative Adversarial Networks (GANs) [5], it reweights the true data empirical distribution to encourage the classifier to generate ad-versarial examples. We provide a proof of concept of our method by generating adversarial hyperspectral signatures on a remote sensing dataset.
IMPLEMENTING OF ANALYTICAL STRUCTURAL METHODS SYNTHETIC (SAS) TO IMPROVE THE ABILITY OF READING BEGINNING OF ELEMENTARY SCHOOL STUDENTS

This research is motivated by the problem of the low ability to read the beginning of students, so that the SAS method is applied so that it can help students in the success of reading the beginning. The purpose of this study was to determine the differences in pretest and posttest by applying synthetic analytical structural methods (SAS) in reading the beginning of elementary school students and to determine the increase in pretest and posttest in reading the beginning of students. This type of research is a pre-experimental type of pretest-posttest one group design, which is an experiment that is only carried out in only one group, without comparison. This study was conducted in class 1 with 34 students. Based on the results of the study it was found that the average reading ability before being treated (pretest) was 51.77 while the average reading ability after treatment (posttest) was 88.41 and the average value of gain normality was 0.76 which including the high category and the results of hypothesis testing using t-test with tcount=51,080 and ttable=2,0345 can be concluded tcount&gt;ttable, then reject H0 means significant. This means that there is an increase in initial reading in class I students who are significant between before (pretest) and after (posttest) applying the Synthetic Analytical Structural (SAS) method.SAS method, read beginning, elementary school
Scientific programming with Python

This chapter offers a brief introduction to scientific programming with Python with an emphasis on some mathematical operations that will form the basis of many algorithms. This will specifically include working with matrices and convolutions. Python is a high-level programming language similar to Matlab and R that has gained increasing popularity in the machine learning community. The main reason this book uses Python is that it is freely available and now provides considerable support for machine learning with packages such as sklearn and Keras that are discussed and utilized in this book. Some familiarity with programming concepts is assumed, and the chapter concentrates on a brief introduction to the specific environment and supporting libraries used throughout as well as some basic operations such as convolutions that will be important in later algorithms.
LibGuides: Community Needs Assessment: Surveys

Gathering information and data about the community happens before the planning process begins.
A Review on Different Deep Learning Approaches For Semantic Segmentation

Image semantic segmentation is increasingly being of enthusiasm for computer vision and AI specialists. Numerous applications on the ascent need precise and productive segmentation components: automatic driving, indoor navigation, and augmented reality frameworks to give some examples. This interest corresponds with the ascent of profound learning approaches in pretty much every field or application target identified with PC vision, including semantic division or scene understanding. This paper gives a survey on profound learning strategies for semantic division applied to different application regions. Right off the bat, we depict the phrasing of this field just as obligatory foundation ideas. Next, the primary datasets and difficulties are presented to support specialists choose which are the ones that best suit their needs and their objectives. At that point, existing techniques are investigated, featuring their commitments and their centrality in the field. At long last, quantitative outcomes are given for the portrayed techniques and the datasets in which they were assessed, catching up with a discourse of the outcomes. Finally, we pay attention to many promises for future work and make our own decision related to best techniques for semantic segmentation using deep neural network.
Testing convexity of functions over finite domains

We establish new upper and lower bounds on the number of queries required to test convexity of functions over various discrete domains. 1. We provide a simplified version of the non-adaptive convexity tester on the line. We re-prove the upper bound [MATH HERE] in the usual uniform model, and prove an [MATH HERE] upper bound in the distribution-free setting. 2. We show a tight lower bound of [MATH HERE] queries for testing convexity of functions f : [n]ϵ → R on the line. This lower bound applies to both adaptive and non-adaptive algorithms, and matches the upper bound from item 1, showing that adaptivity does not help in this setting. 3. Moving to higher dimensions, we consider the case of a stripe [3] × [n]. We construct an adaptive tester for convexity of functions f : [3] × [n] → R with query complexity O(log2n). We also show that any non-adaptive tester must use [MATH HERE] queries in this setting. Thus, adaptivity yields an exponential improvement for this problem. 4. For functions f : [n]d → R over domains of dimension d ≥ 2, we show a non-adaptive query lower bound [MATH HERE].
Internet crime as a threat to children and adolescents

Universal access to the Internet - whose role in society in the last few years has become significant - enables the development of almost every field of life. It can be a tool for acquiring new knowledge or entertainment. However, the specificity of the Internet is also associated with certain risks. The purpose of this article is to identify these types of threats in relation to groups of children and young people. Research shows that Internet use may be associated with negative experiences, such as cyberstalking, cyberbullying or instigation to commit a crime. These phenomena can be a serious threat to people who are not fully aware of the risks associated with the use of the network. Experiencing them carries a serious threat to the mental health and integrity of the young person. For this reason, it is important to expand knowledge in the field of online crime, so that it is possible to identify such events early and prevent their consequences.
Improvement in video inpainting in presence of moving subjects

Inpainting or completion is used with the purpose of restoring damaged images and video frames. This paper proposes an applicable algorithm to inpaint corrupted subjects in video frames. To begin with, background and foreground (moving subject) are separated from each other in each frame, with the aim of getting to a more visually pleasant result. Static background inpainting is done using a patch-based method. To inpaint the corrupted moving subject, a subject-based method that is an improvement on the rigid object-based method is used in this study to consider the special issue of inpainting the human body. To fill in the holes created by occluding subjects the most appropriate template is found using a similarity measure which is based on both contour and pixel values. The inpainted video is acquired by superimposing the completed foreground on the inpainted background
A Study on Impact Of Sarcasm In Online Data on Customer Perception Using Data Mining

Sarcasm transforms the polarity of word or comment in positive or negative into its opposite meaning. It is very difficult to understand the intention of the person about the comment made online. The higher corporates are also interested in detecting the sarcasm so that they can know the customer perception and behavior about their products. This research will throw light on the impact of sarcasm in online tweets or comments which will help to know the intention of the customer. Furthermore, this will help the corporates to understand the problem occurred in sarcasm which will help the companies to gain knowledge about their product so that they can make better decisions and ultimatelyimprove business performance to forecast economic future of their organization
Localisation of Zones of Cancer Detection in Prostate Gland Using Ratio Matrix and Radial Scanning of 2D Trans-rectal Ultrasound Images;

Researchers have continued to proffer various solutions to the challenge of delineating from Trans-rectal ultrasound (TRUS) 2D-images of the prostate the regions of desired property. This paper presents an algorithm that categorises the detected regions suspected to be cancerous, hyper-echoic pixels, in the prostate gland from a 2D Trans-rectal Ultrasound images into three zones. The developed algorithm uses radial scanning of the pixels of the prostate gland image from common seed point both to detect and delineate the suspected cancerous pixels into zones, namely peripheral, transition and central, by applying ratios of the anatomical zones of the prostate gland. Expert knowledge, intensity and gradient features were implemented to delineate regions of interest. MATLAB programming tool was used for creating the codes that implemented the algorithms. Samples of TRUS 2D-images of the prostate for patients with raised PSA values (>10 ng/ml) used in a previous work by Award (2007) were used for testing the algorithm. The test results showed that the algorithm could detect zones of the prostate boundary exhibit image properties for cancer cells and also the percentage of malignancy detected in zones agreed with existing research findings. Comparison of detection results with that of an expert radiologist yielded the following performance parameters; accuracy of 88.55% and sensitivity of 71.65%.
Sentimental Analysis of Twitter Data Set using Machine Learning Techniques

Social media is a platform which signifies the viewpoint of people about government, product, and services. Classifying the views in term of positive or negative division from social media text is task of sentimental analysis. Twitter is one such online social networking website where people can post their views about something. This platform help people to share as well as found views on the topics which are going around the world. Twitter is a huge platform with more than 300 million users. So, tweets can be used as a resource for mining views of people. Process of classifying views as positive, negative or neutral is known as sentimental analysis. Saying people tweet about everything and nothing is not an overemphasis. We need a quicker identification method to classify the information that can be used for training in order to construct the systems to twitter sentimental analysis on any given topic. In this project, we progress a method for building such data using Twitter (for example: #excellent #Nature Lover #epic failure) to identify emotional messages that can be used to classify sentiment.
Infraestrutura organizacional necessária ao repositório arquivístico digital confiável: um diálogo com a ISO 16363

This study examines the Audit and Certification of Trustworthy Digital Repositories (ACTDR) and its relevance in the process of auditing Trustworthy Digital Archival Repositories (TDR-Arqs) with emphasis on aspects related to the organizational infrastructure. Therefore, the objective is to perform the synthesis of the audit requirements to be fulfilled by the TDR-Arq that follows the Open Archival Information System (OAIS) model. In addition, it is intended to clarify the archival requirements implied in the ACTDR. The methodology used is part of a bibliographical survey of previously published materials, which consists of the selection of books, technical publications and scientific articles retrieved through the Google Scholar research tool. Therefore, a review article, with a no systematic character, is able to describe and contextualize the organizational infrastructure requirements, as recommended by the ACTDR, within Archival science. Finally, this study provides insights to stimulate discussion and understanding of organizational infrastructure in the TDR-Arqs audit process. In this way, it reaffirms the convergence between OAIS, ACTDR and Archival science, which is in line with TDR-Arq.
davewiner contemporary marches seem to exist so participants feel better about themselves. Hard to find one that accomplished sonething.

@davewiner contemporary marches seem to exist so participants feel better about themselves. Hard to find one that accomplished sonething.
Strategy-Stealing Is Non-Constructive.

In many combinatorial games, one can prove that the first player wins under best play using a simple but non-constructive argument called strategy-stealing. This work is about the complexity behind these proofs: how hard is it to actually find a winning move in a game, when you know by strategy-stealing that one exists? We prove that this problem is PSPACE-hard already for Minimum Poset Games and Symmetric Maker-Maker Games, which are simple classes of games that capture two of the main types of strategy-stealing arguments in the current literature.
The logic of imaginary scenarios

Abstract Imagining is something we use everyday in our lives and in a wide variety of ways. In spite of the amount of works devoted to its study from both psychology and philosophy, there are only a few formal systems capable of modelling it; besides, almost all of those systems are static, in the sense that their models are initially predefined, and they fail to capture the dynamic process behind the creation of new imaginary scenarios. In this work, we review some influential theories of imagination and use their insights to distil an algorithm describing such process. Then, we use this algorithm to define a dynamic logical system built upon on a single-agent epistemic logic that provides the necessary tools to capture how the agent voluntarily creates new imaginary worlds; in other words, our system allows the model to be expanded dynamically at any time as a result of the agent performing an act of imagination. Furthermore, we provide an axiomatization and prove that the system is sound and complete.
Analysis and Development of Information Security Framework for Distributed E-Procurement System

This paper proposes an information security framework for distributed E-Procurement system in Indonesia. E-Procurement in Indonesia has been implemented since 2008, and has provided many benefits. However, there are also information security issues in the use of IT. Developing an information security program is needed to overcome the issues. We compare and analyze the LPSE and ISO 27001 Standards to develop framework. The results show there are some gaps between LPSE Standard and ISO 27001. By implementing the proposed framework, LPSE as a provider of distributed EProcurement system can be easier to implement the LPSE and ISO 27001 Standards simultaneously as an obligation to comply with government regulations
Efficient, Coercion-free and Universally Verifiable Blockchain-based Voting.

Abstract Most electronic voting systems today satisfy the basic requirements of privacy, unreusability, eligibility and fairness in a natural and rather straightforward way. However, receipt-freeness, incoercibility and universal verifiability are much harder to implement and in many cases they require a large amount of computation and communication overhead. In this work, we propose a blockchain-based voting system which achieves all the properties expected from secure elections without requiring too much from the voter. Coercion resistance and receipt-freeness are ensured by means of a randomizer token – a tamper-resistance source of randomness which acts as a black box in constructing the ballot for the user. Universal verifiability is ensured by the append-only structure of the blockchain, thus minimizing the trust placed in election authorities. Additionally, tallying the votes has linear overhead, hence our system is scalable and practical for large scale elections.
Anlysis and Design of Activity Degree Monitoring Algorithm

DAU (Daily Active User) is the number of daily active users, often used to reflect the operation of websites, Internet APPs and games [1]. DAU usually counts the number of users who have logged in or used a product (removing users who are repeatedly logged in) within one day (statistical day), which is similar to the concept of visitors (UV) in the traffic statistics tool. As we all know, the revenue source of some Internet applications lies in the revenue of advertising, and the amount of advertising revenue depends on the size of DAU. Therefore, the design strategy and algorithm to monitor the fluctuations of DAU can better help people analyze and improve our products, thus bringing improvements to the products. Therefore, this paper will design a variety of algorithm construction models to monitor the fluctuation of DAU, and achieve alarm announcement, analysis and location of the abnormal fluctuation of DAU, so as to explore of the value of DAU.
Traceable Inner Product Functional Encryption.

Functional Encryption (FE) has been widely studied in the last decade, as it provides a very useful tool for restricted access to sensitive data: from a ciphertext, it allows specific users to learn a function of the underlying plaintext. In practice, many users may be interested in the same function on the data, say the mean value of the inputs, for example. The conventional definition of FE associates each function to a secret decryption functional key and therefore all the users get the same secret key for the same function. This induces an important problem: if one of these users (called a traitor) leaks or sells the decryption functional key to be included in a pirate decryption tool, then there is no way to trace back its identity. Our objective is to solve this issue by introducing a new primitive, called Traceable Functional Encryption: the functional decryption key will not only be specific to a function, but to a user too, in such a way that if some users collude to produce a pirate decoder that successfully evaluates a function on the plaintext, from the ciphertext only, one can trace back at least one of them.
On Data Transferring speed among various Cloud Providers: An Experimental Study

Due to the rapid growth of data, individuals and enterprises are driving towards the cloud storage due to scalability, cost proficient, all time availability, accessibility, etc. The major challenges for the cloud storage service providers are to store the data efficiently and also to provide users high-speed data transfer and accessibility of data. Our aim in this paper is to compare various cloud storage service providers on the basis of time consumption in uploading and downloading the data of various types under the homogeneous conditions. Under this research an experiment was conducted on five cloud storage service providers: Google drive, OneDrive, Dropbox, PCloud and Sync. We have used t-test to compare the internet speed before and after the experiment to ensure the homogenous condition throughout the experiment. We have used Multivariate Analysis of Variance to compare the average uploading and downloading data time consumption on cloud among various cloud storage service providers. Here we have also done the POST-HOC Analysis Tukey’s test for pair wise comparison among various cloud storage service providers. Statistical analysis is done using the Statistical Product and Service Solutions (SPSS) software.
Classification and Comparison of Cloud Renderfarm Services for Recommender Systems

In India, some of the bollywood movies like the “Lord of the rings”; “Avatar” etc. are exemplifications of the magic that the Visual Effects (VFX) and the 3D animation techniques could create in a movie. In order to create this magic, the animation frames undergo the “rendering” process which takes a long time and contributes to long production time as it is a computationally intensive task. In order to decrease the production time and to satiate the high quality demand; the animation studios have resorted to the cloud renderfarm services. However, the animator’s community betrothed to use the services are baffled by the plethora of cloud renderfarm service models and need a classification framework and a recommender system to recommend appropriate services. This work classifies the cloud renderfarm service models by identifying three key characteristics of the services and their components based on which the services could be recommended. The three key characteristics identified are: (a) Functional characteristics, (b) Non Functional characteristics and (c) Cost. The classification helps to analyze and compare the cloud renderfarm services based on these three key characteristics and identify and recommend the suitable, cost effective services for a particular rendering job. The proposed classification framework is explained and verified using the analysis and comparison of 11 popular cloud renderfarm services which could be incorporated in building a recommender system for cloud renderfarm services.