Reviewerly/exHarmony huggingface dataset:
Международная платформа iScience4All: интеграция в единое цифровое пространство научных знаний стран-членов МЦНТИ

The article describes the possibilities and prospects of integrating the international platform iScience4All into a single digital space of scientific knowledge of the member countries of the International Center for Scientific and Technical Information (ICSTI), the uniqueness and advantages of this project, its contribution to the transformation of the digital economy, as well as to the formation of a digital ecosystems of scientific and technical information of the ICSTI community.
Hyperparameter Tuning for Overlapped Software Defect Prediction Data sets

The machine learning algorithms has become increasingly widely in many applications andresearch. The classifiers in machine learning build a model from data that allows computers toimprove future predictions.Despite the popularity of most machine learning algorithms require knowledge to take the decisionsabout the appropriate model and parameter settings for a particular domain of problem. It’s verydifficult to identify the machine learning classifier that is most well suited for specific characteristicsof data.In this work, we present a method to tune the hyperparameters of best machine learning algorithmfor a overlapped software defect data-set. The results obtained shows that using the machine learningalgorithm and hyperparameter tuning suggested by our method improves the predictive performanceof a classifier than its default settings on overlapped data-sets
A teacher training program in argentina analysed by profiles

In this article, we analyse a 400-hour teacher training program conducted in Argentina targeted to in-service K-12 teachers with no CS background. We research if a single program can be effective for teaching CS contents and pedagogy to teachers with very heterogeneous profiles and also what they expect to do with the contents they learn. To assess these questions anonymous examinations and questionnaires were given and interviews were conducted with program attendees. When analysing the results by profile, we found that the majority reached the expected minimum bar regarding CS contents and pedagogy, but strong differences appear in their self-perception as regards career opportunities in CS teaching.
Талабаларнинг аргументатив компетентлигини ривожлантиришда бахс-мунозара методининг ахамияти

This article discusses the importance of using debate as a teaching method in mastering key technologies to improve students’ argumentative competence. At the same time, the ability to argue constructively is interpreted as the ability to perform various argumentative actions to substantiate or refute certain points of view. The article presents the characteristics of argumentative skills, as well as the conditions for the formation of logical and psychological components of debate, which is one of the specific manifestations of argumentation.
A Survey on Extractive and Abstractive Text Summarization

In recent years, the volume of text data from a variety of sources has boomed. This amountof text is an important source of information and knowledge that needs to be summarised efficientlyfor use. Extraction and abstraction are the two separate automated description methods. Extractivetext summarization is based on the principle of sentence salience in order to recognise the mostrelevant phrases in a document or document collection. Usually, salience is defined in terms of theexistence of specific important words, or in terms of similarity to a pseudo-sentence centroid. Thispaper presents a systematic survey of the new extractive ways to deal with text summarizationdelivered in the most recent decade. There are likewise a couple of abstractive and extractive ways todeal with summarizing text. Another overwhelming challenge in this context is summary evaluation.Consequently, both natural and outward strategies for summary evaluation are characterized top tobottom alongside gatherings and workshops on text summary evaluation
Visual narrative viewers’ event models have greater effects on their attention in Slideshows than Films

Viewers’ attention in film is strongly driven by stimulus features (e.g., motion), while comprehension processes (e.g., the viewer’s event model), have relatively minimal influence, which we call the "Tyranny of Film". Conversely, in picture stories, viewers’ event models have a larger impact on attentional selection. Thus, does removing motion, but maintaining the other filmic features (e.g., shot composition), attenuate the "Tyranny of Film", thereby increasing the influence of comprehension processes on visual selective attention? Or, is removing motion insufficient to allow viewers’ event models to guide attentional selection? This study used the opening scene of "Touch of Evil". The scene shows a time-bomb put in a car, a couple unknowingly gets in the car and drives away, and the scene ends just before the bomb explodes. Previous work using this clip showed few effects of viewers’ event models on attention--supporting the "Tyranny of Film". This eye-tracking study removed motion by presenting the narrative as a slideshow of single frames from the film, while maintaining the narrative content and viewing duration. Context participants watched the full slideshow. No-context participants didn’t see the bomb or the couple in the car, instead started later when another couple was shown walking down the street. At the end of the slideshow, we asked participants to predict what would happen next. Context participants were more likely to infer the bomb would explode, showing a critical difference in their event models. Critically, Context participants were more likely to fixate the car throughout the shared viewing period, breaking the "Tyranny of Film". Nevertheless, there were no significant effects of inference generation on this effect. Thus, the Context participants’ greater looks at the car were likely due to treating the couple in the car as protagonists/agents, rather than maintaining the bomb in their event models.
Keberterimaan Mahasiswa Jurusan Akuntansi terhadap Pembelajaran Online pada Masa Pandemic Covid-19

Nowdays, world was shocked by a corona virus, then WHO established this condition as a pandemic. This condition has a major impact on various sectors, as well as education. Therefore Minister of Education and Culture of the Republic of Indonesia released decreet about The learning system through online learning is a personal computer (PC) device connected to an internet. The change of learning process, has an impact to student accepetence.This research is explanatory research, to explain the causal relationship between the research variables and test the hypotheses that were formulated. Based on the TAM Model developed by Davis in 1989, testing the user's acceptance of an information system technology. The population used is Accounting Department student’s class of 2019-2020 Politeknik Negeri Ujung Pandang. Purposive sampling is used for sampling. The data collection technique used the distribution of questionnaires with 150 samples. The result was tested by SEM PLS.The results of this study indicate that Accounting Department students accept online learning well. This indicated by all the hypotheses in this study which are accepted. Accounting Department students who were sampled in this study were generation Z who were sensitive to technology.
Coronavirus Mirrored in Weibo: SNS Big Data Analytics of COVID-19-related Keywords

This paper investigates some of dominant linguistic and social trends of China in the context of COVID-19, through a comprehensive analysis of vocabulary usage patterns on Weibo, the biggest SNS platform in China.First, we introduce generative and informatic features of Weibo to highlight its distinctive characteristics from other media. Next, based on the actual utterances of 516 million Weibo users, we examined the individual linguistic expressions and collective mentality of Chinese people under COVID-19. In detail, we established COVID-19 Weibo big data which consists of 136,585 Weibos of 22,985,364 characters, and gave a qualitative and quantitative analysis from four different functional perspectives - information seeking, emotional release, citizenship behavior and social connection. In this analysis, we clearly demonstrated some widespread linguistic and cultural phenomena, such as the spread process of Chinese naming for COVID-19 and ‘GuanZhuangBingDu’ being widely used, broad textual tendency to avoid negative emotional expressions and prefer conceptual metaphors, and common types of social connections through hashtags.
Minerals Inventory as a part of Mineral Intelligence for Europe

&amp;lt;p&amp;gt;A significant task of the Mintell4EU project is to improve the quality and spatial coverage of the European inventory of primary and secondary mineral resources. The process of refining the minerals inventory that is currently within the Minerals4EU (M4EU) database includes (a) identification of data gaps in spatial coverage, (b) setting up the quality control application to identify gaps and errors in data, (c) identification of technical errors in the process of harvesting data and (d) establishing connections with other relevant projects.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;After almost 2 years, the spatial coverage of the minerals inventory is extended with data from Western Balkan countries in cooperation with the RESEERVE project and new or modified data from existing data providers are available. Besides, a Mintell4EU Quality Control Application (QCA) was developed to visually check the latest reported data from data providers. Related to this, a harvesting system for collecting and validating mineral resources data is being established and improved access to raw materials data technical routines intervening during the harvesting phase are being implemented to ensure a rigorous control of the data quality.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The subsequent task is to enable new data providers to deliver data in a harmonized way to ensure consistency in the way data is displayed. In order to achieve this in the best way possible, a workshop will be held in Ljubljana in May 2020, drawing on experiences from REESERVE and from guidelines developed by the ORAMA project.&amp;lt;/p&amp;gt;
Mental geometry of 3D size estimation in obliquely viewed pictures.

A 3D object seen from different views, forms quite different retinal images. There exists a single viewpoint from which a photograph forms the same retinal image as the physical 3D scene, but other viewpoints generate distorted retinal images of the scene. Koch et al (PNAS 2018) showed that humans are almost perfect at estimating 3D poses in real scenes by using the optimal geometrical back-transform from retinal orientation to 3D pose, albeit with a systematic fronto-parallel bias. In oblique views of pictures, the scene is perceived as rigidly rotated to the observer’s viewpoint, consistent with their using the same observer-centered back-transform as for real scenes. However, oblique views do lead to changes in perceived shape and sizes. We showed previously (VSS 2019) that size inconstancy is perceived in 3D scenes despite observers using the correct geometric back-transform, if the retinal image evokes a misestimate of viewing elevation. Now we examine 3D size estimation in oblique views of pictures. We presented 4 different oblique views of pictures of 3 sizes of rectangular parallelepipeds lying on the ground in 16 poses each. 6 observers adjusted the height of a view-invariant orthogonally attached narrow cylinder to equate the physical lengths of the two limbs. 3D sizes at fronto-parallel poses were seriously underestimated in oblique views compared to the frontal view. By contrast, there was almost no change for objects perceived as pointing to or from the viewer. Observers’ corrections for size, as a function of pose, were modeled with the optimal geometric back-transform, subject to a systematic underestimation of the tilt of the display, which was confirmed by perceived display tilt measurements. The excellent fit of the model shows that observers use the back-transform from projective geometry, but underestimate the tilt of the display, similar to the fronto-parallel bias for object pose perception.
Saliency Map Predictions of DeepGaze II are Influenced by the Convolutional Neural Network Texture Bias

Though line drawings depict edges of objects and not texture nor colour, which is typically present in the natural environment, humans can recognize scenes depicted in line drawings just as well as those in colour photographs. It has been shown recently that most convolutional neural networks (CNNs) rely on texture more than they rely on edges and shapes of the objects depicted in an image. But what are the effects of these model constraints on modelling visual attention? Here we show that, like humans, a leading CNN-based model of spatial attention, DeepGaze II, generalizes well between photographs and edge-extracted images. Seemingly innocuous low-level changes, however, such as reversing the contrast polarity of the edge-extracted images, cause vastly different predictions by the attention model. This is not the case for human observers, since contrast polarity reversal maintains the structure and global properties of the objects within an image. These results provide further evidence of the reliance of CNNs on texture-based visual information for generating its predictions. To further explore these questions, we recorded eye movements of participants viewing edge-extracted versions of the images from the MIT1003 dataset, which serves as ground truth for DeepGaze II. Comparisons of predicted gaze maps generated from line drawings and from photographs with human fixations on line drawings and photographs showed that both humans and the model generalize well between those drastically different representations of the scenes. Changing contrast polarity of the drawings, on the other hand, drastically changed the predicted gaze maps. Our unique eye movements data set and analysis procedures allow us to further explore the limitations of the CNN texture bias, and to further investigate the capacity of CNNs to learn global shape representations as they apply to directing visual attention.
Identification Of Diseases In Rice Plant Using Chatbot With Methode Artificial Intelligence Markup Language and Normalization

&lt;em&gt;Information Services in agriculture are entering the era of industrial revolution 4.0, always associated with the use of automation machines integrated with the internet network. The technological sophistication of this era makes many conditions change. The chatbot application is one of the right solutions to solve farmer problems, this farmer chatbot application is about the information on handling rice plants, and this application uses the Artificial Intelligence Markup (AIML) method. The purpose of this study was to test the accuracy of the answers to the chatbot. This research method uses question data with words under 5 words and above 5 words, and uses question data according to keywords and outside keywords in this chatbot, with 50 question data, with each question data tested four times than taken the average. average. The results of this study are to get an accuracy of 90.9%, while the response time for answering questions of less than 5 words is 0.01 seconds, and for more than 5 words is 0.02 seconds with a data set of 1000 lines.&lt;/em&gt;
Dynamic Epistemic Logic with ASP Updates: Application to Conditional Planning.

Dynamic Epistemic Logic (DEL) is a family of multimodal logics that has proved to be very successful for epistemic reasoning in planning tasks. In this logic, the agent's knowledge is captured by modal epistemic operators whereas the system evolution is described in terms of (some subset of) dynamic logic modalities in which actions are usually represented as semantic objects called event models. In this paper, we study a variant of DEL, that wecall DEL[ASP], where actions are syntactically described by using an Answer Set Programming (ASP) representation instead of event models. This representation directly inherits high level expressive features like indirect effects, qualifications, state constraints, defaults, or recursive fluents that are common in ASP descriptions of action domains. Besides, we illustrate how this approach can be applied for obtaining conditional plans in single-agent, partially observable domains where knowledge acquisition may be represented as indirect effects of actions.
Organizational and managerial solutions for online (distance) interaction in the educational process at the school

Introduction. The digitalization of education in the context of a pandemic served as an important factor in the transformation of traditional forms of communication, transferring all the variety of interactions between participants in educational relations into a distance format. In a situation of variability and uncertainty, the problem of finding effective organizational and managerial solutions to fulfill the tasks facing the school with the use of modern distant technologies has become keener. Research methods. To identify organizational and managerial decisions, to assess their effectiveness, a set of methods was used: questioning of teachers based on the research of Heldt, Lorenz and Eickelmann; statistical methods in the analysis of personal data (mean, standard deviation), a method for comparing the design of distant interaction between educational institutions in Russia and Germany. Results. Organizational and managerial solutions have been identified that ensured effective communication in solving the problems of the gymnasium in a distant format; it was found that the effectiveness of remote work is provided by a set of management decisions aimed at providing different types of support – technical, pedagogical, organizational; substantiated that the development and adoption of a media concept, regulations, instructions, recommendations for the organization and implementation of digital communication, the creation of unified media channels in the interaction systems "students – teacher", "parent – teacher", "teacher – school administration", "parent – school administration”, the participation of teachers in advanced training programs in aggregate ensured the solution of a complex of problems in the context of an aggravated epidemiological situation and in order to preserve health; defined the forms of corporate digital dialogue: exchange of effective communication practices, a system of support and mutual assistance, mutual training.
LibGuides: KCU Young Library Policies and Procedures During Covid-19: Using the Copier

This guide will help you determine how to access Young Library resources and assistance during the Covid-19 Pandemic. Keeping copier use Covid safe
LibGuides: Free Cataloging Resources: Classification

A collection of free cataloging resources for New Mexico libraries, librarians and library staff Classification resources for subject cataloging, LC call numbers and Dewey Decimal call numbers
DATABASE BACKUP AND RECOVERY MANAGEMENT IN CLOUD

Managing large chain of hotels and ERP database comprises of core areas such as HRMS & PIP.HRMS (Human Resource Management System), which further includes areas such as Soft Joining, Promotion, Transfer, Confirmation, Leave Attendance and Exit, etc. PIP (Payroll Information Portal), wherein employees can view their individual Salary details, submit investment declaration, Reimbursement claim & CTC structuring, etc. Management of Large Chain of Hotels and ERP Database in AWS Cloud involves continuous monitoring with regards to the areas such as Performance of resource usages and optimization techniques relating to the use of PL/SQL. High Availability (HA) of data is accomplished through the Backup and Recovery mechanism and security of the data by Encryption & Decryption mechanism.
Reflections on the Lack of Adoption of Domain Specific Languages

Given all the different benefits that domain specific languages are reported to produce, why are they not a widely adopted practice? The essence of the question has roots in industrial experience of the two authors of this report, but it was put out as a discussion starter at OOPSLE 2020. During the discussion session itself, there were some possible reasons voiced and possibly related concerns expressed. In this report, we try to condense those in a short coherent text. Not claiming any generality and fully acknowledging the anecdotal nature of our evidence, we still think that such a conversation is useful to have within the SLE community.
OptiLIME: Optimized lime explanations for diagnostic computer algorithms

Local Interpretable Model-Agnostic Explanations (LIME) is a popular method to perform interpretability of any kind of Machine Learning (ML) model. It explains one ML prediction at a time, by learning a simple linear model around the prediction. The model is trained on randomly generated data points, sampled from the training dataset distribution and weighted according to the distance from the reference point - the one being explained by LIME. Feature selection is applied to keep only the most important variables. LIME is widespread across different domains, although its instability - a single prediction may obtain different explanations - is one of the major shortcomings. This is due to the randomness in the sampling step, as well as to the flexibility in tuning the weights and determines a lack of reliability in the retrieved explanations, making LIME adoption problematic. In Medicine especially, clinical professionals trust is mandatory to determine the acceptance of an explainable algorithm, considering the importance of the decisions at stake and the related legal issues. In this paper, we highlight a trade-off between explanation's stability and adherence, namely how much it resembles the ML model. Exploiting our innovative discovery, we propose a framework to maximise stability, while retaining a predefined level of adherence. OptiLIME provides freedom to choose the best adherence-stability trade-off level and more importantly, it clearly highlights the mathematical properties of the retrieved explanation. As a result, the practitioner is provided with tools to decide whether the explanation is reliable, according to the problem at hand. We extensively test OptiLIME on a toy dataset - to present visually the geometrical findings - and a medical dataset. In the latter, we show how the method comes up with meaningful explanations both from a medical and mathematical standpoint.
406TF A RASHonal Approach to the Diagnosis of Dermatologic Emergencies

Dermatologic complaints are common presentations to the emergency department. Though most causes of rash are benign, it is critical that emergency physicians be able to recognize the dermatologic manifestations of critical illness. In 1984 Lynch et al proposed an algorithmic approach to the identification of dermatologic processes, and since then several modified Lynch algorithms have been developed and expanded. This learning module will introduce learners to an established series of algorithms designed to organize and narrow the differential diagnosis of serious rash.