THE IMPORTANCE OF BUSINESS INTELLIGENCE TOOLS IN THE DIGITAL TRANSITION ERA: THE PRINTRIA CASE

As a result of the digital transition, planned or imposed, it is important that companies introduce control tools that allow measuring, validating, and improving the operations performed automatically, by the various technologies involved in the processes. For SMEs the challenge is increased by the lack of available resources. The additional benefits that the introduction of these tools can bring are several and diversified, but there are also several challenges to their implementation. One of the main obstacles will be the time factor, which in this case covers several dimensions. We intend to demonstrate that in the first stage of the implementation process, the ChatGPT technology can be important in presenting these benefits and challenges to managers of SMEs as well as higher education institutions, increasing the training of future managers in business intelligence and data analysis platforms that are mostly open source and low/no code for cost reduction. We focus our attention on a small Portuguese company where the advances in the digital transition were imposed by the pandemic but now faces the challenges of uncertainty in the quality of its process data and has to make choices between the visible and invisible costs of implementing business intelligence tools.

Documentation Of Cultural And Spiritual Experiences In The Face Of The COVID-19 Crisis

Introduction and objective: Undoubtedly, employees face a variety of organizational problems and issues during the service period. Employees' decisions and actions in facing these problems and issues will be based on their knowledge and mental patterns. This study aimed to document and record the cultural and spiritual experience in the face of the COVID-19 crisis. Method: This research was an applied, inductive and exploratory-descriptive case study, which was conducted as a one-time crosssectional survey in a qualitative manner. Data was collected using documentary reviews and questionnaires. The statistical population of this study included managers, experts, and administrators of Baqiyatallah University located in Tehran, Iran. According to the qualitative approach of the research, purposeful sampling was used and data collection was continued until the required data was collected. Data analysis was performed in MaxQDA 2020 and Excel 2019 software. Results: The results showed a total of 282 open source codes. Code frequencies were as follows: 25 codes for event dimension, 54 for issue dimension, 61 for measures and decisions dimension, 71 for output-outcome dimension, 35 for suggestions dimension, 17 for scenario planning and modeling dimension, and 19 for lessons learned dimension. Conclusion: The results of this study can be used as a basis for managers to plan and implement experience documentation in cultural and spiritual areas in the face of the COVID-19 crisis. [ FROM AUTHOR] Copyright of Journal of Pharmaceutical Negative Results is the property of ResearchTrentz and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Teaching through Flipped Classroom and Learning Communities: Analisys of a Computer Programming Course during Pandemic

Due to the COVID-19 pandemic, education underwent an important change, forcing courses to follow an online modality. This article shows the case study of a Computer Programming course at the National University of Colombia, using the flipped classroom methodology, where students through an LMS could view content and perform exercises, and subsequently had synchronous sessions with teachers. Additionally, learning communities were created through the use of instant messaging that allowed interaction between students with classmates and teachers. The results and reflections obtained with the proposed methodology are presented from the review of the use of tools but also from student progress and perceptions. © 2022 IEEE.

Monitoring Programming Styles in Massive Open Online Courses Using Source Embedding

In recent years there has been an exponential growth of distance learning, provided by both public and private institutions. As a matter of fact, the number of students enrolled in courses delivered through the Network, has dramatically grown, also due to the COVID-19 pandemic, which has forced millions of people not to move. Consequently, more and more courses delivered in a remote modality have been attended by a huge number of people, producing an increasing number of Massive Open Online Courses (MOOC)s. These kind of courses are imposing new challenges for teachers, especially for monitoring and assessing the community learning processes. On the one hand, the learning assessment cannot be carried out based solely on closed-ended tests, while, on the other hand, teachers cannot evaluate thousands of open-Answer assignments: They should have at their disposition a set of tools helping them monitor the community learning progress. This paper investigates the possibility of using some of the Source Code Embedding techniques, to give teachers useful information about their learners' programming styles in Massive Open Online Courses. We propose a method to visualize each student's program, included the teacher's one, as a point in a 2-D space, using the doc2vec embeddings technique. Thanks to this representation, teachers can identify in the 2-D space groups of students having similar programming styles and reason on them to start a suitable didactic feedback. Moreover, teachers can reason on the relationship between each point compared to their own point as well, considered as the truth programming style. A first experimentation using Python as the programming language is performed with encouraging results. © 2022 IEEE.

Deep Learning Applied to Chest Radiograph Classification—A COVID-19 Pneumonia Experience

Featured ApplicationThe open-source deep learning algorithm presented in this work can identify anomalous chest radiographs and support the detection of COVID-19 cases. It is a complementary tool to support COVID-19 identification in areas with no access to radiology specialists or RT-PCR tests. We encourage the use of the algorithm to support COVID-19 screening, for educational purposes, as a baseline for further enhancements, and as a benchmark for different solutions. The algorithm is currently being tested in clinical practice in a hospital in Espírito Santo, Brazil.Due to the recent COVID-19 pandemic, a large number of reports present deep learning algorithms that support the detection of pneumonia caused by COVID-19 in chest radiographs. Few studies have provided the complete source code, limiting testing and reproducibility on different datasets. This work presents Cimatec_XCOV19, a novel deep learning system inspired by the Inception-V3 architecture that is able to (i) support the identification of abnormal chest radiographs and (ii) classify the abnormal radiographs as suggestive of COVID-19. The training dataset has 44,031 images with 2917 COVID-19 cases, one of the largest datasets in recent literature. We organized and published an external validation dataset of 1158 chest radiographs from a Brazilian hospital. Two experienced radiologists independently evaluated the radiographs. The Cimatec_XCOV19 algorithm obtained a sensitivity of 0.85, specificity of 0.82, and AUC ROC of 0.93. We compared the AUC ROC of our algorithm with a well-known public solution and did not find a statistically relevant difference between both performances. We provide full access to the code and the test dataset, enabling this work to be used as a tool for supporting the fast screening of COVID-19 on chest X-ray exams, serving as a reference for educators, and supporting further algorithm enhancements.

Code Protection Techniques when Distributed in Source Format: An Adobe Connect PodWritten in Javascript

The purpose of this article is to describe techniques for protecting code when distributed in source format. This situation occurs when, for instance, the client component of a web application, whose source code is easily extractable from the browser even by inexperienced users. The case study proposed uses the Adobe Connectc platform, an emerging technology in the field of video communication, content sharing and e-learning environments, which allows to easy integration of applications written in javascript language. The astonishing ease of realization of embedded applications within AdobeR's ecosystem contrasts with the impossibility of protecting the work done, which is visible and redistributable simply by copying the file containing it. The unwary author may thus run the risk of seeing his work thwarted by losing any intellectual property rights arising from the use of the software he has created. For this reason we have realized a form of intellectual property protection when software is distributed in source format. Copyright for this paper by its authors. Use permitted under Creative.

Dolos: Language-agnostic plagiarism detection in source code

Background Learning to code is increasingly embedded in secondary and higher education curricula, where solving programming exercises plays an important role in the learning process and in formative and summative assessment. Unfortunately, students admit that copying code from each other is a common practice and teachers indicate they rarely use plagiarism detection tools. Objectives We want to lower the barrier for teachers to detect plagiarism by introducing a new source code plagiarism detection tool (Dolos) that is powered by state-of-the art similarity detection algorithms, offers interactive visualizations, and uses generic parser models to support a broad range of programming languages. Methods Dolos is compared with state-of-the-art plagiarism detection tools in a benchmark based on a standardized dataset. We describe our experience with integrating Dolos in a programming course with a strong focus on online learning and the impact of transitioning to remote assessment during the COVID-19 pandemic. Results and Conclusions Dolos outperforms other plagiarism detection tools in detecting potential cases of plagiarism and is a valuable tool for preventing and detecting plagiarism in online learning environments. It is available under the permissive MIT open-source license at https://dolos.ugent.be. Implications Dolos lowers barriers for teachers to discover, prove and prevent plagiarism in programming courses. This helps to enable a shift towards open and online learning and assessment environments, and opens up interesting avenues for more effective learning and better assessment. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Efficient and Automated Deployment Architecture for OpenStack in TianHe SuperComputing Environment

Recently, with the large-scale outbreak of the global financial crisis and public safety incidents (such as COVID-19), high-performance computing has been widely applied to risk prediction, vaccine development, and other fields. In scenarios where high-performance computing infrastructure responds to the instantaneous explosion of computing demands, a crucial issue is to provide large-scale flexible allocation and adjustment of computing capability by rapidly constructing computing clusters. Existing large-scale computing cluster deployment solutions usually utilize source code deployment or other deployment tools. The great challenge of existing deployment methods is to reduce excessive image distribution time and refrain from configuration defects. In this article, we design an intelligent distributed registry deployment (IDRD) architecture based on the OpenStack cloud platform, which adaptively places distributed image repositories using the containerized deployment of multiple registries. We propose a server load priority algorithm to solve multiple registries placement problems in IDRD. Furthermore, we devise a clustering algorithm based on demand density that can optimize the global performance of IDRD and improve large-scale cluster load balancing capabilities, which has been implemented in the TianHe Supercomputing environment. Extensive experimental results demonstrate that IDRD can effectively reduce [Formula Omitted]-[Formula Omitted] of the distribution time of component images and significantly improve the efficiency of large-scale cluster deployment.