Reviewerly/exHarmony huggingface dataset:
EVALUATION OF PRE-PANDEMIC AND PANDEMIC EDUCATION FROM THE PERSPECTIVE OF A UNIVERSITY INSTITUTE STUDENTS

"The circumstances of the COVID-19 pandemic have brought a turbulent change to all levels of education around the world. Even in these cases, however, it is necessary to maintain accountability, i.e. the responsibility of educational institutions for both consequences and quantity and quality of their services. For this purpose, various evaluation processes are implemented across universities to identify and ensure the quality of teaching and learning. In this context, the paper deals with the first results of a comparison of higher education in both pre-pandemic and pandemic period, causing the closure of all types of schools and a complete, forced transfer to a distant, untested forms of education. In both monitored periods, the same research tool was used, namely an online evaluation questionnaire, in which the respondents had the opportunity to express their opinions on the lectures and seminars studied, on the conditions of teaching and learning, on the approach of the teacher, and on the overall assessment of the course. The target group were students of bachelor study programmes of a university institute in the Czech Republic. This study provides evidence that, although the pandemic struck without warning and very fast, the university institute concerned was able to cope with the necessary reforms in the organization of education, and from the students' point of view, while maintaining the quality of teaching provided. Even, the complete distance teaching and learning at the time of the pandemic was on average rated better than the pre-pandemic."
Age Estimation of Occluded Faces using EfficientNet-B3 CNN Model

Age estimation is an important task in different information systems’ security areas and has received a lot of research attention. Recently, SARS-CoV-2 pandemic and mask stipulations increased the difficulty of age estimation. This research gives a preliminary analysis of how wearing face masks affects the performance of age estimation systems. The EfficientNet-B3 convolutional neural network (CNN) model is used and was tested on the UTKFace, APPA-Real and FG-NET datasets.
Distributed, multi-domain option generation across legacy planners

This paper presents an effective pipeline for generating cross-domain plan options for Multi-Domain Operations through the coordinated use of legacy planning systems. We accomplish this by combining the intuitive interface of our Distributed InteRactivE C2 Tool (DIRECT) with the efficient distributed AI reasoning of our Multi-Domain Adaptive Request Service (MARS). Existing military planning processes are typically stove-piped and use a variety of manual and domain-specific legacy planning tools. This makes it challenging to generate a plan that draws on resources from different domains for different tasks, resulting in underutilized resources and unfulfilled tasks. MARS uses a marketplace approach to address this problem by conducting auctions that enable legacy planners to participate in a cross-domain planning process. Moreover, MARS provides a framework for developing new automated domain planners in cases with limited existing automated planning support. The process works for either pre-execution or in-execution planning. DIRECT provides an interface to the operator to navigate and compare these cross-domain plan options in the context of the original plan to provide transparency and build trust with the operator. Our experiments demonstrate the efficacy of the approach using a combination of our Air Planner and surrogate legacy planners for space and ground fires, as compared to stove-piped systems. For example, we are able to increase the percentage of requests fulfilled for sequencing strike and battle damage assessment effects from 38% using only single domain air assets to 54% when using the auction to draw in air, land, and space assets.
Generative adversarial neural networks augment marker and pathway analysis of treatment resistant HPV+ head and neck squamous cell carcinoma.

e18059 Background: Biological behaviors of cancers are extremely complex and governed via nonlinear interplay of thousands of genes. This complexity has become a major hurdle to generating mechanistic conclusions in rare clinical scenarios, such as treatment resistant HPV+ head and neck squamous cell carcinoma, when observations are limited. Recently, data augmentation algorithms have served to increase the number and diversity of training observations while preserving the underlying structure and nonlinear relationships of data features. Deep learning techniques such as generative adversarial neural networks (GANs) have shown promise as robust tools for data augmentation of nonimage data. This study aims to evaluate the utility of employing separate GANs to highlight gene expression differences between treatment sensitive (TS) vs treatment resistant (TR) HPV+ head and neck squamous cell carcinoma. Methods: We performed RNAseq on 17 patients with HPV-positive oropharyngeal squamous cell carcinoma (OPSCC). We defined TR patients as those with locally advanced OPSCC at presentation, who rapidly recurred following receipt of standard of care therapy (n = 11). In most cases, patients failed to respond to salvage therapies and rapidly died. In contrast, we defined TS patients as those with locally advanced OPSCC at presentation, who received standard of care therapy, and were followed for 5 years without any evidence of recurrence (n = 6). These two cohorts were used to train two separate GANs, which synthesized 500 robust synthetic samples of each phenotype for downstream analysis. Results: Analysis of 17 samples yielded less than 100 differentially expressed (DE) genes passing FDR cutoffs, inhibiting pathway enrichment analysis (PAE). DE testing between synthetic samples yielded 1196 DE genes. Validating our deep learning approach, these genes belong to coherent pathways as determined by PAE. Namely, the VEGFA-VEGFR2 pathway was strongly enriched in the TR population (p = 1.5 x 10 -4 ). Further, the NKT cell signature (p = 3.0x10 -62 ), and the gene library of T-cell receptor induction of apoptosis (p = 5.4 x 10 -9 ), was enriched in the TS population. Lastly, to validate the experimental method, DE testing on GAN augmented cohorts of renal papillary vs renal clear cell samples from The Cancer Genome Atlas, trained on 10 samples per condition, reliably predicted DE genes when comparing the full cohort of renal papillary n = 534 vs renal clear cell n = 295 samples, with an accuracy of 93% and F1 score of 88%. Conclusions: To our knowledge this is the first study to utilize separate GANs to augment structural gene expression changes occurring in rare clinical scenarios such as treatment resistant HPV+ OPSCC. Utilizing this method uncovers changes in angiogenesis and T-cell function between treatment sensitive and resistant HPV+ OPSCC.
Egeon: Software-Defined Data Protection for Object Storage

With the growth in popularity of cloud computing, object storage systems (e.g., Amazon S3, OpenStack Swift, Ceph) have gained momentum for their relatively low per-G B costs and high availability. However, as increasingly more sensitive data is being accrued, the need to natively integrate privacy controls into the storage is growing in relevance. Today, due to the poor object storage interface, privacy controls are enforced by data curators with full access to data in the clear. This motivates the need for a new approach to data privacy that can provide strong assurance and control to data owners. To fulfill this need, this paper presents Egeon, a novel software-defined data protection framework for object storage. Egeon enables users to declaratively set privacy policies on how their data can be shared. In the privacy policies, the users can build complex data protection services through the composition of data transformations, which are invoked inline by Egeon upon a read request. As a result, data owners can trivially display multiple views from the same data piece, and modify these views by only updating the policies. And all without restructuring the internals of the underlying object storage system. The Egeon prototype has been built atop OpenStack Swift. Evaluation results shows promise in developing data protection services with little overhead directly into the object store. Further, depending on the amount of data filtered out in the transformed views, end-to-end latency can be low due to the savings in network communication.
Analisis Multidimensional Profil Mahasiswa Studi Kasus : Program Studi Sistem Informasi UKDW

In teaching and learning activities, analysis of students is needed. This is done to determine the proper way of learning. The right way of learning can increase the motivation of students. If the motivation of students increases, the academic value will also increase. One way that can be done is to classify students based on predetermined categories. The UKDW SI Study Program does not yet have a system that can classify the categories of students. This research was conducted to answer the above problems. Then machine learning will be built, which can automatically determine the categories of students. The method used to classify students is the Support Vector Machine (SVM). SVM has the advantage that it can be applied to cases that have high dimensions. The conclusion from this research is that the SVM method is very appropriate to be implemented in this study. This is evident in the machine learning model accuracy test on the system, which is 92.3%. With the existence of machine learning to classify students, teachers make it easier to do analysis. So that it is expected to provide an overview of the appropriate learning methods to be applied to students.
Textural and Paleoenvironmental Characterization of the Campano-Maastrichtian Patti Sandstone, Southern Bida Basin, Northcentral Nigeria

Field and granulometric investigation of Patti Sandstone were carried out with a view towards establishing the lithological sequence, geological processes, textural characteristics and paleoenvironment of deposition of the sediments. Field study entailed basic field procedures and sedimentological descriptions which encompass physical factors such as textural parameter, composition, colour and structures; biological inference in terms of fossil content and chemical character in terms of chemical composition and post depositional diagenesis effect. Granulometric laboratory procedure includes weighing and sieving of samples with set of sieves; calculated cumulative percentages, software input of data to generate statistical parameters such as mean, standard deviation (sorting), skewness, kurtosis and ternary diagrams. Field observations show that the lower part of the outcrop section has alternation of claystone and shale; the mid-section shows coarsening-up sequence of shale, clay and sandstone facies of a retrogradational system, while the upper part is composed of intercalation of claystone and sandstone in repetitive pattern, various thicknesses and colours. The textural characteristics of the sandstone are generally coarse-fine grains, reddish to greyish in colour, rounded to subrounded and moderately to well sorted. Structurally, the outcrops are planar cross bedded, wavy laminated and concretional. Granulometric result categorized the sandstones into fine, medium and coarse grained particles; well sorted to moderately well sorted, characterized by lateral retrogradational sequence from the shoreline. The skewness varies from fine skewed, very fine skewed and symmetrical. Kurtosis values suggest multi-sourced particles deposited in marine and fluviatile paleoenvironments.
Surface 3D Scanner Using Time of Flight Ranging Sensor with Cylindrical Coordinate System

&lt;p class="TTPParagraphothers"&gt;3D scanner that uses image sensors requires the role of a computer that includes a data generator, data acquisition, and visual display. In a simply system, it can be designed the sensory system uses non-imagery sensor so the role of the data generator can be handled by the microcontroller. This research aims to make a simple 3D scanner using inexpensive non-imagery Time of Flight VL53L0X sensor and data processing can be processed directly by the microcontroller. The results of sensor distance measurements are processed on the microcontroller and desktop application. The distance and angle values are converted into Cartesian coordinate using cylindrical coordinate system. The scan results of the cubes, prisms and cylinder are similar with the reference object, but the results of the pyramid test at the top cannot be scanned properly due to the narrow surface. The laser beam from the emitter cannot bounce back to the collector properly makes distance reading is inaccurate and causes error in the point cloud conversion. The comparison error between the side of the scan results and the reference object is between 2.54-39.8%. The surface of objects with bright color has a smaller error than those with dark color. The comparison error of the height of the scan results with the reference object is between 5-32%. The angle of the emitter exclusion cone and the collector exclusion cone sensor affects the error at the side and height of the scan results.&lt;/p&gt;
Invitin Project: Scrum Framework Implementation in a Software Development Project Management

The printed invitation is commonly used in our community to invite others to special events. In Indonesian society, this has happened inviting someone from door-to-door by sending printed invitations. Due to rapid information technology development, we proposed a website-based online invitation platform, Invitin. In general, the Invitin project has three main actors, which are customer, customer service, and admin. The methodology that we used was based on the SDLC (Software Development Life Cycle) and applied Scrum framework. Implementing the Invitin project using Scrum helps to organize development tasks easier and provides other benefits in some cases. Thus, Scrum increases our team productivity to develop a high-quality dynamic website application.
GOES: GO EMERGENCY SERVICE, ANDROID-BASED SYSTEM AS A LABOR SAVING EFFORT IN REMOTE AREAS WITH NETWORK REACH

Maternal mortality occurs because they do not have access to quality health services, especially emergency health services on time which are motivated by being late in recognizing danger signs, being late in making decisions, being late in reaching health facilities, and being late in getting services at health facilities and influenced by direct causes such as obstetric complications. 90%) is known as the classic triad (bleeding, infection and preeclampsia, or complications during pregnancy, birth, and postpartum that are not handled properly and on time). Many Indonesians live in remote areas, unlike people who live in urban areas, where access is easy to get medical assistance for delivery if they experience contractions and/or during labor, while people who live in remote areas must take a long time and long distances to get health services, so it is undeniable that many still use village shamans or village shamans as the closest alternative to getting assistance during childbirth. The purpose of this program is as an effort to save childbirth in remote areas that can make it easier for people who are far from the facility to get help more quickly during childbirth and are assisted by midwives who are experts in their fields. The method used in this activity is to create a framework where the framework will outline the sequence to be implemented. Based on the results of the activity, using the GOES application makes it very easy to access health services that are fast and appropriate for mothers and babies to more easily get help from medical personnel who are experts in their fields.
Applying Data Mining Clustering on Web Server Logs to Identify and Analyze Robots’ Behavior

Web servers logs have a large amount of data, which can be explored in order to find access patterns and understand the behavior of users and robots. These robots generate several requests and can compromise the server. Therefore, it is important to identify their requests and take measures to mitigate possible problems. Given this scenario, the objective of this study was to use the Data Mining Clustering task to analyze web server logs, identify requests made by humans and robots, and analyze robots’ behavior. For this purpose, the K-means, DBSCAN, and Agglomerative algorithms were used. Although the algorithms generated different numbers of clusters, it was observed that three clusters contain Bot sessions with similar characteristics: requests with an empty referrer, few requests to Javascript and CSS, and low image per page ratio. It is concluded that the process of labeling the sessions must be carried out with criteria and can be improved based on the analysis of the generated clusters.
Educational Innovations: Formation of the Categorical-Conceptual Apparatus, Stages and Laws of Flow

Modern society is distinguished by ongoing innovation processes, since innovation activity is a necessary condition for economic growth and improving the quality of life in any country. In this regard, education has not bypassed innovative activity. This article discusses the retrospective of the formation of the concept of «innovation». The progenitor of the definition of «innovation» was the term «novation». For the first time, the concept of «novation» appeared in legal documents of the 13th century. and denoted the obligations of the parties to replace one agreement they signed with another, which did not imply the invention of something, but simply novelty. As a self-sufficient term, the definition of «innovation» began to assert itself in the 19th century, in connection with the development of science, technology, and the industrial revolution. As a scientific term, the concept of «innovation» received its purpose at the beginning of the 20th century in the works of the Austrian economist Joseph Alois Schumpeter, which was the result of an analysis of «innovative combinations», changes in the development of economic systems. With the development of the economy, the definition of «innovation» has expanded its boundaries of understanding. Since the end of the 1950s, innovation processes in pedagogy have also been studied. In relation to the pedagogical process, innovation means the introduction of innovation in the goals, content, methods, forms of education and upbringing, as well as in the organization of joint activities of the teacher and students. There are four laws of the flow of innovation processes: irreversible destabilization of the pedagogical innovation environment; final implementation of the innovation process; stereotyping of pedagogical innovations; cyclic repeatability, recurrence of pedagogical innovations.
Cybercomplexity Root Causes

AbstractThe objective of cybersecurity risk management is to identify and address the risk factors for information compromise or information-related business disruption via the application of security controls in accordance with an organization’s tolerance for risk.
Fundamentals of Machine Learning and AI

You learned about AIOps, its architecture, and its components in previous chapters, and you will be venturing deeper into AIOps in the next few chapters. Before you start implementing algorithms for AIOps, though, you need to learn a few basic things about machine learning and artificial intelligence. This chapter will cover the fundamentals of artificial intelligence, machine learning, and deep learning. It will then go on to discuss specific techniques that are used in AIOps.
Exploiting Inherent Elasticity of Serverless in Irregular Algorithms

Serverless computing, in particular the Function-as-a-Service (FaaS) execution model, has recently shown to be effective for running large-scale computations, including MapReduce, linear algebra and machine learning jobs, to mention a few. Despite this wide array of applications, little attention has been paid to highly-parallel applications with unbalanced and irregular workloads. Typically, these workloads have been kept out of the cloud due to the impossibility of anticipating their computing resources ahead of time, frequently leading to severe resource over- and underprovisioning situations. Our main insight in this article is, however, that the elasticity and ease of management of serverless computing technology can be a key enabler for effectively running these problematic workloads for the first time in the cloud. More concretely, we demonstrate that with a simple serverless executor pool abstraction, a data scientist can achieve a better cost-performance trade-off than a Spark cluster of static size built upon large EC2 virtual machines (VMs). To support this conclusion, we evaluate three irregular algorithms: the Unbalanced Tree Search (UTS), the Mandelbrot Set using the Mariani-Silver algorithm, and the Betweenness Centrality on a random graph. For instance, our serverless implementation of UTS is able to outperform Spark by up to 55% with the same cost. We also show that a serverless environment can outperform a large EC2 VM in the Betweenness Centrality algorithm by a 10% using the same amount of virtual CPUs. This provides the first concrete evidence that highly-parallel, irregular workloads can be efficiently executed using purely stateless functions with almost zero burden on users — i.e., no need for users to understand non-obvious system-level parameters and optimizations. Furthermore, we show that UTS can benefit from the FaaS' pay-as-you-go billing model, which makes it worth for the first time to enable certain application-level optimizations that can lead to significant improvements (e.g., of 41%) with negligible increase in cost.
The Effects of Job Autonomy, Learning Culture, and Organizational Cynicism On Learning Transfer in MBA

Purpose:To propose and test a model of learning transfer in MBA, assessing the influence of job autonomy, learning culture, and organizational cynicism on learning transfer.Theoretical Framework: Based on the taxonomy of use of Yelon, Ford, and Bhatia (2014), we developed and tested a learning transfer scale structured in five dimensions: perform, assess, explain, instruct and lead. Methodology:We conducted a quantitative study with 306 students in the concluding phase and graduates of MBA courses of Brazilian business schools.The data were analyzed using partial least squares structural equations modeling (PLS-SEM). Results:The results confirmed that learning culture influences learning transfer, while job autonomy and organizational cynicism do not influence it.In addition, we analyzed the influence of five control variables and two of them had weak but significant effects on learning transfer: age and job position of the student. Practical and Social Implications of theResearch: Despite the increase in academic research on learning transfer in recent decades and the growing investments in training and development (T&D) actions by companies at a global level, there is a lack of studies involving long-duration educational programs.So, this study presents an opportunity to investigate how MBA students transfer what they have learned in the course to the work context.Contributions: This study contributes to the theory by proposing and testing a multidimensional learning transfer scale.The results also revealed that students