Reviewerly/exHarmony huggingface dataset:
Influence of body visualization in VR during the execution of motoric tasks in different age groups

Virtual reality (VR) has become a common tool and is often considered for sport-specific purposes. Despite the increased usage, the transfer of VR-adapted skills into the real-world (RW) has not yet been sufficiently studied, and it is still unknown how much of the own body must be visible to complete motoric tasks within VR. In addition, it should be clarified whether older adults also need to perceive their body within VR scenarios to the same extent as younger people extending the usability. Therefore, younger (18–30 years old) and elderly adults (55 years and older) were tested (n = 42) performing a balance-, grasping- and throwing task in VR (HMD based) accompanied with different body visualization types in VR and in the RW having the regular visual input of body’s components. Comparing the performances between the age groups, the time for completion, the number of steps (balance task), the subjective estimation of difficulty, the number of errors, and a rating system revealing movements’ quality were considered as examined parameters. A one-way ANOVA/Friedmann with repeated measurements with factor [body visualization] was conducted to test the influence of varying body visualizations during task completion. Comparisons between the conditions [RW, VR] were performed using the t-Tests/Wilcoxon tests, and to compare both age groups [young, old], t-Tests for independent samples/Mann-Whitney-U-Test were used. The analyses of the effect of body visualization on performances showed a significant loss in movement’s quality when no body part was visualized (p &lt; .05). This did not occur for the elderly adults, for which no influence of the body visualization on their performance could be proven. Comparing both age groups, the elderly adults performed significantly worse than the young age group in both conditions (p &lt; .05). In VR, both groups showed longer times for completion, a higher rating of tasks’ difficulty in the balance and throwing task, and less performance quality in the grasping task. Overall, the results suggest using VR for the elderly with caution to the task demands, and the visualization of the body seemed less crucial for generating task completion. In summary, the actual task demands in VR could be successfully performed by elderly adults, even once one has to reckon with losses within movement’s quality. Although more different movements should be tested, basic elements are also realizable for elderly adults expanding possible areas of VR applications.
Liderazgo Y Tensiones En La Universidad: El Reto De Integrar La Educación Digital

The university is facing, once again, a deep transformation process. On this occasion, the continuous increase in the digital dimension of society and its consequent impact on teaching and research plays a fundamental role. The effects of the pandemic, which have made digital education the only option to avoid the complete interruption of higher education in most of the world, have further accentuated this impact. This article combines two investigations. A qualitative one on the rationale behind the transformation processes of the university, analyzing in-depth interviews with eleven international university experts, who identified ten tensions that the university is experiencing at this time. The other one is a mixed methodology study that, based on three case studies located in three universities, determines the leadership literacies necessary for the integration of digital education in the transformation process of universities.The results make it possible to relate the leadership competencies necessary to solve the ten tensions raised, concluding with the importance of developing these leadership literacies for digital education proposed by the DELLHE framework.
Evidence from hacking: A few tiresome problems

Most nations allow investigators to hack into computers of interest but usually the results are not used in evidence but only as intelligence. This appears to be changing. The article examines what is involved, practically and legally, to turn material acquired through hacking into forensically sound evidence.
Snowball Framework for Web Service Composition in SOA Applications

Service Oriented Architecture (SOA) has emerged as a promising architectural style that provides software applications with high level of flexibility and reusability.However, in several cases where legacy software components are wrapped to be used as web services the final solution does not completely satisfy the SOA aims of flexibility and reusability.The literature review and the industrial applications show that SOA lacks a formal definition and measurement for optimal granularity of web services.Indeed, wrapping several business functionalities as a coarse-grained web services lacks reusability and flexibility.On the other hand, a huge number of fine-grained web services results in a high coupling between services and large size messages transferred over the Internet.The main research question still concerns with "How to determine an optimal level of service granularity when wrapping business functionalities as web services?"This research proposes the Snowball framework as a promising approach to integrate and compose web services.The framework is made up three-step process.The process uses the rules in deciding the web services that have an optimal granularity that maintains the required performance.To demonstrate and evaluate the framework, we realized a car insurance application that was already implemented by a traditional approach.The results show the efficiency of snowball framework over other approaches.
Counterfactual inference with latent variable and its application in mental health care

This paper deals with the problem of modeling counterfactual reasoning in scenarios where, apart from the observed endogenous variables, we have a latent variable that affects the outcomes and, consequently, the results of counterfactuals queries. This is a common setup in healthcare problems, including mental health. We propose a new framework where the aforementioned problem is modeled as a multivariate regression and the counterfactual model accounts for both observed and a latent variable, where the latter represents what we call the patient individuality factor ( $$\upvarphi $$ ). In mental health, focusing on individuals is paramount, as past experiences can change how people see or deal with situations, but individuality cannot be directly measured. To the best of our knowledge, this is the first counterfactual approach that considers both observational and latent variables to provide deterministic answers to counterfactual queries, such as: what if I change the social support of a patient, to what extent can I change his/her anxiety? The framework combines concepts from deep representation learning and causal inference to infer the value of $$\upvarphi $$ and capture both non-linear and multiplicative effects of causal variables. Experiments are performed with both synthetic and real-world datasets, where we predict how changes in people’s actions may lead to different outcomes in terms of symptoms of mental illness and quality of life. Results show the model learns the individually factor with errors lower than 0.05 and answers counterfactual queries that are supported by the medical literature. The model has the potential to recommend small changes in people’s lives that may completely change their relationship with mental illness.
The negative impacts of student use of online tools during emergency remote teaching and learning on teacher-student relationships.

This article focuses on the importance of establishing and maintaining relationships between teachers and students for the mutual benefit of both groups and explores this in one context during the Emergency Remote Teaching and Learning (ERTL) period in the first COVID-19 pandemic lockdown in 2020. Ten instructors of university English as a foreign language (EFL) academic writing were interviewed about their experiences during this period. All questioned the academic honesty and ethics of their students due to what seemed to be their impossibly rapid progress in linguistic complexity and correctness, and the depth of ideas expressed growing very quickly. Due to communication between teachers and students being limited to text-based messages on the Learning Management System, teachers were unable to investigate without potentially causing university disciplinary action to fall on their students. This led to a worsening of relationships and spiraling levels of distrust. Forty first-year students were then asked to submit three-minute videos explaining how they undertook their homework assignments during the 15-week semester. Participants reported using a variety of technological assistance, particularly Machine Translation (MT) and online grammar checkers, to prepare assignments for submission. Findings suggest that educating instructors about how appropriate usage of such technology can benefit student learning could help prevent misunderstandings about unethical technology usage. Similarly, by recognizing the value of the relationship between instructors and students, time can be spent on building and fostering these bonds.
Information Bottleneck in Deep Learning - A Semiotic Approach

&#x0D; &#x0D; &#x0D; The information bottleneck principle was recently proposed as a theory meant to explain some of the training dynamics of deep neural architectures. Via information plane analysis, patterns start to emerge in this framework, where two phases can be distinguished: fitting and compression. We take a step further and study the behaviour of the spatial entropy characterizing the layers of convolutional neural networks (CNNs), in relation to the information bottleneck theory. We observe pattern formations which resemble the information bottleneck fitting and compression phases. From the perspective of semiotics, also known as the study of signs and sign-using behavior, the saliency maps of CNN’s layers exhibit aggregations: signs are aggregated into supersigns and this process is called semiotic superization. Superization can be characterized by a decrease of entropy and interpreted as information concentration. We discuss the information bottleneck principle from the perspective of semiotic superization and discover very interesting analogies related to the informational adaptation of the model. In a practical application, we introduce a modification of the CNN training process: we progressively freeze the layers with small entropy variation of their saliency map representation. Such layers can be stopped earlier from training without a significant impact on the performance (the accuracy) of the network, connecting the entropy evolution through time with the training dynamics of a network.&#x0D; &#x0D; &#x0D;
Determinants of e-learning adoption in higher education in Iraq an academics and students’ perspective

E-learning allows the students to access, repeat and use the scientific materials wherever and whenever the users want. Both academics and learners are the leading and essential actors in the process of the learning process. Moreover, the acceptance of a technology is the positive react for users by using such technology. Due to the COVID-19 pandemic, e-learning has become mandatory over the world, thus, there is a necessity to investigate the determinants of e-learning adoption in higher education institutions (HEIs) in Iraq. Consequently, this paper aims to investigate elearning adoption and identify the determinants of e-learning in HEIs. An instrument was developed with eight dimensions based on technology organization and environmental (TOE) elements. The sample selection was of 580 students and 130 university lecturers who spread over several colleges. A clustering sampling method was used in the sampling selection process. Some hypotheses were proposed and tested via the paired T-test tool. To evaluate the relationship between the TOE variables and e-learning adoption, a regression analysis was carried out. The study findings classified into two categories, first, it helps to determine to what extent e-learning be adopted. Second, it provides meaningful guidance for higher education institutions that should follow as a pre-requested before adopting e-learning
Printing and scanning investigation for image counter forensics

Abstract Examining the authenticity of images has become increasingly important as manipulation tools become more accessible and advanced. Recent work has shown that while CNN-based image manipulation detectors can successfully identify manipulations, they are also vulnerable to adversarial attacks, ranging from simple double JPEG compression to advanced pixel-based perturbation. In this paper we explore another method of highly plausible attack: printing and scanning. We demonstrate the vulnerability of two state-of-the-art models to this type of attack. We also propose a new machine learning model that performs comparably to these state-of-the-art models when trained and validated on printed and scanned images. Of the three models, our proposed model outperforms the others when trained and validated on images from a single printer. To facilitate this exploration, we create a data set of over 6000 printed and scanned image blocks. Further analysis suggests that variation between images produced from different printers is significant, large enough that good validation accuracy on images from one printer does not imply similar validation accuracy on identical images from a different printer.
Uncertainty-aware membranous nephropathy classification: A Monte-Carlo dropout approach to detect how certain is the model

Membranous nephropathy (MN) is among the most common glomerular diseases that cause nephrotic syndrome in adults. To aid pathologists on performing the MN classification task, we proposed here a pipeline consisted of two steps. Firstly, we assessed four deep-learning-based architectures, namely, ResNet-18, MobileNet, DenseNet, and Wide-ResNet. To achieve more reliable predictions, we adopted and extensively evaluated a Monte-Carlo dropout approach for uncertainty estimation. Using a 10-fold cross-validation setup, all models achieved average F1-scores above 92%, where the highest average value of 93.2% was obtained by using Wide-ResNet. Regarding uncertainty estimation with Wide-ResNet, high uncertainty scores were more associated with erroneous predictions, demonstrating that our approach can assist pathologists in interpreting the predictions with high reliability. We show that uncertainty-based thresholds for decision referral can greatly improve classification performance, increasing the accuracy up to 96%. Finally, we investigated how the uncertainty scores relate to complexity scores defined by pathologists.
Multi-Scale Adaptive Aggregate Graph Convolutional Network for Skeleton-Based Action Recognition

Skeleton-based human action recognition has attracted extensive attention due to the robustness of the human skeleton data in the field of computer vision. In recent years, there is a trend of using graph convolutional networks (GCNs) to model the human skeleton into a spatio-temporal graph to explore the internal connections of human joints that has achieved remarkable performance. However, the existing methods always ignore the remote dependency between joints, and fixed temporal convolution kernels will lead to inflexible temporal modeling. In this paper, we propose a multi-scale adaptive aggregate graph convolution network (MSAAGCN) for skeleton-based action recognition. First, we designed a multi-scale spatial GCN to aggregate the remote and multi-order semantic information of the skeleton data and comprehensively model the internal relations of the human body for feature learning. Then, the multi-scale temporal module adaptively selects convolution kernels of different temporal lengths to obtain a more flexible temporal map. Additionally, the attention mechanism is added to obtain more meaningful joint, frame and channel information in the skeleton sequence. Extensive experiments on three large-scale datasets (NTU RGB+D 60, NTU RGB+D 120 and Kinetics-Skeleton) demonstrate the superiority of our proposed MSAAGCN.
Acknowledgment to Reviewers of Journal of Cybersecurity and Privacy in 2021

Rigorous peer-reviews are the basis of high-quality academic publishing [...]
Human Resources in the Digital Age A Manager’s Realities and Perspectives

Technological improvement has an impact on the working environment and in particular on human resources management.Also, this area of inquiry is the object of study for consultancy companies and research institutes (Deloitte, 2017).The Global Human Capital Trends 2017 report, published by Deloitte, reflects significant changes in the business world.This new era of digitalization, also called the "Fourth Industrial Revolution" is characterized by an accelerated transformation, creating new rules for business and implicitly for human resource management.So, organizations face a radically changing concept for the workforce, the workplace, and the world of work.These changes modified the rules for almost every organizational practice, from management learning to the definition of work itself.(Deloitte, 2017).Companies are preparing avid in this area and are trying to find new tools and opportunities in digital absorption in HR: automatic posting of vacancies on social media, issuance of different certificates, timesheet and tax calculation, automatically identify online resources in order to prepare a pre-profile for the candidate before supporting an interview and even analyzing the candidate's
A New Method of Image Classification Based on Domain Adaptation

Deep neural networks can learn powerful representations from massive amounts of labeled data; however, their performance is unsatisfactory in the case of large samples and small labels. Transfer learning can bridge between a source domain with rich sample data and a target domain with only a few or zero labeled samples and, thus, complete the transfer of knowledge by aligning the distribution between domains through methods, such as domain adaptation. Previous domain adaptation methods mostly align the features in the feature space of all categories on a global scale. Recently, the method of locally aligning the sub-categories by introducing label information achieved better results. Based on this, we present a deep fuzzy domain adaptation (DFDA) that assigns different weights to samples of the same category in the source and target domains, which enhances the domain adaptive capabilities. Our experiments demonstrate that DFDA can achieve remarkable results on standard domain adaptation datasets.
EVALUASI HASIL BELAJAR SISWA SMA KRISTEN BARANA’ PADA PEMBELAJARAN TATAP MUKA DI MASA NEW NORMAL

The purpose of this study is to evaluate the learning outcomes of Barana Christian High School students' face-to-face learning in the new normal. Face-to-face learning is carried out because many students, parents and teachers complain about learning conducted online from home due to the difficulty of students receiving inadequate learning and networks. This research method uses qualitative method through literature study, observation and problem tree analysis approach with a sample number of 366 students. The results of this study show that the learning objectives have not been achieved due to the decrease in student learning outcomes during this new normal, so it is necessary to increase the interest and motivation of students' learning and teacher skills in supporting the improvement of student learning outcomes.&#x0D; &#x0D; Keywords: evaluation, learning outcomes, face-to-face learning, new normal
Learning Lessons From the Scrum Adoption in the Brazilian Air Force

This article shares the experience of Scrum adoption in a strongly hierarchical organization of the Brazilian Air Force. There is an apparent paradox between Scrum teams and groups formed by military personnel. Hierarchy is not exclusive to the military. The turning point to succeed was institutional support. This article also shares some good teachings from military pilots that helped to spread the Agile Culture. The main contribution is the learning lessons that may inspire other organizations to work with Scrum or Agile adoption. Once a strongly hierarchical organization has succeeded in its journey, why not others? The journey is still underway. While Scrum adoption became an Agile transformation, culture and mindsets are changing. The main goal is to turn the Brazilian Aeronautics Computing Center of São José dos Campos into a reference in Agile Methods within the Brazilian Air Force.
Design of Optimal Scheduling Model for Emergency Medical Supplies by Blockchain Technology

The study aims to explore the scheduling plan for the emergency of blockchain technology in the medical industry. Network security architecture for medical supplies management based on the Hyperledger Fabric optimized consensus mechanism is established by studying the characteristics of blockchain technology and its data structure composition. The supply chain model for medical device scheduling based on intelligent contracts is selected for the particularity of the nature and shape of medical devices in medical supplies. Ant colony algorithm is used to solve it. Case analysis and verification results show that the improved Hyperledger Fabric consensus mechanism has better security performance. Under the condition of 10,000 transactions, the probability of an attacker with the optimized consensus mechanism successfully controlling the transaction is only 7.2%. The optimized solution is about 50% higher than the original solution in terms of transaction processing speed. Over 1000 transactions, the transaction latency optimization rate is more than doubled. The total order completion time of the medical device scheduling model adopted by the intelligent contract is 26.3% higher than the historical service time of 19 days. The performance of the medical emergency material scheduling program that is added to the supply chain technology is better.
Online identification of symmetric pure states

We consider online strategies for discriminating between symmetric pure states with zero error when $n$ copies of the states are provided. Optimized online strategies involve local, possibly adaptive measurements on each copy and are optimal at each step, which makes them robust in front of particle losses or an abrupt termination of the discrimination process. We first review previous results on binary minimum and zero error discrimination with local measurements that achieve the maximum success probability set by optimizing over global measurements, highlighting their online features. We then extend these results to the case of zero error identification of three symmetric states with constant overlap. We provide optimal online schemes that attain global performance for any $n$ if the state overlaps are positive, and for odd $n$ if overlaps have a negative value. For arbitrary complex overlaps, we show compelling evidence that online schemes fail to reach optimal global performance. The online schemes that we describe only require to store the last outcome obtained in a classical memory, and adaptiveness of the measurements reduce to at most two changes, regardless of the value of $n$.