Reviewerly/exHarmony huggingface dataset:
A framework for risk stratification of burnout due to Covid-19 in nurses using personas" creation

Abstract Funding Acknowledgements Type of funding sources: None. Background Covid-19 pandemic has generated a huge amount of stress upon all healthcare workers, with an increased risk of developing burnout syndrome and Post Traumatic Stress Disorder (PTSD). Mobile health could provide tools for preventive interventions, whose efficacy could be improved by applying User-Centered Design (UCD) techniques to create personas to stratify the associated risk and tailor those interventions. Purpose We propose a framework for the definition of personas applied to an Italian nurse population to highlight different levels of risk relevant to burnout, anxiety, depression, and PTSD induced by Covid-19. Methods Data was gathered by web-questionnaire delivered to nurses in Italy between April and May 2020. Quantitative information about respondents’ lifestyle, professional experience, impact of Covid-19 pandemic on both work conditions and personal life were collected, together with psychological indexes as Maslach Burnout Inventory (MBI), Impact of Event Scale (IES) and Patient Health Questionnaire (PHQ), used to define the risk level of burnout, PTSD, and anxiety and depression, respectively. Collected variables (55) were first reduced using Principal Component Analysis; then, k-medoids clustering with Partitioning Around Medoids algorithm was applied, where the best value of k was determined by silhouette and total within sum of square differences heuristics, together with evaluation from domain experts. Statistical analysis was applied to define which variables were able to differentiate among the k clusters, constituting the basis for the definition of a Persona card (i.e., a template with textual and graphical information) for each of the obtained clusters. Results From the 175 respondents nurses, a total of three clusters (personas) were identified. They presented very strong dissimilarities both in lifestyle and in how nurses reacted to the pandemic, showing distinct profiles associated to three different levels of risk of developing burnout and PSTD: the highest (representing 38.3% of all respondents), associated to a married nurse with children living with family, also using Personal Protective Equipment (PPE) at home, with workload highly impacted by COVID-19; the middle one, associated to an engaged nurse living alone with workload highly impacted by COVID-19; the lowest, associated to a married nurse living only with the husband/wife and wearing PPE at home, with workload moderately impacted by the pandemic. All personas showed a good amount of knowledge about burnout. Conclusions The application of the proposed framework to nurses showed three clusters that evidenced three risk profiles associated to different personas, each requiring distinctive treatment. This approach constitutes the first step for the development of tailored m-health interventions aimed at monitoring and preventing burnout and PTSD among healthcare workers in critical situations, such as the one provoked by Covid-19 pandemic.
A Journey of Transforming to an Online Government Documents Collection

As a selective depository library in Tennessee for decades, the Paul Meek Library has received and housed publications of the Government Printing Office (GPO) in a variety of formats as part of the library’s collection. Over the years, lack of consistent oversight and lack of staff have led to deficiencies in the development and maintenance of the government documents collection. The purpose of this paper is to share the library’s experience with using the Cataloging Record Distribution Program (CRDP) to provide a sustainable and accessible collection. The paper describes how the library collaborated with GPO and MARCIVE, Inc. to implement the project and transform part of the collection to online resources.
Block chain technology using food supply chain management

Agriculture is the primary source of livelihood for about 58 percent of India population. Food supply chain its very complicated by fragmented inbound and outbound networks[9]. It consists of multiple agents or intermediaries inthe marketing channel to realize a profit and successfully pass on the losses to the producer[3]. Food producers are facing multiple obstacles, from seasonal changes to the broken supply chain also their occupation is very laborious and demanding[3]. Most of the losses produced here are due to missing formation, miscommunication and lack of trust between the different intermediaries[3]. Especially the Indian food supply chain is highly dividable.main it helps formers to sell their products with a valuable profit so people will never loose their trust on agriculture .so that it helps whole world to get their foods and whatever they need they can buy. Former is happy then he feeds whole world. This all is possible only because of using block chain technology.The proposed system uses blockchain technology which facilitates the transfer of data or useful information  and transparent manner pertaining to food supply management. This system ensures trust and brings transparency across various stakeholders.
Autonomous Agents on the Web : Executive Summary

The vision of autonomous agents on the Web is almost as old as the Web itself: in his keynote at WWW’941 , Sir Tim Berners-Lee was noting that documents on the Web describe real objects and relationships among them, and if the semantics of these objects are represented explicitly then machines can browse through and manipulate reality.2 These ideas were published under the Semantic Web vision in 2001 [1]. Yet in 2007, after having spent the better half of a decade advancing this vision, James Hendler was looking back to conclude that most ideas in the original article were already seeing widespread deployment on the Web except for agent-based systems – and raised the question: “where are all the intelligent agents?” [2].
Student Academic Mark Clustering Analysis and Usability Scoring on Dashboard Development Using K-Means Algorithm and System Usability Scale

Learning activities are one of the processes of delivering information or messages from teachers to students. SMPN 4 Sidoarjo is a State Junior High School (JHS) located in Sidoarjo Regency. During the learning process, the collected academic score data were still not well organized by teachers and school principals in monitoring student learning performance. The score data is from Bahasa Indonesia subject from a teacher with 222 data included at 2019/2020 school year. The method used in student clustering is K-Means. The number of clusters are determined using the elbow method and displayed in graphic form. Clustering result can be used as a reference for teachers in determining study groups and determining the best treatment for each cluster. The best clustering results are proven by validation score using Davies-Bouldin Index, Silhouette Width, and Calinski-Harabasz Index. Three clusters were obtained for each class level of data, while the cluster ranges from two to five for the data for each study group. The dashboard is used in order to visualize the clustering result. Usability testing using System Usability Scale (SUS) has a score value of 87.5, which means that the dashboard can be accepted by SMPN 4 Sidoarjo.
Formal Methods to Verify and Ensure Self-coordination Abilities in the Internet of Vehicles

The emerging Internet of Vehicles (IoV) is a distributed multiagent network that utilises the potentials for collaboration of vehicles with the aim to improve the reliability and safety of transportation and logistic systems. IoV systems require operational methods to reason about the capacity of the involved (human and artificial) agents to form strategically capable coalitions as a means to ensure safety. In this work, we (1) develop a logic-based machinery to represent and reason about strategic abilities in IoV systems, (2) provide a process to verify whether a given IoV system is capable to safely self-coordinate, and (3) introduce a mechanism to ensure such an ability in a temporal, strategic, and normative setting.
Design and Development of Business English Major Practical Skill Education

Business English Major practical skill education is of importance for both the development of export-oriented economy and international trade of companies. Traditional Business English major talent cultivation curriculum neglects the inner relation between courses and significance of real practice. DNUI English Department has designed and developed a new Business English Major practical skill education system which observe the inner connection between courses and has made good effects. With this paper, the author wishes to present a new way of teaching Business English Major practical skill.
A Systematic Review on Spatial Domain Steganography & Cryptography Techniques

The establishment of secure communication between two communicating parties is becoming a difficult problem due to the possibility of attacks and other unintentional changes during active communication over an unsecured network. Secret information, on the other hand, can be safeguarded using either cryptography or steganography. Steganography is the practise of concealing a message (with no traceability) in such a way that it has no meaning to anyone other than the intended recipient, whereas cryptography is the art of converting a plaintext (message) into an unreadable format. Thus, steganography hides the existence of a secret message, whereas cryptography modifies the message format itself. Steganography and cryptographic techniques are both powerful and resilient. The primary goal of this paper is to examine various methods for combining steganography and cryptographic techniques to create a hybrid system. Furthermore, some distinctions between cryptographic and steganography techniques were presented.
The Wave Of Innovation Artificial Intelligence And I P Rights

The paper provides a concise overview of the interplay between law and artificial intelligence. Based on the analysis of legal resources, it identifies key topics, organizes them in a systematic manner and describes them in general, essentially regardless of specificities of individual jurisdictions. The paper depicts how artificial intelligence is related to copyright  and patent law , how law regulates artificial intelligence, with regard to the developments in artificial intelligence.
Attendance Record Using Computer Vision

The customary strategy for gauging participation is done physically by the instructor or the executive which requires significant measure of time and endeavors additionally including blunders and intermediary participation. As the quantity of understudies are expanding step by step, it is a difficult assignment for colleges or universities to screen and keep up the record of the understudies. Computerized frameworks including utilization of biometrics like unique finger impression what's more, iris acknowledgment are all around created in the ongoing years be that as it may, it is meddling and cost required for arrangement on expansive scale gets expanded considerably. To conquer these issues, biometric highlight like facial acknowledgment can be utilized which includes the stages, for example, picture securing, face location, highlight extraction, face characterization, face acknowledgment and in the end denoting the participation. Different constant situations should be thought about, for example, scaling, brightening, impediments and posture. The issue of repetition in manual records and keeping participation is unraveled by this framework. Quantitative examination is finished based on PSNR values.
Relationship between Organizational Behavior and Organizational Performance

The study investigates the relationship between Organizational Behavior and Organizational performance and to test the relationship between the most influent factors (organization culture, technology, interpersonal relationship , structure) and their contribution to the overall performance which is the main focus of the hypothesis by using a measure of two critical process elements which are Job Satisfaction and Motivation to decide whether the relationship is positive or negative and how to implement these inputs in order to maximize performance. Also this study will be using the Outputs to measure the strength and efficiency of the relationship and determine if it’s good or bad , since a healthy and positive relationship will give a maximized level of performance and will positively influence the two process factors and vice versa.
DIFFERENCE IMPLEMENTATION OF T1WI SE AND T1WI FSPGR BRAVO SEQUENTS IN MRI BRAIN TUMOR

Tumor is basically an uncontrolled growth of cancerous cells in any part of the body, whereas a brain tumor is an uncontrolled growth of cancerous cells in the brain. Useing of MRI in diagnose tumors can be done with various sequences. Contrast medium is needed to evince tumor enhancement, as well as sequences that support to produce tumor in post contrast, one of which uses a conventional sequence T1WI SE. This sequences often lose information in providing images in cases of brain tumors and generally more time consuming. FSPGR BRAVO is a 3D volumetric acquisition that captures thin section images with near isotropic or isotropic spatial resolution. This sequence displays anatomy, especially brain parenchymal anatomy, in fine detail. The type of research in this mini research is a qualitative study with an observational approach which aims to find out sequences that can optimize post-MRI images in contrast to brain tumor cases with a short time and get the right diagnosis. Convensional sequence of MRI TIWI SE can’t detection of lessions in cerebral cortex. FSPGR BRAVO in producing images in 3D format in one-time retrieval of one particular piece and able to display anatomy especially the anatomy of the brain parenchyma in fine detail. FSPGR BRAVO can be used to assist cause to be uprise the diagnosis of MRI brain tumor by displaying the anatomy of the brain parenchyma in more fine detail, without the need for extended time.
Colour-Managed LED Walls for Virtual Production

We present DNEG’s approach to colour-managing LED walls for in-camera VFX in virtual production. By characterising the entire imaging pipeline end-to-end with a closed loop, we enable filmmakers and visual effects artists to prepare virtual environments in advance of shooting, confident that their colour intent will be preserved in the final footage. Our system flexibly adapts to the Cinematographer’s choice of exposure and white-balance, allowing them to focus more on story-telling and less on technical constraints of the wall.
Sensor System For Intruder Detection Based On Ground Vibration

The ground vibration device is used to detect intruders by measuring the vibration of the ground. The movement of an intruder or a human over the ground causes seismic waves to be generated in the soil, which are analyzed by geophones. Personnel monitoring and seismic detection. In light of this, we propose a sensor node that uses a programming language in the current study based livestock detection and identification system based on ground vibration.
Blockchain and International Identifiers for Music

The context of revenue generation streams in the music industry has been intermittently discussed since the transition from sales to streaming started when Spotify launched in 2008. Although revenues in the industry have reached new heights, musicians express dissatisfaction with lower royalty payouts. Moreover, it has become increasingly more difficult and convoluted to understand the royalty calculations in the music industry. With today’s complicated licensing agreements, money flows through a complex chain of third parties before it reaches the hand of musicians. The industry struggles with transparency and efficiency, and the musicians are paying the price. Meanwhile, the rise of blockchain technology has developed since its first application with Bitcoin in 2008. Today, more and more advanced blockchains can run decentralized transparent applications that utilize the technology’s efficient transaction system. With the industry issues and the promises of blockchain in mind, this paper demonstrates how blockchain technology can be applied to solve value chain problems within the music space. This paper identifies core issues in the music industry, proposes a decentralized application (dApp) that attempts to solve these issues and implements the proposed solution. This paper exhibits the business logic by developing smart contracts on theEthereum blockchain and uses IPFS (InterPlanetary File System) for decentralised file storage to make an associated web application choosing a JavaScript framework. The dApp works as a global copyrights database where musicians can register and license musical works. Author exploits Ethereum’s efficient transactional system to manage license purchases.
Cumulative effects in differential argument encoding and long-distance extraction

Only few grammatical theories can faithfully incorporate cumulative effects. In Optimality Theory, two different means have been suggested to account for cumulativity of constraint interaction, viz., local constraint conjunction and Harmonic Grammar. The present paper considers cumulative effects in morphology (with differential argument encoding) and syntax (with long-distance extraction) and shows that existing analyses in terms of local constraint conjunction cannot be transferred to Harmonic Grammar analyses. Whereas cumulative constraint interaction can be selectively switched off in local conjunction analyses, this is impossible for principled reasons in Harmonic Grammar analyses. Consequently, even though Harmonic Grammar is explicitly designed to capture cumulative effects, it turns out that this approach is systematically unable to derive a certain kind of cumulativity because it cannot prevent unwanted concurrent cumulative interaction.
Improving Cognitive Domain of Graduate Students on Corpus Linguistics Course through the Implementation of Blended Learning

Face to face learning method caused the students to be uninterested in class, discouraged, bored, stop trying in accomplishing tasks given (Felder and Silverman, 1988) however blended learning that is prove to be more effective in improving student satisfaction and knowledge (Marchalot et al (2018), Khodeir (2018), Alsalhi et al (2019), Li et a (2019), Yigzaw et al (2019),Yao  (2019), Cocquyt et al (2019), Law et al (2019), (Asarta & Schmidt, 2020). This cross-sectional study aims to evaluate the effectiveness of blended learning in improving graduate students cognitive domain until level 5 (synthesis) on corpus linguistic. The population of this study were first grader university students who enrolled in 2018/2019 (group 1) and third grader university students who enrolled in 2019/2020 (group 2) in corpus linguistics course. The data were analysed using the statistical program that available in SPSS. The similarity of the groups was analysed using crosstabulation and Mann Whitney U test. The different mean (average) scores of the two groups were analysed using an unpairing student t-test/ Mann Whitney U test depend on the equality of variant. The results of the testing revealed that the average score achieved by the students using face to face learning methods was 84.4737 while average scores achieved by the students with blended learning was 90.0000. Both scores were highly significant different with p < 0.01. In conclusion, blended learning is more effective compared to face to face learning
An approach of classifying waste using transfer learning method

One of the most critical issues facing the world is waste management, regardless of whether the region is being established or becoming established. There is a waste partitioning process in waste management, and the main challenge is that the garbage space is flooded long before the beginning of the following cleaning process at clear spots. Only unskilled workers conduct waste separation, which is less accurate, time-consuming, and not utterly possible due to the enormous amount of waste. Using the Convolutional Neural Network, we propose an artificial waste classification problem to compile and organize a dataset into seven categories consisting of metal, plastic, glass, paper, cardboard, trash, and E-waste. We then distinguished between specific transfer learning algorithms for our project: Xception, DenseNet121, Resnet-50, MobilenetV2, and EffiecienNetB7. DenseNet121 achieved a high precision characterization of about 93.3% for our model, while Mobilenet also demonstrated an incredible conversion to different forms of waste of 93% and Resnet-50, Xception and EfffiecienNetB7 achieved 92%, 92.5%, and 87%, respectively. In the future, we would like to increase the accuracy by using some other hyperparameter tuning, and we would like to deploy the project on mobile devices. We will use dockers or Kubernetes to deploy and YOLO real-time object detection as a framework for the post.
Komunikasi Data Menggunakan Vpn

The development of technology and information has been very rapid in recent years. The internet allows communication without any distance restrictions, but this communication requires security so that communication runs smoothly without any interference. One type of network security is a Virtual Private Network (VPN). The advantage of using a VPN is that it uses a public network as if it were a private network. One type of VPN is the Point to Point Tunneling Protocol (PPTP), where communication occurs in a tunnel created on the internet and to use it requires a VPN account. Each PPTP VPN account can only be used for one sender and one recipient. The advantage of this type of VPN is that it can be implemented in various Operating Systems (OS).Keywords : Computer Networks, VPN, PPTP, Tunneling
A Systematic Implementation of Deepfake Multimedia Video Generation and Detection using Deep Learning

In the following years, technology has progressed in so many ways that it has provided the cyber society with a resource that only computers can excel at, such as the art of counterfeit of media, which was before unavailable. Deepfakes are a term used to describe this kind of deception. The majority of well-documented Deep Fakes are produced using Generative Adversarial Network (GAN) Models, which are essentially two distinct Machine Learning Models that perform the roles of attack and defence. These models create and identify deepfakes until they reach a point where the morphing no longer detects the deepfakes anymore. Using this algorithm/model, it is possible to discover and create new media that has a similar demographic to the training set, resulting in the development of the ideal Deep Fake media. Because the alterations are carried out utilising advanced characteristics, they cannot be seen with the human eye. However, it is completely feasible to develop an algorithm that can automatically identify this kind of tampering carried out via the internet. This not only enables us to broaden the scope of our search beyond a single media item, but also beyond a large library of mixed media. The more it learns, the better it becomes as artificial intelligence takes over in full force with automation. In order to create better deep fakes, new models are being developed all the time, making it more difficult to distinguish between genuine and morphing material.