Reviewerly/exHarmony huggingface dataset:
Energy-Aware Live VM Migration Using Ballooning in Cloud Data Center

The demand for digitization has inspired organizations to move towards cloud computing, which has increased the challenge for cloud service providers to provide quality service. One of the challenges is energy consumption, which can shoot up the cost of using computing resources and has raised the carbon footprint in the atmosphere; therefore, it is an issue that it is imperative to address. Virtualization, bin-packing, and live VM migration techniques are the key resolvers that have been found to be efficacious in presenting sound solutions. Thus, in this paper, a new live VM migration algorithm, live migration with efficient ballooning (LMEB), is proposed; LMEB focuses on decreasing the size of the data that need to be shifted from the source to the destination server so that the total energy consumption of migration can be reduced. A simulation was performed with a specific configuration of virtual machines and servers, and the results proved that the proposed algorithm could trim down energy usage by 18%, migration time by 20%, and downtime by 20% in comparison with the existing approach of live migration with ballooning (LMB).
OA-STRUKTKOMM – Open Access Structured Communication

Based on the Open access workflow model developed in the predecessor project OA-HVerlag, the research team addressing the question of how data exchange between the systems involved in the workflow can be made more robust and compatible.In this project, the academic approach of a communication structure is being developed.This data transfer container will ensure access to project-specific content and systematize and facilitate the transformation of metadata standards for the distribution of the publication on different platforms.In addition, a toolbox of consulting materials for modelling OA workflows were created and a production system for producing OA monographs was developed.
An Entrepreneurial Definition of the Blockchain Technology and a Stacked Layer Model of the ICO Marketplace Using the Text Mining Approach

The landscape of ICOs and its underlying Blockchain technology needs more clarity, given that several overlapping and opposing views exist from governmental institutions, institutional investors, economists, and academia. Those positions stem from confusion, bias, and vested interest. Having consensus from the pioneer entrepreneurs who define Blockchain technology usage, and its marketplace address this need. Furthermore, an agreement on the problems blockchain is solving from the industry perspective would further the understanding of the technology direction and its “raison d’être.” or “reason of existence”. The paper analyzes 4367 businesses that requested funding using ICO whitepapers and raising more than $20 billion US dollars during the most active ICO period. Using Latent Semantic Analyses (LSA), the paper identifies a one-factor solution that explains 98.15% of all ICOs. The paper conducts a second-order analysis that generates an 18-factor solution. Through the empirical analysis, the paper presents its findings as an ICO marketplace stacked layer model. The model is comprised of four layers: (1) Trust; (2) Value exchange; (3) Automation; and (4) Applications to enable value exchange, and an era of new business models. The paper then presents an unbiased, unified entrepreneurial definition of the Blockchain technology usage.
Characteristics of Patients with Pulmonary Embolism at Dr. Soetomo General Hospital Surabaya, from 2019-2021

Background: A complication of venous thromboembolism is pulmonary embolism.Pulmonary embolism is a type of cardiovascular disease caused by a thrombus in the pulmonary artery.Nonspecific symptoms and a wide range of clinical to asymptomatic presentations make diagnosing this disease challenging.Objective: In this research, the patient profiles of pulmonary embolism patients admitted at Dr. Soetomo General Hospital in Surabaya from 2019 until 2021 were analyzed.Materials and Methods: This descriptive study utilizes a retrospective methodology using secondary data from the medical record of individuals who suffered pulmonary embolisms during 2019 and 2021.Result: A total of 33 pulmonary embolism patients met the inclusion criteria.Patients were dominated by men (66.7%) and age groups 36-45 and 56-65 years (30.3%).The most symptom was shortness of breath (43.3%).The Geneva score was dominated by the intermediate group (78.8%).PESI scores were overwhelmed by the class III group (36.4%).Vital signs are within normal limits, except respiratory rate results above normal with an average of 24x/min.Complete blood is within normal limits, except white blood cell results above normal with an average of 12.2 10 3 / l. %).Based on the CT scan results, most thrombi were found in the distal pulmonary artery (60%).The anticoagulant group is the most widely administered pharmacological therapy (76.5%). Conclusion:The majority of pulmonary embolism patients are men over 40, with the most frequent symptoms being shortness of breath and the most thrombus locations found in the distal pulmonary artery
Course Materials for Uncertain Times

Acquisitions and Course Reserves librarians have worked throughout the pandemic to provide course materials in a constantly changing learning environment. At the Candler School of Theology, student demand for online courses has increased, and in response, faculty have implemented more creative pedagogy. The library has, in turn, changed our service models throughout the past two years to continue promoting student success. This article offers a look into Course Reserve collecting policies from 2019 to 2022 to show how changes in instructional methods have impacted everything from staffing needs to collection development. We will then discuss how our decision-making around electronic resource purchasing has evolved and demonstrate how to update Course Reserve and collection development policies to serve a hybrid learning environment.
Enhancing use of BERT information in neural machine translation

Although BERT has achieved excellent results in various natural language processing tasks, it does not exhibit the same high performance in cross-lingual tasks, especially machine translation tasks. We propose a BERT enhanced neural machine translation (BE-NMT) model to improve the use of the information that is contained in BERT by NMT. The model consists of three aspects: (1) A MASKING strategy is applied to alleviate the knowledge forgetting that is caused by the fine-tuning of BERT on the NMT task.(2) Serial and parallel processing are combined for the multi-attention models when incorporating BERT into the NMT model. (3) The multiple hidden layer outputs of BERT are fused to supplement the missing linguistic information of its final hidden layer output. Experiments demonstrate that our method achieves good improvements in various NMT tasks compared with the baseline model.
Predicting the Frequent Item Sets for Supermarket Data

This Python notebook uses the Apriori algorithm to analyze datasets from various supermarkets, retail organizations, and minimarkets, resulting in a more accurate analysis of customer behaviour and better product prediction and forecasting. The dataset that is used in this model typically involves customer purchases in supermarkets or any other organization. The datasets contain item details as well as the number of transactions purchased by customers. This model can be used by retailers and supermarkets of all sizes in both urban and rural areas. This algorithm implementation enables accurate forecasting and allows products to be sold efficiently and profitably in stores. Supermarkets, for example, can use the resulting data to forecast future sales volume using a variety of machine-learning techniques. It displays the most frequently purchased items or associated items by the user. This prediction is primarily focused on figuring out the rules of the association. It identifies the set of items or attributes that occur together or frequently in the dataset using association rules. If this apriori model meets a minimum threshold value for support and confidence, it produces a set of items known as a frequent itemset. This Python notebook implements a prediction model based on the apriori algorithm, which improves the efficiency of level-wise generation of frequent item sets by utilizing an important property known as the Apriori property, which aids in reducing the search space.
Page Rank Algorithm in Hadoop By MapReduce Framework

One of the most popular algorithms in processing internet data i.e. web pages is a site ranking algorithm that is intended to decide the importance of web pages by attribution weight value based on any incoming link to this site.Large amounts of internet data can lead to computational load in processing the page ranking algorithm.To take this burden into account, in this article we present a an algorithm for processing site rankings through a distributed system A Hadoop MapReduce framework called MR PageRank. This paper intended for the first analysis of input raw web pages create the page name and its outbound links as key and value of the pair, respectively, as well as the total weight of the hanging nodes and total number of pages. Next, we calculate the probability each page and divide this probability by each outgoing link evenly. Each outgoing weight is mixed and aggregated based on page similarity to new update weight value of each page. Keywords: pagerank, hadoop, mapreduce, Attribution weight...
Error-mitigated deep-circuit quantum simulation of open systems: Steady state and relaxation rate problems

Deep-circuit quantum computation, like Shor's algorithm, is undermined by error accumulation, and near-future quantum techniques are far from adequate for full-fledged quantum error correction. Instead of resorting to shallow-circuit quantum algorithms, recent theoretical research suggests that digital quantum simulation (DQS) of closed quantum systems are robust against the accumulation of Trotter errors, as long as local observables are concerned. In this paper, we investigate the error-mitigation problem of open systems via DQS. First, we prove that the deviation in the steady state obtained from digital quantum simulation depends only on the error in a single Trotter step, which indicates that error accumulation may not be disastrous. By numerical simulation of the quantum circuits for the DQS of the dissipative XYZ model, we then show that the correct results can be recovered by quantum error mitigation as long as the error rate in the DQS is below a sharp threshold. We explain this threshold behavior by the existence of a dissipation-driven quantum phase transition. Finally, we propose a error-mitigation technique based on the scaling behavior in the vicinity of the critical point of a quantum phase transition. Our results expand the territory of near-future available quantum algorithms and stimulate further theoretical and experimental efforts in practical quantum applications.
Sentiment Analysis of iPusnas Application Reviews on Google Play Using Support Vector Machine

The iPusnas application is a digital library platform that allows users to borrow and read books online, free, and legally. Digital libraries provide literacy facilities without having to come to the library, take part in reducing the spread of the Covid-19 virus, and one of the government's efforts to increase reading interest in Indonesia. User experience in using the application usually conveyed in form of positive or negative reviews on Google Play which can be used to determine the quality of an application. However, too many reviews often make it difficult for readers to conclude the contents of the reviews. Based on that, this research aims to determine user sentiment of iPusnas application by conducting sentiment analysis using Support Vector Machine. The dataset used is iPusnas application reviews on Google Play with 6.946 clean data. Classification produces 94,24% accuracy, 92,38% precision, 83,86% recall, and 87,82% f1-score. This application has 75.1% positive sentiment and 24.9% negative sentiment with a list of frequently appearing words displayed in wordcloud. The final result of this analysis can be used as an evaluation to determine the steps for developing the application to be more optimal so that it can provide a better user experience.
An Analysis of Speech Act on Civil War Movie

This research aims to analyze speech act found in Civil War movie. This research focuses on the conversation between Captain America and Iron Man. The method used in this research is qualitative using conversation analysis. The data were collected from conversation script between Captain America and Iron Man. The result of this research shows that in the conversation between Captain America and Iron Man. It was found that there are three kind of speech acts used between Captain America and Iron Man. They are locutionary, illocutionary, and perlocutionary acts. The most speech act frequently used is locutionary act with total number is 33 (46%). While the second speech act frequently used is illocutionary act that are 23 (33%). For perlocutionary act is only found 15 (21%). In locutionary act, the conversations consisted of rhetic and phatic act. Illocutionary act dominated by asking and answering and perlocutionary act involved sarcasm, joke and caution.
Information Security : The Landscape of Management In Electronic Record

Protecting information based on physical boundaries and firewall technologies are outdated and inadequate in this area. Today the more efforts to establish a successful information security practice by developing rules regulating and making the users understand how to follow the prescribed information security rules and policies also is not likely to be successful. Thus, a better understanding on holistic process of information security protection in healthcare industry is very important to manage information security management. Nowadays, the increasing role of information technology platform in organizing health information has led to the need of review on the confidentiality, privacy, and security of electronic information. The widespread use of electronic health records (EHRs) in healthcare industry is prevailing. Once information is electronically stored and shared, it opens the door for hackers and other malicious attackers to access the records.. Therefore, this paper is to scrutinize the landscape of security management elements that contribute on successful of implementing security management in healthcare industry. This paper also includes overview on identifying the simple process of security management that will helps any organization in healthcare industry to formulate, implement and manage any medical or hospital information system.
A Self-Compassion Experience in Immersive Video: Opportunities and Pitfalls

We have designed an immersive video experience providing participants with theexperience of an intimate encounter with themselves using immersive video. Seventy three participants (35 men, 38 women) ages 22-65 (M = 37.82, SD = 8.1) were randomly assigned to one of two conditions: meeting Self (n=38) and meeting Stranger (n=35), and the experience was carefully designed to increase self-compassion. The participants in the Self condition found the experience significantly more meaningful than those in the control condition (t(72)=-3.923, p&amp;lt;0.001) and reported higher active positive affect (t(72)=-2.353, p=0.021). However, our results indicate that the experience (interacting with self) negatively affected self-esteem, self-kindness and self-isolation (reverse coded) as compared to the control group (interacting with a stranger). Furthermore, the results indicate that older individuals may benefit more from the experience than younger individuals, and suggest that BMI is also an important moderating variable. Overall, our conclusion is that meeting yourself in VR can be a very powerful experience, but further studies are necessary in order to understand the contribution of experience design decisions and moderating variables,if this is intended as a positive experience.
Digital Literacy of Open Access Services to Support Online Learning During The Covid-19 Pandemic

The covid-19 pandemic has changed the pattern of learning and information tracing in the world of education. This condition becomes a challenge for academics amid various limitations and new habits. In addition to the problem of online learning, currently, students are also faced with difficulties in finding and accessing references to support online learning given the limitations of library services that have not provided loans and reading services online. This condition is faced by the partner of the service program, namely students of Fisip UHO. Problems faced by partners, first limited knowledge in access ebook services and open access e-journal. Second, the ability to use Mendeley as a means of citation scientific work. Solving the problem is done through ebook access training, e-journal and the use of Mendeley continued with the practice of finding open access reference sources and doing the citation independently in Mendeley The results showed significant changes in knowledge and skills for access to ebooks and e-journal services as well as Mendeley. There are only 60% of partners understand ebook access, 64% understand e-journal access, and there are 33.3% of partners who ady understand using Mendeley. This percentage value was obtained through the distribution of questionnaires before the training activities to 22 participants. After the training was carried out there was a change of one part to 100% of partners already understunderstooding ebook and e-journal services, but only 93.8% were able to use Mendeley.
In, out &amp; shake it all about: Ingesting and disseminating OA books through OAPEN and DOAB

<p class="first" id="d8704880e61">The OAPEN Library and the Directory of Open Access Books are infrastructures to disseminate OA books as efficient as possible. What services do they offer to publishers, and how are the books disseminated? The many different roads from publisher to reader are described, and the important role metadata conversion plays in this process.
CONSTRAINTS FACED BY FARMERS IN SAURASHTRA REGION IN USING SMART PHONES FOR AVAILING AGRICULTURAL INFORMATION

The present era is a 'Smart Phone Era'. The mobile phone became an inevitable gadget for everyone and also for farmers to know the latest agricultural technology. But there were many problems which hinder the maximum use of smart phone to get the scientific information of agricultural field. Keeping this in view, the present study was undertaken to identify the constraints faced by farmers in the use of smart phone for agricultural information. This study was undertaken in Porbandar district of Gujarat state. 120 farmers who had minimum three years' experience in the use of smart phone were selected from 12 randomly selected villages of Porbandar district. Results indicated that, unavailability/fluctuation of network connectivity in rural area is the major constraint in the use of smart phone. Some other important constraints were; lack of technical know-how to operate smart phone properly, language barrier i.e. majority of information is available in English not in vernacular language and the cost of smart mobile phones is high. The network service provider should give priority for strong network connectivity in rural areas, farmers should be trained to improve their smart phone use efficiency, agricultural information should be available in local/vernacular language and the government should provide smart phones at low cost to farmers were crucial suggestions given by the respondents to overcome the constraints faced by farmers in the use of smart phone for agricultural information.
Language Learning Strategies for English Speaking Skill among Level 2 Primary School Pupils

Speaking skills plays a vital role in improving the ability to communicate effectively using a language especially to second language learners.Language Learning Strategies (LLS) are beneficial for language learners as it helps them to learn and use a language effectively.However, the lack of confidence has always been a big barrier for them to perform well in speaking English.Hence, a descriptive study has been conducted which aimed to identify the most preferred LLS of speaking skill used by level 2 primary school pupils and ways for educators to improve their speaking skill by using the LLS.A survey questionnaire which has been adopted from the Oxford (1990) Strategy Inventory for Language Learning (SILL) was distributed to 50 respondents of rural primary 6 pupils in Sarawak by using random sampling.The data and findings are collected and analyzed by using descriptive analysis through mean score and percentage as the indicators.As a result, the study discovered that the respondents are prone to apply metacognitive strategies most and compensation strategies least when learning English speaking skill.With this knowledge in mind, it is hoped that teachers, typically in the rural areas, would take part in contributing to increase pupils' awareness of their preferred LLS so as to better enhance their English speaking skill and indirectly boost their confidence level.
Hierarchical Task Knowledge Constrains and Simplifies Action Understanding

Human social interactions require understanding and predict- ing other people’s behavior. A growing body of work has found that these inferences are structured around an assump- tion that agents act rationally and efficiently in space. While powerful, this view treats action understanding in a vacuum, ignoring that much social inference happens in the context of familiar, hierarchically structured events (e.g.: buying gro- ceries, ordering in a restaurant). We propose that social and world knowledge is critical for efficiently interpreting behavior and test this idea through a simple block-building paradigm, where participants infer an agent’s sub-task (study 1a), next action (study 1b), and higher-level goal (study 1c), from very sparse observations. We compare these inferences against a Bayesian model of goal inference that exploits task structure to interpret agents’ actions. This model fit participant judg- ments with high quantitative accuracy, highlighting how world knowledge may help support social inferences in a rich and powerful way.
Extensible Steganalysis via Continual Learning

To realize secure communication, steganography is usually implemented by embedding secret information into an image selected from a natural image dataset, in which the fractal images have occupied a considerable proportion. To detect those stego-images generated by existing steganographic algorithms, recent steganalysis models usually train a Convolutional Neural Network (CNN) on the dataset consisting of paired cover/stego-images. However, it is inefficient and impractical for those steganalysis models to completely retrain the CNN model to make it effective for detecting a new emerging steganographic algorithm while maintaining the ability to detect the existing steganographic algorithms. Thus, those steganalysis models usually lack dynamic extensibility for new steganographic algorithms, which limits their application in real-world scenarios. To address this issue, we propose an accurate parameter importance estimation (APIE)-based continual learning scheme for steganalysis. In this scheme, when a steganalysis model is trained on a new image dataset generated by a new emerging steganographic algorithm, its network parameters are effectively and efficiently updated with sufficient consideration of their importance evaluated in the previous training process. This scheme can guide the steganalysis model to learn the patterns of the new steganographic algorithm without significantly degrading the detectability against the previous steganographic algorithms. Experimental results demonstrate the proposed scheme has promising extensibility for new emerging steganographic algorithms.