Reviewerly/exHarmony huggingface dataset:
A Risk Management Framework for Large Scale Scrum using Metadata Outer Request Management Methodology

Recently, most software projects became naturally Distributed Agile Development (DAD) projects.The main benefits of DAD projects are cost-saving and being close to markets due to their distributed nature, such as in large-scale Scrum (LeSS).Developing LeSS projects leads to the emergence of challenges in risk management, especially the team collaboration challenges, where there is no standardized process for teams to communicate collaboratively.Team collaboration and the knowledge sharing is a vital resource for a large Scrum team's success.Hence, finding a dynamic technique that facilitates team collaboration in the LeSS environment is necessary.This paper proposes a risk management framework for LeSS using outer metadata requests.The proposed framework manages the outer requests amongst the distributed team.Therefore, it avoids missing team collaboration, risks, and threats to project completion.It also contributes to exchanging team skills and experience.The proposed framework is evaluated by applying it to two different case studies for large-scale Scrum projects.The evaluation results are given.The evaluation proved the effectiveness of the proposed framework.
INTERIOR IDEAL FUZZY SEMIRING

Semiring is one of the ring extensions, which eliminates the inverse axiom in the first operation. One of the topics on the semiring is the ideal interior. This study introduces the concept of the ideal interior semiring and the ideal interior fuzzy semiring. Further, it examined the properties of the ideal fuzzy semiring interior and the nature of the existence of the ideal interior semiring if the ideal fuzzy interior is given.
Operational Characterization of Infinite-Dimensional Quantum Resources

Recently, various non-classical properties of quantum states and channels have been characterized through an advantage they provide in specific quantum information tasks over their classical counterparts. Such advantage can be typically proven to be quantitative, in that larger amounts of quantum resources lead to better performance in the corresponding tasks. So far, these characterizations have been established only in the finite-dimensional setting. In this manuscript, we present a technique for extending the known results to the infinite-dimensional regime. The technique relies on approximating infinite-dimensional resource measures by their finite-dimensional counterparts. We give a sufficient condition for the approximation procedure to be tight, i.e. to match with established infinite-dimensional resource quantifiers, and another sufficient condition for the procedure to match with relevant extensions of these quantifiers. We show that various continuous variable quantum resources fall under these conditions, hence, giving them an operational interpretation through the advantage they can provide in so-called quantum games. Finally, we extend the interpretation to the max relative entropy in the infinite-dimensional setting.
17 Credibility and reach of nutrition influencers on social media

<h3>Background</h3> Nutrition influencers can reach large segments of the public, regardless of formal training or credentials. Though social media is a popular source of nutrition information, it may not be credible. Furthermore, the perceived credibility of nutrition information may be enhanced through social validation (i.e., popularity of the public figure), yet this phenomenon has not been examined. <h3>Objective</h3> To examine the credibility of nutrition influencers’ websites in relation to their social media reach. <h3>Methods</h3> Nutrition influencers identified through a key word search on popular search engines: Yahoo! Google, and Bing who had active public websites and Instagram accounts were included. ‘Tips to Spot Misinformation’ developed for the public by the Dietitians of Canada and PEN: Practice Evidence-Based Nutrition were used to create a credibility score for each website. Based on scores, websites were categorized as having low, moderate, or high credibility. The reach of each influencer was ascertained by combining the total number of followers/subscribers from five popular social media platforms (Instagram, Facebook, Twitter, YouTube, and Pinterest). <h3>Results</h3> Of the 39 websites, there were 12 (31%) high, 13 (33%) moderate, and 14 (36%) low credibility sites, and the average number of followers for each group were 186 775, 547 088 and 2 153 515, respectively. There was a significant difference in followers between the three groups (p = 0.017) and a significantly lower number of followers for influencers with high credibility websites compared to low credibility websites (p = 0.022), with more than 10 times fewer followers. <h3>Discussion</h3> Popular influencers with low credibility websites have enormous reach whereas influencers with highly credible websites lack the ability to reach large segments of the population. Further research is needed to understand how social validation influences the public’s eating behaviors and to identify strategies that will increase the visibility of highly credible information.
A Data Security Algorithm for the Cloud Computing based on Elliptic Curve Functions and Sha3 Signature

The rapid development of distributed system technologies enforces numerous challenges.For example, one of the most critical challenges facing cloud computing is ensuring the security of confidential data during both transfer and storage.Indeed, many techniques are used to enhance data security on cloud computing storage environment.Nevertheless, the most significant method for data protection is encryption.Thus, it has become an interesting topic of research and different encryption algorithms have been put forward in the last few years in order to provide data security, integrity, and authorized access.However, they still have some limitations.In this paper, we will study the security concept in Cloud Computing applications.Then, an ECC (Elliptical Curve Cryptography) based algorithm is designed and tested to ensure cloud security.The experimental results demonstrate the efficiency of the proposed algorithm which presents a strong security level and reduced execution time compared to widely used existing techniques.
PUGH Decision Trapezoidal Fuzzy and Gradient Reinforce Deep Learning for Large Scale Requirement Prioritization

Objective: To prioritize requirements for large scale software projects within time involving uncertainty in the opinions among different stakeholders.Methods: We propose Pugh Trapezoidal Fuzzy and Gradient Reinforce Learning (PTF-GRL) methods for large scale software requirement prioritization.A Pugh Decision-based Trapezoidal Fuzzy Requirement Selection model is designed, inputting the functional and non-functional requirements of the corresponding stakeholders.With the assistance of Trapezoidal Fuzzy Inference, the qualitative factors are mapped with the corresponding numeric factors, which increases the computational efficiency.Findings: Performance is analyzed based on four parameters: The first parameter is accuracy and our method showed improvement of 4%, 7% and 3% compared to JRD-SCRUM, IFS and SRP-Tackle respectively.The second parameter is prioritization time and found that our method had reduced time of 30%, 37% and 39% compared with existing methods.The third parameter is precision and it was found that our method improves precision by 6%, 10% and 5% compared with the other two methods.The final parameter we consider is the test suite execution and our method showed improvement of 12%, 19% and 5% compared with the existing two methods.Novelty/Applications: The originality of this work indicates the better performance along with the optimal test suite execution even considering the uncertainty factor in the proposed method compared with existing similar methods.
Physical Activity Recognition Based on Machine Learning

The following paper presents a comparison study of various machine learning techniques in recognition of activities of daily living (ADL), with special attention being given to movements during human falling and the distinction among various types of falls.The motivation for the development of physical activity recognition algorithm includes keeping track of users' activities in real-time, and possible diagnostics of unwanted and unexpected movements and/or events.The activities recorded and processed in this study include various types of daily activities, such as walking, running, etc., while fall activities include falling forward, falling backward, falling left and right (front fall, back fall and side fall).The algorithm was trained on two publicly available datasets containing signals from an accelerometer, a magnetometer and a gyroscope.
Agglomerative Nesting (AGNES) Method and Divisive Analysis (DIANA) Method For Hierarchical Clustering On Some Distance Measurement Concepts

Clustering data through hierarchical approach could be performed by Agglomerative Nesting (AGNES) Method and Divisive Analysis (DIANA) Method. The objective of this research is to compare both the methods based on Euclid and Manhattan distance measurements. Of this research the clustering procedures of agglomerative method are conducted by exploring all techniques including single linkage, complete linkage, average linkage, and Ward. The data used are the National Socio-Economic Survey (SUSENAS) data which are selected specifically for the percentage of over 5 year old residents in each province, for both living in urban or rural, who access the internet in the last 3 months in 2017 but classified according purpose of accessing. By applying Mean Square Error (MSE) for 2 and 3 clusters, it can be concluded that the single linkage technique is the best performance of clustering procedure for both Euclidean and Manhattan distances.
Out of the Box Artificial Intelligence (OBAI): The Beginning of a New Era in Artificial Intelligence

The main purpose of writing this article is to unify all the OUT OF THE BOX ideas (under Artificial Intelligence) invented by the corresponding author of this work during the period (2013-2022) under a single umbrella titled &amp;ldquo;Out of the BOX Artificial Intelligence Field (OBAI Field)&amp;rdquo;. All the OUT OF THE BOX ideas which are proposed under Artificial Intelligence will come under new field titled OBAI Field which is defined in this work. A new Artificial Intelligence field titled &amp;ldquo;Artificial Cartoon Algorithms (ACA)&amp;rdquo; is invented in this work. ACA is a sub-field of OBAI field as it is an OUT OF THE BOX idea. Four new algorithms titled &amp;ldquo;Artificial Cartoon Popeye Algorithm&amp;rdquo;, &amp;ldquo;Artificial Cartoon Chhota Bheem Algorithm&amp;rdquo;, &amp;ldquo;Artificial Cartoon Jerry Algorithm&amp;rdquo; and &amp;ldquo;Artificial Cartoon Happy Kid Algorithm&amp;rdquo; are designed in this work.
A comparison of non-experts and experts using DSIS method

Subjective evaluations are necessary to learn how expected viewers perceive the quality of a system.Traditionally, non-expert subjective tests are preferred rather than expert tests.In this study, we conducted subjective evaluation experiments for nonexperts and experts on compressed 8K videos using the double stimulus impairment scale (DSIS) method and analyzed the experimental results expressed in terms of the mean opinion score (MOS), which is the average of individual scores.Furthermore, we investigated the differences between non-experts and experts by considering a new method in P.913 that estimates an improved MOS and a new experimental method using experts, called expert viewing protocol (EVP).Our contribution shows advantages of conducting expert subjective tests, such as EVP: expert tests allow to perform experiments with fewer subjects, to distinguish between original and distorted images, to determine a lower threshold for the image quality, to distribute scores in an appropriate range, and to constantly gain MOS values equal to improved MOS values.
Comparison of simulation-based algorithms for parameter estimation and state reconstruction in nonlinear state-space models

This study aims at comparing simulation-based approaches for estimating both the state and unknown parameters in nonlinear state-space models. Numerical results on different toy models show that the combination of a Conditional Particle Filter (CPF) with Backward Simulation (BS) smoother and a Stochastic Expectation-Maximization (SEM) algorithm is a promising approach. The CPFBS smoother run with a small number of particles allows to explore efficiently the state-space and simulate relevant trajectories of the state conditionally to the observations. When combined with the SEM algorithm, this algorithm provides accurate estimates of the state and the parameters in nonlinear models, where the application of EM algorithms combined with a standard particle smoother or an ensemble Kalman smoother is limited.
iLearning education based on gamification blockchain

&lt;span&gt;learning process implemented in universities due to COVID-19 has brought significant disruption to education. Interest and motivation to learn are very important in the learning process. It takes a platform that supports students to be motivated in running the activity hours well. Learning techniques that can be chosen are collaborative and creative learning in achieving effective graduate outcomes with the support of the Merdeka Campus program. This research aims to make gamification techniques to make students more creative, innovative, and interactive by utilizing the concepts of learning and playing. Furthermore, a learning platform based on blockchain technology can support quality universities as a layer of control in implementing security and fun learning. With gamification techniques in collaboration with blockchain technology, all devices connected to the network will become nodes or servers. This study uses agile development methods based on iterative and incremental models to create a distributed storage blockchain algorithm, and IPFS used to build the GamiChain iLearning education model (gamification blockchain). It can be concluded that GamiChain (gamification blockchain) can support the Merdeka Campus program, which can provide facilities for students to develop their potential and prepare excellent and outstanding graduates.&lt;/span&gt;
The Prediction of Gold Price Movement by Comparing Naive Bayes, Support Vector Machine, and K-NN

Gold is a yellow precious metal that can be forged so it is easy to form with various forms of jewelry such as pendants, earrings, rings, bracelets and others, gold has a high value. Gold itself is an exchange rate used in ancient times before the existence of money as it is today. Gold also can be used as an investment that is profitable for the investor and it has less risks. Investment is a form of fund management to give benefit by putting fund in allocation that is predicted will give additional benetifs. Prediction of gold price movements or predictions of gold price in gold stock investment, this research uses 3 (three) algorithms that will be implemented in analysis and increase accuracy, in the discussion or research that was made using the Naïve Bayes algorithm, Support Vector Machine and K-Nearest Neighbor, the dataset is obtained from the website, namely www.finance.yahoo.com the data was then tested using Rapid miner tools so that the average value of the Support Vector Machine algorithm with an accuracy rate of 57.59%, precision 58 ,73% and recall 51,78%. The next is the Naïve Bayes algorithm so that it is known to have an accuracy rate of 55.59%, precision 54.55% and recall 51.70%. Based on the comparison of the three algorithms, it is known that the one with the best accuracy, precision, and recall is the K-NN algorithm with 61.90% accuracy, 60.98% precision, and 60.35% recall. Furthermore, the results of testing the K-Nearst Neighbor algorithm have good results compared to the 3 (three) other algorithm tests and the Naïve Bayes algorithm testing has a low level of accuracy, namely 55.59%, precision 54.55% and recall 51.70%. The research uses 3 algorithms, namely naive bayes, K-nearst neighbor and Support Vector Machine, because the three algorithms are well-established algorithms to be applied to research, especially in time series gold price research and are very good, especially for classification
Political Intervention and Monetary Transmission: A Theoretical Note

Within a theoretical framework, the paper analyzes the impact of government intervention in the credit market on bank lending.The results indicate that in the short-run, a monetary expansion lowers lending that is not susceptible to political intervention by a magnitude that is higher than loans that are impacted by political influence.In the long run, a monetary expansion is observed to uniformly raise bank lending, consistent with the jawboning hypothesis.
An improved hybrid cloud workflow scheduling algorithm based on ant colony optimization

At present, with the enormous development of cloud-based applications, the demand of related enterprises for computing and a large number of storage resources is increasing day by day. Due to the characteristics of ultra-large scale and low cost, cloud computing has been gradually applied to complex workflow scheduling problems in various fields. The traditional scheduling algorithms have lot of limitations such as inefficient task management and unreasonable resource allocation. The workflow scheduling problem in the cloud computing environment has been proved to be an NP-complete problem. To solve these problems, this paper proposes an improved ant colony optimization workflow scheduling for workflow execution of tasks within a user-specified deadline. On this basis, a hybrid cloud deadline-constrained cost workflows scheduling algorithm under the hybrid cloud is proposed, which will prioritize scheduling execution in the private cloud, and use the public cloud to schedule part of the workflow when the task execution time exceeds the task deadline constraint. The simulation results show that the proposed workflow scheduling algorithm reduces the cost and attain faster execution time.
On the data requirements of probing

As large and powerful neural language models are developed, researchers have been increasingly interested in developing diagnostic tools to probe them.There are many papers with conclusions of the form "observation X is found in model Y ", using their own datasets with varying sizes.Larger probing datasets bring more reliability, but are also expensive to collect.There is yet to be a quantitative method for estimating reasonable probing dataset sizes.We tackle this omission in the context of comparing two probing configurations: after we have collected a small dataset from a pilot study, how many additional data samples are sufficient to distinguish two different configurations?We present a novel method to estimate the required number of data samples in such experiments and, across several case studies, we verify that our estimations have sufficient statistical power.Our framework helps to systematically construct probing datasets to diagnose neural NLP models.
Software development process evolution and paradigm shift - a case study of Malaysian companies

Due to the consequences of global software development (GSD), the traditional approaches of software development evolved into lightweight agile methods. The agile methods got overwhelming response from software development companies due to their obvious support to GSD. In this regard, limited research work has been presented on software process evolution and process paradigm shift in context of GSD. Most of the work presented on GSD mainly focus on the companies in Europe, America, Australia and other western countries. Existing research work highlight the standard benefits and challenges of GSD but do not investigate its effect on software development processes and associated reasons causing the affect particularly in Malaysian software development companies. The research work presented in this paper addresses this issue and investigates the effects of GSD on software processes, software process evolution and paradigm shift and finds the current software processes being used in Malaysian companies. Also, it further determines the GSD factors and reasons behind the change and selection of a software development process. GSD factors affecting the software processes have been termed as challenges. Structured interviews have been conducted to collect qualitative data from industry professionals involved in GSD. General inductive approach has been used for qualitative data analysis and findings. The results show that after GSD, Malaysian companies are mostly following agile methods. The traditional and ad-hoc approaches used before GSD have been replaced by the lightweight agile methodologies. Few of the companies are still following ad-hoc approaches mainly due to size of the project and company as most of the Malaysian companies are small and medium size. The research is significant that it provides clear insight into software process paradigm in Malaysian companies. The outcome of the research provides foundations for the standardization of software processes, process improvement, selection and quality enhancement approaches in Malaysian companies.
DEVELOPING PUZZWORLD GAME FOR WRITING SKILL

The use of ICT in education is a must and can be said to be a necessity in the digital era. This is because it is an effort to create quality learning. PuzzWorld Game (Puzzle World Game) is a technology-based learning media developed as a game that contains various learning materials that have been adapted to the needs of students. However, its existence is still rarely used in the world of education. The existence of ICT in the form of Puzzle Games in English lessons can support learning success because not all students easily understand every subject matter presented. PuzzWorld Game is a solution to student problems because it can facilitate student learning activities. This study examines the use of Puzzle Game media in optimizing English learning in junior high schools, especially in writing skills. A study on the use of the Puzzle Game was conducted in 2021 at Madrasah Tsanawiyah Nurul Qur'an Ploso Jombang with a total of 26 students. This type of research is development research. To analyze the results of the study used descriptive qualitative analysis. Data were obtained through questionnaires and learning outcomes tests. Researchers tested the effect of Puzzle Game Media. The results get 95 was in “very good” category. Through Puzzle Games, students can evaluate learning outcomes well and make it easier to understand the material being studied. The results of this study are very relevant in learning, especially in improving student learning outcomes towards learning English at Junior High School level.