Reviewerly/exHarmony huggingface dataset:
Risk Management Metrics

This chapter describes how risk management deals with the detection, the evaluation and the precedence of the risks in the process of project management. There is always an uncertainty factor related to the decisions of an investment while managing a project. Risk management is a proactive approach to deal with such future events that can lead to slow performance of the software project management. For successful risk management; there are different metrics that have been used in the past and are being getting used in the present for inspecting the progress of a project at specific points in a timeline that help in reducing the amount of risk. For the adoption of effective metrics for risk management, data is required. All of the metrics can be applied to the different domains of project, process and product. The chapter also covers strategies to advance, distinguish, estimate, and forecast the risk management process. A review of the key point indicators (KPIs) are also integrated along with the project metrics to signify the future and the present renderings.
Pelatihan Model Pembelajaran Speaking dengan Communicative Approach Bagi Guru-Guru Bahasa Inggris di Kabupaten Lampung Selatan

The speaking learning model with communicative approach has been widely adopted in various countries as a method of learning English because speaking learning with communicative approach proved effective to facilitate students actively involved in the learning process of English, especially speaking. The effectiveness / success of speaking learning by communicative approach as a way of English education in helping students improve speaking skills is evidenced by the discovery of some research tried in various countries where English as a foreign language. However, in Indonesia this method is still not popular and very few teachers or practitioners apply this method. Therefore, this community service activity aims to help English teachers in South Lampung Regency improve insight and knowledge about the development / creation of a speaking learning model based on communicative approach through training and workshops by presenting material development of task-based speaking teaching materials theoretically; and conduct simulations of the development of task-based speaking teaching materials. The results of the training showed that the participants of this training gained real efficacy is an increase in professionalism in order to be able to correct the quality of the English education process, especially speaking so that the dialogue skills of students in schools face an increase. Prolonged similar training in the future still really needs to be tried.
Face Unit Radial Basis Function Networks

This chapter introduces a different way of learning the face recognition task through the reorganization of the standard radial basis function (RBF) networks into a group of smaller ‘face recognition units’, each trained to recognize a single person. The face unit network is essentially a normal RBF network with two output units which produces a positive signal only for the particular person it is trained to recognize. Face unit networks allow a flexible approach to learning in dynamic environments compared to other neural networks models which have to be completely retrained if the training data is altered in any way. The face unit network only uses a few of the total number of classes in a problem to train, so operations on any of the other classes not used for training will leave it unaffected. As the number of classes increases, the chance of each face unit network needing retraining due to an operation on another class will become less.
Automatic National Anthem Playing Unit using RTC &amp; Microcontroller

As all school, begins on time and if we could play automatic national anthem then it gives energy throughout a day, feels energetic whole day. As we plays it manually there should be always a person is required to turn ON/OFF media player. This manual method is hectic and it encouraged us to play it automatically. So this motivated us to design a system where national anthem plays on predefined time after playing of national anthem it should be automatically stops the playing. So considering these parameter we designed and implemented automatic national anthem player since 2016 and till date it is working smoothly. All required material such microcontroller board, media player, speaker were purchase from local market. Media player main facility of playing again from initial as supply resumes. So, each day national anthem plays from beginning point.
Revisiting the Orthogonal Lattice Algorithm in Solving General Approximate Common Divisor Problem

Due to the property of supporting arbitrary operation over the encrypted data, fully homomorphic encryption (FHE) has drawn considerable attention since it appeared. Some FHE schemes have been constructed based on the general approximate common divisor (GACD) problem, which is widely believed intractable. Therefore, studying the GACD problem's hardness can provide proper security parameters for these FHE schemes and their variants. This paper aims to study an orthogonal lattice algorithm introduced by Ding and Tao (Ding-Tao algorithm) to solve the GACD problem. We revisit the condition that Ding-Tao algorithm works and obtain a new bound of the GACD samples' number based on geometric series assumption. Simultaneously, we also give an analysis of the bound given in the previous work. To further verify the theoretical results, we conduct experiments on Ding-Tao algorithm under our bound. We show a comparison with the experimental results under the previous bound, which indicates the success probability under our bound is higher than that of the previous bound with the growth of the bound.
Communication Efficient Distributed Learning Using Variational Auto Encoders

Advancements in mobile technology led to the manufacturing of smart devices that have started generating massive amounts of data. This triggered a new interest in developing intelligent applications that can harness this data's power and provide meaningful insights from it. This data from various devices needs to be aggregated at a central location to provide more accurate predictions, raising severe bandwidth, privacy, and latency issues. This paper presents a novel distributed learning approach that trains a Variational Auto Encoder at each client and then uses this model's generative capabilities to create a sample set of points on the server. The server aggregates these samples from all the clients and then trains a single global model whose parameters are communicated back to the clients. We show through our results that the amount of data transmitted between clients and the server is significantly lower when compared to the federated learning as well as a central learning approach. We also show that our approach outperforms the federated learning approach in terms of accuracy by more than 3% when the data is distributed in a non-iid fashion and at the same time sees a small drop in accuracy which less than 2% in an iid scenario when compared to federated learning and central learning methods. We have also shown how the amount data transmitted and the variation in accuracies when the number of clients vary between 2, 5 and 10.
Medical prescription model based on a Web Application

In this paper, we propose a medical prescription model for the issuance of an automatic prescription that will be approved by a doctor in charge, based on the selected symptoms. The model is implemented within a web application that allows the collection of the patient&#x0027;s medical record and symptomatology to facilitate their care and the prescription of medical prescriptions. It should be noted that the patient is autonomous to record their medical record and symptoms, which are compared with the database of the web application to generate the automatic medical prescription. The problem is based on the limitations that patients present to schedule a medical appointment in a health center and the high rate of self-medication present in Peru. The proposal uses a learning algorithm that is deployed on a Cloud platform to access medical information quickly contemplating the phases: 1. Data capture, 2. Data integration, 3. Cloud storage and 4. Presentation and follow-up. The medical prescription model implemented has been validated and tested through the execution of acceptance tests by patients and doctors from Lima, Peru, resulting in the reduction of the prescription time of a medical prescription by 68&#x0025;.
Relationship Between Software and Hardware Attributes of Intelligent Terminal and Micro Implicit Interaction Behavior

User browsing behavior is an important implicit feedback data reflecting users' interests and preferences in the field of recommendation system. How to make full use of user browsing behavior data and combined with other context information to improve recommendation efficiency has become a research hotspot. Firstly, the user browsing behavior and the software and hardware attribute data of intelligent terminal are cleaned and clustered. On this basis, the implicit feedback behavior of micro network is analyzed. Then, the influence of the software and hardware attribute context of intelligent terminal on the implicit feedback behavior of user micro network is studied by using binary and multiple regression analysis. The results show that the type of app store and browser used are very important factors. In addition, the type of operating system and screen resolution also affect the implicit feedback behavior of micro network to a certain extent.
Analysis of methods of image feature extraction

The article considers the methods of extraction and representation of image features, which play an important role in the processing of multimedia data. An overview of the latest developments in the field of image feature extraction is performed: extraction of color features from images where color has many functions, such as color moments(CM), color coherence vector (CCV), etc., extraction of texture features using the Gabor filter and extraction of shape features using methods based on contours and regions. An overview of the methods for representing image features is performed. The efficiency of merging global and local features in image processing was analyzed, as well as another way of presenting features like a visual word package. In the course of further work, the authors plan to study the relationship between the number of functions and the final performance. Intuitively, the larger the number of functions, the better the final performance. Secondly, the study of the relationship between the representation of functions and the final performance is also a very interesting and complex topic. It includes methods for representing objects (global, block, and regional). In the case of using block and regional functions, it is necessary to investigate the performance, which depends on the size of the partition or segmentation. Third, it is supposed to investigate the relationship between a suitable combination of image functions and the final performance in order to assess whether this combination can further improve performance. The results of the research are supposed to be used in the geolocation system of photos based on the automatic analysis of their images. Improving the accuracy and performance of image feature extraction will help improve the quality of the geopositioning system proposed in the article [28].
Performance Comparison of Machine Learning Algorithms in Symbol Detection Using OFDM

Nowadays, vast amounts of data transmission and data retrieval are crucial and are done using many ways. Orthogonal Frequency-Division Multiplexing (OFDM) is one of the efficient ways to transmit data with the help of orthogonal sub-carriers, which is used in applications such as WiFi, WiMax, and cellular communication. In this paper, instead of conventional detection techniques, machine learning (ML)-based methods are adopted to detect the symbols after data is being received through the Additive White Gaussian channel (AWGN). Detection is one of the areas in which the bit error rate (BER) performance of the OFDM system can be improved. Machine learning algorithms only depend on the training data to predict the outputs; hence, we can detect the symbol even without the use of cyclic prefix or channel estimation which can save a lot of time and data if the input data is large. A comparative study on the performance of receivers based on K-means clustering, k-nearest neighbors classifier, support vector machine, linear discriminant analysis, and quadratic discriminant analysis is done. The modulation techniques such as BPSK and QAM with various modulation orders ranging from 4 to 64 are used in this analysis. Performance comparison of aforementioned detection techniques using employing machine learning is done using BER vs signal-to-noise ratio (SNR) in the range of 0–30 dB.
Development of Smart Solar Tracker-An Approach towards Self Governed Tracking Mechanism

This paper presents a novel smart dual axis solar tracker to optimize the energy consumption in tracking mechanism. In conventional approach high torque motors are employed to continuously hold and mobilize the solar PV panels, which lead to excess energy consumption by motors, resulting in less efficient tracking systems. This issue is addressed by optimizing the panel rotation and detachment of motor shaft after directing the panel to sun's direction. The tracker includes two parts; main SPV plant of 60 W and miniaturized tracker with 10 W panels. LDR Sensors are located on miniaturized panel to track the sun's direction and this tracking mechanism of miniature model is utterly dependent on direction of irradiance only. Further, DC motor coupled to main SPV panels will rotate as per the signals of the miniaturized model and thus miniaturized model acts as decision making unit. Comparative analysis of results revealed that, performance of the system has increased by 15-35% at varying irradiance values.
Volunteer Facilitators Experience Intensive, Ongoing Training

The Volunteer Management ReportVolume 25, Issue 12 p. 2-2 Specialized Training Volunteer Facilitators Experience Intensive, Ongoing Training First published: 03 November 2020 https://doi.org/10.1002/vmr.31570Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume25, Issue12December 2020Pages 2-2 RelatedInformation
Feature-reduction Fuzzy c-means Clustering for Basketball Players Positioning

One of the best-known clustering methods is the fuzzy c-means clustering algorithm, besides k-means and hierarchical clustering. Since FCM treats all data features as equally important, it may obtain a poor clustering result. To solve the problem, feature selection with feature weighting is needed. Besides feature selection by assigning feature weights, there is also feature selection by assigning feature weights and eliminating the unrelated feature(s). THE Feature-reduction FCM (FRFCM) clustering algorithm can improve the FCM clustering result by weighting the features and discarding the unrelated feature(s) during the clustering process. Basketball is one of the famous sports, both international and national. There are five players in basketball, each with a different position. A player can generally be in guard, forward, or center position. Those three general positions need different characteristics of playersâ€™ physical conditions. In this paper, FRFCM is used to select the related physical feature(s) for basketball players, consisting of height, weight, age, and body mass index. to determine the basketball playersâ€™ position. The result shows that FRFCM can be applied to determine the basketball playersâ€™ position, where the most related physical feature is the playerâ€™s height. FRFCM gets one incorrect playerâ€™s position, so the error rate is 0.0435. As a comparison, FCM gets five incorrect playerâ€™s positions, with an error rate of 0.2174. This method can help the coach decide the basketball new playerâ€™s position.
Extruded material based on Bi0.85Sb0.15 for thermoelectric converters

In order to create a sufficiently strong material with thermo- and magneto-thermoelectric figure of merit close to single-crystal samples of material based on Bi-Sb systems, extruded samples of Bi0.85Sb0.15 solid solution were obtained and the effect of composition, heat treatment mode, grain size and tellurium impurities was investigated on their thermoelectric properties in the temperature range ~ 77-300 K and magnetic field strength H up to ~ 74∙104A/m. We managed to obtain extruded material with thermo- and magneto-thermoelectric figure of merit ~6.2∙10-3 K-1 and ~7.2∙10-3K-1 at ~ 77 K, respectively, with strengths ~ 3 times higher than the strength of single-crystal samples of this composition, which are suitable for use in low-temperature thermoelectric converters.
A Framework for the Generation of Class Diagram from Text Requirements using Natural language Processing

The software development procedure begins with identifying the requirement analysis. The process levels of the requirements start from analysing the requirements to sketch the design of the program, which is very critical work for programmers and software engineers. Moreover, many errors will happen during the requirement analysis cycle transferring to other stages, which leads to the high cost of the process more than the initial specified process. The reason behind this is because of the specifications of software requirements created in the natural language. To minimize these errors, we can transfer the software requirements to the computerized form by the UML diagram. To overcome this, a device has been designed, which plans can provide semi-automatized aid for designers to provide UML class version from software program specifications using natural Language Processing techniques. The proposed technique outlines the class diagram in a well-known configuration and additionally facts out the relationship between instructions. In this research, we propose to enhance the procedure of producing the UML diagrams by utilizing the Natural Language, which will help the software development to analyze the software requirements with fewer errors and efficient way. The proposed approach will use the parser analyze and Part of Speech (POS) tagger to analyze the user requirements entered by the user in the English language. Then, extract the verbs and phrases, etc. in the user text. The obtained results showed that the proposed method got better results in comparison with other methods published in the literature. The proposed method gave a better analysis of the given requirements and better diagrams presentation, which can help the software engineers. Key words: Part of Speech,UM
Full Issue

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 58, Issue 5 p. I-I Full Issue Full Issue First published: 04 July 2021 https://doi.org/10.1111/j.1467-6346.2021.10044.xAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume58, Issue5July 2021Pages I-I RelatedInformation
TEACHERS EXPERIENCE WITH INTRODUCING PROGRAMMING IN DIFFERENT COURSES FOR NON-COMPUTER SCIENCE STUDENTS

Digital literacy has become more and more important in the last decade, and many people predict that in the future, the need for digital skills will be even more crucial than it is today. The dynamic development and use of technology are becoming increasingly common in all areas of life, changing demands of modern life and the labor market, which makes it necessary to educate students from many different study-programs on how to use different digital tools and how to program. Depending on different professions, there are different requirements on what it means to have digital literacy. For some it is most important to know how technologies are created or to use the product effectively, for others it is the security of data transfer that is essential. The different professions have different needs for digital literacy and different use for programming skills. Teaching computer programming can be particularly difficult in the case of introducing programming for non-computer scientists. While computer science itself (programming) is relatively well described in the subject’s literature, the use of programming in other professions is not well defined. There are different suggestions, recommendations according to the level of education (primary, secondary, higher) or the study-programs the students take. There is no definition of what digital literacy is in different professions, what it means to know computer programming in different professions, and to what extend the students from non-computer science courses should master digital literacy and programming. That can cause challenges for the teachers and students in non-computer science professions that are required to know computer programming for their future jobs. There is no doubt that academic computer science skills for non-programmers can mean/contain different knowledge depending on course curriculum, teachers' experience, chosen literature, but the level of obtaining digital skills should be comparable, adequate, and relevant for the modern citizen. This article presents requirements, some descriptions/cases of introduction to programming for non-computer scientists from a teacher’s perspective. An adaptation of the general programming knowledge into the specific need of different subjects. The data is collected from higher education teachers that have different backgrounds and are teaching at different study-programs to get various views and experiences. The analysis of the findings uses SOLO-taxonomy to compare to what extend the different courses introduce programming to students.