Reviewerly/exHarmony huggingface dataset:
Analysis of Maturity Level Project Management of Software Development In Scrum Framework: Case Research On Tribe Enterprise PT. XYZ

One of the most widely used product developments now is Agile Development Method. In Agile Development there are several frameworks, one of them is Scrum. This research examines the maturity level of software development project management that applies Scrum framework. The research was conducted using quantitative research methodologies using Scrum Maturity Model. Data was collected by distributing questionnaires to employees at a company that works as a Scrum Master. In addition to the data obtained from the questionnaire, interviews were also conducted to confirm answers from respondents. The interview aims to ensure the answers given by respondents are consistent with documentary evidence carried out through the research of project documents. Then the data analysis is done by assessing the level of maturity of each process in the Scrum framework using Agile Maturity Model (AMM) approach. The results from the analysis of Maturity Level Project Management of Software Development are used to provide recommendations for improvement to achieve a higher level of maturity.
The Performance Of The National Narcotics Agency In Illegal Drugs Prevention Efforts Of Palu City, Central Sulawesi, Indonesia

This study aims to determine the performance of Badan Narkotika Nasional or the National Narcotics Agency of Palu in the narcotics prevention efforts, determined by the support of the main tasks and functions of the National Narcotics Agency Office of Palu. The theory used to assess performance is with five dimensions of theory to measure the performance of public bureaucracy which are Productivity, Service Quality, Responsiveness, Responsibility, and Accountability as proposed by Agus Dwiyanto (2002). This research utilized descriptive-qualitative approach. Data collection techniques used are but not limited to: Observation, Interview, and Documentation. Informants in this study are determined purposively. The results of the study revealed that based on the five dimensions of performance theory from Agus Dwiyanto, it can be concluded that the performance of the National Narcotics Agency of Palu is not insufficient. Thus, the vision proclaimed by the Palu City Government which was the Realization of Government and Community Synergy Towards Drug-Free Palu City in 2015, was not achieved
Using SDR Platform to Extract the RF Fingerprint of the Wireless Devices for Device Identification

Due to the advent of the Internet of Things era, the number of related wireless devices is increasing, making the abundant and complex information networks formed by communication between devices.Therefore, security and trust between devices a huge challenge.In the traditional identification method, there are identifiers such as hash-based message authentication code, key, and so on, often used to mark a message that the receiving end can verify it.However, this kind of identifiers is easy to tamper.Therefore, recently researchers address the idea that using RF fingerprint, also called radio frequency fingerprint, for identification.Our paper demonstrates a method that extracts properties and identifies each device.We achieved a high identification rate, 99.9% accuracy in our experiments where the devices communicate with Wi-Fi protocol.The proposed method can be used as a stand-alone identification feature, or for two-factor authentication.
Blockchain Use Cases for Inclusive FinTech: Scalability, Privacy, and Trust Distribution

Few blockchain centric projects have gone beyond their white paper or proofs-of-concept. While many have fallen below expectations and failed to address the fundamental issues of scalability, privacy, and trust distribution, there are a few “imperfect” projects that are making an impact on society. We describe the lessons learned from three projects and highlight their “improvisions” in achieving their vision of serving the underserved, and identify areas of possible improvements. Our research has shown that mass adoption of blockchain technology will accelerate in financial industry and supply chain with private permissioned blockchains, but these e-inclusion projects using “Inclusive” Blockchain will take a longer time with OnChain/OffChain complexities. A long-term view is needed to build a Noah’s Ark as the rush to build the Tower of Babel to harness short term gain may not bring net benefits to the economy and society.
Peripheral Blood Smear Morphology- A Red Flag in COVID-19

Covid-19 pandemic has affected the whole word medically, economically and emotionally. It is being considered as the biggest pandemic after the Spanish flu, with very high degree of morbidity and mortality in those with complications. The diagnostic and treatment criteria of this novel virus are being updated frequently as nothing much is known about it. This highlights the importance of hematology lab parameters in Covid diagnosis and prediction of disease progression. Multiple studies on complete blood counts and it’s derived parameters have been conducted in patients of Covid-19 however limited literature is available which discusses the morphology of circulating blood cells in Covid-19 cases. This short communication is presented with the purpose of highlighting the peripheral blood findings of 50 lab confirmed Covid-19 cases admitted at Super Specialty Pediatric Hospital and Post Graduate Teaching Hospital, NOIDA.
Attendance Management System using Face Recognition

In each and every field there is a need of maintaining attendance management system.The system will maintain a log details of each and every in their respective field.From past few years Automatic Face Recognition (AFR) technologies have fair improvements in performance, and such systems are now widely used for security and commercial applications. Automated face recognition system is helpful for recognizing face of humans in a real time background mainly for college to mark the attendance of their students. Automatic Face Recognition System is a real world solution which is useful in everyday activities. The matched face is used to mark attendance of the student. This system maintains the attendance records of students. The traditional approach of manually entering of attendance in logbooks is a difficult task and it also wastes the time. So this system is going to design an efficient module that comprises of face recognition to manage the attendance records of students. The new module enrols the students details those are collection of images. This enrolment is an onetime process and the students facewill be stored in the database.This system will store data using roll number which will be unique for each student.The presence of each student will be updated into the database.The results showed improved performance over manual attendance management system and system log details.Attendance is marked after student identification.One of the classification process used is Convolution Neural Network (CNN).
Enhancing Bias Detection in Political News Using Pragmatic Presupposition

Usage of presuppositions in social media and news discourse can be a powerful way to influence the readers as they usually tend to not examine the truth value of the hidden or indirectly expressed information.Fairclough and Wodak (1997) discuss presupposition at a discourse level where some implicit claims are taken for granted in the explicit meaning of a text or utterance.From the Gricean perspective, the presuppositions of a sentence determine the class of contexts in which the sentence could be felicitously uttered.This paper aims to correlate the type of knowledge presupposed in a news article to the bias present in it.We propose a set of guidelines to identify various kinds of presuppositions in news articles and present a dataset consisting of 1050 articles which are annotated for bias (positive, negative or neutral) and the magnitude of presupposition.We introduce a supervised classification approach for detecting bias in political news which significantly outperforms the existing systems.
Survey on EFL Students’ Writing Anxiety: Level, Types, and Causes

This study attempts to investigate the EFL learners’ writing anxiety. Being more specific, the researcher was interested to measure the EFL learners’ writing anxiety levels, to find out types of writing anxieties (i.e., Somatic Anxiety, Cognitive Anxiety, Avoidance Behavior), and elaborate the triggering factors of writing anxiety. The research subjects were 28students who registered for Essay Writing in English Study Program; Two different questionnaires were administered to gather the data; SLWAI questionnaire designed by Cheng (2004) and another questionnaire developed by Zhang (2011). To investigate the level of writing anxiety and its types, the data from the SLWAI questionnaire were analyzed and calculated to find the mean. Moreover, to find out the causes of writing anxiety, the data from the second questionnaire were ranked from 1-7 based on the most chosen items. The findings showed that students experience high level of anxiety (M=65.86, &gt;65). Then the most common type of anxiety was Cognitive Anxiety, and the mean score is 23.9 in which it was the highest of all the types. Furthermore, linguistics difficulties (82%), lack of topical knowledge (68%), and insufficient writing technique (60%) were the dominant sources of students’ writing anxiety
eQuilibrium: A Prototype of a Sensor-Based Balance Training and Monitoring System

Integrated sensor devices are becoming a valuable tool for monitoring, evaluation, and real-time feedback during various exercises in sports, physical rehabilitation, and injury prevention. In this paper, we present the design and development of an inertial sensor system integrated into a fitness ball called eQuilibrium. The main application of this system would be balance training, analysis, and monitoring. The sensor system is composed of an inertial measurement unit sensor with a microcontroller and a WiFi communication module. The wireless sensor device is sending data to LabView application running on a laptop computer. The gyroscope and accelerometer provide a variety of signals, and we defined and evaluated three possible metrics to match the signals with the actual execution of the exercises. The metrics include i) angle rotation range, ii) mean absolute angular velocity, mean absolute acceleration deviation, and iv) mean absolute acceleration gradient. For the initial evaluation of the system, we specified test protocol and tasks that were executed by test subjects and monitored by a trained tester. There are four tasks in the protocol, and each task was being executed for at least 5 seconds. The first task was a reference task, standing on the feet of both legs. In the remaining tasks, the subject was standing on the tips of the toes (both legs, single leg), with the heels as high as possible off the ground. We conducted initial tests to analyze the operation of the system and obtain the initial measurement results. Our preliminary results indicate that several scalar metrics derived from motion sensor data can be used to evaluate motion intensity in the control of body balance. Rotation angle range, mean absolute rotation speed, mean absolute acceleration standard deviation, and mean absolute acceleration gradient are scalar values and independent of sensor axis orientation. In the future, we plan to perform more test executions and compare the sensor results with subjective self-reports of the test subjects, as well as the physical rehabilitation expert evaluations.
Twitter Sentiment Analysis on Citizenship Amendment Act in India

Sentiment analysis is an opinion mining process, in which computational analysis and categorization of opinion of a piece of text is done to obtain an unbiased understanding of the writer's opinion towards any specific topic. In this paper, Sentiment Analysis of the twitter user demographic towards Citizenship Amendment Act, which came into effect in India from January 10 th , 2020, has been done. CAA was considered, as it had garnered mixed opinions from different sections of the Indian demographic, so there was no clear understanding of the overall sentiment of the public towards it. It had also led to protests and riots in various parts of India, which the Government struggled to handle as it was unexpected. This paper presents a faster approach of sentiment analysis of a large demographic, by using various classifiers for categorization of opinion of data into positive, negative or neutral. VADER was used for faster and accurate POS tagging of data. Support Vector machine obtained the highest accuracy at 77.32% out of all the remaining classifiers.
On-line Path Planning and Visual Tracking of Fast-Moving Objects by Robot Manipulators with High-Speed Camera Arrays

In this paper, we investigate the problem of tracking a fast-moving object by a robot manipulator. The tracking task considered in this paper includes visually locating and predicting the trajectory of an object, approaching and physically contacting the object with the robot arm. Since the object moves with high and varying speeds, tight integration of a high-speed vision system and the robot motion controller is critical for accomplishing this tracking task. We combine multiple cameras and embedded computers to construct a camera array with low latency and an equivalent frame rate as high as the sampling rate of the motion control law. Furthermore, the camera array is triggered and synchronized by the motion controller. The path of the robot is re-planned on-line at every sampling time whenever the camera array updates the latest position of the object. Experiments were conducted and the results showed that the proposed robotic tracking system can accurately predict the trajectory of a fast-moving object, and successfully contact it with the tip of the robot arm.
VSA-CGAN: An Intelligent Generation Model for Deep Learning Sample Database Construction

In order to solve the problem of model accuracy reduction caused by the difficulty of obtaining specific training samples or the insufficient number of samples in the application of existing object detection and recognition model based on deep learning, this article proposes a conditional generative adversarial network model (VSA-CGAN), which integrates the self-attention mechanism of visual perception to optimize the inference of object attention feature maps, so as to learn the global information of the image and the detailed features of the object.It is designed to add conditional features in the generator and the discriminator, associate the specific dimensions of the data with the semantic features, and explicitly indicate the model to generate the corresponding object signature category information, so as to generate the feature representation of the image which is more suitable for the distribution of the original data.The model in this article has completed numerical experiments on several general standard data sets, and compared with several mainstream generative adversarial network models in image data augmentation performance.The experimental results show that the generation model in this article has excellent object simulation ability and strong application prospects.
Flow Experience in Blended Learning: Behind the Inconsistent Effects of Flexibility and Interaction

Despite the growing importance of blended learning environments in management education, the extant research provides inconsistent results. While many studies find positive effects of blended environments or their central characteristics (i.e., flexibility and interaction) on learning effectiveness, dropout rates remain high and learners prefer face-to-face encounters over online activities. As the cognitive mechanisms behind learning effectiveness have been neglected thus far, we draw on educational psychology to derive an explanation for these contradictions. We introduce flow theory, which describes a learner’s complete immersion in an activity, and we propose that experiencing flow mediates the positive effects of flexibility and interaction on blended learning effectiveness. The results of an experiment with 115 graduate management students support our hypotheses. We contribute to management education research on blended learning environments by looking beyond the direct effects of format characteristics on learning outcomes. We reveal cognitive characteristics, especially flow experience, as an important mediator. Consequently, management education scholars should consider indirect effects from related disciplines when investigating blended learning environments. Furthermore, instructors should ensure that they integrate flexibility and interaction into blended learning environments, and they should account for cognitive learner characteristics.
A random subspace based conic functions ensemble classifier

Classifiers overfit when the data dimensionality ratio to the number of samples is high in a dataset. This problem makes a classification model unreliable. When the overfitting problem occurs, one can achieve high accuracy in the training; however, test accuracy occurs significantly less than training accuracy. The random subspace method is a practical approach to overcome the overfitting problem. In random subspace methods, the classification algorithm selects a random subset of the features and trains a classifier function trained with the selected features. The classification algorithm repeats the process multiple times, and eventually obtains an ensemble of classifier functions. Conic functions based classifiers achieve high performance in the literature; however, these classifiers cannot overcome the overfitting problem when it is the case data dimensionality ratio to the number of samples is high. The proposed method fills the gap in the conic functions classifiers related literature. In this study, we combine the random subspace method and a novel conic function based classifier algorithm. We present the computational results by comparing the new approach with a wide range of models in the literature. The proposed method achieves better results than the previous implementations of conic function based classifiers and can compete with the other well-known methods.
Effect of the nearby levels on the resonance fluorescence spectrum of the atom–field interaction

We study the resonance fluorescence in the Jaynes–Cummings model when nearby levels are taking into account. We show that the Stark shift produced by such levels generates a displacement of the peaks of the resonance fluorescence due to an induced effective detuning and also induces an asymmetry. Specific results are presented assuming a coherent and a thermal fields.
Examining the Digital Competencies of Pre-service Teachers

This study aims to examine the variables that may influence pre-service teachers' digital competencies at the Faculty of Education in Jagodina.If pre-service teachers are expected to integrate information and communication technology (ICT) into future teaching processes, they need to possess a high level of adequate competencies.In this study, digital competencies were identified as: (a) competencies to support pupils for ICT use in the class and (b) competencies to use ICT for instructional design.Four variables (support from teacher training institution (TTI), perceived usefulness, perceived ease of use and attitudes towards ICT use) were hypothesized to have direct and positive influences on digital competencies.This study also investigated whether support from TTI, perceived ease of use, perceived usefulness and digital competencies had any effect on the pre-service teachers' intention to use technology in their future teaching practice.Moreover, the development of pre-service teachers' digital competencies while studying at the Faculty (i.e differences between freshmen, juniors and master students) were examined.With data gathered from 131 participants, using a survey questionnaire, linear regression analysis revealed positive effects of the strategies, perceived usefulness, attitudes, and perceived ease of use on pre-service teachers' digital competencies.Furthermore, the results revealed a positive impact of pre-service teachers' perceived usefulness, perceived ease of use and digital competencies in their intention to use digital technology in future teaching.On the other hand, the strategies do not affect intention to use ICT.Various contributions to research and implications for teacher training are discussed.
Efficient Deployment of Conversational Natural Language Interfaces over Databases

Many users communicate with chatbots and AI assistants in order to help them with various tasks.A key component of the assistant is the ability to understand and answer a user's natural language questions for questionanswering (QA).Because data can be usually stored in a structured manner, an essential step involves turning a natural language question into its corresponding query language.However, in order to train most natural languageto-query-language state-of-the-art models, a large amount of training data is needed first.In most domains, this data is not available and collecting such datasets for various domains can be tedious and time-consuming.In this work, we propose a novel method for accelerating the training dataset collection for developing the natural language-to-query-language machine learning models.Our system allows one to generate conversational multi-term data, where multiple turns define a dialogue session, enabling one to better utilize chatbot interfaces.We train two current state-of-the-art NL-to-QL models, on both an SQL and SPARQL-based datasets in order to showcase the adaptability and efficacy of our created data.
Pydra - a flexible and lightweight dataflow engine for scientific analyses

This paper presents a new lightweight dataflow engine written in Python: Pydra.Pydra is developed as an open-source project in the neuroimaging community, but it is designed as a general-purpose dataflow engine to support any scientific domain.The paper describes the architecture of the software, as well as several useful features, that make Pydra a customizable and powerful dataflow engine.Two examples are presented to demonstrate the syntax and properties of the package.
Spirituality in OT Practice: Where Is Our Spirituality Now?

Abstract Date Presented 03/26/20 This presentation will explore the meaning and importance of spirituality, as well as provide insight into the current landscape of spirituality in practice through the perspectives of OT practitioners across diverse settings. Primary Author and Speaker: Loree Pham
Launching Vehicle Acoustic Data Compression Study Using Lossy Audio Formats

The field of data compression has evolved over the last decades. In this way, several techniques to reduce the amount of acquired data from the sensor required to be transmitted have been developed. Those techniques are usually classified by lossless or lossy, where, for the lossless techniques, all acquired data is recovered, while the lossy techniques introduce errors to these data. Each of these techniques presents advantages and drawbacks, being the analyst responsible for choosing the appropriate technique for a specific application. This work presents a comparative study using lossy audio formats to be applied on a launch vehicle on-board acoustic data. The Opus format achieved a higher compression rate in comparison with standard compression techniques by saving up to 254 times the required amount of data to be transmitted through a telemetry link on launcher vehicle, and the lowest discrepancy from original data measured by the mean square error metric.