Reviewerly/exHarmony huggingface dataset:
Approaches for Inserting Neurodynamics into the Training of Healthcare Teams

Team neurodynamicsTeam Neurodynamics is the study of the changing rhythms and organizations of teams from the perspective of neurophysiology. As a discipline, team neurodynamicsTeam Neurodynamics is located at the intersection of collaborative learning, psychometrics, complexity theory, and neurobiology with the resulting principles and applications both drawing from and contributing to these specialties. This article describes the tools for studying team neurodynamicsTeam Neurodynamics and illustrates the potential and the challenges these methods and models have for better understanding healthcare team training and performance. The fundamental metric is neurodynamic organizationNeurodynamic Organization, which is the tendency of teams and its members to enter into prolonged metastable relationships when they experience and resolve uncertainty. The patterns of these relationships are resolved by symbolic modeling of electroencephalographic (EEG) power levels of the team members, and the informationInformation in these patterns are calculated using informationInformation theory tools. The topics discussed in this chapter anticipate the time when dynamic biometric data can contribute to our understanding of how to rapidly determine a team’s functional status, and how to use this informationInformation to optimize outcomes and training. The rapid, dynamic, and task neutral measures make the lessons learned in healthcare applicable to other complex group and team environments, and provide a foundation for incorporating these models into machines to support the training and performance of teams.
Detection and measurement of obstacles on a track using color segmentation with background subtraction and morphological operation

Abstract The process of detecting objects in a path of the image frame is a complex process. Generally, the identification process to find out the condition of a track in the form of objects and the size of objects is done manually by humans, namely using measuring instruments such as rulers, etc. It requires long processing time, and is less efficient, besides that it cannot be done remotely in the form of monitoring on the computer from the camera in terms of measurement. This study aims to create a system of detection and measurement of obstacle on a track using color segmentation method with background subtraction and morphological operation as a result of decision support. This detection and measurement system consists of webcam image acquisition, video extraction, converting frames of RGB to HSV images, color segmentation with background subtraction, gray scaling and thresholding, morphological analysis to improve the results of segmentation, detection and measurement displayed in the form of a Graphical User Interface (GUI). The performance of the system was tested on two background color references in the form of black, namely the asphalt track and white, namely the concrete cement track. The result of testing with the black background, produces an accuracy value of 96%, and with a white background of 100%. The system results in measuring obstacle have an average length of accuracy of 96.16% and a height of 85% on a black background in the form of an asphalt track, and on a white background in the form of cement concrete track results in a length measurement accuracy of 95.99% and a height of 90.88% with the camera moving or fixed.
Scoring of Streaming Data in Game-Based Assessments

In this chapter we describe an exploration of the problem of automated scoring of process data from a game or simulation. Unlike a traditional test administration, this assumes that the test take is working on a remote computer which is streaming event log data to the scoring server. There are two complications that arise in processing streaming event data. First, because of networking issues, data may not all arrive at the scoring server or may be out of order or delayed. Second, the event log data are usually voluminous, and need to be summarized as a collection of observables before they are entered into psychometric models for scoring (e.g., Bayes nets, item response theory, or diagnostic classification models). The prototype software used to solve these problems for the educational game Physics Playground and the lessons learned from that application are discussed.
Applying Formal Methods to SOA and SaaS Service Compositions for ERP Systems

Service oriented architectures (SOA) and software as a service (SaaS) models are accelerating the use of the service concept. They have been rapidly adopted by enterprise resource planning (ERP) providers and offers to organizations modern environments favorable for making strategic decisions. However, as the SaaS concept is often confused with the SOA one, a solution is needed to clarify this problem before moving to the introduction of a framework that aggregates several services. Moreover, most of the services-based solutions rely on a particular composition and only a few are equipped with appropriate formal tools for automating the service verification. They focus on specific formal models and are based on particular properties. This article proposes a formal framework that reveals a set of general steps starting from defining the service composition before shifting to the use of the chosen formal methods so as to verify the ERP system properties.
Mediclaim Fraud Detection and Management Using Predictive Analytics

Medical insurance fraud detection has become very significant and a demanding task. Fraudulent activities occurring in insurance claims has resulted a huge loss to the Health insurance companies. The goal of this proposed system is to collect and analyze insurance data including insurance claims, hospital records, past insurance claim data, patient's data and to provide a single platform for checking and providing a list of suspicious claims using predictive analytics. Predictive analytics helps to assess risk associated with certain conditions, and guides in decision making. The medical insurance dataset is viewed as a collection of data of hospitals, insurance company, doctors, patients, pharmacies, and other fields. These data can be huge with many doctors, hospitals and millions of patients details involved. In this work, a mediclaim fraud detection system is designed using predictive analytics, which detects fraudulent medical claims resulting from substantial monetary loss in healthcare systems. An analytical approach to detect medical claim insurance fraud is done using Logistic regression. The fraudulent claims are to be detected using the data from various sectors like insurance company, hospitals, pharmacy, and insurance policyholder. So a multi criteria decision support system is developed to predict if a claim is fraudulent or legitimate. The experimental results demonstrates that proposed model using logistic regression and multi criteria decision analysis performs better than the existing system.
Advertisement Recommendation System Based on User Preference in Online Broadcasting

Today, advertisement has become a part of media content and it is exposed to viewers in ways such as home shopping, PPL (Product Placement), and T-commerce. Even though media contents and platforms have been developed into user-interactive services, the interaction between advertisement systems and users is still limited. For advertising services to interact with users, a recommendation system should be introduced. Recommendation systems have been studied such as content-based, collaborative filtering, and hybrid methods. Since conventional recommendation systems require high computational complexity in the preference prediction process and do not consider the time factor, there are limitations in performing recommendation considering user preference. In this paper, we propose an advertisement recommendation system that predicts the ATU’s (Active Target User) preference for items in real-time. The proposed system creates PRETREE by modeling ATU's history data with a tree data structure for reducing computational complexity. A recommendation is triggered when the item that the ATU is looking at in the media content is detected. If there are rating records for the item in the history of the ATU, the preferences are predicted on the basis of PRETREE. If there is no history for the item, the proposed system predicts preference on the basis of similar groups and the LCC (Longest Common Category) node at the ATU's PRETREE. Finally, we evaluate the performance of the proposed recommendation system using real- world data.
Data Invariants: On Trust in Data-Driven Systems.

The reliability and proper function of data-driven applications hinge on the data's continued conformance to the applications' initial design. When data deviates from this initial profile, system behavior becomes unpredictable. Data profiling techniques such as functional dependencies and denial constraints encode patterns in the data that can be used to detect deviations. But traditional methods typically focus on exact constraints and categorical attributes, and are ill-suited for tasks such as determining whether the prediction of a machine learning system can be trusted or for quantifying data drift. In this paper, we introduce data invariants, a new data-profiling primitive that models arithmetic relationships involving multiple numerical attributes within a (noisy) dataset and which complements the existing data-profiling techniques. We propose a quantitative semantics to measure the degree of violation of a data invariant, and establish that strong data invariants can be constructed from observations with low variance on the given dataset. A concrete instance of this principle gives the surprising result that low-variance components of a principal component analysis (PCA), which are usually discarded, generate better invariants than the high-variance components. We demonstrate the value of data invariants on two applications: trusted machine learning and data drift. We empirically show that data invariants can (1) reliably detect tuples on which the prediction of a machine-learned model should not be trusted, and (2) quantify data drift more accurately than the state-of-the-art methods. Additionally, we show four case studies where an intervention-centric explanation tool uses data invariants to explain causes for tuple non-conformance.
A Study of the Upgrade of Competency-Based Major Curriculum System: With a Focus on the Case of D University

본 연구는 전공역량 기반 교육과정 체계 고도화를 위해 전공교육과정의 체계, 교육과정에 맵핑된 전공역량 분석, 타 대학 사례조사, 교육과정 실태조사 등을 활용하여 문제점과 한계점을 탐색하여 개선방안을 도출하였다. 연구결과를 바탕으로 전공역량 기반 교육과정 체계 구축 시 중점을 두어야 할 사안을 정리하면 다음과 같다. 첫째, 입학부터 졸업까지 아우를 수 있는 전반적인 교육의 질 관리체계 고도화가 필요하다. D대학은 2014학년도 역량에 대한 정의를 시작으로 전공역량 기반 교육과정구성, 교육과정 평가로 이어지는 체계를 구축하여 운영해왔다. 그러나 교육과정 체계의 상호보완성, 교육의 정체성과 비전을 정의하고 이를 평가 할 수 있는 평가체계(평가·환류)가 미흡한 것으로 나타났다. 따라서 대학 교육목표정립, 대학 핵심역량설정, 사회수요 및 전공특성이 반영된 전공특화역량 설정, 전공역량이 반영된 교육과정 체계구축, 교육성과 측정으로 이어지는 전반적인 질 관리체계 개선이 필요하다. 둘째, 전공역량 기반 교육과정 체계 개선이 필요하다. D대학은 대부분 학과에서 대학 핵심역량 기반 전공교육과정 체계 구축을 통하여 핵심역량이 전공 교육과정에 맵핑되었다. 그러나 대학 핵심역량을 기반으로 한 전공역량 설정이 교과목과의 연관성이 미흡하다는 지적에 따라 직무역량을 기본으로 학과특성이 반영된 전공특화역량을 2017학년도에 지정하였지만, 여전히 전공특화역량 지정에 어려움을 겪고 있는 것으로 나타났다. 따라서 D 대학은 이러한 한계와 전공특화역량의 교과목과의 부적절한 연관성을 개선하기 위한 방안을 다각도로 모색하고 있다.This study derived improvement measures to upgrade the competency-based system of major courses by exploring problems and limitations through the curriculum system, an analysis of major competences mapped to the curriculum, case studies of other universities, and surveys of the status of major courses. Based on the research results, the following issues should be addressed to focus on establishing a competency-based curriculum system. First, it is necessary to upgrade quality control of the education system over all from admission to graduation. D University has established and operated such a system beginnings with the definition of competence during the 2014 school year and leadings to competency-based curriculum composition and evaluation. However, the evaluation system (assessment and feedback) does not adequately define or evaluate the complementarity of the curriculum system or identify the vision of education. Therefore, in the University, it is necessary to establish educational goals core competences and major competences reflecting social needs and major characteristics. Furher, the curriculum system must reflect major competences, and improve the overall quality management system leading to educational performance and evaluation measures. Second, improvement in the system of competency-based major education curriculum courses is necessary. At D University, core competences were mapped to the major curriculum through the establishment of a system of major curriculum courses based on the core competences of the University in most departments. However, it was revealed that D University still has difficulty designating the special abilitiesy for majors, al though they has been designated based on job competencies in 2017 and reflect the characteristics of departments. It was pointed out that the establishment of major competences based on the core competences of D University was not sufficiently related to major subjects. Therefore, D University is looking at various ways to improve such limitations and inappropriate associations with the subjects of major specialty competences.
Estimation of User-Specific Visual Attention Based on Gaze Information of Similar Users

This paper proposes an estimation of user-specific visual attention based on gaze information of similar users. The proposed method estimates the user-specific visual attention by using the eye gaze data of other similar users. Then the similar users are selected based on the past eye gaze data of the target user. Although introducing the eye gaze data of the similar users into the estimation of user-specific visual attention is a simple approach, it can break the limitation of estimation performance. This approach is the main contribution of this paper. Experimental results show the effectiveness.
Automatic Grading for Program Tracing Exercises

Understanding how a programming construct executes is a prerequisite for coding. Tracing exercises are often used to help students develop accurate mental models of how different constructs execute. Besides asking students for the final result, a full tracing exercise, similarly to an instructor's white-board demo, checks whether students understand the execution flow and how the memory/output changes with each step of execution. Such exercises can be time-consuming to grade or set up. For paper-based full tracing exercises, once submitted, they represent the final state of the memory/output. It is time-consuming to infer whether students have traced each step correctly. For an auto-gradable quiz, if the questions are too specific (e.g. the value of a variable), they already reveal partially what the program will do. It takes time to set up auto-gradable questions that avoid this problem. We are developing a web-based system that allows instructors to set up auto-gradable full tracing exercises easily. Our approach is to augment pythontutor.com, a popular open-source code visualization system that graphically demonstrates the execution flow and memory/output changes of a complete program. Before demonstrating a step, the new system will prompt a student to determine which line is executed next and what changes happen in memory/output. The system will be auto-gradable, making it easy to deliver full tracing exercises. We will demonstrate implemented features, discuss future plans, and gather feedback from those present.
Prototype of an Automated Feedback Tool for Intro CS Theory

Computing theory is an important part of computer science education, introducing students to computational models of increasing power to study possibilities and limitations of computation. The subject is, however, very abstract and mathematical, and students often struggle with it. Students must master various computational models, but there is often a lengthy delay from the time a model is introduced until a student gets feedback on their related assignment. During this time, the course has typically moved far ahead, and students become progressively more lost. To alleviate this problem, we developed a prototype of an automated feedback tool for CS theory, which extends the widely used JFLAP software. Our tool currently handles student submissions of deterministic and non-deterministic finite automata, regular expressions, context-free grammars, and push-down automata homework, where an instructor specifies the target language and the students receive immediate feedback on their submissions. Currently, for incorrect submissions, the feedback is in the form of a "witness'' string, specifying a string on which the submission fails. Beyond regular languages, our tool attempts to solve undecidable problems; fortunately, the undecidability does not occur on typical homework assignments. We are collecting preliminary evaluation data from students using the prototype tool in their course. In our future work, we will analyze the data, and we aim to produce automated partial credit (along with the witness feedback) using SAT and QBF solvers.
Predicting Bug Estimation Time for Newly Reported Bug Using Machine Learning Algorithms

In commercial projects, number of defects raised directly depends upon the releases. In order to get the idea of progress of the project it is necessary to estimate the average time required to fix the bug. This time is referred as bug estimation time. It is essential to estimate the time of software bug for a proper project planning. In this paper, a new algorithm is proposed to determine bug estimation time with the help of machine learning algorithms. New developer is predicted and its average bug estimation time is calculated based on the bug estimation time of already existing developer. A comparative study for accuracy for different machine learning algorithms is carried out.
Multisensor JiFi Tracking of Extended Objects

A multisensor, multiobject tracking filter for extended objects is proposed. Objects are observed via measured point highlights. No restrictions are made about object shape. The number of objects is given. The sensors may be heterogeneous. It is assumed that the spatial diversity of the sensor field relative to the objects under track is such that sensors observe different sets of highlights from each object. Examples are given for two idealized kinds of sensor reports-position-only contacts and bearings-only contacts.
Design and Implementation of Machine Learning Evaluation Metrics on HPCC Systems

The HPCC Systems Production Machine Learning bundles provide a diverse set of features that allow the parallelized creation and training of Machine learning models and a large set of evaluation metrics that can be used to test the trained model to ascertain its performance. To help monitor the models more closely however, a new set of evaluation methods that incorporate the analysis of clusters and the selection of features, as well as other commonly used tests, have been proposed, implemented, and tested. The implementations are written completely in Enterprise Control Language and support the various features provided by the Machine Learning bundles such as the Myriad Interface. This paper provides a comprehensive summary of the evaluation metrics currently available in the library, before presenting the details of the design and implementation of the new evaluation methods. It the goes on to present the results of testing these implementations against implementations present in the python scikit-learn library, and a few data visualisations demonstrating some uses of the implemented evaluation metrics.
Image Super-Resolution Reconstruction Based on Multi-scale Convolutional Neural Network

For image super-resolution based on convolutional neural network, there are many problems such as large amount of calculation, many parameters, and unresolved images. This paper proposes an image super-resolution reconstruction algorithm based on multi-scale convolutional neural network. The multi-scale convolution kernel method is introduced into convolutional neural networks. Multi-scale feature extraction is achieved for different sizes of convolutional layers, at the same time, the learning parameters are improved and the network parameters are reduced. Maxout is used as an activation function to introduce competing elements. At the same time, the Skip Connection in the residual network is added to the network model to accelerate the training of deep neural networks. Experiments show that the subjective visual and objective evaluation of this algorithm has been improved to a certain extent. The edge effect of the reconstructed high-resolution image is more clear when reducing the number of network parameters, and more detailed image information is recovered.
Applying a mixed light field generated from a two-level atomic ensemble to two-photon interference

A mixed light field generated from a two-level atomic ensemble can be used for two-photon interference. In this mixed light field, correlated paired photons generated from a four-wave mixing process provide a signal of two-photon interference, while Rayleigh scattered photons of the pump laser provide a stable reference to calibrate the normalized second-order correlation function. We demonstrate two-photon interference using the Hong–Ou–Mandel and Hanbury Brown–Twiss interferometers. A direct quantitative comparison between theoretical predictions and experimental data is performed under perturbed experimental conditions, which reveal this kind of light source has potential application for quantum metrology.
Schemes for Verification of Resources in the Cloud: Comparison of the Cloud Technology Providers

Cloud technology providers differ from each other not only by the level of their computing cloud abstraction level but also by different computing environment conditions. The most efficient, one-use verification scheme is a hash-based function scheme that processes the entire single file per single-use. Data possession verification schemes are feasible even on mobile devices. One solution for single-use would be to delegate cyclical verification to a private server or other cloud computing. Response times to challenges can be used as proof of having resources such as computing power. Due to the powerful computing and storage, high availability and security, easy accessibility and adaptability, reliable scalability and interoperability, cost and time effective cloud computing is the top, needed for the current fast-growing business world. This work provides classification, which may help to survey of several existing cloud computing services developed by various projects globally such as Amazon, Google, Azure, Open Stack and Eucalyptus. Similarities and differences in the architecture approaches of cloud computing were identified in this chapter.
Archiving 4.0: Application of Image Processing and Machine Learning for the Robben Island Mayibuye Archives

Image processing is becoming one of the key technologies of the fourth industrial revolution (4IR) which provides different ways of detecting and recognizing faces, persons and other objects in an image. This can benefit large archives with historical images such as the Robben Island Mayibuye Archives where many figures of the Apartheid struggle appearing in pictures are still unknown. This paper analyzes the findings of the first step of Archiving 4.0, a project that aims of using 4IR techniques to support and improve archiving in South Africa. It aims at finding the best among different models for face detection by evaluating their performance on a subset of the images from the Robben Island Mayibuye Archives. For a range of selected face detection models, the Intersection over Union and the Average Precision are calculated. The models are evaluated and compared on the basis of these metrics and their speed. The results show the strengths and weaknesses of the different approaches and reveal which of the approaches is best to be used in which situation.