Reviewerly/exHarmony huggingface dataset:
Various Methods for Fraud Transaction Detection in Credit Cards

Cashless payments have become effortless with the advent of new technology and the internet.But, for online transactions, you don't have to be in a certain place where the transaction occurs, making it vulnerable to fraudulent attacks.A cyber-attacker can pretend to be the owner of a credit card and make a fraudulent transaction.There are several techniques to determine the nature of the transaction, for instance, by comparing the current transaction with previous transactions.If the monetary difference between current transaction and previous transaction is too large, then there is a greater probability of current transaction being a fraudulent transaction.This method is not reliable for anomaly detection.In some countries like India and China, banks deploy a two-step verification process which strengthens the security of the transaction.While in other countries, employees in the bank manually segregate the transactions to be fraud or not.These methods are highly dependent on human intervention.Machine Learning can be utilized to automate the process of anomaly detection.Supervised algorithms such as Logistic Regression can be used to build a model that will predict the output in the form of binary classes i.e. 0 for a valid transaction and 1 for a fraudulent transaction.Autoencoder Neural Network is one of the unsupervised algorithms using which better accuracy can be obtained for anomaly detection.In this paper, we explain different machine learning algorithms viz; Hidden Markov Model, Artificial Neural Network, and Convolutional Neural Network.Moreover, Logistic Regression is implemented, and the results obtained are highlighted.
UPAYA MENINGKATKAN KETERAMPILAN MENGAJAR GURU BERBASIS MODEL PEMBELAJARAN TEAM GAME TOURNAMENT MELALUI BIMBINGAN BERKELOMPOK DI SDN 016 BANJAR LOPAK

This study aims to improve teacher teaching skills based on team game tournament learning models through group guidance. This research was conducted in September - October 2019 at SDN 016 Banjar Lopak with 14 subjects as teachers. The study design uses school action research that has stages of planning, implementation, observation, and reflection. The research instrument used observation sheets. The results showed an increase in teacher teaching skills based on team game tournament learning models in the first cycle by 71% which is enough to be 87% which is included in the excellent category. Berdasarkan hasil dan pembahasan dapat disimpulkan bahwa melalui bimbingan berkelompok dapat meningkatkan keterampilan mengajar guru berbasis model pembelajaran team game tournament.
Human-in-the-Loop Tasks for Data Management, Citizen Sensing, and Actuation in Smart Environments

Humans are playing critical roles in the management of data at large scales, through activities including schema building, matching data elements, resolving conflicts, and ranking results. The application of human-in-the-loop within intelligent systems in smart environments presents challenges in the areas of programming paradigms, execution methods, and task design. This chapter examines current human-in-the-loop approaches for data management tasks, including data integration, data collection (e.g. citizen sensing), and query refinement. A comparison of approaches (Augmented Algorithms, Declarative Programming, and Stand-alone Platforms) that can enable human tasks within data management is presented. The chapter also covers spatial tasks where users within the smart environment are requested to take physical actions in the environment in the form of citizen actuation.
Evaluation of the Quality of Documentation of Basic Professional Educational Programs in the Electronic Environment

The article reveals the issues of assessing the quality of the preparation of documentation for basic professional educational programs in an electronic information and educational environment. The assessment levels documentation are disclosed. Aspects of a formal assessment are described — criteria and indicators, potentially possible problem situations, ways of implementing an assessment using the instrumental resources of a development system and evaluating the quality of educational programs in an electronic environment. The possibilities of eff ective assessment of the content of the educational program through the passports of competencies, understood as documents containing a summary of the formation of competencies, are revealed. The structure of such passports of competencies is substantiated, the possibilities of determining on their basis substantive repetitions of disciplines and practices, incompleteness of the formed content of competencies, violation of the logic of formation of competencies, inconsistency of forms of educational work and assessment tools to the nature of competencies are described.
Understanding and Optimizing Delay-Sensitive Interactive Mobile Application Systems

Mobile video services are usually offered through centralized cloud servers and account for over half of the total data consumption of mobile networks. In addition to the traditional video applications, new types of latency critical video services have emerged in recent years which also require or would benefit from cloud deployment. These applications include mobile cloud gaming, mobile augmented and virtual reality and deep-learning based applications which utilize object detection and recognition models. The latency from user input to response needs to be low for all of the example applications to ensure a good quality of experience. This makes the deployment of such services difficult as the service provider needs to consider the entire delay pipeline from the user device to the cloud server. In this thesis, we dissect the entire delay pipeline in latency critical mobile video applications through extensive measurements. We introduce novel ways to measure the client hardware delays and inject timing hooks into real applications to determine the largest latency bottlenecks in latency critical mobile applications. For deep learning based object detection and recognition, we compare mobile and server-side performance and show multiple latency-throughput trade-offs when tuning the system parameters. In our latency studies, we use real application scenarios and also provide optimization strategies for remote rendered applications. Regarding cloud gaming, we perform a Europe-wide latency study to determine the impact of data center location count to the overall latency. We vary the data center location count, network access method, and control input. We show that in specific scenarios it is possible to deploy a cloud gaming service in which the user cannot perceive the delay from user input to response on the device display even with current networks and devices. We also study the server-side architecture possibilities and show how containers can be a more lightweight solution than traditional virtual machines in remote rendering applications. In addition, we present specific methods to extend remote rendering also to virtual reality applications which have even stricter latency requirements than traditional cloud gaming. Our optimization strategies hide the latencies related to user head movement from the user and hinders the server-side computational requirements by utilizing dynamic object placement and various graphics optimization methods. Using the findings of this thesis, service providers can design the architecture and distribution of their systems based on the specific latency requirements of their application.; Matkapuhelinverkkojen liikenteesta yli puolet aiheutuu videopalveluista, jotka palveluntarjoaja yleensa tarjoaa keskitettyjen pilvipalveluiden kautta. Perinteisten videopalveluiden rinnalle on syntynyt viime vuosina uusi latenssikriittisten sovellusten joukko, jotka voivat myos hyotya pilvijakelusta. Naita sovelluksia ovat esimerkiksi mobiilipilvipelaaminen, lisattya ja virtuaalitodellisuutta…
LibGuides: Orientation: Module 1

This tutorial and online quiz is created by the Thurgood Marshall Library to orient new students to the library's services and resources.
ADVANCING PROGRAM REACH BY TAKING TRAINING ONLINE

Abstract The National Recreation and Park Association (NRPA) is one of four national organizations to receive funding from CDC to disseminate arthritis-appropriate evidence-based interventions (AAEBIs). NRPA has partnered with Fit &amp; Strong!, a CDC-approved AAEBI and National Council on Aging falls prevention program, to disseminate through local park and recreation agencies across the country. To meet the needs of this dissemination effort, Fit &amp; Strong! adapted its in-person instructor training into an online curriculum which became available March 2018. After initial pilot-testing, Fit &amp; Strong! created a five-module training curriculum using Moodle teaching software. The training curriculum uses slides, graphics, videos, and interactive questions to assess mastery. After completing the training, fidelity checks are performed on all instructors by having them video record a class session and share it with the Fit &amp; Strong! team for review. After evaluating the videos, the team meets with the instructors by phone to debrief and advise on any needed improvements. Through this partnership, Fit &amp; Strong! trained an initial cohort of 39 instructors across 18 states within a month’s time. Training evaluation results have been positive. To date, over 900 participants have been reached. A second cohort of approximately 40 new instructors will be trained in Spring 2019. This presentation will review the processes of creating and executing the online training, potential cost and time-saving benefits to CBOs, instructor feedback, and the ease with which instructors can complete the training and offer the program anywhere across the country.
Using Regression Analysis to Identify the Predictive Ability of the Achievement Test and the Secondary School Rate in the Prediction of the Cumulative Rate

this study aimed to identify the predictive ability of the achievement test and the secondary school rate in the prediction of the cumulative rate of scientific specialization for students of the Faculty of Science at Prince Sattam Bin Abdul-Aziz University.The data of the study was collected from the Deanship of Admissions and Registration for the academic year (2017)(2018).The sample of the study was (180) students.Linear regression was adopted in the data analysis.The results revealed a positive correlation between the secondary school rate and the university cumulative rate.A positive and weak correlation between the achievement test and the university cumulative rate was found.In addition, the secondary school rate and achievement tests were able to predict the cumulative rate in the specializations of mathematics, chemistry, and biology.The study recommended reconsidering the policy of admitting students in Saudi universities by adding other criteria besides the secondary school rate and the achievement test suited to the nature of each university.
SPECIFICATIONS OF APPROXIMATION k-VALUE FUNCTIONS AND THEIR PROPERTIES

This article discribes different characteristics of proximity specifications k -value function. Most of the authors consider characteristics of proximity specifications in boolean case as Hamming distance only, but we can suggest several different approaches to definitions of this terms. The solution to this problem will allow to expand the number of methods for analysis and building information processing and security data system.
Novel triangular pattern generation method for fast and accurate three-dimensional shape measurement

Three-dimensional (3D) shape measurement of fringe projection profilometry (FPP) with high speed and accuracy is required in many applications. The measurement speed of FPP system is mainly limited by the low frame rate of pattern projection. Although defocusing binary patterns can be used to increase the measurement speed greatly, it will decrease the measurement depth. To this end, a novel triangular pattern generation method is proposed in this paper. By designing and projecting binary strip patterns with focused projector, images of triangular patterns can be then acquired with high frame rate. Fast and accurate 3D shape measurement can be finally obtained by using phase-shifting triangular patterns. Compared with the traditional sinusoidal patterns, the proposed method can improve the projection rate more than 4 times. Moreover, the proposed method will not decrease the measurement depth. Experimental results are given to confirm the validity of the proposed method.
Enabling Intelligent Systems, Applications, and Analytics for Smart Environments Using Real-time Linked Dataspaces

The design of next-generation smart environments poses significant technical challenges with data management, data integration, and real-time processing of dynamic data, and non-technical challenges such as engaging end-users and supporting cultural and organisational changes. Real-time Linked Dataspaces (RLD) are data platforms designed explicitly to tackle these challenges. In this chapter, we provide an overview of how a Real-time Linked Dataspace enables the creation of intelligent systems within several smart environments to demonstrate and evaluate their effectiveness.
Predictive Healthcare : Cervical Cancer Screening Risk Stratification and Genetic Disease Markers

The use of Machine Learning is rapidly expanding into previously uncharted waters. In the medicine fields there are vast troves of data available from hospitals, biobanks and registries that now ar ...
Design and Implementation of Intelligent Construction Engineering Information Management System Supporting Mobile Platform

This paper introduces the contents and requirements of construction project management, the guiding ideology, popular mode and characteristics of the development of MIS. The intelligent construction project management system uses object-oriented software development method and UML as modeling tool to develop the system. According to the standard process of software engineering, this paper elaborates the whole process of development from the aspects of requirement analysis, design, code implementation and testing. The system modeling runs through the whole development process and UML use case diagrams, activity diagrams, class diagrams are provided. The designed system adopts B/S architecture to support various mainstream browsers, and it also supports Android smart mobile terminal to provide mobile office platform. The test and trial running results show that the system runs reliably, stably and easily, which reduces manpower cost and improves management level achieving the expected object.
An Analysis of Higher English Teaching Mode for TCM“5+3” under the Background of Innovative Teaching Concept

With the development of the new concept and thesocial globalization, TCM has spread all over the world. As an important language tool for spreading TCM, theEnglish in TCM should be attached great importance to in colleges and universities. We should comprehensively promote education modernization era of new requirements, based on the full implementation of fundamental task of the new era mission, continue to deepen reform of undergraduate education and teaching, to speed up the construction of between teachers and students community, vigorously promote the modern information technology and education depth fusion, guide the province's colleges and universities actively explore the reform of curriculum innovation, speed up the implementation by teachers teaching as the centre to learning centred idea transformation, makes the Jilin gold classroom.
Model of Online-Teaching Platform Use in Higher Education Business School

This study aims to depict a model of Information and Communication Technology use, especially an online teaching-learning platform known as eLearning, in a Higher Education Business Schools.The investigation focuses on two aspects, how and why HEBS lecturers use eLearning in teaching.Sixty five lecturers participated in the study.Data was collected through interviews and physical artefact study by accessing eLearning accounts of each lecturer.Using a qualitative descriptive analysis approach, the results shows that although all participants had accessed eLearning, but the utilization was not as expected.Mostly the lecturers used eLearning just for uploaded their course material.Their main reason for using eLearning platform is the pedagogical reason, it is using online teaching platform to improve learning process.However, this reason is inconsistent with the fact that not all lecturers use eLearning actively and optimally to improve the quality of lectures.This research contributes to the development of Faculty policies, particularly in the improvement of the quality eLearning use which can improve teaching and learning.
Chemistry, a step ahead in Rock Physics applied to Time-lapse Seismic

In carbonate rocks, oil production monitoring by seismic is always defiant, and things become much more difficult when this kind of reservoir is found at big depths, as in brazilian pre-salt offshore fields.Due mainly to the high stiffness and low porosity, we need leading edge geophysical technologies in order to get some indirect information about reservoir dynamics.Knowing that, the very first step to support the decision of investing in a 4D seismic project is the feasibility study, i.e., the numerical simulation of changes in rock properties and, consequently, seismic attributes.In this way, assuming that rock-physics is not all we need, the purpose of this paper is to propose the incorporation some chemical theory in order to build, in the future, a time-lapse seismic modeling methodology that could take into account the effect of acidification in carbonates.A methodology that could be informally denominated "rock physical chemistry".
Distributed Model Predictive Pose Control of Multiple Nonholonomic Vehicles.

This paper investigates a method for pose control of multiple nonholonomic vehicles using time-optimal control in a Model Predictive Control (MPC) framework. The vehicles are driving on a limited space considering field boundaries to build-up a formation. Distributed Model Predictive Control (DMPC) in a priority-based manner reduces computation time and avoids vehicle collisions. Priorities are automatically set by evaluating the target poses of the vehicles. We demonstrate our method in simulations. It produces near-optimal trajectories and reduces the compuation time in comparison to centralized MPC.
Active Contour Models Based on Block Similarity for Multiple Objects Segmentation

For the model of active contours with group similarity (ACGS), a rank constraint for a group of evolving contours is defined to keep the shape similarity. ACGS obtains robust results in extracting a single object with missing or misleading features. However, with one initial contour, it could not extent to multiple objects segmentation because low-rank property will not hold in some image sequences. Besides, ACGS is affected by nontarget objects. In this paper, an active contour model based on block similarity of shapes is proposed to extend the ACGS model to realize multiple objects extraction. For a sequence of image with multiple objects, a model for multiple objects extraction is constructed by combining sparse decomposition and ACGS; second, a block low-rank constraint is proposed to constrain the similarity of these evolving contours in every block; finally, segmentation results are obtained through iterative evolutions. Experimental results show the proposed method could segment images with multiple targets, and it improves the robust segmentation performance of sequence of image when the features of multiobjects are missing or misleading.
The automatic categorisation of space mission requirements for the Design Engineering Assistant

To enhance Knowledge Reuse in the field of space mission design, the implementation of Information Retrieval (IR) is key. Topic Modeling (TM) is used to identify, learn and extract topics from a corpus of documents, and can therefore support several IR tasks such as categorisation. This study relies on a common TM method, Latent Dirichlet Allocation (LDA), a probability-based approach. An extensive Wikipedia-based corpus focused on space mission design is collected, parsed, preprocessed, and used to train a general ’Space Mission Design’ LDA model. The LDA model is optimised based on the perplexity measure for a range of topics numbers. The topics dictionaries of the retained model are labelled by human annotators, with labels corresponding to spacecraft subsystems. The performances of the general model are evaluated against a set of space mission requirements with a categorisation task. The general model is then used as a base to generate specific LDA models focused on one topic, or spacecraft subsystem. The general LDA model developed in this study proves to be a solid base for the generation of focused LDA models, yielding very high accuracy scores and Mean Reciprocal Ranking.Finally, a semi-supervised LDA model, fed with lexical priors is trained, leading to improved performances of a general model
Sharing Knowledge is Cool: OA experiments in context

This symposium was supported by the Arcadia Fund, a charitable fund of Lisbet Rausing and Peter Baldwin
A Self-generating Prototype Method Based on Information Entropy Used for Condensing Data in Classification Tasks

This paper presents a new self-generating prototype method based on information entropy to reduce the size of training datasets. The method accelerates the classifier training time without significantly decreasing the quality in the data classification task. The effectiveness of the proposed method is compared to the K-nearest neighbour classifier (kNN) and the genetic algorithm prototype selection (GA). kNN is a benchmark method used for data classification tasks, while GA is a prototype selection method that provides competitive optimisation of accuracy and the data reduction ratio. Considering thirty different public datasets, the results of the comparisons demonstrate that the proposed method outperforms kNN when using the original training set as well as the reduced training set obtained via GA prototype selection.
Research on Service Quality of "12306 China Railway" Mobile Ticketing Software

In recent years, with the vigorous development of mobile application software market and the continuous growth of China railway, the official mobile application "12306 China Railway" has been increasingly used by the people to purchase train tickets.This paper aims to study the service quality of "12306" mobile ticketing software using the SERVQUAL scale.For this, the in-depth interview method combined with the service characteristics of mobile ticketing software to modify the original dimension and question of SERVQUAL scale, and then determine the adjusted SERVQUAL scale containing a total of 20 questions and 5 dimensions.Based on the revised SERVQUAL scale, the questionnaire survey was conducted to analyse the respondents' expected value and actual value of the "12306" mobile app through the data collection, and calculate the difference between perception and expectation of the users in the process of software ticketing.Finally, the problems with the service quality of 12306 mobile app was found through the reliability and validity analysis, and target suggestions were given.