Reviewerly/exHarmony huggingface dataset:
Supply Chain Management in E-Commerce Using Blockchain

With the increase in the Internet and online shopping era, online shopping and E-commerce have become the most convenient way for customer and seller to buy and sell the product online using Internet. With this increasing E-commerce services, it has become mandatory to provide the trusted proof of delivery system to the customer and seller. Existing system is centralized system which completely relies on the third party for the proof of delivery which can lead to a single point of failure. Existing proof of delivery systems leads back in credibility, transparency, and traceability. Blockchain is the decentralized distributed ledger which provides the system to be transparable, traceable, and creditable. Supply chain management using Ethereum blockchain is one of the solutions that can be given. This system is the decentralized proof of delivery system that uses Ethereum smart contract to prove the delivery of a shipped item between seller and buyer. Each customer, seller, and courier services registers on the Web application. Later, seller uploads catalog of the commodities to be sold which customers choose to buy. Courier services are assigned this parcel based on the sentiment analysis, and later, at the time of delivery, payment is done using ethers.
Time-sensitive clustering evolving textual data streams

Clustering a stream of text documents is an emerging subject of interest since it is widely used in analysing the content in social media and e-journals. The aim is to find a certain structure for unlabelled data based on a similarity criterion. However, few works have focused on this field and fall in this perspective, that's why a new document clustering approach adapted to a stream of text data and test it on news articles data sets is proposed. A distributed representation of words is used, and a bottom-up approach is used to represent documents as vectors on a unit hyper-sphere. The proposed approach gains its roots from the SPherical k-means (SPKM) algorithm and its underlying mixture of von-Mises Fisher (vMF) distributions. The proposed approach yields comparable results to baseline batch algorithm for stable data streams and superior results for rapidly evolving data streams.
Design of an Automatic Stone Flicking Machine

Stone flicking is a game played on the baduk board. In this study, a stone flicking machine called R-robot is developed, which is an intelligent robotic system that can compete against a human player. The robot photographs the stadium and processes the images to find the best route for efficient competition. The targeting stone is identified using a camera and a path-searching algorithm selects the stone-flicking position. Based on inverse kinematics, the rotation angle of the motor is controlled to move the robot according to the given coordinates. A linear actuator adjusts the height of the flicking part, while a solenoid controls the power. ANSYS simulates the stress distribution of the robotic arm. The flicking direction is selected using C# and OPENCV. The Dynamixel RC servo motor controls the main joints of the robot. Arduino is applied for data processing and actuator control. Finally, the R-bot is manufactured.
Application of Statistical Techniques in Project Monitoring and Control

Projects monitoring and control is a critical aspect in the execution of projects and delivering the high-quality product. Product quality and delivery are dependent on the processes defined and followed. In these days, projects are of complex nature and monitoring of these projects requires the application of statistical techniques. For this purpose, periodic project data collection is required to generate metrics. These metrics are baseline by deriving process performance baseline. Further, the regression analysis is carried out and process performance model is created. The purpose of this paper is to discuss the challenges faced before the application of these techniques and analyze the improvement, impact using statistical techniques. To end with, the paper will discuss how these techniques have benefited in delivering a quality product within budgeted effort and schedule.
Handling concurrent requests in a secret sharing based storage system using Petri Nets

Data can be stored securely in various storage servers. But in the case of a server failure, or data theft from a certain number of servers, the remaining data becomes inadequate for use. Data is stored securely using secret sharing schemes, so that data can be reconstructed even if some of the servers fail. But not much work has been carried out in the direction of updation of this data. This leads to the problem of updation when two or more concurrent requests arrive and thus, it results in inconsistency. Our work proposes a novel method to store data securely with concurrent update requests using Petri Nets, under the assumption that the number of nodes is very large and the requests for updates are very frequent.
THE IMPACT OF ISO PRINCIPLES OF TOTAL QUALITY MANAGEMENT ON HIGHER EDUCATION QUALITY

Total quality management (TQM) represents a set of practices for continuously improvement within organizations. The government of UAE has taken large steps towards the implementation of TQM. Despite that, many organizations in UAE are not able to apply the principles of TQM. The aim of this study is to investigate the effect of TQM principles developed by the International Organization for Standardization (ISO) on education quality in UCMOTHER and Higher College of Technology - Sharjah Women’s Colleges. This study used quantitative approach to answer research questions. The result of this study indicates that educational institutions need to implement the principles of TQM according to the seven principles of ISO to achieve high education quality. This study recommends that universities in UAE should establish a department of quality office in each college headed by specialist managers in TQM who will be responsible for following-up and monitoring the application of TQM like continuous improvement, teamwork spirit, and sharing decision-making. Article visualizations:
Library Research Guides: Journalism, advertising, PR and communication: Research tips

This guide will assist you to find information and library resources for journalism, advertising, public relations and communication studies, and provide research and referencing tips.
Study on the intelligent evaluation model after stoke based on random forest

Abstract According to the summary of “China cardiovascular report 2018”, in recent years, there are 2 million new stroke patients in China every year, and nearly 70% of them have lost hand function due to stroke. Therefore, the rehabilitation evaluation of postoperative hand function of stroke patients is complicated with low intelligence. In this paper, data collection methods are introduced firstly, and the original data are artificially classified. Then, starting from the decision tree method, the motion data of stroke patients are intelligently evaluated by establishing a random forest, and the evaluation results are returned to the patients for review in the form of floating point number based on their probability proportion. Finally, the results of the model experiment and its advantages and disadvantages are analyzed, and the future development direction and prospect are put forward.
Tensor Products, K-Theory and Rotation Algebras

A primer on C*-tensor products can be found in [RW98, Appendix B].
Detectando conluio em compras governamentais: Uma abordagem utilizando Red Flags e a Teoria Dempster-Shafer

In both public and private sectors, there is a growing number of fraudulent techniques and schemes perpetrated by individuals whose purpose is to misappropriate the assets of the target entities. Fraud detection is a complex activity as these individuals try to hide their actions so that they are not discovered. Given this, the objective of the present work is to present an approach that allows identifying and aggregating evidence related to red flag signals from the use of different data mining techniques, deriving a general measure of probative value that can be used to recognize bids in which collusion may have occurred between bidders. The results show that the proposal can help the investigation activities conducted by the inspection entities, as it helps to direct efforts to areas that concentrate a larger set of evidential elements.
Recognition and Optimizing Process Lagging Indicators During Software Development

Software industry has grown tremendously since the last few years. Customers today have very high expectations from systems delivered to them, in terms of quality, cost and time. In order to satisfy the customer, the project manager needs to monitor and control these three so-called major project management constraints during project execution. It is mostly seen that it is not possible for project managers to control all these three factors together and satisfy the customer needs. The task of measuring and monitoring the project data is considered as an overhead, and so, the project manager becomes selective in his approach. Project managers choose the high priority factor based on the customer requirements or as per the project signed agreement. Thereafter, the relevant data needs to be measured, collected, monitored and controlled using statistical techniques. But focusing and controlling only one of the factors many times is not sufficient for the overall success of the project. The manager needs to enhance and improve project monitoring to encompass the other factors also so that the project does not fail from any loose ends. The paper describes stepwise approach followed to achieve this: Firstly, how the importance of controlling more than one factor was accepted by the project managers; secondly, statistical techniques used in implementing the same; and thirdly, improvement in the final product as a result.
Conditioning of extreme learning machine for noisy data using heuristic optimization

Abstract This article provides a tool that can be used in the exact sciences to obtain good approximations to reality when noisy data is inevitable. Two heuristic optimization algorithms are implemented: Simulated Annealing and Particle Swarming for the determination of the extreme learning machine output weights. The first operates in a large search space and at each iteration it probabilistically decides between staying at its current state or moving to another. The swarm of particles, it optimizes a problem from a population of candidate solutions, moving them throughout the search space according to position and speed. The methodology consists of building data sets around a polynomial function, implementing the heuristic algorithms and comparing the errors with the traditional computation method using the Moore–Penrose inverse. The results show that the heuristic optimization algorithms implemented improve the estimation of the output weights when the input have highly noisy data.
Educational Big-Data Practice: A Model for Combining Requirements and Technologies

Institutional research (IR) has revealed the need for data processing in a university, and big-data technology can be regarded as a technical approach. The combination of the two methods can help universities introduce data-driven decision-making models. On the demand side of big-data processing on campus, we proposed three groups of university stakeholders—university, student, and society—with student learning outcomes as the main research topic. On the technical side, we proposed a data processing model from the perspective of big-data processing, including data gathering, pre-processing, storage and integration, analysis and exploration, and visual presentation. We applied the proposed learning outcomes and the big-data processing model to Tzu-Chi University, exploring student learning outcomes with big-data technology. In an initial experiment, this application model is feasible. Finally, we proposed several relevant conclusions to promote educational big-data research as a reference for related research.
Offline Estimation and Online Update Algorithm of Clutter Intensity in Multi-target Tracking

Aimed at the problem that the offline estimation algorithm of clutter intensity has lower accuracy in multitarget tracking, we propose an online update algorithm of clutter intensity. In our method, the update of the clutter intensity involves the average clutter number per scan and clutter spatial distribution. During the tracking procedure, the average clutter number per scan is updated via the clutter number. Moreover, the confidence degree method is adopted to divide the clutter set and the parameters of clutter spatial distribution are updated. In addition, the k-means method is further employed to modify the clutter spatial distribution. At last, the smoothing operation is introduced to improve the robustness of the proposed algorithm. Numerical simulations show that our algorithm has higher estimate accuracy of clutter intensity and better performance in multi-target tracking over the existing algorithm.
PENGEMBANGAN BAHAN AJAR PERBANDINGAN BERBALIK NILAI BERBASIS ANDROID UNTUK PEMBELAJARAN SMP KELAS VII

A B STRAK Penelitian ini dilatarbelakangi karena belum tersedianya bahan ajar berbasis android pada mata pelajaran matematika khususnya untuk materi perbandingan berbalik nilai. Oleh karena itu dilakukan penelitian pengembangan bahan ajar berbasis android yang bertujuan untuk menghasilkan bahan ajar materi perbandingan berbalik nilai berbasis android yang valid dan praktis. Penelitian ini adalah penelitian pengembangan menggunakan model pengembangan ADDIE . Data penelitian ini diperoleh melalui hasil walktrough , lembar angket, dan observasi. Hasil dari penelitian ini menunjukkan bahwa validasi bahan ajar perbandingan berbalik nilai berbasis android yang dilihat dari aspek isi, konstruk, dan bahasa yang dinilai dari hasil validasi ahli materi dan ahli media serta komentar siswa pada saat uji coba perorangan. Sedangkan kepraktisan bahan ajar perbandingan berbalik nilai berbasis android terlihat dari hasil lembar penilaian angket dan observasi pada tahap uji coba kelompok kecil. Dari hasil penilaian angket dan observasi diperoleh perhitungan nilai sebesar 82,17 % berkategori praktis dan 89,17% berkategori sangat praktis. Kesimpulan dari penelitian ini adalah bahan ajar perbandingan berbalik nilai berbasis android telah valid dan praktis. Kata K unci: Pengembangan ; Bahan Ajar ; ADDIE THE DEVELOPMENT OF ANDROID BASED INSTRUMENTS ON THE MATERIAL OF REVERSE VALUATION FOR 7TH GRADE ABSTRAC T The research is facing a background because there is not a more-based teaching material available in mathematical subjects especially for materials of comparative value. And so it is done to rsearch the development of lame-based teaching materials aimed at producing materials that actually contras valid and practical android value. This research is development research using ADDIE development models. These research data are obtained walkthrough, routing sheets, and observations. The results of this study show that the validation of materials rebased android contrasts is seen in content, constructive, andlanguage aspects assessed by both material and media experts validation and student comments during individual trials. The bare-based comparison of value-based values can be seen from the result of the top-rated assessment sheet and the observation at the trial stage of a small group. Based on the value assessments and observations, the rating is 82.17% practical valise and 89.17% valegori are extremely practical. The conclusion of this research is that the reverent-based android ratio is valid and practical. Keywords : Develop m ent; Teaching M aterials; ADDIE
Generalized Multiple Instance Learning for Cancer Detection in Digital Histopathology

We address the task of detecting cancer in histological slide images based on training with weak, slide- and patch-level annotations, which are considerably easier to obtain than pixel-level annotations. we use CNN based patch-level descriptors and formulate the image classification task as a generalized multiple instance learning (MIL) problem. The generalization consists of requiring a certain number of positive instances in positive bags, instead of just one as in standard MIL. The descriptors are learned on a small number of patch-level annotations, while the MIL layer uses only image-level patches for training. We evaluate multiple generalized MIL methods on the H&E stained images of lymphatic nodes from the CAMELYON dataset and show that generalized MIL methods improve the classification results and outperform no-MIL methods in terms of slide-level AUC. Best classification results were achieved by the MI-SVM(k) classifier in combination with simple spatial Gaussian aggregation, achieving AUC 0.962. However, MIL did not outperform methods trained on pixel-level segmentations.
Research and Practice of College English Blended Learning Model

This paper explores the blended learning mode of college English through empirical research. This model divides college English teaching into three stages: the first stage is for students to independently conduct web-based self-learning; the second stage is for on-line collaborative learning among learners; the third stage is for teachers to pass classroom teachers through classrooms. Conduct face-to-face teaching for students and interact with teachers and students. The quality of learning at each stage has a corresponding evaluation and monitoring. The study found that the college English mixed learning mode can solve the contradiction between the limited classroom teaching capacity and the students’ individualized learning needs, and effectively improve the quality of college English teaching.
Machine Learning for Automatic Encoding of French Electronic Medical Records: Is More Data Better?

The encoding of Electronic Medical Records is a complex and time-consuming task. We report on a machine learning model for proposing diagnoses and procedures codes, from a large realistic dataset of 245 000 electronic medical records at the University Hospitals of Geneva. Our study particularly focuses on the impact of training data quantity on the model's performances. We show that the performances of the models do not increase while encoded instances from previous years are exploited for learning data. Furthermore, supervised models are shown to be highly perishable: we show a potential drop in performances of around -10% per year. Consequently, great and constant care must be exercised for designing and updating the content of such knowledge bases exploited by machine learning.
ANALYSIS AND DESIGN OF INTEGRATED ONE DOOR SERVICE IN THE MINISTRY OF RELIGIOUS AFFAIRS, KUTAI TIMUR

The One Door Integrated Service Application is a service delivery for several types of services that are organized in an integrated manner in one place and controlled by a management control system. In this application there are two types of services namely customer service (Cs) and front office (FO), in both types of services have different service functions CS functions to provide services to the public and employees who want to do consultations include, Hajj and Umrah services , Marriage, Endowments, Zakat, Promotion, Staffing while FO Providing services to the community in the form of requests to transfer schools, legalizing certificates, mosque recommendations, passport recommendations, Requests for Qibla direction, requests for prayer, requests for clergy, Requests for place of worship data, Requests for marriage data. The application was developed using the Prototype system development method, data collection methods by interviewing, observing and studying literature, analyzing system design using UML (Unifield Modeling Language). In building applications information is used in the PHP programming language, MySQL Database, Apache Local Web Server, Adobe Dreamweaver CS6 as a web editor, and Adobe Photoshop as an image editor. Testing methods The information application uses the Blackbox testing method and Beta testing. The results of this study is that with this web-based application it will facilitate office staff in archiving services to record history and make it easier to serve the general public as well as providing convenience to the community when it will perform services
A Book-Finding Application Based on iBeacon-A Case Study of CCU Library

Finding a book or a thesis in a large library is never an easy job, even though librarians and researchers provides a call number system or an on-line webcat system. Since indoor positioning technologies are getting popular and cost-reasonable, it might be useful in the library to assist. In order to help readers finding books, this paper aims to design a book-finding application with iBeacon. Provide readers with more convenient and effective services to help them locate their books quickly in the library.
Exploring Augmented Reality Teaching Aids in Children’s Emotional Learning

How can a preschool teacher combine tablet with real teaching aids to improve the learning efficiency of children? How can a teacher apply science and technology to record the learning route of children? This research is aimed to develop the teaching aids of points of interests (POI) and MAKAR database in order to record children’s learning preference and effectiveness in the learning center of drama. Thirty-seven 4–6 years old were randomly selected. This research used tablet PC to develop nine emotional theaters. Each theater was designed by four POIs for creative thinking including Inquiring, Thinking, Trial and Sharing (ITTS). System would record autonomously the learning process when children manipulated the tablets. When children made decisions, system would autonomously record the decision they made, including the using time and frequency, then updated to database. Findings indicated that children are more interested in MAKAR teaching. MAKAR teaching has extremely significant effect on emotional ability (p = .000). The overall reliability analysis showed a 74.7% consistency. The learning records will be used as foundation for future revision and improvement. The result of this research supported that the learning effectiveness by using Unity and Vuforia Augmented Reality (AR) is much better than traditional teaching. MAKAR facilitates children’s ability not only emotional learning but also creative problem-solving.
Information separation of multi-surface based on wavelength phase-shifting interferometry

The simultaneous non-contact measurement of the front and rear surfaces of parallel plates by using wavelength phase-shifting interference technology was of great significance in the field of optical determination. A time-domain weighting algorithm was proposed to solve the overlapping interference signals of the front and rear surfaces of parallel plates: weighted 36-step sampling algorithm. Firstly, based on the principle and constraints of the algorithm, the basic distribution parameters of the weighted multi-step sampling algorithm were designed, and then the sampling weights of the interference signals of front surface, rear surface and thickness variance were obtained. Through the calculation of the basic distribution parameters, the sampling weight of each surface can be obtained, and the initial phase distribution of each interference information can be obtained by using the weight operation. Based on Zernike polynomials, the simulation of multi-surface interference phase solution was carried out, and the maximum error between the simulation result and the true value was not more than 0.06 nm. In addition, the influence of various errors on the measurement results was analyzed. A parallel plate with a thickness of 20 mm has been measured. The experiment verifies the effectiveness of the algorithm in the actual measurement process.