Reviewerly/exHarmony huggingface dataset:
PENGARUH MODEL PEMBELAJARAN KELILING KELOMPOK (ROUND CLUB) TERHADAP KETERAMPILAN MENULIS LAPORAN PERJALANAN SISWA KELAS V SDN 01 NAGARI BUKIK SIKUMPA

This research is motivated by the low desire of students in writing travel reports. The lack of students' knowledge of the report concept, the lack of students' knowledge of the 5W + 1H report points of good and correct language, the lack of students' knowledge of the spatial, time and topic pattern and not yet reached KKM 75. The method used by the teacher has not been interesting, lecture method. The purpose of this study is to describe the skills of writing travel reports by using Round Club learning model which is viewed from the aspect of understanding the report concept, the use of 5W +1H report points, the spatial, time, and topic pattern.The population of this study is the students of class V Lessons Year 2017/2018 which amounted to 2 classes with the number 80. The sample of research as much as two classes taken by the sample of propotional.Class V.1 as experimental class and class V.2 as control class. The research instrument used is performance test. Provide an assessment by specifying the subject of the 5W + 1H report, as well as the spatial, time and topic pattern. Data were analyzed by 't' test by first testing normality, homogeneity, and hypothesis testing.The results showed that the average control class 68 with more than enough qualifications with standard deviation 16.96. 83 experimental class with good qualification and standard deviation of 15.42 and there is a significant influence on the result of writing skill of class V SDN 01 Nagari Bukik SikumpaSubdistrict, Lima Puluh Kota. This is evidenced by the average value of writing skills in the experiment class higher than the average value in the control class. Normality test results indicate that the two sample classes of Lo values in the control class -0.2141 are smaller than the normal 0.190 Lt distributed. Homogeneity test results that the variation of this study is homogeneous at a real level of 0.05, because Ftable 2.16 &gt; Fhitung 1.21 and the results of data analysis then obtained = 2.78 &gt; 1.70 t table, so H0 rejected and H1 accepted. It can be concluded that there is Influence. Using Learning Model of Student Group Writing Skills Travel Report of students of class V SDN 01 Nagari Bukik Sikumpa Subdistrict, Kabupaten Lima Puluh Kota.KeyWords: model pembelajaran round club, menulis laporan perjalanan.
Measurement of Load on a Mobile LCD Screen Using Moiré Pattern

Stress and strain measurements face challenges frequently both in industry and academics. This chapter presents a novel technique to measure these parameters easily measuring moiré fringe shift on the display of a mobile phone, typically made from liquid crystals (LCD). Moiré fringes are generated from overlapping of two periodic patterns of nearly equal frequencies. When an LCD screen is imaged by CCD/CMOS, two 2D periodic patterns superpose with each other and a screen image, superposed with moiré fringes, is observed. In addition, if different loads are applied on the mobile screen, the corresponding moiré fringe shift can be measured and calibrated. An unknown load, therefore, can be measured provided it is within certain limit based on LCD and the load. The method is cost-effective, fast and accurate since it integrates digital methods to the existing measurement principle.
Construction of Practice Teaching Evaluation System for Undergraduate Introduction to Logistics Based on AHP Method and Gagne's Learning Theory

In order to enhance the scientificity of practice teaching evaluation, this paper selected three primary indexes and 17 secondary indexes to construct a practice teaching evaluation system of the course Introduction to Logistics which based on analytic hierarchy process (AHP) and guided by Gagne's learning theory as well as three-stage control principle. This paper provided a relatively objective weight by analysis of quantitative results and put forward some suggestions to improve practice teaching level according to the weight information.
Oracle Autonomous Linux

In 2006, with the objective of proposing a more complete end-to-end software stack along with their database, Oracle decided to have their own distribution of Linux by modifying and compiling the Red Hat Enterprise Linux binaries. This new distribution is now called Oracle Linux and it’s the operating system (OS) of choice for Oracle Cloud Infrastructure, Oracle Cloud at Customer, Oracle Engineered Systems like Exadata, and others. For your on-premise needs, you can also download this product for free from Oracle Software Delivery Cloud.
Development of an Assistive Device via Smart Glasses

This study used smart glasses as the carrier to develop three applications aimed at the elderly. Taiwan entered an elderly society in 2018, with the proportion of the elderly population exceeding 14%. It is estimated that the elderly population will exceed 20% in 2026, and Taiwan will enter an ultra-elderly society. To improve the quality of life of the elderly and attach importance to the health care of the elderly, more and more scientific and technological products are used to improve the life and consumption patterns of the elderly, thereby promoting the popularization of smart wearable devices and sensing technologies. In view of the purpose, this research develops related functional applications with smart glasses. First, the memory recall mechanism is carried out based on face recognition. When the user uses the function, an embedded camera in glasses is turned on to capture the target personnel. Afterwards, a KNN (K Nearest Neighbor) machine learning approach is applied to identify the target, and the personal data is performed in the glasses through searching on the developed cloud database. Besides, the user can add, delete and modify the personal data through the computer, which the entrance is carried out by scanning the two-dimension code shown in the computer program with glasses for security purpose. Secondly, the image enhancement function shows that the captured texts can be expanded and enlarged in the projection monitor of glasses. Finally, the construction of voice commands is performed through the microphone within the suite of smart glasses. The results show that the accuracy rate of KNN face recognition is 93.3%, which can be applied to general life situations.
Fostering of Entrepreneurs by KSP Inc.

KSP Inc. was established in 1986 as the core entity of the Kanagawa Science Park as a result of a project designed to realize the “Brain Center Framework” policy of Kanagawa Prefecture, and as the aim of Kawasaki City to become a mecca for science, technology, and research and development in Japan and Asia. KSP Inc. carries out the comprehensive management of the Kanagawa Science Park. Incubation managers of KSP assist the tenants of Kanagawa Science Park and other startups for business outside the park. In addition, KSP supports training of entrepreneurs and the commercialization of technical seeds by signing MOUs with various universities for concerning cooperation. Also we have “KSP Business Innovation School (KSP BIS)” for developing entrepreneurs for businesspersons for 28 years. More than 650 people graduated from the KSP BIS. The core concept of this school’s curriculum is the “How a business plan should be written?” One can experience real-time open innovations and join business networking in various industries, aiming at development of entrepreneurship.
“You Really Get Me”: Conversational AI Agents That Can Truly Understand and Help Users

Have you watched the movie Her? Have you ever wondered or wished to have an AI companion like Samantha, who could tell you what you really are, whom your best teammate may be, and which career path would be best for you? In this talk, Michelle will present a framework for building hyper-personalized, conversational Artificial Intelligent (AI) agents who can deeply understand users and responsibly guide user behavior in both virtual and real world. Through live demos, she will highlight two technical advances of the framework: (1) evidence-based personality inference and (2) model-based conversation generation. Michelle will discuss real-world applications of these agents and the wider implications of enabling hyper-personalized conversational AI agents for businesses and individuals.
Notifications and Monitoring

The purpose of this chapter is to provide information on two Oracle Cloud Infrastructure Services tightly related, Notifications and Monitoring services. We have seen, so far, great Oracle Cloud Infrastructure Services, going from Compute, Autonomous Database (ADB), Block Volume, Object Storage, and so on. Now we will see the monitoring and notification mechanisms Oracle Cloud Infrastructure uses to manage your system. This helps you to react as quickly when a particular situation happens. We will see how the Monitoring service monitors cloud resources and has the ability to publish messages using the Notification service.
An Empirical Analysis of the Influential Factors of College Students' Academic Performance

Based on the scores of selected undergraduates with two kinds of educational system in an ordinary university, with the help of variance analysis and multiple regression, the effects of different factors and their interaction on the total scores and the scores of moral, intellectual and physical courses are studied by using the ideas of discovering (the difference of students' scores in different categories), verifying (the effect of influential factors and their interaction on students' scores) and analyzing (trends and causes of scores changed based on different factors ). The key results are as follows: (1) College students' scores are significantly correlated with time, gender, birthplace, starting point for admission and their interaction, but the influence of different factors on each course is diverse. (2) The scores of female students are higher than those of male students, especially public courses; in addition to public courses, the scores of technical-school-source students are higher than those of high-school-source students, especially moral courses; students with high entrance score remain excellent in college, especially specialized courses. (3) The students in the 1st and 2nd years have the lowest academic performance, the reason of which are that many students failed; the grades of specialized course determine the total grades. (4) Curriculum classification plays a key role in studying influential factors of college students' scores. The research can provide reference for practical teaching work.
A pseudonymisation method for language documentation corpora: An experiment with spoken Komi

This article introduces a novel and creative application of the Constraint Grammar formalism, by presenting an automated method for pseudonymising a Zyrian Komi spoken language corpus in an effective, reliable and scalable manner.The method is intended to be used to minimize various kinds of personal information found in the corpus in order to make spoken language data available while preventing the spread of sensitive personal data about the recorded informants or other persons mentioned in the texts.In our implementation, a Constraint Grammar based pseudonymisation tool is used as an automatically applied shallow layer that derives from the original corpus data a version which can be shared for open research use.
Overview of Our API

In this (very short!) chapter, I’ll take you through the API that you’re going to build and the problem it’s attempting to solve. We’ll also cover the REST API pattern at a high level.
A recommendations model with multiaspect awareness and hierarchical user-product attention mechanisms

Neural network methods have been trained to satisfactorily learn user/product representations from textual reviews. A representation can be considered as a multiaspect attention weight vector. However, in several existing methods, it is assumed that the user representation remains unchanged even when the user interacts with products having diverse characteristics, which leads to inaccurate recommendations. To overcome this limitation, this paper proposes a novel model to capture the varying attention of a user for different products by using a multilayer attention framework. First, two individual hierarchical attention networks are used to encode the users and products to learn the user preferences and product characteristics from review texts. Then, we design an attention network to reflect the adaptive change in the user preferences for each aspect of the targeted product in terms of the rating and review. The results of experiments performed on three public datasets demonstrate that the proposed model notably outperforms the other state-of-the-art baselines, thereby validating the effectiveness of the proposed approach.
Detection of Anomalies in Operation of Continuous Plants and Systems

Anomaly detection during real time operation of continuous plants and systems is a key activity of the operator that aims at determining the health status of the system. For such detection the information from real time multidimensional data streams supplied by multiple sensors has to be analyzed and com-pared for similarity analysis. This paper presents a methodology and algorithm for developing a computer monitoring system for anomaly detection in online mode. The proposed method includes creating Data Cloud models based on portions of data samplings with equal lengths and estimating the Similarity Level between them as a bounded value within the interval [0, 1]. Data cloud models use the concept of mesh of grid cells that capture the local density of the data points around the cells. The Similarity analysis is based on estimating the difference between the local densities of a pair of data clouds. The whole methodology is explained and illustrated in the paper by using numerous examples with real data. The algorithm and the monitoring system use the moving window concept for continuous analysis of the similarity level in online mode. The final section of the paper shows experimental results for anomaly detection based on using real operation data (temperatures) from a Petrochemical plant.
Towards the Ontologization of the Outsider Art Domain: Position Paper

The purpose of this paper is to present a prospective and interdisciplinary research project seeking to ontologize knowledge of the domain of Outsider Art, that is, the art created outside the boundaries of official culture. The goal is to combine ontology engineering methodologies to develop a knowledge base which i) examines the relation between social exclusion and cultural productions, ii) standardizes the terminology of Outsider Art and iii) enables semantic interoperability between cultural metadata relevant to Outsider Art. The Outsider Art ontology will integrate some existing ontologies and terminologies, such as the CIDOC - Conceptual Reference Model (CRM), the Art & Architecture Thesaurus and the Getty Union List of Artist Names, among other resources. Natural Language Processing and Machine Learning techniques will be fundamental instruments for knowledge acquisition and elicitation. NLP techniques will be used to annotate bibliographies of relevant outsider artists and descriptions of outsider artworks with linguistic information. Machine Learning techniques will be leveraged to acquire knowledge from linguistic features embedded in both types of texts.
Using Context to Help Predict Speaker’s Emotions in Social Dialogue

Emotion plays a vital role in social interaction. Often, the speaker’s attitude is as essential as, if not more important than, his/her words for communication purposes. In this paper, we present experiments for using conversational context to help text-based emotion detection. We used data from the Dialogue Emotion Recognition Challenge – EmotionX. BERT is used for encoding the input sentences. We explore four ways for encoding the input by varying whether to concatenate a dialogue history with the current sentence and whether to add the speaker’s name as part of the input. Our results indicate that adding context can improve the results of emotion detection when the emotion categories do not overlap with each other.
Privacy and Security Issues in Cloud Computing

Many organizations in education, healthcare, banking and the corporate sector are tending towards the use of cloud computing technology due to its efficiency and human resource savings. Organizations do not need to purchase infrastructure or data centres and can rent access for various applications to use over a network. The services provided by cloud computing are pay-per-use based, which reduces the overall cost. Cloud computing depends entirely on internet service over a network, with customers able to access data through data centres maintained by ISPs such as Google or Amazon. Cloud computing has great potential but faces many challenges to ensure security and privacy. This chapter describes various models of cloud computing, such as IaaS, PaaS and SaaS. Security and privacy concerns faced by the cloud computing environment are also addressed.
Research Guides: Dissertations or Theses in SOAR at USI: Copyright Basics

Information about submitting your electronic thesis or dissertation (ETD) in the Scholarly Open Access Repository (SOAR) at USI.
Application Analysis of Blockchain in the Field of Finance

In response to the impact of technology, financial institutions quickly seized the opportunity. Following the wave, financial technology was born. This article starts from the background of financial technology and introduces the impact of financial technology on the industry. It also introduces a series of applications with great potential in the financial sector, including digital currency and cross-border payment settlement. At the same time, given the problems in the blockchain and its application, this paper proposes a series of solutions, including learning from the experience of foreign “regulatory sandbox” and strengthening cooperation mechanisms to promote the healthy development of blockchain and other financial technologies. In general, the research on blockchain is still in its infancy, and the future situation is optimistic. However, in the process of development and application, financial and legal supervision needs to be improved.
Augmented Reality Based Collaborative Product Enhancement

The paper presents an application of Augmented Reality to support collective product development among members of different domains across the product development including end-users. Portable software tool that works on smart devices (smartphones, tablets, laptops, PCs) is developed for collaborative product development using Augmented Reality to nurture, maintain and increase a company’s market share by fulfilling consumer’s demand. Portable software tool is presented with the proposed methodology for a collaborative platform to work on mobile devices to capture the synergistic dynamics and to customize and interrelate with the product in customer(s) own environment. The product shown to the customer using the developed tool maybe customizable as per the customers’ requirement in his/her own environment remotely and to record comments. The recorded customization and comments are then stored in the database and used in the product development and enhancement of the product.
Apps with Containers

Containers is one of the buzzwords used across the industry these days. Containers provide the flexibility to ship your code with the runtime so that it can run its own environment without any dependency on the OS. Basically, it makes your app cross-platform ready.
A Comparative Study of Data Augmentation Methods for Brand Logo Classifiers

Social networks have become a widely used way to share information, including images. This information can be useful for companies, which need to know their customers’ opinions and their interest in their brands. For these purposes, an image classifier has a great utility. Currently, deep learning using convolutional neural networks are heavily employed for image classification. However, they require a large number of training image samples, which are not always accessible. In order to solve this problem, we can use data augmentation, which is a regularization technique that is based on expand the original dataset to increase classification accuracy and avoid overfitting. This present work aims to compare the use of different data augmentation methods for brand logo classification. For tests, seven methods (flip, crop, rotation, gaussian filter, gaussian noise, scale, and shear) were selected based on previous studies. Two convolutional neural networks were used, AlexNet and SmallerVGGNet, this way we could see if some combinations lead to better results in different artificial network architectures. Ten different combinations of those methods show that the combination of flip, crop, rotation, and scale is a more effective combination for a brand logo classifier in the both convolutional neural networks used, improving accuracy by 22.09% using AlexNet and 12.57% using SmallerVGGNet. Other good results are found with the combination of all seven methods; flip, crop, rotation, plus gaussian noise; and the combination of flip, crop, and rotation. Further research is intended to use another regularization technique along with data augmentation to improve even more the accuracy and reduce overfitting.
Combining Expert Knowledge with Frequency Information to Infer CEFR Levels for Words

Traditional approaches to set goals in second language (L2) vocabulary acquisition relied either on word lists that were obtained from large L1 corpora or on collective knowledge and experience of L2 experts, teachers, and examiners. Both approaches are known to offer some advantages, but also to have some limitations. In this paper, we try to combine both sources of information, namely the official reference level description for French language and the FLElex lexical database. Our aim is to train a statistical model on the French RLD that would be able to turn the distributional information from FLElex into one of the six levels of the Common European Framework of Reference for languages (CEFR). We show that such approach yields a gain of 29% in accuracy compared to the method currently used in the CEFRLex project. Besides, our experiments also offer deeper insights into the advantages and shortcomings of the two traditional sources of information (frequency vs. expert knowledge).
Classification and Measuring Accuracy of Lenses Using Inception Model V3

Convolution network is widely used nowadays for the most computer vision solutions. Deep convolution network has become mainstream and is yielding various benchmarks since 2014. Deep learning has shown tremendous results in the field of image processing. Artificial neural networks are inspired by biological neurons and allow us to map various complex functions at a very fast speed. These neural networks have to train on large sets of data and then take a certain amount of time to do so. Eventually, they give immediate quality gains for most of the tasks in use cases of lens vision and big data scenarios. In this paper, Inception model V3 has been used that aims at utilizing the added computation as efficiently as possible in case of lens images. This model takes bulk images of lenses and fragments them into six different classes depending upon their properties. Inception model achieves accuracy for recognizing images with more than 1000 classes. Histogram shows the comparative study of six different classes on basis of classification rate and accuracy. Confusion matrix has been made which gives the correct predictions for the images of lens. Finally, the results have been compiled in two parts. In the first part contain, we find out the precision and recall of lens images. In the second part, overall accuracy of the model has been compiled which comes out to be 95.9%.