Reviewerly/exHarmony huggingface dataset:
Simulasi Driller’s Method Sebagai Metode Penanganan Kick pada Operasi Pemboran Darat

Oil and gas drilling operations are activities that use high technology, high investment and high riskl. The success of a drilling operation much influenced by the success, speed and accuracy in handling problems in the drilling operation. Drilling problems handling is very dependent on the human competence of the drilling operations, therefore they are needed correct and appropriate knowledge. The drilling problem that most important to be addressed and handling with properly is if this problem cannot be handled will result in a bigger problem that is Blow out. In this discussion, the author tries to provide knowledge about one of the methods used in handling the problem of kick, namely Driller's Method.
The Effect of Security Awareness Training on the Use of Biometric Authentication : Focusing on the Protection Motivational Behaviors

The purpose of this study is to investigate the behavioral factors affecting the security attitude and intention to use biometrics password based on the protection motivation theory. This study also investigates security awareness training to understand trust, privacy, and security vulnerability regarding biometric authentication password. This empirical analysis reveals security awareness training boosts the protection motivational factors that affect on the behavior and intention of using biometric authentication passwords. This study also indicates that biometric authentication passwords can be used when the overall belief in a biometric system is present. After all, security awareness training enhances the belief of biometric passwords and increase the motivation to protect security threats. The study will provide insights into protecting security vulnerability with security awareness training.
Pengaruh Mata Pelajaran Produktif terhadap Prestasi Prakerin Siswa Kelas XII SMK Negeri 1 Bukittinggi

This study aims to study whether there is a relationship between subjects and work performance of the XII class program in electrical installation engineering expertise at SMK Negeri 1 Bukittinggi 2019/2020 academic year both simultaneously and partially. This research is research involving. The number of members of the study population is less than 100, so the entire population is made the object of research. The population of this research is the XII grade students of electric power installation engineering expertise program at SMK Negeri 1 Bukittinggi who have carried out industrial work practices in April - September 2019 which obtained 82 students. The data collection method uses the instruction method. Data analysis techniques with descriptive frequency and for hypotheses using simple linear regression using SPSS 14.0. Based on the descriptive analysis of the frequency, it shows that the achievement of earning training is included in the category of getting good with a weighting percentage of 92.1%. Industrial practice awards are in the good category with an average of 81.46. the results of the analysis using simple regression data processed in the Sig. = 0.830 is greater than the value of 0.05 and with a comparison between tcount with ttable, where ttable = 1.99006 and tcount -0.216 which can be used both simultaneously and partially on industrial work practices.
A Survey of Challenges Facing Streaming Data

This survey performs a thorough enumeration and analysis of existing methods for data stream processing. It is a survey of the challenges facing streaming data. The challenges addressed are preprocessing of streaming data, detection and dealing with concept drifts in streaming data, data reduction in the face of data streams, approximate queries and blocking operations in streaming data.
Libraries: How to Use the BC Catalog to Find ERC Materials: Getting Started

Use this guide to understand the features of the BC Catalog to discover materials in the ERC.
Theoretical Domains Framework Applied to Cybersecurity Behaviour

The challenge of changing user cybersecurity behaviour is now in the foreground of cybersecurity research. To understand the problem, cybersecurity behaviour researchers have included, into their studies, theories from the Psychology domain. Psychology makes use of several behavioural theories to explain behaviour. This leads to the question, which of these theories are best suited to firstly understand cybersecurity behaviour and secondly to change the behaviour for the better. To answer this question, as a prelude to the current paper, previous publications have 1) established a definition for the different categories of cybersecurity behaviour, 2) identified and applied a framework, the Theoretical Domains Framework, that ties different behavioural theories together into one behaviour change framework. The current study is aimed to show the link between the behavioural constructs discussed in the Theoretical Domains Framework to the different cybersecurity behaviour categories. The contribution of the study is towards the implementors of initiatives that aim to change cybersecurity behaviour.
Research Guides: Capturing Video Clips from Library Databases and Other Sources: Capturing Video Clips Using Zoom and Panopto

This guide shows methods of capturing video clips from library databases, such as Films on Demand, as well as other online, streaming sources and physical media.
MULTIMEDIA INTERAKTIF BERBASIS LECTORA INSPIRE PADA SUBTEMA SUMBER ENERGI SISWA KELAS IV SEKOLAH DASAR

The purpose of this research is to produce and develop a product maximally in order to increase its usefulness by utilizing the facilities and infrastructure in Muhammadiyah Karangploso elementary school. The research to be carried out is in the form of thematic learning media products using Lectora Inspire media. Data collection techniques used expert validation assessment sheets (media experts, material experts and learning experts) and student response assessment questionnaire sheets. The data analysis used is quantitative and qualitative descriptive analysis by analyzing quantitative data that has been obtained from expert validation sheets and student response assessment sheets to learning media which were originally a score, then converted into qualitative data (interval data) on a scale of five. The results of this study indicate that the media using Lectora Inspire multimedia is suitable for use in learning. Interactive multimedia based on Lectora Inspire on the sub-theme of energy sources used in class four elementary schools to assist teachers in providing understanding to students on energy sources with interactive multimedia based on Lectora Inspire is very supportive when used for thematic learning on the sub-theme of energy sources for fourth grade elementary school students.
A Tool for Facilitating OCR Postediting in Historical Documents

Optical character recognition (OCR) for historical documents is a complex procedure subject to a unique set of material issues, including inconsistencies in typefaces and low quality scanning. Consequently, even the most sophisticated OCR engines produce errors. This paper reports on a tool built for postediting the output of Tesseract, more specifically for correcting common errors in digitized historical documents. The proposed tool suggests alternatives for word forms not found in a specified vocabulary. The assumed error is replaced by a presumably correct alternative in the post-edition based on the scores of a Language Model (LM). The tool is tested on a chapter of the book An Essay Towards Regulating the Trade and Employing the Poor of this Kingdom (Cary, 1719). As demonstrated below, the tool is successful in correcting a number of common errors. If sometimes unreliable, it is also transparent and subject to human intervention.
Cross-Modal N-Pair Network for Generalized Zero-Shot Learning

Generalized Zero-Shot Learning (GZSL), which aims at transferring information from seen categories to unseen categories and recognizing both during the test stage, has been attracting a lot of attention in recent years. As a cross-modal task, the key to aligning visual and semantic representations is accurately measuring the distance in the projection space. Although many methods achieve GZSL by utilizing metric learning, few of them leverage intra- and inter-category information sufficiently and would suffer from domain shift problem. In this paper, we introduce a novel Cross Modal N-Pairs Network (CMNPN) to alleviate this issue. Our CMNPN firstly maps visual features and semantic prototypes into a common space with an embedding network, and then employs two N-pairs networks, VMNPN and SMNPN, to optimize an N-pair loss in both visual and semantic spaces, where we utilize a cross-modal N-pair mining strategy to mine information from all classes. Specifically, we select the hard visual representation for its corresponding semantic prototype and combines the two features with ( $$N-1$$ ) negative samples to form an N-pair. In VMNPN, the negative samples are hard visual representations of the other ( $$N-1$$ ) categories, while in SMNPN, they are the other ( $$N-1$$ ) semantic prototypes. Extensive experiments on three benchmark datasets demonstrate that our proposed CMNPN achieves the state-of-the-art results on GZSL tasks.
IMPLEMENTATION OF MULTILAYER PERCEPTRON FOR STUDENT FAILURE PREDICTION

University is one of the educational institutions and can be established by the government or the individual. At this time, Indonesia has hundreds of universities spread throughout the region. As an educational institution, university of course must be able to educate its students and issue quality graduates with the academically and non-academically qualified. In its implementation, there are many problems that should be resolved as well as possible, such as when there are students who intentionally stop or disappear before completing their education or are even unable to complete their education and issued by institution (dropout). Based on these problems, this research makes a model for predicting students who have the potential to fail or dropout during their studies using one of the data mining methods namely Multilayer Perceptron by referring to personal and academic data. The results obtained from this research are 86.9% an accuracy rate with the 54.7% sensitivity, and 95.4% specificity. This research is expected to be used to determine the need strategies to minimize the number of students who stop or dropout.
Temporally Distributed Networks for Fast Video Segmentation

We present TDNet, a temporally distributed network designed for fast and accurate video semantic segmentation. We observe that features extracted from a certain high-level layer of a deep CNN can be approximated by composing features extracted from several shallower sub-networks. Leveraging the inherent temporal continuity in videos, we distribute these sub-networks over sequential frames. Therefore, at each time step, we only need to perform a lightweight computation to extract a sub-features group from a single sub-network. The full features used for segmentation are then recomposed by application of a novel attention propagation module that compensates for geometry deformation between frames. A grouped knowledge distillation loss is also introduced to further improve the representation power at both full and sub-feature levels. Experiments on Cityscapes, CamVid, and NYUD-v2 demonstrate that our method achieves state-of-the-art accuracy with significantly faster speed and lower latency.
A Study of Social Media linked MCGDM Skill under Pentagonal Neutrosophic Environment in the Banking Industry

Social media is a new observable fact in computer-based technology and neutrosophic theory.Researchers are now thinking of the power of social media in banks as it is the fastest expandingonline noticeable fact and banks with poor presence in social media are facing identity crisis underuncertainty fields. Through social media we can share ideas and information through establishingvirtual networks. Initially it was evident that people used it for personal interaction with friends andrelatives but with changing time it is established that business houses and financial institutionsincluding Banks are using this popular technology to reach out to the prospective customers.Especially in the banking industry digital communication is becoming most popular and powerfulas here consumers' interface is obligatory. Online communication has become a powerful mediumbetween banks and consumers. The power of social media is to connect and share information withpeople across globe. Social Media in Indian Bank is not only a medium of advertising but it alsohelps the Banks to be a part of their customers’ life as this relation involves conversation beyondbusiness under neutrosophic environment. The aim of this study is to find out the best social mediaas per users’ preference and explore its impact on Banks’ business in pentagonal neutrosophic(PNN) arena by increasing customer satisfaction and augment customer relationship managementin banking industry.
Педагогическая интернатура в России: опыт, проблемы, пути развития

Demidovich M. I. Pedagogical Internship in Russia: Experience, Problems, Ways of the Development. The article reveals the phenomenon of pedagogical internship as an innovative approach to the organization of practical training of future teachers and the formation of professional competence of a bachelor student, analyzes the history of its creation in the Russian Federation, regulatory and legal, organizational aspects of the role and place in the system of pedagogical education, opportunities in the training of bachelors in the context of GEF, issues related to its implementation in universities.
Managing Security Risks in Post-Trade Matching and Confirmation Using CorDapp

Blockchain technology is ready to revolutionise the financial industry. The financial industry has various security challenges (e.g., tampering, repudiation, denial of service, etc). The Corda platform provides suitable technological infrastructure to build the blockchain-based application (CorDapp) in the financial industry to overcome these challenges. In this paper, we take a case of the capital market post-trade matching and confirmation process to perform security risk management. We compare the countermeasures of centralised application and CorDapp that mitigate the security risks. Furthermore, we explain what security risks appear within the CorDapp.
CLOUTIDY: A CLOUD-BASED SUPPLY CHAIN MANAGEMENT SYSTEM USING SEMAR AND BLOCKCHAIN SYSTEM

Supply chain management (SCM) system is an essential requirement for companies and manufacturers to collaborate in doing business. There are many techniques to manage supply chains, such as using Excel sheets and web-based applications. However, these techniques are ineffective, insecure, and prone to human error. In this paper, we propose CLOUTIDY, a cloud-based SCM system using SEMAR (Service Market) and Blockchain system. We modify JUGO architecture to develop SEMAR as a broker between users and cloud service providers. Also, we apply the Blockchain concept to store the activity log of the SCM system in a decentralized database. CLOUTIDY system can solve several common cases: service selection, resource provisioning, authentication and access control. Also, it improves the security of data by storing each activity log of the supply chain management system in the Blockchain system.
LibGuides: SCALE: Welcome

The guide for students in degree programmes offered by NUS School of Continuing & Lifelong Learning (SCALE).
Striving for a More Diverse and Inclusive Field in Engineering

Environmental Engineering ScienceVol. 37, No. 8 AEESP President's LetterStriving for a More Diverse and Inclusive Field in EngineeringKarl G. LindenKarl G. Linden*Corresponding author: University of Colorado Boulder, Boulder, Colorado, USA. E-mail Address: karl.linden@colorado.eduUniversity of Colorado Boulder, Boulder, Colorado, USA.†Member of AEESP.Search for more papers by this authorPublished Online:11 Aug 2020https://doi.org/10.1089/ees.2020.0261AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View article"Striving for a More Diverse and Inclusive Field in Engineering." Environmental Engineering Science, 37(8), pp. 519–520FiguresReferencesRelatedDetails Volume 37Issue 8Aug 2020 InformationCopyright 2020, Mary Ann Liebert, Inc., publishersTo cite this article:Karl G. Linden.Striving for a More Diverse and Inclusive Field in Engineering.Environmental Engineering Science.Aug 2020.519-520.http://doi.org/10.1089/ees.2020.0261Published in Volume: 37 Issue 8: August 11, 2020PDF download
LibGuides: Off Campus/Remote Library Access: Fine Arts

This guide is intended to be a quick, helpful place to answer basic questions about Library Resources -- and how you can access them off campus. Select online arts resources. See subject guides for more recommendations.
DEVELOPMENT OF LOAD BALANCING MECHANISMS IN SDN DATA PLANE FAT TREE USING MODIFIED DIJKSTRA’S ALGORITHM

SDN is a computer network approach that allows network administrators to manage network services through the abstraction of functionality at a higher level, by separating systems that make decisions about where traffic is sent (control plane), then forwarding traffic to the chosen destination (data plane). SDN can have problems with network congestion, high latency, and decreased throughput due to unbalanced traffic allocation on available links, so a load-balancing load method is needed. This technique divides the entire load evenly on each component of the network on the path or path that connects the data plane and S-D (Source Destination) host. The Least Loaded Path (LLP) of our proposed concept, which is a Dijkstra development, selects the best path by finding the shortest path and the smallest traffic load, the smallest traffic load (minimum cost) obtained from the sum of tx and rx data in the switchport data plane involved in the test, this result which will then be determined as the best path in the load balancing process.
Lifelong Learning And Value Orientation Development In Personalityâ€™S System

Reviewing the process of initiating lifelong learning and the need to form value orientations within the personality is one of the major perspectives for the entire educational system. The content of the article highlights constituent parts of lifelong learning and value orientation, reflects personal opinions on the relationship between lifelong learning and the process of forming value guidelines for personality supported by arguments derived from a theoretical-scientific study aiming at practical application. Consequently, the reflection on the process of value orientations formation resulted in developing a mechanism for the formation of value orientations within the personality as correlated with the educational process, sociocultural implications and as projected in lifelong learning. The mechanism of orientation formation towards values and through values is established from the perspective of the personality development in ontogenesis and it allows for its extension throughout various educational stages.
Current Status and Direction Search of General Education Based on Character Education in the University : Focusing on the Global Citizenship Education and Leadership

The purpose of this study is to investigate the direction of a general education model based on ‘Global citizenship education and leadership’. For the purpose of collecting information, we organized FGIs in 2020. The FGI was conducted with the expert group of 6 participants from January 20th to May 6th. To extract data of detail and depth, the study implemented three series of focus group interview. The semi-structured interview items were passed through a formal review process of the institutional Ethics Committee(IRB). Furthermore, under the participants’ pre-consents of recording, all recorded data were categorized through transcripts process. The results of the study are as follows. As a result of the study, a total of 9 subcategories were derived from 3 areas of categories, including developing a Character competency based on ‘Global Citizenship Education and Leadership’, ‘construction an education system’, ‘Reinforcing an effective education’. In sum, this research provided that Global Citizenship Education and Leadership is very meaningful education model, which can contribute on reinforcing university student’s positive self-image and capability and mission as a global citizen. In turn, this investigation would lead to a discussion on the implications and possibilities of cultivating future global leaders.
The Use of the Recommended Learning Path in the Personalized Adaptive E-Learning System

This paper promotes the idea of the learning process management in the e-learning system. A personalized adaptive e-learning system is used in this research that comprises three developed topic acquisition sequences: teacher, learner or optimal topic sequences. The learner has the ability to switch between the aforementioned topic sequences. The system stores data about the course acquisition process. The analysis of the stored data demonstrated that a bit more than half of the students used the teacher topic sequence; higher grades in topics got those students who chose the learner or optimal topic sequence; the grades of the half of the students who used the optimal and teacher topic sequences were in the same level. The obtained results were used as the justification for the improvement of the existing optimal topic sequence development method. As a result, an algorithm for the recommended learning path development is proposed in this paper. The topics of the course and links in between are described using a weighted directed graph. The weight of every edge and vertex of the graph is calculated based on the parameter values describing the topic. Afterwards, the recommended learning path is assumed to be the path with the lowest weight that is found in the weighted oriented graph using a search.
LARGE-CONTEXT POINTER-GENERATOR NETWORKS FOR SPOKEN-TO-WRITTEN STYLE CONVERSION

This paper introduces a spoken-to-written style conversion method that is suitable for handling a series of text such as discourses and conversations. Spoken-to-written style conversion can increase the readability of automatic speech recognition (ASR) outputs because ASR systems transcribe input speech into text in a literal manner; however, it generates several disfluencies and redundant expressions. The most successful method of text style conversion is sequence-to-sequence mapping using pointer-generator networks that possess a copy mechanism from an input sequence. However, pointer-generator networks cannot process a series of text serially because they are developed to handle isolated text. In fact, pointer-generator networks cannot consider relationships between current processing text and all preceding text. Therefore, this paper proposes large-context pointer-generator networks that combine pointer-generator networks with large-context encoder-decoder networks. In the proposed networks, all preceding written-style text can be considered to convert current spoken-style text into written-style text. In addition, the proposed networks introduce a large-context copy mechanism that can copy tokens from both current spoken-style text and preceding written-style text. Our experiments demonstrate the proposed networks yield better performance than conventional pointer-generator networks and large-context encoder-decoder networks.
Multi Cohort Intelligence Algorithm

Multi-Cohort Intelligence (Multi-CI) algorithm has been proposed by Shastri and Kulkarni in [14]. The algorithm implements intra-group and inter-group learning mechanisms. It focuses on the interaction amongst different cohorts. The performance of the algorithm was validated by solving 75 unconstrained test problems with dimensions up to 30.