Reviewerly/exHarmony huggingface dataset:
A Method for Deriving High-Priority Financial Data Items that Must be Improved to Increase Market Capitalization

Market capitalization is one of the most important indicators for gauging the value of a company. Normally, improving financial data increases market capitalization. However, because there are numerous financial data, it is important to derive high-priority financial data items whose improvement can increase market capitalization. To achieve this, in this study, a method was developed using a remodeled customer satisfaction analysis model with financial data of the companies that make up the Nikkei 225. A graph was created with the correlation coefficient on the horizontal axis and the deviation value of each financial data item on the vertical axis, and then the financial data items plotted in the lower right corner of the graph were extracted. Using this method, it is possible to derive high-priority financial data items to increase market capitalization from numerous financial data.
Learning Strategy Development Coeurs De Dieu to Appreciate Poetry to Short Story

This research is to find out learning strategy development that can used for the subject Poetry Appreciation to the short story. Learning strategy mean is the implementation guidebook strategy Coeurs De Dieu to explain poetry appreciation subject to short story. This research is a development research to develop learning strategy. The instruments that used in this research is student needs questionnaire, observation notes, and teaching process observations sheets. This learning strategy development guidebook arrange through steps, namely stage checking, theoretical adjustment, analysis, empirical test, analysis, and reflection. The result of analysis, evidently the Coeurs De Dieu strategy worthy to used for poetry appreciation subject being short story.
Characteristics Recognition and Soft Multimedia System for Japanese Machine Translation and Edge-Driven Hardware Implementations

Abstract With the development of economic globalization, international exchanges and cooperation are increasingly frequent and in-depth. In this process, there is a serious obstacle, that is, language differences. Therefore, the development of high quality and practical machine translation system is of great significance. Japan has a close relationship with China, so it is necessary to acquire and process Japanese information. Japanese translation is the basis of Japanese information processing, which plays an important role in cross language information retrieval, machine translation, information extraction and other practical applications. In recent years, machine translation has made remarkable progress, but there is still much room for improvement in the quality of translation. Multimedia assisted instruction is an important application of computer technology in teaching field. In recent years, the wide application of multimedia courseware in all kinds of teaching has received good teaching results. With the development and deepening of Japanese teaching reform, contextualized teaching has been paid more and more attention. In this context, Japanese intensive teaching is also increasingly showing the trend of network, multimedia assisted. Therefore, this paper realizes the feature recognition and hardware structure of Japanese machine translation.
Mixed Reality for Virtual Jewellery Fitting

The Hong Kong jewellery industry is facing challenges in adopting e-commerce, especially for product visualisation, personalised marketing, and building customer faith. To tackle these challenges, a mixed reality for virtual jewellery fitting (MRVJF) system is developed for jewellery retailing. The MRVJF is designed and implemented in consideration of time and budget limitations, availability of functionalities, and potential benefits. It incorporates mixed reality, face recognition, and hand recognition technologies to improve jewellery visualisation and user-friendliness with interactive virtual fitting on smartphones. The MRVJF is aimed to increase the competitiveness of the Hong Kong jewellery retailers in the global commodity chain.
The Method Balance Consistency Availability on Systems Virtual Machine

In this time, a few associations are putting away their information on distributed storage so as to meet the prerequisites of effective activity like soundness, versatility, and accessibility of administrations. Information replication benefits in distributed storage frameworks are there to improve execution. In this unique situation, the prerequisites for guaranteeing information consistency have turned out to be progressively significant. In this paper, we have proposed a virtual server solution for refreshing replication, effectively settling assets, distinguishing and avoiding gridlocks at the server sides. In this original study, we have demonstrated that the proposed arrangement yields the outcomes for the construction which guarantees information consistency in regard to expenses and idleness. we utilize the Open Stack apparatus, which joins a proposed calculation on Adjusting Consistency Accessibility in Physical Machine for keeping up information consistency in Distributed storage Frameworks.
Effectiveness for Securing Sensitive Data and Supporting Multi Data Applications in Cloud Forensics Environment

Distributed computing turned into the most discussed imaginative innovation in Information and correspondence innovation in current patterns. The significant worry of the various associations currently is the security of a client subtleties or archives. In computerized legal examinations and log observing is by and by a significant and open concern. The effect is that patient's subtleties doesn't work be really handled and dissipated nature. The vast majority of the applications need to associate with a few different information stockpiling process contingent upon the sort of information they need to keep up: existing information types, result report, factual information from interpersonal organizations and so on. The significance of utilizing provenance information is critical it gives the criminological agents information history as far as patients, substances and exercises engaged with creating related patients wellbeing information reports. It is difficult to find the client's information where expected to be put away. In this paper, propose a lot of techniques and encryption plot is targeting lightening designers task for creating, sending and moving different information stockpiling progressively assignments and furthermore to upgrade a protected provenance conspire dependent on cutting edge VM measurable computerized assessment investigation apparatuses and square chain innovation. It oversees and screen the internal area of information, both where it originates from as it goes through the information provenance by means of hypervisor.
MODEL-DRIVEN TRANSFORMATION OF HETEROGENEOUS SOURCES INTO LINKED DATA

There are a lot of data sources that can be used to make a reasonable decision in the diversity of fields. But the linking of data from these sources into a solid view must come first. To represent linked data RDF language is used. Unfortunately, there are lots of those who publish data in non-RDF formats. Obviously, considering current data volumes integration task cannot be done in analytics’ heads. A number of instruments to link heterogeneous data were proposed. The most flexible ones are based on special mapping languages. However, these solutions lack scaling and may not be effective for processing large data. In the presented paper, an approach for a possible solution is proposed. The idea of the approach is to abstract data linking process model separating it from a mapping as well as from a code implementation. The model is automatically generated based on a mapping of a set of source ontology elements into a set of domain ontology elements. The model of a process becomes available for separate manipulations relatively independent of other activities. The model can be used to optimize or to custom the linking process or can be treated as a specification for different implementations. As it based on the data-flow concepts it can be naturally translated into contemporary highperformance computation concepts. In other words, it is demonstrated how a process model can be implemented on one of such platforms. The obtained results make it reasonable to continue investigating and developing the proposed approach.
Application of the Swarm Intelligence for the Semantic Annotation of the Web of Things

Entity Linking (EL) is an important tool for many researches including the Internet of Things (IoT).Particularly, it helps to automate the annotation of the IoT devices and services with semantic vocabulary.This semantic annotation improves the interoperability between IoT devices during services discovery and composition.This paper describes an ELbased semantic annotation framework.The latter processes a web table to extract the mentions, generates candidate entities from the Knowledge base (KB) for each mention and then disambiguates the mentions to the correct entry in the KB.The main contribution of this paper is the use of both genetic algorithm (GA) and Artificial Bee Colony (ABC) to handle the combinatorial problem of the collective disambiguation.The proposed framework implements, in addition, some text preprocessing techniques such as the bag of words (BoW) model and provides a tool for IoT data collection.The framework is a first step to demonstrate how swarm intelligence (SI) can exceed the numerical approaches and even some meta-heuristic techniques like the probabilistic based ones.
Review of WAVETRISK-1.0: an adaptive wavelet hydrostatic dynamical core

This manuscript describes the elements used to build a wavelet-adaptive, global, hydrostatic atmospheric model.The numerical scheme is based on a previous, nonadaptive version of the model and is only described briefly in the manuscript.The main ingredients required to make this model wavelet-adaptive are discussed: including important aspects such as conservation of quantities during interpolations between refinement levels.The hexagonal/icosahedral grid structure is presented also in the context of wavelets and general details on the implementation and parallel scheme are given.The algorithm and criteria used for adaptive refinement are presented, including a discussion and analysis of trend-versus solution-based criteria.Validation and performance tests are then conducted, including idealised cases and a more complete, climate-scale Held-Suarez circulation model.
Analysis of Level of Awareness and Usage of Social Media in Healthcare Sector in Tricity Chandigarh

Digital marketing has changed the shape of advertisement. Social media, one of the popular tool of digital marketing is extensively utilized in various sectors. Though it looks challenging in some of the sectors like healthcare sector. Social media has given an opportunity to the marketers to spread awareness about various services offered by the healthcare service providers. Marketers can connect to the customers and are able to manage their relationship with them. It also helps to attract more customers and enables customers to be co-producer of services. According to ICUBE report 2018, India had 483 million internet users (growth of 18%) and was expected to be 627 million by 2019. This figure is projected to grow to 666.4 million internet users in 2023. According to study, the number of social media users in India stood at 326.1 million in 2018 and is expected to grow to 448 million by 2021. A social media user in India spends averagely 17 hours per week on social media. This rapidly increasing awareness of social media inIndia shows the more prospects of advertisement to the marketers.This research paper aims to analyse the difference in the level of awareness and usage of social media in healthcare services by social media users in tricity (Chandigarh, Mohali and Panchkula).The study was done by randomly selecting 130 respondents from each city through structured questionnaire.
Automatic Speech Recognition (ASR) System for Isolated Marathi Words: Using HTK

The present manuscript focuses on building automatic speech recognition (ASR) system for Marathi language (M-ASR) using Hidden Markov Model Toolkit (HTK). The M-ASR system gives the detail about experimentation and implementation using the HTK Toolkit. In this work total 106 speaker independent Marathi isolated words were recognized. These unique Marathi words are used to train and evaluate M-ASR system. The speech corpus (database) is created by us using isolated Marathi words uttered with mixed gender people. The system uses Mel Frequency Cepstral Coefficient (MFCC) for the purpose of extracting features using Gaussian mixture model (GMM). Viterbi algorithm based on token passing is used for decoding to recognize unknown utterances. The proposed M-ASR system is speaker independent. The proposed system has reported 96.23% word level recognition accuracy.
A Fast GPU Algorithm for Complex Fenestration Systems Optimization

Choosing the right Complex Fenestration System (CFS) is a challenging problem when design goals and Climate-Based Daylight Modeling (CBDM) are taken into account.Current software tools, such as Radiance or EnergyPlus, only allow the evaluation of a given fenestration system, but lack support for automatically finding optimal designs.Furthermore, these tools are computationally expensive for daylight simulation.In this work, we propose an optimization algorithm based on the Variable Neighborhood Search metaheuristic, and the daylight coefficient method for annual daylight simulation.Two strategies to compute the daylight coefficients are presented and implemented on a Graphics Processing Unit (GPU) on top of the Nvidia OptiX library.Experimental results show our parallel GPU implementation provides fast and accurate results, which leads to significant reductions in simulation time (up to one order of magnitude) with respect to Radiance.The optimization test cases were successfully solved, and satisfactory solutions were found in reasonable time, allowing interactive design cycles.
Kesulitan Menyimak dan Strategi Mahasiswa dalam Memahami Bagian Listening Comprehension pada Tes TOEIC

This study is aimed to identify the difficulties to comprehend listening activities experienced by students and their strategies in understanding Listening Comprehension section of TOEIC test. Descriptive qualitative method was employed to find out and analyze the difficulties and strategies the students implemented. The research was carried out at English Department of Politeknik Negeri Bandung with the population of the third semester students in Academic Year 2017/2018. There were 22 students as the subject in this study. Data were collected by questionnaires and interview to confirm students’ answers. The results showed that the major problems encountered by students were unfamiliar vocabulary, accents of the speaker, speed of speech, lack of concentration, and quality of recorded materials. Listening strategies implemented by students were mostly metacognitive strategy since in this strategy they obtain the awareness of the materials they are listening to in a more cautious way. This method is about the ways of planning, monitoring and assessing the collected information from the listening section the same as pre listening activities. Students applying metacognitive strategies have the benefits such as they can use appropriate learning strategy to master learning materials; they comprehend learning materials more quickly and integrate the knowledge seriously; they put themselves to be loyal receivers and can be properly adaptable to all conditions; they have high confidences in themselves to get help when needed from their peers, teachers, or family; they pay attention to the materials and assess themselves as to how to be successful learners; and they properly deal with the problems in doing their task. Keywords: Students’ Difficulties, Listening Comprehension, Listening Strategies
Implementation of End to End Automation for BTS commissioning using Python

Network Management Systems (NMSs) are the innovative products used by telecommunication companies to monitor, control, analyze and manage telecommunication networks.Base Transceiver Station (BTS) is a network element which facilitate wireless communication between user equipment and network.BTSs can be integrated with NMS and all related applications available in NMS can be used over BTS.One such application is software upgrade or downgrade in BTS which is termed here as BTS commissioning.This BTS commissioning process involves multiple steps to be performed for successful software upgrade or downgrade in BTS manually through NMS.In this paper methodology to implement end to end automation of BTS commissioning using python is discussed.GUI is developed using JavaScript, jQuery, Flask, Python, HTML and CSS.The reason for using Python for this website is because it is powerful, simple and has many libraries.Developed feature is tested by integrating a test BTS with Nokia's NMS named NetAct.
Endowments, foundations shifting strategies to weather pandemic

Nonprofit Business AdvisorVolume 2020, Issue 369 p. 6-6 Nonprofit Research Endowments, foundations shifting strategies to weather pandemic First published: 14 May 2020 https://doi.org/10.1002/nba.30776Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume2020, Issue369June 2020Pages 6-6 RelatedInformation
The Adequacy Assessment of Test Sets in Machine Learning using Mutation Testing

The accuracy is computed by applying the test dataset to the model that has been trained using the training dataset. Thus, The test dataset in machine learning is expected to be able to validate whether a trained model is sufficiently accurate for use. This study addresses this issue in the form of the research question, “how adequate is the test dataset used in machine learning models to validate the models.” To answer this question, the study takes seven most-popular datasets registered in the UCI machine learning data repository, and applies the data sets to the six difference machine learning models. We do an empirical study to analyze how adequate the test sets are, which are used in validating machine learning models. The testing adequacy for each model and each data set is analyzed by mutation analysis technique.
IMPLEMENTASI CLOUD COMPUTING DAN APLIKASI ONLYOFFICE DENGAN KEAMANAN INTRUSION DETECTION SYSTEM PADA RASPBERRY PI

The development of increasingly advanced technology affects a system and efficiency in the world of entrepreneurship. Store owners have difficulties in creating, processing, storing data and lack of network security. Therefore, cloud computing is very much needed as a storage medium, in addition to the importance of cloud computing in Toko Titik, it also really needs data processing applications to make it easier to work and network security to avoid external attacks. To overcome this, the author will design a cloud computing with Nextcloud software, onlyoffice application as a data processor and network security with IDS (Intrusion Detection System) on the Raspberry Pi. From the results of research conducted, it has been proven that with cloud computing, storage media is more effective and efficient. Nextcloud can also be accessed on Android anytime and anywhere as long as there is internet, and also data processing using the Onlyoffice application installed on NextCloud. Of course this is very easy for entrepreneurs to store data and process data, coupled with network security so that data security is maintained
Journal Information

Learned PublishingVolume 32, Issue 4 p. 412-412 Issue InformationFree Access Journal Information First published: 02 October 2019 https://doi.org/10.1002/leap.1179AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume32, Issue4October 2019Pages 412-412 RelatedInformation
Tugas Akhir Adm Muthia Febio 18006041

Tugas Akhir Adm Muthia Febio 18006041
Factorisation machines considering the latent characteristics behind target data

A factorisation machine (FM) is often used as a regression-type prediction model that considers the interactions between features. In recent years, many researchers have focused on the differences between interactions from the perspective of prediction accuracy. In some cases, it can be assumed that there are several groups with different characteristics in terms of the relationships between input and output data for a target population. Therefore, there is a possibility that the effects of interactions will differ between input data groups, which are used to construct latent clusters. However, there has been little research on FMs considering the latent characteristics behind target data. Therefore, this paper proposes a novel type of FM that considers latent data characteristics. The proposed model can express the differences between the interaction effects of latent groups and it is expected to improve the prediction accuracy of FMs for complex problems. Based on a demonstration experiment using the MovieLens dataset, the effectiveness of our proposed model is verified.
Pemahaman Mahasiswa kedokteran Universitas Sebelas Maret pada Penanganan Awal Fraktur

Medical students really need to know the initial handling of fractures, because medical students have become a community paradigm that is considered as a prospective doctor who can maintain and prosper the public health environment. Medical students are required to understand the handling of emergencies, especially the initial handling of fractures. For this reason, this study took a sample of 20 university medical students from eleven March to determine the level of understanding of students in the initial handling of fractures.
Chapter 6 Measuring professionals’ perception on blockchain-based futuristic accounting

This research measures the disruption in accounting triggered by emerging technology: blockchain technology (BT). It analyzes its role in accounting with accounting professionals’ awareness of these technologies and its impact over their working. It also measures the advantages and challenges of adopting BT in accounting. For that purpose, it explores the possibility of accounting professionals’ perception after collecting data through the opinion survey method. To analysis data, t-test with SPSS software was used. For testing the reliability of collected data through opinion survey, Cronbach’s alpha reliability test has been conducted. The study concluded that accounting professions are aware of these technologies. Results also show that BT makes real-time accounting possible soon. Results revealed that this technology may improve monopoly of the big corporation. The study revealed that professionals’ perceived mind-set may become a big challenge to adopt BT in accounting.