Reviewerly/exHarmony huggingface dataset:
The Use of Various Indonesian and Elements of Code Experts, Code Mixing as a Mean of Communication in University Level: Case Study of Indraprasta University, Jakarta

Language is always used as a mean of communication among students, lecturers, staff, and structural officials at the Indraprasta University, Jakarta, English Education Department and Mathematics Education Department. The requirement to use Indonesian is not completed maximally due to the influence local and foreign language. As language users among students, lecturers, academic staff and structural officials, they are required to use Indonesian based on its context. The use of language can be seen from the use of standard language indicating good and correct use of Indonesian. The methods of the study were conducted by observation, interviews, and documentation with a sub-focus domain of the various Indonesian use and elements of code experts as well as code mixing as a means of communication. Data were collected by conducting research documents on the use of the Indonesian with a study of language functions and ethnographic methods starting with the selection of a culture. The results of the study show that the use of the formal and informal domain dominate the use of Indonesian. In addition, there is an element of code expert and code mixing both spoken and written at the English Language Education Study Program and Mathematics Education Study Program at Indraprasta University, Jakarta.
Real-time eye tracking analysis for training in a dynamic task

A dynamic task refers to a task in which the state of the system can dynamically change when a user interacts with the system's components. For example, when an air traffic controller detects aircraft on converging flight paths, the controller can select from multiple altitude, heading and speed clearances to maintain safe separation between them. Some clearance options for those two aircraft, however, may lead to losses of separation with other aircraft. One viable non-intrusive approach to characterize a user's interaction with a system is through real-time analysis of eye movements at the time when the state of the system is changing. The presentation of data from such analyses could be an effective way to enhance user training techniques. In this article, we provide a framework of how to analyze eye-tracking data to identify useful characteristics along with associated algorithms, followed by a simple case study to validate our framework.
How open science can benefit bilingualism research: A lesson in six tales

Bilingualism is hard to define, measure, and study. Sparked by the “replication crisis” in the social sciences, a recent discussion on the advantages of open science is gaining momentum. Here, we join this debate to argue that bilingualism research would greatly benefit from embracing open science. We do so in a unique way, by presenting six fictional stories that illustrate how open science practices – sharing preprints, materials, code, and data; pre registering studies; and joining large-scale collaborations – can strengthen bilingualism research and further improve its quality.
Maritime Incident Information Extraction using Machine and Deep Learning Techniques

In this paper, we present several Natural Language Processing (NLP) techniques for extracting information about piracy from unstructured maritime news articles. The first technique is a document segmentation algorithm, which, given an article that may discuss several unrelated topics, separates the document into a list of maritime incidents. This technique takes advantage of the notion that maritime articles tend to include many precise dates and achieves an F1-score of 0.82 on a dataset of 52 articles. The second technique extracts the names and types of ships involved in a maritime incident using a combination of Named Entity Recognition (NER) and database lookups; an F1-score of 0.89 is achieved. The third and final technique determines the incident type, the severity of the incident, the response taken by the victim, and the eventual impact of this response. To determine these details, five binary classification models were trained and tested. The overall best algorithm was gradient boosting via the CatBoost library, which achieved an average accuracy of 91%.
CloudBridge Waste Segregator Automation using Machine Learning

There is a huge problem in creating space today because of growing population and research is going on profusely in finding space to dump waste. The waste has been dumped to rivers, underground and mixed with soil and by other methods. But all these methods are harmful to environment in long term. Our research is done on finding efficient way to segregate waste followed by recycling of wastes. The difficulties in isolation of various products are dealt using machine learning approach. The framework used to robotize the procedure of waste isolation to deal with the junk effectively and productively is one of the Machine Learning strategies called Convolutional Neural Network (CNN). The experiments showed that the performance of CNN is better because it recognizes the components in an image and recombines these components to recognize other structures while other methods learn to recognize as they go through it. The work will be segregated into 6 bins consisting of biodegradable, non- biodegradable. Here we have used the TensorFlow algorithm which uses Python. The applications of TensorFlow are Python application itself. The application of our research includes waste segregation in society, in industries, in agricultural fields. The recycled wastes can be used as organic material in many places.
Special Issue of INFORMS Journal on Computing—Scalable Reinforcement Learning Algorithms

Free AccessAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to SectionFree Access HomeINFORMS Journal on ComputingVol. 34, No. 1 Special Issue of INFORMS Journal on Computing—Scalable Reinforcement Learning AlgorithmsJ. Paul Brooks, Ted Ralphs, Nicola SecomandiJ. Paul Brooks, Ted Ralphs, Nicola SecomandiPublished Online:27 Jan 2022https://doi.org/10.1287/ijoc.2021.1153"Special Issue of INFORMS Journal on Computing—Scalable Reinforcement Learning Algorithms." INFORMS Journal on Computing, 34(1), pp. 2–3 Previous Back to Top Next FiguresReferencesRelatedInformation Volume 34, Issue 1January-February 2022Pages 1-669, C2 Article Information Metrics Downloaded 339 times in the past 12 months Information Published Online:January 27, 2022 Copyright © 2022, INFORMSCite asJ. Paul Brooks, Ted Ralphs, Nicola Secomandi (2022) Special Issue of INFORMS Journal on Computing—Scalable Reinforcement Learning Algorithms. INFORMS Journal on Computing 34(1):2-3. https://doi.org/10.1287/ijoc.2021.1153 PDF download
Bank Credit and SMEs Financing in Nigeria

This study examined the extent SMEs are funded by deposit money banks in Nigeria.This was done through extensive descriptive, graphical and empirical analysis of Bank credit to SMEs data.The study generated and analyzed Bank credit to SMEs gap.It further explored Nigeria's "financing gap" which measures the need of external funds for the corporate sector.Bank credit to SMEs was found to be decreasing over the period covered (i.e. 1992 -2020).Bank Loans to SMEs as percent of Total Bank Loans reached a maximum of 47.7% in 2003 from where it had continued to decline to 0.56% in 2019 ended.Loans to Small Scale Enterprises as a percentage of Total Credit to the Private sector had been on the decline from a maximum of 27.04 % in 1992 to 0 0.54% in 2019.The analyzed SMEs Credit Gap shows that by 1992, SMEs Credit Gap was N113.664 million but by 2009, the SMEs Credit Gap has grown to N4261.46 billion and N7725.51 billion in 2016.The SMEs Credit Gap as at 2019 ended stood at N7565.51 billion.For the corporate sector, the financing gap is alarming as results reveal a financing gap of N3, 176,147,942,428.47 trillion in 2019.Thus, policies and actions aimed at reversing the downward trend in credit to SMEs as well as closing the financing gap should be intensified.Deposit money banks not just Microfinance banks should be encouraged to lend more to SMEs and remove all obstacles (hidden charges that increase the interest rate) while providing more technical assistance to SMEs.
Risk and Protecting Factors of Cyberbullying in Malaysia: A Comparative Analysis

Technology communication allowed the increased number of users over the internet. Each user is able to communicate with each other’s via mobile phone, notebook and any platform as long as connected with the internet. Therefore, they do not have to use a physical interaction while communicating. However, the development of applications such as Facebook, Telegram, WhatsApp etc. resulted in positive and negative effects for victims, Perpetrators, Parents and Stakeholders. This leaves a gap that needs to be addressed since there is a limited model that explains how to prevent cyberbullying. This study aims to identify the risk factor and protecting factor used in cyberbullying. Methodology: This study was conducted using a Systematic Literature Review (SLR). Thus, this study provides a gap of study for existing work of cyberbullying for risk factors that contribute to cyberbullying and analyse the protection factor of cyberbullying. Based on this study, we found that communication skills, parental involvement and law enforcement are a necessity to reduce the number of cyberbullying in Malaysia.
Printed Textbook and its Potential in Remote Learning during The Covid-19 Crisis

Remote learning which is mostly done online has become a new norm as schools are forced to close due to COVID-19.This learning setting has exposed a huge digital inequality among learners around the world, where not all of them are privileged to join remote learning sessions and they might be left behind for a long time as the pandemic is not showing any sign of slowing down.However, a solution to this pressing situation might be found through the always reliable printed textbook.As a printed textbook does not require an internet connection or a device to be utilized, it is thought that it can ensure a continuous access to education in this challenging time since it provides convenience in terms of time, money, and connectivity, while also offering credible content, standardization, and becoming a good vehicle for self-study among learners.However, the currently available printed textbook in the market is not intended to be used in a time of crisis like this; a better printed textbook must be urgently produced to suit the unprecedented time we are living in.
Students’ Perspectives in Using XReading as an Extensive Reading Platform for Higher Education Students

Abstract: This study investigated students' perspectives in using XReading website in an Extensive Reading course during the pandemic era of Covid-19. The method employed in the study was a mixed method research design used to describe the quantitative and qualitative data integrated in a single survey of students’ perspective on the use of XReading as an Extensive Reading platform for higher education students. A questionnaire and an interview guide were utilized as the instrument of this study. The results of the questionnaire were calculated in the form of frequency distribution and percentages which were then combined with the result of the interview and being elaborated into sentences in the form of narrative. The participants showed positive responses toward the use of XReading in an Extensive Reading course. They also claimed that the website is an adequate online source that is not only providing reading materials, but also allowing students with opportunities to develop their English language skills such as reading ability as well as vocabulary acquisition and listening skills. Keywords: students’ perspective, XReading, extensive reading Abstrak: Penelitian ini meneliti perspektif mahasiswa dalam menggunakan situs web XReading untuk program Extensive Reading yang mereka lakukan di era pandemi Covid-19. Metode yang digunakan dalam penelitian ini adalah desain penelitian metode campuran yang didefinisikan untuk menggambarkan data kuantitatif dan kualitatif yang tergabung dalam sebuah survei mengenai perspektif mahasiswa tentang penggunaan XReading sebagai platform Membaca untuk mahasiswa pendidikan tinggi. Kuesioner dan wawancara digunakan sebagai instrumen pada penelitian ini. Hasil angket dihitung dalam bentuk distribusi frekuensi dan persentase yang dijabarkan ke dalam kalimat dalam bentuk format naratif. Peserta yang mengikuti penelitian ini menunjukkan respon positif terhadap penggunaan program XReading for Extensive Reading. Mereka juga berpendapat bahwa situs web tersebut merupakan sumber online yang memadai yang tidak hanya menyediakan bahan bacaan, tetapi juga memberikan banyak kesempatan kepada siswa untuk mengembangkan kemampuan bahasa Inggris mereka seperti kemampuan membaca serta penguasaan kosa kata dan keterampilan mendengarkan. Kata kunci: perspektif mahasiswa, XReading, extensive reading
USE OF THE GEOTHERMAL GRADIENT VARIATIONS IN ZONING OF POSTMIOCENE SEQUENCE IN THE NILE DELTA, EGYPT

Among the most important thermal parameters of the rocks and their composing minerals is the Geothermal Gradient.Geothermal gradient plays an important role in expressing the subsurface thermal conditions in any area.After carrying out the required corrections for the thermal data in hand, it was used for estimating and illustrating the Geothermal Gradient variations with depth.Using such results, an attempt was carried out to use such variations to identify the subsurface lithologic compositional variations in the study area based on the thermal characteristics of the different subsurface lithologies.Four main subsurface units were identified and mapped within the Nile Delta.
Trusted Data Space for Creating Value from Data in a Chain Reaction Manner

The Smart World holds the possibility of maximizing the value of data throughout society by enabling diverse organizations to bring each other data for analysis and create data for new purposes in a chain reaction manner beyond the walls of companies and industries.In reality, however, the use of data among organizations goes no further than the provision of limited data to limited parties, which prevents a value chain from being achieved.This article introduces a new mechanism for data sharing called "trusted data space" as an initiative to solve this problem and describes key technologies for making it a reality.
How Dense Autoencoders can still Achieve the State-of-the-art in Time-Series Anomaly Detection

Time series data has become ubiquitous in the modern era of data collection. With the increase of these time series data streams, the demand for automatic time series anomaly detection has also increased. Automatic monitoring of data allows engineers to investigate only unusual behavior in their data streams. Despite this increase in demand for automatic time series anomaly detection, many popular methods fail to offer a general purpose solution. Some demand expensive labelling of anomalies, others require the data to follow certain assumed patterns, some have long and unstable training, and many suffer from high rates of false alarms. In this paper we demonstrate that simpler is often better, showing that a fully unsupervised multilayer perceptron autoencoder is able to outperform much more complicated models with only a few critical improvements. We offer improvements to help distinguish anomalous subsequences near to each other, and to distinguish anomalies even in the midst of changing distributions of data. We compare our model with state-of-the-art competitors on benchmark datasets sourced from NASA, Yahoo, and Numenta, achieving improvements beyond competitive models in all three datasets.
Human Resource Analysis Based on Used Libraries in Eclipse Projects on GitHub

As current software development increasingly relies on libraries and frameworks, the knowledge and experience to use libraries is considered an important skill in software development. This paper attempts to analyze and identify the types of skills of contributors in five Eclipse projects, focusing on the libraries used by the contributors. We found that standard util libraries and I/O libraries are used in all projects, while specific libraries such as maven libraries and servlet libraries are used in one of the projects. Also, in library category analysis, we found that two projects require the security specialist that can use security libraries. Finally, in library provider analysis, we found that Oracle and JUnit libraries are used in all projects, which indicates that developers are recommended to learn these libraries.
Temporalizing static graph autoencoders to handle temporal networks

Graph autoencoders (GAE), also known as graph embedding methods, learn latent representations of the nodes of a graph in a low-dimensional space where the structural information is preserved. While real-world graphs are generally dynamic, only a few embedding methods handle the temporal dimension: Even though they have proven their reliability, the majority of the embedding techniques address the case of static networks and present poor performances when applied to temporal ones.
iQurNet: A Deep Convolutional Neural Network for Text Classification on the Indonesian Holy Quran Translation

The holy book of Muslims has been translated into more than a hundred different languages. In Indonesia, the translation appeared around the Walisongo era or the 15th century and was widely spread for Islamic teaching. However, the Indonesian translation has not been grouped by the main content of the verses. In this research, a deep learning model is proposed for text classification called iQurNet. Our model incorporates an embedding layer, several layers of one-dimensional convolutional layers, and is fully connected with 3 hidden layers. The dataset was collected from the website of the Ministry of Religion of the Republic of Indonesia. Prior to computing, we conduct preprocessing to remove any trivial words, special characters, and weigh the output class. We also compare some machine learning models namely Na&#x00EF;ve Bayes, Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Neural Network (MLP). The results, in Na&#x00EF;ve Bayes with accuracy at 66%, Decision Tree at 59%, SVM at 71%, KNN at 68%, MLP at 73 %, and iQurNet at 84%. In conclusion, our model become the best model in performance for its accuracy.
Use of ANN to Identify Fake Profiles

Abstract: There is a huge expansion in advancements nowadays. Mobiles are becoming shrewd. Innovation is related with online informal organizations which has turned into a section in each one's life in making new companions and keeping companions, their inclinations are known simpler. Be that as it may, this expansion in systems administration online makes numerous issues like faking their profiles, online pantomime having become increasingly more in present days. Clients are taken care of with more superfluous information during riding which are posted by counterfeit clients. Explores have seen that 20% to 40% profiles in internet based informal organizations like Facebook are phony profiles. Subsequently, this identification of phony profiles in internet based informal communities’ results into arrangement utilizing systems. Keywords: Online Social Networks, Fake profiles, Classification, Neural Network.
An Effective Classification Algorithm for Breast Cancer using Dyadic Projection

Breast Cancer is a wide spread reason for the deaths of women in the world. Nowadays, CAD systems have become the most increasing interest in its detection. In this paper, a new computer-aided diagnosis method is introduced to help oncologists to classify it as benign or evil breast tumors in ultrasound. In the proposed model, bi clustering is done for feature acquisition and then dyadic transform is applied. Biclustering mining is used as a key to identify the regularity patterns in columns on the working out data, Biclustering mining is utilized as a key. At last, to identify the perfect combinations and put them into a strong classifier, AdaBoost learning is applied. Using a dataset the proposed method is evaluated validated and the results are compared with the results of existing methods. The results of the proposed model showed the best calculation, proving it to be effective in laboratory applications.
Enhancement of Face Recognition using Deep Learning

Our aim in this paper is to increase the accuracy of existing facial recognition system on a comparative smaller dataset as per the requirements of present day. Namely in sensitive regions. The methodology that has been adopted is by combining more than one algorithms. The feature detection capability of harr cascade along with Ada boost to fetch to Bilinear CNN so that on a comparative smaller dataset can produce comparative result as on bigger dataset.
Printer flaw threatens all Windows machines

An extremely serious vulnerability in the Windows printer spooler means that all PCs running any version of the operating system could be subject to remote code execution (RCE).
ASHES 2020: 4th Workshop on Attacks and Solutions in Hardware Security

The workshop on "Attacks and Solutions in HardwarE Security"(ASHES) welcomes any theoretical and practical works on hardware security, including attacks, solutions, countermeasures, proofs, classification, formalization, and implementations. Besides mainstream research, ASHES puts some focus on new and emerging scenarios: This includes the internet of things (IoT), nuclear weapons inspections, arms control, consumer and infrastructure security, or supply chain security, among others. ASHES also welcomes dedicated works on special purpose hardware, such as lightweight, low-cost, and energy-efficient devices, or non-electronic security systems. The workshop hosts four different paper categories: Apart from regular and short papers, this includes works that systematize and structure a certain (sub-)area (so-called "Systematization of Knowledge" (SoK) papers), and so-termed "Wild and Crazy" (WaC) papers, which distribute seminal ideas at an early conceptual stage. This summary gives a brief overview of the fourth edition of the workshop, which will take place virtually on November 13, 2020, as a post-conference satellite workshop of ACM CCS.
Relational Database Service

Relational Database Service (RDS) is a service that makes it easy to create and manage a database in the cloud. RDS supports MySQL, MariaDB, PostgreSQL, Oracle, and SQL Server. While you could install and run any of these on an EC2 instance, RDS greatly simplifies the effort. RDS instances are managed by AWS, eliminating time-consuming activities, such as patching and backups, and allowing you to focus on your application.