Reviewerly/exHarmony huggingface dataset:
Aplikasi Kehadiran Karyawan Berbasis Android Menggunakan QR Code Scanning dan Location Based Service

Employee attendance is an administrative process that is carried out every day. Use of a manual system for events prone to damage or loss. Not to mention employees who forget attendance records, which will cause delays in the flow of employee attendance information. As a solution to the existing problems is by designing an application to make presence using Android-based QR Code Scanning and Location-Based Services (LBS). QR Code technology is very efficient because the code can be read in a very short time using a smartphone while the Location Based Service (LBS) is to prevent employees from making presence outside the area determined by the company. The design of this application produces a paperless system and prevents manipulation of attendance data because in this application it is implemented that one employee only asks for one device ID or smartphone IMEI
Correction to: Unsupervised and scalable subsequence anomaly detection in large data series

A correction to this paper has been published: https://doi.org/10.1007/s00778-021-00678-1
FbG as an around the clock support for an essay writing course amidst the pandemic

This study aimed to explore the potential of Facebook Group (FbG) for teaching essay writing skills in the period of social distancing due to covid-19 pandemic. The study involved three groups of students who had been attending Essay Writing Course. Data was collected through questionnaires, interview, home assignments, and documents. Questionnaires were distributed via Google Form. Interviews were conducted with students through WhatsApp chats and calls. Descriptive statistics were used to analyse data obtained from closed questionnaires, home assignments, and mid-term test. Data from open questionnaires, interviews, and documents was analysed using qualitative inductive approach to identifying themes � topics, ideas, and patters of meaning. Study findings indicated that FbG has been an appropriate place for developing essay writing skills amidst the pandemic. FbG as networking site has made possible an effective, efficient, and practical essay writing learning for the following reasons.� Firstly, FbG discussion forum provided invaluable learning input. Secondly, FbG was easy to operate. The students could learn independently at their own pace.� Lastly, use of FbG brought them convenience and raised confidence to better engage in learning online. FbG has supported students� essay writing learning process that has led to good writing performance. This can be seen not only from the conducive learning process in comparison to traditional classroom instruction prior to the pandemic, but also from learning outcome apparently observed in writing portfolios as well as mid-term test.
Motivasi Karir, Motivasi Gelar, Persepsi Biaya Pendidikan, Parental Influence dan Minat Mengikuti PPAk

Accounting profession education is important because it can produce professional accountants, but the interest in following it is currently still low. This study aims to determine the effect of career motivation, degree motivation, perceived cost of education, and parental influence on interest in pursuing accounting profession education. The population is all active students of accounting in the morning and evening regular FEB Unud class of 2017 as many as 287. The sample collection technique is the purposive sampling method. The number of samples is 143 with the data collection method using a questionnaire. The data analysis technique is multiple linear regression analysis. The results of the analysis show that career motivation and degree motivation have an effect on interest in attending accounting profession education, perceptions of educational costs and parental influence have no effect on interest in PPAk.&#x0D; Keywords: Career Motivation; Degree Motivation; Perceptions of Education Costs; Parental Influence; Accountant Professional Education.
A novel imbalanced data classification approach using both under and over sampling

The performance of the data classification has encountered a problem when the data distribution is imbalanced. This fact results in the classifiers tend to the majority class which has the most of the instances. One of the popular approaches is to balance the dataset using over and under sampling methods. This paper presents a novel pre-processing technique that performs both over and under sampling algorithms for an imbalanced dataset. The proposed method uses the SMOTE algorithm to increase the minority class. Moreover, a cluster-based approach is performed to decrease the majority class which takes into consideration the new size of the minority class. The experimental results on 10 imbalanced datasets show the suggested algorithm has better performance in comparison to previous approaches.
Pronunciation augmentation for Mandarin-English code-switching speech recognition

Abstract Code-switching (CS) refers to the phenomenon of using more than one language in an utterance, and it presents great challenge to automatic speech recognition (ASR) due to the code-switching property in one utterance, the pronunciation variation phenomenon of the embedding language words and the heavy training data sparse problem. This paper focuses on the Mandarin-English CS ASR task. We aim at dealing with the pronunciation variation and alleviating the sparse problem of code-switches by using pronunciation augmentation methods. An English-to-Mandarin mix-language phone mapping approach is first proposed to obtain a language-universal CS lexicon. Based on this lexicon, an acoustic data-driven lexicon learning framework is further proposed to learn new pronunciations to cover the accents, mis-pronunciations, or pronunciation variations of those embedding English words. Experiments are performed on real CS ASR tasks. Effectiveness of the proposed methods are examined on all of the conventional, hybrid, and the recent end-to-end speech recognition systems. Experimental results show that both the learned phone mapping and augmented pronunciations can significantly improve the performance of code-switching speech recognition.
ANALISA DAN PREDIKSI IKLAN LOWONGAN KERJA PALSU DENGAN METODE NATURAL LANGUAGE PROGRAMING DAN MACHINE LEARNING

This research was conducted using the data provided by Kaggle. This data contains features that describe job vacancies. This study used location-based data in the US, which covered 60% of all data. Job vacancies that are posted are categorized as real or fake. This research was conducted by following five stages, namely: defining the problem, collecting data, cleaning data (exploration and pre-processing) and modeling. The evaluation and validation models use Naïve Bayes as a baseline model and Small Group Discussion as end model. For the Naïve Bayes model, an accuracy value of 0.971 and an F1-score of 0.743 is obtained. While the Stochastic Gradient Descent obtained an accuracy value of 0.977 and an F1-score of 0.81. These final results indicate that SGD performs slightly better than Naïve Bayes.Keywords—NLP, Machine Learning, Naïve Bayes, SGD, Fake Jobs
DEVELOPING POP-UP BOOK MEDIA IN EARLY READING AT LOW CLASS

This research was motivated by the lack of media in early reading at the low class. Pop-up book was a media that provided a more attractive visualization for students. Pop-up books provoked students' enthusiasm in reading and stimulated students' imagination. This type of research was development (R&amp;D), using the ADDIE model consisting of 5 stages, namely analysis, design, development, implementation, evaluation . The data were obtained from the questionnaire filled in by validators and field experts viewed from 5 aspects, namely simplicity, emphasis, balance, integration, and media design. Then, the feasibility test by media experts obtained a percentage of 89.55% (very feasible), by material experts was 88.55% (very feasible), and by field experts on media design was 86.25% (very feasible), and on the material was 85.4% (very feasible). Overall, pop-up book media for early reading was very feasible.
QUANTUM COMPUTING AND COMMUNICATIONS

Today, like never before, we are witnessing a pervasive diffusion of ultra-broadband fixed-mobile connectivity, the deployment of Cloud-native 5G network and service platforms, and the wide adoption of Artificial Intelligence. It has the so-called Digital Transformation of our Society: as a matter of fact, the transformative role of Telecommunications and Information Communication Technologies (ICT) has long been witnessed as a precursor of scientific progress and economic growth in the modern world. Nevertheless, this transformation is still laying its foundations on Electronics and the impending end of Moore’s Law: therefore, a rethinking of the long-term ways of doing computation and communications has been already started. Among these different ways, quantum technologies might trigger the next innovation breakthrough in the medium long-term. In this direction, the paper provides an overview of the state of the art, challenges, and opportunities posed by an expected second wave of quantum technologies and services.
Інформаційний вплив як об’єкт наукового дослідження

International scientific and practical conference «Pedagogy, psychology and teaching methods: international experience» : conference proceedings, July 16–17, 2021. Riga, Latvia : «Baltija Publishing», 2021. 295 pages.
Geological Survey of Western Australia: Geological Survey of Western Australia’s Accelerated Geoscience Program

During 2020–21, the Geological Survey of Western Australia (GSWA) reprioritized its work programme due to the impact of travel and operational restrictions imposed by the COVID-19 pandemic. By usin...
Development of mobile and desktop applications for a fingerprint-based attendance management system

Mobile application technology has been at the forefront of technological advancement in recent years. This has made life easier, and tasks that were considered herculean have been made easier and executable in a much shorter time than ever. One of such tasks is the process of taking attendance during events (such as lectures and conferences) by scribbling one’s signature and other personal details on a central register. This manual process is cumbersome and inconvenient, especially when a large number of participants are involved. To address this problem, this paper presents an automated solution in which a Java-based mobile application was developed and connected wirelessly to a central database that was created using My structured query language (MySQL) application whose task, among others, was to record attendance information. The database was connected to the backend of the web-based software program which was coded in hypertext pre-processor (PHP) programming language. Authentication was achieved through username, password, and fingerprint information. The system was deployedin a university to log students’ details, time absent, time present and cumulative attendances per month and it was realised that the system was highly effective, efficient and 5 times faster than the conventional paper-based attendance logging technique.
Implementation Of Bubble Sort Algorithm On 2 Fruit Models Of Data Selection Using The Java Program Language

There are various kinds of data sorting methods that we know of which are the Bubble Sort, Selection Sort, Insertion Sort, Quick Sort, Shell Sort, Heap Sort, and Radix Sort methods. All of these methods have advantages and disadvantages of each, whose use is determined based on needs. Each method has a different algorithm, where different algorithms affect the execution time. One interesting algorithm to be implemented on 2 variant models of data sorting is the Bubble Sort algorithm, the reason is that this algorithm has a fairly long and detailed process flow to produce an ordered data sequence from a previously unordered data sequence. Two (2) data sorting variant models that will be implemented using the Bubble Sort algorithm are: Ascending data sorting variants moving from left to right, and Descending data sorting variants moving from left to right. And the device used in implementing the Bubble Sort algorithm is the Java programming language.
A Hardware Platform for Ensuring OS Kernel Integrity on RISC-V

The OS kernel is typically preassumed as a trusted computing base in most computing systems. However, it also implies that once an attacker takes control of the OS kernel, the attacker can seize the entire system. Because of such security importance of the OS kernel, many works have proposed security solutions for the OS kernel using an external hardware module located outside the processor. By doing this, these works can realize the physical isolation of security solutions from the OS kernel running in the processor, but they cannot access the inner state of the processor, which attackers can manipulate. Thus, they elaborated several methods to overcome such limited capability of external hardware. However, those methods usually come with several side effects, such as high-performance overhead, kernel code modifications, and/or excessively complicated hardware designs. In this paper, we introduce RiskiM, a new hardware-based monitoring platform to ensure kernel integrity from outside the host system. To deliver the inner state of the host to RiskiM, we have devised a hardware interface architecture, called PEMI. Through PEMI, RiskiM is supplied with all internal states of the host system essential for fulfilling its monitoring task to protect the kernel. To empirically validate our monitoring platform’s security strength and performance, we have fully implemented PEMI and RiskiM on a RISC-V based processor and FPGA, respectively. Our experiments show that RiskiM succeeds in the host kernel protection by detecting even the advanced attacks which could circumvent previous solutions, yet suffering from virtually no aforementioned side effects.
GEOSPATIAL ASSESSMENT FOR PLANNING A SMART ENERGY CITY USING ROOFTOP SOLAR PHOTOVOLTAIC IN BANDUNG CITY, INDONESIA

Abstract. Increasing the production of clean and environmentally friendly energy has become one of the world agendas as a strategic effort in dealing with long-term climate change. Seeing the potential of the energy produced, the ease in the installation process, with the small risk of harm generated, solar energy has received significant attention from many countries in the world. The potential for solar energy in Indonesia alone reaches 207 GWp, but only 145.81 MWp has been utilized. Currently, the Indonesian government has set a target to build a Solar Power Plant capacity in 2025 of 6.5 GWh. Urban areas are areas with higher energy demand than rural areas, but the availability of vacant land in urban areas is very minimal for installing solar power plants. Therefore, rooftop solar PV(Photovoltaic) can be a solution in dense areas such as cities. Good planning by looking at the potential resources and energy needs in spatial is needed to manage and utilize energy optimally and sustainably in urban areas. This study aims to develop a geospatial assessment for plan smart energy city that uses rooftop solar PV's potential energy in every building that is effective and efficient. The novelty in the analysis of the distribution of the potential for rooftop solar PV development in urban areas integrates meteorological and spatial aspects and socio-economic aspects. Integration of multi-dynamic spatial data uses in determining the rooftop solar PV construction location, such as meteorological data for solar energy potential, increasing energy needs of each building, and socio-economy data. The data source used comes from statistical data and remote sensing data. The analysis will be carried out temporally (2008, 2013, and 2018) to see the pattern of changes in aspects used in a certain period so that the development plan can be carried out more optimally. This research's output is the formation of a priority analysis of solar PV rooftop construction in urban areas, especially the city of Bandung. The result of energy can also produce by the construction of rooftop solar PV in a potential area. This research is expected to be utilized by policymakers to develop renewable energy in the city of Bandung and increase community participation in switching to renewable energy.
Complementary Representation of ALBERT for Text Summarization

Pretraining has proved to be an effective strategy to learn the parameters of the deep neural network.It captures the world knowledge that can be adapted to downstream tasks.Text summarization based on ALBERT [1] outperformed previous work by a large margin.However, they only use the final layer as a contextualized representation of the input text.Multiple studies have proven that intermediate layers also encode the rich hierarchy of linguistic information.In this paper, we propose a Fast Complementary Representation Network (FCRN), which dynamically incorporates linguistic knowledge spread across the entire ALBERT for extractive selection.Different from previous work, we measure the importance of hidden layers by all sentence representations rather than all token embeddings, which can filter nonsignificant words and takes six times less time during training.FCRN first obtains the importance of each layer by sentence embeddings and then automatically absorbs the supplementary information to ALBERT's output.We conduct experiments on CNN/DailyMail and XSum datasets.The results show that our model obtains higher ROUGE scores.
Development of a Road Condition Information System using Augmented Reality Technology on Smartphones

Recently, accidents involving ground transportations are getting worse and more serious. Indonesian State Police (Korlantas POLRI) recorded the number of accidents in 2018 as many as 109,215 accidents. The number has incresed 4.69 percent compared to 2017 as many as 104,327 events. Road traffic accidents are caused by human error, the driver in this case. The driver's mistake is influenced by several factors, one of them is they cannot expect the road condition when they drive a vehicle at high speed. To solve this problem, drivers need information that can show road conditions. So, we present a new approach for detecting damaged roads by applying augmented reality technology. This research produces a road condition information system to help drivers get information about road conditions via smartphone. This system uses augmented reality technology with a markerless GPS Based Tracking method. The development of this system requires several stages such as collecting the data, data conversion, data classification, and views road condition. The researchers gathered the road condition data from the Public Work Department Semarang. This department itself undertakes a task to control the road condition in Semarang The trial of this system includes all drivers in Semarang city. Based on the results of the questionnaire responded to by 93 respondents, this test obtained an average value of 68%. So this system gets a pretty good response from the driver. Through this system, all drivers can avoid the damaged road condition which can cause traffic-congested and accident.
A Flexible and Accurate Calibration Method for Line Structured Light

Line structured light calibration establishes the relationship between the camera and the laser plane. Different from conventional calibration algorithms, the proposed method needs only one scan of the stair block, and then we can obtain the optimal laser plane function. We first demonstrate our line structured light system's mathematical model. Secondly, a constrained nonlinear multivariable function is constructed with the aid of the stair block. Finally, we can obtain the optimal laser plane function by solving the optimization function. In the experimental section, Our calibration method is proved with high accuracy and flexibility by several experiments.
Cross-Site Scripting Attack Detection using Rule-Based Signature

Rule-Based Signature or also known as Misuse Detection is IDS which rely on matching data captured on retrieval of attack pattern which in system that allow attacks. If the attack activity detected according to existing signature, then it will be read by system and called as attack. The advantage of this Signature-Based IDS is the accuracy of detecting matched attack which in the system with low false-positive result and high true-positive. Cross-Site Scripting is type of attack which is perform by injecting code (usually) JavaScript to a site. XSS is very often utilized by attacker to steal web browser resource such as cookie, credentials, etc. Dataset which used in this research is dataset which created by injecting script into a website. Once obtained the dataset, then feature extraction is performed to separate the attribute which used. XSS attack pattern can be easily recognized from URI, and then detected using engine which has been created. Detection result of algorithm which used is evaluated using confusion matrix to determine detection accuracy value which performed. Obtained accuracy detection of research result reached 99.4% with TPR 98.8% and FPR 0%.
Students Vocabulary Mastery toward Crossword Puzzle Games

This research investigated the improvement of students’ participation and vocabulary achievement by using crossword puzzles in the classroom. The research design was Classroom Action Research (CAR) conducted in two cycles. The research subjects were the seventh-grade students and the research area was at a private Islamic Junior High School in Kendari. The data were collected by using observation and vocabulary achievement tests. The collected data were analyzed statistically by using a percentage formula. The students’ activity improved from 78,66% in cycle 1 to 97,66% in cycle 2. Meanwhile, the students’ vocabulary mastery improved from 54% in cycle 1 to 90% in cycle 2. In conclusion, using crossword puzzles improved the students’ vocabulary achievement and students' participation in the teaching and learning English.
Visibility estimation of foggy weather based on continuous video information

Low visibility in foggy weather can easily cause accidents and affect normal life. Accurate visibility estimate is critical to transportation, aviation, marine and other fields. For visibility estimation, this paper proposes a visibility estimation algorithm Mean Square Error Based on Convolution Neural Network (MSEBCNN) based on continuous surveillance video, which is an improved neural network deep learning algorithm. In this paper, the images of airport surveillance video were extracted every 15 s and the Region of Interest (ROI) in each image was selected as the measurement area. The improved Convolution Neural Network (CNN) was constructed by replacing the Softmax layer of the last layer of the convolutional neural network with the Mean Square Error (MSE) layer of the objective regression function, and the gradient descent method was used for fitting training to achieve the continuous prediction of visibility. By experiment, the accuracy of prediction at a range of 0–500 m is 97.51%, while the accuracy of prediction at a range of 500–1600 m is 91.67%, which obtained a high prediction accuracy.