Reviewerly/exHarmony huggingface dataset:
Monitoring Jaringan dan Notifikasi dengan Telegram pada Dinas Komunikasi dan Informatika Kota Padang

Handling of network problems at Dinas Komunikasi dan Informatika Kota Padang is still done conventionally. This conventional method has several weaknesses, among others: the process of finding out slow network problems, information on problematic network conditions is conveyed using voice calls from smartphones, the slow network checking process and inefficient use of time. From this problem, a system for network handling that utilizes information technology is developed called Network Monitoring by implementing the Cacti and Telegram software functions. The result of this research is a network monitoring system based on web browser and mobile by implementing Cacti with SNMP features as network monitoring and Telegram as notification. The aim is to reduce weaknesses in the handling and repair of the network that has been used in Dinas Komunikasi dan Informatika Kota Padang
Pragmatic Analysis of Flouting Maxim in Donald Trump’s Interview with TIME in the Oval Office 2020

&lt;p align="justify"&gt;&lt;em&gt;The purpose of this research is to define a linguistic feature based on Donald Trump’s interview with TIME since Trump is a politician who has the power to influence others, and it can be extremely essential to analyze how the hidden meaning in his utterance images him as a public figure. This research uses the descriptive qualitative method because the object of the study is a transcript containing utterance and conversation. Grice's (1975) theory is used in this study to define the flouting maxim that occurs during the interview. The result of this study is that 11 utterances containing flouting maxim of quantity, 3 flouting the maxim of relation, 2 flouting the maxim of manner, and no flouting maxim of quality is found in this study. After analyzing the finding, the most dominant found types is flouting maxim of quantity which means that Trump frequently gives more information than required to express his interest and to show his power as a president and his nationalism as representative of American. &lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;
Multi-Word Lexical Simplification

In this work we propose the task of multi-word lexical simplification, in which a sentence in natural language is made easier to understand by replacing its fragment with a simpler alternative, both of which can consist of many words.In order to explore this new direction, we contribute a corpus (MWLS1), including 1462 sentences in English from various sources with 7059 simplifications provided by human annotators.We also propose an automatic solution (Plainifier) based on a purpose-trained neural language model and evaluate its performance, comparing to human and resource-based baselines.
Development of Bot Detection Applications on Twitter Social Media Using Machine Learning with a Random Forest Classifier Algorithm

A Twitter bot is a Twitter account programmed to automatically do social activities by sending tweets through a scheduling program. Some bots intend to disseminate useful information such as earthquake and weather information. However, not a few bots have a negative influence, such as broadcasting false news, spam, or become a follower to increase an account's popularity. It can change public sentiments about an issue, decrease user confidence, or even change the social order. Therefore, an application is needed to distinguish between a bot and non-bot accounts. Based on these problems, this paper develops bot detection systems using machine learning for multiclass classification. These classes include human classes, informative, spammers, and fake followers. The model training used guided methods based on labeled training data. First, a dataset of 2,333 accounts was pre-processed to obtain 28 feature sets for classification. This feature set came from analysis of user profiles, temporal analysis, and analysis of tweets with numeric values. Afterward, the data was partitioned, normalized with scaling, and a random forest classifier algorithm was implemented on the data. After that, the features were reselected into 17 feature sets to obtain the highest accuracy achieved by the model. In the evaluation stage, bot detection models generated an accuracy of 96.79%, 97% precision, 96% recall, and an f-1 score of 96%. Therefore, the detection model was classified as having high accuracy. The bot detection model that had been completed was then implemented on the website and deployed to the cloud. In the end, this machine learning-based web application could be accessed and used by the public to detect Twitter bots.
Exploiting Node Content for Multiview Graph Convolutional Network and Adversarial Regularization

Network representation learning (NRL) is crucial in the area of graph learning.Recently, graph autoencoders and its variants have gained much attention and popularity among various types of node embedding approaches.Most existing graph autoencoder-based methods aim to minimize the reconstruction errors of the input network while not explicitly considering the semantic relatedness between nodes.In this paper, we propose a novel network embedding method which models the consistency across different views of networks.More specifically, we create a second view from the input network which captures the relation between nodes based on node content and enforce the latent representations from the two views to be consistent by incorporating a multiview adversarial regularization module.The experimental studies on benchmark datasets prove the effectiveness of this method, and demonstrate that our method compares favorably with the state-of-the-art algorithms on challenging tasks such as link prediction and node clustering.We also evaluate our method on a real-world application, i.e., 30-day unplanned ICU readmission prediction, and achieve promising results compared with several baseline methods.
EMOTIONAL LEADERSHIP AS A FACTOR OF EFFICIENT MANAGEMENT OF MILITARY UNITS: THE ANALYSIS OF FOREIGN RESEARCH

The article purpose is to present the theoretical analysis and determine how to use military personnel’s emotional intelligence skills for management of a military unit. We used theoretical research methods: the analysis of theoretical sources discussing service members’ leadership formation based on the concept of emotional leadership, a comparative analysis of the reviewed theoretical materials. Foreign authors’ works devoted to emotional intelligence and emotional leadership among military personnel were analysed. The basic principles of a leader’s emotional intelligence were revealed, which meant a leader’s capability to use emotional intelligence in order to improve the managing efficiency for military units. A number of issues that need to be addressed were identified, namely: influence of military leaders’ emotional intelligence skills on post-traumatic stress overcoming and mitigating of negative consequences associated with a psychological trauma; influence of leaders’ emotional intelligence on military unit management and relations between military personnel’s emotional competence, leadership attributes and technical and tactical skills; programs for assessment and development of service members’ emotional intelligence and emotional leadership in order to improve unit management by control over service members’ emotions, other people’s emotions and groups’ emotions and implementation of such programs. The study results has confirmed that service members’ emotional leadership skills should be developed to improve unit management efficiency, since such skills are necessary for management, emotional leadership improves management skills at all, social responsiveness and interactions with a team.
Aplikasi Monitoring Penderita Kardiovaskular dan Obesitas Berbasis Mobile Internet of Things (MIoT)

According to the World Health Organization (WHO), coronary heart disease is the biggest cause of death in Indonesia. In 2016, the death rate from heart disease was 122 people per 100,000 population. This figure is higher than other causes, such as stroke, tuberculosis, and diabetes. The number is increasing every year due to changes in lifestyle of Indonesian people who like to eat high-fat foods and lifestyle factors that affect the risk of cardiovascular disease, including lack of physical activity, smoking, unhealthy diet, and alcohol consumption habits. This study aims to monitor the heart rate of cardiovascular sufferers with the mobile internet of things (MIoT) approach. Using the ESP8266 Wifi module for communication to the database server and heart rate sensor to detect heart rate then convert it to Bit per Minute (BPM). Every patient with cardiovascular disease can be monitored using a sensor connected to a smartphone to record any changes that occur. The research method consists of several stages, namely: Prepare, Plan, Design, Implement, Operate and Optimize (PPDIOO). The results obtained in the form of a aplication heart rate monitoring for patients with cardiovascular for healthcare services.
ELECTROPHYSIOLOGICAL INDICES OF THE VECTION ILLUSION IN VIRTUAL REALITY

Relevance. The study of the self-motion (“vection”) illusion is an important task for modern psychology and neuroscience due to the widespread use of virtual reality systems. The study of psychophysiological mechanisms of this phenomenon has particular importance as an example of intersensory interactions. Objective. To study the psychophysiological mechanisms of the self-motion illusion in a virtual reality system using electroencephalography. Methodology. Eleven healthy subjects took part in the experiment. The stimulation was a virtual opto-kinetic drum that rotated clockwise and counterclockwise around a vertical axis with angular velocities of 30, 45 and 60 angular deg/s. The duration of each rotation was 60 seconds. The subjects were presented with 18 rotations; there was a series with instructions for free viewing of the visual scene (3 speeds × 2 directions × 2 repetitions) and a series with instructions to fix the gaze in the center of the virtual scene (3 speeds × 2 directions × 1 repetition). After each rotation, the subjects filled out the “Simulator Sickness Questionnaire” and evaluated the intensity of the illusion on a 10-point scale. Stimulation was presented in the HTC Vive virtual reality helmet. Electroencephalogram recording during the observation of cylinder rotations was performed using Mitsar-EEG-10/70-201. Results. Significant differences were found in the intensity of the illusion, the total score on the questionnaire, and the power of the alpha rhythm in the parietal zones, depending on the speed of rotation. The higher the rotational speed, the greater the values of these dependent variables. Large values for beta-rhythm power in the occipital areas were found in the series with fixed eyes, in the subjects with high values for the intensity of the illusion. Conclusions. Differences were shown in the bioelectrical activity of the brain during the experience of the self-motion illusion, related to mechanisms of visual-vestibular integration and greater attention to the performance of the motor task of gaze fixation.
An Empirical Analysis of Cybercrime Trends and Its Impact on Moral Decadence Among Secondary School Level Students in Nigeria

Cybercrimes in various forms are on the raise daily; these crimes pose a potential threat to our moral society as well as economy and nation building at large. They are namely: social engineering, malvertising, ransom ware, Spamming, Botnets, fake bank alert messages (SMS) /unsolicited SMS requesting you to provide bank details as Bank Verification Number (BVN), fraud, identity theft, piracy, pornography, hacking, fraudulent e-mail related SMS, forgery such as fake documents as Certificate etc. Thus, Cybercrime is gradually becoming a threat to our moral society at large. Hence, the increase number of cybercrime rate is an open field of ongoing research studies. Therefore, we have proposed a new approach that emphases on the prominent cybercrimes carried out around the world, and presents a precise study in Nigeria within some secondary school students. The study revealed a new approach to fighting Cybercrimes in order to combat cybercriminals. Keywords: Cybercrime, Botnets, Malvertising, Ransom ware, Pornography
Penerapan Metode Rapid Application Development (RAD) dalam Perancangan Sistem Pemesanan Menu menggunakan Quick Response (QR) Code

Cafe business that provides food and beverage menus is a fast growing business today. 99's Coffee located in Tanjung Pura is a cafe that wants to further optimize service to consumers by simplifying the process of ordering food and drinks. The purpose of this research is to design an ordering system to make it easier for customers to order menus and business owners to manage orders. The method used in designing this system is Rapid Application Development (RAD). The stages in the RAD consist of Requirements planning, Design Workshop and Implementation. The system was built using the PHP programming language with the Codeigniter Framework, the system display uses the Bootstrap CSS Framework and data storage uses MariaDB. The result of this research is a system that allows customers to order food and beverage menus by simply scanning the Quick Response (QR) Code that has been provided at each customer's table.
Principles of Evidence-Based Education in the Development of Giftedness

The article considers the perspective of using the principles of evidence-based education in the process of studying the phenomenon of giftedness. The limitations and potential of this approach are identified. The author analyzes the trends in the development of modern education, describes the principles of creating a laboratory of evidence-based education in a secondary school.
PELATIHAN PEMBUATAN KEMASAN SABUN BUNGA MATAHARI DI DESA BOJONGHALEUANG SEBAGAI PROGRAM COMMUNITY EMPOWERMENT

Maranatha Christian University as a private university in Bandung has a work program that is reflected in the vision and mission of higher education tri dharma. The vision and mission of Maranatha Christian University which is carried out through the fields of teaching, research, and community service, one of which is focused on community empowerment programs and social problems around, involving several teaching staff (lecturers) from representatives of the Visual Communication Design Study Program and D-3 Art Design as a material formulator as well as a skills trainer or teacher as a form of social responsibility. The community service program which was initiated in the form of training on making soap product packaging from sunflower materials creatively and has aesthetic value was carried out by providing materials online and practicing packaging directly to the training participants for five meetings. This activity is targeted at community partners who are not economically productive yet, such as the Kota Baru Parahyangan (KBP) assisted community groups that are members of the Maju Bersama Sejahtera Joint Business Group (KUBE). The potential of KUBE Maju Bersama Sejahtera in Bojonghaleuang Village, West Bandung Regency is sunflower and the honey bee. The processing and utilization of sunflower seeds as herbal soap material need to be supported by the creative efforts of KUBE Maju Bersama Sejahtera to package sunflower soap products in a unique, attractive, and marketable way. This community service activity succeeded in providing insight into the packaging technique for soap products from sunflower to the Maju Bersama Sejahtera Joint Business Group (KUBE) as a provision for home industry skills that can support the household economy. These results can be seen after participating in the activity, the participants can make various forms of packaging in a unique and creative way. Keywords: community empowerment, creative, packaging, soap, sunflower
Probability distributions of COVID-19 tweet posted trends uses a nonhomogeneous Poisson process

The influence of social media in disseminating information, especially during the COVID-19 pandemic, can be observed with time interval, so that the probability of number of tweets discussed by netizens on social media can be observed. The nonhomogeneous Poisson process (NHPP) is a Poisson process with dependent on time parameters and the exponential distribution having unequal parameter values and, independently of each other. The probability of no accurence an event in the initial state is one and the probability of an event in initial state is zero. Using of non-homogeneous Poisson in this paper aims to predict and count the number of tweet posts with the keyword coronavirus, COVID-19 with set time intervals every day. Posting of tweets from one time each day to the next do not affect each other and the number of tweets is not the same. The dataset used in this study is crawling of COVID-19 tweets three times a day with duration of 20 minutes each crawled for 13 days or 39 time intervals. Result of this study obtained predictions and calculated for the probability of the number of tweets for the tendency of netizens to post on the situation of the COVID-19 pandemic.
AUTOMATIC MODULATION CLASSIFIER: REVIEW

The automatic modulation classification (AMC) is highly important to develop intelligent receivers in different military and civilian applications including signal intelligence, spectrum management, surveillance, signal confirmation, monitoring, interference identification, as well as counter channel jamming. Clearly, without knowing much information related to transmitted data and various indefinite parameters at receiver, like timing information, carrier frequency, signal power, phase offsets, and so on, the modulation’s blind identification has been a hard task in the real world situations with multi-path fading, frequency-selective in addition to the time-varying channels. There are 2 methods could be utilized to decide the classification signal technique: Feature-based (FB) approach and the Maximum likelihood functions (LB) method. With regard to the FB (referred to as pattern-recognition) classification method used in the study. In the presented work, thorough study is provided to find easy method to identify and classify the digital modulation signals at low SNRs. Spectral-based features, high-order statistic features, wavelet-based features, also cyclic features on the basis of cyclostationary typically utilized to determine and discriminate modulation types have been examined. The number of the classifiers which have been utilized in the process of discrimination have been studied thoroughly and compared for helping researchers in determining and finding the drawbacks with pattern-recognition according to past works. The presented study serving as guide with regard to studies of AMC for determining adequate algorithms and features.
FRAMEWORK BASED SUPERVISED VOICE ACTIVITY DETECTION USING LINEAR AND NON-LINEAR FEATURES

Voice activity detection (VAD) methods based on linear features are limited by linearity assumption and correct estimation of the pitch.The nonlinear dynamic methods can analyse irregular vocal cord behaviours and are found to be useful in the areas of voice study, clinical treatment evaluation, voice classification as per the Titze.The development of VAD using nonlinear features has required more attention.However, speech recordings in most of the practical scenarios like, in hospitals, research centers, video conferencing, and forensics are affected by babble noise.Moreover, correct estimation of signal to noise ratio (SNR) also depends on reliable voice activity detection.By looking at such challenges, the paper presents a framework based VAD using the combination of linear and nonlinear features with the two-step noise reduction (TSNR) for the possible speech enhancement.Speech segments are classified by using a supervised support vector machine (SVM).The framework is evaluated and compared with different time and frequency domain based VADs on the speech containing continuous and sustained vowels with babble noise.For this purpose, the experimental study is carried on continuous NOIZEUS corpus with babble noise at varying SNR levels.As the vocal disorder is more prominent, laryngeal pathologies based database from Saarbruecken Voice Database (SVD) and Laryngeal cancer data are used for sustained vowels.The study revealed the importance of bio-inspired linear and nonlinear features in the VAD.Finally, the proposed VAD is found to be more robust as far as vowels and continued speech are concerned.
Competencias digitales y habilidades investigativas en estudiantes de estudios generales de una universidad privada de Lima

The development of this research work has as main objective to establish whether the digital skills are related to the research abilities in students of general studies from a Private University of Lima during the year 2019. A non-experimental correlational research design was used. The studied sample was probabilistic and was composed by a total of 348 students, to whom two evaluation instruments were applied: the Questionnaire of digital skills of Santiago Mengual, and the Questionnaire of research abilities of Jaime Rodríguez. Both instruments have consistent levels of validity and reliability. The results show that the digital skills and the research abilities are significantly correlated (r = 0,84). Furthermore, significant correlations between the diverse digital skills and the diverse research abilities were found, which demonstrates the importance these variables have in the academic development of the students.
Microcontroller Based Solar Tracking System Design and Grid Connected Photovoltaic Systems

In the era of ever-rising demands of electrical energy, the concerns related to scarcity and environmental damages associated with the application of fossil fuels have increased. Renewable energy resources have proved to be a blessing. With their world-class ability to produce zero greenhouse emissions and diversifying the energy supply in turn reducing the dependency on fossil fuels, renewable energy resources have been gaining quite a lot of popularity in the electricity generation sector. Aiming to create a more well developed, efficient, and reliable system, this paper focuses on the concept of employing the best possible application of the most abundant source of energy - Solar Energy. The system is based on mechanically tracking the position of the Sun and orienting the Solar Panel with respect to the Sun’s radiation for achieving the maximum output by incorporating the application of Microcontroller. The proposed system focuses on the design and simulation of a Microcontroller-based solar tracking system on Proteus 8 professional software. The paper also incorporates the concept of ‘Grid-connected Photovoltaic systems’ as a dominant phenomenon and advancement for increasing the efficiency of the solar tracking system.
Joint Persian Word Segmentation Correction and Zero-Width Non-Joiner Recognition Using BERT

Words are properly segmented in the Persian writing system; in practice, however, these writing rules are often neglected, resulting in single words being written disjointedly and multiple words written without any white spaces between them.This paper addresses the problems of word segmentation and zero-width non-joiner (ZWNJ) recognition in Persian, which we approach jointly as a sequence labeling problem.We achieved a macro-averaged F 1 -score of 92.40% on a carefully collected corpus of 500 sentences with a high level of difficulty.