Reviewerly/exHarmony huggingface dataset:
Topic modeling of disaster based on Indonesia tweet using latent dirichlet allocation

Twitter is one of the most used social media to spread information, which can be modeled using topic modeling techniques. In this study, disaster-related topics would be modeled. Several phases were performed to get the desired topics: data collection, stop word removal, stemming, case folding, normalizing slang words, and normalizing abbreviated words. The sampling of six topics was done as the LDA method parameter. Keywords used in data collection were taken from official Twitter accounts, such as The Meteorology, Climatology, and Geophysics Agency (BMKG) and the National Disaster Management Agency (BNPB). Keywords used were in Indonesian: banjir (flood), kecelakaan (accident), longsor (landslide), and others. Topics with high coherence values and the most discussed were coronaviruses, accidents, drowning, storms, windstorms, cyclones, and hurricanes. World Cloud was used to visualize the disaster-related topics.
The expert evaluation systems in drug development

This study investigates the unique properties and criteria of use intellectual systems in drug delivery. The traditional methods of pharmaceutical drug development are limited in the field of usability. In connection with the above mentioned, the task of development and creation of the intellectual system allowing to choose active pharmaceutical ingredients, auxiliary substances, which will be based on the expert system and information base of knowledge in the field of pharmaceutical production and it should be of current scientific and practical relevance. The most commonly used type of intelligent systems today are expert systems, which are computer systems capable of partially replacing a highly qualified specialist in his or her field of competence through the knowledge previously obtained from him or her. That is why expert systems are accepted to be considered together with knowledge bases, which are models of specialists’ behavior in a certain field, and for decision-making procedures of logical conclusion are applied. The field of application of expert systems is absolutely unlimited. A vivid example is their use in diagnostic tasks of modern medicine.
Students’ Readiness in Implementing Mobile Learning for English Language Learning Purposes

Background:&#x0D; The application of mobile learning (m-learning), especially in the area of language learning, has gained interest in the last several years due to the benefits offered by mobile devices. However, to maximize the effectiveness of m-learning, students are required to be ready. Thus, it is crucial to assess studentsâ€™ readiness before implementing m-learning. By focusing on rural schools, this quantitative study aims to examine studentsâ€™ readiness in implementing m-learning in their English language learning.&#x0D; Methodology: &#x0D; This study employed a survey design to measure m-learning readiness of senior high school students by investigating three factors, namely mobile self-efficacy, optimism, and self-directed learning. Through an online questionnaire, 140 responses were successfully collected from students in two public senior high schools (one high school and one vocational high school) located in a rural area in Soppeng, Indonesia. The data collected were subsequently analyzed using SPSS Statistics ver. 25.&#x0D; Findings: &#x0D; The results revealed that senior high school students from a rural area in Soppeng, Indonesia, had a high level of self-efficacy and optimism in implementing m-learning in their English language learning. However, their self-directed learning was still at a moderate level. Moreover, further analysis showed that type of school significantly affected studentsâ€™ mobile self-efficacy and optimism, while self-directed learning was not affected.&#x0D; Conclusion: &#x0D; Students from rural senior high schools in Soppeng, Indonesia, showed readiness in implementing m-learning for English language learning purposes.
A simple and efficient ensemble classifier combining multiple neural network models on social media datasets in Vietnamese.

Text classification is a popular topic of natural language processing, which has currently attracted numerous research efforts worldwide. The significant increase of data in social media requires the vast attention of researchers to analyze such data. There are various studies in this field in many languages but limited to the Vietnamese language. Therefore, this study aims to classify Vietnamese texts on social media from three different Vietnamese benchmark datasets. Advanced deep learning models are used and optimized in this study, including CNN, LSTM, and their variants. We also implement the BERT, which has never been applied to the datasets. Our experiments find a suitable model for classification tasks on each specific dataset. To take advantage of single models, we propose an ensemble model, combining the highest-performance models. Our single models reach positive results on each dataset. Moreover, our ensemble model achieves the best performance on all three datasets. We reach 86.96% of F1- score for the HSD-VLSP dataset, 65.79% of F1-score for the UIT-VSMEC dataset, 92.79% and 89.70% for sentiments and topics on the UIT-VSFC dataset, respectively. Therefore, our models achieve better performances as compared to previous studies on these datasets.
An Exploration of the Essence of Open Education

A platform to help the public climb up the social ladder, open education promotes education equality, providing every member of society with the opportunity to receive higher education and serving for people’s lifelong learning and development. In practice, people with different educational backgrounds are admitted to this education system, which appears to be seemingly unpersuasive. Despite a history of 40 years, the Open University of China does not seem to be widely recognized by the society. Therefore, it is necessary explore the essence of open education from its very beginning. This paper reviews the evolution of open education and explores its essence, definition and academic debates with the aim to reach the core of open education.
Strong-Weak Feature Alignment for 3D Object Detection

Object detection in 3D point clouds is still a challenging task in autonomous driving. Due to the inherent occlusion and density changes of the point cloud, the data distribution of the same object will change dramatically. Especially, the incomplete data with sparsity or occlusion can not represent the complete characteristics of the object. In this paper, we proposed a novel strong–weak feature alignment algorithm between complete and incomplete objects for 3D object detection, which explores the correlations within the data. It is an end-to-end adaptive network that does not require additional data and can be easily applied to other object detection networks. Through a complete object feature extractor, we achieve a robust feature representation of the object. It serves as a guarding feature to help the incomplete object feature generator to generate effective features. The strong–weak feature alignment algorithm reduces the gap between different states of the same object and enhances the ability to represent the incomplete object. The proposed adaptation framework is validated on the KITTI object benchmark and gets about 6% improvement in detection average precision on 3D moderate difficulty compared to the basic model. The results show that our adaptation method improves the detection performance of incomplete 3D objects.
Inquiry Learning Model to improve Social Studies Learning Outcomes in Class V SD Negeri 59 North Bengkulu

This Research aim to to increase process the study IPS of to be can please and make the student become active. Applying model the study Inquiry can improve the result learn the IPS of student in the class V SD Negeri 59 Bengkulu Utara of teaching year 2593 / 2594. This Research type is Classroom Action Research (PTK) by using model of Kemmis and Mc. Taggart, by subjek is research that is students in the class V SDN 59 Bengkulu Utara amounting to 23 student and object in this research is the make-up of result learn the student at subject IPS. Action conducted by during this research is study execution by using model Inquiry. Technique of data collecting used by is problem tes of[is breakdown of and Technique observation analyse the data conducted by dekriptif qualitative and quantitative descriptive Result of research indicate that through applying model the Inquiry in study IPS can improve the result learn the student. Make-Up of result learn this student is seen from the increasing of student ability in searching answer from problems of through various step in model Inquiry that is orient, formulating problem, formulating hypothesis, collecting data or information, conducting hypothesis test, and formulate the conclusion. With refer to the step in model inkuri of besides improving result learn also improve the student livelines in course of study of where student instructed to go it alone to to look for answers to question which have dirumusakan. Make-Up of result learn the student seen from the increasing of complete percentage result of tes student of where at cycle I from 74% and mount again at cycle II become 91%.Keywords: Model the Inquiry, Result of learning IPS.
The Teaching Procedures for Online Public Speaking Class

This Research aimed to discuss the teaching procedures for online public speaking class during Covid-19 Pandemic situation. Teaching and learning methods during COVID-19 pandemic have forced the teachers and students to adopt new learning method called online learning. Currently learning from home is very popular as the best strategy to decrease the number of humans contact or distancing from social activities. Basically, public speaking or oratory is one of communication skills performed by the speakers in front of audiences, however this pandemic has shifted the basic meaning of public speaking. Due to the social distancing, public speakers tend to speak in front of the camera which literally do not involve the teacher and the audiences in the process of public speaking practice. The trend of public speaking practices have improved numerously during this COVID-19 Pandemic era, the teaching procedures occur some differences from usual public speaking taught by the teachers in real classroom. The sample of participants of this research are 30 EFL students in Budi Darma University Medan who registered in the first semester.
A smart guidance navigation robot using petri net, database location, and radio frequency identification

The objective of this research is, to explain a new framework to navigate the movement of the robot towards a target goal. This involves the need for the robot to move from the initial position to 1 out of 30 rooms. Therefore the strategy used involves the combination of the room database stored in the RFID data using the petri net (PN) method to simulate and model the movement of the robot for navigation after which the dynamic behavior of the robot is moving to the desired location was analyzed. The process started from the creation of an environmental map determined by the user followed by modeling through PN and the result was used to produce a marking value which explains and navigates the movement of the robot towards the selected room. The marking value was also used as the database for the robot's movement and later substituted with the RFID to be used as the sensor input in the implementation stage. It was concluded that the robot has the ability to move to the target position according to the database stored in RFID and designed to move forward and turn left and right. For example, it followed the marking value M1 M2 M3M13M12 M11 M10 M9 M8 to Room 1 and M1 M2 M46 M47 to Room 29.
A Query Path Recommendation Method Supporting Exploratory Search Based on Search Goal Shift Graphs

Increasing new knowledge or learning about new skills is often the main factor that triggers search users to conduct exploratory searches. Due to the lack of knowledge of their problem domain, it is difficult to form a learning path about new knowledge or new functions in the minds of searchers in the early stage of performing exploratory search, often searching for unnecessary information, resulting in wasting search time, the search is inefficient. However, the current search system does not provide enough support to solve this problem. For this reason, this paper designs a query path recommendation method supporting exploratory search based on a search goal shift graph. In the initial stage of exploratory search, directly recommends a set of query paths for searchers based on the searcher’s initial query, helping the searcher to find appropriate learning objects and build efficient learning paths, avoiding the search for irrelevant information, thereby shortening the search time and improving the search efficiency.
Use Of Game Technologies In The Process Of Professional Training

In this article possibilities of application of game technologies in professional training of students are opened, types of the games generally connected with future professional activity of today's students are designated and characterized; the factors promoting the effective organization of occupations with use of research, problem games and games of administrative character are defined. In article the technique of preparation and carrying out games is stated, requirements to observance of stages of game occupations are noted. The special attention is paid to need of communication of the scenario of game with the content of studied scientific disciplines and with real life situations. By the author value of application of game technologies for development of cognitive activity of students, increase of interest to a profession, strengthening of communication of theoretical knowledge and practical skills is emphasized.
THE TIME SPIRAL AND IRREVERSIBILITY IN TOM MCCARTHY’S NOVELS

The article examines the work of the contemporary English writer Tom McCarthy in the framework of the original author’s concept of time and space. Based on observations about the consonance of McCarthy’s postmodern novels “Remainder” (2005), “C” (2010), and “Satin Island”(2015) and the entropy systems theory, as well as certain philosophical concepts of postmodernism (“Mobius strip”, ellipse, ruin), the conclusion is made about the conjugation of the personal and the cosmic. The cyclical nature of the plot episodes in McCarthy’s novels is associated with the idea of the character’s involvement in the processes of world disintegration and total irreversibility, being in a loop, in atemporal space, in the entropy funnel of time. The traumatized “Self” of the main character symbolizes the universal trauma of infinite non-anthropological time. McCarthy’s work significantly develops images, compositional and narrative tools, associated with non-linearity and fragmentation, which are characteristic for modernist and postmodernist writing (Joyce, Faulkner, Rob-Grillet, Pynchon). Cyclicity in McCarthy’s postmodern art is no longer associated with absolute mythological time and “everyday cyclic time” (Bakhtin), but with the “temporality of the accelerated cycle” (Baudrillard), with some loss, disintegration, “ellipse” (Derrida), with an endless spiral like a shell. The McCarthy’s cyclicity appears as a repetition, which cuts off the road to the future.
Performance Evaluation of Multi-Tier Application by using the Comprehensive Workload Modelling in the Cloud

Cloud-computing is the on-request accessibility of computer system-assets, especially data-storage and computing power, without client direct dynamic administration. Proponents similarly cloud computing ensures that ventures allow them to get more of their applications faster, more reasonably, with more rationality, and with less support, which empowers asset groups to quickly change assets to meet fluctuations and exceptional needs. The current framework centers around how to diminish the dynamic remaining task at hand and increment the reaction time prerequisites in the multi-application by utilizing the auto-scaling virtual machine cloud assets. The proposed framework is to improve the exhaustive remaining task at hand model which considers the space of the specific information document in the cloud for multi-level applications. The total framework uses the RDF Classifier model through which the Request Time and Average Response Time are determined. The proposed strategy plays out the auto-scaling utilization of the remaining task at hand conducted approach which assists with foreseeing application reaction time.
Black-Box Optimization of Object Detector Hyper-Parameters

Object detectors have improved considerably in the last years by using advanced Convolutional Neural Networks (CNNs) architectures. However, many detector hyper-parameters are not generally tuned, and they are used with values set by the detector authors. Blackbox optimization methods have gained more attention in recent years because of its ability to optimize the hyper-parameters of various machine learning algorithms and deep learning models. However, these methods are not explored in improving CNN-based object detector's hyper-parameters. In this research work, we propose the use of blackbox optimization methods such as Gaussian Process based Bayesian Optimization (BOGP), Sequential Model-based Algorithm Configuration (SMAC), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to tune the hyper-parameters in Faster R-CNN and Single Shot MultiBox Detector (SSD). In Faster R-CNN, tuning the input image size, prior box anchor scales and ratios using BOGP, SMAC, and CMA-ES has increased the performance around 1.5% in terms of Mean Average Precision (mAP) on PASCAL VOC. Tuning the anchor scales of SSD has increased the mAP by 3% on PASCAL VOC and marine debris datasets. On the COCO dataset with SSD, mAP improvement is observed in the medium and large objects, but mAP decreases by 1% in small objects. The experimental results show that the blackbox optimization methods have proved to increase the mAP performance by optimizing the object detectors. Moreover, it has achieved better results than the hand-tuned configurations in most of the cases.
Tag Recommendation Method for Enhancing Web Novel Retrieval

A huge number of web novels i.e. user-generated novels, exist on the Internet. The increase in their number has cased crucial problems for both writers and readers; even if noteworthy, newly submitted novels are buried amidst existing ones and readers have difficulty locating suitable, relevant novels. Most web novel sites attach several tags to each novel to describe its themes, characters, genre and so on. These tags generally vary widely because they are attached by the individual writer without specific rules or uniformity. In this paper, we propose tag recommendation methods that enable readers to easily retrieve suitable novels. These recommended tags should express the contents of the novel, differ from each other, and be selected from tags attached to a sufficiently limited number of novels. To satisfy these requirements, we analyze the tag distributions in an actual web novel site. We vectorize the novels using Dov2Vec methods, and vectorize the tags from the novel's vectors attached to the novels.
Effective Approach to Develop a Sentiment Annotator For Legal Domain in a Low Resource Setting.

Analyzing the sentiments of legal opinions available in Legal Opinion Texts can facilitate several use cases such as legal judgement prediction, contradictory statements identification and party-based sentiment analysis. However, the task of developing a legal domain specific sentiment annotator is challenging due to resource constraints such as lack of domain specific labelled data and domain expertise. In this study, we propose novel techniques that can be used to develop a sentiment annotator for the legal domain while minimizing the need for manual annotations of data.
Social Networks and the Challenge of Hate Disguised as Fear and Politics

This case study examines social networks as the modern intersections of radical discourse and political extremism. But, as this research will show, extremist content in social networks, even that which has telegraphed violent hate crimes, is seldom communicated in textbook forms bigotry or provocations of violence. Today, the true challenge for social networks like Facebook and Twitter is addressing hate speech that reads more like fear mongering and identity politics, and thus, does not get flagged by monitors. From accounts dedicated to inciting fear over the “threat of immigrants” or “black crime,” to groups that form around hashtags declaring that a “#whitegenocide” is underway. These narratives represent the more ubiquitous versions of hate culture that permeate these popular spaces and radicalize cultural discourses happening there. This case study explores how such rhetoric has the same capacity to deliver messages of hate, and even incite violence, by investigating six hate crimes from 2019 that were preceded by social media diatribes. The comparative analysis will show how these examples mostly featured nonviolent expressions of cultural paranoia, rather than avowals of violence or traditional hate speech, thus making them harder to detect by programs seeking out such threats in plain sight. The research then examines the user policies of leading social networks to assess whether their guidelines on hateful and violent content are purposed to address the kinds of language that were espoused by these violent extremists. The study considers the strategies being employed by social networks to expose hateful content of all forms, and the need for more prominent counter narratives.
Segmentasi Pelanggan Berdasarkan Perilaku Penggunaan Kartu Kredit Menggunakan Metode K-Means Clustering

Credit card is one of the payment media owned by banks in conducting transactions. Credit card issuers provide benefits for banks with interest that must be paid. Credit card issuers also provide losses to banks that have agreed to pay not to pay their credit card bills. To request a loan from the bank, a cluster model is needed. This study, proposing a segmentation system in research using credit cards to determine marketing strategies using the K-Means Clustering method and conducting experiments using the 4 methods namely K-Means, Agglomerative Clustering, GMM, and DBSCAN. Clustering is done using 9000 active credit card user data at banks that have 18 characteristic features. The results of cluster quality accuracy obtained by using the K-Means method are 0.207014 with the number of clusters 3. Based on the results obtained by considering 4 of these methods, the best method for this case is K-Means.
Blockchain Empowered College Fees Transaction System

A Blockchain web application for college is an application, it can be accessed throughout the institution and with adequate login provided, especially by the students. The blockchain model uses various cryptocurrencies to pay students the college fee. In this system, Ether cryptocurrency is used which is generated by the Ethereum blockchain platform and ether is a crucial element of decentralized applications. The cryptocurrency ether can be shifted between financial records and used for computational output payment of contributor mining nodes. Meanwhile, the ether blockchain is used to record digital currency (Ether) ownership, whereas the Ethereum based blockchain focuses on running decentralized code applications. This paper focuses on the developing of the blockchain model for college fee transactions and it reveals the significance of decentralized applications. Reviewed the up-to-date dApps and discussed the fulfilment of decentralized applications characters with blockchain development. The readers will get the outline of decentralized applications and aware of the blockchain model.