Reviewerly/exHarmony huggingface dataset:
Transition Words Progression of Written Texts Made by the Second-Semester Students of the English Study Program of FKIP Saint Thomas University in Academic Year 2019/2020

This research is written to achieve an objective as follow: (1) to find out the progression of transitional in writing text has been made by the second semester of English Education Study Program in academic 2018/2019. The writer used descriptive qualitative research to give description systematically and factually about facts of a certain population. The researcher determines that the design is descriptive quantitative method because it describes the data that is taken from the sample, and the results of the data were tabulated in the form of percentage. There were 18 students (90%) agreed that transition words were used to connect the sentences. There were 20 (100%) students (90%) agreed that transition words were used to organize the sentences. There were 17 students (85%) agreed that transition words were used to remember about the topic. There were 20 (100%) students agreed that transition words were used to tell the experience, and there were 20 (100%) students agreed transition words were used to enhance the connections between facts.
Different Language Usage on Social Media

The research presented the effects of Social Media on the formation of new words that are being used by the Social Media users that often includes in the formal use of language in the academe.The emergence of different Social Networking Sites (SNSs) such as Facebook, Twitter, and E-mail have driven a more advanced change in the way people communicate.The study aimed to assess how Social Media affects the formal English Language used in the academe.The result of the study also highlighted how often do Social Media users of the Central Luzon State University, College of Education use the proper abbreviations, exclamatory spelling of emoticons, use letter homophones, acronyms, commit misspelled words, use shortening of words, use numbers to represents words, and use combination of two different language in their papers.The qualitative method of research used the survey technique and was utilized for gathering data.The questionnaires serve as the instrument for collecting data.50 students of the College of Education, English Majors of the said university are the respondents.
Development of Integrated Thematic Student Worksheets (LKPD) Using the Discovery Learning Model in Class IV Elementary Schools

This research is motivated because of the problems found in schools, namely, the LKPD used in schools is generally less attractive, both in terms of the space for filling in the less extensive answers, the color of the LKPD which does not have an attractive color, the structure is incomplete and the supporting images for the LKPD are inadequate. This research is a development research, namely Research and Development (R&amp;D) which consists of four stages, namely: define, design, development, disseminate. in this development research using the 4-D model proposed by Thiagarajan (in Ratumanan, 2019: 61). The results showed that the development of discovery learning-based LKPD collected through a validation questionnaire was used to measure the feasibility of student worksheets (LKPD). The results showed that the discovery learning-based student worksheets developed were feasible and valid for learning. This is evident from the results of obtaining questionnaires by expert experts and educators who state that the development product is feasible based on the results of the linguist expert's validity test (92.5%) is categorized as very valid, aspects of rpp Validator 1 (92.5%) are categorized as very valid, Validator 2 (95.00%) which is categorized as very valid, expert design expert (98.07%) is categorized as very valid. This proves that the Integrated Thematic Student Worksheet Development (LKPD) with the Discovery Learning Model in Class IV of Elementary School is stated very valid.
Blockchain Technology: Use in the Fight Against Coronavirus

A total of 480 subjects at the baseline from the Well Elderly study 2 (ICPSR data #33641).Interventions: Not applicable.Main Outcome Measures: Not applicable.Results: The PCA demonstrated that approximately 44% of the total variance in the COPE-12 items was explained by one dimension with no critical unexplained variances remaining in 1st contrast (eigenvalue Z 1.78).One item (ST_DIS "I kept myself from getting distracted by other thoughts or activities") was flagged as misfit (Infit MnSq Z 1.33, Infit ZSTD Z 5.05, Outfit MnSq Z 1.56, Outfit ZSTD Z 7.15), which was removed from the measure.Our Rasch analysis identified that the item difficulties of the 11 remaining items were consistent with the conceptual theories without any significant DIF for gender and age groups.The person strata were 3.10, equivalent to a reliability of 0.81.Conclusions: The findings indicated that 11 items in COPE-12 could accurately and precisely measure stress coping skills.These findings were found to be consistent regardless of the respondent's sex or age.
Multi-channel Convolutional Neural Network Feature Extraction for Session Based Recommendation

A session-based recommendation system is designed to predict the user’s next click behavior based on an ongoing session. Existing session-based recommendation systems usually model a session into a sequence and extract sequence features through recurrent neural network. Although the performance is greatly improved, these procedures ignore the relationships between items that contain rich information. In order to obtain rich items embeddings, we propose a novel Recommendation Model based on Multi-channel Convolutional Neural Network for session-based recommendation, RMMCNN for brevity. Specifically, we capture items' internal features from three dimensions through multi-channel convolutional neural network firstly. Next, we merge the internal features with external features obtained by a GRU unit. Then, both internal features and external features are merged by an attention mechanism together as the input of the transformation function. Finally, the probability distribution is taken as the output after the softmax function. Experiments on various datasets show that our method's precision and recommendation performance are better than those of other state-of-the-art approaches.
A Patience-Aware Recommendation Scheme for Shared Accounts on Mobile Devices

As sharing of accounts is quite common among family members or roommates, the design of efficient recommender schemes for shared accounts has raised much attention recently. Generally speaking, after each login, it is essential for a recommender system to identify the current user behind and leverage this information to make recommendations. One naive approach is first to identify the identity of the current user and then make recommendations. However, this two-stage based approach may not achieve satisfactory performance. The key is that the recommended items favoring identifying users in the first stage may not be interesting to the users, which can deplete the user's patience quickly and cause early termination of users. To address the problem, we propose a novel recommendation scheme that makes a tradeoff between recommending discriminating items (helpful for identifying the user) and recommending interesting ones to the user (helpful for increasing the number of clicks). Under this scheme, we develop a patience model to capture the user's dynamic patience level during the recommendation process. Moreover, considering the increasing popularity of mobile devices, we also incorporate mobile sensor data (i.e., angle, accelerometer, gyroscope, etc.) into our approach to further improve the performance of the system. We implemented the above system in an App on mobile devices and carried out extensive experiments. The results demonstrate that our proposed scheme significantly outperforms the existing state-of-the-art approaches.
On expected error of randomized Nyström kernel regression

Kernel methods are a class of machine learning algorithms which learn and discover patterns in a high (possibly infinite) dimensional feature space obtained by often nonlinear, possibly infinite mapping of an input space. A major problem with kernel methods is their time complexity. For a data set with n input points a time complexity of a kernel method is O(n3), which is intractable for a large data set. A method based on a random Nystr?m features is an approximation method that is able to reduce the time complexity to O(np2+p3) where p is the number of randomly selected input data points. A time complexity of O(p3) comes from the fact that a spectral decomposition needs to be performed on a p x p Gram matrix, and if p is a large number even an approximate algorithm is time consuming. In this paper we will apply the randomized SVD method instead of the spectral decomposition and further reduce the time complexity. An input parameters of a randomized SVD algorithm are p x p Gram matrix and a number m &lt; p. In this case time complexity is O(nm2+p2m+m3), and linear regression is performed on a m-dimensional random features. We will prove that the error of a predictor, learned via this method is almost the same in expectation as the error of a kernel predictor. Aditionally, we will empirically show that this predictor is better than the ONE that uses only Nystr?m method.
Geolocation Apps using A* Algorithm for Android Based Traders

The development of technology has a positive effect on the trade sector, creating smartphones that can be utilized in all activities combined with the internet network. Activity that is currently growing is a mobile trader in the city of Pekanbaru. This development caused much competition, for example, in the Pekanbaru city area, especially in Sialangmunggu village. Traders around is difficult to find consumers because consumers do not have precise location and time information. Therefore, researchers aim to design and build applications by utilizing the functions of google maps and GPS (Global Positioning System) where the Algorithm to be applied is the A* algorithm whose function is to find the nearest location between buyers to mobile merchants, to accommodate data from mobile merchants where buyers can know the nearest position of the traveling merchant. Process analysis will be divided into running analysis that discusses the workings of the process of mobile traders and buyers in the field. Then the proposed system analysis of the analysis will be made by the author to maximize the process on the current analysis. By making analysis and design, the author will know the needs needed in the creation of the system. The result of using method A* is applied to displaying the merchant's route with the user, and the result can provide the fastest route to get to the trader. The use of method A* is also done to find the trader whose location is closest to the user's location, and the result can display the nearest trader.
Unsupervised Text Feature Extraction for Academic Chatbot using Constrained FP-Growth

In the edge where conversation merely involves online chatting and texting one another, an automated conversational agent is needed to support certain repetitive tasks such as providing FAQs, customer service and product recommendations. One of the key challenges is to identify and discover user’s intention in a social conversation where the focus of our work in the academic domain. Our unsupervised text feature extraction method for Intent Pattern Discovery is developed by applying text features constraints to the FP-Growth technique. The academic corpus was developed using a chat messages dataset where the conversation between students and academicians regarding undergraduate and postgraduate queries were extracted as text features for our model. We experimented with our new Constrained Frequent Intent Pattern (cFIP) model in contrast with the N-gram model in terms of feature-vector size reduction, descriptive intent discovery, and analysis of cFIP Rules. Our findings show significant and descriptive intent patterns was discovered with confidence rules value of 0.9 for cFIP of 3-sequence. We report an average feature-vector size reduction of 76% compared to the Bigram model using both undergraduate and postgraduate conversation datasets. The usability testing results depicted overall user satisfaction average mean score is 4.30 out of 5 in using the Academic chatbot which supported our intent discovery cFIP approach.
Decision Support System for Community Housing Subsidy Recipients

Human needs consist of primary, secondary, and tertiary needs. In primary needs, the house is one of the most critical primary needs in supporting one’s life. However, not all residents in Indonesia can meet these needs. Therefore, the government provides subsidized housing ownership programs for people with low income. This study aims to propose a decision support system in determining the proper housing subsidy recipients. The method used in weighting is Analytic Hierarchy Process (AHP). Previous research was still limited to the selection of subsidized housing for developers and potential buyers. This method is projected to provide results in the form of a priority sequence of alternative solutions based on test results. The results were considered capable of providing a better solution for selecting prospective recipients of the housing subsidy program.
A Detector-Oblivious Multi-Arm Network for Keypoint Matching

This paper presents a matching network to establish point correspondence between images. We propose a Multi-Arm Network (MAN) to learn region overlap and depth, which can greatly improve the keypoint matching robustness while bringing little computational cost during the inference stage. Another design that makes this framework different from many existing learning based pipelines that require re-training when a different keypoint detector is adopted, our network can directly work with different keypoint detectors without such a time-consuming re-training process. Comprehensive experiments conducted on outdoor and indoor datasets demonstrated that our proposed MAN outperforms state-of-the-art methods. Code will be made publicly available.
Prediction of Sudden Health Crises Owing to Congestive Heart Failure with Deep Learning Models

Artificial Intelligence (AI) has its roots in every area in the present scenario. Healthcare is one of the markets in which AI has greatly grown in recent years. The tremendous increase in health data generation and the substantial evolution of the robust data analysis tools have contributed to AI improvement in health care and research, leading to increased service efficiency. Health reporting is stored as Electronic Health Records (EHR), providing information on the patients sought temporarily. EHR data have different issues, such as heterogeneity, missing values, distortion, noise, time, etc. This study reflects the irregularity of appointment that refers to the irregular timing of the operations (patient visits). Congestive heart failure (CHF) is a grave clinical disorder caused by an insufficient blood supply in the bloodstream owing to a heart muscle dysfunction. Most people suffer from CHF which result in death or immediate recognition. A multi-layer perceptron (MLP) model was used to treat visit stage abnormalities. The studies on the Medical Knowledge Mart for Intensive Care-III (MIMIC-III) dataset and the findings obtained indicate that the lack of a visit stage affects the estimation of the clinical outcome. It has been demonstrated that the readmission and reduction of the prediction model for mortality conditions is beneficial. Compared with baseline models, the proposed model is successful.
Toward Improving Image Retrieval via Global Saliency Weighted Feature

For full description of images’ semantic information, image retrieval tasks are increasingly using deep convolution features trained by neural networks. However, to form a compact feature representation, the obtained convolutional features must be further aggregated in image retrieval. The quality of aggregation affects retrieval performance. In order to obtain better image descriptors for image retrieval, we propose two modules in our method. The first module is named generalized regional maximum activation of convolutions (GR-MAC), which pays more attention to global information at multiple scales. The second module is called saliency joint weighting, which uses nonparametric saliency weighting and channel weighting to focus feature maps more on the salient region without discarding overall information. Finally, we fuse the two modules to obtain more representative image feature descriptors that not only consider the global information of the feature map but also highlight the salient region. We conducted experiments on multiple widely used retrieval data sets such as roxford5k to verify the effectiveness of our method. The experimental results prove that our method is more accurate than the state-of-the-art methods.
Introducing the Information Literacy Model “Empowering 8” in South Asia with special reference to India and Sri Lanka

Information literacy (IL) is the ability to identify, locate, evaluate, organize and effectively create, use and communicate information to address an issue or a problem. It is a basic human right to lifelong learning. Information itself is becoming a transforming strategic resource of the emerging information society. Without information literacy, the information society will not be able to achieve its full potential. Therefore, the concept and practice of information literacy must be promoted from an early age among the school going children. Information literacy is more relevant for the developing nations to enable themselves to bridge the growing info-divide between the developed and the developing nations. In this context, a regional workshop was held at the University of Colombo to create awareness of information skills for learning among the participants from South and Southeast Asian countries.
PERCEPTION OF STUDENTS FROM BTESL TOWARDS ENHANCING VOCABULARY THROUGH SOCIAL MEDIA IN MSU

Vocabulary is fundamental and one of the important components in acquiring a second language. The implementation of various platforms and applications in carrying a lesson is another way or substitute towards enhancing the students’ vocabulary knowledge. The current study aims to investigate the perceptions of students from Bachelor in Teaching English as a Second Language (BTESL) towards Enhancing Vocabulary through Social Media in Management and Science University (MSU), Malaysia. Another aim of this study is to examine the differences in the perceptions of the BTESL students towards enhancing vocabulary through social media according to the year of study. This research involves 263 BTESL students consisting of 78.1% of Female respondents and 21.9% of Male respondents. A quantitative research was used in this study by using a survey method for the data collection. The questionnaire was created after reviewing the existing related literature and the draft was given to the experts in the field of Linguistics. The data was tabulated in the SPSS software and it was interpreted through descriptive analysis. The findings affirm that majority of the students view enhancing vocabulary through social media positively and it was observed that there are differences in the students’ perceptions according to the year of study. Thus, this study provides pedagogic implications to foster the teaching and learning processes based on the 21st Century learning.
Hotel Recommender System based on Knowledge Graph and Collaborative Approach

With the rapid increase of Information technology, online services and social media, recommendation system becomes an important issue and a need for both the customer and business sectors. The main aim of traditional and online recommendation systems is to recommend the desired and the necessary services that are appropriate recommendations to users. Traditional recommendation systems often suffer from inefficient data analysis techniques, rating the different services without regard to the previous preferences of the users and do not meet the personal demands of the users. Therefore, in this paper we used a hybrid approach based on Knowledge graph and Machine Learning similarity function as a recommendation system. We used real datasets to conduct the experiment. We built the knowledge graph for the visitors, hotels and their ranks, and we used the knowledge graph and similarity scores to recommend a hotel or a set of hotels for the visitors based on former preferences and ratings of other visitors. The results show significant accuracy and good quality of service recommender systems with 93.5% for f-measure.
Android-based Dental Anatomy Learning Application Using Mayer’s Multimedia Learning Principles

Anatomy learning methods are still dominated by using books, in which the material is presented in pictures and writings. The anatomy learning method using books is carried out by redrawing parts and memorizing the location and its terms. Thus, students have difficulties when they forget and want to find terms on the anatomy because it is uneasy and takes a long time. This research aims to create a system to study anatomy, especially dental anatomy, through an android-based application, and provide ease of anatomy of tooth learning using Mayer’s principle of multimedia learning on application. Based on system testing and user testing results, this application can be used as a medium of information and learning about tooth anatomy. It is evidenced by black-box testing and questionnaire results.
E-GOVERNMENT GATEWAY: A USER STUDY FOR USABILITY AND TRUST ANALYSIS

E-Government Gateway is a web application that aims to increase efficiency in public services by enabling citizens to access all services electronically from a single point. This application has started to take a critical place in our lives, especially during the Covid-19 pandemic period. However, we yet know very little about how citizens perceive and use it. This study aims to shed light on users' experience with the application, and to address potential issues they face in terms of usability and trust. To this end, a task-based user study was conducted with 10 volunteer participants, followed by a post- study survey and an interview. The findings of the study, based on both quantitative and qualitative data, show that the participants found the application very useful and easy to use in overall. However, the results also indicate a few design problems. In particular, it has been observed that the difficulty of accessing the information provided by the application regarding data privacy has led to a decrease in user trust. Building on the findings, this article presents a number of design suggestions to improve user experience and trust.
Pelatihan Penggunaan Sistem Informasi Inventaris Bagi Petugas Poliklinik Di Kawasan Pemulung Rangkah Surabaya

The “Scavenger” polyclinic is a polyclinic that provides free medical treatment and medicine for people in the scavenger village area. From the operational side, the polyclinic is assisted by medical personnel from the Indonesian Red Crescent (BSMI) and medicines from the Al-Falah Social Fund Foundation (YDSF), while the polyclinic admin comes from residents. Based on the results of interviews and observations, it is concluded that what partners need is insight and skills in inventory activities, especially inventory management, archive and asset management, and an application that can help polyclinic administrators recapitulate assets and making weekly reports. Based on these problems, this community service activity aims to provide training and provide knowledge about the correct and optimal inventory process using the Goods Inventory Information System and also Microsoft Excel. The information system is web-based and set up offline for simplicity and flexibility. From the pre-test and post-test results, it is concluded that there has been an increase in insight and knowledge about the information system and excel material being taught.