Reviewerly/exHarmony huggingface dataset:
Cohesion and Coherence of Discourse in the Story of "Layangan Putus" on Social Media Facebook

This research is to find out how the use of cohesion and coherence in the story of "Layangan Putus", which was viral some time ago. Because cohesion and coherence are important criteria and principles in a text. This research is expected to contribute to the development of the quality of story writing. So the story can be understood and useful for the readers. Thus, the purpose of writing stories can inspire readers. The method used to analyze cohesion and coherence was a descriptive analysis seen in each paragraph. The cohesion aspects analyzed were lexical and grammatical cohesion aspects. The results of this study indicated that the story of "Layangan Putus" had aspects of lexical cohesion, grammatical cohesion, and coherence. Aspects of cohesion and constructive coherence in which discourse can convey a message to readers. In this way, the contents of the story can make the reader into feeling what the writer feel.
Enhancing the Students’ Writing Skill through Technological Writing Feature of Wridea

This study experimental research investigated the effect of using Wridea on enhancing EFL writing performance in PGRI University. It shed a light on how Wridea could enhance the writing skill. Wridea helped many of the participants to be more metacognitively aware of their English writing process. The results of this study showed that Wridea could be utilized as a service for improving the writing skill outside of the classrooms.
Arabic Word Recognition System for Historical Documents using Multiscale Representation Method

In the last decades, huge efforts have been made to develop automated handwriting recognition systems.The task of recognition usually involves several complex processes including image pre-processing, segmentation, features extracting and matching.This task usually gets harder by processing historical documents as they involve skews, document degradation and structure noise.Although, the success that has been achieved in English language, the recognition of handwritten Arabic still constitutes a major challenge for many reasons.The characteristic of Arabic language, as a Semitic language, differs from other languages (e.g., European languages) in several aspects such as complex structure, implicit characters, concatenation and, writing styles and direction.This work proposes a full recognition system for the task of word recognition from from Arabic historical documents.In the proposed system, a novel feature extraction method is presented to define robust features from Arabic words.Prior Feature extraction, each input image is pre-processed and segmented resulting in segmented words.After that, the features of each word/sub-word are defined based on Multiscale Convexity Concavity(MCC) analysis of contour word shape.For feature matching, a circular shift method is proposed to burn the computational cost instead of using traditional dynamic time warping (DTW) which exhibits high computational cost.Finally, the proposed algorithm has been evaluated under well-known dataset, namely, Ibn Sina, and showed high performance for historical documents with low computational cost.
ANIMAL CLASSIFICATION USING DEEP LEARNING

Kaggle's Dogs vs. Cats contest is trying to solve the CAPTCHA challenge, which is based on the issue of distinguishing dog and cat images.It's easy for humans, but evidence suggests that the automatic separation of cats and dogs is particularly difficult.Many people have been working or working on building classifiers for machine learning to address this issue.A color-based classifier had an accuracy of 56.9% on the Asirra dataset.A SVM classifier achieved an accuracy of 82.7 percent based on a combination of color and texture characteristics.And in, they used the features of SIFT (Scale-Invariant Feature Transform) to train a classifier then finally got a 92.9 percent accuracy.We also want to solve this problem in our plan and achieve higher efficiency.We've tried various strategies.We tried Dense-SIFT features, combining Dense SIFT and color features, and features learned from CNN, for example.We also used SVMs on the learned features and finally achieved 94.00 percent of our best classification accuracy.
Optimization of Neural Networks Based on Modified Multi-Sonar Bat Units Algorithm

The motivation behind this paper is to explore an algorithm that has the ability to optimize the free parameters required to design a neural network without being diligent in determining its values.The method uses modified version of Multi-Sonar Bat Units (MSBU) algorithm which is based on the idea that bats leave their roost shelter in groups and spread in space searching for their best food targets.The algorithm considers the population of the bats is equal to the training samples, each bat flies to the location of one training sample which represents its first target.After that, there will be frequent competitions between the bats to capture the next targets using their sonar beam.The bat got the highest association value with the target wins the competition.Each winning bat modifies the architecture of the designed network, and reflects its effect on the network weight values and on the positions of the remaining bats.The proposed method has been used to design several classification neural networks which are then utilized to diagnose various pathologies.Although some of the used training datasets are of nonlinear and heterogeneous nature, the designed networks using the proposed algorithm showed high performance outputs.The results obtained were compared with pattern recognition NNs.The comparison showed very promise output using the proposed algorithm.
Enhancing Intrinsic Motivation in Physical Activity through Quantified-self Data Sharing

INTRODUCTION: Quantified-self application is widely used in sports and health management; the type and amount of data that can be detected and fed back to the user are growing rapidly.However, only a few studies discussed the social attributes of quantified-self data, especially in the context of cycling.OBJECTIVES: To design and evaluate SocialBike -a digitally augmented bicycle that aims to increase user's motivation in physical activity by showing their quantified-self data to each other.METHODS: We conducted a within-subject experiment on a cycling simulation system with 20 participants.The on-bike display of SocialBike was compared to a control display to evaluate its impact on two dependent variables, including user's intrinsic motivation in physical activity and social relatedness with other cyclists nearby.Intrinsic Motivation Inventory (IMI) was used to measure users' self-reported intrinsic motivation and social relatedness.The user's cycling behaviour data were also recorded by the simulation system.We extracted six indicators about the two dependent variables from the behaviour data.Qualitative data were collected through semi-structured interviews.We conducted a paired sample T-test on both types of quantitative data.Qualitative data were analysed by the method of thematic analysis.RESULTS: The results of the Intrinsic Motivation Inventory show that SocialBike's on-bike display has significant positive effects on four subscales, including intrinsic motivation, perceived competence, values/usefulness, and relatedness.The results of user cycling behaviour indicate that SocialBike increased user's social relatedness with other cyclists nearby by significantly affecting three indicators.Three themes were identified from qualitative data, including motivation, social interaction, and data visualization.The qualitative result provides supplementary evidence for the quantitative result and additional insights into the overall design of SocialBike.CONCLUSION: SocialBike as a platform for sharing quantified-self data have positive effects in enhancing users' intrinsic motivation in physical activity and social relatedness with other cyclists nearby.
Non-null testing of the aspheric surface using a quadriwave lateral shearing interferometer

A non-null aspheric surface testing system using a quadriwave lateral shearing interferometer (QWLSI) based on the randomly encoded hybrid grating is proposed. The system uses a non-null lens to partially compensate for the longitude aberration of the aspheric surface, which is more versatile than the null configuration. QWLSI is a common-path and self-reference system and requires no reference surface. Compared with the conventional Twyman–Green interferometer (TGI), QWLSI has higher dynamic range, better antivibration ability, and higher stability. For a non-null testing system based on the QWLSI and TGI, the compensation ranges of different aspheric surfaces and the allowable range of aspheric axial positions are analyzed under the condition that the interference fringe satisfies the Nyquist sampling theorem. The iterative reverse optimization retrace error-correction algorithm based on the exact system modeling and the localization algorithm of aspheric axial position are discussed. Simulation results show that the root mean square value of the aspheric surface reconstruction residual is less than <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>1</mml:mn> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:mn>200</mml:mn> </mml:mrow> <mml:mspace width="thickmathspace" /> <mml:mi>λ<!-- λ --></mml:mi> </mml:math> , which indicates that the system has extremely high theoretical testing accuracy. In the experiment, the QWLSI non-null system and the autocollimation null method with a ZYGO interferometer agree with the testing results of the same aspheric surface, which demonstrates the feasibility and accuracy of the QWLSI aspheric non-null testing method.
Designing English Listening Materials through YouTube Video Editing: Training for English Teachers of Islamic Junior High Schools, Parepare, South Sulawesi

This study was conducted to help English teachers in designing English listening materials in form of audio and textbook through YouTube video editing. 18 English teachers of 10 Islamic junior high schools in Parepare were trained to write English listening materials in form of textbook and to edit video(download ,import, cut, merge, and export video) in form of audio.150 students were observed and tested to evaluate teachers’ products. Training materials consist of: (1) searching and download video through YouTube, (2) editing video that includes import, cut, merge, and export video, and (3) writing worksheet that contains phoneme discrimination dan listening comprehension exercise in form of multiple choice,true false,and completion. Training activities include: (1) explanation, (2) practice, (3) grouping, (4) assignment/design, and (5) evaluation and revision. After following training, teachers’ ability was categorized into good and fair in designing English listening materials. More than 50% teachers were categorized into good in editing video and 72% teachers were categorized into good in writing listening exercise. Students’ English listening learning outcome was categorized into good after learning by using teachers’ products and students have positive attitude in learning English listening skill.
The Effectiveness of Using Picture Book to Motivate Students Especially Young Learners in Reading

&lt;p&gt;This journal aims to investigate the effectiveness of PICTUREBOOK in resolve the problem of low interest in reading, especially for young learners. Reading is something that is learned. Reading means gaining an understanding of the written symbols represented by the sounds of oral language. This is an important skill to learn as it opens the doors to further studying, better job opportunities and to a whole other world that happens in print. There are many strategies that can be used to teach reading for young learners one of them is by using a picture book. A picture book is a book contained illustrations on each page accompanied by texts which tell about the same story told by the illustrations. In this journal, the authors use a qualitative research method, by reviewing some journals from other researchers. The result of this research proves that picture books can improve and motivate young learners to enhance and also motivate young learners to prefer reading. Picture books can visualization literacy skills and train children to comprehend the story from multiple perspectives.&lt;/p&gt;
Method of Artificial Neural Networks Teaching

The technique of artificial neural networks training has been developed.A distinctive feature of the proposed technique is that it provides training not only for the synaptic weights of the artificial neural network but also for the type and parameters of the membership function.If it is impossible to provide, the specified quality of functioning of artificial neural networks due to the learning of the parameters of the artificial neural network, the architecture of artificial neural networks is trained.The choice of architecture, type and parameters of the membership function takes into account the computing resources of the tool and taking into account the type and amount of information that is coming to the input of an artificial neural network.Also, while using the proposed method, there is no accumulation of error learning artificial neural networks as a result of processing information, which is supplied to the input of artificial neural networks.The development of the proposed methodology is due to the need to train artificial neural networks, in order to process more information, with the uniqueness of the made decisions.According to the results of the research, it is established that the mentioned training method provides on average 16-23 percent higher efficiency of training of artificial neural networks and does not accumulate errors during training.This technique will allow to train artificial neural networks; identify effective measures to improve the performance of artificial neural networks.Also, the developed technique will increase the efficiency of the functioning of artificial neural networks by learning the parameters and architecture of artificial neural networks.The technique proposed by the authors reduces the use of computing resources for support and decision-making systems.Using the developed methodology will develop measures that are aimed at improving the efficiency of artificial neural networks training and increase the speed of the processing information.
Domain Specific Lexicon Generation through Sentiment Analysis

Sentiment analysis (SA) is used to extract opinions from a huge amount of data and these opinions are comprised of multiple words. Some words have different semantic meanings in different fields and we call them domain specific (DS) words. A domain is defined as a special area in which a collection of queries about a specific topic are held when user do queries in the data regarding the domain appear. But Single word can be interpreted in many ways based on its context-dependency. Demonstrate each word under its domain is extremely important because their meanings differ from each other so much in different domains that a word meaning from A in one context can change into Z in another context or domain. The purpose of this research is to discover the correct sentiment in the message or comment and evaluate it either it is positive, negative or neutral. We collected tweets dataset from different domains and analyze it to extract words that have a different definition in those specific domains as if they are used in other fields of life they would be defined differently. We analyzed 52115 words for finding their DS meaning in seven different domains. Polarity had been given to words of the dataset according to their domains and based on this polarity they have been recognized as positive negative and neutral and evaluated as domain-specific words. The automatic way is used to extract the words of the domain as we integrated and afterward the comparison to identify that either this word differs from other words as far as domain is concerned. This research contribution is a prototype that processes your data and extracts their domain-specific words automatically. This research improved the knowledge about the context-dependency and found the core-specific meanings of words in multiple fields.
Pengembangan Pop Up Book Bahasa Indonesia Berbasis Budaya Slempitan

Innovation in education world is a nuance that must always be improved, considering that time goes with the changing of era. So in the field of education there has to be a special touch in the form of innovation which is able to become an educational identity as a space for freedom from backwardness. Education is now designed to return to the local wisdom of each region so that it can produce scholars with extensive knowledge and cultured character. Slempit Village is one of the villages in Gresik Regency with unique local culture and now has to get special attention for some of them are starting to get abandoned as modern access which is marked by the construction of the Gresik-Mojokerto toll road. Various elements of society have the same responsibility for cultural preservation and especially in the world of education which focuses directly on the inculcation of values towards the nation's next generations. Based on this, the researchers conducted the Development of Indonesian Language Pop Up Book Based on Slempitan Culture. The development process is carried out with a research method of development which then gives birth to a pop-up book which is effective and relevant to be applied in the learning process, especially at SDN I Slempit regarding local culture of Slempit. The development model is done by adapting instructional development model which, according to Sadiman, 1996 (et al) consists of seven systematic stages. This study used validation instruments from the data analysis. The validation of Slempitan Culture-Based Indonesian Pop-Up Book consists of presentation / material validator as well as graphic validator. The results of the validation of the presentation obtained a score of 90% in the very feasible category. While the score obtained from the results of the graphic is 90.9% in the excellent category.
Personalized Learning: Empowering a Student with Autism Spectrum Disorder in Basic Grammar Course

&lt;p&gt;This paper provides illustrations of how a personalized learning approach is implemented in the Basic Grammar course to empower a student with an autism spectrum disorder is taking the ownership of learning and meeting his learning needs based on his strengths, weaknesses, and interests. Building upon literature and pedagogical practices of personalized learning, this paper details the innovations implemented in the Basic Grammar course for a student with an autism spectrum disorder to take more control of his learning, facilitate decision-making and communication skills, and engage in the learning process. Additionally, the innovations implemented in this course mirror the essence and principles of personalized learning. This paper is expected to enrich educators in the attempts to adopt a personalized learning approach and to give new insight into how to implement personalized learning in an inclusive classroom. Moreover, it is hoped that the idea of equity in inclusive education by implementing personalized learning could be an omnipresent practice to improve the quality of education for all in Indonesia.&lt;/p&gt;
Leveraging Concept-Enhanced Pre-Training Model and Masked-Entity Language Model for Named Entity Disambiguation

Named Entity Disambiguation (NED) refers to the task of resolving multiple named entity mentions in an input-text sequence to their correct references in a knowledge graph. We tackle NED problem by leveraging two novel objectives for pre-training framework, and propose a novel pre-training NED model. Especially, the proposed pre-training NED model consists of: (i) concept-enhanced pre-training, aiming at identifying valid lexical semantic relations with the concept semantic constraints derived from external resource Probase; and (ii) masked entity language model, aiming to train the contextualized embedding by predicting randomly masked entities based on words and non-masked entities in the given input-text. Therefore, the proposed pre-training NED model could merge the advantage of pre-training mechanism for generating contextualized embedding with the superiority of the lexical knowledge (e.g., concept knowledge emphasized here) for understanding language semantic. We conduct experiments on the CoNLL dataset and TAC dataset, and various datasets provided by GERBIL platform. The experimental results demonstrate that the proposed model achieves significantly higher performance than previous models.
METHOD OF DATA DEPERSONALIZATION IN PROTECTED AUTOMATED INFORMATION SYSTEMS

Context. The problem of data depersonalization in information systems is considered. The analysis of modern approaches to depersonalization of data is carried out, it is revealed and proved by need of creation of the new method allowing to increase security of the processed data and their reliability. The object of the study was a model of data depersonalization, allowing to reduce the cost of protecting information systems. Objective. The goal of the work is the analysis of modern methods of depersonalization and the creation of a method that eliminates the identified shortcomings, with an increased level of confidentiality and use of hashing of critical data and a private key.Method. A method of personal data depersonalization is proposed, based on the method of entering identifiers using hashing of critical data and a private key, which allows to increase the confidentiality of information processed in information systems. Methods are proposed for selecting key critical attributes from primary documents that uniquely identify the subject of personal data, the method of generating initial sets, which divides the source data into two disjoint subsets, the method of generating a hash identifier from a unique sequence and a private key that depersonalizes information and enhances its confidentiality.Results. The developed method is implemented in software and researched while solving the problems of depersonalization.Conclusions. The carried out experiments confirmed the efficiency of the proposed method and allow to recommend it for implementation in automated information systems for processing personal data for solving problems of depersonalization. Prospects for further research may be in the creation of hardware streamlined data depersonalization allowing to increase the speed of processing and confidentiality of data in the information systems.
HUMAN ACTIVITY DETECTION AND ACTION RECOGNITION IN VIDEOS USING CONVOLUTIONAL NEURAL NETWORKS

Human activity recognition from video scenes has become a significant area of research in the field of computer vision applications. Action recognition is one of the most challenging problems in the area of video analysis and it finds applications in human-computer interaction, anomalous activity detection, crowd monitoring and patient monitoring. Several approaches have been presented for human activity recognition using machine learning techniques. The main aim of this work is to detect and track human activity, and classify actions for two publicly available video databases. In this work, a novel approach of feature extraction from video sequence by combining Scale Invariant Feature Transform and optical flow computation are used where shape, gradient and orientation features are also incorporated for robust feature formulation. Tracking of human activity in the video is implemented using the Gaussian Mixture Model. Convolutional Neural Network based classification approach is used for database training and testing purposes. The activity recognition performance is evaluated for two public datasets namely Weizmann dataset and Kungliga Tekniska Hogskolan dataset with action recognition accuracy of 98.43% and 94.96%, respectively. Experimental and comparative studies have shown that the proposed approach outperformed state-of the art techniques.