Reviewerly/exHarmony huggingface dataset:
Diagnosa Dini Penyakit Mata Menerapkan Metode Case Based Reasoning (CBR)

The eye is a sense organ found in humans. The eye has the ability to adjust the amount of light that enters, focus attention on objects near and far and produce a continuous image and is conveyed to the brain. Distractions that cause damage to the eyes, can occur due to eye fatigue, lack of sleep, exposure to dust, too long in front of the computer and so on. This study aims to analyze and design an expert system for early diagnosis of diseases of the eye that includes disease information, both symptoms and solutions, and plays a role in replacing and mimicking the reasoning process of an expert in solving specification problems. The method used for reasoning is case base reasoning (CBR) and the results of the study are an expert system for early diagnosis of computerized eye diseases that can be used to provide useful information in diagnosing disease
Improvement security in e-business systems using hybrid algorithm

E-business security becomes an important issue in the development of technology, to ensure the safety and comfort of transactions in the exchange of information is privacy. This study aims to improve security in e-business systems using a hybrid algorithm that combines two types of keys, namely symmetric and asymmetric keys. Encryption and decryption of messages or information carried by a symmetric key using the simple symmetric key algorithm and asymmetric keys using the Rivest Shamir Adleman (RSA) algorithm. The proposed hybrid algorithm requires a high running time in the decryption process compared to the application of a single algorithm. The level of security is stronger because it implements the process of message encryption techniques with two types of keys simultaneously.
Text2Video: Text-driven Talking-head Video Synthesis with Phonetic Dictionary

With the advance of deep learning technology, automatic video generation from audio or text has become an emerging and promising research topic. In this paper, we present a novel approach to synthesize video from the text. The method builds a phoneme-pose dictionary and trains a generative adversarial network (GAN) to generate video from interpolated phoneme poses. Compared to audio-driven video generation algorithms, our approach has a number of advantages: 1) It only needs a fraction of the training data used by an audio-driven approach; 2) It is more flexible and not subject to vulnerability due to speaker variation; 3) It significantly reduces the preprocessing, training and inference time. We perform extensive experiments to compare the proposed method with state-of-the-art talking face generation methods on a benchmark dataset and datasets of our own. The results demonstrate the effectiveness and superiority of our approach.
Analysis of the impact of parameters in TextGCN

Deep learning models uses many parameters to work properly. Asthey become more complex, the authors of these novel models cannotexplore in their papers the variation of each parameter of theirmodel. Therefore, this work describes an analysis of the impact offour different parameters (Early Stopping, Learning Rate, Dropout,and Hidden 1) in the TextGCN Model. This evaluation used fourdatasets considered in the original TextGCN publication, obtainingas a side-effect small improvements in the results of three of them.The most relevant conclusion is that these parameters influence theconvergence and accuracy, although they individually do not constitutestrong support when aiming to improve the model’s resultsreported as the state-of-the-art.
When does text prediction benefit from additional context? An exploration of contextual signals for chat and email messages

Email and chat communication tools are increasingly important for completing daily tasks.Accurate real-time phrase completion can save time and bolster productivity.Modern text prediction algorithms are based on large language models which typically rely on the prior words in a message to predict a completion.We examine how additional contextual signals (from previous messages, time, and subject) affect the performance of a commercial text prediction model.We compare contextual text prediction in chat and email messages from two of the largest commercial platforms Microsoft Teams and Outlook, finding that contextual signals contribute to performance differently between these scenarios.On emails, time context is most beneficial with small relative gains of 2% over baseline.Whereas, in chat scenarios, using a tailored set of previous messages as context yields relative improvements over the baseline between 9.3% and 18.6% across various critical serviceoriented text prediction metrics.
EFEKTIVITAS STRATEGI READ ALOUD DALAM MENGENALKAN BAHASA INGGRIS PADA ANAK USIA DINI

In order to face the global era, the ability to speak foreign languages, in this case English, is very important. So that the introduction of English should be pursued from an early age. However, the lack of knowledge of teachers and parents regarding strategies to introduce English to children has an impact on the lack of English language skills of children, therefore it is necessary to seek effective ways so that children's English language skills develop optimally during their best development period. The aim of this study is to determine the effectiveness of the strategy. Read Aload in introducing English to children. The research approach is a quantitative experiment with a pretest-posttest control group design through data collection techniques, observation and documentation, and analysis of hypothesis testing using the t test. The t test result in the control group was -17.245 while in the experimental group -5.844. There is a significant difference between language learning using the Read Aload strategy and others, proving that this strategy is effective for use in the introduction of English to children.
Correction to: Arabic text summarization using deep learning approach

An amendment to this paper has been published and can be accessed via the original article.
MEDIA PEMBELAJARAN BERBAHAN LOOSE PART DALAM PEMBELAJARAN EKSAK DI MI KEDUNGWULUH LOR

Media is a tool that is used to convey informatiabson from the sender of the message to the recipient of the message. Learning media is defined as a tool used by teachers to convey learning material to students, and a means of communication between students and students. There are problems such as the use of learning media at MI Muhammadiyah Kedungwuluh Lor is not optimal. This activity seeks to provide assistance in creating and using learning media, especially in exact subjects so that student learning outcomes are getting better.
Susceptibility of trapped-ion qubits to low-dose radiation sources

We experimentally study the real-time susceptibility of trapped-ion quantum systems to small doses of ionizing radiation. We expose an ion-trap apparatus to a variety of $\alpha$, $\beta$, and $\gamma$ sources and measure the resulting changes in trapped-ion qubit lifetimes, coherence times, gate fidelities, and motional heating rates. We found no quantifiable degradation of ion trap performance in the presence of low-dose radiation sources for any of the measurements performed. This finding is encouraging for the long-term prospects of using ion-based quantum information systems in extreme environments, indicating that much larger doses may be required to induce errors in trapped-ion quantum processors.
Adaptive weighting for estimation of the mean of the merged measurement for multi‐target bearing tracking

In multi-target tracking, sensor with finite resolution generates merged measurements, which means that a group of targets might produce only one measurement, and such phenomenon could lead to degraded tracking performance if it is not considered. The generalised labelled multi-Bernoulli filter for merged measurement (GLMB-M) provides a promising solution for such problem. However, the merged measurement likelihood it used was modelled as a Gaussian density with its mean being the uniform weighted mean of the measurements generated by the merging targets. Such uniform weighting strategy might fail if the merged measurements deviate severely from the uniform weighted position due to significant difference of the strengths of the merging targets. To avoid such deviation, an adaptive weighting strategy for estimation of the mean of the merged bearing measurement is proposed, which uses the bearing measurement error to obtain the adaptive weighting factors. Simulation results show that the proposed adaptive weighting strategy outperforms the uniform weighting strategy when the strengths of the targets vary greatly, although both strategies provide similar performance when the strengths of the target are equal.
Utilising Visual Attention Cues for Vehicle Detection and Tracking

Advanced Driver-Assistance Systems (ADAS) have been attracting attention from many researchers. Vision-based sensors are the closest way to emulate human driver visual behaviour while driving. In this paper, we explore possible ways to use visual attention (saliency) for object detection and tracking. We investigate: 1) How a visual attention map such as a subjectness attention or saliency map and an objectness attention map can facilitate region proposal generation in a 2-stage object detector; 2) How a visual attention map can be used for tracking multiple objects. We propose a neural network that can simultaneously detect objects as and generate objectness and subjectness maps to save computational power. We further exploit the visual attention map during tracking using a sequential Monte Carlo probability hypothesis density (PHD) filter. The experiments are conducted on KITTI and DETRAC datasets. The use of visual attention and hierarchical features has shown a considerable improvement of ≈8% in object detection which effectively increased tracking performance by ≈4% on KITTI dataset.
A-SA SOS: A Mobile- and IoT-based Pre-hospital Emergency Service for the Elderly and Village Health Volunteers

In Thailand, emergency illnesses are lifethreatening conditions that constitute serious health problems and quick access to definitive care can improve the survival rate of the elderly dramatically.Currently, the pre-hospital emergency medical services have limitations which prevent the treatment from getting to the accident site on time.In this research, we proposed the A-SA SOS application, a mobile-and IoT-based pre-hospital emergency service for the elderly.The system helps the elderly to send the request to the nearest village health volunteers via a mobile application and smart device.After reaching the elderly, the village health volunteers help carry out basic life support to increase the survival rate before sending the patients directly to the Emergency Management System (EMS) agency.To evaluate the system, we tested it for three months in the Sub-district of Suthep in Chiang Mai city.Finally, the incident report showed that the average time to reach the scene (4.91±0.56) to help elderly patients was less than the standard criteria of an average 3 minutes.
Edukasi Pemanfaatan Limbah Sisa Makanan Menjadi Produk Yang Bernilai Ekonomis

Poor waste management has become a problem in Kuta Jeumpa Village of Jeumpa Sub-district of Aceh Barat Daya Regency. The people of Kuta Jeumpa Village still throw their household wasteinto the river. As the result, the water of Putroe Aloh River in Kuta Jeumpa Village is filled with household waste. To overcome this situation, waste management education is needed to be carried out for the people of Kuta Jeumpa to manage their waste properly. The people of Kuta Jeumpa village need to be taught on how to turn waste into a source of income, for example by introducing on how to make processed food from household waste. After the the people of Kuta Jeumpa received the waste management education, they have succeeded in making new processed food products from household waste with the help of university students and lecturers. The products produced can provide profit value for the community. In addition, the community was also educated to no longer throw their household waste into the river.&#x0D;
EDUCATIONAL WORKERS METHODOLOGICAL COMPETENCE FORMATION IN THE CONDITIONS OF INCLUSIVE LEARNING

The article analyzes the scientific achievements on the problem of educators’ methodological competence forming in the context of SEI inclusive education. The definitions of the concepts "inclusive education", "inclusive learning", "Methodical competence of a teacher" have been clarified. It is clarified that inclusive education means that all students receive a quality education in a general secondary education institution, each student is unique, has different abilities and has a different level of knowledge that a teacher helps him develop, receives individual assistance from a teacher and an educational institution in general, he is a personality being developed throughout life.Among the main advantages of inclusive education are: the development of individual strengths and talents of the student; admission of all children without exception into the general school system and society as a whole; active involvement of parents in the learning process and school life; development of personality culture in all its manifestations; development of the student's ability to learn throughout life and the formation of a sense of respect for the various abilities and talents of peers; ability to create friendly relationships with peers.Key words: competence, methodical competence, educational institution, pedagogical workers, inclusive education.
Spline and Kernel Mixed Nonparametric Regression for Malnourished Children Model in West Nusa Tenggara

Health sector development is essential to improve human life quality, especially in West Nusa Tenggara (NTB) Province. Based on data from the NTB Provincial Health Office from 2011 to 2016, children under five suffering from malnutrition continued to increase, caused by several factors that affected the incident. Therefore, appropriate analysis is needed to model children who suffer from malnutrition in NTB Province in 2016, consisting of 10 districts based on the variables that influence it. The analysis in this study was carried out using a nonparametric regression mixed-model spline truncated and kernel. The estimation of the nonparametric regression curve depends on the optimal knot points and bandwidths parameter. Therefore, in determining the optimal knot points and bandwidths obtained from Generalized Cross-Validation (GCV). Based on the analysis that has been done, we obtained a nonparametric regression mixed-model spline truncated and kernel optimal knot points, such as for each variable and optimum bandwidths, such as and , with the value of GCV. The mixed model acquired has a good model by considering the values of and MSE. Besides, the MAPE value indicated a high degree of accuracy, so that the model obtained has an excellent forecast.
Measure extendibility/extensibility quality attribute using object oriented design metric

Software design is one of the very important phases of the software engineering. The costs of software can be minimized if improvements or corrections made during this stage. Several of the current computer aided software engineering (CASE) tools like enterprise architect (EA) v12 do not have the capability to improve the design. This work aims to develop an algorithm that helps the software engineers evaluating the design quality utilizing one of the object-oriented (OO) design models namely quality metrics for object-oriented design (QMOOD) which represents as hierarchical model that describes the relationship between quality attributes such as reusability, extendibility and properties of the design of OO design. This algorithm describesed how the assessment of the extendibility/ extensibility using the software metrics has been done and the impact of the involved metrics in the extendibility value. Results obtained demonstrate the effect of OO design metrics such as inheritance, polymorphism, abstraction and coupling in quality characteristics like extensibility. The results show that lower values of abstraction and coupling, obtain higher value of extendibility which means the class diagram is ready to accept additional improvements. The proposed algorithm has been tested on two different systems (test cases) that vary in their class diagrams, functionalities, and complexities.
Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes

In this study aims to compare the performance of several classification algorithms namely C4.5, Random Forest, SVM, and naive bayes. Research data in the form of JISC participant data amounting to 200 data. Training data amounted to 140 (70%) and testing data amounted to 60 (30%). Classification simulation using data mining tools in the form of rapidminer. The results showed that . In the C4.5 algorithm obtained accuracy of 86.67%. Random Forest algorithm obtained accuracy of 83.33%. In SVM algorithm obtained accuracy of 95%. Naive Bayes' algorithm obtained an accuracy of 86.67%. The highest algorithm accuracy is in SVM algorithm and the smallest is in random forest algorithm
EFL Teachers Perception toward the Use of Beyond International Textbook

This study aims to explore the teachers perception on using the beyond textbook that is from an international publisher. The study used descriptive qualitative design. The open-ended questionnaire was used to explore the teachers perception. The participants of the study were three English teachers, two of them are from SMPIT Al Muqorrobin-Depok and one is from SMPI Al Ikhlas-South Jakarta. The finding revealed that the teacher has a positive perception of the textbook. It has accordance with the requirements of the good book. The content is aligned with the Indonesia curriculum. It also involved four basic language skills as well as its language knowledge. However, the textbook has very minimal local cultural understanding and doesnt facilitate all learners cognitive level. For further research, it is suggested to have the study in Bilingual or senior school in order to find the maximum result toward the implementation of the textbook.
Expanded Neo-Fuzzy Adaptive Decayed Brain Emotional Learning Network for Online Time Series Predication

The Neo-Fuzzy integrated Adaptive Decayed Brain Emotional Learning (NF-ADBEL) network has recently been proposed for online time series predicting problems. The NF-ADBEL network is suitable for online time series prediction with shorter update intervals and offers features such as fast learning, accuracy, simplicity, and lower computational complexity. However, the neo-fuzzy neuron network in NF-ADBEL was integrated only in the orbitofrontal cortex (OFC) part of the ADBEL network. This paper aims to further improve the performance of the NF-ADBEL network by integrating the neo-fuzzy neuron network into the amygdala (AMY) section as well, inspired by a fully integrated version of a neo-fuzzy-based pattern recognizer. As is known, the AMY has two outputs: one response is based on imprecise information received from the thalamus, and the second response is based on information received from the sensory cortex. In this study, the imprecise response generation is operated as previously, while the other AMY process is treated by neo-fuzzy neurons. The resultant network is called Expanded Neo-Fuzzy integrated Adaptive Decayed Brain Emotional Learning (ENF-ADBEL). The modified network is still simple and meets the requirement for online prediction problems. A few chaotic and stochastic nonlinear systems, namely the Mackey-Glass, Lorenz, Rossler, disturbance storm time index, Narendra dynamic plant identification, wind speed and wind power series, are used to evaluate the performance of the proposed network in terms of the root mean squared error (RMSE) and correlation coefficient (COR) criteria in a MATLAB programming environment.
Implementasi Speech Recognition Pada Aplikasi E-Prescribing Menggunakan Algoritme Convolutional Neural Network

The process of manually prescribing drugs by doctors can cause several problems, including doctors not knowing what drugs are available and it takes time to find out what drugs are available in the pharmacy. Speech recognition is now widely used in various ways, which can help facilitate work. The application of speech recognition can be done in the e-prescribing application with the neural network method using the Convolutional Neural Network (CNN) algorithm, which is the basic method of deep learning. This study aims to facilitate physicians in filling out drug data in e-prescribing applications using speech recognition. The data used in this study were obtained from the open source dataset provided by Google and collected independent datasets. From the results of experiments that have been carried out, the accuracy achieved with 40 epochs and 40 direct impressions with different words is 90%. Where words are successfully recognized 36 words out of 40 words
Advanced Intelligent Data Hiding Using Video Stego and Convolutional Neural Networks

Steganography is a technique of concealing secret data within other quotidian files of the same or different types. Hiding data has been essential to digital information security. This work aims to design a stego method that can effectively hide a message inside the images of the video file. In this work, a video steganography model has been proposed through training a model to hiding video (or images) within another video using convolutional neural networks (CNN). By using a CNN in this approach, two main goals can be achieved for any steganographic methods which are, increasing security (hardness to observed and broken by used steganalysis program), this was achieved in this work as the weights and architecture are randomized. Thus, the exact way by which the network will hide the information is unable to be known to anyone who does not have the weights. The second goal is to increase hiding capacity, which has been achieved by using CNN as a strategy to make decisions to determine the best areas that are redundant and, as a result, gain more size to be hidden. Furthermore, In the proposed model, CNN is concurrently trained to generate the revealing and hiding processes, and it is designed to work as a pair mainly. This model has a good strategy for the patterns of images, which assists to make decisions to determine which is the parts of the cover image should be redundant, as well as more pixels are hidden there. The CNN implementation can be done by using Keras, along with tensor flow backend. In addition, random RGB images from the "ImageNet dataset" have been used for training the proposed model (About 45000 images of size (256x256)). The proposed model has been trained by CNN using random images taken from the database of ImageNet and can work on images taken from a wide range of sources. By saving space on an image by removing redundant areas, the quantity of hidden data can be raised (improve capacity). Since the weights and model architecture are randomized, the actual method in which the network will hide the data can't be known to anyone who does not have the weights. Furthermore, additional block-shuffling is incorporated as an encryption method to improved security; also, the image enhancement methods are used to improving the output quality. From results, the proposed method has achieved high-security level, high embedding capacity. In addition, the result approves that the system achieves good results in visibility and attacks, in which the proposed method successfully tricks observer and the steganalysis program.