Reviewerly/exHarmony huggingface dataset:
AN ANALYSIS TEACHER’S MISTAKES IN TEACHING ENGLISH AT KINDERGARTEN AR-ROIHAN LAWANG

In Indonesia, English is taught even in the most basic educational institutions, that is Kindergarten. But, most of the Kindergarten teacher did not graduate from English department, so the English they taught often contain the mistakes. This study aimed to identify the mistakes that was made by the teacher during teaching and learning English. The designed of this study is descriptive-qualitative research. The data of this study was collected from the sentences containing mistakes spoken by the teacher in teaching and learning activity at Kindergarten Ar-Roihan Lawang. The result of this study showed that the teacher did some mistakes when she taught English in the class. The researcher found 32 mistakes made by the teacher. The most mistakes made by the teacher is structure of the sentences with 18 mistakes, the second is the plural of nouns with 8 mistakes, and the last is tenses with 6 mistakes.
Monomial Evaluation of Polynomial Functions Protected by Threshold Implementations

In the context of side-channel countermeasures, threshold implementations (TI) have been introduced in 2006 by Nikova et al. to defeat attacks in presence of hardware effects called glitches. On several aspects, TI may be seen as an extension of another classical side-channel countermeasure, called masking, which is essentially based on the sharing of any internal state of the processing into independent parts (or shares). Among the properties of TI, uniform distribution of input and output shares is generally the most complicated to satisfy. Usually, this property is achieved by generating fresh randomness throughout the execution of the protected algorithm (e.g. the AES block cipher). In this paper, we combine the changing of the guards technique published by Daemen at CHES 2017 (which reduces the need for fresh randomness) with the work of Genelle et al. at CHES 2011 (which combines Boolean masking and multiplicative one) to propose a new TI without fresh randomness well suited to Substitution-Permutation Networks. As an illustration, we develop our proposal for the AES block cipher, and more specifically its non-linear part implemented thanks to a field inversion. In this particular context, we argue that our proposal is a valuable alternative to the state of the art solutions. More generally, it has the advantage of being easily applicable to the evaluation of any polynomial function, which was usually not the case of previous solutions.
MOTIVES AND MOTIVATION OF STUDENTS OF HIGHER EDUCATIONAL INSTITUTIONS TO RESEARCH ACTIVITY AND WAYS OF ITS INCREASE

This article examines and summarizes the views of the authors involved in the study of student participation in research activities. The concept of "motivation for research activity" is specified, the content of scientific research as a special type of intellectual activity of a person is characterized. Further, the authors on the basis of the method of interviewing collected, analyzed and summarized the material, which allowed to identify the motives that encourage students of higher educational institutions to participate in activities of a scientific and research nature. The main motives were defined: the remunerations received by the student as a result of participation in scientific research; the desire to realize the existing abilities for scientific activity; disclosure of other abilities or satisfaction of other needs for which research activity is a means to achieve goals. Also, the authors identified the lack of interest on the part of students to research activities, formulated and justified the reasons for the lack of motivation of students to this activity. According to the results of the study, recommendations were developed, the introduction of which in the educational process will increase the motivation of students to research activities carried out independently or under the guidance of experienced teachers and researchers of higher educational institutions.
A Partially Pre-Trained Dense Network for Image Super-Resolution

In recent years, convolutional neural networks (CNN) have made remarkable achievements in image super-resolution (SR). But there are two problems with the networks that limit their performance. Firstly, the tasks for SR neural networks are not specific enough. Current works require the neural networks to complete the whole SR task by supervised learning, which makes the networks waste their modeling ability on low-level SR tasks. Secondly, the existing networks do not take full advantage of the features of convolution block extraction, nor have they established complete information links. In view of the problems above, this paper puts forward Partially Pre- Trained Dense Network for Image Super-Resolution (PTDSR). PTDSR first pre-trains a miniature super-resolution network, which requires very little computation. Then PTDSR fixes the parameters of the mini-network and uses a large-scale CNN to learn the residual part of SR image. This training strategy will enable the core CNN to concentrate on high-level tasks and achieve better results. PTDSR also links all features extracted by convolution blocks and fully utilize the feature information. Experiment results on benchmark data sets show that the results of PTDSR are better than those of state-of-the-art methods.
Eco-cultural approach integrating Islamic values with artha wantilan concept on the fish market design in Kedonganan Bali, Indonesia

Bali is an island that has many coastal areas, one of which is Kedonganan and there is a fish market that is quite famous among the surrounding population and the tourists. This study aims to re-design the Fish Market with an eco-cultural approach. This design approach considers the continuation of nature and also local traditional Balinese Architecture. However, there are several values in Hinduism that contradict the values of the Islamic religion. The values of the Islamic integration argument are used to examine aspects adapted to the artha wantilan concept based on the characteristics of Bale Wantilan so that its application in the design of the fish market still contains elements of Balinese architecture and remains integrated with Islamic values. The used method is descriptive qualitative, by collecting the literature review, examining several aspects related to the object of research, and applying the results of the study in object design. So that the results of this study can know the shape of the roof, building body and foot of the building, and ornaments that are applied and have been adapted to the needs of the fish market building and also integrated Islamic values.
АВТОМАТИЗОВАНА СИСТЕМА ЗАХИСТУ ПРОГРАМНОГО ЗАБЕЗПЕЧЕННЯ ДЛЯ ОПЕРАЦІЙНОЇ СИСТЕМИ ANDROID

The article pays the attention to the authentication of data on mobile devices running the Android operation system. We use smartphones and applications which are installed on them, every day. We can use personal information in some applications, that we do not wish to disclose. Such built-in user authentication mechanisms as unlocking with a PIN code, password, fingerprint, or with the new Face ID technology are most commonly used. Most often, smartphone users, and not only use the same password to access different sites, authenticate a user to the device, rarely they use two different passwords, and very rarely use different passwords for different authentication systems. These systems are well protected from computer hacking, but not from humans. What does it mean? At the time of authentication, another person may be present, and he can look at the password (it's called the shoulder surfing) and remember it after that. That's why it has been decided to create an authentication that would be difficult to remember and would confuse the user who wanted to gain unauthorized access to personal data. The article introduces the implementation of Android application lock, which is an improved version of the usual use of passwords, using a change in the way details are introduced. That is, instead of the user seeing the usual input field every time, the user will be able to enter a combination from a specially created table, whose order of elements will be changed every time, thus not allowing easy password reproduction. Using this method of blocking apps, you can be sure that nobody in your environment, or just random people who wanted to know your sensitive information when you have just left your smartphone, would be able to access by simple typing of easy password right away. In addition, the testing has provided valid results regarding the reliability of the developed authentication method.
Rabbit Type Classification Using Multi-SVM Based on Feature Extraction

Rabbits reputation of being cute, fluffy, cuddly critters lend then to being a popular choice for children pets. But in raising a rabbit is not easy, this depends on the type of rabbit. Rabbits that commonly pet are Rex Rabbits, American Rabbits, and Giant Rabbits. Rex Rabbits itself has some species including Rex Amber and Rex Lilac species, Giant Rabbits rabbits includes Giant Chekered rabbit, Harleyquin, dan American White Rabbit. Classification technology can be used to help the classification process of rabbits are Multi-SVM method and image feature extraction to classify rabbit species. Feature extraction used in this study is mean, variance, skewness, kurtosis, entropy. The five features are classified with Multi-SVM. The data used in this study are 125 images, consisting of 100 training images and 25 test images. The accuracy of this method reached 92%. Keywords – Classification, Multi-SVM, Rabbit, Feature Extraction
Peningkatkan Penguasaan Kosakata Bahasa Inggris Melalui Metode VCD Pada Siswa Kelas II di SDN Rorotan 03 Pagi Kelurahan Rorotan Cilincing Jakarta Utara

The purpose of this study was to improve the mastery of English vocabulary through the VCD media method on class II students in SD N Rorotan 03 Morning Village in Rorotan Sub-District, Cilincing District, North Jakarta. This research was conducted on 32 year old children. This study was an action research with Kemmis and Taggart models consisting of 4 stages namely (plan, action, observation and reflection). This study consisted of 2 cycles, with each cycle as many as four meetings. Data collection techniques are using observation, field notes, and documentation. Data analysis uses quantitative and qualitative. Quantitative data analysis is carried out with statistical descriptions to compare pre cycles to the second cycle. The stages of qualitative analysis are data reduction, display data and verification. The results showed that there was an increase in English vocabulary mastery through the VCD media method. Theresults of the study quantitatively addressed the learning outcomes of vocabulary improvement in 32 children with an average of 58. 53% so that the results had not reached KKM, then continued in the first cycle to experience the average vocabulary ability is 71. 31% and then in cycle II the average vocabulary is 84.06%.&#x0D; Keywords: Mastery of vocabulary English words and VCD methods
CONDUCTING ANOVAS WITHOUT DATA

We present a simple method for conducting factorial ANOVAs in the absence of data.The method relies on descriptive statistics, namely the mean, variance, and sample size for each cell in the design.We briefly describe how this method can easily generalized to any number of factors, allowing us to analyze n-way factorial ANOVAs with any number of interactions.We then introduce the idea that this method allows us to (a) perform ANOVAs from existing studies that did not themselves perform ANOVAs and (b) combine descriptives from multiple studies in order to cumulate them into a sort of meta-analytic ANOVA.
Survey on Secure and Efficient Content-Based Retrieval in Cloud Image Repositories

Many cloud stages ascend to meet sincere prerequisites for broad volume of individual picture store, sharing and search. Truth be told the vast majority of those pictures contain delicate secure information (e.g., Human, territory and any event) and people's insurance concerns ruin their venture into untrusted administrations, the present cloud stages give little help to picture security confirmation that is security assurance. Confronting extensive scale images from numerous clients i.e. user's, it is extremely challenging for the cloud to keep up the record structure and schedule parallel calculation without learning up anything about the image contents and indices. In this work, present a novel that is Privacy-protecting Content based Image Search framework on Cloud, or, in other words towards possible cloud services which give secure content based extensive scale image search with fine-grained access control using Shape-Based Invariant Texture Index (SITI). Users can search on others' images on the off chance that they are approved by the image owners. Larger part of the computationally serious part is handled by the cloud, and a querier can now simply send the query and get the search outcome. Specially, to manage enormous images, will designed our framework suitable for distributed and parallel calculation and introduce several optimizations with further facilitate the search procedure. Our security examination and model system assessment results will exhibit that this work adequately verifies the picture assurance expecting practically no exertion of computation and correspondence.
MemNetAR: Memory Network with Adversative Relation for Target-Level Sentiment Classification

Target-level sentiment classification aims to identify the sentiment of multiple targets in a sentence. Although existing approaches based on neural network have achieved good performance in this task, we find that many approaches tend to give the same predictions for instances that have multiple targets, and this tendency can make a low accuracy for those instances that have different classes for different targets. Based on this observation, we propose MemNetAR, a memory network which can explicitly leverage the adversative relation among multiple targets in a sentence. Specifically, we add an adversative loss to the cross-entropy loss when there are adversative words between targets. The experimental results on public laptop and restaurant datasets prove that our model can improve 0.84% and 0.8% on total test dataset, and improve 2.97% and 2.68% on the dataset consisting of those instances with multiple targets but different classes by leveraging this new adversative information.
Evelyn Simanjuntak, Wahjuningsih Usadiati, Elanneri Karani

AbstractTeachers are required to have good reading material with various questions excercicesto meet students’ interest to improve their reading ability. This developmental research wasundertaken to develop a model of instructional material of HOTs questions for narrativereading texts for IX grade students of SMPN 2 Jorong. The development of the model in R &amp;D research involved the following steps: needs assessment, reference study, materialdevelopment, expert validation, tryout, revision, and final product. The subject of theresearch was the students of grade IX of SMPN 2 Jorong. The instruments to take the datawere observation sheet, questionaire sheet, students’ test result, and test sheet. After all of thesteps were done, the final product was in form of instructional material consisting of: Cover;Introduction, and Content. The content of the product consisted of Unit 1 and Unit 2, whichcontained narrative reading texts entitled Hansel and Grethel, Malin Kundang, and Rapunzeltaken and modified from the internet. Each story was inserted with HOTS questions to ask thestudents to generalise, analyse, compare, distinguish, and to correlate.Based on the result ofthe expert validation, the model of HOTS questions for narrative reading texts for IX gradestudents of SMPN 2 Jorong was very effective and advisable to be implemented.Key words: HOTs questions, narrative text
Teaching Students to Use the Gauss Method for Integer Matrices when Implemented on a Computer

The paper is written on the basis of a part of “Analysis of algorithms” course for students of the Computer science department of the Division of mathematics and mechanics of Saint Petersburg State University. The example of the computer implementation of the Gauss method illustrates the difference between the algebraic complexity (the number of arithmetic operations) of processing integers and the computational complexity which depends on the length of the input data. A formula which specifies the increase in the length of matrix coefficients, along with the implementation the Gauss method, is proved. The problems arising in the processing of large integers associated with “chopping” numbers are shown. To overcome the indicated problems, the possibility of using multi-valued integers is proposed. The upper bounds of the number of steps for processing the multivalued integers is shown to coincide with such bounds for a multi-tape Turing machine.
Can Students’ Smartphones Replace Data-show Inside the Classroom? Action Research (AR)

Due to the shortage of adequate data-show and projectors at the Public Authority for Applied Education and Training (PAAET) in Kuwait, smartphones and their accompanying apps provide a platform which, if exploited appropriately, could benefit both teachers and students. This research focuses on the use of smartphones as a technological alternative to inadequate data-show and projectors. Furthermore, the use of smartphones as a platform for pursuing collaborative work amongst pre-service teachers is explored here. For this purpose, action research (AR) was applied as a methodology, with several different approaches and technologies being adopted by the teacher and participants, i.e. collaborative learning (CL); group discussion; lectures; relevant video material (via personal data-show and smartphones); a summative activity through ClassMarker; an e-survey on SurveyMonkey, and finally, WhatsApp to connect participants with each other outside the lecture hall. Text messages from WhatsApp were analysed using interaction analysis (IA). In addition, two methods of data collection and analysis were applied, namely participant observation and an e-survey. Within AR, these allow for the ongoing and immediate analysis of data in recurring cycles of planning, data-gathering, evaluation and action.
TRAINING PROBABILISTIC NEURAL NETWORKS ON THE SINGLE CLASS PATTERN MATRIX AND ON CONCATENATION OF PATTERN MATRICES

A possibility to optimize the probabilistic neural network is studied by building an efficient training set. Commonly, the training dataset for a probabilistic neural network is a matrix whose columns represent classes. If every class has only one column, the matrix is called the single class pattern matrix. However, the simple architecture of the probabilistic neural network does not imply that the class pattern must be single. First, the range of values for a class feature can be too wide. Then it is desirable to break it into subranges, each of which will give its own average and thus a few patterns for this class will be formed. Second, a class feature can have a finite number of its values, where every value has the same importance. Then it would be incorrect to calculate an average and use it in the respective single class pattern. Thus, it is studied whether concatenation of pattern matrices into a long pattern matrix is reasonable. In fact, the goal of the research is to ascertain whether it is efficient to build probabilistic neural networks on long pattern matrices. The criterion of the efficiency is performance of the probabilistic neural network, i. e. either its accuracy or percentage of errors. To achieve the goal, performance of the probabilistic neural network is estimated on the case when the class is described by a few class patterns. The probabilistic neural networks are then tested for the two subcases: when objects generated by different class patterns are fed to the input, and when objects are generated by a generalized single class pattern. Eventually, it is ascertained that training probabilistic neural networks on the single class pattern matrix (obtained either by averaging over the available pattern matrices or just by using one pattern per class) is more efficient when the objects to be classified do not inherit any class pattern numerical properties. On the contrary, when the objects to be classified may have some distinct numerical properties of a few class patterns, then training probabilistic neural networks on long pattern matrices is more efficient ensuring noticeably higher accuracy. The smooth training method appears inefficient in improving the performance.
A Study of Treatment of Lizard Phobia Using Visible and Invisible Marker-based AR Application

Augmented reality refers to the combination of computer-generated objects with real world environment. This technology is empowered by the evolution of hardware and software in the past decade. Virtual objects are placed through device screens in a user’s environment relying on the device camera and capability of graphics hardware. This advancement opens a wide horizon for application development in the field of entertainment, education, medical, etc.&#x0D; One of the main advantages of augmented reality (AR) is in the treatment of various phobias though exposure treatment. A phobia is characterized by irrational fear or reaction to a normal situation. Thus, gradually exposing patients to their phobias helps in reducing their uncontrolled reaction and aids in reducing their fear of their respective phobia. Herpetophobia is the extreme fear from aversion reptiles such as lizards. The lizard phobia is a common form of phobia amongst people.&#x0D; In this article, an application of lizard phobia exposure treatment is proposed to help patients. The proposed application relies on marker-based AR. This is implemented using Visible marker AR system (VMARS) and Invisible marker AR system (IMARS), comparing the results of both systems to a group a subject to identify the best approach for Herpetophobia treatment.
Pixel-based Character Image Compression for Data Transfer from ARM Controller to FPGA System

This paper proposes a pixel-based compression algorithm for character digital image in improving the storage of characters in memory during system operation.In particular, in this algorithm, each character binary image in text is grouped by binary numbers and then encoded to reduce the character image capacity of the character compared to the original character.In addition, a novel point in this algorithm is that one character image type is differently grouped binary numbers for compressing.Therefore, the compressed character image is stored in a memory using an ARM microcontroller system and transferred to an FPGA module for decoding before printing.Moreover, the compression ratio of each character is high or low depending on the font type of image characters.Therefore, the high compression ratio using this compression algorithm will allow saving memory space in the memory system.Simulation results show to illustrate the effectiveness of the proposed algorithm and also this compression algorithm was implemented to texts with characters for encoder data transfer from an ARM microcontroller into an FPGA system for effectively printing the text/logo/barcode/QR code/expired date on products with high speed after decoding.Moreover, this compression algorithm can be developed to apply to many different font types and sizes, as well as be utilized different microcontrollers/Microprocessors connected to FPGA systems for processing with high speed.It means that one industrial system using this algorithm can obtain very high performance related to processing digital image characters.