Reviewerly/exHarmony huggingface dataset:
Efektifitas Penggunaan Buku Cerita Bergambar terhadap Kecerdasan Visual Anak Prasekolah

Multiple intelligences must be optimally stimulated. Its development will be very good for the development of early childhood abilities. This research will discuss the effectiveness of the use of picture books on preschool children's visual intelligence. The research aims to look for the effectiveness of the use of picture books on children's visual intelligence aged of 4-5 years. This study used quasi-experiment method. The subjects were children of TK-KB ABA Pringwulung, Yogyakarta. The sampling technique uses purposive sampling, including two groups, A1 as the experimental group and A2 as the control group. The subjects used in this study examined 40 children. The data analysis technique used a t-test using the SPSS for Windows version 16 computer program to see children's visual intelligence between the two cohorts. The results indicated that there was a significant impact on children's visual intelligence through the use of picture book media in group A at TK-KB ABA Pringwulung Yogyakarta. It can be used by parents, educators, and educational practitioners so as to build an appropriate strategy in developing early childhood visual intelligence.
Design of Airport Obstacle-Free Zone Monitoring UAV System Based on Computer Vision

In recent years, a rising number of incidents between Unmanned Aerial Vehicles (UAVs) and planes have been reported at airports and airfields. A design scheme for an airport obstacle-free zone monitoring UAV system based on computer vision is proposed. The system integrates the functions of identification, tracking, and expelling and is mainly used for low-cost control of balloon airborne objects and small aircrafts. First, a quadcopter dynamic model and 2-Degrees of Freedom (2-DOF) Pan/Tilt/Zoom (PTZ) model are analyzed, and an attitude back-stepping controller based on disturbance compensation is designed. Second, a low and slow small-target self-identification and tracking technology is constructed against a complex environment. Based on the You Only Look Once (YOLO) and Kernel Correlation Filter (KCF) algorithms, an autonomous target recognition and high-speed tracking plan with great robustness and high reliability is designed. Third, a PTZ controller and automatic aiming strategy based on Anti-Windup Proportional Integral Derivative (PID) algorithm is designed, and a simplified, automatic-aiming expelling device, the environmentally friendly gel ball blaster, which features high speed and high accuracy, is built. The feasibility and stability of the project can be verified through prototype experiments.
Determining Effective Subjects Online Learning (Study and Examination) with Multi-Attribute Utility Theory (MAUT) Method

The Corona pandemic in Indonesia forced the learning system into a drastic change into online learning. Many campuses that were previously quite comfortable with face-to-face learning were forced to be helpless because they had never prepared a backup plan when something unexpected happened, one of which was when the campus was forced to not be able to face-to-face. If the learning system continues to be done online, it might cause the quality of learners to decrease dramatically compared to face-to-face learning. Moreover, the courses that must be filtered include theories and practice courses. Most assignments given to students are not seriously done because of a lack of significant evaluation and supervision. The Faculty of Computer Science, Santo Thomas Catholic University, Medan supports this government policy by implementing online learning using the Zoom application for face-to-face and Edmodo for supplementary lecture material for online learning media. In one semester apart from studying there is a test period which is the Midterm Examination (UTS) and Final Examination Semester (UAS) where the current leadership must wisely determine the type and nature of the exams to be conducted online. To find out the effectiveness of the form of exam questions in online implementation, a Multi-Attribute Utility Theory (MAUT) method was conducted, the test results found that online learning with Zoom and Edmodo was very effective for theoretical courses with a value of 0.88, then the results of this calculation it is recommended that online tests be conducted in the form of theories such as multiple-choice, essay and analysis
Improving English Language Speaking Skills Using “Absyak” On-Line Learning Model for Second Semester in Higher Education

This study aims to improve students' speaking skills. The research sample consisted of 30 students (20 female students and 8 male students) all of whom were studying English, the majority of the sample were second semester students, the form of this study was to analyze (1) teaching and learning plans to speak through the task of information gaps that exist in "Absyak" on-line learning media websites, (2) the implementation of teaching speaking through structured tasks and information gaps with "absyak" on-line learning media websites, (3) teaching teaching evaluation systems through the task gap "absyak" information media website-based online learning, (4) improvement of student speaking using the information gap task on "absyak" website-based on-line learning. This is a classroom action research that takes place in 2 cycles. The subjects of the study were the second semester students of Surabaya Pharmacy Academy, Regular Class A consisting of 30 students in the academic year 2019/2020. Data collected by tests, observation notes and documentation. Data were analyzed descriptively qualitatively. The results showed that learning that was designed using the information gap technique with "absyak". Website-based online learning with cycle procedures. Cycle I results are still not optimal, then continue to the second cycle. In cycle II the results are maximum. Student activities increase every cycle II for learning, student activity, performance indicators, learning processes, completeness scores increase, decrease the number of unfinished and success indicators. Based on the results of research 11 which has been conducted 11 with 1 application of information gap techniques and "absyak" on-line learning website in learning Speaking in the second semester students of the Surabaya pharmacy academy that there is a significant increase in speaking skills from the results of learning achievements of cycle I and cycle II. This means that the students' speaking skills from the stages are increasingly increasing and succeeding well.
Mean-removed product quantization for large-scale image retrieval

Product quantization (PQ) and its variations are popular and attractive in approximating nearest neighbor search (ANN) due to their lower memory usage and faster retrieval speed. PQ belongs to a divide-and-conquer algorithm, which decomposes the high-dimensional vector space into disjoint low-dimensional subspaces and employs k-means on them. However, when there is large variance in the average amplitude of the components of the data points, directly utilizing PQ on the data points would result in poor performance. To the end, we propose a novel approach, namely, mean-removed product quantization (MRPQ) to address this issue in this paper. In fact, the average amplitude of a data point or the mean of a data point can be regarded as statistically independent on its residuals. Thus we can learn a separate scalar quantizer of the means of the data points and apply the PQ to their residuals. It is worth noting that our approach can achieve substantial improvements in terms of Recall and MAP over some known methods as shown in our comprehensive experiments. Moreover, our approach is general which can be combined with PQ and its variations. Due to the unbalanced variances of the means among different subspaces, we have developed an adaptive subspace mean-removed product quantization (ASMRPQ) to achieve better performance.
PENGEMBANGAN INSTRUMEN LITERASI DIGITAL SEKOLAH, SISWA DAN GURU

This study aims to develop digital literacy instruments for schools, teachers and students whose function is to measure the extent of the digital literacy levels of schools, teachers, and students. It is not possible for a school to follow the development of science and technology if the school's potential is inadequate and its human resources are not ready. Therefore, the development of digital literacy instruments needs to be made in order to identify the level of readiness of schools, teachers and students in facing the digital era in the industrial revolution 4.0. Subjects of the study were 90 respondents consisting of 30 teachers and 60 students in SMA Negeri 14 Pekanbaru. The questionnaire that was made was first validated by 2 expert validators, after being declared valid by the expert validator, then the questionnaire was conducted a limited test to calculate the level of validity and reliability of each question item. The questionnaire uses a Likert scale with 3 categories of answer choices (often, rarely, never or there is, is being planned, no, etc.). Based on the results and conclusions, conclusions can be drawn, namely: the items that can be used as data collection tools on the school digital literacy questionnaire instrument are 23 questions, while the items that can be used as data collection tools on the teacher's digital literacy questionnaire are 52 items and instrument digital student literacy questionnaire as many as 31 questions. Based on the reliability criteria, the three digital literacy questionnaires show high and very high reliability criteria with a reliability level greater than 0.684.
Effort Estimation Approach through Extracting Use Cases via Informal Requirement Specifications

Sometimes unclearly describing the requirement specifications of satisfied customer’s needs, means it may be difficult to develop the production of high-quality software systems. A persistent issue of requirement engineering is how to clearly understand the requirements of the large and complex software project, and also how to analyze them exactly. To solve this problem, we propose a linguistic analysis method based on the semantic analysis of the Fillmore’s textual approach. This method extracts use-cases from informal requirement specifications. For applied requirement engineering with this method, we suggest extracting a use-case diagram, as well as calculating the software effort estimation with the original use-case point (UCP). To simply explanations of our use-case extraction method, we use one example of a simple postal information system.
Border following–based reversible watermarking algorithm for images with resistance to histogram overflowing

Histogram shifting is an effective manner to achieve reversible watermarking, which works by shifting pixels between the peak point and its nearest zero point in histogram to make room for watermark embedding. However, once zero point is absent, the algorithm suffers from overflowing problem. Even though some works attempt to deal with this risk by introducing auxiliary information, such as a location map, they occupy a lot of embedding capacity inevitably. In this article, in order to deal with overflowing problem efficiently, we propose a border following–based reversible watermarking algorithm for images. With the help of border following algorithm and pre-processing, available regions with at least one zero point are recognized to embed watermark so that auxiliary information is not needed any more. And the algorithm utilized also ensures the same border can be re-recognized from the watermarked image without error, thus the correctness is also guaranteed. The performance of the proposed algorithm is evaluated using classic image datasets in this area, and the results not only validate the effectiveness of the proposed algorithm but also indicate its advantages compared with the classic histogram shifting–based reversible watermarking algorithm as well as the state of the art.
Local Differential Privacy Protection of High-Dimensional Perceptual Data by the Refined Bayes Network

Although the Crowd-Sensing perception system brings great data value to people through the release and analysis of high-dimensional perception data, it causes great hidden danger to the privacy of participants in the meantime. Currently, various privacy protection methods based on differential privacy have been proposed, but most of them cannot simultaneously solve the complex attribute association problem between high-dimensional perception data and the privacy threat problems from untrustworthy servers. To address this problem, we put forward a local privacy protection based on Bayes network for high-dimensional perceptual data in this paper. This mechanism realizes the local data protection of the users at the very beginning, eliminates the possibility of other parties directly accessing the user's original data, and fundamentally protects the user's data privacy. During this process, after receiving the data of the user's local privacy protection, the perception server recognizes the dimensional correlation of the high-dimensional data based on the Bayes network, divides the high-dimensional data attribute set into multiple relatively independent low-dimensional attribute sets, and then sequentially synthesizes the new dataset. It can effectively retain the attribute dimension correlation of the original perception data, and ensure that the synthetic dataset and the original dataset have as similar statistical characteristics as possible. To verify its effectiveness, we conduct a multitude of simulation experiments. Results have shown that the synthetic data of this mechanism under the effective local privacy protection has relatively high data utility.
Solving the missing cone problem by deep learning

<i>Advanced Photonics</i>, co-published by SPIE and Chinese Laser Press, is a highly selective, Gold Open Access, international journal publishing innovative research in all areas of optics and photonics, including fundamental and applied research.
Tracking down coronavirus

What is it like to be a covid-19 contact tracer and what do they do?
Defeating State-of-the-Art White-Box Countermeasures with Advanced Gray-Box Attacks

The goal of white-box cryptography is to protect secret keys embedded in a cryptographic software deployed in an untrusted environment. In this article, we revisit state-of-the-art countermeasures employed in white-box cryptography, and we discuss possible ways to combine them. Then we analyze the different gray-box attack paths and study their performances in terms of required traces and computation time. Afterward, we propose a new paradigm for the gray-box attack against white-box cryptography, which exploits the data-dependency of the target implementation. We demonstrate that our approach provides substantial complexity improvements over the existing attacks. Finally, we showcase this new technique by breaking the three winning AES-128 white-box implementations from WhibOx 2019 white-box cryptography competition.
Speech Act Analysis of Whatsapp Statuses Used by Jordanians

This qualitative study aims at investigating the WhatsApp statuses as used by Jordanians. It also investigates the types of speech acts used in these statuses. For this purpose, the study has collected and analyzed 200 statuses. The population of the study included all English language students of Jadadra University, where the sample of the study included (50) students, representing 20 % of the whole population. The results showed that data were classified into six main topics; religious, social, political, personal, romantic and national. Additionally, five themes emerged from the data, namely, expressive, directive, assertive, commissive and declaration. Expressive speech acts represent (37 %) of the total speech acts types analyzed. The directive took the second place, representing (25%) of the total status update analyzed. The assertive and commisive fall into the third and fourth position representing (23%) and (15%) respectively. The declarative type has the no occurrences representing (0 %) of the analyzed data. Some of the recommendations suggested are that further research needs to be conducted into the speech acts used by Jordanians on different social networking platforms.
ВЕБ ОРІЄНТОВАНА СИСТЕМА ДОСТУПУ ДО ВІДДАЛЕНОГО РОБОЧОГО СТОЛУ ТА ПРОГРАМНОГО КОМПЛЕКСУ GRAN У ПРОЦЕСІ НАВЧАННЯ МАТЕМАТИКИ В ШКОЛІ

The article deals with Gran software and the access to it through the remote desktop. The peculiarities of web-based remote desktop access and Gran software system for teaching mathematics at school are revealed. The paper also describes the application of open source software (Ulteo OVD) that allows users to remotely access their desktops and applications using one of the Desktop as a Service cloud components through a client device browser. The modes of using Ulteo OVD client software are described. Two servers (Application Server and Session Manager Server) have been organized to use Ulteo OVD by means of PROXMOX web-based virtual environment. The authors propose the way to use Ulteo OVD to access software installed on a remote server, in particular Gran software. The article also describes some examples of graphical problem solving using Gran1 that can be used in maths at school. The considered issues are quite complex and rather time-consuming without Gran1 software for graphical analysis of various tasks and approximate calculation of numerical measures of different objects (lengths of surfaces, areas of surfaces, volumes of bodies, probabilities of occurrence of different events, statistical analysis of observational data) of their solving. The range of tasks that can be solved with the use of the Gran software package, in particular Gran1 programme, is quite broad. By applying a creative approach, their analysis and solution can have a positive effect on the learners’ mental and cultural development. It is emphasized that the use of various technologies in educational process, as well as the selection of tasks and the content of training in general, should be pedagogically weighted, based on the harmonious combination of pedagogical heritage of the past and modern achievements of scientific and technological progress.
Simulation and Experimental Verification of Electrical Power Output of a Microcontroller Based Solar Tracking Photovoltaic Module

The study evaluated the performance of a microcontroller based solar tracking PV system. The system utilized a stepper motor, two light dependent resistors and a worm gear to intelligently control the rotation of the photovoltaic modules such that it automatically provides the best alignment of the solar panel with the sun for maximum power output. The performance of the PV system was evaluated through simulation and experiment. The effect of different levels of solar irradiance and cell temperature was investigated on the power output of the solar tracking PV system. An increase in maximum power output (Pmax, m), actual light generated current (Ipv) and actual short circuit current (Isc) has been observed with the increase in solar irradiance. With the decreasing cell temperature, the power produced decreases. The percentage relative error between the optimum simulated and the experimental values for Pmax and Ipv was found below 5%, indicating that the simulation model found good agreement with the experimental values. The results are expected to provide insights into the interaction between weather data (irradiance and temperature) and PV module power output during conversion of solar energy into electricity.
The Development of Digital Flipbook Media Based on the 5 Hours Battle of Kalianda upon High School History Materials

The purpose of this study is to develop a digital flipbook learning media by integrating the local history material of ‘The 5 Hours Battle of Kalianda’ in one of the high schools in Kalianda City. This study uses research and development (R&amp;D) as the research methods. The stages of the research include: the stages of analysis, design, development, implementation, and evaluation. The subjects in this study consisted of two material and two media experts as validator, 40 eleventh-graders students, and a history teacher. The results of the study showed that the Flipbook digital media based on ‘The 5 Hours Battle of Kalianda’ had proven to be valid and feasible according to the material and media expert assessments, while the results of small group trials, limited group trials, and extensive trials indicated digital Flipbook learning media based on ‘The 5 Hours Battle of Kalianda’ was proven to be effective and suited with the needs analysis.
New Emoji Requests from Twitter Users

As emojis become prevalent in personal communications, people are always looking for new, interesting emojis to express emotions, show attitudes, or simply visualize texts. In this study, we collected more than 30 million tweets mentioning the word emoji in a 1-year period to study emoji requests on Twitter. First, we filtered out bot-generated tweets and extracted emoji requests from the raw tweets using a comprehensive list of linguistic patterns. To our surprise, some extant emojis, such as fire 🔥 and hijab 🧕, were still frequently requested by many users. A large number of non-existing emojis were also requested, which were classified into one of eight emoji categories by Unicode Standard. We then examined patterns of new emoji requests by exploring their time, location, and context. Eagerness and frustration of not having these emojis were evidenced by our sentiment analysis, and we summarize users’ advocacy channels. Focusing on typical patterns of co-mentioned emojis, we also identified expressions of equity, diversity, and fairness issues due to unreleased but expected emojis, and we summarized the significance of new emojis on society. Finally, time-continuity-sensitive strategies at multiple time granularity levels were proposed to rank petitioned emojis by the eagerness, and a real-time monitoring system to track new emoji requests was implemented. To the best of our knowledge, the proposed tracking system is the first to rank the new desired emojis on a large scale and in a real-time manner.
Deep Neural Networks Combined with STN for Multi-Oriented Text Detection and Recognition

Developing systems for interpreting visuals, such as images, videos is really challenging but important task to be developed and applied on benchmark datasets.This study solves the very challenge by using STN-OCR model consisting of deep neural networks (DNN) and Spatial Transformer Networks (STNs).The network architecture of this study consists of two stages: localization network and recognition network.In the localization network it finds and localizes text regions and generates sampling grid.Whereas, in the recognition network, text regions will be input and then this network learns to recognize text including low resolution, curved and multioriented text.Deep learning-based approaches require a lot of data for training effectively, therefore, this study has used two benchmark datasets, Street View House Numbers (SVHN) and International Conference on Document Analysis and Recognition (ICDAR) 2015 to evaluate the system.The STN-OCR model achieves better results than literature on these datasets.