Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN DESAIN MEDIA PEMBELAJARAN BERBASIS KATALOG PENINGGALAN SEJARAH LOKAL UNTUK MENGUATKAN PEMAHAMAN SEJARAH LOKAL SISWA DI SMA NEGERI 3 MENGGALA TULANG BAWANG

The purpose of this research is to find the media of Learning Media catalog which can give information briefly, regularly and clearly about the material of history of war against colonialism, the time of arrival process and the power of VOC, and the impact of Dutch government in class XI, and in class X the impact in spreading Islam and also aims to strengthen the understanding of local history of the students. This type of research is a Level 1 development which is where in the preparation of this medium with the development method Sugiyono level 1 which where only test the feasibility of the product with internal validating stage by the team of experts not to the stage test try to students, with the preparation must also be adjusted with the analysis KI, KD and the subject matter, after the drafting process is completed then the next stage can be validated internally by a team of material and media experts.Based on the results of research, has been obtained instructional media based local historical heritage in Menggala Tulang Bawang which has been through the validation stage, validation is a media feasibility test developed, validation is through two stages of stage 1 and 2, which of course there are suggestions and comments in order to revise the product back up to the criteria worth at least score 81 and from the data obtained on the validation of material and media experts there is an increase in scores at stages 1 and 2, in stage 1 the material got a percentage of 78% and in stage 2 with the value 88% percentage. While the validation of media experts in stage 1 got the percentage of 80% and in stage 2 got a percentage of 94%. From these data can already be declared feasible tested
The effects of income and inflation on financial development: evidence from heterogeneous panels

This paper examines four unresolved issues regarding the effects of GDP and inflation on financial development: (i) Does GDP have uniform impact on financial development in heterogeneous income countries? (ii) Is the relationship non-linear? (iii) Does financial development vary with inflation rates? (iv) Does inflation moderate the effect of GDP on financial development? The authors employ the newly developed dynamic Common Correlated Effects (CCE) and dynamic panel system Generalized Method of Moments (GMM) on data from 125 countries. These techniques enable us to control for crosssectional dependence, heterogeneity and endogeneity. They show that GDP has a positive impact on financial development in high and middle-income countries, and the relationship is non-linear in over 60% of the countries. The authors also reveal that inflation has a negative effect on financial development in high- and medium-inflationary countries. Besides, high inflation moderates the effect of GDP on financial development in over 70% of the countries. They also show the countries where higher GDP is better for financial development and where it is not. They recommend some policy options based on the findings.
Dynamic, Anytime Task and Path Planning for Mobile Robots

The study of combined task and motion planning has mostly been concerned with feasibility planning for high-dimensional, complex manipulation problems. Instead this paper gives its attention to optimal planning for low-dimensional planning problems and introduces the dynamic, anytime task and path planner for mobile robots. The proposed approach adopts a multi-tree extension of the T-RRT* algorithm in the path planning layer and further introduces dynamic and anytime planning components to enable low-level path correction and high-level re-planning capabilities when operating in dynamic or partially-known environments. Evaluation of the planner against existing methods show cost reductions of solution plans while remaining computationally efficient, and simulated deployment of the planner validates the effectiveness of the dynamic, anytime behavior of the proposed approach.
PROBLEMATIKA PEMBELAJARAN BAHASA INDONESIA DI JENJANG SEKOLAH DASAR

Indonesian language is an important language for Indonesian peoples as a differentiator from other nations. Therefore, it is the compulsory lesson in schools for all types and levels of schools in this country. This has become the formal language for citizens but there are students who have difficulty in learning this language. There are many problems in teaching Indonesian language experienced by teachers in schools. This phenomenon requires teachers to think hard to find solutions and try to overcome those problems. This article will examine those problems and offer solutions that can be done to resolve them specifically for elementary school students.
Developing a Bidiretional Mutual Gaze Mechanism for Human Robot Interaction

Establishing mutual gaze is one of the most fundamental capabilities to initiate any interaction in both human-human and human-robot communications. This capability of mutual gaze is particularly important when people collaborate over a shared visual space. This paper proposes a bidirectional mutual gaze mechanism for human-robot communication. It is assumed that look at each other makes the mutual gaze. However, simply looking at each other alone does not ensure mutual gaze. Both agents also need to be aware of being watched by the other. In order to perform mutual gaze effectively, we propose two functions: gaze crossing and gaze awareness. For gaze crossing, the system detects the human face and his/her eye region. The system also tracks the eyeball of the target human by locating the position of eyeball. After gaze crossing, the system detects the smile of human which ensure the gaze awareness. Continuing the flow of communication, the robot performs eye blinking, head nodding and verbal actions in response to human. Several software modules are developed to perform the bidirectional mutual gaze mechanism and evaluate the effectiveness of each module with several experimental trials. Moreover, the system is tested in human initiative and robot initiative mutual gaze cases.
The Role of NonSQL Databases in Big Data

This chapter aims to show the role that NonSQL databases play in the Big Data. It also shows the processing and persistence needs of information in the Big Data. The chapter describes the limitations of relational databases to be used in Big Data solutions. It also describes the main characteristics of the NonSQL databases, and how they solve the storage and information processing needs that arise in the Big Data. The Big Data Operational refers to the set of tools that have been developed to solve some of the technological problems that have arisen and have been commented. In the field of Big Data, the information that needs to be stored can be very diverse, from structured, semi-structured data, to even data without structure. Therefore, the Big Data phenomenon appears due to the business opportunities that arise from having huge amounts of data to be processed and exploited.
Speed-up of SCA Attacks on 32-bit Multiplications

Many crypto-algorithms, Deep-Learning, DSP compute on words larger than 8-bit. SCA attacks can easily be done on Boolean operations like XOR, AND, OR, and substitution operations like s-box, p-box or q-box, as 8-bit hypothesis or less are enough to forge attacks. However, attacking larger hypothesis word increases exponentially required resources: memory and computation power. Considering multiplication, 32-bit operation implies $$2^{32}$$ hypotheses. Then a direct SCA attack cannot be efficiently performed. We propose to perform instead 4 small 8-bit SCA attacks. 32-bit attack complexity is reduced to 8-bit only complexity.
Digital book for assessment and evaluation courses based on Kvisoft-kelase asynchronous pattern

This study was conducted with the aim of obtaining information about the design of assessment and evaluation digital book as well as deployment process of the digital book through asynchronous patterns. This study uses development method with the 4D design which focuses only on define and design phase. Parties involved in conducting design trial consist of two experts namely expert of education evaluation and expert of informatics engineering education, with a trial tool in the form of questionnaires. The technique was used to analyze the trial results data from those experts was descriptive quantitative with descriptive percentages calculation. In this research, the design result of assessment and evaluation digital book with Kvisoft Flipbook Maker and the initial design of deployment with an kelase asynchronous pattern are included in the good category with a percentages of 89.38% based on experts review.
A novel local variance-based filtering method for denoising remote sensing images

In this paper, a novel filtering method is designed for denoising remote sensing image. Firstly, the image domain of noisy image is partitioned into blocks for estimating the variance of Gaussian white noise. Secondly, based on the fact that the variance of the textural region is always larger than that of the homogeneous region, the noisy image is roughly divided into homogeneous and textural regions. Thirdly, a novel filter is designed and is used to reduce the noises. To this end, adaptive windows with appropriate shape and size are selected for each pixel. With the pixels in the window(s), the noise intensity of the central pixel is estimated and further qualified as a noise level. Based on noise levels, pixel values within the filter window are first updated and then filtered by using the proposed filter. Compared with other filtering methods, better performance is achieved in both noise smoothing and detail conserving.
Applying Topic Modeling and Similarity for Predicting Bug Severity in Cross Projects

Recently, software has increased in complexity and been applied in various industrial fields.As a result, the presence of software bugs cannot be avoided.Various bug severity prediction methodologies have been proposed, but their performance needs to be further improved.In this study, we propose a novel technique for bug severity prediction in cross projects such as Eclipse, Mozilla, WireShark, and Xamarin by using topic modeling and similarity (i.e., KL-divergence).First, we construct topic models from bug repositories in cross projects using Latent Dirichlet Allocation (LDA).Then, we find topics in each project that contain the most numerous similar bug reports by using a new bug report.Next, we extract the bug reports belonging to the selected topics and input them to a Naïve Bayes Multinomial (NBM) algorithm.Finally, we predict the bug severity in the new bug report.In order to evaluate the performance of our approach and to verify the difference between cross projects and single project, we compare it with the Naïve Bayes Multinomial approach; the Lamkanfi methodology, which is a well-known bug severity prediction approach; and an emotional similarity-based bug severity prediction approach.Our approach exhibits a better performance than the compared methods.
Comparative study of oboe and clarinet

The oboe and clarinet are relatively similar instruments in their size and shape yet create such different sounds. This study is an exploration of the reasoning behind their differences through a detailed acoustical analysis and geometrical study of these instruments, comparing and contrasting their properties. The oboe and clarinet are comparable in size but have some key differences. The oboe’s conical shape allows for all the harmonics to be present, while the clarinet is mostly cylindrical, except for the bell, so that mostly odd harmonics are present. The oboe’s keys have holes much smaller than those of the clarinet. Looking to find differences in acoustical spectra based on their geometric differences, samples of low, medium, and high ranges on both instruments using the same concert pitches have been taken. Data have been taken in a regular lab setting as well as an anechoic chamber in order to look for differences in harmonic content for each instrument. The data are consistent with the notion that the shape of the instruments does contribute to the spectra.
The musical saw: Musical acoustics of trapped vibrational modes in a curved blade

The musical saw is a popular folk instrument consisting of a flexible hand saw that is bent into an “S"-shape by the performer. When the smooth edge of the saw is bowed, skilled musicians can produce intricate melodies, as well as vibrato and glissando effects, by varying the curvature along the blade. The acoustically significant vibrational modes are trapped near the inflection point between regions of alternating curvature along the saw, exhibiting very low damping as a result of this confinement. Experimental data, taken using electronic speckle-pattern interferometry, illustrate the relevant mode frequencies, shapes, and positions relative to the blade curvature for various configurations of the saw. These data are presented and interpreted along with results from a finite element model of the tapered and curved saw blade.
ViewSeeker: An interactive view recommendation tool

View recommendation has emerged as a powerful tool to assist data analysts in exploring and understanding big data. Existing view recommendation approaches proposed a variety of utility functions in selecting useful views. Even though each utility function might be suitable for specific scenarios, identifying the most appropriate ones along with their tunable parameters, which represent the user’s intention during an exploration, is a challenge for both expert and non-expert users. This paper presents the first attempt towards interactive view recommendation by automatically discovering the most appropriate view utility functions in an exploration based on the user’s preferences. In particular, our proposed ViewSeeker uses a novel active learning method to discover the view utility function by interactively refining the set of k view recommendations. The effectiveness and efficiency of ViewSeeker was experimentally evaluated using both synthetic and real data sets.
Decentralized Food Supply Bazar Using Blockchain

Blockchain is an expanding piece of technology helping us in designing and development of decentralized distributed solutions. It has impacted many industries by reducing intermediate authorities. So, we decided to explore its feasibility in the Food supply industry. The Food Agro sector has been responsible for several catastrophes, and as the industry expands globally it is strenuous to keep check and credence of and certify the food data. The fertile land for agriculture is diminishing and more yields need to be produced to content the need of the country. We propose a Blockchain based Decentralized market place where we implement a collaborative agro model between farmers and consumers. The farmers will be able to record the potential crops and the anticipated output on his farm land on the decentralized ledger. The consumers can check the information and the farmer integrity based on the previous supplies. This creates a far open and full proof digital market framework for agro-products and will help to reduce hoarding. This research paper explores one possible model of blockchain in the Food Supply Market by creating a breach proof ledger of records. We have used HTML, CSS, Javascript for frontend, Ethereum and Solidity based frameworks for backend working of this model.
Integrated Cryptography for Internet of Things Using TBF Approach

In current trends, Internet-based portable system is on the huge demand, and in coming future, it will be the most demanded technology among the smart society. It is very difficult to say that entire world will use the smart technology but majority of the developed and developing countries will base on smart work, using smart technology. The people can control their smart devices from remote location and can be causation free with security issues, which is associated with their valuables and other belongings. But all these are not so easy to implement practically to solve security pitfall and other issues to maintain reliable usage of smart devices. Here, in our proposed system, we are providing data security on data layer by using the concept of excessive cryptography and implementing the integrated approach of two cryptography techniques, i.e. TBF-transposition and blowfish cryptography techniques which can make powerful security barrier against the vulnerability and can stop the illegal usage of data. Here first of all, the plain text will go through transposition after that blowfish block cipher technique will be applied on consequential value from transposition outcome. This application will use in highly sensitive security purpose over the smart secure communication as we know that security issues are major challenges among smart devices where the data is more sensitive, and if it will go to wrong hands, there will be loss of human life in some cases. Finally, our work will contribute to integrate the crypto techniques for maintaining more security without breach of services.
Eye Movement Pre-Algebra and Visual Semantic Algebra

This article proposes a new denotational mathematics entity, i.e., the eye movement pre-algebra (EMpA), which may be considered as a pre-algebraic structure in a certain sense generating, according to universal algebra, Husserlian phenomenological theory and structuralism, another basic algebraic structure of denotational mathematics, said to be visual semantic algebra (VSA).
PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS ICT MENGGUNAKAN MACROMEDIA FLASH PADA MATA PELAJARAN IPA MI/SD

The purpose of this reseach to describe the process of planning or designing, the level of practicality, and the effectiveness of applying ICT-based learning media using Macromedia Flash in class science subjects IV SD / MI. The method used in this study is research and development (R &amp; D) using a four-dimensional model (4-D) including the definition, design, development and dissemination stages. Data collection methods using interviews, observations, expert validation questionnaires and respondents' questionnaires, as well as tests of learning outcomes. Data analysis in this study uses percentage analysis. The results of this study are as follows: (1) The design or design of the media includes the cover page, media, program, material (text, videos, exercises) and tests. (2) The level of practical application very practical average with a total average score of 87.5 on the response of teachers and learners answers obtained Total score 154 with a total average of 90.58 classified as very practical. (3) Effective use of the media is very effective in achieving student learning outcomes. The results of votes accumulated three cognitive, emotional and psychomotor aspects obtained the number of 1423 and an average of 83.70 with both categories. In addition, from the data value of all learners who are known to have reached a value of scientific subjects of completeness at least 100% classified as very effective.
A general approach to prior transformation

We present a general method for setting prior distributions in Bayesian models where parameters of interest are re-parametrized via a functional relationship. We generalize the results of Heck and Wagenmakers (2016) by considering the case where the dimension of the auxiliary parameter space does not equal that of the primary parameter space. We present numerical methods for carrying out prior specification for statistical models that do not admit closed-form solutions. Taken together, these results provide researchers a more complete set of tools for setting prior distributions that could be applied to many cognitive and decision making models. We illustrate our approach by re-analyzing data under the Selective Integration model of Tsetsos et al. (2016). We find, via a Bayes factor analysis, that the selective integration model with all four parameters generally outperforms both the three-parameter variant (omitting early cognitive noise) and the w=1 variant (omitting selective gating), as well as an unconstrained competitor model. By contrast, Tsetsos et al. found the three parameter variant to be the best performing in a BIC analysis (in the absence of a competitor). Finally, we also include a pedagogical treatment of the mathematical tools necessary to formulate our results, including a simple “toy” example that illustrates our more general points.
Low-Light Remote Sensing Images Enhancement Algorithm Based on Fully Convolutional Neural Network

Low-light remote sensing is a powerful complement to daytime optical remote sensing and can greatly expand the time domain of high-resolution earth observations, and make day and night imaging possible. However, when a low-light sensor is used in the morning dusk and dawn, the captured images have characteristics of low contrast, low brightness, and low signal-to-noise ratio, which severely restrict the identification and interpretation of ground objects. Traditional low-light image enhancement algorithms such as histogram equalization, gamma conversion, and contrast-limited adaptive histogram equalization algorithm, and so on can enhance the low-light remote sensing image and solve the problem of contrast enhancement, but the noise amplification effect brought by the enhancement will degrade the signal-to-noise ratio of the enhanced image. Therefore, in this paper, a data-driven low-light remote sensing image enhancement algorithm is studied. First of all, lots of low-light raw image data pairs corresponding to very low illumination are captured. Then, these raw image data are used to train a deep fully convolutional neural network composed of an encoder–decoder structure. After that, the low-light remote sensing images could be enhanced by the pretrained net structure. The numerical results demonstrate that the fully convolutional neural network based on enhancement algorithm greatly improves the brightness and the contrast of low-light images compared with the traditional enhancement algorithms while a high enough signal-to-noise ratio could be preserved, which will make interpretation and identification much easier.
Influence of astigmatism and field curvature on the correction zones of an optical imaging system

By a detailed analysis of the dependence of aberrations on the numerical aperture and the field of view of the optical system, it is possible to find such values of the numerical aperture and the field of view, where the residual wave aberration is zero. These values can be called correction zones. Our work presents a theoretical analysis of the described problem, and general formulas are derived for the expression of wave aberration coefficients using correction zones for astigmatism and field curvature of the third and fifth order. Finally, an analysis of optimum values of correction zones and the optimum position of the image plane has been performed using derived equations with respect to maximization of the Strehl definition. The analysis of the correction zone and the position of the image plane is presented on two examples of the same optical system, where the image plane is located either in the paraxial image plane or in the optimum image plane.
ℓ0-Regularized Sparsity for Probabilistic Mixture Models

This paper revisits a classical task of learning probabilistic mixture models.Our major goal is to sparsely learn the mixture weights to automatically determine the right number of clusters.The key idea is to use a novel Bernoulli prior on the mixture weights in a Bayesian learning framework, and formalize the task of determining the mixture weights as an 0-regularized optimization problem.By leveraging a specific mathematical structure, we derive a quadratic time algorithm for efficiently solving the non-convex 0-based problem.In experiments, we evaluate the performance of our proposed approach over existing methods in recovery capability and anomaly detection for synthetic as well as realworld data sets.