Reviewerly/exHarmony huggingface dataset:
Mobile Application Implementing Location Based Services Framework with Google Cloud Platform Integration: SSRU Development Case

Finding a place in a new location where visitors have no experience and clueless about it.This seems like getting lost in a maze and there is no easy way to reach out destination.To solve this problem, Location Based Services (LBS) plays an important role in providing visitors with assistance, direction, guideline and other relevant information.With LBS, visitors will be given walking instructions to get to a desirable place correctly.Moreover, visitors are enabled to find out the best route to approach destination efficiently.Then, no one wastes time and gets lost.This work applied Location Based Services framework integrated with Google Maps Platform to develop a mobile application called SSRU Map, run on Android Operating System.This application provides a series of geolocation information and solution featured on the location of Suan Sunandha Rajabhat University (SSRU), Thailand.Each year, SSRU has thousand visitors come to this university with various purposes e.g.applicants for admission or seminar participants.SSRU geographical landscape is not a rectangular shape and it is not a grid layout either.That is why it is harder for visitors to locate a building they are looking for by themselves.This mobile application is devoted for the main campus of SSRU in Bangkok with its area around 2 square kilometers.Main features of this application cover all locations and routes of major and minor buildings on this campus.Besides navigation feature, it also provides each building information e.g.pictures of interior and exterior of each building, website information, and contact information.This application will serve thousand visitors every year while.
Interactions between soccer teams reveal both design and emergence: Cooperation, competition and Zipf-Mandelbrot regularity

Considering soccer matches as complex systems facilitates the identification of properties that emerge from the interactions between players. Such properties include the regularities and statistical patterns that characterize couplings and sets between players established during matches. Empirical studies on the statistical distributions of number of items (e.g., words in texts) have shown that these distributions follow scaling properties according to empirical laws known as Zipf-Mandelbrot. Here we investigate whether the (re)occurrence of pitch location of sets of players in a soccer match also obeys these empirical laws. Data collected from 10 soccer matches shows that, for most sets of players, this seems to be the case. Exceptions were found in particular types of sets, such as goalkeeper and goal, and left defender and right attacker from opposite teams. Rather than challenging the hypothesis that a Zipf-Mandelbrot law defines this system, these exceptions may be explained by the players configuration design, which is a trait of human interaction within complex systems. This design expresses match strategy, before the team enters in such dynamical processes (the game).
Proposed Model of Speech Recognition using MFCC and DNN

Speech Recognition is the ability of the machine to identify the word or phrases in human language and convert it into machine understandable form.Speech Recognition allows you to supply input to an application together with your voice.Speech recognition systems aim to form human machine communication quickly and simply .The main focus of the project would be to convert the speech of a human into text.In this paper, we propose a system architecture that will fetch speech data, process it and give out an effective text outcome.For Demonstration, we use Polygon smoothing algorithm to preprocess the data, and then use MFCC for feature extraction and we did the comparative study of classification techniques like SVM models and DNN.Although the system provides classification using SVM, in comparison, our system proves to be more precise and accurate if we consider big data to be processed.Over the last few years, deep neural networks (DNNs) have become increasingly popular in many areas including ASR, so we have carried out a detailed survey about Speech Recognition using DNN.
Geometry and context guided refinement for stereo matching

The disparity refinement phase of existing end-to-end stereo matching networks refines the disparity by learning the mapping from the concatenated coarse disparity and corresponding features to fine disparity. It depends on the scenarios' characteristics, such as the distribution of disparity and semantic categories contained in the domain, which makes the network fail to work on unseen domain. In this paper, we propose a geometry and context guided refinement network (GCGR-Net) containing a Fine Matching module and an Upsampling module. GCGR-Net learns to utilize pixels' relationship to get high resolution dense disparity, which is independent of the data's content. The Fine Matching module performs a minimum range search based on the relationship between the possible matching pixel pairs, i.e. the called geometry information, to recover the internal structure of the object. The Upsampling module obtains context information, the relationship between central pixel and the pixels in its neighborhood, to upsample the lower resolution disparity. The final disparity map is obtained step by step through an iterative refinement model. Experiment results show that our method not only has good performance in the training scenarios, but also outperforms previous methods on the unseen domain without fine-tuning.
School Readiness of Preschool Children Entering Elementary School Based on Nijmeegse Schoolbekwaamheids Test (Nst) (Case Study at Early Childhood Education Al Husna Pengging Boyolali In 2017)

This is descriptive research to find out school readiness in terms of N.S.T (Nijmeegse Schoolbekwaamheids Test) test results. Research uses quantitative approach. Subject of the research is students of early childhood education Al Husna Kindergarten B level in Pengging Boyolali which will enter elementary school. Number of research subject is 32 students. Measuring instrument used on the research is N.S.T (Nijmeegse Schoolbekwaamheids Test) and also observation. Analysis of data will be done quantitatively with simple statistic technique. Result of the research shows that from 32 students, 50% stated has readiness as much 16 students, 40,6% is quite ready as much 13 students meanwhile not ready is 9,4% as much 3 students.The most prominent aspect in its level of maturity is aspect of form observations and the ability to distinguish which is 84,4% for 27 students. The least aspect on the task of understanding the story is 25% for 8 students. Qualitatively, school readiness aspect from cognitive aspect is observation and the ability to distinguish, notions of big, number and comparison, concentrate and memory already reaches a fairly optimal level of maturity. Meanwhile related aspect with understanding stories and pictures of people skill the level of preparedness is not yet optimal.
SISTEM INFORMASI DONOR DARAH BERBASIS ANDROID PADA UNIT TRANSFUSI DARAH PALANG MERAH INDONESIA (UTD PMI) KOTA PEKANBARU

Blood is one of the most important parts in the human body because blood has a function to circulate the juice, carry oxygen, circulate hormones, and others. If humans reduce blood caused by accidents or anemia, so that humans lack blood or store dirty blood in the body, then it can cause diseases that endanger the health of even someone. Blood donor activity is an activity of the process of blood transfer from donors to people who lack blood. The process is carried out by those who carry out blood processing processes such as the Blood Transfusion Unit (UTD) under the auspices of the Indonesian Red Cross (PMI). UTD PMI Pekanbaru City only has a web-based blood donor information system, so UTD PMI Pekanbaru City wants to keep up with the times where many users use smartphones so they want to upgrade from the web version to the Android version. The reason for the improvement of this system aims to reduce hosting costs to be paid when using a web system and Android-based information system does not require typing too much when the user will carry out an order to the system, its use is not limited by space and place, and does not really need a connection that is prime to run the system. Android-Based Donor Information System in the Indonesian Red Cross Blood Transfusion Unit (UTD PMI) Pekanbaru City can help all people to find out about information about blood donations well by using Android-based smartphones as interesting media.
Factores relacionados con el uso académico de Internet en educación superior

The following article aims to establish what academic activities higher education students carry out with the internet and determine which factors explain the differences in the use of internet for these activities. In order to carry out the research, a survey was applied to 435 higher education students in Temuco, La Araucan&iacute;a, Chile. The results show that students use the internet frequently for activities associated with their studies. Among these, the most frequent are collaborative activities, such as the preparation of group reports, asking classmates for study materials, or requesting help to solve exercises or understand some activity. On the other hand, the variables that best explain the gaps in the academic use of the internet are cultural capital and the type of higher education institution that students attend. &nbsp
Dimensionality Reduction with Sparse Locality for Principal Component Analysis

Various dimensionality reduction (DR) schemes have been developed for projecting high-dimensional data into low-dimensional representation. The existing schemes usually preserve either only the global structure or local structure of the original data, but not both. To resolve this issue, a scheme called sparse locality for principal component analysis (SLPCA) is proposed. In order to effectively consider the trade-off between the complexity and efficiency, a robust L 2, p -norm-based principal component analysis (R2P-PCA) is introduced for global DR, while sparse representation-based locality preserving projection (SR-LPP) is used for local DR. Sparse representation is also employed to construct the weighted matrix of the samples. Being parameter-free, this allows the construction of an intrinsic graph more robust against the noise. In addition, simultaneous learning of projection matrix and sparse similarity matrix is possible. Experimental results demonstrate that the proposed scheme consistently outperforms the existing schemes in terms of clustering accuracy and data reconstruction error.
Correction to: An Application of Time Truncated Single Acceptance Sampling Inspection Plan Based on Generalized Half‑Normal Distribution

In the original publication of the article, under the real life application section, commas in the observation of real examples are misplaced and the observations are changed.
Augmented Reality: A New Way to View the World

Technology in education can influence students to find out actively and may motivate them, resulting in an efficient process of learning.Although the physical world is threedimensional, mostly we opt to use two-dimensional media in education.The AR technology strengthens the effectiveness and attractiveness of teaching and learning for students in real scenarios.Since its introduction, augmented reality (AR) has been shown to own good potential in making the training process more active, effective and meaningful.This is possible because of its advanced technology that enables users to interact with virtual and real-time applications and brings them a natural experience.Augmented Reality could be a new medium, combining aspects from ubiquitous computing, tangible computing, and social computing.AR offers unique combining of physical and virtual worlds, with continuous and implicit control by the users for the purpose of viewing and interactivity.This study comprises of short introduction to augmented reality, AR components, working, and its comparison with virtual reality, advantages and its use in the field of education.
Information Thresholds for Non-Parametric Structure Learning on Tree Graphical Models

We provide high probability finite sample complexity guarantees for non-parametric structure learning of tree-shaped graphical models whose nodes are discrete random variables with either finite or countable alphabets, both in the noiseless and noisy regimes. We study a fundamental quantity called the (noisy) information threshold, which arises naturally from the error analysis of the Chow-Liu algorithm and, as we discuss, provides explicit necessary and sufficient conditions on sample complexity, by effectively summarizing the difficulty of the tree-structure learning problem. Specifically, we show that finite sample complexity of the Chow-Liu algorithm for ensuring exact structure recovery is inversely proportional to the information threshold (provided it is positive), and scales almost logarithmically relative to the number of nodes over a given probability of failure, also matching relevant asymptotic results in the literature. Conversely, in the noiseless case, we show that, for arbitrarily small information thresholds, the structure recovery task given any finite number of samples becomes impossible for any algorithm whatsoever. Consequently, strict positivity of the information threshold characterizes the feasibility of tree-structure learning, in general terms. Lastly, as a consequence of our analysis, we resolve the problem of tree structure learning in the presence of non-identically distributed observation noise, providing conditions for convergence of the Chow-Liu algorithm under this setting, as well.
Research on the Status Quo of the Curriculum of Public Physical Education in High Schools

With the advancement of high-quality education, the importance of physical education courses has been increasing. Traditionally, the teaching of sports which mainly teaches students' sports skills has mandatory learning characteristics. With the development and improvement of the physical education curriculum, the current public physical education curriculum in high schools is considered more scientific in terms of teaching content and form, focusing on the cultivation of students' interest in learning, and improving the students' various abilities and physical fitness. This article mainly analyzes the status quo of the curriculum of public physical education in high schools and proposes corresponding countermeasures.
Exploring Techniques to Improve Activity Recognition using Human Pose Skeletons

Human pose skeletons provide an explainable representation of the orientation of a person. Neural network architectures such as OpenPose can estimate the 2D human pose skeletons of people present in an image with good accuracy. Naturally, the human pose is a very attractive choice as a representation for building systems aimed at human activity recognition. However, raw pose keypoint representations suffer from various problems such as variance to translation and scale of the input images. Keypoints are also often missed by the pose estimation framework. These, and other factors lead to poor generalization and learning of networks that may be trained directly on these raw representations. This paper introduces various methods aimed at building a robust representation for training models related to activity recognition tasks, such as the usage of handcrafted features extracted from poses with the intent of introducing scale and translation invariance. Additionally, the usage of train-time techniques such as keypoint dropout are explored to facilitate better learning of models. Finally, we conduct an ablation study comparing the performance of deep learning models trained on raw keypoint representation and handcrafted features whilst incorporating our train-time techniques to quantify the effectiveness of our introduced methods over raw representations.
6-Month Infant Brain Mri Segmentation Guided by 24-Month Data Using Cycle-Consistent Adversarial Networks

Due to the extremely low intensity contrast between the white matter (WM) and the gray matter (GM) at around 6 months of age (the isointense phase), it is difficult for manual annotation, hence the number of training labels is highly limited. Consequently, it is still challenging to automatically segment isointense infant brain MRI. Meanwhile, the contrast of intensity images in the early adult phase, such as 24 months of age, is a relatively better, which can be easily segmented by the well-developed tools, e.g., FreeSurfer. Therefore, the question is how could we employ these high-contrast images (such as 24-month-old images) to guide the segmentation of 6-month-old images. Motivated by the above purpose, we propose a method to explore the 24-month-old images for a reliable tissue segmentation of 6-month-old images. Specifically, we design a 3D-cycleGAN-Seg architecture to generate synthetic images of the isointense phase by transferring appearances between the two time-points. To guarantee the tissue segmentation consistency between 6-month-old and 24-month-old images, we employ features from generated segmentations to guide the training of the generator network. To further improve the quality of synthetic images, we propose a feature matching loss that computes the cosine distance between unpaired segmentation features of the real and fake images. Then, the transferred of 24-month-old images is used to jointly train the segmentation model on the 6-month-old images. Experimental results demonstrate a superior performance of the proposed method compared with the existing deep learning-based methods.
Adaptive Subset-Based Digital Image Correlation for Fatigue Crack Evaluation

This paper proposes a fatigue crack evaluation technique based on digital image correlation (DIC) with statistically optimized adaptive subsets. In conventional DIC analysis, a uniform subset size is typically utilized throughout the entire region of interest (ROI), which is determined by experts’ subjective judgement. The basic assumption of the conventional DIC analysis is that speckle patterns are uniformly distributed within the ROI of a target image. However, the speckle patterns on the ROI are often spatially biased, augmenting spatially different DIC errors. Thus, a subset size optimization with spatially different sizes, called adaptive subset sizes, is needed to improve the DIC accuracy. In this paper, the adaptive subset size optimization algorithm is newly proposed and experimentally validated using an aluminum plate with sprayed speckle patterns which are not spatially uniform. The validation test results show that the proposed algorithm accurately estimates the horizontal displacements of 200 μ m , 500 μ m and 1 mm without any DIC error within the ROI. On the other hand, the conventional subset size determination algorithm, which employs a uniform subset size, produces the maximum error of 33% in the designed specimen. In addition, a real fatigue crack-opening phenomenon, which is a local deformation within the ROI, is evaluated using the proposed algorithm. The fatigue crack-opening phenomenon as well as the corresponding displacement distribution nearby the fatigue crack tip are effectively visualized under the uniaxial tensile conditions of 0.2, 1.0, 1.4 and 1.7 mm , while the conventional algorithm shows local DIC errors, especially at crack opening areas.
Implementation of combined new optimal cuckoo algorithm with a gray wolf algorithm to solve unconstrained optimization nonlinear problems

&lt;p&gt;In this article, a combined optimization algorithm was proposed which combines the optimal adaptive Cuckoo algorithm (OACS) which is Nature-inspired algorithm with Gray Wolf optimizer algorithm (GWO). Sometimes considering the cuckoo algorithm alone, may fail to find the local minimum-point and also fails to reach to the solution because of the slow speed of its convergence property. Therefore, considering the new proposed adaptive combined algorithm gave a strong improvement for using this to reach the minimum point in solving (23) nonlinear test problems. This is suitable to solve a large number of nonlinear unconstraint optimization test functions with obtaining good and robust numerical results.&lt;/p&gt;
The Use of Project Gutenberg and Hexagram Statistics to Help Solve Famous Unsolved Ciphers

Project Gutenberg, begun by Michael Hart in 1971, is an attempt to make public domain electronic texts available to the public in an easily available and useable form.The number of available texts reached 60,000 by 2019.Classical cryptanalysis methods rely on the development and use of high-quality frequency tables of letter arrangements from a variety of sources.As the amount of text grows, frequency tables of higher orders can be developed and may provide more solving power for classical cryptographic algorithms.As a sideeffect of the availability of a wide range of public domain texts, we were able to develop hexagram frequency tables of letters in the English language which were then a crucial factor to solving an unsolved transposition cipher of Mahon and Gillogly (2008).The texts themselves were then used as input to solve a book cipher of Thouless (1948) using the same scoring method.
A Lossless Compression Algorithm Based on Differential and Canonical Huffman Encoding for Spaceborne Magnetic Data

Lossless compression for spaceborne magnetic data is significant, especially for small, low-cost satellites or deep space exploration spacecrafts. However, most of existing lossless compression algorithms are difficult to adapt to the embedded platform or the existing embedded algorithms have low compression ratio. In light of this, taking full advantage of the characteristics that the spaceborne magnetic data change slowly in a short time, a lossless compression algorithm based on differential encoding and canonical Huffman encoding is proposed. The original magnetic values are replaced with differential values with variable byte length to shorter the length of raw data. Canonical Huffman encoding can be performed twice after differential operation to further improve the compression ratio if the memory is sufficient. Experiments are carried out on spaceborne magnetic data of a satellite on an onboard computer. This algorithm can get compression ratio of 1.76 and 1.83 with canonical Huffman encoding disabled and enabled respectively, and the results demonstrate the superiority of the proposed algorithm in comparison with embedded LZW algorithms.
Cryptocurrencies: formation of returns from the CRIX index

This paper examines the formation prices in the cryptocurrency market using the CAPM model based on OLS and Regime-Switching approaches. Following Baek & Elbeck’s argument that internal factors drove cryptocurrency returns, CAPM was built, taking the CRIX index as the market and ten cryptocurrencies as assets. The results suggest that the market risk factor can partially explain cryptocurrency returns. Moreover, the regime change estimation positively impacts the market risk determination power for cryptocurrencies.