Reviewerly/exHarmony huggingface dataset:
DISTRIBUTION TREN OF SENIOR HIGH SCHOOL AS ORIGIN SCHOOL OF COLLEGE STUDENT STIKOM BALI BASE GIS

GIS (Geographic Information System) is an information system that can store and integrate spatial data and non-spatial data can be used for interactive mapping of STIKOM Student School origin in Denpasar. During this time the spread of origin of high school / vocational school / equivalent STIKOM Bali students has never been mapped to find out the trend of increase or decrease in the number of origin of STIKOM Bali student schools from 2013-2018 and predict the number of students in accordance with the origin of schools in the city of Denpasar. This study aims to provide information to the management of STIKOM Bali regarding the distribution trends of the interests of prospective students to continue to tertiary level, especially STIKOM Bali. This research will collaborate between statistical science and the concept of GIS (Geographic Information System). Statistically the number of STIKOM Bali students is based on the origin of schools in Denpasar City and predicts it for the next 3 years using a trend analysis of semi-average methods (Semi Average Methods) as a material for evaluating the performance of STIKOM Bali management in improving the performance of campus promotions. This method makes trends by finding the average group of data which consists of grouping data into 2 parts, calculating average arithmetic, calculating the difference, formulating the value of change and making equations for subsequent trends. The results of these calculations are mapped with the concept of GIS (Geographic Information System) using ArcView as software to implement that integrates spatial data with non-spatial data.
Hierarchical Clustering with Hard-batch Triplet Loss for Person Re-identification

For most unsupervised person re-identification (re-ID), people often adopt unsupervised domain adaptation (UDA) method. UDA often train on the labeled source dataset and evaluate on the target dataset, which often focuses on learning differences between the source dataset and the target dataset to improve the generalization of the model. Base on these, we explore how to make use of the similarity of samples to conduct a fully unsupervised method which just trains on the unlabeled target dataset. Concretely, we propose a hierarchical clustering-guided re-ID (HCR) method. We use hierarchical clustering to generate pseudo labels and use these pseudo labels as monitors to conduct the training. In order to exclude hard examples and promote the convergence of the model, We use PK sampling in each iteration, which randomly selects a fixed number of samples from each cluster for training. We evaluate our model on Market-1501, DukeMTMC-reID and MSMT17. Results show that HCR gets the state-of-the-arts and achieves 55.3% mAP on Market-1501 and 46.8% mAP on DukeMTMC-reID. Our code will be released soon.
Students’ soft skills as a factor of their professional competitiveness improvement

Planning and Teaching Engineering Staff for the Industrial and Economic Complex of the Region 2019: Proceedings of 2019 XVIII Russian Scientific and Practical Conference on Planning and Teaching Engineering Staff for the Industrial and Economic Complex of the Region (PTES)
Absolute interferometric testing of a flat surface based on the Laplacian method

In the present study, a method is introduced for absolute interferometric testing. An interferometer is used to measure the phase difference distributions at five positions of the reference surface with respect to the test surface. The Laplacian of the phase distribution is calculated from the measured phase difference distributions. Then, the reference phase distribution is reconstructed by solving the Laplacian equation by the Fourier method. The accuracy of the method with respect to the amount of the displacement and noise is investigated by simulation and compared with the conjugate differential method. It is shown that the spatial frequencies of the phase distributions reconstructed by the Laplacian method are less filtered out with respect to the conjugate differential methods. Finally, the experimental result is presented.
Haberleşme Sistemlerinde Derin Öğrenme

Deep learning has become the most successful learning method in machine learning.While deep learning provides a clear advantage over other machine learning methods, especially when the amount of data is high, it can produce an approximate result to other machine teaching methods when data is low.This new learning method has the potential to contribute to many innovations, from redesigning the physical layers used in communication technologies to modeling wireless networks.It is particularly useful in communication systems where mathematical modeling is difficult, for example, 5G and molecular communication.Therefore, many types of research on the application of deep learning in communication systems have been conducted recently.However, the distance of institutions and researchers about communication technologies to deep learning methods has limited the number and impact of these studies.Therefore, it is necessary to collectively examine the studies that involve the application of deep learning to communication technologies, to evaluate the achievements, and to contribute to the determination of new research topics.For this purpose, in this study, firstly, the achievements of deep learning and usage areas are summarized and then the studies that contribute to the development of communication technologies are classified and examined comparatively.To make deep learning more effective in communication, what needs to be done were discussed and deep learning based research areas that could lead to next generation communication systems were determined.
Book Review: Blockchain Technology Applications in Education

This book is edited by Ramesh Chander Sharma, Hakan Yildirim, Gulsun Kurubacak. The 1st edition of the book was published in 2020, IGI Global. The book has a total of 336 pages. The metadata of the book is as followings: ISBN13: 9781522594789, ISBN10: 1522594787, EISBN13: 9781522594796, and DOI: 10.4018/978-1-5225-9478-9.
Distortion-free camera calibration

A universal pixel-by-pixel distortion-free camera calibration technique is described. All camera lenses will cause image distortions. An LCD flat panel can be used as an active calibration panel for camera calibration. Each sensor pixel has its ray vector in space and can be calibrated with the LCD. A set of phase shifting fringes (PSF) can be used to establish the mapping relationship between the LCD pixels and the sensor pixels. For primary camera calibration, a virtual sensor can be created on the LCD and a set of inverse mapping parameters (IMP) for each virtual pixel can be determined. The captured images can be rectified by resampling with the IMP. The output images will be distortion-free with zero geometric distortions and zero chromatic aberrations. For advanced camera calibration, all pixel ray vectors in space can be calibrated. A virtual sensor can be created on any expected planar or curvature surface in space and the IMP can be determined accordingly. After image rectification or 3D reconstruction, for every 2D pixel or 3D point cloud, the mean error will be 0, the std error will be 1/1,000 pixel pitch or smaller. The cameras can be used as non-contact 2D/3D rulers. The distortion-free calibration technique can be applied to any cameras and projectors, no matter how complex their lens structures can be. Interactive and comprehensive intensity calibrations can be made between LCD, cameras and projectors.
AN ANALYTICAL STUDY ON CLOUD COMPUTING

Cloud computing is regarded as massively extensible, an on-demand configurable resourcescomputing model. It approaches the cloud infrastructure in a distributed rather than dedicatedinfrastructure where users can have full access to the extensible, reliable resources. Datagenerated by IoT attached objects is high, cloud is a key to store the incalculable datagenerated by these attached devices and it is the forward stepped towards the green computing,it removes the setups and installation steps as the cloud user accessing the hardware resourcesco-exist on different platform in distributed way.Cloud computing environment furnished a great flexibility and availability of computingresources at a very lower cost. This arriving technology opens a new era of e-services indifferent disciplines. In this paper, we seen cloud computing with its applications, mostcommon Cloud Service Provider such as Google, Microsoft, Amazon, HP, and Sales force andwe present innovative applications for cloud computing in Enterprise Resource Planning.
Digital tools against COVID-19: Framing the ethical challenges and how to address them.

Data collection and processing via digital public health technologies are being promoted worldwide by governments and private companies as strategic remedies for mitigating the COVID-19 pandemic and loosening lockdown measures. However, the ethical and legal boundaries of deploying digital tools for disease surveillance and control purposes are unclear, and a rapidly evolving debate has emerged globally around the promises and risks of mobilizing digital tools for public health. To help scientists and policymakers navigate technological and ethical uncertainty, we present a typology of the primary digital public health applications currently in use. Namely: proximity and contact tracing, symptom monitoring, quarantine control, and flow modeling. For each, we discuss context-specific risks, cross-sectional issues, and ethical concerns. Finally, in recognition of the need for practical guidance, we propose a navigation aid for policymakers made up of ten steps for the ethical use of digital public health tools.
Model-Based Tool Support for Service Design

This paper introduces a modelling environment for service design that currently supports 5 different notations (Business Model Canvas, e3value, Service Blueprint, Process Chain Network and BPMN). Besides, the tool supports the generation of partial views of models based on a particular notation from models made with another one, along with the corresponding relations model.
Application of generalized method of least modules in problems of processing and analysing images

The article describes the use of nonlinear smoothing filter for image processing and analysis. Description of the model of the smoothing filter based on the generalized method of the least absolute values is given. The filter constructed on the basis of the offered model efficiently reduces the noise on brightness difference. Along with noise reduction in the contrast images, this method can be used for the solving problems of machine vision, medical diagnostics, etc. It has been found that nonlinear filtration on the basis of the generalized method of the least modules allows to solve such problems as clarification of the boundaries of contrast objects and segmentation of the image. There has been shown the possibility of recovering the boundaries of the images in which the contrast borders were blurry. X-ray image of an animal hand with defocusing was used as an example. After filtering, the contrast boundary was restored to the place where it was originally located. When processing a fluorography image, the filter removed various artifacts from the image and increased the contrast. Removal of artifacts along with the recoveries of the boundaries of contrast objects improves the overall “readability” of the fluorography image and also allows seeing earlier not distinguishable details on the image. Examples of the filter application in the clustering problem using the k-means algorithm are given. Due to the lack of this algorithm, applying it directly to the image does not give an acceptable result. However, after processing the original image with a nonlinear filter, the application of the k-means algorithm yields the desired result.
A GPU Homotopy Path Tracker and End Game for Mechanism Synthesis

This paper presents an implementation of a path tracking algorithm and end game for the homotopy solution of mechanism synthesis equations using a graphical processing unit, or GPU. The goal is to have the GPU execute a large number of path tracking solutions in parallel in order to identify design candidates that satisfy a set of kinematic synthesis polynomial equations. Effective use of the GPU requires that the processors execute the same instruction set. This imposes constraints on the structure of the path tracker and the end game. In this paper, we present that our implementation of GPU-based polynomial homotopy solver and end game. We demonstrate the implementation by solving the five position planar four bar synthesis problem for a given set of task positions.
The expressiveness of looping terms in the semantic programming

We consider the language of ∆0-formulas with list terms interpreted over hereditarily finite list superstructures.We study the complexity of reasoning in extensions of the language of ∆0-formulas with non-standard list terms, which represent bounded list search, bounded iteration, and bounded recursion.We prove a number of results on the complexity of model checking and satisfiability for these formulas.In particular, we show that the set of ∆0-formulas with bounded recursive terms true in a given list superstructure HW (M) is non-elementary (it contains the class kExpTime, for all k 1).For ∆0-formulas with restrictions on the usage of iterative and recursive terms, we show lower complexity.
Representation Bayesian Risk Decompositions and Multi-Source Domain Adaptation

We consider representation learning (hypothesis class $\mathcal{H} = \mathcal{F}\circ\mathcal{G}$) where training and test distributions can be different. Recent studies provide hints and failure examples for domain invariant representation learning, a common approach for this problem, but the explanations provided are somewhat different and do not provide a unified picture. In this paper, we provide new decompositions of risk which give finer-grained explanations and clarify potential generalization issues. For Single-Source Domain Adaptation, we give an exact decomposition (an equality) of the target risk, via a natural hybrid argument, as sum of three factors: (1) source risk, (2) representation conditional label divergence, and (3) representation covariate shift. We derive a similar decomposition for the Multi-Source case. These decompositions reveal factors (2) and (3) as the precise reasons for failure to generalize. For example, we demonstrate that domain adversarial neural networks (DANN) attempt to regularize for (3) but miss (2), while a recent technique Invariant Risk Minimization (IRM) attempts to account for (2) but does not consider (3). We also verify our observations experimentally.
Compositionality as Creative Identity Building

In this paper, we examine the concept of Compositionality, proposed by Brazilian composer and scholar Paulo Costa Lima, from the interpenetration of five vectors: inseparability, creation of worlds, criticality, reciprocity and compositional processes. Three of these vectors will be compared with the Theory of Compositional Systems in an attempt to demonstrate that the inseparable flow between theory and practice is essential for the developing of a compositional process that comes with the creation of worlds. The systemic modeling of excerpts of Ponteio Op. 35, by Paulo Costa Lima, will be used as a case study for the compositional planning of a small fragment for flute, clarinet, and bassoon.
TRAINING OF FUTURE TEACHERS OF MATHEMATICS TO THE FORMATION OF RESEARCH SKILLS OF STUDENTS

This article describes the preparation of future teachers of mathematics to the formation of research skills of students. Targeted formation of research skills of students in accordance with the modern model of education of Kazakhstan in the conditions of modernization of secondary education requires: improving the research training of students, ready for effective work in schools. The purpose of the study is to prepare future teachers of mathematics to the formation of research skills of students through solving math problems. The study used research methods as analysis of scientific-methodical and special literature on the research problem; study and analysis of curricula and standards of professional-pedagogical skills, curricula, textbooks and learning materials; pedagogical observation; interview; survey; testing. As a result, students showed willingness to formation of research skills of students through solving math problems. The paper shows methods of solving mathematical problems. The article proposes a solution to the problem of formation of research skills of students on the basis of the decision search task.
A Study of Neural Training with Non-Gradient and Noise Assisted Gradient Methods.

In this work we demonstrate provable guarantees on the training of depth-2 neural networks in new regimes than previously explored. (1) We start with exhibiting a non-gradient iterative algorithm Neuro-Tron which gives a first-of-its-kind poly-time approximate solving of a neural regression (here in the $\ell_\infty$-norm) problem at finite net widths and with non-realizable data. (2) Next we give a simple stochastic algorithm that can train a ReLU gate in the realizable setting with significantly milder conditions on the data distribution than previous results. Leveraging some additional distributional assumptions we also show near-optimal guarantees of training a ReLU gate when an adversary is allowed to corrupt the true labels. (3) Lastly we analyze the behaviour of noise assisted gradient descent on a ReLU gate in the realizable setting. While making no further distributional assumptions, we locate a ball centered at the origin such that all the iterates remain inside it with high probability.
Assessment of Geo-Accumulation Index (Igeo) In Soil Samples of Fika Agrarian Community of Yobe State, Nigeria, Using Mp-Aes Analytical Technique

This study examined the concentration level and geo-accumulation index of trace element in composite soil samples in Fika local government area of Yobe, Nigeria.Soil samples from ten locations were collected at a depth of 0 to 20 cm using soil auger in the study area through purposive sampling technique and global Positioning System (GPS) device was used to mark the sample locations and elevations above sea level.Microwave Plasma Atomic Emission Spectrophotometer (MP-AES) was used to determine the concentrations of trace elements in the study area.From the data obtained elemental concentration (EC) and geo-accumulation index (Igeo) of each element was determined.Based on the result obtained, the mean elemental concentration in the study area was of the order of Pb(81.585 ± 25.591) > Fe(53.907± 4.593) > Al(3.941 ± 2.595) > Mn(2.311 ± 0.291) > Zn(0.500 ± 0.167) > Cr(0.151 ± 0.015) > Co(0.002 ± 0.000) > Cd(0.001 ± 0.00) in ppm respectively.Of all the trace elements detected, only Pb in FKA1 was found to be above the International benchmark.Moreover, strong Igeo value correlations were obtained between (Zn -Pb) and (Al -Fe) at 0.5 significance level.The result further revealed that all the mean Igeo values of trace element were < 0 which imply that all the soil samples analyzed were uncontaminated and unpolluted.The study recommends that there is need to extend the scope of the study to cover radiological contaminants in soil, plants and river sediments in the study area so as to obtain a composite baseline data.
Practical Perspectives on Quality Estimation for Machine Translation

Sentence level quality estimation (QE) for machine translation (MT) attempts to predict the translation edit rate (TER) cost of post-editing work required to correct MT output. We describe our view on sentence-level QE as dictated by several practical setups encountered in the industry. We find consumers of MT output---whether human or algorithmic ones---to be primarily interested in a binary quality metric: is the translated sentence adequate as-is or does it need post-editing? Motivated by this we propose a quality classification (QC) view on sentence-level QE whereby we focus on maximizing recall at precision above a given threshold. We demonstrate that, while classical QE regression models fare poorly on this task, they can be re-purposed by replacing the output regression layer with a binary classification one, achieving 50-60\% recall at 90\% precision. For a high-quality MT system producing 75-80\% correct translations, this promises a significant reduction in post-editing work indeed.
2kenize: Tying Subword Sequences for Chinese Script Conversion

Simplified Chinese to Traditional Chinese character conversion is a common preprocessing step in Chinese NLP. Despite this, current approaches have poor performance because they do not take into account that a simplified Chinese character can correspond to multiple traditional characters. Here, we propose a model that can disambiguate between mappings and convert between the two scripts. The model is based on subword segmentation, two language models, as well as a method for mapping between subword sequences. We further construct benchmark datasets for topic classification and script conversion. Our proposed method outperforms previous Chinese Character conversion approaches by 6 points in accuracy. These results are further confirmed in a downstream application, where 2kenize is used to convert pretraining dataset for topic classification. An error analysis reveals that our method's particular strengths are in dealing with code-mixing and named entities.
Usability testing on touchscreen based electronic kiosk machine in convenience store

Innovative information technologies have been commonly used in retail business. One of them is electronic kiosk that operated in various convenience store in Indonesia. However, this is a newly established kind of retail service and many users found the user interface too confusing, thus making this machine difficult to operate. This research is aiming to measure the usability of electronic kiosk so the user can use it easily. To measure the usability, usability testing methods such as performance measurement, Retrospective Think Aloud, System Usability Scale (SUS), and Questionnaire for User Interaction Satisfaction (QUIS) were used. The result of this research was a recommended user interaction design for electronic kiosk that will allow the user to use electronic kiosk machine easily.
Mobile Edge Computing and Artificial Intelligence: A Mutually-Beneficial Relationship

This article provides an overview of mobile edge computing (MEC) and artificial intelligence (AI) and discusses the mutually-beneficial relationship between them. AI provides revolutionary solutions in nearly every important aspect of the MEC offloading process, such as resource management and scheduling. On the other hand, MEC servers are utilized to avail a distributed and parallelized learning framework, namely mobile edge learning.