Reviewerly/exHarmony huggingface dataset:
Complex Models of Ordering Multi-Sequences with Fuzzy Parameters

Purpose. The aim of the article is to develop complex constructive mathematical models of ordering processes for multi-sequences of elements with fuzzy parameters. At the same time, the following requirements for fuzzy ordering of multi-sequences with complexity evaluation (FOMSCE) were established: accounting fuzzy estimates of the formation operations complexity, the need to define fuzzy classes for ordering the initial elements, as well as building individual fuzzy models for the processes of receiving orders from different sources. Methodology. To solve the problems of optimal planning of non-deterministic processes of clinical monitoring of the patients’ treatment, the formation of complex constructive mathematical models of the processes of ordering multi-sequences of elements with fuzzy FMLCPM parameters was applied. For forming models of FOMSCE tasks, a methodology is used to create models with multilayer structures. To implement fuzzy problems, methods and procedures for discretizing a system of fuzzy quantities using sets of α-levels are applied. Findings. The article proposes an approach to solving the problems of analysis and optimal planning of the processes of clinical monitoring of the patients’ treatment, represented as flow control in service systems under uncertainty. For its formalization and implementation, complex multilayer constructive-production models for ordering multi-sequences with fuzzy parameters have been developed. Originality. The work has developed constructive-production methods for modeling complex systems, presented in the form of a multilayer model FMLCPM, which are designed for the processes of ordering multi-sequences of elements with fuzzy parameters. In FMLCPM, layer models are proposed that provide accounting for fuzzy estimates of the complexity of ordering operations, classification of fuzzy parameters of output elements, the formation and analysis of individual fuzzy models of the processes of receipt of orders in service systems. Practical value. The practical value of the results obtained lies in the spectrum development of applications of the problems of optimal planning of the processes in the service systems, presented as an ordering of multi-sequences with fuzzy parameters. The complex models of FOMSCE processes developed in the article are suitable and effective for formalizing the tasks of analysis and optimal planning of clinical monitoring processes, as well as a wide range of other tasks for monitoring non-deterministic transport processes, logistics and service systems.
A stochastic gradient method for a class of nonlinear PDE-constrained optimal control problems under uncertainty

The study of optimal control problems under uncertainty plays an important role in scientific numerical simulations. This class of optimization problems is strongly utilized in engineering, biology and finance. In this paper, a stochastic gradient method is proposed for the numerical resolution of a nonconvex stochastic optimization problem on a Hilbert space. We show that, under suitable assumptions, strong or weak accumulation points of the iterates produced by the method converge almost surely to stationary points of the original optimization problem. Measurability and convergence rates of a stationarity measure are handled, filling a gap for applications to nonconvex infinite dimensional stochastic optimization problems. The method is demonstrated on an optimal control problem constrained by a class of elliptic semilinear partial differential equations (PDEs) under uncertainty.
An Interpretation Of Regularization By Denoising And Its Application With The Back-Projected Fidelity Term

The vast majority of image recovery tasks are ill-posed problems. As such, methods that are based on optimization use cost functions that consist of both fidelity and prior (regularization) terms. A recent line of works imposes the prior by the Regularization by Denoising (RED) approach, which exploits the good performance of existing image denoising engines. Yet, the relation of RED to explicit prior terms is still not well understood, as previous work requires too strong assumptions on the denoisers. In this paper, we make two contributions. First, we show that the RED gradient can be seen as a (sub)gradient of a prior function—but taken at a denoised version of the point. As RED is typically applied with a relatively small noise level, this interpretation indicates a similarity between RED and traditional gradients. This leads to our second contribution: We propose to combine RED with the Back-Projection (BP) fidelity term rather than the common Least Squares (LS) term that is used in previous works. We show that the advantages of BP over LS for image deblurring and super-resolution, which have been demonstrated for traditional gradients, carry on to the RED approach.
Acquisition of point spread function of dose distribution in intense pulsed Gamma radiation field

The intense pulsed radiation field provides experimental environment for the study of various radiation effects, and the radiation field parameters are the important basis for the experimental study. The dose intensity distribution of intense pulsed radiation field must be provided. The analysis object is the information of intensity distribution image. The effect of reconstructing intensity distribution image is the most important means to judge whether the image information is good or bad. In the dose distribution of intense pulse gamma radiation field, the main factor of image blurring is the existence of point spread function. How to obtain the point spread function and establish the point spread degradation model are the premise of image reconstruction. In this paper, the degradation model modeling and reconstruction algorithm are used to analyze the dose distribution image of intense pulse gamma radiation field. A good degradation model can improve the image quality, and the point spread function acquisition is particularly important.
Anchor neighborhood embedding based single-image super-resolution reconstruction with similarity threshold adjustment

Anchored neighborhood super-resolution (SR) reconstruction algorithms reconstruct a high-resolution (HR) image from a single low resolution (LR) image effectively by exploiting the non-local similarity in images. In this paper, we propose an anchored neighborhood reconstruction algorithm with a similarity threshold adaptive scheme to improve the reconstruction for the mapping matrix. In the proposed method, a similarity adjustment matrix is introduced to improve the similarity of the image blocks with high deviation in the neighborhood. Besides, a threshold function is applied to determine the weights for similar blocks. Following this function, larger weights are assigned to samples with low deviations and low coefficients are assigned to blocks with low similarity. This scheme is employed to prevent the blocks from being assigned inappropriate weights and benefit the reconstruction. Experimental results show that the proposed algorithm improves the image reconstruction quality with a low computational cost.
auto{API} – A Web-Based Tool for Specification of an API Endpoint to Return JSON Data From an XML Source

auto{API} is a web-based tool for the specification and management of API endpoints to online XML data sources. The APIs generated return the data in JSON format. The tool allows users to specify the field names to be used in the JSON data and to control which elements of the original data set are returned. Options are provided for the returned data to skip null values and to flatten the structure that is retrieved. auto{API} is written in Python/Flask and is available under the MIT licence. The software is available from GitHub (https://github.com/aamoore/autoAPI) and can be seen deployed at https://autoapi-app.herokuapp.com/?http://apis.opendatani.gov.uk/translink/3042AA.xml.
Research on multi-modal hateful meme detection

Research shows that the increase of social media usage is directly proportional to mental health. With the popularity of social networks, people increasingly like to vent their negative emotions on social platforms. These negative emotions, such as anxiety, tension, anger, depression, sadness and pain, have seriously affected our rational thinking. Long term impact of these negative emotions will affect the smooth progress of people's work and life, and even affect people's physical and mental health. At present, technology can detect negative comments that may have an impact on society. However, according to investigations and studies, social platforms are flooded with malicious pictures and texts composed of "harmless sentences" and "harmless images" to vent their negative emotions. The detection of these malicious pictures and text messages can help social media to correctly identify the expression of negative emotions and deal with them in a timely manner, thereby forming a good social atmosphere. The effective mode of current technology is for a single text or picture, so it is difficult and troublesome to solve this problem. Only text or image as a model can work well, but the detection of the meaning expressed by the combination of text and image cannot give appropriate results. This multimodality and benign confounding factors make it more difficult to detect malignant memes.
Pedestrian Skeleton Tracking Using OpenPose and Probabilistic Filtering

An essential task to prevent pedestrian injuries by an autonomous vehicle is the ability to correctly detect and predict its movement. A deep learning-based 2D human poses detector, as OpenPose, provides a skeleton of people present in an image captured by cameras mounted in the car. Nevertheless, these kinds of algorithms give a frame solution but do not capture the movement between them. Then, parts of the body are missed or the skeleton leaped to another part of the image where the infrastructure resembles a person. In this context, an algorithm based in the Kalman Filter algorithm to estimate the real skeleton including correlations in time and between parts of the body is presented. The algorithm was tested on videos using data provided by a vehicle moving in real scenarios. Results are presented that shown the capability of the algorithm to correct the mentioned loss of tracking.
Website Based Information System for Sales and Purchasing of Traditional Fish for Kalanganyar Residents

The purpose of this research is to create an information system that can be used for buying and selling websites. The research method used is the waterfall method, but the data collection techniques used are observation, interviews, and literature study. Where to get the data, it is necessary to come directly to the location which is the place of research. Later, the result of this research is that circles of people can sell products online and can expand the reach of website based marketing, buyers only need to access the website to make purchase transactions
Statement spectrum with two dimensional eigenvalues for intelligent software fault localization

The existing Software Fault Localization Frameworks (SFLF) based on program spectrum for estimation of statement suspiciousness have the problems that the feature type of the spectrum is single and the efficiency and precision of fault localization need to be improved. To solve these problems, a framework 2DSFLF proposed in this paper and used to evaluate the effectiveness of software fault localization techniques (SFL) in two-dimensional eigenvalues takes both dynamic and static features into account to construct the two-dimensional eigenvalues statement spectrum (2DSS). Firstly the statement dependency and test case coverage are extracted by the feature extraction of 2DSFLF. Subsequently these extracted features can be used to construct the statement spectrum and data flow spectrum which can be combined into the optimized spectrum 2DSS. Finally an estimator which takes Radial Basis Function (RBF) neural network and ridge regression as fault localization model is trained by 2DSS to predict the suspiciousness of statements to be faulty. Experiments on Siemens Suit show that 2DSFLF improves the efficiency and precision of software fault localization compared with existing techniques like BPNN, PPDG, Tarantula and so fourth.
Experimental Investigation of Mutual Collision Avoidance Behavior for Multiple Mobile Robots

Introductions of multiple mobile robots into real environments have been reported. In the environments where multiple robots co-exist together, a mutual collision-free motion planning with awareness of each other’s movements is important. This study focuses on a distributed system where each robot avoids obstacles and other robots without communication. Dynamic avoidance methods based on the prediction of the movement of moving objects have been proposed. However, when robots applying such methods confront each other, an oscillation problem where robots synchronously jiggle their avoiding direction is prevalent. This study proposes a way for realizing multi-robot collision avoidance eliminating or alleviating oscillation problems by extending the dynamic path planning method. We aimed to eliminate or alleviate the oscillation problems by finding the key parameters that affects and leads to different interaction behavior when plugged with different values. In this study, three parameters: sensing range, update frequency of path planning and robot’s maximum angular velocity, that may affect the robot’s behavior of obstacle avoidance are picked up. We experimentally verified the behavior of robots applying different parameters of the dynamic obstacle avoidance method when they face each other. From the experimental results, it was confirmed that the oscillation problem can be eliminate or alleviate by applying different parameters to the confronting robots.
Towards Improving Car Point-Cloud Tracking Via Detection Updates

Most autonomous driving applications leverage RGB images representing the surrounding environment that contain useful appearance features but with a cost in terms of geometric features. On the other side, 3D point clouds generated by LIDAR sensors can provide more geometric 3D information with high accuracy and robustness but with a loss on appearance features. Regardless of the adopted technology, object tracking in autonomous driving scenarios suffers from the so-called error drift in detecting objects over time/frames. This work investigates the car tracking problem in an urban scenario, leveraging 3D point clouds. In particular, we have set our goal to mitigate the typical error drift that characterizes the classic tracking algorithm and, to this aim, proposed a system able to reduce the drift error by detection. An extensive experimental evaluation on the KITTI dataset shows the improvement in our solution's performance compared to state-of-the-art approaches.
Research on the Quality Evaluation of Teaching Reform Project in Colleges Based on Computer

Abstract With the rapid development and change of society, the teaching of colleges should continue to follow up the reform. In addition, with the deepening of teaching reform in colleges, there is an urgent need to develop a quality evaluation system suitable for teaching reform projects. This paper first introduces the theoretical basis of the quality evaluation of teaching reform projects in colleges, then analyzes the construction of the quality evaluation index system of teaching reform projects based on computer, and finally introduces the quality evaluation methods of teaching reform projects.
The Improved Method for Image Encryption Using Fresnel Transform, Singular Value Decomposition and QR Code

In this paper, we fortify the double random phase encoding procedure (DRPE) for encrypting the input images along with QR code in Fresnel domain. In the proposed scheme, the wavelength (λ) and propagation distances (z1, z2) are encrypted keys. In the said scheme, there is no need of lenses. Therefore, this scheme is also called as lens-less technique. By this process images to be encrypted are used. In decryption, we need to use separate encryption keys, because each step is encrypted individually. Here the random phase mask (RPM), structure phase mask (SPM) and Fresnel parameters act as the decryption as well as encryption keys. The proposed idea is simulated using the MATLAB in computer system (Intel(R) Core (TM) i3-2328 CPU @ 2.20 GHz-2.71 GHz, 2GB RAM running Windows 10 on MATLAB R2019a 5(9.6..0.1174912) 64-bit (win64), LM: 40664749).
Source-side Reordering to Improve Machine Translation between Languages with Distinct Word Orders

English and Hindi have significantly different word orders. English follows the subject-verb-object (SVO) order, while Hindi primarily follows the subject-object-verb (SOV) order. This difference poses challenges to modeling this pair of languages for translation. In phrase-based translation systems, word reordering is governed by the language model, the phrase table, and reordering models. Reordering in such systems is generally achieved during decoding by transposing words within a defined window. These systems can handle local reorderings, and while some phrase-level reorderings are carried out during the formation of phrases, they are weak in learning long-distance reorderings. To overcome this weakness, researchers have used reordering as a step in pre-processing to render the reordered source sentence closer to the target language in terms of word order. Such approaches focus on using parts-of-speech (POS) tag sequences and reordering the syntax tree by using grammatical rules, or through head finalization. This study shows that mere head finalization is not sufficient for the reordering of sentences in the English-Hindi language pair. It describes various grammatical constructs and presents a comparative evaluation of reorderings with the original and the head-finalized representations. The impact of the reordering on the quality of translation is measured through the BLEU score in phrase-based statistical systems and neural machine translation systems. A significant gain in BLEU score was noted for reorderings in different grammatical constructs.
EaGWO: Extended algorithm of Grey Wolf Optimizer

Abstract The Grey Wolf Optimizer (GWO) and their very recent improved algorithms have some limitations such as stagnation in local optima, slow convergence rate, and so forth. In order to overcome these limitations, we have proposed an algorithm of GWO, named Extended algorithm of GWO (EaGWO), that modifies the hunting equation and encircling equation of the original GWO. The proposed algorithm has been examined on twenty-three very renowned test functions and compared the results with basic GWO and other very recent variants of GWO. This research work also considers the Friedman ANOVA test to verify the effectiveness of the proposed methodology. Consequently, the findings of this work justify quite competitive performance compared to other meta-heuristic algorithms.
The Relationship Of Online Learning With The Learning Results Of Students Of Class V Theme 8 

This study aims to determine the relationship between online learning and student learning outcomes in class V Theme 8 at SDIT EL HAQ Buduran. The research method used is non-experimental quantitative. The population used was all fifth grade students of Al Hazen SDIT EL HAQ and the sample in this study was 28 students. Data collection techniques used in the form of a questionnaire (questionnaire) and student learning outcomes tests. Data collection techniques through Microsoft Forms. The instrument used in the questionnaire (questionnaire) using a Likert scale, as well as learning outcomes tests in the form of multiple choice tests. The results of the study were then analyzed using the data normality test, linearity test and product moment correlation. Based on the results of the analysis, it can be concluded that there is a relationship between online learning and the learning outcomes of class V Theme 8 students at SDIT EL HAQ Buduran. From the results of this study, it is in the category of having a fairly strong relationship.
The Effects of Learning Media Based on Illustrated Story Books on Understanding Mathematical Concepts of Primary Schools

The purpose of this study is to determine the effect of illustrated story book based learning media on the understanding of mathematical concepts by third grade primary school students. The method used in this study is quasi-experimental research. The aspect measured in this study is comparing two study groups. The experimental group is the one using instructional media based on illustrated story books and the control group is that using conventional learning media. The population of data in this study are third grade primary school students (54 students) at Sukoharjo district elementary school in the 2018/2019 school year. The population is divided into two classes, namely 28 students for the experimental class and 26 students for the control class. Data sampling was done by cluster random sampling. Data collection techniques consisted of tests and observations. The data were analysed using t-test and descriptive statistics. The results revelaed that illustrated story books based learning media influence the understanding of mathematical concepts. The learning outcomes of third grade primary school students showed a significant value of less than 0.05 which is equal to 0,000.
Optimization of Imam Santri Scheduling at the Griya Tilawah Islamic Boarding School in the Areas Around Malang Using Genetic Algorithms

Every holy month of Ramadhan, the students will be placed in certain mosques around Malang to become imams. While every year there are always additional students, and the ability of each student is different. Therefore, every year the priest must re-schedule. One method that can be used to solve these problems is to use genetic algorithms. After forming the initial population, the individuals will be selected whether they are in accordance with what is expected or not. From the results of the selection will form a new population that is better. So it is expected that the resulting schedule is optimal.
The Effect of Using Literary Film to Improve Speaking Ability of the Tenth Grade Students at MA Al-Imaroh Wongsorejo

The thesis is about the effect of using literary film to improve speaking ability of the tenth grade student at MA Al Imarah Wongsorejo in the 2019/2020 academic year. The objective of this research is to know whether or not there is the effect of using literary film to improve speaking ability of the tenth grade student at MA Al Imarah Wongsorejo in the 2019/2020 academic year.The method used in this research is experimental quantitative method. The respondents of this research are 39 students of the tenth grade students. This research used purposive method to get the respondents of the research and the interview test to collect the data. The result of T- test is 13,420 and the T table value is 2,021 in the significant level 5% and degree of freedom was 37. It shows that the T-test value is higher than the value of T table. This data analysis is also strengthened by the average scores of students. The average score of the respondents’ posttest result was 73,974 which were higher than 39 in the experimental group that had gotten pretest. Based on the result of the data analysis and discussion, it can be concluded that, there is significant effect of literary film to improve speaking ability of Tenth Grade Students of MA Al Imarah Wongsorejo in 2019/2020 Academic year.
Twitter disables Trump campaign’s George Floyd video tribute

Last month, Twitter placed fact-check warnings on two tweets from Trump’s own account that called mail-in ballots “fraudulent” and predicted problems with the November U.S. elections.