Reviewerly/exHarmony huggingface dataset:
Couple Double-Stage FPNs with Single Pipe-Line for Solar Speckle Images Deblurring

Solar speckle images acquired by ground-based optical telescope usually are blurred or degraded seriously with more noise and fuzzy local details. Most of the deep learning deblurring methods are suitable for the natural images which have sufficient contextualized and gradient information, but for solar speckle images may cause some problems such as over-smoothing, high-frequency loss, and artifacts generated. In this paper, we propose a deblurring method based on coupling double-stage feature pyramid networks (FPN) with a single pipe-line (DSFSP) to reconstruct high-resolution solar speckle images. In stage1, one FPN is used to recover structure features; In stage2, another FPN is used to enhance the structural contextualized, and the single pipe-line coupled with this FPN is used to extract gradient information. After fusing these to generate a reconstructed image, discriminators are used to make it closer to the reference. Experiments show that DSFSP has a strong ability to strengthen the gradient spatial and contextualized information, improve image clarity, restore high-frequency details and drop artifacts.
Regression Models, Fantastic Beasts, and Where to Find Them: A Simple Tutorial for Ecologists Using R

Regression modeling is a workhorse of statistical ecology that allows to find relationships between a response variable and a set of explanatory variables. Despite being one of the fundamental statistical ideas in ecological curricula, regression modeling can be complex and subtle. This paper is intended as an applied protocol to help students understand the data, select the most appropriate models, verify assumptions, and interpret the output. Basic ecological questions are tackled using data from a fictional series, "Fantastic beasts and where to find them," with the aim to show how statistical thinking can foster curiosity, creativity and imagination in ecology, from the formulation of hypotheses to the interpretation of results.
Hybrid Feature and Sequence Extractor based Deep Learning Model for Image Caption Generation

Generating the captions from the input images is a crucial problem, it includes both computer vision and natural language processing. In this paper, we introduced a hybrid feature and sequence extractor-based deep learning model that helps in generating captions for the input images. The proposed generative model is designed using a deep convolution neural network (VGG-19) for extracting the salient feature vectors from the images. While producing the informative captions from these feature vectors, the LSTM (Long Short Term Memory) network is utilized. The model is trained to expand the probability of the target description sentence delivered to the training image. In the analysis, the model is trained with very renowned data sets like FLICKR-8K, and the correctness of the model is calculated utilizing the BLEU score granting the range 0 to 100. We compare our model based on the BLEU score with four other models.
Rancang Bangun Sistem Informasi Bank Sampah di Desa Paguyangan

Garbage is the residual material resulting from a production process, both industry and households. Based on Brebes district's statistical data, the Paguyangan sub-district is in fourth place with an average of 249.62 m3 of garbage per day. Paguyangan is one of the villages in the Paguyangan sub-district. Paguyangan has a garbage problem where people still have the habit of throwing garbage in the river and yards around the house. Based on this, the Paguyangan Village Hall plans to build a garbage bank to overcome the problem. A garbage bank managed activities such as recording saving's transactions. This study conducted a web-based design of the Garbage Bank Information System (SIBS). SIBS is an information system used to help process garbage transactions at a garbage bank. The purpose of this SIBS is to facilitate officers and customers in processing services at the garbage bank. The methodology used in system development is the Waterfall method. SIBS development uses PHP as a programming language and MySql as the DBMS. The result of this research is an information system that can be used in the management of Waste Management transactions. The results of system testing using Black box Testing and Mean Opinion Score (MOS) obtained that all components in this system are running well according to needs.
Malicious URLs detection using data streaming algorithms

As a result of advancements in technology and technological devices, data is now spawned at an infinite rate, emanating from a vast array of networks, devices, and daily operations like credit card transactions and mobile phones. Datastream entails sequential and real-time continuous data in the inform of evolving stream. However, the traditional machine learning approach is characterized by a batch learning model. Labeled training data are given apriori to train a model based on some machine learning algorithms. This technique necessitates the entire training sample to be readily accessible before the learning process. The training procedure is mainly done offline in this setting due to the high training cost. Consequently, the traditional batch learning technique suffers severe drawbacks, such as poor scalability for real-time phishing websites detection. The model mostly requires re-training from scratch using new training samples. This paper presents the application of streaming algorithms for detecting malicious URLs based on selected online learners: Hoeffding Tree (HT), Naïve Bayes (NB), and Ozabag. Ozabag produced promising results in terms of accuracy, Kappa and Kappa Temp on the dataset with large samples while HT and NB have the least prediction time with comparable accuracy and Kappa with Ozabag algorithm for the real-time detection of phishing websites.
A Comparative Study of Educational Texts for Native, Foreign, and Bilingual Young Speakers of Russian: Are Simplified Texts Equally Simple?

Studies on simple language and simplification are often based on datasets of texts, either for children or learners of a second language. In both cases, these texts represent an example of simple language, but simplification likely involves different strategies. As such, this data may not be entirely homogeneous in terms of text simplicity. This study investigates linguistic properties and specific simplification strategies used in Russian texts for primary school children with different language backgrounds and levels of language proficiency. To explore the structure and variability of simple texts for young readers of different age groups, we have trained models for multiclass and binary classification. The models were based on quantitative features of texts. Subsequently, we evaluated the simplification strategies applied to readers of the same age with different linguistic backgrounds. This study is particularly relevant for the Russian language material, where the concept of easy and plain language has not been sufficiently investigated. The study revealed that the three types of texts cannot easily be distinguished from each other by judging the performance of multiclass models based on various quantitative features. Therefore, it can be said that texts of all types exhibit a similar level of accessibility to young readers. In contrast, binary classification tasks demonstrated better results, especially in the R-native vs. non R-native track (with 0.78 F1-score), these results may indicate that the strategies used for adapting or creating texts for each type of audience are different.
Auxiliary Fiber Art Creation Design Based on Conductive Fiber Textile Wireless Structure Sensor

The integration of fiber materials into modern pottery creation is an attempt to explore its boundaries as a specific material and art form. Fiber materials, such as fabric and paper, are not resistant to high temperatures, and the clay attached to them can retain the surface texture, texture, and original three-dimensional form of the fiber materials intact during the kiln firing process, making up for the defects of single material molding and maintaining the visual effect of ultrathin and highly translucent works. The light source inside the work is more conducive to creating a specific artistic atmosphere. The purpose of this paper is to explore how fiber materials become the basis of ceramic works and the source of decorative expression, so that this expression and process can be systematically analyzed and interpreted in the application of ceramic art creation. Along with the rapid development of nanotechnology, electronics, and optical technology, people’s clothing fabrics have been increasing in demand in terms of function and appearance. This paper focuses on the research and development of fiber textiles from the field of science and technology and discusses the current status of fiber textiles and the possibility of combining fiber art with science and technology. In this paper, wood cellulose-multiwalled carbon nanotube/wood cellulose composite films were prepared, as well as wood cellulose films and wood cellulose/multiwalled carbon nanotube composite films. The optimal reaction time for the preparation of the films was 2 h, and the optimal reaction temperature was 70°C. Experimental results show that the dispersibility of multilayer carbon nanotubes in wood cellulose multilayer carbon nanotubes/wood cellulose composite films in wood cellulose multilayer carbon nanotubes composite films is superior. If the amount of multilayered carbon nanotubes was 3 wt%, the fracture point extension and accessibility of the wood cellulose multilayer carbon nanotubes/wood cellulose composite film are 12.2% and 106.7 MPa, respectively. It is 93.7%, respectively. 10.7% is higher than wood cellulose/multilayered carbon nanotube composite films.
A Corpus for Dimensional Sentiment Classification on YouTube Streaming Service

The streaming service platform such as YouTube provides a discussion function for audiences worldwide to share comments. YouTubers who upload videos to the YouTube platform want to track the performance of these uploaded videos. However, the present analysis functions of YouTube only provide a few performance indicators such as average view duration, browsing history, variance in audience’s demographics, etc., and lack of sentiment analysis on the audience’s comments. Therefore, the paper proposes multi-dimensional sentiment indicators such as YouTuber preference, Video preferences, and Excitement level to capture comprehensive sentiment on audience comments for videos and YouTubers. To evaluate the performance of different classifiers, we experiment with deep learning-based, machine learning-based, and BERT-based classifiers to automatically detect three sentiment indicators of an audience’s comments. Experimental results indicate that the BERT-based classifier is a better classification model than other classifiers according to F1-score, and the sentiment indicator of Excitement level is quite an improvement. Therefore, the multiple sentiment detection tasks on the video streaming service platform can be solved by the proposed multi-dimensional sentiment indicators accompanied with BERT classifier to gain the best result.
Тhe problem of continuity in the study of the topic "function" between school and university

The article deals with the problem of succession in the study of the topic "Function" between school and university mathematics courses and presents analysis and interpretation of the main results of this test study. The problem of continuity in teaching mathematics is connected with the tasks of implementing intra-subject and meta-subject connections, with the sequence of presentation of educational material, the levels of increasing its complexity, with the search for optimal forms and methods of organizing the process of teaching mathematics at different educational stages. In this regard, the article deals with the problem of continuity in the study of the topic "Function" between the school and university courses of mathematics, presents an analysis and interpretation of the main results of the knowledge and skills of first-year students in three different streams with the Russian language of instruction.
Storytelling in a frameless screen: Screenwriting for VR and AR at Pentridge Heritage Precinct

Cinematic virtual reality (CVR) and augmented reality (AR) technologies are emerging as storytelling media that enable practitioners to move from traditional 2D filmmaking to explore narratives within a frameless screen. This article describes the challenges of writing an interactive extended reality (XR) documentary for a site-specific location. It focuses on the award-winning, practice-led research project, A Miscarriage of Justice XR , produced at Melbourne’s Pentridge Prison heritage precinct in 2020. It claims that the screenwriter is the principal designer of augmented and virtual reality documentaries and that conceiving and composing XR experiences requires taking an “experience design” (XD) approach, exploiting the “user’s experience” (UX).
Decision Support System for Foundation Beneficiaries Using the Simple Additive Weighting (SAW) Method

Receipt of compensation funds for underprivileged students needs to be selected so that they are not wrong in providing assistance, so that the recipients of assistance are students who deserve to receive. The decision support system is an effective way of selecting students who need compensation funds. The criteria are as a stage for selecting students who are entitled to grant aid, with these criteria all students who participate in receiving the compensation funds will be ranked. The results of the ranking are based on calculations, the first order is Farhan Rifai with a value of0.8; second place is Sidratul Muntaha with a value of 0.7666
The Role of Product Innovation on an Improvement of the Organization's Reputation /A Field Study in State Organization for Marketing of Oil (SOMO)

This paper aims to verify the existence of relationships between product innovation and the reputation of the organization. The study problem is that the State Organization for Marketing of Oil (SOMO) system is inflexible in terms of marketing procedures and needs innovative, unconventional methods in innovating its products and improving performance. The reputation of the organization. The importance of the study lies in that it is an attempt to raise the interest of SOMO in its approach to the research variables in order to enhance its competitive position in the future and improve the marketing business environment, which contributes to enhancing the reputation of the organization by product innovation. The study sample included 221 of SOMO employees and employees. Data and information were collected using questionnaires and personal interviews. As well as, the field presence of the researchers. The data were analyzed using the statistical program (SPSS v.24). The most prominent findings of the study showed that any change in product innovation can contribute to bringing about a change in the reputation of the organization
Study on the Continuation Task of China’s New College Entrance Examination Based on the Interactive Alignment Theory

With the extensive implementation of China’s new college entrance examination policy, the Continuation Task in the new college entrance examination has attracted wide attention from the academic circle, which also gives rise to plentiful relevant research. In the past, most of the research only focused on university foreign language education, which was rarely involved in English teaching in primary and secondary schools. Based on the Interactive Alignment Theory, this paper is going to analyze and study the Continuation Task, and try to put forward some teaching strategies for the teaching of the new task, so as to improve student's English proficiency level. According to the research, this paper finds out that "The Continuation Task" can give full play to the alignment effect between reading and writing, promote writing through reading, and thus promote the improvement of students' foreign language proficiency. Therefore, this essay can provide some insights into the teaching of English in high schools.
VIDEO CONTROLLING USING HAND GESTURE FOR DISABLED PEOPLE USING CONVEX HULL TECHNIQUE

The development of human-computer interaction (HCI) is a non-tangible way of communication between a human and a computer.Human hand gesture recognition (HGR) is one of the main topics in the area of computer science as an effective approach for HCI.Our project aims at developing a video player controlled by human hand gestures using the Convex Hull technique.This application uses the webcam to capture gestures by the user and perform basic operations such as play and pause.The gesture would serve as the direct command for operations such as play or pause the video based on the user's gestures onto the screen.The project is implemented using Python, OpenCV.We can use our hand gestures to control certain functions of a computer like playing or pausing a video, increasing and decreasing volume, etc.
Comparison Between Two Multinomial Overdispersion Models Through Simulation

A key assumption when using the multinomial distribution is that the observations are independent. In many practical situations, the observations could be correlated or clustered and the probabilities within each cluster might vary, which may lead to overdispersion. In this paper we discuss two well-known approaches to model overdispersed multinomial data, the Dirichlet-multinomial model and the finite-mixture model. The difference between these two models has been illustrated via simulation study. The forest pollen data is considered as a practical example of overdisperse multinomial data. The overdispersion parameter,φ, has been estimated using two classical estimators.&#x0D; Dhaka Univ. J. Sci. 68(1): 45-48, 2020 (January)
New One-Dimensional Search Iteration Algorithm and Engineering Application

In structural optimization design, obtaining the optimal solution of the objective function is the key to optimal design, and one-dimensional search is one of the important methods for function optimization. The Golden Section method is the main method of one-dimensional search, which has better convergence and stability. Based on the solution of the Golden Section method, this paper proposes an efficient one-dimensional search algorithm, which has the advantages of fast convergence and good stability. An objective function calculation formula is introduced to compare and analyse this method with the Golden Section method, Newton method, and Fibonacci method. It is concluded that when the accuracy is set to 0.1, the new algorithm needs 3 iterations to obtain the target value. The Golden Section method takes 11 iterations, and the Fibonacci method requires 11 iterations. The Newton method cannot obtain the target value. When the accuracy is set to 0.01, the number of iterations of the new method is still the least. The optimized design of the T-section beam is introduced for engineering application research. When the accuracy is set to 0.1, the new method needs 3 iterations to obtain the target value and the Golden Section method requires 13 iterations. When the accuracy is set to 0.01, the new method requires 4 iterations and the Golden Section method requires 18 iterations. The new method has significant advantages in the one-dimensional search optimization problem.
Bounded Affine Permutations II. Avoidance of Decreasing Patterns

We continue our study of a new boundedness condition for affine permutations, motivated by the fruitful concept of periodic boundary conditions in statistical physics. We focus on bounded affine permutations of size N that avoid the monotone decreasing pattern of fixed size m. We prove that the number of such permutations is asymptotically equal to $$(m-1)^{2N} N^{(m-2)/2}$$ times an explicit constant as $$N\rightarrow \infty $$ . For instance, the number of bounded affine permutations of size N that avoid 321 is asymptotically equal to $$4^N (N/4\pi )^{1/2}$$ . We also prove a permuton-like result for the scaling limit of random permutations from this class, showing that the plot of a typical bounded affine permutation avoiding $$m\cdots 1$$ looks like $$m-1$$ random lines of slope 1 whose y intercepts sum to 0.
A Data-Centric Approach to Improve Machine Learning Model’s Performance in Production

Machine learning teaches computers to think in a similar way to how humans do. An ML models work by exploring data and identifying patterns with minimal human intervention. A supervised ML model learns by mapping an input to an output based on labeled examples of input-output (X, y) pairs. Moreover, an unsupervised ML model works by discovering patterns and information that was previously undetected from unlabelled data. As an ML project is an extensively iterative process, there is always a need to change the ML code/model and datasets. However, when an ML model achieves 70-75% of accuracy, then the code or algorithm most probably works fine. Nevertheless, in many cases, e.g., medical or spam detection models, 75% accuracy is too low to deploy in production. A medical model used in susceptible tasks such as detecting certain diseases must have an accuracy label of 98-99%. Furthermore, that is a big challenge to achieve. In that scenario, we may have a good working model, so a model-centric approach may not help much achieve the desired accuracy threshold. However, improving the dataset will improve the overall performance of the model. Improving the dataset does not always require bringing more and more data into the dataset. Improving the quality of the data by establishing a reasonable baseline level of performance, labeler consistency, error analysis, and performance auditing will thoroughly improve the model's accuracy. This review paper focuses on the data-centric approach to improve the performance of a production machine learning model.