Reviewerly/exHarmony huggingface dataset:
The planning rain water harvestingintegrated wells in public facilities in the village of Kayu Besi

Abstract Continuous and excessive use of groundwater causes a decrease in the water table, as well as drought during the dry season. Land use change causes reduced water infiltration, leading to flooding and inundation during the rainy season. This happened in KayuBesi Village, which is located in PudingBesar District, Bangka Regency. The purpose of this planning is to determine the dimensions of the rain water harvesting pool integrated with infiltration wells in the village office as the center of the village government and the mosque as the center of worship. The method used to calculate the volume of rainwater collected in a rain water harvesting pond is the result of substitution and modification made by Maryono from the relationship between discharge, volume of rainwater, and duration of rain with the relationship between discharge, speed of rainwater, and the cross-sectional area of the gutter pipe. The method used to calculate the depth of the infiltration well is the Sunjoto method. The results of the planning for the KayuBesi Village Office obtained the dimensions of the rain water harvesting pool with a pool length of 5 m, a pool width of 4 m and a pool height of 1.5 m, as well as an infiltration well with a diameter of 1 m and a depth of 2.5 m, while for the KayuBesi Village Mosque obtained the dimensions of the rain water harvesting pond with a pool length of 6 m, a pool width of 5.5 m, and a pool height of 1.6 m, as well as an infiltration well with a diameter of 1 m and a depth of 4.5 m.
A Scrambled Image Blind Evaluation Method Based on Space and Frequency Domain

Abstract Most of the image scrambling degree evaluation algorithms rely on the statistical features of the original image, which cannot achieve blind evaluation. Based on the uniform distribution model of the statistical characteristics of ideal scrambled images, a blind evaluation algorithm of image scrambling degree combining space and frequency domain is proposed in this paper. In the space domain, the uniform distribution characteristics of the gray histogram of the ideal scrambled image are used, and the uniform distribution characteristics of the Discrete Fourier Transform (DFT) spectrogram in the frequency domain are combined with the gray correlation analysis theory. The two are weighted to realize the space and frequency domain evaluation of the scrambled image performance. The experimental results indicate that the evaluation algorithm in this paper can consider the performance evaluation of both pixel value and pixel position dislocation, avoiding the disadvantage of ineffective spatial domain evaluation when only the pixel position scrambling is performed. It has very sensitive to the histogram distribution and frequency domain features of encrypted images, and has good agreement with Human Visual System (HVS). The original image is completely independent, and it enables blind evaluation objectively.
Usage of Saliency Prior Maps for Detection of Salient Object Features

Current headway on significant article identification is broad, progressing for the most part from the unstable extension of Convolutional Neural Networks (CNNs). In Computer Vision (CV) society, the examination on saliency location is more mainstream contrasted with the conventional manual item discovery. As of late striking article location is being utilized in different exercises such item following, and picture acknowledgment and so on Profound Learning CNNs have been successfully utilized to the errand of Salient Object Detection to a generally decent degree. In this paper, we propose a clever notable item identification method grounded on a blend of Recurrent Neural Networks (RNNs) and CNNs. Comparative with the current profound learning procedures, the created framework is fit for utilizing the manual customary strategy of Object Detection which utilizes saliency maps for extra exact inductions. The created framework utilizes two discrete frameworks to recognize striking articles and approve limit data. In this paper, we propose a clever article recognition network by thinking about the distinction between the two undertakings. In the proposed framework the consideration map for object order is created by zeroing in on the most recognizable article part as for object arrangement
unittest

The last chapter discussed the concepts of test automation. You learned about docstring and doctest and their use in writing simple, static, yet elegant test cases for Python 3 programs. However, due to the lack of features, like API, configurable tests, and test fixtures, doctest enjoys very limited popularity. You need to explore a powerful API library for automating complex real-life projects and learning Python's built-in unittest module is your first step toward it.
Iterative threshold segmentation method based on information entropy

Light strip extraction is important in a structured light measurement system. However, the extraction of light strip is incomplete, losing the information, in the detection of highly reflective surface. This paper presents an iterative threshold segmentation algorithm based on information entropy. Firstly, the image is initially segmented, then using the eightneighborhood detection to remove the noise and retain the light strip, after which the iterative segmentation is carried out with the inflection point of the information entropy of the extracted image as the termination condition to improve the integrity of the light strip segmentation and avoid the excessive segmentation of the background. Compared with the results of Otsu algorithm, the proposed method can retain more complete light strip information, with fewer breaking points.
Self help books and their titles – a lexical and syntactical analysis

The article presents results of an analysis of titles of English self-help books as animportant phenomenon of pop culture. The focus was placed on the role of the title bothas a key factor for the consumer decision-making process and as a carrier of culturalthought or an element of culture. Individual titles of self-help publications were dividedaccording to, among others, their length, structural and semantic features. The lexicallayer was discussed with particular emphasis on the elements that stand out from theselected research material. A syntactic analysis was undertaken in order to identify themost common structures to determine whether there is a rule for titling self-help booksthat would ensure their commercial success. The article also mentions the problemof intertextual references and rhetorical procedures that make titles more attractiveand perform advertising functions. The subject under scrutiny spans several branchesof science, including psychology and sociology.
Handling of The Environmental Drainage System In Jembrana District, Bali Province

Abstract The rapid growth of the urban population should be followed by the provision of basic infrastructure and facilities inadequate residential areas. One of them is the facilities and infrastructure for the environmental drainage system in residential areas to be free from standing water, especially during the rainy season. In Jembrana Regency, especially in Negara city and its surroundings, there are fourteen inundation points spread over 3 (three) districts. Drainage treatment is carried out by ranking priority scales based on inundation parameters. The parameters are inundation of height, length, frequency, and also the loss parameters due to inundation of economic facilities, public facilities, transportation facilities, housing, and casualties. With limited funds available, detailed planning is carried out on three inundation points with the highest priority score. The analysis shows that three inundation points receive priority for handling, namely Baler Bale Agung village’s environmental drainage system, Dauh Waru village, and environmental drainage Loloan Timur village. The drainage construction system uses an open channel with a U-ditch type and a box culvert with freecast concrete’s quality value of K-350.
The choice of vantage points in vp-tree construction for copy-move detection in digital images

Binary space partitioning trees are a known way to speed up searches in multidimensional spaces. VP-trees have a high efficiency among various types of binary space partitioning trees and have proven their advantages in solving the problem of finding duplicate fragments. In this paper, we investigate several ways to select vantage points in vp-tree construction. The study was conducted using several different types of images and feature extraction techniques.
On Smoothed MWSD Estimation of Mixing Proportion

The problem considered in the present paper is estimation of mixing proportions of mixtures of two (known) distributions by using the minimum weighted square distance (MWSD) method. The two classes of smoothed and unsmoothed parametric estimators of mixing proportion proposed in a sense of MWSD due to Wolfowitz(1953) in a mixture model F(x)=p (x)+(1-p) (x) based on three independent and identically distributed random samples of sizes n and , =1,2 from the mixture and two component populations. Comparisons are made based on their derived mean square errors (MSE). The superiority of smoothed estimator over unsmoothed one is established theoretically and also conducting Monte-Carlo study in sense of minimum mean square error criterion. Large sample properties such as rates of a.s. convergence and asymptotic normality of these estimators are also established. The results thus established here are completely new in the literature.
PAID HIGHER EDUCATION IN THE PERCEPTION OF STUDENTS AND TEACHERS: THE EXPERIENCE OF SOCIOLOGICAL ANALYSIS

In a dynamically developing society, the modernization of the higher education system needs an up-to-date understanding of trends, problems and prospects for implementation in each direction. The purpose of the work is to identify the specifics of the attitude to the introduction of paid higher education of students and teachers, that is, the main agents of the provision and consumption of educational services. The implementation of research tasks was achieved using a quantitative method of sociological research - a questionnaire among students and teachers of the Volgograd Institute of Management - a branch of the RANEPA. The results of the study demonstrate an ambiguous attitude to this aspect of modernization: 53% of the surveyed teachers consider the introduction of paid higher education a necessary measure, 67.9% of students do not share this position. Respondents unanimously believe that fee-based education can improve the financial situation of the university, its material, technical and research base. The main risk of the development of paid higher education is associated with the difficulties of students and their families related to the financial provision of tuition on a fee-based basis. The identified problems can be compensated by the development of alternative sources of financing for higher education, including targeted training, as well as the expansion of opportunities for obtaining social support for students and their families.
A Movie Score Prediction Model Based on XGBoost Algorithm

With the improvement of people’s quality of life, filmmaking has become an increasingly important part of the business industry. The score prediction before the movie is released has great reference value for the research of the related recommendation work. Although several studies have been done in this field, it is still necessary to improve the prediction performance of the movie scoring model. In this paper, an effective model is developed to predict movie score by constructing the feature engineering and using XGBoost for prediction. The whole experimental procedure strictly complies with the real-world scenario. The empirical evaluation results demonstrate the outperformance of our approach has good prediction ability on the real data.
Linear Additives

We introduce LAM, a subsystem of IMALL2 with restricted additive rules able to manage duplication linearly, called linear additive rules. LAM is presented as the type assignment system for a calculus endowed with copy constructors, which deal with substitution in a linear fashion. As opposed to the standard additive rules, the linear additive rules do not affect the complexity of term reduction: typable terms of LAM enjoy linear strong normalization. Moreover, a mildly weakened version of cut-elimination for this system is proven which takes a cubic number of steps. Finally, we define a sound translation from proofs of LAM into linear lambda terms of IMLL2, and we study its complexity.
Expert System for Assessing the Indoor Climate

The paper proposes an approach to the development of an expert system for assessing the room comfort. The core of the expert system is a fuzzy system, for which a knowledge base is created in the form of a set of production rules. To form an integrated assessment of comfort, the room is divided into significant and insignificant zones. Variants of aggregation functions for each zone are proposed.
Recommending Electricity Plan: A Significant Nearest Neighbors Collaborative Filtering Method

With the development of electricity market trading, building owners will possess the freedom to choose their preferred electricity retailers. This paper inspects the application of recommender system. Different from the traditional KNN (K-nearest neighbors)-based collaborative filtering method, we chooses the SNN (significant nearest neighbors)-based collaborative filtering method. The users with top relative similarity index values with respect to the target user are called SNN. This method effectively reduces the computational cost and improves the performance of the personalized recommendation system. By using weekly operation duration times of some typical electrical appliance data, the recommendation system will rate a large number of different plans, so that the homeowner can choose a more suitable electricity package. Different tests can evaluate the performance of the method. The instructions of the proposed method on electricity retailer helps to improve the competitive operation.
Research on Optimized Pedestrian Detection Based on Sliding Window

Pedestrian detection is a key part of image processing technology, which needs to accurately identify the pedestrian’s images. How to improve the robustness of the detection algorithm while maintaining high detection efficiency has always been a research topic. This paper first introduces the application of pedestrian detection, then discusses the current research, and introduces the principle of Histogram of Oriented Gradient feature extraction and Support Vector Machine classifier based on a sliding window. This study helps to enhance the contrast of the test image by histogram equalization and then uses multiple training methods to improve the performance of the model. Experiments are carried out by using a self-built training set and test set, and the test results show good results.
Dilemmas in the integration of technologies in a Primary school classroom: the dialogue between teacher agency, curriculum and digital technologies

The current study describes and analyse the day-to-day integration of digital technologies in a Year Three Primary Education classroom in Spain. The process of integrating technologies in the classroom and the difficulties and contradictions encountered are analysed in accordance with two key theories: teacher agency and the prescribed curriculum conveyed through textbooks. It is a qualitative research with an intrinsic case study, consisted of participant and non-participant observation, in-depth interviews, focus groups and document analysis. Results show that interactions between various personal, contextual and structural aspects, and their various combinations, forges and restructures teachers’ ICT practices, revealing the difficulties teachers experience in introducing technology-based innovations to the current curriculum. The observation of the process exposes a series of beliefs and contradictory practices that compromise the teacher’s agency in terms of her capacity to meet her initial objective of autonomously managing the curriculum.
Parameter Efficient Neural Networks With Singular Value Decomposed Kernels

Traditionally, neural networks are viewed from the perspective of connected neuron layers represented as matrix multiplications. We propose to compose these weight matrices from a set of orthogonal basis matrices by approaching them as elements of the real matrices vector space under addition and multiplication. Making use of the Kronecker product for vectors, this composition is unified with the singular value decomposition (SVD) of the weight matrix. The orthogonal components of this SVD are trained with a descent curve on the Stiefel manifold using the Cayley transform. Next, update equations for the singular values and initialization routines are derived. Finally, acceleration for stochastic gradient descent optimization using this formulation is discussed. Our proposed method allows more parameter-efficient representations of weight matrices in neural networks. These decomposed weight matrices achieve maximal performance in both standard and more complicated neural architectures. Furthermore, the more parameter-efficient decomposed layers are shown to be less dependent on optimization and better conditioned. As a tradeoff, training time is increased up to a factor of 2. These observations are consequently attributed to the properties of the method and choice of optimization over the manifold of orthogonal matrices.
Metafunction Analysis of Net Language

Net language resulting from the development of internet and social media serves a special tool for netizens to communicate and bond with each other in computer-mediated communication which involves not only synchronic but also asynchronous ways. Systemic Functional Linguistics believes that any language performs three metafunctions: ideational, interpersonal, and textual. This paper analyzes these three metafunctions of net language and elaborates on the specific field, tenor and mode of net language.
Joint Augmented Reality Video Analytics and Artificial Intelligence Supervision

Augmented Reality (AR) and Artificial Intelligence (AI) technologies have become ubiquitous in the public’s daily lives. In the AR space, many notable industry players are engaged in bringing smaller, more ergonomic, and more powerful AR headsets and smart-glasses to the market, with the eventual goal of these devices to replace mobile phones for a wide variety of tasks. With the improvements in computing power on AR devices, a similar surge in AI-powered technologies running on these devices is also evident.
‘This is a House’: Large Image Collections and Their Platform Embeddings

This paper analyses the active role of image collections in supporting platforms and their operations. Large image collections are increasingly present on media, scientific and other platforms. A case study of Facebook’s predictive modelling of satellite images of human settlement exemplifies how image collections are changing. The treatment of images in a predictive model – a deep neural network – constructs a condensed indexical field, a field that allows the platform to generate referential statements about the world. Under platform conditions, image collections function less as archives or records and more as densely woven indexical fields that orient, position and embed the platform. In describing the transformation of image collections, the paper points to important changes in how platforms use images to position themselves in the world.
Based on the Inception and the ResNet Module Improving VGG16 to Classify Commodity Images

With the advent of the new retail era, intelligent identification of goods in the shelf image has become an important technology for managing unmanned supermarkets. In recent years, classification methods based on convolutional neural networks have been widely used in image classification. In this paper, a deep neural network based on VGG16-IR is designed to improve the classification accuracy of low-resolution commodity images. First, Inception and residual ideas are integrated, and four modules are designed, namely Inception ResNet module 1, Inception ResNet module 2, ResNet module 3, and ResNet module 4. These modules replace the convolution phase of the original VGG16, and VGG16-IR replaces the new network. Secondly, a small batch gradient descent algorithm with momentum is used. Finally, the activation function of ELU is used to avoid the phenomenon of partial neuron necrosis. The experiment is conducted in Microsoft PI100 product image training and testing. The experimental results show that the model's accuracy in the training set reaches 99.69%, and the accuracy of the test set reaches 90.63%. Compared with 89.10% of VGG16, the model in this paper improves 1.5% and increases the generalization ability to a certain extent.
Application of Fuzzy Mathematics Algorithm in Computer Speech Recognition

Abstract Speech recognition, as one of the key artificial intelligence technologies in modern development, plays an important role in any aspect. However, there are still problems in practical application, such as poor anti-interference and low degree of perfection. Therefore, this paper aims at the content of existing computer speech recognition technology, grasps fuzzy mathematical algorithm, and analyzes how to use this algorithm to better study computer speech recognition.
Secure Analysis of Disaster Area Using Satellite Imagery and Deep Learning

Deep learning is a subset of machine learning based on learning data representations, in contrast to task-specific algorithms. Deep learning models derive inspiration from how information is processed in the nervous system of the human body that consists of trillions of neurons communicating with each other. During a disaster, it is necessary to ensure that containment and rescue operations are conducted as quickly as possible with a primary focus on affected areas, as an improper organization might lead to wastage of resources such as money, materials, and time. To properly plan during disasters, satellite images of the affected location can be analyzed to identify the areas demanding immediate attention. A model can be designed using convolutional neural networks (CNNs) to help categorize the areas by the degree of destruction. To secure data fed into the model, a layer of security can be added between the input and output layers of the CNN. The model can be trained using old satellite images of the cities. New images fed into the model can be analyzed to obtain information on the level of devastation.
A New Semantic Segmentation Network with FPFN and Dense ASPP

Aiming at the problem that the DeepLabv3+ network does not fully use the multi-scale feature information generated by the backbone network and the excessive atrous rate in the atrous spatial pyramid pooling (ASPP) leads to poor segmentation results for small targets, the FPDA DeepLabv3+ network is proposed. First, the backbone network was replaced with MobileNetv3 to reduce the number of model parameters, and then the ASPP connection method was changed to a dense connection to capture a larger receptive field. Finally, the Feature Pyramid Fusion Network (FPFN) was used to integrate the high-resolution features effectively. The experimental results show that the mIoU and mPA of the FPDA DeepLabv3+ network on the semantic segmentation data set AeroScapes verification set reached 82.98% and 91.36%, respectively, which fully verified that the network could effectively improve the prediction results on the data set AeroScapes.