Reviewerly/exHarmony huggingface dataset:
A Criminisi-DnCNN Model-Based Image Inpainting Method

Existing image inpainting methods achieve unideal results in dealing with centralized inpainting areas. For this reason, in this study, a Criminisi-DnCNN model-based image inpainting method is proposed. Inspired by the manual inpainting technology, the pointwise mutual information (PMI) algorithm was adopted to obtain the marginal structural map of the images to be repaired. Then, the Criminisi algorithm was used to restore the marginal structure to obtain the complete marginal structure image guided by the superficial linear structure. Finally, the problem of texture inpainting was converted into the counterpart of image denoising through the separation of variables by using the denoising convolutional neural network image denoiser (DnCNN). Compared with the existing inpainting methods, this model has improved the clarity of the marginal structure and reduced the blurring of the area to be repaired.
Strategies for Effective Government Advocacy

The Membership Management ReportVolume 18, Issue 9 p. 4-4 Member Advocacy Efforts Strategies for Effective Government Advocacy Kim Pawlak, Kim PawlakSearch for more papers by this author Kim Pawlak, Kim PawlakSearch for more papers by this author First published: 02 August 2022 https://doi.org/10.1002/mmr.31983Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume18, Issue9September 2022Pages 4-4 RelatedInformation
Outlier quantification for multibeam data

Abstract This paper discusses the challenges of applying a data analytics pipeline for a large volume of data as can be found in natural and life sciences. To address this challenge, we attempt to elaborate an approach for an improved detection of outliers. We discuss an approach for outlier quantification for bathymetric data. As a use case, we selected ocean science (multibeam) data to calculate the outlierness for each data point. The benefit of outlier quantification is a more accurate estimation of which outliers should be removed or further analyzed. To shed light on the subject, this paper is structured as follows: first, a summary of related works on outlier detection is provided. The usefulness for a structured approach of outlier quantification is then discussed using multibeam data. This is followed by a presentation of the challenges for a suitable solution, and the paper concludes with a summary.
gRosSo: mining statistically robust patterns from a sequence of datasets

Abstract Pattern mining is a fundamental data mining task with applications in several domains. In this work, we consider the scenario in which we have a sequence of datasets generated by potentially different underlying generative processes, and we study the problem of mining statistically robust patterns , which are patterns whose probabilities of appearing in transactions drawn from such generative processes respect well-defined conditions. Such conditions define the patterns of interest, describing the evolution of their probabilities through the datasets in the sequence, which may, for example, increase, decrease, or stay stable, through the sequence. Due to the stochastic nature of the data, one cannot identify the exact set of the statistically robust patterns by analyzing a sequence of samples, i.e., the datasets, taken from the generative processes, and has to resort to approximations. We then propose gRosSo , an algorithm to find rigorous approximations of the statistically robust patterns that do not contain false positives or false negatives with high probability. We apply our framework to the mining of statistically robust sequential patterns and statistically robust itemsets. Our extensive evaluation on pseudo-artificial and real data shows that gRosSo provides high-quality approximations for the problem of mining statistically robust sequential patterns and statistically robust itemsets.
Kalman Filter-Based Differential Privacy Federated Learning Method

The data privacy leakage problem of federated learning has attracted widespread attention. Using differential privacy can protect the data privacy of each node in the federated learning, but adding noise to the model parameters will reduce the accuracy and convergence efficiency of the model. A Kalman Filter-based Differential Privacy Federated Learning Method (KDP-FL) has been proposed to solve this problem, which reduces the impact of the noise added on the model by Kalman filtering. Furthermore, the effectiveness of the proposed method is verified in the case of both Non-IID and IID data distributions. The experiments show that the accuracy of the proposed method is improved by 0.3–4.5% compared to differential privacy federated learning.
Two-Stage and Two-Channel Attention Single Image Deraining Network for Promoting Ship Detection in Visual Perception System

Image deraining ensures the visual quality of images to prompt ship detection for visual perception systems of unmanned surface vessels. However, due to the insufficiency of captured rain streaks features and global information, current image deraining methods often face the issues of rain streaks remaining and image blurring. Consider that the visual perception system captures the same useful information during rainy and hazy days, and only the way in which the image degrades is different. In addition, rainy days are usually accompanied by hazy days at the same time. In this paper, a two-stage and two-channel attention single image deraining network is proposed. Firstly, the subpixel convolution up-sampling module is introduced to increase the range of captured features and improve the image clarity. Secondly, the attention mechanism is integrated with the pyramid multi-scale pooling layer, so that the network can accumulate context information in a local to global way to avoid the loss of global information. In addition, a new composite loss function is designed, in which a regular term loss is introduced to maintain the smoothness and a perceptual loss function is employed to overcome the problem of large differences in the output of the loss function due to outliers. Extensive experimental results on both synthetic and real-world datasets demonstrate the superiority of our model in both quantitative assessments and visual quality by comparing with other state-of-the-art methods. Furthermore, the proposed deraining network is incorporated into the visual perception system and the detection accuracy of ships on rainy seas can be effectively improved.
Which Finite Rings Have the Most or Fewest Units?

Among finite rings of a fixed order that are not fields, we classify those that have the most units. Our methods also give the finite rings that have the fewest units.
Design of LED lighting system using solar powered PV cells for a proposed business complex

The paper outlines the concepts and design of an upcoming stand-alone solar photovoltaic system to supply the energy needs of a new proposed business complex. The purpose of this study is to develop a prediction method for the use of solar energy for commercial purposes. Firstly, the annual energy demand for the illumination and utilities in a business complex is calculated. LED lights are considered in the complex for the illumination to minimize the cost of energy. Based on the load estimation, the number of solar panels are predicted as 6097, to generate the power for the proposed area. Secondly, the important part of the design in the estimation of solar radiation and optimal tilt angle of a photovoltaic panel has been calculated for the maximum energy harvest. In this case the optimal tilt angle is 49.34°. The installation of PV panels for optimal and feasible operation is also predicted. The calculated parameters are used in a simulation with a software to test their practicality in the business complex. The technique is used to determine the amount of energy produced and system's performance ratio. A cost estimate is also provided for the solar PV system. In the end, an analysis of these simulations and estimations is presented.
Measuring nesting

Nesting is a fundamental and very commonly used programming construct. In many cases, the wise use of nesting contributes significantly to a programming team elegantly designing a solution to a difficult problem. However, the ease with which nesting constructs may be created and the essentially unlimited depth and breadth to which they may exist also enable a programming team to create programs that may be extremely difficult to understand and maintain. In this paper, nesting and nesting metrics are examined. The authors begin the nesting examination/discussion with a precise definition of the scope of programme selection nodes. The term ‘nodes’ instead of ‘statements’ is used because this work is done on control flow graphs of programme modules. Although there has been much work done on metrics related to nesting, there has not been significant work done on metrics that focus entirely on nesting. Explicit definitions for nesting depth and nesting breadth metrics are given for programme modules. The metrics are validated by comparing them to probably the best known nesting metric, the MaCabe cyclomatic metric. Also, the metrics are compared to the standard lines of code metric. Further, a new nesting concept, the nesting tree, is discussed and it is proposed that nesting trees themselves could be useful nesting measurement values.
Noise-Regularized Advantage Value for Multi-Agent Reinforcement Learning

Leveraging global state information to enhance policy optimization is a common approach in multi-agent reinforcement learning (MARL). Even with the supplement of state information, the agents still suffer from insufficient exploration in the training stage. Moreover, training with batch-sampled examples from the replay buffer will induce the policy overfitting problem, i.e., multi-agent proximal policy optimization (MAPPO) may not perform as good as independent PPO (IPPO) even with additional information in the centralized critic. In this paper, we propose a novel noise-injection method to regularize the policies of agents and mitigate the overfitting issue. We analyze the cause of policy overfitting in actor–critic MARL, and design two specific patterns of noise injection applied to the advantage function with random Gaussian noise to stabilize the training and enhance the performance. The experimental results on the Matrix Game and StarCraft II show the higher training efficiency and superior performance of our method, and the ablation studies indicate our method will keep higher entropy of agents’ policies during training, which leads to more exploration.
Internet of agents system for age and gender classification using grasshopper optimization with deep convolution neural networks

Abstract The internet of agents (IoA) is an emergent model, which mainly intends to resolve the limitations of the internet of things (IoT) devices with respect to reasoning and social competencies for improving proactivity, intelligence, and interoperability. This article presents a novel grasshopper optimization with deep learning enabled multi‐agent system for age and gender classification (GOADL‐MASAGC) model for IoA. The proposed GOADL‐MASAGC technique intends to categorize age as well as gender. The proposed GOADL‐MASAGC technique applies a multi‐agent system, which incorporates distinct processes for age and gender classification like pre‐processing, feature extraction, and classification. Besides, the GOADL‐MASAGC technique enables to performance concurrent process of classification and regression to identify age and gender respectively. In addition, the GOA with Capsule Network (CapsNet) model was executed for deriving a suitable group of feature vectors and the GOA is employed as a hyperparameter optimizer. Finally, wavelet kernel extreme learning machine (WKELM) was employed as a classifier for gender classification and deep belief network (DBN) is used as a regression approach for age recognition. For demonstrating the improved performance of the GOADL‐MASAGC model, a series of simulations were executed and the outcomes are examined in various aspects. The extensive comparative analysis reported the enhanced outcomes of the GOADL‐MASAGC approach over the existing methods.
Developed an Automated Design Sorting System According to Outer Edges of Object

The automated sorting systems are used in the industrial sectors to increase the rate of production. This research developed the sorting system by using a vision machine to detect the matching of capturing image with the storage base image. The system will be matching and sorting in real-time with 5 cm/s conveyor belt speed. The vision system is based on three stages to arrive at the sorting decision. The first stage is to covert the capturing image to a binary image, the second stage is applying edge detection of the product, and the third stage is matching this result with the base image. The system was successful to sort any product with complexity in shape and with high efficiency. The system sorting can be detected and sorted any product/machine element at any position or orientation. The system uses real-time analysis in order to provide the required results. The results arrived at the sorting gate at the end conveyer belt of the system if open that means the product matching. Three different products were selected in order to investigate the response and the accuracy of the results. It was found that the maximum of error to detect the product is not exceeding 2% for all cases.
Understanding the Roles of Video and Sensor Data in the Annotation of Human Activities

Human activities can be recognized in sensor data using supervised machine learning algorithms. In this approach, human annotators must annotate events in the sensor data which are used as input to supervised learning algorithms. Annotating events directly in time series graphs of data streams is difficult. Video is often collected and synchronized to the sensor data to aid human annotators in identifying events in the data. Other work in human activity recognition (HAR) minimizes the cost of annotation by using unsupervised or semi-supervised machine learning algorithms or using algorithms that are more tolerant of human annotation errors. Rather than adjusting algorithms, we focus on the performance of the human annotators themselves. Understanding how human annotators perform annotation may lead to annotation interfaces and data collection schemes that better support annotators. We investigate the accuracy and efficiency of human annotators in the context of four HAR tasks when using video, data, or both to annotate events. After a training period, we found that annotators were more efficient when using data alone on three of four tasks and more accurate when marking event types when using video alone on all four tasks. Annotators were more accurate when marking event boundaries using data alone on two tasks and more accurate using video alone on the other two tasks. Our results suggest that data and video collected for annotation of HAR tasks play different roles in the annotation process and these roles may vary with the HAR task.
Instantaneous global horizontal irradiance and clearness index data with high temporal resolution for 1016 days

Global Horizontal Irradiance was measured using a thermopile-type pyranometer during more than three years using a sample time of two seconds, with the purpose of capturing fast transient events of irradiance which are notable in tropical regions as the one where these data were collected: Bogotá, Colombia. The date and time of each measurement were registered along with the irradiance values. In addition, other related quantities were calculated and included for each one of the measurement instants: Optical Air Mass, Zenith angle, Extraterrestrial Solar Irradiance, and Clearness Index. Daily aggregated statistics of irradiance were calculated and are provided here too. Data points corresponding to nights were discarded. The raw data was analyzed to remove incomplete days, to guarantee that daily statistics are accurate and meaningful. After this data cleaning process, 1016 complete days remain, having a total of 21,959,912 data points. These data are useful for studying the effect of irradiance transients over photovoltaic systems, including power electronics, batteries and electric loads; it can also be used in studies about the stability of the radiative regime or the variability of irradiance such as Avila et al. (2019) (where part of these data was effectively used) and other related works cited there.
A partial evaluation methodology for optimizing rewrite theories incrementally

Partial evaluation (PE) is a branch of computer science that achieves code optimization via specialization. This article describes a PE methodology for optimizing rewrite theories that encode concurrent as well as nondeterministic systems by means of the Maude language. The main advantages of the proposed methodology can be summarized as follows:•An automatic program optimization technique for rewrite theories featuring several PE criteria that support the specialization of a broad class of rewrite theories.•An incremental partial evaluation modality that allows the key specialization components to be encapsulated at the desired granularity level to facilitate progressive refinements of the specialization.•All executability theory requirements are preserved by the PE transformation. Also the transformation ensures the semantic equivalence between the original rewrite theory and the specialized theory under rather mild conditions.
FQRS: Farmer Query Redressal System Using Open-Source Framework

The Farmer query redressal system (FQRS) is an application designed as a chatbot. The FQRS is developed with the help of machine learning and deep learning techniques. The FQRS interacts with the farmers and this system can understand and respond totext or speech. The FQRS is developed using the nlu-based open-source framework called Rasa. The Rasa framework uses building blocks such as the nlu and nlu core. The Rasa nlu and Rasa core are the building blocks for the development of a conversational chatbot. The FQRSwill use natural language processing and mimic conversations with the customers or users in a manner identical to a human being. The FQRS is implemented in Rasa 3.0. In this paper, to address farmer-related query, a chatbot has been created and the results show that the Rasa framework is more versatile than other chatbot developments platforms for creating chatbots. The results show that farmers&rsquo;queries can be effectively addressed with the proper training of the model.
Analysis of Pruning Optimization Technology Based on Deep Learning

Neural network models have become a major topic in many areas in recent years due to their excellent problem-solving ability. However, during the training process in the real application, neural network models usually have relatively high requirements on the performance of the platform, which limits their applications, especially in battery-driven devices or embedded platforms with relatively weak performance. Therefore, the importance of optimizing and pruning neural network models is very important. This paper analyzes four categories of pruning techniques, including channel pruning, neuron pruning, weight pruning and layer pruning. This paper investigates its main ideas and particular pruning methods and compares the optimised effects of these pruning techniques in different neural network models through experiments. This paper discusses neuron pruning on the VGG-16 model, weight pruning on ResNet and AlexNet, and layer pruning on the YOLOV3 model, respectively. The parameter amount and inference speed before and after pruning are compared, and the accuracies drop before and after pruning are verified on data sets such as CIFAR-10.
Research on Marketing Strategy of Electric Vehicle Based on Bayesian Processing and Natural Language Analysis

The automobile industry is an important pillar of the national economy, and the new energy automobile industry is a strategic emerging industry. This paper carries on the mathematical modeling analysis of the new energy electric vehicle. First of all, using the naive Bayesian processor in natural language processing, make a judgment through theme analysis and emotion analysis, define the coefficient into the model, and finally compare target customers' satisfaction with different brands of cars. Then use DBSCAN and UMAP algorithm to divide the fluctuating data index on the sequence, analyze its linkage by establishing linkage factor to study its correlation degree, and then quantify the problem, establish abnormal coefficient, analyze the persistence of data run out for a single index, and establish an algorithm for evaluating abnormal coefficient. According to the distribution of different data indicators, the impact index of different factors on the sales of different brands of electric vehicles is determined.
INVESTIGATING HIGH-SCHOOL EFL LEARNERS’ FOREIGN LANGUAGE ANXIETY: A CASE STUDY AT SMA 4 BANDA ACEH

This study aimed to investigate the level of foreign language anxiety (FLA) experienced by senior-high-school English language learners (ELLs) and to explore plausible factors that contribute to the learners’ anxiety. This research employed a qualitative descriptive method with a case study approach, involving thirty-four twelfth-grade ELLs from SMA 4 Banda Aceh, Indonesia, as participants. In gathering the data, Horwitz et al.'s (1986) Foreign Language Speaking Anxiety Scale (FLCAS) and semi-structured interview were used as instruments. The findings revealed that the majority of the ELLs were at a moderate level of anxiety, with more than half of them being at “Mildly Anxious” level and with no “Very Anxious” learners. The findings also indicated that the ELLs’ FLA was caused not only by teacher factors but also student factors. The teacher factors include oral presentation task, incomprehensible input, and teachers’ attitude, while student factors comprise learners’ fear of being laughed at, their’ beliefs about L2, and lack of preparation.
亚院二年级学生使用方位词“里”与“中”的偏误分析 KESALAHAN PENGGUNAAN KATA BENDA PENUNJUK ARAH “LI” DAN “ZHONG” PADA MAHASISWA STBA-PIA SEMESTER IV T.A. 2018/2019

This study aims to describe the types of errors and the factors that cause errors in the use of directional nouns “li” and “zhong” made by STBA-PIA students in the fourth semester of academic year 2018/2019. The research method used is descriptive qualitative, namely to describe the types of errors and the factors of errors. The instruments used in this research are elicitation techniques and interview techniques. The research subject was taken using snowball sampling techniques, which was as many as 25 subjects. The results of the research conducted by research on STBA-PIA students in fourth semester of academic year 2018/2019 are 10 omission errors, 6 addition errors, 14 misformations and 3 misordering. Meanwhile, the factors that cause errors are namely interlingual errors, which are 7 respondents and intralingual errors, which are 15 respondents. Keywords: Language Errors, Directional Nouns, “li”, and “zhong”