Reviewerly/exHarmony huggingface dataset:
Impact on Estimation Accuracy of Training Data used in CNN-based Solar Irradiance Estimation Method

This paper verifies impacts that the training data have on estimation accuracy of CNN-based solar irradiance estimation method proposed by the authors. The all datasets, training data and test data, are composed of camera images and solar irradiance data. In this paper, training data is created from three perspectives: the size of data, the bias of data based on solar irradiance and the used days of data. CNN learns the estimation model for solar irradiance using training data. Estimation accuracy is evaluated by average MAE using test data. As a result, first, increasing the size of data improves average MAE. At least, 20&#x0025; of size of data composed of ten days should be used for training data to achieve high estimation accuracy and short calculation time. Second, the bias of data, average MAE improves as the bias of data gets mitigated. Unbiased data should be used for training. Last, increasing the used days of data has a significant impact on MAE. In addition, the combination of weather changed by the used days also has a significant impact on MAE. As a result, many used days&#x0027; data should be used for training data to achieve high estimation accuracy. After validating the training data from three perspectives, the training data is evaluated comprehensively. CNN learns the estimation model using the training data composed of selected 19,390 samples from ten days&#x0027; data. This size of training data is equivalent to 20 &#x0025; of ten days&#x0027; data. As a result, MAE reaches 0.0240 kW/m<sup>2</sup> which is almost same to the estimation accuracy trained by all data of ten days. The calculation time for training reduces by 75&#x0025;.
Representing voting rules in Łukasiewicz’s three-valued logic

We show how voting rules like the simple and the absolute majority rules, unanimity, consensus, etc. can be represented as logical operators in Łukasiewicz’s three-valued logic. First, we prove that the binary simple majority operator can be extended to express the majority choice of groups formed of n voters. Consequently, decisions in larger groups can be represented as more complex decisions in groups formed of pairs of voters. This property is not shared, however, by the consensus and the absolute majority operators. Secondly, we prove that the simple majority operator can be defined in terms of the consensus operator, but that the converse does not hold. Finally, we construct the classes of logical operators definable in terms of the simple majority and of the consensus and point to some more general implications of these results.
DNA based service data security in cloud computing environment

Cloud computing is the type of technology preferred to maintain computing power, computer resources, and majorly used to handle the cloud storage process. Also, it helps to manage the data security process during different applications. The data encryption, data classification, and feature extraction approaches are majorly considered for increasing the data security process. The main aim of the work is to review the DNA based cloud computing process for improving data security. The main purpose of this work is to review the current research article focus on the data security and computing process. Further, taxonomy is introduced that helps in the evaluation and analysis process. The secondary research approach is used during review work. Further, the deep learning with sequencing and modification process is reviewed in the work to manage the security and sequence analysis process. In addition, the block-chain-based random technique is reviewed to manage data detection and probability rate. The data optimization and data indexing process with block-chain is preferred to enhance data security and handle the feasibility, accuracy, and transfer time. Moreover, the OPNET model, Dynamic and static model approach, and data access algorithms are used together for maintaining the data mining security process. The IoT network topology and attributes based encryption process are reviewed in developed methods for maintaining data optimization and data security process. These approaches and factors are analyzed with the help of current research articles in order to review the state of the art in the developed method.
Conceptual spaces and scientific data models

This chapter will more substantially develop our approach to Conceptual Space Theory in natural (as well as programming) language contexts, that was initiated in earlier chapters. We present further philosophical motivations for the structural details of our proposed “Syntagmatic Graph” representations and examine techniques for integrating conceptual spaces with linguistic paradigms such as Conceptual Role Semantics and situational semantics. Our central argument is that the classical linguistic concept of “thematic roles” provides an alternative framework for analyzing the semantic integration of multiple conceptual spaces, contrasted with “quantitative blend” models endemic to conceptual space theory proper. Therefore we propose “role-indexed” Conceptual Space models, which have distinct semantic and syntactic patterns, juxtaposing this theory to existing formalizations of conceptual spaces in (for example) Quantum NLP. With that natural-language foundation as a motivation, we then consider semantic models as they could be more concretely applied to scientific data sets.
Interleaved Data Processing Scheme for Optimizing Tensorflow Framework

Nowadays, technologies based on machine learning (ML) are becoming a popular solution for building recommendation services on consumer electronics (CE) devices. Therefore, in this paper, we introduce i.data (interleaved data) processing scheme to more efficiently enable ML models on CE devices. The key idea of i.data is to interleave the training and inference steps in a parallel way so as to improve the GPU utilization and overall performance. We also compared i.data with Numpy and tf.data that are widely used to build ML models in various environments including CE and the internet of things (IoT). Our experimental results clearly show that i.data improves the performance compared with the traditional data processing approaches.
Triangular Regions Representations for Matching Images With Viewpoint Changes

This paper proposes triangular region representations based on keypoints detected in images with viewpoint changes. The strongest keypoints in the reference and query images are allocated individually using a previously published contourlet-based approach to determine the keypoints. These selected keypoints serve as vertices of the triangular regions to be transformed into rectangular representations as simple numeric matrices. The suggested representation methods used to form rectangular matrices are full triangle representation (FTR) and a lighter representation called triangle medians and sides representation (TMSR). For the former, the intensity values along the lines connecting the anchor keypoint to the points between the other two triangular vertices form the rows of the representation matrix. These two triangle vertices are located in an allotted window around the anchor keypoint from the nearby keypoints. For the latter, the intensity values on the six triangular lines, medians, and sides formed the resulting rectangular matrix. The proposed representations are validated for image-matching applications using a descriptor-less matching method. Moreover, the performances of these algorithms are compared with those of traditional algorithms. The results confirmed the superiority of the proposed method over these algorithms.
Tracing the source of unsteady-state low-frequency noise based on wavelet coherence analysis

The original partial coherence analysis method can be used to analyze the contributions between different noise sources and field points under steady-state conditions. Nevertheless, coherence analysis cannot be used to deal with unsteady-state signals because the signals are time-varying. In this paper, the method of panel contribution under unsteady-state conditions based on wavelet coherence analysis is proposed to determine the largest noise contribution in the cab. First, the unsteady-state simulation signals are analyzed by wavelet coherence theory. This step not only verifies the effectiveness but also points out that wavelet coherence is more superior to crossed wavelet. Second, the measured unsteady-state signals of the eight measuring points are processed by time-frequency wavelet coherent analysis. According to the obtained two-dimensional time-frequency diagrams, the contribution of different panels to the noise of measuring field points in the continuous frequency range can be found out. Then, the total contribution coefficient sum of each panel in a certain frequency band is calculated. Finally, the above analysis results are used to determine the panels with the largest contribution to the cab noise. The corresponding noise reduction scheme is also proposed. The results show that the overall sound pressure level is obviously decreased after optimization. The noise in the cab is reduced by 4.5dBA ∼ 7dBA. The effectiveness of this method has been further verified by the above results.
Influence of Weights for Selected Cognitive Abilities: An Online Approach

The existing literature emphasizes the importance of cognitive abilities in performing a specific task. However, an experimental study to identify the importance of these abilities and their order of preferences have received limited attention. A detailed assessment in a virtual environment with subjects performing online is also a new technique for cognitive ability assessment. Each cognitive ability comprises certain tasks which are given specific scores, thereby a total score is given to individual for all the eight cognitive skills considered. A multicriteria decision analysis was conducted to identify the variation in the performance index of subjects. This research compares the weights of the individual cognitive abilities of fifteen subjects using Wilcoxon test. A total score suitable for our study was obtained with a pilot study previously conducted. The results prove that the method of study can be used for assessment of cognitive skills and further worker allocation based on the skills can also be accomplished. The appropriate work station design with complex robot mechanisms can be considered as a major application in this field of study.
Multi-Scale Adaptive Feature Fusion Network for Raindrop Removal

Raindrop removal from a single image is an important low-level computer vision task for autonomous driving and object detection. Current CNN-based methods achieve encouraging performance, while are limited to single-scale and then make the network more deeper to achieve good performance. To address this issue, we present a Multi-scale Adaptive Feature Fusion Network (MSAFF-Net) in end-to-end manner, to exactly capture raindrop features with multi-scale extraction and information aggregation. For better extracting the features, a novel Multi-scale Extraction Block (MSEB) is proposed to get local and global features across different scales through dilated convolution with different dilation rate. Besides, we design an Adaptive Feature Fusion module (AFF) to aggregate different features instead of directly concatenating or adding. Extensive experiments on datasets demonstrate the effectiveness of the designed MSAFF-Net by comparing with recent state-of-the-art raindrop removal algorithms.
Ontology Model for Scientific Information Representation

The article presents the results of research dedicated to the representation of information of scientific organizations in order to subsequently assess the quality of their functioning. To represent and structure scientific information it is proposed to use ontological models as a modern means of intelligent information processing. The article gives a brief overview of approaches to the evaluation of scientific information, such as Bibliometrics and Citation analysis. Also, a system of ontologies for representing scientific information is proposed and the results obtained during the direct development of such an ontology using TEDAOS platform are given.
Analysis and Prediction of the Influence of Moving Object Trajectory in Indoor Environment Based on Infrared Camera Tracking

This paper mainly studies the trajectory simulation generation of indoor moving objects and the prediction of indoor moving object trajectory in the analysis of the influence of indoor moving object trajectory based on the external red camera. First of all, based on infrared imaging, this paper implements a moving object trajectory simulation tool that can simulate different indoor environment positioning deployment maps. Then, this paper analyzes the trajectory of a large number of indoor moving objects, proposes an indoor hotspot area query method based on moving objects, and uses the indoor moving object trajectory dataset generated by the tool to verify the method. The results show that the trajectory prediction accuracy reaches 97.2%
Comprehensive Evaluation of the Large-Scale Charging Pile Based on the Cloud Platform

At present, the existing charging pile detection and evaluation index system only considers the technical indicators, economic indicators, environmental indicators and safety indicators, but ignores the impact of special environmental factors and historical operation data on equipment performance testing, and fails to comprehensively evaluate the performance of charging piles. This paper proposes a charging pile historical maintenance data based on cloud storage, as well as charging pile brand, model, environmental temperature and humidity indexes. The membership degree of each index is solved by the gray cloud model, and then the evaluation score after testing is revised based on the weight value of the AHP analytic hierarchy process of each index. It can improve the accuracy and reliability of the testing of charging piles, and then it can be applied to the testing and evaluation of large-scale charging piles.
Photovoltaic Generation Scenario Analysis Considering Irradiation Uncertainty and Output Derating Probability

The performance of PV system is influenced by both uncertain external factors and component reliability. This paper proposes a scenario generation approach of PV system combing the irradiation forecasting and system derating probability. Firstly, the catboost machine learning method is adopted to achieve the irradiation forecasting of different weather conditions as well as the predicting error distribution. Meanwhile, the PV system is divided into several levels according to the failure impact degree. The reliability states are sampled by using the sequential Monte Carlo simulation to analyze the distribution of PV derating output state. Finally, combining the error distribution and derating probability, the typical PV output scenario is generated. The results show that the typical photovoltaic scene obtained by the proposed algorithm can cover a larger fluctuation range and effectively eliminate the negative effects caused by prediction errors, providing a more reasonable basis for scheduling.
Brain-Computer Interface Analysis and Data Mining Measurement of Communicative Awareness in Online English Guiding in Colleges from a Cross-Cultural Perspective

With the acceleration of the globalization process, international exchanges are becoming more frequent. As one of the main lingua franca in the world, English has become more widely used. This paper analyzes the brain-computer interface analysis and data mining measurement of English communicative awareness in big vocational universities based on a cross-cultural perspective. Because of the rapid development of my country's economy, the rapid growth of diplomatic relations has put up with higher satisfication for our foreign language education. Finally, with data mining analysis of the construction of English teaching mode in big vocational universities from a cross-cultural perspective, the importance of cross-cultural communication awareness is analyzed through a data mining model based on principal component analysis, which improves communication efficiency by 6.7%.
Using a Modified Enrichment Factor Based on Multivariate Statistics to Assess the Source of Geochemical Anomalies in the Sediments of the Changjiang River (China)

The concentrations of a selection of major and trace elements were determined in 101 sediment samples collected from a core located in a mid-channel of the Changjiang River (China), about 150 km far away from the river mouth. The whole core refers to a sedimentation period of about 150 years (1850 ~ 2007). The median diameter (D50), as a main grain-size parameter of sediment, total organic carbon (TOC) and total nitrogen (TN) were also determined and used as proxies for weathering processes and hydrological events occurred in the catchment basin. A modified Enrichment Factor (mEF) based on groups of elements instead of individual variables was proposed to determine geochemical anomalies in the core sediments. Compositional Data Analysis (CoDA) and multivariate statistics, including Robust Principal Component Analysis (RPCA) and Fuzzy Clustering (FC), were applied to determine the group of elements to be used as a reference and select samples to estimate baseline values. Applying the method to the sediments of the Changjiang River demonstrated how it could be effective in discriminating the occurrence of natural and anthropogenic phenomenaÂ in such a big catchment. Results showed, in fact, that flood events that took place in the last 150 years drove the largest geochemical depletion and enrichment processes of the lower Changjiang catchment basin. The pressure exerted by human activities on the catchment following the two main events that favored the recent socio-economic development of China were also highlighted by mEF.
Estimation of Global Solar Radiation Using NNARX Neural Networks Based on the UV Index

Context: This work presents different models based on artificial neural networks, among them NNARX, for estimating global solar radiation from UV index measurements. The objective is to determine the efficiency of the models studied to estimate global solar radiation in terms of the coefficient of determination (R2), the root-mean-square error (RMSE), and the mean absolute error (MAE). Methodology: It is divided into four stages: i) conformation of the training dataset (in this case, it uses a training set of 213.019 data collected over five years in the city of Pasto, Colombia, with the Davis Vantage Pro 2.0 station); ii) pre-processing of data to remove erroneous and unusual data; iii) definition of models based on recurrent and conventional artificial neural networks according to an analysis of topologies, e.g. NNFIR and NNARX; iv) training of the models and evaluation of the estimation efficiency through metrics such as R2, RMSE, and MAE. To validate the model, a new dataset collected during the last year was used, which was not included in the data training. Results: The global solar radiation estimation models based on NNARX show the best estimation efficiency compared to conventional neural networks. The NNARX221 model has an RMSE of 54,32 and a MAE of 18,06 w/m2. Conclusions: NNARX models are highly efficient at estimating global solar radiation, with a coefficient of determination of 0,9697 in the best of cases. The most efficient models are characterized by using two past times and the current UV index instant, and they feed from two past times of their own estimated radiation output. Furthermore, the numerical results show that the contribution of temperature and relative humidity is not relevant to improving the efficiency of the estimation of global solar radiation. These models can be particularly important since they only use measurements made with UV index sensors, which are less expensive than solar radiation ones.
Small Signal Stability Assessment of Power System Based on Sade-ELM Algorithm

Along with the increasing scales of power system, the traditional numerical method to calculate the damping ratio of system to evaluate the small signal stability cannot meet the requirements of real-time analysis. Therefore, we propose a small signal stability assessment method for power system based on Extreme Learning Machine (ELM) optimized by Differential Evolution Algorithm with Strategy-Adaptation (SaDE). The node power, generator power and branch power are taken as inputs of the model in this method. Meanwhile, the minimum damping ratio of the system is taken as the output, and fits input data and output data. The results of 3-machine 9-node and 10-machine 39-node systems show that the method can estimate the minimum damping ratio of the system after off-line training of historical data without considering the change of control parameters. Compared with the prediction results of Back Propagation (BP) and ELM models, SaDE-ELM has the lowest error, the highest prediction accuracy and the strongest generalization ability.
A Multi-Sensor Fusion Discrimination Method Based on Phase Modulation Feature

In order to solve the problem of multi-sensor fusion discrimination of the same radar emitter, a multi-sensor fusion discrimination method based on phase modulation feature is proposed in this paper. First, the pulse sequences received by different sensors are reconstructed, and then the phase modulation feature of the pulses is used to determine whether the same source is used. The simulation results show the effectiveness of the proposed method.
A Public-Blockchain based De-Centralized Application Framework for Agri Supply Chain Management System

Traditional supply chain system for agri -products has problems due to centralized management, lack of storage, transportation problem, number of intermediaries in the market, malpractice, lack of grading, untrustworthiness and lack of security. To solve these problems, agri-supply chain system is implemented with blockchain technology. Blockchain is a serverless technology, which allows immutability, security and transparency. Blockchain is a shared ledger which focus on smart contracts and all interactions and transactions among all the network nodes are broadcasted using the postman tool. Smart contract is based on the agreement and contract between all the stakeholders in the agri-supply chain management system. In this proposed system, a generic framework for de-centralized application is built between network of computers to ensure immutable trustworthy and secured transactions.
Joint Feature Euclidean Distance Sampling for Semi-Supervised 3d Object Detection

Current 3D object detection methods based on point cloud largely rely on large-scale high-quality 3D annotation. To reduce the number of labeled data, we propose a new 3D object detection method based on self-ensembling semi-supervised 3D object detection (SESS). First, we design a joint feature Euclidean distance sampling method, this method takes the joint matrix of the spatial coordinates and features of the voting points as input and calculates the Euclidean distance between points, then the Euclidean distance is used to sample the farthest point, which increases the probability of foreground points being sampled. Second, we introduce Transformer in the voting module, this module will enhance the mutual attention between point and its neighborhood points and aggregate the local features of point cloud. Finally, we use channel attention to enhance the weight of foreground points in seed points and cluster points, and then we fuse the point cloud features of two parts to realize the aggregation of global features and local features. Our method significantly improves the accuracy of semi-supervised 3D object detection on ScanNetV2 and SUN RGB-D datasets.