Reviewerly/exHarmony huggingface dataset:
Extracting High Profit Sequential Feature Groups of Products Using High Utility Sequential Pattern Mining

Creating a set of product features obtained through mining users’ opinions helps retailers identify the attributes (features or aspects) more accurately and discover the most preferred features of a certain product. High Profit Feature Groups are created by extracting such product feature groups such as ‘{batterylife, camera} of a smartphone,’ which results in higher profit for manufacturers and increased consumer satisfaction. The accuracy of opinion-feature extraction systems can be improved if more complex sequential patterns of customer reviews are included in the user-behavior analysis to obtain relevant feature groups. An existing system referred to in this paper as HPFG19_HU uses High Utility Itemset Mining and Aspect-Based Sentiment Analysis to obtain high profit aspects considering the high utility values, but it does not consider the order of occurrences (sequences) of features formed in customers’ opinion sentences that help distinguish similar users and identify more relevant and related high profit product features. This paper proposes a High Profit Sequential Feature Groups based on the High Utility Sequences (HPSFG_HUS) system, which identifies sequential patterns in features. It combines Opinion Mining with High Utility Sequential Pattern Mining. This approach provides more accurate high feature groups, sales profit, and customer satisfaction, as shown by the retailer’s graphs of extracted High Profit Sequential Feature Groups. Experiments with evaluation results of execution time and evaluation metrics show that this system generates higher revenue than the tested existing systems.
An Adaptive Unsupervised Method for Powerline Aerial Image Quality Assessment

When helicopters or unmanned aerial vehicles are patrolling power transmission lines, due to the variability of camera angles and interference from sunlight, atmospheric environment, and other factors, the collected images are distorted, which adversely affects the subsequent image processing. To solve the above problems, a method for evaluating the image quality of transmission lines based on self-adaptively generated weights is proposed, and it is applied to image preselection. First, ResNet-50 (Residual Network-50) is used as a feature extraction network to extract deep semantic features; second, a weight generation network composed of convolutional networks is used to generate adaptive weight parameters; finally, the image and the weight parameters are sent to the quality score prediction network formed by the fully connected layer to generate a score map. The aerial image quality score predicted by the proposed method is highly consistent with human visual perception.
Towards Reinforcement Learning Control of an Electromechanical Pinball Machine

In recent years, the application of artificial intelligence in production processes is becoming increasingly important. Initially focused on the quality management process, artificial intelligence has experienced more usage in production control processes and other complex dynamic systems. In addition to learning speed, the robustness of the resulting algorithm is an important quality criterion. This paper describes the steps towards a reinforcement learning trained $$Q$$ -network that operates an electromechanical pinball machine. The aim is that the algorithm generates inputs for the system such that the resulting playing time and the associated point gain is maximized and shows a superior performance compared to a human player. This paper presents the planned learning concept and contains exemplary measurements to determine transition probabilities. Based on these results a Monte Carlo simulation should be used to train the $$Q$$ -network and thus be able to get an optimal initialization for the practical realization.
Feature and Performance Based Comparative Study on Serverless Frameworks

We conduct experiments on the different public clouds provided in this paper to bring out a comparative study, to help the developers understand the diversity of the platforms. This study will help them choose a suitable platform for their desired application. The contemporary and future usage of this diverse nature of cloud computing is hugely demanding. Thus, we elaborate our study on serverless cloud computing to suffice the demand. Serverless prevents a great deal of unessential consumption of power and is a pay-as-you-go service. This technology has added a great impact on software and application development. Although the major obstacle to this development field is that there is not enough documentation on how the big companies provide this facility and how their architecture is built. The comparative study on this diverse platform is missing in the literature. Therefore, our research is based on the on-demand serverless use cases and comparative study with necessary measures. This can be effective and efficient to use for further serverless implementation. Hence, we and others can follow our research for understanding the technical complexity.
A Comparison of Wearable Sensor Configuration Methods for Human Activity Recognition Using CNN

The number and location configuration methods of wearable sensors for human activity recognition (HAR) are analytically discussed. Based on the publicly available Daily and Sports Activities data set, a convolutional neural network (CNN) was built to recognize 19 kinds of daily and sports activities, and then the model was optimized for better performance. The results of numerous comparative experiments show that deep learning-based HAR is better than machine learning-based HAR in terms of accuracy, and its improvement in accuracy is not directly related to the increase of sensor quantity. Due to its strong capability of feature extraction, deep learning extracts not only activity-related features but also individual differences, therefore, the location with less individual randomness should be selected according to practical engineering. Moreover, the results are also influenced by the limb symmetry in the data set. Finally, the feasibility of achieving higher accuracy with fewer sensors is proved.
An ADMM-based approach for multi-class recursive parameter estimation

Due to the increasing popularity of cloud-based architectures, it is of paramount importance to understand how to benefit from shared information for solving collaborative estimation problems and exploit the additional computational resources available. Meanwhile, it is crucial to devise solutions that allow connected devices to retain private data and to carry out the desired tasks on their own, when disconnected from the cloud.In this paper, we present a cloud-aided iterative solution for multi-class parameter estimation for a set of mass-produced devices. The method exploits the similarity between systems operating under comparable conditions and their connection to the cloud, while allowing devices to retain and process raw data privately. The effectiveness of the strategy is assessed on a numerical example, showing its potential.
Between financial and algorithmic dynamics of cryptocurrencies: An exploratory study

Abstract This article aims at investigating the extent to which the algorithmic nature (i.e., mining process) of cryptocurrencies might influence their dynamics and interaction with some major economic indicators. Our study observes that proof‐of‐stake based cryptocurrencies are less correlated with other crypto‐assets offering more opportunities for diversifying portfolio strategy. We also observe a positive correlation between the proof‐of‐work based cryptocurrencies and the oil price. This article discusses these matters and suggests that the differences in cryptocurrencies' dynamics are more related to their service or purpose rather than their mining protocol. This claim contributes to the current debates on the intrinsic value of cryptocurrencies and it is illustrated with a discussion of the Stellar (XLM) and Ether (ETH) cases. Beyond our empirical results, our article suggests that, the liquidity and the returns dynamics of cryptocurrencies might be affected by two different aspects. Precisely, the former appears to be influenced by the economic service for which these cryptocurrencies are used, while cryptocurrencies' returns are more reactive to the way their cryptographic validation is operated. Our findings also suggest that an analysis through the economic service/purpose of cryptocurrencies is actually appropriate to understand their dynamics in relation to economic indicators. This perspective implicitly questions the monetary aspect often associated with cryptocurrencies and it calls for a more categorized research (by economic purpose) of cryptocurrencies whose potential intrinsic value would then be related to their economic purpose.
Security Baseline Evaluation and Standard Research of Power Intelligent Internet of Things Terminal

The power Internet of Things is the evolution direction of the power industry. While improving the convenience of power grid operation, it also brings emerging network security risks to traditional industries. Baseline assessment is based on a set of minimum security requirements specification sets as a method to grasp the security status of information systems. The application of security baseline assessment in the Internet of Things is still few. This article first studies the security standards of the Internet of Things, establishes a security baseline, designs a set of security baseline evaluation models for power intelligent Internet of Things terminals, and gives the operation process and scoring algorithm. The analysis result shows that our method is suitable for security evaluation of power intelligent internet of things terminal.
Encoder decoder based CNN for single image dehazing with a semi-supervised approach

In the present era of computer vision, the dehazing technique has a substantial role in numerous real-world applications, including outdoor surveillance, video-assisted conveyance, and support system for the driver. Previous approaches have utilized prior-based learning algorithms or methods depending on the light scattering model. These conventional techniques are not good in practice since they rely on statistical resources where the presumed priors do not hold for a particular appearance. We suggested a well-organized trainable end-to-end Convolutional Neural Network (CNN) with a semi-supervised framework independent of any statistical model to restore visibility in tempestuous weather conditions. The proposed deep learning model consists of decoupling CNNs and uses both synthetics and real-world hazy images as the training set. The supervised part is trained with multiple losses such as multi-structure similarity index measure-based loss and mean absolute error (MS-SSIM + 11) combined with perceptual loss. In contrast, the unsupervised part is trained with total variation loss. Specifically, we designed CNN with encoder-decoder architecture. In the reconstruction part of hazy images, we used attention mechanism to remove the distortion and artifacts in the focused part of the hazy images. We observed that the attention mechanism helps to achieve better results. Our wide range simulation shows that our designed framework performed compared to other state-of-art single image dehazing models on real-world data and synthetics hazy data.
Classification

The linear regression model for continuous variable prediction has been introduced previously. Now let’s dive into the classification problem. A typical application of the classification problem is to teach computers how to automatically recognize objects in images. Let’s consider one of the simplest tasks in image classification: 0–9 digital picture recognition, which is relatively simple and also has a very wide range of applications, such as postal code, courier number, and mobile phone number recognition. We will take 0–9 digital picture recognition as an example to explore how to use machine learning to solve the classification problem.
Foreign Shadow Robust Makeup Transfer via Hierarchical Deep Aggregation and Disentangled Representation

Facial makeup transfer aims to transfer the makeup style from a reference makeup image to a source image. In daily life, the reference makeup images from various scenes are uneasy about guaranteeing quality. In contrast, the source images photoed by ourselves are likely to control illumination and avoid shadows cast by occlusion. To this end, we propose a novel robust makeup transfer network (SRMT) for facial makeup and de-makeup under the foreign shadow. The reference image is hierarchically aggregated multi-context features for predicting makeup style which is input into a disentangled network with source image for achieving shadow robust makeup transfer. In particular, we first incorporate a shadow manipulation module(SMM) to manipulate the reference makeup, which furtherly restores the original color distribution on the face. Then we propose a makeup transfer module(MTM) based on disentangled representation, producing a high-quality source image from the predicted reference makeup. Extensive experiments show the superiority of our method in terms of visual effects. Moreover, a carefully designed makeup dataset with paired shadow-deshadow makeup images is available at https://github.com/lucuspring/Deshadow-dataset.
Role of Data Mining and Machine Learning in Software Reusability

Machine learning and data mining have opened new streams in research and software development. Likewise, integration of machine learning with software development life cycle has provided opportunities for well-organized and effective development. Software reusability is an important aspect of SDLC. Therefore, automation of software reusability plays a dynamic role in SDLC. It curtails the cost and effort required for development of a software product. This paper provides a taxonomical mapping of reuse metrics and corresponding machine learning techniques applicable. Artificial intelligence techniques i.e., neural networks and clustering are nominated in the paper to check their applicability for automation of software reusability. A model is created to identify best machine learning practice amongst the nominated techniques for automation of software reusability. Evaluation of model based on survey results identifies ANN: the best technique among neural networks and clustering.
BIIR: Blind Inpainting based Image Reconstructon for Texture Defect Detection

Image reconstruction is an important method for texture defect detection, and the existing image reconstruction algorithms based on Autoencoder and GAN cannot suppress the reconstruction of defect information, which affects the detection accuracy. To solve this problem, this paper proposes a novel image reconstruction algorithm based on image inpainting, which includes two modules of defect estimation network and defect inpainting network. Firstly, the defect estimation network used the pre-training model to extract the deep features of the defect image and applied the Gaussian distance to estimate the background area. and then the image inpainting network applied the contextual attention mechanism to repair the non-background area of the defect image. Through the experimental analysis which compared with other state-of-the-art image reconstruction algorithms on the public Mvtec texture dataset, the superiority of the proposed algorithm is effectively verified.
Hierarchical Model of Social Responsibility of Heavy Industry Enterprises in the Age of Artificial Intelligence

After the conventional model assigns social responsibility to enterprises, the progress of enterprises' implementation of social responsibility is slow. To solve this problem, this paper proposes a hierarchical model of the social responsibility of heavy industry enterprises in the era of artificial intelligence. Combined with the characteristics of the artificial intelligence era, the evaluation index of heavy industry enterprises' social responsibility fulfillment ability is selected, and the index weight is determined by combining the entropy weight method and analytic hierarchy process. The evaluation value of corporate social responsibility fulfillment ability is calculated, and the ability level is divided. Corporate social responsibility is divided into three levels: advanced, intermediate and basic. According to the level of enterprises' fulfillment ability, the social responsibility level to be fulfilled is determined, and the corresponding level of social responsibility is assigned to enterprises, thus completing the construction of the grading model. A certain iron and steel co., ltd. is selected as the model application object, and a comparative experiment is set up. The results show that the designed model judges that the enterprise has high performance ability and can allocate high-level social responsibility to the enterprise. Compared with the two conventional models, the designed model can accelerate the allocation of corporate social responsibility.
Improving Performance of a Pre-trained ResNet-50 Based VGGFace Recognition System by Utilizing Retraining as a Heuristic Step

Deep learning has remodeled the research aspect of facial recognition throughout the last decade by utilizing multiple processing layers to extract significant facial features. Although this emerging technology has achieved high performance for the face recognition problems, the dilemma of achieving low performance while training with a few samples per class has not been resolved yet. In this study, it has been shown that by utilizing retraining as a heuristic step, ResNet-50 based VGGFace architecture can enhance the performance of the face recognition scheme significantly. Multi-task Cascaded Convolutional Neural Networks have been utilized to crop faces first. The first training phase was completed by considering train samples from a combined dataset of 5-celebrity dataset, Georgia tech database, and three variants of KomNet datasets. The retraining of individual datasets further produced 94.41% test accuracy for the KomNet social media dataset and 100% test accuracy for the other four datasets.
Teachers' perception on the use of Show and Tell technique in teaching English speaking

The objectives of this research were to find out the teachers’ perception on the use of Show and Tell technique and to find out what are the obstacles when applying this technique. The subjects were 3 English teachers of SMPN 4 Natar. This research conducted by the researcher using qualitative descriptive method and interview questions were used to collect the data. The result showed that there was no significant difference of teachers’ perception toward this technique, the teachers perceived the Show and Tell technique positively. It happened because the technique was related to the students’ daily life and relevant to the curriculum standard the school applied. It can be concluded, Show and Tell technique is one of good techniques in Teaching English Speaking. The more the techniques or material related to students’ daily life, the more the students would be more easy to understand the material and they would get better scores. Thus, it is suggested the English teachers to use Show and Tell technique to make the teaching English speaking be more interesting and make the students be more active in the learning process. In addition, the teachers have to consider the obstacles toward the technique so the students will get better learning achievement.
Quantum tomography for quantum systems optimization

Debugging quantum states transformations is an important task of modern quantum computing. The use of quantum tomography for these purposes significantly expands the range of possibilities. However, the presence of preparation and measurement errors complicates the practical use of this procedure. In this work, we investigate the possibility of estimating these errors from experiment. These estimates are subsequently used to build a robust quantum tomography model. The model allows one to accurately reconstruct unitary errors of single-qubit gates. We show that, having such imperfect single-qubit gates with pre-estimated errors, one can obtain transformations close to ideal ones. A similar approach can also significantly mitigate single-qubit gates crosstalk.
Online Adaptation of a Compensatory Neuro-Fuzzy Controller Parameters Using the Extended Kalman Filter: Application on an Inverted Pendulum

This paper presents the implementation of a Compensatory Adaptive Neuro-Fuzzy Inference System (CANFIS) controller to control an inverted pendulum. This controller is developed in order to readjust the parameters relating to the membership functions and the fuzzy rules used as well as to optimize the dynamics of the latter, using a learning algorithm based on the extended Kalman filter. The CANFIS controller is developed on the Simulink environment of the MATLAB software and is implemented on a Raspberry Pi 3 board, with a view to analyzing its real behavior, and testing its speed as well as its robustness through the use of the &ldquo;Processor-In-the-Loop&rdquo; (PIL) technique. The results obtained through PIL tests showed the effectiveness of the neuro-fuzzy controller equipped with a compensator.
Construction of English-Assisted Teaching Mode Based on Multimedia Technique in Network Environment

Modern educational technology, embodied by multimedia network technology, is now widely used in education and teaching, and it offers distinct benefits in terms of improving classroom teaching effectiveness. Through videos, sounds, images, and animations, multimedia English teaching can show students all kinds of information more intuitively, vividly, and quickly. This paper attempts to apply multimedia-assisted teaching to the English classroom, combining the characteristics of the English subject and the requirements of curriculum standards, based on related theories of psychology and information technology. The learning effect can be established with the CET effective classroom environment construction and the three dimensions of the teaching process reflected by the evaluation scale and the path model with a good fitting degree. Teachers’ correct guidance will play a positive role in learners’ correct use of multimedia English learning resources, according to teaching practice. A simulated language environment can be created using multimedia technology, allowing students’ sensory organs to be activated more effectively, their attention to be drawn in, and their interest in English learning to improve.
Research on Clearness Index Support Vector Regression Method for Solar Radiation Forecasting

Aiming at the problem of intermittent and fluctuating solar radiation, which is difficult to forecast accurately, a short-term forecasting method of solar radiation intensity based on support vector regression is proposed. First, the trend curve of the daily clearness index is calculated by using the K-means algorithm to cluster the curves to obtain the cluster center of the curve under the general weather type. Then, the gradient boosting decision tree is used to judge the forecasting weather type, and the corresponding weather type is used. The meteorological information builds a support vector regression model from time to time to obtain the clearness index and calculate the solar radiation intensity, which avoids the problem of low forecasting accuracy caused by using the same fitting model in different weather conditions. Finally, the proposed forecasting method is verified with measured data. Experimental results show that the proposed method has high forecasting accuracy.