Reviewerly/exHarmony huggingface dataset:
Analysis of Customers’ Reviews Using Soft Computing Classification Algorithms: A Case Study of Amazon

Recently, text and sentiment analysis has received tremendous attention, especially due to the availability of gigantic data in the form of unstructured text available on social media, E-commerce websites, E-mails, blogs, and other similar sources. It involves analyzing large volumes of unstructured text, extracting relevant information, and determining people’s opinions and expressions like positive, negative, neutral etc. Nowadays, the majority of business firms are using text and sentiment analysis techniques to understand the feedbacks of their customers and to gain information about the degree of customers’ inclination towards their products and services. Therefore, sentiment analysis provides valuable insights and helps the firms to formulate effective business strategies. However, the massive data derived from social media and other sources are unstructured, highly dimensional, and involve uncertainty and imprecision. Thanks to soft computing techniques, we are equipped to handle uncertainty imprecision, partial truth, and approximation. The present chapter is based on text and sentiment analysis of customers’ reviews collected from the Amazon customer review portal. We propose a three-tier model that takes raw data from this portal as input and generates a comparative report over certain parameters. We fetch data variables from this portal, apply data preprocessing and cleaning techniques to repair and/or remove dirty data in the first phase. In the second phase, we filter out those input variables which exhibit the strongest relationship with output variables using statistical feature selection techniques. In the final phase, we analyze processed dataset using machine learning algorithms to classify positive, negative and neutral reviews. For classification, we apply Random Forest, Naïve Bayes, and Support Vector Machine algorithms in particular. These algorithms are applied to the processed dataset to study a few parameters like accuracy, precision, F-measure, true positive, false negative, etc. Finally, our study compares the outputs of these three classifiers over the above-mentioned parameters.
Self-calibration of cameras using affine correspondences and known relative rotation angle

This paper proposes a flexible method for camera self-calibration using affine correspondences and known relative rotation angle, which applies to the case that camera and inertial measurement unit (IMU) are tightly fixed. An affine correspondence provides two more constraints for the self-calibration problem than a traditional point correspondence, and the relative rotation angle can be derived from the IMU. Therefore, calibrating intrinsic camera parameters needs fewer correspondences, which can reduce the iterations and improve the algorithm's robustness in the random sample consensus framework. The proposed method does not require rotational alignment between the camera and the IMU. This advantage makes our method more convenient and flexible. The experimental results of both synthetic data and publicly available real datasets demonstrate that our method is effective and accurate for camera self-calibration.
Research and application of bolt defects detection technology based on machine learning

Abstract With the development of computer technology, the technology based on computer machine learning plays an important role in various fields. Using drones for collecting image data and using machine learning to analyze the collected image data have become the current general method of intelligent detection technology. As the main machine learning method, deep learning is commonly used in image analysis, but it requires many high-quality training samples and high-performance embedded system. In the engineering quality and safety detection with few training samples, the detection effect of this method is not satisfactory. To solve this problem, computer vision and machine learning technology are introduced into image analysis of bolt, based on the analysis and mining of historical image samples, the recognition and judgment of new collected images can be realized by matching the newly collected image samples and historical samples. Taking the bolt on a steel structure bridge as an example, this method is used to recognize the bolt appearance image collected by UAV. The results show that the method can effectively identify the appearance state of bolts, with fast calculation speed and high recognition accuracy.
Dynamics signature based anomaly detection

Abstract Identifying anomalies, especially weak anomalies in constantly changing targets, is more difficult than in stable targets. In this article, we borrow the dynamics metrics and propose the concept of dynamics signature (DS) in multi‐dimensional feature space to efficiently distinguish the abnormal event from the normal behaviors of a variable star. The corresponding dynamics criterion is proposed to check whether a star's current state is an anomaly. Based on the proposed concept of DS, we develop a highly optimized DS algorithm that can automatically detect anomalies from millions of stars' high cadence sky survey data in real‐time. Microlensing, which is a typical anomaly in astronomical observation, is used to evaluate the proposed DS algorithm. Two datasets, parameterized sinusoidal dataset containing 262,440 light curves and real variable stars based dataset containing 462,996 light curves are used to evaluate the practical performance of the proposed DS algorithm. Experimental results show that our DS algorithm is highly accurate, sensitive to detecting weak microlensing events at very early stages, and fast enough to process 176,000 stars in less than 1 s on a commodity computer.
Resource Allocation based on Genetic Algorithm for Cloud Computing

With the fast development of Cloud computing, many applications and services have become convenient. When resource allocation is unbalanced in Cloud, the processing time becomes longer. Hence, the resource allocation strategy for the virtual machine is a critical problem for the cloud computing environment. In this paper, we propose two phases to overcome the problem with resource allocation. First, we predict the workload with each virtual machine to estimate the resource is significant than the threshold. Then, the genetic algorithm allocates the task to solve the optimal solution for resource allocation. The experiment results show that using the proposed method achieves the load balance and reduces Cloud’s processing time for cloud computing.
RETRACTED: Power Intelligent Customer Service Robot Based on Artificial Intelligence

Abstract With the rapid development of speech recognition technology, voice chat robot has become a breakthrough of artificial intelligence. Voice chat robot should be a typical application field of customer service, providing customers with efficient and convenient service all day. The traditional customer service center is mainly based on telephone service, facing the problems of large number of customers, high maintenance cost, slow knowledge update, limited service time, low training cost and so on. So, at the same time, the use habits of customers have also changed fundamentally. The vast majority of services and transactions can be carried out through the Internet, such as Taobao and Jingdong. However, the quality and cost of voice services can be greatly reduced through the interaction between robots and channelization voice service centers. Through the research and development of natural language technology, an intelligent and centralized mobile communication service application platform is constructed by using we-chat platform. Through natural language processing, machine learning, big data computing and other technological innovation, we focus on the use of online robot recognition to understand customer problems and timely feedback customer needs. The results show that in the statistics of customer service machine problems, the highest proportion of consumers’ problems about payment is 37%, and the lowest is 29%.
Research on the Teaching of Network Engineering Training Courses Under the Online Teaching Mode in the Era of Big Data

This paper first analyzes the problems of traditional training courses for network engineering students. And discusses the teaching measures of network engineering professional training courses under the online teaching mode in the era of big data. Based on the online platform, case analysis, task driven and virtual simulation experiment, a new teaching mode of practical training course is constructed. Compared with the traditional practice teaching mode, the ability of students to explore, think, practice and solve problems is significantly improved, and the cultivation of students’ autonomous learning ability and comprehensive application ability is promoted.
Fracture Phenomena in Swarms

In this paper, we describe qualitatively material deformations and fracture phenomena using a discrete 2D kinematic model. This model is based on the swarm intelligence approach, where simple laws among the agents can lead the whole group to achieve complex behaviors; it seems to be promising because it is able to not only qualitatively reproduce standard deformations and fracture phenomena but also a lot of exotically ones that other methods in literature cannot reproduce or their computational costs are too high. Instead, in our model the fracture arises more naturally. Furthermore, it has a low computational cost and it is parallelizable, allowing us to take profit of CUDA $$^{\circledR }$$ architecture. Some numerical simulations are provided and discussed using two different kind of lattices and changing some model’s parameters.
Online exploitation of children and the role of intermediaries: an Indian legislative and policy perspective

The Intermediary landscape in India is extensive and regulating; it is often laborious considering the expanding market and ensuing policy changes. The advent of globalization heralds the inrush of entities that operate transnationally, which often beget legal uncertainties in outlining the intermediary liabilities conundrum. Many jurisdictions oscillated from the notions of liability to safe harbor in fixing the intermediary liabilities. Children are often an easy target of online abuse and Child Sexual Abuse Materials (CSAM) is the most circulated object in the cyberspace. This paper has adopted both empirical and doctrinal research methods. The paper primarily addresses the accountability and response on the part of intermediaries for abusive content against children transmitted in their platform and is an attempt to provide an insight into the reasons behind the online exploitation of children in India. The study analyses the public opinion on the online exploitation of children based on a survey conducted among 270 respondents aged from 14 to 50 years. The findings gave support to the suggestions that the exploitation and abuse of children frequently transpire online, and the public perception regarding the gender of child victims varies between girls and boys.
Handling Class Imbalance in Fraud Detection Using Machine Learning Techniques

A disproportion between the fraudulent and the non-fraudulent class is generally termed as class imbalance. In class imbalance problems the instances of the positive class (minority) are much smaller than the number of instances of the negative class (majority). The non-fraudulent class being more dominant and the fraudulent class being relatively rare the latter would be considered as outliers thereby resulting into a misclassification of the minority class. Hence the results of imbalanced classes are not always accurate. The applications of class imbalance ranges from fraud detection, anomaly detection, oil spillage detection, network intrusion to medical diagnosis. In all these scenarios we have cases where we have two different kinds of labels and one of them is highly skewed. The proposed model aims at using the sampling techniques like Random under sampling and Random oversampling to sample the dataset after which the balanced dataset will be given to the classifier and the results will be then compared and evaluated using the performance evaluation metrics.
A Suitable Proposal of S-Boxes (Inverse-Like) for the AES, Their Analysis and Performances

Vectorial Boolean functions are widely applied to block ciphers’ design in cryptography, where they are used to create the so-called substitution boxes, or S-boxes, whose input and output are then both sequences of bits. A prominent example of a block cipher is the AES, or Rijndael, which contains an (8, 8)-function at its core. This paper presents a simple (but efficient) method for building S-boxes for AES with good cryptographic properties and analyzing their security. Using our approach, we exhibit an S-box example and explore its main algebraic and statistical properties such as Strict Avalanche Criterion (SAC), Distance to SAC, and algebraic complexity. We also analyze its resistance to block ciphers’ most popular attacks, namely differential attack, boomerang attack, linear attack, and differential-linear attack. Although the proposed S-box is finally proved to be affinely equivalent to the inverse function, when it is involved in a block cipher, it has enhanced properties at a better level compared to AES (for example, it has better algebraic and statistical criteria such as periodicity, algebraic complexity, strict avalanche, and distance to SAC) by preserving the same security level concerning several main cryptanalyses (for example, it is differentially 4-uniform as well). Consequently, the proposed S-box is more resistant to any attack based on statistical cryptanalysis. We present its strength, including in terms of efficient implementation, and we emphasize that our proposed S-box offers, in addition, more flexibly thanks to the choices of the parameter in its conception, leading to an attractive and suitable alternative for an S-box to be involved in a block cipher.
On impropriety for a large-sized discrete fourier transform of a real-valued stationary process

It is known that a finite-sized discrete Fourier transform (DFT) of a real-valued colored stationary random process results in improperly distributed complex random variables in the frequency domain. It is also well-known that a large-sized DFT of a colored random process approximates the behavior of the Karhunen–Loève (KL) transform, in terms of achieving approximate decorrelation between the frequency domain terms. We show that this decorrelating behavior also manifests itself in terms of the approximate complex circular (proper) nature of the majority of the transform domain terms, except near the band edges, where the nature of the complex distribution may be non-circular. We derive an analytical result relating the DFT size with the degree of impropriety, and also demonstrate the same via simulations. The result is important in the context of wideband xDSL (Digital Subscriber Line) discrete multi-tone (DMT) receivers affected by extraneous spectrally colored noise sources, besides being of general interest.
Features Selection in Microscopic Printing Analysis for Source Printer Identification with Machine Learning

Source printer identification for printed documents has been studied extensively in recent years. Applying machine learning to features extracted from the artifacts of the printed papers is a potential approach in this field. Due to the fact that extracting features is a manual task that requires domain knowledge from the expert, which is one of the most resource-intensive tasks, In this work, we aim to reduce the number of training features on many different machine learning models but guarantee the high performance of the identifying results. Following the work of the authors from [], our proposed features selection methods show that we can achieve about the same accuracy while significantly reduced the number of features.
Analysis of the emergence of trust in the information field as a decision-making tool

The presented material analyzes the phenomenon of trust, its place in the value system of social regulation. In the development of modern science, there is a clear trend towards the need to diagnose the potential and risks of new technologies, to assess the social and economic consequences of their implementation. Using the language of synergetics as a branch of philosophy, the authors consider the problem of trust in information as a special kind of interaction of certain variables in information processes and systems, raise the question of indicating, reducing and eliminating the risks of the social interaction in the context of trust in information in fiscal policy.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si52.svg"><mml:mrow><mml:msup><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math>R loss: A weighted loss by multiplicative factors using sigmoidal functions

In neural networks, the loss function represents the core of the learning process that leads the optimizer to an approximation of the optimal convergence error. Convolutional neural networks (CNN) use the loss function as a supervisory signal to train a deep model and contribute significantly to achieving the state of the art in some fields of artificial vision. Cross-entropy (CE) and Center loss (CL) functions are commonly used to increase the discriminating power of learned functions and increase the generalization performance of the model. Center loss minimizes the class intra-class variance and at the same time penalizes the long distance between the deep features inside each class. However, the total error of the center loss will be heavily influenced by the majority of the instances and can lead to a freezing state in terms of intra-class variance. To address this, we introduce a new loss function called sigma squared reduction loss (σ2R loss), which is regulated by a sigmoid function to inflate/deflate the error per instance and then continue to reduce the intra-class variance. Our loss has clear intuition and geometric interpretation, furthermore, we demonstrate by experiments the effectiveness of our proposal on several benchmark datasets showing the intra-class variance reduction and overcoming the results obtained with center loss and soft nearest neighbour functions.
Finding Secret Treasure? Improving Memorized Secrets Through Gamification

Users tend to bypass systems that are designed to increase their personal security and privacy while limiting their perceived freedom. Nudges present a possible solution to this problem, offering security benefits without taking away perceived freedom. We have identified a lack of research comparing concrete implementations of nudging concepts in an emulated real-world scenario to assess their relative value as a nudge. Comparing multiple nudging implementations in an emulated real-world scenario including a novel avatar nudge with gamification elements, this publication discusses the advantages of nudging for stronger user-created passwords regarding efficacy, usability, and memorability. We investigated the effect of gamification in nudges, performing two studies (N1 = 16, N2 = 1, 000) to refine and evaluate implementations of current and novel nudging concepts. Our research found a gamified nudge, which integrates a personalizable avatar guide into the registration process, to perform less effectively than state-of-the-art nudges, independently of participants’ gaming frequency.
Collaborative Cloud Computing using Dedicated Shared Resources

A collaborative cloud based on Open-Source OpenStack Cloud Framework where different partners will come together and will plug in their dedicated Infrastructure. This contributed Infrastructure from different partners will create a big pool of Infrastructure Resources. All the Infrastructure resource owners will be eligible to receive credits whenever someone else is using their idle resources. And whenever somebody requires more resources than their available dedicated resources can hire the resources available in the pool from other partners. This will increase the usage of Infrastructure which is generally left idle on private clouds. And it will also make it easy for Partners to meet their demands at times whenever they require more resources than their available dedicated resources.
Objectively Measure Player Performance on Olympic Weightlifting

In Olympic-style weightlifting athlete attempts to lift the weight plates on a barbell and scores are determined by a combination of the successful highest weight achieved in snatch and the clean-and-jerk actions. However, the current method does not objectively measure the player techniques. In this paper, we introduce a novel method to objectively measure player performance on weightlifting using human action recognition in videos. We introduce a method to assess player techniques in weightlifting by using skeleton-based human action recognition. In order to achieve our goal, we further introduce a new video dataset for action recognition in weightlifting which is annotated to frame level and introduce an automated scoring system through action recognition. We conclude our paper with qualitative and quantitative experimental results using non-Olympic players and 2016 Gold, Silver, and Bronze medalists in different weight categories (both men and women).
Evaluation of Stereo Images Matching

Image matching and finding correspondence between a stereo image pair is an essential task in digital photogrammetry and computer vision. Stereo images represent the same scene from two different perspectives, and therefore they typically contain a high degree of redundancy. This paper includes an evaluation of implementing manual as well as auto-match between a pair of images that acquired with an overlapped area. Particular target points are selected to be matched manually (22 target points). Auto-matching, based on feature-based matching (FBM) method, has been applied to these target points by using BRISK, FAST, Harris, and MinEigen algorithms. Auto matching is conducted with two main phases: extraction (detection and description) and matching features. The matching techniques used by the prevalent algorithms depend on local point (corner) features. Also, the performance of the algorithms is assessed according to the results obtained from various criteria, such as the number of auto-matched points and the target points that auto-matched. This study aims to determine and evaluate the total root mean square error (RMSE) by comparing coordinates of manual matched target points with those obtained from auto-matching by each of the algorithms. According to the experimental results, the BRISK algorithm gives the higher number of auto-matched points, which equals 2942, while the Harris algorithm gives 378 points representing the lowest number of auto-matched points. All target points are auto-matched with BRISK and FAST algorithms, while 3 and 9 target points only auto-matched with Harris and MinEigen algorithms, respectively. Total RMSE in its minimum value is given by FAST and manual match in the first image, it is 0.002651206 mm, and Harris and manual match provide the minimum value of total RMSE in the second image is 0.002399477 mm.