Reviewerly/exHarmony huggingface dataset:
Automatic Microseismic Event Location Using Deep Neural Networks in Anisotropic Media

Summary Accurate microseismic event location offers invaluable insights into the subsurface conditions not only for oil and gas production but also for seismic hazard assessment. Conventional microseismic event location methods face considerable drawbacks like requiring manual traveltime picking or large computational cost for simulating the wavefields. In fact, the need to locate microseismic events in real time leaves a gap for an automatic and efficient approach. Building on a previously developed method which is based on a deep Convolutional Neural Network for microseismic event location, we propose an extension of such an approach to include the anisotropic nature of the Earth and irregular receiver sampling. Example application on a 2D SEAM time-lapse model illustrates both the accuracy and efficiency of this method. Moreover, we validate the practicability of this approach for both isotropic and anisotropic media considering that the Earth is predominantly anisotropic. Equally important, we demonstrate that this approach is not only feasible for the cases with a uniform receiver distribution, but also applicable to cases where the passive seismic data are acquired with irregularly spaced receivers.
Prediction of Rockburst Using Supervised Machine Learning

The rockburst is one of the serious mining hazards, that cause injury, death, damage to mining equipment, and leads to financial problems in mining constructions. At the same time, mining is the major and essential resource of mineral commodities, that all mining locations uncover important for sustaining and enhancing their needs of dwelling. Moreover, mining contributes a significant portion of the GDP (Gross Domestic Production) growth of nations such as China, Australia, Russia, and the USA. These countries play major roles in exporting mineral commodities, which has been creating a huge demand to find out conventional methods for predicting the rockburst occurrence during mining. Moreover, determining rockburst occurrence using formulation or machine-based equipment has not been ideal in showcasing the result, due to changing geological parameters and the mining environment. In recent years, various soft computing and machine learning tools have been developed significantly, which help us to improve predicting models more accurate. In this research, original Rockburst data set have been collected in the last 20 years. Then standard empirical formulation method which is currently used in mining and supervised machine learning models has been used to predict rockburst occurrence. Finally, an interacting machine learning model developed in a way to predict the Rockburst occurrence result in three Indicators (high, medium and low Rockburst) are presented.
Outlier Detection for Categorial Data Using Clustering Algorithms

Detecting outliers is a widely studied problem in many disciplines, including statistics, data mining and machine learning. All anomaly detection activities are aimed at identifying cases of unusual behavior when compared to the remaining set. There are many methods to deal with this issue, which are applicable depending on the size of the dataset, the way it is stored and the type of attributes and their values. Most of them focus on traditional datasets with a large number of quantitative attributes. While there are many solutions available for quantitative data, it remains problematic to find efficient methods for qualitative data. The main idea behind this article was to compare categorical data clustering algorithms: $$ \textit{K-modes} $$ and ROCK. In the course of the research, the authors analyzed the clusters detected by the indicated algorithms, using several datasets different in terms of the number of objects and variables, and conducted experiments on the parameters of the algorithms. The presented study has made it possible to check whether the algorithms detect the same outliers in the data and how much they depend on individual parameters such as the number of variables, tuples and categories of a qualitative variable.
Social Distance Measurement and Face Mask Detection Using Deep Learning Models

The coronavirus disease 2019 has caused a worldwide catastrophe with its destructive spreading and causing death of more than 2.47 million people around the globe. In the current circumstance, most of the countries are trying to implement social distancing, wearing masks, extensive testing, and contact tracing strategies to curb the virus outbreaks. Maintaining adequate social or physical distance is believed to be a sufficient precautionary measure (standard) against the spread of the pandemic infection. This research paper has two different contributions of social distance measurement and face mask detection using various deep learning approaches. In the first section, we have monitored the social distance where we have detected people by examining a video feed with SSD-MobileNet and Faster R-CNN ResNet50 deep learning algorithms. Next, the image is converted into an overhead view to measure the specific distance among people to ensure safe physical distancing. In the second section, we have detected the face masks used by the people by implementing MobileNetV2 convolutional neural network architecture. Hence, we have used computer vision to find the region of interest of a face, and finally, we have found that the mask is in the face or not. Both of our social distance measurement and face mask detection systems offer high accuracy. As for the social distance monitoring, the accuracy greatly depends on the people detection, and the execution time is 30 ms and 89 ms for SSD-MobileNet and Faster R-CNN ResNet50, respectively. For the face mask detection, we obtained 99% accuracy, and it is checked in real-time so that we can prove that our model is not overfitting and it performs well outside our dataset in real-time camera.
A Team Formation Approach to Supplier Network Design, Considering Multi-Objective Modeling, Fuzzy Inference, and Social Network Analysis

This research developed an approach to reliable team formation and supplier network design, focusing on a multi-objective model integrating fuzzy-set theory and social network analysis. The approach simultaneously focuses on reliable collaboration network, order allocation, capabilities (skills, expertise, knowledge, or competency), backup team, and capacity. Fuzzy logic is used to calculate the relative importance value of relationships between members. The multi-objective model is used for reliable supplier selection and order allocation. Social network analysis is used to suggest network leader(s). The augmented epsilon constraint and grey wolf optimizer (MOGWO) was developed to validate the model. Since the model is NP_HARD, three meta-heuristic algorithms are called a non-dominated sorting genetic algorithm (NSGA-III), non-dominated ranking genetic algorithm (NRGA), and particle swarm optimization algorithm (MOPSO), was developed with a new chromosome structure to validate the solutions. The results have been compared based on the AHP-PROMETHEE with eight performance criteria, showing that the MOGWO works better than the other three algorithms. The results of the experiments in numerical studies demonstrate the efficiency and applicability of the model to solve, large, and small-size problems.
The Impact of Social Advertising on Student Adaptation in Higher Education Institutions

The article considers the organization of psychological and pedagogical work with first-year students. It is established that during the period of studentship young people are included in the system of relationships in a new social adult environment. In this regard, it is very important that young people are actively involved in the educational process in higher education institutions, successfully coping with the requirements for learning, receiving a set of necessary competencies that can be successfully applied in practice in the future. The main problems arising in the first-year students are outlined: the new system of education; a different mode, loading and requirements; a new social role; relationships with fellow students and teachers; problems in social and everyday life; independent life in the new conditions for nonresident students.Analysis of the scientific literature allowed us to conclude that a significant potential for solving the problem of student adaptation in higher education is social advertising.
Sarcastic Image Detection Using Radial Basis Function

There have been a lot of new ways to figure out how people feel about things on Twitter and other microblogging platforms in the last few years. Using both types of semantics, experiments are carried out to see how they perform in three popular tasks of sentiment investigation on Twitter: sentiment analysis at the level of entities, sentiment analysis at the level of tweets, and context-sensitive sentiment lexicon adaptation. These three tasks include: Find and categorise sarcastic texts were the objectives of this module. After the initial sentiment classification, texts that were initially classified as positive will be classified as moreover positive sarcastic or true positive. Texts classified as negative by the primary sentiment classification module can be confidential in two ways. Negative sarcasm and true negativity are two different types of negativity. A radial basis function (RBF) model was used to identify and categorise sarcastic words. Using data from Amazon and Twitter, it was tested to see if it worked. Detailed information on the dataset, experiments, and findings are presented in the section on experimental setup and results.
Quantum Computing

The chapter introduces the core ideas of quantum computing and quantum information theory. Most importantly, the concept of the qubit as the basic indivisible unit of quantum information is covered in detail, along with the circuit approach to modeling quantum computations. The chapter introduces the most common quantum gates—both single- and multi-qubit ones—and draws parallels to the quantum mechanical concepts covered earlier in the book.
Prospective Customer Preference Toward Service Robots: A Discrete Choice Model Approach

Abstract. One major technological development pertains to the service robot industry. In Indonesia, the development of robots is increasing. Telkom will begin to explore new opportunities with companion robots, which can respond to and chat with humans and move around. Therefore, more analysis is needed based on market research and prospective user surveys. The present study aims to obtain information on the features of service robots significantly influencing customers’ desire to purchase them and on the types of service robots they are likely to choose. The discrete choice model method is used to understand and predict a decision from a discrete alternative, based on a set of choices constructed with the help of the Ngene software and the D-efficient design method. We processed questionnaire results using the Python-Biogeme software to look for significant attributes and their elasticity. The significant attributes of companion robots defined by T-test are basic robot look (3.86), battery life (5.45), robot price (9.16), walking-computer look (6.10), and robot weight (8.38). The suggested strategy, among the three types of companion robots, is to focus on the personal assistant robot with the highest T-test score while paying attention to the resources a company already has. Keywords: Service Robot, Discrete Choice Model, Significant Attribute, Elasticity
Exploration and Practice on Teaching Reform of Organic Chemistry Laboratory

With the further deepening of education and teaching reform, in view of problems in traditional organic chemistry laboratory courses, the Chemistry Experiment Teaching Center in SUSTech has carried out a comprehensive course reform in three aspects: course contents, teaching methods, and assessment modes.Years of practices have proved that the teaching effects of laboratory courses have been significantly improved, and the qualities of students have been comprehensively improved.Some cases in this article may provide references for the teaching reform of organic chemistry laboratory in other universities.
Responding to the Loneliness in the New Era of the Internet: Exploring the Role of Hugging and Fitting in Alleviating Loneliness

The development of the new era has given the new generation new psychological characteristics of loneliness. The purpose of this paper is to explore a new way to relieve loneliness, and to demonstrate that the interaction method and form of hugging with objects is useful for relieving loneliness.The new design and analysis method is used to verify that the ergonomic design of human fit and inductive hugging interaction function are useful for alleviating loneliness, proving that the emotional needs of users can be addressed by attaching hugging function and ergonomic redesign to specific products, providing a new direction for alleviating the psychological situation of contemporary lonely people and creating better products for alleviating loneliness.In this paper, a literature review is conducted in the early stage to outline the current research on the Internet and loneliness. Based on the starting point of hugging and fitting to relieve loneliness, a human-computer pillow with hugging function is designed. University students who meet the characteristics of the target user group are selected as the experimental research objects, and the questionnaire method is used to collect objective intelligence information by distributing online questionnaires to the research target group, and the interview method is used to conduct in-depth research on the target user group so as to deeply understand the users' psychology and identity. After collecting the relevant data, the data were statistically processed using mathematical tools, analyzed using SPSS and other analytical tools, and considered for its credibility, so as to verify the effect of hugs on alleviating loneliness. The experimental method and interview method were used to ask the test subjects about their feelings, so as to prove that the feeling of closeness has an effect on alleviating loneliness.This paper finds that the interaction action of hugging with objects and the ergonomically based sense of fit are useful for alleviating loneliness and can alleviate people's inner loneliness to a certain extent, which helps to focus on the new trend of loneliness psychology among young people based on the contemporary Internet society, design better loneliness relief products for lonely people and pay attention to the deep needs of users.Keywords: ergonomics, psychology, SPSS, loneliness, Internet, z era, interaction
Server-side adaptive bitrate method based on perceptual video quality in networks with controllable bandwidth

In HTTP adaptive bitrate (ABR) streaming, the ABR algorithm is an important factor determining the quality of experience. Traditional ABR methods control the bitrate of individual clients autonomously so that the quality variation occurs even when the bandwidth of a delivery network is managed. Here, we propose a server-side ABR method achieving quality fairness among clients and suppressing a quality variation over time in networks where external applications dynamically control the quality of service. The method controls both the bitrate and delivery bandwidth based on a perceptual quality score. We evaluated the basic performance of the proposed method on an experimental network whose bandwidth could be controlled by the API. We confirmed that the proposed method could maintain the target quality within the upper-limit bandwidth and suppress the quality variation among clients and over time.
Detecting Hate Speech and Offensive Language Using Transformer Techniques

Social media has become part of our everyday lives, and it has its pros and cons. Hate speech online is a new-born problem in our modern society that is growing at a steady pace, using the weaknesses of cohesive regimes that spill over several social media platforms. Due to the inconsistent and large size of social media, the detection of hate content has become a major concern to avoid conflicts and prevent unwanted activities. Hence, it is crucial to devise an automated system to detect the same. There have been many studies in the field of Artificial Intelligence to detect hate speech. Nonetheless, the efficiency of the models has not been up to the mark. In this study, we compare two different state-of-the-art models. The first is BERT-CNN model based on transformer, and the second is an LSTM model based on attention. Both these models work on a publicly available dataset containing messages categorized into different hate emotions. This study shows that the BERT-CNN model exhibits the highest accuracy, while outperforming the other.
Multi-step Ahead Forecasting of River Water Temperature Using Advance Artificial Intelligence Models: Voting Based Extreme Learning Machine Based on Empirical Mode Decomposition

River water temperature (Tw) is highly dynamic in space and time and can vary over the seasons and between day and night, and can affect the health condition of the aquatic organisms in multiple ways. Remarkably, modelling water Tw has received great attention during the last few years, especially, using machine learning models, however, few studies have focused to the forecasting of water Tw at short-term, medium-term and long-term. To make water Tw forecasting more reliable and accurate, a new hybrid model is introduced in this chapter. The proposed modelling strategy is composed of three main steps. First, water Tw time series was analyzed using the autocorrelation function (ACF) and the partial autocorrelation function (PACF), and the relevant time lag were selected as input variables for model development. Second, the selected input variables, i.e., the time lag of water Tw, were decomposed using the empirical mode decomposition (EMD) signal processing and the relevant intrinsic mode functions (IMF) were selected as a new input variable. Third, a new machine learning model called voting based extreme learning machine (VELM) was introduced as a robust tool for multi-step-ahead forecasting of river water temperature, and compared to the standard ELM and the MLPNN. The VELM model was compared to the standard ELM and the MLPNN according to several performances metrics namely, the RMSE, the MAE, the R, and the Nash–Sutcliffe efficiency (NSE). Numerical results show that river water Tw can be reliably forecasted up to 30 days in advance (t + 30), and the R and NSE values were above 0.920 and 0.840 using the hybrid models (i.e., MLPNN_EMD, VELM_EMD and ELM_EMD) for the horizon (t + 30), compared to the perfect values obtained for the horizon (t + 1). Overall accuracy of the developed models demonstrated that the EMD contributed significantly to the improvement of the machine learning models.
New type I binary $[72, 36, 12]$ self-dual codes from $M_6(\mathbb{F}_2)G$ - Group matrix rings by a hybrid search technique based on a neighbourhood-virus optimisation algorithm

&lt;p style='text-indent:20px;'&gt;In this paper, a new search technique based on a virus optimisation algorithm is proposed for calculating the neighbours of binary self-dual codes. The aim of this new technique is to calculate neighbours of self-dual codes without reducing the search field in the search process (this technique is known in the literature due to the computational time constraint) but still obtaining results in a reasonable time (significantly faster when compared to the standard linear computational search). We employ this new search algorithm to the well-known neighbour method and its extension, the &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ k^{th} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-range neighbours, and search for binary &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ [72, 36, 12] $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; self-dual codes. In particular, we present six generator matrices of the form &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ [I_{36} \ | \ \tau_6(v)], $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; where &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ I_{36} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; is the &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$ 36 \times 36 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; identity matrix, &lt;inline-formula&gt;&lt;tex-math id="M6"&gt;\begin{document}$ v $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; is an element in the group matrix ring &lt;inline-formula&gt;&lt;tex-math id="M7"&gt;\begin{document}$ M_6(\mathbb{F}_2)G $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; and &lt;inline-formula&gt;&lt;tex-math id="M8"&gt;\begin{document}$ G $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; is a finite group of order 6, to which we employ the proposed algorithm and search for binary &lt;inline-formula&gt;&lt;tex-math id="M9"&gt;\begin{document}$ [72, 36, 12] $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; self-dual codes directly over the finite field &lt;inline-formula&gt;&lt;tex-math id="M10"&gt;\begin{document}$ \mathbb{F}_2 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;. We construct 1471 new Type I binary &lt;inline-formula&gt;&lt;tex-math id="M11"&gt;\begin{document}$ [72, 36, 12] $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; self-dual codes with the rare parameters &lt;inline-formula&gt;&lt;tex-math id="M12"&gt;\begin{document}$ \gamma = 11, 13, 14, 15, 17, 19, 20, 21, 22, 23, 25, 26, 28, 29, 30, 31, 32 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; in their weight enumerators.&lt;/p&gt;
A Hybrid Material Generation Algorithm with Probabilistic Neural Networks for Solving Classification Problems

Classification is based on machine learning, in which each element in a set of data is classified into one of a predetermined set of groups.In data mining, an artificial neural network (ANN) is the most significant methodology because of the exact results obtained through this algorithm and applied in solving many classification problems.ANN consists of a group of types of feed-forward networks, feed-back network, RFB networks, and the probabilistic neural networks (PNN).For classification issues, the PNN is frequently utilized.The primary goals of this research are to fine-tune the weights of neural networks to enhance the classification accuracy.To accomplish this goal, the Material Generation Algorithm (MGA) was investigated with PNN in a hybrid model.Newly, the hybridization of algorithms is ubiquitous and it has led to the development of unique procedures that outperform those that use a single algorithm.Several distinct classification tasks are used to test the efficiency of the suggested (MGA-PNN) approach.The MGA algorithm's efficiency is evaluated using the PNN training outcomes generated, and its outcomes are compared to that of other optimization strategies.By 11 benchmark datasets, the suggested algorithm's performance in terms of classification accuracy is evaluated.The outcomes display that the MGA outperforms the biogeography based optimization, firefly method in terms of classification accuracy.