Reviewerly/exHarmony huggingface dataset:
Metamorphic reactions as a basis of gold mineralization

Metamorphic reactions considered in terms of the aqua-complex concept have been proved to be responsible for extraction, mobilization and migration of metals. Extracted from basic rocks, Au and Fe are mobilized in the [2SiO3-OH2-(Au,Mn+)2(Fe,Mm+)0’4] (n =1,2; m≥3; O’-volatiles) aqua-complex. The metals migrate step by step via redistribution of chemical bonds in the mineral continuum. Quartz-gold-sulfide (pyrite) association stems from mineralized constituent parts of the (Au-Fe) aqua-complexes disintegrating under inversion of redox potential and decreased pressure-temperature in the mineral system. The approach reveals the genetic relationship between gold-ore deposits of various style.
Deep learning based high similarity automatic retrieval algorithm for vocabulary interpretation of workers of Food Sector in china

In order to build a high similarity English vocabulary interpretation domain knowledge base and ensure the automatic retrieval effect of high similarity English vocabulary interpretation, this paper standardizes the automatic retrieval specification of authoritative interpretation of high similarity English vocabulary knowledge, and takes high similarity English vocabulary as the source corpus of the knowledge base. On the basis of the existing work, this paper attempts to propose an automatic retrieval algorithm of high similarity English word interpretation based on deep learning. The goal is to diversify the sources of high similarity English word knowledge and achieve the accuracy of automatic retrieval of word interpretation while ensuring a certain knowledge coverage. A suitable domain knowledge base of machine-readable dictionary is constructed through a new method It can not only provide accurate knowledge information for high similarity English vocabulary, but also provide retrieval verification for user needs analysis and high similarity English vocabulary indexing of snippet. The experimental results show that the algorithm based on deep learning is effective and can fully meet the research requirements.
Dynamics and Control

Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the growing intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics. With a focus on integrating dynamical systems modeling and control with modern methods in applied machine learning, this text includes methods that were chosen for their relevance, simplicity, and generality. Topics range from introductory to research-level material, making it accessible to advanced undergraduate and beginning graduate students from the engineering and physical sciences. The second edition features new chapters on reinforcement learning and physics-informed machine learning, significant new sections throughout, and chapter exercises. Online supplementary material – including lecture videos per section, homeworks, data, and code in MATLAB®, Python, Julia, and R – available on databookuw.com.
Perceptual Impacts of Wireless Network Impairments on Video Streaming QoE using Taguchi Approach

Video streaming applications have grown tremendously in recent years due to the technological advancement of wireless networks and smart devices. Unlike voice content, videos possess strict network performance demands to deliver a quality view. The Quality of Experience (QoE) refers to the level of users&#x2019; satisfaction with the delivered service and is one of the important aspects of managing wireless networks. Normally, wireless networks inherently exhibit various impairments such as packet loss, delay, and jitter. Therefore, understanding QoE on services delivered through the wireless networks is a basic requirement for the quality management process. Hence, this study adopts the Taguchi design of experiments to investigate the simultaneous impacts of network impairments on video streaming QoE. Experiments were conducted in a laboratory using a wireless network testbed. Different network conditions were emulated and real people assessed their impacts on videos streamed using smart devices. This study found that delay and jitter significantly affected video streaming QoE at $\mathrm{p}\lt 0.05$. Moreover, the effects of packet loss were not significant but exhibited outstanding interaction effects with jitter on video streaming QoE, which must be considered for effective network management practices.
Pixels On The Road - Where Are They Headed?

This paper describes how “pixels on the road” have grown by the numbers over the past decade and provides a personal outlook on where they are headed in the coming years.
Forge Audio Detection Using Keypoint Features on Mel Spectrograms

Audio copy-move-forgery audio is one of the most popular methods in the field of audio forensic. This type of forgery is created by copying one or more audio segments and pasting it in another position within the same audio. In this study, for detection of the audio copy-move forgery, a new method using a keypoint-based scheme on the Mel spectrogram model of audio is presented. Firstly, Mel spectrogram image is generated from the suspicious audio. Then, SURF keypoints are obtained from each RBG color channel of Mel spectrogram image. The obtained keypoints from each channel are matched via feature vectors to reveal whether the audio file is forged or original. Finally, the proposed post-processing step is applied to eliminate possible false matches. In the method, providing sufficient final matched keypoints according to the threshold value of the number of matches which is determined by experimental studies reveals that the audio file is forged. Experimental studies are carried out on publicly available the pitch-based dataset. The performance results prove that the proposed method is more robust against even under post-processing operations like noise addition, filtering operation, and compression operation.
The Use of Bilingual Advertising Targeting Hispanics

The proliferation of the Spanish-language media has changed the way Hispanic consumers and the marketing industry use Spanish. Many television networks, radio stations, newspapers, and magazines target Hispanics who prefer Spanish. In addition, the spectrum of Spanish-language media outlets has dramatically expanded over the last few years with multiple cable offerings. The virtual interactive world of the Internet has also expanded, providing additional platforms for advertising, which also use Spanish and bilingual content in order to appeal Hispanic consumers. While there is considerable lexical variation across Spanish-speaking cultures, most Spanish speakers in the world can communicate effectively with each other. Yet, developing a campaign that addresses the diversity of new immigrants and the diversity of those who represent a higher acculturated segment of Hispanics in the U.S. clearly represents a challenge. Understanding how the use of language can affect advertising to U.S. Hispanic audiences is crucial in these ever-changing demographics. To explain the role of Spanish in advertising and marketing communication, we must understand the significant relationship language has with the cognition and socialization of a group within a culture (Hogg & Terry, 2000) as well as within sub-cultures. Indeed, the self-identity of the Hispanic consumers and their experiences affect the way they react to messages (Hofstede & Hofstede, 2005). This chapter explains the role of language in advertising. We examine data that shows the effect of bilingual messages on attitudes and behaviors and exposes the cultural relevance of Spanish in advertising for Hispanic communities in the United States.
Language and Intelligence, Artificial vs. Natural or What Can and What Cannot AI Do with NL?

In this talk, I argue that there are certain pragmatic features of natural language (that I will call 'productivity' and 'malleability', on top of syntactical generativity and semantical compositionality), which are not only hard, but even impossible to capture in an artificial language used by an AI system, and the reason for this is to be found in certain deep, metaphysical differences between artificial and natural intelligence, accounting for the differences in their respective processes of concept-formation.
Towards the Detection of Hateful Sentiment in Social Networks

Hate speech in social networks is affected and therefore increased thanks to anonymity. The use of opinion mining and Natural Language Processing (NLP) have increased their activity during the last few years because they provide an approach to analyze people's opinion, attitude ratings and emotions in the evolution of web 2.0. This article shows the analysis of hateful sentiment in social networks, specifically on Twitter. For this purpose, the tweets have been obtained through the source of information provided by the Tweepy API, thus forming a corpus with tweets that will be labeled as hate and non-hate, as the input of the analysis. To carry it out, a series of tasks are performed: preprocessing, feature extraction, vectorization, training of the Naive Bayes classification algorithm and validation of the algorithm along several metrics. We conclude the validity of the method, which could be used to make a more precise specification of hate speech, with the aim of identifying social biases, such as gender or racial discrimination, among others.
Encodability and Separation for a Reflective Higher-Order Calculus

The ρ-calculus (Reflective Higher-Order Calculus) of Meredith and Radestock is a π-calculus-like language with some unusual features, notably, structured names, runtime generation of free names, and the lack of an operator for scoping visibility of names.These features pose some interesting difficulties for proofs of encodability and separation results.We describe two errors in a previously published attempt to encode the π-calculus in the ρ-calculus by Meredith and Radestock.Then we give a new encoding and prove its correctness, using a set of encodability criteria close to those proposed by Gorla, and discuss the adaptations necessary to work with a calculus with runtime generation of structured names.Lastly we prove a separation result, showing that the ρ-calculus cannot be encoded in the π-calculus.
A Generic Type System for Higher-Order Ψ-calculi

The Higher-Order Ψ-calculus framework (HOΨ) is a generalisation of many first-and higher-order extensions of the π-calculus.It was proposed by Parrow et al. who showed that higher-order calculi such as HOπ and CHOCS can be expressed as HOΨ-calculi.In this paper we present a generic type system for HOΨ-calculi which extends previous work by Hüttel on a generic type system for first-order Ψ-calculi.Our generic type system satisfies the usual property of subject reduction and can be instantiated to yield type systems for variants of HOπ, including the type system for termination due to Demangeon et al..Moreover, we derive a type system for the ρ-calculus, a reflective higher-order calculus proposed by Meredith and Radestock.This establishes that our generic type system is richer than its predecessor, as the ρ-calculus cannot be encoded in the π-calculus in a way that satisfies standard criteria of encodability.
Face Inverse Rendering via Hierarchical Decoupling

Previous face inverse rendering methods often require synthetic data with ground truth and/or professional equipment like a lighting stage. However, a model trained on synthetic data or using pre-defined lighting priors is typically unable to generalize well for real-world situations, due to the gap between synthetic data/lighting priors and real data. Furthermore, for common users, the professional equipment and skill make the task expensive and complex. In this paper, we propose a deep learning framework to disentangle face images in the wild into their corresponding albedo, normal, and lighting components. Specifically, a decomposition network is built with a hierarchical subdivision strategy, which takes image pairs captured from arbitrary viewpoints as input. In this way, our approach can greatly mitigate the pressure from data preparation, and significantly broaden the applicability of face inverse rendering. Extensive experiments are conducted to demonstrate the efficacy of our design, and show its superior performance in face relighting over other state-of-the-art alternatives. {Our code is available at \url{https://github.com/AutoHDR/HD-Net.git}}
Evaluating a contact tracing course: How universities can develop the public health workforce

To describe the participants of a university-based COVID-19 contact tracing course and determine whether the course changed knowledge, attitudes, and intention to participate in contact tracing.Faculty, staff, and students were eligible.Surveys evaluated the impact of the course on participant intentions to engage in contact tracing. Logistic regression identified characteristics associated with increased likelihood of participating in contact tracing.Nearly 800 individuals participated, of whom 26.2% identified as Hispanic/Latino and 14.0% as Black. Nearly half (48.8%) planned to conduct contact tracing. While attitudes did not change, knowledge improved (67.9% vs. 93.8% scores on assessments; p < 0.001). Younger participants and Black individuals were more more likely to be confident that they would participate in contact tracing.Course completion was associated with increased knowledge. Participants were racially and ethnically diverse, highlighting how universities can partner with health departments to develop workforces that reflect local communities.
Generating Synthetic Image Segmentation Dataset Using Realistic City Backgrounds

Creating a dataset for the image segmentation problem is a time-consuming process. For this reason, generating synthetic datasets that are quite close to reality has great importance. In this study, for the problem of segmentation of various objects encountered in the city, a method for synthetically generating a set of images that contain objects in an city background employing images that do not have city backgrounds is proposed. This method creates a synthetic segmentation dataset of the desired size with realistic city backgrounds, using city videos from video-sharing platforms and a segmentation dataset related to the object to be segmented. The proposed approach has been tested for a pedestrian segmentation problem and the detailed results have been compared with the results of a real dataset. Results show that using the synthetic dataset increases the AP value by 8 percentage points and the AR value by 4 percentage points compared to not using it.
Robustness to Spelling Errors for Intent Detection

Intent detection is determining the requested action of the user from the query text in chatbot applications. Generally, the query for this task is comprised of only a few words. In real life use cases there can be spelling errors when the communication is through a written channel. The employed text classification method is based on fine tuning of the pre-trained BERT model on the specific problem. It is proposed to augment the training data with automatically generated erroneous samples to compensate the performance degradation caused by the queries containing spelling errors. Test sets are generated by replacing one or two words in query with its corresponding realistic human errors. Intent detection accuracies are reduced by %6.9 and %15.3 relative to the clean set accuracy respectively for single and double error test sets. The proposed error augmentation approach compensated the performance decrease caused by spelling errors %70,3 and %66,0 relatively.
Automatic Keyword Extraction From Dialogue Text

Keyword extraction is the automatic process of extracting keywords or keyphrases that are most relevant to a text using various algorithms. In this study, we have extracted keywords from customer service records of various companies. We reached %70 recall score by supporting tf-idf with textrank and positionrank algorithms. This corresponds to %10 relative improvement to tf-idf alone. This method is created to get accurate results especially from small-medium length and preferably dialogue texts. The system can be applied to different types of texts by optimizing its parameters. Our method also generates minimum three word phrases which is an easily understandable and a very short summary of the dialog, from the extracted keywords.
Facial Expressions Detection of Children with Hearing Impairment

Facial emotions play a critical part in daily life and understanding and analyzing emotions are of great importance in terms of human-computer interaction. Although facial expressions between children and adults differ, many face recognition systems use models that are exclusively trained on adult data. Children with hearing impaired (HI) have hardship in social life due to differences in expressing emotions. Therefore, the facial expressions recognition of children with HI is a challenging problem. This study aims to develop a high performance emotion classification system for children with HI. For training Convolutional Neural Networks (CNN) from scratch, a large amount of data is required, but the children&#x2019;s dataset is limited. Firstly, fine-tuning the model with pre-trained CNN via transfer learning is discussed as a method for the classification of children&#x2019;s emotions. As a result of the experiments, the most accurate result was obtained by fine-tuning the dataset of children with 3 emotion labels with the trained-model of adults with 8 emotion labels.
Skeletal-based Classification for Human Activity Recognition

Human activity recognition (HAR) is critical for determining human interactions and interpersonal relationships. Among the various classification techniques, two things become the main focus of HAR, namely the type of activity and its localization. Most of the tasks in HAR involve identifying a human scene from a series of frames in a video, where the subject being monitored is free to perform an activity. For some of the current HAR approaches, 3D sensors are used as input extractors for the skeleton/body pose of the subject being monitored. It is much more precise than using only 2D information obtained from conventional cameras. Of course, the use of 3D sensors is a significant limitation for implementing video-based surveillance systems. In this research, we use the Deep learning OpenPose 3D method as a substitute for 3D sensors that can estimate the 3D frame/pose of the subject's body identified from conventional camera 2D input sources. It is then combined with other machine learning methods for the activity classification process from the obtained 3D framework. Classifiers that can be used include Support Vector Machine (SVM), Neural Network (NN), Long short-term memory (LSTM), and Transformer. Thus, HAR can be applied flexibly in various scopes of supervision without the help of 3D sensors. The experiment results inform that Transformer is the best in accuracy while SVM is in speed.
A Study on the Establishment of Elementary Education Institution by Jeju Residents in Japanese colonial era

In Japanese colonial era, Jeju residents established a number of Kaeryang seodangs on their own. These Kaeryang seodangs are equipped with facilities equivalent to ordinary schools. Jeju residents promoted the school to ordinary School. School establishment activities were most actively carried out in coastal villages in Bukjeju. On the other hand, the villages in Bukjeju and Namjeju were relatively inactive. The reason for this difference is that the difference in economic power between villages should be considered first. At that time, Jeju Island had two large sources of economic income. One was the material of the haenyeo and the other was the remittance of Jeju residents in Japan. Coastal villages were more active in these two factors than in other regions, so their economic power was relatively dominant, and based on this, they led the establishment of elementary education institutions.&#x0D; The strong community of Jeju Island's village also helped establish a school. Modified Seodang was built on a village-by-town basis and was impossible to operate without the cooperation of villagers. The fact that many improved seodangs were built shows that the bond between the residents of Jeju Island was strong. As such, the establishment of an elementary education institution developed in Jeju Island, Japanese colonial era, is significant in that it itself shows the educational enthusiasm and educational activities of Jeju residents. And furthermore, such activities were possible in the economic, socio-cultural background of Jeju Island.
Enhancing the Security and Performance of Cloud-Based Distance Education System

There are many methods that enable a learner to receive education online from distant locations, but online education is growing fast. There remains the issue of the security of digital content during transmission over the cloud. However, it was noted that the security of digital information has to be increased. The emphasis of the current study is on the safety and efficiency improvement of distant learning systems in the cloud. Several kinds of cloud computing research have been conducted to offer remote learning. Previous research found issues with data performance and security. There is a need for a mechanism that could transfer educational content from one place to another in less time securely. During data transmission, the education materials have to be protected and compressed. In order to decrease the size of the packet, research utilized a content replacement method. The proposed work has improved security with the use of the substitute mechanism Exclusive OR (XOR), since other types of encryption are time-consuming, such as the Advance encryption standard (AES) and the Data encryption standard (DES). The technology is safe and quick, since data is first compressed and encrypted on the sender’s side before sending it. During packet transmission, proposed work has provided a solution to manage packet loss, errors, packet hijacking and many different attacks. Finally, data is decrypted and decompressed at the receiver end after receiving. The probability of cracking encrypted files also gets reduced as the data is encrypted after compression. The comparison of the proposed work is made to the previous Rivest–Shamir–Adleman (RSA), Deoxyribo Nucleic Acid (DNA) cryptography mechanism. The impact of attacks on packets in the case of the proposed work, RSA and DNA cryptography has been simulated. The simulation results confirm that the proposed work is more secure than previous RSA and DNA cryptography.
The Role of Coatings in Reflective Augmented Reality Glasses

Reflective Augmented reality glasses have two main parts: an image projection element from a Liquid Crystal Display (LCD) or OLED and a combiner element for coupling the image to the eye. The role of optical coatings in both parts will be addressed in this paper.