Reviewerly/exHarmony huggingface dataset:
Quotes within Quotes

Abstract “They inspire me to be more confident every day. When I wake up in the morning, I feel just like any other insecure 24-year-old girl. But then I say, ‘[Expletive], you’re Lady Gaga, you better . . . get up and walk the walk today,’ because they need that from me.”
Using Commas before Short Bits at the Ends of Sentences

Abstract I play John Wayne in every picture regardless of the character, and I’ve been doing all right, haven’t I?
Dynamics of Fuzzy Cognitive Maps with Uncertain Weights

In real-life problems, the weights of the connections between the concepts are often not known exactly. This uncertainty can be modelled in different ways, based on different considerations. In this chapter, we discuss two models, regarding the nature of uncertainty. Fuzzy grey cognitive maps (FGCMs) employ grey system theory to represent vagueness of the causal connections. This approach assumes that we do know the range of the unknown weight, but within this range we do not have any further information. In other words, the model assumes known extension, but unknown intension. In classical fuzzy cognitive maps the weights are single numbers, so the fuzziness is not present there. Fuzzy set valued fuzzy cognitve maps handle this known issue by assigning fuzzy sets to the connections, so in this model the intension of a weight is known, but the extension is not exactly clear. These models behave somewhat similarly to classical fuzzy cognitive maps. The final conclusion about the modelled system is based on the limiting behaviour of the iteration process. This iteration may converge to an equilibrium point, may arrive to limit cycles or may produce unstable behaviour. In this chapter, sufficient conditions for the existence and uniqueness of fixed points of fuzzy grey and fuzzy set valued FCMs are provided.
Robust Feature Extraction Technique for Hand Gesture Recognition System

Hand gesture recognition (HGR) is a vital and versatile technology widely employed in various applications such as gaming, robotics, and interactive media. Beyond its broad utility, HGR systems play a crucial role in facilitating interactions for individuals with disabilities, empowering them with newfound capabilities. This paper introduces an innovative approach to feature extraction, leveraging Autoencoders to derive essential characteristics from the data. The extracted features are subsequently utilized for a comparative analysis of the accuracy achieved across different data sizes within the same dataset. By mapping the visual vocabulary of images, the Autoencoder generates a unified vector, which is then fed into Support Vector Machine (SVM), Naive Bayes, and K-Nearest Neighbor (KNN) classifiers. Through this research, we aim to enhance the efficiency and effectiveness of hand gesture recognition systems, fostering advancements in human-computer interaction and augmenting the quality of life for individuals with disabilities.
Text Mining Based on the Literature of Waste Management as the Source of a Reconstructed Time Series

As no statistical time series was available for the new refined, “split” concepts, the next goal of the research was generating a time series for all 33 factors, based on information in the literature, relying on the available expert judgment. Relying only on expert knowledge would have led to rather unreliable results, thus, an objective method for analysing the texts of the literature had to be proposed. Text mining seemed to be the easiest way to go, especially, as it promised to be the most suitable way to extract the time series in the time period covered by these literatures for all the new concepts. We assumed that this approach would allow the extraction of structured knowledge from the available unstructured or only slightly structured text files. Thus, the next step was to gather and transform the textual knowledge and information from these different document sources with applying proper machine intelligence. A specialised text mining expert company offered help as “sub-contractors” to the team’s research.
Comparison for Handwritten Character Recognition and Handwritten Text Recognition and Tesseract Tool on IJAZAh’s Handwriting

Handwriting is a form of being able to recognize various types of writing in various existing fonts. Unlike consistent computer letters, each human handwriting is unique in its form and consistency. These problems can be found in a document where the data is in the form of handwriting. Segmentation of the data location will use a run length smoothing algorithm with points as segmentation features. The Handwriting Text Recognition (HTR) technique requires segmented data into words. The Handwriting Character Recognition (HCR) technique requires segmented data into various characters. The process of this HCR technique uses the LeNet5 model using the EMNIST dataset. HTR uses the tesseract tool and a convolutional iterative neural network using the IAM database. Experiment on 10 samples of scan images, segmentation obtained an average accuracy of 95.6%. The HCR technique failed in the letter segmentation process in cursive handwriting. The easiest technique to use is the HTR with the helps of tesseract tool, tesseract tool also has a good performance. Tesseract managed to get word accuracy above 70% tested on 5 scan samples, 15 data fields.
Solving interval type-2 fuzzy relation equations via semi-tensor product of interval matrices

&lt;abstract&gt;&lt;p&gt;This paper mainly studied the problem of solving interval type-2 fuzzy relation equations $ \widetilde A \circ \widetilde X = \widetilde B $. First, to solve the interval type-2 fuzzy relation equations, we extend the semi-tensor product of matrices to interval matrices and give its specific definition. Second, the interval type-2 fuzzy relation equation was divided into two parts: primary fuzzy matrix equation $ {\widetilde A_\mu } \circ {\widetilde X_\mu }{\rm{ = }}{\widetilde B_\mu} $ and secondary fuzzy matrix equation $ {\widetilde A_f} \circ {\widetilde X_f} = {\widetilde B_f} $. Since all elements of $ {\widetilde X_f} $ equal to one, only the principal fuzzy matrix equation needs to be considered. Furthermore, it was proved that all solutions can be obtained from the parameter set solutions if the primary fuzzy matrix equation is solvable. Finally, with semi-tensor product of interval matrices, the primary fuzzy matrix equation was transformed into an algebraic equation and the specific algorithm for solving an interval type-2 fuzzy relation equation was proposed.&lt;/p&gt;&lt;/abstract&gt;
Enhancing optical character recognition: Efficient techniques for document layout analysis and text line detection

Abstract In recent years, automatic document and text analysis has gained significant importance, driven by advancements in optical character recognition (OCR) technology and the need for efficient processing of large volumes of printed or handwritten documents. This article specifically focuses on document layout analysis (DLA) and text line detection (TLD), both of which are crucial components of OCR systems. Our objective is to develop an effective method for extracting both textual and non‐textual regions, addressing challenges unique to the Persian (and Persian‐like) language(s). In the DLA stage, we employ deep learning models and a voting system to accurately determine the regions of interest. Additionally, we introduce methods such as optimum font size concepts, angle correction, and a line curvature elimination algorithm in the TLD process to enhance OCR accuracy. Comparative evaluations against state‐of‐the‐art methods demonstrate the superiority of our approach, showcasing a 2.8% improvement in the accuracy of Tesseract‐OCR 5.1.0 (a well‐established commercial OCR system) on the official Iranian newspapers dataset. These findings underscore the importance of addressing DLA and TLD challenges to advance OCR technology for Persian language documents and provide a solid foundation for future research in this domain.
Detection Model of Substation Personnel Safety Protection Tools Based on Improved YOLOv5

Abstract Substation operation and maintenance personnel face the hazards of high voltage and strong current in their work. Therefore, it is crucial to accurately and promptly detect the condition of safety protection tools for these personnel. This paper proposes a detection model that enhances YOLOv5‘s substation personnel safety protection tool detection capabilities. The CBAM attention mechanism was integrated into the YOLOv5 model using this approach, while the feature fusion network PANet was replaced with the CB-BiFPN structure. Finally, DIoU was used instead of GIoU loss function to improve the convergence speed and detection accuracy of the model. Experimental results demonstrate that our improved YOLOv5 model achieves high accuracy and fast processing speed with a mean average precision (mAP) of 89.23% at 32 frames per second (FPS). Our proposed method outperforms other related object detection models such as Faster RCNN, SSD, and YOLOv5 in terms of performance metrics. This study improves safety protection tool detection accuracy and provides valuable insights for real-time safety management and control in substations.
The Dreaded Comma Splice

Abstract If I asked you to guess the most common writing problem that students have, what would you say? The truth is, my most frequent comment on students’ papers isn’t about grammar or usage, or even about punctuation. It’s that they need to express their ideas more clearly and directly, in a more natural voice. But aside from that, their most persistent error is probably the comma splice.
Using Commas with Transition Words and Phrases

Abstract You must realize that honorary degrees are given generally to people whose SAT scores were too low to get them into schools the regular way. As a matter of fact, it was my SAT scores that led me into my present vocation in life: comedy.
Commas in Direct Address

Abstract And if we should ever separate, my little plum, I want to give you one little piece of fatherly advice: Never give a sucker an even break.
Zeroth, first, and second-order phase transitions in deep neural networks

We investigate deep-learning-unique first-order and second-order phase transitions, whose phenomenology closely follows that in statistical physics. In particular, we prove that the competition between prediction error and model complexity in the training loss leads to the second-order phase transition for deep linear nets with one hidden layer and the first-order phase transition for nets with more than one hidden layer. We also prove the linear origin theorem, which states that common deep nonlinear models are equivalent to a linear network of the same depth and connection structure close to the origin. Therefore, the proposed theory is directly relevant to understanding the optimization and initialization of neural networks and serves as a minimal model of the ubiquitous collapse phenomenon in deep learning.
En Dashes

Abstract The en dash is longer than a hyphen but shorter than the em dash. It has uses, but before we go any further, I’m going to make a shocking confession: I’ve never used an en dash. You may not need to either, because, except in published texts, it’s normally okay to use a hyphen instead. Newspapers typically use hyphens, not en dashes. The New York Times Manual of Style and Usage tells journalists not to use the en dash, except as a minus sign. But in other types of published work, en dashes are a sign of careful editing. If a publisher accepts your work, it’s certainly good to apply the guidelines below to put en dashes in your typescript. But, as one veteran editor at a university press told me, if the copy editor replaces your hyphens with en dashes when appropriate, you probably don’t need to worry too much about doing it.
Ten simple rules for starting FAIR discussions in your community

This work presents 10 rules that provide guidance and recommendations on how to start up discussions around the implementation of the FAIR (Findable, Accessible, Interoperable, Reusable) principles and creation of standardised ways of working. These recommendations will be particularly relevant if you are unsure where to start, who to involve, what the benefits and barriers of standardisation are, and if little work has been done in your discipline to standardise research workflows. When applied, these rules will support a more effective way of engaging the community with discussions on standardisation and practical implementation of the FAIR principles.
Edge Detection Algorithm for Infrared Images of Power Equipment Based on Improved Grey Wolf Algorithm and Corrosion Algorithm

Abstract The enhanced grey wolf algorithm is merged with the corrosion algorithm, and a grey wolf corrosion optimization algorithm is proposed to precisely identify the boundaries within infrared images of power equipment. The refined grey wolf algorithm employs exponential entropy as its fitness function and incorporates an individual wolf selection mechanism, enabling binary segmentation with adaptively chosen thresholds and high resemblance to the original image. Simulation results of actual infrared images captured in substations reveal that, compared to conventional edge detection algorithms such as Sobel, Roberts, Prewitt, Log, and Canny, the algorithm proposed in this paper yields comprehensive and well-defined boundaries with a high degree of conformity to the original infrared image. This proposed algorithm demonstrates significant applicability in the domain of infrared image processing and target recognition for power equipment.
When Not to Hyphenate

Abstract We saw in the previous lesson that you shouldn’t hyphenate open compounds like home run or closed compounds like football. We also saw that you shouldn’t use hyphens with compounds or prefixes if your dictionary doesn’t, or if the hyphen would create ambiguity. This lesson discusses other contexts in which you don’t need hyphens.
High temporal resolution of pedestal dynamics via machine learning on density diagnostics

Abstract At the Joint European Torus, the reference diagnostic to measure electron density is Thomson scattering. However, this diagnostic has a low sampling rate, which makes it impractical to study the temporal dynamics of fast processes, such as edge localized modes. In this work, we use machine learning to predict the density profile based on data from another diagnostic, namely reflectometry. By learning to transform reflectometry data into Thomson scattering profiles, the model is able to generate the density profile at a much higher sampling rate than Thomson scattering, and more accurately than reflectometry alone. This enables the study of pedestal dynamics, by analyzing the time evolution of the pedestal height, width, position and gradient. We also discuss the accuracy of the model when applied on experimental campaigns that are different from the one it was trained on.
The Oxford Comma

Abstract A lot of people go a little crazy about whether to use the Oxford comma. Roy Peter Clark, the author of The Glamour of Grammar, told me that if he blogs about the conflict in Syria, he might get three responses. But if he defends the Oxford comma, his readers will fight about it for weeks. Mignon Fogarty, aka Grammar Girl, says that this is the comma that people ask her about the most.1 And when people learned that I was writing a book on punctuation, several of them immediately asked me one burning question: Where do I stand on the Oxford comma? Obviously it’s a celebrity punctuation mark.
<scp>PKD‐Net</scp>: Distillation of prior knowledge for image completion by multi‐level semantic attention

Summary Prior knowledge plays a crucial role in image completion. Although almost all of the existing image completion methods use prior knowledge to complete the image to be repaired from different perspectives, the learning and modeling of the prior knowledge is still a challenging problem. In order to address this issue, we propose a novel prior knowledge distillation framework (PKD‐Net) which could distill prior knowledge of structure and style from multiple semantic space and generates not only plausible content but also consistent style with surrounding image area. Our PKD‐Net replaces the skip connection in the vanilla U‐Net with a semantic shift attention module. The semantic shift attention module takes features from encoder layer and those from decoder layer as input pairs to output shifted features which take into account the long‐range dependency of encoder layer features and corresponding decoder layer features from the perspective of local structure and style. Semantic shift attention module models the global interdependencies in local spatial structures (patches centered at each position) and style (appearance texture) dimensions respectively, which could implement distillation of prior knowledge from two aspects: structure and style. Experiments on multiple datasets including faces (CelebA, CelebA‐HQ) and natural images (ImageNet, Places2, Paris Street View) demonstrate that our proposed approach generates higher quality completion results than existing ones.
Parentheses and Square Brackets

Abstract The word parentheses originally referred to words that were inserted into texts as examples, explanations, or asides. By the late sixteenth century, though, the term came to refer to the curved upright lines that were placed around those inserted words. The British call them round brackets or just brackets.
Computing with Anyons

Abstract We introduce some basic ideas of quantum computing including the idea of a quantum circuit model and the idea of a universal quantum computer — implementing arbitrary gates arbitrarily accurately. We discuss how braiding of certain anyons can be used as a universal quantum computer, and we illustrate this using Fibonacci anyons as an example. We then turn to the task of designing braids to perform particular computations, which is known as Topological Quantum compiling.
Quality of paving blocks soaking water made from medical waste incineration ash

Abstract Given the ash level of medical waste incineration utilized as a raw material mixture that contains metal compounds, dumping the paving block immersion water into rivers could result in a drop in water quality. The purpose of this study is to evaluate water quality during immersion of paving blocks made from medical waste incineration ash. Water quality was measured by five measurements in water before immersion, soaking for 7 days, 14 days, 21 days, and 28 days. The test method used is in accordance with SNI 6989-84:2019. The metal compounds examined in this study were Pb (lead), Copper (Cu), Nickel (Ni), and cadmium (Cd). Laboratory tests were conducted at the Jasa Tirta I Environmental Laboratory and the Sukoharjo Environmental Service. It can be concluded that there is no pollution due to heavy metals contained in paving blocks. The results show that the soaking water for 7 to 28 days is still below the specified quality, so paving block-making activities using a mixture of medical waste incineration ashes can be carried out. The need for other studies measuring the levels of other metals such as Cr, Hg, Sn, Ti, and Zn.
Semicolons (;)

Abstract A lot of people think we have a weight problem in this country; I don’t agree with them. I don’t believe we’ll have a weight problem until we’re all physically touching each other all the time.
Introduction

Abstract If you’ve ever watched the sitcom Modern Family, you know that one of the main characters, Phil, can be a loveable klutz. In one episode, he gets jealous when his wife laughs at another man’s jokes, and he blurts out
Stochastic Gradient Descent with Deep Learning-assisted Object Detection and Classification for Visually Challenged People

Object detection and classification systems can be devised to support visually challenged persons in communicating and understanding their environments. Such systems use computer vision methods for classifying and detecting objects in real time. Deep learning (DL) can be adopted to help visually challenged persons in object classification and detection tasks, allowing them to communicate and understand their surroundings more efficiently. By leveraging DL for object detection and classification, visually challenged individuals can receive real-time data regarding their interaction, surroundings, and overall independence and facilitate their navigation. With this motivation, the study presents a novel Stochastic Gradient Descent with Deep Learning-assisted Object Detection and Classification (SGDDL-ODC) technique for visually challenged people. The main intention of the SGDDL-ODC technique concentrates on the accurate and automated detection of objects to help visually challenged people. To obtain this, the SGDDL-ODC technique focused on the development of the optimal hyperparameter tuning of the DL models effectively. To accomplish this, the SGDDL-ODC technique follows the YOLOv6 model for object detection purposes. To adjust the hyperparameter values of the YOLOv6 method, the SGD model can be applied. At the final stage, the deep neural network method can be exploited for the classification of the recognized objects. A series of simulations were performed to validate the improved performance of the SGDDL-ODC approach. The simulation results illustrate the superior efficiency of the SGDDL-ODC technique over other techniques under diverse datasets in terms of different measures.
Pelatihan IT Untuk Tenaga Pendidik dan Tenaga Kependidikan UPTD SMP Negeri 3 Towuti, Kec. Towuti, Kab. Luwu Timur, Prov. Sulawesi Selatan

Information technology training was carried out at UPTD SMP Negeri 3 Towuti, Towuti sub-district, East Luwu district, South Sulawesi province. The training was carried out for two days, namely from Friday to Saturday, precisely on 27 - 28 October 2023. The implementation of this activity was carried out using the DIA method. This method was chosen because it is considered quite effective in a teaching and learning process, considering that the participants in this activity are people who are adults in terms of reasoning, thinking and acting. DIA is an abbreviation for Delivery, Interaction, Assessment. Based on the activity evaluation, the results showed that 100% of the 24 teaching staff gained knowledge, 100% of the teaching staff thought that IT training was very useful and they hoped that the training would continue at another time.
Machine learning and topological data analysis identify unique features of human papillae in 3D scans

The tongue surface houses a range of papillae that are integral to the mechanics and chemistry of taste and textural sensation. Although gustatory function of papillae is well investigated, the uniqueness of papillae within and across individuals remains elusive. Here, we present the first machine learning framework on 3D microscopic scans of human papillae ([Formula: see text]), uncovering the uniqueness of geometric and topological features of papillae. The finer differences in shapes of papillae are investigated computationally based on a number of features derived from discrete differential geometry and computational topology. Interpretable machine learning techniques show that persistent homology features of the papillae shape are the most effective in predicting the biological variables. Models trained on these features with small volumes of data samples predict the type of papillae with an accuracy of 85%. The papillae type classification models can map the spatial arrangement of filiform and fungiform papillae on a surface. Remarkably, the papillae are found to be distinctive across individuals and an individual can be identified with an accuracy of 48% among the 15 participants from a single papillae. Collectively, this is the first evidence demonstrating that tongue papillae can serve as a unique identifier, and inspires a new research direction for food preferences and oral diagnostics.