Reviewerly/exHarmony huggingface dataset:
Investigating the Geometry of an Unnamed Fault System South of Socorro, New Mexico, Using an Integrated Geophysical and Remote Sensing Approach

Active faults within continental rift zones, such as the Rio Grande rift (RGR), pose hazards to society.Evaluations of earthquake hazard along active faults require models of subsurface geometry and estimates of fault activity rates.South of the city of Socorro, New Mexico, a series of normal faults -including the Socorro Canyon Fault Zone (SCFZ) -mark the eastern boundary of the Chupadera Mountains in the RGR.Despite the seismic hazard posed by these fault systems to surrounding communities, their subsurface geometry is not well-constrained.Here we use a variety of interdisciplinary geophysical techniques, including ground penetrating radar (GPR) and seismic reflection surveying, to image the subsurface architecture of this complex fault system.We also use recently released high-resolution lidar imagery to relate scarp morphology in unconsolidated alluvial fans to relative fan age in order to establish Quaternary fault activity.Our geophysical imaging surveys show offset of reflectors consistent with normal faulting.Relative fan age estimates and analysis of scarp morphology indicate that this system has remained active throughout the Quaternary.This recent activity warrants further investigation into the seismic hazard posed by this system, including possible linkages between this and surrounding structures in large earthquakes.Future work includes extending the study area along-strike and cosmogenic nuclide dating of alluvial fans to further constrain the slip history of the faults.
The Emergent Literacy Activities Through the Storytelling Method with Flashcard Media for Early Childhood Education

Teachers have an important role in the development of children's language at school. The study period at Early Childhood Education is an excellent opportunity to improve literacy skills when children are experiencing a sensitive language learning period. Language skills have a role in improving social aspects and developing emotional and cognitive aspects. Children aged 2-4 years go through a concrete learning period. Using storytelling teaching methods with flashcard media can create a fun learning atmosphere and encourage children to learn optimally. This study aims to look at the application of the flashcard storytelling method to the language skills of children aged 2-4 years in support of an emergent literacy program.
Mouse-to-human transfer learning for glomerulus segmentation

Mice and humans share many features of internal organs. Therefore, mice are often used in experimental models of human diseases. Although this is commonplace in medicine, there is an avenue to go to explore it in computational pathology, where digital whole-slide images (WSIs) are the main objects of investigation. Considering the absence of research about knowledge transfer between mice and humans in machine learning modelling, we propose investigating the possibility of segmenting glomeruli in human WSIs by training deep learning models on mouse data only. A set of semantic segmenters were evaluated, which had their performance assessed on two data sets comprised of 18 mouse WSIs, and 42 human WSIs. Results demonstrated that U-Net 3+ achieved superior results on intra-data set: On the mouse data set, it reached the highest average score on HE-stained images, while on the human data set, this network achieved the highest average on all stains. U-Net 3+ also obtained the best results after being trained only on the mouse data set and predicting on the entire (train and test) human data set. Although all networks proved to be capable of segmenting intra-stain images, it was not possible to confirm the same results on inter-stain ones.
Syllabus Design of English Club: Fostering Joyful Extracurricular for Tenth Graders

The transition from online to onsite English classroom resulted in the challenge for tenth graders to be more skillful in actively engaging into English conversation activities. However, it requires better planning of English Club as curricular lesson to enhance student’s participation. This study was intended to develop the syllabus of English Club to be a joyful extracurricular for tenth graders at a senior high school in Daha Kediri. The need analysis used questionnaire and interview to find the required data of student’s speaking practice in English Club activities. The findings show that English Club plays an important role in supporting students to practice speaking. The students also thought that the English Club had a great way of encouraging their motivation to practice speaking with friends. In other words, English Club gives them more joyful experience and knowledge from the learning atmosphere which is fun and comfortable making them more interested in practicing speaking.
Implementation of Quantum Key Distribution Algorithm in Real Time IBM Quantum Computers

Abstract: We are Currently in the NISQ(noisy intermediate scale quantum) era. Current Quantum computers have a high noise error rate. With the increasing number of qubits every year we can develop fault tolerant quantum systems which have immense power to change our current computing power. Current quantum systems have shown to have the potential to break the classical RSA algorithm using shors algorithm. With more development and increase in number of qubits available it could easily break the current security systems of our computing. In future we could transmit data safely using the same quantum principles in the post quantum cryptography era. Quantum key distribution could be the future protocol used to transfer our secure information without getting hacked even by using quantum computing and break other quantum principles like shors algorithm which can break current encryption techniques like the RSA algorithms. We need to develop Quantum network infrastructure so we can implement the quantum key distribution algorithm in the future to enable secure transmission of data.
Evaluation of Music Art Teaching Quality Based on Grey Neural Network

The study and implementation of music art teaching quality monitoring systems as a major teaching management project in Chinese colleges and universities has been in continuation for a decade. An essential challenge in the management of colleges and universities is that how to assess the teaching abilities of music and art professors fairly and objectively. The system is designed and implemented by separating the front end and back end of browser + server to provide examination services in the way of cloud computing. This system primarily gathers the students’ multidimensional data including sound intensity, pitch, and beat. Then a grey neural network is used to analyze the students’ sound intensity, pitch, and beat. After evaluation process, the scores of the final examination in colleges and universities are uploaded to the backstage server in real-time.
Underwater Image Enhancement Method Based on CGAN and Parallel Attention Mechanism

Due to the absorption and scattering of light in water, the underwater image has some problems such as colour distortion, serious colour difference and ambiguity, which seriously affects the detection of underwater resources and object recognition. At present, the latest underwater image enhancement methods are all based on deep learning, but they do not take into account the diversity of channel information and pixel features. In order to achieve better enhancement effect, a parallel attention mechanism combining Channel Attention and Pixel Attention is introduced based on Conditional Generative Adversarial Network consisting of multi-scale generator and double discriminator. Compared with other latest methods, the enhancement effect of the proposed method is closer to the real image, and the necessity of each module of the proposed model is proved by ablation study.
Long Text Multi-label Classification

In recent years, the evaluation of non-technical literacy such as sense of worth, outlook on life, and sustainable development concept, has become increasingly important in university education in China. They are often evaluated by questionnaires or interviews. However, the feedback texts from students often contain long paragraphs of students' opinion responses in Chinese, with long text, large amount of information and complex structure, which reduce the accuracy of long Chinese text classification. In this paper, we propose a multi-label classification model named MLformer, which uses the Longformer model to obtain feature representations containing information about the global semantic and local contextual features of the text to effectively classify students' views. The experimental results on our long text dataset in Chinese about engineering sustainability show that our method outperforms the typical deep learning classification model BERT in terms of accuracy, precision, and F1 score, thus, it can provide a powerful reference for students' opinion analysis and evaluation.
Discriminative Candidate Selection for Image Inpainting Applications to the Fine Arts

Within the field of Cultural Heritage, image inpainting is a conservation process that fills in missing or damaged parts of an artwork to present a complete image. Multi-modal diffusion models have brought photo-realistic results on image inpainting where content can be generated by using descriptive text prompts. However, these models fail to produce content consistent with a particular painter’s artistic style and period, being unsuitable for the reconstruction of fine arts and requiring laborious expert judgement. Moreover, generative models produce many plausible outputs for a given prompt. This work presents a methodology to improve the inpainting of fine art by automating the selection process of inpainted candidates. We propose a discriminator model that processes the output of inpainting models and assigns a probability that indicates the likelihood that the restored image belongs to a certain painter.
Career Ladder: Erik Luber

1980s Doing the math From the outset, Erik Luber was interested in solving puzzles—especially ones he could take apart and try to put back together. His first word, according to his mother, was “puzzle.” “It would be a lie to say my mom’s not prone to exaggeration and storytelling, but that is the way she tells it,” Luber says. By the time Luber was 6 years old, he had discovered his lifelong interest in math. “I would sit down and start writing numbers. I was fascinated. Yeah, that’s what I did for fun growing up,” Luber says. That love of using math to solve complex problems would become a key thread running through his career. 2002 And on to physics After drawing on inspiration from a number of science teachers, Luber opted to major in engineering physics as an undergraduate at the University of Alberta. “I was really drawn to
Prevention of phishing website attacks in online banking systems using visual cryptography

Piracy operations and attackers endeavor to lure victims by handing over their sensitive information, such as their bank account numbers and private passwords, which are increasing daily using different ways. One is counterfeiting websites, especially electronic payment sites, including online banking and other websites. Those websites are characterized by a lack of security awareness among users. This paper presents an approach of protection implemented at the authentication level to limit these phenomena by utilizing Visual Cryptography (VC) technology. Our proposed approach would help the end-user, especially those unfamiliar with the cyber security field, distinguish between a phishing website and the genuine one. Visual Cryptography is considered simple and does not require any deployment of complicated encryption and decryption processes.
Unsupervised Functional Data Clustering Based on Adaptive Weights

In recent years, functional data has been widely used in finance, medicine, biology and other fields.The current clustering analysis can solve the problems in finite-dimensional space, but it is difficult to be directly used for the clustering of functional data.In this paper, we propose a new unsupervised clustering algorithm based on adaptive weights.In the absence of initialization parameter, we use entropy-type penalty terms and fuzzy partition matrix to find the optimal number of clusters.At the same time, we introduce a measure based on adaptive weights to reflect the difference in information content between different clustering metrics.Simulation experiments show that the proposed algorithm has higher purity than some algorithms.
Efficient Two-Party Authentication Key Agreement Protocol Using Reconciliation Mechanism from Lattice

It is crucial and challenging to design a quantum-secure and efficient authentication key agreement scheme for IoT. The reasons are that not only there are various security requirements need to meet, but also communication party is resource-constrained. Recently, a large number of 2PAKA schemes for IoT have been presented, yet most of them are subject to quantum attack. In this paper, we put forward a quantum-secure 2PAKA protocol using lattice cryptography. The proposed LB-ID-2PAKA protocol makes use of identity-based signature to avoid the complicated certificate management of PKI-based protocol. At the same time, based on the Kyber.KE, we apply Peikert’s reconciliation mechanism to save the communication cost. Our LB-ID-2PAKA protocol can be resistance against various attack and provide desired security property, especially support perfect forward secrecy. Moreover, the provable security analysis shows that our LB-ID-2PAKA protocol is provably secure under RO model and the hardness assumption of MLWE.
A Novel Hybrid Optimization Model for Task Scheduling in Cloud Environment

Resource management is a fundamental design issue for big data processing systems in the cloud".Various resource allocation strategies pose diverse impacts on fairness and performance.Thus, task scheduling is said to be an effective and classic method to enhance resource efficiency in the cloud.Since several big data appliances with diverse resource requirements are deployed in the cloud, there were huge prospects for allocating resources.However, no clear solution was developed for tackling the issues that occur during task scheduling.Therefore, this paper aims to introduce a new task scheduling model using a novel hybrid optimization algorithm termed Sea Lion Updated Grey Wolf Optimization Algorithm (SU-GWO).The newly developed model takes account of multi-objective functions like security and makespan that enhances the allocation of tasks in virtual machines (VM).In the end, the performance of the presented approach is examined by determining make span, security analysis, and total cost as well.
How feasible is it to segment human glomerulus with a model trained on mouse histology images?

Many genetic, physiological and structural characteristics of internal organs are shared by mice and humans. Hence, mice are frequently used in experimental model of human diseases. Although this is an indisputable truth in medicine, there is an avenue to go in computational pathology, where digital images are the main objects of investigation. Considering the lack of study about knowledge transfer between mice and humans concerning machine learning models, we propose investigating if it is possible to segment glomeruli in human WSIs by training deep learning models on mouse data only. Three different semantic segmenters were evaluated, which had their performance assessed on two data sets comprised of 18 mouse WSIs and 30 human WSIs. The results found corroborate our hypothesis validation.
On the algorithms in phase-shift interferometry

A number of phase-shift algorithms exist in literature.These are categorized as 3-step, 4-step, 5-step etc. with different but constant values of phase steps.In this paper, it is shown that many of them can be deduced from the generalized theory under each category in literature.
A Study of the Verification of the Effectiveness of Multiple Endings in Learning Novel Games

Today, digital games that incorporate multi-ending scenarios are not uncommon in the entertainment field. However, there is no such application of the multiple endings to learning games and educational situations. Therefore, there are no studies that have examined the learning effects of the multiple endings in learning games. The purpose of this study was to examine the effects of a multiple endings learning game on learners' motivation and learning effectiveness. To test effectiveness of multiple endings, a multiple endings learning novel game was designed in the experiment. The results of the experiment showed that the learners of group with multiple endings practiced more often. In addition, the multi group performed better. And the results also showed that the learners in the multi group had a higher sense of achievement and self-determination than those in the single group. Based on these experiments, this study clarified that the multiple endings can improve the learning effect.
SUPLEMENTAÇÃO DE ÁCIDO GRAXO POLI-INSATURADO ÔMEGA 3 DERIVADOS DE ÓLEO DE PEIXE NO TRATAMENTO DA COVID-19 E DOENÇAS CRÔNICAS NÃO TRANSMISSÍVEIS: UMA REVISÃO SISTEMÁTICA

This study aims at carrying out a literature review on the use of acupuncture in the treatment of attention deficit hyperactivity disorder (ADHD).In order to perform it, we searched articles in the PUBMED, LILACS and Elsevier databases.Five articles were selected in which the effects of acupuncture and the main acupoints for treatment were verified.Data were analyzed according to the following theories: Yin/ Yang, Zang/ Fu, five movements and the psycho one..Given the literature search, it was found that acupuncture has benefits in the treatment of ADHD and it should be used as complementary therapy in patients with such diagnosis.
Content Warning

Journal Article Content Warning Get access Music Theory Spectrum, Volume 45, Issue 1, Spring 2023, Page 100, https://doi.org/10.1093/mts/mtad003 Published: 26 April 2023
Enabling Secure and Efficient Crypto Transfer with SectorX.crypto: A Web 3.0 and Blockchain Solution

Web3.0 has ushered in a new era of decentralized applications that provide users with greater control over their data and transactions. The impact of this technology could be profound in the realm of cryptocurrency. A comprehensive website that provides information on all major cryptocurrencies could be a game-changer for the industry. This platform could provide a one-stop-shop for cryptocurrency enthusiasts and traders, where they can access news updates, trading strategies, market exchanges, and analysis from industry experts and influencers. Moreover, this website could also serve as a platform for users to transfer and receive cryptocurrency from one wallet to another, thereby eliminating the need for multiple accounts on different exchanges. The use of blockchain technology and smart contracts could ensure greater security, transparency, and efficiency in transactions
Facing the Unknown Unknowns of Data Analysis

Empirical claims are inevitably associated with uncertainty, and a major goal of data analysis is therefore to quantify that uncertainty. Recent work has revealed that most uncertainty may lie not in what is usually reported (e.g., p value, confidence interval, or Bayes factor) but in what is left unreported (e.g., how the experiment was designed, whether the conclusion is robust under plausible alternative analysis protocols, and how credible the authors believe their hypothesis to be). This suggests that the rigorous evaluation of an empirical claim involves an assessment of the entire empirical cycle and that scientific progress benefits from radical transparency in planning, data management, inference, and reporting. We summarize recent methodological developments in this area and conclude that the focus on a single statistical analysis is myopic. Sound statistical analysis is important, but social scientists may gain more insight by taking a broad view on uncertainty and by working to reduce the “unknown unknowns” that still plague reporting practice.
An Investigation Teaching Reading Strategies at SMA Negeri 3 Kota Ternate

This research discussed about investigation teaching reading strategies. The aim of this research is to know the teacher’s use of teaching reading strategies at SMA Negeri 3 Kota Ternate. The researcher focuses on teaching reading strategies by teacher. The method of this research is qualitative by using descriptive analysis. The subjects of this research were two English teachers. The instruments used are observation, interview and documentation. Descriptive qualitative is used to analyzed and describe the data of the respondents. The results of this research show that there are several reading strategies that used by the teachers. Teacher A implemented four reading strategies, they include skimming, scanning, guessing the meaning of new words, and summarizing. Teacher B implemented two reading strategies they are guessing the meaning of new words and summarizing. The teaching reading strategies in the classroom used by the two teachers (A and B) indicated guessing the meaning of new words and sumarizing. Teaching reading strategies is very important to be applied to English Teachers. Because, it can make it easier for the students be able to read in English. Therefore, the researchers are very interested in conducting research related to the teaching reading strategies
Classification of happiness in North Kalimantan using decision tree-based CHAID

The concept of welfare is closely related to economic development. The 2021 happiness index in Indonesia shows 5 provinces with the highest index have a GRDP that tends to be low. This study aims to examine the factors that influence happiness in North Kalimantan as the youngest province, which, according to the SPTK 2021, has the second-highest happiness index. The study employs a quantitative method as well as inferential analysis with a decision tree based CHAID to classify happiness. This method is advantageous since it can handle both categorical and continuous variables, as well as data that is not regularly distributed. The happiness level is the dependent variable in this study, with 19 independent variables indicating various factors such as life satisfaction, affect, and eudaimonia. According to the report, 70.2 percent of households in North Kalimantan are in the happy category. According to the findings of this study, the majority of happy households are experiencing pleasure and satisfied with their income. In households where they lack of pleasure, there are other factors that explain happiness such as job satisfaction and not feeling stressed. The accuracy from the CHAID model is 78.85 percent, indicating the model is fit to classify happiness.&#x0D; Keywords: Happiness index, CHAID, North Kalimantan, SPTK.MSC2020: 62C86
Ticketless Entry for Monuments &amp; Museums

Abstract: The traditional method of booking tickets for monuments and museums involves lengthy lines and the inconvenience of carrying printed tickets. Ticketless booking has grown in popularity among visitors since the advent of technology. This paper examines the existing literature on ticketless entry for monument and museum booking, highlighting the different technologies used and their effect on user experience.