Reviewerly/exHarmony huggingface dataset:
Boosting Adapter Transfer Learning via Weak Parameter Sharing

Adapter tuning is a parameter-efficient way of transfer learning for natural language processing, where adapter modules are inserted in a pretrained model and only the modules are updated, with the pretrained model fixed. This also allows easier continual learning, as the number of parameter linearly grows with the number of tasks. In this paper, we apply the concept of adapter tuning and further improve the method by sharing parameters among adapters plugged in different encoder layers. We attempt a lot of different configurations of parameter sharing to find the optimal setup for each GLUE task and find the settings where the model is on par with or even outperforms the default adapter tuning with just 1, 2, 3, or 4 adapters in a BERT-base model. In the experiment, we also analyze the training results to find some patterns among the configurations and their meanings in the perspective of improving transfer learning.
User-Specified Image Color Transfer

This paper proposes a flexible example-based color transfer system, providing an automatic mode and an advanced mode, for novices and expert users, respectively. The experimental results show that the proposed color transfer system not only can introduce natural results with simple operation by novices in the first use, but also can produce various desired results by users with short-term learning.
MSFSA-GAN: Multi-Scale Fusion Self Attention Generative Adversarial Network for Single Image Deraining

Bad weather such as rainy days will seriously affect the image quality and the accuracy of visual processing algorithm. In order to improve the image deraining quality, a multi-scale fusion self attention generation adversarial network (MSFSA-GAN) is proposed. This network uses different scales to extract input characteristics of rain lines. First, Gaussian pyramid rain maps with different scales are generated by Gaussian algorithm. Then, in order to extract the features of rain lines with different scales, the coarse fusion module and fine fusion module are designed respectively. Next, the extracted features are fused at different scales. In this process, the self attention mechanism is introduced to make the network focus on the extracted features of different scales. And before the fusion, the rain pattern reconstruction operation is also carried out, so that the network can reproduce the input image more perfectly. Finally, it is input into the discriminator network with dense blocks to obtain the image that removes the rain lines. We used R100H and R100L datasets to train and test our network. The results show that our method as high as 27.79 in PSNR and UQI is 0.94, which is superior to the existing methods in performance. Meanwhile, we also compared the cost of time, the result of our network is only 0.02s.
The Analysis of Reading Motivation of High School Students Based on Certain Variables

Reading habit, the ability to sustain the reading activity, is required for quality reading. Those who tend to read need to be motivated one way or another. Reading motivation is closely associated with the reading goals and trends of the individuals, and the time they devote to reading. The present study aims to determine the reading motivation of high school students based on certain variables. Thus, the reading motivation levels of the high school students, and the differences between their motivation levels based on gender, grade level, school type, socioeconomic level, education level and occupation of the parents, newspaper and magazine subscription, internet access, and the reasons for not reading variables are determined. The study was conducted with 1070 students attending nine high schools in Sinop province in Turkey and screening method during the 2019-2020 academic year. The study findings demonstrated that there were significant differences between reading motivation levels of the high school students based on gender, school type, newspaper and magazine subscription, internet access and the reason for not reading variables, while no significant differences were determined based on the grade level, socioeconomic status, education level, and the occupation of the parents.
Using Research to Expand the Transformative Agreement: A LYRASIS Case Study

In 2020, LYRASIS conducted a survey of their members to better understand predominantly U.S. institutional attitudes towards open content, including Open Access (OA) scholarship. The survey revealed several findings indicating that U.S. institutions cannot conform to the same OA models and strategies as their international counterparts. Likewise, a significant portion of institutions cannot participate in transformative agreements, at least not by the current definition. This presentation described the survey findings and how strategists from LYRASIS used what they learned to introduce OA into their negotiations in a way that increased participation from institutions of different sizes, missions, and research outputs, thus expanding the definition of the transformative agreement.
LATINIZATION OF KAZAKH IN THE ERA OF DIGITALIZATION

Статья «Латинизация казахского языка в условиях цифровизации» посвящена переходу казахского языка на латинский алфавит в условиях цифровизации. Автор данной статьи анализирует соответствие латинских букв орфографии и фонетике казахского языка. Он также подчеркивает, что цифровизация облегчает переход на латинский алфавит The article entitled Latinization of Kazakh in the Era of Digitalization is devoted to the transition of the Kazakh language to the Latin alphabet in the era of digitalization. Here the author analyzes the correlation of Latin letters with the Kazakh language orthography and phonetics. He also emphasizes that digitalization facilitates a transition to the Latin alphabet
New Particle Swarm Optimization Variant with Modified Neighborhood Structure

Numerous particle swarm optimization (PSO) variants were proposed in past decades to tackle different types optimization problems more robustly.Nevertheless, the imbalance of explorative and exploitative search behaviors remains as an on-going research challenge that can restrict the performance of PSO.In this paper, a new variant known as PSO with time-varying topology connectivity (PSO-TVTC) is proposed.A time-varying topology connectivity (TVTC) module is designed to achieve the proper regulation on explorative and exploitative behaviors of PSO via dynamic modifications of particle's topology connectivity throughout the optimization process.Experimental results reveal that the proposed PSO-TVTC has exhibited prominent performance among its competitors by producing 7 best mean fitness out of 8 benchmark functions.
MAPPING OF Fe2O3 , Nb and TiO2 , AS A SUPPORT TO CLASSIFY OUTCROPPING MATERIALS IN&amp;#8221; MORRO DOS SEIS LAGOS&amp;#8221; CARBONATITE COMPLEX, BRAZILIAN AMAZON

&amp;lt;p&amp;gt;The study goal was the preliminary mapping of Fe&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;O&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt;, Nb and TiO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; contents to support of the classification of outcropping materials (focused on laterite types), in &amp;amp;#8220;Morro dos Seis Lagos&amp;amp;#8221;, in Brazilian Amazon. The methodological procedures were based on machine learning tools, gathering Sentinel-2 MSI and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor data, numerical terrain models (all with 20 m spatial resolution), and geochemical legacy dataset from the Geological Survey of Brazil (CPRM). The input geochemical dataset was subdivided in training dataset (341 samples) and validation dataset (85 samples) to apply the Random Forest (RF), in 60 loops of iteration, and the model&amp;amp;#8217;s performance were evaluated through the average values of the metrics (R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;, RMSE and MAE). Subsequently, the resulting average maps were combined using cluster analysis (k-means) via unsupervised classification, performed at the R environment through the Vegan package, where the number of resulting classes was optimized taking into account the &amp;amp;#8220;Simple Structure Index&amp;amp;#8221; (SSI) criterion. Different cluster grouping was tested considering classes number (6 to 10) and interactions (0 to 8000), and the resulting classes (zones) were contrasted with available geological map. &amp;amp;#160;The results showed the best performance in modeling via the Random Forest (RF) model associated with Recursive Feat Elimination (RFE) for the elements Nb (R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=0.08, RMSE = 0.86, MAE = 0.66), TiO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; (R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=0.14, RMSE = 3.90, MAE= 2.56) and Fe&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;O&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt; (R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=0.23, RMSE =19.77, MAE = 14.12). Based on the results obtained via preliminary cluster analysis, the best optimization was achieved grouping in 9 classes, according to SSI criterion. The results showed agreement when compared to the classes of the geological map available for the area, but with better detailing of the laterite facies. The conclusions of the preliminary study pointed out that advances regarding the scale detail provided better understanding the behavior of the variability in laterite and talus deposits with the support of machine-learning tools and covariates from remote sensing data. However, improvement in the cluster classification can be achieve by adding other geochemical compounds and testing different predictive models.&amp;lt;/p&amp;gt;
An Analysis of Domestic Research Trends on Multicultural Dance Education

The purpose of this study is to analyze research trends in the field of multicultural dance education in Korea and to suggest future research directions and tasks. To this end, in this study, 71 studies related to multicultural dance education published from 2008 to 2020 were selected. This study classify and analyze by The Year, Research Methods, Research Subjects, Major Educational Contents and Major Research Results. The analysis results are as follows. First, the number of doctoral dissertations was significantly insufficient compared to the master
Horizon Multi-functional Student Administration System

This paper is made for mainly colleges and schools for maintaining discipline and to preserve time of working faculty. This paper is mainly focused on cloud or digital storage option as well as it is focused or simply made with the intension on centralized management system for schools or colleges. We can conserve time and maintain student discipline using "Centralized Student Administration System”. Student administration system provides an organized IoT based User Interface as compared to all traditional system till now. Another objective of this paper is to increase convenience for teachers and other staff with modern technology focused on reducing lengthy work and increasing convenience. After researching in the field of Information Technology about College Management System, we found that traditional systems are way too time consuming and lengthy. There are many ideas about College Management System but only few of them are implemented because of difficult and vast requirement and its time-consuming process. It proposes efficient and convenient wireless sensor network technique to acquire the neatness and discipline in college or school. It proposes a Centralized Management System of modern technology and innovation. Now a days, technologies like smart cards are getting used world- wide for making the office excess works like attendance, entries less time consuming. We are aiming to develop convenience to rather all workers co-workers and other employees. If we are considering these professional characters then we also consider students who are usually going through their most important phase of their career. This paper is made for mainly colleges and schools for maintaining discipline and to preserve time of working faculty. This paper is mainly focused on cloud or digital storage option as well as it is focused or simply made with the intention on centralized management system for schools or colleges. We can conserve time and maintain student discipline using “Centralized Student Administration System”. Centralized student administration system provides an organized IoT based User Interface as compared to all traditional system till now. Another objective of this paper is to increase convenience for teachers and other staff with modern technology focused on reducing lengthy work and increasing convenience. Data management can be difficult for traditional systems but because of this paper it will be easy to maintain student data Traditional academic management system lack in support for mobile devices, so we will try to make this paper available on most common platform-like mobile devices.
Interpretation and Reconstruction of Depositional Environment and Petroleum Source Rock Using Outcrop Gamma-ray Log Spectrometry From the Huai Hin Lat Formation, Thailand

&amp;lt;div&amp;gt; &amp;lt;div&amp;gt; &amp;lt;div&amp;gt; &amp;lt;p&amp;gt;Gamma ray logs are most useful in identifying subsurface lithology and interpreting depositional environments. This study highlights the use of outcrop gamma-ray logs along with outcrop observations and total organic carbon (TOC) analysis to provide the stratigraphic framework of the organic-rich rocks of Huai Hin Lat Formation in central Thailand. The study reveals five sedimentary facies including (1) structureless sandstone, (2) structured sandstone, (3) interbedded sandstone and siltstone, (4) interbedded mudstone and siltstone and (5) calcareous mudstone. These facies can be grouped into two facies associations; mudstone-dominated and sandstone-dominated facies associations. The depositional environment was interpreted as lacustrine basin- fill subdivided into deep lacustrine environment and sublacustrine fan associated with the turbidity currents. The total gamma-log characteristics are closely related to the lithologies controlled primarily by clay mineral compositions. Whist, the use of spectral gamma-ray can reveal more details on depositional environments and conditions. In this study, uranium (U) concentrations is proven to be useful in highlighting organic-rich rocks in low potassium (K) and thorium (Th) concentration successions due to its ability to be fixed in clay minerals and organic materials under an anoxic condition. Thus, the U spectral gamma ray is suggested to combine with conventional gamma ray log for depositional environment and recognition of organic-rich rocks.&amp;lt;/p&amp;gt; &amp;lt;/div&amp;gt; &amp;lt;/div&amp;gt; &amp;lt;/div&amp;gt;
Placed-Based Songwriting

On May 20, 2021, I gathered with six participants in an ICQI pre-conference workshop to explore together a songwriting methodology I use in group settings. As a performing songwriter I am constantly trying to make sense of the world around me through song. I write funny songs, thought provoking songs, sad songs, and songs that sometimes just tell a story. It is the story song, more specifically the place-based story song, that was the focus of the workshop.
New Developments for Journal Package Analysis and Data Visualization

The presenters demonstrated the most recent results and insights from a long-running, iterative Collection Management Technology (CMT) project. Demonstrations included a journal package level analysis report, an interactive subject analysis report, several data visualization designs for journal package analysis, and a program to automate report production, including data visualizations. Finally, the current, cyclical journal collection review process was compared to a past cycle to highlight how the CMT team’s development priorities have changed. Instead of focusing primarily on efficiency and information yield in the tools they build, the team has now prioritized the development of tools that enable interaction with the data and broaden understanding.
A design approach for a distributed system to handle Chinese

In designing systems to handle non-Roman alphabet languages, unique problems in information retrieval are encountered. The concept of "P-trees” will be explained and compared to B-trees. Experiences in applying P-trees to non-Roman alphabet retrieval environments will be covered in detail.
Supported by artificial intelligence intellectual decision systems

Bu məqalə biznesin proqnozlaşdırılması prosesi üçün Süni İntellekt tərəfindən dəstəklənən qərar dəstək sistemi (DSS) metodunu təqdim edir. Neyron şəbəkə modeli ilə biznes məlumatlarının proqnozlaşdırılmasının effektivliyində ən yaxşı təlim yanaşması müəyyən edilir. Bu tədqiqat neyron şəbəkələrə və öyrənmə yanaşmalarına əsaslanan birgə proqnozlaşdırma prosesi yaradır. Əlaqə öyrənmə modeli müəyyən edilmiş irəli gizli təbəqə şəbəkəsində istifadə olunur, logistik funksiya isə müvafiq olaraq çıxış və gizli təbəqə ötürmə funksiyası kimi istifadə olunur. Bu model DSS məhsulu­nun istehsalçısı üçün şərti məlumatlar üzərində işləyir. Verilənlər bazasından istifadə etməklə, qrafiklər və hesabatlar əldə etməklə və təhlil aparmaqla proqnoz menecerlərinə kömək edən istifadəçi dostu bir modeldir. Açar sözlər: Qərarlara Dəstək Sistemləri, biznes zəkası, maşın öyrənmə Natig Elkhan Huseynov Supported by artificial intelligence intellectual decision systems Summary This article introduces the Decision Support System (DSS) method supported by Artificial Intelligence for the business forecasting process. The neural network model identifies the best training approach to the effectiveness of business data forecasting. This research creates a collaborative prediction process based on neural networks and learning approaches. The communi­ca­tion learning model is used in a defined forward secret layer network, and the logistics function is used as an output and hidden layer transfer function, respectively. This model works on the conditional data for the manufacturer of the DSS product. It is a user-friendly model that helps forecast managers use databases, obtain graphs and reports, and analyze. Key words: Decision Support Systems, business intelligence, machine learning
Intelligent Maintenance Recommender System

Recommendation engine's techniques have proved their performance in different fields such as Amazon and Netflix. This paper discusses the usage of the recommendation engine concept in the industrial field, especially in maintenance operations. Nowadays, the plant maintenance team needs to make a maintenance plan against sudden asset failure, to reduce unscheduled production downtime. However, the planning takes a lot of time, because the appropriate maintenance countermeasures are chosen from many options depending on the failure condition and asset environment. Therefore, we try to suggest a reliable countermeasure against the failure conditions to make the planning time short. In this work, we propose two approaches for the maintenance recommender systems based on artificial intelligence techniques to recommend the maintenance actions. The first approach is a single-stage recommender system that reads the defect information and its description entered by the operator to recommend the maintenance action for similar defects found in the historical data. The second approach is a multi-stage recommender system where the system starts by estimating one of the maintenance attributes which be used as an input for the next stage to estimate the next maintenance attribute. Finally, we will evaluate the accuracy of the recommendation by using past maintenance report which contains defect condition and maintenance actions adopted actually in the past. We found that the multi-stage system outperformed the single-stage system in terms of accuracy, and the multistage system is possibly helped the maintenance team against the sudden asset failure with the maintenance action recommendation.
Quantum Analog Annealing of Gain‐Dissipative Ising Machine Driven by Colored Gaussian Noise (Adv. Theory Simul. 3/2022)

Quantum Analog Annealing of Gain-Dissipative Ising Machine Driven by Colored Gaussian Noise In article number 2100497, Zhiqiang Liao, Hiroyasu Yamahara, and co-workers utilize noise dispersion as a hyperparameter to modulate the performance of a gain-dissipative Ising machine. The work proves the reddening noise can suppress random spin switching, so that the Ising machine can better find the ground state. The cover depicts the superior performance of red-noise-driven Ising machine in domain clustering dynamics.
CALCIFICATION CLUSTERS AND LESIONS ANALYSIS IN MAMMOGRAM USING MULTI-ARCHITECTURE DEEP LEARNING ALGORITHMS

Today, radiologists observe a mammogram to determine whether breast tissue is normal. However, calcifications on the mammogram are so small that sometimes radiologists cannot locate them without a magnified observation to make a judgment. If clusters formed by malignant calcifications are found, the patient should undergo a needle localization surgical biopsy to determine whether the calcification cluster is benign or malignant. However, a needle localization surgical biopsy is an invasive examination. This invasive examination leaves scars, causes pain, and makes the patient feel uncomfortable and unwilling to receive an immediate biopsy, resulting in a delay in treatment time. The researcher cooperated with a medical radiologist to analyze calcification clusters and lesions, employing a mammogram using a multi-architecture deep learning algorithm to solve these problems. The features of the location of the cluster and its benign or malignant status are collected from the needle localization surgical biopsy images and medical order and are used as the target training data in this study. This study adopts the steps of a radiologist examination. First, VGG16 is used to locate calcification clusters on the mammogram, and then the Mask R-CNN model is used to find micro-calcifications in the cluster to remove background interference. Finally, an Inception V3 model is used to analyze whether the calcification cluster is benign or malignant. The prediction precision rates of VGG16, Mask R-CNN, and Inception V3 in this study are 93.63%, 99.76%, and 88.89%, respectively, proving that they can effectively assist radiologists and help patients avoid undergoing a needle localization surgical biopsy.
"TRIPTYCH" BY PYOTR FOMENKO: BASIC PRINCIPLES AND EXAMPLES OF DRAMATIZATION

The article focuses on Pyotr Fomenko's (1932–2012) last theatre production — "Triptych", staged at the "Pyotr Fomenko Workshop" in 2009. The director combined three texts by Alexander Pushkin: "Count Nulin", "The Stone Guest" and "Scene from Faust". The performance reflected the director's main principles of working with a literary text, such as the introduction of a conditional narrator, the use of rough drafts and texts by other authors, and musical and lyrical fragments. Fractional structure of the performance gave it a poetic order and allowed to consider it within the traditions of poetic theater. While the internal connection of each of the literary texts is causal, the director complicated the structural connections and combined the texts by association, freely comparing one story with another.