Reviewerly/exHarmony huggingface dataset:
Tutorial: a geophysical perspective on machine learning

Machine learning is fast becoming omnipresent in all aspects of science involving large volumes of data. The terminology and ideas involved are not immediately transparent or obvious to people not already immersed in the minutia of the implementation of machine learning procedures. Here, I try to relate the concepts and terminology used in machine learning to things that geoscientists will already be familiar with, so as to form a bridge between their current knowledge and an understanding of machine learning.
An Appropriate Parameterized Utility Technique On Heterogeneous Server Dependencies

A new server-based approach incorporated in Heterogeneous Servers. Current cloudinfrastructures are mostly homogeneous composed of a large number of machines of the same type – centrally managed and made available to the end user.In a cloud computing pattern, multiple resources types were utilizing. Users may have diverse resource needs. Furthermore, diversity in server properties/capabilities may mean that only a subset of servers may be usable by a given user. In platforms with such heterogeneity, we identify important limitations in existing multi-resource fair allocation mechanisms, notably Dominant Resource Fairness and its follow-up work. To overcome such limitations, we propose a new server-based approach; each server allocates resources by maximizing a per-server utility function. We propose a specific class of utility functions which, when appropriately parameterized, adjusts the trade-off between efficiency and fairness, and captures a variety of fairness measures. We establish conditions for the proposed mechanism to satisfy certain properties that are generally deemed desirable, e.g., envy-freeness, sharing incentive, bottleneck fairness, and Pareto optimality. To implement resource parameterized mechanism, we develop an iterative algorithm which is shown to be globally convergent on Heterogeneous server dependencies.
Asynchronous Digital-Based Learning Transformation of Speaking Skill Through Instagram

This article is the result of a research related to Asynchronous digital-based learning in speaking skill through instagram. This study aims to determine the transformation of Asynchronous digital-based learning and to analyze the students' speaking skill using the assessment rubric that has been designed. The data were collected through oral practice in the form of videos related to the theme Freizeitbeschäftigung / Hobby. Each student was asked to upload a 2-3 minute video and comment on two videos of other students. The results of this study indicated that the assessment rubric covered the fulfillment of tasks and pronunciation. All the given assignment points were fulfilled quite well, although there were six students who still made grammatical errors. The pronunciation ability of seven students was very good, while the other seven students were still influenced by Indonesian accent. Students' comments regarding other students’ videos were varied and positive. Students gave supportive comments to motivate each other in doing their work and they did not have any difficulties in using instagram. Keywords: Asynchronous, German language learning, instagram, speaking skill
Face Recognition from Art Face Images based on Deep Learning

The research of face recognition is one of the hot topics of computer vision. Recent models based on deep learning are very useful in many image processing problems. However, there are few researches on face recognition in art works. Based on the face data set in a newly released Japanese art work, we try to use deep learning to recognize the gender and identity of characters. We propose a stacked model of six deep learning models that achieves the best classification accuracies when compared with using these models separately. Our results achieve the best performance on this dataset so far.
Video Object Segmentation Based on Location and RoIAlign in Weight Modulated Multi-Scale Network

As a binary labeling problem, video object segmentation targets at segmenting one or more specific objects throughout a video sequence. Since 2016, many video object segmentation methods based on deep learning have been developed. Most of these methods use the ground truth segmentation mask of the first frame to fine-tune the subsequent segmentation frames of the video, so that the deep network can remember the appearance of the objects that need to be segmented in a given video. Satisfactory segmentation accuracy can be achieved ultimately. But the fine-tuning process fails to meet the requirements of real-time applications. In this work, we propose a fast and effective method which does not rely on fine-tuning. Our network is mainly divided into two parts: location and segmentation. We also extract the semantic information of the annotation object in the first frame to generate corresponding channel-wise weights so as to re-target the network to locate and segment the specific object accurately. The experimental results show that on the DAVIS 2016 dataset, our method without fine-tuning exhibits more competitive results than the most advanced methods using online fine-tuning. The J&F average index reached 79.7%, and the running time per frame was only 0.11s.
EVALUASI AKSESIBILITAS WEBSITE COVID19.KALTIMPROV.GO.ID MENGGUNAKAN SORTSITE 5.3.5

T his article aims to evaluate the accessibility of a government website, covid19.kaltimprov.go.id which created for disseminating the official information regarding the spread of covid-19 in East Kalimantan. Accessibility is an important aspect of a website because it indicates how far the range of information dissemination of such website. In this article we use SortSite 5.3.5, an online website testing software. The result shows that accessibility of covid19.kaltimprov.go.id still score below average with so many pages with accessibility issues. It is therefore need attention from East Kalimantan government to improve the accessibility level of their website. Keywords: Accessibility, Website, Covid-19, East Kalimantan
Estudo sobre a eficácia dos óleos essenciais no crescimento capilar: uma revisão integrativa

Abstract: Introduction: Hair loss is a problem that alters self-esteem and causes psychological damage. Essential oils compared to medicines available on the market for alopecia are more accessible and safer. Objective: To evaluate the use of essential oils in hair growth, through the studies obtained and the results achieved. Method: The present study was a descriptive research, with a qualitative approach and a bibliographic procedure, with databases: PubMed, LILACS and SciELO. The inclusion criteria defined for the selection of articles were: articles published in Portuguese and English, articles that portrayed the theme referring to essential oils in hair growth and alopecia, which have been published in the period from 2010 to 2020. Results: We found 43 articles, of which only three met the criteria for the study. Being found the following assets to stimulate hair growth: essential oil of Chamaecyparis obtusa (it is a species of cypress native to central Japan), Peppermint oil (peppermint) and another study used a solution with essential oils of Thyme vulgaris (thyme), Lavanndula agustifolia (lavender), Rosmainus officialis (rosemary) and Cedrus atlântica (cedar), mixed in jojoba and grape carrier oil. Conclusion: It was possible to verify the effectiveness of essential oils for alopecia, inducing hair growth, increasing the thickness and volume of hair strands.
Role of Triple Assessment of Breast Lump in Premenopausal and Postmenopausal Women -A Comparative and Descriptive Study

Triple assessment of breast is based on clinical examination, histological and radiological study where in our study, the pattern of breast swelling in a premenopausal and postmenopausal woman was compared. A total of 150 female patients with palpable breast lump went through clinical examination, FNAC, USG breast while mammogram &amp; core needle biopsy was done in selected patients. Histopathological examination of the specimen was done for all post-operative patients. Benign &amp; malignant breast lesion accounted for 82(54.6%) &amp; 68(45.3%) respectively. Based on the incidence of benign breast lump &amp; malignant breast lump among pre menopausal women, it was 79% &amp; 21% respectively while in post menopausal women it was 21.9% &amp; 78.1%. The most common type of breast lump among the pre and post menopausal women was fibroadenoma 56.9% &amp; Invasive ductal carcinoma 59.3% respectively. The most common benign breast lump &amp; malignant breast lesion based on the histological pattern among the premenopausal women was fibroadenoma 72% &amp; Invasive ductal carcinoma 50% while in postmenopausal women it was fibroadenosis 71.4% &amp; Invasive Ductal Carcinoma 76%. Based on the hormonal status(ER/PR) of pre and postmenopausal women positivity was seen majorly in post menopausal women 82.3% compared to premenopausal women 17.7%. In our study, the malignant breast disease was more common in post menopausal, while benign breast disease was common in pre menopausal women. Triple assessment in evaluating a breast lump is per se a reliable &amp; valid tool in the armamentarium of treating clinician with very high diagnostic accuracy.
Prepare and run test on available dataset v1

This protocol shows how to perform tests whose results are presented in the manuscript dedicated to the ReVisE open source visualization system. The system implements interactive visualization of large datasets hosted on a remote server or a supercomputer. The protocol also refers to another protocol that describes how to build and install the ReVisE system; that step should be taken if ReVisE has not been built yet on the server machine. The entire process, as described in the protocols, has been tested on Ubuntu 18.04 and Ubuntu 20.04 Linux distributions.
Requirement elicitation: Requirements conflict resolution and communication model for Telecommunication Sector

Requirements elicitation (RE) phase is very critical and crucial to the success of IS projects in telecommunication sector.Unfortunately, this phase of IS projects development is susceptible to a large degree of errors, affected by key factors embedded in the applied communication techniques, which results in a requirement conflicts that widen the gap of what is being build and what is being desired by the stakeholders.The aim of this paper is to present a Requirements conflict resolution and communication model to address the lack of a systematic mechanism to quantify the communication obstacles and to classify the requirements conflicts in the RE phase.The proposed model will introduce the conflict detection and resolution mechanism based on the normalized cross correlations function (NCCF), standard deviation (SD) and the standard error (SE) functions to detect and quantify the conflicted requirements based on the calculation of the requirements correlations and accuracy.The proposed model will overcome the reoccurring issues of the requirement elicitation phase.
Condition Monitoring of Industrial Motors using Machine Learning Classifiers

Rotating machines namely induction motors are most common load used in modern and service industries due to their reliability, ruggedness and negligible maintenance. However, it is essential to monitor the health of rotating machine to avoid production delays, capital loss to company etc. Recently, the condition monitoring methods are evolved to reduce abrupt failures of rotating machine. In this work, the induction motor parameters such as voltage, current, temperature and vibration are utilized for monitoring the condition of Induction Motors (IM). Further, the two different Machine Learning (ML) algorithms namely k-means and extreme learning machine are utilized for the analyses. Also, the performance of both the ML algorithms are analyzed and the best ML algorithm is utilized as a ML classifier for developed real-time intelligent system. Results demonstrate that the performance of Extreme Learning Machine (ELM) classifier is superior to the k-means classifier. This work appears to be of high industrial significance since the condition of the industrial motors can be identified using developed real-time intelligent system
The Life and Activities of Lee Gyu-won

Maneun Lee Gyu-won was a military officer in the late Joseon Dynasty. His family clan is from Jeonju and his adult name was Sungoh. In 1851, he passed the military service examination at the age of 19 and when he received the position of Seonjeongwan, he served as provincial governors as the Dancheonbusa at Hamgyeong-do and Tongjinbusa at Gyeonggi-do. In 1881, upon receiving information that the Japanese were logging at Ulleung-do, he was appointed as the inspector of Ulleung-do where he suppressed the unauthorized loggers at Ulleung-do. After a nine-day investigation, he drew maps called ‘Ulleung-do Wae-do’ and ‘Ulleung-do Nae-do’ and offered it to King Gojong. His achievements did not stop here, but he also marked his page in history as the moksa of Jeju. Lee Gyu-won was appointed as Challisa and Jeju-moksa in August of 1891 where he spent his time working on stabilizing Jeju. He prohibited fishing activities of the Japanese and focused on the safety of the common people. In 1894, he was appointed as the Gunmuamundaeshin and in 1900 he served as the gwangchalsa of Hamgyeongbuk-do. In 1910, he was given the posthumous epithet of Jang-hee. His name meant to maintain dignity with military force (Jang) and to serve with care and respect (Hee).
3D-Unet deep learning algorithm for denoising OCTA volumes acquired at MHz A-scan rates

Increasing the FOV of OCTA images while keeping the acquisition time moderate requires high A-scan rates. Therefore, OCTA images appear to be noisier. Deep learning methods can be used for noise reduction. In OCTA volumes small vessels with an orientation perpendicular to the image plane are often removed by deep learning denoising algorithms, due to their small appearance. To overcome this a 3-dimensional Unet was developed to utilize volumetric information. With the knowledge of also the third dimension, the algorithm is able to distinguish between noise and vessel contrast and is therefore less likely to remove vessels.
A novel improved cuckoo optimisation algorithm for engineering optimisation

After the introduction of the imperialist competitive algorithm, a new algorithm named cuckoo optimisation algorithm, which is more capable of finding the optimum solution was proposed. This algorithm is one of the latest and powerful evolutionary optimisation algorithms. Cuckoo optimisation algorithm inspired by the life of a bird group, named the cuckoo. This algorithm was developed in 2009 by Yang and Deb. The cuckoo algorithm was investigated in more detail by Rajabioun. In this article, the cuckoo algorithm is investigated and some methods to improve the performance of this algorithm will be proposed. The improved algorithm is implemented for several test problems with nonlinear functions. The results are compared to the original algorithm. The results show that the proposed algorithm has good results and the improved algorithm is better than the original algorithm.
Gender, discourse and ideology in Italian

Language, Gender and Sexuality is a new series of books which highlights the role of language in analyzing and interpreting issues, identities and relationships in relation to genders and sexualiti...
Pengembangan Media Pembelajaran Galaksi Statistika Berbasis Komputer pada Materi Statistika untuk Siswa Kelas VII SMP

This research aims to find out the feasibility and response of students to the learning media of “Galaksi Statistika”. This type of research is development research. The subjects of this study were 8 students of Grade VII N Colomadu Junior High School. The data collection techniques in this study used questionnaires and interviews. Data analysis techniques are performed with the media feasibility data analysis stage by media experts and materials experts, then analysis of student responses after using “Galaksi Statistika” learning media independently. Based on the results of the study obtained response questionnaire results from 8 students overall the average student response to computer-based “Galaksi Statistika” learning media was 87.6% which means In accordance with the criteria of student response in chapter III, that is, if the percentage of the student response is more than 50% then it can be said positive. So in accordance with the theory described earlier, the response of students after using the media of learning the “Galaksi Statistika” independently is very positive and the media is expressed to interest students.
LibGuides: Library Course Reserves: Introduction

Library Course Reserves use Leganto. Created by Ex Libris, Leganto is an online application that supports the creation and management of course resource reading lists.
PENGELOLAAN ARSIP SEBAGAI SUMBER INFORMASI BAGI SUATU ORGANISASI MELALUI ARSIP MANUAL DAN ARSIP DIGITAL

In the era of information technology and digital, the government or private institution carries out an activity for an organization. These activities include the creation and creation, storage, recording, and destruction of archives. An archive is processed and created and stored to be more effective and efficient. To perform archive management consists of two functions, namely static and dynamic archives, each of which has an important role in success in managing records. The archive aims to facilitate the search for documents for the required period. In the era of globalization, archives are divided into two parts, namely manual archives and digital archives. To manage records in an organization it is divided into three parts, namely centralization, decentralization, and a combination of centralization and decentralization. So with the existence of a digital era, there have been many changes in the formulation of an archive policy.
Abstract PO-076: Unsupervised learning of image embeddings enables new opportunities to extract novel information from digital pathology H&amp;E images

Abstract Introduction: In digital pathology, large amounts of time and money are spent annotating whole slide images (WSIs). By using an AI algorithm trained on thousands of WSIs, it is possible to remove the process of annotating from the workflow whilst simultaneously finding potential imaging features that correlate with drug response, molecular phenotypes, histologic features (e.g. Tertiary Lymphoid Structures), inflammation at the tumor-stroma interface, artifacts, etc. Method: A pipeline was constructed for ingesting WSI from the set of diagnostic slides available in the cancer genome archive (TCGA). The WSIs were masked for tissue areas and divided into patches extracted at multiple magnification levels. These patches were used to train a U-Net (initialised with a pre-trained ResNet34) to complete a self-supervised in-painting task and evaluated with perceptual loss using VGG16. After training, embeddings were extracted from the bottleneck layer of the U-Net, and these were evaluated for their capacity to cluster according to tissue type and magnification level. Results: The trained embeddings showed strong clustering by tissue type, magnification level, and separation of artefacts. The results were qualitatively evaluated by pathologist with consensus and exceeded the performance of a baseline pre-trained ImageNet model. Conclusion: These results highlight a novel methodology for AI algorithm development that removes the need for numerous pathologists to annotate pathology images by hand and converts their role to reviewing and quality-checking images. AI algorithms based on this approach can have a significant impact in the pathologic evaluation of tissue for oncology clinical trials by extracting a richer set of features than glass microscopy alone would permit. Furthermore, self-supervised methods, such as in-painting allow for the training of embeddings that encapsulate a richer understanding of the data and are consequently more repurposable than supervised/labelled approaches. Citation Format: Jason Hipp, Mona Xu, Lucas Bordeaux, Feng Gu, Carlos Pedrinaci, Khan Baykaner. Unsupervised learning of image embeddings enables new opportunities to extract novel information from digital pathology H&amp;E images [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-076.
PENGELOMPOKAN KECAMATAN DI KABUPATEN JEMBER BERDASARKAN TANAMAN PANGAN DENGAN ALGORITMA FUZZY CMEANS DAN METODE ELBOW

Jember Regency is known as one of the largest food crop producing regions in Indonesia. Jember has become one of the national food barns. The natural resources owned by Jember Regency make this area a chance to become an agrarian-based industrial city, according to the head of the economic development team of the Bank Indonesia Jember representative office, the growth of the agricultural sector in Jember in 2018 is 0.08% over the past five years growth has continued to decline this has an impact on GRDP (Gross Regional Domistic Product). Based on previous research, this study was conducted to classify sub-districts in Jember Regency based on agricultural production of food crops with the Fuzzy C-Means algorithm. For optimum cluster measurement in determining the best cluster, the method used is the Elbow method. The data used are data on agricultural production of food crops in 2018 in 31 districts in Jember Regency. From a series of tests ranging from 2 clusters to 10 clusters, optimum clusters are produced in 3 clusters based on the distance of SSE (Sum of Squares Error) on the Elbow method. Cluster 1 consists of 12 subdistricts, while Cluster 2 consists of 14 sub-districts and Cluster 3 consists of 5 sub-districts.
Video Sentiment Analysis with Bimodal Information-augmented Multi-Head Attention

Sentiment analysis is the basis of intelligent human-computer interaction. As one of the frontier research directions of artificial intelligence, it can help computers better identify human intentions and emotional states so that provide more personalized services. However, as human present sentiments by spoken words, gestures, facial expressions and others which involve variable forms of data including text, audio, video, etc., it poses many challenges to this study. Due to the limitations of unimodal sentiment analysis, recent research has focused on the sentiment analysis of videos containing time series data of multiple modalities. When analyzing videos with multimodal data, the key problem is how to fuse these heterogeneous data. In consideration that the contribution of each modality is different, current fusion methods tend to extract the important information of single modality prior to fusion, which ignores the consistency and complementarity of bimodal interaction and has influences on the final decision. To solve this problem, a video sentiment analysis method using multi-head attention with bimodal information augmented is proposed. Based on bimodal interaction, more important bimodal features are assigned larger weights. In this way, different feature representations are adaptively assigned corresponding attention for effective multimodal fusion. Extensive experiments were conducted on both Chinese and English public datasets. The results show that our approach outperforms the existing methods and can give an insight into the contributions of bimodal interaction among three modalities.
A studies on comparative linguistics: Comparing Hausa and Gera sounds in northern Nigeria

This paper compared the sounds of Hausa and Gera, who both belong to the Chadic family. Hausa has 31 consonants while Gera has 37. There are 23 consonants shared by both Hausa and Gera. There are 14 Gera consonants not existing in Hausa and there are 9 Hausa consonants not existing among the Gera sounds. Hausa has 5 long and short vowels while Gera have 3 long and 2 short vowels, and an additional vowel, the schwa. Hausa has 2 diphthongs while Gera has 8.