Reviewerly/exHarmony huggingface dataset:
PS for You: A Contactless, Remote, Cloud-Based Body Measurement Technology Powered by Artificial Intelligence

Contactless and remote measurement technologies have recently been at the core of the digital shopping experience, not only for their potential to provide better customization but to revolutionize the way customers shop -from minimizing returns due to misfits to reducing environmental impact by producing on-demand.For small-batch retailers, this is key in ensuring minimal waste and streamlining the consumer journey.From the consumer's point of view, fit is also an unaddressed issue.After conducting user interviews on 60+ women, we identified 70% of respondents reported difficulties finding proper fitting apparel by designers.Reports by Mintel [1] and Deloitte [2] also confirm that the issue of fit is paramount in the industry.Here we introduce "PS for you", our AI-powered solution to provide a fast and scalable platform for designers to obtain reliable measurements and for shoppers to find the right fit.Our solution uses computer vision models paired with a proprietary layer of post-processing algorithms to extract personalized measurements from photos that the user can upload from any device.The solution is deployed on a serverless CPU using Amazon Web Services (AWS) for easier interoperability and faster run times.To test the effectiveness of this approach, we collected 40 images from 20 volunteers of different body shapes and sizes (including petite, short-stature, and athletic).The system was able to detect measurements for waist, hips, and bust with an average difference of less than 3% with respect to tapemeasured data.The median difference remained <1in, a significant improvement which is 3x more reflective of true size than the current universal sizing methods [3].Working on this technology allows us to foster a new connection between shoppers interested in customization, sustainability, and comfort and designers who are interested in decreasing returns, eliminating waste, and understanding more about their consumers.Technology will not just help but can create opportunities to build new systems, without heavy manual support that connects and fosters experiences anywhere in the World.By using the right technology and data, responsibly, we're building a product that will open up opportunities in connected markets as well as enhancing existing ones.
THE EFFECT OF USING EDMODO APPLICATION ON THE STUDENTS’ WRITING SKILL OF ANALYTICAL EXPOSITION TEXT AT SMAN 4 PALANGKA RAYA

This research was conducted to find out the effect using Edmodo application on the students’ writing skill of analytical exposition text at SMAN 4 Palangka Raya. Data of the students score of writing Analytical Exposition text was collected using writing test, and the hypothesis testing was examined based on t-test for normally because the data distribution was normal. A test in form of writing analytical exposition text was used the instrument to collect the data needed. Then, the data were analyzed by using parametric statistic dependent sample T – test because the data were normally distributed. The writer took conclusion based on statistical result. The result of this research shows that the critical value for students T distribution was 1.734 the critical value t0 was t05 {19} = 1.729. Because 1.734 is greater than 1.729, null hypothesis was rejected. Thus, it can be concluded that there was significant effect of using Edmodo Application in writing Analytical Exposition Text gained by eleventh grade of SMAN 4 Palangka Raya. Thus, Edmodo Application had been successful in improving students’ writing ability of Analytical Exposition text. The researcher suggest that the teacher should provide an appropriate media in teaching English, especially for Analytical Exposition Text and made Edmodo Application as reference to him/her to use it for making students’ attracted to the material.
Translation of imperative sentences and its mistakes: strategy analysis in pragmatic studies

Pragmatic studies focus on the use of a language in relation to its context. One of the notions of pragmatics is a study of how language is used to communicate, especially the relationship between sentences and the context and situations in which they are used. The speech act is one of the pragmatic sub-studies. Speech acts are closely related to human activities that cannot be separated from language, either individually or in groups. In speech acts, speakers and speech partners must understand each other the rules of the language that govern this, so that speech act activities can run well. Each speech participant is responsible for each of these lingual interactions, where a context has a role in forming a speech act. Furthermore, speech acts are the utterance of sentences to state so that the intent of the speaker is known to the listener. He distinguishes three aspects related to speech, namely locational, illoccionary, and per focusary. Locusionary is merely the act of speaking, namely the act of saying something with words and sentences in accordance with the meaning of the word (in the dictionary) and the meaning of the sentence is in accordance with the syntactic rules.
Pengaruh Kebiasaan Membaca Buku dan Kecerdasan Emosional Terhadap Prestasi Belajar Bahasa Indonesia

The study aims to analyze and test the validity of the hypothesis regarding the effect of Reading Books and Emotional Intelligence on Indonesian Language Learning Achievement of students. The research hypothesis tested includes: 1) Effect of Book Reading Habits on Students' Indonesian Language Learning Achievement. 2) Effect of Emotional Intelligence on Students' Indonesian Language Learning Achievement. 3) Interactive Influence on the Habit of Reading Books and Emotional Intelligence on Students' Indonesian Learning Achievement. The research was conducted by survey method with correlational techniques. The population used in this study is private vocational school students in Depok city. The study sample was 52 students. The results of the study: 1) There is a significant influence on the habit of reading books and emotional intelligence together on the learning achievement of Indonesian students of Depok Vocational High Schools. This can be proven by the Sig. 0.013 &lt;0.05, and Fh = 4.786. the independent variables of reading habits and emotional intelligence together contribute 16.3% to the variation in the achievement of learning Indonesian. 2) There is a significant influence on the habit of reading books on the learning achievement of Indonesian language students of private vocational schools in Depok. This can be proven by the value of Sig.0,010 &lt;0,05 and th = 2,697. 3) There is a significant influence of emotional intelligence together on the learning achievement of Indonesian students of Depok Private Vocational Schools. This can be proven by the Sig. 0.006 &lt;0.05 and th = -2.864.
Fractional Wavelet-Based Generative Scattering Networks

Generative adversarial networks and variational autoencoders (VAEs) provide impressive image generation from Gaussian white noise, but both are difficult to train, since they need a generator (or encoder) and a discriminator (or decoder) to be trained simultaneously, which can easily lead to unstable training. To solve or alleviate these synchronous training problems of generative adversarial networks (GANs) and VAEs, researchers recently proposed generative scattering networks (GSNs), which use wavelet scattering networks (ScatNets) as the encoder to obtain features (or ScatNet embeddings) and convolutional neural networks (CNNs) as the decoder to generate an image. The advantage of GSNs is that the parameters of ScatNets do not need to be learned, while the disadvantage of GSNs is that their ability to obtain representations of ScatNets is slightly weaker than that of CNNs. In addition, the dimensionality reduction method of principal component analysis (PCA) can easily lead to overfitting in the training of GSNs and, therefore, affect the quality of generated images in the testing process. To further improve the quality of generated images while keeping the advantages of GSNs, this study proposes generative fractional scattering networks (GFRSNs), which use more expressive fractional wavelet scattering networks (FrScatNets), instead of ScatNets as the encoder to obtain features (or FrScatNet embeddings) and use similar CNNs of GSNs as the decoder to generate an image. Additionally, this study develops a new dimensionality reduction method named feature-map fusion (FMF) instead of performing PCA to better retain the information of FrScatNets,; it also discusses the effect of image fusion on the quality of the generated image. The experimental results obtained on the CIFAR-10 and CelebA datasets show that the proposed GFRSNs can lead to better generated images than the original GSNs on testing datasets. The experimental results of the proposed GFRSNs with deep convolutional GAN (DCGAN), progressive GAN (PGAN), and CycleGAN are also given.
How cybercriminal communities grow and change: An investigation of ad-fraud communities

Firms spend enormous resources on digital advertising and promoting their brand online. In the meantime, ad-fraud undertaken by cybercriminals cost $42 billion in 2019 and could reach $100 billion by 2023. However, while digital advertisers continue to wrestle with how to effectively counteract ad-fraud, the topic of advertising fraud itself has received little academic attention. Here, we investigate this gap between practice and research through an exploration of ad-fraud communities. Our research implemented a multimethod approach for data collection in a longitudinal (18 months, October 2017 to April 2019) online investigation of this phenomenon. Integrating qualitative and quantitative analysis, we examined (1) internal interactions within ad-fraud communities and (2) ad-fraud communities’ performance and growth. Our online investigation extends our conceptual understanding of ad-fraud and explains how ad-fraud communities innovate. Our findings indicate that capabilities enacted by some communities foster requisite variety and enable the coordination of complex, iterative, and incremental dynamics (cocreation of artificial intelligence-based bots, customer involvement, and reinforcing capabilities). This research has both theoretical and practical implications for innovation in cybercriminal communities. Furthermore, we provide practical guidance for policy-makers and advertisers regarding how to improve their response to business threats. Indeed, a better understanding of how ad-fraud communities innovate enables organizations to develop countermeasures and intelligence capabilities.
GIS-based machine learning algorithms for mapping beach placer deposits in the southwest coast of India using Landsat-8 OLI images

Beach placer deposits are economically enriched with Fe-Ti oxide, Zr, and rare earth minerals such as garnet, rutile, zircon, monazite, ilmenite, and sillimanite. Landsat 8-OLI image provides the distinct spectral signature of placer deposits in its multispectral bands that can be used as a key source for mapping placer mineral deposits. GIS-based machine learning algorithms (MLAs) are used for executing the subpixel-based classification of high-dimensional data products (multispectral images, i.e., Landsat 8-OLI image) based on its spectral signatures for mapping the placer mineral deposits. The Landsat 8-OLI images are executed using the four widely used MLAs such as maximum likelihood classification, random forest classifier (RFC), support vector machine, and artificial neural network for mapping placer mineral deposits in the southwest coastal stretches of Thiruvananthapuram district in the Kerala state of India. The resultant map shows the occurrence of placer deposits with a major concentration of ilmenite. The accuracy assessment reveals that RFC shows better performance for mapping beach minerals with an overall accuracy of 84.4925% and kappa coefficient of 0.8184. Our study proves that the MLAs are the vital tools for mapping the placer mineral deposits in the sandy beaches, and thereby, this approach is being used as a scientific key tool for opening the gateways for beach mineral mapping using multispectral satellite images.
A Systematic Literature Review on Extraction of Parallel Corpora from Comparable Corpora

In today's Globalized Scenario, the requirement for translation is high and increasing rapidly in the number of fields, but it is difficult to translate everything manually.Machine Translation, which is dependent on corpora availability, is a medium for meeting this high demand for translation.Parallel corpora are used to gain most translation knowledge.But, the number and quality of parallel corpora are critical.Because parallel corpora are not readily accessible for many different language pairs, comparable corpora that are widely accessible can be used to extract parallel corpora.A systematic literature survey is performed on 188 research articles that are published in premier journals, conferences, workshops and book chapters.The research process is carried out while considering the research questions.Different MT systems along with their features are identified.Several datasets and techniques for bilingual lexicon extraction, parallel sentence and fragment extraction are revealed.A proposed architecture and a mind map are also showcased in this review article to provide better clarity regarding parallel data extraction using comparable corpora.The study of the paper will increase readers' understanding of parallel data mining through bilingual lexicons, parallel sentences and fragments.
An Error Analysis of Tense Errors in English Major Undergraduates’ Translations

Tense errors in translation works are of great importance because it affects the transmission of the original texts and plays a big part in research of Second Language Acquisition.The authors of this article used the method of Error Analysis to analyze errors of tenses in translation works of English major undergraduates from China.Then, they conducted interviews with 3 students who provided their works and made a questionnaire to deeply explore causes of errors.The authors found that the target students still made errors about tense even if they have a better command of English and are aware of the difference between Chinese tenses and English tenses.Six possible reasons are demonstrated: (1) different tense morphological features; (2) lack of adverbial of time; (3) difficulties to contextualize; (4) translators' misunderstanding about tenses; (5) translators' habits and preferences; (6) overlaps of concepts between some tenses.The acquisition of tenses in English is related to both the transfer between Chinese and English and students' learning experience.Inefficient correction methods should be highlighted because it is difficult for students to find errors in tenses themselves.However, self-study still serves as the most common and effective way for them to learn more about tenses.
A New Chaos-based Approach for Robust Image Encryption

Chaotic encryptions offered various advantages over traditional encryption methods, like high security, speed, reasonable computational overheads. This paper introduces novel perturbation techniques for data encryption based on double chaotic systems. A new technique for image encryption utilizing mixed the proposed chaotic maps is presented. The proposed hybrid system parallels and combines two chaotic maps as part of a new chaotification method. It based on permutation, diffusion and system parameters, which are then involved in pixel shuffling and substitution operations, respectively. Many statistical test and security analysis indicate the validity of the results, e.g., the average values for NPCR and UACI are 99.67145% and 33.63288%, respectively. The proposed technique can achieve low residual intelligibility, high sensitivity and quality of recovered data, high security performance, and it show that the encrypted image has good resistance against attacks.
What Can “Marriage Announcements” Tell Us? A Content Analysis of News Articles on Library-Press Collaboration

More university presses and academic libraries have started to collaborate in the scholarly publishing field, and it becomes important to investigate how this message of collaboration has been delivered to the academic community, since this community includes both creators and users of scholarly works. This study collects 23 news articles on the collaboration between university presses and academic libraries from the formal news sites of the affiliated institutions and then conducts content analysis on these articles, focusing on their content coverage and purposes. These news articles covered topics including the background and benefits of collaboration, how the collaboration worked/would work, and how libraries and presses can contribute to the collaboration. The analysis reveals that these news articles aim not only to inform their readers about the collaboration, but also to provide rationales to the collaboration. The news articles also demonstrate an unbalanced depiction between press and library by focusing more on the press, and this study interprets this phenomenon as a result of two assumptions held by the news articles: the audience’s unfamiliarity with the press and more challenging financial viability issues the press encountered.
Scholars and Literati at the Saint Petersburg Academy of Sciences (1724–1800)

This note is a summary description of the set of scholars and literati who participated in the activities of the Saint Petersburg Academy of Sciences, from its inception in 1724 to the eve of the Industrial Revolution (1800).
An Optimal Clustering with Hybrid Metaheuristic Algorithm for Sentiment Analysis and Classification

Sentimental Analysis (SA) becomes a familiar topic among business people, which is commonly applied for the classification of sentiments from online reviews. It is generally treated as a sentiment classification (SC) problem where the online reviews are categorized into positive or negative polarities using the words that exist in the online reviews. With this motivation, this paper presents a new K-means clustering with hybrid metaheuristic algorithm (KMC-HMA) for SA and classification. The proposed KMC-HMA technique initially performs data preprocessing to remove the unwanted words from the product reviews. In addition, K-means clustering technique is used for the clustering of the massive quantity of the applied product reviews. Moreover, the clustered data are fed into the classification model based on hybrid ant colony optimization (ACO) with dragonfly algorithm (DFA). The ACO algorithm is used for the classification of product reviews and the performance of the ACO algorithm can be optimally tuned by the use of DFA. The performance validation of the KMC-HMA technique is validated using two datasets such as Canon and ipod. The experimental values pointed out the superior performance of the KMC-HMA technique over the recent state of art techniques.
APPLICATION OF REMOTE SENSING IN LITHOLOGICAL DISCRIMINATION OF PRECAMBRIAN BASEMENT ROCKS OF ZUNGERU AREA, PART OF SHEET 163 (ZUNGERU NW), NORTH CENTRAL NIGERIA

Remote sensing technology has advanced significantly, making it useful for geological applications such as structural and lithological mapping, as well as mineral prospecting and mapping. This study looks at how certain enhancement techniques on Landsat 7 ETM+ data can be used to map surface geology, resulting in color composite imagery that can be interpreted and validated through field mapping exercises. Because some of these rocks are poorly exposed and some portions of the research region are inaccessible, field mapping was supplemented by remote sensing lithological mapping techniques. False Color Composite images for bands (7:4:1 and7:5:4), Principal Component analysis (PC1, 2, 3 and PC4, 5, 6) and RGB composite images of Landsat band ratios (1/3:5/7:3/5 and5/1:5/7:4) proved useful in determining the approximate boundaries of the various lithology in the research area. Gneiss and quartzites occur in the central and western quadrants of the study area, mylonites and schist in the eastern quadrant, and amphibolite in the southern and southeastern quadrants, according to GPS sample locations of the individual rock types found in the study area plotted on the processed images. The contact relationships between these rocks are mostly gradational and interlayered. As a result, a thorough examination of satellite optical imagery, such as Landsat data, can greatly aid lithological inquiry and the creation of more detailed geological maps in areas that are inadequately understood.
Visual Emotion Representation Learning via Emotion-Aware Pre-training

Despite recent progress in deep learning, visual emotion recognition remains a challenging problem due to ambiguity of emotion perception, diverse concepts related to visual emotion and lack of large-scale annotated dataset. In this paper, we present a large-scale multimodal pre-training method to learn visual emotion representation by aligning emotion, object, attribute triplet with a contrastive loss. We conduct our pre-training on a large web dataset with noisy tags and fine-tune on visual emotion classification datasets. Our method achieves state-of-the-art performance for visual emotion classification.
Evaluating Registrations of Serial Sections With Distortions of the Ground Truths

Registration of histological serial sections is a challenging task. Serial sections exhibit distortions and damage from sectioning. Missing information on how the tissue looked before cutting makes a realistic validation of 2D registrations extremely difficult. This work proposes methods for ground-truth-based evaluation of registrations. Firstly, we present a methodology to generate test data for registrations. We distort an innately registered image stack in the manner similar to the cutting distortion of serial sections. Test cases are generated from existing 3D data sets, thus the ground truth is known. Secondly, our test case generation premises evaluation of the registrations with known ground truths. Our methodology for such an evaluation technique distinguishes this work from other approaches. Both under- and over-registration become evident in our evaluations. We also survey existing validation efforts. We present a full-series evaluation across six different registration methods applied to our distorted 3D data sets of animal lungs. Our distorted and ground truth data sets are made publicly available.
Improvising Weakly Supervised Object Detection WSOD using Deep Learning Technique

Object detection is closely related with video and image analysis. Under computer vision technology, object detection model training with image-level labels only is challenging research area.Researchers have not yet discovered accurate model for Weakly Supervised Object Detection (WSOD). WSOD is used for detecting and localizing the objects under the supervision of image level annotations only.The proposed work usesself-paced approach which is applied on region proposal network of Faster R-CNN architecture which gives better solution from previous weakly-supervised object detectors and it can be applied for computer visionapplications in near future.