Reviewerly/exHarmony huggingface dataset:
Analysis of Unsupervised Learning Algorithms for Anomaly Mining with Bitcoin

Bitcoin technology is fast-growing virtual currency technology in the present electronic transactions system. It has become as popular as online currency transfer along with a worldwide acceptance of this technology. It is being used in any business environment for selling and purchasing any deal similar to stock trading. Because of its global reputation, there are many cases where serious instances of anomalous behavior are detected which cause serious risks to cryptocurrencies. In our proposed work, machine learning applied techniques are recommended for detecting anomaly over cryptocurrency by taking into consideration, the dataset of Bitcoin. To design an effective model we have compared and analyzed results from numerous practical algorithms in our proposed work. The algorithms used for detecting outliers in the given dataset include multivariate Gaussian distribution, One-Class SVM, and Two-Phase Clustering and Isolation forest procedures. All the mentioned algorithms pertain to the family of unsupervised family of Machine Learning Algorithms. Results with the help of graphical representation, are depicted, showing how each algorithm performs with the data set, and the accuracy measure is also evaluated to understand which outperformed the others. In the acclaimed results, it was evident that in the unsupervised family, the multivariate Gaussian provided a better accuracy over the others.
Breast Cancer Detection Using Deep Belief Network by Applying Feature Extraction on Various Classifiers

Among various types of cancer, breast cancer is considered as second largest hazardous diseases that cause death. Initially, small lump like structure grow from breast cells which are considered as malignant lumps. To find the available of malignancy in breast area, several checkups such as self-test and periodic has to be done to reduce the death rate due to breast cancer. But, the classification of breast cancer by medical physicians using available techniques is not sufficient, so it is important to improve the classification technique using neural network. The four important phases namely preprocessing, segmentation, feature extraction, and classification can be done in constructed Deep Belief Network (DBN) whereas preprocessing makes to remove the noise and artifacts of mammogram image and then the glands are enhanced. The preprocessed output is given to fuzzy c-means segmentation process with the help of masking. Again the feature extractors such as Scale Invariant Feature Transform (SIFT) and Speeded up Robust Transform (SURF) made to apply on two types of classifiers such as gradient boosting tree classifier and adaboost classifier. The examination is done interms of parameters such as accuracy, precision, recall and F1 score.
On the (In)Effectiveness of Images for Text Classification

Images are core components of multi-modal learning in natural language processing (NLP), and results have varied substantially as to whether images improve NLP tasks or not.One confounding effect has been that previous NLP research has generally focused on sophisticated tasks (in varying settings), generally applied to English only.We focus on text classification, in the context of assigning named entity classes to a given Wikipedia page, where images generally complement the text and the Wikipedia page can be in one of a number of different languages.Our experiments across a range of languages show that images complement NLP models (including BERT) trained without external pre-training, but when combined with BERT models pre-trained on largescale external data, images contribute nothing.
Extended Evolutionary Algorithms with Stagnation-Based Extinction Protocol

Extinction has been frequently studied by evolutionary biologists and is shown to play a significant role in evolution. The genetic algorithm (GA), one of popular evolutionary algorithms, has been based on key concepts in natural evolution such as selection, crossover, and mutation. Although GA has been widely studied and implemented in many fields, little work has been done to enhance the performance of GA through extinction. In this research, we propose stagnation-driven extinction protocol for genetic algorithm (SDEP-GA), a novel algorithm inspired by the extinction phenomenon in nature, to enhance the performance of classical GA. Experimental results on various benchmark test functions and their comparative analysis indicate the effectiveness of SDEP-GA in terms of avoiding stagnation in the evolution process.
Online Products Fake Reviews Detection System Using Machine Learning

Nowadays online analyses gotten single indispensable components clients web based shop. Associations plus people utilize data near purchase correct items then settle on business choices.affect spammers otherwise unscrupulous agents make bogus surveys also elevate items near rivalries. To handle this issue, examines have been directed to define successful approaches to recognize the spam surveys. Different spam recognition strategies have been presented in which a large portion of them separates significant highlights from the content or utilized AI proceduresIn this paper, named spam recognition framework, which uses spam highlights for showing review enlightening lists as heterogeneous information structures to configuration spam ID strategy into a gathering of issue. Utilizing the criticalness, us to acquire great results with respect to various measurements on survey informational indexes. The commitment effort remains after people search enquiry show wholly n-no of things similarly as recommendation of the thing.
A Study on Estimation and Prediction of Vector Time Series Model Using Financial Big Data (Interest Rates)

Due to the global economic downturn, the Korean economy continues to slump. Hereupon the Bank of Korea implemented a monetary policy of cutting the base rate to actively respond to the economic slowdown and low prices. Economists have been trying to predict and analyze interest rate hikes and cuts. Therefore, in this study, a prediction model was estimated and evaluated using vector autoregressive model with time series data of long- and short-term interest rates. The data used for this purpose were call rate (1 day), loan interest rate, and Treasury rate (3 years) between January 2002 and December 2019, which were extracted monthly from the Bank of Korea database and used as variables, and a vector autoregressive (VAR) model was used as a research model. The stationarity test of variables was confirmed by the ADF-unit root test. Bidirectional linear dependency relationship between variables was confirmed by the Granger causality test. For the model identification, AICC, SBC, and HQC statistics, which were the minimum information criteria, were used. The significance of the parameters was confirmed through t-tests, and the fitness of the estimated prediction model was confirmed by the significance test of the cross-correlation matrix and the multivariate Portmanteau test. As a result of predicting call rate, loan interest rate, and Treasury rate using the prediction model presented in this study, it is predicted that interest rates will continue to drop.
Research on Human Motion Recognition Based on Data Redundancy Technology

Aiming at the problems of low recognition rate and slow recognition speed of traditional body action recognition methods, a human action recognition method based on data deduplication technology is proposed. Firstly, the data redundancy technology and perceptual hashing technology are combined to form an index, and the image is filtered from the structure, color, and texture features of human action image to achieve image redundancy processing. Then, the depth feature of processed image is extracted by depth motion map; finally, feature recognition is carried out by convolution neural network so as to achieve the purpose of human action recognition. The simulation results show that the proposed method can obtain the optimal recognition results and has strong robustness. At the same time, it also fully proves the importance of human motion recognition.
Creation Of Multilingual Teaching Materials Focused On Content And Background Of Ngada Culture For Primary 1st Grade

This research was conducted in line with the development of existing technology. The teaching materials are developed using the ADDIE model, which consists of five steps, namely: 1) analyze, 2) design, 3) development, 4) implementation, 5) evaluation. In this study, sources of data were obtained from teachers as material / content experts, lecturers as language experts, and design experts. Components assessed by material experts are content feasibility, linguists on the appropriateness of language use, design experts on product design appropriateness. The suitability of the developed teaching materials in terms of the results of the assessment or validation of content or material experts obtained a score of 79 with an average of 4.4, and local language experts received a score of 44 with an average of 3.6, Indonesian language experts got a score of 43 with an average of 4.7. Product design experts obtained a score of 43 with an average of 3.9. Thus, multilingual teaching materials based on content and the local cultural context of the Ngada ethnic group on Theme 6 Clean, Healthy, and Beautiful Environment can be used.
Contiguous Loss for Motion-Based, Non-Aligned Image Deblurring

In general dynamic scenes, blurring is the result of the motion of multiple objects, camera shaking or scene depth variations. As an inverse process, deblurring extracts a sharp video sequence from the information contained in one single blurry image—it is itself an ill-posed computer vision problem. To reconstruct these sharp frames, traditional methods aim to build several convolutional neural networks (CNN) to generate different frames, resulting in expensive computation. To vanquish this problem, an innovative framework which can generate several sharp frames based on one CNN model is proposed. The motion-based image is put into our framework and the spatio-temporal information is encoded via several convolutional and pooling layers, and the output of our model is several sharp frames. Moreover, a blurry image does not have one-to-one correspondence with any sharp video sequence, since different video sequences can create similar blurry images, so neither the traditional pixel2pixel nor perceptual loss is suitable for focusing on non-aligned data. To alleviate this problem and model the blurring process, a novel contiguous blurry loss function is proposed which focuses on measuring the loss of non-aligned data. Experimental results show that the proposed model combined with the contiguous blurry loss can generate sharp video sequences efficiently and perform better than state-of-the-art methods.
Searching for Search Errors in Neural Morphological Inflection

Neural sequence-to-sequence models are currently the predominant choice for language generation tasks.Yet, on word-level tasks, exact inference of these models reveals the empty string is often the global optimum.Prior works have speculated this phenomenon is a result of the inadequacy of neural models for language generation.However, in the case of morphological inflection, we find that the empty string is almost never the most probable solution under the model.Further, greedy search often finds the global optimum.These observations suggest that the poor calibration of many neural models may stem from characteristics of a specific subset of tasks rather than general ill-suitedness of such models for language generation.
OPUS-CAT: Desktop NMT with CAT integration and local fine-tuning

OPUS-CAT is a collection of software which enables translators to use neural machine translation in computer-assisted translation tools without exposing themselves to security and confidentiality risks inherent in online machine translation.OPUS-CAT uses the public OPUS-MT machine translation models, which are available for over a thousand language pairs.The generic OPUS-MT models can be fine-tuned with OPUS-CAT on the desktop using data for a specific client or domain.
Thinking and Research on the Management Skills of Timbre in Contemporary Chinese Pop Music Production

With the rapid development of music culture in China this year, people have higher and higher requirements for the quality of music. Professional music production technology also ushered in a period of rapid development. However, the rapid development of systematization also leads to different quality of technical means. More and more musicians realize the importance of learning how to scientifically manage and customize their own timbre library. In this paper, we will think about and study the management skills of timbre in Contemporary Chinese pop music production.
Assessment of Facial Homogeneity with Regard to Genealogical Aspects Based on Deep Learning Approach

The current research work encompasses the assessment of similarity based facial features of images with erected method so as to determines the genealogical similarity. It is based on the principle of grouping the closer features, as compared to those which are away from the predefined threshold for a better ascertainment of the extracted features. The system developed is trained using deep learning-oriented architecture incorporating these closer features for a binary classification of the subjects considered into genealogic non-genealogic. The genealogic set of data is further used to calculate the percentage of similarity with erected methods. The present work considered XX datasets from XXXX source for the assessment of facial similarities. The results portrayed an accuracy of 96.3% for genealogic data, the salient among them being those of father-daughter (98.1%), father-son(98.3%), mother-daughter(96.6%), mother-son(96.1%) genealogy in case of the datasets from “kinface W-I”. Extending this work onto “kinface W-II” set of data, the results were promising with father-daughter(98.5%), father-son(96.7%), mother-daughter(93.4%) and mother-son(98.9%) genealogy. Such an approach could be further extended to larger database so as to assess the genealogical similarity with the aid of machine-learning algorithms.
Study of linear phase shift algorithms and application to deflectometry

For a number of optical methods, including deflectometry, information is encoded in fringe patterns. To extract the phase data from intensity values, a number of phase shift algorithms have been designed. In deflectometry, the nonlinearity of the fringe display implies that the displayed fringe pattern presents a harmonic content even if the input pattern is perfectly sinusoidal. The propagation of these harmonics through phase shift algorithms creates parasitic fringe patterns, reminiscent of the initial fringe pattern on the estimated phase. This phenomenon, known as print-through, has been identified as a serious performance limitation. In this paper, we revisit Surrel’s work on harmonic insensitive phase shift algorithms and demonstrate that the class of Discrete Fourier Transform (DFT) phase shift algorithms he defines is very appropriate for the field of deflectometry. We show how to choose the most suitable one depending on the application by performing a complete modeling of the harmonic print-through phenomenon for these DFT algorithms and studying the error propagation for shot noise and temporal perturbations. In a deflectometry context, we demonstrate by means of simulations that carefully chosen DFT algorithms can simultaneously be robust to print-through and perform better with respect to noise than the state of the art nonlinear phase shift algorithms. Lastly, by comparing experimental mirror shape measurements of the matrix of the secondary mirror of the European Extremely Large Telescope made on the one hand by DFT deflectometry and on the other hand by phase shift interferometry, we demonstrate that the use of DFT algorithms can substantially improve the high spatial frequency measurement capabilities of a deflectometry setup, without the need for a calibration of the display’s nonlinearities.
Design of English Intelligent Simulated Paper Marking System

In this paper, we analyze the intelligent subdesign of the simulated marking system through an in-depth study of it. This paper proposes a correlation analysis-based quantification of N-element sense values and a rationality enhancement-based scoring fitting algorithm for English essays. This paper also extracts word features, sentence features, and chapter structure features in essays to fit English composition scores. Since not all students can complete the essays according to the topic requirements, a triage scoring model is used to separate the normal essays from the low-scoring essays. Statistically, it was found that the essay scores also showed a certain normal distribution. The standard support vector regression algorithm is prone to data skewing problems, so this paper addresses this problem by using a rationality enhancement method that gives a corresponding penalty factor according to the distribution of the dataset. The results show that the English essay scoring fitting algorithm proposed in this paper can well improve the prediction accuracy of some data and solve the problem of skewed data where the scores show a normal distribution. This paper designs and implements an online mock examination system that incorporates an intelligent scoring system for essays, enabling it to meet the needs of teachers and students for online examinations and intelligent scoring.
MadDog: A Web-based System for Acronym Identification and Disambiguation

Acronyms and abbreviations are the shortform of longer phrases and they are ubiquitously employed in various types of writing.Despite their usefulness to save space in writing and time in reading, they also provide challenges for understanding the text especially if the acronym is not defined in the text or if it is used far from its definition in long texts.To alleviate this issue, there are considerable efforts both from the research community and software developers to build systems for identifying acronyms and finding their correct meanings in the text.However, none of the existing works provide a unified solution capable of processing acronyms in various domains and to be publicly available.Thus, we introduce MadDog, the first web-based acronym identification and disambiguation system which can process acronyms from various domains including scientific, biomedical, and general domains.The web-based system is publicly available at http://iq.cs.uoregon.
A model-based adaptive method for speckle noise reduction in ultrasound images of ovarian tumours: a new approach

Ultrasound imaging is widely used in medical diagnostics. The existence of speckle noise tends to impair ultrasound image quality, which has a negative effect on the computer-aided diagnostic pipeline. As a result, a content-preserving noise reduction is an essential part of ultrasound image pre-processing. This paper argues that conventional one-fit-all preprocessing methods on all images irrespective of their quality and/or their content have many limitations. The paper demonstrates that the negative effects of the speckle noise are more significant in regions where solid tissues are present. Consequently, we propose an adaptive approach of using trained classification models to detect such regions within the image and targeting the speckle noise of the detected regions instead of the whole image. The detection is achieved by placing a sliding window over the image and feeding individual windows to a trained classifier. In this study, we first analyse the content of the images to identify the complexity of the speckle noise by training a linear support vector machine classifier on histogram-based measurements such as skewness and kurtosis to determine whether the image partially or fully needs pre-processing. To evaluate the effectiveness of the new adaptive pre-processing methods, a hybrid two-model solution in which the first trainable model decides if an image requires pre-processing or not and applies it respectively on the whole image. The second model takes a step further to check which parts of the images requires pre-processing and adaptively applies it using the block-based trainable system. The results, based on 138 benign and 104 malignant ovarian ultrasound images, show that the two models performed better than other state-of-the-art pre-processing techniques, which confirms the need for the adaptive system that applies pre-processing only when needed.
CDA: a Cost Efficient Content-based Multilingual Web Document Aligner

We introduce a Content-based Document Alignment approach (CDA), an efficient method to align multilingual web documents based on content in creating parallel training data for machine translation (MT) systems operating at the industrial level.CDA works in two steps: (i) projecting documents of a web domain to a shared multilingual space; then (ii) aligning them based on the similarity of their representations in such space.We leverage lexical translation models to build vector representations using TF×IDF.CDA achieves performance comparable with state-of-the-art systems in the WMT-16 Bilingual Document Alignment Shared Task benchmark while operating in multilingual space.Besides, we created two web-scale datasets to examine the robustness of CDA in an industrial setting involving up to 28 languages and millions of documents.The experiments show that CDA is robust, cost-effective, and is significantly superior in (i) processing large and noisy web data and (ii) scaling to new and low-resourced languages.
The Role of Syntactic Planning in Compositional Image Captioning

Image captioning has focused on generalizing to images drawn from the same distribution as the training set, and not to the more challenging problem of generalizing to different distributions of images.Recently, Nikolaus et al. ( 2019) introduced a dataset to assess compositional generalization in image captioning, where models are evaluated on their ability to describe images with unseen adjective-noun and noun-verb compositions.In this work, we investigate different methods to improve compositional generalization by planning the syntactic structure of a caption.Our experiments show that jointly modeling tokens and syntactic tags enhances generalization in both RNNand Transformer-based models, while also improving performance on standard metrics.
Application of Artificial Intelligence (Neural Networks) in Education

This article focuses on the use of artificial intelligence in the educational environment. We consider the latest technology, which already plays a huge role for both teachers and students. Currently, there are many systems for the development of training systems, among which artificial neural networks occupy a substantial place. The article presents an example of the use of artificial neural networks, which can play a significant role in developing educational systems.
Project-then-Transfer: Effective Two-stage Cross-lingual Transfer for Semantic Dependency Parsing

This paper describes the first report on crosslingual transfer for semantic dependency parsing.We present the insight that there are two different kinds of cross-linguality, namely surface level and semantic level, and try to capture both kinds of cross-linguality by combining annotation projection and model transfer of pre-trained language models.Our experiments showed that the performance of our graph-based semantic dependency parser almost achieved the approximated upper bound.