Reviewerly/exHarmony huggingface dataset:
Building a Chinese-English Parallel Corpus for Machine Translation: A Big Data-Driven Approach

In recent years, the use of massive text corpora to tackle language challenges, including Business English translation, has become commonplace. A large-scale English-Chinese corpus has not yet been established due to particular obstacles and the work required to solve them. This research is an effort to create an English-Chinese parallel corpus formed of digital texts and Web documents containing little or no noise. The abundant translations of existing texts available on the Web make it an ideal instrument for Business English translation in this regard. Creating a Chinese-English Parallel Corpus benefits users by allowing them to satisfy their expectations, boosting translation accuracy. The purpose of the research is to create a big data platform that will allow translation corpora to be mapped onto a BE language. This paper investigates the establishment of Chinese-English Parallel Corpus, explaining the feasibility of the Corpus’s design application, validating the corpus’s lexical collocation, statistical word frequency, and lexical context examination, and responding to questions about its collection process. In this process, word count and translation accuracy are assessed from the data, as well as mistranslations introduced by the vocabulary extraction and construction of the BE translation corpus. A lower accuracy resulted in the presence of more words in a sentence. In terms of accuracy, 100 words have an accuracy rate of 92.31%, but only 500 words have an accuracy rate of 68.74%.
Addition-Based Algorithm to Overcome Cover Problem During Anonymization of Transactional Data

Transactional data (such as diagnostic codes, customer shopping lists) are shared or published on the Internet for use in many applications. However, before sharing, it is protected by anonymization techniques such as disassociation. Disassociation makes data confidential without suppressing or altering it. However, it has been found to have a cover problem in disassociated data, which weakens its level of privacy. To overcome these shortcomings, we propose an algorithm based essentially on the addition of items. The performance evaluation results show that our algorithm completely suppresses the cover problem without significant information loss.
Text Processing for Marathi Text-To-Speech Synthesis

A Text To Speech (TTS) synthesizer is a computer based system which converts arbitrary input text into speech. Text processing is the first step which converts the entered text to a sequence of synthesis units, in this paper we have presented the Marathi text processing using python, which is the prerequisite for implementing a TTS system.
Comparative analysis of wavelet-based copyright protection techniques

Copyright protection of digital content is the need of the hour. The watermarking in the form of wavelet in digital images is becoming popular nowadays. We have implemented and compared different wavelets family-based techniques for copyright protection. We have used Haar, Daubechies, biorthogonal and reverse biorthogonal wavelets to calculate the MSE and PSNR values for sample cover image and watermark images with constant values of approximate coefficient and intensity gain factor. We have also proposed to choose local threshold value of approximate coefficient of cover image in insertion algorithm. The local threshold value of cover image can improve the PSNR value significantly.
Comparison of daily step counts by pedometers under free‒living conditions in young children

This study examined whether daily step counts under free‒living conditions differed among four types of pedometers as used by 5‒to‒6 year old children. In Study one, we compared the Kenz Lifecorder EX and Omron Active style Pro as accelerometer‒based pedometers (n=11). In Study two, we compared the Kenz Lifecorder EX (n=22) or the Omron Active style Pro (n=24) and the Yamax SW‒200 (widely used in research) and the YAMASA EX‒200 (pants pocket‒type pedometer). In Study one, the correlation was strong (r=0.825) ; the average difference between these two pedometers was -27.7％. In Study two, correlations between the Kenz Lifecorder EX and the Yamax SW‒200 or the YAMASA EX‒200 were moderate (r=0.657) or strong (r=0.817) ; the average differences between pedometers were -17.3％ and -15.6％, respectively. Correlations between the Omron Active style Pro and the Yamax SW‒200 or YAMASA EX‒200 were moderate (r=0.688) or strong (r=0.880) ; the average differences between pedometers were +28.7％ and +8.6％, respectively. In conclusion, the choice of pedometer had a substantial impact on step counts in young children.
Seeking Attention: Using Full Context Transformers for Better Disparity Estimation

Until recently, convolutional neural networks have dominated various machine vision fields-including stereo disparity estimation-with little to no competition. Vision transformers have shaken up this domination with the introduction of multiple models achieving state of art results in fields such as semantic segmentation and object detection. In this paper, we explore the viability of stereo transformers, which are attention-based models inspired from NLP applications, by designing a transformer-based stereo disparity estimation as well as an end-to-end transformer architectures for both feature extraction and feature matching. Our solution is not limited by a pre-set maximum disparity and manages to achieve state of the art on SceneFlow dataset.
Deepfakes for Video Conferencing Using General Adversarial Networks (GANs) and Multilingual Voice Cloning

Covid-19 pandemic led to remote working and hence resulting in more video conferences among all sectors. Even important international conferences between different nations are being conducted on online video conferencing platforms. Hence, a methodology capable of performing real-time end-to-end speech translation has become a necessity. In this paper, we have proposed a complete pipeline methodology, wherein the real-time video conferencing will become interactive, and it can be used in the educational section for generating videos of instructors from just their images and textual notes. We are using automatic voice translation (AVT), text-to-stream machine translation (MT), and text-to-voice generator for voice cloning and translation in real time. For video generation, we use general adversarial networks (GANs), encoder-decoder, and various other previously implemented generative models. The proposed methodology has been implemented and tested with some raw data and is quite effective for the specified application.
Reconstructing a Multiscale Digital Record of Clinical Pathology Slide Review Using Onboard-Cameras

In this pilot study, we leveraged an onboard microscope camera to turn clinical glass slide reviews into data-efficient variable resolution images and attention maps, enabling pathologists to see “the whole picture” of their diagnoses.
Improving Prediction Models’ Propriety in Intensive-Care Unit, by Enforcing an Advance Notice Period

Intensive-Care-Units (ICUs) are time-critical, and sufficient reaction time is crucial. Previous studies of systems for alerting life-threatening events in the ICU, suffer from “immediate” events bias. In this research, we present a new approach for outcome prediction in ICU admissions, which takes into consideration the constraint of an advance notice of a predicted outcome. We showcase the approach over mortality and sepsis-3 predictions and compare it to existing approaches. We’ve created a set of Neural Network models that implement and evaluate the existing and the suggested approaches using the MIMIC-III data. We show that the performance is affected significantly when enforcing a notice period for mortality prediction, but not affected for sepsis-3 prediction. Further, we examine whether models need to be trained for a specific notice period, or whether the approach could be incorporated at the evaluation level. We found that adding notice enforcement post-model training, has no significant performance loss compared to incorporating the notice period during training, within the bounds of the trained lookahead. The concept of adding Alert-Interval could be applied to other clinical scenarios, where having advance notice is essential.
Analysis of the Micro Implicit Feedback Behavior of User Network Exploring Based on Mobile Intelligent Terminal

Abstract In the face of the information recommendation requirements in mobile Internet applications, in order to better use the user micro implicit feedback behavior obtained by the mobile intelligent terminal to improve the recommendation efficiency, this paper intends to carry out the analysis of the implicit feedback behavior by analyzing the behavior distribution and behavior correlation. The analytical results reveal the particularity of the implicit feedback behavior in mobile intelligent terminal.
FL4J—Federated Learning Framework for Java

The article suggests an approach to implementing a federated machine learning framework for the Java platform. The subject of the research is federated machine learning, a brief description is given in the introduction, and the object is frameworks for federated machine learning systems development. As an experiment, it is shown the possibility of running a system with Java FL client for training linear ANN with one hidden layer with DeepLearning4Java backend together with Python FL client with PyTorch backend. The results model’s accuracies are compared.
Securing Open Systems

Security is one of the crucial aspects of the software development process. From conceptualization to a product’s release, developers need to ensure that a proactive approach exists to build a secure product. As the software complexity increases, flaws and defects in the software also increase. Bad actors may take advantage of these flaws to exploit the software. Hence security needs to be at the forefront of a software development life cycle.
A novel genetic search scheme based on nature-inspired evolutionary algorithms for binary self-dual codes

&lt;p style='text-indent:20px;'&gt;In this paper, a genetic algorithm, one of the evolutionary algorithm optimization methods, is used for the first time for the problem of computing extremal binary self-dual codes. We present a comparison of the computational times between the genetic algorithm and a linear search for different size search spaces and show that the genetic algorithm is capable of computing binary self-dual codes significantly faster than the linear search. Moreover, by employing a known matrix construction together with the genetic algorithm, we are able to obtain new binary self-dual codes of lengths 68 and 72 in a significantly short time. In particular, we obtain 11 new binary self-dual codes of length 68 and 17 new binary self-dual codes of length 72.&lt;/p&gt;
Design of Intelligent Recognition English Translation Model Based on Improved Machine Translation Algorithm

Whether it is learning a language or traveling abroad, translation is a skill that must be mastered. Traditional human translation is too slow and expensive. Therefore, in the integration and development of artificial intelligence and translation industry, intelligent translation has become the main direction of scientific research and exploration. On the basis of understanding the current development status of intelligent translation, this paper designs the content of intelligent recognition English translation model based on improved machine translation algorithm and analyzes its application value from the perspective of practical application. In this paper, an intelligent recognition model for English translation is proposed to solve the problem of the low accuracy of the traditional algorithm model. Based on the improved algorithm framework, an intelligent recognition model of automatic search, data acquisition, processing, and output planning is designed to collect and process English signals in a planned way and extract characteristic parameters to realize intelligent recognition of English translation. Experimental analysis shows that the designed English translation recognition model has high accuracy and can meet the needs of English translation work.
Face Mask Monitoring for Educational Institutions and Organizations

In the year 2020, we had seen a disastrous effect on the society by the outbreak of coronavirus which has demanded the world to mandatorily wear a mask. Therefore, one major protection for people is to wear mask in public areas. Thus, we proposed a face mask monitoring system, which uses convolution neural networks to train the model, Caffe-based DL detector with MobileNetV2 architecture to extract the facial features for mask detection and allowing our monitoring application to be deployed on any embedded system easily. The main objective of this paper is to automate face mask monitoring so that it reduces time and human effort administered in this process and, thus, helps in controlling the spread of virus.
Parallel Chaos Search Based Incremental Extreme Learning Machine Based Empirical Wavelet Transform: A New Hybrid Machine Learning Model for River Dissolved Oxygen Forecasting

In the present work, a novel hybrid model based on signal processing decomposition, extreme learning machine and parallel chaos search were proposed for forecasting DO several days in advance. First, the correlation between DO data at several times lags were calculated using the autocorrelation function (ACF) and the partial autocorrelation function (PACF). Second, the DO concentration time series were decomposed using the empirical wavelet transform technique (EWT), and the multiresolution analysis (MRA) components were then provided. Third, the obtained MRA components using the EWT technique were used as input variables for the extreme learning machine (ELM) and the hybrid parallel chaos search based incremental extreme learning machine (PCELM) models. The proposed models were tested during the validation stage using the correlation coefficient (R), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and the mean absolute error (MAE). Comparison between simple and hybrid models reveals the importance of the signal decomposition in improving the models performances and also it was demonstrated that the models performances decreased by the increase of the forecasting time step. While we found that the PCELM coupled with the EWT models gave slightly accurate estimates of DO at different forecasting horizons compared to the ELM coupled with the EWT, and yielded RMSE values ranging from 0.357 mg/L to 0.577 mg/L, MAE values ranging from 0.194 to 0.378 mg/L, compared to the values obtained using the ELM who have exhibited a RMSE values ranging from 0.376 to 1.104 mg/L, MAE ranging from 0.180 to 0.790 mg/L, it was found that the MLPNN estimated DO significantly more accurately than the two ELM models, with a mean RMSE of 0.440 mg/L, and a mean MAE of 0.274 mg/L, respectively. Comparison of the obtained results with different forecasting horizons indicates a high potential for the development of robust forecasting models.
Product Answer Generation from Heterogeneous Sources: A New Benchmark and Best Practices

It is of great value to answer product questions based on heterogeneous information sources available on web product pages, e.g., semistructured attributes, text descriptions, userprovided contents, etc.However, these sources have different structures and writing styles, which poses challenges for (1) evidence ranking, (2) source selection, and (3) answer generation.In this paper, we build a benchmark with annotations for both evidence selection and answer generation covering 6 information sources.Based on this benchmark, we conduct a comprehensive study and present a set of best practices.We show that all sources are important and contribute to answering questions.Handling all sources within one single model can produce comparable confidence scores across sources and combining multiple sources for training always helps, even for sources with totally different structures.We further propose a novel data augmentation method to iteratively create training samples for answer generation, which achieves close-to-human performance with only a few thousand annotations.Finally, we perform an in-depth error analysis of model predictions and highlight the challenges for future research.
Enhanced Image Segmentation by a Novel Test Time Augmentation and Super-Resolution

Image segmentation in computer vision applications plays a critical role in the video processing workflow. In real applications, where interesting elements are moving in the presence of moving objects in the background, complex models are required in the segmentation process to obtain better results. In this paper, a methodology based on super-resolution and test time augmentation is proposed to improve the precision and effectiveness of the segmentation process. Our proposal avoids both modification and retraining of the model. Experiments show that our approach can increase the mean average precision of images segmentation in sequences from well-known benchmark datasets with a significant improvement.
Multi-instance Learning for Semantic Image Analysis

Semantic image analysis is an active topic of research in computer vision and pattern recognition. In the last two decades, a large number of works on semantic image analysis have emerged, among which the multi-instance learning (MIL) is one of the most commonly used methods due to its theoretical interest and its applicability to real-world problems. However, compared with various MIL methods and their corresponding applications in the field of semantic image analysis, there is a lack of surveys or review researches about MIL related studies. So the current paper, to begin with, elaborates the basic principles of multi-instance learning, subsequently summarizes it with applications to semantic based- image annotation, image retrieval and image classification as well as several other related applications comprehensively. At length, this paper concludes with a summary of some important conclusions and several potential research directions of MIL in the area of semantic image analysis for the future.
Bespoke and Repetitive: Converging Technologies in the Design of Custom Products

The growth in Industry 4.0 marks a change in some ways that products will be created as well as the ways in which professions like industrial design will continue to grow. Advances in technology, data, and analytics will increasingly give designers the ability to develop customized or bespoke products for a range of end users. Specifically, the intersection of 3D surface scanning, generative design and digital fabrication could trigger this growth. This paper examines how the intersection of these technologies is currently being leveraged by designers for bespoke solutions and identifies growing opportunities and attributes that make the design of customized repetitive use products a strong candidate for adoption of these digital tools.
The impact of different interaction methods of virtual objects on mobile AR experience

Currently, immersive experience is the main highlight of mobile AR applications, but the impact of different interaction methods of virtual objects, which are the main experience content, is less studied.The purpose of this study is to investigate the impact of different interaction methods of virtual objects on the immersive experience, usability and satisfaction of mobile AR applications.In this study, 30 users experienced two different AR model presentation methods by customizing the independent variables (two different model interaction viewing methods, steering viewing Demo1 and free viewing Demo2), using a one-way within-group experimental design, and the data were processed by repeated measures ANOVA.The experimental report showed that Demo1 outperformed Demo2 in terms of spatial sense of immersion experience and ease of learning in usability; Demo2 outperformed Demo1 in terms of realism of immersion experience and efficiency of interaction with virtual objects; and Demo1/2 differed less in terms of fluency of the experiment.Our conclusion is that the interaction method of turning to view Demo1 is more immersive and spatial, and can be used in exhibition AR applications, and the interaction method of free view Demo2 is more novel and efficient in operation, and can be used in new AR games to improve user satisfaction. In general, AR applications should consider the age range of audience users when designing the viewing method of virtual objects, and choose the operation method that is more suitable for most people, easier to learn and more convenient.
An Efficient Text Mining Technique and Its Application to SMS Spam Detection

Ranjith Reddy, K. Chaudhary, SanjayToday, there is a lot of business information which exists in the form of text. Text mining permits us to manage the conflicting crude data, unstructured data and then again permits taking care of with fragmented data, ambiguity, vulnerability, and fluffiness. Along these lines, to remove significant and valuable examples from the text, some pre-preparing techniques and calculations are required. In this way, as a rule, text mining is the most common way of separating significant information and information from the accessible unstructured text. In this paper, we propose a technique for an efficient text mining technique that we use natural language processing (NLP) technique that is called as bag-of-words to classify messages as ham or spam. We use bag-of-words and feature engineering techniques and application to SMS spam detection, experimented the proposed technique on standard datasets, and presented the results in the paper.
Bento Packaging Activity Recognition Based on Statistical Features

Due to the fast advancements of low-cost micro-embedded sensors and MoCap sensors, human action recognition has become an essential study topic and is garnering a lot of interest in different sectors. Recently, it is drawing a lot of attention in human-robot collaboration to assist human to preform regular tasks step-wise because it is difficult to obtain human labor at a lower wage to monitor industrial works. In this work, we have presented a straightforward machine learning paradigm to recognize ten different Bento (lunch-box) packaging activities in real-time world. Unlike other skeleton-based human activity recognition domain, it is a very challenging task due to the absence of lower-body marker information. Under these circumstances, we have provided an in-depth statistical analysis of different Bento packaging activities. After feature extraction process, we have used several machine algorithms and obtained best results in random forest classifier using hyperparameter tuning. We have achieved 64.9% validation accuracy using leave-one-out method.
Confidence-weighted Learning for Feature Evolution

Several recent works have studied feature evolvable learning.They usually assume that features would not vanish or appear in an arbitrary way; instead, old features vanish and new features emerge as the hardware device collecting the data features is replaced.However, the existing learning algorithms for feature evolution only utilize the first-order information of data streams and ignore the second-order information which can reveal the correlations between features and thus significantly improve the classification performance.We propose a Confidence-Weighted learning for Feature Evolution (CWFE) algorithm to solve the aforementioned problem.First, second-order confidence-weighted learning is introduced to update the prediction model.Next, to make full use of the learned model, a linear mapping is learned in the overlapping period to recover the old features.Then, the existing model is updated with the recovered old features and, at the same time, a new prediction model is learned with the new features.Furthermore, two ensemble methods are introduced to utilize the two models.Finally, experimental studies show that the proposed algorithms outperform existing feature evolvable learning algorithms.