Reviewerly/exHarmony huggingface dataset:
Presenting Multiple Viewpoints Inspired by Art

The paper presents an innovative method to artfully present multiple human and nonhuman viewpoints on a user&#x2019;s current viewpoint. The method is built on a digital platform for capturing a large number of human and nonhuman viewpoints in the world, and enabling us to collectively share the viewpoints. Inspired by the art, the presentation method enables people to watch the world in a novel way.
Efficient Open Domain Question Answering With Delayed Attention in Transformer-Based Models

Open Domain Question Answering (ODQA) on a large-scale corpus of documents (e.g. Wikipedia) is a key challenge in computer science. Although Transformer-based language models such as Bert have shown an ability to outperform humans to extract answers from small pre-selected passages of text, they suffer from their high complexity if the search space is much larger. The most common way to deal with this problem is to add a preliminary information retrieval step to strongly filter the corpus and keep only the relevant passages. In this article, the authors consider a more direct and complementary solution which consists in restricting the attention mechanism in Transformer-based models to allow a more efficient management of computations. The resulting variants are competitive with the original models on the extractive task and allow, in the ODQA setting, a significant acceleration of predictions and sometimes even an improvement in the quality of response.
Style Transfer Generator for Dataset Testing Classification

The development of the Generative Adversarial Network is currently very fast. First introduced by Ian Goodfellow in 2014, its development has accelerated since 2018. Currently, the need for datasets is sometimes still lacking, while public datasets are sometimes still lacking in number. This study tries to add an image dataset for supervised learning purposes. However, the dataset that will be studied is a unique dataset, not a dataset from the camera. But the image dataset by doing the augmented process by generating from the existing image. By adding a few changes to the augmentation process. So that the image datasets become diverse, not only datasets from camera photos but datasets that are carried out with an augmented process. Camera photos added with painting images will become still images with a newer style. There are many studies on Style transfer to produce images in drawing art, but it is possible to generate images for the needs of image datasets. The resulting force transfer image data set was used as the test data set for the Convolutional Neural Network classification. Classification can also be used to detect specific objects or images. The image dataset resulting from the style transfer is used for the classification of goods transporting vehicles or trucks. Detection trucks are very useful in the transportation system, where currently many trucks are modified to avoid road fees
Credit Card Fraud Detection Using Neural Network Auto Encoders

Nowadays, the Internet has become a crucial part of almost every area of our lives. Online transactions, purchases, and transactions are growing exponentially on e-commerce sites. Similarly, the number of online scammers increased, and the recent increase in credit card payments makes credit card fraud a very serious threat. Institutions lose huge sums of money every year due to fraud. As these agencies tighten their security measures, scammers are constantly looking for new ways to use their nefarious methods. Well-known online payment methods include credit and debit cards, internet banking, and mobile banking. Because of its easy usage, people tend to prefer credit card payments, and as a result, fraudulent transactions related to these types of transactions are increasing. In this article, we propose a method to detect credit card fraud transactions using a neural network autoencoder. The credit card fraud detection problem is generally considered a classification problem. This requires rebalancing any data imbalances present in the dataset. In this article, we propose to treat this problem as an outlier detection problem that does not require a solution. This article also describes various parameters to consider when training a model. We also published the experimental results and conclusions. The goal of our project is to develop a robust fraud detection system that is inherently dynamic and rapidly adapts to changing fraud patterns and requires little or no manual processing during model inference and training. The proposed system architecture provides estimates of the ROC AUC(area under the receiver operating curve) at 0.9212, 0.8976, 0.9378, 0.9489, and 0.9512 at all multiples, with a cumulative ROC AUC of 0.9314 without manual coding.
Applying User-Centered Design to U.S. Military Acquisition Requests

This study presents the concept testing results of a low-fidelity user-centered design (UCD) tool applied to the U.S. military acquisition request process. A purposive, random, small-sampled population of United States Marines used a UCD-based Design Worksheet to aid their own design process as they prepared a simulated acquisition request for equipment modifications. The results indicated that Marines engaged in the robust discovery and design exploration while using the Design Worksheet. While Marines had some challenges with understanding how to navigate the Design Worksheet, they found it valuable as a prompt for a useful, more productive thinking process. Importantly, Marines indicated that they would adopt the Design Worksheet to complete a real acquisition design request. Considerations for how the Design Worksheet supported their equipment modification analysis, and whether similar HCI or UCD tools could be pursued and adopted in comparable design environments are discussed.
Prediction of Hospitalization Cost and Length of Stay for Patients with Heart Failure Using Deep Learning

Heart failure is a leading cause of hospitalization in Japan, and the related length of hospital stay is much longer than that in western countries. This brings a heavy burden on society. To improve the allocation of medical resources, a deep learning model with weighted predictors is developed for the prediction of hospitalization cost and length of stay using electronic health record. Through 5-fold cross validation, the proposed model demonstrates a better prediction performance than that of linear regression.
Woodwind and Brass Instruments

Abstract This chapter briefly reviews the basic science of wind and brass instrument operation and the player-instrument interaction. It begins with how controlled, elevated pressure in the lungs produces air flow is modulated at the instrument input by a vibrating element—an air jet (flutes), a reed (other woodwinds), or the player’s lips (brass). This modulation inputs sound waves to the bore (and also back into the vocal tract), where resonances produce standing waves that interact with the vibrating element. Under suitable conditions of embouchure and bore configuration, the standing waves and vibrating element interact to produce sustained oscillations, some energy of which is radiated as output sound. The chapter next explains how the resonances are related to the shapes and configurations of the bore, why some of the resonances are harmonically related, and how these are controlled by the player. In all cases, note production, spectrum, and timbre are related to the musician’s control parameters: breath pressure, embouchure forces, tongue, vocal tract and finger gestures. The difficult issue of air speed is discussed, as are the effects of CO2 humidity, and temperature on playing pitch and techniques including vibrato, altissimo playing, and multiphonics.
Quantum Estimation of Kerr Nonlinearity in Driven-Dissipative Systems

We mainly investigate the quantum measurement of Kerr nonlinearity in the driven-dissipative system. Without the dissipation, the measurement precision of the nonlinearity parameter $\chi$ scales as ``super-Heisenberg scaling" $1/N^2$ with $N$ being the total average number of particles (photons) due to the nonlinear generator. Here, we find that ``super-Heisenberg scaling" $1/N^{3/2}$ can also be obtained by choosing a proper interrogation time. In the steady state, the ``super-Heisenberg scaling" $1/N^{3/2}$ can only be achieved when the nonlinearity parameter is close to 0 in the case of the single-photon loss and the one-photon driving or the two-photon driving. The ``super-Heisenberg scaling" disappears with the increase of the strength of the nonlinearity. When the system suffers from the two-photon loss in addition to the single-photon loss, the optimal measurement precision will not appear at the nonlinearity $\chi=0$ in the case of the one-photon driving. Counterintuitively, in the case of the two-photon driving we find that it is not the case that the higher the two-photon loss, the lower the measurement precision. It means thatthe measurement precision of $\chi$ can be improved to some extent by increasing the two-photon loss.
Automatic Extraction of English-Chinese Translation Templates Based on Deep Learning

Translation templates are an important cause of knowledge in machine translation (MT) systems. Their quality and scale directly influence the performance of MT systems. How to obtain high-quality and efficient translation templates from corpora has become a hot topic in recent study. In this paper, a tree to String alignment template (TAT) based on syntactic structure is proposed. This template describes the alignment between the source language syntax tree and the target language string. The syntactic structure, a large number of construction tags, and variables are introduced into the template, which enables the syntactic model to deal with discontinuous phrases and has the ability of generalization. Templates can be used in syntactic statistics, case-based, and rule-based MT systems according to different decoders. ATTEBSC algorithm is a basic method to learn translation templates by comparing sentence pairs. It demands that sentence pairs be constructed in a precise comparison structure ahead of time, but there are no strict guidelines on how to do it. In this paper, we propose a method to calculate the specific comparison scheme using the longest common subsequence (LCS) and use the normalized LCS distance to screen sentences with high similarity and then use the ATTEBSC algorithm to automatically remove the template. Experiments show that this method is easy and effective, and many expensive templates can be learned.
Machine Learning Based Predictive Analytics on Social Media Data for Assorted Applications

With 140-character messages, Twitter advertises itself as a "real-time information network," connecting people from all over the world. It's free to use, and all you need is a computer and an Internet connection (or a phone with SMS capability). Twitter became popular. Twitter began as a way to let people know what one was up to. Celebrities like Ashton Kutcher helped bring attention to the fact that it might be used in this way. Afterwards, politicians used to Twitter to express their ideas and make demands for change. Businesses quickly followed suit with product debuts, coupons, and other Twitter-specific promotions, as well as the merging of their messaging with those on other social media platforms. It's also been used to backchannel during meetings and to publish job announcements. Today, businesses keep a close eye on it for public relations and customer service objectives. For all intents and purposes, Twitter is a dynamic and evolving communication medium.
Steganographic Methods for Embedding and Detecting Hidden Messages Using GIF Images as Container Files

Over the past few decades, the development of new methods of storing, transferring, analyzing, and reproducing data has become increasingly important, considering the widespread development of information technologies and multimedia means. These methods also include means of ensuring the reliability, security, safety, and confidentiality of information when it is transmitted through various communication channels. Today, the fulfillment of the necessary data confidentiality requirements is developing in the direction of cryptography and steganography.&#x0D; The article discusses a method of ensuring data confidentiality by means of Digital Steganography, using GIF images as container files. The features of this method are considered, the main steps of the algorithm are described, and the resulting file is analyzed. The second part of the article studies a method that estimates the probability of hiding information in the blocks of a container file. This method utilizes Pearson’s goodness-of-fit test. A number of test tests of the algorithm have been carried out to check the efficiency of this method. As a result, a computer program has been created using the Microsoft Visual Studio 2019 Community programming environment and the C# programming language (.NET Framework).
Power Monitoring Image Encryption Transmission Method Based on Internet of Things

In order to be able to achieve state maintenance, keep historical data of the operation of each system, improve the precision of computer room management, improve work efficiency, reduce the work intensity of computer room managers, and reduce manual errors and potential accidents, this paper combines the Internet of Things technology to construct an image encryption transmission method that can be used for power monitoring. Moreover, this paper constructs a power system intelligent monitoring system based on the Internet of Things, and encrypts relevant data. In addition, this paper proposes a surveillance video encryption algorithm based on foreground and background separation. This video encryption algorithm is different from ordinary video encryption methods. It fully taps the characteristics of the surveillance video taken by a still camera. According to the experimental research results, it can be seen that the power monitoring image encryption transmission system based on the Internet of Things constructed in this paper has good performance in power monitoring image processing, image encryption, image transmission, etc. The evaluation results of the three functional parameters are all above 80%, which have great advantages compared with traditional algorithms. Therefore, the method proposed in this paper has good practical effects.
Leveraging Human Segmentation Guided Frames in Videos for Activity Recognition

Human Activity Recognition (HAR) has been an essential task in Machine Learning for identifying the concurrent activity through some form of sequential data like in video frames or activity sensors. This research provides a framework, working on video data, that aids the use of an independent deep learning pipeline working on human image segmentation for pre-processing each passing video frame sent into a Convolutional-LSTM HAR pipeline for determining the ongoing activity. The applied pre-processing makes the foreground pixel intensities lower than that of the human subject making the subject appear in better focus. The applied novel pre-processing technique enhances the results compared to ones using standard RGB frames sent to the conventional HAR pipeline. The work is then validated over two publicly available datasets, namely: KARD and MSR-Daily Activity. The results show some superior accuracy compared to other state-of-the-art works over the validated datasets with accuracy as&#x2014;99.07% (KARD) and 98.75% (MSR-Daily Activity).
A Sequence Obfuscation Method for Protecting Personal Genomic Privacy

With the technological advances in recent decades, determining whole genome sequencing of a person has become feasible and affordable. As a result, large-scale individual genomic sequences are produced and collected for genetic medical diagnoses and cancer drug discovery, which, however, simultaneously poses serious challenges to the protection of personal genomic privacy. It is highly urgent to develop methods which make the personal genomic data both utilizable and confidential. Existing genomic privacy-protection methods are either time-consuming for encryption or with low accuracy of data recovery. To tackle these problems, this paper proposes a sequence similarity-based obfuscation method, namely IterMegaBLAST, for fast and reliable protection of personal genomic privacy. Specifically, given a randomly selected sequence from a dataset of genomic sequences, we first use MegaBLAST to find its most similar sequence from the dataset. These two aligned sequences form a cluster, for which an obfuscated sequence was generated via a DNA generalization lattice scheme. These procedures are iteratively performed until all of the sequences in the dataset are clustered and their obfuscated sequences are generated. Experimental results on benchmark datasets demonstrate that under the same degree of anonymity, IterMegaBLAST significantly outperforms existing state-of-the-art approaches in terms of both utility accuracy and time complexity.
Moving beyond <i>S</i> Minus <i>P</i> Earthquake Locations as “THE” Lab in Seismology Education

Abstract Locating earthquakes is an important activity that underpins both seismological research and seismology education broadly. The seismology community tends to view the earthquake location process as a fundamental element of the field, and locating earthquakes is arguably the most common seismology education activity that middle school through introductory college students encounter. As a result, nearly all students’ first, and primary, exposure to the field of seismology consists of paper seismograms, drawing compasses, and 2D maps. This occurs in an era of computers available for every student, high-speed Internet, publicly available digital waveforms, complex algorithms to solve for earthquake locations, and the Next Generation Science Standards driving public education. This article explores the origins of the S minus P location method both as a scientific solution and as an educational activity in Earth Science classrooms, and reflects on technical, pedagogical, and curricular aspects of common implementations, underpinning beliefs, and their implications. From this reflection, three opportunities are identified for the seismology community to expand the seismological horizons of Earth Science students and instructors. These include working with instructors across a range of educational levels to correct the record on how earthquakes are located, to rethink both the content and pedagogy used both to teach about the process of earthquake locations, and to define several key ways the seismology community might lead geoscience educators to move beyond earthquake locations as “THE” seismology lab used with students.
A Unique Key Integrity Agreement Approach for Big Data Storage and Privacy Access in Cloud

The development of in-network services in the IT industry influences cloud usage rapidly for storage and distribution. As such new computing and service technology needed data owners to entrust their important data to place in the cloud service provider (CSP), in such case, the concerns surrounding the data security and privacy for the outsourced data is the biggest challenge. But it has become a widespread and apparent trend to utilize cloud services for data outsourcing. However, in consideration of the widespread interest in cloud computing, data security is the primary concern regarding the storage of cloud data is related to reliability and privacy, which are considered to be major difficulties to the acceptance of the cloud. To facilitate these issues and challenges, in this paper a Unique Key Integrity Agreement (UKIA) Approach has been presented. It initially investigates the issues and challenges to provide secure and reliable data storage and access methods in cloud computing. The UKIA proposes a framework to have secure and private data exchange between the data owner and consumer in support of an intermediate domain service provider to offer data storage and privacy access in the cloud. It implements a secure authorization model for the intermediate domain service providers, which will confine the required reliability and privacy assurance for the data owners for data storage, and a UKIA method for secure and private data access. The experiment analysis performs an asymptotic complexity comparison of the results to indicate the security goals with the practical efficiency of the computation in the security and privacy needs.
Classification of Rich-Classes but Scarce-Samples Images via Multi-modeling: the Humpback Whale Epitome

This paper focuses on demonstrating classification techniques for image datasets with rich classes but scarce samples per category. Although the machine learning technique is an effective way to solve such problems, it is difficult to define a particular method as optimal among traditional machine learning methods. We propose the combination of multiple Siamese Networks with a voting model for the classification of such complex datasets. Experimental results show that our approach can outperform the results of several traditional Siamese classification models working independently when dealing with rich classes but sparsely sampled image data.
Development of a Web Based Online Financial Aggregator Service

Based on the joint realization of financial technology and search algorithms, an online banking aggregation service was developed. A large database containing all data from participating financial institutions and precise search algorithms is the primary key behind the specific aggregation results. Users, regardless of occupation, position, or age, need to handle bank accounts, credit cards, and loans for different purposes. Using this algorithm, users can find the most suitable option for them.
An Introduction to Wavelet Analysis with Applications to Image and JPEG 2000

Image processing based on continuous or discrete transforms are classic topics for researchers, for it is widely used in telecommunications, information and electronics. Nowadays, the wavelet theories make up very popular methods of the image compression, denoising, etc. Considering that other literature focus more on the pure mathematics, this paper presents an overview of the wavelet, including the continuous wavelet transform and the discrete wavelet transform and discusses about its application in image processing, consisting of the multiresolution analysis. Then, the Joint Photographic Experts Group 2000 (JPEG 2000) - a new standard for image compression is paid much attention to. Finally, we use Matlab to verify the theories before and implement some basic image processing function, like: image compression, enhancement and passivation.
A Mobile Scanner Application for Embedding Data inside 3D Fabricated Objects

This paper presents a mobile application for scanning hidden embedding data, such as, watermarks, from the 3D printed objects. The application facilitates to collect, retrieve, and manage a database of 3D objects from each creator. To conveniently searching the products of each. The application achieves to scan code from the trial products with the barcode, QR, and the optical character recognition (OCR) very well. Moreover, in terms of satisfaction, up to 95% of users were very satisfied with &#x2019;very good&#x2019; and &#x2019;good&#x2019; experiences ratings.