Reviewerly/exHarmony huggingface dataset:
Criteria of evaluating augmented reality applications

Introduction. The field of augmented reality (AR) is growing rapidly and has great advances in interaction, navigation and tracking. Nowadays there are a lot of trends for AR applications in different areas (education, entertainment, business, medicine, etc.). However, there is a lack of research to provide the evaluating AR apps framework to support developers when creating suitable AR applications for specific needs. We provide a practical approach to quantify some of the AR applications features. We focus on the development of criteria for evaluating augmented reality applications. We discuss the criteria of choosing dimensions for that space such as standards for AR, tools for AR development, navigation and tracking, content management, usability. We provide analysis and evaluation of AR apps through each characteristic using guidelines which we have developed. Materials and Methods. An AR application is a software application that integrates digital visual, audio and other types of content into a real-world environment. The software quality and performance are the main characteristics of the application, which are key factors for AR applications. The analysis of scientific papers, documents and standards made it possible to determine characteristics that are the most significant quality indicators based on well-grounded users’ needs and demands. Results. The criteria we have developed for evaluating applications with augmented reality enable developers to create their own software products in stages, based on step-by-step requirements for them, evaluating the development process by characteristics. This approach will allow you to create high-quality software products using standardized, modern development tools. Discussion and Conclusions. In addition, developers will have a detailed understanding of each stage of creating the application and the necessary development tools and technologies to obtain the highest quality result. That will give an opportunity to decide on specific development tools, methods, models and technologies before starting work on a project. As a result, it will provide the final high-quality software product with good extensibility and compliance with the modern requirements of the digital industry market.
Credit Card Fraud Detection in Payment Using Machine Learning Classifiers

The fraud detection in payment is a classification problem that aims to identify fraudulent transactions based individually on the information it contains and on the basis that a fraudster's behaviour patterns differ significantly from that of the actual customer. In this context, the authors propose to implement machine learning classifiers (Naïve Bayes, C4.5 decision trees, and Bagging Ensemble Learner) to predict the outcome of regular transactions and fraudulent transactions. The performance of these classifiers is judged by the following ways: precision, recall rate, and precision-recall curve (PRC) area rate. The dataset includes more than 297K transactions via credit cards in September 2013 and November 2017 that have been collected from Kaggle platform, of which 3293 are frauds. The performance PRC ratio of machine learning classifiers is between 99.9% and 100%, which confirms that these classifiers are very good at identifying binary classes 0 in the dataset. The results of the tests have proved that the best classifier is C4.5 decision trees. This classifier has the best accuracy of 94.12% in prediction of fraudulent transactions.
Content-aware Facial Image Compression with Deep Learning Method

Recently, online office work and education through video conferences have become a typical part of people's daily life, increasing the need for bandwidth and storage of wireless communication networks. To avoid video stalling and blurring, this paper proposes a content-aware facial image compression method based on deep learning networks at low bit rates for higher quality of experience (QoE). In this paper, the average face calculated from the database collected by the server is incorporated into the face representation model, which is used as prior knowledge to improve the performance of the video communication system. With the face representation model, facial images are decomposed into shape components and texture components, and different compression methods are applied to these components according to different redundancy levels. Specifically, a convolutional neural network (CNN) method is proposed to process texture components. The texture components are optimized by end-to-end rate-distortion learning with mean squared error (MSE) and structural similarity (SSIM) as optimization objectives for high fidelity reconstruction. The shape components use quantizer and entropy coding to preserve the details. Compared with traditional compression methods JPEG and JPEG2000, our proposed method could achieve a better performance in terms of PSNR and SSIM values at low bit rates.
Intelligent obstacle avoidance algorithms for autonomous underwater vehicle

Abstract Real-time obstacle avoidance is the basis to ensure the safe operation of the autonomous underwater vehicle (AUV), and it also represents the intelligent level of AUV. The main factors that affect AUV obstacle avoidance are environment perception ability, obstacle avoidance decision-making ability, and trajectory tracking control ability. This paper starts from the perspective of obstacle avoidance decision-making ability, that is, obstacle avoidance algorithm. Firstly, we summarized the structure and influencing factors of AUV real-time obstacle avoidance. Then, we introduced in detail the research progress of AUV intelligent obstacle avoidance algorithms, including fuzzy logic algorithms, neural network algorithms, and reinforcement learning algorithms. And we analysed the improvement methods of each algorithm from three-dimensional underwater environment, ocean current, AUV motion characteristics, and dynamic obstacles. Finally, we prospected the development of the AUV intelligent obstacle avoidance algorithm.
Traffic Control System Using Image Proccesing

The problem of traffic congestion needs to be addressed in an efficient way, specially in the country like India where this is a very big problem due to huge population. The present traffic control systems that are used are either manually controlled or automatic controlling system with static time allocation, both have certain drawbacks, one requires more man power to operate and is less efficient and other uses static time allocation which results in wastage of time. In this paper image processing technique is used to control traffic congestion by dynamically allocating time to release vehicles depending upon on the density of vehicles on roads. The proposed work uses Canny Edge detection, Gaussian Blur and Density calculation algorithms.
Dimensionality Reduction Methods Used in Machine Learning

Abstract In most cases, a dataset obtained through observation, measurement, etc. cannot be directly used for the training of a machine learning based system due to the unavoidable existence of missing data, inconsistencies and high dimensional feature space. Additionally, the individual features can contain quite different data types and ranges. For this reason, a data preprocessing step is nearly always necessary before the data can be used. This paper gives a short review of the typical methods applicable in the preprocessing and dimensionality reduction of raw data.
Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments

Deep reinforcement learning (RL) agents are able to learn contact-rich manipulation tasks by maximizing a reward signal, but require large amounts of experience, especially in environments with many obstacles that complicate exploration. In contrast, motion planners use explicit models of the agent and environment to plan collision-free paths to faraway goals, but suffer from inaccurate models in tasks that require contacts with the environment. To combine the benefits of both approaches, we propose motion planner augmented RL (MoPA-RL) which augments the action space of an RL agent with the long-horizon planning capabilities of motion planners. Based on the magnitude of the action, our approach smoothly transitions between directly executing the action and invoking a motion planner. We evaluate our approach on various simulated manipulation tasks and compare it to alternative action spaces in terms of learning efficiency and safety. The experiments demonstrate that MoPA-RL increases learning efficiency, leads to a faster exploration, and results in safer policies that avoid collisions with the environment. Videos and code are available at this https URL .
Agricultural Product Recommendation Model based on BMF

Abstract In this article, based on the collaborative deep learning (CDL) and convolutional matrix factorisation (ConvMF), the language model BERT is used to replace the traditional word vector construction method, and the bidirectional long–short time memory network Bi-LSTM is used to construct an improved collaborative filtering model BMF, which not only solves the phenomenon of ‘polysemy’, but also alleviates the problem of sparse scoring matrix data. Experiments show that the proposed model is effective and superior to CDL and ConvMF. The trained MSE value is 1.031, which is 9.7% lower than ConvMF.
Deep Active Learning in Remote Sensing for data efficient Change Detection

We investigate active learning in the context of deep neural network models for change detection and map updating. Active learning is a natural choice for a number of remote sensing tasks, including the detection of local surface changes: changes are on the one hand rare and on the other hand their appearance is varied and diffuse, making it hard to collect a representative training set in advance. In the active learning setting, one starts from a minimal set of training examples and progressively chooses informative samples that are annotated by a user and added to the training set. Hence, a core component of an active learning system is a mechanism to estimate model uncertainty, which is then used to pick uncertain, informative samples. We study different mechanisms to capture and quantify this uncertainty when working with deep networks, based on the variance or entropy across explicit or implicit model ensembles. We show that active learning successfully finds highly informative samples and automatically balances the training distribution, and reaches the same performance as a model supervised with a large, pre-annotated training set, with $\approx$99% fewer annotated samples.
Offline Handwritten Signature Recognition using Convolutional Neural Network Approach

Handwritten signature is one of the essential biometric parameters widely used for document validation and verification. Other methods such as fingerprints, iris/retina scanning, face, and voice recognition, although more accurate, need special equipment. The purpose of the research is to demonstrate an appropriate and reliable technology organizations may use to recognize signatures automatically. Convolutional neural networks are trained on preprocessed signature images. The code was developed using MATLAB, and results indicate our method to provide promising results and have contributed by extending the technique to be reliable. The CNN is tested with 4 different datasets with N number of individuals and M number of signatures for each individual and contains signatures that differ from each other in many aspects like the type of signature, its readability, etc. We used our CNN to train and test on all the datasets to observe the performance and make interesting observations of our implementation. The network performed reasonably well on all datasets, which is presented in the results section.
Unsupervised KB-to-Text Generation with Auxiliary Triple Extraction using Dual Learning

KB-to-text task aims at generating texts based on the given KB triples. Traditional methods usually map KB triples to sentences via a supervised seq-to-seq model. However, existing annotated datasets are very limited and human labeling is very expensive. In this paper, we propose a method which trains the generation model in a completely unsupervised way with unaligned raw text data and KB triples. Our method exploits a novel dual training framework which leverages the inverse relationship between the KB-to-text generation task and an auxiliary triple extraction task. In our architecture, we reconstruct KB triples or texts via a closed-loop framework via linking a generator and an extractor. Therefore the loss function that accounts for the reconstruction error of KB triples and texts can be used to train the generator and extractor. To resolve the cold start problem in training, we propose a method using a pseudo data generator which generates pseudo texts and KB triples for learning an initial model. To resolve the multiple-triple problem, we design an allocated reinforcement learning component to optimize the reconstruction loss. The experimental results demonstrate that our model can outperform other unsupervised generation methods and close to the bound of supervised methods.
Study of the Effect of Combining Activation Functions in a Convolutional Neural Network

Convolutional Neural Networks (CNNs) have proven to be an effective approach for solving image classification problems. The output, the accuracy and the computational efficiency of a CNN are determined mainly by the architecture, the convolutional filters, and the activation functions. Based on the importance of an activation function, in this paper, nine new activation functions based on combinations of classical functions such as ReLU and sigmoid are presented. Also, a study about the effects caused by the activation functions in the performance of a CNN is presented. First, every new function is described, also, their graphs, analytic forms and derivatives are presented. Then, a traditional CNN model with each new activation function is used to classify three 10-class databases: MNIST, Fashion MNIST and a handwritten digit database created by us. Experimental results illustrate that some of the proposed activation functions lead to better performances on classifying than classical activation functions. Moreover, our study demonstrated that the accuracy of a CNN could be increased by 1.18% with the new proposed activation functions.
Expert Concept-Modeling Ground Truth Construction for Word Embeddings Evaluation in Concept-Focused Domains

We present a novel, domain expert-controlled, replicable procedure for the construction of concept-modeling ground truths with the aim of evaluating the application of word embeddings.In particular, our method is designed to evaluate the application of word and paragraph embeddings in concept-focused textual domains, where a generic ontology does not provide enough information.We illustrate the procedure, and validate it by describing the construction of an expert ground truth, QuiNE-GT.QuiNE-GT is built to answer research questions concerning the concept of naturalized epistemology in QUINE, a 2-million-token, single-author, 20th-century English philosophy corpus of outstanding quality, cleaned up and enriched for the purpose.To the best of our ken, expert conceptmodeling ground truths are extremely rare in current literature, nor has the theoretical methodology behind their construction ever been explicitly conceptualised and properly systematised.Expert-controlled concept-modeling ground truths are however essential to allow proper evaluation of word embeddings techniques, and increase their trustworthiness in specialised domains in which the detection of concepts through their expression in texts is important.We highlight challenges, requirements, and prospects for future work.
Detecting Near-Surface Coherent Structure Characteristics Using Wavelet Transform with Different Meteorological Elements

In the present study, three wavelet basis functions, i.e., Mexican-hat, Morlet, and Wave, were used to analyze the atmospheric turbulence data obtained from an eddy covariance system in order to determine the effect of six meteorological elements including three-dimensional wind speed, temperature, and CO<sub>2</sub> and H<sub>2</sub>O concentrations on the time scale of coherent structures. First, we used the degree of correlation between original and reconstructed waveforms to test the three wavelets'performance when determining the time scale of coherent structures. The Wave wavelet's reconstructed coherent structure signal best matched the original signal; thus, it was used to further analyze the time scale, number, and time cover of the meteorological elements. We found similar results for all elements, though there was some internal variation, suggesting that coherent structures are not inherently dependent on these elements. Our results provide a basis for proper coherent structure detection in atmospheric turbulence and improve the understanding of similarities and differences between coherent structure characteristics of different meteorological elements, which is helpful for further research into atmospheric turbulence and boundary layers.
Application of Computer Accounting System in Internal Control of Enterprise Accounting

Abstract In the process of rapid development of computer technology, to promote the effective application of corporate accounting internal control in computers, accounting information system software can be constructed. The use of computer accounting information system software can improve the efficiency and level of corporate internal accounting control. The system software can not only improve the corporate financial management system, but also protect the accounting information system of the calculation class. When carrying out the internal control of the computer accounting information system, we need to strengthen computer hardware and software protection, and correctly face the security threats that exist in the computer for accounting control. In addition, we also need to take effective measures to improve the application level of corporate internal control on computers.
Performance Measurement of GraphQL API in Home ESS Data Server

This paper implemented an API server using GraphQL to transport only the data necessary for user of home ESS(Energy Storage System) management application. In addition, this paper measured the response time and mobile data consumption for the request of GraphQL API client and the performance was compared with the legacy API (RESTful API) server.
SpanAlign: Sentence Alignment Method based on Cross-Language Span Prediction and ILP

We propose a novel method of automatic sentence alignment from noisy parallel documents.We first formalize the sentence alignment problem as the independent predictions of spans in the target document from sentences in the source document.We then introduce a total optimization method using integer linear programming to prevent span overlapping and obtain non-monotonic alignments.We implement cross-language span prediction by fine-tuning pre-trained multilingual language models based on BERT architecture and train them using pseudo-labeled data obtained from unsupervised sentence alignment method.While the baseline methods use sentence embeddings and assume monotonic alignment, our method can capture the token-to-token interaction between the tokens of source and target text and handle non-monotonic alignments.In sentence alignment experiments on English-Japanese, our method achieved 70.3 F 1 scores, which are +8.0 points higher than the baseline method.In particular, our method improved by +53.9 F 1 scores for extracting non-parallel sentences.Our method improved the downstream machine translation accuracy by 4.1 BLEU scores when the extracted bilingual sentences are used for fine-tuning a pre-trained Japanese-to-English translation model.
Model Selection for Predicting Breast Cancer using Supervised Machine Learning Algorithms

Breast Cancer is the most common malignancy in women affecting 2.1 million women every year and causing the maximum number of deaths in women due to cancer. It occurs as a result of the unusual development of cells in the breast tissue, which is generally referred to as a Tumor. A tumor does not signify cancer. It may be not cancerous (benign), pre-cancerous (pre-malignant), or cancerous (malignant). Various types of tests such as mammograms, MRIs, ultrasound, and biopsy are frequently used to identify breast cancer. Early detection and treatment will help to improve breast cancer outcomes as well as survival. Therefore, this paper consists of a relative study of the breast cancer prediction using different supervised machine learning algorithms like Logistics Regression, K-Nearest Neighbors, Decision Tree Classifier, Gaussian NB, and Support Vector Machine on the UCI repository dataset. Concerning the performance of all the models, the accuracy score, precision, recall, and F-score of each model have been compared. After using various models, we got to see that Logistic Regression is a well-suited algorithm for Breast cancer prediction and came up with better accuracy and other performance indices as compared with other models.
Re-markable: Stealing Watermarked Neural Networks Through Synthesis

With the advent of the deep learning paradigm, the state-of-the-art neural network models have achieved unprecedented success in a variety of tasks, particularly pertaining to image, text and speech processing. The key to their high performance involves efforts in systematic curation of relevant data, optimization of neural architectures and heavy investments in hardware infrastructure essential for training models. This necessitates the requirement of frameworks for establishing ownership on trained models by associated stakeholders. Watermarking neural networks have been used as a mechanism for establishing such Intellectual Property rights, verifiable when necessary, created with the goal of preventing theft and unauthorised use. In this paper, we explore the vulnerabilities of watermarking neural networks by backdooring, which spawned a wide range of variations around the same principle due to its potential robustness. We propose a model-modification attack to successfully extract the trained neural network and demonstrate the removal of watermarking signatures. We use synthesis with GANs to generate samples, which can be used to re-train the marked models and achieve comparable accuracies of performance (within about 3% of actual model). The proposed attack successfully removes verifiable watermarks, as tested on highly benchmarked datasets including MNIST, Fashion MNIST and CIFAR-10, thus jeopardizing the reliability of the state-of-the-art watermarking scheme.