Reviewerly/exHarmony huggingface dataset:
UAVs Control Using 3D Hand Keypoint Gestures

Over past few years, unmanned aircraft vehicles (UAVs) have been becoming more and more popular for various purposes such as surveillance, automated industry, robotics, vehicle guidance, traffic monitoring and control system. It is very important to have multiple methods of UAVs controlling to fit in UAVs usages. The goal of this work was to develop a new technique to control an UAV by using different hand gestures. To achieve this, a hand keypoint detection algorithm was used to detect 21 keypoints in the hand. Then this keypoints were used as the input to an intelligent system based on Convolutional Neural Networks (CNN) that was able to classify the hand gestures. To capture the hand gestures, the video camera of the UAV was used. A database containing 2400 hand images was created and used to train the CNN. The database contained 8 different hand gestures that were selected to send specific motion commands to the UAV. The accuracy of the CNN to classify the hand gestures was 93%. To test the capabilities of our intelligent control system, a small UAV, the DJI Ryze Tello drone, was used. The experimental results demonstrated that the DJI Tello drone was able to be successfully controlled by hand gestures in real time.
SODA: Self-Organizing Data Augmentation in Deep Neural Networks Application to Biomedical Image Segmentation Tasks

In practice, data augmentation is assigned a predefined budget in terms of newly created samples per epoch. When using several types of data augmentation, the budget is usually uniformly distributed over the set of augmentations but one can wonder if this budget should not be allocated to each type in a more efficient way. This paper leverages online learning to allocate on the fly this budget as part of neural network training. This meta-algorithm can be run at almost no extra cost as it exploits gradient based signals to determine which type of data augmentation should be preferred. Experiments suggest that this strategy can save computation time and thus goes in the way of greener machine learning practices.
Ensure Safe Internet for Children and Teenagers Using Deep Learning

Modern technology provides us with incredible resources that change how we live our lives daily. In today&#x2019;s world, every single person uses a mobile phone. The child and teenagers also use mobile phones with the internet to communicate with their parents when they are in the office. Children and teenagers also use mobile phones for study, gaming, and social media. Sometimes the inappropriate content will appear before children and teenagers. Sometimes they cannot understand and click on it. We developed a proposed architecture based on CNN, RNN, OpenCV, haar cascade classifier, and MySQL for internet safety children and teenagers. When children and teenagers click on inappropriate content, the video camera will open and detect a child, teenager, adult, or old. If it is a child or teenager, the content will be hidden. OpenCV has been used for opening the video camera. Haar cascade classifier used for face detection. XAMPP MySQL database has been used for matching website links and blocking them. We generate a child, teenager, adult, and old(CTAO) dataset that contains 5000 images. The proposed architecture has been assessed using the CTAO dataset. We obtained 88.50% accuracy, 86.12% precision, 87.10% recall, and 86.60% f1 score.
Multi-Frame Super-Resolution With Raw Images Via Modified Deformable Convolution

In this paper we propose a novel model towards multi-frame super-resolution, which leverages multiple RAW images and yields a super-resolved RGB image. To facilitate the pixel misalignment in burst photography, we apply a refined Pyramid Cascading and Deformable Convolution (PCD) feature alignment module. A new 3D deformable convolution fusion module is proposed subsequently to merge the information from all frames adaptively. In addition, we employ an encoder-decoder network to restore color and details in sRGB space after super-resolving images in linear space. Extensive experiments demonstrate the superiority of our architecture and the strength of multi-frame super-resolution with RAW images.
BSOLO: Boundary-Aware One-Stage Instance Segmentation SOLO

Current one-stage instance segmentation methods ignore the boundary information of masks, resulting in coarse masks that are far from the ground truth. In this paper, we propose a boundary-aware method to refine boundary information, called BSOLO. The core idea of BSOLO is to design a Hungarian-Algorithm-based boundary loss to calculate matching costs between boundaries. This loss effectively measures the difference between boundaries and suits for boundary regression, contributing to generating refined instance masks with high-quality boundaries. Besides, we propose a Feature Fusion Network (FFN) to capture long-range dependency. Through constructing the relationship between pixels, such a module is beneficial for predicting masks with large or uncontinuous region. Furthermore, we introduce a Prototype Attention Module (PAM) for mask assembling through channel attention, which enhances informative features and spotlights important prototypes. To evaluate the performance of BSOLO, we conduct extensive experiments. Experimental results show that BSOLO achieves 39.3 AP on MS COCO test-dev2017, outperforming SOLO and other methods by a large margin. We hope that BSOLO broadens the perspective for designing more valid boundary constraints.
Finding The Available Website Name By Using Naive Bayes Classification

This study aims to positive good text or negative text sentiment on websites. The property consists of the URL of the website and results in a positive website and a negative website that should be blocked in access to that. In this research, a computational approach for website classification based on features retrieved from URLs is proposed to must compare the concept of classification with the Multinomial Naive Bayes of independence property data by comparing the ideal models for discrete data Multinomial Naive Bayes. In addition, have been used other machine learnings are Decision Tree and Logistic Regression to compare which method is suitable for the analysis of this data set and which has the most accuracy with use data divided into 3 sets as follows: Training data, Test data, and External data about racist and sexist tweets sentiment to provide machine learning. The result of the Multinomial Naive Bayes has the highest accuracy of 95.14 percent for the training model.
From Shallow to Deep: Compositional Reasoning over Graphs for Visual Question Answering

In order to achieve a general visual question answering (VQA) system, it is essential to learn to answer deeper questions that require compositional reasoning on the image and external knowledge. Meanwhile, the reasoning process should be explicit and explainable to understand the working mechanism of the model. It is effortless for human but challenging for machines. In this paper, we propose a Hierarchical Graph Neural Module Network (HGNMN) that reasons over multi-layer graphs with neural modules to address the above issues. Specifically, we first encode the image by multi-layer graphs from the visual, semantic and commonsense views since the clues that support the answer may exist in different modalities. Our model consists of several well-dasigned neural modules that perform specific functions over graphs, which can be used to conduct multi-step reasoning within and between different graphs. Compared to existing modular networks, we extend visual reasoning from one graph to more graphs. We can explicitly trace the reasoning process according to module weights and graph attentions. Experiments show that our model not only achieves state-of-the-art performance on the CRIC dataset but also obtains explicit and explainable reasoning procedures.
Spurious Cotton Seed Control Management

There have been several news stories in the last couple of years about police seizing fake cotton seeds. Because no effective steps or activities have been done to address this issue it has gotten worse. The customers who buy the seeds and the authentic producers who generate the seeds gain equally from the use of this technology. Customers may scan the QR code on the package to ensure that the goods they are purchasing are from a trusted manufacturer. Genuine cotton seed package makers have suffered huge losses as a result of the surge of counterfeit cotton seed packages. Genuine producers will be able to receive their fair portion of the money as a result of the suggested system, which would lead to the eradication of phony goods. As previously stated, no meaningful steps have been done against this, other than the arrest of those responsible for this heinous act. However, they are just measures implemented after the event. The system proposed is intended to be a preventative measure.
Sign Language Translation Assistant using Machine Learning

One of the most important and greatest that have been achieved in the past few years is the invention of sign language. But unfortunately, it is not widely known by the common people and only the trained ones will be able to understand it thereby making it harder for the deaf-mute people to communicate with the common world. For solving this many inventions like sign language translating systems were also made. But for the existing systems there are many faults and misconceptions that reduce the accuracy of the system. In this model, camera of a smartphone device has been used to capture the images of the hand gestures as input and based on CNN model used to train the dataset of the proposed system it classifies the hand gesture inputs and give the text translations as the output of the system.
A Bibliometric Analysis of Blockchain and Its Applications in the Insurance Industry

The objective of this research paper is a comprehensive study on how Blockchain can revolutionize Insurance sectors by improvising efficiency in gains and cost reductions and eliminating the duration taken for uncovering the information or fraudulent claims. This paper uses a narrative literature review approach. The critical aspect of this research paper lies in how effectively Insurance Industry is transforming and adapting to digital technology by employing blockchain in their process. In order to save time and lower their transactional costs by ensuring zero threat from cyberattacks, which is a key area to improve their customer experience and enhance their profits by retaining the old customers with utmost satisfaction. This study benefits insurers by saving their transactional costs, eliminating fraudulent crimes, and uncovering information.
Improving Phonetic Realizations in TTS by Using Phoneme-Aligned Graphemes

Most text-to-speech acoustic models, such as WaveNet, Tacotron, ClariNet, etc., use either a phoneme sequence or a letter sequence as the fundamental unit of speech. Although the letter (or grapheme) sequence closely matches the actual runtime input of the TTS system, it often fails to represent the fine-grained phonetic variations. A purely phonemic input seems to perform better in practice, though is heavily dependent on a meticulously crafted phonology and lexicon. This reliance poses issues (with quality and consistency) which can lead to the need for a trade-off between quality and scalability. To overcome this, we propose using a mix of the two inputs, namely providing phoneme-aligned graphemes to the model. In this paper, we show that this approach can help the model learn to disambiguate some of the more subtle phonemic variations (such as the realization of reduced vowels), and that this effect improves the fidelity to the accent of the original voice talent. For evaluation, we present a way of generating an unbiased targeted test using phoneme spectral diffs, and using that, show improvement over the baseline approach for multiple voice technologies and multiple locales.
HAT: Hybrid Adversarial Training to Make Robust Deep Learning Classifiers

Deep learning has become state-of-the-art in real-life applications. However, current studies show that deep learning models are susceptible to adversarial attacks. Adversarial attacks are well-crafted perturbed inputs that fool the deep learning models. An adversarial attack can easily fool the classifier thus posing a threat for deep learning models while deploying them in real-world applications. Our work explores various adversarial attacks and defenses against adversaries available in the literature. We find that existing defense strategies show good results on greyscale images like MNIST and FMNIST but, the robustness of the same defense models radically decreases on RGB images like the CIFAR10 dataset. Also, the robustness of a model greatly depends on the type of adversarial examples on which the model is trained for achieving robustness. We devise a defense technique based on adversarial training, called Hybrid Adversarial Training (HAT). During training, we augment HAT with state-of-art adversarial examples crafted by combining DeepFool and FGSM attack hence increasing the robustness of deep learning models in the stipulated amount of time against a variety of attacks. Empirically performance of HAT is evaluated on cutting-edge adversarial attacks using various benchmark datasets. Our model shows good performance in terms of robustness and time than existing defense models. Our defense model can withstand the strong adversarial attack on the CIFAR10, a benchmark RGB image dataset. HAT outperforms the existing models as our model shows 15&#x0025; more robustness than existing defenses. HAT is also proficient to maintain the natural accuracy of classifiers.
Implementing a TSP Path Controller for Autonomous Robot

In this paper the cooperation of hybrid robots with digital map is presented. Based on ground robot Fuzzy Controller is designed to achieve the desired navigation accuracy, to estimate the optimized path. The approach combines Path Controller, Travel Sales Problem and Fuzzy Controller. A combined algorithm is demonstrated on multiple inputs to achieve higher accuracy and efficiency.
Validation of Secure Wiping Applications for Android Phones

Identity theft and financial fraud are happening very frequently, and data privacy becomes one of the biggest challenges for Android phone users. Cyber thefts are particularly perceptive when recovering confidential information from user phones after users have deleted/erased their data from phone memory using the factory restore or by using data wiping applications available on Google Play Store and Internet. This research work proposed and developed an efficient data wiping application for Android phones according to the National Institute of Standard and Technology (NIST) SP800-88 Standard of USA. The proposed application is based on the data overwrite technique. It has two layers, data erasing and data overwriting. After erasing the user data from phone memory, it overwrites deleted data locations by three overwrite phases, zeros, ones, and random characters. The data overwrite layer makes deleted data permanently unrecoverable from phone memory.To validate existing wiping applications, we selected the following data wiping applications from Google Play Store: Secure Erase with iShredder-6, Secure Delete, and Shreddit-Data Eraser for data wiping experiments and picked the following data recovery applications and software to validate the above-selected wiping applications: DiskDigger, Dr Fone, FonePaw and EaseUS MobiSaver for data recovery experiments on following selected Android phones: Samsung Galaxy SM-J600F, Vivo 1908 and Huawei Honor 9-lite LLD-21 having Android version Oreo 8.0 and 8.1. The experiments result show that the chosen wiping applications are not working according to the standard, and the erased data is recoverable through the above-mentioned data recovery applications and software. On the other hand, data could not be recovered from the phones which are wiped with the proposed framework. We maintained the record of the recovered data from wiped Android phones and proposed an efficient overwrite-based data wiping application for Android phones based on the experiment results. Recommendation: A preinstall secure data wiping application must meet standards such as NIST SP800-88 for complete data erasure and should be available on all android phones for users.
LB-CNN: Convolutional Neural Network with Latent Binarization for Large Scale Multi-class Classification

Reese, Timothy Michael Zhu, Yu Convolutional Neural Networks (CNNs) demonstrate state-of-the-art performance in large-scale multi-class image classification tasks. CNNs consist of convolution layers that progressively construct features and a classification layer. Typically, a softmax function is used in the classification layer to learn joint probabilities for the classes, which are subsequently used for class prediction. We refer to such an approach as the joint approach to multi-class classification. There exists another approach in the literature which determines the multi-class prediction outcome through a sequence of binary decisions, and is christened the class binarization approach. A popular type of class binarization is error-correcting output codes. In this paper, we propose to incorporate error-correcting output codes into convolutional neural networks by inserting a latent binarization layer in a CNN classification layer. This approach encapsulates both encoding and decoding steps of error-correcting output codes into a single CNN architecture that is capable of discovering an optimal coding matrix during training. The latent binarization layer is motivated by the family of latent-trait and latent-class models used in behavioral research. We call the proposed convolutional neural networks with Latent Binarization as LB-CNNs, and develop algorithms combining expectation maximization algorithms and back-propagation to train LB-CNNs. The proposed models and algorithms are applied to several image recognition tasks, producing excellent results. Furthermore, our model enhances the interpretability of the decision process of standard convolutional neural networks.
Temporal Clustering with External Memory Network for Disease Progression Modeling

Disease progression modeling (DPM) involves using mathematical frameworks to quantitatively measure the severity of how certain disease progresses. DPM is useful in many ways such as predicting health state, categorizing disease stages, and assessing patients’ disease trajectory, etc. Recently, with the wider availability of electronic health records (EHR) and the broad application of data-driven machine learning methods, DPM has attracted much attention yet remains two major challenges: (i) Due to the existence of irregularity, heterogeneity, and long-term dependency in EHRs, most existing DPM methods might not be able to provide comprehensive patient representations. (ii) Lots of records in EHRs might be irrelevant to the target disease. Most existing models learn to automatically focus on the relevant information instead of explicitly capture the target-relevant events, which might make the learned model suboptimal. To address these two issues, we propose Temporal Clustering with External Memory Network (TC-EMNet) for DPM that groups patients with similar trajectories to form disease clusters/stages. TC-EMNet uses a variational autoencoder (VAE) to capture internal complexity from the input data and utilizes an external memory work to capture long-term distance information, both of which are helpful for producing comprehensive patient health states. Last but not least, the k-means algorithm is adopted to cluster the extracted comprehensive patient representation to capture disease progression. Experiments on two real-world datasets show that our model demonstrates competitive clustering performance against state-of-the-art methods and is able to identify clinically meaningful clusters. The visualization of the patient representations shows that the proposed model can generate better patient health states than the baselines.
How fraud detection technologies can help to detect damages in aircraft structures

A software architecture based on Machine Learning (ML) and Finite Element Method (FEM) and aimed at improving the detection of damages in aircraft structure subjected to complex variable loadings is presented here. Firstly, the software relies on statistical tools used among others in fraud detection (One-Class Support Vector Machine, Local Outlier Factors, Isolation Forest, DBSCAN) to identify anomalies in a vast amount of data recorded over time by multiple strain gauges located on the structure of the aircraft. Once an anomaly is detected at a given time and for a specific set of strain gauges, it can be classified as insignificant or critical by the user. If the anomaly is critical, the data of the associated strain gauges can be used as input data for a FEM optimization. This static optimization allows to visually assess the position and geometry of possible cracks in the structure.
Scored and Error-annotated Essay Dataset of Chinese EFL/ESL Learners

A certain scale of finely annotated essay dataset of EFL/ESL (English as a foreign language or the second language) learners is not only an important language resource for language research and teaching, but also contributing materials for language-related computing science. Unfortunately, this type of data open on the Internet are not only of small quantity but also of uneven quality, especially such data of Chinese learners. We collected 147 essays of Chinese EFL/ESL learners and had four teachers score them under the same criteria and one teacher annotate major errors, and have them scored in Pigai scoring system. We then structured the score file, error-annotated files, essay files together with context information, and built the Scored and Error-annotated Essay Dataset of Chinese EFL/ESL Learners (SeedCel) which is open on the Internet and will be incrementally updated. This paper explains how SeedCel is constructed, what the details of SeedCel are, and where SeedCel will be used.
Combine Sliced Joint Graph with Graph Neural Networks for Smart Contract Vulnerability Detection

Smart contract security has drawn extensive attention in recent years because of the enormous economic losses caused by vulnerabilities. Even worse, fixing bugs in a deployed smart contract is difficult, so developers must detect security vulnerabilities in a smart contract before deployment. Existing smart contract vulnerability detection efforts heavily rely on fixed rules defined by experts, which are inefficient and inflexible.To overcome the limitations of existing vulnerability detection approaches, we propose a GNN based approach for smart contract vulnerability detection. First, we construct a graph representation for a smart contract function with syntactic and semantic features by combining abstract syntax tree (AST), control flow graph (CFG), and program dependency graph (PDG). To further strengthen the presentation ability of our approach, we perform program slicing to normalize the graph and eliminate the redundant information unrelated to vulnerabilities. Then, we use a Bidirectional Gated Graph Neural-Network model with hybrid attention pooling to identify potential vulnerabilities in smart contract functions.