Reviewerly/exHarmony huggingface dataset:
GPS/INS Integration During GPS Outages Using Machine Learning Augmented with Kalman Filter

Nowadays a tremendous progress has been witnessed in Global Positioning System (GPS) and Inertial Navigation System (INS). The Global Positioning System provides information as long as there is an unobstructed line of sight and it suffers from multipath effect. To enhance the performance of an integrated Global Positioning System and Inertial Navigation System (GPS/INS) during GPS outages, a novel hybrid fusion algorithm is proposed to provide a pseudo position information to assist the integrated navigation system. A new model that directly relates the velocity, angular rate and specific force of INS to the increments of the GPS position is established. Combined with a Kalman filter the hybrid system is able to predict and estimate a pseud GPS position when GPS signal is unavailable. Field test data are collected to experimentally evaluate the proposed model. In this paper, the obtained GPS/INS datasets are pre-processed and semi-supervised machine learning technique has been used. These datasets are then passed into Kalman filtering for the estimation/prediction of GPS positions which were lost due to GPS outages. Hence, to bridge out the gaps of GPS outages Kalman Filter plays a major role in prediction. The comparative results of Kaman filter and extended Kalman filter are computed. The simulation results show that the GPS positions have been predicted taking into account some factors/measurements of a vehicle, the trajectory of the vehicle, the entire simulation was done using Anaconda (Jupyter Notebook).
From Continuous-Time Chaotic Systems to Pseudo Random Number Generators: Analysis and Generalized Methodology

The use of chaotic systems in electronics, such as Pseudo-Random Number Generators (PRNGs), is very appealing. Among them, continuous-time ones are used less because, in addition to having strong temporal correlations, they require further computations to obtain the discrete solutions. Here, the time step and discretization method selection are first studied by conducting a detailed analysis of their effect on the systems’ statistical and chaotic behavior. We employ an approach based on interpreting the time step as a parameter of the new “maps”. From our analysis, it follows that to use them as PRNGs, two actions should be achieved (i) to keep the chaotic oscillation and (ii) to destroy the inner and temporal correlations. We then propose a simple methodology to achieve chaos-based PRNGs with good statistical characteristics and high throughput, which can be applied to any continuous-time chaotic system. We analyze the generated sequences by means of quantifiers based on information theory (permutation entropy, permutation complexity, and causal entropy × complexity plane). We show that the proposed PRNG generates sequences that successfully pass Marsaglia Diehard and NIST (National Institute of Standards and Technology) tests. Finally, we show that its hardware implementation requires very few resources.
Prediction of Financial Statement Fraud using Machine Learning Techniques in UAE

The purpose of this study is to predict the potential occurrence of financial statement fraud in the United Arab Emirates (UAE) companies using machine learning (ML) techniques in Python, including Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Neural Network (NN) techniques. The data was collected from the UAE Securities and Commodities Authority. The Bloomberg and Osiris databases were also used to collect data for UAE companies, excluding financial firms, for the period from 2010 to 2018. ML techniques in Python were used to predict the potential of financial statement fraud in UAE companies. The results show that SVM, with 89.54% accuracy and a 77.18% F1 score, outperforms all other classifiers, including LR, DT and NN. The findings of the study are significant to businesses that wish to determine the importance of prediction of financial statement fraud using ML techniques. This research project is different from other existing studies because it is conducted within the context of the UAE.
Improving resistance to adversarial deformations by regularizing gradients

Improving the resistance of deep neural networks against adversarial attacks is important for deploying models in realistic applications. Nowadays, most defense methods are designed to resist intensity perturbations, and location perturbations have not yet attracted enough attention. However, these two types should be equally important for deep model security. In this paper, we focus on adversarial deformations, a typical class of location perturbations, and propose a defense method named flow gradient regularization to improve the resistance of models against such attacks. By theoretical analysis, we prove that regularizing flow gradients is able to get a tighter bound than regularizing input gradients. Through verifying over multiple datasets, network architectures, and adversarial deformations, our empirical results indicate that training with flow gradients performs better than training with input gradients by a large margin, and also better than adversarial training. Moreover, the proposed method can be used to combine with adversarial deformation training to improve the resistance further. Our method is now available at https://github.com/xpf/Flow-Gradient-Regularization.
Learning user-item paths for explainable recommendation

Knowledge graph based explainable recommendation system is a kind of personalized recommendation which uses side information to solve the reason why recommending an item. Previous study has not fully explored the connection between users and items in knowledge graph, especially the problem of overall semantic representation, and can not capture the high level semantic representation of the path, it is difficult for existing path-based methods to clarify the overall semantics of paths. Especially when the path contains similar entities but different relationship. In this paper, we propose a model named Meta-Path-based Explainable Recommendation System (MPERS) to represent the paths in the knowledge graph through the semantic information of entities and relationships, and distinguish the different contributions that different paths make to conduct users' preference. The experimental study demonstrates the superiority of our method compared with the state-of-the-art ones
Pattern detection in the activation space for identifying synthesized content

Generative Adversarial Networks (GANs) have recently achieved unprecedented success in photo-realistic image synthesis from low-dimensional random noise. The ability to synthesize high-quality content at a large scale brings potential risks as the generated samples may lead to misinformation that can create severe social, political, health, and business hazards. We propose SubsetGAN to identify generated content by detecting a subset of anomalous node-activations in the inner layers of pre-trained neural networks. These nodes, as a group, maximize a non-parametric measure of divergence away from the expected distribution of activations created from real data. This enable us to identify synthesised images without prior knowledge of their distribution. SubsetGAN efficiently scores subsets of nodes and returns the group of nodes within the pre-trained classifier that contributed to the maximum score. The classifier can be a general fake classifier trained over samples from multiple sources or the discriminator network from different GANs. Our approach shows consistently higher detection power than existing detection methods across several state-of-the-art GANs (PGGAN, StarGAN, and CycleGAN) and over different proportions of generated content.
Noise in the Machine: Sources of Physical and Computation Error in Eye Tracking with Pupil Core Wearable Eye Tracker

Developments in wearable eye tracking devices make them an attractive solution for studies of eye movements during naturalistic head/body motion. However, before these systems’ potential can be fully realized, a thorough assessment of potential sources of error is needed. In this study, we examine three possible sources for the Pupil Core eye tracking goggles: camera motion during head/body motion, choice of calibration marker configuration, and eye movement estimation. In our data, we find that up to 36% of reported eye motion may be attributable to camera movement; choice of appropriate calibration routine is essential for minimizing error; and the use of a secondary calibration for eye position remapping can improve eye position errors estimated from the eye tracker.
Improving Facial Attribute Recognition by Group and Graph Learning

Exploiting the relationships between attributes is a key challenge for improving multiple facial attribute recognition. In this work, we are concerned with two types of correlations that are spatial and non-spatial relationships. For the spatial correlation, we aggregate attributes with spatial similarity into a part-based group and then introduce a Group Attention Learning to generate the group attention and the part-based group feature. On the other hand, to discover the non-spatial relationship, we model a group-based Graph Correlation Learning to explore affinities of predefined part-based groups. We utilize such affinity information to control the communication between all groups and then refine the learned group features. Overall, we propose a unified network called Multi-scale Group and Graph Network. It incorporates these two newly proposed learning strategies and produces coarse-to-fine graph-based group features for improving facial attribute recognition. Comprehensive experiments demonstrate that our approach outperforms the state-of-the-art methods.
An Improved Hand Gesture Recognition Algorithm based on image contours to Identify the American Sign Language

Abstract It is an open era of research to provide the assistance communication to hearing and visually impaired people. For the same, this paper proposed a recognition and classification of hand gesture to identify the correct denotation with maximum accurateness for standard American Sign Language. The proposal intelligently used the information based on image contours to identify the character’s representation of hand gesture. The proposal optimizes the performance overhead through identifications of 17 characters and 6 symbols based on image contours and convexity measurement of Standard American Sign Language without using complex algorithms and specialized hardware devices. Accuracy measurement done through simulation, which shows how our proposal provide more accuracy with minimum complexity in comparison to other state-of-art works.
An Iterative Model for Text Mining Using Big Data Technology

Big Data is a term used to refer an enormous volume of structured, unstructured data or combination of both which is so large that it becomes very difficult and complex to process them in a relational database management system or in a legacy software system. In enterprise layout the volume of data is too large set and/or it moves too rapidly and/or it exceeds present processing capacity. The figuring of Big Data begins with the raw data that isn’t clustered and is most usually impossible to cache in the memory of a single computer. Storing and retrieving the data is the most tedious challenge in Big Data. Some application domains of big data which are Banking, Health care, Text Mining, Education etc. When data is in text format then manual grouping of data has significant complications. Therefore there is an intense obligation of document clustering for appropriate grouping of text articles so that the right sentiment of the writer can be revealed. It involves algorithms of data mining, machine learning, statistics, and natural language processing, attempts to extract high quality of useful information from textual data. In this paper we have proposed a text mining model which will make the large volume text mining process easy as well proficient. Clustering and big data technologies are the real work horses of the model. It will eliminate the irrelevant context (Stop Words) and represent the document in a quantitative form. The accuracy of the model is increased by following an iterative approach of analysis. Eventually the name of model is appealed as “Advanced Text Mining Model”.
Aspect-Based Recommendation Model for Fashion Merchandising

An aspect-based recommendation model (ARM) was proposed to detect local and global aspect representations in customer reviews available on ecommerce websites for fashion merchandising. This model was constructed with two independent paths to process user/item reviews simultaneously, and each path had a convolutional neural network (CNN), a long-short time memory network (LSTM) with attention mechanism to separately capture local aspect features and global aspect features. To enhance the generalization of the ARM model, the local and global aspect features from both user and item reviews were merged through mutual operations prior to the rating prediction. The Clothing, Shoes & Jewelry dataset from Amazon 5-core was used to train and test ARM. The significance of the extracted aspects regarding user preferences and item properties from the reviews were examined as opposed to several state-of-the-art fashion recommenders.
Deep Elastica For Image Segmentation

Image segmentation is a fundamental topic in image processing and has been studied for many decades. Deep learning-based supervised segmentation models have achieved state-of-the-art performance, but most of them are limited by using pixel-wise loss functions for training without geometrical constraints. Inspired by the Euler's Elastica model and recent active contour models introduced into deep learning, we propose a novel active contour with an elastic (ACE) loss function. The ACE loss function incorporates Elastica knowledge as geometrically-natural constraints for the image segmentation tasks. In the ACE loss function, we introduce the mean curvature, i.e. the average of all principal curvatures, as a more compelling image prior to represent the curvature. Furthermore, based on the mean curvature definition, we propose a fast solution (Fast-ACE) to approximate our ACE loss with Laplace operators for three-dimensional (3D) image segmentation. We evaluate our ACE loss and Fast-ACE loss functions on one two-dimensional (2D) dataset and one 3D biomedical image dataset. Our results show that the proposed loss function outperforms other mainstream loss functions when different segmentation networks are used. Our source code is available at https://github.com/HiLab-git/ACELoss.
Internet of things image recognition system based on deep learning

At present, many exciting results have been achieved in the application of deep learning to image recognition. However, there are still many problems to be overcome before deep learning is used in practical applications such as image retrieval, image annotation, and image-text conversion. This paper studies the structure of deep learning, improves the commonly used training algorithms, and proposes two new neural network models for different application scenarios. This paper uses Support Vector Machine (SVM) as the main classifier for Internet of Things image recognition and uses the database of this paper to train SVM and CNN. At the same time, the effectiveness of the two for image recognition is tested, and the trained classifier is used for image recognition. The result surface: In the labeled data set, the rank-1 accuracy of CNN is 85.77%, which is higher than 90.28% of the SVM method. In the detection data, CNN’s rank-1 accuracy rate is 83.11%, which also exceeds SVM’s 80.22%. SVM+CNN has a rank 1 value of 84.69% for the detection data set. This shows that deep learning can map the feature representation of the image and the feature representation of the word to the same space, making the calculation of the similarity and correlation between the image and the text easier and more straightforward.
Intelligent multiple Vehicule Detection and Tracking Using Deep-learning and Machine Learning: An Overview

Autonomous vehicles are in full development and vehicles classification is a fundamental part of this new technology in this years. The AVs is very is important solution in smart cities. The electric vehicle EV is one of the solutions recommended by the vehicle manufacturers and research organizations to reduce noise pollution, fuel consumption, real time and time execution for tasks in networking EV. The transport and mobility sector has for more than a decade seen a fundamental change in its organization due to a double technological revolution and use a new forms of mobility. This motion has profound consequences on the relationship that citizens maintain with mobility. It also proposes a physical platform able to perform a form of platooning using Artificial Intelligence (AI) with scheduler to create platoons with miniature vehicle. Platform called Autonomous Learning Intelligent Vehicles is the association of multiple physical cars coupled with an infrastructure, which handles the data of every single car and makes decisions based on the augmented environmental. In this solution, we present tracking and detection of EVs in parking or in station. So, we change communications EVs, giving priority to charging demands over other types of sms lower priority messages. Finally, we propose an efficient admission control mechanism to manage EVs traffic and to provide quality of Service to charging demand messages in terms of strict delay to avoid both a long latency of EV users and a network overload in high offered load conditions.
Discrete-time quantum walk on circular graph: Simulations and effect of gate depth and errors

We investigate the counterparts of random walks in universal quantum computing and their implementation using standard quantum circuits. Quantum walks have been recently well investigated for traversing graphs with certain oracles. We focus our study on traversing a 1D graph, namely a circle, and show how to implement a discrete-time quantum walk in quantum circuits built with universal CNOT and single qubit gates. We review elementary quantum gates and circuit decomposition techniques and propose a generalized version of all CNOT-based circuits of the quantum walk. We simulated these circuits on five different qubits IBM-Q quantum devices. This quantum computer has nonperfect gates based on superconducting qubits, and, therefore, we analyzed the impact of the CNOT errors and CNOT-depth on the fidelity of the circuit.
Unsupervised Representation Learning From Pathology Images With Multi-Directional Contrastive Predictive Coding

Digital pathology tasks have benefited greatly from modern deep learning algorithms. However, their need for large quantities of annotated data has been identified as a key challenge. This need for data can be countered by using unsupervised learning in situations where data are abundant but access to annotations is limited. Feature representations learned from un-annotated data using contrastive predictive coding (CPC) have been shown to enable classifiers to obtain state of the art performance from relatively small amounts of annotated computer vision data. We present a modification to the CPC framework for use with digital pathology patches. This is achieved by introducing an alternative mask for building the latent context and using a multi-directional PixelCNN autoregressor. To demonstrate our proposed method we learn feature representations from the Patch Camelyon histology dataset. We show that our proposed modification can yield improved deep classification of histology patches.
Analysis Of Digital And Mathematical Competences In Elementary School Students

In basic education it is important to develop digital and mathematical competences in students; both necessary to solve computing and calculation problems. Regarding this reflection, the objective of this study is to analyze digital and mathematical competences in students participating in a virtual education program as a consequence of the social isolation generated by Covid - 19. Thus, in terms of methodology, the study is oriented according to the quantitative approach of descriptive design in which 40 students from the fifth and sixth grade elementary education from a private school (Lima - Peru) participated. From our results, it could be concluded that the fifth-grade students were more competent in solving mathematical problems of shapes, movement and location, unlike the sixth-grade students. And also, according to gender, it was possible to identify that women excel in the communication and digital collaboration competences.
Electrolint and security of electron applications

JavaScript applications today are not limited to just client-side web applications and server-side code powered by Node.js. They became the standard for desktop application development with the emergence and popularity of the Electron framework. Combining the features of client-side and server-side applications, the Electron applications possess a completely different security posture. The attacks typical for front-end applications can now be escalated to the back-end attacks, for example, making a cross-site scripting result in a remote code execution on the user’s machine. The goal of our study is to analyze the typical security vulnerabilities of an Electron application, study common mitigation controls, and propose new remediation solutions that are easy to implement for developers. In this study we analyze security vulnerabilities in over a hundred open source Electron applications using automated and manual static analysis. We explore the mitigation controls existing in the Electron framework, and propose changes to the framework that will prevent many of the common vulnerabilities. Based on these results, we develop an IDE plugin for Electron applications that automatically suggests remediations to common security defects within a developer’s work environment, thus shifting the fixing of a vulnerability to earlier in the software development life cycle. We show the effectiveness of the IDE plugin by applying the plugin’s suggestions to the analyzed open source applications and demonstrating that they stop being exploitable after the applied fix.
Imperceptibility Analysis for Watermarking Technique Based on Image Block Division Scheme

Image watermarking techniques proposed different algorithm schemes to improve performance quality. For a medical image, the watermarking techniques are required to give significant attention to achieve the highest imperceptibility performance to remain visual quality. That is why this paper analyzes two different watermarking algorithm schemes: image block division 88 and image block division 22 to determine the imperceptibility performance for each. Both schemes used the fitness evaluation to evaluate the imperceptibility performance of each embedding location. Thereafter, the imperceptibility, embedding capacity, and watermark data bit error of both schemes were measured. The experiment results show that the image block division 88 scheme outperformed in imperceptibility performance under Peak Signal-to-Noise Ratio (PSNR) values for the three different watermarked medical images is 106.1014 to110.5222. The image block division 22 scheme outperformed in embedding capacity and watermark data bit error where the embedding capacity is 1449 to 31953 bit and watermark data bit error is zero for the three different watermarked medical images. This finding indicates that the watermarking technique with image block division 22 scheme can achieve more accurate performance.