Reviewerly/exHarmony huggingface dataset:
Factors influencing the choice of studying at UAE universities: an empirical research on the adoption of educational marketing strategies

This study empirically examines different factors that influence students to select universities in the UAE. The primary data were collected based on a survey of students at two public universities – United Arab Emirates University and Zayed University, as well as three private universities – New York University, Abu Dhabi University, and Alain University. The data were analyzed using an ANOVA test, an exploratory factor analysis (EFA), and confirmatory factor analysis (CFA) using SPSS and Smart PLS software. This study found high reliability and validity of the scale under five dimensions, which determine the student’s choice of the university. The findings show that several factors, such as the academic reputation, grants and funding, location and proximity, facilities and services, promotional and marketing channels, drive students’ choices for studying at universities in UAE. This finding will help the administrators of private and public universities in the UAE, in particular, and other regional and international universities, in general, address the growing competition among the institutions of higher education. It will also help policymakers to design effective policies and strategies to make the UAE an international education hub.
Deep Depth Estimation on 360° Images with a Double Quaternion Loss

While 360° images are becoming ubiquitous due to popularity of panoramic content, they cannot directly work with most of the existing depth estimation techniques developed for perspective images. In this paper, we present a deep-learning-based framework of estimating depth from 360° images. We present an adaptive depth refinement procedure that refines depth estimates using normal estimates and pixel-wise uncertainty scores. We introduce double quaternion approximation to combine the loss of the joint estimation of depth and surface normal. Furthermore, we use the double quaternion formulation to also measure stereo consistency between the horizontally displaced depth maps, leading to a new loss function for training a depth estimation CNN. Results show that the new double-quaternion-based loss and the adaptive depth refinement procedure lead to better network performance. Our proposed method can be used with monocular as well as stereo images. When evaluated on several datasets, our method surpasses state-of-the-art methods on most metrics.
Path planning in autonomous aerial vehicles

Path planning is an important task in unmanned aerial vehicle systems. Path-planning tasks are particularly difficult and cumbersome in UAV systems because the algorithms are developed in three-dimensional configuration spaces which may be affected with different forms of uncertainties. In this chapter, we discuss different path-planning problems for UAVs.
Deception in Supervisory Control

The use of deceptive strategies is important for an agent that attempts not to reveal his intentions in an adversarial environment. We consider a setting, in which a supervisor provides a reference policy and expects an agent to follow the reference policy and perform a task. The agent may instead follow a different deceptive policy to achieve a different task. We model the environment and the behavior of the agent with a Markov decision process, represent the tasks of the agent and the supervisor with reachability specifications, and study the synthesis of optimal deceptive policies for such agents. We also study the synthesis of optimal reference policies that prevent deceptive strategies of the agent and achieve the supervisor's task with high probability. We show that the synthesis of optimal deceptive policies has a convex optimization problem formulation, while the synthesis of optimal reference policies requires solving a nonconvex optimization problem. We also show that the synthesis of optimal reference policies is NP-hard.
Generation of Indian sign language by sentence processing and generative adversarial networks

This paper proposes generating Indian Sign Language from sentences using sentence preprocessing and Generative Adversarial Networks (GANs). The proposed method focuses on converting sentences to brief notations in the form of glosses and generating synthetic video frames for each gloss using a Generative Adversarial Network (GAN) architecture, thus resulting in a video representation for the input sentence. The glosses are mapped to a corresponding minimal skeletal representation, which is used as an input and a base image for the GAN model. The GAN outputs the corresponding video frames, which are further processed to produce a video. The dataset consists of video representations of words present in the Indian Sign Language. The proposed paper measures the performance of the method by using the SSIM index and a side-by-side comparison of real and synthetically generated video frames.
Online learning of neural networks using random projections and sliding window: A case study of a real industrial process

Online Learning of non-stationary data streams is a challenging task. This work presents an online training method for a Single hidden Layer Feedforward neural Network (SLFN) that learns sample-by-sample, using an adjustable sliding window to adapt the network when data has changed. The method presents a fast training procedure, estimating hidden and output layer parameters independently. Tests with four synthetic datasets showed a good accuracy and quick recovery after drift occurrences. The proposed method is also applied to a real dataset from an industrial process in order to address the anomaly detection task, with the network acting as a classifier. Results show that the method is able to detect drifts prior to anomalies in the pre-fault periods, in the real situation that appeared in the industrial dataset.
Measuring stimulation and cognitive reactions in middle schoolers after using immersive technology: Design and validation of the TINMER questionnaire

Immersive Technology has dramatically changed the concept of teaching and learning. Its academic study is recent, so there is little to no background information that can explain how its stimuli and cognitive reactions can improve students' learning experiences. Therefore, this study set out to analyze the psychometric properties of a questionnaire to measure the stimuli and cognitive reactions inherent in Immersive Technology. Rigorous criteria were considered in the questionnaire's design and validation in order to achieve this objective: theoretical review, expert judgment, cognitive interview, pilot test, and exploratory and confirmatory factor analysis. The instrument was administered to 231 middle school students in Concepción, Chile. Participants responded to the instrument after observing and interacting with science content using augmented reality and virtual reality technologies. After conducting the analyses, a latent structure with a good fit for the data was obtained, consisting of 16 items that underlie the latent variables, interactivity, presence, and flow. The questionnaire, called TINMER, presents a satisfactory level of reliability (0.85). Guidelines for its use are discussed to deepen the potential of Immersive Technology in learning experiences.
Laplacian networks: bounding indicator function smoothness for neural networks robustness

For the past few years, deep learning (DL) robustness (i.e. the ability to maintain the same decision when inputs are subject to perturbations) has become a question of paramount importance, in particular in settings where misclassification can have dramatic consequences. To address this question, authors have proposed different approaches, such as adding regularizers or training using noisy examples. In this paper we introduce a regularizer based on the Laplacian of similarity graphs obtained from the representation of training data at each layer of the DL architecture. This regularizer penalizes large changes (across consecutive layers in the architecture) in the distance between examples of different classes, and as such enforces smooth variations of the class boundaries. We provide theoretical justification for this regularizer and demonstrate its effectiveness to improve robustness on classical supervised learning vision datasets for various types of perturbations. We also show it can be combined with existing methods to increase overall robustness.
Additive-to-multiplicative moiré fringe transition in simultaneous dual-wavelength interferometry

Simultaneous dual-wavelength interferometry (SDWI) could extend the measured range of each single-wavelength interferometry, and has higher efficiency when the two single wavelengths work simultaneously. However, the directly measured phase demodulation at the long synthetic wavelength (λS) from the generated moiré fringe patterns in SDWI is rather arduous. To reduce the difficulty of the processing for the moiré fringe patterns, a synthetic wavelength temporal phase retrieval method (STP) with the π/(2k) phase shift at λS was proposed in this paper. By the additive-to-multiplicative moiré fringe transition, the synthetic-wavelength interferometry information exists in the form of the superposition with other wavelength information. And then for the longer separation distance of the phase lobes in spectrum, the synthetic-wavelength spectrum in STP method could be filter out more easily than traditional temporal Fourier transform (FT) method. After a low-pass filtering in the spectrum of the multiplicative moiré fringe intensity and adopting the π/(2k) phase shift at λS, the synthetic-wavelength phase is retrieved by the conventional phase-shift algorithm from the obtained synthetic-wavelength phase-shift interferograms with the interval of π/2 phase shift. Compared with the spatial FT method with the introduction of spatial carrier, STP method releases the restriction of spatial carrier for spectrum separation and the requirement of quadratic polar coordinate transform for the circle carrier. Finally, the feasibility and applicability of the STP method are verified using simulation and experimental results.
Isolated Handwritten Tamil Character Recognition using Convolutional Neural Networks

In this digital era, the demand for character recognition systems in various languages has been increasing steadily. Tamil is one of the oldest languages spoken till date and the need for efficient Tamil character recognition systems are now more than ever. This research work proposed a novel architecture of convolutional neural networks for the recognition of handwritten Tamil characters. The approach of using convolutional neural networks for the recognition of handwritten characters differs from other standard approaches in feature extraction. Proposed model was trained with a large dataset containing thousands of handwritten Tamil characters and has produced best results in both training and testing. Proposed trained model has also shown promising recognition rates when tested with different handwritten Tamil characters in both real-time and offline modes. The proposed work achieved a training accuracy of approximately 97%, which was significantly higher when compared to other approaches.
Optimization of Decision Tree Algorithm in Text Classification of Job Applicants Using Particle Swarm Optimization

Job interview is one of the stages that must be passed by job applicants before getting a job. However, the manual interview process resulted in a large amount of cost and time of selection, so we need a system that can provide recommendations of which applicants are qualified. Currently there are many studies of text classification using the Naive Bayes method, K-Nearest Neighbor, Support Vector Machine, and Deep Learning. Therefore, algorithms that are used less frequently will be tested in this study, such as the Decision Tree. Then the Swarm Intelligence method, Particle Swarm Optimization, is implemented to improve the performance of the method. So that this research focuses on testing and comparing ordinary Decision Tree and Decision Tree methods that are optimized with Particle Swarm Optimization. From the test results, the accuracy of the optimized model increased by 7.1%, and the highest accuracy achieved was 74.3%.
Exposing DeepFakes Using Convolutional Neural Networks and Transfer Learning Approaches

Advancements in Artificial Intelligence - oriented computing power and the ever-growing reach of social media have proven to be catalysts in emergence and spread of a new vein of AI generated fake videos known as 'DeepFake' videos. Such videos are synthesized using generative machine learning models like Generative Adversarial Networks or Variational AutoEncoders and they can achieve high degrees of realism. Spread of sensitive political or obscene content in form of such videos may lead to social distress to the target entity(s). This paper presents a study pertinent to the detection of DeepFake videos using Convolutional Neural Networks (CNNs) with transfer learning. A comparative study of the performance of various models in the detection of tampered videos has been presented. These models are trained (fine-tuned) and tested on a custom dataset encompassing randomly selected labelled frames from videos in the DeepFake Detection Dataset by Google AI and FaceForensics++ dataset.
Techniques for the Automatic Detection and Hiding of Sensitive Targets in Emergency Mapping Based on Remote Sensing Data

Emergency remote sensing mapping can provide support for decision making in disaster assessment or disaster relief, and therefore plays an important role in disaster response. Traditional emergency remote sensing mapping methods use decryption algorithms based on manual retrieval and image editing tools when processing sensitive targets. Although these traditional methods can achieve target recognition, they are inefficient and cannot meet the high time efficiency requirements of disaster relief. In this paper, we combined an object detection model with a generative adversarial network model to build a two-stage deep learning model for sensitive target detection and hiding in remote sensing images, and we verified the model performance on the aircraft object processing problem in remote sensing mapping. To improve the experimental protocol, we introduced a modification to the reconstruction loss function, candidate frame optimization in the region proposal network, the PointRend algorithm, and a modified attention mechanism based on the characteristics of aircraft objects. Experiments revealed that our method is more efficient than traditional manual processing; the precision is 94.87%, the recall is 84.75% higher than that of the original mask R-CNN model, and the F1-score is 44% higher than that of the original model. In addition, our method can quickly and intelligently detect and hide sensitive targets in remote sensing images, thereby shortening the time needed for emergency mapping.
Photonic pattern reconstruction enabled by on-chip online learning and inference

Abstract Recent investigations in neuromorphic photonics exploit optical device physics for neuron models, and optical interconnects for distributed, parallel, and analog processing. Integrated solutions enabled by silicon photonics enable high-bandwidth, low-latency and low switching energy, making it a promising candidate for special-purpose artificial intelligence hardware accelerators. Here, we experimentally demonstrate a silicon photonic chip that can perform training and testing of a Hopfield network, i.e. recurrent neural network, via vector dot products. We demonstrate that after online training, our trained Hopfield network can successfully reconstruct corrupted input patterns.
Learning Graph Representation of Bug Reports to Triage Bugs using Graph Convolution Network

Bug triage is a software engineering problem, which is being solved by classification methods. The social network analysis, mining repositories, statistical modeling, topic modeling, machine learning, and deep learning techniques have been used to triage the bugs. These existing methods showed promising results but still far from perfection, which requires improvement. This paper proposes a graph representation method for the bug reports dataset, which solves the bug triage problem with the node classification problem. The heterogeneous graph is built using the word to word and word to document co-occurrences for the whole bug dataset. The graph convolution network (GCN) is trained on the generated graph to learn the bug reports' graph representation. The proposed method is validated on the open-source project's bug data. Top-K accuracy is used as an evaluation metric to evaluate the performance of the model. The reported results show promising results compared to previous studies.
A novel approach to fully representing the diversity in conditional dependencies for learning Bayesian network classifier

Bayesian network classifiers (BNCs) have proved their effectiveness and efficiency in the supervised learning framework. Numerous variations of conditional independence assumption have been proposed to address the issue of NP-hard structure learning of BNC. However, researchers focus on identifying conditional dependence rather than conditional independence, and information-theoretic criteria cannot identify the diversity in conditional (in)dependencies for different instances. In this paper, the maximum correlation criterion and minimum dependence criterion are introduced to sort attributes and identify conditional independencies, respectively. The heuristic search strategy is applied to find possible global solution for achieving the trade-off between significant dependency relationships and independence assumption. Our extensive experimental evaluation on widely used benchmark data sets reveals that the proposed algorithm achieves competitive classification performance compared to state-of-the-art single model learners (e.g., TAN, KDB, KNN and SVM) and ensemble learners (e.g., ATAN and AODE).
A “data sharing trust” model for rapid, collaborative science

<h2>Summary</h2> Complex datasets provide opportunities for discoveries beyond their initial scope. Effective and rapid data sharing and management practices are crucial to realize this potential; however, they are harder to implement than post-publication access. Here, we introduce the concept of a "data sharing trust" to maximize the value of large datasets.
CatBoost for Fraud Detection in Financial Transactions

Financial fraud is an ever growing menace with severe consequences in the financial industry. Machine learning plays an active role in the fraud detection in financial transactions. However, fraud detection is still a challenging problem due to two major reasons. First, either fraudulent or non-fraudulent behaviors change fast and constantly. Secondly, currently online transactions happen so fast, which require detection algorithms to be efficient and accurate. This paper introduces a machine learning method based on CatBoost for fraud detection. To improve detection accuracy, we apply feature engineering to generate highly important features and feed them into CatBoost for classification. Another key contribution of our work is using memory compression to speed up detection. The performance of our method is evaluated on a publicly IEEE-CIS Fraud dataset, provided by Kaggle competition platform. The experimental results demonstrate that our model based on CatBoost has obtained optimal accuracy of 0.983.
BERT-CoQAC: BERT-Based Conversational Question Answering in Context

As one promising way to inquire about any particular information through a dialog with the bot, question answering dialog systems have gained increasing research interests recently. Designing interactive QA systems has always been a challenging task in natural language processing and used as a benchmark to evaluate machine's ability of natural language understanding. However, such systems often struggle when the question answering is carried out in multiple turns by the users to seek more information based on what they have already learned, thus, giving rise to another complicated form called Conversational Question Answering (CQA). CQA systems are often criticized for not understanding or utilizing the previous context of the conversation when answering the questions. To address the research gap, in this paper, we explore how to integrate the conversational history into the neural machine comprehension system. On one hand, we introduce a framework based on publicly available pre-trained language model called BERT for incorporating history turns into the system. On the other hand, we propose a history selection mechanism that selects the turns that are relevant and contributes the most to answer the current question. Experimentation results revealed that our framework is comparable in performance with the state-of-the-art models on the QuAC ( http://quac.ai/ ) leader board. We also conduct a number of experiments to show the side effects of using entire context information which brings unnecessary information and noise signals resulting in a decline in the model's performance.
Insensitivity of Nadaraya–Watson estimators to design correlation

We show that Nadaraya–Watson estimators in nonparametric regression may be uniformly consistent without any specification of the design correlation structure. In contrast to the predecessors’ results, the design is not required to be fixed or consisted of independent or weakly dependent random variables under various dependence conditions, and the design random observations are not necessarily identically distributed and nondegenerate. We suggest a new simple sufficient condition on the design which provides the uniform consistency of such estimators. Key words and phrases: nonparametric regression, Nadaraya–Watson estimators, uniform consistency, strongly dependent data.
Handwritten Form Recognition Using Artificial Neural Network

Form Recognizer aims to build a deep learning model to extract handwritten text from a scanned Permanent Account Number (PAN) application form and convert them into digital format or editable text and store it in an excel file for further processing like statistical analysis or machine learning. The Learning model is based on the Convolution Neural Network (CNN) for the feature extraction and higher end classification. To accomplish this task, the handwritten forms are scanned, preprocessed to remove noise and handwritten fields are extracted. OpenCV is used to get the contours of the characters in the extracted images. This approach gives better accuracy than using plain CNN without out contours. The CNN model gives an accuracy of 91% on merger of numbers, uppercase and lower-case alphabets of EMINST dataset. Further, handwritten form recognizer system is built by incorporating this learning model, which is in turn integrated with preprocessing and segmentation methods. Finally, the output of the system is stored in a CSV file.
Asymmetric bidirectional quantum state exchange between Alice and Bob through a third party

In this paper we present a quantum communication protocol which utilizes a multipartite multi-qubit entangled channel for mutual transfer of three-qubit generalized W-state and arbitrary two-qubit entangled state between two parties. The protocol is perfect with no cases of failure and is supervised by a third party. The work is in the direction of the recently initiated line of research on exploring the possibility of simultaneous quantum multitasking using multipartite entanglement.