Reviewerly/exHarmony huggingface dataset:
Statistical theory of digital interferometry: A tutorial with applications to fringe projection profilometry

The signal-to-noise ratio (SNR) and Shannon information (SI) are crucial concepts in statistical communication theory (SCT). Understanding their relation has been crucial for the exponential increase of communication speed during the past 70 years. However, these concepts remain essentially unexplored in the context of digital interferometry (DI). This is despite DI uses common concepts with SCT such as quadrature filtering for phase demodulation. In this tutorial, we show the relevance of SNR and SI for DI, and the utility of these figures of merit when assessing phase demodulation algorithms and techniques. The statistical theory of DI presented is independent of the experiment used to obtain the fringe patterns. These fringe images may come from optical shop testing, speckle interferometry, digital holography, microscopy interferometry, isochromatic-stress, fringe projection profilometry (FPP), Talbot deflectometry, etc. Particularly, here we show that dual-sensitivity phase measurement produces a significantly higher SI entropy versus increasing only the SNR on low-sensitivity FPP. Our claims are supported by mathematical analysis, numerical simulations, and experimental results.
Guess the Age 2021: Age Estimation from Facial Images with Deep Convolutional Neural Networks

Guess The Age 2021 is an international contest meant for teams able to propose methods based on modern Deep Convolutional Neural Networks (DCNNs) for age estimation from facial images. In order to allow the teams to train effective models, the Mivia Age Dataset, including 575.073 images annotated with age labels, was provided as training set; it is among the biggest publicly available datasets of faces in the world with age annotations. The performance of the methods submitted by the teams have been evaluated on a test set of more than 150.000 images, different from the ones available in the training set; a new index, called AAR, which takes into account the age estimation accuracy, namely the average error, and the regularity, i.e. the standard deviation of the error, has been adopted for drawing up the final ranking. The BTWG team, winner of the contest, achieved an impressive AAR equal to 7.94, with a novel method demonstrating an impressive accuracy and regularity in facial age estimation.
Understanding the Middle East through the eyes of Japan’s Newspapers: A topic modelling and sentiment analysis approach

Abstract For Japan—a country that has always been described with virtually no major natural resources such as oil, gas, and coal—the Middle Eastern region has a special place in its economic and foreign policy. In 2017, 39% of Japan’s energy came from oil, and 87% of Japan’s imported oil came from the Middle East, predominantly Saudi Arabia and the UAE. The above facts are enough to discern the critical significance of the Middle Eastern region for Japan. For Japan to have an unhindered supply of oil and other natural resources, it is pertinent that this region remains peaceful. In this scenario, the Middle East-related articles in Japan’s newspapers can help understand Japan’s perspective towards the Middle East. This paper would first apply the topic modelling approach non-negative matrix factorization (NMF) on Middle East-related articles from three newspapers of Japan. After discovering crucial topics, we would utilize traditional supervised machine learning algorithms to determine the overall and topic-specific sentiments from the collected headlines. Our topic modelling results discovered that the Japanese media widely reported issues like Islamic State, the refugee crisis, the Syrian civil war, Qasem Soleimani killing, and Iran nuclear deal. Further, the news related to Saudi Arabia, Syria, and Trump garnered high negative sentiment.
Unsupervised Domain Adaptation of Deep Networks for ToF Depth Refinement

Depth maps acquired with ToF cameras have a limited accuracy due to the high noise level and to the multi-path interference. Deep networks can be used for refining ToF depth, but their training requires real world acquisitions with ground truth, which is complex and expensive to collect. A possible workaround is to train networks on synthetic data, but the domain shift between the real and synthetic data reduces the performances. In this paper, we propose three approaches to perform unsupervised domain adaptation of a depth denoising network from synthetic to real data. These approaches are respectively acting at the input, at the feature and at the output level of the network. The first approach uses domain translation networks to transform labeled synthetic ToF data into a representation closer to real data, that is then used to train the denoiser. The second approach tries to align the network internal features related to synthetic and real data. The third approach uses an adversarial loss, implemented with a discriminator trained to recognize the ground truth statistic, to train the denoiser on unlabeled real data. Experimental results show that the considered approaches are able to outperform other state-of-the-art techniques and achieve superior denoising performances.
Representation Learning via Variational Bayesian Networks

We present Variational Bayesian Network (VBN) - a novel Bayesian entity representation learning model that utilizes hierarchical and relational side information and is particularly useful for modeling entities in the "long-tail'', where the data is scarce. VBN provides better modeling for long-tail entities via two complementary mechanisms: First, VBN employs informative hierarchical priors that enable information propagation between entities sharing common ancestors. Additionally, VBN models explicit relations between entities that enforce complementary structure and consistency, guiding the learned representations towards a more meaningful arrangement in space. Second, VBN represents entities by densities (rather than vectors), hence modeling uncertainty that plays a complementary role in coping with data scarcity. Finally, we propose a scalable Variational Bayes optimization algorithm that enables fast approximate Bayesian inference. We evaluate the effectiveness of VBN on linguistic, recommendations, and medical inference tasks. Our findings show that VBN outperforms other existing methods across multiple datasets, and especially in the long-tail.
GPR Image Noise Removal Using Grey Wolf Optimisation in the NSST Domain

Hyper-wavelet transforms, such as a non-subsampled shearlet transform (NSST), are one of the mainstream algorithms for removing random noise from ground-penetrating radar (GPR) images. Because GPR image noise is non-uniform, the use of a single fixed threshold for noisy coefficients in each sub-band of hyper-wavelet denoising algorithms is not appropriate. To overcome this problem, a novel NSST-based GPR image denoising grey wolf optimisation (GWO) algorithm is proposed. First, a time-varying threshold function based on the trend of noise changes in GPR images is proposed. Second, an edge area recognition and protection method based on the Canny algorithm is proposed. Finally, GWO is employed to select appropriate parameters for the time-varying threshold function and edge area protection method. The Natural Image Quality Evaluator is utilised as the optimisation index. The experiment results demonstrate that the proposed method provides excellent noise removal performance while protecting edge signals.
Efficient Face Recognition System for Education Sectors in COVID-19 Pandemic

COVID-19 has completely startled the education sectors. Educational organisations are seeking technological innovations to execute their academic activities smoothly e.g. online classes, exams, meetings etc. Face recognition system is one of the main technological innovations that is helping the education sectors to verify the student's identification. In this paper, the authors aimed to explore and develop an efficient face recognition system. These systems require enormous visual information. Visual information holds a large set of features that needs large spatial occupancy. Convolutional neural networks (CNNs) are the most suitable techniques to classify large spatial datasets. We proposed an experimentally based 8 layered tuned CNN model comprising four convolution layers, three max-pooling layers, and one dense layer. The proposed model is trained on 14 face classes and analysed the recognition performance based on different image resolutions, activation functions, batch sizes and optimisers. The proposed model performance has been compared to the state-of-the-art methods and achieved high face recognition.
Explore Modeling Relation Information and Direction Information in KBQA

Knowledge Bases (KB) are composed of multiple entities and relations, which can be organized by an entity-relation-entity pattern. Question Answering models over Knowledge Bases (KBQA) aims to retrieve the answers to a factual question from a given KB. The relation information has gained more attention from existing models due to its ability to generate the key reasoning path. Although effective, the main concern of existing models is that relation information is not fully explored, based on the observation that they utilize the fixed relation representation across different question-aware subgraphs. Thus the relation representation will overlook the rich structured information of different subgraphs. Besides, the direction information, which is inspired by human behaviors, can help the model find the answers more accurately, but it is not fully explored. To address the above two challenges, in this paper, we propose a novel reasoning model, which is capable of making the relation aware of subgraphs and implanting the Direction information into reasoning. Specifically, we convert all relations in each subgraph to additional nodes to learn structure information. Additionally, we utilize direction information to enhance the reasoning ability. We verify the effectiveness and robustness of our proposed model with different KB sizes on two widely used datasets.
Deep Learning-Based Real-Time Object Classification and Recognition Using Supervised Learning Approach

Due to the rapid technological advancements in recent years, humans have gained the ability to design and implement the knowledge into machines and it also allows them to perform various functions such as autonomous thinking ability, understanding skills, and problem-solving. Moreover, machine learning [ML] plays an important role in developing the image-processing models and application. In real-time applications, the labels of objects may be unfamiliar to those who are unaware of them, or there may be several identical objects but are labeled differently. In this paper, the approach that would be useful to detect and classify various objects is presented with a trial study by utilizing various datasets. This method can be used to improve accuracy in finding the classification of similar objects. In the process of classifying real-time objects, this system uses a supervised learning technique, where various datasets are trained and compared with the queried object. Here, the Support Vector Machine (SVM) algorithm is utilized for performing analysis and decision making in object classification domain. The result of this project is to read the given objects with the help of computer vision and allowing the machine to perform the prediction or classification for the given object. More than 800 images obtained from standard datasets of trained labeled classes are implemented in our application to classify the objects.
A new reliability‐based task scheduling algorithm in cloud computing

Summary In the last decade, the scale of heterogeneous computing (HC) systems such as heterogeneous cloud computing environments was growing like never before. So network failures are unavoidable in such systems, which affect system reliability. Since the task scheduling algorithm in HC is challenging, we investigate a new reliability‐aware task scheduling algorithm (RATSA) in this paper. RATSA is designed to schedule tasks on directed acyclic graphs (DAGs) by using the shuffled frog‐leaping algorithm (SFLA) and genetic algorithm (GA) as evolutionary algorithms. The population‐based SFLA‐GA is applied to optimize makespan in the RATSA as an NP‐complete problem. Moreover, the proposed algorithm exploits a new heuristic‐based earliest finish time technique for task mapping to virtual machines (VMs) section to decrease the failure rate. Experimental results on random DAGs indicate that RATSA improves some current algorithms in terms of reliability and has acceptable performance in makespan. The results reveal that the RATSA decreases the overall failure rate by 43% compared to some current task scheduling algorithms.
A Robust UAV Hyperspectral Image Stitching Method Based on Deep Feature Matching

Unmanned aerial vehicle (UAV) hyperspectral imaging has been extensively applied in various fields. However, due to the limited imaging width, hyperspectral images (HSIs) captured by UAV need to be stitched, so as to effectively cover the study area. In this article, an effective seamless stitching method with deep feature matching and elastic warp is proposed for HSIs, which consists of the following major steps. First, for each input HSI, a single-band gray-scale image is obtained by fusing the bands corresponding to the red, green, and blue wavelengths. Second, the feature points of each HSI are obtained with a robust VGG-style network and matched with a graph neural network. After point pairs are obtained, the next step is to estimate the transformation matrix of adjacent images, and a spectral correction method based on intrinsic decomposition is proposed to ensure the spectral consistency of adjacent images. In the final stage, a seam-cutting and multiscale blending strategy is adopted to ensure the spatial consistency of the stitching results. Experimental results on real HSIs show that the proposed method is superior to six representative image stitching approaches.
Medical Image Encryption Using SCAN Technique and Chaotic Tent Map System

In today’s digital world, security during medical image transmission is very important. The proposed method uses the concept of SCAN method and chaotic tent map for medical image encryption. It consists of bit plane decomposition, pixel rearranging stage and diffusion operation. Amount of information in the image is distributed into eight binary planes with a different proportional. Higher four bit planes are shuffled using spiral SCAN pattern, and lower bit planes are shuffled using diagonal SCAN pattern. Then perform pixel wise xor operation between shuffled image and the random image generated from the chaotic tent map. Experimental results show that proposed method achieves high level of security with lesser computation.
Distilling Knowledge from BERT into Simple Fully Connected Neural Networks for Efficient Vertical Retrieval

Distilled BERT models are more suitable for efficient vertical retrieval in online sponsored vertical search with low-latency requirements than BERT due to fewer parameters and faster inference. Unfortunately, most of these models are still far from ideal inference speed. This paper presents a novel and effective method to distill knowledge from BERT into simple fully connected neural networks (FNN). Results of extensive experiments on English and Chinese datasets demonstrate that our method achieves comparable results with existing distilled BERT models while the inference is accelerated by more than ten times. We have successfully applied our method on our online sponsored vertical search engine and get remarkable improvements.
Artificial Intelligence Based Solar Radiation Predictive Model Using Weather Forecasts

Solar energy has gained attention in the past two decades, since it is an effective renewable energy source that causes no harm to the environment.Solar Irradiation Prediction (SIP) is essential to plan, schedule, and manage photovoltaic power plants and grid-based power generation systems.Numerous models have been proposed for SIP in the literature while such studies demand huge volumes of weather data about the target location for a lengthy period of time.In this scenario, commonly available Artificial Intelligence (AI) technique can be trained over past values of irradiance as well as weatherrelated parameters such as temperature, humidity, wind speed, pressure, and precipitation.Therefore, in current study, the authors aimed at developing a solar irradiance prediction model by integrating big data analytics with AI models (BDAAI-SIP) using weather forecasting data.In order to perform long-term collection of weather data, Hadoop MapReduce tool is employed.The proposed solar irradiance prediction model operates on different stages.Primarily, data preprocessing take place using various sub processes such as data conversion, missing value replacement, and data normalization.Besides, Elman Neural Network (ENN), a type of feedforward neural network is also applied for predictive analysis.It is divided into input layer, hidden layer, loadbearing layer, and output layer.To overcome the insufficiency of ENN in choosing the value of weights and hidden layer neuron count, Mayfly Optimization (MFO) algorithm is applied.In order to validate the performance of the proposed model, a series of experiments was conducted.The experimental
Unsupervised Cross-View Feature Selection on incomplete data

Unsupervised multi-view feature selection (UMV-FS) deals with the dimension reduction problem wherein instances are unlabeled and represented by heterogeneous features. Existing mainstream UMV-FS methods incorporate instance-wise view interactions based on graphs to guide feature selection, in which within-view selection decisions are independently learned to piece up a global feature subset. However, this strategy induces a globally sub-optimal feature selection decision in the sense that unexpected redundant features across views proliferate. Furthermore, existing studies are performed in view-complete frameworks, which hardly satisfies real-world applications. To address these issues, we propose a novel cross-view feature selection (CVFS) framework in an unsupervised manner, which can process large-scale/streaming data. This is the first attempt to approach incomplete multi-view feature selection by devising and fusing two-wise view interactions. Specifically, we incorporate the traditional instance-wise view interactions based on graphs to find discriminative features in each view and model a novel kind of feature-wise view interactions to enforce selection diversity and reduce feature redundancy. These techniques can yield a globally optimal feature subset across all views. Comprehensive experiments validate the effectiveness and efficiency of the proposed CVFS.
Modal Logics and Group Polarization

Abstract This paper proposes different ways of modally defining properties related to the concept of balance in signed social networks where relations can be either positive or negative. The motivation is to be able to formally reason about the social phenomenon of group polarization based on balance theory. The starting point is a recently developed basic modal logic that axiomatizes the class of social networks that are balanced up to a certain degree. This property is not modally definable but can be captured using a deduction rule. In this work, we examine different possibilities for extending this basic language to define frame properties such as balance and related properties such as non-overlapping positive and negative relations and collective connectedness as axioms. Furthermore, we define the property of full balance rather than balanced-up-to-a-degree. We look into the complexity of the model checking problem and show a non-compactness result of the extended language. Along the way, we provide axioms for weak balance. We also look at a full hybrid extension and reason about network changes with dynamic modalities. Then, to explore the measures of how far a network is from polarization, we consider variations of measures in relation to balance.
SECURE AND TRANSPARENT CROWDFUNDING USING BLOCKCHAIN

Crowdfunding is the practice by which an individual or an organization can raise funds for a project, where a large number of people contribute small amount of money, typically via the internet. In today's world whenever an entrepreneur wants to implement his idea into the real world, he needs some money to bring his idea to life. Earlier when crowdfunding platform were not available the entrepreneur were able to target only a limited number of people for funding of the project also it was very difficult for them to reach out to the people who can fund the project. Now a day's a number of crowdfunding platform like Kickstarter exists which allows the entrepreneur to post their ideas on the platform where backers can see the project and can contribute some amount of money to that project. These crowdfunding platforms made it easier for the entrepreneur to reach out to a large crowd worldwide who can support their project. In the existing crowdfunding system, despite having many advantages there are some problem related to these systems such as charging a huge amount of money for maintenance, transparency in the system, and trust. Our application can remove these problems related to the existing crowdfunding system by providing a more transparent system where every transaction can be stored on the blockchain using the Smart Contracts.
Single-DARTS: Towards Stable Architecture Search

Differentiable architecture search (DARTS) marks a milestone in Neural Architecture Search (NAS), boasting simplicity and small search costs. However, DARTS still suffers from frequent performance collapse, which happens when some operations, such as skip connections, zeroes and poolings, dominate the architecture. In this paper, we are the first to point out that the phenomenon is attributed to bi-level optimization.We propose Single-DARTS which merely uses single-level optimization, updating network weights and architecture parameters simultaneously with the same data batch. Even single-level optimization has been previously attempted, no literature provides a systematic explanation on this essential point. Replacing the bi-level optimization, Single-DARTS obviously alleviates performance collapse as well as enhances the stability of architecture search. Experiment results show that Single-DARTS achieves state-of-the-art performance on main-stream search spaces. For instance, on NAS-Benchmark-201, the searched architectures are nearly optimal ones. We also validate that the single-level optimization framework is much more stable than the bi-level one. We hope that this simple yet effective method will give some insights on differential architecture search. The code is available at https://github.com/PencilAndBike/Single-DARTS.git.