Reviewerly/exHarmony huggingface dataset:
Who are My Family Members? A Solution Based on Image Processing and Machine Learning

Image creation and retention are growing at an exponential rate. Individuals produce more images today than ever in history and often these images contain family. In this paper, we develop a framework to detect or identify family in a face image dataset. The ability to identify family in a dataset of images could have a critical impact on finding lost and vulnerable children, identifying terror suspects, social media interactions, and other practical applications. We evaluated our framework by performing experiments on two facial image datasets, the Y-Face and KinFaceW, comprising 37 and 920 images, respectively. We tested two feature extraction techniques, namely PCA and HOG, and three machine learning algorithms, namely K-Means, agglomerative hierarchical clustering, and K nearest neighbors. We achieved promising results with a maximum detection rate of 94.59% using K-Means, 89.18% with agglomerative clustering, and 77.42% using K-nearest neighbors.
Trust-enhancing technologies: Blockchain mathematics in the context of Industry 4.0

Abstract Studying how traditional cybersecurity assumptions, models, and frameworks are affected and changed in the future, Industry 4.0 revolution is a pending challenge. In particular, the traditional idea of “security” is not enough, and some new concepts such as trust or privacy must be considered. These new mechanisms are supported by technologies such as Blockchain, where behavior is stochastic, instead of deterministic as in standard security solutions based, for example, in rules. Thus, cybersecurity in Industry 4.0 will be described by a new mathematical framework, involving innovative probabilistic and stochastic mechanisms and the graph theory, employed to describe complex relations and interactions between components in Industry 4.0 solutions. In this chapter, this new framework is described and analyzed, with an especial interest on how “trust” and “privacy” are provided using Blockchain technologies and considering also new regulations such as the European General Data Protection Regulation.
Exploring Placer Gold Deposits through Integrated Geophysical and Geochemical Techniques at the Confluence of the Indus and Kabul Rivers, NW Pakistan

Abstract Analysis of the anomalous magnetic mineral intensities and geochemistry for placer gold deposits are presented for those of the Attock area at the confluence of the Indus and Kabul rivers in northwestern Pakistan. Two grids covering an area of 10×18 m 2 and 8×10 m 2 were analyzed using a G‐858 Cesium Vapor Magnetometer. The anomalous zones obtained were plotted on contour maps, 2D and 3D magnetic intensity maps. Based on the magnetic anomalies, grid‐1 of the study area was sampled at three different anomalous zones for geochemical analysis. These zones contain gold concentrations, ranging from 2.11 ppm to 6.109 ppm with an average of 4.01 ppm. Increase in gold concentration in the subsurface within the anomalous zones indicates that magnetometer survey followed by a geochemical analysis can potentially narrow down the gold‐bearing anomalous zones.
Investigation of Level Set Segmentation Procedures in Brain MR Images

The task in this research is to evaluate the efficiency of the six level-set algorithms in 2D brain segmentation on a given MRI image. For both algorithms and the comparison contour used for the computation of the dice criteria, the initialization used is the same MATLAB tool-backed application is used to measure the efficiency, particularly in biomedical image processing, of different level-based segmentation algorithms. This work includes a comparative study of clustering algorithms according to their performance. Although some findings indicate that MRI images segmentation of the brain tumor is time-consuming, it is an essential work.
Clustering-based software modularisation models for resource management in enterprise systems

Software module clustering approaches can provide a better understanding of large and complex software systems through decomposing the enterprise resources into classified modules which are smaller, and therefore easier-to-handle. As the dimensions and complexity of enterprise software projects are continuously increasing, handling a large software project is going to be more challenging. The challenge would be more complex if the experienced personnel is considered as well. Therefore, appropriate automatic software modularization clustering methods are required in resource management. This paper provides a Systematic Literature Review (SLR) on the software modularization clustering models. We studied a wide range of papers from 2001 to 2020 to provide our SLR. Also, a technical taxonomy is presented to classify the existing papers on software modularization clustering models and algorithms. The software module clustering methods are categorized into three main classes. Finally, new challenges and forthcoming issues of software modularization clustering models are presented.
Multi-scale Dense Object Detection in Remote Sensing Imagery Based on Keypoints

Object detection is a popular topic of computer vision and has attracted increasing attention in the field of remote sensing. However, object detection in remote sensing imagery is a very challenging task. Many existing detection methods depend on densely tiled anchor boxes, which require redundant computation resources and careful fine-tuning. In recent years, some anchor-free detectors have been proposed and show excellent performance. In this paper, we propose a novel anchor-free method for remote sensing object detection, named as multi-scale dense object detector based on keypoints (MDKD). Our method detects object in remote sensing imagery as a single keypoint, which needs neither predefined anchor boxes nor fallible pairing operation. For remote sensing scenario, we dedicate to improve the recall of detection results by allowing more keypoint samplings. In addition, we take advantage of balanced L1 loss to predict bounding boxes more accurately. Multi-scale test improves the performance further more. Our proposed MDKD method is verified on two widely used benchmarks, i.e. NWPU VHR-10 and DOTA datasets. The experimental results indicate that our keypoint based detector is effective on remote sensing imagery and outperforms several popular anchor-based methods.
Digital electronic neural networks with analog nanophotonic frontends - a numerical study

There is recent interest in integrating optics into neural computing systems considering the potential energy savings and speed enhancements. Optical neural networks relying on free-space diffractive effects can potentially permit the use of low resolution imagers and off load a portion of electronic computation. They are of interest in microscopy and edge computing applications. A numerical analysis of ideal few-layered diffractive optical neural networks with trainable phase masks (in real and Fourier spaces) under monochromatic coherent illumination is presented. Stacked all-dielectric transmissive metasurfaces are particularly suitable for the realization of such networks at optical frequencies. Six different kinds of networks (with and without Fourier space phase masks; electronic dense layers; and intensity-dependent optical nonlinearity) are considered for a comparative analysis. The networks are assessed on their testing accuracy on the MNIST, Fashion MNIST and the grayscale CiFAR-10 datasets. Optical variants were found to perform well on MNIST (&ge;96% testing accuracy) and F-MNIST (&ge;85%) datasets where objects are clearly demarcated from the background. However, the optical variants performed poorly on the CiFAR dataset (≈44%) in comparison to state-of-the-art electronic deep convolutional neural networks. The presence of electronic dense layers or optical nonlinearity provided marginal improvements of about 2% in test accuracy. Furthermore, network scaling by widening the phase plates and/or cascading more layers is found to only marginally improve test accuracy.
Cryptanalysis of phase-truncated Fourier-transforms-based optical cryptosystem using an untrained neural network

Optical cryptosystem based on phase-truncated-Fourier-transforms (PTFT) is one of the most interesting optical cryptographic schemes due to its unique mechanism of encryption/decryption. Conventional learning-based attack method need a large number of plaintext-ciphertext pairs to train a neural network and then predict the plaintexts from subsequent ciphertexts. In this work, we propose an alternative method of attack on PTFT-based optical asymmetric cryptosystem by using an untrained neural network. We optimize the parameters of a neural network with the help of the encryption model of PTFT-based cryptosystem, hoping to get the ability of retrieving any plaintext from the corresponding unknown ciphertext but without help of the decryption keys. The proposed untrained-neural-network-based attack approach eliminates the requirement of tens of thousands of training images and might open up a new avenue for optical cryptanalysis.
Data Augmentation with Transformers for Text Classification

The current deep learning revolution has established to transformer based architectures as the state of the art in several natural language processing tasks. However, it is not clear whether such models can be also used for enhancing other aspects of the learning pipeline in the NLP context. This paper presents a study in such a direction, in particular, we explore the suitability of transformer models as a data augmentation mechanism for text classification. We introduce four ways of using transformer models for augmenting data in text classification. Each of these variants take the outputs of a transformer model, feed with training documents, and use such outputs as additional training data. The proposed strategies are evaluated in benchmark data using CNN and LSTM based classifiers. Experimental results are promising: improvements over a model training on the plain documents are consistent.
Object Detection Based on Sparse Representation of Foreground

Objects detection can be regard as the segmentation of foreground from background. In this paper, we propose a foreground segmentation method based on sparse representation of direction features for threat object detection in X-ray images. The threat objects are supposed as foreground and all other contents in the images are background. We extract the direction features to make up foreground dictionary firstly. Then we search the foreground area in the test image through sparse representation of their direction features by foreground dictionary. The experimental results show that this proposed method is robust to the X-ray images with different backgrounds.
Using Active Learning to Improve Distantly Supervised Entity Typing in Multi-source Knowledge Bases

Entity typing in the knowledge base is an essential task for constructing a knowledge base. Previous models mainly rely on manually annotated data or distant supervision. However, human annotation is expensive and distantly supervised data suffers from label noise problem. In addition, it suffers from semantic heterogeneity problem in the multi-source knowledge base. To address these issues, we propose to use an active learning method to improve distantly supervised entity typing in the multi-source knowledge base, which aims to combine the benefits of human annotation for difficult instances with the coverage of a large distantly supervised data. However, existing active learning criteria do not consider the label noise and semantic heterogeneity problems, resulting in much of annotation effort wasted on useless instances. In this paper, we develop a novel active learning pipeline framework to tackle the most difficult instances. Specifically, we first propose a noise reduction method to re-annotate the most difficult instances in distantly supervised data. Then we propose a data augmentation method to annotate the most difficult instances in unlabeled data. We propose two novel selection criteria to find the most difficult instances in different phases, respectively. Moreover, we propose a hybrid annotation strategy to reduce human labeling effort. Experimental results show the effectiveness of our method.
Dynamic Reasoning Network for Multi-hop Question Answering

Multi-hop reasoning question answering is a sub-task of machine reading comprehension (MRC) which aims to find the answer of a given question across multiple passages. Most existing models usually obtain the answer by visiting the question only once so that models may not obtain adequate text information. In this paper, we propose a Dynamic Reasoning Network (DRN), a novel approach to obtain correct answers by multi-hop reasoning among multiple passages. We establish a query reshaping mechanism which visits a query repeatedly to mimic people's reading habit. The model dynamically reasons over an entity graph with graph attention (GAT) and the query reshaping mechanism to promote its ability of comprehension and reasoning. The experimental results on the HotpotQA and TriviaQA datasets show that our DRN model achieves significant improvements as compared to prior state-of-the-art models.
Cascaded Adaptive Nonlinear Functional Link Networks for Modeling and Predicting Crude Oil Prices Time Series Data

Contrast to traditional neural networks functional link neural network (FLNN) is preferred for its single layer structural design, lower computational complexity and higher convergence rate. It achieves high dimensional representation space of input patterns through functional expansion of input signals. However, its nonlinear approximation capability is limited up to certain extent. Further improvement in performance may require enlargement in dimensionality of the input pattern, which increases the computational overhead significantly. Chebyshev FLNN (CFLNN) is a special case of FLNN and has universal approximation capacity along with faster convergence. Legendre neural network (LeNN) uses simple polynomial expansion functions and posses computational gain over FLNN. This paper develops two cascaded neural networks in order to improve the performance of FLNN. The first model combines the input expansion capacity of FLNN and better approximation of CFLNN to develop a model termed as CCFLNN. Similarly, the second model takes the advantages of FLNN and LeNN to develop another model termed as CLeFLNN. The weight and bias vectors are adjusted by gradient descent based back propagation learning method. The proposed models are evaluated on forecasting crude oil prices. Extensive simulation outcome and comparative performance investigation suggests suitability of the proposed model.
New discrete logarithm computation for the medium prime case using the function field sieve

&lt;p style='text-indent:20px;'&gt;The present work reports progress in discrete logarithm computation for the general medium prime case using the function field sieve algorithm. A new record discrete logarithm computation over a 1051-bit field having a 22-bit characteristic was performed. This computation builds on and implements previously known techniques. Analysis indicates that the relation collection and descent steps are within reach for fields with 32-bit characteristic and moderate extension degrees. It is the linear algebra step which will dominate the computation time for any discrete logarithm computation over such fields.&lt;/p&gt;
Neural Networks based Hand-crafted genetic learning approach to simulate Space Mario Game

Genetic algorithm is a search heuristic inspired by Charles Darwin's theory of natural selection where the fittest individuals pass on their traits to the next generation. The notion behind Natural Selection is to select the fittest individuals from a population-based on a fitness score and crossing over their features to create better off-springs and adding some mutation. The process repeats itself until the desired generation score is reached. To simulate a space Mario game, our research proposes a handcrafted genetic learning model that selects the best surviving entities from a population, crossing them over according to fitness priorities. The fittest entity from the previous generation is also passed along with some mutation. This continues until the fittest entities reach the target score in fewer generations possible. Our model crosses the target score of 2000 in the 16th generation.
Highly Scalable Processor Architecture for Reinforcement Learning

Q-Learning is also referred as a reinforcement learning method. Its main advantage is that of obtaining a near optimal policy while interfacing with an unknown model. This paper presents a hardware architecture for Q-Learning, which is efficient in terms of running a larger set of Q-Learning problem sizes. The proposed research work presents the method and resource utilization, when implemented in FPGA, and algorithm involved in the proposed hardware. The proposed algorithm consumes a negligible area when compared to the parallel implementation of Q learning algorithm.
A scalable Kalman filter algorithm: Trustworthy analysis on constrained least square model

Summary Kalman filter (KF) is one of the most important and common estimation algorithms. We introduce an innovative designing of Kalman filter algorithm based on domain decomposition (we call it DD‐KF). DD‐KF involves decomposition of the whole computational problem, partitioning of the solution and a slight modification of KF algorithm allowing a correction at run‐time of local solutions. The resulted parallel algorithm consists of concurrent copies of KF algorithm, each one requiring the same amount of computations on each subdomain and an exchange of boundary conditions between adjacent subdomains. Main advantage of this approach is that it can be potentially applied in a moderately nonintrusive manner to existing codes for tracking and controlling systems in location, navigation, in computer graphics and in much more state estimation problems. To highlight the capability of DD‐KF of exploiting the computing power provided by future designs of microprocessors based on multi/many‐cores CPU/GPU technologies, we consider DD both at physical core level and at microprocessor level and we discuss scalability of DD‐KF algorithm at coarse and fine grained level. Throughout the present work, we derive and discuss DD‐KF algorithm for solving constrained least square model, which underlies any data sampling and estimation problem.
Noise-Robust Projection Rule for Rotor and Matrix-Valued Hopfield Neural Networks

A complex-valued Hopfield neural network (CHNN) has weak noise tolerance due to rotational invariance. Some alternatives of CHNN, such as a rotor Hopfield neural network (RHNN) and a matrix-valued Hopfield neural network (MHNN), resolve rotational invariance and improve the noise tolerance. However, the RHNN and MHNN with projection rules have a different problem of self-feedbacks. If the self-feedbacks are reduced, the noise tolerance is expected to be improved further. For reduction in the self-feedbacks, the noise-robust projection rules are introduced. The stability conditions are extended, and the self-feedbacks are reduced based on the extended stability conditions. Computer simulations support that the noise tolerance is improved. In particular, the noise tolerance is more robust against an increase in the number of training patterns.
Professional profile of the distance education expert

The purpose studio is to present a referential framework focused on defining what is the professional profile of distance education expert, a specialist in higher education. For this, a qualitative-descriptive methodology will be used, based on literature scientific review, as well as empirical studies. To achieve, first the perspective of the field in the current labor market will be described. Second, the most common activities will be defined. Third, an interview process will be presented to fill the position. Fourth, the prospective of the field of work will be determined. Finally, the final considerations of this document will be outlined.
Entanglement and nonlocality of four-qubit connected hypergraph states

We study entanglement and nonlocality of connected four-qubit hypergraph states. One obtains the Stochastic Local Operations and Classical Communication (SLOCC) classification from the known local unitary (LU)-orbits. We then consider Mermin’s polynomials and show that the nonlocal behavior of hypergraph states can be detected by a suitable choice of Mermin’s inequality. Finally, we implement some of the corresponding inequalities on the IBM Quantum Experience and report the detection of the nonlocality of a three-qubit hypergraph states.
Text Sentiment Analysis Network Model Based on Self-attention Mechanism

At present, sentiment analysis appeared to be an important tool to extract great value and support the decision-making of institutions. Traditional machine learning algorithms, such as Naïve Bayes and Maximum Entropy, are time-consuming, difficult to train by using manually designed features selection method. This paper presents an efficient architecture to address these issues and improve the model. The model combines the self-attention mechanism and stacked BiLSTM network, which increases the depth of the model and captures the relationships better between words for text sentiment analysis. Through the test on the Internet Movie and Chinese Weibo database, this model can effectively improve the accuracy of emotion classification in English and Chinese and is expected to be used for other corresponding applications.
Digital Banking Accelerator: A Service-Oriented Architecture Starter Kit for Banks

This article proposes a digital banking accelerator-a "starter kit" for helping traditional banks having little or no service-oriented architecture (SOA) competencies to rapidly implement an effective SOA to accelerate their digital banking capability.
Fast motion tracking based on moth-flame optimization and kernel correlation filter

Kernel Correlation Filter (KCF) tracker has shown great potential on precision, robustness and efficiency. However, the candidate region used to train the correlation filter is fixed, so tracking is difficult when the target escapes from the search window due to fast motion. In this paper, an improved KCF is put forward for long-term tracking. At first, the moth-flame optimization (MFO) algorithm is introduced into tracking to search for lost target. Then, the candidate sample strategy of KCF tracking method is adjusted by MFO algorithm to make it has the capability of fast motion tracking. Finally, we use the conservative learning correlation filter to judge the moving state of the target, and combine the improved KCF tracker to form a unified tracking framework. The proposed algorithm is tested on a self-made dataset benchmark. Moreover, our method obtains scores for both the distance precision plot (0.891 and 0.842) and overlap success plots (0.631 and 0.601) on the OTB-2013 and OTB-2015 data sets, respectively. The results demonstrate the feasibility and effectiveness compared with the state-of-the-art methods, especially in dealing with fast or uncertain motion.
Weighted Pre-trained Language Models for Multi-Aspect-Based Multi-Sentiment Analysis

In recent years, aspect-based sentiment analysis has attracted the attention of many researchers with its wide range of application scenarios. Existing methods for fine-grained sentiment analysis usually explicitly model the relations between aspects and contexts. In this paper, we tackle the task as sentence pair classification. We build our model based on pre-trained language models (LM) due to their strong ability in modeling semantic information. Besides, in order to further enhance the performance, we apply weighted voting strategy to combine the multiple results of different models in a heuristic way. We participated in NLPCC-2020 shared task on Multi-Aspect-based Multi-Sentiment Analysis (MAMS) and won the first place in terms of two sub-tasks, indicating the effectiveness of the approaches adopted.