Reviewerly/exHarmony huggingface dataset:
Automatic Race Estimation from Facial Images Using Shape and Color Features

In this work, an attempt has been made to estimate the human race from facial images. This work is done for three major ethnicities: African, American, and Asian. The performances of the classifiers were tested with the faces images of African, American, and Asian population belonging to different age groups and genders. For this, first the facial part such namely the nose region is detected using the well-known Viola–Jones object detection technique. From the detected nose, 9 Zernike moments, 81 HoG features, and 2 color features are extracted to estimate the race using classifiers namely ANN and SVM. Four hundred and fifty sample images taken from the FERET database were considered for this study, out of which 330 images were used for training and for 120 testing. The accuracy of the model obtained through artificial neural network is 91.06%, whereas the accuracy obtained by applying SVM is 95.1%. From the results obtained, it is evident that SVM outperforms ANN in identifying the race of a person from his/her facial image and could be effectively employed in automatic race estimation systems based on facial images.
Evaluating IVR in Primary School Classrooms

In recent years, the availability of affordable mobile Virtual Reality (VR) viewers has resulted in strong interests to incorporate Immersive Virtual Reality (IVR) within classrooms. However, studies on the effect of IVR on primary schoolers' learning are few, and they have often used equipment and settings far removed from everyday classroom instruction. We explored the role of interactivity and immersion in learning in a primary school classroom with 36 children aged 11-13 years, using commercially available devices that are ready-to-scale. We co-created content with different levels of immersion and interactivity together with teachers and investigated student engagement and learning. We present and discuss the use of multiple data sources (performance, physiological responses, observations and self-report) in a real-world classroom evaluation.
Combined penalized weights based GM-PHD for point target tracking in starry-sky background

It is well known that the point targets observed by the space-based platform are extremely difficult to track due to the interference of a large number of stellar targets and background noise. The traditional GM-PHD, due to its probability accumulation, is easy to mistakenly identify the star as a derived new target when the real target passes closely to the star. In order to achieve precise tracking of multiple moving point targets for space-based observations in a complex background environment such as a starry sky background, this paper proposes a simple and effective combined penalized weights based GM-PHD for filtering. First, a unique label is given for each target to determine which real target the estimate came from. Next, the moving region of target is discretized into 10 irregular intervals to construct ten different motion templates, numbered one by one, for discretization of the direction of the target motion. Next, the true target at time k − 1 and all the estimated values having the same label at time k are used to calculate the direction penalty factor and the speed penalty factor to obtain the penalty strength matrix and penalized weight matrix, respectively. Finally, the penalized weight matrix is used to output the target with the highest weight in each label as the real target at time k. And a given penalty threshold is used to remove some fuzzy weights with greater penalty in the penalty strength matrix, so as to selectively filter out some less relevant targets to further avoid the stars participating in the next iteration. In order to verify the effectiveness of the proposed algorithm, we used the Tycho-2 catalog to simulate four kinds of starry-sky data sets with different complexity and conducted comparative experiments on each data set. The experimental results show that the proposed algorithm can effectively track multiple space-based infrared weak point targets in complex starry-sky scenarios.
Statistical Power Analysis for IoT Device Oriented Encryption with Glitch Canceller

Big data which is collected by IoT devices is utilized in various businesses. For security and privacy, some data must be encrypted. IoT devices for encryption require not only to tamper resistance but also low latency and low power. PRINCE is one of the lowest latency cryptography. A glitch canceller reduces power consumption, although it affects tamper resistance. Therefore, this study evaluates the tamper resistance of dedicated hardware with glitch canceller for PRINCE by statistical power analysis and T-test. The evaluation experiments in this study performed on field-programmable gate array (FPGA), and the results revealed the vulnerability of dedicated hardware implementation with glitch canceller.
Collaborative Learning of High-Precision Quantum Control and Tomography

High-precision control of quantum states and gate operations is essential to the hardware implementation of quantum computation. Recently, online calibration has become an important tool for correcting errors induced by parameter shifts or environmental noises in the underlying quantum control systems. However, the experimental cost for acquiring information through quantum tomography (for state or gate reconstruction) is very high, especially when many iterations are to be done. In this paper, we propose a novel scheme that integrates the gradient-descent optimization of quantum control pulses with the adaptive learning of quantum tomography as two interactive processes, which updates the control iteratively with the progressively refined state tomography. This scheme, which we call c-GRAPE, can greatly improve the calibration efficiency by substantial reduction the experimental cost for tomography without sacrificing the control precision.
Classification and Clustering of Emotive Microblogs in Albanian: Two User-Oriented Tasks

HumanPrifti, Marjana emotion analysis has continuously stimulated studies in differentBiba, Marenglen disciplines and it is spurring interest among the computer scientists too. Particularly, the growing popularity of Micro-blogging platforms, has generated large amounts of data, which in turn represent an attractive source to study social media users, especially in user-generated content analysis, such as opinion mining and sentiment analysis. In this paper, we propose to analyze micro-blogging content in order to characterize the users individually when writing posts with emotional content. The analysis is two-fold and considers the emotional content at different granularity levels, one refers to the textual units and allows us to capture the emotional state expressed by the user, the other one refers to the collections of textual units and allows us to summarize the lexicon used by the user. In particular, in the first case, we focus on a sentence-based emotion detection problem, aimed at classifying the textual units into a set of pre-defined emotion categories. The second analysis is performed through a keyword extraction approach, aimed at finding representative generic word sets in the form of prototypes of textual unit clusters. Extensive experiments conducted under different perspectives, yet always centered around the user, reveal interesting findings in terms of classification accuracy, clustering incoherence versus classifications perspectives and valuable efforts in user emotion profiling.
Tracking of Targets in Mobile Robots Based on Camshift Algorithm

This paper presents a robustness and real-time target tracking system for mobile robots applications based on the Camshift algorithm. Camshift is a continuously adaptive Meanshift algorithm. It is an improved method of meanshift. It can adjust the window size of tracking object automatically to fit the object area when the size of tracking object is changing. The Camshift algorithm is introduced and improvements of Camshift algorithm are analyzed in this paper. The hardware and software structures of the target tracking system are designed. The experiment shows the tracking result and the tracking performance is reliable.
Optimal Sizing of Stand-alone PV System Using Grey Wolf optimization

Photovoltaic systems are among the leading renewable energy sources. Their main drawback is their high initial cost. Optimizing the size of the system is one of the possible options to minimise the cost. Sizing of the system is the proper selection of the rating and the number of the system components such as the solar panel, the battery, the charger, and the DC/AC inverter. In recent years, the artificial intelligence algorithms have been deployed in optimizing the size of renewable energy systems. In this paper, we optimize the size of stand-alone photovoltaic system by applying grey wolf optimization algorithm. The inputs to the algorithm are the load data, the solar radiation and the ambient temperature. The stand-alone photovoltaic system is modelled and implemented in Matlab. The life cycle cost of the system is selected as our objective function in the grey wolf optimization algorithm. Using different PV system components with different specifications and prices, the results of the grey wolf optimization algorithm sizing are optimum as proven by simulations.
HRPGAN: A GAN-based Model to Generate High-resolution Remote Sensing Images

Abstract Generative adversarial networks (GAN) has been mainly used in the generation of natural images such as MNIST, CIFAR10 as well as Imagenet datasets and achieves satisfying generation results. However, GAN always fails in generating high quality high-resolution remote sensing images because remote sensing images are large in size and have various ground objects. To address this issue, a novel framework called High-Resolution PatchGAN (HRPGAN) is introduced in this paper. The structure of HRPGAN follows PatchGAN, but the batch normalization layers are removed and the ReLU activation is replaced by the SELU activation. In addition, a new loss function consisting of the adversarial loss, perceptual reconstruction loss and regularization loss is used in HRPGAN. Experiment results show that the proposed HRPGAN model generates the more diverse and lifelike images in HR remote sensing generation than Bicubic method and TGAN model.
Optimized machine learning model using Decision Tree for cancer prediction

Decision making is used for the selection oriented process. But can we use it in the field of healthcare is the biggest question. The answer is yes here also we can have with machine learning (ML) classifiers like Decision Tree (DT), random forest (RF), support vector machine (SVM), and neural network (NN). In the case of breast cancer (BC) disease, we can predict using machine learning classifiers. For this, original BC samples experiment. Our model named clustering based feature selection (CFS) intends to increase the accuracy and to reduce the feature dimension. We experiment the classifiers in three stages: 1. all features of BC, 2. DT based cluster, and 3. Feature selection. In all feature, RF achieves 99.10% accuracy, in DT based cluster RF, achieves 99.37% accuracy, and finally in cluster-based feature selection (CFS) with only four features the RF achieves 99.50% accuracy. The experimental results of our CFS approach along with DT, RF, SVM, and NN classifier along with its performance are presented.
Enhancing fractal image compression speed using peer adjacent mapping with sum of absolute difference for computed radiography images

The encoding phase in full-search fractal image compression (FIC) is time-intensive as a sequential search must be performed through a massive domain pool to find the best-matched domain for each block of ranges. In this paper, a peer adjacent with the sum of absolute difference (SAD) mapping has been suggested to enhance the FIC speed while retaining the reconstructed image quality. The SAD similarity measure applied in searching the most matching domain between domain pool for a range before transformation in order to shorten the mapping process. Therefore, instead of performing a complete search in the next level, one requires to only search a close neighbourhood of the region computed from the previous search. The efficiency of the proposed method is evaluated using standard test image, SMPTE test pattern and standard computed radiography digital images from JSRT database, from which the peak signal-to-noise ratio (PSNR), compression time and compression ratio are calculated. The experimental results validate the effectiveness of the proposed method.
Normative and Fuzzy Aspects of Medical AI

Normative and fuzzy aspects of Medical AI are examined and a framework is presented for the purpose of designing architectures that can accommodate the lifelong growth of data and knowledge pertaining to an individual's health, together with relevant privileges and permissions for access, growth and utilization of such a system. Considering disease through the normativistic perspective advanced by Sadegh-Zadeh, an action-theoretical analysis is adopted suitable for Medical AI. In this approach diseases are viewed as social artifacts mapped into deontic constructs. In a sense, they are action provoking states of affairs amenable to fuzzy computational approaches that in the past have found broad applicability to industrial process control.
Generative Object Detection: Erasing the Boundary via Adversarial Learning with Mask

Recently, object detection has presented superior performance using the deep convolutional neural network (CNN). However, most CNN-based models need the bounding box information of the input image in pairs. To overcome this limitation, we propose the Generative Object Detection which learns with only cropped images that are not in pairs. Our model based on Generative Adversarial Networks (GAN) creates cropped images by making a mask that represents the object region. To achieve this goal, we devise a novel mask mean loss (MML) that helps the GAN be able to estimate the distribution of training data and uses dilated convolution for a wider reception field in the generator. The experimental results show that Generative Object Detection improves the mIoU and accuracy.
Performance analysis of vehicle detection based on spatial saliency and local image features in H.265 (HEVC) 4K video for developing a relationship between iou and subjective evaluation value

A vehicle detection method fusing spatial saliency and local image features is presented in this paper to analyze the detection performance in compressed domain. Most of the existing visual saliency models use the input images, in which salient objects are to be detected and are free from complex background and overlapping areas. Moreover, they are very sensitive to the complex scene and different illuminations. In this method, the Scale Invariant Feature Transform and Harris features in combination with spatial saliency model play an important role in detecting vehicles from the scene. A one‐to‐one symmetric search is performed on the descriptors to select a set of matched interest point pairs for vehicle detection. We use 4K video of a road scene with different types of vehicles. The proposed method is able to detect desired overlapping objects from the road scene without heavy computation like other training‐based methods. In addition, the detection performance is analyzed in a H.265 compressed domain to help construct a relationship between objective and subjective evaluation value. The performance of this method is demonstrated by the experimental results. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.
Quality Evaluation Assurance Levels for Deep Neural Networks Software

Quality of machine learning software products or services is dependent on datasets used for training. However, defining quality of datasets is difficult, which might bring about risks in business situations. Independent, third-party testing laboratories would mitigate the risks. This paper proposes quality evaluation assurance levels, which is a basis of a third-party evaluation and certification framework. Moreover, quality of machine learning software is indeed viewed from three perspectives: prediction performance quality, training mechanism quality, and lifecycle support quality enabling continuous operations.
The improved DnCNN for linear noise attenuation

Seismic data are often highly corrupted by different kinds of noise, including linear noise. Therefore, the attenuation of linear noise has been an essential step in seismic data processing. Traditional methods of linear noise suppression are mostly based on the difference of signals and noise in transform domains. However, the application of these traditional methods is limited to some particular assumptions. For this reason, we utilize an algorithm based on deep convolutional neural network (DnCNN) to attenuate linear noise. DnCNN is proposed to suppress Gaussian noise in images. In term of the characteristics of linear noise, we make some improvements to the original DnCNN, like patch size, convolutional kernel number. Tests on two types of synthetic data both indicate that the improved DnCNN algorithm is capable of linear noise attenuation in the seismic data.
Robust Small-Footprint Keyword Spotting Using Sequence-To-Sequence Model With Connectionist Temporal Classifier

Aiming at the low computational complexity and frame-by-frame real-time processing conditions for small embedded devices, this paper propose a small-footprint Keyword Spotting (KWS) system using sequence-to-sequence model based on Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) with Connectionist Temporal Classifier (CTC), and this system which use the Per-channel energy normalization (PCEN) mel feature is more robust for the noisy speech recognition. The LSTM and GRU network can not only remember continuous information in the speech sequence signal, but also can process speech frame-by-frame in low-computation devices. Sequence-to-sequence model can output the pinyin posterior probability of each frame, and then perform de-duplication processing to get the final recognition result. The experimental results show that the single-layer small sequence-to-sequence model can achieve good results but not increasing computation complexity compare with deep neural network.
Real-Time Tracking and Lane Line Detection Technique for an Autonomous Ground Vehicle System

Real-time detection and tracking of a moving car in sequence of image frames acquired by a static camera and lane line detection using an onboard camera have been elucidated in this paper. Vehicle tracking has been performed by exploiting modelling behaviour of Kalman filter, and lane line detection has been done using modified Hough transform. In case of car tracking, image acquisition, detection of vehicle and locating its centroid are done using adaptive background subtraction technique. Parameter estimation of car’s position is further performed using Kalman filter. In case of lane detection, pre-processing of image, edge detection and masking results’ detection of broken lane lines are done. Hough transform, for detecting curvatures by exploiting the duality between points on a curve and curve parameters, is applied to join all broken parts and to get solid lane lines. The entire system has been implemented on a ‘VEEROBOT’ robotic car in laboratory environment. Real-time image has been captured using a ‘Logitech’ web camera. All relevant processing has been carried out by ‘Raspberry pi 3’. All the analyses of experimental results are elaborately presented in this paper.
Earthquake Knowledge Graph Constructing Based on Social Intercourse Using BiLSTM-CRF

Abstract Nowadays, the earthquake has become a very serious topic. The earthquake-related information always appears first in social media. Constructing an earthquake knowledge graph can help dealing with earthquake news social media text data. This paper proposed the BiLSTM-CRF (Bi-directional Long Short Term Memory-Conditional Random Field) model to construct earthquake knowledge graph using news text data. The BiLSTM-CRF model identified the entities and then writes entities and their types to the table so that the entities and relationships between entities can be extracted for earthquake. The entities and relationships between entities have been combined with the form of RDF (Resource Description Framework) to construct earthquake knowledge graph on Neo4j database.
Design a Compact Non-linear S-Box with Multiple-Affine Transformations

The present paper introduces a new method of S-Box construction which work as a byte-oriented substitution scheme. The S-Box design considers the core part for the building most block cipher algorithms and play a major role in generating confusion property. Modern ciphers build with different types of S-Box of half-byte (nibble of 4-bit) or full-byte 8-bit as lookup tables with distinct mathematical Boolean functions (BFs). The proposed method generates the S-box based on multiplications of multiple different affine transforms with multiple distinct overlapped vectors of 8-bit to achieve high nonlinearity. The construction of S-box focused on use an irreducible polynomial of order eight over Galois Field GF (28). The proposed S-box quit similar to the S-box of Advance Encryption Standard (AES) but with more algebraic complexity and high non-linearity factor.
Learning Optimal Parameterized Policy for High Level Strategies in a Game Setting

Complex and interactive robot manipulation skills such as playing a game of table tennis against a human opponent is a multifaceted challenge and a novel problem. Accurate dynamic trajectory generation in such dynamic situations and an appropriate controller in order to respond to the incoming table tennis ball from the opponent is only a prerequisite to win the game. Decision making is a major part of an intelligent robot and a policy is needed to choose and execute the action which receives highest reward. In this paper, we address this very important problem on how to learn the higher level optimal strategies that enable competitive behaviour with humans in such an interactive game setting. This paper presents a novel technique to learn a higher level strategy for the game of table tennis using P-Q Learning (a mixture of Pavlovian learning and Q-learning) to learn a parameterized policy. The cooperative learning framework of Kohenon Self Organizing Map (KSOM) along with Replay Memory is employed for faster strategy learning in this short horizon problem. The strategy is learnt in simulation, using a simulated human opponent and an ideal robot that can perform hitting motion in its workspace accurately. We show that our method is able to improve the average received reward significantly in comparison to the other state-of-the-art methods.
Scalable Revocable Identity-Based Signature Scheme with Signing Key Exposure Resistance from Lattices

In 2014, a new security definition of a revocable identity-based signature (RIBS) with signing key exposure resistance was introduced. Based on this new definition, many scalable RIBS schemes with signing key exposure resistance were proposed. However, the security of these schemes is based on traditional complexity assumption, which is not secure against attacks in the quantum era. Lattice-based cryptography has many attractive features, and it is believed to be secure against quantum computing attacks. We reviewed existing lattice-based RIBS schemes and found that all these schemes are vulnerable to signing key exposure. Hence, in this paper, we propose the first lattice-based RIBS scheme with signing key exposure resistance by using the left-right lattices and delegation technology. In addition, we employ a complete subtree revocation method to ensure our construction meeting scalability. Finally, we prove that our RIBS scheme is selective-ID existentially unforgeable against chosen message attacks (EUF-sID-CMA) under the standard short integer solutions (SIS) assumption in the random oracle model.