Reviewerly/exHarmony huggingface dataset:
Stereocorrelation Formalism Considering Brightness and Contrast Effects: Application to Torsional Loadings

Many multiaxial experimental setups rely on stereocorrelation (SC) techniques to resolve surface strains and deformations. Torsional loadings can affect the quality and accuracy of SC results due to changes in lighting conditions and the non-conservation of gray levels with the angle of twist. In this note, the gray level correction method implemented for 2D DIC cases is introduced in a 3D surface NURBS SC formalism and applied for torsional loadings. In particular, tension-torsion loading of AISI 1144 steel rods is used to demonstrate the approach. Different surface parameterizations, with and without gray level corrections, are tested in order to enhance the SC quality (defined via gray level residuals). It is found that both gray level corrections and relaxing the regularization of an overly constrained NURBS surface definition (6 vs. 36 knots), lower SC residuals.
A Centralized Key Management Scheme Based on McEliece PKC for Space Network

Constrained by the limited resource, high-latency and high bit error rate, the existing group key management schemes for the space network are inefficient. To solve this problem, we proposed a centralized and identity-based key management scheme by using McEliece public-key cryptosystem (PKC). In this scheme, the node identity is used as the parameter to generate the public key. Therefore, the authentication can be embedded into the verification of the public key without needing the PKI. The group key is distributed with the protection of the public key so that the group key management scheme can be implemented safely. Furthermore, the McEliece public-key cryptosystem can resist the quantum attack and provide error correction capacity. It improves the efficiency of the group key distribution over the noisy channel. The proposed key management scheme is simulated on OPNET. The security of public-key generation, forward secrecy, backward secrecy and performance are analyzed. The results show that our scheme can provide confidentiality, integrity, authentication, non-repudiation, failure tolerance and error correction. In addition, the computation overhead and rounds of interaction are lower than former work.
English as a Foreign Language (EFL) Learners' Predominant Language Use for Online Informal Learning Activities through Smartphones in Indonesian Context

In recent years, researchers have become increasingly interested in mobile-assisted language learning (MALL) studies for online informal learning of English as a foreign language (EFL).Smartphones, and other mobile devices, have been used as a powerful tool in informal contexts of learning since that form of technology can help students engage in out-of-class language learning experiences.However, to our knowledge, no research on EFL learners' predominant language use for their online informal learning activities has been conducted in Indonesian context.Therefore, this study attempted to investigate EFL learners' predominant language use for their online informal learning of English through smartphones, in which the data collected were quantitatively analyzed through descriptive statistics.The results indicate that EFL learners predominantly perform their online informal learning activities in Indonesian, in English, and in both Indonesian and English.Finally, suggestions and recommendations for future research are also discussed.
Diagnosing Imperfections in Quantum Sensors via Generalized Cramér-Rao Bounds

Quantum metrology derives its capabilities from the careful employ of quantum resources for carrying out measurements. This advantage, however, relies on refined data postprocessing, assessed based on the variance of the estimated parameter. When Bayesian techniques are adopted, more elements become available for assessing the quality of the estimation. Here we adopt generalized classical Cram\'er-Rao bounds for looking in detail into a phase-estimation experiment performed with quantum light. In particular, we show that the third-order absolute moment can give a superior capability in revealing biases in the estimation, compared to standard approaches. Our studies point to the identification of an alternative strategy that brings a possible advantage in monitoring the correct operation of high-precision sensors.
Computing Complex Graph Properties with SQL Queries

In big data analytics, graph problems are as important as machine learning. There exist many algorithms to analyze large graphs, but most of them are limited by the main memory. On the other hand, a lot of data stored on DBMSs needs to be analyzed as graphs. Even data is not in DBMSs, it is fast to load large data sets into DBMSs. More importantly, DBMSs can work in parallel and they do not have RAM limitations. Based on these reasons, we propose several algorithms that compute metrics and properties of the graph as well as help us to understand the graph structure specifically diameter, betweenness centrality. This work is a big step beyond transitive closure and recursive queries. We propose SQL queries that can work on a large graph stored in relational form as triples. And we prove these queries can compute complex graph metrics in a more flexible and efficient manner. Moreover, we study how to optimize the SQL queries combining demanding joins and aggregations that remove the main memory limitation and also can work in parallel. We provide an experimental evaluation to evaluate the accuracy and performance of our SQL algorithms by comparing our algorithms with popular platforms including Python and Spark. In general, the experiments show our SQL algorithms are accurate and efficient, without memory limitation.
Performance evaluation and security analysis of ground‐to‐satellite FSO system with CV‐QKD protocol

This study evaluates the performance of a secure ground-to-satellite free-space optical (FSO) system using a bipolar pulse amplitude modulation over modulated gamma fading channel. A closed-form expression is derived for the joint probability of a satellite-based continuous-variable quantum key distribution (CV-QKD) protocol that uses dual-threshold detection. Furthermore, to study the system behaviour, closed-form expressions for quantum bit-error-rate (QBER) and quantum bit-discard rate (QBDR) are given. The accuracy of the proposed derivations is validated using Monte-Carlo simulations and numerical analysis. The dual-threshold range is optimised to satisfy design criteria in terms of QBER and QBDR. Finally, a sufficient selection of transmitter and receiver parameters ensures a guarantee of system security. Additionally, the information capacity is maximised by a scaling coefficient of .
A survey of neural network accelerator with software development environments

Abstract Recent years, the deep learning algorithm has been widely deployed from cloud servers to terminal units. And researchers proposed various neural network accelerators and software development environments. In this article, we have reviewed the representative neural network accelerators. As an entirety, the corresponding software stack must consider the hardware architecture of the specific accelerator to enhance the end-to-end performance. And we summarize the programming environments of neural network accelerators and optimizations in software stack. Finally, we comment the future trend of neural network accelerator and programming environments.
A Proposed Framework for Fingerprint-based Voting System in Bangladesh

The objective of this project is to improve the existing voting system that will be accurate, transparent, and faster and will ensure a single vote for a single person. Our proposed system has covered all of these issues successfully. This product is affordable by many organizations where preferential elections conducted. The product is a prototype and can be implemented for vast use. Voting is an onerous task for the election commission to conduct free and fair polls in our country, the largest democracy in the world. A lot of money has been spent on this to make sure that the elections are rampage free. But, now- a -days it has become very usual for some forces to indulge in rigging which may eventually lead to a result contrary to the actual verdict given by the people. In order to provide inexpensive solutions to the above, this project is implemented with the biometric system i.e. fingerprint scanning. This is used to ensure the security to avoid fake, repeated voting, etc. It also enhances the accuracy and speed of the process. The system uses a thumb impression for voter identification as we know that the thumb impression of every human being has a unique pattern. Thus it would have an edge over the present-day voting systems. The purpose of such a system is to ensure that the voting rights are accessed only by a legitimate user and no one else. In this, creation of a database consisting of the thumb impressions of all the eligible voters in a constituency is done as a pre-poll procedure. During elections, the thumb impression of a voter is entered as input to the system. This is then compared with the available records in the database. If the particular pattern matches with anyone in the available record, access to cast a vote is granted. But in case the pattern doesnâ€™t match with the records of the database or in case of repetition, access to cast a vote is denied or the vote gets rejected. The result is instantaneous and counting is done. The overall cost for conducting elections gets reduced and so does the maintenance cost of the systems.
Visual Object Detection For Autonomous UAV Cinematography

The popularization of commercial, battery-powered, camera-equipped, Vertical Take-off and Landing (VTOL) Unmanned Aerial Vehicles (UAVs) during the past decade, has significantly affected aerial video capturing operations in varying domains. UAVs are affordable, agile and flexible, having the ability to access otherwise inaccessible spots. However, their limited resources burden computation cinematography techniques on operating with high accuracy and real-time speed on such devices. State-of-the-art object detectors and feature extractors are, thus, studied in this work, aiming to find a trade-off between performance and speed that will allow UAV exploitation for intelligent cinematography purposes. Experimental evaluation on three newly introduced datasets of rowing boats, cyclists and parkour athletes is performed and evidence is provided that even limited-resource autonomous UAVs can indeed be used for cinematography applications.
GCBlock: A Grouping and Coding Based Storage Scheme for Blockchain System

To achieve the tamper-proof, reliability and traceability of transactions in a trustless environment, the blockchain requires each peer node to store the whole global ledger. However, as transactions keep increasing over time, the storage cost of each node increases. In addition, many schemes have been proposed to boost rapid transactions which will even lead transactions to grow explosively. The problem of storage is becoming one challenge of blockchain since the storage overhead of each node increase rapidly. Reducing the storage overhead of each participant is very urgent and worthy. In this paper, we present GCBlock: a grouping overlay network storage scheme for the blockchain which can reduce the storage overhead of nodes and cut the whole storage cost of the blockchain greatly while keeping the underlying protocols. In our scheme, we try to group the nodes according to their physical fuzzy distance to reduce the overall delay when tracing. We set rules of autonomous check to deal with evil behavior within the group. To further enhance the stability of our scheme, we propose the transcript fractional repetition code which is newly constructed based on the fractional repetition code to encode data. Finally, we make a comprehensive evaluation of GCBlock and the results show that it is workable and reasonable.
Convolutional Neural Network for Automatic Speech Recognition of Filipino Language

Researches focusing on deep learning for speech recognition are integral for the successful implementation of natural language processing (NLP).Successful implementations of NLP would allow even non-technical and illiterate users to have access to technology by simply using their native tongue.Currently, there are very limited studies on the use of the Convolutional Neural Network (CNN) for Automatic Speech Recognition of Filipino Language.This paper presents a CNN model using SqueezeNet architecture for the Filipino language with 99.58% training accuracy, and 84.71% testing accuracy.Experimental method was employed to achieve this accuracy by adjusting the learning rate of the model.SqueezeNet architecture was chosen because it requires less resource but maintains the same level of accuracy as compared to other neural network architectures.Mel Frequency Cepstral Coefficient was also utilized to convert the audio inputs into Mel Spectrograms.The study also presented the precision rate of each Filipino class identified in the data set and how CNN was used to increase prediction accuracy.This CNN model in this study can be used as basis for further improvement of the speech recognition rate of other Filipino words and other new languages.
Blockchain in Cybersecurity Realm: An Overview

Cybersecurity is a pressing need for governments, businesses, and individuals, which is compounded by the fast-pace technological changes and changing cyberthreat landscape. A large number of solutions have been proposed to address the changing security requirements, and one of such solutions is based on blockchain. Blockchain is a promising infrastructural technology, with potential to be leveraged in different aspects of cybersecurity. For example, blockchain characteristics such as decentralization, verifiability and immutability can facilitate one to achieve authenticity, reliability and integrity of data. Yet, little is known about factors related to the adoption decisions and how it can systemically be put into use to remedy current security’s issues in the digital world. In order to advance the body of practical and theoretical knowledge, this book seeks to provide a sample of cutting-edge research from both academia and industry to perceive blockchain cybersecurity and its current solutions in a clearer context and pave the way for the future.
GraphConvLSTM: Spatiotemporal Learning for Activity Recognition with Wearable Sensors

Wearable activity recognition is an important area for healthcare applications, especially for children and elderly people. Current research proves that deep learning techniques are capable of learning deep features from the sensor raw signal. However, this task faces two challenges. First, it is hard to capture the dynamic and non-linear interactions between different sensor modalities and time slots. Besides, the spatial relationship between sensors convey significant information for wearable activity recognition, thus how to model the sensor relationship is one of the problems that need to be solved. In this work, we propose GraphConvLSTM, a scalable framework that model spatial relationship, local interaction, and long-term temporal dependency simultaneously. We evaluate our model on two real-world activity recognition datasets, and it achieves 1.75%, 2.68% accuracy improvements over existing methods (accuracy: 90.83%, 93.08%) for wearable activity recognition task, which demonstrates the ability of the proposed approach to learn spatial, local and global temporal features.
Structure-Aware Texture Filtering Based on Local Histogram Operator

Smoothing the multi-scale textures with strong gradient while maintaining the weak structures is still a challenging work for the existing texture filtering methods. In this paper, we propose a variant of the bilateral filter to address this issue. Unlike the classical bilateral filter, which usually uses a fixed range kernel for texture filtering, the variant uses an adaptive range kernel, that is, if a pixel is located near a structure, the range kernel assigned to the pixel is small, otherwise, the range kernel is large. This mechanism ensures the preservation of sharp edges. In order to perform structure measurement, we propose a local histogram operator, which can identify structures from textures by comparing the difference in color distribution between the left and right half neighboring windows centered on the pixel of interest. In addition, after once bilateral filtering, instead of multiple iterations, a novel anisotropic filter is designed to remove residual textures, which can avoid edge blurring to some extent. Experimental results demonstrate the effectiveness of our method by comparing it with the state-of-the-art methods in preserving weak structures as well as suppressing textures with strong gradient or varying scales.
Deep neural networks for automatic speech processing: a survey from large corpora to limited data

Abstract Most state-of-the-art speech systems use deep neural networks (DNNs). These systems require a large amount of data to be learned. Hence, training state-of-the-art frameworks on under-resourced speech challenges are difficult tasks. As an example, a challenge could be the limited amount of data to model impaired speech. Furthermore, acquiring more data and/or expertise is time-consuming and expensive. In this paper, we focus on the following speech processing tasks: automatic speech recognition, speaker identification, and emotion recognition. To assess the problem of limited data, we firstly investigate state-of-the-art automatic speech recognition systems, as this is the hardest task (due to the wide variability in each language). Next, we provide an overview of techniques and tasks requiring fewer data. In the last section, we investigate few-shot techniques by interpreting under-resourced speech as a few-shot problem. In that sense, we propose an overview of few-shot techniques and the possibility of using such techniques for the speech problems addressed in this survey. It is true that the reviewed techniques are not well adapted for large datasets. Nevertheless, some promising results from the literature encourage the usage of such techniques for speech processing.
Real-time quantification of damage in structural materials during mechanical testing

A novel methodology is introduced for quantifying the severity of damage created during testing in composite components. The method uses digital image correlation combined with image processing techniques to monitor the rate at which the strain field changes during mechanical tests. The methodology is demonstrated using two distinct experimental datasets, a ceramic matrix composite specimen loaded in tension at high temperature and nine polymer matrix composite specimens containing fibre-waviness defects loaded in bending. The changes in the strain field owing to damage creation are shown to be a more effective indicator that the specimen has reached its proportional limit than using load-extension diagrams. The technique also introduces a new approach to using experimental data for creating maps indicating the spatio-temporal distribution of damage in a component. These maps indicate where damage occurs in a component, and provide information about its morphology and its time of occurrence. This presentation format is both easier and faster to interpret than the raw data which, for some tests, can consist of tens of thousands of images. This methodology has the potential to reduce the time taken to interpret large material test datasets while increasing the amount of knowledge that can be extracted from each test.
Predicting Unnecessary Nodule Biopsies from a Small, Unbalanced, and Pathologically Proven Dataset by Transfer Learning

This study explores an automatic diagnosis method to predict unnecessary nodule biopsy from a small, unbalanced, and pathologically proven database. The automatic diagnosis method is based on a convolutional neural network (CNN) model. Because of the small and unbalanced samples, the presented method aims to improve the transfer learning capability via the VGG16 architecture and optimize the related transfer learning parameters. For comparison purpose, a traditional machine learning method is implemented, which extracts the texture features and classifies the features by support vector machine (SVM). The database includes 68 biopsied nodules, 16 are pathologically proven benign and the remaining 52 are malignant. To consider the volumetric data by the CNN model, each image slice from each nodule volume is selected randomly until all image slices of each nodule are utilized. The leave-one-out and 10-folder cross validations are applied to train and test the randomly selected 68 image slices (one image slice from one nodule) in each experiment, respectively. The averages over all the experimental outcomes are the final results. The experiments revealed that the features from both the medical and the natural images share the similarity of focusing on simpler and less-abstract objects, leading to the conclusion that not the more the transfer convolutional layers, the better the classification results. Transfer learning from other larger datasets can supply additional information to small and unbalanced datasets to improve the classification performance. The presented method has shown the potential to adapt CNN architecture to improve the prediction of unnecessary nodule biopsy from small, unbalanced, and pathologically proven volumetric dataset.
The Algorithm and Structure for Digital Normalized Cross-Correlation by Using First-Order Moment

Normalized cross-correlation is an important mathematical tool in digital signal processing. This paper presents a new algorithm and its systolic structure for digital normalized cross-correlation, based on the statistical characteristic of inner-product. We first introduce a relationship between the inner-product in cross-correlation and a first-order moment. Then digital normalized cross-correlation is transformed into a new calculation formula that mainly includes a first-order moment. Finally, by using a fast algorithm for first-order moment, we can compute the first-order moment in this new formula rapidly, and thus develop a fast algorithm for normalized cross-correlation, which contributes to that arbitrary-length digital normalized cross-correlation being performed by a simple procedure and less multiplications. Furthermore, as the algorithm for the first-order moment can be implemented by systolic structure, we design a systolic array for normalized cross-correlation with a seldom multiplier, in order for its fast hardware implementation. The proposed algorithm and systolic array are also improved for reducing their addition complexity. The comparisons with some algorithms and structures have shown the performance of the proposed method.
Unsupervised Learning of Camera Pose with Compositional Re-estimation

We consider the problem of unsupervised camera pose estimation. Given an input video sequence, our goal is to estimate the camera pose (i.e. the camera motion) between consecutive frames. Traditionally, this problem is tackled by placing strict constraints on the transformation vector or by incorporating optical flow through a complex pipeline. We propose an alternative approach that utilizes a compositional re-estimation process for camera pose estimation. Given an input, we first estimate a depth map. Our method then iteratively estimates the camera motion based on the estimated depth map. Our approach significantly improves the predicted camera motion both quantitatively and visually. Furthermore, the re-estimation resolves the problem of out-of-boundaries pixels in a novel and simple way. Another advantage of our approach is that it is adaptable to other camera pose estimation approaches. Experimental analysis on KITTI benchmark dataset demonstrates that our method outperforms existing state-of-the-art approaches in unsupervised camera ego-motion estimation.