Reviewerly/exHarmony huggingface dataset:
Compression of High-Resolution Satellite Images Using Optical Image Processing

This chapter presents a novel method for compressing satellite imagery using phase grating to facilitate the optimization of storage space and bandwidth in satellite communication. In this research work, each Satellite image is first modulated with high grating frequency in a fixed orientation. Due to this modulation, three spots (spectrum) have been generated. From these three spots, by applying Inverse Fourier Transform in any one band, we can recover the image. Out of these three spots, one is center spectrum spot and other spots represent two sidebands. Care should be taken during the spot selection is to avoid aliasing effect. At the receiving end, to recover image we use only one spectrum. We have proved that size of the extracted image is less than the original image. In this way, compression of satellite image has been performed. To measure quality of the output images, PSNR value has been calculated and compared this value with previous techniques. As high-resolution satellite image contains a lot of information, therefore to get detail information from extracted image, compression ratio should be as minimum as possible.
Development of Add-On Planar Translational Driving System for Aerial Manipulation with Multirotor Platform

We propose an add-on planar translational driving system (ATD) which can be equipped on a multirotor platform for aerial manipulation. The device is lightweight and consists of three ducted fans controlled via an on-board CPU. It uses a simple control method and enables a multirotor to perform positioning and generate force in two dimensions while keeping the airframe horizontal. By translating the multirotor without changing attitude, it can more smoothly and easily perform many types of aerial manipulation tasks with higher positioning accuracy. In this paper, we mainly show the design, modeling, and control of the ATD. Several preliminary experiments were performed to verify the positioning accuracy and effectiveness of the system. In addition, we successfully performed the push and pull task using a rigid arm.
Gender Prediction Based on Vietnamese Names with Machine Learning Techniques

As biological gender is one of the aspects of presenting individual human, much work has been done on gender classification based on people names. The proposals for English and Chinese languages are tremendous; still, there have been few works done for Vietnamese so far. We propose a new dataset for gender prediction based on Vietnamese names. This dataset comprises over 26,000 full names annotated with genders. This dataset is available on our website for research purposes. In addition, this paper describes six machine learning algorithms (Support Vector Machine, Multinomial Naive Bayes, Bernoulli Naive Bayes, Decision Tree, Random Forrest and Logistic Regression) and a deep learning model (LSTM) with fastText word embedding for gender prediction on Vietnamese names. We create a dataset and investigate the impact of each name component on detecting gender. As a result, the best F1-score that we have achieved is up to 96% on LSTM model and we generate a web API based on our trained model.
WITHDRAWN: Contrasting the performance metrics of discrete transformations on digital image steganography using artificial intelligence

Image steganography is the impressive art of hiding or embedding a secret image within cover image without any loss when subject to human vision system. The digital image steganography is initially performed by using Least Significant Bit (LSB) technique in spatial domain. Later, the image steganography is implemented by using various frequency domain techniques in which the secret images are transformed to frequency domain and their payload bits are embedded to the frequency domain of the cover image. In this work, the most commonly used transformation techniques like Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are applied to the colour images of different formats such as portable network graphics (png), bitmap (bmp), joint photographic experts group (jpeg) and tagged image file format (tiff) and their performance metrics are compared. Additional to the various transformation techniques and colour image formats, various payloads of the secret images are also used. From the overall experimental results and the comparison of performance metrics like mean square error (MSE), peak signal to noise ratio (PSNR), structural similarity index (SSIM) and structural dissimilarity (DSSIM), it is evident that DWT is highly recommendable transformation technique for image steganography compared to other techniques expect for lesser payloads, where DFT exhibits better PSNR and for JPEG images, DCT shows better PSNR.
EVIDENCE BASED LEARNING: AN ANALYSIS OF IMPACT OF EXPERIENTIAL LEARNING ON ATTAINMENT OF INTENDED SPECIFIC LEARNING OUTCOME

The intension of the present study is to enhance the conceptual understanding amongst Grade 12 School students. As a teacher, it is very challenging to make the learners to be strong and have deep understanding in the theoretical concept for its application in day- to-day life. The experiential learning activities enhance the deep learning. The purpose of the study is to investigate the effect of experiential learning activities designed for the concept “corrosion” on the academic performance amongst Senior Secondary School students in Chemistry. Totally 40 students participate in the study. The samples are drawn using Purposive sampling technique. The samples are divided in to two groups: study and the control group based of their performance of formative assessment before the treatment. Students of the study group are taught by using experiential learning activities. The present study is experimental in nature. Data is collected using formative assessment before and after the treatment. Mean, mean difference and standard deviation are the statistical tools applied to analyze the data. Results of the study reveal that experiential learning activities have a positive impact on the deep conceptual understanding to achieve the intended specific learning outcome. Based on the results few recommendations are made.
Implicit Normalizing Flows

Normalizing flows define a probability distribution by an explicit invertible transformation $\boldsymbol{\mathbf{z}}=f(\boldsymbol{\mathbf{x}})$. In this work, we present implicit normalizing flows (ImpFlows), which generalize normalizing flows by allowing the mapping to be implicitly defined by the roots of an equation $F(\boldsymbol{\mathbf{z}}, \boldsymbol{\mathbf{x}})= \boldsymbol{\mathbf{0}}$. ImpFlows build on residual flows (ResFlows) with a proper balance between expressiveness and tractability. Through theoretical analysis, we show that the function space of ImpFlow is strictly richer than that of ResFlows. Furthermore, for any ResFlow with a fixed number of blocks, there exists some function that ResFlow has a non-negligible approximation error. However, the function is exactly representable by a single-block ImpFlow. We propose a scalable algorithm to train and draw samples from ImpFlows. Empirically, we evaluate ImpFlow on several classification and density modeling tasks, and ImpFlow outperforms ResFlow with a comparable amount of parameters on all the benchmarks.
What senior academics can do to support reproducible and open research: a short, three-step guide

Increasingly, policies are being introduced to reward and recognise open research practices, while the adoption of such practices into research routines is being facilitated by many grassroots initiatives. However, despite this widespread endorsement and support, open research is yet to be widely adopted, with early career researchers being the notable exception. For open research to become the norm, initiatives should engage academics from all career stages, particularly senior academics (namely senior lecturers, readers, professors) given their routine involvement in determining the quality of research. Senior academics, however, face unique challenges in implementing policy change and supporting grassroots initiatives. Given that - like all researchers - senior academics are in part motivated by self-interest, this paper lays out three feasible steps that senior academics can take to improve the quality and productivity of their research, that also serve to engender open research. These steps include a) change hiring criteria, b) change how scholarly outputs are credited, and c) change to funding and publishing with open research. The guidance we provide is accompanied by live, crowd-sourced material for further reading.
Ship Detection for SAR Imagery Based on Deep Learning: A Benchmark

SAR Ship Detection Dataset (SSDD) has been widely used by researchers because it can achieve a good tradeoff between model performance and time consumption of experiments. However, there is still a lack of performance benchmark for state-of-the-art methods on SSDD. On the one hand, this makes it difficult for researchers to grasp the performance of mainstream methods in general; on the other hand, research results of SAR ship detection based on deep learning cannot be compared horizontally in a unified experiment setup. To solve the lack of this solid work in the field of ship detection in SAR images based on deep learning, this paper benchmarks state-of-the-art detection methods on SSDD to provide an overview for future research work.
Finite-key analysis of loss-tolerant quantum key distribution based on random sampling theory

The core of security proofs of quantum key distribution (QKD) is the estimation of a parameter that determines the amount of privacy amplification that the users need to apply in order to distill a secret key. To estimate this parameter using the observed data, one needs to apply concentration inequalities, such as random sampling theory or Azuma's inequality. The latter can be straightforwardly employed in a wider class of QKD protocols, including those that do not rely on mutually unbiased encoding bases, such as the loss-tolerant (LT) protocol. However, when applied to real-life finite-length QKD experiments, Azuma's inequality typically results in substantially lower secret-key rates. Here, we propose an alternative security analysis of the LT protocol against general attacks, for both its prepare-and-measure and measure-device-independent versions, that is based on random sampling theory. Consequently, our security proof provides considerably higher secret-key rates than the previous finite-key analysis based on Azuma's inequality. This work opens up the possibility of using random sampling theory to provide alternative security proofs for other QKD protocols.
Adaptive hyperparameter updating for training restricted Boltzmann machines on quantum annealers

Restricted Boltzmann Machines (RBMs) have been proposed for developing neural networks for a variety of unsupervised machine learning applications such as image recognition, drug discovery, and materials design. The Boltzmann probability distribution is used as a model to identify network parameters by optimizing the likelihood of predicting an output given hidden states trained on available data. Training such networks often requires sampling over a large probability space that must be approximated during gradient based optimization. Quantum annealing has been proposed as a means to search this space more efficiently which has been experimentally investigated on D-Wave hardware. D-Wave implementation requires selection of an effective inverse temperature or hyperparameter ([Formula: see text]) within the Boltzmann distribution which can strongly influence optimization. Here, we show how this parameter can be estimated as a hyperparameter applied to D-Wave hardware during neural network training by maximizing the likelihood or minimizing the Shannon entropy. We find both methods improve training RBMs based upon D-Wave hardware experimental validation on an image recognition problem. Neural network image reconstruction errors are evaluated using Bayesian uncertainty analysis which illustrate more than an order magnitude lower image reconstruction error using the maximum likelihood over manually optimizing the hyperparameter. The maximum likelihood method is also shown to out-perform minimizing the Shannon entropy for image reconstruction.
Last-iterate Convergence of Decentralized Optimistic Gradient Descent/Ascent in Infinite-horizon Competitive Markov Games

We study infinite-horizon discounted two-player zero-sum Markov games, and develop a decentralized algorithm that provably converges to the set of Nash equilibria under self-play. Our algorithm is based on running an Optimistic Gradient Descent Ascent algorithm on each state to learn the policies, with a critic that slowly learns the value of each state. To the best of our knowledge, this is the first algorithm in this setting that is simultaneously rational (converging to the opponent's best response when it uses a stationary policy), convergent (converging to the set of Nash equilibria under self-play), agnostic (no need to know the actions played by the opponent), symmetric (players taking symmetric roles in the algorithm), and enjoying a finite-time last-iterate convergence guarantee, all of which are desirable properties of decentralized algorithms.
A novel bidirectional clustering algorithm based on local density

Abstract With the widely application of cluster analysis, the number of clusters is gradually increasing, as is the difficulty in selecting the judgment indicators of cluster numbers. Also, small clusters are crucial to discovering the extreme characteristics of data samples, but current clustering algorithms focus mainly on analyzing large clusters. In this paper, a bidirectional clustering algorithm based on local density (BCALoD) is proposed. BCALoD establishes the connection between data points based on local density, can automatically determine the number of clusters, is more sensitive to small clusters, and can reduce the adjusted parameters to a minimum. On the basis of the robustness of cluster number to noise, a denoising method suitable for BCALoD is proposed. Different cutoff distance and cutoff density are assigned to each data cluster, which results in improved clustering performance. Clustering ability of BCALoD is verified by randomly generated datasets and city light satellite images.
Cache Blocking Technique to Large Scale Quantum Computing Simulation on Supercomputers

Classical computers require large memory resources and computational power to simulate quantum circuits with a large number of qubits. Even supercomputers that can store huge amounts of data face a scalability issue in regard to parallel quantum computing simulations because of the latency of data movements between distributed memory spaces. Here, we apply a cache blocking technique by inserting swap gates in quantum circuits to decrease data movements. We implemented this technique in the open source simulation framework Qiskit Aer. We evaluated our simulator on GPU clusters and observed good scalability.
Research on laser stripe characteristics and center extraction algorithm for desktop laser scanner

Abstract This study presents laser stripe center extraction algorithm for desktop-level 3D laser scanners. The laser stripe center extraction accuracy is an important factor affecting 3D scanning result. Desktop-level devices should have adaptability of a wide range of scanning objects. In this paper, laser stripe energy distribution characteristics with different laser stripe width, ambient light, materials and colors are obtained by experiments. Experiment results show that waveforms of bright spot, low brightness stripe and stripe with large width are complex or easily disturbed, so the center extraction algorithm of them are studied. The extraction effects of extremum method, gradient method and gray centroid method under different conditions are compared. Based on traditional grayscale value, a weighted grayscale value is proposed to extract laser stripe center. Standard deviations of extracted pixel position and fitting pixel position are calculated by different method with different weighted grayscale value. For different conditions, especially for different ambient light intensity, weight matrix plays an important role to extraction result.
PFA-GAN: Pose Face Augmentation Based on Generative Adversarial Network

In this work, we propose a novel framework based on Generative Adversarial Networks for pose face augmentation (PFA-GAN).It enables a controlled pose synthesis of a new face image from a source face given a driving one while preserving the identity of the source face.We introduce a method for training the framework in a fully self-supervised mode using a large-scale dataset of unconstrained face images.Besides, some augmentation strategies are presented to expand the training set.The face verification experimental results demonstrate the effectiveness of the presented augmentation strategies as all augmented datasets outperform the baseline.
Options for Accessing Online Electronic Resources in Difficult Fiscal Times

Accessing electronic books and electronic journals both on- and off-campus is no longer a perk, but rather an expectation of community members. Providing access off-campus is expensive and requires constant monitoring. Further, costs for electronic journals continue to increase and publisher pricing models and products have changed. A 2019 survey of hospital, corporate, and academic librarians showed that libraries plan to continue using the decision-making procedures and policies they created a decade ago. If existing policies and procedures in libraries are not revisited and updated to meet challenges presented by the Digital Age, options for accessing electronic information will diminish.
Handwritten Text Recognition using Deep Learning

There are many researchers working on handwritten text recognition (HTR) and also contributing to HTR domain. Even though many research methods are existing for HTR, there is a need for some more improvements in the accuracy of the HTR systems. This paper is a contribution of the application of the Deep Learning algorithm for the HTR system. In this paper first we will collect the data for training the handwritten texts, later features have been extracted from those text datasets and perform training of the model using Deep Learning approach. In this work we are going to use the strategy to recognize in terms of words rather those characters so that accuracy will be improved. The built model using LSTM deep model achieves a very good accuracy. Lastly, this developed approach of the HTR system is integrated into the OCR system and comparison of results are reported in this paper. Two approaches have been compared in this paper on IAM handwritten data set, and found that 2DLSTM based approach outperforms the other approach.
An Improved Baseline for Sentence-level Relation Extraction

Sentence-level relation extraction (RE) aims at identifying the relationship between two entities in a sentence. Many efforts have been devoted to this problem, while the best performing methods are still far from perfect. In this paper, we revisit two problems that affect the performance of existing RE models, namely entity representation and noisy or ill-defined labels. Our improved RE baseline, incorporated with entity representations with typed markers, achieves an F1 of 74.6% on TACRED, significantly outperforms previous SOTA methods. Furthermore, the presented new baseline achieves an F1 of 91.1% on the refined Re-TACRED dataset, demonstrating that the pretrained language models (PLMs) achieve high performance on this task. We release our code to the community for future research.
SQL-Like Interpretable Interactive Video Search

Concept-free search, which embeds text and video signals in a joint space for retrieval, appears to be a new state-of-the-art. However, this new search paradigm suffers from two limitations. First, the search result is unpredictable and not interpretable. Second, the embedded features are in high-dimensional space hindering real-time indexing and search. In this paper, we present a new implementation of the Vireo video search system (Vireo-VSS), which employs a dual-task model to index each video segment with an embedding feature in a low dimension and a concept list for retrieval. The concept list serves as a reference to interpret its associated embedded feature. With these changes, a SQL-like querying interface is designed such that a user can specify the search content (subject, predicate, object) and constraint (logical condition) in a semi-structured way. The system will decompose the SQL-like query into multiple sub-queries depending on the constraint being specified. Each sub-query is translated into an embedding feature and a concept list for video retrieval. The search result is compiled by union or pruning of the search lists from multiple sub-queries. The SQL-like interface is also extended for temporal querying, by providing multiple SQL templates for users to specify the temporal evolution of a query.
MATLAB-based general approach for square-root extended-unscented and fifth-degree cubature Kalman filtering methods

A stable square-root approach has been recently proposed for the unscented Kalman filter (UKF) and fifth-degree cubature Kalman filter (5D-CKF) as well as for the mixed-type methods consisting of the extended Kalman filter (EKF) time update and the UKF/5D-CKF measurement update steps. The mixed-type estimators provide a good balance in trading between estimation accuracy and computational demand because of the EKF moment differential equations involved. The key benefit is a consolidation of reliable state mean and error covariance propagation by using delicate discretization error control while solving the EKF moment differential equations and an accurate measurement update according to the advanced UKF and/or 5D-CKF filtering strategies. Meanwhile the drawback of the previously proposed estimators is an utilization of sophisticated numerical integration scheme with the built-in discretization error control that is, in fact, a complicated and computationally costly tool. In contrast, we design here the mixed-type methods that keep the same estimation quality but reduce a computational time significantly. The novel estimators elegantly utilize any MATLAB-based numerical integration scheme developed for solving ordinary differential equations (ODEs) with the required accuracy tolerance pre-defined by users. In summary, a simplicity of the suggested estimators, their numerical robustness with respect to roundoff due to the square-root form utilized as well as their estimation accuracy due to the MATLAB ODEs solvers with discretization error control involved are the attractive features of the novel estimators. The numerical experiments are provided for illustrating a performance of the suggested methods in comparison with the existing ones.