Reviewerly/exHarmony huggingface dataset:
Machine Learning for Long-Distance Quantum Communication

Machine learning can help us in solving problems in the context big data analysis and classification, as well as in playing complex games such as Go. But can it also be used to find novel protocols and algorithms for applications such as large-scale quantum communication? Here we show that machine learning can be used to identify central quantum protocols, including teleportation, entanglement purification and the quantum repeater. These schemes are of importance in long-distance quantum communication, and their discovery has shaped the field of quantum information processing. However, the usefulness of learning agents goes beyond the mere re-production of known protocols; the same approach allows one to find improved solutions to long-distance communication problems, in particular when dealing with asymmetric situations where channel noise and segment distance are non-uniform. Our findings are based on the use of projective simulation, a model of a learning agent that combines reinforcement learning and decision making in a physically motivated framework. The learning agent is provided with a universal gate set, and the desired task is specified via a reward scheme. From a technical perspective, the learning agent has to deal with stochastic environments and reactions. We utilize an idea reminiscent of hierarchical skill acquisition, where solutions to sub-problems are learned and re-used in the overall scheme. This is of particular importance in the development of long-distance communication schemes, and opens the way for using machine learning in the design and implementation of quantum networks.
Fully Convolutional Neural Network with Augmented Atrous Spatial Pyramid Pool and Fully Connected Fusion Path for High Resolution Remote Sensing Image Segmentation

Recent developments in Convolutional Neural Networks (CNNs) have allowed for the achievement of solid advances in semantic segmentation of high-resolution remote sensing (HRRS) images. Nevertheless, the problems of poor classification of small objects and unclear boundaries caused by the characteristics of the HRRS image data have not been fully considered by previous works. To tackle these challenging problems, we propose an improved semantic segmentation neural network, which adopts dilated convolution, a fully connected (FC) fusion path and pre-trained encoder for the semantic segmentation task of HRRS imagery. The network is built with the computationally-efficient DeepLabv3 architecture, with added Augmented Atrous Spatial Pyramid Pool and FC Fusion Path layers. Dilated convolution enlarges the receptive field of feature points without decreasing the feature map resolution. The improved neural network architecture enhances HRRS image segmentation, reaching the classification accuracy of 91%, and the precision of recognition of small objects is improved. The applicability of the improved model to the remote sensing image segmentation task is verified.
Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning

Dimensionality reduction (DR) is frequently used for analyzing and visualizing high-dimensional data as it provides a good first glance of the data. However, to interpret the DR result for gaining useful insights from the data, it would take additional analysis effort such as identifying clusters and understanding their characteristics. While there are many automatic methods (e.g., density-based clustering methods) to identify clusters, effective methods for understanding a cluster's characteristics are still lacking. A cluster can be mostly characterized by its distribution of feature values. Reviewing the original feature values is not a straightforward task when the number of features is large. To address this challenge, we present a visual analytics method that effectively highlights the essential features of a cluster in a DR result. To extract the essential features, we introduce an enhanced usage of contrastive principal component analysis (cPCA). Our method, called ccPCA (contrasting clusters in PCA), can calculate each feature's relative contribution to the contrast between one cluster and other clusters. With ccPCA, we have created an interactive system including a scalable visualization of clusters' feature contributions. We demonstrate the effectiveness of our method and system with case studies using several publicly available datasets.
Deep Hierarchical Encoder–Decoder Network for Image Captioning

Encoder-decoder models have been widely used in image captioning, and most of them are designed via single long short term memory (LSTM). The capacity of single-layer network, whose encoder and decoder are integrated together, is limited for such a complex task of image captioning. Moreover, how to effectively increase the “vertical depth” of encoder-decoder remains to be solved. To deal with these problems, a novel deep hierarchical encoder-decoder network is proposed for image captioning, where a deep hierarchical structure is explored to separate the functions of encoder and decoder. This model is capable of efficiently exerting the representation capacity of deep networks to fuse high level semantics of vision and language in generating captions. Specifically, visual representations in top levels of abstraction are simultaneously considered, and each of these levels is associated to one LSTM. The bottom-most LSTM is applied as the encoder of textual inputs. The application of the middle layer in encoder-decoder is to enhance the decoding ability of top-most LSTM. Furthermore, depending on the introduction of semantic enhancement module of image feature and distribution combine module of text feature, variants of architectures of our model are constructed to explore the impacts and mutual interactions among the visual representation, textual representations, and the output of the middle LSTM layer. Particularly, the framework is training under a reinforcement learning method to address the exposure bias problem between the training and the testing by the policy gradient optimization. Qualitative analyses indicate the process that our model “translates” image to sentence and further visualization presents the evolution of the hidden states from different hierarchical LSTMs over time. Extensive experiments demonstrate that our model outperforms current state-of-the-art models on three benchmark datasets: Flickr8K, Flickr30K, and MSCOCO. On both image captioning and retrieval tasks, our method achieves the best results. On MSCOCO captioning Leaderboard, our method also achieves superior performance.
Eye Movements Actively Reinstate Spatiotemporal Mnemonic Content

Eye movements support memory encoding by binding distinct elements of the visual world into coherent representations. However, the role of eye movements in memory retrieval is less clear. We propose that eye movements play a functional role in retrieval by reinstating the encoding context. By overtly shifting attention in a manner that broadly recapitulates the spatial locations and temporal order of encoded content, eye movements facilitate access to, and reactivation of, associated details. Such mnemonic gaze reinstatement may be obligatorily recruited when task demands exceed cognitive resources, as is often observed in older adults. We review research linking gaze reinstatement to retrieval, describe the neural integration between the oculomotor and memory systems, and discuss implications for models of oculomotor control, memory, and aging.
Chaotic image encryption algorithm using frequency‐domain DNA encoding

A digital image encryption algorithm based on dynamic deoxyribonucleic acid coding and chaotic operations using hyper digital chaos in frequency-domain is proposed and demonstrated, where both the amplitude and phase components in frequency-domain are diffused and scrambled. The proposed encryption algorithm is evaluated through various evaluations of key parameters such as histogram uniformity, entropy, and correlation. Excellent performance of the encrypted image is achieved to resist the statistical attacks, which implies that the statistical properties of the original image are completely destroyed. In the encryption procedure, each cipher pixel is affected by all of the plain-pixels as well as cipher-pixels, due to the implementation of chaotic diffusion and scrambling operations, which increases the sensitivity of the encrypted image to the plain-text, and improves the security against any differential attacks. Moreover, due to the high sensitivity introduced by the hyper digital chaos, a huge key space is provided for the encrypted image to ensure the high security level, thus the encryption algorithm has a strong secure capability against the brute-force attacks.
A Survey of Opinion Mining in Arabic

Opinion-mining or sentiment analysis continues to gain interest in industry and academics. While there has been significant progress in developing models for sentiment analysis, the field remains an active area of research for many languages across the world, and in particular for the Arabic language, which is the fifth most-spoken language and has become the fourth most-used language on the Internet. With the flurry of research activity in Arabic opinion mining, several researchers have provided surveys to capture advances in the field. While these surveys capture a wealth of important progress in the field, the fast pace of advances in machine learning and natural language processing (NLP) necessitates a continuous need for a more up-to-date literature survey. The aim of this article is to provide a comprehensive literature survey for state-of-the-art advances in Arabic opinion mining. The survey goes beyond surveying previous works that were primarily focused on classification models. Instead, this article provides a comprehensive system perspective by covering advances in different aspects of an opinion-mining system, including advances in NLP software tools, lexical sentiment and corpora resources, classification models, and applications of opinion mining. It also presents future directions for opinion mining in Arabic. The survey also covers latest advances in the field, including deep learning advances in Arabic Opinion Mining. The article provides state-of-the-art information to help new or established researchers in the field as well as industry developers who aim to deploy an operational complete opinion-mining system. Key insights are captured at the end of each section for particular aspects of the opinion-mining system giving the reader a choice of focusing on particular aspects of interest.
Pseudorandom number generator based on enhanced Hénon map and its implementation

This paper presents a pseudorandom number generator (PRNG) based on enhanced Hénon map (EHM) and its implementation in software and hardware for chaos-based cryptosystems with high processing such as image or video encryption. The proposed EHM presents better statistical properties and higher key sensitivity in comparison with classic Hénon map (CHM) by means of numerical tests such as bifurcation diagrams, largest Lyapunov exponent, Gottwald-Melbourne test, and histograms. The proposed 8-bit PRNG-EHM algorithm is implemented in MATLAB (software) and in FPGA technology (hardware) for experimental results. In hardware implementation, we use VHDL language and the Altera DE2-115 FPGA board with RS-232 serial port communication for data extraction, which are analyzed with MATLAB. In both software and hardware level, the proposed PRNG-EHM passes the randomness NIST 800-22 statistical tests. For first time in literature, a comprehensive security analysis from a cryptographic point of view is presented for hardware implementation such as key space analysis, key sensitivity, floating frequency, histograms, autocorrelation, correlation, entropy, and performance. Comparisons of proposed PRNG-EHM with recent similar schemes show main advantages in security capabilities for cryptographic applications. According with the results, the proposed scheme can be used in chaos-based cryptographic applications at software or hardware implementation.
A scoping review of augmented reality in nursing

Augmented reality (AR) has the potential to be utilized in various fields. Nursing fulfils the requirements of smart glass use cases, and technology may be one method of supporting nurses that face challenges such as demographic change. The development of AR to assist in nursing is now feasible. Attempts to develop applications have been made, but there has not been an overview regarding the existing research.The aim of this scoping review is to provide an overview of the current research regarding AR in nursing to identify possible research gaps. This led to the following research question: "To date, what research has been performed regarding the use of AR in nursing?". A focus has been placed on the topics involving cases, evaluations, and devices used.A scoping review was carried out with the methodological steps outlined by Arksey and O'Malley (2005) and further enhanced by Levac et al. (2010). A broad range of keywords were used systematically in eight databases including PubMed, Web of Science and ACM to search for topics in nursing.The search led to 23 publications that were included in the final analysis. The majority of the identified publications describe pilot studies. The methods used for identifying use cases and evaluating applications differ among the included studies. Furthermore, the devices used vary from study to study and may include smart glasses, tablets, and smart watches, among others. Previous studies predominantly evaluated the use of smart glasses. In addition, evaluations did not take framing conditions into account. Reviewed publications that evaluated the use of AR in nursing also identified technical challenges associated with AR.These results show that the use of AR in nursing may have positive implications. While current studies focus on evaluating prototypes, future studies should focus on performing long-term evaluations to take framing conditions and the long-term consequences of AR into consideration. Our findings are important and informative for nurses and technicians who are involved in the development of new technologies. They can use our findings to reflect on their own design of case identification, requirements for elicitation and evaluation.
An Improved CNN Model for Within-Project Software Defect Prediction

To improve software reliability, software defect prediction is used to find software bugs and prioritize testing efforts. Recently, some researchers introduced deep learning models, such as the deep belief network (DBN) and the state-of-the-art convolutional neural network (CNN), and used automatically generated features extracted from abstract syntax trees (ASTs) and deep learning models to improve defect prediction performance. However, the research on the CNN model failed to reveal clear conclusions due to its limited dataset size, insufficiently repeated experiments, and outdated baseline selection. To solve these problems, we built the PROMISE Source Code (PSC) dataset to enlarge the original dataset in the CNN research, which we named the Simplified PROMISE Source Code (SPSC) dataset. Then, we proposed an improved CNN model for within-project defect prediction (WPDP) and compared our results to existing CNN results and an empirical study. Our experiment was based on a 30-repetition holdout validation and a 10 * 10 cross-validation. Experimental results showed that our improved CNN model was comparable to the existing CNN model, and it outperformed the state-of-the-art machine learning models significantly for WPDP. Furthermore, we defined hyperparameter instability and examined the threat and opportunity it presents for deep learning models on defect prediction.
Two-qubit entangling gates within arbitrarily long chains of trapped ions

Ion trap systems are a leading platform for large scale quantum computers. Trapped ion qubit crystals are fully-connected and reconfigurable, owing to their long range Coulomb interaction that can be modulated with external optical forces. However, the spectral crowding of collective motional modes could pose a challenge to the control of such interactions for large numbers of qubits. Here, we show that high-fidelity quantum gate operations are still possible with very large trapped ion crystals, simplifying the scaling of ion trap quantum computers. To this end, we present analytical work that determines how parallel entangling gates produce a crosstalk error that falls off as the inverse cube of the distance between the pairs. We also show experimental work demonstrating entangling gates on a fully-connected chain of seventeen $^{171}{\rm{Yb}}^{+}$ ions with fidelities as high as $97(1)\%$.
Path Planning via an Improved DQN-Based Learning Policy

The path planning technology is an important part of navigation, which is the core of robotics research. Reinforcement learning is a fashionable algorithm that learns from experience by mimicking the process of human learning skills. When learning new skills, the comprehensive and diverse experience help to refine the grasp of new skills which are called as the depth and the breadth of experience. According to the path planning, this paper proposes an improved learning policy based on the different demand of the experience's depth and breadth in different learning stages, where the deep Q-networks calculated Q-value adopts the dense network framework. In the initial stage of learning, an experience value evaluation network is created to increase the proportion of deep experience to understand the environmental rules more quickly. When the path wandering phenomenon happens, the exploration of wandering point and other points are taken into account to improve the breadth of the experience pool by using parallel exploration structure. In addition, the network structure is improved by referring to the dense connection method, so the learning and expressive abilities of the network are improved to some extent. Finally, the experimental results show that our model has a certain improvement in convergence speed, planning success rate, and path accuracy. Under the same experimental conditions, the method of this paper is compared with the conventional intensive learning method via deep Q-networks. The results show that the indicators of this method are significantly higher.
Spotted Hyena Optimization Algorithm With Simulated Annealing for Feature Selection

The purpose of this paper is to propose a new hybrid metaheuristic to solve the problem of feature selection. Feature selection problem is the process of finding the most relevant subset based on some criteria. A hybrid metaheuristic is a new trend in the development of optimization algorithms. In this paper, two different hybrid models are designed based on spotted hyena optimization (SHO) for feature selection problem. The SHO algorithm can find the optimal or nearly optimal feature subset in the feature space to minimize the given fitness function. In the first model, the simulated annealing algorithm (SA) is embedded in the SHO algorithm (called SHOSA-1) to enhance the optimal solution found by the SHO algorithm after each iteration. In the second model, SA enhances the final solution obtained by the SHO algorithm (called SHOSA-2). The performance of these methods is evaluated in 20 datasets in the UCI repository. The experiments show that SHOSA-1 performs better than the native algorithm and SHOSA-2. And then, SHOSA-1 is compared with six state-of-the-art optimization algorithms. The experimental results confirm that SHOSA-1 improves the classification accuracy and reduces the number of selected features compared with other wrapper-based optimization algorithms. That proves the excellent performance of SHOSA-1 in spatial search and feature attribute selection.
DEPLEST: A blockchain-based privacy-preserving distributed database toward user behaviors in social networks

Social networks record a significant amount of user behavior data every day. By analyzing this behavior data, companies or attackers use it for marketing or more questionable purposes. We propose a blockchain-based model to protect the privacy of users’ data in such big data environments. Traditional blockchain methods require too many resources for this task, so we propose a model that secures sensitive user information in a distributed blockchain and passes nonsensitive information through to the primary system in order to manage the blockchain size. Our DEPLEST algorithm performs these synchronization operations to keep local database storage and computational capacity within the limits of individual users’ devices. We also propose a consensus protocol for blockchain ledger maintenance that runs well on typical client systems and prove that this protocol has excellent Byzantine fault tolerance (BFT). Our experimental results show that DEPLEST meets the architectural and performance needs and that our consensus protocol outperforms the existing proof of work (PoW) and proof of stake (PoS) methods in this application.
Mapping Listvenite Occurrences in the Damage Zones of Northern Victoria Land, Antarctica Using ASTER Satellite Remote Sensing Data

Listvenites normally form during hydrothermal/metasomatic alteration of mafic and ultramafic rocks and represent a key indicator for the occurrence of ore mineralizations in orogenic systems. Hydrothermal/metasomatic alteration mineral assemblages are one of the significant indicators for ore mineralizations in the damage zones of major tectonic boundaries, which can be detected using multispectral satellite remote sensing data. In this research, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral remote sensing data were used to detect listvenite occurrences and alteration mineral assemblages in the poorly exposed damage zones of the boundaries between the Wilson, Bowers and Robertson Bay terranes in Northern Victoria Land (NVL), Antarctica. Spectral information for detecting alteration mineral assemblages and listvenites were extracted at pixel and sub-pixel levels using the Principal Component Analysis (PCA)/Independent Component Analysis (ICA) fusion technique, Linear Spectral Unmixing (LSU) and Constrained Energy Minimization (CEM) algorithms. Mineralogical assemblages containing Fe2+, Fe3+, Fe-OH, Al-OH, Mg-OH and CO3 spectral absorption features were detected in the damage zones of the study area by implementing PCA/ICA fusion to visible and near infrared (VNIR) and shortwave infrared (SWIR) bands of ASTER. Silicate lithological groups were mapped and discriminated using PCA/ICA fusion to thermal infrared (TIR) bands of ASTER. Fraction images of prospective alteration minerals, including goethite, hematite, jarosite, biotite, kaolinite, muscovite, antigorite, serpentine, talc, actinolite, chlorite, epidote, calcite, dolomite and siderite and possible zones encompassing listvenite occurrences were produced using LSU and CEM algorithms to ASTER VNIR+SWIR spectral bands. Several potential zones for listvenite occurrences were identified, typically in association with mafic metavolcanic rocks (Glasgow Volcanics) in the Bowers Mountains. Comparison of the remote sensing results with geological investigations in the study area demonstrate invaluable implications of the remote sensing approach for mapping poorly exposed lithological units, detecting possible zones of listvenite occurrences and discriminating subpixel abundance of alteration mineral assemblages in the damage zones of the Wilson-Bowers and Bowers-Robertson Bay terrane boundaries and in intra-Bowers and Wilson terranes fault zones with high fluid flow. The satellite remote sensing approach developed in this research is explicitly pertinent to detecting key alteration mineral indicators for prospecting hydrothermal/metasomatic ore minerals in remote and inaccessible zones situated in other orogenic systems around the world.