Reviewerly/exHarmony huggingface dataset:
Designing tomorrow's quantum internet

Principles of quantum mechanics promise a future quantum internet that connects a wide variety of quantum devices together in a coherent and secure fashion. It is well known that due to the size of this quantum internet, quantum repeaters will be a critical part in a similar fashion to the importance of repeaters in today's telecommunications internet. Given the inherent differences between classical and quantum physics, it is essential to establish how a quantum internet will function including how we route information as well as the functionality quantum repeaters will need to provide. Our considerations here go far beyond quantum key distribution and instead focus on a true network of connected quantum devices, including computers and sensors. We show how the efficient operation of such quantum networks relies on the seamless integration of both quantum and classical communication resources.
Blockchain for Future Wireless Networks: A Decade Survey

The emerging need for high data rate, low latency, and high network capacity encourages wireless networks (WNs) to build intelligent and dynamic services, such as intelligent transportation systems, smart homes, smart cities, industrial automation, etc. However, the WN is impeded by several security threats, such as data manipulation, denial-of-service, injection, man-in-the-middle, session hijacking attacks, etc., that deteriorate the security performance of the aforementioned WN-based intelligent services. Toward this goal, various security solutions, such as cryptography, artificial intelligence (AI), access control, authentication, etc., are proposed by the scientific community around the world; however, they do not have full potential in tackling the aforementioned security issues. Therefore, it necessitates a technology, i.e., a blockchain, that offers decentralization, immutability, transparency, and security to protect the WN from security threats. Motivated by these facts, this paper presents a WNs survey in the context of security and privacy issues with blockchain-based solutions. First, we analyzed the existing research works and highlighted security requirements, security issues in a different generation of WN (4G, 5G, and 6G), and a comparative analysis of existing security solutions. Then, we showcased the influence of blockchain technology and prepared an exhaustive taxonomy for blockchain-enabled security solutions in WN. Further, we also proposed a blockchain and a 6G-based WN architecture to highlight the importance of blockchain technology in WN. Moreover, the proposed architecture is evaluated against different performance metrics, such as scalability, packet loss ratio, and latency. Finally, we discuss various open issues and research challenges for blockchain-based WNs solutions.
An Efficient AdaBoost Algorithm with the Multiple Thresholds Classification

Adaptive boost (AdaBoost) is a prominent example of an ensemble learning algorithm that combines weak classifiers into strong classifiers through weighted majority voting rules. AdaBoost’s weak classifier, with threshold classification, tries to find the best threshold in one of the data dimensions, dividing the data into two categories-1 and 1. However, in some cases, this Weak Learning algorithm is not accurate enough, showing poor generalization performance and a tendency to over-fit. To solve these challenges, we first propose a new Weak Learning algorithm that classifies examples based on multiple thresholds, rather than only one, to improve its accuracy. Second, in this paper, we make changes to the weight allocation scheme of the Weak Learning algorithm based on the AdaBoost algorithm to use potential values of other dimensions in the classification process, while the theoretical identification is provided to show its generality. Finally, comparative experiments between the two algorithms on 18 datasets on UCI show that our improved AdaBoost algorithm has a better generalization effect in the test set during the training iteration.
Quantum arbitrary waveform generator

Controlling the waveform of light is the key for a versatile light source in classical and quantum electronics. Although pulse shaping of classical light is a mature technique and has been used in various fields, more advanced applications would be realized by a light source that generates arbitrary quantum light with arbitrary temporal waveform. We call such a device a quantum arbitrary waveform generator (Q-AWG). The Q-AWG must be able to handle versatile quantum states of light, which are fragile. Thus, the Q-AWG requires a radically different methodology from classical pulse shaping. In this paper, we invent an architecture of Q-AWGs that can operate semi-deterministically at a repetition rate over GHz in principal. We demonstrate its core technology via generating highly non-classical states with waveforms that have never been realized before. This result would lead to powerful quantum technologies based on Q-AWGs such as practical optical quantum computing.
Sustainable Teaching and Learning through a Mobile Application: A Case Study

Currently, mobile devices are widely used as a support in education for teaching and learning of multiple academic subjects. An example of this is educational mobile applications, which in recent years have been massively developed and have generated multiple downloads for use in the classroom. Despite their features and benefits, the use of mobile devices such as smartphones is not usually allowed in classrooms due to the distraction they can generate. This paper aims to evidence the use of mobile devices in education and why it should be used as a support in the educational model. To do this, it is proposed to compare two teaching-learning methodologies and identify whether the use of mobile applications can influence the specific education of an engineering subject. The methodologies were tested in the classroom, focusing on IP addressing and network numbering systems, with two groups of students: an experimental group and a control group. At the end of the experiment, their performance was evaluated using a questionnaire. The answers of this questionnaire were subjected to an analysis of variance (ANOVA) and hypotheses were proposed to identify whether the use of a mobile application used as a support in the educational model has benefits in learning. The results indicate that educational mobile applications can be helpful in the teaching-learning process and at present, education can benefit from the use of this innovative learning methodology.
Knowledge distillation with ensembles of convolutional neural networks for medical image segmentation

Purpose: Ensembles of convolutional neural networks (CNNs) often outperform a single CNN in medical image segmentation tasks, but inference is computationally more expensive and makes ensembles unattractive for some applications. We compared the performance of differently constructed ensembles with the performance of CNNs derived from these ensembles using knowledge distillation, a technique for reducing the footprint of large models such as ensembles. Approach: We investigated two different types of ensembles, namely, diverse ensembles of networks with three different architectures and two different loss-functions, and uniform ensembles of networks with the same architecture but initialized with different random seeds. For each ensemble, additionally, a single student network was trained to mimic the class probabilities predicted by the teacher model, the ensemble. We evaluated the performance of each network, the ensembles, and the corresponding distilled networks across three different publicly available datasets. These included chest computed tomography scans with four annotated organs of interest, brain magnetic resonance imaging (MRI) with six annotated brain structures, and cardiac cine-MRI with three annotated heart structures. Results: Both uniform and diverse ensembles obtained better results than any of the individual networks in the ensemble. Furthermore, applying knowledge distillation resulted in a single network that was smaller and faster without compromising performance compared with the ensemble it learned from. The distilled networks significantly outperformed the same network trained with reference segmentation instead of knowledge distillation. Conclusion: Knowledge distillation can compress segmentation ensembles of uniform or diverse composition into a single CNN while maintaining the performance of the ensemble.
A Big Wave of Deep Learning in Medical Imaging - Analysis of Theory and Applications

Deep learning models have become the province of cutting-edge machine learning models that are widely used in medical imaging in multiple kinds of ranging from image recognition to natural language processing. This article presents a brief overview on the recent developments and some relevant issues in medical image processing and image analysis as they related to machine learning. The proposed article will not cover the entire application landscape because it is becoming a very large and rapidly growing field, but will instead focus primarily on deep learning theory and techniques in medical imaging. Our goal is threefold: (i) To provide such a brief overview to deep medical imaging learning; (ii) To represent different approaches as well as comparisons of how deep learning has been accomplished, from image retrieval to segmentation to disease prediction; (iii) Provide the current medical imaging-related deep learning applications.
A Bitcoin price prediction model assuming oscillatory growth and lengthening cycles

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Multi-channel multiplexing quantum teleportation based on the entangled sideband modes

Quantum teleportation is a key primitive across a number of quantum information tasks and represents a fundamental ingredient for many quantum technologies. Channel capacity, other than the fidelity, becomes another focus of quantum communication. Here, we present a 5-channel multiplexing continuous-variable quantum teleportation protocol in the optical frequency comb system, exploiting five-order entangled sideband modes. Because of the resonant electro-optical modulation (EOM) that is specifically designed, the fidelities of five channels are greater than 0.78, which are superior to the no-cloning limit of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m1"> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>/</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:math> . This work provides a feasible scheme for implementing efficient quantum information processing.
Efficient Long Short-Term Memory-Based Sentiment Analysis of E-Commerce Reviews

In today's modern era, e-commerce is making headway through the process of bringing goods within everyone's grasp. Consumers are not even required to step out of the comfort of their homes for buying things, which makes it very convenient for them. Moreover, there is a wide variety of brands to choose from. Since more customers depend on online shopping platforms these days, the value of ratings is also growing. To buy these products, people rely solely on the reviews that are being provided about the products. To analyze these reviews, sentiment analysis needs to be performed, which can prove useful for both the buyers and the manufacturer. This paper describes the process of sentiment analysis and its requirements. In this paper, Amazon Review dataset 2018 has been used for carrying out our research and Long Short-Term Memory (LSTM) has been combined with word2vec representation, resulting in improving the overall performance. A gating mechanism was used by LSTM during the training process. The proposed LSTM model was evaluated on four performance measures: accuracy, precision, recall, and F1 score, and achieved overall higher results when compared with other baseline models.
Investigating the Impact of Min-Max Data Normalization on the Regression Performance of K-Nearest Neighbor with Different Similarity Measurements

K-nearest neighbor (KNN) is a lazy supervised learning algorithm, which depends on computing the similarity between the target and the closest neighbor(s). On the other hand, min-max normalization has been reported as a useful method for eliminating the impact of inconsistent ranges among attributes on the efficiency of some machine learning models. The impact of min-max normalization on the performance of KNN models is still not clear, and it needs more investigation. Therefore, this research examines the impacts of the min-max normalization method on the regression performance of KNN models utilizing eight different similarity measures, which are City block, Euclidean, Chebychev, Cosine, Correlation, Hamming, Jaccard, and Mahalanobis. Five benchmark datasets have been used to test the accuracy of the KNN models with the original dataset and the normalized dataset. Mean squared error (MSE) has been utilized as a performance indicator to compare the results. It’s been concluded that the impact of min-max normalization on the KNN models utilizing City block, Euclidean, Chebychev, Cosine, and Correlation depends on the nature of the dataset itself, therefore, testing models on both original and normalized datasets are recommended. The performance of KNN models utilizing Hamming, Jaccard, and Mahalanobis makes no difference by adopting min-max normalization because of their ratio nature, and dataset covariance involvement in the similarity calculations. Results showed that Mahalanobis outperformed the other seven similarity measures. This research is better than its peers in terms of reliability, and quality because it depended on testing different datasets from different application fields.
Assessment of solar radiation resource from the NASA-POWER reanalysis products for tropical climates in Ghana towards clean energy application

In order to expand the output of solar power systems for efficient integration into the national grid, solar energy resource assessment at site is required. A major impediment however, is the widespread scarcity of radiometric measurements, which can be augmented by satellite observation. This paper assessed the suitability of satellite-based solar radiation resource retrieved from the NASA-POWER archives at [Formula: see text] spatial resolution over Ghana-West Africa, to develop a long-term source reference. The assessment is based on the criteria of comparison with estimations from sunshine duration measurement for 22 synoptic stations. Overall, the satellite-based data compared well with ground-based estimations by r = 0.6-0.94 ± 0.1. Spatiotemporally, the agreement is strongest over the northern half Savannah-type climate during March-May, and weakest over the southern half Forest-type climate during June-August. The assessment provides empirical framework to support solar energy utilization in the sub-region.
Machine Learning-Based Systems for the Anticipation of Adverse Events After Pediatric Cardiac Surgery

Pediatric congenital heart disease (CHD) patients are at higher risk of postoperative complications and clinical deterioration either due to their underlying pathology or due to the cardiac surgery, contributing significantly to mortality, morbidity, hospital and family costs, and poor quality of life. In current clinical practice, clinical deterioration is detected, in most of the cases, when it has already occurred. Several early warning scores (EWS) have been proposed to assess children at risk of clinical deterioration using vital signs and risk indicators, in order to intervene in a timely manner to reduce the impact of deterioration and risk of death among children. However, EWS are based on measurements performed at a single time point without incorporating trends nor providing information about patient's risk trajectory. Moreover, some of these measurements rely on subjective assessment making them susceptible to different interpretations. All these limitations could explain why the implementation of EWS in high-resource settings failed to show a significant decrease in hospital mortality. By means of machine learning (ML) based algorithms we could integrate heterogeneous and complex data to predict patient's risk of deterioration. In this perspective article, we provide a brief overview of the potential of ML technologies to improve the identification of pediatric CHD patients at high-risk for clinical deterioration after cardiac surgery, and present the CORTEX traffic light, a ML-based predictive system that Sant Joan de Déu Barcelona Children's Hospital is implementing, as an illustration of the application of an ML-based risk stratification system in a relevant hospital setting.
Efficient Entropic Security with Joint Compression and Encryption Approach Based on Compressed Sensing with Multiple Chaotic Systems

This paper puts forward a new algorithm that utilizes compressed sensing and two chaotic systems to complete image compression and encryption concurrently. First, the hash function was utilized to obtain the initial parameters of two chaotic maps, which were the 2D-SLIM and 2D-SCLMS maps, respectively. Second, a sparse coefficient matrix was transformed from the plain image through discrete wavelet transform. In addition, one of the chaotic sequences created by 2D-SCLMS system performed pixel transformation on the sparse coefficient matrix. The other chaotic sequences created by 2D-SLIM were utilized to generate a measurement matrix and perform compressed sensing operations. Subsequently, the matrix rotation was combined with row scrambling and column scrambling, respectively. Finally, the bit-cycle operation and the matrix double XOR were implemented to acquire the ciphertext image. Simulation experiment analysis showed that the compressed encryption scheme has advantages in compression performance, key space, and sensitivity, and is resistant to statistical attacks, violent attacks, and noise attacks.
Internet of things based electrocardiogram monitoring system using machine learning algorithm

&lt;span&gt;In Bangladesh’s rural regions, almost 30% of the population lives in poverty. Rural residents also have restricted access to nursing and diagnostic services due to obsolete healthcare infrastructure. Consequently, as cardiac failure occurs, they usually fail to call the services and adopt the facilities. The internet of things (IoT) offers a massive advantage in addressing cardiac problems. This study proposed a smart IoT-based electrocardiogram (ECG) monitoring system for heart patients. The system is divided into several parts: ECG sensing network (data acquisition), IoT cloud (data transmission), result analysis (data prediction) and monetization. P, Q, R, S, and T are ECG signal properties fetched, pre-processed, analyzed and predicted to age level for future health management. ECG data are saved in the cloud and accessible via message queuing telemetry transport (MQTT) and hypertext transfer protocol (HTTP) servers. The linear regression method is utilized to determine the impact of electrocardiogram signal characteristics and error rate. The prediction was made to see how much variation there was in PQRST regularity and its sufficiency to be utilized in an ECG monitoring device. Recognizing the quality parameter values, acceptable outcomes are achieved. The proposed system will diminish future medical costs and difficulties for heart patients.&lt;/span&gt;
Medical visual question answering based on question-type reasoning and semantic space constraint

Medical visual question answering (Med-VQA) aims to accurately answer clinical questions about medical images. Despite its enormous potential for application in the medical domain, the current technology is still in its infancy. Compared with general visual question answering task, Med-VQA task involve more demanding challenges. First, clinical questions about medical images are usually diverse due to different clinicians and the complexity of diseases. Consequently, noise is inevitably introduced when extracting question features. Second, Med-VQA task have always been regarded as a classification problem for predefined answers, ignoring the relationships between candidate responses. Thus, the Med-VQA model pays equal attention to all candidate answers when predicting answers. In this paper, a novel Med-VQA framework is proposed to alleviate the above-mentioned problems. Specifically, we employed a question-type reasoning module severally to closed-ended and open-ended questions, thereby extracting the important information contained in the questions through an attention mechanism and filtering the noise to extract more valuable question features. To take advantage of the relational information between answers, we designed a semantic constraint space to calculate the similarity between the answers and assign higher attention to answers with high correlation. To evaluate the effectiveness of the proposed method, extensive experiments were conducted on a public dataset, namely VQA-RAD. Experimental results showed that the proposed method achieved better performance compared to other the state-of-the-art methods. The overall accuracy, closed-ended accuracy, and open-ended accuracy reached 74.1 %, 82.7 %, and 60.9 %, respectively. It is worth noting that the absolute accuracy of the proposed method improved by 5.5 % for closed-ended questions.
Is Your Data Relevant?: Dynamic Selection of Relevant Data for Federated Learning

Federated Learning (FL) is a machine learning paradigm in which multiple clients participate to collectively learn a global machine learning model at the central server. It is plausible that not all the data owned by each client is relevant to the server's learning objective. The updates incorporated from irrelevant data could be detrimental to the global model. The task of selecting relevant data is explored in traditional machine learning settings where the assumption is that all the data is available in one place. In FL settings, the data is distributed across multiple clients and the server can't introspect it. This precludes the application of traditional solutions to selecting relevant data here. In this paper, we propose an approach called Federated Learning with Relevant Data (FLRD), that facilitates clients to derive updates using relevant data. Each client learns a model called Relevant Data Selector (RDS) that is private to itself to do the selection. This in turn helps in building an effective global model. We perform experiments with multiple real-world datasets to demonstrate the efficacy of our solution. The results show (a) the capability of FLRD to identify relevant data samples at each client locally and (b) the superiority of the global model learned by FLRD over other baseline algorithms.