Reviewerly/exHarmony huggingface dataset:
Quantum Channel Characteristics from the Point of View of Stability

The article presents a series of measurements conducted on the fully-operated Quantum Key Distribution system. These measurements primarily focus on the Quantum Bit Error Rate (QBER), which is the most important parameter of the quantum channel. This parameter was observed and measured for 16 days under the quantum channel’s operating conditions to determine any correlations between the QBER and other quantum link parameters, such as secret key rate. A thorough statistical analysis of the measured data was performed as a part of this investigation and is presented in the paper.
Artificial Bee Colony Optimization-Based Load Balancing in Distributed Computing Systems—A Survey

Distributed computing allows the interoperability of components in distributed system in which software or hardware components located at networked computers coordinate and communicate their actions by message passing. The tasks in such a system are carried out independently. In distributed computing systems, load balancing is one of the issues, which is a means to distribute the tasks such that the computational nodes are neither overloaded nor underloaded, and the performance of the system is improved. Among the various solutions proposed for load balancing, metaheuristic-based algorithms are one of them. This paper discusses the variants and recent developments of artificial bee colony optimization algorithm for solving load balancing in distributed systems. As a result of load balancing, various performance metrics measured are throughput, response time, makespan, CPU utilization, memory utilization, and network utilization. The performance of the optimization algorithms is also measured with the time taken to converge in finding the optimal solution for load balancing.
Performance Analysis of Cooperative Automatic Modulation Classification using Higher Order Statistics

Over the past few decades, many automatic modulation classification (AMC) algorithms were discussed based on their ability to classify the modulation scheme. However, in practical scenarios, the AMC algorithm should have low complexity, high speed, and better resource utilization. The majority of the current modulation classification methods have inferior performance in low SNRs. In this paper, a cooperative AMC (CAMC) approach using soft decision fusion based on the combination of fourth and sixth order cumulants over additive white Gaussian noise channel and multipath fading channel is proposed. This proposed approach improves the classification accuracy of the modulation schemes at low SNR comparatively.
Arabic Sign Language Recognition Using Deep Learning Models

Arabic Sign Language (ArSL) is a family of sign languages that are spread throughout the Arab world. This paper focuses on developing a robust deep learning model that is trained on the ArSL2018 dataset to convert images of the ArSL alphabets into Arabic alphabets. The ArSL2018 dataset consists of 54,049 images of 32 alphabets collected from 40 signers. We have implemented and validated several deep learning models, including Convolutional Neural Network (CNN), VGG-16, and ResNet-18. The best result is attained by the modified ResNet-18 model which achieved an average test accuracy of 99.47%.
Multi-Task Federated Edge Learning (MTFeeL) With SignSGD

The paper proposes a novel Federated Learning (FL) algorithm involving signed gradient as feedback to reduce communication overhead. The Multi-task nature of the algorithm provides each device a custom neural network after completion. Towards improving the performance, a weighted average loss across devices is proposed which considers the similarity between their data distributions. A Probably Approximately Correct (PAC) bound on the true loss in terms of the proposed empirical loss is derived. The bound is in terms of (i) Rademacher complexity, (ii) discrepancy, and (iii) penalty term. A distributed algorithm is proposed to find the discrepancy as well as the fine tuned neural network at each node. It is experimentally shown that this proposed method outperforms existing algorithms such as FedSGD, DITTO, FedAvg and locally trained neural network with good generalization on various data sets.
Semantic Masking: A Novel Technique to Mitigate the Class-Imbalance Problem in Real-Time Semantic Segmentation

In the field of computer vision and scene under-standing, semantic segmentation is considered to be one of the most challenging task. This is due to the fact that it has to solve all the three standard vision problems, multi-class classification, object detection and image segmentation. One of the most promising areas of application of semantic segmentation is autonomous driving. The advent of deep-learning and the availability of large-scale datasets has enabled the research com-munity to reach to unprecedented performance heights compared to traditional machine learning algorithms. However, despite all the progress, existing learning methods still face the problem of class-imbalance because of which large classes get more attention and consequently the network becomes biased towards them. The problem of class-imbalance is particularly more prominent in urban road-scene datasets. This is because the layout of the scene captured by the camera mounted on a fixed location, naturally causes certain less important classes to occupy more area in the dataset. Trees, sky and buildings are some of the examples of large classes which frequently occur and occupy large areas despite the fact that they are less important with regards to driving related decision making. To tackle this problem, in this work, we have done the statistical analysis of the famous Cityscapes dataset to uncover the hidden patterns that large and small classes follow. Based on these patterns, we propose a semantic masking technique, that enables our proposed network MaskNet to pay more attention to regions where the smaller classes are more likely to occur. In this way, we see a significant performance increase with regards to smaller classes and the problem of class-imbalance is mitigated to a good extent.
Semantic Segmentation on Low Resolution Cytology Images of Pleural and Peritoneal Effusion

Automation in the detection of malignancy in effusion cytology helps to save time and workload for cytopathologists. Cytopathologists typically consider a low-resolution image to identify the malignant regions. The identified regions are scanned at a higher resolution to confirm malignancy by investigating the cell level behaviour. Scanning and processing time can be saved by zooming only the identified malignant regions instead of entire low-resolution images. This work predicts malignancy in cytology images at a very low resolution (4X). Annotation of cytology images at a very low resolution is challenging due to the blurring of features such as nuclei and texture. We address this issue by upsampling the very low-resolution images using adversarial training. This work develops a semantic segmentation model trained on 10X images and reuse the network to utilize the 4X images. The prediction results of low resolution images improved by 15% in average F-score for adversarial based upsampling compared to a bicubic filter. The high resolution model gives a 95% average F-score for high resolution images. Also, the sub-area of the whole slide that requires to be scanned at high magnification is reduced by approximately 61% while using adversarial based upsampling compared to a bicubic filter.
Comparison Study of Traffic Signs Recognition Using Deep Learning Architectures

Self-driving vehicles are considered one of the most significant advanced technologies in computer science and artificial intelligence. The primary objective of autonomous driving is the elimination of human supervision from the workflow of autonomous cars. The advantages of this technology are to improve people’s lives by minimizing traffic, eliminating parking spaces in cities, boosting collective fuel efficiency, and lowering accidents. As autonomous driving becomes more incorporated into our daily lives, effective solutions are essential to its challenges. One of the most important tasks for autonomous driving is the automatic recognition of traffic signs. Artificial intelligence is broadly used in the object detection fields and especially in traffic sign detection tasks, while Convolutional Neural Networks (CNNs) are utilized in many computer vision tasks. In this paper, we conduct a comparison study of object recognition challenges for self-driving automobiles, in particular for traffic signs and traffic signals detection. Two standard datasets and six different deep CNNs architectures are utilized to carry out experiments. The data augmentation method is employed to solve the unbalanced datasets problem. Experiments show that for the GTSRB dataset, the DenseNet201 model with Adam optimizer achieved the top accuracy (98.66%). For the LISA TL dataset, the InceptionResNetV2 model with Adam optimizer achieved the highest accuracy among the other networks (98.84%).
Improved Adaptive Kalman-Median Filter for Line-Scan X-ray Transmission Image

With their wide application in industrial fields, the denoising and/or filtering of line-scan images is becoming more important, which also affects the quality of their subsequent recognition or classification. Based on the application of single source dual-energy X-ray transmission (DE-XRT) line-scan in-line material sorting and the different horizontal and vertical characteristics of line-scan images, an improved adaptive Kalman-median filter (IAKMF) was proposed for several kinds of noises of an energy integral detector. The filter was realized through the determination of the off-line noise total covariance, the covariance distribution coefficient between the process noise and measurement noise, the adaptive covariance scale coefficient, calculation scanning mode and single line median filter. The experimental results show that the proposed filter has the advantages of simple code, good real-time control, high precision, small artifacts, convenience and practicality. It can take into account the filtering of high-frequency random noise, the retention of low-frequency real signal fluctuation and the preservation of shape features. The filter also has a good practical application value and can be improved and extended to other line-scan image filtering scenarios.
Variational Autoencoder for Zero-Shot Recognition of Bai Characters

When talking about Bai nationality, people are impressed by its long history and the language it has created. However, since fewer people of the young generation learn the traditional language, the glorious Bai culture becomes less known, making understanding Bai characters difficult. Based on the highly precise character recognition model for Bai characters, the paper is aimed at helping people read books written in Bai characters so as to popularize the culture. To begin with, a data set is built with the support of Bai culture fans and experts. However, the data set is not large enough as knowledge in this respect is limited. This makes the deep learning model less accurate since it lacks sufficient data. The popular zero-shot learning (ZSL) is adopted to overcome the insufficiency of data sets. We use Chinese characters as the seen class, Bai characters as the unseen class, and the number of strokes as the attribute to construct the ZSL format data set. However, the existing ZSL methods ignore the character structure information, so a generation method based on variational autoencoder (VAE) is put forward, which can automatically capture the character structure information. Experimental results show that the method facilitates the recognition of Bai characters and makes it more precise.
Analysis of University Teaching Evaluation in the Era of BD

College teaching evaluation is an important part of teaching because it not only summarizes the semester's teaching results but also guides the next semester's teaching. However, with the implementation of China's quality-oriented education reform and the arrival of the era of big data (BD), new requirements for college teaching evaluation are being put forward, which also brings many reform conditions. Teaching evaluation in the traditional sense is typically based solely on the course's student scoring data. This method is based on a single data source and thus cannot evaluate the teaching effect comprehensively, objectively or fairly. To address this issue, a teaching evaluation method in colleges and universities operating within the BD environment is proposed. This paper investigates college and university teaching evaluation in the post-BD era. The observed value of the Bartlett sphericity test statistic is 6619.943, and the corresponding probability <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>p</mi> </math> is nearly equal to 0. If the significance level an is 0.05, the null hypothesis should be rejected because the probability <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>p</mi> </math> is less than the significance level a, indicating that the correlation coefficient matrix cannot be the unit matrix. This also implies that there must be some correlation between the original variables, making factor analysis possible. BD can combine scattered and single evaluation data to create continuous and systematic data. BD can provide a large amount of data support for teaching evaluation, making it more scientific and fair; at the same time, driven by BD, teaching evaluation can return the results of teaching evaluation more quickly.
Quantum Fourier Convolutional Network

The neural network and quantum computing are both significant and appealing fields, with their interactive disciplines promising for large-scale computing tasks that are untackled by conventional computers. However, both developments are restricted by the scope of the hardware development. Nevertheless, many neural network algorithms had been proposed before GPUs became powerful enough for running very deep models. Similarly, quantum algorithms can also be proposed as knowledge reserve before real quantum computers are easily accessible. Specifically, taking advantage of both the neural networks and quantum computation and designing quantum deep neural networks (QDNNs) for acceleration on the Noisy Intermediate-Scale Quantum (NISQ) processors is also an important research problem. As one of the most widely used neural network architectures, convolutional neural network (CNN) remains to be accelerated by quantum mechanisms, with only a few attempts having been demonstrated. In this article, we propose a new hybrid quantum-classical circuit, namely, Quantum Fourier Convolutional Network (QFCN). Our model achieves exponential speedup compared with classical CNN theoretically and improves over the existing best result of quantum CNN. We demonstrate the potential of this architecture by applying it on different deep learning tasks, including traffic prediction and image classification.
New light fields based on integration theory within the Weyl ordering product of operators

The development of quantum optics theory based on the method of integration within an ordered product of operators (IWOP) has greatly stimulated the study of quantum states in the light field, especially non-Gaussian states with various non-classical properties. In this paper, the two-mode squeezing operator is derived with integral theory within the Weyl ordering product of operators using a combinatorial field in which one mode is a chaotic field and the other mode is a vacuum field. The density operator of the new light field, its entanglement property and photon number distribution are analyzed. We also note that tracing a three-mode pure state can yield this new light field. These methods represent a theoretical approach to investigating new density operators of light fields.
Research on intelligent analysis strategies to improve athletes' psychological experience in the era of artificial intelligence

In order to improve the psychological quality of athletes, this paper combines artificial intelligence technology to quantitatively analyze the psychological experience of athletes. Moreover, in view of the insufficiency of the ASI method to standardize the data, this study proposes to use the normalization method for processing. In addition, in order to verify the feasibility and effectiveness of the research and development of the multi-dimensional dual-objective CD-CAT topic selection strategy, this paper adopts the Monte Carlo simulation experiment method to analyze the data. Finally, this paper constructs an analysis system of athlete's psychological experience and intelligent decision-making based on artificial intelligence. The experimental data analysis results show that the intelligent decision-making system based on artificial intelligence for athletes' psychological experience proposed in this paper has a good effect and can effectively promote the psychological training effect of athletes.
A Survey on Textual Entailment: Benchmarks, Approaches and Applications

Textual Entailment Recognition (TER), also known as natural language inference, is a crucial task in natural language processing that combines many fundamental aspects of language understanding. TER focuses on predicting the inference relationship between text fragments. Given two sentences (known as premise and hypothesis), the goal is to determine if the meaning of the hypothesis can be entailed/inferred from the premise. Understanding this relationship between two texts can be helpful in several tasks, such as information retrieval, semantic parsing, and common-sense reasoning. This survey paper provides an overview of TER and its variants and applications. We then highlighted TER benchmark datasets for various languages and the main approaches that have been proposed to tackle the problem for a better understanding of the progress this task has reached.
End-to-End Machine Learning Solution for Recognizing Handwritten Arabic Documents

The research for offline handwriting recognition (HWR) solutions for various languages has recently gained rising attention, especially in the Arabic language. This is connected to the growing necessity to digitize Arabic documents in several applications such as exploring large documents, automated sorting of express mail, editing of earlier printed documents, and bank control processing. Regrettably, notwithstanding decades of research, there is no satisfactory solution for recognizing cursive handwriting like the Arabic language because of its difficulty. This paper presents end-to-end machine learning solution by applying deep learning techniques like CRNN-BLSTM using the MADCAT dataset to accurately recognize the Arabic handwritten documents after simultaneously learning text detection, segmentation, and finally conversion to editable text. Our integrated method, resulting from integrating several distinct neural networks models, achieved high accuracy in parsing the full page, converting it to lines, and predicting the writing within each line. This approach has been evaluated using a large-scale set of Arabic handwritten documents that contains various problems that need to be addressed, the achieved character error rate is 3.96%.
Hyperelliptic Covers of Different Degree for Elliptic Curves

In elliptic curve cryptography (ECC) and hyperelliptic curve cryptography (HECC), the size of cipher-text space defined by the cardinality of Jacobian is a significant factor to measure the security level. Counting problems on Jacobians of elliptic curve can be solved in polynomial time by Schoof–Elkies–Atkin (SEA) algorithm. However, counting problems on Jacobians of hyperelliptic curves are solved less satisfactorily than those on elliptic curves. So, we consider the construction of the cover map from the hyperelliptic curves to the elliptic curves to convert point counting problems on hyperelliptic curves to those on elliptic curves. We can also use the cover map as a kind of cover attacks. Given an elliptic curve over an extension field of degree <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>n</mi> </math> , one might try to use the cover attack to reduce the discrete logarithm problem (DLP) in the group of rational points of the elliptic curve to DLPs in the Jacobian of a curve of genus <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>g</mi> <mo>≥</mo> <mi>n</mi> </math> over the base field. An algorithm has been proposed for finding genus 3 hyperelliptic covers as a cover attack for elliptic curves with cofactor 2. Our algorithms are about the cover map from hyperelliptic curves of genus 2 to elliptic curves of prime order. As an application, an example of an elliptic curve whose order is a 256-bit prime vulnerable to our algorithms is given.
Deformation Measurements of Helicopter Rotor Blades Using a Photogrammetric System

As an important part of the helicopter, the rotor directly affects flight safety and flight quality. Knowledge of the rotor dynamic behaviors is significant for validating and optimizing the performance of the helicopter rotor system. In this study, a photogrammetric system, based on 3D point tracking and stereo photogrammetry technology, is presented to solve the full-field dynamic motion and deformation parameters of rotating blades by identifying the retro-reflective targets arranged on the rotor. The photogrammetric system is demonstrated in the wind tunnel tests of a 2 m-diameter model rotor, conducted at the 5.5 m × 4 m Aeroacoustic Wind Tunnel of the China Aerodynamics Research and Development Center (CARDC). With the targets attached on the special hat installed directly over the rotor hub, a unified rotor coordinate system, that was stationary with respect to the rotor, could be established at any measuring instant. Therefore, by transforming the 3D coordinates of all measured targets to the rotor coordinate system, the blade displacements and deformations at different test conditions could be calculated consistently. Experimental results from current study were compared to simulation results calculated by the comprehensive analytical model of rotorcraft aerodynamics and dynamics (CAMRAD), which shows quite good agreements.
ICT competency and practicum of preservice teachers as digital natives: a mixed-method study

Along with the global trend of developing students’ 21st-century skills, teacher education must enable preservice teachers (PSTs) to improve students’ learning outcomes with Information and Communications Technology (ICT), and school practicum plays an essential role in the process. This study investigated Hong Kong PSTs’ ICT competency with or without teaching practicum completed and explored factors contributing to ICT competency in and beyond the practicum. This mixed-methods study began with a questionnaire to give a broad picture of the ICT competence of Hong Kong PSTs of English (N = 127), who were found to perceive themselves as competent with educational technology and their competencies of understanding ICT related policy and using ICT for professional development was enhanced after school practicum. A purposeful subset of the sample (N = 10) was interviewed based on their ICT competency scores, exploring the factors shaping interviewees’ readiness and ICT use in their teaching practicum. It highlights that digital native students do not naturally transform into teachers with ICT competency to facilitate 21st-century learning, and the practicum could play a more critical role in enhancing their ICT competence to future-proof their skills. Promoting ICT-friendly school culture and designing better teaching practicum programmes are suggested.