Reviewerly/exHarmony huggingface dataset:
Almost Tight Sample Complexity Analysis of Quantum Identity Testing by Pauli Measurements

This paper studies the quantum identity testing problem, a quantum analogue of distribution identity testing. The goal is to determine whether a quantum state is identical to another fixed quantum state, using as few state samples as possible. Rather than general entangled measurements, we consider the less powerful but experimentally friendly Pauli measurements. We follow the standard setting where Pauli measurements are regarded as two-outcome measurements, i.e., each <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -qubit Pauli measurement has a one-bit outcome. We prove that for an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -qubit quantum system, the sample complexity of this problem is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Theta \left({\mathrm {poly}(n)\cdot \frac {4^{n}}{\epsilon ^{2}}}\right)$ </tex-math></inline-formula> if only Pauli measurements are allowed. In other words, we provide simple algorithms to determine whether two <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -qubit quantum states, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula> , are identical or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> -far in trace distance using Pauli measurements, using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O\left({\min \{n^{4},n^{3}+\log ^{3}(1/\epsilon)\}\cdot \frac {4^{n}}{\epsilon ^{2}}}\right)$ </tex-math></inline-formula> copies of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula> . Interestingly, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}\left({\frac {4^{n}}{\epsilon ^{2}}}\right)$ </tex-math></inline-formula> copies are not sufficient under this setting.
Stokastik Fraktal Arama Algoritması ve CMA-ES tabanlı yeni bir hibrit algoritma

In this study, a novel hybridization approach, which is called CMASFS and is based on the covariance matrix adaptation evolution strategy (CMA-ES) and the stochastic fractal search (SFS) algorithms. To make the proposed algorithm dynamic, Gaussian walk equations involved in the diffusion process of SFS have been updated and the algorithm decide to use which the Gaussian walk equations. The effectiveness of the proposed algorithm is tested using CEC2017 benchmark functions having unimodal, multimodal, hybrid, and composition functions in 10, 30, 50, and 100 dimensions. The performance of the CMASFS algorithm is compared with 17 metaheuristic algorithms given in the literature over the CEC2017 benchmark functions. According to the results, it is seen that CMASFS is generally obtained better mean error values. Moreover, to show the superiority of the proposed algorithm, Friedman analysis and the Wilcoxon rank-sum test are applied to the test results of the algorithms. The results of the Wilcoxon signed-rank test show that the improvement with the CMASFS algorithm is statistically significant on the majority of the CEC2017. The results of Friedman test verify that the CMASFS is obtained the best rank compared to both the original SFS and other compared algorithms.
Dual Consistency-enhanced Semi-supervised Sentiment Analysis towards COVID-19 Tweets

In the context of COVID-19, numerous people present their opinions through social networks. It is thus highly desired to conduct sentiment analysis towards COVID-19 tweets to learn the public's attitudes, and facilitate the government to make proper guidelines for avoiding the social unrest. Although many efforts have studied the text-based sentiment classification from various domains ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , delivery and shopping reviews), it is hard to directly use these classifiers for the sentiment analysis towards COVID-19 tweets due to the domain gap. In fact, developing the sentiment classifier for COVID-19 tweets is mainly challenged by the limited annotated training dataset, as well as the diverse and informal expressions of user-generated posts. To address these challenges, we construct a large-scale COVID-19 dataset from Weibo and propose a dual COnsistency-enhanced semi-superVIseD network for Sentiment Anlaysis (COVID-SA). In particular, we first introduce a knowledge-based augmentation method to augment data and enhance the model's robustness. We then employ BERT as the text encoder backbone for both labeled data, unlabeled data, and augmented data. Moreover, we propose a dual consistency ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , label-oriented consistency and instance-oriented consistency) regularization to promote the model performance. Extensive experiments on our self-constructed dataset and three public datasets show the superiority of COVID-SA over state-of-the-art baselines on various applications.
Adaptive Ensemble Clustering With Boosting BLS-Based Autoencoder

Ensemble clustering has an advantage in producing a more promising and robust clustering result by combining multiple partitions strategically. The quality of both base partitions and co-association matrix plays an essential role in improving the consensus partition. However, the current ensemble clustering methods have several limitations: 1) The noise in high-dimensional feature space is ignored; 2) The independent base partition generation process does not pay attention to ambiguous samples; 3) The co-association matrix and the weights of base partitions commonly lack of theoretical optimization. In order to address these issues, we propose an adaptive ensemble clustering framework with boosting BLS-based autoencoder (BoostAEC). In the generation step, a boosting BLS-based autoencoder (BoostBLSAE) is designed to generate base partitions sequentially, which learns compressed feature subspaces for ambiguous samples and adaptively evaluates the corresponding weights of reliability. In the integration step, we construct a fuzzy membership function to capture the inter-cluster correlation and explicitly propose a consensus objective function to optimize the unified co-association matrix by considering the weighted base partitions. Extensive experiments on various real-world datasets demonstrate the superior performance of BoostAEC to the state-of-the-art ensemble clustering methods.
Multimedia Semantic Communications: Representation, Encoding and Transmission

The sixth generation (6G) mobile communication systems could serve new multimodal services, such as virtual reality (VR), augmented reality (AR), holographic projection. These new services are accompanied by new demands for intelligence, personalization and collaboration. Semantic communications are increasingly attracting attention due to its potential to support the aforementioned multimodal services. Therefore, in this article, we discuss key technologies for semantic communications, which involve semantic representation, semantic encoding and decoding, and transmission of semantic information. First, we present existing work on semantic communications, such as sketch graph structure representation of images, semantic reconstruction, and end-to-end semantic communication systems. Second, we identify challenges in semantic communication systems and propose the unified semantic representations, semantic codecs, and multiple access techniques for support the semantic information transmission. Last, we present the results of semantic encoding and decoding methods and the coexistence of semantics and bits transmission supported by the non-orthogonal multiple access technique (NOMA).
Research on Network Traffic Classification Method Based on CNN–RNN

With the continued development of the information society, the scale of the Internet grows ever larger, and the enormous amount of traffic data throughput in the Internet is so diverse that the difficulty of the network traffic classification problem is constantly challenged, and the speed and accuracy of the classifier are put forward to higher standards. It is critical to understand how to continuously optimize network traffic classification techniques using existing technologies for implementing network security censorship, strengthening network security management, detecting and defending against network intrusion, and so on. The traditional port-based classification method is no longer reliable in today's increasingly complex network environment, and the deep packet inspection technique is inapplicable to the classification of encrypted network traffic. The machine learning-based classification method has good classification performance and solves the problem that deep packet inspection cannot identify encrypted network traffic, which has been a hot research topic in network traffic classification in the recent years. To address the shortcomings of existing methods, this paper proposes a classification method combining recurrent neural network and convolutional neural network within the framework of machine learning.
Optimization of Cross-Lingual Voice Conversion With Linguistics Losses to Reduce Foreign Accents

Cross-lingual voice conversion (XVC) transforms the speaker identity of a source speaker to that of a target speaker who speaks a different language. Due to the intrinsic differences between languages, the converted speech may carry an unwanted foreign accent. In this paper, we first investigate the intelligibility of the converted speech and confirm the performance degradation caused by the accent/intelligibility issue. With the goal of generating native-sounding speech, this paper further proposes a novel training scheme with two additional linguistic losses for speech waveform generation: 1) a frame-wise phonetic content loss derived from bottleneck features, and 2) an automatic speech recognition loss on characters. Experiments were conducted between English and Mandarin Chinese conversions. The experimental results confirmed that the generated speech sounds more natural with the proposed linguistic losses and the proposed solution significantly improves speech intelligibility.
Traffic Sign Recognition from Digital Images by Using Deep Learning

Traffic signs are essentially needed to obey the traffic rules. Once a driver ignores the signs, especially those critical signs, due to the complexity of actual traffic scenes or the influence of inclement weather conditions, it will lead to violating traffic regulations or traffic accidents, causing casualties and property losses. Therefore, Traffic Sign Recognition (TSR) is an essential part of autonomous vehicles and has important academic significance. The main contributions of this paper are as follows: (1) We apply an algorithm to the dark channel prior, and we also provide a guided image filtering algorithm for image defogging. Our results show that the guided image filtering method is very effective in image defogging. (2) This paper presents a number of deep learning solutions towards the aforementioned problems. Based on the experiments conducted, we discover that YOLOv5 is very suitable for real-time TSR.
Statistical Relational Extension of Answer Set Programming

This tutorial presents a statistical relational extension of the answer set programming language called $$\textrm{LP}^\mathrm{{MLN}}$$ , which incorporates the concept of weighted rules into the stable model semantics following the log-linear models of Markov Logic. An $$\textrm{LP}^\mathrm{{MLN}}$$ program defines a probability distribution over “soft” stable models, which may not satisfy all rules, but the more rules with larger weights they satisfy, the higher their probabilities, thus allowing for an intuitive and elaboration tolerant representation of problems that require both logical and probabilistic reasoning. The extension provides a natural way to overcome the deterministic nature of the stable model semantics, such as resolving inconsistencies in answer set programs, associating probability to stable models, and applying statistical inference and learning with probabilistic stable models. We also present formal relations between $$\textrm{LP}^\mathrm{{MLN}}$$ and other related formalisms, which produce ways of performing inference and learning in $$\textrm{LP}^\mathrm{{MLN}}$$ .
An SIFT-Based Fast Image Alignment Algorithm for High-Resolution Image

To solve the problem of high-resolution image alignment time overhead, an SIFT-based fast image alignment algorithm is presented. The overlap region of images is computed in detail by phase correlation algorithm to avoid a lot of useless calculations of non-overlapping region. After the distribution of feature points determined in difference of Gaussian through formula derivation, the total number of feature points is limited. The more stable spatially distributed for the feature points is obtained due to the expanded detection range of extreme points and added non-maximum suppression. It is noteworthy that the range of the descriptor is calculated by the method of down-sampling. And the circular descriptor is constructed with only 56-dimensional in the feature point descriptor generation stage, which makes the time of the descriptor generation and feature point matching shorter. This indicates that the total descriptor calculation is faster in lower dimensions by the new algorithm. In addition, experimental results show that the average time (9.60s, 13.46s, and 15.81s) of the proposed algorithm is 0.86%, 0.43%, and 0.10% of the SIFT algorithm, respectively. The overall speed is 2–3 orders of magnitude faster than the SIFT algorithm, which indicates that the new algorithm can solve the problem of high-resolution image alignment time overhead. The new algorithm provides a good stitching quality and shows an excellent performance for high-resolution image compared with several existing image stitching algorithms at the current. It indicates that the algorithm has potential application value in real-time image stitching.
Density-Distance Outlier Detection Algorithm Based on Natural Neighborhood

Outlier detection is of great significance in the domain of data mining. Its task is to find those target points that are not identical to most of the object generation mechanisms. The existing algorithms are mainly divided into density-based algorithms and distance-based algorithms. However, both approaches have some drawbacks. The former struggles to handle low-density modes, while the latter cannot detect local outliers. Moreover, the outlier detection algorithm is very sensitive to parameter settings. This paper proposes a new two-parameter outlier detection (TPOD) algorithm. The method proposed in this paper does not need to manually define the number of neighbors, and the introduction of relative distance can also solve the problem of low density and further accurately detect outliers. This is a combinatorial optimization problem. Firstly, the number of natural neighbors is iteratively calculated, and then the local density of the target object is calculated by adaptive kernel density estimation. Secondly, the relative distance of the target points is computed through natural neighbors. Finally, these two parameters are combined to obtain the outlier factor. This eliminates the influence of parameters that require users to determine the number of outliers themselves, namely, the top-n effect. Two synthetic datasets and 17 real datasets were used to test the effectiveness of this method; a comparison with another five algorithms is also provided. The AUC value and F1 score on multiple datasets are higher than other algorithms, indicating that outliers can be found accurately, which proves that the algorithm is effective.
Extended Chebyshev Chaotic Map Based Message Verification Protocol for Wireless Surveillance Systems

Wireless networks have continued to evolve to offer connectivity between users and smart devices such as drones and wireless sensor nodes. In this environment, insecure public channels are deployed to link the users to their remote smart devices. Some of the application areas of these smart devices include military surveillance and healthcare monitoring. Since the sensor data collected and transmitted to the users is highly sensitive and private, any leakages can have adverse effects. As such, strong entity authentication should be implemented before any access is granted to these wireless networks. Although numerous protocols have been developed for this purpose, simultaneous attainment of robust security and privacy at low latencies, execution time and bandwidth remains a mirage. In this paper, a message verification protocol for wireless networks is presented. This protocol amalgamates fuzzy extraction with extended Chebyshev chaotic maps to boost the integrity of the exchanged payload. The security analysis shows that this scheme offers entity anonymity, mutual authentication, key negotiation, and backward and forward key secrecy. In addition, it is demonstrated to be robust against attacks such as secret ephemeral leakages, side—channeling, man-in-the-middle (MitM) and impersonations. From the performance perspective, the proposed scheme requires the least communication overheads and relatively low execution time during the authentication and key negotiation process.
Comparison of the applicability of synergistic models with dense neural networks on the example of mobile device security

The dominant approach in contemporary science and industry applications is deep learning. Deep learning consists of many different architectures, including dense, convolutional and recurrent neural networks. In this article we compare deep learning, particularly dense neural networks (DNNs) with the other approach, synergetic models. As a task to compare the mobile application security was chosen. Two datasets were created, sensor values for human beings interacting with smartphones and malicious bot records using emulators. Deep learning and synergetic models were tasked to distinguish them from each other using binary classification. Both models tackled a problem comparatively well, archiving 99% and 85% accuracy respectively. While the deep learning model performed better, the synergetic model excelled in training speed, versatility and results transparency. Pros and cons of both models were addressed in the results section.
EFL Educators’ Insights into Online Education and its Impact on Teaching during the COVID-19 Pandemic

&lt;p style="text-align:justify"&gt;After months of 100% online teaching due to the pandemic produced by COVID-19, the return to face-to-face classes is being experienced. This study focused on finding the English as a foreign language (EFL) University professors’ perspectives of online education during that time. The information was collected from three Ecuadorian universities: The Technical University of Ambato, The Higher Polytechnic School of Chimborazo, and The University of Cuenca. The survey was designed with 26 questions about professors’ perceptions of online teaching and its impact on their pedagogical practices, affective factors, and student achievement. Software R, and the Cronbach's alpha tests were used as statistical tools, along with Kendall's Tau_b, and the Kruskal-Wallis test. The results showed that teaching online not only represents a solution in cases where face-to-face education is not possible, but also offers teachers the opportunity to experience the benefits of using technological tools and innovative strategies. Although some drawbacks, such as lack of time and real interaction were encountered, online teaching resulted in an alternative methodology that engaged learners.&lt;/p&gt;
Context awareness based Sketch-DeepNet architecture for hand-drawn sketches classification and recognition in AIoT

A sketch is a black-and-white, 2-D graphical representation of an object and contains fewer visual details as compared to a colored image. Despite fewer details, humans can recognize a sketch and its context very efficiently and consistently across languages, cultures, and age groups, but it is a difficult task for computers to recognize such low-detail sketches and get context out of them. With the tremendous increase in popularity of IoT devices such as smartphones and smart cameras, etc., it has become more critical to recognize free hand-drawn sketches in computer vision and human-computer interaction in order to build a successful artificial intelligence of things (AIoT) system that can first recognize the sketches and then understand the context of multiple drawings. Earlier models which addressed this problem are scale-invariant feature transform (SIFT) and bag-of-words (BoW). Both SIFT and BoW used hand-crafted features and scale-invariant algorithms to address this issue. But these models are complex and time-consuming due to the manual process of features setup. The deep neural networks (DNNs) performed well with object recognition on many large-scale datasets such as ImageNet and CIFAR-10. However, the DDN approach cannot be carried out for hand-drawn sketches problems. The reason is that the data source is images, and all sketches in the images are, for example, 'birds' instead of their specific category (e.g., 'sparrow'). Some deep learning approaches for sketch recognition problems exist in the literature, but the results are not promising because there is still room for improvement. This article proposed a convolutional neural network (CNN) architecture called Sketch-DeepNet for the sketch recognition task. The proposed Sketch-DeepNet architecture used the TU-Berlin dataset for classification. The experimental results show that the proposed method beats the performance of the state-of-the-art sketch classification methods. The proposed model achieved 95.05% accuracy as compared to existing models DeformNet (62.6%), Sketch-DNN (72.2%), Sketch-a-Net (77.95%), SketchNet (80.42%), Thinning-DNN (74.3%), CNN-PCA-SVM (72.5%), Hybrid-CNN (84.42%), and human recognition accuracy of 73% on the TU-Berlin dataset.