Reviewerly/exHarmony huggingface dataset:
Impact of deep learning-based image super-resolution on binary signal detection

Purpose: Deep learning-based image super-resolution (DL-SR) has shown great promise in medical imaging applications. To date, most of the proposed methods for DL-SR have only been assessed using traditional measures of image quality (IQ) that are commonly employed in the field of computer vision. However, the impact of these methods on objective measures of IQ that are relevant to medical imaging tasks remains largely unexplored. We investigate the impact of DL-SR methods on binary signal detection performance. Approach: Two popular DL-SR methods, the super-resolution convolutional neural network and the super-resolution generative adversarial network, were trained using simulated medical image data. Binary signal-known-exactly with background-known-statistically and signal-known-statistically with background-known-statistically detection tasks were formulated. Numerical observers (NOs), which included a neural network-approximated ideal observer and common linear NOs, were employed to assess the impact of DL-SR on task performance. The impact of the complexity of the DL-SR network architectures on task performance was quantified. In addition, the utility of DL-SR for improving the task performance of suboptimal observers was investigated. Results: Our numerical experiments confirmed that, as expected, DL-SR improved traditional measures of IQ. However, for many of the study designs considered, the DL-SR methods provided little or no improvement in task performance and even degraded it. It was observed that DL-SR improved the task performance of suboptimal observers under certain conditions. Conclusions: Our study highlights the urgent need for the objective assessment of DL-SR methods and suggests avenues for improving their efficacy in medical imaging applications.
A lightweight federated learning based privacy preserving B5G pandemic response network using unmanned aerial vehicles: A proof-of-concept

The concept of an intelligent pandemic response network is gaining momentum during the current novel coronavirus disease (COVID-19) era. A heterogeneous communication architecture is essential to facilitate collaborative and intelligent medical analytics in the fifth generation and beyond (B5G) networks to intelligently learn and disseminate pandemic-related information and diagnostic results. However, such a technique raises privacy issues pertaining to the health data of the patients. In this paper, we envision a privacy-preserving pandemic response network using a proof-of-concept, aerial–terrestrial network system serving mobile user entities/equipment (UEs). By leveraging the unmanned aerial vehicles (UAVs), a lightweight federated learning model is proposed to collaboratively yet privately learn medical (e.g., COVID-19) symptoms with high accuracy using the data collected by individual UEs using ambient sensors and wearable devices. An asynchronous weight updating technique is introduced in federated learning to avoid redundant learning and save precious networking as well as computing resources of the UAVs/UEs. A use-case where an Artificial Intelligence (AI)-based model is employed for COVID-19 detection from radiograph images is presented to demonstrate the effectiveness of our proposed approach.
MSFYOLO: Feature fusion-based detection for small objects

At present, the effect of object detection algorithm in small object detection is very poor, mainly because the low-level network lacks semantic information and the characteristic information expressed by small object inspection data is very lack. In view of the above difficulties, this paper proposes a small object detection algorithm based on multi-scale feature fusion. By learning shallow features at the shallow level and deep features at the deep level, the proposed multi-scale feature learning scheme focuses on the fusion of concrete features and abstract features. It constructs object detector (MSFYOLO) based on multi-scale deep feature learning network and considers the relationship between a single object and local environment. Combining global information with local information, the feature pyramid is constructed by fusing different depth feature layers in the network. In addition, this paper also proposes a new feature extraction network (CourNet), through the way of feature visualization compared with the mainstream backbone network, the network can better express the small object feature information. The proposed algorithm is valuated on the MS COCO and achieved leading performance with 11.7% improvement in FPS, 17.0% improvement in AP, 81.0% improvement in ARS, and 23.3% reduction in computational FPLOs compared to YOLOv3. This study shows that the combination of global information and local information is helpful to detect the expression of small objects in different illumination. MSFYOLO uses CourNet as the backbone network, which has high efficiency and a good balance between accuracy and speed.
An Optimizing Diffusion Kernel-Based Binary Encoding Strategy With Genetic Algorithm for Fringe Projection Profilometry

The binary encoding fringe projection profilometry has the potential of achieving high-speed 3-D measurement. Error diffusion is one of the most widely investigated binary encoding techniques. However, the existing error diffusion usually utilizes the same diffusion kernel to encode sinusoidal (S) fringe patterns without considering the fringe pitch-widths, leading to inevitable measurement error due to the demodulated phase error. In this article, a fringe binary encoding strategy based on genetic algorithm is proposed to reduce the phase error recovered by the temporal phase unwrapping (TPU) method. We design an optimization equation that combines phase error and intensity error to calculate the encoding error, which ensures that the optimized diffusion kernel is robust to defocusing changes. Both simulation and experiments have proved the effectiveness and accurateness of the proposed method. Comparative experiments indicate that the extracted phase error is greatly reduced for two types of extensively adopted TPU methods at different defocusing levels. Especially for multi wavelength method, the phase error can be reduced by up to 68%.
Computer-assisted pronunciation training—Speech synthesis is almost all you need

The research community has long studied computer-assisted pronunciation training (CAPT) methods in non-native speech. Researchers focused on studying various model architectures, such as Bayesian networks and deep learning methods, as well as on the analysis of different representations of the speech signal. Despite significant progress in recent years, existing CAPT methods are not able to detect pronunciation errors with high accuracy (only 60\% precision at 40\%-80\% recall). One of the key problems is the low availability of mispronounced speech that is needed for the reliable training of pronunciation error detection models. If we had a generative model that could mimic non-native speech and produce any amount of training data, then the task of detecting pronunciation errors would be much easier. We present three innovative techniques based on phoneme-to-phoneme (P2P), text-to-speech (T2S), and speech-to-speech (S2S) conversion to generate correctly pronounced and mispronounced synthetic speech. We show that these techniques not only improve the accuracy of three machine learning models for detecting pronunciation errors but also help establish a new state-of-the-art in the field. Earlier studies have used simple speech generation techniques such as P2P conversion, but only as an additional mechanism to improve the accuracy of pronunciation error detection. We, on the other hand, consider speech generation to be the first-class method of detecting pronunciation errors. The effectiveness of these techniques is assessed in the tasks of detecting pronunciation and lexical stress errors. Non-native English speech corpora of German, Italian, and Polish speakers are used in the evaluations. The best proposed S2S technique improves the accuracy of detecting pronunciation errors in AUC metric by 41\% from 0.528 to 0.749 compared to the state-of-the-art approach.
Society 5.0: Internet as if People Mattered

While the primary focus of 5G has been on industry verticals, future 6G mobile networks are anticipated to become more human-centered. Emerging cyber-physical-social systems (CPSSs) aim at functionally integrating human beings into today's cyber-physical systems at the social, cognitive, and physical levels. CPSSs are instrumental in realizing the human-centered Society 5.0 vision. Society 5.0 envisions human beings increasingly interacting with social robots and embodied artificial intelligence in their daily lives. In this article, we build on our recent work on robonomics in the 6G era. Robonomics is an emerging field that investigates social human-robot interaction and its sociotechnical impact as well as blockchain technologies and cryptocurrencies, not only coins but - more interestingly - also tokens. Specifically, we study the tokenization process of creating tokenized digital twins of assets and access rights in the physical and digital world, paying close attention to its central role in ushering in the future Web3 and its underlying token economy, the successor of today's information and platform economies. After introducing our CPSS-based bottom-up multilayer token engineering framework for Society 5.0, we experimentally demonstrate how the collective human intelligence of a blockchain-enabled decentralized autonomous organization can be enhanced via purpose-driven tokens.
Do Pre-trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation and a Reasonable Approach

In recent years, pre-trained language models (PLMs) have been shown to capture factual knowledge from massive texts, which encourages the proposal of PLM-based knowledge graph completion (KGC) models.However, these models are still quite behind the SOTA KGC models in terms of performance.In this work, we find two main reasons for the weak performance: (1) Inaccurate evaluation setting.The evaluation setting under the closedworld assumption (CWA) may underestimate the PLM-based KGC models since they introduce more external knowledge; (2) Inappropriate utilization of PLMs.Most PLM-based KGC models simply splice the labels of entities and relations as inputs, leading to incoherent sentences that do not take full advantage of the implicit knowledge in PLMs.To alleviate these problems, we highlight a more accurate evaluation setting under the open-world assumption (OWA), which manually checks the correctness of knowledge that is not in KGs.Moreover, motivated by prompt tuning, we propose a novel PLM-based KGC model named PKGC.The basic idea is to convert each triple and its support information into natural prompt sentences, which are further fed into PLMs for classification.Experiment results on two KGC datasets demonstrate OWA is more reliable for evaluating KGC, especially on the link prediction, and the effectiveness of our PKCG model on both CWA and OWA settings.
All You May Need for VQA are Image Captions

Visual Question Answering (VQA) has benefited from increasingly sophisticated models, but has not enjoyed the same level of engagement in terms of data creation.In this paper, we propose a method that automatically derives VQA examples at volume, by leveraging the abundance of existing image-caption annotations combined with neural models for textual question generation.We show that the resulting data is of high-quality.VQA models trained on our data improve state-of-the-art zero-shot accuracy by double digits and achieve a level of robustness that lacks in the same model trained on human-annotated VQA data.
Design and Embedded Implementation of Secure Image Encryption Scheme Using DWT and 2D-LASM

In order to further improve the information effectiveness of digital image transmission, an image-encryption algorithm based on 2D-Logistic-adjusted-Sine map (2D-LASM) and Discrete Wavelet Transform (DWT) is proposed. First, a dynamic key with plaintext correlation is generated using Message-Digest Algorithm 5 (MD5), and 2D-LASM chaos is generated based on the key to obtain a chaotic pseudo-random sequence. Secondly, we perform DWT on the plaintext image to map the image from the time domain to the frequency domain and decompose the low-frequency (LF) coefficient and high-frequency (HF) coefficient. Then, the chaotic sequence is used to encrypt the LF coefficient with the structure of "confusion-permutation". We perform the permutation operation on HF coefficient, and we reconstruct the image of the processed LF coefficient and HF coefficient to obtain the frequency-domain ciphertext image. Finally, the ciphertext is dynamically diffused using the chaotic sequence to obtain the final ciphertext. Theoretical analysis and simulation experiments show that the algorithm has a large key space and can effectively resist various attacks. Compared with the spatial-domain algorithms, this algorithm has great advantages in terms of computational complexity, security performance, and encryption efficiency. At the same time, it provides better concealment of the encrypted image while ensuring the encryption efficiency compared to existing frequency-domain methods. The successful implementation on the embedded device in the optical network environment verifies the experimental feasibility of this algorithm in the new network application.
Optimizing Shadow Tomography with Generalized Measurements

Advances in quantum technology require scalable techniques to efficiently extract information from a quantum system. Traditional tomography is limited to a handful of qubits, and shadow tomography has been suggested as a scalable replacement for larger systems. Shadow tomography is conventionally analyzed based on outcomes of ideal projective measurements on the system upon application of randomized unitaries. Here, we suggest that shadow tomography can be much more straightforwardly formulated for generalized measurements, or positive operator valued measures. Based on the idea of the least-square estimator shadow tomography with generalized measurements is both more general and simpler than the traditional formulation with randomization of unitaries. In particular, this formulation allows us to analyze theoretical aspects of shadow tomography in detail. For example, we provide a detailed study of the implication of symmetries in shadow tomography. Moreover, with this generalization we also demonstrate how the optimization of measurements for shadow tomography tailored toward a particular set of observables can be carried out.
Customer attitudes towards online shopping: A systematic review of the influencing factors

The primary goal of this study is to investigate the major elements that impact customer attitudes regarding internet purchasing in Jordan. This study employed a qualitative systematic literature review methodology, with 100 existing peer-reviewed articles completed in Jordan chosen for evaluation based on an inclusion/exclusion criterion. The findings of this study were collected utilizing a thematic method, which involved extracting previous researchers' findings from the literature, categorizing similar themes and findings, and drawing conclusions. According to the findings of this survey, the most important elements impacting customer attitudes about online buying in Jordan are trust, cultural hurdles such as uncertainty avoidance and a lack of understanding, security, perceived ease-of-use, and perceived utility. It was also discovered that Hofstede's cultural dimensions’ theory and the technology acceptance model (TAM) can help online businesses identify what factors drive online shopping adoption in Jordan. This study discovered, through a review of available literature, that the online shopping sector in Jordan is currently undeveloped, necessitating more effective growth techniques. Finally, the outcomes of this study provide online merchants with insight into what has to be prioritized in order to entice Jordanian customers to make online purchases. From the findings of this investigation, three areas of inquiry were uncovered for further study. Findings from this study also include suggestions for online retailers and policymakers looking to boost Jordanians' comfort level with making purchases over the Internet. The findings of this study will be invaluable to international online retailers that are considering entering the Jordanian market. They will reveal not only the most important aspects affecting consumers' perceptions of online shopping in Jordan, but also the level of maturity of this sector.
Improved Ship Detection Algorithm from Satellite Images Using YOLOv7 and Graph Neural Network

One of the most critical issues that the marine surveillance system has to address is the accuracy of its ship detection. Since it is responsible for identifying potential pirate threats, it has to be able to perform its duties efficiently. In this paper, we present a novel deep learning approach that combines the capabilities of a Graph Neural Network (GNN) and a You Only Look Once (YOLOv7) deep learning framework. The main idea of this method is to provide a better understanding of the ship’s presence in harbor areas. The three hyperparameters that are used in the development of this system are the learning rate, batch sizes, and optimization selection. The results of the experiments show that the Adam optimization achieves a 93.4% success rate when compared to the previous generation of the YOLOv7 algorithm. The High-Resolution Satellite Image Dataset (HRSID), which is a high-resolution image of a synthetic aperture radar, was used for the test. This method can be further improved by taking into account the various kinds of neural network architecture that are commonly used in deep learning.
CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification

Few-shot classification is a challenging problem that aims to learn a model that can adapt to unseen classes given a few labeled samples. Recent approaches pre-train a feature extractor, and then fine-tune for episodic metalearning. Other methods leverage spatial features to learn pixel-level correspondence while jointly training a classifier. However, results using such approaches show marginal improvements. In this paper, inspired by the transformer style self-attention mechanism, we propose a strategy to cross-attend and re-weight discriminative features for fewshot classification. Given a base representation of support and query images after global pooling, we introduce a single shared module that projects features and cross-attends in two aspects: (i) query to support, and (ii) support to query. The module computes attention scores between features to produce an attention pooled representation of features in the same class that is later added to the original representation followed by a projection head. This effectively re-weights features in both aspects (i & ii) to produce features that better facilitate improved metric-based metalearning. Extensive experiments on public benchmarks show our approach outperforms state-of-the-art methods by 3%~5%.
A Structured Dictionary Perspective on Implicit Neural Representations

Implicit neural representations (INRs) have recently emerged as a promising alternative to classical discretized representations of signals. Nevertheless, despite their practical success, we still do not understand how INRs represent signals. We propose a novel unified perspective to theoretically analyse INRs. Leveraging results from harmonic analysis and deep learning theory, we show that most INR families are analogous to structured signal dictionaries whose atoms are integer harmonics of the set of initial mapping frequencies. This structure allows INRs to express signals with an exponentially increasing frequency support using a number of parameters that only grows linearly with depth. We also explore the inductive bias of INRs exploiting recent results about the empirical neural tangent kernel (NTK). Specifically, we show that the eigenfunctions of the NTK can be seen as dictionary atoms whose inner product with the target signal determines the final performance of their reconstruction. In this regard, we reveal that meta-learning has a reshaping effect on the NTK analogous to dictionary learning, building dictionary atoms as a combination of the examples seen during meta-training. Our results permit to design and tune novel INR architectures, but can also be of interest for the wider deep learning theory community.
A systematic review on the metaverse-based blended English learning

Over the past two decades, various digital technologies have been applied to sustain higher education. As the latest emerging information technology, the metaverse has been a recurring theme to be considered as a new direction to promote blended English learning. This study aims to investigate metaverse-based blended English learning. Through a systematic review based on bibliographic and content analysis, the study attempts to integrate the evidence to generate a model that links the education-based metaverse. The metaverse platforms in which learners' academic success can be significantly enhanced due to a high degree of learner engagement in immersive virtual environments. In addition, the virtual learning experience is restricted by the degree of digital literacy at the same time. To improve instructors' and learners' digital literacy levels, necessary support is indispensable by educational institutions and designers of the metaverse platforms. Meanwhile, this study addresses potential challenges that may hinder sustaining metaverse-based blended English learning, and provides some suggestions based on the previous literature. In future research, we will keep updating and polishing the metaverse-based blended English learning research to provide more detailed guidance for researchers and educators.
Hierarchical Consensus Hashing for Cross-Modal Retrieval

Cross-modal hashing (CMH) has gained much attention due to its effectiveness and efficiency in facilitating efficient retrieval between different modalities. Whereas, most existing methods unconsciously ignore the hierarchical structural information of the data, and often learn a single-layer hash function to directly transform cross-modal data into common low-dimensional hash codes in one step. This sudden drop of dimension and the huge semantic gap can cause the discriminative information loss. To this end, we adopt a coarse-to-fine progressive mechanism and propose a novel Hierarchical Consensus Cross-Modal Hashing (HCCH). Specifically, to mitigate the loss of important discriminative information, we propose a coarse-to-fine hierarchical hashing scheme that utilizes a two-layer hash function to refine the beneficial discriminative information gradually. And then, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{2,1}$</tex-math></inline-formula> -norm is imposed on the layer-wise hash function to alleviate the effects of redundant and corrupted features. Finally, we present consensus learning to effectively encode data into a consensus space in such a progressive way, thereby reducing the semantic gap progressively. Through extensive contrast experiments with some advanced CMH methods, the effectiveness and efficiency of our HCCH method are demonstrated on four benchmark datasets. We have released the source code at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/sunyuan-cs</uri> .