Reviewerly/exHarmony huggingface dataset:
Incomplete multi-view subspace clustering based on missing-sample recovering and structural information learning

The incomplete multi-view clustering (IMVC) focuses on exploring hidden information from missing samples and complementary information from multiple views to enhance clustering performance. Most of existing methods for IMVC only attempt to obtain a shared consistent representation or graph from the non-missing samples based on matrix factorization. However, these methods rely too much on view alignment information, neglecting the specific-view structural information of different views and the hidden information of missing samples. To address the above problems, a novel method, called incomplete multi-view subspace clustering based on missing-sample recovering and structural information learning (MSR_SIL), is proposed in this paper. Overall, MSR_SIL incorporates the missing-sample recovering in feature space and self-expressiveness coefficient learning in representation space into a unified framework. Specifically, a reconstruction term is introduced to recover missing samples with the help of non-missing ones so that incomplete multi-view is completed. Next, the self-expressiveness coefficient of the recovered complete multi-view is learned. Then, each self-expressiveness coefficient is decomposed into a consistent part to capture the similarity information of view-paired and a specific part to hold the unique information of each view. Especially, to retain the global manifold structure of the multi-view data, Schatten-p norm is applied. Finally, the clustering results can be obtained on the basis of the consistent and specific representation. Experimental results on eleven well-known benchmark multi-view datasets demonstrate that the proposed MSR_SIL yields significant improvements on clustering performance.
Quantum Information Protection Scheme Based on Reinforcement Learning for Periodic Surface Codes

Quantum information transfer is an information processing technology with high speed and high entanglement with the help of quantum mechanics principles. To solve the problem of quantum information getting easily lost during transmission, we choose topological quantum error correction codes as the best candidate codes to improve the fidelity of quantum information. The stability of topological error correction codes brings great convenience to error correction. The quantum error correction codes represented by surface codes have produced very good effects in the error correction mechanism. In order to solve the problem of strong spatial correlation and optimal decoding of surface codes, we introduced a reinforcement learning decoder that can effectively characterize the spatial correlation of error correction codes. At the same time, we use a double-layer convolutional neural network model in the confrontation network to find a better error correction chain, and the generation network can approach the best correction model, to ensure that the discriminant network corrects more nontrivial errors. To improve the efficiency of error correction, we introduced a double-Q algorithm and ResNet network to increase the error correction success rate and training speed of the surface code. Compared with the previous MWPM 0.005 decoder threshold, the success rate has slightly improved, which can reach up to 0.0068 decoder threshold. By using the residual neural network architecture, we saved one-third of the training time and increased the training accuracy to about 96.6%. Using a better training model, we have successfully increased the decoder threshold from 0.0068 to 0.0085, and the depolarized noise model being used does not require a priori basic noise, so that the error correction efficiency of the entire model has slightly improved. Finally, the fidelity of the quantum information has successfully improved from 0.2423 to 0.7423 by using the error correction protection schemes.
<scp><i>Need for speed</i></scp>, but how much does it cost? Unpacking the <scp>fee‐speed</scp> relationship in Bitcoin transactions

Abstract The growing popularity of blockchain‐based cryptocurrencies is driven by the flexibility in transaction fee offerings, among other factors. To achieve service‐level differentiation among their users, many cryptocurrencies allow users to “name your own price,” giving rise to a large variation in fee offerings and hence, variation in confirmation times. Yet, the time it takes a cryptocurrency transaction to be confirmed in the blockchain is not only affected by the fee offered, but also by the contemporaneous congestion level and the inherent randomness in the verification process. Although it is generally expected that higher fees lead to quicker confirmation, the uniqueness of the cryptocurrency setting adds important nuances to the fee‐speed relationship. Using Bitcoin—the original and most heavily used cryptocurrency by far—as our empirical context, we stylize the transaction confirmation processes, propose a theoretical framework that maps the causal path from fee to speed, and estimate this framework using Bitcoin transaction data under periods of high volatility. Our results show strong evidence for two characteristics of fee's impact: congestion dependence and tail shrinkage. Our finding that the speed acceleration effect of fee is particularly strong on the tail of the confirmation time distribution motivates a target service level approach to fee recommendation. To put this finding into practice, we develop an efficient computational procedure that helps Bitcoin users accurately estimate fees based on their confirmation delay preferences. We discuss the implications of our analyses on future cryptocurrency development and the long‐term adoption of this revolutionary technology.
MsmcNet: A Modular Few-Shot Learning Framework for Signal Modulation Classification

With the development of deep learning (DL) technology, the feature extraction capability of networks has gradually been enhanced, and high accuracy has been achieved in signal modulation classification (SMC) tasks. DL requires numerous training samples to achieve high classification accuracy. However, in non-cooperative cases, only a few labeled data are usually available. To solve this problem, we propose a modular few-shot learning framework for SMC, called MsmcNet. MsmcNet comprises an IQ fusion (IQF) module, 1D signal feature processing (1D-SFP) modules, and a classifier. The number of the 1D-SFP modules is variable and is determined by a GCN-based method. Experimental results on RadioML2016.04c and RadioML2016.10a show that the proposed approach outperforms the baselines in all aspects. In particular, for the RadioML2016.10a dataset, when the SNR is 6&#x00A0;dB and the training data account for more than 2&#x0025; of the test data, the classification accuracy of MsmcNet is greater than 70&#x0025;. To further meet the requirements in the non-cooperative case, we explore the performance of the proposed MsmcNet in the one-shot learning case and find that it maintains its high performance advantage. In addition to the SMC task, we explore the performance of MsmcNet in the transmitter identification task to verify the universality of the framework. Compared to the baselines, MsmcNet achieves outstanding results.
Evidence-based guiding principles to build public trust in personal data use in health systems

Public trust in health systems is pivotal for their effective and efficient functioning. In particular, public trust is essential for personal data use, as demonstrated in debates in many countries, for example, about whether data from COVID-19 contact tracing apps should be pooled or remain on individuals' smartphones. Low levels of public trust pose a risk not only to health system legitimacy but can also harm population health.Synthesising our previous qualitative and theoretical research in the English National Health Service which enabled us to conceptualise the nature of public trust in health systems, we present guiding principles designed to rebuild public trust, if lost, and to maintain high levels of public trust in personal data use within the health system, if not.To build public trust, health system actors need to not rush trust building; engage with the public; keep the public safe; offer autonomy to the public; plan for diverse trust relationships; recognise that trust is shaped by both emotion and rational thought; represent the public interest; and work towards realising a net benefit for the health system and the public.Beyond policymakers and government officials, the guiding principles address a wide range of actors within health systems so that they can work collectively to build public trust. The guiding principles can be used to inform policymaking in health and health care and to analyse the performance of different governments to see if those governments that operate in greater conformity with the guiding principles perform better.
TypeFormer: Multiscale Transformer With Type Controller for Remote Sensing Image Caption

Image captioning in remote sensing can help us understandthe inner attributes of the objects and the outer relations between different objects. However, the existing image captioning algorithms lack the ability of global representation, and cannot obtain object relations over long distances. In addition, these algorithmics generate captions randomly without consideration of the specific demands. To this end, we propose a pure transformer architecture with caption type controller for remote sensing image captioning. Specifically, a multi-scale vision transformer is adopted for the image representation, where the global and detailed content can be captured with multi-head self-attention layers. A transformer decoder is then introduced to successively translate the image features into comprehensive sentences. The optional block called the caption type controller is designed to consider the types of captions through caption type matrix sets according to the demands, embedding the learnable sentence feature with the required type. The comparison and ablation experiments conducted on the Remote Sensing Image Captioning Dataset (RSICD) dataset demonstrate that the proposed framework outperforms the current state-of-the-art image captioning methods. The experiments conducted on the FloodNet caption dataset further illustrate that the proposed methods can effectively generate specific types of captions.
Secured Cyber Security Algorithm for Healthcare System Using Blockchain Technology

Blockchain technology is critical in cyber security.The most recent cryptographic strategies may be hacked as efforts are made to build massive electronic circuits.Because of the ethical and legal implications of a patient's medical data, cyber security is a critical and challenging problem in healthcare.The image secrecy is highly vulnerable to various types of attacks.As a result, designing a cyber security model for healthcare applications necessitates extra caution in terms of data protection.To resolve this issue, this paper proposes a Lionized Golden Eagle based Homomorphic Elapid Security (LGE-HES) algorithm for the cybersecurity of blockchain in healthcare networks.The blockchain algorithm preserves the security of the medical image by performing hash function.The execution of this research is carried out by MATLAB software.The suggested framework was tested utilizing Computed Tumor (CT) pictures and MRI image datasets, and the simulation results revealed the proposed model's profound implications.During the simulation, 94.9% of malicious communications were recognized and identified effectively, according to the total outcomes statistics.The suggested model's performance is also compared to that of standard approaches in terms of Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), time complexity, and other factors.
Real-Time Facemask Detection for Preventing COVID-19 Spread Using Transfer Learning Based Deep Neural Network

The COVID-19 pandemic disrupted people’s livelihoods and hindered global trade and transportation. During the COVID-19 pandemic, the World Health Organization mandated that masks be worn to protect against this deadly virus. Protecting one’s face with a mask has become the standard. Many public service providers will encourage clients to wear masks properly in the foreseeable future. On the other hand, monitoring the individuals while standing alone in one location is exhausting. This paper offers a solution based on deep learning for identifying masks worn over faces in public places to minimize the coronavirus community transmission. The main contribution of the proposed work is the development of a real-time system for determining whether the person on a webcam is wearing a mask or not. The ensemble method makes it easier to achieve high accuracy and makes considerable strides toward enhancing detection speed. In addition, the implementation of transfer learning on pretrained models and stringent testing on an objective dataset led to the development of a highly dependable and inexpensive solution. The findings provide validity to the application’s potential for use in real-world settings, contributing to the reduction in pandemic transmission. Compared to the existing methodologies, the proposed method delivers improved accuracy, specificity, precision, recall, and F-measure performance in three-class outputs. These metrics include accuracy, specificity, precision, and recall. An appropriate balance is kept between the number of necessary parameters and the time needed to conclude the various models.
An adaptive polyak heavy-ball method

The heavy-ball (HB) method has become a well-known practice for large-scale machine learning problems, and it can achieve the fastest local convergence rate when objective functions are smooth and strongly convex using Polyak’s optimal hyper-parameters. However, such convergence rates are based on specific uncertain and time-invariant hyper-parameters that limit its potential. In this paper, we propose an adaptive HB that estimates the Polyak’s optimal hyper-parameters at each iteration. Our adaptive approach employs the absolute differences of current and previous model parameters and their gradients. Such representation allows for a computationally efficient optimizer. We show that our method guarantees a global linear convergence rate for smooth and strongly convex objective functions. Whereas in the stochastic setting, we show that proposed stochastic algorithm converges almost surely for non-convex smooth functions with bounded gradient. We validate the effectiveness of our method on image classification datasets with no empirical tuning, and its superiority on quadratic and non-convex functions while comparing its performance to the state-of-the-art optimizers.
Influence of dynamic content on visual attention during video advertisements

Purpose Dynamic advertising, including television and online video ads, demands new theory and tools developed to understand attention to moving stimuli. The purpose of this study is to empirically test the predictions of a new dynamic attention theory, Dynamic Human-Centred Communication Systems Theory, versus the predictions of salience theory. Design/methodology/approach An eye-tracking study used a sample of consumers to measure visual attention to potential areas of interest (AOIs) in a random selection of unfamiliar video ads. An eye-tracking software feature called intelligent bounding boxes (IBBs) was used to track attention to moving AOIs. AOIs were coded for the presence of static salience variables (size, brightness, colour and clutter) and dynamic attention theory dimensions (imminence, motivational relevance, task relevance and stability). Findings Static salience variables contributed 90% of explained variance in fixation and 57% in fixation duration. However, the data further supported the three-way interaction uniquely predicted by dynamic attention theory: between imminence (central vs peripheral), relevance (motivational or task relevant vs not) and stability (fleeting vs stable). The findings of this study indicate that viewers treat dynamic stimuli like real life, paying less attention to central, relevant and stable AOIs, which are available across time and space in the environment and so do not need to be memorised. Research limitations/implications Despite the limitations of small samples of consumers and video ads, the results of this study demonstrate the potential of two relatively recent innovations, which have received limited emphasis in the marketing literature: dynamic attention theory and IBBs. Practical implications This study documents what does and does not attract attention to video advertising. What gets attention according to salience theory (e.g. central location) may not always get attention in dynamic advertising because of the effects of relevance and stability. To better understand how to execute video advertising to direct and retain attention to important AOIs, advertisers and advertising researchers are encouraged to use IBBs. Originality/value This study makes two original contributions: to marketing theory, by showing how dynamic attention theory can predict attention to video advertising better than salience theory, and to marketing research, showing the utility of tracking visual attention to moving objects in video advertising with IBBs, which appear underutilised in advertising research.
Constraint-handling techniques used with evolutionary algorithms

Evolutionary Algorithms (EAs) have been found successful in the solution of a wide variety of optimization problems. However, EAs are unconstrained search techniques. Thus, incorporating constraints into the fitness function of an EA is an open research area.
Labeled projective dictionary pair learning: application to handwritten numbers recognition

Dictionary learning was introduced for sparse image representation. Today, it is a cornerstone of image classification. We propose a novel dictionary learning method to recognise images of handwritten numbers. Our focus is to maximise the sparse-representation and discrimination power of the class-specific dictionaries. We, for the first time, adopt a new feature space, i.e., histogram of oriented gradients (HOG), to generate dictionary columns (atoms). The HOG features robustly describe fine details of hand-writings. We design an objective function followed by a minimisation technique to simultaneously incorporate these features. The proposed cost function benefits from a novel class-label penalty term constraining the associated minimisation approach to obtain class-specific dictionaries. The results of applying the proposed method on various handwritten image databases in three different languages show enhanced classification performance (∼98%) compared to other relevant methods. Moreover, we show that combination of HOG features with dictionary learning enhances the accuracy by 11% compared to when raw data are used. Finally, we demonstrate that our proposed approach achieves comparable results to that of existing deep learning models under the same experimental conditions but with a fraction of parameters.
Non-convex fractional-order TV model for impulse noise removal

For the pursuit of high-quality restoration effects, we introduce a novel non-convex extended scheme to reconstruct blurred images under impulse noise in this paper. Because of the remarkable advantages of the combination of non-convex ℓp-norm and fractional-order total variation regularization, our developed strategy possesses a brilliant ability to overcome the staircase-like aspects and preserve neat contours. To obtain the minimizer of the optimization problem, we adopt the alternating direction method of multipliers closely integrating with the iteratively reweighted ℓ1 algorithm. Theoretically, a convergence proof is provided for the developed algorithm. By considering salt-and-pepper noise and random-valued impulse noise, comprehensive simulation comparisons with some popular methods are carried out for image restoration. And the experimental results illustrate that our proposed model behaves better with respect to visual comparison and quantitative measurement.
Network-Aware Container Placement in Cloud-Edge Kubernetes Clusters

With the diffusion of Fog and Edge Computing paradigms, new application categories have emerged with specific quality of service requirements, in terms of communication latency and throughput. The placement of these applications on distributed Cloud-Edge environments is a challenging task, because of the continuously varying node-to-node network latency and bandwidth on Edge infrastructure. Although Kubernetes is the de-facto standard for container orchestration on Cloud data centers, its scheduling strategy is not suitable for the placement of time critical applications on Edge environments because it does not take into account current network conditions, neither communication requirements between microservices during its scheduling decisions. In this work we propose a network-aware scheduler plugin that extends the default Kubernetes scheduler, in order to deal with variable network conditions on cloud-edge clusters and run-time communication requirements of microservices. A custom descheduler is also proposed that periodically monitors run-time network state and traffic exchanged between microservices and evicts Pods from cluster nodes if better placement decisions can be done. An evaluation of our scheduling and descheduling strategies has been carried out on a sample microservices-based application deployed on a test bed environment.