Reviewerly/exHarmony huggingface dataset:
Machine Learning techniques in breast cancer prognosis prediction: A primary evaluation

Abstract More than 750 000 women in Italy are surviving a diagnosis of breast cancer. A large body of literature tells us which characteristics impact the most on their prognosis. However, the prediction of each disease course and then the establishment of a therapeutic plan and follow‐up tailored to the patient is still very complicated. In order to address this issue, a multidisciplinary approach has become widely accepted, while the Multigene Signature Panels and the Nottingham Prognostic Index are still discussed options. The current technological resources permit to gather many data for each patient. Machine Learning (ML) allows us to draw on these data, to discover their mutual relations and to esteem the prognosis for the new instances. This study provides a primary evaluation of the application of ML to predict breast cancer prognosis. We analyzed 1021 patients who underwent surgery for breast cancer in our Institute and we included 610 of them. Three outcomes were chosen: cancer recurrence (both loco‐regional and systemic) and death from the disease within 32 months. We developed two types of ML models for every outcome (Artificial Neural Network and Support Vector Machine). Each ML algorithm was tested in accuracy (=95.29%‐96.86%), sensitivity (=0.35‐0.64), specificity (=0.97‐0.99), and AUC (=0.804‐0.916). These models might become an additional resource to evaluate the prognosis of breast cancer patients in our daily clinical practice. Before that, we should increase their sensitivity, according to literature, by considering a wider population sample with a longer period of follow‐up. However, specificity, accuracy, minimal additional costs, and reproducibility are already encouraging.
Modality-specific and shared generative adversarial network for cross-modal retrieval

Cross-modal retrieval aims to realize accurate and flexible retrieval across different modalities of data, e.g., image and text, which has achieved significant progress in recent years, especially since generative adversarial networks (GAN) were used. However, there still exists much room for improvement. How to jointly extract and utilize both the modality-specific (complementarity) and modality-shared (correlation) features effectively has not been well studied. In this paper, we propose an approach named Modality-Specific and Shared Generative Adversarial Network (MS2GAN) for cross-modal retrieval. The network architecture consists of two sub-networks that aim to learn modality-specific features for each modality, followed by a common sub-network that aims to learn the modality-shared features for each modality. Network training is guided by the adversarial scheme between the generative and discriminative models. The generative model learns to predict the semantic labels of features, model the inter- and intra-modal similarity with label information, and ensure the difference between the modality-specific and modality-shared features, while the discriminative model learns to classify the modality of features. The learned modality-specific and shared feature representations are jointly used for retrieval. Experiments on three widely used benchmark multi-modal datasets demonstrate that MS2GAN can outperform state-of-the-art related works.
Design and Application of an Adaptive Fuzzy Control Strategy to Zeroing Neural Network for Solving Time-Variant QP Problem

Zeroing neural network (ZNN), as an important class of recurrent neural network, has wide applications in various computation and optimization fields. In this article, based on the traditional-type zeroing neural network (TT-ZNN) model, an adaptive fuzzy-type zeroing neural network (AFT-ZNN) model is proposed to settle time-variant quadratic programming problem via integrating an adaptive fuzzy control strategy. The most prominent feature of the AFT-ZNN model is to use an adaptive fuzzy control value to adaptively adjust its convergence rate according to the value of the computational error. Four different activation functions are injected to analyze the convergence rate of the AFT-ZNN model. In addition, different membership functions and different ranges of the fuzzy control value are discussed to study the character of the AFT-ZNN model. Theoretical analysis and numerical comparison results further show that the AFT-ZNN model has better performance than the TT-ZNN model.
Mining Hard Samples Globally and Efficiently for Person Reidentification

Person reidentification (ReID) is an important application of Internet of Things (IoT). ReID recognizes pedestrians across camera views at different locations and time, which is usually treated as a ranking task. An essential part of this task is the hard sample mining. Technically, two strategies could be employed, i.e., global hard mining and local hard mining. For the former, hard samples are mined within the entire training set, while for the latter, it is done in mini-batches. In literature, most existing methods operate locally. Examples include batch-hard sample mining and semihard sample mining. The reason for the rare use of global hard mining is the high computational complexity. In this article, we argue that global mining helps to find harder samples that benefit model training. To this end, this article introduces a new system to: 1) efficiently mine hard samples (positive and negative) from the entire training set and 2) effectively use them in training. Specifically, a ranking list network coupled with a multiplet loss is proposed. On the one hand, the multiplet loss makes the ranking list progressively created to avoid the time-consuming initialization. On the other hand, the multiplet loss aims to make effective use of the hard and easy samples during training. In addition, the ranking list makes it possible to globally and effectively mine hard positive and negative samples. In the experiments, we explore the performance of the global and local sample mining methods, and the effects of the semihard, the hardest, and the randomly selected samples. Finally, we demonstrate the validity of our theories using various public data sets and achieve competitive results via a quantitative evaluation.
Super-resolution of real-world rock microcomputed tomography images using cycle-consistent generative adversarial networks

Digital rock imaging plays an important role in studying the microstructure and macroscopic properties of rocks, where microcomputed tomography (MCT) is widely used. Due to the inherent limitations of MCT, a balance should be made between the field of view (FOV) and resolution of rock MCT images-a large FOV at low resolution (LR) or a small FOV at high resolution (HR). However, large FOV and HR are both expected for reliable analysis results in practice. Super-resolution (SR) is an effective solution to break through the mutual restriction between the FOV and resolution of rock MCT images, for it can reconstruct an HR image from a LR observation. Most of the existing SR methods cannot produce satisfactory HR results on real-world rock MCT images. One of the main reasons for this is that paired images are usually needed to learn the relationship between LR and HR rock images. However, it is challenging to collect such a dataset in a real scenario. Meanwhile, the simulated datasets may be unable to accurately reflect the model in actual applications. To address these problems, we propose a cycle-consistent generative adversarial network (CycleGAN)-based SR approach for real-world rock MCT images, namely, SRCycleGAN. In the off-line training phase, a set of unpaired rock MCT images is used to train the proposed SRCycleGAN, which can model the mapping between rock MCT images at different resolutions. In the on-line testing phase, the resolution of the LR input is enhanced via the learned mapping by SRCycleGAN. Experimental results show that the proposed SRCycleGAN can greatly improve the quality of simulated and real-world rock MCT images. The HR images reconstructed by SRCycleGAN show good agreement with the targets in terms of both the visual quality and the statistical parameters, including the porosity, the local porosity distribution, the two-point correlation function, the lineal-path function, the two-point cluster function, the chord-length distribution function, and the pore size distribution. Large FOV and HR rock MCT images can be obtained with the help of SRCycleGAN. Hence, this work makes it possible to generate HR rock MCT images that exceed the limitations of imaging systems on FOV and resolution.
Personalized Ranking with Importance Sampling

As the task of predicting a personalized ranking on a set of items, item recommendation has become an important way to address information overload. Optimizing ranking loss aligns better with the ultimate goal of item recommendation, so many ranking-based methods were proposed for item recommendation, such as collaborative filtering with Bayesian Personalized Ranking (BPR) loss, and Weighted Approximate-Rank Pairwise (WARP) loss. However, the ranking-based methods can not consistently beat regression-based models with the gravity regularizer. The key challenge in ranking-based optimization is difficult to fully use the limited number of negative samples, particularly when they are not so informative. To this end, we propose a new ranking loss based on importance sampling so that more informative negative samples can be better used. We then design a series of negative samplers from simple to complex, whose informativeness of negative samples is from less to more. With these samplers, the loss function is easy to use and can be optimized by popular solvers. The proposed algorithms are evaluated with five real-world datasets of varying size and difficulty. The results show that they consistently outperform the state-of-the-art item recommendation algorithms, and the relative improvements with respect to [email protected] are more than 19.2% on average. Moreover, the loss function is verified to make better use of negative samples and to require fewer negative samples when they are more informative.
Classification of Garments from Fashion MNIST Dataset Using CNN LeNet-5 Architecture

Recently, deep learning has been used extensively in a wide range of domains. A class of deep neural networks that give the most rigorous effects in solving real-world problems is a Convolutional Neural Network (CNN). Fashion businesses have used CNN on their e-commerce to solve many problems such as clothes recognition, clothes search and recommendation. A core step for all of these implementations is image classification. However, clothes classification is a challenge task as clothes have many properties, and the depth of clothes categorization is highly complicated. This complicated depth makes different classes to have very similar features, and so the classification problem becomes very hard. In this paper, CNN based LeNet-5 architecture is proposed to train parameters of the CNN on Fashion MNIST dataset. Experimental results show that LeNet-5 model achieved accuracy over 98%. Therefore, it outperforms both the classical CNN model and the other existing state-of-the-art models in literatures.
A Novel Image Steganographic Method based on Integer Wavelet Transformation and Particle Swarm Optimization

Image steganography is a technique of hiding secret data into a cover image and so as to prevent the intruders from accessing the secret data. The efficiency of image steganography techniques are usually evaluated based on perceptual transparency, payload capacity, security, temper resistance and computational costs. Though there has been significant improvement in related research over the decades, available steganographic techniques usually satisfy only a subset of these criteria. This paper presents a novel IWT (Integer Wavelet Transform) based steganography method using PSO (Particle Swarm Optimization) to find the optimal substitution matrix for converting secret data into their substituted forms. In the proposed method, optimal pixel adjustment process is used to improve perceptual transparency so that the obtained stego image has low distortion. The proposed method improves the security, imperceptibility, and robustness of the secret data by hiding them into the wavelet coefficients of an image. Thus, the paper provides a detailed study of the use of PSO in three different image steganographic methods based on (i) LSB (Least Significant Bit) substitution, (ii) DWT (Discrete Wavelet Transform), and (iii) IWT. Experiments are conducted using well-known benchmark images and results are comparatively analyzed. It is found that our proposed approach of PSO based IWT outperforms both PSO based LSB and PSO based DWT from the context of standard quality metrics, statistical analysis, security level estimation, payload capacity, imperceptibility etc. We have also provided a comparative overview of the existing data hiding methods including the proposed approach.
An intelligent electric vehicle charging system for new energy companies based on consortium blockchain

With the concerns of environment protection, electric vehicle (EV) is regarded as a promising transportation tool for green cities project. Since the amount of EV is rising shapely, the EV charging demands is also rapidly generated. However, seeking suitable charging facilities is not easy for EV users, new energy companies run charging station separately for self-interests, and charging pile information is not transparent for drivers. This dilemma is not solved until the merging of blockchain technology. In this paper, a novel EV charging system is proposed for the cooperation of new energy companies and providing convenient charging services for users. In this system, charging information is managed and recorded by the company alliance based on consortium blockchain, which is tamper-resistant and multi-centralized. Meanwhile, a new smart contract is designed to balance the allocation of company’ charging users, so that the profits of different new energy companies could be fairer. To equilibrate the interest of companies and EV users, a Bio-Objective Mixed-Integer Programming model (BOMILP) is proposed as the mathematical logic of smart contracts. Furthermore, we proposed a new algorithm named Limited Neighborhood Search with Memory (LNSM) to support the implementation of smart contracts, which could make the smart contract running faster and has a better performance. At last, the proposed EV charging system and the smart contract are validated through a real case study with the EV charging data in Beijing, China.
Drug Governance: IoT-based Blockchain Implementation in the Pharmaceutical Supply Chain

Pharmaceutical drugs are essential in diagnosing and treating patients. However, over the past decade, the issue of fraud and abuse drugs in the pharmaceutical supply chain has become increasingly apparent. To solve these problems, a transformation of the current pharmaceutical supply chain to include tracking technologies, from the source of the ingredients to the consumer, is recommended. In this paper, we investigate the novel pharmaceutical governance based on IoT and Blockchain technology. Internet of Things (IoT) based blockchain is a type of distributed ledger (DLT) that maintains an immutable record of all transaction information that is incapable of being falsified and is visible to all participants. Implementing an IoT-based blockchain system would provide the tools for the pharmaceutical industry to improve drug governance along the supply chain, thus making healthcare more efficient and reliable.
Distilled Semantics for Comprehensive Scene Understanding from Videos

Whole understanding of the surroundings is paramount to autonomous systems. Recent works have shown that deep neural networks can learn geometry (depth) and motion (optical flow) from a monocular video without any explicit supervision from ground truth annotations, particularly hard to source for these two tasks. In this paper, we take an additional step toward holistic scene understanding with monocular cameras by learning depth and motion alongside with semantics, with supervision for the latter provided by a pre-trained network distilling proxy ground truth images. We address the three tasks jointly by a) a novel training protocol based on knowledge distillation and self-supervision and b) a compact network architecture which enables efficient scene understanding on both power hungry GPUs and low-power embedded platforms. We thoroughly assess the performance of our framework and show that it yields state-of-the-art results for monocular depth estimation, optical flow and motion segmentation.
Variational quantum state eigensolver

Extracting eigenvalues and eigenvectors of exponentially large matrices will be an important application of near-term quantum computers. The Variational Quantum Eigensolver (VQE) treats the case when the matrix is a Hamiltonian. Here, we address the case when the matrix is a density matrix $\rho$. We introduce the Variational Quantum State Eigensolver (VQSE), which is analogous to VQE in that it variationally learns the largest eigenvalues of $\rho$ as well as a gate sequence $V$ that prepares the corresponding eigenvectors. VQSE exploits the connection between diagonalization and majorization to define a cost function $C=\Tr(\tilde{\rho} H)$ where $H$ is a non-degenerate Hamiltonian. Due to Schur-concavity, $C$ is minimized when $\tilde{\rho} = V\rho V^\dagger$ is diagonal in the eigenbasis of $H$. VQSE only requires a single copy of $\rho$ (only $n$ qubits) per iteration of the VQSE algorithm, making it amenable for near-term implementation. We heuristically demonstrate two applications of VQSE: (1) Principal component analysis, and (2) Error mitigation.
Adversarial Attacks on GMM I-Vector Based Speaker Verification Systems

This work investigates the vulnerability of Gaussian Mixture Model (GMM) i-vector based speaker verification systems to adversarial attacks, and the transferability of adversarial samples crafted from GMM i-vector based systems to x-vector based systems. In detail, we formulate the GMM i-vector system as a scoring function of enrollment and testing utterance pairs. Then we leverage the fast gradient sign method (FGSM) to optimize testing utterances for adversarial samples generation. These adversarial samples are used to attack both GMM i-vector and x-vector systems. We measure the system vulnerability by the degradation of equal error rate and false acceptance rate. Experiment results show that GMM i-vector systems are seriously vulnerable to adversarial attacks, and the crafted adversarial samples are proved to be transferable and pose threats to neural network speaker embedding based systems (e.g. x-vector systems).
Real-Time, Universal, and Robust Adversarial Attacks Against Speaker Recognition Systems

As the popularity of voice user interface (VUI) exploded in recent years, speaker recognition system has emerged as an important medium of identifying a speaker in many security-required applications and services. In this paper, we propose the first real-time, universal, and robust adversarial attack against the state-of-the-art deep neural network (DNN) based speaker recognition system. Through adding an audio-agnostic universal perturbation on arbitrary enrolled speaker's voice input, the DNN-based speaker recognition system would identify the speaker as any target (i.e., adversary-desired) speaker label. In addition, we improve the robustness of our attack by modeling the sound distortions caused by the physical over-the-air propagation through estimating room impulse response (RIR). Experiment using a public dataset of 109 English speakers demonstrates the effectiveness and robustness of our proposed attack with a high attack success rate of over 90%. The attack launching time also achieves a 100× speedup over contemporary non-universal attacks.
Investigating the process of mine rescuers' safety training with immersive virtual reality: A structural equation modelling approach

Immersive virtual reality (VR) is a powerful tool for vocational training - especially for safety-critical vocations where real-world training is often too complicated, expensive, or risky. Despite its apparent utility in this context, there is not yet a coherent, systematic framework that captures the key features of user learning experiences in immersive virtual reality environments. With the use of Structural Equation Modelling (SEM), this article presents a comprehensive framework which brings different dimensions (assessment criteria) together. This framework is informed by the analysis of 45 VR training sessions conducted by Mines Rescue Pty Ltd and total of 284 mines rescue brigades partook in this study and received training on specific mines rescue operation. The result of this study shows that actual and perceived learning was enhanced by trainee engagement with the scenario, their perception of the fidelity of the scenario, their sense of co-presence with other trainees, the perceived usability of the system, an overall positive attitude towards the technology, and the involvement of skilled trainers. These results suggest that: 1) there are multiple paths by which immersive VR training can have a positive impact on learning, and 2) immersive VR training will not replace the requirement for skilled trainers, but rather it can serve as an effective vehicle to convey their expertise.
A combination model based on transfer learning for waste classification

Summary The increasing amount of solid waste is becoming a significant problem that needs to be addressed urgently. The reliable and accurate classification method is a crucial step in waste disposal because different types of wastes have different disposal ways. The existing waste classification models driven by deep learning are not easy to achieve accurate results and still need to be improved due to the various architecture networks adopted. Their performance on different datasets is varied, and there is also a lack of specific large‐scale datasets for training. We propose a new combination classification model based on three pretrained CNN models (VGG19, DenseNet169, and NASNetLarge) for processing the ImageNet database and achieve high classification accuracy. In our proposed model, the transfer learning model based on each pretrained model is constructed as a candidate classifier, and the optimal output of three candidate classifiers is selected as the final classification result. The experiments based on two waste image datasets demonstrate that the proposed model achieves 96.5% and 94% classification accuracy and outperforms several counterpart methods.
Risk spillover between Bitcoin and conventional financial markets: An expectile-based approach

In order to challenge the existing literature that points to the detachment of Bitcoin from the global financial system, we use daily data from August 17, 2011–February 14, 2020 and apply a risk spillover approach based on expectiles. Results show reasonable evidence to imply the existence of downside risk spillover between Bitcoin and four assets (equities, bonds, currencies, and commodities), which seems to be time dependent. Our main findings have implications for participants in both the Bitcoin and traditional financial markets for the sake of asset allocation, and risk management. For policy makers, the findings suggest that Bitcoin should be monitored carefully for the sake of financial stability.
BERT for Evidence Retrieval and Claim Verification

We investigate BERT in an evidence retrieval and claim verification pipeline for the task of evidence-based claim verification. To this end, we propose to use two BERT models, one for retrieving evidence sentences supporting or rejecting claims, and another for verifying claims based on the retrieved evidence sentences. To train the BERT retrieval system, we use pointwise and pairwise loss functions and examine the effect of hard negative mining. Our system achieves a new state of the art recall of 87.1 for retrieving evidence sentences out of the FEVER dataset 50K Wikipedia pages, and scores second in the leaderboard with the FEVER score of 69.7.