Reviewerly/exHarmony huggingface dataset:
Non-orthogonal bases for quantum metrology

Abstract Many quantum statistical models are most conveniently formulated in terms of non-orthogonal bases. This is the case, for example, when mixtures and superpositions of coherent states are involved. In these instances, we show that the analytical evaluation of the quantum Fisher information matrix may be greatly simplified by avoiding both the diagonalization of the density matrix and the orthogonalization of the basis. The key ingredient in our method is the Gramian matrix (i.e. the matrix of scalar products between basis elements), which may be interpreted as a metric tensor for index contraction. As an application, we derive novel analytical results for several estimation problems involving noisy Schrödinger cat states.
Improved Artificial Electric Field Algorithm Using Nelder-Mead Simplex Method for Optimization Problems

Artificial electric field (AEF) algorithm was developed to be an alternative method for tackling with real-world engineering problems as a physics inspired meta-heuristic algorithm. Due to its stochastic nature, AEF suffers from poor exploitation, which needs to be improved. Therefore, this study attempts to obtain an effective structure by using AEF together with Nelder-Mead (NM) simplex search method. The proposed method has been named as artificial electric field with Nelder-Mead algorithm (AEF-NM). The proposed algorithm performs global search via AEF whereas NM is used to achieve better local search ability. In this way, a better performing algorithm has been achieved. Four well-known benchmark functions (Rosenbrock, Ackley, Sphere, Schwefel) were adopted to test the proposed algorithm. Comparative statistical analyses were carried out using the state-of-the-art algorithms such as sinecosine, atom search optimization, salp swarm and original AEF algorithms to observe the capability of the proposed AEF-NM. The proposed algorithm was shown to be have better performance than other compared algorithms.
WITHDRAWN: English smart classroom teaching system based on 5G network and internet of things

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
Deep Transfer Learning for Source Code Modeling

In recent years, deep learning models have shown great potential in source code modeling and analysis. Generally, deep learning-based approaches are problem-specific and data-hungry. A challenging issue of these approaches is that they require training from starch for a different related problem. In this work, we propose a transfer learning-based approach that significantly improves the performance of deep learning-based source code models. In contrast to traditional learning paradigms, transfer learning can transfer the knowledge learned in solving one problem into another related problem. First, we present two recurrent neural network-based models RNN and GRU for the purpose of transfer learning in the domain of source code modeling. Next, via transfer learning, these pre-trained (RNN and GRU) models are used as feature extractors. Then, these extracted features are combined into attention learner for different downstream tasks. The attention learner leverages from the learned knowledge of pre-trained models and fine-tunes them for a specific downstream task. We evaluate the performance of the proposed approach with extensive experiments with the source code suggestion task. The results indicate that the proposed approach outperforms the state-of-the-art models in terms of accuracy, precision, recall, and F-measure without training the models from scratch.
Securing logistics system and supply chain using Blockchain

Abstract Past international trade practices have been associated with opaque information flows that have hindered traceability and created hurdles in hassle‐free trade. Blockchain and allied technologies have been investigated as a panacea for the problems faced by supply chain and logistics industry. Network analysis also uncovered twenty types of legal and antisocial entities operating on bitcoin and provided a path for uncovering these antisocial entities. However, earlier works of literature have focused on limited aspects of a typical supply chain such as monitoring assets, securing traceability, data integrity negligence, and data access. To overcome such drawbacks, this article proposes permissioned Blockchain with relevant processes and functions to obtain a holistic framework for securing the supply chain and logistic operations. The efficacy of the proposed framework was demonstrated in the case study. Comparative study was performed with the existing frameworks where the proposed framework was found to be better at four counts compared on the basis of data integrity in supply chain, decentralized decision making and traceability. It was found that critical loopholes in a current supply chain can be overcome using the proposed framework.
Robot skill learning in latent space of a deep autoencoder neural network

Just like humans, robots can improve their performance by practicing, i. e. by performing the desired behavior many times and updating the underlying skill representation using the newly gathered data. In this paper, we propose to implement robot practicing by applying statistical and reinforcement learning (RL) in a latent space of the selected skill representation. The latent space is computed by a deep autoencoder neural network, with the data to train the network generated in simulation. However, we show that the resulting latent space representation is useful also for learning on a real robot. Our simulation and real-world results demonstrate that by exploiting the latent space of the underlying motor skill representation, a significant reduction of the amount of data needed for effective learning by Gaussian Process Regression (GPR) can be achieved. Similarly, the number of RL epochs can be significantly reduced. Finally, it is evident from our results that an autoencoder-based latent space is more effective for these purposes than a latent space computed by principal component analysis.
Spiral Generative Network for Image Extrapolation

In this paper, motivated by human natural ability to perceive unseen surroundings imaginatively, we propose a novel Spiral Generative Network, SpiralNet, to perform image extrapolation in a spiral manner, which regards extrapolation as an evolution process growing from an input sub-image along a spiral curve to an expanded full image. Our SpiralNet, consisting of ImagineGAN and SliceGAN, disentangles image extrapolation problem into two independent sub-tasks as semantic structure prediction (via ImagineGAN) and contextual detail generation (via SliceGAN), making the whole task more tractable. The design of SliceGAN implicitly harnesses the correlation between generated contents and extrapolating direction, divide-and-conquer while generation-by-parts. Extensive experiments on datasets covering both objects and scenes under different cases show that our method achieves state-of-the-art performance on image extrapolation. We also conduct ablation study to validate efficacy of our design. Our code is available at https://github.com/zhenglab/spiralnet .
On Joint Optimization of Automatic Speaker Verification and Anti-Spoofing in the Embedding Space

Biometric systems are exposed to spoofing attacks which may compromise their security, and voice biometrics based on automatic speaker verification (ASV), is no exception. To increase the robustness against such attacks, anti-spoofing systems have been proposed for the detection of replay, synthesis and voice conversion-based attacks. However, most proposed anti-spoofing techniques are loosely integrated with the ASV system. In this work, we develop a new integration neural network which jointly processes the embeddings extracted from ASV and anti-spoofing systems in order to detect both zero-effort impostors and spoofing attacks. Moreover, we propose a new loss function based on the minimization of the area under the expected (AUE) performance and spoofability curve (EPSC), which allows us to optimize the integration neural network on the desired operating range in which the biometric system is expected to work. To evaluate our proposals, experiments were carried out on the recent ASVspoof 2019 corpus, including both logical access (LA) and physical access (PA) scenarios. The experimental results show that our proposal clearly outperforms some well-known techniques based on the integration at the score- and embedding-level. Specifically, our proposal achieves up to 23.62% and 22.03% relative equal error rate (EER) improvement over the best performing baseline in the LA and PA scenarios, respectively, as well as relative gains of 27.62% and 29.15% on the AUE metric.
FMixCutMatch for semi-supervised deep learning

Mixed sample augmentation (MSA) has witnessed great success in the research area of semi-supervised learning (SSL) and is performed by mixing two training samples as an augmentation strategy to effectively smooth the training space. Following the insights on the efficacy of cut-mix in particular, we propose FMixCut, an MSA that combines Fourier space-based data mixing (FMix) and the proposed Fourier space-based data cutting (FCut) for labeled and unlabeled data augmentation. Specifically, for the SSL task, our approach first generates soft pseudo-labels using the model's previous predictions. The model is then trained to penalize the outputs of the FMix-generated samples so that they are consistent with their mixed soft pseudo-labels. In addition, we propose to use FCut, a new Cutout-based data augmentation strategy that adopts the two masked sample pairs from FMix for weighted cross-entropy minimization. Furthermore, by implementing two regularization techniques, namely, batch label distribution entropy maximization and sample confidence entropy minimization, we further boost the training efficiency. Finally, we introduce a dynamic labeled–unlabeled data mixing (DDM) strategy to further accelerate the convergence of the model. Combining the above process, we finally call our SSL approach as "FMixCutMatch", in short FMCmatch. As a result, the proposed FMCmatch achieves state-of-the-art performance on CIFAR-10/100, SVHN and Mini-Imagenet across a variety of SSL conditions with the CNN-13, WRN-28-2 and ResNet-18 networks. In particular, our method achieves a 4.54% test error on CIFAR-10 with 4K labels under the CNN-13 and a 41.25% Top-1 test error on Mini-Imagenet with 10K labels under the ResNet-18. Our codes for reproducing these results are publicly available at https://github.com/biuyq/FMixCutMatch.
Quantum particles-enhanced multiple Harris Hawks swarms for dynamic optimization problems

Dynamic optimization problems (DOPs) have been a subject of considerable research interest mainly due to their widespread application potential. In the literature, various mechanisms have been reported to cope with the challenges of DOPs. The proposed mechanisms have usually been adopted by well-known population-based optimization algorithms, such as genetic algorithms or particle swarm optimization. Although new generation swarm-intelligence algorithms are continuously being developed and have much to offer in DOPs, their performance is usually tested on stationary optimization problems. In this study, a recently introduced optimization algorithm, Harris Hawk Optimizer, is redesigned as a multi-population based algorithm to deal with possible multiple optima. Thus, the proposed modification is allowed to search diverse parts of the search space more efficiently, particularly in multimodal environments. Next, it is further enhanced by using quantum particles to tackle with diversification and intensification challenges in DOPs. As shown in the present work, this mechanism can maintain population diversity and intensification depending on a user-supplied parameter. Finally, based on different algorithmic components, four different variants of HHO are proposed. The performances of the developed algorithms are tested on both stationary and dynamic test problems. Dynamic test functions introduced in the IEEE Congress on Evolutionary Computation 2009 (CEC 2009) are used and further extended to test the proposed algorithms' performances. Finally, appropriate statistical analysis is conducted to demonstrate significant improvements over the existing algorithms.
A Bit(coin) of happiness after a failure: An empirical examination of the effectiveness of cryptocurrencies as an innovative recovery tool

This research provides the first examination of the effectiveness of cryptocurrencies as an innovative recovery tool. Through four experiments, we assess the effects of crypto-compensation against traditional compensation types (voucher/cash) on customer recovery satisfaction. Study 1 findings indicate that crypto-compensation is more effective than voucher and cash in improving customer recovery satisfaction. Further, it shows that consumer innovativeness moderates the effectiveness of crypto-compensation. After establishing the effectiveness of crypto-compensation, Study 2 finds a moderating effect of consumer choice in influencing crypto-compensation effectiveness. Study 3 reveals the differential effect of communicating different crypto-compensation benefits on customer recovery satisfaction. Finally, Study 4 concludes that familiar cryptocurrencies (e.g., Bitcoin vs. EOS) best restore satisfaction after a failure and that compensation message framing (i.e., cryptocurrency monetary value vs. real nominal value) moderates this relationship. Theoretical and practical implications are discussed.
A systematic review of PIN-entry methods resistant to shoulder-surfing attacks

Although conventional PIN-entry methods are widely used in many daily authentication procedures, they are highly susceptible to shoulder-surfing attacks. A plethora of PIN-entry methods have been proposed in the literature to mitigate such attacks. Unfortunately, none of these methods is capable of replacing the conventional PIN-entry method. This study presents the results of a systematic review of PIN-entry methods resistant to shoulder-surfing attacks so that the main challenges that impede their adoption can be provided along with opportunities for future research. A systematic search was conducted on seven databases using predefined criteria. A test–retest approach was performed by a single author to extract data. A total of 55 articles were included in this review. The review results manifest that PIN-entry methods are classified mainly into direct and indirect inputs. The user study was the standard research method, and error rate and PIN-entry time were the most frequently adopted usability measures. The review argues that a recording-based shoulder-surfing attack is a major threat to PIN-entry methods. Error rate and PIN-entry time are widely adopted criteria for usability. The review indicates that most PIN-entry methods require a high error rate and PIN-entry time than the conventional method. Moreover, the lack of a standard evaluation framework should be addressed.
Speculation Invariance (InvarSpec): Faster Safe Execution Through Program Analysis

Many hardware-based defense schemes against speculative execution attacks use special mechanisms to protect instructions while speculative, and lift the mechanisms when the instructions turn non-speculative. In this paper, we observe that speculative instructions can sometimes become Speculation Invariant before turning non-speculative. Speculation invariance means that (i) whether the instruction will execute and (ii) the instruction's operands are not a function of speculative state. Hence, we propose to lift the protection mechanisms on these instructions early, when they become speculation invariant, and issue them without protection. As a result, we improve the performance of the defense schemes without changing their security properties. To exploit speculation invariance, we present the InvarSpec framework. InvarSpec includes a program analysis pass that identifies, for each relevant instruction i, the set of older instructions that are Safe for i-i.e., those that do not prevent i from becoming speculation invariant. At runtime, the InvarSpec micro-architecture loads this information and uses it to determine when speculative instructions can be issued without protection. InvarSpec is one of the first defense schemes for speculative execution that combines cooperative compiler and hardware mechanisms. Our evaluation shows that InvarSpec effectively reduces the execution overhead of hardware defense schemes. For example, on SPEC17, it reduces the average execution overhead of fence protections from 195.3% to 108.2%, of Delay-On-Miss from 39.5% to 24.4%, and of InvisiSpec from 15.4% to 10.9%.
TopicBERT for Energy Efficient Document Classification

Prior research notes that BERT's computational cost grows quadratically with sequence length thus leading to longer training times, higher GPU memory constraints and carbon emissions.While recent work seeks to address these scalability issues at pre-training, these issues are also prominent in fine-tuning especially for long sequence tasks like document classification.Our work thus focuses on optimizing the computational cost of fine-tuning for document classification.We achieve this by complementary learning of both topic and language models in a unified framework, named TopicBERT.This significantly reduces the number of self-attention operations -a main performance bottleneck.Consequently, our model achieves a 1.4x ( 40%) speedup with 40% reduction in CO 2 emission while retaining 99.9% performance over 5 datasets.
Towards scaling Twitter for digital epidemiology of birth defects

In recent work, we identified and studied a small cohort of Twitter users whose pregnancies with birth defect outcomes could be observed via their publicly available tweets. Exploiting social media's large-scale potential to complement the limited methods for studying birth defects, the leading cause of infant mortality, depends on the further development of automatic methods. The primary objective of this study was to take the first step towards scaling the use of social media for observing pregnancies with birth defect outcomes, namely, developing methods for automatically detecting tweets by users reporting their birth defect outcomes. We annotated and pre-processed approximately 23,000 tweets that mention birth defects in order to train and evaluate supervised machine learning algorithms, including feature-engineered and deep learning-based classifiers. We also experimented with various under-sampling and over-sampling approaches to address the class imbalance. A Support Vector Machine (SVM) classifier trained on the original, imbalanced data set, with n-grams, word clusters, and structural features, achieved the best baseline performance for the positive classes: an F1-score of 0.65 for the "defect" class and 0.51 for the "possible defect" class. Our contributions include (i) natural language processing (NLP) and supervised machine learning methods for automatically detecting tweets by users reporting their birth defect outcomes, (ii) a comparison of feature-engineered and deep learning-based classifiers trained on imbalanced, under-sampled, and over-sampled data, and (iii) an error analysis that could inform classification improvements using our publicly available corpus. Future work will focus on automating user-level analyses for cohort inclusion.
Audio Inpainting: Revisited and Reweighted

We deal with the problem of sparsity-based audio inpainting, i.e. filling in the missing segments of audio. A consequence of the approaches based on mathematical optimization is the insufficient amplitude of the signal in the filled gaps. Remaining in the framework based on sparsity and convex optimization, we propose improvements to audio inpainting, aiming at compensating for such an energy loss. The new ideas are based on different types of weighting, both in the coefficient and the time domains. We show that our propositions improve the inpainting performance in terms of both the SNR and ODG.
An overview of cryptographic primitives for possible use in 5G and beyond

Abstract This survey overviews the potential use of cryptographic primitives in the fifth-generation mobile communications system (aka 5G) and beyond. It discusses the new security challenges that come with 5G and presents the upcoming security architecture. It shows the use of current cryptographic algorithms and discusses new algorithms or modifications of existing ones, that can be relevant. It also discusses the need for lightweight algorithms to meet the new use cases as well as the general demand for algorithms secure even when large quantum computers are available.
Multi-Scale Gradients Self-Attention Residual Learning for Face Photo-Sketch Transformation

Face sketch synthesis, as a key technique for solving face sketch recognition, has made considerable progress in recent years. Due to the difference of modality between face photo and face sketch, traditional exemplar-based methods often lead to missed texture details and deformation while synthesizing sketches. And limited to the local receptive field, Convolutional Neural Networks-based methods cannot deal with the interdependence between features well, which makes the constraint of facial features insufficient; as such, it cannot retain some details in the synthetic image. Moreover, the deeper the network layer is, the more obvious the problems of gradient disappearance and explosion will be, which will lead to instability in the training process. Therefore, in this paper, we propose a multi-scale gradients self-attention residual learning framework for face photo-sketch transformation that embeds a self-attention mechanism in the residual block, making full use of the relationship between features to selectively enhance the characteristics of specific information through self-attention distribution. Simultaneously, residual learning can keep the characteristics of the original features from being destroyed. In addition, the problem of instability in GAN training is alleviated by allowing discriminator to become a function of multi-scale outputs of the generator in the training process. Based on cycle framework, the matching between the target domain image and the source domain image can be constrained while the mapping relationship between the two domains is established so that the tasks of face photo-to-sketch synthesis (FP2S) and face sketch-to-photo synthesis (FS2P) can be achieved simultaneously. Both Image Quality Assessment (IQA) and experiments related to face recognition show that our method can achieve state-of-the-art performance on the public benchmarks, whether using FP2S or FS2P.