Reviewerly/exHarmony huggingface dataset:
Efficient Estimation of Pauli Channels

Pauli channels are ubiquitous in quantum information, both as a dominant noise source in many computing architectures and as a practical model for analyzing error correction and fault tolerance. Here we prove several results on efficiently learning Pauli channels, and more generally the Pauli projection of a quantum channel. We first derive a procedure for learning a Pauli channel on $n$ qubits with high probability to a relative precision $\epsilon$ using $O\bigl(\epsilon^{-2} n 2^n\bigr)$ measurements, which is efficient in the Hilbert space dimension. The estimate is robust to state preparation and measurement errors which, together with the relative precision, makes it especially appropriate for applications involving characterization of high-accuracy quantum gates. Next we show that the error rates for an arbitrary set of $s$ Pauli errors can be estimated to a relative precision $\epsilon$ using $O\bigl(\epsilon^{-4} \log s\log s/\epsilon\bigr)$ measurements. Finally, we show that when the Pauli channel is given by a Markov field with at most $k$-local correlations, we can learn an entire $n$-qubit Pauli channel to relative precision $\epsilon$ with only $O_k\bigl(\epsilon^{-2} n^2 \log n \bigr)$ measurements, which is efficient in the number of qubits. These results enable a host of applications beyond just characterizing noise in a large-scale quantum system: they pave the way to tailoring quantum codes, optimizing decoders, and customizing fault tolerance procedures to suit a particular device.
The future is big graphs

Ensuring the success of big graph processing for the next decade and beyond.
Stacked Broad Learning System: From Incremental Flatted Structure to Deep Model

The broad learning system (BLS) has been proved to be effective and efficient lately. In this article, several deep variants of BLS are reviewed, and a new adaptive incremental structure, Stacked BLS, is proposed. The proposed model is a novel incremental stacking of BLS. This invariant inherits the efficiency and effectiveness of BLS that the structure and weights of lower layers of BLS are fixed when the new blocks are added. The incremental stacking algorithm computes not only the connection weights between the newly stacking blocks but also the connection weights of the enhancement nodes within the BLS block. The Stacked BLS is considered as the increment of “layers” and “neurons” dynamically during the training for multilayer neural networks. The proposed architecture along with the training algorithms that utilizes the residual characteristic is very versatile in comparison with traditional fixed architecture. Finally, experimental results on UCI datasets, MNIST dataset, NORB dataset, CIFAR-10 dataset, SVHN dataset, and CIFAR-100 dataset indicate that the proposed method outperforms the selected state-of-the-art methods on both accuracy and training speed, such as deep residual networks. The results also imply that the proposed structure could highly reduce the number of nodes and the training time of the original BLS in the classification task of some datasets.
BBAS: Towards large scale effective ensemble adversarial attacks against deep neural network learning

Recent decades have witnessed rapid development of deep neural networks (DNN). As DNN learning is becoming more and more important to numerous intelligent system, ranging from self driving car to video surveillance system, significant research efforts have been devoted to explore how to improve DNN model’s robustness and reliability against adversarial example attacks. Distinguish from previous study, we address the problem of adversarial training with ensemble based approach and propose a novel boosting based black-box attack scheme call BBAS to facilitate high diverse adversarial example generation. BBAS not only separates example generation from the settings of the trained model but also enhance the diversity of perturbation over class distribution through seamless integration of stratified sampling and ensemble adversarial training. This leads to reliable and effective training example selection. To validate and evaluate the scheme from different perspectives, a set of comprehensive tests have been carried out based on two large open data sets. Experimental results demonstrate the superiority of our method in terms of effectiveness.
Automatic Detection and Classification of Mammograms Using Improved Extreme Learning Machine with Deep Learning

Breast cancer, the most intrusive form of cancer affecting women globally. Next to lung cancer, breast cancer is the one that provides a greater number of cancer deaths among women. In recent times, several intelligent methodologies were come into existence for building an effective detection and classification of such noxious type of cancer. For further improving the rate of early diagnosis and for increasing the life span of victims, optimistic light of research is essential in breast cancer classification. Accordingly, a new customized method of integrating the concept of deep learning with the extreme learning machine (ELM), which is optimized using a simple crow-search algorithm (ICS-ELM). Thus, to enhance the state-of-the-art workings, an improved deep feature-based crow-search optimized extreme learning machine is proposed for addressing the health-care problem. The paper pours a light-of-research on detecting the input mammograms as either normal or abnormal. Subsequently, it focuses on further classifying the type of abnormal severities i.e., benign type or malignant. The digital mammograms for this work are taken from the Curated Breast Imaging Subset of DDSM (CBIS-DDSM), Mammographic Image Analysis Society (MIAS), and INbreast datasets. Herein, the work employs 570 digital mammograms (250 normal, 200 benign and 120 malignant cases) from CBIS-DDSM dataset, 322 digital mammograms (207 normal, 64 benign and 51 malignant cases) from MIAS database and 179 full-field digital mammograms (66 normal, 56 benign and 57 malignant cases) from INbreast dataset for its evaluation. The work utilizes ResNet-18 based deep extracted features with proposed Improved Crow-Search Optimized Extreme Learning Machine (ICS-ELM) algorithm. The proposed work is finally compared with the existing Support Vector Machines (RBF kernel), ELM, particle swarm optimization (PSO) optimized ELM, and crow-search optimized ELM, where the maximum overall classification accuracy is obtained for the proposed method with 97.193% for DDSM, 98.137% for MIAS and 98.266% for INbreast datasets, respectively. The obtained results reveal that the proposed Computer-Aided-Diagnosis (CAD) tool is robust for the automatic detection and classification of breast cancer.
ResNet or DenseNet? Introducing Dense Shortcuts to ResNet

ResNet or DenseNet? Nowadays, most deep learning based approaches are implemented with seminal backbone networks, among them the two arguably most famous ones are ResNet and DenseNet. Despite their competitive performance and overwhelming popularity, inherent drawbacks exist for both of them. For ResNet, the identity shortcut that stabilizes training might limit its representation capacity, and DenseNet mitigates it with multi-layer feature concatenation. However, the dense concatenation causes a new problem of requiring high GPU memory and more training time. Partially due to this, it is not a trivial choice between ResNet and DenseNet. This paper provides a unified perspective of dense summation to analyze them, which facilitates a better understanding of their core difference. We further propose dense weighted normalized shortcuts as a solution to the dilemma between them. Our proposed dense shortcut inherits the design philosophy of simple design in ResNet and DenseNet. On several benchmark datasets, the experimental results show that the proposed DSNet achieves significantly better results than ResNet, and achieves comparable performance as DenseNet but requiring fewer computation resources.
Meta-learning in natural and artificial intelligence

Meta-learning, or learning to learn, has gained renewed interest in recent years within the artificial intelligence community. However, meta-learning is incredibly prevalent within nature, has deep roots in cognitive science and psychology, and is currently studied in various forms within neuroscience. The aim of this review is to recast previous lines of research in the study of biological intelligence within the lens of meta-learning, placing these works into a common framework. More recent points of interaction between AI and neuroscience will be discussed, as well as interesting new directions that arise under this perspective.
Inequality, information technology and inclusive education in sub-Saharan Africa

This study examines linkages between inequality, information and communication technology (ICT) and inclusive education in order to establish inequality thresholds that should not be exceeded in order for ICT to promote inclusive education in 42 countries in sub-Saharan Africa for the period 2004–2014. The empirical evidence is based on the Generalized Method of Moments. The following findings are established. First, a Gini coefficient and an Atkinson index of respectively, 0.400 and 0.625 are income inequality thresholds that should not be exceeded in order for internet penetration to positively influence inclusive education. Second, a Gini coefficient, an Atkinson index and a Palma ratio of respectively, 0.574, 0.676 and 9.000 are thresholds of income inequality that if exceeded, fixed broadband subscriptions will no longer positively affect inclusive education. As a main policy implication, the established inequality thresholds should not be exceeded in order for ICT to promote inclusive education in sampled countries. Other implications in the light of Sustainable Development Goals (SDGs) are discussed.
Does Twitter Affect Stock Market Decisions? Financial Sentiment Analysis During Pandemics: A Comparative Study of the H1N1 and the COVID-19 Periods

Investors are constantly aware of the behaviour of stock markets. This affects their emotions and motivates them to buy or sell shares. Financial sentiment analysis allows us to understand the effect of social media reactions and emotions on the stock market and vice versa. In this research, we analyse Twitter data and important worldwide financial indices to answer the following question: How does the polarity generated by Twitter posts influence the behaviour of financial indices during pandemics? This study is based on the financial sentiment analysis of influential Twitter accounts and its relationship with the behaviour of important financial indices. To carry out this analysis, we used fundamental and technical financial analysis combined with a lexicon-based approach on financial Twitter accounts. We calculated the correlations between the polarities of financial market indicators and posts on Twitter by applying a date shift on tweets. In addition, correlations were identified days before and after the existing posts on financial Twitter accounts. Our findings show that the markets reacted 0 to 10 days after the information was shared and disseminated on Twitter during the COVID-19 pandemic and 0 to 15 days after the information was shared and disseminated on Twitter during the H1N1 pandemic. We identified an inverse relationship: Twitter accounts presented reactions to financial market behaviour within a period of 0 to 11 days during the H1N1 pandemic and 0 to 6 days during the COVID-19 pandemic. We also found that our method is better at detecting highly shifted correlations by using SenticNet compared with other lexicons. With SenticNet, it is possible to detect correlations even on the same day as the Twitter posts. The most influential Twitter accounts during the period of the pandemic were The New York Times, Bloomberg, CNN News and Investing.com, presenting a very high correlation between sentiments on Twitter and stock market behaviour. The combination of a lexicon-based approach is enhanced by a shifted correlation analysis, as latent or hidden correlations can be found in data.
Sub-Graph Contrast for Scalable Self-Supervised Graph Representation Learning

Graph representation learning has attracted lots of attention recently. Existing graph neural networks fed with the complete graph data are not scalable due to limited computation and memory costs. Thus, it remains a great challenge to capture rich information in large-scale graph data. Besides, these methods mainly focus on supervised learning and highly depend on node label information, which is expensive to obtain in the real world. As to unsupervised network embedding approaches, they overemphasize node proximity instead, whose learned representations can hardly be used in downstream application tasks directly. In recent years, emerging self-supervised learning provides a potential solution to address the aforementioned problems. However, existing self-supervised works also operate on the complete graph data and are biased to fit either global or very local (1-hop neighborhood) graph structures in defining the mutual information based loss terms. In this paper, a novel self-supervised representation learning method via Sub-graph Contrast, namely Subg-Con, is proposed by utilizing the strong correlation between central nodes and their sampled subgraphs to capture regional structure information. Instead of learning on the complete input graph data, with a novel data augmentation strategy, Subg-Con learns node representations through a contrastive loss defined based on subgraphs sampled from the original graph instead. Compared with existing graph representation learning approaches, Subg-Con has prominent performance advantages in weaker supervision requirements, model learning scalability, and parallelization. Extensive experiments verify both the effectiveness and the efficiency of our work compared with both classic and state-of-the-art graph representation learning approaches on multiple realworld large-scale benchmark datasets from different domains.
Advances in immersive virtual reality interventions for mental disorders: A new reality?

Immersive virtual reality (VR) has been identified as a potentially revolutionary tool for psychological interventions. This study reviews current advances in immersive VR-based therapies for mental disorders. VR has the potential to make psychiatric treatments better and more cost-effective and to make them available to a larger group of patients. However, this may require a new generation of VR therapeutic techniques that use the full potential of VR, such as embodiment, and self-led interventions. VR-based interventions are promising, but further well-designed studies are needed that use novel techniques and investigate efficacy, efficiency, and cost-effectiveness of VR interventions compared with current treatments. This will be crucial for implementation and dissemination of VR in regular clinical practice.
On the Design and Implementation of a Blockchain Enabled E-Voting Application Within IoT-Oriented Smart Cities

A smart city refers to an intelligent environment obtained by deploying all available resources and recent technologies in a coordinated and smart manner. Intelligent sensors (Internet of Things (IoT) devices) along with 5G technology working mutually are steadily becoming more pervasive and accomplish users' desires more effectively. Among a variety of IoT use cases, e-voting is a considerable application of IoT that relegates it to the next phase in the growth of technologies related to smart cities. In conventional applications, all the devices are often assumed to be cooperative and trusted. However, in practice, devices may be disrupted by the intruders to behave maliciously with the aim of degradation of the network services. Therefore, the privacy and security flaws in the e-voting systems in particular lead to a huge problem where intruders may perform a number of frauds for rigging the polls. Thus, the potential challenge is to distinguish the legitimate IoT devices from the malicious ones by computing their trust values through social optimizer in order to establish a legitimate communication environment. Further, in order to prevent from future modifications of data captured by smart devices, a Blockchain is maintained where blocks of all legitimate IoT devices are recorded. This article has introduced a secure and transparent e-voting mechanism through IoT devices using Blockchain technology with the aim of detecting and resolving the various threats caused by an intruder at various levels. Further, in order to validate the proposed mechanism, it is analyzed against various security parameters such as message alteration, Denial of Service (DoS) and Distributed Denial of Service (DDoS) attack and authentication delay.
Going from bad to worse: from Internet voting to blockchain voting

Abstract Voters are understandably concerned about election security. News reports of possible election interference by foreign powers, of unauthorized voting, of voter disenfranchisement, and of technological failures call into question the integrity of elections worldwide. This article examines the suggestions that “voting over the Internet” or “voting on the blockchain” would increase election security, and finds such claims to be wanting and misleading. While current election systems are far from perfect, Internet- and blockchain-based voting would greatly increase the risk of undetectable, nation-scale election failures. Online voting may seem appealing: voting from a computer or smartphone may seem convenient and accessible. However, studies have been inconclusive, showing that online voting may have little to no effect on turnout in practice, and it may even increase disenfranchisement. More importantly, given the current state of computer security, any turnout increase derived from Internet- or blockchain-based voting would come at the cost of losing meaningful assurance that votes have been counted as they were cast, and not undetectably altered or discarded. This state of affairs will continue as long as standard tactics such as malware, zero day, and denial-of-service attacks continue to be effective. This article analyzes and systematizes prior research on the security risks of online and electronic voting, and shows that not only do these risks persist in blockchain-based voting systems, but blockchains may introduce ‘additional’ problems for voting systems. Finally, we suggest questions for critically assessing security risks of new voting system proposals.
Using ontologies to enhance human understandability of global post-hoc explanations of black-box models

The interest in explainable artificial intelligence has grown strongly in recent years because of the need to convey safety and trust in the ‘how’ and ‘why’ of automated decision-making to users. While a plethora of approaches has been developed, only a few focus on how to use domain knowledge and how this influences the understanding of explanations by users. In this paper, we show that by using ontologies we can improve the human understandability of global post-hoc explanations, presented in the form of decision trees. In particular, we introduce Trepan Reloaded, which builds on Trepan, an algorithm that extracts surrogate decision trees from black-box models. Trepan Reloaded includes ontologies, that model domain knowledge, in the process of extracting explanations to improve their understandability. We tested the understandability of the extracted explanations by humans in a user study with four different tasks. We evaluate the results in terms of response times and correctness, subjective ease of understanding and confidence, and similarity of free text responses. The results show that decision trees generated with Trepan Reloaded, taking into account domain knowledge, are significantly more understandable throughout than those generated by standard Trepan. The enhanced understandability of post-hoc explanations is achieved with little compromise on the accuracy with which the surrogate decision trees replicate the behaviour of the original neural network models.
Semi-supervised GAN-based Radiomics Model for Data Augmentation in Breast Ultrasound Mass Classification

The capability of deep learning radiomics (DLR) to extract high-level medical imaging features has promoted the use of computer-aided diagnosis of breast mass detected on ultrasound. Recently, generative adversarial network (GAN) has aided in tackling a general issue in DLR, i.e., obtaining a sufficient number of medical images. However, GAN methods require a pair of input and labeled images, which require an exhaustive human annotation process that is very time-consuming. The aim of this paper is to develop a radiomics model based on a semi-supervised GAN method to perform data augmentation in breast ultrasound images. A total of 1447 ultrasound images, including 767 benign masses and 680 malignant masses were acquired from a tertiary hospital. A semi-supervised GAN model was developed to augment the breast ultrasound images. The synthesized images were subsequently used to classify breast masses using a convolutional neural network (CNN). The model was validated using a 5-fold cross-validation method. The proposed GAN architecture generated high-quality breast ultrasound images, verified by two experienced radiologists. The improved performance of semi-supervised learning increased the quality of the synthetic data produced in comparison to the baseline method. We achieved more accurate breast mass classification results (accuracy 90.41%, sensitivity 87.94%, specificity 85.86%) with our synthetic data augmentation compared to other state-of-the-art methods. The proposed radiomics model has demonstrated a promising potential to synthesize and classify breast masses on ultrasound in a semi-supervised manner.
The mediating role of digital informal learning in the relationship between students' digital competence and their academic performance

Students' digital competencies are important for their academic performance. Although scholars have highlighted the importance of students' digital informal learning in developing their digital competence, the mediating role of digital informal learning between digital competence and academic performance have remained unexplored. Thus, this study aimed to investigate the mediating role of digital informal learning between higher education students' digital competence and their academic performance. The data were collected from 319 students from Shiraz University in Iran. Data were analysed using structural equation modeling via AMOS. The result showed a positive effect of the students' digital competence on their digital informal learning and academic performance. Moreover, digital informal learning as the mediator variable had a positive effect on the relationship between digital competence and students' academic performance. In conclusion, to improve students’ academic performance, educators and curriculum designers should consider both their digital competence and their digital informal learning.