Reviewerly/exHarmony huggingface dataset:
3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training

While making a tremendous impact in various fields, deep neural networks usually require large amounts of labeled data for training which are expensive to collect in many applications, especially in the medical domain. Un-labeled data, on the other hand, is much more abundant. Semi-supervised learning techniques, such as co-training, could provide a powerful tool to leverage unlabeled data. In this paper, we propose a novel framework, uncertainty-aware multi-view co-training (UMCT), to address semi-supervised learning on 3D data, such as volumetric data from medical imaging. In our work, co-training is achieved by exploiting multi-viewpoint consistency of 3D data. We generate different views by rotating or permuting the 3D data and utilize asymmetrical 3D kernels to encourage diversified features in different sub-networks. In addition, we propose an uncertainty-weighted label fusion mechanism to estimate the reliability of each view's prediction with Bayesian deep learning. As one view requires the supervision from other views in co-training, our self-adaptive approach computes a confidence score for the prediction of each unlabeled sample in order to assign a reliable pseudo label. Thus, our approach can take advantage of unlabeled data during training. We show the effectiveness of our proposed semi-supervised method on several public datasets from medical image segmentation tasks (NIH pancreas & LiTS liver tumor dataset). Meanwhile, a fully-supervised method based on our approach achieved state-of-the-art performances on both the LiTS liver tumor segmentation and the Medical Segmentation Decathlon (MSD) challenge, demonstrating the robustness and value of our framework, even when fully supervised training is feasible.
A Survey on Blockchain-Based Self-Sovereign Patient Identity in Healthcare

Convergence of physical and digital identity and integration of various individual records, such as patient data, into a united repository remains a serious challenge. On one hand, collecting relevant data can help clinicians, specialists and healthcare service providers to facilitate care for patients. On the other hand, Self-Sovereign identity and the right to control personal data comes into question, because patients do not handle their data explicitly. Distributed Ledger Technology (DLT) is a novel method which would allow to securely record time-stamped data and enable patient-driven health and identity records. In this paper, we review the state-of-the-art in Blockchain (BC)-based self-sovereignty and patient data records in healthcare. Our motivation is to investigate the potential of BC technology for use in the patient data and identity management. As a distributed decentralized technology, BC can be very beneficial, giving patients control over their own data and self-sovereign identity. To the extent of our knowledge, there is no literature covering the same concerns. More specifically, the focus is on solutions that aim the realization of holistic BC-based Electronic Health Records (EHR) and Patient Health Records (PHR). EHR and PHR are used to record patient data, such as the doctor's notes upon a visit and radiology images. Hence, they include critical information regarding patient's privacy and identity. Therefore, development of pure decentralized Healthcare Information Systems (HIS) is a great challenge in terms of architectural and technical structure of the systems. Designing robust and reliable EHR and PHR, which represent the foundation of many other healthcare services, relies on carefully finding the balance in a trade-off between many factors, such as level of decentralization, privacy, scalability and data throughput. In this paper, we review the state-of-the-art and provide an analysis on the design trade-offs.
Smart contracts on the blockchain – A bibliometric analysis and review

Smart contracts are decentrally anchored scripts on blockchains or similar infrastructures that allow the transparent execution of predefined processes. Using smart contracts, business logic can be automated and assets such as money become programmable, which opens up previously inaccessible application potential. To date, smart contracts control billions in value. This paper analyzes 468 articles on the topic of smart contracts and their 20,188 references, providing a summary and analysis of the current state of research on smart contracts and identifying intellectual structures and emerging trends. Using exploratory factor analysis for co-citation analysis, six different strands of research are identified that concern technical, social, economic and legal disciplines: I) technical foundations, development and open questions of blockchain networks, II) blockchain and smart contracts for the Internet of Things, III) smart contract standardization, verification and security, IV) blockchain and smart contracts for the disruption of existing processes and industries, V) potentials and challenges of smart contracts, and VI) smart contracts and the law. The interrelations between these groups and individual high-impact publications are visualized using social network analysis. A structured overview of the main strands of research concerning smart contracts, their development over time, the relevance of smart contract platforms in research, and conceptual connections between publications and discourses is obtained. Based on the results, starting points for future research are derived, which offer researchers and practitioners a substantial basis for their work on smart contracts.
Estimating the gradient and higher-order derivatives on quantum hardware

For a large class of variational quantum circuits, we show how arbitrary-order derivatives can be analytically evaluated in terms of simple parameter-shift rules, i.e., by running the same circuit with different shifts of the parameters. As particular cases, we obtain parameter-shift rules for the Hessian of an expectation value and for the metric tensor of a variational state, both of which can be efficiently used to analytically implement second-order optimization algorithms on a quantum computer. We also consider the impact of statistical noise by studying the mean squared error of different derivative estimators. In the second part of this work, some of the theoretical techniques for evaluating quantum derivatives are applied to their typical use case: the implementation of quantum optimizers. We find that the performance of different estimators and optimizers is intertwined with the values of different hyperparameters, such as a step size or a number of shots. Our findings are supported by several numerical and hardware experiments, including an experimental estimation of the Hessian of a simple variational circuit and an implementation of the Newton optimizer.
Ready or Not for Contact Tracing? Investigating the Adoption Intention of COVID-19 Contact-Tracing Technology Using an Extended Unified Theory of Acceptance and Use of Technology Model

To diminish the risk of spreading COVID-19 as society exits the lockdowns, several apps have been developed for contact tracing. These apps register which users have been in proximity of each other. If a user is diagnosed with COVID-19, app users who have been recently in proximity to this person are notified. The effectiveness of these apps highly depends on public support. Therefore, this study investigated the factors that influence app use intention, based on an extended unified theory of acceptance and use of technology model. A survey was administered in Belgium (Flanders) to 1,500 participants aged 18 to 64 years old. Structural equation modeling was used to test the relationships among the model's constructs. Our results indicated that 48.70 percent of the respondents wanted to use the app. The model explained 39 percent of the variance in app use intention. The most important predictor was performance expectancy, followed by facilitating conditions and social influence. Effort expectancy was not related to intention. Moreover, individuals' innovativeness was positively related with app use intention, whereas app-related privacy concerns negatively influenced intention. Based on the results, suggestions are made for policy makers and developers.
MTI-Net: Multi-scale Task Interaction Networks for Multi-task Learning

In this paper, we argue about the importance of considering task interactions at multiple scales when distilling task information in a multi-task learning setup. In contrast to common belief, we show that tasks with high affinity at a certain scale are not guaranteed to retain this behaviour at other scales, and vice versa. We propose a novel architecture, namely MTI-Net, that builds upon this finding in three ways. First, it explicitly models task interactions at every scale via a multi-scale multi-modal distillation unit. Second, it propagates distilled task information from lower to higher scales via a feature propagation module. Third, it aggregates the refined task features from all scales via a feature aggregation unit to produce the final per-task predictions. Extensive experiments on two multi-task dense labeling datasets show that, unlike prior work, our multi-task model delivers on the full potential of multi-task learning, that is, smaller memory footprint, reduced number of calculations, and better performance w.r.t. single-task learning. The code is made publicly available ( https://github.com/SimonVandenhende/Multi-Task-Learning-PyTorch ).
Faster quantum simulation by randomization

Product formulas can be used to simulate Hamiltonian dynamics on a quantum computer by approximating the exponential of a sum of operators by a product of exponentials of the individual summands. This approach is both straightforward and surprisingly efficient. We show that by simply randomizing how the summands are ordered, one can prove stronger bounds on the quality of approximation for product formulas of any given order, and thereby give more efficient simulations. Indeed, we show that these bounds can be asymptotically better than previous bounds that exploit commutation between the summands, despite using much less information about the structure of the Hamiltonian. Numerical evidence suggests that the randomized approach has better empirical performance as well.
A Complete VADER-Based Sentiment Analysis of Bitcoin (BTC) Tweets during the Era of COVID-19

During the COVID-19 pandemic, many research studies have been conducted to examine the impact of the outbreak on the financial sector, especially on cryptocurrencies. Social media, such as Twitter, plays a significant role as a meaningful indicator in forecasting the Bitcoin (BTC) prices. However, there is a research gap in determining the optimal preprocessing strategy in BTC tweets to develop an accurate machine learning prediction model for bitcoin prices. This paper develops different text preprocessing strategies for correlating the sentiment scores of Twitter text with Bitcoin prices during the COVID-19 pandemic. We explore the effect of different preprocessing functions, features, and time lengths of data on the correlation results. Out of 13 strategies, we discover that splitting sentences, removing Twitter-specific tags, or their combination generally improve the correlation of sentiment scores and volume polarity scores with Bitcoin prices. The prices only correlate well with sentiment scores over shorter timespans. Selecting the optimum preprocessing strategy would prompt machine learning prediction models to achieve better accuracy as compared to the actual prices.
Deep learning techniques for recommender systems based on collaborative filtering

Abstract In the Big Data Era, recommender systems perform a fundamental role in data management and information filtering. In this context, Collaborative Filtering (CF) persists as one of the most prominent strategies to effectively deal with large datasets and is capable of offering users interesting content in a recommendation fashion. Nevertheless, it is well‐known CF recommenders suffer from data sparsity, mainly in cold‐start scenarios, substantially reducing the quality of recommendations. In the vast literature about the aforementioned topic, there are numerous solutions, in which the state‐of‐the‐art contributions are, in some sense, conditioned or associated with traditional CF methods such as Matrix Factorization (MF), that is, they rely on linear optimization procedures to model users and items into low‐dimensional embeddings. To overcome the aforementioned challenges, there has been an increasing number of studies exploring deep learning techniques in the CF context for latent factor modelling. In this research, authors conduct a systematic review focusing on state‐of‐the‐art literature on deep learning techniques applied in collaborative filtering recommendation, and also featuring primary studies related to mitigating the cold start problem. Additionally, authors considered the diverse non‐linear modelling strategies to deal with rating data and side information, the combination of deep learning techniques with traditional CF‐based linear methods, and an overview of the most used public datasets and evaluation metrics concerning CF scenarios.
Detection of DoH Tunnels using Time-series Classification of Encrypted Traffic

Computer networks have fallen easy prey to cyber attacks in the ever-evolving internet services. Domain Name System (DNS) has also not remained untouched with these cybercrime attempts. Encrypted HyperText Transfer Protocol (HTTP) traffic over Secure Socket Layer (SSL), alternatively called HTTPS, has succeeded to prevent DNS attacks to a great extent. To secure DNS traffic, the security community has introduced the concept of DNS over HTTPS (DoH) to improve user privacy and security by combating eavesdropping and DNS data manipulation on the way to prevent Man-in-the-Middle (MitM) attacks. This paper discusses one of the persistent security concerns, abuse of DNS protocol to create covert channels by tunneling data through DNS packets. We identify tunneling activities that utilize DNS communications over HTTPS by presenting a two-layered approach to detect and characterize DoH traffic using time-series classifiers.
Variational Quantum Generators: Generative Adversarial Quantum Machine Learning for Continuous Distributions

Abstract A hybrid quantum–classical approach to model continuous classical probability distributions using a variational quantum circuit is proposed. The architecture of this quantum generator consists of a quantum circuit that encodes a classical random variable into a quantum state and a parameterized quantum circuit trained to mimic the target distribution. The model allows for easy interfacing with a classical function, such as a neural network, and is trained using an adversarial learning approach. It is shown that the quantum generator is able to learn using either a classical neural network or a variational quantum circuit as the discriminator model. This implementation takes advantage of automatic differentiation tools to perform the optimization of the variational circuits employed. The framework presented here for the design and implementation of the variational quantum generators can serve as a blueprint for designing hybrid quantum–classical models for other machine learning tasks.
The convergence of IoT and distributed ledger technologies (DLT): Opportunities, challenges, and solutions

Digital revolution is characterized by the convergence of technologies — from cloud computing to edge/fog computing, Artificial Intelligence (AI), big data, Intelligent Internet of Things (IoT), and Distributed Ledger Technology (DLT) — that is blurring the lines between physical and digital worlds. In this context, the IoT tsunami, the ubiquitous adoption of intelligent connected devices, and the public embracement of DLT, of which blockchain is a popular example, are increasingly becoming an integral feature of many modern systems, particularly, IoT-based smart and connected healthcare. Although the IoT and DLT/blockchain are two very different technologies and distinct from each other, the fusion of blockchain and IoT technologies is an unprecedented paradigm shift that is expected to disrupt both current and future systems in various fields. Blockchain has exactly what is needed to fix the weaknesses and vulnerabilities of IoT. It solves the security fault line among intelligent IoT where most of the IoT devices are connected to each other through the public trustless environment. Moreover, its distributed peer-to-peer nature can address the shortcomings of client/server models in Cloud-IoT solutions. Although the convergence of IoT and blockchain (Blockchain-IoT) can potentially tackle major shortcomings of today's solutions, its adoption is still in infancy, suffering from various issues and thus there is a necessity to address significant challenges including scalability, consensus algorithms, data privacy, efficiency, availability, storage, interoperability, and standardization, among others. In addition, there is no consensus towards any reference model or best practices that specify how blockchain should be utilized in IoT. This paper aims to present a holistic reference architecture as well as fundamentals, recent advancements, promises, and challenges in order to foster the investigations on cutting-edge research and allowing one to contribute to advancing the convergence of blockchain and IoT.
Explainability in Graph Neural Networks: A Taxonomic Survey

Deep learning methods are achieving ever-increasing performance on many artificial intelligence tasks. A major limitation of deep models is that they are not amenable to interpretability. This limitation can be circumvented by developing post hoc techniques to explain predictions, giving rise to the area of explainability. Recently, explainability of deep models on images and texts has achieved significant progress. In the area of graph data, graph neural networks (GNNs) and their explainability are experiencing rapid developments. However, there is neither a unified treatment of GNN explainability methods, nor a standard benchmark and testbed for evaluations. In this survey, we provide a unified and taxonomic view of current GNN explainability methods. Our unified and taxonomic treatments of this subject shed lights on the commonalities and differences of existing methods and set the stage for further methodological developments. To facilitate evaluations, we provide a testbed for GNN explainability, including datasets, common algorithms and evaluation metrics. Furthermore, we conduct comprehensive experiments to compare and analyze the performance of many techniques. Altogether, this work provides a unified methodological treatment of GNN explainability and a standardized testbed for evaluations.
Body, Avatar, and Me

Self-presentation in online digital social spaces has been a long standing research interest in HCI and CSCW. As online social spaces evolve towards more embodied digital representations, it is important to understand how users construct and experience their self and interact with others' self in new and more complicated ways, as it may introduce new opportunities and unseen social consequences. Using findings of an interview study (N=30), in this paper we report an in-depth empirical investigation of the presentation and perception of self in Social Virtual Reality (VR) - 3D virtual spaces where multiple users can interact with one another through VR head-mounted displays and full-body tracked avatars. This study contributes to the growing body of CSCW literature on social VR by offering empirical evidence of how social VR platforms afford new phenomena and approaches of novel identity practices and by providing potential design implications to further support such practices. We also expand the existing research agenda in CSCW on the increasing complexity of people's self-presentation in emerging novel sociotechnical systems.
Conditional Wasserstein GAN-based oversampling of tabular data for imbalanced learning

Class imbalance impedes the predictive performance of classification models. Popular countermeasures include oversampling minority class cases by creating synthetic examples. The paper examines the potential of Generative Adversarial Networks (GANs) for oversampling. A few prior studies have used GANs for this purpose but do not reflect recent methodological advancements for generating tabular data using GANs. The paper proposes an approach based on a conditional Wasserstein GAN that can effectively model tabular datasets with numerical and categorical variables and pays special attention to the down-stream classification task through an auxiliary classifier loss. We focus on a credit scoring context in which binary classifiers predict the default risk of loan applications. Empirical comparisons in this context evidence the competitiveness of GAN-based oversampling compared to several standard oversampling regimes. We also clarify the conditions under which oversampling in general and the proposed GAN-based approach in particular raise predictive performance. In sum, our findings suggest that GAN architectures for tabular data and our extensions deserve a place in data scientists’ modelling toolbox.
Secure and Energy Efficient-Based E-Health Care Framework for Green Internet of Things

This paper proposes a secure and energy-efficient Internet of Things (IoT) model for e-health. The main objective is to secure the transmission and retrieval of biomedical images over IoT networks. To achieve this, the compressive sensing and five-dimensional hyper-chaotic map (FDHC) are utilized to encrypt the biomedical images. However, FDHC suffers from the hyper-parameter tuning problem. To resolve this, a θ-non-dominated sorting genetic algorithm III ( θ-NSGA-III) based FDHC is proposed. Initially, the initial attributes of FDHC are tuned using θ-NSGA-III. Then, the tuned attributes are used by FDHC to generate the secret keys. The obtained secret keys are then utilized to permute and diffuse the input biomedical images. Permutation and diffusion operations are applied row-wise and column-wise on the input biomedical image to perform encryption. The proposed technique is sensitive towards the input images as the permutation and diffusion of each row and column depends upon the initial scrambled row and column. Experimental results reveal that the proposed technique outperforms the state-of-art image encryption techniques. The proposed framework encrypts and decrypts images at a higher speed, therefore, it can be used to secure the communication in green IoT networks.