Reviewerly/exHarmony huggingface dataset:
A Smart-Contract-Based Access Control Framework for Cloud Smart Healthcare System

In current healthcare systems, electronic medical records (EMRs) are always located in different hospitals and controlled by a centralized cloud provider. However, it leads to single point of failure as patients being the real owner lose track of their private and sensitive EMRs. Hence, this article aims to build an access control framework based on smart contract, which is built on the top of distributed ledger (blockchain), to secure the sharing of EMRs among different entities involved in the smart healthcare system. For this, we propose four forms of smart contracts for user verification, access authorization, misbehavior detection, and access revocation, respectively. In this framework, considering the block size of ledger and huge amount of patient data, the EMRs are stored in cloud after being encrypted through the cryptographic functions of elliptic curve cryptography (ECC) and Edwards-curve digital signature algorithm (EdDSA), while their corresponding hashes are packed into blockchain. The performance evaluation based on a private Ethereum system is used to verify the efficiency of proposed access control framework in the real-time smart healthcare system.
Feedback Loop and Bias Amplification in Recommender Systems

Recommendation algorithms are known to suffer from popularity bias; a few popular items are recommended frequently while the majority of other items are ignored. These recommendations are then consumed by the users, their reaction will be logged and added to the system: what is generally known as a feedback loop. In this paper, we propose a method for simulating the users interaction with the recommenders in an offline setting and study the impact of feedback loop on the popularity bias amplification of several recommendation algorithms. We then show how this bias amplification leads to several other problems such as declining the aggregate diversity, shifting the representation of users' taste over time and also homogenization of the users. In particular, we show that the impact of feedback loop is generally stronger for the users who belong to the minority group.
Side-Aware Boundary Localization for More Precise Object Detection

Current object detection frameworks mainly rely on bounding box regression to localize objects. Despite the remarkable progress in recent years, the precision of bounding box regression remains unsatisfactory, hence limiting performance in object detection. We observe that precise localization requires careful placement of each side of the bounding box. However, the mainstream approach, which focuses on predicting centers and sizes, is not the most effective way to accomplish this task, especially when there exists displacements with large variance between the anchors and the targets. In this paper, we propose an alternative approach, named as Side-Aware Boundary Localization (SABL), where each side of the bounding box is respectively localized with a dedicated network branch. To tackle the difficulty of precise localization in the presence of displacements with large variance, we further propose a two-step localization scheme, which first predicts a range of movement through bucket prediction and then pinpoints the precise position within the predicted bucket. We test the proposed method on both two-stage and single-stage detection frameworks. Replacing the standard bounding box regression branch with the proposed design leads to significant improvements on Faster R-CNN, RetinaNet, and Cascade R-CNN, by 3.0%, 1.7%, and 0.9%, respectively. Code is available at https://github.com/open-mmlab/mmdetection .
Covid-19 and the digital revolution

Since the 1980s, the digital revolution has been both a negative and positive force. Within a few weeks of the Covid-19 outbreak, lockdown accelerated the adoption of digital solutions at an unprecedented pace, creating unforeseen opportunities for scaling up alternative approaches to social and economic life. But it also brought digital risks and threats that placed new demands on policymakers. This article assembles evidence from different areas of social science expertise about the impacts of Covid-19 in digitised societies and policy responses. The authors show how the pandemic supported changes in data collection techniques and dissemination practices for official statistics, and how seemingly insuperable obstacles to the implementation of e-health treatments were largely overcome. They demonstrate how the ethics of artificial intelligence became a primary concern for government legislation at national and international levels, and how the features enabling smart cities to act as drivers of productivity did not necessarily give them an advantage during the pandemic. At the micro-level, families are shown to have become 'digital by default', as children were exposed to online risks and opportunities. Globally, the spread of the pandemic provided a fertile ground for cybercrime, while digital disinformation and influencing risked becoming normalised and domesticated.
Avoiding local minima in variational quantum eigensolvers with the natural gradient optimizer

We compare the BFGS optimizer, ADAM and Natural Gradient Descent (NatGrad) in the context of Variational Quantum Eigensolvers (VQEs). We systematically analyze their performance on the QAOA ansatz for the Transverse Field Ising Model (TFIM) as well as on overparametrized circuits with the ability to break the symmetry of the Hamiltonian. The BFGS algorithm is frequently unable to find a global minimum for systems beyond about 20 spins and ADAM easily gets trapped in local minima. On the other hand, NatGrad shows stable performance on all considered system sizes, albeit at a significantly higher cost per epoch. In sharp contrast to most classical gradient based learning, the performance of all optimizers is found to decrease upon seemingly benign overparametrization of the ansatz class, with BFGS and ADAM failing more often and more severely than NatGrad. Additional tests for the Heisenberg XXZ model corroborate the accuracy problems of BFGS in high dimensions, but they reveal some shortcomings of NatGrad as well. Our results suggest that great care needs to be taken in the choice of gradient based optimizers and the parametrization for VQEs.
Artificial intelligence and the new forms of interaction: Who has the control when interacting with a chatbot?

Advances in artificial intelligence provide new tools of digital assistance that retailers can use to support consumers while shopping. The aim of this research is to examine how consumers react as a function of assistants’ appearance (human- vs. not human-like) and activation (automatic vs. human-initiated). We advance a model of sequential mediation whose empirical validation on 400 participants in two studies shows that non-anthropomorphic digital assistants lead to higher psychological reactance. In turn, reactance affects perceived choice difficulty, which positively reflects on choice certainty, perceived performance and—ultimately—satisfaction. Thus, although reactance might appear as a negative outcome, it eventually leads to higher satisfaction. Furthermore, initiation (system vs. user initiation) does not activate the chain of effects, but significantly interacts with anthropomorphism so that individuals exhibit lower reactance when confronted with human-like digital assistants activated by the consumer. Overall, reactance is highest for non-human like digital assistants that are computer-initiated.
BorderDet: Border Feature for Dense Object Detection

Dense object detectors rely on the sliding-window paradigm that predicts the object over a regular grid of image. Meanwhile, the feature maps on the point of the grid are adopted to generate the bounding box predictions. The point feature is convenient to use but may lack the explicit border information for accurate localization. In this paper, We propose a simple and efficient operator called Border-Align to extract “border features” from the extreme point of the border to enhance the point feature. Based on the BorderAlign, we design a novel detection architecture called BorderDet, which explicitly exploits the border information for stronger classification and more accurate localization. With ResNet-50 backbone, our method improves single-stage detector FCOS by 2.8 AP gains (38.6 v.s. 41.4). With the ResNeXt-101-DCN backbone, our BorderDet obtains 50.3 AP, outperforming the existing state-of-the-art approaches.
Machine learning for site-adaptation and solar radiation forecasting

Optimal management for solar energy systems requires quality data to build accurate models for predicting the behavior of solar radiation. Solar irradiance and environmental data are provided by satellite and in-situ measurements. It is usual that satellite measurements present high temporal resolution with limited spatial resolution, and in-situ measurements provide high accuracy but significant missing data. This paper proposes a methodology based on machine learning algorithms that: i) takes the best of both data sources to obtain an improved spatio-temporal resolution, known as site-adaptation; and ii) provides highly accurate forecasting solar-radiation models based on deep learning on the improved data. Through a study case with real data, we show the benefits of using the proposed methodology based on machine and deep learning techniques to integrate data from different sources and to construct precise solar radiation forecasting models in regions where solar energy systems are required. Results show that machine learning models for site-adaptation performed up to 38% better than traditional methods.
Mapping and Taking Stock of the Personal Informatics Literature

The research community on the study and design of systems for personal informatics has grown over the past decade. To take stock of what the topics the field has studied and methods the field has used, we map and label 523 publications from ACM's library, IEEE Xplore, and PubMed. We surface that the literature has focused on studying and designing for health and wellness domains, an emphasis on understanding and overcoming barriers to data collection and reflection, and progressively fewer contributions involving artifacts being made. Our mapping review suggests directions future research could explore, such as identifying and resolving barriers to tracking stages beyond collection and reflection, engaging more with domain experts, and further discussing the privacy and ethical concerns around tracked data.
Security in quantum cryptography

Secure communications is a vital need of the world, and the basic features of quantum mechanics seemingly offer fundamental new tools for achieving this security. But do these tools really work? This review gives due attention to the real-world problems with experimental quantum cryptography, but the heart of the review is an in-depth survey of the analyses developed to prove the security of the basic quantum techniques introduced by Wiesner, Ekert, Bennett, and Brassard. A broader set of cryptographic tasks have quantum aspects which are also discussed here.
Blockchain technology in the healthcare industry: Trends and opportunities

The growth in the use of blockchain technology in healthcare is remarkable and has a significant impact on the healthcare industry. In this work, the gap between the healthcare industry and blockchain technologies was addressed by evaluating previous activities. Bibliometric analysis of dataset distribution, venues, keywords and citations was conducted to identify the trend of blockchain technology in healthcare. Case studies of telecare medicine information system and E-health were also reviewed and evaluated in terms of security and privacy. This study discussed potential future challenges such as scalability and storage capacity, blockchain size, universal interoperability and standardisation. This work highlighted the motivations of employing blockchain technology in the healthcare industry. Prospects in health data and sharing process, clinical trials, the pharmaceutical industry, big data, artificial intelligence, 5 G ultrasonic device, security and privacy were highlighted.
Graph Self-Supervised Learning: A Survey

Deep learning on graphs has attracted significant interests recently. However, most of the works have focused on (semi-) supervised learning, resulting in shortcomings including heavy label reliance, poor generalization, and weak robustness. To address these issues, self-supervised learning (SSL), which extracts informative knowledge through well-designed pretext tasks without relying on manual labels, has become a promising and trending learning paradigm for graph data. Different from SSL on other domains like computer vision and natural language processing, SSL on graphs has an exclusive background, design ideas, and taxonomies. Under the umbrella of graph self-supervised learning, we present a timely and comprehensive review of the existing approaches which employ SSL techniques for graph data. We construct a unified framework that mathematically formalizes the paradigm of graph SSL. According to the objectives of pretext tasks, we divide these approaches into four categories: generation-based, auxiliary property-based, contrast-based, and hybrid approaches. We further describe the applications of graph SSL across various research fields and summarize the commonly used datasets, evaluation benchmark, performance comparison and open-source codes of graph SSL. Finally, we discuss the remaining challenges and potential future directions in this research field.
A systematic literature review on applications of information and communication technologies and blockchain technologies for precision agriculture development

This review examines literature from 2011 to 2020 on information and communications technologies (ICTs) and blockchain technologies (BTs) in agriculture. To describe the status of the subjects and identify issues for future research, we utilized bibliometric and content analyses of leading journals. The main findings are: (1) ICT technique adoption is affected by application design and socio-demographic factors, while research in BTs in this area only focuses on application design factors; (2) ICTs and BTs have similar applications in agricultural production (information provision for efficient decisions and production improvement), logistics (tracing), and supply chain (trust-building); (3) ICT techniques are applied more to production improvement, while BTs focus more on using visibility, traceability, and automation to improve transparency and transaction efficiency in whole sectors; (4) in relation to consumption, ICTs focus on balancing supply and demand to achieve market stability, while BTs investigate effects on consumer purchasing decisions. Insights are also proposed for future research based on common challenges in the application of ICT and blockchain in agriculture in the following areas: (i) security and privacy protection; (ii) solutions for scalability and interoperability; (iii) solutions for high cost and high consumption, and demands for high knowledge in technology application; (iv) formulation of policies and regulations related to standard criteria for market stability. This review provides a basis for studying the combination of ICTs and BTs in agriculture and furthers the understanding of their usage by comparison; this has significant implications for technological development and agricultural sustainability.
Internet, social media and online hate speech. Systematic review

This systematic review aimed to explore the research papers related to how Internet and social media may, or may not, constitute an opportunity to online hate speech. 67 studies out of 2389 papers found in the searches, were eligible for analysis. We included articles that addressed online hate speech or cyberhate between 2015 and 2019. Meta-analysis could not be conducted due to the broad diversity of studies and measure units. The reviewed studies provided exploratory data about the Internet and social media as a space for online hate speech, types of cyberhate, terrorism as online hate trigger, online hate expressions and most common methods to assess online hate speech. As a general consensus on what is cyberhate, this is conceptualized as the use of violent, aggressive or offensive language, focused on a specific group of people who share a common property, which can be religion, race, gender or sex or political affiliation through the use of Internet and Social Networks, based on a power imbalance, which can be carried out repeatedly, systematically and uncontrollably, through digital media and often motivated by ideologies.
A novel oversampling technique for class-imbalanced learning based on SMOTE and natural neighbors

Developing techniques for the machine learning of a classifier from class-imbalanced data presents an important challenge. Among the existing methods for addressing this problem, SMOTE has been successful, has received great praise, and features an extensive range of practical applications. In this paper, we focus on SMOTE and its extensions, aiming to solve the most challenging issues, namely, the choice of the parameter k and the determination of the neighbor number of each sample. Hence, a synthetic minority oversampling technique with natural neighbors (NaNSMOTE) is proposed. In NaNSMOTE, the random difference between a selected base sample and one of its natural neighbors is used to generate synthetic samples. The main advantages of NaNSMOTE are that (a) it has an adaptive k value related to the data complexity; (b) samples of class centers have more neighbors to improve the generalization of synthetic samples, while border samples have fewer neighbors to reduce the error of synthetic samples; and (c) it can remove outliers. The effectiveness of NaNSMOTE is proven by comparing it with SMOTE and extended versions of SMOTE on real data sets.
Opposition-based learning grey wolf optimizer for global optimization

Grey wolf optimizer is a novel swarm intelligent algorithm. It has received lots of interest from the heuristic algorithm community for its superior optimization capacity and few parameters. However, it is also easy to trap into the local optimum when solving complex and multimodal functions. In order to boost the performance of GWO, an opposition-based learning grey wolf optimizer (OGWO) is proposed. The opposition-based learning approach is incorporated into GWO with a jumping rate, which can help the algorithm jump out of the local optimum and not increase the computational complexity. What is more, the coefficient a→ is dynamically adjusted by the nonlinear function to balance exploration and exploitation. The serial experiments have revealed that the proposed algorithm is superior to the conventional heuristic algorithms, it is also better than GWO and its variants.
Backdoor Attacks to Graph Neural Networks

In this work, we propose the first backdoor attack to graph neural networks (GNN). Specifically, we propose a subgraph based backdoor attack to GNN for graph classification. In our backdoor attack, a GNN classifier predicts an attacker-chosen target label for a testing graph once a predefined subgraph is injected to the testing graph. Our empirical results on three real-world graph datasets show that our backdoor attacks are effective with a small impact on a GNN's prediction accuracy for clean testing graphs. Moreover, we generalize a randomized smoothing based certified defense to defend against our backdoor attacks. Our empirical results show that the defense is effective in some cases but ineffective in other cases, highlighting the needs of new defenses for our backdoor attacks.
Delving into Localization Errors for Monocular 3D Object Detection

Estimating 3D bounding boxes from monocular images is an essential component in autonomous driving, while accurate 3D object detection from this kind of data is very challenging. In this work, by intensive diagnosis experiments, we quantify the impact introduced by each sub-task and found the ‘localization error’ is the vital factor in restricting monocular 3D detection. Besides, we also investigate the underlying reasons behind localization errors, analyze the issues they might bring, and propose three strategies. First, we revisit the misalignment between the center of the 2D bounding box and the projected center of the 3D object, which is a vital factor leading to low localization accuracy. Second, we observe that accurately localizing distant objects with existing technologies is almost impossible, while those samples will mislead the learned network. To this end, we propose to remove such samples from the training set for improving the overall performance of the detector. Lastly, we also propose a novel 3D IoU oriented loss for the size estimation of the object, which is not affected by ‘localization error’. We conduct extensive experiments on the KITTI dataset, where the proposed method achieves real-time detection and outperforms previous methods by a large margin. The code will be made available at: https://github.com/xinzhuma/monodle.
Accessing Artificial Intelligence for Clinical Decision-Making

Advancements in computing and data from the near universal acceptance and implementation of electronic health records has been formative for the growth of personalized, automated, and immediate patient care models that were not previously possible. Artificial intelligence (AI) and its subfields of machine learning, reinforcement learning, and deep learning are well-suited to deal with such data. The authors in this paper review current applications of AI in clinical medicine and discuss the most likely future contributions that AI will provide to the healthcare industry. For instance, in response to the need to risk stratify patients, appropriately cultivated and curated data can assist decision-makers in stratifying preoperative patients into risk categories, as well as categorizing the severity of ailments and health for non-operative patients admitted to hospitals. Previous overt, traditional vital signs and laboratory values that are used to signal alarms for an acutely decompensating patient may be replaced by continuously monitoring and updating AI tools that can pick up early imperceptible patterns predicting subtle health deterioration. Furthermore, AI may help overcome challenges with multiple outcome optimization limitations or sequential decision-making protocols that limit individualized patient care. Despite these tremendously helpful advancements, the data sets that AI models train on and develop have the potential for misapplication and thereby create concerns for application bias. Subsequently, the mechanisms governing this disruptive innovation must be understood by clinical decision-makers to prevent unnecessary harm. This need will force physicians to change their educational infrastructure to facilitate understanding AI platforms, modeling, and limitations to best acclimate practice in the age of AI. By performing a thorough narrative review, this paper examines these specific AI applications, limitations, and requisites while reviewing a few examples of major data sets that are being cultivated and curated in the US.