Reviewerly/exHarmony huggingface dataset:
Association of “#covid19” Versus “#chinesevirus” With Anti-Asian Sentiments on Twitter: March 9–23, 2020

Objectives. To examine the extent to which the phrases, "COVID-19" and "Chinese virus" were associated with anti-Asian sentiments.Methods. Data were collected from Twitter's Application Programming Interface, which included the hashtags "#covid19" or "#chinesevirus." We analyzed tweets from March 9 to 23, 2020, corresponding to the week before and the week after President Donald J. Trump's tweet with the phrase, "Chinese Virus." Our analysis focused on 1 273 141 hashtags.Results. One fifth (19.7%) of the 495 289 hashtags with #covid19 showed anti-Asian sentiment, compared with half (50.4%) of the 777 852 hashtags with #chinesevirus. When comparing the week before March 16, 2020, to the week after, there was a significantly greater increase in anti-Asian hashtags associated with #chinesevirus compared with #covid19 (P < .001).Conclusions. Our data provide new empirical evidence supporting recommendations to use the less-stigmatizing term "COVID-19," instead of "Chinese virus."
A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence

We define hybrid intelligence (HI) as the combination of human and machine intelligence, augmenting human intellect and capabilities instead of replacing them and achieving goals that were unreachable by either humans or machines. HI is an important new research focus for artificial intelligence, and we set a research agenda for HI by formulating four challenges.
Robotic transformative service research: deploying social robots for consumer well-being during COVID-19 and beyond

Purpose Besides the direct physical health consequences, through social isolation COVID-19 affects a considerably larger share of consumers with deleterious effects for their psychological well-being. Two vulnerable consumer groups are particularly affected: older adults and children. The purpose of the underlying paper is to take a transformative research perspective on how social robots can be deployed for advancing the well-being of these vulnerable consumers and to spur robotic transformative service research (RTSR). Design/methodology/approach This paper follows a conceptual approach that integrates findings from various domains: service research, social robotics, social psychology and medicine. Findings Two key findings advanced in this paper are (1) a typology of robotic transformative service (i.e. entertainer, social enabler, mentor and friend) as a function of consumers' state of social isolation, well-being focus and robot capabilities and (2) a future research agenda for RTSR. Practical implications This paper guides service consumers and providers and robot developers in identifying and developing the most appropriate social robot type for advancing the well-being of vulnerable consumers in social isolation. Originality/value This study is the first to integrate social robotics and transformative service research by developing a typology of social robots as a guiding framework for assessing the status quo of transformative robotic service on the basis of which it advances a future research agenda for RTSR. It further complements the underdeveloped body of service research with a focus on eudaimonic consumer well-being.
Efficient Verifiable Delay Functions

We construct a verifiable delay function (VDF). A VDF is a function whose evaluation requires running a given number of sequential steps, yet the result can be efficiently verified. They have applications in decentralised systems, such as the generation of trustworthy public randomness in a trustless environment, or resource-efficient blockchains. To construct our VDF, we actually build a trapdoor VDF. A trapdoor VDF is essentially a VDF which can be evaluated efficiently by parties who know a secret (the trapdoor). By setting up this scheme in a way that the trapdoor is unknown (not even by the party running the setup, so that there is no need for a trusted setup environment), we obtain a simple VDF. Our construction is based on groups of unknown order such as an RSA group, or the class group of an imaginary quadratic field. The output of our construction is very short (the result and the proof of correctness are each a single element of the group), and the verification of correctness is very efficient.
Virtual Reality (VR) in Assessment and Treatment of Addictive Disorders: A Systematic Review

Background: Substance Use Disorder (SUD) and behavioral addictions are common and require a multidisciplinary approach. New technologies like Virtual Reality could have the potential to improve assessment and treatment of these disorders. Objective: In the present paper, we therefore present an overview of Virtual Reality (Head Mounted Devices) in the field of addiction medicine for craving assessment and treatment. Method: We conducted a systematic review by querying PubMed database for the titles of articles published up to March 2019 with the terms [virtual] AND [addictive] OR [addiction] OR [substance] OR [alcohol] OR [cocaine] OR [cannabis] OR [opioid] OR [tobacco] OR [nicotine] OR [methamphetamine] OR [gaming] OR [gambling]. Results: We screened 319 abstracts and analyzed 37 articles, dividing them into two categories, the first for assessment of cue reactivity (craving, psychophysiological response and attention to cue) and the second for intervention, each drug (nicotine, cocaine, alcohol, cannabis, gambling) being detailed within each category. Conclusions: This overview suggest that VR provide benefits in the assessment and treatment of substance use disorders and behavior addictions and achieve high levels of ecological validity. While, craving provocation in VR is effective across addiction disorders, treatments based exclusively on virtual exposure to drug related cues as shown heterogenous results.
Mitigating loneliness with companion robots in the COVID-19 pandemic and beyond: an integrative framework and research agenda

Purpose Loneliness and isolation are on the rise, globally threatening the well-being across age groups; global social distancing measures during the COVID-19 crisis have intensified this so-called “loneliness virus”. The purpose of this paper is to develop an integrative framework and research agenda on the role of companion robots in mitigating feelings of loneliness. Design/methodology/approach A netnographic analysis of 595 online visual and textual descriptions offer empirical insights about the role of the companion robot Vector during the COVID-19 pandemic. Findings The contributions of this study are twofold. First, it postulates that companion robots have the potential of mitigating feelings of loneliness (i.e. indicator of well-being). Second, this study contributes to transformative service by developing an integrative framework introducing the roles (personal assistant, relational peer and intimate buddy) that companion robots can fulfill to mitigate feelings of loneliness through building different types of supportive relationships. Research limitations/implications The proposed research agenda encourages future service scholars to investigate 1) the role of robots in addressing loneliness, 2) design features that drive adoption of robots, 3) social support for different groups, 4) the operationalization and the measurement of loneliness and 5) an impact analysis of companion robots. Practical implications Service providers and policy makers can leverage the insights about how companion robots can help reduce a sense of loneliness. Originality/value The integrative framework on loneliness reduction, based on 595 unprompted online contributions issued during the COVID-19 pandemic, offers initial evidence for the impact of companion robots in reducing people's feelings of loneliness.
Barriers to the adoption of blockchain technology in business supply chains: a total interpretive structural modelling (TISM) approach

Blockchain is an emerging technology with a wide array of potential applications. This technology, which underpins cryptocurrency, provides an immutable, decentralised, and transparent distributed database of digital assets for use by firms in supply chains. However, not all firms are appropriately suited to adopt blockchain in the existing supply chain primarily due to their lack of knowledge on the benefits of this technology. Using Total Interpretive Structural Modelling (TISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC), this paper identifies the adoption barriers, examines the interrelationships between them to the adoption of blockchain technology, which has the potential to revolutionise supply chains. The TISM technique supports developing a contextual relationship-based structural model to identify the influential barriers. MICMAC classifies the barriers in blockchain adoption based on their strength and dependence. The results of this research indicate that the lack of business awareness and familiarity with blockchain technology on what it can deliver for future supply chains, are the most influential barriers that impede blockchain adoption. These barriers hinder and impact businesses decision to establish a blockchain-enabled supply chain and that other barriers act as secondary and linked variables in the adoption process.
From Pre-Quantum to Post-Quantum IoT Security: A Survey on Quantum-Resistant Cryptosystems for the Internet of Things

Although quantum computing is still in its nascent age, its evolution threatens the most popular public-key encryption systems. Such systems are essential for today's Internet security due to their ability for solving the key distribution problem and for providing high security in insecure communications channels that allow for accessing websites or for exchanging e-mails, financial transactions, digitally signed documents, military communications or medical data. Cryptosystems like Rivest-Shamir-Adleman (RSA), elliptic curve cryptography (ECC) or Diffie-Hellman have spread worldwide and are part of diverse key Internet standards like Transport Layer Security (TLS), which are used both by traditional computers and Internet of Things (IoT) devices. It is especially difficult to provide high security to IoT devices, mainly because many of them rely on batteries and are resource constrained in terms of computational power and memory, which implies that specific energy-efficient and lightweight algorithms need to be designed and implemented for them. These restrictions become relevant challenges when implementing cryptosystems that involve intensive mathematical operations and demand substantial computational resources, which are often required in applications where data privacy has to be preserved for the long term, like IoT applications for defense, mission-critical scenarios or smart healthcare. Quantum computing threatens such a long-term IoT device security and researchers are currently developing solutions to mitigate such a threat. This article provides a survey on what can be called post-quantum IoT systems (IoT systems protected from the currently known quantum computing attacks): the main post-quantum cryptosystems and initiatives are reviewed, the most relevant IoT architectures and challenges are analyzed, and the expected future trends are indicated. Thus, this article is aimed at providing a wide view of post-quantum IoT security and give useful guidelines to the future post-quantum IoT developers.
Federated Learning for 6G: Applications, Challenges, and Opportunities

Standard machine-learning approaches involve the centralization of training data in a data center, where centralized machine-learning algorithms can be applied for data analysis and inference. However, due to privacy restrictions and limited communication resources in wireless networks, it is often undesirable or impractical for the devices to transmit data to parameter sever. One approach to mitigate these problems is federated learning (FL), which enables the devices to train a common machine learning model without data sharing and transmission. This paper provides a comprehensive overview of FL applications for envisioned sixth generation (6G) wireless networks. In particular, the essential requirements for applying FL to wireless communications are first described. Then potential FL applications in wireless communications are detailed. The main problems and challenges associated with such applications are discussed. Finally, a comprehensive FL implementation for wireless communications is described.
NNV: The Neural Network Verification Tool for Deep Neural Networks and Learning-Enabled Cyber-Physical Systems

This paper presents the Neural Network Verification (NNV) software tool, a set-based verification framework for deep neural networks (DNNs) and learning-enabled cyber-physical systems (CPS). The crux of NNV is a collection of reachability algorithms that make use of a variety of set representations, such as polyhedra, star sets, zonotopes, and abstract-domain representations. NNV supports both exact (sound and complete) and over-approximate (sound) reachability algorithms for verifying safety and robustness properties of feed-forward neural networks (FFNNs) with various activation functions. For learning-enabled CPS, such as closed-loop control systems incorporating neural networks, NNV provides exact and over-approximate reachability analysis schemes for linear plant models and FFNN controllers with piecewise-linear activation functions, such as ReLUs. For similar neural network control systems (NNCS) that instead have nonlinear plant models, NNV supports over-approximate analysis by combining the star set analysis used for FFNN controllers with zonotope-based analysis for nonlinear plant dynamics building on CORA. We evaluate NNV using two real-world case studies: the first is safety verification of ACAS Xu networks, and the second deals with the safety verification of a deep learning-based adaptive cruise control system.
Motor Learning and Generalization Using Broad Learning Adaptive Neural Control

Human neural motor system has the intelligence to learn new skills, and then to generalize these skills naturally. But it is not easy for a robot to demonstrate such intelligent behaviors. Inspired by the neural motor behaviors, a framework of broad learning based novel adaptive neural control is proposed in this article, such that in the presence of dynamic disturbance, robots can learn a set of basic skills and then generalize these skills to the neighboring movements naturally as our human motor system. This is achieved by incorporating the deterministic learning with the broad learning system that can accumulate and reuse the learned knowledge. The broad learning enabled adaptive neural control has been rigorously established in theory and tested in both simulation and experimental studies. Simulation results and performance of the Baxter robot in the experiments have shown the effectiveness and superiority of the proposed method in comparison to the conventional adaptive neural control.
Open Science, Open Data, and Open Scholarship: European Policies to Make Science Fit for the Twenty-First Century

Open science will make science more efficient, reliable and responsive to societal challenges. The European Commission has sought to advance Open Science policy from its inception in a holistic and integrated way, covering all aspects of the research cycle from scientific discovery and review to sharing knowledge, publishing and outreach. We present the steps taken with a forward-looking perspective of the challenges laying ahead, in particular the necessary change of the rewards and incentives system for researchers (for which various actors are co-responsible and which goes beyond the mandate the European Commission). Finally, we discuss the promising role of Artificial Intelligence within an Open Science perspective.
Picking Winning Tickets Before Training by Preserving Gradient Flow

Overparameterization has been shown to benefit both the optimization and generalization of neural networks, but large networks are resource hungry at both training and test time. Network pruning can reduce test-time resource requirements, but is typically applied to trained networks and therefore cannot avoid the expensive training process. We aim to prune networks at initialization, thereby saving resources at training time as well. Specifically, we argue that efficient training requires preserving the gradient flow through the network. This leads to a simple but effective pruning criterion we term Gradient Signal Preservation (GraSP). We empirically investigate the effectiveness of the proposed method with extensive experiments on CIFAR-10, CIFAR-100, Tiny-ImageNet and ImageNet, using VGGNet and ResNet architectures. Our method can prune 80% of the weights of a VGG-16 network on ImageNet at initialization, with only a 1.6% drop in top-1 accuracy. Moreover, our method achieves significantly better performance than the baseline at extreme sparsity levels.
Structuring Content in the Façade Interactive Drama Architecture

The process of building Façade, a first-person, real-time, one-act interactive drama, has involved three major research efforts: designing ways to deconstruct a dramatic narrative into a hierarchy of story and behavior pieces; engineering an AI system that responds to and integrates the player's moment-by-moment interactions to reconstruct a real-time dramatic performance from those pieces; and understanding how to write an engaging, compelling story within this new organizational framework. This paper provides an overview of the process of bringing our interactive drama to life as a coherent, engaging, high agency experience, including the design and programming of thousands of joint dialog behaviors in the reactive planning language ABL, and their higher level organization into a collection of story beats sequenced by a drama manager. The process of iteratively developing the architecture, its languages, authorial idioms, and varieties of story content structures are described. These content structures are designed to intermix to offer players a high degree of responsiveness and narrative agency. We conclude with design and implementation lessons learned and future directions for creating more generative architectures.
Wavelength division multiplexing of continuous variable quantum key distribution and 18.3 Tbit/s data channels

Abstract Quantum key distribution (QKD) can offer communication with unconditional security and is a promising technology to protect next generation communication systems. For QKD to see commercial success, several key challenges have to be solved, such as integrating QKD signals into existing fiber optical networks. In this paper, we present experimental verification of QKD co-propagating with a large number of wavelength division multiplexing (WDM) coherent data channels. We show successful secret key generation over 24 h for a continuous-variable QKD channel jointly transmitted with 100 WDM channels of erbium doped fiber amplified polarization multiplexed 16-ary quadrature amplitude modulation signals amounting to a datarate of 18.3 Tbit/s. Compared to previous co-propagation results in the C-band, we demonstrate more than a factor of 10 increase in the number of WDM channels and more than 90 times higher classical bitrate, showing the co-propagation with Tbit/s data-carrying channels.
CrypTFlow: Secure TensorFlow Inference

We present CRYPTFLOW, a first of its kind system that converts TensorFlow inference code into Secure Multi-party Computation (MPC) protocols at the push of a button.To do this, we build three components.Our first component, Athos, is an end-to-end compiler from TensorFlow to a variety of semihonest MPC protocols.The second component, Porthos, is an improved semi-honest 3-party protocol that provides significant speedups for TensorFlow like applications.Finally, to provide malicious secure MPC protocols, our third component, Aramis, is a novel technique that uses hardware with integrity guarantees to convert any semi-honest MPC protocol into an MPC protocol that provides malicious security.The malicious security of the protocols output by Aramis relies on integrity of the hardware and semi-honest security of MPC.Moreover, our system matches the inference accuracy of plaintext TensorFlow.We experimentally demonstrate the power of our system by showing the secure inference of real-world neural networks such as RESNET50 and DENSENET121 over the ImageNet dataset with running times of about 30 seconds for semi-honest security and under two minutes for malicious security.Prior work in the area of secure inference has been limited to semi-honest security of small networks over tiny datasets such as MNIST or CIFAR.Even on MNIST/CIFAR, CRYPTFLOW outperforms prior work.
Generative Neural Networks for Anomaly Detection in Crowded Scenes

Security surveillance is critical to social harmony and people's peaceful life. It has a great impact on strengthening social stability and life safeguarding. Detecting anomaly timely, effectively and efficiently in video surveillance remains challenging. This paper proposes a new approach, called S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -VAE, for anomaly detection from video data. The S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -VAE consists of two proposed neural networks: a Stacked Fully Connected Variational AutoEncoder (S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</sub> -VAE) and a Skip Convolutional VAE (S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</sub> -VAE). The S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</sub> -VAE is a shallow generative network to obtain a model like Gaussian mixture to fit the distribution of the actual data. The S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</sub> -VAE, as a key component of S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -VAE, is a deep generative network to take advantages of CNN, VAE and skip connections. Both S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</sub> -VAE and S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</sub> -VAE are efficient and effective generative networks and they can achieve better performance for detecting both local abnormal events and global abnormal events. The proposed S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -VAE is evaluated using four public datasets. The experimental results show that the S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -VAE outperforms the state-of-the-art algorithms. The code is available publicly at https://github.com/tianwangbuaa/.