Reviewerly/exHarmony huggingface dataset:
Future of digital health and community care: Exploring intended positive impacts and unintended negative consequences of COVID-19

Response to COVID-19 has both intentionally and unintentionally progressed the digitization of health and community care, which can be viewed as a human rights issue considering that access to health and community care is a human right. In this article, we reviewed two cases of digitization of health and community care during the pandemic; one in Scotland, United Kingdom and another in British Columbia, Canada. An integrated analysis revealed that digitization of health and community care has intended positive and unintended negative consequences. Based on the analysis, we suggest five areas of improvement for equity in care: building on the momentum of technology advantages; education and digital literacy; information management and security; development of policy and regulatory frameworks; and the future of digital health and community care. This article sheds light on how health practitioners and leaders can work to enhance equity in care experiences amid the changing digital landscape.
VarCLR

Variable names are critical for conveying intended program behavior. Machine learning-based program analysis methods use variable name representations for a wide range of tasks, such as suggesting new variable names and bug detection. Ideally, such methods could capture semantic relationships between names beyond syntactic similarity, e.g., the fact that the names average and mean are similar. Unfortunately, previous work has found that even the best of previous representation approaches primarily capture "relatedness" (whether two variables are linked at all), rather than "similarity" (whether they actually have the same meaning).
Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation

Recently, pre-training methods have shown remarkable success in task-oriented dialog (TOD) systems. However, most existing pre-trained models for TOD focus on either dialog understanding or dialog generation, but not both. In this paper, we propose SPACE, a novel unified pre-trained dialog model learning from large-scale dialog corpora with limited annotations, which can be effectively fine-tuned on a wide range of downstream dialog tasks. Specifically, SPACE consists of four successive components in a single transformer to maintain a task-flow in TOD systems: (i) a dialog encoding module to encode dialog history, (ii) a dialog understanding module to extract semantic vectors from either user queries or system responses, (iii) a dialog policy module to generate a policy vector that contains high-level semantics of the response, and (iv) a dialog generation module to produce appropriate responses. We design a dedicated pre-training objective for each component. Concretely, we pre-train the dialog encoding module with span mask language modeling to learn contextualized dialog information. To capture the structured dialog semantics, we pre-train the dialog understanding module via a novel tree-induced semi-supervised contrastive learning objective with the help of extra dialog annotations. In addition, we pre-train the dialog policy module by minimizing the ℒ2 distance between its output policy vector and the semantic vector of the response for policy optimization. Finally, the dialog generation model is pre-trained by language modeling. Results show that SPACE achieves state-of-the-art performance on eight downstream dialog benchmarks, including intent prediction, dialog state tracking, and end-to-end dialog modeling. We also show that SPACE has a stronger few-shot ability than existing models under the low-resource setting.
Facial-sketch Synthesis: A New Challenge

Abstract This paper aims to conduct a comprehensive study on facial-sketch synthesis (FSS). However, due to the high cost of obtaining hand-drawn sketch datasets, there is a lack of a complete benchmark for assessing the development of FSS algorithms over the last decade. We first introduce a high-quality dataset for FSS, named FS2K, which consists of 2 104 image-sketch pairs spanning three types of sketch styles, image backgrounds, lighting conditions, skin colors, and facial attributes. FS2K differs from previous FSS datasets in difficulty, diversity, and scalability and should thus facilitate the progress of FSS research. Second, we present the largest-scale FSS investigation by reviewing 89 classic methods, including 25 handcrafted feature-based facial-sketch synthesis approaches, 29 general translation methods, and 35 image-to-sketch approaches. In addition, we elaborate comprehensive experiments on the existing 19 cutting-edge models. Third, we present a simple baseline for FSS, named FSGAN. With only two straightforward components, i.e., facial-aware masking and style-vector expansion, our FSGAN surpasses the performance of all previous state-of-the-art models on the proposed FS2K dataset by a large margin. Finally, we conclude with lessons learned over the past years and point out several unsolved challenges. Our code is available at https://github.com/DengPingFan/FSGAN .
OAG-BERT: Towards a Unified Backbone Language Model for Academic Knowledge Services

Academic Knowledge Services have substantially facilitated the development of human science and technology, providing a plenitude of useful research tools. However, many applications highly depend on ad-hoc models and expensive human labeling to understand professional contents, hindering deployments in real world. To create a unified backbone language model for various knowledge-intensive academic knowledge mining challenges, based on the world's largest public academic graph Open Academic Graph (OAG), we pre-train an academic language model, namely OAG-BERT, to integrate massive heterogeneous entity knowledge beyond scientific corpora. We develop novel pre-training strategies along with zero-shot inference techniques. OAG-BERT's superior performance on 9 knowledge-intensive academic tasks (including 2 demo applications) demonstrates its qualification to serve as a foundation for academic knowledge services. Its zero-shot capability also offers great potential to mitigate the need of costly annotations. OAG-BERT has been deployed to multiple real-world applications, such as reviewer recommendations for NSFC (National Nature Science Foundation of China) and paper tagging in the AMiner system. All codes and pre-trained models are available via the CogDL.
Learning free software using cloud services

The article deals with the use of cloud technology services in the study of free software. Free software is a social phenomenon based on the philosophy of freedom and the right to intellectual creative activity. To date, a significant number of software products have been created that are licensed under free software and not used in educational activities. The conducted research revealed the factors promoting and hindering the use of free software in educational activities. Conducted questionnaires, analysis of open data, research of scientists made it possible to conclude on the expediency of using free software in educational activities. Cloud technology is not only a modern trend of effective use of information and communication technologies in professional activity, but also a proven tool for educational activities. To get acquainted with the free software, the use of cloud technologies has been helpful, which is the goal of our research.
Defensive Distillation-Based Adversarial Attack Mitigation Method for Channel Estimation Using Deep Learning Models in Next-Generation Wireless Networks

Future wireless networks (5G and beyond), also known as Next Generation or NextG, are the vision of forthcoming cellular systems, connecting billions of devices and people together. In the last decades, cellular networks have dramatically grown with advanced telecommunication technologies for high-speed data transmission, high cell capacity, and low latency. The main goal of those technologies is to support a wide range of new applications, such as virtual reality, metaverse, telehealth, online education, autonomous and flying vehicles, smart cities, smart grids, advanced manufacturing, and many more. The key motivation of NextG networks is to meet the high demand for those applications by improving and optimizing network functions. Artificial Intelligence (AI) has a high potential to achieve these requirements by being integrated into applications throughout all network layers. However, the security concerns on network functions of NextG using AI-based models, i.e., model poising, have not been investigated deeply. It is crucial to protect the next-generation cellular networks against cybersecurity threats, especially adversarial attacks. Therefore, it needs to design efficient mitigation techniques and secure solutions for NextG networks using AI-based methods. This paper proposes a comprehensive vulnerability analysis of deep learning (DL)-based channel estimation models trained with the dataset obtained from MATLAB’s 5G toolbox for adversarial attacks and defensive distillation-based mitigation methods. The adversarial attacks produce faulty results by manipulating trained DL-based models for channel estimation in NextG networks while making models more robust against attacks through mitigation methods. This paper also presents the performance of the proposed defensive distillation mitigation method for each adversarial attack. The results indicate that the proposed mitigation method can defend the DL-based channel estimation models against adversarial attacks in NextG networks.
Representations of solar photovoltaic triple-diode models using artificial hummingbird optimizer

The true modeling of the solar photovoltaic units can boost their performances. However, on account of the lack of accurate solar cell parameters, cell modeling is erroneous. This is because the required parameters for modeling a trustworthy solar photovoltaic cell are not given in the manufacturer’s data sheets. As a consequence, it is significant to accurately estimate the essential parameters of solar photovoltaic triple-diode models. Diverse optimization techniques have addressed this problem; nevertheless, due to premature convergence and local minima, most of the techniques obtain suboptimal results. Accordingly, artificial hummingbird technique (AHBT) is attempted for estimating SPV uncertain parameters in this current effort. Furthermore, the AHBT approach improves quality of solution by preserving a history of past locations that may be compared to the present ones. To demonstrate the competency of the AHBT, its performance is compared to other well-known frameworks and newly developed techniques (e.g. African vulture’s optimization technique, Tuna swarm technique, and teaching learning studying-based technique) that are applied for the first time in this article. Some actual results of the minimum values of the errors in measured and estimated currents by employed ABHT method are 1.6945 mA for STM6-40/36 module, 0.5144 mA for mSi cell, and 0.4447 mA for KC200GT module. The solar photovoltaic modules are tested with varying temperature and irradiance sunshine levels. In addition to this, the calculated parameters are compared to experimental findings, including statistical analysis to validate the presentation of the AHBT. At last stage of this current effort, the principal performance of triple-diode models is investigated under varied operating temperatures and varied sun irradiances as well. Based on the findings, it can be concluded that the AHBT are effective at estimating solar photovoltaic models. The performance of a solar photovoltaic generating unit might be improved by precise modeling.
Optimizing Continuous-Variable Quantum Key Distribution with Phase-Shift Keying Modulation and Postselection

A numerical security proof technique is used to analyse the security of continuous-variable quantum key distribution (CV-QKD) protocols with phase-shift keying modulation against collective attacks in the asymptotic limit. We argue why it is sufficient to consider protocols with a maximum number of eight signal states and analyse different postselection strategies for protocols with four (QPSK) and eight (8PSK) signal states for untrusted ideal and trusted nonideal detectors. We introduce a \emph{cross-shaped} postselection strategy, and show that both cross-shaped and radial and angular postselection clearly outperform a radial postselection scheme (and no postselection) for QPSK protocols. For all strategies studied, we provide analytical results for the operators defining the respective regions in phase space. We outline several use-cases of postselection, which can easily be introduced in the data processing of both new and existing CV-QKD systems: Motivated by the high computational effort for error-correction, we studied the case when a large fraction of the raw key is eliminated by postselection and observed that this can be achieved while increasing the secure key rate. Postselection can also be used to partially compensate the disadvantage of QPSK protocols over 8PSK protocols for high transmission distances, while being experimentally less demanding. Finally, we highlight that postselection can be used to reduce the key rate gap between scenarios with trusted and untrusted detectors while relying on less assumptions on Eve's power.
Security and privacy in the internet of things healthcare systems: Toward a robust solution in real-life deployment

The internet of things (IoT) technology can be nowadays used to track user activity in daily living and health-related quality of life. IoT healthcare sensors can play a great role in reducing health-related costs. It helps users to assess their health progression. Nonetheless, these IoT solutions add security challenges due to their direct access to numerous personal information and their close integration into user activities. As such, this IoT technology is always a viable target for cybercriminals. More importantly, any adversarial attacks on an individual IoT node undermine the overall security of the concerned networks. In this study, we present the privacy and security issues of IoT healthcare devices. Moreover, we address possible attack models needed to verify the robustness of such devices. Finally, we present our deployed AMbient Intelligence (AMI) Lab architecture, and we compare its performance to current IoT solutions.
Hierarchical Multi-Attention Transfer for Knowledge Distillation

Knowledge distillation (KD) is a powerful and widely applicable technique for the compression of deep learning models. The main idea of knowledge distillation is to transfer knowledge from a large teacher model to a small student model, where the attention mechanism has been intensively explored in regard to its great flexibility for managing different teacher-student architectures. However, existing attention-based methods usually transfer similar attention knowledge from the intermediate layers of deep neural networks, leaving the hierarchical structure of deep representation learning poorly investigated for knowledge distillation. In this paper, we propose a hierarchical multi-attention transfer framework (HMAT) , where different types of attention are utilized to transfer the knowledge at different levels of deep representation learning for knowledge distillation. Specifically, position-based and channel-based attention knowledge characterize the knowledge from low-level and high-level feature representations, respectively, and activation-based attention knowledge characterize the knowledge from both mid-level and high-level feature representations. Extensive experiments on three popular visual recognition tasks, image classification, image retrieval, and object detection, demonstrate that the proposed hierarchical multi-attention transfer or HMAT significantly outperforms recent state-of-the-art KD methods.
A Privacy-Preserving KYC-Compliant Identity Scheme for Accounts on All Public Blockchains

Cryptocurrencies have the potential to enable socioeconomic growth throughout the world by offering easier access to capital and financial services. However, many virtual asset service providers (VASPs) that offer cryptocurrency services lack identity management and can be accessed anonymously, which has led to their services being exploited by criminal activities such as money laundering and illegal foreign exchange. Such crimes have a negative impact on socioeconomic sustainability. Building identity systems on blockchains can help VASPs improve their identity management to combat cryptocurrency-based crimes so VASPs can better serve the social economy and achieve their sustainability goals. However, existing solutions have privacy problems because the identity provider can associate users’ identities with their wallet accounts. In addition, there is currently no solution that can support all public blockchains unconditionally, as current solutions can only support EVM-compliant blockchains or require additional work to support new blockchains. This article proposes a KYC (know your customer)-compliant identity scheme based on Ethereum using Merkle trees and smart contracts. The identity and wallet accounts are linked by the user rather than the KYC provider so, in general, no one but the user knows the association between the wallet accounts and the identity, which protects privacy. For suspicious accounts, supervisors can trace their identities and thus achieve supervision. In addition, the scheme supports identifying accounts on all public blockchains by using Merkle trees and smart contracts to bind accounts on multiple blockchains to one identity and no extra work is required. Moreover, the scheme supports users to prove that their attributes meet the requirements of VASPs by adopting the BBS+ signature and the Sigma protocol.
Canonically Consistent Quantum Master Equation

We put forth a new class of quantum master equations that correctly reproduce the asymptotic state of an open quantum system beyond the infinitesimally weak system-bath coupling limit. Our method is based on incorporating the knowledge of the reduced steady state into its dynamics. The correction not only steers the reduced system toward a correct steady state but also improves the accuracy of the dynamics, thereby refining the archetypal Born-Markov weak-coupling second-order master equations. In case of equilibrium, we use the exact mean-force Gibbs state to correct the Redfield quantum master equation. By benchmarking it with the exact solution of the damped harmonic oscillator, we show that our method also helps correct the long-standing issue of positivity violation, albeit without complete positivity. Our method of a canonically consistent quantum master equation opens a new perspective in the theory of open quantum systems leading to a reduced density matrix accurate beyond the commonly used Redfield and Lindblad equations, while retaining the same conceptual and numerical complexity.
Optimization of the Trust Propagation on Supply Chain Network Based on Blockchain Plus

The decentralization of blockchain technology greatly improves the trust relationship in the supply chain network.In view of the lack of trust, uncertainty, and asymmetry in the supply chain network, this paper integrates the blockchain technology to build a network dynamics model of trust representation, calculation, and propagation, and explores how the blockchain influences the supply chain network.The result indicates that the network scale increased by 115.89%, the network connectivity increased by 60.31%, and the average shortest path decreased by 4.95%, after the blockchain trust framework had been deployed in the agricultural supply chain.Meanwhile, the network topology performance such as degree distribution and average clustering coefficient were optimized to varying degrees.Taking agricultural supply chain as an example, the practical significance of topological change was explained.Overall, the blockchain trust mechanism improves the topology of the supply chain network by affecting the trust relationship between nodes.
What Comes After Telepresence? Embodiment, Social Presence and Transporting One’s Functional and Social Self

Advances in robotics and multisensory displays allow extending telepresence ambitions beyond only "the feeling of being present at a remote location".In this paper, we discuss what may lie beyond telepresence and how we can transport both the functional and social self of a user.We introduce the embodiment illusion and its potential contribution to task performance and list important cues to evoke this illusion, including synchronicity in multisensory information, a firstperson visual perspective, and a human-like visual appearance and anatomy of the telepresence robot.We also introduce the concept of social presence and the important bidirectional social cues it needs, including eye contact, facial expression, posture, gestures, and social touch.For all these multisensory and social cues, we explain how they can be implemented in a telepresence system and describe our solution consisting of a closed control pod and a humanoid telepresence robot.
HHSKT: A learner–question interactions based heterogeneous graph neural network model for knowledge tracing

Knowledge tracing (KT) has evolved into a crucial component of the online education system with the rapid development of online adaptive learning. A key component of the online education system, knowledge tracing (KT) assesses the state of knowledge by tracing each learner’s learning activities. The deep KT model, however, is unable to completely extract the features of the questions and skills due to the heterogeneity of the knowledge structure and the sparsity of the interaction records. The model’s capacity to handle diverse data is also restricted by over parameterization. Additionally, rather than focusing solely on a precise fit, Intelligent Tutoring System (ITS) should stress interpretable feedback to the learner. The deep KT approach’s item parameters are still unable to give students useful feedback. This paper proposes to trace learner’s short-term attentional knowledge based on heterogeneous hierarchical differentiation, named HHSKT. Hierarchical heterogeneous knowledge structures and short-term memory enhancement will be used to model the effects of different interaction sequences on learners. Specifically, knowledge structure features are extracted by constructing a heterogeneous graph-based graph information augmentation component. Question differentiation parameters are derived by transforming the TrueSkill system. Besides, learners’ history-related practices are emphasized by windowing attention. Comparing regression-based and deep-based knowledge tracing experiments shows that HHSKT significantly outperforms the state-of-the-art approach on three real-world benchmark datasets (with an average AUC improvement of up to 3%), demonstrating the superiority of the proposed model.