Reviewerly/exHarmony huggingface dataset:
Immersion, presence, and episodic memory in virtual reality environments

Although virtual reality (VR) represents a promising tool for psychological research, much remains unknown about how properties of VR environments affect episodic memory. Two closely related characteristics of VR are immersion (i.e., the objective degree to which VR naturalistically portrays a real-world environment) and presence (i.e., the subjective sense of being "mentally transported" to the virtual world). Although some research has demonstrated benefits of increased immersion on VR-based learning, it is uncertain how broadly and consistently this benefit extends to individual components of immersion. Moreover, it is unclear whether presence may mediate the effect of immersion on memory. Three experiments assessed how presence and memory were affected by three manipulations of immersion: field of view, unimodal (visual only) vs. bimodal (audiovisual) environments, and the realism of lighting effects (e.g., the occurrence or absence of shadows). Results indicated that effects of different manipulations of immersion are heterogeneous, affecting memory in some instances and presence in others, but not necessarily both. Importantly, no evidence for a mediating effect of presence emerged in any of these experiments, nor in a combined cross-experimental analysis. This outcome demonstrates a degree of independence between immersion and presence with regard to their influence on episodic memory performance.
User Experiences of the NZ COVID Tracer App in New Zealand: Thematic Analysis of Interviews

Background For mobile app–based COVID-19 contact tracing to be fully effective, a large majority of the population needs to be using the app on an ongoing basis. However, there is a paucity of studies of users, as opposed to potential adopters, of mobile contact tracing apps and of their experiences. New Zealand, a high-income country with western political culture, was successful in managing the COVID-19 pandemic, and its experience is valuable for informing policy responses in similar contexts. Objective This study asks the following research questions: (1) How do users experience the app in their everyday contexts? and (2) What drives the use of the app? Methods Residents of New Zealand’s Auckland region, which encompasses the country’s largest city, were approached via Facebook, and 34 NZ COVID Tracer app users were interviewed. Interview transcripts were analyzed using thematic analysis. Results Interviews ranged in duration from 15 to 50 minutes. Participants ranged in age from those in their late teens to those in their early sixties. Even though about half of the participants identified as White New Zealanders of European origin, different ethnicities were represented, including New Zealanders of South Pacific, Indian, Middle Eastern, South American, and Southeast Asian descent. Out of 34 participants, 2 (6%) identified as Māori (Indigenous New Zealanders). A broad range of careers were represented, from top-middle management to health support work and charity work. Likewise, educational backgrounds ranged broadly, from high school completion to master’s degrees. Out of 34 participants, 2 (6%) were unemployed, having recently lost their jobs because of the pandemic. The thematic analysis resulted in five major themes: perceived benefits, patterns of use, privacy, social influence, and need for collective action. Benefits of using the app to society in general were more salient to the participants than immediate health benefits to the individual. Use, however, depended on the alert level and tended to decline for many participants at low alert levels. Privacy considerations played a small role in shaping adoption and use, even though the participants were highly aware of privacy discourse around the app. Participants were aware of the need for high levels of adoption and use of the app to control the pandemic. Attempts to encourage others to use the app were common, although not always successful. Conclusions Appeals to civic responsibility are likely to drive the use of a mobile contact tracing app under the conditions of high threat. Under the likely scenario of COVID-19 remaining endemic and requiring ongoing vigilance over the long term, other mechanisms promoting the use of mobile contact tracing apps may be needed, such as offering incentives. As privacy is not an important concern for many users, flexible privacy settings in mobile contact tracing apps allowing users to set their optimal levels of privacy may be appropriate.
BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation

Semantic segmentation aims to predict pixel-level labels. It has become a popular task in various computer vision applications. While fully supervised segmentation methods have achieved high accuracy on large-scale vision datasets, they are unable to generalize on a new test environment or a new domain well. In this work, we first introduce a new Unaligned Domain Score to measure the efficiency of a learned model on a new target domain in unsupervised manner. Then, we present the new Bijective Maximum Likelihood <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> (BiMaL) loss that is a generalized form of the Adversarial Entropy Minimization without any assumption about pixel independence. We have evaluated the proposed BiMaL on two domains. The proposed BiMaL approach consistently outperforms the SOTA methods on empirical experiments on "SYNTHIA to Cityscapes", "GTA5 to Cityscapes", and "SYNTHIA to Vistas".
COVID-19 Mortality Prediction From Deep Learning in a Large Multistate Electronic Health Record and Laboratory Information System Data Set: Algorithm Development and Validation

Background COVID-19 is caused by the SARS-CoV-2 virus and has strikingly heterogeneous clinical manifestations, with most individuals contracting mild disease but a substantial minority experiencing fulminant cardiopulmonary symptoms or death. The clinical covariates and the laboratory tests performed on a patient provide robust statistics to guide clinical treatment. Deep learning approaches on a data set of this nature enable patient stratification and provide methods to guide clinical treatment. Objective Here, we report on the development and prospective validation of a state-of-the-art machine learning model to provide mortality prediction shortly after confirmation of SARS-CoV-2 infection in the Mayo Clinic patient population. Methods We retrospectively constructed one of the largest reported and most geographically diverse laboratory information system and electronic health record of COVID-19 data sets in the published literature, which included 11,807 patients residing in 41 states of the United States of America and treated at medical sites across 5 states in 3 time zones. Traditional machine learning models were evaluated independently as well as in a stacked learner approach by using AutoGluon, and various recurrent neural network architectures were considered. The traditional machine learning models were implemented using the AutoGluon-Tabular framework, whereas the recurrent neural networks utilized the TensorFlow Keras framework. We trained these models to operate solely using routine laboratory measurements and clinical covariates available within 72 hours of a patient’s first positive COVID-19 nucleic acid test result. Results The GRU-D recurrent neural network achieved peak cross-validation performance with 0.938 (SE 0.004) as the area under the receiver operating characteristic (AUROC) curve. This model retained strong performance by reducing the follow-up time to 12 hours (0.916 [SE 0.005] AUROC), and the leave-one-out feature importance analysis indicated that the most independently valuable features were age, Charlson comorbidity index, minimum oxygen saturation, fibrinogen level, and serum iron level. In the prospective testing cohort, this model provided an AUROC of 0.901 and a statistically significant difference in survival (P&lt;.001, hazard ratio for those predicted to survive, 95% CI 0.043-0.106). Conclusions Our deep learning approach using GRU-D provides an alert system to flag mortality for COVID-19–positive patients by using clinical covariates and laboratory values within a 72-hour window after the first positive nucleic acid test result.
Compressed Domain Deep Video Super-Resolution

Real-world video processing algorithms are often faced with the great challenges of processing the compressed videos instead of pristine videos. Despite the tremendous successes achieved in deep-learning based video super-resolution (SR), much less work has been dedicated to the SR of compressed videos. Herein, we propose a novel approach for compressed domain deep video SR by jointly leveraging the coding priors and deep priors. By exploiting the diverse and ready-made spatial and temporal coding priors ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.,</i> partition maps and motion vectors) extracted directly from the video bitstream in an effortless way, the video SR in the compressed domain allows us to accurately reconstruct the high resolution video with high flexibility and substantially economized computational complexity. More specifically, to incorporate the spatial coding prior, the Guided Spatial Feature Transform (GSFT) layer is proposed to modulate features of the prior with the guidance of the video information, making the prior features more fine-grained and content-adaptive. To incorporate the temporal coding prior, a guided soft alignment scheme is designed to generate local attention off-sets to compensate for decoded motion vectors. Our soft alignment scheme combines the merits of explicit and implicit motion modeling methods, rendering the alignment of features more effective for SR in terms of the computational complexity and robustness to inaccurate motion fields. Furthermore, to fully make use of the deep priors, the multi-scale fused features are generated from a scale-wise convolution reconstruction network for final SR video reconstruction. To promote the compressed domain video SR research, we build an extensive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Compressed Videos with Coding Prior</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CVCP</i> ) dataset, including compressed videos of diverse content and various coding priors extracted from the bitstream. Extensive experimental results show the effectiveness of coding priors in compressed domain video SR.
Edge Device Identification Based on Federated Learning and Network Traffic Feature Engineering

With the ubiquitous deployment and applications of Internet of Things (IoT), security issues pose a critical challenge to IoT devices. External attackers often utilize vulnerable IoT devices to invade the target’s internal network and then further cause a security threat to the whole network. To prevent such attacks, it is necessary to develop a security mechanism to control the access of suspicious IoT devices and manage the internal devices. In recent years, deep learning (DL) algorithm has been widely used in the field of edge device identification (EDI), and has made great achievements. However, these previous methods are essentially centralized learning-based EDI (CentEDI) that trains all data together, which can not guarantee data security and not conducive to deployment on edge devices. To address this problem, we introduce a federated learning-based EDI (FedeEDI) method via network traffic to automatically identify edge devices connected to the whole network. Experimental results show that the training efficiency of our proposed FedeEDI method is much higher than that of the CentEDI method, although its classification accuracy is slightly reduced. In contrast to the CentEDI method, the proposed FedeEDI method has two main advantages: faster training speed and safer training process.
Affordances and challenges of teaching language skills by virtual reality: A systematic review (2010–2020)

The emergence of newer educational technologies provides opportunities for computer-assisted language learning practitioners and software developers to capitalize on the affordances for the second language (L2) learning purposes. This article explored and brought together the reported affordances and challenges of virtual reality to teach the L2 receptive and productive skills by reviewing seventy-five peer-reviewed articles published from 2010 to 2020. The results revealed an imbalanced trend of research on language skills learning by virtual reality tools and affordances in favor of listening and speaking skills compared to reading and writing skills. The obtained data were tabularized in terms of language skills. The results were visualized by hierarchical database formats. Pedagogical implications of the study inform teachers about affordances available to teach L2 skills in educational virtual reality environments toward a more digitally enhanced L2 pedagogy. Theoretical implications of the study inform researchers about the challenges that need to be addressed.
Privacy-Preserving Blockchain-Based Federated Learning for Marine Internet of Things

The marine Internet of things (MIoT) is the application of the Internet of things technology in the marine field. Nowadays, with the arrival of the era of big data, the MIoT architecture has been transformed from cloud computing architecture to edge computing architecture. However, due to the lack of trust among edge computing participants, new solutions with higher security need to be proposed. In the current solutions, some use blockchain technology to solve data security problems while some use federated learning technology to solve privacy problems, but these methods neither combine with the special environment of the ocean nor consider the security of task publishers. In this article, we propose a secure sharing method of MIoT data under an edge computing framework based on federated learning and blockchain technology. Combining its special distributed architecture with the MIoT edge computing architecture, federated learning ensures the privacy of nodes. The blockchain serves as a decentralized way, which stores federated learning workers to achieve nontampering and security. We propose a concept of quality and reputation as the metrics of selection for federated learning workers. Meanwhile, we design a quality proof mechanism [proof of quality (PoQ)] and apply it to the blockchain, making the edge nodes recorded in the blockchain more high-quality. In addition, a marine environment model is built in this article, and the analysis based on this model makes the method proposed in this article more applicable to the marine environment. The numerical results obtained from the simulation experiments clearly show that the proposed scheme can significantly improve the learning accuracy under the premise of ensuring the safety and reliability of the marine environment.
Congested crowd instance localization with dilated convolutional swin transformer

Crowd localization is a new computer vision task, evolved from crowd counting. Different from the latter, it provides more precise location information for each instance, not just counting numbers for the whole crowd scene, which brings greater challenges, especially in extremely congested crowd scenes. In this paper, we focus on how to achieve precise instance localization in high-density crowd scenes, and to alleviate the problem that the feature extraction ability of the traditional model is reduced due to the target occlusion, the image blur, etc. To this end, we propose a Dilated Convolutional Swin Transformer (DCST) for congested crowd scenes. Specifically, a window-based vision transformer is introduced into the crowd localization task, which effectively improves the capacity of representation learning. Then, the well-designed dilated convolutional module is inserted into some different stages of the transformer to enhance the large-range contextual information. Extensive experiments evidence the effectiveness of the proposed methods and achieve the state-of-the-art performance on five popular datasets. Especially, the proposed model achieves F1-measure of 77.5% and MAE of 84.2 in terms of localization and counting performance, respectively.
Modeling Sub-Actions for Weakly Supervised Temporal Action Localization

As a challenging task of high-level video understanding, weakly supervised temporal action localization has attracted more attention recently. Due to the usage of video-level category labels, this task is usually formulated as the task of classification, which always suffers from the contradiction between classification and detection. In this paper, we describe a novel approach to alleviate the contradiction for detecting more complete action instances by explicitly modeling sub-actions. Our method makes use of three innovations to model the latent sub-actions. First, our framework uses prototypes to represent sub-actions, which can be automatically learned in an end-to-end way. Second, we regard the relations among sub-actions as a graph, and construct the correspondences between sub-actions and actions by the graph pooling operation. Doing so not only makes the sub-actions inter-dependent to facilitate the multi-label setting, but also naturally use the video-level labels as weak supervision. Third, we devise three complementary loss functions, namely, representation loss, balance loss and relation loss to ensure the learned sub-actions are diverse and have clear semantic meanings. Experimental results on THUMOS14 and ActivityNet1.3 datasets demonstrate the effectiveness of our method and superior performance over state-of-the-art approaches.
MedQ: Lossless ultra-low-bit neural network quantization for medical image segmentation

Implementing deep convolutional neural networks (CNNs) with boolean arithmetic is ideal for eliminating the notoriously high computational expense of deep learning models. However, although lossless model compression via weight-only quantization has been achieved in previous works, it is still an open problem about how to reduce the computation precision of CNNs without losing performance, especially for medical image segmentation tasks where data dimension is high and annotation is scarce. This paper presents a novel CNN quantization framework that can squeeze a deep model (both parameters and activation) to extremely low bitwidth, e.g., 1∼2 bits, while maintaining its high performance. In the new method, we first design a strong baseline quantizer with an optimizable quantization range. Then, to relieve the back-propagation difficulty caused by the discontinuous quantization function, we design a radical residual connection scheme that allows gradients to flow through every quantized layer freely. Moreover, a tanh-based derivative function is used to further boost gradient flow and a distributional loss is employed to regularize the model output. Extensive experiments and ablation studies are conducted on two well-established public 3D segmentation datasets, i.e., BRATS2020 and LiTS. Experimental results evidence that our framework not only outperforms state-of-the-art quantization approaches significantly, but also achieves lossless performance on both datasets with ternary (2-bit) quantization.
A Framework of Severity for Harmful Content Online

The proliferation of harmful content on online social media platforms has necessitated empirical understandings of experiences of harm online and the development of practices for harm mitigation. Both understandings of harm and approaches to mitigating that harm, often through content moderation, have implicitly embedded frameworks of prioritization - what forms of harm should be researched, how policy on harmful content should be implemented, and how harmful content should be moderated. To aid efforts of better understanding the variety of online harms, how they relate to one another, and how to prioritize harms relevant to research, policy, and practice, we present a theoretical framework of severity for harmful online content. By employing a grounded theory approach, we developed a framework of severity based on interviews and card-sorting activities conducted with 52 participants over the course of ten months. Through our analysis, we identified four Types of Harm (physical, emotional, relational, and financial) and eight Dimensions along which the severity of harm can be understood (perspectives, intent, agency, experience, scale, urgency, vulnerability, sphere). We describe how our framework can be applied to both research and policy settings towards deeper understandings of specific forms of harm (e.g., harassment) and prioritization frameworks when implementing policies encompassing many forms of harm.
Meta‐learning and the new challenges of machine learning

In the last years, organizations and companies in general have found the true potential value of collecting and using data for supporting decision-making. As a consequence, data are being collected at an unprecedented rate. This poses several challenges, including, for example, regarding the storage and processing of these data. Machine Learning (ML) is also not an exception, in the sense that algorithms must now deal with novel challenges, such as learn from streaming data or deal with concept drift. ML engineers also have a harder task when it comes to selecting the most appropriate model, given the wealth of algorithms and possible configurations that exist nowadays. At the same time, training time is a stronger restriction as the computational complexity of the training model increases. In this paper we propose a framework for dealing with these challenges, based on meta-learning. Specifically, we tackle two well-defined problems: automatic algorithm selection and continuous algorithm updates that do not require the retraining of the whole algorithm to adapt to new data. Results show that the proposed framework can contribute to ameliorate the identified issues.
What do blockchain technologies imply for digital creative industries?

As the technology that powers cryptocurrencies like bitcoin, blockchains are associated with volatile and (as yet) largely unregulated financial trade, but they are also about more than money. This capacity to help automate, incentivize and authenticate global trade has numerous potential applications. Blockchain technologies promise efficient transactions, greater accountability of trade and increased/direct payment for creative enterprise. As such, despite their lingering technical challenges, these nascent technologies are already being employed within a wide variety of creative innovation processes. Based upon research into their potential applications within Scotland's digital creative industries, this study explores the ways in which these emerging technologies might disrupt digital creative industries, such as digital media production, digital art, web/interface/experience design, application development, extended reality and gaming, both in Scotland and beyond. Of particular interest are the ways that these emerging technologies might transform value exchange and intellectual property management. Early results indicate that blockchain technologies are poised to substantially disrupt the sale and distribution of creative digital works. Yet, whilst these emerging technologies can encourage open innovation, it also seems likely that they will just as often be used to streamline existing systems designed to control and potentially exploit creativity. The implications for digital disruption theories are discussed, highlighting the need for frameworks that can also account for second‐order disruptions.