Reviewerly/exHarmony huggingface dataset:
Towards an Ethics for the Healthcare Metaverse

Harnessing the metaverse for medical and healthcare purposes is currently heralded as the “next frontier in healthcare.” However, to ensure the “amazing possibilities” of the so-called “healthcare metaverse” can be fully realized, it’s vital to proactively identify and address potential challenges, especially those of an ethical nature. This paper aims to spotlight these ethical challenges within the healthcare metaverse and chart a course for confronting these issues and cultivating an ethics framework for this emerging field. We will demonstrate that the ethical quandaries within the healthcare metaverse bear striking similarities to those seen in digital medicine, an arena that grapples with vast quantities of data and the application of artificial intelligence. Reflecting on these parallels, we explore how six central ethical challenges in digital medicine – 1) accessibility, 2) fairness, 3) discrimination and bias, 4) responsibility, 5) privacy, data safety and security, and data ownership, as well as 6) environmental issues – unfold within the healthcare metaverse’s virtual boundaries and show that the metaverse’s immersive nature further intensifies these ethical challenges. In view of this, we advocate that the next phase in forging ethics for the healthcare metaverse involves a nuanced reconsideration of current ethical methods and principles in digital medicine, with an emphasis on the immersive element of the metaverse. We foresee the concept of embodiment within virtual settings to be at the heart of this immersion-focused reassessment.
Cross-Domain Recommendation via Progressive Structural Alignment

Cross-domain recommendation, as a cutting-edge technology to settle data sparsity and cold start problems, is gaining increasingly popular. Existing research paradigms primarily focus on leveraging the representation of overlapping entities, such as representation aggregation or cross-domain consistency constraints, to facilitate knowledge transfer and enhance the performance of single-domain or dual-domain recommender systems. Even though these approaches bring significant promotions, they still suffer from optimization bottlenecks when faced with sparse overlapping users, which often occurs in reality. Unlocking the full potential of overlapping user information and exploring novel sources of cross-domain knowledge are pivotal in addressing this challenge effectively. On account of this, this paper proposes an innovative cross-domain recommendation framework, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SEAGULL</i> , to promote dual-target recommendation performance in line with these two perspectives. We bolster the utilization of overlapping user knowledge and extract non-overlapping user interests by refining the message passing mechanism in a unified heterogeneous cross-domain graph and facilitating the transfer of latent structural relationships among users. Specifically, we first construct the interaction of two domains as a unified cross-domain heterogeneous graph and design a novel attention mechanism to incorporate cross-domain collaboration signals between users and items. Second, we perform user structure alignment from global and local levels to extend semantic transfer and information augmentation. Finally, unlike previous work that directly incorporates mixed cross-domain knowledge, we employ a gentle and progressive cross-domain transfer strategy to reduce empirical risk loss. Extensive experiments on five tasks derived from three data sets fully demonstrate the effectiveness of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SEAGULL</i> .
The Overestimation of Heart Failure in Cause-of-Death Statistics

Heart Failure is one of the top ten leading causes of death in mortality statistics. Mortality statistics report underlying causes of death, i.e. starting points of causal chains leading to death. Heart failure always has a cause and therefore cannot be such a starting point. Therefore, the aim of this study is to explain the occurrence of heart failure in mortality statistics. Survey among a sample of 750 certifiers who mentioned heart failure on a death certificate. Comparison of the physician's opinion on the role of heart failure as a cause of death with the outcome of automated coding for statistics. The survey response was 56%. When a doctor mentions heart failure on a death certificate, the disease is considered underlying cause of death in 21.4% of the cases. In statistics, the disease is assigned this role in 39.2% of the (same) cases. Heart failure is overestimated by 17.8 percentage points or 45% (95%-CI: 40-50%) in mortality statistics. In cause-of-death statistics, heart failure is more often regarded as underlying cause of death than intended by the certifying physician. This overestimation is caused by incorrectly completed death certificates, conceptual confusion and a real difference in opinion on the role of heart failure as underlying cause of death.
Can digital technologies help Africa to leapfrog its massive education gap?

A sound education system is one of the keys to personal mastery and growth and a country's socio-economic development more broadly. Globally, Africa is the furthest behind in terms of educational achievement levels, with serious shortcomings in teaching quality, infrastructure, access and relevance. Yet attempting to catch up with other countries using traditional, government-led approaches will take too long and will be too costly. Whether or not Africa can 'leapfrog' its education gap by using digital technologies is up for debate. Africa has undergone a 'mobile revolution' in recent years, yet many countries on the continent lack the basics for efficient online teaching and learning, including a reliable source of electricity and affordable internet access. In this chapter we discuss the plausibility of leapfrogging in an educational context and provide some policy recommendations that should underscore Africa's attempts to bridge its educational divide in the digital age.
Blockchain Data Mining With Graph Learning: A Survey

Blockchain data mining has the potential to reveal the operational status and behavioral patterns of anonymous participants in blockchain systems, thus providing valuable insights into system operation and participant behavior. However, traditional blockchain analysis methods suffer from the problems of being unable to handle the data due to its large volume and complex structure. With powerful computing and analysis capabilities, graph learning can solve the current problems through handling each node's features and linkage relationships separately and exploring the implicit properties of data from a graph perspective. This paper systematically reviews the blockchain data mining tasks based on graph learning approaches. Firstly, we investigate the blockchain data acquisition method, integrate the currently available data analysis tools, and divide the sampling method into rule-based and cluster-based techniques. Secondly, we classify the graph construction into transaction-based blockchain and account-based methods, and comprehensively analyze the existing blockchain feature extraction methods. Thirdly, we compare the existing graph learning algorithms on blockchain and classify them into traditional machine learning-based, graph representation-based, and graph deep learning-based methods. Finally, we propose future research directions and open issues which are promising to address.
Active Learning Empowered Sentiment Analysis: An Approach for Optimizing Smartphone Customer’s Review Sentiment Classification

Product reviews are critical in informing customers about products and services and are crucial to customer inclinations and business success. Active learning enables Artificial Intelligence (AI) algorithms to learn more efficiently by actively selecting the most informative data samples for training. This study proposes a state-of-the-art approach that utilizes active learning (AL) based machine learning algorithms for Flipkart’s smartphone customers’ reviews sentiments analysis. The proposed study adopts rigorous data cleaning, data balancing, and Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction techniques, followed by five AL-based machine learning algorithms, including Random Forest (AL-RF), Decision Tree (AL-DT), K-Nearest Neighbor (AL-KNN), Logistic Regression (AL-LR), and Gradient Boost (AL-GB), to classify sentiment analysis based on product reviews. The experiment spanned over three iterations with a chunk size of 200, and an active learning strategy was applied to the process. The results demonstrate the effectiveness of active learning in improving the accuracy of sentiment analysis on unbalanced datasets. Logistic Regression has demonstrated an accuracy rating of 89%. Collectively, the study showcases the potential of active learning techniques for sentiment analysis in product reviews and serves as a valuable tool for guiding customers in making informed decisions regarding their choice of products.
Multi-view Ensemble Federated Learning for Efficient Prediction of Consumer Electronics Applications in Fog Networks

Federated Learning (FL) collaboratively trains a model while preserving privacy and providing intelligence. This makes it ideal for Consumer Electronics (CE) applications, involving continuous streaming of data. However, the existence of a single central server for orchestrating the entire process and the participation of an abundant number of clients introduce major bottlenecks like a single-point failure of the server and communication inefficiency. Additionally, in most CE applications, local data is used by the clients as a single dataset, which fails to retrieve deep insights from data features for better decision-making. In this light, we propose a multi-view ensemble FL framework for CE applications in fog networks, namely EnsembleFed. The proposed approach derives multiple views from raw data to gain deeper insights from it and trains a model for each view. Later, the output of these models is ensembled for robust predictions. The distributed fog nodes are incorporated into the FL framework to combat single-point failure risk and improve communication efficiency. Empirical results demonstrate that the proposed approach outperforms state-of-the-art techniques on a real-time dataset in terms of increased accuracy by 4.02%, 4.32%, and 5.27%, compared to FedYogi, FedAdam, and FedAvg, respectively.
Chaotic signal denoising based on energy selection TQWT and adaptive SVD

Abstract Aiming at the problem of denoising chaotic signals with low signal-to-noise ratio and unknown dynamic system parameters, a new chaotic signal denoising algorithm is proposed, which combines adjustable Q-factor wavelet transform (TQWT) and adaptive singular value decomposition (ASVD). This method uses TQWT to decompose the noisy chaotic signal. According to the maximum wavelet entropy theory and energy threshold rule, the subband of TQWT is accurately divided into signal subband and noise subband. For noise subbands, adaptive SVD is used to denoise them, to achieve preliminary denoising. In ASVD, the standard deviation of the singular value subset is used to determine the effective reconstruction order to improve the noise suppression effect. To further remove noise in the signal subband, TQWT reconstruction is performed on the preliminarily denoised signal, and ASVD is used to denoise the reconstructed signal again to obtain the chaotic signal after secondary denoising. Chua's simulated signal and four kinds of underwater radiated noise measured by TQWT-ASVD were denoised, and compared with the SVD denoising method, TQWT denoising method, complete ensemble empirical mode decomposition with adaptive noise and threshold denoising method (CEEMDAN-WT) and modified ensemble empirical mode decomposition combined with least squares denoising method (MEEMD-LMS), The experimental results show that the TQWT-ASVD method can reduce the noise of chaotic signals more effectively. Compared with SVD, TQWT, CEEMDAN-WT, MEEMD-LMS, and Chua's signal denoising method, the signal-to-noise ratio (SNR) of this method increased by 23.22%, 26.46%, 18.79%, 16.11% the root mean square error (RMSE) decreased by 32.53%,39.48%, 30.96%, 27.94%, and the row entropy (PE) decreased by 40.44%, 41.96%, 22.78%, 20.59%; After reducing the radiation noise of cargo ships, the PE value of this method is reduced by 13.91%, 10.18%, 10.88%, 8.68% respectively, and the FE value is reduced by 33.66%, 31.42%, 26.98%, 21.32% respectively.
In Search of a Head Start: Marketing Opportunities in the Metaverse

Abstract The concept of the metaverse, a virtual world with immersive 3-D environments, has gained significant attention and is expected to affect how we interact and communicate. While there is no consensus on its future, many brands are preparing for its potential. The metaverse offers unique marketing opportunities, enabling brands to provide offerings that are impossible in the real world. It can serve as a third space for retailing, where brands can create immersive experiences and overcome physical limitations. Metaverse advertising should be interactive and immersive to maximize its potential, while brand communities and events in the metaverse offer new avenues for engagement. The metaverse also allows companies to unlock new revenue streams through virtual products, NFT collections and digital twins. However, the metaverse brings challenges, including technical issues, privacy concerns, avatar misbehavior and the need for virtual and real coexistence. Brands must navigate these challenges and conduct further research to understand and maximize the benefits while minimizing risks in the metaverse.
Quantum controlled teleportation with OR-logic-gate-like controllers in noisy environment

Abstract We evaluate a one-way, bidirectional, and cyclic quantum controlled teleportation with two controllers. We show that the controllers’ agreement mimics the OR logic gate, i.e., the teleportation succeeds with only one of the controllers’ cooperation, and it does not matter which one. This result is generalized to multi-way quantum controlled teleportation with N controllers. The proposed protocols are evaluated by considering a noisy environment in the form of phase-damping noise and amplitude-damping noise and the dependence of fidelity only on the decoherence rate and the initial state’s amplitude parameter were established. It is interesting to note that for the phase-damping noise case, there are states with perfect fidelity (and the entropy equal to zero) even in a noisy environment. We discuss the novelty of the proposed protocols and highlight that they can be useful if the information to be teleported needs an OR-logic-gate-like controllers’ agreement combination, which cannot be done with other existing protocols.
Multi-Level Content-Aware Boundary Detection for Temporal Action Proposal Generation

It is challenging to generate temporal action proposals from untrimmed videos. In general, boundary-based temporal action proposal generators are based on detecting temporal action boundaries, where a classifier is usually applied to evaluate the probability of each temporal action location. However, most existing approaches treat boundaries and contents separately, which neglect that the context of actions and the temporal locations complement each other, resulting in incomplete modeling of boundaries and contents. In addition, temporal boundaries are often located by exploiting either local clues or global information, without mining local temporal information and temporal-to-temporal relations sufficiently at different levels. Facing these challenges, a novel approach named multi-level content-aware boundary detection (MCBD) is proposed to generate temporal action proposals from videos, which jointly models the boundaries and contents of actions and captures multi-level ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., frame level and proposal level) temporal and context information. Specifically, the proposed MCBD preliminarily mines rich frame-level features to generate one-dimensional probability sequences, and further exploits temporal-to-temporal proposal-level relations to produce two-dimensional probability maps. The final temporal action proposals are obtained by a fusion of the multi-level boundary and content probabilities, achieving precise boundaries and reliable confidence of proposals. The extensive experiments on the three benchmark datasets of THUMOS14, ActivityNet v1.3 and HACS demonstrate the effectiveness of the proposed MCBD compared to state-of-the-art methods. The source code of this work can be found in https://mic.tongji.edu.cn.
The Finance Money Crisis and Cryptocurrencies

The speculative financial services sector and the systemic failures of financial globalisation have prompted a growing call for delinking from the hegemony of the US dollar. These systemic failures have been illustrated by financial mismanagement and the financial crisis of 2007-8. This event paints a dire picture of the consequences associated with poor regulation of the financial sector. This study seeks to interpret these events in terms of Gramsci’s Prison Notebooks and his concept of the ‘morbid symptoms of the interregnum’. According to Babic (2020), these occur when a hegemony and its institutions are ‘dying’, thereby hampering their power. This notion also highlights that the future remains murky, despite public calls for change. Gramsci calls this the ‘new that cannot be born’.This study seeks to extend this framework to the current state of the dollar hegemony. The ‘morbid symptoms’ we will examine include the call to delink from the speculative high-risk US economy, the emergence of cryptocurrencies, and the politics surrounding fiat money. Some have argued that the emergence of cryptocurrencies could be the ‘new’. However, this study argues that the negative characteristics associated with cryptocurrencies such as cybersecurity concerns and price volatility create ambiguity about whether cryptocurrency is the ideal trajectory. Therefore, it argues that ambiguity and calls for change qualify our current monetary situation to be classified as a Gramscian interregnum.It also examines the current fiat money discourse by highlighting how the dollar hegemony may be succeeded by another fiat currency. However, it argues that the inflationary shortfalls of fiat money make this a fallible option, thereby perpetuating the ambiguities within the Gramscian interregnum.
Acculturation Strategies of Indigenous Students in Learning English: A Qualitative Inquiry

This qualitative descriptive study aimed to identify the factors that contribute to social and psychological distance of indigenous English learners, their strategies to reduce these factors, and their insights about acculturation. To this end, ten indigenous English major students at the University of Mindanao were interviewed. Through thematic analysis, findings revealed that the factors involved in the social distance of the participants were incongruence and dissimilarity between 2LL and TL group, negative attitudinal orientation towards the TL group, and cohesiveness of the 2LL group, while the psychological factors were language shock, high affective filter, and ego permeability. To address these factors, the participants implemented the following strategies: positive intergroup relationship, accommodation of the TL, perceptual distortions in favor of outgroup vitality, cultivation of positive self-image, and development of TL skills. In terms of the insights of the participants, this study observed empowerment of indigenous community, enhancement of exposure to second language input, instructional scaffolding in L2 teaching, implementation of intercultural approach, immersion to the outgroup, and alleviation of affective filter. The results evinced how the acculturation dilemmas faced by indigenous English learners prevented effective second language acquisition. From this, appropriate solutions may be formulated to ensure successful acculturation in the classroom.
Image Captioning With Controllable and Adaptive Length Levels

Image captioning is one of the fundamental problems of computer vision and has drawn great attention over the years. However, most existing methods in image captioning focus on improving the quality of the image captions, while ignoring the ability of controlling the caption style. In this work, we aim to improve the controllability of image captioning methods, especially, by choosing to describe the image either roughly or in detail. We find this can be achieved by adding a simple length level embedding into existing models, which enables them to generate length-controllable captions describing the image at a specified level of detail, and further improve the diversity. On top of it, we propose to learn a length-level reranking transformer that captures the correlation between the semantic complexities of the image and text modalities, which can be used to select the most suitable length level for each image to make the captions informative while not being redundant. Moreover, when the length of the generated captions grows, existing methods usually suffer from a linearly increased computational complexity due to their autoregressive (AR) nature. To aid this, we devise a non-autoregressive (NAR) approach that generates captions in a length-irrelevant complexity. Besides, we propose a refinement-enhanced sequence training scheme and also adopt a sequence-level knowledge distillation technique for the training of our NAR model to bridge its performance gap with the AR models. In the experiments, our length-controllable models not only achieve SOTA performance in terms of caption quality on the MS COCO dataset but more importantly, generate controllable and diverse image captions. Specifically, our NAR model outperforms the AR baselines in terms of controllability and diversity, and also significantly improves the decoding efficiency for longer captions. By further applying the sequence-level training schemes, the caption quality of our NAR model improves clearly and is competitive with the state-of-the-art AR baselines.