Reviewerly/exHarmony huggingface dataset:
Robust and optimal neighborhood graph learning for multi-view clustering

In recent years, researchers have proposed many graph-based multi-view clustering (GMC) algorithms to solve the multi-view clustering (MVC) problem. However, there are still some limitations in the existing GMC algorithm. In these algorithms, a graph is usually constructed to represent the relationship between the samples in a view; however, it cannot represent the relationship very well since it is often preconstructed. In addition, these algorithms ignore the robustness problem of each graph and high-level information between different graphs. Then, in the paper, we propose a novel MVC method, i.e., robust and optimal neighborhood graph learning for MVC (RONGL/MVC). Specifically, we first build an initial graph for each view. However, these initial graphs cannot represent the relationship between the samples in each view well, so we look for the optimal graph with k connected components in the neighborhood of each initial graph, where k is the number of clusters. Then, to improve the robustness of RONGL/MVC, we reconstruct the optimal graph with the self-representation matrix. Furthermore, we stack all the self-representation matrices into a tensor and impose the tensor low-rank constraint, which can maximize consistent features and explore the high-order relationship between optimal graphs. In addition, we provide an optimization strategy by utilizing the Augmented Lagrange Multiplier (ALM) method. Experimental results on several datasets indicate that RONGL/MVC outperforms state-of-the-art methods.
Hieraledger: Towards malicious gateways in appendable-block blockchain constructions for IoT

Appendable-block blockchain was established in an effort to standardize a blockchain architecture for heterogeneous Internet of Things devices. In current constructions, the main disadvantage lies in the full trust of gateways, which can launch coordinated and individual eclipse attacks against IoT sensing devices without being caught. This paper introduces Hieraledger, a novel commitment protocol for exposing malicious gateways in appendable-block blockchain constructions. To minimize the likelihood of eclipse attacks, each sensing device selects more than one gateway based on a novel reputation scoring model and the self-valuation of its privacy. To expose malicious gateways, our protocol employs fail-stop signatures thwarting signature forging, cryptographic hash functions, and a novel Merkle appendable tree. For each sensing device, we generate a resource-aware repudiable cryptographic proof of origin evidence. For gateway nodes, we generate a chain of commitment-based non-repudiable cryptographic proofs of delivery evidence. To demonstrate the security and robustness of our scheme, we analyze it under a solid theoretical security model. Based on our findings, our protocol enables sensing devices to seek arbitration and expose malicious gateways to the network. The present study is a further addition to the growing body of research into privacy preserving blockchain solutions for the Internet of Things
Two‐Qubit Steerability, Nonlocality, and Average Steered Coherence under Classical Dephasing Channels

Abstract When two qubits are prepared in a mixture of two Bell states and exposed to local transmission channels, the dynamics of steerability, Bell nonlocality, and average steered coherence are investigated. Disorders are assumed to influence the channels, resulting in either Markovian Ornstein–Uhlenbeck noise or non‐Markovian static noise in two models: a single noisy channel or two local noisy channels. Their findings show that the type and number of classical channels, noise, and initial state must be in an optimal setting in order to preserve quantum correlations.
Enhancing Text Classification by Graph Neural Networks With Multi-Granular Topic-Aware Graph

Text classification based on graph neural networks (GNNs) has been widely studied by virtue of its potential to capture complex and across-granularity relations among texts of different types from learning on a text graph. Existing methods typically construct text graphs based on words-documents to capture relevant intra-class document representations among the same documents via words-words and words-documents propagation. However, a natural problem is that polysemy words in documents may become an information medium between documents of different categories, promoting heterophily information propagation. The performance of text classification will be somewhat constrained by this issue. This paper proposes a novel text classification method based on GNN from multi-granular topic-aware perspective, referred to as Text-MGNN. Specifically, topic nodes are introduced to build a triple node set of “word, document, topic,” and multi-granularity relations are modeled on a text graph for this triple node set. The introduction of topic nodes has three significant advantages. The first is to strengthen the propagation of topics, words, and documents. The second is to enhance class-aware representation learning. The final is to mitigate the effect of heterophily information caused by polysemy words. Extensive experiments are conducted on three real-world datasets. Results validate that our proposed method outperforms 11 baselines methods.
Understanding the effects of hand design on embodiment in virtual reality

Abstract Understanding user perceptions of interacting with the virtual world is one of the research focuses in recent years, given the rapid proliferation of virtual reality (VR) and driven to establish the metaverse. Users can generate a familiar connection between their bodies and the virtual world by being embodied in virtual hands, and hand representations can induce users’ embodiment in VR. The sense of embodiment represents the cognitive awareness of one's manifestation and includes three subcomponents: the sense of body ownership, agency and self-location. There is insufficient evidence in the literature about the effects of hand designs on the embodiment, especially based on studying its three subcomponents. This study investigates how virtual hand designs with five realism levels influence the three subcomponents of embodiment in VR. This research employs a self-report questionnaire commonly used in the literature to assess embodiment and evaluates agency and self-location by introducing implicit methods (intentional binding and proprioceptive measurement) derived from psychology. Besides, the objective data of eye tracking is used to explore the connection between embodiment and hand designs, and classifying participants’ eye tracking data to help analyze the link between embodiment and user attention. Overall, this research makes a major contribution through a systematic exploration of users’ embodied experience in VR and offers important evidence of the effects of virtual hand designs on body ownership, agency, and self-location, respectively. In addition, this study provides a valuable reference for further investigation of embodiment through implicit and objective methods, and practical design recommendations for virtual hand design in VR applications.
Toward Highly Efficient Multimode Superconducting Quantum Memory

Microwave quantum memory promises advanced capabilities for noisy intermediate-scale superconducting quantum computers. Existing approaches to microwave quantum memory lack complete combination of high efficiency, long storage time, noiselessness and multi-qubit capacity. Here we report an efficient microwave broadband multimode quantum memory. The memory stores two spectral modes of single photon level microwave radiation in on-chip system of eight coplanar superconducting resonators. Single mode storage shows a power efficiency of up to $60\pm 3\%$ at single photon energy and more than $73\pm 3\%$ at higher intensity. The demonstrated efficiency is an order of magnitude larger than the previously reported multimode microwave quantum memory. The noiseless character of the storage is confirmed by coherent state quantum process tomography. The demonstrated results pave the way to further increase in efficiency and hence building a practical multimode microwave memory for superconducting quantum circuits.
Machine Talk: How Verbal Embodiment in Conversational AI Shapes Consumer–Brand Relationships

Abstract This research shows that AI-based conversational interfaces can have a profound impact on consumer–brand relationships. We develop a conceptual model of verbal embodiment in technology-mediated communication that integrates three key properties of human-to-human dialogue—(1) turn-taking (i.e., alternating contributions by the two parties), (2) turn initiation (i.e., the act of initiating the next turn in a sequence), and (3) grounding between turns (i.e., acknowledging the other party’s contribution by restating or rephrasing it). These fundamental conversational properties systematically shape consumers’ perception of an AI-based conversational interface, their perception of the brand that the interface represents, and their behavior in connection with that brand. Converging evidence from four studies shows that these dialogue properties enhance the perceived humanness of the interface, which in turn promotes more intimate consumer–brand relationships and more favorable behavioral brand outcomes (greater recommendation acceptance, willingness to pay a price premium, brand advocacy, and brand loyalty). Moreover, we show that these effects are reduced in contexts requiring less mutual understanding between the consumer and the brand. This research highlights how fundamental principles of human-to-human communication can be harnessed to design more intimate consumer–brand interactions in an increasingly AI-driven marketplace.
Expl(AI)ned: The Impact of Explainable Artificial Intelligence on Users’ Information Processing

Although future regulations increasingly advocate that AI applications must be interpretable by users, we know little about how such explainability can affect human information processing. By conducting two experimental studies, we help to fill this gap. We show that explanations pave the way for AI systems to reshape users' understanding of the world around them. Specifically, state-of-the-art explainability methods evoke mental model adjustments that are subject to confirmation bias, allowing misconceptions and mental errors to persist and even accumulate. Moreover, mental model adjustments create spillover effects that alter users' behavior in related but distinct domains where they do not have access to an AI system. These spillover effects of mental model adjustments risk manipulating user behavior, promoting discriminatory biases, and biasing decision making. The reported findings serve as a warning that the indiscriminate use of modern explainability methods as an isolated measure to address AI systems' black-box problems can lead to unintended, unforeseen problems because it creates a new channel through which AI systems can influence human behavior in various domains.
A Method based on Evolutionary Algorithms and Channel Attention Mechanism to Enhance Cycle Generative Adversarial Network Performance for Image Translation

A Generative Adversarial Network (GAN) can learn the relationship between two image domains and achieve unpaired image-to-image translation. One of the breakthroughs was Cycle-consistent Generative Adversarial Networks (CycleGAN), which is a popular method to transfer the content representations from the source domain to the target domain. Existing studies have gradually improved the performance of CycleGAN models by modifying the network structure or loss function of CycleGAN. However, these methods tend to suffer from training instability and the generators lack the ability to acquire the most discriminating features between the source and target domains, thus making the generated images of low fidelity and few texture details. To overcome these issues, this paper proposes a new method that combines Evolutionary Algorithms (EAs) and Attention Mechanisms to train GANs. Specifically, from an initial CycleGAN, binary vectors indicating the activation of the weights of the generators are progressively improved upon by means of an EA. At the end of this process, the best-performing configurations of generators can be retained for image generation. In addition, to address the issues of low fidelity and lack of texture details on generated images, we make use of the channel attention mechanism. The latter component allows the candidate generators to learn important features of real images and thus generate images with higher quality. The experiments demonstrate qualitatively and quantitatively that the proposed method, namely, Attention evolutionary GAN (AevoGAN) alleviates the training instability problems of CycleGAN training. In the test results, the proposed method can generate higher quality images and obtain better results than the CycleGAN training methods present in the literature, in terms of Inception Score (IS), Fréchet Inception Distance (FID) and Kernel Inception Distance (KID).
The Effectiveness of Educational Robots in Improving Learning Outcomes: A Meta-Analysis

Numerous studies have been conducted to investigate the potential effect of educational robots, but what appears to be missing is an up-to-date and thorough review of the learning effectiveness of educational robots and the various influencing factors. In this study, a meta-analysis was conducted to systematically synthesize studies’ findings on the effects of educational robots on students’ learning outcomes. After searching for randomized studies describing educational robots interventions to improve learning outcomes, 34 effect sizes described in 17 articles met the selection criteria. The results of our work evidence a moderate but significantly positive effect of educational robots on learning outcomes (g = 0.57, 95% CI [0.49, 0.65], p &lt; 0.00001). Moreover, moderator analyses were conducted to investigate important factors relating to the variation of the impact, including educational level and assessment type. Based on the findings of this study, we provide researchers and practitioners with insights into what characteristics of educational robot interventions appear to benefit students’ learning outcomes and how pedagogical approaches can be applied in various educational settings to guide the design of future educational robot interventions.
The correlation between proprioceptive drift and subjective embodiment during the rubber hand illusion: A meta-analytic approach

In the rubber hand illusion (RHI), participants see a fake hand touched synchronously with their real hand, which is hidden from view. The three-way interaction between vision, touch, and proprioception induces the sensation that the dummy hand belongs to oneself (i.e., subjective embodiment) and the illusory displacement of the real hand towards the fake one (i.e., proprioceptive drift). In the literature, there are mixed results (some positive and some null) regarding the existence of a relationship between subjective embodiment and proprioceptive drift. We conducted a Bayesian meta-analysis to tackle this issue quantitatively. Evidence strongly favours the presence of a correlation between subjective embodiment and proprioceptive drift, supporting the model proposed by Botvinick and Cohen in 1998. However, the correlation is around .35, a value suggesting that the two indices capture different facets of the RHI. This result clarifies the association between the illusory effects produced by the RHI and may be helpful for designing studies having appropriate statistical power.
Identifying the Factors to Enhance Digital Competence of Students at Vocational Training Institutes

Digital competence is a quite complex and evolving concept, difficult to be defined by using a single definition. The European Digital Competence Framework (DigComp) provides a consistent framework to guide cross-country measurements of digital competence. Even though there are many studies on measuring the digital competence level, there is a limited focus on the factors that may influence each DigComp area. The aim of this study is to examine the factors that affect the different areas of digital competence level of students at Vocational Training Institutes. For the purposes of the study, a set of laboratory tests was developed. The study employs parametric tests—t-test, one-way ANOVA, and Bonferroni correction—to examine those factors. According to the findings: (1) ‘age’ affects the ‘Communication and collaboration’ area of DigComp; (2) ‘educational level’ affects ‘Digital content creation’; (3) ‘specialization’ affects ‘Communication and collaboration’; (4) ‘possession and use of PC’ affects ‘Information and data literacy’, as well as ‘Problem solving’; (5) ‘possession and use of smartphones’ affects ‘Communication and collaboration’; and (6) ‘Internet use’ affects ‘Information and data literacy’. The results suggest that educational institutes should update their curricula to achieve a sufficient level of digital competence for their graduates by developing tailored made courses to reinforce the skills related to those areas separately, and employ educational practices to confront influential factors, such as age and use of technology. Employees’ training processes should also consider updating reskilling and upskilling programs towards improving the five DigComp areas.
Combining Diverse Meta-Features to Accurately Identify Recurring Concept Drift in Data Streams

Learning from streaming data is challenging as the distribution of incoming data may change over time, a phenomenon known as concept drift. The predictive patterns, or experience learned under one distribution may become irrelevant as conditions change under concept drift, but may become relevant once again when conditions reoccur. Adaptive learning methods adapt a classifier to concept drift by identifying which distribution, or concept , is currently present in order to determine which experience is relevant. Identifying a concept requires some representation to be stored for comparison, with the quality of the representation being key to accurate identification. Existing concept representations are based on meta-features, efficient univariate summaries of a concept. However, no single meta-feature can fully represent a concept, leading to severe accuracy loss when existing representations cannot describe concept drift. To avoid these failure cases, we propose the first general framework for combining a diverse range of meta-features into a single representation. We solve two main challenges, first presenting a method of efficiently computing, storing, and querying an arbitrary set of meta-features as a single representation, showing that a combination of meta-features may successfully avoid failure cases seen with existing methods. Second, we present the first method for dynamically learning which meta-features distinguish concepts in any given dataset, significantly improving performance. Our proposed approach enables state-of-the-art feature selection methods, such as mutual information, to be applied to concept representation meta-features for the first time. We investigate tradeoffs between memory budget and classification performance, observing accuracy increases of up to 16% by dynamically weighting the contribution of each meta-feature.
Supporting mental health self-care discovery through a chatbot

Good mental health is imperative for one's wellbeing. While clinical mental disorder treatments exist, self-care is an essential aspect of mental health. This paper explores the use and perceived trust of conversational agents, chatbots, in the context of crowdsourced self-care through a between-subjects study (N = 80). One group used a standalone system with a conventional web interface to discover self-care methods. The other group used the same system wrapped in a chatbot interface, facilitating utterances and turn-taking between the user and a chatbot. We identify the security and integrity of the systems as critical factors that affect users' trust. The chatbot interface scored lower on both these factors, and we contemplate the potential underlying reasons for this. We complement the quantitative data with qualitative analysis and synthesize our findings to identify suggestions for using chatbots in mental health contexts.
Social media discourse and voting decisions influence: sentiment analysis in tweets during an electoral period

Abstract In a time where social media is fundamental for any political campaign and to share a message with an electoral audience, this study searches for a conclusion of the actual persuasion capacity of social media in the electors when they need to decide whom to vote for as their next government. For this, it compares the sentiment that Social Media users demonstrated during an electoral period with the actual results of those elections. For this analysis, it was used, as a case study, tweets mentioning the two major English parties, Conservative and Labor, their respective candidates for the position of prime minister, and terms that identified their political campaign during the electoral period of the General Elections of the United Kingdom that occurred on December 12, 2019. Data were collected using R. The treatment and analysis were done with R and RapidMiner. Results show that tweets’ sentiment is not a reliable election results predictor. Additionally, results also show that it is impossible to state that social media impacts voting decisions. At least not from the polarity of the sentiment of opinions on social media.
Enhancing Small Medical Dataset Classification Performance Using GAN

Developing an effective classification model in the medical field is challenging due to limited datasets. To address this issue, this study proposes using a generative adversarial network (GAN) as a data-augmentation technique. The research aims to enhance the classifier’s generalization performance, stability, and precision through the generation of synthetic data that closely resemble real data. We employed feature selection and applied five classification algorithms to thirteen benchmark medical datasets, augmented using the least-square GAN (LS-GAN). Evaluation of the generated samples using different ratios of augmented data showed that the support vector machine model outperforms other methods with larger samples. The proposed data augmentation approach using a GAN presents a promising solution for enhancing the performance of classification models in the healthcare field.
Deep Learning Based Social Bot Detection on Twitter

While social bots can be used for various good causes, they can also be utilized to manipulate people and spread malware. Therefore, it is crucial to detect bots running on social media platforms. However, social bots are increasingly successful in creating human-like messages with the recent developments in artificial intelligence. Thus, we need more sophisticated solutions to detect them. In this study, we propose a novel deep learning architecture in which three long short-term memory (LSTM) models and a fully connected layer are utilized to capture complex social media activity of humans and bots. Since our architecture involves many components connected at different levels, we explore three learning schemes to train each component effectively. In our extensive experiments, we analyze the impact of each component of our architecture on classification accuracy using four different datasets. Furthermore, we show that our proposed architecture outperforms all baselines used in our experiments.