Reviewerly/exHarmony huggingface dataset:
Examining self-congruence between user and avatar in purchasing behavior from the metaverse to the real world

The aim of this study was to examine the role of “avatar” in the metaverse, specifically, by exploring how appearance similarity with an avatar affects virtual product purchase intention, which is mediated by self-congruence and flow. Further, this study also attempted to find how human behavior in the virtual world could affect behavior in the real world. An online survey was administered to a total of 164 users of a popular South Korean metaverse platform ZEPETO. The findings indicated that the appearance similarity with the avatar influenced virtual product purchase intention, and this relationship was fully mediated by avatar-self congruence and flow. This study also found that virtual product purchase intention could influence real product purchase intention. Based on these findings, it is suggested high-quality avatar customization is critical for users, and retail brands can utilize metaverse as a new marketing tool and as a testbed for new products.
H2Former: An Efficient Hierarchical Hybrid Transformer for Medical Image Segmentation

Accurate medical image segmentation is of great significance for computer aided diagnosis. Although methods based on convolutional neural networks (CNNs) have achieved good results, it is weak to model the long-range dependencies, which is very important for segmentation task to build global context dependencies. The Transformers can establish long-range dependencies among pixels by self-attention, providing a supplement to the local convolution. In addition, multi-scale feature fusion and feature selection are crucial for medical image segmentation tasks, which is ignored by Transformers. However, it is challenging to directly apply self-attention to CNNs due to the quadratic computational complexity for high-resolution feature maps. Therefore, to integrate the merits of CNNs, multi-scale channel attention and Transformers, we propose an efficient hierarchical hybrid vision Transformer (H2Former) for medical image segmentation. With these merits, the model can be data-efficient for limited medical data regime. The experimental results show that our approach exceeds previous Transformer, CNNs and hybrid methods on three 2D and two 3D medical image segmentation tasks. Moreover, it keeps computational efficiency in model parameters, FLOPs and inference time. For example, H2Former outperforms TransUNet by 2.29% in IoU score on KVASIR-SEG dataset with 30.77% parameters and 59.23% FLOPs.
Robust Semantic Communications With Masked VQ-VAE Enabled Codebook

Although semantic communications have exhibited satisfactory performance on a large number of tasks, the impact of semantic noise and the robustness of the systems have not been well investigated. Semantic noise refers to the misleading between the intended semantic symbols and received ones, thus causes the failure of tasks. In this paper, we first propose a framework for the robust end-to-end semantic communication systems to combat the semantic noise. In particular, we analyze sample-dependent and sample-independent semantic noise. To combat the semantic noise, the adversarial training with weight perturbation is developed to incorporate the samples with semantic noise in the training dataset. Then, we propose to mask a portion of the input, where the semantic noise appears frequently, and design the masked vector quantized-variational autoencoder (VQ-VAE) with the noise-related masking strategy. We use a discrete codebook shared by the transmitter and the receiver for encoded feature representation. To further improve the system robustness, we develop a feature importance module (FIM) to suppress the noise-related and task-unrelated features. Thus, the transmitter simply needs to transmit the indices of these important task-related features in the codebook. Simulation results show that the proposed method can be applied in many downstream tasks and significantly improve the robustness against semantic noise with remarkable reduction on the transmission overhead.
An Ontology-based Engineering system to support aircraft manufacturing system design

During the conceptual design phase of an aircraft manufacturing system, different industrial scenarios need to be evaluated against performance indicators in a collaborative engineering process. Domain experts’ knowledge and the motivations for decision-making is a crucial asset for enterprises which is challenging to be captured and capitalised. Ontology-based Engineering (OBE) systems emerge as a new generation of Knowledge-based Engineering techniques with advancements of ontology engineering methods and computer science technologies. Ontologies enable to capture both explicit and implicit domain knowledge from historical records and domain experts. These Ontology-based Engineering systems can stand highly complex collaborative design processes involving multidisciplinary stakeholders and various digital tools. This paper proposes a tradespace framework with Ontology-based Engineering features included on top of existing Model-Based System Engineering and interoperability capabilities. These additional Ontology-based Engineering features reuse formalised knowledge via knowledge graph technologies and generative algorithms, changing the cognitive process from the designer, to an automatic process which generates design alternatives for the designer. The tradespace framework is demonstrated in a case study to design the aircraft fuselage orbital joint process, helping the designer to take better strategic decisions at conceptual phase and proving to be an advantageous paradigm for the design process.
An Analysis of Artificial Intelligence Techniques in Surveillance Video Anomaly Detection: A Comprehensive Survey

Surveillance cameras have recently been utilized to provide physical security services globally in diverse private and public spaces. The number of cameras has been increasing rapidly due to the need for monitoring and recording abnormal events. This process can be difficult and time-consuming when detecting anomalies using human power to monitor them for special security purposes. Abnormal events deviate from normal patterns and are considered rare. Furthermore, collecting or producing data on these rare events and modeling abnormal data are difficult. Therefore, there is a need to develop an intelligent approach to overcome this challenge. Many research studies have been conducted on detecting abnormal events using machine learning and deep learning techniques. This study focused on abnormal event detection, particularly for video surveillance applications, and included an up-to-date state-of-the-art that extends previous related works. The major objective of this survey was to examine the existing machine learning and deep learning techniques in the literature and the datasets used to detect abnormal events in surveillance videos to show their advantages and disadvantages and summarize the literature studies, highlighting the major challenges.
The Affordances and Contradictions of AI-Generated Text for Second Language Writers

The public release of ChatGPT has provoked much debate as to the potential consequences of artificial intelligence (AI) for writing education. For second language educators and researchers in particular, AI-generated writing poses a major concern for student learning. However, it is also important to recognize the powerful affordances of ChatGPT for second language writers in their studies and careers. We argue that the value of ChatGPT and similar AI-based writing tools for second language learners must be understood through three contradictions: the “imitation” contradiction, the “rich get richer” contradiction, and the “with or without” contradiction. To most effectively address these contradictions, we propose a five-part pedagogical framework focusing on understanding, accessing, prompting, corroborating, and incorporating AI in the writing process. By facilitating conversations about how to use AI in the classroom, we can better support second language learners’ writing development and prepare these students for a world that increasingly values one’s ability to understand and use AI.
Understanding Blockchain Technology: How It Works and What It Can Do

Blockchain is a chain of blocks that are connected together and are continuously growing by storing transactions on the blocks. This platform uses a decentralized approach that allows the information to be distributed and that each piece of distributed information or commonly known as data have shared ownership. Blockchains holds batches of transactions that are hashed thus providing them security and they are managed by peer-to-peer networks. Blockchain, an information administration method, has the potential to encourage responsibility and openness. In a blockchain, each user of the computer network can access the same copy of the transaction ledger. The blockchain technology has the potential to handle various security attacks as it can eliminate the need of the centralized authority to perform various operations. In the blockchain technology, a number of users participate in transaction verification and validation.
A Comparative Study of Machine Learning Algorithms for Detecting Breast Cancer

Breast cancer poses a major hazard to women, with high morbidity and fatality rates, because there is a lack of reliable prognostic models, clinicians find it challenging to develop a treatment regimen that could increase patient life expectancy. There are required to detect breast cancer early stages so the necessary steps should be taken as early as possible to stop this disease first we need more research in this field. So, in this work, we are trying to build a sustainable machine-learning model which can detect the type of breast cancer whether benign or malignant. Through the detection, we proposed the best model which can detect this outbreak efficiently. In our study, we examined the performance of five machine learning algorithms (XGBoost, Naïve Bayes, Decision Tree, Random Forest, and Logistic Regression) in predicting human health behavior. Among these algorithms, XGBoost had the highest accuracy (95.42%) and performed well in terms of sensitivity (98.5%), specificity (97.5%), and F-1 score (99%). Our findings suggest that XGBoost has promising potential in predicting breast cancer, but further research is needed to develop and apply it for commercial use in the healthcare industry.
Quantum Machine Learning in Healthcare: Developments and Challenges

Machine learning is playing a very significant role to process voluminous data and its classification in a variety of domains. Due to better performance and rapid development in the last decade, quantum computing is also benefiting many areas. With the amalgamation of these two technologies, a new domain for processing big data more efficiently and accurately has evolved, known as quantum machine learning. Healthcare is one of the prominent domains where a huge volume of data is produced from several processes. Efficient processing of healthcare data and records is very important to facilitate many biological and medical processes to provide better treatments to patients. The fundamental aim of this paper is to present a state-of-the-art review of quantum computing concepts, quantum machine learning framework, and the various applications of quantum machine learning in the domain of healthcare. The comparison of QML healthcare models with ML-based healthcare applications is discussed in this work. The authors also present the various challenges faced in the deployment of quantum machine learning algorithms in the domain of healthcare and possible future research directions.
MEMS based Sensor Robot for Immobilized Persons

For directing the robot via motion, a variety of approaches are used. Some of the work suffers from layout coordination, which prevents the mobility inputs from becoming dynamic. To continuously signal the robot, the proposed framework uses a machine interface device. The study provides an example of the efficacy of MEMS -based gesture-controlled Robots that communicate wirelessly and can be controlled with our hands or gestures rather than the standard keypad or buttons. In the upcoming years, it's possible to create bots that can interact with people naturally. As a result, our target uses gesture interfaces based on hand motion. A novel technique-based gesture recognition is created to identify the distinctive action indicators made through hand movement. MEMS sensors are utilized for this task, while ultrasonic sensors are utilized for certain operations. This study has designed a wheelchair prototype that is only gesture- controlled, adaptable to different users, and resistant to vibration, light changes, and user movement. The camera installed on the user's hand and the user's hand angle changes detection are the key to this flexibility. For wheelchair control, a gesture control sensor examines the user's hand position. Results of our gesture detection technology, in comparison to others, demonstrated its superiority. Additionally, the impact of changing the illumination has been assessed.
A Study on Cervical Cancer Prediction using Various Machine Learning Approaches

Cervical cancer is a major cause of mortality for women, and early detection is crucial for successful treatment Recent studies have investigated the use of machine learning for early detection of cervical cancer, but challenges remain. This paper evaluates the performance of different machine learning algorithms, including logistic regression, bagging, random forest, and XG Boost, for predicting cervical cancer. The study analyzes challenges in working with cervical cancer data, such as dealing with imbalanced datasets and limited data availability. To address these challenges, the paper proposes an approach that combines the strengths of the different algorithms to develop a more accurate and reliable model for early detection of cervical cancer. To assess the effectiveness of the proposed approach, the study uses standard metrics, including accuracy, precision, recall, and F1 score. The findings indicate that the proposed approach outperforms the individual machine learning algorithms in terms of predictive accuracy and precision. The paper emphasizes the need for further research in this area and highlights the potential of machine learning to enhance the early detection of cervical cancer. By proposing a new approach that addresses the challenges faced by existing methods, the paper aims to contribute to efforts to improve cervical cancer detection and treatment.
Collaborating Across Realities: Analytical Lenses for Understanding Dyadic Collaboration in Transitional Interfaces

Transitional Interfaces are a yet underexplored, emerging class of cross-reality user interfaces that enable users to freely move along the reality-virtuality continuum during collaboration. To analyze and understand how such collaboration unfolds, we propose four analytical lenses derived from an exploratory study of transitional collaboration with 15 dyads. While solving a complex spatial optimization task, participants could freely switch between three contexts, each with different displays (desktop screens, tablet-based augmented reality, head-mounted virtual reality), input techniques (mouse, touch, handheld controllers), and visual representations (monoscopic and allocentric 2D/3D maps, stereoscopic egocentric views). Using the rich qualitative and quantitative data from our study, we evaluated participants' perceptions of transitional collaboration and identified commonalities and differences between dyads. We then derived four lenses including metrics and visualizations to analyze key aspects of transitional collaboration: (1) place and distance, (2) temporal patterns, (3) group use of contexts, (4) individual use of contexts.
Evaluating Large Language Models in Generating Synthetic HCI Research Data: a Case Study

Collecting data is one of the bottlenecks of Human-Computer Interaction (HCI) research. Motivated by this, we explore the potential of large language models (LLMs) in generating synthetic user research data. We use OpenAI's GPT-3 model to generate open-ended questionnaire responses about experiencing video games as art, a topic not tractable with traditional computational user models. We test whether synthetic responses can be distinguished from real responses, analyze errors of synthetic data, and investigate content similarities between synthetic and real data. We conclude that GPT-3 can, in this context, yield believable accounts of HCI experiences. Given the low cost and high speed of LLM data generation, synthetic data should be useful in ideating and piloting new experiments, although any findings must obviously always be validated with real data. The results also raise concerns: if employed by malicious users of crowdsourcing services, LLMs may make crowdsourcing of self-report data fundamentally unreliable.
The impact of central bank digital currency news on the stock and cryptocurrency markets: Evidence from the TVP-VAR model

This study employs a non-linear framework to investigate the impacts of central bank digital currency (CBDC) news on the financial and cryptocurrency markets. The time-varying vector autoregressive (TVP-VAR) model developed by Primiceri (2005) is estimated based on weekly data from the first week of January 2015 to the last week of December 2021. The vector of endogenous variables in the VAR estimation contains the Central Bank Digital Currency uncertainty index (CBDCU), cryptocurrency policy uncertainty index, S&P 500 index, VIX, and Bitcoin price. The TVP-VAR model's time-varying responses demonstrated that the reactions of the cryptocurrency market to central bank digital currency announcements vary remarkably over time. The impacts of the CBDC shocks on the financial market have been increasingly visible during the COVID-19 pandemic. According to the time-varying forecast error decompositions, CBDCU and VIX shocks have accounted for most of the variance in cryptocurrency uncertainty and Bitcoin return shocks, notably during the COVID-19 period.
Privacy protection in intelligent vehicle networking: A novel federated learning algorithm based on information fusion

Federated learning is an effective technique to solve the problem of information fusion and information sharing in intelligent vehicle networking. However, most of the existing federated learning algorithms generally have the risk of privacy leakage. To address this security risk, this paper proposes a novel personalized federated learning with privacy preservation (PDP-PFL) algorithm based on information fusion. In the first stage of its execution, the new algorithm achieves personalized privacy protection by grading users’ privacy based on their privacy preferences and adding noise that satisfies their privacy preferences. In the second stage of its execution, PDP-PFL performs collaborative training of deep models among different in-vehicle terminals for personalized learning, using a lightweight dynamic convolutional network architecture without sharing the local data of each terminal. Instead of sharing all the parameters of the model as in standard federated learning, PDP-PFL keeps the last layer local, thus adding another layer of data confidentiality and making it difficult for the adversary to infer the image of the target vehicle terminal. It trains a personalized model for each vehicle terminal by “local fine-tuning”. Based on experiments, it is shown that the accuracy of the proposed new algorithm for PDP-PFL calculation can be comparable to or better than that of the FedAvg algorithm and the FedBN algorithm, while further enhancing the protection of data privacy.
FedMed-GAN: Federated domain translation on unsupervised cross-modality brain image synthesis

Utilizing multi-modal neuroimaging data is proven to be effective in investigating human cognitive activities and certain pathologies. However, it is not practical to obtain the full set of paired neuroimaging data centrally since the collection faces several constraints, e.g., high examination cost, long acquisition time, and image corruption. In addition, these data are dispersed into different medical institutions and thus cannot be aggregated for centralized training considering the privacy issues. There is a clear need to launch federated learning and facilitate the integration of dispersed data from different institutions. In this paper, we propose a new benchmark for federated domain translation on unsupervised brain image synthesis (FedMed-GAN) to bridge the gap between federated learning and medical GAN. FedMed-GAN mitigates the mode collapse without sacrificing the performance of generators, and is widely applied to different proportions of unpaired and paired data with variation adaptation properties. We treat the gradient penalties using the federated averaging algorithm and then leverage the differential privacy gradient descent to regularize the training dynamics. A comprehensive evaluation is provided for comparing FedMed-GAN and other centralized methods, demonstrating that the proposed algorithm outperforms the state-of-the-art. Our code is available at: https://github.com/M-3LAB/FedMed-GAN.
MetaOmniCity: Toward Immersive Urban Metaverse Cyberspaces Using Smart City Digital Twins

The movie — The Matrix (1999) — boosted our imagination about how further we can be immersed within the cyber world, i.e., how further the cyber world can be indistinguishable from the real world. Nobody, including the creators, had expected that the aspirational fictional virtual worlds such as “ActiveWorlds (1995)” and “Second Life (2003)” would impact the way of experiencing our real urban environments with many urban experiences embedded into a rich featured 3D environment. Are we going to stay as ourselves while existing in a mirror world? Are the fabricated, created imaginary worlds becoming a part of real worlds or vice versa? The recent once-in-a-lifetime pandemic has confirmed the importance of location and time-independent Digital Twins (DTs) (i.e., virtual scale models) of cities and their automated services which can provide everybody with equity and accessibility. This study analyses how the metaverse (3D elevation of linear Internet), that aims to build high-fidelity virtual worlds with which to interact with, can be engaged within the Smart City (SC) ecosystem with high immersive Quality of Experiences (QoE) leading to increased Quality of Life (QoL). An urban metaverse ecosystem framework — MetaOmniCity — is designed to demonstrate a variety of insights and orchestrational directions for policymakers, city planners and all other stakeholders about how to transform data-driven SCs with DTs into virtually inhabitable cities with a network of shared urban experiences from a metaverse point of view. MetaOmniCity, allowing the metaversification of cities with granular virtual societies, i.e., MetaSocieties, and eliminating the boundaries between the real world and their virtual counterparts can be shaped to the particular requirements and features of cities. Urban metaverse shared worlds, an extension of residents and urban society, where the virtual and the physically real blend and are more organically integrated, would impact urban ways of living significantly with many practical implementations. This would open doors for potential businesses with tremendous global economic value by democratising skills/assets within an urban environment, paving the way for immersive globalisation with the bigger and richer metaverse of Country (MoC) and metaverse of World (MoW) being an immersive DT of the broader universe with tangibly connected cities. MetaOmniCity is expected to accelerate the building, deployment, and adoption of immersive urban metaverse networks for citizens to interface with as an extension of real social and individual experiences.