Reviewerly/exHarmony huggingface dataset:
Fashion marketing in the metaverse

ABSTRACTThe metaverse is a shared virtual environment with significant implications for several aspects of businesses. This conceptual paper focuses on how fashion brands can benefit from the metaverse's vast opportunities. Reviewing the key publications in this field, it identifies the key stakeholders who shape fashion marketing in the metaverse: fashion brands, tech developers, and consumers. Furthermore, the paper outlines the opportunities and challenges of fashion marketing in the metaverse. The study presents a conceptual framework that shows the collaborative relationships between critical stakeholders involved in fashion marketing to take such opportunities and overcome potential challenges. The framework provides a theoretical foundation for research and practice in fashion marketing within the metaverse. The study contributes to the expanding body of research on the intersection of the metaverse and fashion marketing, offering valuable managerial insights for fashion brands and tech developers and a rich agenda for future research.KEYWORDS: Metaversefashion marketingconceptual frameworkstakeholder collaborationtechnological infrastructureimmersive time Disclosure statementNo potential conflict of interest was reported by the author(s).
Using Adaptiveness and Causal Superpositions Against Noise in Quantum Metrology

We derive new bounds on achievable precision in the most general adaptive quantum metrological scenarios. The bounds are proven to be asymptotically saturable and equivalent to the known parallel scheme bounds in the limit of a large number of channel uses. This completely solves a long-standing conjecture in the field of quantum metrology on the asymptotic equivalence between parallel and adaptive strategies. The new bounds also allow us to easily assess the potential benefits of invoking nonstandard causal superposition strategies, for which we prove, similarly to the adaptive case, the lack of asymptotic advantage over the parallel ones.
Real-Time Onboard Object Detection for Augmented Reality: Enhancing Head-Mounted Display with YOLOv8

This paper introduces a software architecture for real-time object detection using machine learning (ML) in an augmented reality (AR) environment. Our approach uses the recent state-of-the-art YOLOv8 network that runs onboard on the Microsoft HoloLens 2 head-mounted display (HMD). The primary motivation behind this research is to enable the application of advanced ML models for enhanced perception and situational awareness with a wearable, hands-free AR platform. We show the image processing pipeline for the YOLOv8 model and the techniques used to make it real-time on the resource-limited edge computing platform of the headset. The experimental results demonstrate that our solution achieves real-time processing without needing offloading tasks to the cloud or any other external servers while retaining satisfactory accuracy regarding the usual mAP metric and measured qualitative performance.
Study on the brightness and graphical display object directions of the Single-Gaze-Gesture user interface

Single gaze gestures (SGGs), as the basic unit of gaze gestures, interact with graphical display objects (GDOs) on a graphical user interface (GUI) through user saccades to control the system. The brightness design of the interface and saccade direction can significantly affect interaction performance and user experience. Therefore, this study adopts the method of ergonomic experiments combined with subjective evaluation, proposing the design suggestions for the background brightness (BB), brightness contrast (BC), GDO (completion field) directions, and target brightness (TB) of the gaze gesture interface: (1) Background brightness: To ensure the efficiency and experience in actual use, the background of the interface should choose a brightness value slightly lower than 50%; (2) Brightness contrast: At 25% BB, it is advisable to choose a brightness value between 21.5% and 43.5%, and the best choice is 33.25%. Under the condition of BB above 50%, the brightness contrast value of 50% can meet the requirements of the operation task; (3) Target brightness: In the gaze gesture interface design, light color should be used to mark the target; (4) GDO Directions: The GDO should be placed on the upper side of the initiation field (IF), and it is best to be directly above the IF. The contribution of this research is the proposal of an interface design strategy that is beneficial for improving the performance and experience of gaze gesture interaction and can provide future directions and recommendations for interface design.
Deepfake in the Metaverse: Security Implications for Virtual Gaming, Meetings, and Offices

The metaverse has gained significant attention from various industries due to its potential to create a fully immersive and interactive virtual world. However, the integration of deepfakes in the metaverse brings serious security implications, particularly with regard to impersonation. This paper examines the security implications of deepfakes in the metaverse, specifically in the context of gaming, online meetings, and virtual offices. The paper discusses how deepfakes can be used to impersonate in gaming scenarios, how online meetings in the metaverse open the door for impersonation, and how virtual offices in the metaverse lack physical authentication, making it easier for attackers to impersonate someone. The implications of these security concerns are discussed in relation to the confidentiality, integrity, and availability (CIA) triad. The paper further explores related issues such as the darkverse, and digital cloning, as well as regulatory and privacy concerns associated with addressing security threats in the virtual world.
Prompting meaning: a hermeneutic approach to optimising prompt engineering with ChatGPT

Abstract Recent advances in natural language generation (NLG), such as public accessibility to ChatGPT, have sparked polarised debates about the societal impact of this technology. Popular discourse tends towards either overoptimistic hype that touts the radically transformative potentials of these systems or pessimistic critique of their technical limitations and general ‘stupidity’. Surprisingly, these debates have largely overlooked the exegetical capacities of these systems, which for many users seem to be producing meaningful texts. In this paper, we take an interdisciplinary approach that combines hermeneutics—the study of meaning and interpretation—with prompt engineering—task descriptions embedded in input to NLG systems—to study the extent to which a specific NLG system, ChatGPT, produces texts of hermeneutic value. We design prompts with the goal of optimising hermeneuticity rather than mere factual accuracy, and apply them in four different use cases combining humans and ChatGPT as readers and writers. In most cases, ChatGPT produces readable texts that respond clearly to our requests. However, increasing the specificity of prompts’ task descriptions leads to texts with intensified neutrality, indicating that ChatGPT’s optimisation for factual accuracy may actually be detrimental to the hermeneuticity of its output.
Deep learning based multi-view stereo matching and 3D scene reconstruction from oblique aerial images

In this paper, we propose a practical three-dimensional (3D) real-scene reconstruction framework named Deep3D, which is paired with a deep learning based multi-view stereo (MVS) matching model named the adaptive multi-view aggregation matching (Ada-MVS) model, to obtain a 3D textured mesh model from multi-view oblique aerial images. Deep3D is the first deep learning based framework for 3D scene reconstruction, in which aerial triangulation and view selection are first performed on the input images, and the depth map of each image is then inferred using the pretrained Ada-MVS model. All the inferred depth maps are then fused into a dense point cloud after filtering the outliers. Finally, the 3D textured mesh is extracted from the dense 3D points as the final product. In the Ada-MVS model, a novel adaptive inter-view aggregation module is specially proposed to address the inconsistent information among oblique views and to fuse the multi-view costs into a robust cost volume. A lightweight recurrent regularization module is also designed for high-efficiency processing of high-capacity aerial images with large depth variations. Moreover, as oblique aerial image datasets are currently lacking, we built a large-scale synthetic multi-view oblique aerial image dataset (WHU-OMVS dataset) for deep learning based model training and methodology evaluation for the task of 3D scene reconstruction. The experimental results show that, firstly, the proposed Ada-MVS model has obvious advantages when used with high-capacity oblique aerial images, compared with several relevant learning-based MVS methods. Secondly, through a comprehensive comparison with popular commercial software packages and open-source solutions, it is shown that the proposed Deep3D framework outperforms all the other solutions in terms of reconstruction quality, and outperforms all the open-source solutions and some of the software packages in terms of efficiency on the WHU-OMVS dataset. Thirdly, the Deep3D framework shows a stable generalization ability and excellent performance when applied to other oblique or nadir aerial images, without any further fine-tuning. The dataset and code will be available at http://gpcv.whu.edu.cn/data.
An extensive benchmark study on biomedical text generation and mining with ChatGPT

In recent years, the development of natural language process (NLP) technologies and deep learning hardware has led to significant improvement in large language models (LLMs). The ChatGPT, the state-of-the-art LLM built on GPT-3.5 and GPT-4, shows excellent capabilities in general language understanding and reasoning. Researchers also tested the GPTs on a variety of NLP-related tasks and benchmarks and got excellent results. With exciting performance on daily chat, researchers began to explore the capacity of ChatGPT on expertise that requires professional education for human and we are interested in the biomedical domain.To evaluate the performance of ChatGPT on biomedical-related tasks, this article presents a comprehensive benchmark study on the use of ChatGPT for biomedical corpus, including article abstracts, clinical trials description, biomedical questions, and so on. Typical NLP tasks like named entity recognization, relation extraction, sentence similarity, question and answering, and document classification are included. Overall, ChatGPT got a BLURB score of 58.50 while the state-of-the-art model had a score of 84.30. Through a series of experiments, we demonstrated the effectiveness and versatility of ChatGPT in biomedical text understanding, reasoning and generation, and the limitation of ChatGPT build on GPT-3.5.All the datasets are available from BLURB benchmark https://microsoft.github.io/BLURB/index.html. The prompts are described in the article.
Toward explicit control between exploration and exploitation in evolutionary algorithms: A case study of differential evolution

Exploration and exploitation are two cornerstones of evolutionary algorithms. An appropriate balance between exploration and exploitation can drive a search process toward global optima with a fast convergence rate. However, this balance is not comprehensively understood, and the issue of how to effectively control it is very challenging. In this paper, a new search framework based on an explicit control strategy that balances the amounts of exploration and exploitation in a search process is proposed. First, an explicit control strategy consisting of three types of transference is proposed to balance exploration and exploitation. Then, exploration and exploitation operators are formally defined by adaptive Gaussian local search with reinitialization and multioffspring-based differential evolution, respectively. Finally, a new triple-transference-based differential evolution method is proposed. The experimental results on 29 benchmark optimization functions show its outstanding performance, especially on complex problems. The balance between exploration and exploitation in the proposed algorithm is discussed in detail. The success of this new framework provides more insights into the principles of balancing exploration and exploitation. It also leads us to believe that exploration and exploitation in evolutionary algorithms can eventually be explicitly controlled.
Research on financing strategy under the integration of green supply chain and blockchain technology

Green supply chain management (GSCM) has now risen to the strategic level of many companies. However, in practice, the introduction of green technology leads to the financial constraints for enterprises and there will be problems such as asymmetric information and low trust when enterprises adopt financing strategies. This paper builds a green supply chain model composed of a capital-constrained manufacturer and a retailer to study the impact of the application of blockchain technology on the manufacturer’s financing strategy. There are four financing strategies considered: no financing (NF) strategy, trade credit financing (TCF) strategy, bank credit financing (BCF) strategy and hybrid financing (HF) strategy. Through theoretical and numerical analysis of the impact of key parameters on the manufacturer's equilibrium financing strategy, we find that: there is no pure Pareto dominating financing strategy for this manufacturer. When the efficiency of blockchain technology investment is low, the NF strategy is optimal, while when the efficiency of blockchain technology investment is high, the TCF strategy is optimal. Base on these results, the green supply chains are suggested improving the investment efficiency of blockchain technology.
Drones in Venice: Exploring business model applications for disruptive mobility and stakeholders’ value proposition

Drones can play a significant role in the paradigm shift desired by several international entities. Although many attempts to transport through the technology have encountered regulatory barriers, drones represent disruptive mobility that can foster social and environmental sustainability. For the successful implementation of this new mobility model, the support of several stakeholders is critical. In this regard, the paper applies the theoretical lens of stakeholder theory embedded within the business 4.0 model for the creation of value propositions. Through several semi-structured interviews, the research analyses findings related to a single case study concerning the unique location of Venice, Italy. The city emerges as having international interest because of its singular transportation system. Specifically, Flying Aid, a start-up venture of the Strategy Innovation ecosystem of the Ca’ Foscari University of Venice, uses drones to transport drugs to remote areas to promote therapeutic access for patients with chronic diseases and transportation difficulties. The paper contributes to understanding how a paradigm shift through drone transportation may improve patient engagement and overcome transportation barriers. It also expands stakeholder engagement by empowering healthcare providers and promoting knowledge translation and systems integration. Moreover, the research contributes to the investigation of environmental and social sustainability by advancing knowledge toward cleaner production and decarbonized mobility. Finally, the paper provides insights into the elements of the business model 4.0, facilitating value creation for stakeholders.
Perspectives of microwave quantum key distribution in the open air

One of the cornerstones of quantum communication is the unconditionally secure distribution of classical keys between remote parties. This key feature of quantum technology is based on the quantum properties of propagating electromagnetic waves, such as entanglement, or the no-cloning theorem. However, these quantum resources are known to be susceptible to noise and losses, which are omnipresent in open-air communication scenarios. In this paper, we theoretically investigate the perspectives of continuous-variable open-air quantum key distribution at microwave frequencies. In particular, we present a model describing the coupling of propagating microwaves with a noisy environment. Using a protocol based on displaced squeezed states, we demonstrate that continuous-variable quantum key distribution with propagating microwaves can be unconditionally secure with communication at room temperature up to distances of around 200 m, limited by the total coupled noise photon number. Moreover, we show that microwaves can potentially outperform conventional quantum key distribution at telecom wavelengths and imperfect weather conditions.
Unmasking log4j’s Vulnerability: Protecting Systems against Exploitation through Ethical Hacking and Cyberlaw Perspectives

In today’s digital landscape, the widespread utilization of the Java library Log4j for storing error messages in applications is prevalent. However, a recently discovered vulnerability in the Apache webserver has raised serious concerns, potentially allowing hackers to gain remote access to devices worldwide. The imminent danger of exploitation is further heightened by the fact that malicious actors are already attempting to exploit this vulnerability. By leveraging a script embedded within a targeted log message, attackers have been successful in gathering LDAP information, enabling them to execute access attacks on the server without the need for login credentials. These attacks involve several stages, including intelligence collection, weaponization, attack delivery, exploitation, and ultimately breaching the system by installing the intended malware or malicious payload. To safeguard against this pervasive threat, it is crucial to regularly update the operating system and all software associated with Iog4j, particularly java-based files. This action should be complemented by implementing additional measures such as installing a robust firewall on both the operating system and the software components.
Cross-modal fine-grained alignment and fusion network for multimodal aspect-based sentiment analysis

Multi-modal Aspect-based Sentiment Analysis (MABSA) aims to forecast the polarity of sentiment concerning aspects within a given sentence based on the correlation between the sentence and its accompanying image. Comprehending multi-modal sentiment expression requires strong cross-modal alignment and fusion ability. Previous state-of-the-art (SOTA) models fail to explicitly align valuable visual clues with aspect and sentiment information in textual representations and overlook the utilization of syntactic dependency information in the accompanying text modality. We present CoolNet (Cross-modal Fine-grained Alignment and Fusion Network) to boost the performance of visual-language models in seamlessly integrating vision and language information. Specifically, CoolNet starts by transforming an image into a textual caption and a graph structure, then dynamically aligns the semantic and syntactic information from both the input sentence and the generated caption, as well as models the object-level visual features. Finally, a cross-modal transformer is employed to fuse and model the inter-modality dynamics.This network boasts advanced cross-modal fine-grained alignment and fusion capabilities. On standard benchmarks such as Twitter-2015 and Twitter-2017, CoolNet consistently outperforms state-of-the-art algorithm FITE with notable improvements in accuracy and Macro-F1 values. Specifically, we observe an improvement in accuracy and Macro-F1 values by 1.43% and 1.38% for Twitter-2015, and 0.74% and 0.88% for Twitter-2017, respectively, demonstrating the superiority of our CoolNet architecture.
Causal inference from cross-sectional earth system data with geographical convergent cross mapping

Causal inference in complex systems has been largely promoted by the proposal of some advanced temporal causation models. However, temporal models have serious limitations when time series data are not available or present insignificant variations, which causes a common challenge for earth system science. Meanwhile, there are few spatial causation models for fully exploring the rich spatial cross-sectional data in Earth systems. The generalized embedding theorem proves that observations can be combined together to construct the state space of the dynamic system, and if two variables are from the same dynamic system, they are causally linked. Inspired by this, here we show a Geographical Convergent Cross Mapping (GCCM) model for spatial causal inference with spatial cross-sectional data-based cross-mapping prediction in reconstructed state space. Three typical cases, where clearly existing causations cannot be measured through temporal models, demonstrate that GCCM could detect weak-moderate causations when the correlation is not significant. When the coupling between two variables is significant and strong, GCCM is advantageous in identifying the primary causation direction and better revealing the bidirectional asymmetric causation, overcoming the mirroring effect.
Secure and Privacy-Preserving Intrusion Detection and Prevention in the Internet of Unmanned Aerial Vehicles

In smart cities, unmanned aerial vehicles (UAVS) play a vital role in surveillance, monitoring, and data collection. However, the widespread integration of UAVs brings forth a pressing concern: security and privacy vulnerabilities. This study introduces the SP-IoUAV (Secure and Privacy Preserving Intrusion Detection and Prevention for UAVS) model, tailored specifically for the Internet of UAVs ecosystem. The challenge lies in safeguarding UAV operations and ensuring data confidentiality. Our model employs cutting-edge techniques, including federated learning, differential privacy, and secure multi-party computation. These fortify data confidentiality and enhance intrusion detection accuracy. Central to our approach is the integration of deep neural networks (DNNs) like the convolutional neural network-long short-term memory (CNN-LSTM) network, enabling real-time anomaly detection and precise threat identification. This empowers UAVs to make immediate decisions in dynamic environments. To proactively counteract security breaches, we have implemented a real-time decision mechanism triggering alerts and initiating automatic blacklisting. Furthermore, multi-factor authentication (MFA) strengthens access security for the intrusion detection system (IDS) database. The SP-IoUAV model not only establishes a comprehensive machine framework for safeguarding UAV operations but also advocates for secure and privacy-preserving machine learning in UAVS. Our model's effectiveness is validated using the CIC-IDS2017 dataset, and the comparative analysis showcases its superiority over previous approaches like FCL-SBL, RF-RSCV, and RBFNNs, boasting exceptional levels of accuracy (99.98%), precision (99.93%), recall (99.92%), and F-Score (99.92%).