Reviewerly/exHarmony huggingface dataset:
What factors influence continuous usage intention of head-mounted display-based virtual reality content?: a cross-sectional survey

The aim of this study was to explore the continuous usage intention of head-mounted display-based virtual reality (HMD-based VR) content among college students. The study also sought to understand how this intention is influenced by factors related to personal cognition, social aspects, VR content, and HMD-related elements.This descriptive correlational study used a self-report questionnaire to survey 217 students from two universities in Korea who had prior experience with HMD-based VR content.The mean score for continuous usage intention of HMD-based VR content was 2.59±0.57 points (range, 1-5 points). Regarding the average frequency of HMD-based VR content usage, 64.5% of participants reported using it 1 to 2 times, while 91.7% indicated a total HMD-based VR usage period of less than 6 months. Factors such as personal cognition, VR content, social aspects, and HMD-related elements had explanatory power of 35.1%, 10.7%, 4.4%, and 2.5%, respectively, for the continuous usage intention of HMD-based VR content. Additionally, engagement (β=.45, p<.001), influential others (β=.37, p<.001), environmental support (β=-.18, p=.030), and cyber sickness (β=-.21, p=.001) were identified as having a significant influence.When developing HMD-based VR content, strategies to improve users' personal cognition should be included. Additionally, it is necessary to develop strategies that enhance enjoyment and interest in the content, while also facilitating ongoing social support. Furthermore, coping strategies should be devised that take into account cyber sickness, a potential side effect of these devices.
Hybrid quantum-classical convolutional neural network for phytoplankton classification

The taxonomic composition and abundance of phytoplankton have a direct impact on marine ecosystem dynamics and global environment change. Phytoplankton classification is crucial for phytoplankton analysis, but it is challenging due to their large quantity and small size. Machine learning is the primary method for automatically performing phytoplankton image classification. As large-scale research on marine phytoplankton generates overwhelming amounts of data, more powerful computational resources are required for the success of machine learning methods. Recently, quantum machine learning has emerged as a potential solution for large-scale data processing by harnessing the exponentially computational power of quantum computers. Here, for the first time, we demonstrate the feasibility of using quantum deep neural networks for phytoplankton classification. Hybrid quantum-classical convolutional and residual neural networks are developed based on the classical architectures. These models strike a balance between the limited function of current quantum devices and the large size of phytoplankton images, making it possible to perform phytoplankton classification on near-term quantum computers. Our quantum models demonstrate superior performance compared to their classical counterparts, exhibiting faster convergence, higher classification accuracy and lower accuracy fluctuation. The present quantum models are versatile and can be applied to various tasks of image classification in the field of marine science.
Software‐in‐the‐loop simulation for developing and testing carbon‐aware applications

Abstract The growing electricity demand of IT infrastructure has raised significant concerns about its carbon footprint. To mitigate the associated emissions of computing systems, current efforts therefore increasingly focus on aligning the power usage of software with the availability of clean energy. To operate, such carbon‐aware applications require visibility and control over relevant metrics and configurations of the energy system. However, research and development of novel energy system abstraction layers and interfaces remain difficult due to the scarcity of available testing environments: Real testbeds are expensive to build and maintain, while existing simulation testbeds are unable to interact with real computing systems. To provide a widely applicable approach for developing and testing carbon‐aware software, we propose a method for integrating real applications into a simulated energy system through software‐in‐the‐loop simulation. The integration offers an API for accessing the energy system, while continuously modeling the computing system's power demand within the simulation. Our system allows for the integration of physical as well as virtual compute nodes, and can help accelerate research on carbon‐aware computing systems in the future.
Short video marketing in tourism: Telepresence, celebrity attachment, and travel intention

Abstract Drawn upon Telepresence theory, this study aims to identify the relationships between existential authenticity, celebrity attachment, telepresence, and travel intention in the short video experience. Survey results show that existential authenticity fosters user attachment to celebrity and then travel intention, but has no impact on telepresence which is also not related to travel intention; Furthermore, celebrity attachment enhances travel intention. Although existential authenticity has no effect on telepresence, celebrity attachment mediates the relationship between existential authenticity and telepresence. This study offers insights to both scholars and practitioners, informing strategies for enhancing destination competitiveness through TikTok marketing campaigns.
An Improved Image Compression Algorithm Using 2D DWT and PCA with Canonical Huffman Encoding

Of late, image compression has become crucial due to the rising need for faster encoding and decoding. To achieve this objective, the present study proposes the use of canonical Huffman coding (CHC) as an entropy coder, which entails a lower decoding time compared to binary Huffman codes. For image compression, discrete wavelet transform (DWT) and CHC with principal component analysis (PCA) were combined. The lossy method was introduced by using PCA, followed by DWT and CHC to enhance compression efficiency. By using DWT and CHC instead of PCA alone, the reconstructed images have a better peak signal-to-noise ratio (PSNR). In this study, we also developed a hybrid compression model combining the advantages of DWT, CHC and PCA. With the increasing use of image data, better image compression techniques are necessary for the efficient use of storage space. The proposed technique achieved up to 60% compression while maintaining high visual quality. This method also outperformed the currently available techniques in terms of both PSNR (in dB) and bit-per-pixel (bpp) scores. This approach was tested on various color images, including Peppers 512 × 512 × 3 and Couple 256 × 256 × 3, showing improvements by 17 dB and 22 dB, respectively, while reducing the bpp by 0.56 and 0.10, respectively. For grayscale images as well, i.e., Lena 512 × 512 and Boat 256 × 256, the proposed method showed improvements by 5 dB and 8 dB, respectively, with a decrease of 0.02 bpp in both cases.
Assessing the alerting capabilities of the Earthquake Network early warning system in Haiti with Monte Carlo simulations

Abstract Smartphone-based earthquake early warning systems implemented by citizen science initiatives are characterised by significant variability in their smartphone network geometry. This has a direct impact on the earthquake detection capability and system performance. Here, a Monte Carlo-based simulation framework is implemented to infer relevant earthquake detection quantities such as detection distance from the epicentre, detection delay, and warning time for people exposed to high ground shaking levels. The framework is applied to Haiti, which has experienced deadly earthquakes in recent decades, and to the Earthquake Network citizen science initiative, which is popular in the country. It is found that warning times of up to 12 s for people exposed to intensities between 7.5 and 8.5 on the modified Mercalli scale are possible starting from a relatively low involvement of citizens in the initiative (i.e., from 1 Haitian in 10,000).
January 6th on Twitter: measuring social media attitudes towards the Capitol riot through unhealthy online conversation and sentiment analysis

While social media can serve as public discussion forums of great benefit to democratic debate, discourse propagated through them can also stoke political polarization and partisanship. A particularly dramatic example is the January 6, 2021 incident in Washington D.C., when a group of protesters besieged the US Capitol, resulting in several deaths. The public reacted by posting messages on social media, discussing the actions of the participants. Aiming to understand their perspectives under the broad concept of unhealthy online conversation (i.e. bad faith argumentation, overly hostile or destructive discourse, or other behaviours that discourage engagement), we sample 1,300,000 Twitter posts taken from the #Election2020 dataset dating from January 2021. Using a fine-tuned XLNet model trained on the Unhealthy Comment Corpus (UCC) dataset, we label these texts as healthy or unhealthy, furthermore using a taxonomy of 7 unhealthy attributes. Using the NRCLex sentiment analysis lexicon, we also detect the emotional patterns associated with each attribute. We observe that these conversations contain accusatory language aimed at the 'other side', limiting engagement by defining others in terms they do not themselves use or identify with. We find evidence of three attribute clusters, in addition to sarcasm, a divergent attribute that we argue should be researched separately. We find that emotions identified from the text do not correlate with the attributes, the two approaches revealing complementary characteristics of online discourse. Using latent Dirichlet allocation (LDA), we identify topics discussed within the attribute-sentiment pairs, linking them to each other using similarity measures. The results we present aim to help social media stakeholders, government regulators, and the general public better understand the contents and the emotional profile of the debates arising on social media platforms, especially as they relate to the political realm.
A spatially variant high-order variational model for Rician noise removal

Rician noise removal is an important problem in magnetic resonance (MR) imaging. Among the existing approaches, the variational method is an essential mathematical technique for Rician noise reduction. The previous variational methods mainly employ the total variation (TV) regularizer, which is a first-order term. Although the TV regularizer is able to remove noise while preserving object edges, it suffers the staircase effect. Besides, the adaptability has received little research attention. To this end, we propose a spatially variant high-order variational model (SVHOVM) for Rician noise reduction. We introduce a spatially variant TV regularizer, which can adjust the smoothing strength for each pixel depending on its characteristics. Furthermore, SVHOVM utilizes the bounded Hessian (BH) regularizer to diminish the staircase effect generated by the TV term. We develop a split Bregman algorithm to solve the proposed minimization problem. Extensive experiments are performed to demonstrate the superiority of SVHOVM over some existing variational models for Rician noise removal.
Human intuition as a defense against attribute inference

Attribute inference-the process of analyzing publicly available data in order to uncover hidden information-has become a major threat to privacy, given the recent technological leap in machine learning. One way to tackle this threat is to strategically modify one's publicly available data in order to keep one's private information hidden from attribute inference. We evaluate people's ability to perform this task, and compare it against algorithms designed for this purpose. We focus on three attributes: the gender of the author of a piece of text, the country in which a set of photos was taken, and the link missing from a social network. For each of these attributes, we find that people's effectiveness is inferior to that of AI, especially when it comes to hiding the attribute in question. Moreover, when people are asked to modify the publicly available information in order to hide these attributes, they are less likely to make high-impact modifications compared to AI. This suggests that people are unable to recognize the aspects of the data that are critical to an inference algorithm. Taken together, our findings highlight the limitations of relying on human intuition to protect privacy in the age of AI, and emphasize the need for algorithmic support to protect private information from attribute inference.
Deterministic generation of arbitrary ultrasmall excitation of quantum systems by composite pulse sequences

In some applications of quantum control, it is necessary to produce very weak excitation of a quantum system. Such an example is presented by the concept of single-photon generation in cold atomic ensembles or doped solids, e.g., by the Duan-Lukin-Cirac-Zoller (DLCZ) protocol, for which a single excitation is shared among thousands and millions of atoms or ions. Another example is the possibility to create a huge Dicke state of $N$ qubits sharing a single or a few excitations. Other examples are using tiny rotations to tune high-fidelity quantum gates, or using these tiny rotations to test high-fidelity quantum process tomography protocols. Ultrasmall excitation of a quantum transition can be generated by either a very weak or far-detuned driving field. However, these two approaches are sensitive to variations in the experimental parameters. Here we propose a different method for generating a well-defined arbitrary preselected very small transition probability---in the range from ${10}^{\ensuremath{-}2}$ to ${10}^{\ensuremath{-}8}$---by using composite pulse sequences. Contrary to other methods, it features both high fidelity and robustness to variations in the pulse area and the pulse duration.
BSEMS—A Blockchain-Based Smart Energy Measurement System

The modern world's increasing reliance on automated systems for everyday tasks has resulted in a corresponding rise in power consumption. The demand is further augmented by increased sales of electric vehicles, smart cities, smart transportation, etc. This growing dependence underscores the critical necessity for a robust smart energy measurement and management system to ensure a continuous and efficient power supply. However, implementing such a system presents a set of challenges, particularly concerning the transparency, security, and trustworthiness of data storage and retrieval. Blockchain technology offers an innovative solution in the form of a distributed ledger, which guarantees secure and transparent transaction storage and retrieval. This research introduces a blockchain-based system, utilising Hyperledger Fabric and smart contracts, designed for the secure storage and retrieval of consumers' energy consumption data. Finally, a user-friendly web portal was designed and developed using the node.js framework, offering an accessible and intuitive interface to monitor and manage energy consumption effectively.
Details Matter: Secluded Areas and Voting Secrecy with French Ballots

To explore the psychology of voting secrecy, we conducted a field experiment to examine voter sensitivity to arrangements for ballot paper selection under the French ballot system (i.e., multiple ballot papers). Working closely with Swedish election authorities, we randomly assigned participants to vote in a fictional election under low, medium-high and high privacy conditions with a follow up paper-and-pen survey to record perceived voting secrecy. Results show that participants perceived an arrangement for selecting ballot papers behind a closed screen as clearly more secret than one where selection was public, and that a third and even more private arrangement, where voters did not have to walk a few meters with ballot papers in hand, potentially visible to onlookers, provided even higher levels of perceived secrecy. The study demonstrates that voters are sensitive even to small changes in the voting environment. Accordingly, election administrators should be aware that details matter for the experience of voting secretly.
Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning

Few-shot learning aims to fast adapt a deep model from a few examples. While pre-training and meta-training can create deep models powerful for few-shot generalization, we find that pre-training and meta-training focus respectively on cross-domain transferability and cross-task transferability, which restricts their data efficiency in the entangled settings of domain shift and task shift. We thus propose the Omni-Training framework to seamlessly bridge pre-training and meta-training for data-efficient few-shot learning. Our first contribution is a tri-flow Omni-Net architecture. Besides the joint representation flow, Omni-Net introduces two parallel flows for pre-training and meta-training, responsible for improving domain transferability and task transferability respectively. Omni-Net further coordinates the parallel flows by routing their representations via the joint-flow, enabling knowledge transfer across flows. Our second contribution is the Omni-Loss, which introduces a self-distillation strategy separately on the pre-training and meta-training objectives for boosting knowledge transfer throughout different training stages. Omni-Training is a general framework to accommodate many existing algorithms. Evaluations justify that our single framework consistently and clearly outperforms the individual state-of-the-art methods on both cross-task and cross-domain settings in a variety of classification, regression and reinforcement learning problems.
Neighbourhood Representative Sampling for Efficient End-to-End Video Quality Assessment

The increased resolution of real-world videos presents a dilemma between efficiency and accuracy for deep Video Quality Assessment (VQA). On the one hand, keeping the original resolution will lead to unacceptable computational costs. On the other hand, existing practices, such as resizing or cropping, will change the quality of original videos due to difference in details or loss of contents, and are henceforth harmful to quality assessment. With obtained insight from the studies of spatial-temporal redundancy in the human visual system, visual quality around a neighbourhood has high probability to be similar, and this motivates us to investigate an effective quality-sensitive neighbourhood representative sampling scheme for VQA. In this work, we propose a unified scheme, spatial-temporal grid mini-cube sampling (St-GMS), and the resultant samples are named <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fragments</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> . In St-GMS, full-resolution videos are first divided into mini-cubes with predefined spatial-temporal grids, then the temporal-aligned quality representatives are sampled to compose the fragments that serve as inputs for VQA. In addition, we design the Fragment Attention Network (FANet), a network architecture tailored specifically for fragments. With fragments and FANet, the proposed <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FAST-VQA</b> and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FasterVQA</b> (with an improved sampling scheme) achieves up to 1612× efficiency than the existing state-of-the-art, meanwhile achieving significantly better performance on all relevant VQA benchmarks.
Shield Against Gradient Leakage Attacks: Adaptive Privacy-Preserving Federated Learning

Federated learning (FL) requires frequent uploading and updating of model parameters, which is naturally vulnerable to gradient leakage attacks (GLAs) that reconstruct private training data through gradients. Although some works incorporate differential privacy (DP) into FL to mitigate such privacy issues, their performance is not satisfactory since they did not notice that GLA incurs heterogeneous risks of privacy leakage (RoPL) with respect to gradients from different communication rounds and clients. In this paper, we propose an Adaptive Privacy-Preserving Federated Learning (Adp-PPFL) framework to achieve satisfactory privacy protection against GLA, while ensuring good performance in terms of model accuracy and convergence speed. Specifically, a leakage risk-aware privacy decomposition mechanism is proposed to provide adaptive privacy protection to different communication rounds and clients by dynamically allocating the privacy budget according to the quantified RoPL. In particular, we exploratively design a round-level and a client-level RoPL quantification method to measure the possible risks of GLA breaking privacy from gradients in different communication rounds and clients respectively, which only employ the limited information in general FL settings. Furthermore, to improve the FL model training performance (i.e., convergence speed and global model accuracy), we propose an adaptive privacy-preserving local training mechanism that dynamically clips the gradients and decays the noises added to the clipped gradients during the local training process. Extensive experiments show that our framework outperforms the existing differentially private FL schemes on model accuracy, convergence, and attack resistance.
Study Report of Tor Antiforensic Techniques

Internet has facilitated ease of communication, providing e-commerce, government and private services, also it has fostered crime to a large extent. Tor browser provides access to the dark web that is famous for market of illicit goods like drugs, weapons, pornography, malicious codes and hacked account details to name a few. All this is possible due to the anonymity and privacy provided by Tor. The anonymity and privacy provided by Tor network has made it a choice for cybercriminals. To avoid detection Tor uses antiforensic techniques like the browser settings, encryption, onion routing, onion addresses. The objective of this chapter is to put forth the antiforensic techniques provided by Tor network, present a detailed analysis of deanonymizing techniques researched till now and propose a simplified model for detecting a suspect using Tor communication.