Reviewerly/exHarmony huggingface dataset:
Controllable Feature-Preserving Style Transfer

This paper proposes a new style transfer quality assessment approach introducing quantifiable metrics to optimize. First, we utilize a pre-trained DualStyleGAN model to generate multiple stylized portraits in the style vector space. Then, we design a custom scoring mechanism that uses the newly proposed CSCI and CCVI metrics to evaluate the results’ structural similarity, color consistency, and edge retention. We select and optimize the top outputs using human aesthetic standards to obtain the most natural, beautiful, and artistic results. Experimental results show that our proposed evaluation pipeline can effectively improve the quality of style transfer.
Multiple Information-Aware Recurrent Reasoning Network for Joint Dialogue Act Recognition and Sentiment Classification

The task of joint dialogue act recognition (DAR) and sentiment classification (DSC) aims to predict both the act and sentiment labels of each utterance in a dialogue. Existing methods mainly focus on local or global semantic features of the dialogue from a single perspective, disregarding the impact of the other part. Therefore, we propose a multiple information-aware recurrent reasoning network (MIRER). Firstly, the sequence information is smoothly sent to multiple local information layers for fine-grained feature extraction through a BiLSTM-connected hybrid CNN group method. Secondly, to obtain global semantic features that are speaker-, context-, and temporal-sensitive, we design a speaker-aware temporal reasoning heterogeneous graph to characterize interactions between utterances spoken by different speakers, incorporating different types of nodes and meta-relations with node-edge-type-dependent parameters. We also design a dual-task temporal reasoning heterogeneous graph to realize the semantic-level and prediction-level self-interaction and interaction, and we constantly revise and improve the label in the process of dual-task recurrent reasoning. MIRER fully integrates context-level features, fine-grained features, and global semantic features, including speaker, context, and temporal sensitivity, to better simulate conversation scenarios. We validated the method on two public dialogue datasets, Mastodon and DailyDialog, and the experimental results show that MIRER outperforms various existing baseline models.
Exploring User Acceptance of Al Image Generator: Unveiling Influential Factors in Embracing an Artistic AIGC Software

This study aims to explore the user acceptance of AI Image Generators and offers an analysis based on their distinct features. Drawing from the Technology Acceptance Model (TAM), we examined the impact of factors such as creativity, accuracy, image quality, interactivity, and efficiency on users’ willingness to adopt. Furthermore, we evaluated the mediating effects of perceived usefulness and perceived ease of use. Data analysis reveals that both perceived usefulness and ease of use play a significant role in users’ intentions to adopt the AI Image Generator technology. The features of the AI Image Generator significantly influence users’ perceptions of its usefulness and ease of use. Additionally, perceived ease of use has a notable effect on perceived usefulness. The insights from this research offer valuable guidance for the design, development, and promotion of AI Image Generators, facilitating the industry in catering better to users’ needs and promoting its adoption in the art world.
Leveraging Machine Learning for Crime Intent Detection in Social Media Posts

Detecting crime intent from user-generated content on social media platforms has become increasingly important for law enforcement and crime prevention. This paper presents a comprehensive approach for crime intent detection from user tweets using machine learning techniques. The study utilizes a dataset of about 400,000 tweets and applies data preprocessing, feature selection, and model training with logistic regression, ridge regression classifier, Stochastic Gradient Descent (SGD) classifier, Random Forests, and support vector machine models. Evaluation metrics such as accuracy, precision, recall, and F1 score are employed to assess the models’ performance. The results reveal that the logistic regression model achieves the highest accuracy ratio of 0.981 in detecting crime intent from tweets. This research showcases the effectiveness of machine learning and advanced transformer-based models in leveraging social media data for crime analysis. The findings provide valuable insights into the potential for early detection and monitoring of crime intent using online platforms, contributing to the field of crime prevention and law enforcement. The utilization of machine learning techniques offers new avenues for understanding and analyzing crime-related sentiments expressed by social media users. By accurately detecting crime intent from user-generated content, law enforcement agencies can enhance their proactive measures, monitor public sentiment towards crime, and shape policies and interventions to address public concerns effectively. The research highlights the significance of leveraging social media data for crime detection and emphasizes the potential impact of advanced machine learning models in improving public safety and crime prevention efforts.
MFAR-VTON: Multi-scale Fabric Adaptive Registration for Image-Based Virtual Try-On

Image-based virtual try-on technology provides a better shopping experience for online consumers. However, existing methods face challenges in effectively capturing high-level semantic information and achieving accurate registration between clothing and the body, particularly in complex body poses or target garments. To address these issues, we propose MFAR-VTON, a novel framework that incorporates a multi-scale enhanced adaptive clothing registration strategy and possesses matching filtering capabilities. Our method enables the generation of highly precise clothing alignment results, leading to seamless integration of try-on images. Additionally, we introduce a deformation energy constraint that effectively preserves intricate garment details. Experimental results demonstrated that MFAR-VTON achieves state-of-the-art performance in terms of accuracy and realism.
Analyzing Multilingual Automatic Speech Recognition Systems Performance

Understanding spoken language, or transcribing the spoken words into text, was one of the earliest goals of computer language processing and falls under the realm of speech processing. Speech processing in itself predates the computer by many decades. Speech being the most important and most common means of communication for most people, is always in need of necessary technology advances. Therefore, in the recent decades there has been great interest in techniques including automatic speech recognition (ASR), text to speech etc. This research is focused around English and the scope needs to be expanded to other languages as well. In this study we explore several open-source ASR systems that offer multilingual (English and Spanish) models. We discuss various models these ASR systems offer, evaluate their performance. Based on our manual observations and using automatic evaluation metrics (the word error rate) we find that Whisper models perform the best for both English and Spanish. In addition, it supports a multilingual model that has the ability to process audio that consists of words from both English and Spanish.
A Novel Adaptive Edge Aggregation and Multiscale Feature Interaction Detector for Object Detection in Remote Sensing Images

Object detection (OD) in remote sensing (RS) images is an important task in the field of computer vision. OD techniques have achieved impressive advances in recent years. However, complex background interference, large-scale variations, and dense instances pose significant challenges for OD. These challenges may lead to misalignment between features extracted by OD models and the features of real objects. To address these challenges, we explore a novel single-stage detection framework for the adaptive fusion of multiscale features and propose a novel adaptive edge aggregation and multiscale feature interaction detector (AEAMFI-Det) for OD in RS images. AEAMFI-Det consists of an adaptive edge aggregation (AEA) module, a feature enhancement module (FEM) embedded in a context-aware cross-attention feature pyramid network (2CA-FPN), and a pyramid squeeze attention (PSA) module. The AEA module employs an edge enhancement mechanism to guide the network to learn spatial multiscale nonlocal dependencies and solve the problem of feature misalignment between the network’s focus and the real object. The 2CA-FPN employs level-by-level feature fusion to enhance multiscale feature interactions and effectively mitigate the misalignment between the scales of the extracted features and the scales of real objects. The FEM is designed to capture the local and nonlocal contexts as auxiliary information to enhance the feature representation of information interaction between multiscale features in a cross-attention manner. We introduce the PSA module to establish long-term dependencies between multiscale spaces and channels for better interdependency refinement. Experimental results obtained using the NWPU VHR-10 and DIOR datasets demonstrate the superior performance of AEAMFI-Det in object classification and localization.
Moving Towards Internet of Behaviors

The Internet of Behaviors (IoB) is an emerging trend that aims to revolutionize human interactions by analyzing and influencing behavior using data from connected devices. This study explores the importance of blockchain technology in addressing the IoB challenges and enhancing the security and privacy of IoB business models. Blockchain technology provides a decentralized and transparent platform for behavioral data storage and sharing, mitigating privacy concerns. Individuals are empowered with control over their personal data through smart contracts and decentralized identifiers, enabling them to decide how their data is accessed and used within IoB systems. In this study, significant blockchain-based solutions and approaches are presented that can enhance IoB business models through examples. Exploration of blockchain potential in IoB systems will ensure new insights for further advancement and evolution of the IoB paradigm.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>α</mml:mi><mml:mo>&gt;</mml:mo></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mi>ε</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:msub><mml:mi>α</mml:mi><mml:mo>&lt;</mml:mo></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mi>ε</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math> for the Margolus-Levitin quantum speed limit bound

The Margolus-Levitin (ML) bound says that for any time-independent Hamiltonian, the time needed to evolve from one quantum state to another is at least $\ensuremath{\pi}\ensuremath{\alpha}(\ensuremath{\epsilon})/2\ensuremath{\langle}E\ensuremath{-}{E}_{0}\ensuremath{\rangle}$, where $\ensuremath{\langle}E\ensuremath{-}{E}_{0}\ensuremath{\rangle}$ is the expected energy of the system relative to the ground state of the Hamiltonian and $\ensuremath{\alpha}(\ensuremath{\epsilon})$ is a function of the fidelity $\ensuremath{\epsilon}$ between the two states. For a long time, only an upper bound ${\ensuremath{\alpha}}_{&gt;}(\ensuremath{\epsilon})$ and a lower bound ${\ensuremath{\alpha}}_{&lt;}(\ensuremath{\epsilon})$ are known, although they agree up to at least seven significant figures. Recently, H\"ornedal and S\"onnerborn [arXiv:2301.10063] proved an analytical expression for $\ensuremath{\alpha}(\ensuremath{\epsilon})$, a fully classified system whose evolution time saturates the ML bound, and gave this bound a symplectic-geometric interpretation. Here I solve the same problem through an elementary proof of the ML bound. By explicitly finding all the states that saturate the ML bound, I show that ${\ensuremath{\alpha}}_{&gt;}(\ensuremath{\epsilon})$ is indeed equal to ${\ensuremath{\alpha}}_{&lt;}(\ensuremath{\epsilon})$. More importantly, I point out a numerical stability issue in computing ${\ensuremath{\alpha}}_{&gt;}(\ensuremath{\epsilon})$ and report a simple way to evaluate it efficiently and accurately.
Embodied Provenance for Immersive Sensemaking

Immersive analytics research has explored how embodied data representations and interactions can be used to engage users in sensemaking. Prior research has broadly overlooked the potential of immersive space for supporting analytic provenance, the understanding of sensemaking processes through users’ interaction histories. We propose the concept of embodied provenance, the use of three-dimensional space and embodied interactions in supporting recalling, reproducing, annotating and sharing analysis history in immersive environments. We design a conceptual framework for embodied provenance by highlighting a set of design criteria for analytic provenance drawn from prior work and identifying essential properties for embodied provenance. We develop a prototype system in virtual reality to demonstrate the concept and support the conceptual framework by providing multiple data views and embodied interaction metaphors in a large virtual space. We present a use case scenario of energy consumption analysis and evaluated the system through a qualitative evaluation with 17 participants, which show the system’s potential for assisting analytic provenance using embodiment. Our exploration of embodied provenance through this prototype provides lessons learnt to guide the design of immersive analytic tools for embodied provenance.
MRI-based structural covariance network in early human brain development

OPINION article Front. Neurosci., 01 November 2023Sec. Brain Imaging Methods Volume 17 - 2023 | https://doi.org/10.3389/fnins.2023.1302069
Interactions across Displays and Space: A Study of Virtual Reality Streaming Practices on Twitch

The growing live streaming economy and virtual reality (VR) technologies have sparked interest in VR streaming among streamers and viewers. However, limited research has been conducted to understand this emerging streaming practice. To address this gap, we conducted an in-depth thematic analysis of 34 streaming videos from 12 VR streamers with varying levels of experience, to explore the current practices, interaction styles, and strategies, as well as to investigate the challenges and opportunities for VR streaming. Our findings indicate that VR streamers face challenges in building emotional connections and maintaining streaming flow due to technical problems, lack of fluid transitions between physical and virtual environments, and not intentionally designed game scenes. As a response, we propose six design implications to encourage collaboration between game designers and streaming app developers, facilitating fluid, rich, and broad interactions for an enhanced streaming experience. In addition, we discuss the use of streaming videos as user-generated data for research, highlighting the lessons learned and emphasizing the need for tools to support streaming video analysis. Our research sheds light on the unique aspects of VR streaming, which combines interactions across displays and space.
Distinguishability and mixedness in quantum interference

We study the impact of distinguishability and mixedness, two fundamental properties of quantum states, on quantum interference. We show that these can influence the interference of multiple particles in different ways, leading to effects that cannot be observed in the interference of two particles alone. This is demonstrated experimentally by interfering three independent photons in pure and mixed states and observing their different multiphoton interference, despite exhibiting the same two-photon Hong-Ou-Mandel interference. In addition to its fundamental relevance, our observation has important implications for quantum technologies relying on photon interference.
3D Finger Rotation Estimation from Fingerprint Images

Various touch-based interaction techniques have been developed to make interactions on mobile devices more effective, efficient, and intuitive. Finger orientation, especially, has attracted a lot of attentions since it intuitively brings three additional degrees of freedom (DOF) compared with two-dimensional (2D) touching points. The mapping of finger orientation can be classified as being either absolute or relative, suitable for different interaction applications. However, only absolute orientation has been explored in prior works. The relative angles can be calculated based on two estimated absolute orientations, although, a higher accuracy is expected by predicting relative rotation from input images directly. Consequently, in this paper, we propose to estimate complete 3D relative finger angles based on two fingerprint images, which incorporate more information with a higher image resolution than capacitive images. For algorithm training and evaluation, we constructed a dataset consisting of fingerprint images and their corresponding ground truth 3D relative finger rotation angles. Experimental results on this dataset revealed that our method outperforms previous approaches with absolute finger angle models. Further, extensive experiments were conducted to explore the impact of image resolutions, finger types, and rotation ranges on performance. A user study was also conducted to examine the efficiency and precision using 3D relative finger orientation in 3D object rotation task.
Understanding the digital economy in China: Characteristics, challenges, and prospects

AbstractThis paper explores the distinct characteristics of China’s digital economy during its rapid growth over the past decade and sheds light on the transformative impact that the digital economy has had on the Chinese society. The findings reveal that China’s digital economy has experienced remarkable expansion in recent decades, driven by the convenience and efficiency it has brought about. Notable achievements include the development of robust digital infrastructure, the emergence of innovative digital finance, and the rapid growth of central bank digital currency. Throughout this digitalisation process, the government has played a pivotal role in driving and regulating the digital economy. Recognising the significance of digital transformation, the government has actively engaged in shaping policies and providing regulatory frameworks to foster a conducive environment for digital advancements and market regulation.Keywords: Digital economydigital infrastructurecentral bank digital currencydigital governance Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 As presented in Figure 2, in 2021, the scale of the US’s digital economy was US$15.3181 trillion and that of Germany was US$2.8767 trillion.2 Available at: https://goingdigital.oecd.org/countries/chn.3 Available at: https://www.bea.gov/data/special-topics/digital-economy.4 Available at: https://www.mofa.go.jp/files/000185874.pdf.5 Available at: http://www.caict.ac.cn/english/research/whitepapers/202208/P020220819505049573088.pdf.6 Available at: https://goingdigital.oecd.org/en/indicator/33.7 See the official website of Alibaba Group, available at: https://ali-home.alibaba.com/document-1490858931906412544 for the year 2021.8 Available at: https://www.gov.cn/xinwen/2023-01/31/content_5739339.htm.9 See ‘China Computing Power Development Index White Paper’, available at: http://www.caict.ac.cn/kxyj/qwfb/bps/202211/P020221105727522653499.pdf.10 Also see ‘China Computing Power Development Index White Paper’.11 Available at: http://xw.cbimc.cn/2020-11/27/content_372523.htm.12 The CBIRC was established in April 2018 by a merger of China’s banking and insurance regulators, namely, the China Banking Regulatory Commission (CBRC) and China Insurance Regulatory Commission (CIRC). It was abolished in March 2023 as part of a plan to overhaul government agencies, and replaced by the National Administration of Financial Regulation.13 Available at: https://static.sse.com.cn/stock/information/c/202008/e731ee980f5247529ea824d20fcdb293.pdf.Additional informationFundingThis research is supported by the National Social Science Fund of China [Grant No. 20ZDA053] and the Youth Programme of National Natural Science Foundation of China [Grant No. 72101267].
Understanding the Effects of Movement Direction on 2D Touch Pointing Tasks

HCI researchers have long recognized the significant effects of movement direction on human performance, and this factor has been carefully addressed to benefit user interface design. According to our previous study (2012), the weights of the two target dimensions, width W and height H, in the extended index of difficulty (ID) for 2D pointing tasks are asymmetric and appear to vary periodically based on movement direction (θ), following a cosine function. However, this periodic effect of movement direction is uncertain for direct 2D touch pointing tasks, and a thorough understanding of the effects of movement direction on direct pointing tasks, such as on touch input surfaces, is still lacking. In this paper, we conducted two experiments on a 24-inch touch screen, with tilted and horizontal orientations respectively, to confirm the periodic effect in the context of direct pointing and illustrate its variations across different pointing tasks. At the same time, we propose a quantification formula to measure the real differences in task difficulty caused by the direction factor. To the best of our knowledge, this is the first study to do so. Using this formula, the ID values in different directions can be unified to the same scale and compared, providing a new perspective for understanding and evaluating human performance in different interaction environments.
Comparative Study of Metaheuristic Optimization of Convolutional Neural Networks Applied to Face Mask Classification

The preventive measures taken to curb the spread of COVID-19 have emphasized the importance of wearing face masks to prevent potential infection with serious diseases during daily activities or for medical professionals working in hospitals. Due to the mandatory use of face masks, various methods employing artificial intelligence and deep learning have emerged to detect whether individuals are wearing masks. In this paper, we utilized convolutional neural networks (CNNs) to classify the use of face masks into three categories: no mask, incorrect mask, and proper mask. Establishing the appropriate CNN architecture can be a demanding task. This study compares four swarm intelligent metaheuristics: particle swarm optimization (PSO), grey wolf optimizer (GWO), bat algorithm (BA), and whale optimization algorithm (WOA). The CNN architecture design involves determining the essential hyperparameters of the CNNs. The results indicate the effectiveness of the PSO and BA in achieving an accuracy of 100% when using 10% of the images for testing. Meanwhile, when 90% of the images were used for testing, the results were as follows: PSO 97.15%, WOA 97.14%, BA 97.23%, and GWO 97.18%. These statistically significant differences demonstrate that the BA allows better results than the other metaheuristics analyzed in this study.