Reviewerly/exHarmony huggingface dataset:
The HoloLens in medicine: A systematic review and taxonomy

The HoloLens (Microsoft Corp., Redmond, WA), a head-worn, optically see-through augmented reality (AR) display, is the main player in the recent boost in medical AR research. In this systematic review, we provide a comprehensive overview of the usage of the first-generation HoloLens within the medical domain, from its release in March 2016, until the year of 2021. We identified 217 relevant publications through a systematic search of the PubMed, Scopus, IEEE Xplore and SpringerLink databases. We propose a new taxonomy including use case, technical methodology for registration and tracking, data sources, visualization as well as validation and evaluation, and analyze the retrieved publications accordingly. We find that the bulk of research focuses on supporting physicians during interventions, where the HoloLens is promising for procedures usually performed without image guidance. However, the consensus is that accuracy and reliability are still too low to replace conventional guidance systems. Medical students are the second most common target group, where AR-enhanced medical simulators emerge as a promising technology. While concerns about human-computer interactions, usability and perception are frequently mentioned, hardly any concepts to overcome these issues have been proposed. Instead, registration and tracking lie at the core of most reviewed publications, nevertheless only few of them propose innovative concepts in this direction. Finally, we find that the validation of HoloLens applications suffers from a lack of standardized and rigorous evaluation protocols. We hope that this review can advance medical AR research by identifying gaps in the current literature, to pave the way for novel, innovative directions and translation into the medical routine.
Customers’ acceptance of artificially intelligent service robots: The influence of trust and culture

This study addresses two critical research gaps in human-robot interaction (HRI): the limited systematic research on the role of trust in customers’ acceptance of artificially intelligent (AI) robots; and the lack of understanding of robot acceptance under different cultural backgrounds. Drawing on the AIDUA framework, this study examines the impacts of trust and moderating effects of both national (the U.S. and China) and individual culture on customers’ intentions to use AI robots in hospitality services by developing a theoretical model. The model is tested on data collected using online data collection platforms from 491 U.S. and 495 Chinese respondents. PLS-SEM and the bootstrapping method were used to test the hypothesized relationships and analyze the moderating effects of culture, respectively. The findings suggest that trust in interaction with AI robots is a significant higher-order construct that influences the intention of use. Furthermore, uncertainty avoidance, long-term orientation, and power distance have been found to exhibit significant moderation effects. The results of this study extend the theoretical frameworks in HRI and provide detailed guidance to promote AI robot applications across different cultures.
The collaborative role of blockchain, artificial intelligence, and industrial internet of things in digitalization of small and medium-size enterprises

Due to digitalization, small and medium-sized enterprises (SMEs) have significantly enhanced their efficiency and productivity in the past few years. The process to automate SME transaction execution is getting highly multifaceted as the number of stakeholders of SMEs is connecting, accessing, exchanging, adding, and changing the transactional executions. The balanced lifecycle of SMEs requires partnership exchanges, financial management, manufacturing, and productivity stabilities, along with privacy and security. Interoperability platform issue is another critical challenging aspect while designing and managing a secure distributed Peer-to-Peer industrial development environment for SMEs. However, till now, it is hard to maintain operations of SMEs' integrity, transparency, reliability, provenance, availability, and trustworthiness between two different enterprises due to the current nature of centralized server-based infrastructure. This paper bridges these problems and proposes a novel and secure framework with a standardized process hierarchy/lifecycle for distributed SMEs using collaborative techniques of blockchain, the internet of things (IoT), and artificial intelligence (AI) with machine learning (ML). A blockchain with IoT-enabled permissionless network structure is designed called "B-SMEs" that provides solutions to cross-chain platforms. In this, B-SMEs address the lightweight stakeholder authentication problems as well. For that purpose, three different chain codes are deployed. It handles participating SMEs' registration, day-to-day information management and exchange between nodes, and analysis of partnership exchange-related transaction details before being preserved on the blockchain immutable storage. Whereas AI-enabled ML-based artificial neural networks are utilized, the aim is to handle and optimize day-to-day numbers of SME transactions; so that the proposed B-SMEs consume fewer resources in terms of computational power, network bandwidth, and preservation-related issues during the complete process of SMEs service deliverance. The simulation results present highlight the benefits of B-SMEs, increases the rate of ledger management and optimization while exchanging information between different chains, which is up to 17.3%, and reduces the consumption of the system's computational resources down to 9.13%. Thus, only 14.11% and 7.9% of B-SME's transactions use network bandwidth and storage capabilities compared to the current mechanism of SMEs, respectively.
Metaverse for climbing the ladder toward ‘Industry 5.0’ and ‘Society 5.0’?

ABSTRACTABSTRACTMetaverse implementations have started to emerge in various industries with the promise of providing better industrial services and a more sustainable society (Society 5.0). At the same time, various studies have revealed several challenges to the use of metaverse. Motivated by the fragmented literature on the implementation of metaverse in industries, this study presents a systematic literature review of research on this topic. Both content and bibliometric analyses are applied to reveal research trends, impacts and challenges of research on metaverse in the industry. The findings have revealed that metaverse adoption in industries is still in its infancy, with most research adopted in education and health industries. Additionally, there is unequal geographical distribution of research on metaverse in industries, calling for more international collaboration in this regard to facilitate metaverse adoption worldwide. The findings have also revealed several concerns that researchers and practitioners should keep in mind while implementing metaverse in industries.摘要元宇宙因其可提供更优质的产业服务及更具可持续性的社会(Society 5.0), 在众多产业中得以应用。与此同时, 诸多研究揭示了元宇宙应用的若干挑战。因关于元宇宙在产业中应用的文献较为零散, 本研究对该主题进行了系统文献综述。通过内容和文献计量分析, 揭示元宇宙在产业应用中的研究趋势、影响及挑战。结果表明, 元宇宙在产业中的应用仍处于起步阶段, 大多数研究关注元宇宙在教育和健康产业中的应用。此外, 相关研究的地理分布不均, 需要更多的国际合作以促进元宇宙在全球范围内的应用。同时, 研究结果也揭示了研究人员和从业人员在元宇宙应用中应关注的若干问题。KEYWORDS: Metaverseresilienceethicssocietysustainability关键字: 元宇宙复原力伦理社会可持续性 Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are available based on a reasonable request from the corresponding author.
Identifying Malignant Breast Ultrasound Images Using ViT-Patch

Recently, the Vision Transformer (ViT) model has been used for various computer vision tasks, due to its advantages to extracting long-range features. To better integrate the long-range features useful for classification, the standard ViT adds a class token, in addition to patch tokens. Despite state-of-the-art results on some traditional vision tasks, the ViT model typically requires large datasets for supervised training, and thus, it still face challenges in areas where it is difficult to build large datasets, such as medical image analysis. In the ViT model, only the output corresponding to the class token is fed to a Multi-Layer Perceptron (MLP) head for classification, and the outputs corresponding to the patch tokens are exposed. In this paper, we propose an improved ViT architecture (called ViT-Patch), which adds a shared MLP head to the output of each patch token to balance the feature learning on the class and patch tokens. In addition to the primary task, which uses the output of the class token to discriminate whether the image is malignant, a secondary task is introduced, which uses the output of each patch token to determine whether the patch overlaps with the tumor area. More interestingly, due to the correlation between the primary and secondary tasks, the supervisory information added to the patch tokens help with improving the performance of the primary task on the class token. The introduction of secondary supervision information also improves the attention interaction among the class and patch tokens. And by this way, ViT reduces the demand on dataset size. The proposed ViT-Patch is validated on a publicly available dataset, and the experimental results show its effectiveness for both malignant identification and tumor localization.
ChatGPT for e-Tourism: a technological perspective

Abstract The interest around the conversational system ChatGPT has exploded since OpenAI launched the GPT-3 prototype at the end of November 2022. Applications of ChatGPT could be disruptive for the digital transformation processes, challenging societies, companies, and people in a possibly unprecedented way. Alongside remarkable performances, there are risks related to its intrinsic limitations. In this viewpoint paper we introduce ChatGPT, highlighting its main characteristics, with the aim of offering some general and preliminary guidelines for its adoption in the tourism field.
Analysis and Design of Surgical Instrument Localization Algorithm

With the help of surgical navigation system, doctors can operate on patients more intuitively and accurately. The positioning accuracy and real-time performance of surgical instruments are very important to the whole system. In this paper, we analyze and design the detection algorithm of surgical instrument location mark, and estimate the posture of surgical instrument. In addition, we optimized the pose by remapping. Finally, the algorithm of location mark detection proposed in this paper and the posture analysis data of surgical instruments are verified and analyzed through experiments. The final result shows a high accuracy.
Weakly supervised machine learning

Abstract Supervised learning aims to build a function or model that seeks as many mappings as possible between the training data and outputs, where each training data will predict as a label to match its corresponding ground‐truth value. Although supervised learning has achieved great success in many tasks, sufficient data supervision for labels is not accessible in many domains because accurate data labelling is costly and laborious, particularly in medical image analysis. The cost of the dataset with ground‐truth labels is much higher than in other domains. Therefore, it is noteworthy to focus on weakly supervised learning for medical image analysis, as it is more applicable for practical applications. In this review, the authors give an overview of the latest process of weakly supervised learning in medical image analysis, including incomplete, inexact, and inaccurate supervision, and introduce the related works on different applications for medical image analysis. Related concepts are illustrated to help readers get an overview ranging from supervised to unsupervised learning within the scope of machine learning. Furthermore, the challenges and future works of weakly supervised learning in medical image analysis are discussed.
Analyzing augmented reality (AR) and virtual reality (VR) recent development in education

Augmented Reality (AR) and Virtual Reality (VR) technologies have revolutionized learning approaches through immersive digital experience, interactive environment, simulation and engagement. Yet, these technologies are in developing stage and require massive investment and mass customization to meet the high demand in education. This comprehensive review aims to frame AR and VR development in education during the last twelve years. By adopting text mining and topic analysis approaches, a total of 1536 articles were selected for further analysis. These articles were selected from Scopus database based on specific criteria where titles, keywords and abstracts were extracted for analysis by WordStat. Hypotheses were formulated based on the prior works of AR and VR in education and being processed and evaluated to unvield state of art of AR and VR literature development, applications, advantages and future directions. Results reveal that adoption of AR and VR in education have exponential growth during recent years where wearable device have gain the large portion of this development. Based on secondary data, results also reveal the gap in implementing and customizing these technologies quickly in educational institutions. As AR and VR technologies rapidly develop and become mature, more educational applications emerge in learning process. Researchers are recommended to keep in pace to discover gaps of AR and VR transition to education and create effective adaptability approaches to gain more benefits of these technologies development.
UAV-YOLOv8: A Small-Object-Detection Model Based on Improved YOLOv8 for UAV Aerial Photography Scenarios

Unmanned aerial vehicle (UAV) object detection plays a crucial role in civil, commercial, and military domains. However, the high proportion of small objects in UAV images and the limited platform resources lead to the low accuracy of most of the existing detection models embedded in UAVs, and it is difficult to strike a good balance between detection performance and resource consumption. To alleviate the above problems, we optimize YOLOv8 and propose an object detection model based on UAV aerial photography scenarios, called UAV-YOLOv8. Firstly, Wise-IoU (WIoU) v3 is used as a bounding box regression loss, and a wise gradient allocation strategy makes the model focus more on common-quality samples, thus improving the localization ability of the model. Secondly, an attention mechanism called BiFormer is introduced to optimize the backbone network, which improves the model's attention to critical information. Finally, we design a feature processing module named Focal FasterNet block (FFNB) and propose two new detection scales based on this module, which makes the shallow features and deep features fully integrated. The proposed multiscale feature fusion network substantially increased the detection performance of the model and reduces the missed detection rate of small objects. The experimental results show that our model has fewer parameters compared to the baseline model and has a mean detection accuracy higher than the baseline model by 7.7%. Compared with other mainstream models, the overall performance of our model is much better. The proposed method effectively improves the ability to detect small objects. There is room to optimize the detection effectiveness of our model for small and feature-less objects (such as bicycle-type vehicles), as we will address in subsequent research.
Planning as an Iterative Process

Activity planning for missions such as the Mars Exploration Rover mission presents many technical challenges, including oversubscription, consideration of time, concurrency, resources, preferences, and uncertainty. These challenges have all been addressed by the research community to varying degrees, but significant technical hurdles still remain. In addition, the integration of these capabilities into a single planning engine remains largely unaddressed. However, I argue that there is a deeper set of issues that needs to be considered -- namely the integration of planning into an iterative process that begins before the goals, objectives, and preferences are fully defined. This introduces a number of technical challenges for planning, including the ability to more naturally specify and utilize constraints on the planning process, the ability to generate multiple qualitatively different plans, and the ability to provide deep explanation of plans.
Splitting on categorical predictors in random forests

One reason for the widespread success of random forests (RFs) is their ability to analyze most datasets without preprocessing. For example, in contrast to many other statistical methods and machine learning approaches, no recoding such as dummy coding is required to handle ordinal and nominal predictors. The standard approach for nominal predictors is to consider all 2 k − 1 − 1 2-partitions of the k predictor categories. However, this exponential relationship produces a large number of potential splits to be evaluated, increasing computational complexity and restricting the possible number of categories in most implementations. For binary classification and regression, it was shown that ordering the predictor categories in each split leads to exactly the same splits as the standard approach. This reduces computational complexity because only k − 1 splits have to be considered for a nominal predictor with k categories. For multiclass classification and survival prediction no ordering method producing equivalent splits exists. We therefore propose to use a heuristic which orders the categories according to the first principal component of the weighted covariance matrix in multiclass classification and by log-rank scores in survival prediction. This ordering of categories can be done either in every split or a priori, that is, just once before growing the forest. With this approach, the nominal predictor can be treated as ordinal in the entire RF procedure, speeding up the computation and avoiding category limits. We compare the proposed methods with the standard approach, dummy coding and simply ignoring the nominal nature of the predictors in several simulation settings and on real data in terms of prediction performance and computational efficiency. We show that ordering the categories a priori is at least as good as the standard approach of considering all 2-partitions in all datasets considered, while being computationally faster. We recommend to use this approach as the default in RFs.
Effects of Game-Based Learning on Attitude and Achievement in Elementary Mathematics

Games-based learning involves instruction with realistic game experiences (Cicchino, 2015).This action research study explored game-based learning as fifth grade mathematics students completed a brief unit on ordered pairs utilizing game-based lessons.Attitude and achievement data were collected mainly by surveys, content tests, student interviews, and field notes.Additional information included in the teacher-researcher analysis consisted of classroom photographs, videos, and student work samples.Results revealed that student attitudes improved both toward the lessons and toward math in general.Similarly, achievement improved for all students across the unit.Analysis of the narrative data produced three themes.First, the students acquired a growth mindset (Boaler, 2016) that fostered a positive work ethic.Second, student partner work helped them to develop problem solving skills.And third, the games engaged the students.In summary, students showed significant improvements both in their attitudes about math and their achievement in ordered pairs.Many students begin to develop negative attitudes about mathematics during their first few years in the elementary classroom (Leroy & Bressoux, 2016).By the time these students reach the fifth grade many of them inherently struggle at math, simply because they do not learn best through the common process of rote memorization.Rather they would better succeed through a process that is exploratory, collaborative, and challenging (Kebritchi, Hirumi, & Bai, 2010).Leroy and Bressoux (2016) note that when elementary students struggle, these attitudes about mathematics can be toxic to a productive atmosphere, fostering low motivation and drive.Boaler (2016), one of the leading researchers on students' mathematics motivation, believes "the difference between those who succeed and those who don't is not in the brains they were born with, but their approach to life, the messages they receive about their potential, and the opportunities they have to learn" (p.5).Researchers assert that most students' dislike for mathematics stems from the way that the subject is presented in the classroom (Boaler, 2014;Dossel, 2016;Hunt, 1985;Reyes, 1984).The current presentation of mathematics in many elementary school classrooms relies on the 1 White and McCoy: Game-Based Learning
Generalized Chaos Synchronization Circuit Simulation and Asymmetric Image Encryption

Generalized chaos systems have more complex dynamic behavior than conventional chaos systems. If a generalized response system can be synchronized with a conventional drive system, the flexible control parameters and unpredictable synchronization state will increase significantly. The study first constructs a four-dimensional nonlinear dynamic equation with quadratic variables as a drive system. The numerical simulation and analyses of the Lyapunov exponent show that it is also a chaotic system. Based on the generalized chaos synchronization (GCS) theory, a four-dimensional diffeomorphism function is designed, and the corresponding GCS response system is generated. Simultaneously, the structural and synchronous circuits of information interaction and control are constructed with Multisim™ software, with the circuit simulation resulting in a good agreement with the numerical calculations. In order to verify the practical effect of generalized synchronization, an RGB digital image secure communication scheme is proposed. We confuse a 24-bit true color image with the designed GCS system, extend the original image to 48-bits, analyze the scheme security from keyspace, key sensitivity and non-symmetric identity authentication, classical types of attacks, and statistical average from the histogram, image correlation. The research results show that this GCS system is simple and feasible, and the encryption algorithm is closely related to the confidential information, which can resist the differential attack. The scheme is suitable to be applied in network images or other multimedia safe communications.
Meteor++ 2.0: Adopt Syntactic Level Paraphrase Knowledge into Machine Translation Evaluation

This paper describes Meteor++ 2.0, our submission to the WMT19 Metric Shared Task.The well known Meteor metric improves machine translation evaluation by introducing paraphrase knowledge.However, it only focuses on the lexical level and utilizes consecutive n-grams paraphrases.In this work, we take into consideration syntactic level paraphrase knowledge, which sometimes may be skip-grams.We describe how such knowledge can be extracted from Paraphrase Database (PPDB) and integrated into Meteor-based metrics.Experiments on WMT15 and WMT17 evaluation datasets show that the newly proposed metric outperforms all previous versions of Meteor.
A Fast Labeled Multi-Bernoulli Filter Using Belief Propagation

A Fast Labeled Multi-Bernoulli Filter Using Belief PropagationWe propose a fast labeled multi-Bernoulli (LMB) filter that uses belief propagation for probabilistic data association.The complexity of our filter scales only linearly in the numbers of Bernoulli components and measurements, while the performance is comparable to or better than that of the Gibbs sampler-based LMB filter.