Reviewerly/exHarmony huggingface dataset:
The Role of Design and Digital Media in Monitoring and Improving the Performance of Taekwondo Athletes

This paper aims to demonstrate how design and digital media can have a relevant contribution to the improvement of Taekwondo athletes’ performance. This study focuses on answering the existing gap of a solution that allows quick and accurate access to data about the performance of martial arts athletes. This access to complex information, previously inaccessible or indecipherable to athletes and coaches, allowed, through digital design, the improvement of communication and a more personalized training feedback. The methodology developed was based on design thinking, in a work process that consisted of user identification, and the conception of a prototype in the user-centred design framework. The results obtained in the usability tests performed with Taekwondo athletes and coaches were demonstrative of the efficiency of the designed solution. These scores are also a stimulus for the potential replication and adaptation of the study in other martial arts.
Reconciling the personalization–privacy paradox via <scp>DoctorBots</scp>: The roles of service robot acceptance model elements and technology anxiety

Abstract The role of artificial intelligence (AI)‐based DoctorBots in improving healthcare and the medical industry is expected to increase significantly in the coming decades. However, contrary to the general view that AI can gradually replace human work, user acceptance of DoctorBots has become an obstacle to the development of AI medical diagnosis. Building on the service robot acceptance model (sRAM), this study investigates the potential of the functional, socioemotional, and relational elements of DoctorBots to reconcile the personalization–privacy paradox, thus enhancing user acceptance. Via two scenario‐based experiments with 398 participants, this study reveals that the negative influence of the personalization–privacy paradox on user acceptance is exacerbated when users' technology anxiety is high. In addition, an online survey of 400 DoctorBot users indicates that ease of use, subjective social norms, social presence, and rapport are effective in addressing both nonpersonalization (NPC) and privacy concerns (PVC). These findings suggest that the healthcare industry can leverage DoctorBots to implement self‐diagnosis. Specifically, DoctorBots' functional elements are effective in mitigating users' NPC, and their relational elements are effective in extenuating users' PVC.
A (Discrete) Homotopy Theory for Geometric Spaces

We define the concepts of homotopy and fundamental group for geometric spaces as a generalization of metric spaces, digital spaces, and graphs; then, we compare them with corresponding concepts in these spaces. Also, we state some properties of the fundamental group of geometric spaces and some theorems to calculate them.
THE BIBLIOMETRIC ANALYSIS OF THE AUGMENTED REALITY RESEARCH CARRIED OUT WITH THE EXPERIMENTAL METHOD PUBLISHED IN SCOPUS BETWEEN 2012-2022

This study aims to make a bibliometric analysis of the augmented reality articles carried out with the experimental method between 2012 and 2022 in the Scopus database. To realize the aim of the research, 255 articles published between 2012-2022 were reached. The bibliometric data of 255 articles from the Scopus database were analyzed using VOS viewer 1.6.18 software and visualized with the Tableau software. According to the research findings, 255 articles were produced by researchers from 49 different countries. According to co-authorship networks, six different clusters were formed. National Taiwan University of Science and Technology is the leading institution in AR research with the experimental method. The 255 articles reviewed were published in 159 different journals. Interactive Learning Environments journal ranks first with 13 publications by far, while Computers and Education journal stands out with 878 citations in terms of the number of citations. When the keywords used in the articles were examined, it was seen that the concepts of virtual reality, mobile learning, motivation, and cognitive load came to the fore. The results obtained within the scope of the research were shared by discussing with other study findings obtained from the literature.
Reentry trajectory design of a hypersonic vehicle based on reinforcement learning

Abstract In this research, we investigate control of a hypersonic vehicle (HV) following its reentry into the Earth’s atmosphere, using deep reinforcement learning (DRL) in a continuous space. Here, we incorporate the basic kinematic and force equations of motion for a vehicle in an atmospheric flight to formulate the reentry trajectory satisfying the boundary constraints and multiple mission related process constraints. The aerodynamic model of the vehicle emulates the properties of a common aero vehicle (CAV-H), while the atmospheric model of the Earth represents a standard model based on US Standard Atmosphere 1976, with significant simplification to the planetary model. In an unpowered flight, we then control the vehicle’s trajectory by perturbing its angle of attack and bank angle to achieve the desired objective, where the control problem is based on different actor-critic frameworks that utilize neural networks (NNs) as function approximators to select and evaluate the control actions in continuous state and action spaces. First, we train the model following each of the methods, that include on-policy proximal policy approximation (PPO) and off-policy twin delayed deterministic policy gradient (TD3). From the trajectory generated, we select a nominal trajectory for each algorithm that satisfies our mission requirements based on the reward model.
Information-Theoretic Cryptography: Life Cycle, Ecosystem, and the Promise of Quantum-Inspired AI

After being introduced by Shannon as a measure of disorder and unavailable information, the notion of entropy has found its applications in a broad range of scientific disciplines. In this chapter, we present a systematic review on the applications of entropy and related information-theoretical concepts in the design, implementation, and evaluation of cryptographic schemes, algorithms, devices, and systems. We make use of the existing literature to establish a life cycle as well as an ecosystem for information-theoretic cryptography. Moreover, we study existing trends and establish a roadmap for the future research in these areas.
The Cross-Impact Between Cryptography and Artificial Intelligence: A Highlight

Here we try to highlight the existence of an inseparable coupling between cryptography and AI visible in recent research literature. To this end, we first try to develop minimal ecosystems for both branches of science. Then we show how AI can be seen almost everywhere in the ecosystem of cryptography, and vice versa.
Kesulitan yang Dihadapi Anak Usia Dini dalam Memahami Teks dan Strategi Mengatasinya

The ability to read is one of the most crucial skills in everyday life. Through reading, a person can acquire knowledge, information, and develop an understanding of the world around them. Good reading skills also form a vital foundation in the learning process at school. This research aims to identify and analyze effective methods for improving early childhood reading abilities, as well as to explore potential challenges faced by young children in understanding text and the factors influencing their reading development. This study is a qualitative case study. The research reveals a significant positive influence of these methods on letter and word recognition, as well as reading comprehension. Therefore, educators and parents can utilize these methods to support and enhance reading abilities, as strong reading skills have long-term benefits for a child's academic development.
F-IVM: analytics over relational databases under updates

Abstract This article describes F-IVM, a unified approach for maintaining analytics over changing relational data. We exemplify its versatility in four disciplines: processing queries with group-by aggregates and joins; learning linear regression models using the covariance matrix of the input features; building Chow-Liu trees using pairwise mutual information of the input features; and matrix chain multiplication. F-IVM has three main ingredients: higher-order incremental view maintenance; factorized computation; and ring abstraction. F-IVM reduces the maintenance of a task to that of a hierarchy of simple views. Such views are functions mapping keys, which are tuples of input values, to payloads, which are elements from a ring. F-IVM supports efficient factorized computation over keys, payloads, and updates. It treats uniformly seemingly disparate tasks: While in the key space, all tasks require general joins and variable marginalization, in the payload space, tasks differ in the definition of the sum and product ring operations. We implemented F-IVM on top of DBToaster and show that it can outperform classical first-order and fully recursive higher-order incremental view maintenance by orders of magnitude while using less memory.
A Vector Space Approach for Measuring Relationality and Multidimensionality of Meaning in Large Text Collections

In this article, we develop a methodological approach for organizational research regarding the construction of multidimensional and relational similarity measures by using the vector space model in natural language processing (NLP). Our vector space approach draws on the well-established premise in organizational research that texts provide a window into social reality and allow measuring theory-based constructs ( e.g., organizations’ self-representations). Using a vector space approach allows capturing the multidimensionality of these theory-based constructs and computing relational similarities between organizational entities ( e.g., organizations, their members, and subunits) in social spaces and with their environments, such as the organization itself, industries, or countries. Thus, our methodological approach contributes to the recent trend in organizational research to use the potential inherent in big (textual) data by using NLP. In an example, we provide guidance for organizational scholars by illustrating how they can ensure validity when applying our methodological contribution in concrete research practice.
From <i>Star Trek</i> to <i>The Hunger Games</i>: emblem gestures in science fiction and their uptake in popular culture

Abstract Research on emblems to date has not drawn on corpus methods that use public data. In this paper, we use corpus methods to explore the use of original fictional gestures in the real world. We look at two examples from popular science fiction, the Vulcan salute from Star Trek and the three-finger salute from The Hunger Games . First, a Twitter corpus of the Vulcan salute emoji shows that it is used to represent Star Trek fandom and wider nerd culture, alongside its use as a greeting. Second, a global news corpus shows the three-finger salute has come to be used as a pro-democracy protest gesture across political and cultural boundaries in South East Asia. These corpus studies show different trajectories for the two gestures, with the three-finger salute escaping the confines of its fictional world, while the Vulcan salute has come to stand in as a reference to the media it originated from. We conclude with a reflection on the opportunities, challenges, and limitations of bringing corpus methods to gesture studies.
Symmetric bidirectional quantum teleportation via eight-qubit cluster state

Abstract This paper proposes a symmetric bidirectional quantum teleportation protocol through an eight-qubit cluster state as a quantum channel to achieve GHZ state interchange between two parties. Alice and Bob must first perform two CNOT gate operations on the unknown GHZ state to be sent in our scheme. Next, they need to complete the Bell state measurements and the corresponding unitary operations to obtain the corresponding GHZ state. Our protocol has lower operational complexity and higher inherent efficiency compared to other protocols. In addition, we have simulated the quantum circuit on the IBM qasm simulator to verify the feasibility of the protocol.
Tuning Query Reformulator with Fine-Grained Relevance Feedback

Pseudo-relevance feedback (PRF) has been empirically validated as an effective query reformulation method to improve retrieval performance. Recent studies formulate query reformulation as a reinforcement learning task to directly optimize the retrieval performance. However, this paradigm computes the feedback signals by comparing the retrieved documents with the manual annotations, and neglects that the annotations severely suffer from the unlabeled problem (the relevant documents of a query may not be fully annotated), causing the model to overfit the training set. Moreover, the training of reinforcement learning is expensive and unstable. To address the above problems, inspired by recent great achievements of reinforcement learning from human feedback (RLHF), we propose a simple fine-grained feedback framework for query reformulation, which computes the feedback signals by a powerful re-ranking model instead of manual annotations. Specially, we first utilize various automation methods to generate annotated data, which allows us to initialize the reformulator and obtain a good starting point. Then we employ a re-ranking model to assign fine-grained scores to the rewritten queries generated by the reformulator. Finally, we refine the reformulator using feedback scores. In this way, the knowledge of the re-ranking model can be effectively transferred to the reformulator, leading to a better generalization performance. Furthermore, our framework can enhance performance by leveraging a large amount of unlabeled data. Experiments on a real-world E-Commerce search engine and three public benchmarks demonstrate the effectiveness of our framework.
Enrichment characteristics and metallogenic significance of rare elements in Zambia: Based on 1:1,000,000 geochemical mapping

The Sino–Zambian Cooperation Project has filled the gap in Zambia national‐scale geochemical mapping, providing basic geochemical guarantees for Zambia's basic geology, mineral development, environmental protection, agricultural production and other aspects. This article aims to reveal the distribution characteristics of its rare elements (Li, Be, Nb, Rb, Sr). The median value of Li, Be, Nb, Rb and Sr is 4.94 × 10 −6 , 0.65 × 10 −6 , 7.50 × 10 −6 , 33.00 × 10 −6 and 21.90 × 10 −6 , respectively. The median and average values of the Irumide Belt, Zambezi Belt, Choma‐Kalomo Block and Hook Granite Complex are higher than other tectonic belts, showing that rare elements are enriched in those areas. The distribution of element anomaly areas is mainly related to the distribution characteristics of granite type rare metal mineralization occurrences, and a small number of anomaly areas are controlled by salt lake brine‐type mineralization occurrences. A total of five rare metal geochemical anomalies have been delineated respectively based on the 85% cumulative percentage of rare elements (Li, Be, Nb, Rb, Sr). The metallogenic prospective area delineated based on the comprehensive anomaly area of rare elements will provide an important reference for searching for rare metal deposits in this area.
AOM: A New Task for Agitative Opinion Mining in We-media

As We-media continues to develop, there is a concerning rise of agitative opinions on We-media platforms, which usually lead to online violence. To formalize the research on identifying whether a sentence contains agitative opinions, we propose a new task AOM for agitative opinion mining in We-media. To clarify the task, we make a clear definition to agitative opinions in which they are categorized into nine types and we manually construct a ten-thousands scale Chinese agitative opinion dataset CAOD based on WeChat public account, for research purpose. Furthermore, a baseline model $$\mathrm{CAOD_{MINER}}$$ based on TextCNN is proposed and sampling methods are adopted in training it. For comparison, we also apply several mainstream text classifiers into CAOD. The comparative experiment and further analysis show that AOM is a soluble but challenging task, where unbalanced data distribution, diversity of expression forms, context dependency, scarcity of external knowledge and implicit expression deserve to be studied in the future.
PNPT: Prototypical Network with Prompt Template for Few-Shot Relation Extraction

Few-shot relation extraction involves predicting the relations between entity pairs in a sentence with a limited number of labeled instances for each specific relation. Prototypical network, which is based on the meta-learning framework, has been widely adopted for this task. Existing prototypical network-based approaches typically obtain the relation representation by concatenating the embeddings corresponding to start tokens of two entity mentions. While these methodologies have demonstrated commendable performance, we argue that the current relation representation fails to fully capture semantic nuances within complex scenes, where the identical entity pairs often convey diverse semantic relationship. In this paper, we propose an innovative relation representation approach that integrates textual context and entity mentions through a prompt template. Furthermore, we introduce a gate mechanism to selectively incorporate external relation knowledge into the origin relation prototype derived from support instances. Experimental results on two benchmark datasets demonstrate the effectiveness of our proposed approach.
Performing complex interdisciplinary procedures: Never on the patient the first time!

Medical EducationEarly View REALLY GOOD STUFF Performing complex interdisciplinary procedures: Never on the patient the first time! Daniel Aiham Ghazali, Corresponding Author Daniel Aiham Ghazali [email protected] orcid.org/0000-0003-0129-4322 Correspondence Dr. Daniel Aiham Ghazali, MD, PhD, Director of Research, Emergency Department and EMS, Amiens Academic Hospital, 1 Rond-Point du Professeur Christian Cabrol Cabrol, 80000 Amiens, France. Email: [email protected] Contribution: Conceptualization, Data curation, Formal analysis, ​Investigation, Methodology, Project administration, Software, Supervision, Validation, Writing - original draftSearch for more papers by this authorPatricia Ilha-Schuelter, Patricia Ilha-Schuelter orcid.org/0000-0002-8510-8920 Contribution: Data curation, ​Investigation, Resources, Software, Visualization, Writing - review & editingSearch for more papers by this authorDenis Oriot, Denis Oriot Contribution: Methodology, Supervision, Visualization, Writing - review & editingSearch for more papers by this author Daniel Aiham Ghazali, Corresponding Author Daniel Aiham Ghazali [email protected] orcid.org/0000-0003-0129-4322 Correspondence Dr. Daniel Aiham Ghazali, MD, PhD, Director of Research, Emergency Department and EMS, Amiens Academic Hospital, 1 Rond-Point du Professeur Christian Cabrol Cabrol, 80000 Amiens, France. Email: [email protected] Contribution: Conceptualization, Data curation, Formal analysis, ​Investigation, Methodology, Project administration, Software, Supervision, Validation, Writing - original draftSearch for more papers by this authorPatricia Ilha-Schuelter, Patricia Ilha-Schuelter orcid.org/0000-0002-8510-8920 Contribution: Data curation, ​Investigation, Resources, Software, Visualization, Writing - review & editingSearch for more papers by this authorDenis Oriot, Denis Oriot Contribution: Methodology, Supervision, Visualization, Writing - review & editingSearch for more papers by this author First published: 13 November 2023 https://doi.org/10.1111/medu.15282Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. REFERENCE 1Ghazali DA, Ilha-Schuelter P, Barreyre L, et al. Development and validation of the first performance assessment scale for interdisciplinary chest tube insertion: a prospective multicenter study. Eur J Trauma Emerg Surg. 2022; 48(5): 4069-4078. doi:10.1007/s00068-022-01928-9 10.1007/s00068-022-01928-9 PubMedWeb of Science®Google Scholar Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation
Paraphrase Generator Using Natural Language Generation and Natural Language Processing

Paraphrasing and summarizing using Artificial intelligence have been able to bring about tremendous changes in various industries by giving different and efficient suggestions of how sentences can be written in different forms but also maintaining the meaning intact to its users. Incorporating AI in the stream of paraphrasing as deemed to be useful to wide range of industries, as we can get sentence recommendations based on the needs of different industries. The model can be trained with datasets relevant to the industries to tune the outputs according to the respective needs. This quality of machine learning can be considered as a boon to people who create content as it paves the way to get automated results having the same efficiency of a human. The core idea of artificial intelligence is to mimic the human thinking process. As computer engineers we have a role to leverage these models and help people who need it the most. The proposed methodology provides greater efficiency and higher accuracy, adequacy and diversity than the other methodologies.
Deep Learning and Optical Character Recognition-Based Automatic UIDAI Details Extraction System

The Automatic UIDAI Details Extraction System extracts details from Aadhaar cards and stores them into a document. The system can be utilized in banking, in government agencies, at vaccination centers. The presented system uses deep learning to detect the name and number on Aadhaar cards and recognize the text in real-time using optical character recognition (OCR). Three sub-blocks make up the proposed system: image acquisition, name and number detection, and OCR. For Image Acquisition, a setup employing a webcam with a reasonable quality was devised. This approach presents the extraction of the name and number from an image using a MobileNet V1 architecture based on Single-Shot Detector (SSD). EasyOCR is utilized for text recognition. The system was deployed on the NVIDIA Jetson Nano hardware platform. The System’s real-time performance has been optimized by adjusting these three sub-block parameters. The system provides accuracy of more than 90%.
Real-Time Full Body Tracking for Life-Size Telepresence

In the final years of the twentieth century, synthetic environment (SE) technology is gaining widespread acceptance among industrialized nations. SE uses a computer to mediate user interaction with a physically separate environment from the user. Telepresence has been highlighted as a particularly well-suited design to SE. This is because telepresence is almost similar to Virtual Reality (VR). What makes it the difference is that VR targets to obtain the illusion of presence in the virtual world. Meanwhile, telepresence targets to obtain the illusion of presence at a remote location. However, it becomes an issue when the size of the telepresence display is smaller. It reduces the impact on the user’s social presence. Therefore, these studies aim to perform real-time full-body human tracking for life-size telepresence. To achieve the aims, this paper explains the life-size telepresence, device tracking, process of full-body tracking, background removal, remote user setting, and results of the studies.