Reviewerly/exHarmony huggingface dataset:
Appropriate use of <i>p</i>‐values

The use of statistics, particularly inferential statistics, is ubiquitous in most areas of medical research. Because we usually only have data from samples, and sometimes quite small samples, the ability to differentiate treatment benefits or risk factor associations that might be reproduced in the broader population of patients from those that may not is crucial. The article by Bangdiwala raises some important issues in how the practice of statistical data analysis frequently differs from how it should be used. Statistical hypothesis tests are an important tool in medical research and when used as intended and in a targeted fashion, the benefits are enormous but misused or overused can give a false confidence in findings that do not actually support the researcher's scientific hypotheses. As Bangidwala points out, the statistical hypothesis testing tools that we use today were developed a long time ago and in a very different context, and mostly nonmedical contexts. The purpose of statistical hypothesis testing then was to test very specific hypotheses, and usually just one. In some areas of medical research, particularly basic science, that can still be the case. In many areas, however, a single manuscript will report multiple effect sizes that represent differences between groups or associations, each with an associated p-value. The intention is good, to give the reader some indication of whether the null hypothesis should be rejected or not. However the discrepancy between principle and practice of statistical hypothesis testing means that they cannot always be taken at face value. The editors of this journal feel that abandoning the use of statistical hypothesis tests would also be harmful, as they do serve a useful purpose. We support the suggestion of Bangdiwala that statistical hypothesis tests be used in a targeted fashion rather than indiscriminately and, where possible, are replaced by other measures, such as interpreting the width of confidence intervals and whether the confidence interval includes the null effect. We also argue that authors should declare whether their use of statistical hypothesis tests is literally to test an a priori hypotheses or is used in a more exploratory way. There is absolutely nothing wrong with exploratory data analysis, but it should be declared rather than dressed up as true hypothesis testing. Michael Jones contributed to the manuscript writing, co-conceived of idea, and approved final manuscript. Kirsteen Browning, Daniel Keszthelyl, Andrea Shin, Rajan Singh, Pierfrancesco Visaggi, and Frank Zerbib provided intellectual input and approved final manuscript. Maura Corsetti co-conceived of idea, provided intellectual input, and approved final manuscript. No competing interests declared.
Multi-object 3D segmentation of brain structures using a geometric deformable model with a priori knowledge

Brain structure segmentation in 3D Magnetic Resonance Images is crucial for understanding neurodegenerative disorders. Manual segmentation is error-prone, necessitating robust automated techniques. In this paper, we introduce a novel and robust approach for the simultaneous segmentation of multiple brain structures in MRI images. Our method involves the concurrent evolution of 3D surfaces toward predefined anatomical targets, employing an efficient multi-object generalized fast marching method (MOGFMM) for simultaneous object detection. Additionally, we propose an effective evolution function that integrates prior knowledge from anatomical and probabilistic atlases, as well as spatial relationships among the segmented structures. Each deformable surface corresponds to a specific structure. To validate our approach, we conducted experiments on a dataset of real brain images (IBSR) and compared the results with several state-of-the-art methods. The obtained results were promising, demonstrating the effectiveness and superiority of our developed method.
Sketching the Mobile-Assisted Language Learning Experiences Designed by Taiwanese High School English Teachers

This study explored the mobile language learning experiences that Taiwanese high school English language teachers created. A total of 119 valid mobile-assisted language learning (MALL) lesson plans from 36 senior high schools in the national mobile learning (m-learning) programme in Taiwan were analysed via content analysis. The results showed that: (a) “direct guided learning” and “video sharing” were the most used m-learning strategies; (b) the major learning activity foci were on “vocabulary/grammar” and “viewing,” while “writing” received less attention; and (c) students often played the roles of “idea sharer,” “worksheet doer” and “information searcher.” Extrapolating the results with the replacement–amplification–transformation framework further revealed students as “worksheet doer” in the replacement level and “adaptive” as the distinguishing feature for the amplification and transformation level. Finally, from a mobile pedagogical framework perspective, the lesson plans showed that a certain level of personalization and collaboration was created. The results of this research could contribute to the understanding of teachers’ MALL lesson design practice and provide possible directions for supporting teachers’ design activities.
Implementation of learning reading and writing activities for 5th class students

The school literacy movement is a social movement that combines various components. The government has launched that schools carry out reading and writing learning activities as a "School Literacy Movement" This study aims to determine the implementation of reading and writing learning activities for grade V students of SD Negeri 37 Kediri City. Study this use study qualitative descriptive. In the survey, qualitative data was collected character qualitative, which describes the research setting, the situation, and the informant/respondent. Both in terms of saying or writing words into complete sentences for fifth-grade students at SD Negeri 37 Kendari City, from the results of observations in the Even Semester of the 2021/2022 Academic Year, it is known that the score The average daily test is only 65. Therefore, it is concluded that the reading ability in students' literacy learning activities is still relatively low or can be said to be below the Minimum Completeness Criteria (KKM) set at SD Negeri 37 Kendari City, which is 70.
A Phenomenological Study of Teachers’ Roles and Learners’ Responses in Learner-Centered Classes Using Metaverse Technology

This paper examines learner-centered media English learning using metaverse-based facilitation through a phenomenological method, which is a type of qualitative research. A private university in Chungcheongbuk-do gave a Liberal Arts English class in Gather.town, one of the Metaverse platforms, and designed a learner-centered course through appropriate facilitation by the professor. This study conducted an in-depth analysis on the experience of students in this new classroom environment, the roles of professors, and the internal and external changes of students by using a phenomenological research method from Mustakas (1994). The results were as follows: first, students enjoyed and felt interested in the new learning; second, the professor played the role of a facilitator who promoted student participation and cooperation; third, the course made students internally motivated to learn actively; and fourth, students externally experienced learner-centered learning through interaction among themselves. Lastly, this study discussed the need for proper facilitation by the instructor for a metaverse-based English class. In conclusion, it was found that English classes using metaverse-based facilitation were interesting, enhancing collaboration, and motivating their learning. Therefore, learner-centered learning based on the metaverse will play a role and function in attracting students’ spontaneous participation, cooperation, and learning motivation when appropriate facilitation is provided.
Optimal allocation of distributed energy storage capacity based on smart node terminal technology

In the context of the poor time performance of some distributed energy storage capacity optimization methods, a distributed energy storage capacity optimization method based on smart node terminal technology is designed. It Quantifies the distributed energy demand response characteristics, extracts the economic index of the energy system, calculates the natural gas consumption of the internal combustion engine and the recovered waste heat respectively, obtains the storage capacity equipment operation coefficient, sets the application scenario selection mode based on the intelligent node terminal technology, improves the load factor of the gas internal combustion engine, and designs the storage capacity optimization allocation method. Results show that the average time required for the distributed energy storage capacity optimization allocation method in the paper is 72.36s, indicating that the designed distributed energy storage capacity optimization allocation method is more suitable for application to practical tasks after the introduction of smart node terminal technology.
Simulation of physical education effectiveness assessment model based on constraint clustering algorithm

During the growth of universities, carrying out Physical Education (PE) courses can not only promote students' physical and psychological health, but also strengthen students' physique, which plays an important role in students' future development. To realize the effectiveness of PE classroom teaching, we must correctly understand the effectiveness of teaching, design teaching scientifically and reasonably, and use instructional assessment reasonably and objectively. Many factors of educational phenomenon can't be classified into absolutely clear categories, and any specific identity is relative, which contains differences and changes, thus showing certain uncertainty. According to the viewpoint of fuzzy mathematics, many things that people regard as the same are a fuzzy set. Combining with Data Mining (DM), this article constructs an assessment model of PE instructional effectiveness based on constrained clustering algorithm. For PE teaching, the construction of the assessment model of PE instructional effectiveness based on constraint clustering algorithm can further improve the monitoring quality of PE teaching, strengthen the information monitoring level and information summarizing ability of PE teaching, and ensure that the relevant information of PE instructional level can be applied to practice more accurately.
Construction of film and television information dissemination model based on grey clustering algorithm

The mode of film and television information dissemination has undergone great changes under the promotion of social network information technology. The disseminators of film and television information have changed from single production information in the past to multi-channel release information now, from simple text information to various forms of multimedia network information interaction. This paper attempts to build a film and television information dissemination model based on grey clustering algorithm and sorts out the application in film and television information dissemination according to the time development order and summarizes the development process and mechanism of film and television information dissemination model. The simulation experiment is used to simulate the film and television information dissemination process of information in this network, which proves the practicability of this model. The research results show the changing trend of the number of final nodes in the unobserved state S state with P value under different μ values. When &mu;=5.5, with the increase of P, that is, the proportion of group effect of group heat information in film and television information network in decision-making gradually increases, the final number of people who have not watched it gradually decreases, and the final spread range of film and television information gradually expands with the increase of the proportion of P value.
Construction of library personalized management system based on collaborative filtering algorithm

In the face of massive book resources, users need to spend a lot of time and energy to obtain the required book resources, while the traditional search query can no longer meet the needs of users. In the process of informatization, the library management system has accumulated a large amount of data. There is a lot of valuable information among these data, which can provide decision support for decision makers of enterprises and departments. Users' demand for resources is becoming more and more detailed and precise, and it is increasingly difficult for users to quickly and accurately obtain the resources they need. Based on this, this article puts forward a personalized recommendation model of library based on Collaborative Filtering (CF) algorithm, which can mine the hidden information behind the data, predict the information needs of readers, organize more and better targeted high-quality information for readers, and provide more personalized services. The experimental results show that the personalized recommendation system can improve the recommendation accuracy and realize dynamic personalized recommendation, which provides a theoretical basis for the construction of university libraries.
Design of security image contraband detection system based on PP-YOLOE+_DCS

Traditional security screening methods mainly use manual identification of security images, there is a low identification of inefficiency, high error of judgement rate, which has become a bottleneck limiting public safety and security. Therefore, to deal with this problem, this paper proposes the security image contraband detection model PP-YOLOE+_DCS, which makes three main improvements on the basis of the PP-YOLOE+ model. To begin with, we introduced deformable convolution within the backbone network that strengthen the models' feature extraction capability. Secondly, we introduced a coordinated attention mechanism among the backbone network and the detection neck for better focusing the model on the object region. Finally, we replaced the original GIOU loss function with the SIOU loss function to improve the detection accuracy and training speed. The improved PP_YOLOE+_DCS model obtained achieves 91.4% detection accuracy, 2.8% improvement compared with the baseline model mAP, only 0.24 M additional parameters, and 420.2 ms inference delay on embedded devices, which provides a new solution for the intelligence of contraband detection.
Research on functional architecture of sports application system based on AI technology

In the field of sports analysis, combining sports action recognition with sports events and analyzing the athletes' sports situation in the video can provide athletes with auxiliary training, find their strengths and weaknesses, optimize the training system and promote their growth. Aiming at the low accuracy of sports video action recognition method, this paper applies the Deep Learning (DL) technology in Artificial Intelligence (AI) to sports action recognition, constructs the functional architecture of sports application system, and conducts comparative experiments on standard sports video action data sets to verify the feasibility and effectiveness of the proposed algorithm from the recognition accuracy and response time respectively. The test results show that the proposed method is superior to the traditional motion recognition model in accuracy and efficiency.
Odgovornost za štetu od veštačke inteligencije

The characteristics of the artificial intelligence (AI) technologies make it complicated to determine who should bear the responsibility in the case of the violation of legally protected human interests (causing damage). In the heart of the article are following questions: Should a person be responsible for the damage caused by the AI technologies, if we bear in mind the "independent decision- making process" of the AI? Should liability be borne by producers, owners or users of AI technologies? Can existing tort liability regimes apply to damage from the AI? Which liability regime should be applied – fault liability or strict liability? The idea of introducing the new category of persons in law – the ePerson – has been left behind for now. Although the AI show certain levels of decision making autonomy, it is the (wo)man who has programed and produced the AI. For that reason, (wo)man should bear legal responsibility for the damage from AI. With that being said, the responsible persons are producers, owners and users of the AI, depending on the circumstances of the case. The last decade has been marked by European Union’s efforts made to find an optimal solution for the AI liability. The proposals have been made for the revision of product liability rules, incorporating this case of damage, as well as for the harmonization and adaptation of the rules of fault liability. If the defect may be found, product liability should be applied towards the producers. If that is not the case, either strict liability under the national law or fault liability should be applied. If the latter should be, there are set of procedural rules proposed by the European Union, regarding the assumption of causal link and guilt of the provider or user of the AI, benefiting the damaged person (claimant).
Coreference disambiguation based on two-layer label dependence analysis

A two-layer labeling-based dependency analysis model is proposed to introduce the location features of words for the coreference disambiguation of entities. Firstly, two layers of labels are used for labeling, the second layer labels the location information of words, the features of sentences are learned using a bidirectional long short-term memory network, the dependency syntax analysis based on deep graph decoding obtains the dependency tree of sentences, and the two layers of labels are fused to improve the performance and accuracy of coreference disambiguation. Experiments are conducted on the text dataset of power security entity relationship extraction, and the results show that the two-layer labeled dependency analysis has an improved effect on co-finger disambiguation, which verifies the effectiveness of the model for co-finger disambiguation on the experimental dataset.
Policy Suggestions for Transplantation of Organs in India: Use of Blockchain Technology to Manage Organ Donation

One of the most honorable acts of humanity is the donation of an organ or organs to save the lives of patients with severe diseases that necessitate organ replacement. Organs can be donated by people who are alive as well as dead. India has seen a noteworthy upsurge in organ donation following the death of the brain in recent years. There are various legal, clinical, ethical, and technical constraints implicated in the current organ donation and transplantation systems in terms of registering recipients, matching donors with recipients, removing organs, delivering organs, and transplanting organs. An end-to-end system is essential to ensure an impartial and competent organ donation and transplantation process. Use of blockchain technology in order to better manage organ donation and allocation can be one of the most efficient ways to remedy this situation. The article proposes blockchain-based solution to ensure complete decentralization, security, traceability, privacy, and trustworthiness in the donation and transplantation of organs. By performing confidentiality, safety, and privacy analyses and linking our projected resolution with the prevailing one, we evaluate the performance of the suggested solution. Adopting the above-mentioned measures will lead to the creation of block chain platforms or hyper ledger fabric frameworks, streamlining organ donation and allocation processes, which will benefit India as it moves toward the digital future in health care.
Transpointflow: Learning Scene Flow from Point Clouds with Transformer

Scene flow estimation is the task of obtaining 3D motion from a dynamic scene. Due to the sparseness of point clouds, extracting features for a local group of points separately may result in different features that may all belong to the same object. This difference makes global correlation prone to producing an unacceptable flow. Local correlation restricts the algorithm to capturing limited movements and fails when fast movement or large deformation of an object occurs. Therefore, we propose a transformer-based scene flow method that can perform global feature modeling through a self-attention layer. Moreover, we propose a cross-attention-based flow embedding layer for global feature matching. We further propose a learnable attention-based up-sampling layer to up-sample the estimated flow to higher resolution based on a single feature scale, eliminating the need to model global dependencies at all scales. Experimental results show that our model produces competitive results on Flyingthings3D and KITTI datasets with efficient performance.
End-to-End Trainable Weakly Non-Negative Factorization

Non-negative matrix factorization (NMF) is widely applied to analyze pattern data in an unsupervised manner. It imposes hard non-negativity constraints on factors to extract intrinsic characteristics from an input matrix, though demanding complicated optimization techniques which hinder the general applicability. Toward flexible formulation, we propose weakly non-negative factorization. In contrast to the strict non-negative approach, our method permits factors to contain small amount of negative values. The relaxation theoretically leads to an efficient factorization formulation which can be implemented by means of off-the-shelf techniques used in a deep learning literature. Thus, the method is flexibly applicable to versatile factorization tasks, such as deep NMF and structured NMF. In the experiments on the NMF-related tasks, we demonstrate that the weak non-negativity produces effective factors similarly to NMF and the method exhibits favorable performance in comparison to the other approaches.
Learned Image Compression Guided Adaptive Quantization for Perceptual Quality

Neural network based image compression has made significant progress in recent years. The learned image codecs are commonly reported to outperform their conventional counterparts in perceptual quality. Despite the superior performance, the learned image codecs are much more complex to decode, which hinders their usage in practice. Without a significant advance in hardware capability, the conventional image codec will likely remain a primary component for large scale image services. It is therefore desirable to improve the quality of conventional image codecs. In this paper, we present an adaptive quantization approach to the conventional image codec with the help of learned image codecs to improve its perceptual quality. It exploits the bit allocation of the neural network based image codec to adapt the quantizers on a block basis. It is experimentally shown that the proposed method provides considerable perceptual quality improvements over other leading contenders.