Reviewerly/exHarmony huggingface dataset:
Enriching a Traditional Learning Activity in Preschool through Augmented Reality: Children’s and Teachers’ Views

Nowadays, Augmented Reality flourishes in educational settings. Yet, little is known about teachers’ and children’s views of Augmented Reality applications in Preschool. This paper explores 71 preschoolers’ opinions of Augmented Reality teaching integrated into a traditional learning activity. Additionally, five educators’ views of Augmented Reality applications in Preschool are captured. Mixed methods with questionnaires and semi-structured interviews were used. The questionnaires record children’s preferences regarding their favorite learning activity between traditional and the Augmented Reality one. Additionally, they explore the activity preschoolers would like to repeat and found most enjoyable: playful. Regarding quantitative data analysis, independent/paired samples t-tests and chi-square test along with bootstrapping with 1000 samples were used. As for the qualitative data collection, educators’ semi-structured interviews focused on three axes: (a) children’s motivation and engagement in Augmented Reality activities, (b) Augmented Reality’s potential to promote skills, and (c) Augmented Reality as a teaching tool in preschool. The emerging results are: Preschoolers prefer more Augmented Reality activities than traditional ones. There are no statistically significant gender differences in preferences for Augmented Reality activities. Educators regard Augmented Reality technology as an innovative, beneficial teaching approach in preschool. However, they express concern regarding the promotion of collaboration among preschoolers via Augmented Reality.
The Effect of Transactional Leadership on Organizational Agility in Tourism and Hospitality Businesses: The Mediating Roles of Organizational Trust and Ambidexterity

The tourism and hospitality industry is dynamic, so it demands agility to gain a competitive edge. This paper aims to examine the effect of transactional leadership on organizational agility in the tourism and hospitality industry. It also investigates the roles of organizational trust and ambidexterity as mediators by adopting the resource-based view (RBV). This study’s data are collected from the full-time employees working in category (A) travel agencies and five-star hotels operating in Egypt. The PLS-SEM approach was used to analyze 532 valid responses using WarpPLS 7.0. The findings showed that there is a positive relationship between transactional leadership and organizational agility, as well as positive relationships between transactional leadership with organizational trust and ambidexterity. Organizational trust and ambidexterity were also found to have positive relationships with organizational agility. Moreover, the findings revealed that organizational trust and ambidexterity play a mediating role between transactional leadership and organizational agility. According to the results, setting specific, measurable, achievable, relevant, and time-bound (SMART) goals is a key aspect of transactional leadership. Managers can collaborate with employees to set goals aligned with organizational agility, encouraging them to proactively identify and seize opportunities.
Developing a Curated Chatbot as an Exploratory Communication Tool for Chemistry Learning

In a technology-centric world, leveraging digital tools such as chatbots allows educators to engage students in ways that may be more accessible for both parties, particularly in large lecture classrooms. This report details the development of an interactive web-based chatbot to curate content for writing about chemistry in context. Students were assigned a 500-word paper where they discuss general chemistry concepts through the lens of a timely, sustainability-related topic, i.e., water footprint, carbon footprint, or embodied carbon. Discussed herein are the development of the decision tree, the chatbot's components, and results from the initial implementation in a large lecture general chemistry classroom. Over 78% of the 347 enrolled students (271) used the chatbot over 350 times in the 3 weeks leading up to the assigned due date of the paper. Eighty-three percent of the interactions were captured for further analysis, which showed that 22% of students used the chatbot more than once. Forty-six percent of recorded interactions were used to aid students in developing or refining their idea for the assignment. The curated chatbot technology reported here for writing assignments in chemistry can be readily adapted to other aspects of coursework in chemistry.
Control and performance analyses of a DC motor using optimized PIDs and fuzzy logic controller

Based on the no-free-lunch theorem, researchers have been proposing optimization algorithms for solving complex engineering problems. This paper analyzes the performance of five metaheuristics: Equilibrium Optimizer (EO), Particle Swarm Optimization (PSO), Teaching-Learning-Based Optimization (TLBO), Differential Evolution (DE), and Genetic Algorithm (GA) in fine-tuning the gains of a Proportional-Integral-Derivative (PID) to control the speed of a DC motor. The selected metaheuristics, in addition to being from distinct classes, are well established in their respective groups. The methods and findings of this study can be summarized in three phases. First, the mathematical model of the DC motor is deduced. Second, detailed descriptions of the aforementioned algorithms are presented. Furthermore, the structures of the applied controllers are discussed. Third, comparisons based on statistical indicators and analyses in the time and frequency domains, in addition to robustness and load disturbance tests, are performed. The results revealed that if a sufficient number of runs is given for each metaheuristic, despite being in different runs, all algorithms are able to propose the same optimal gain values. TLBO presented the highest speed, while GA and DE were the slowest in finding optimal values. Additionally, the results were compared with the Opposition-Based Learning Henry Gas Solubility Optimization (OBL/HBO)-based PID, reported to have better results than some previously published works on this topic, and a Fuzzy Logic Controller (FLC). The five optimized controllers obtained approximately the same results and outperformed the OBL/HGO-based PID, but the FLC was superior compared to the metaheuristic-based PIDs.
High performance image steganography integrating IWT and Hamming code within secret sharing

Abstract Steganography and secret sharing are schemes to increase the security of private information against attackers. Steganography emphasizes secrecy while secret sharing distributes the secret key in shares that are conditionally classified to reconstruct the original secret. This paper introduces a counting‐based secret sharing scheme that aims to reduce the computational complexity for longer keys, thereby providing a practical steganographic approach for efficient sharing. The scheme integrates counting‐based secret sharing with integer wavelet transform (IWT) and steganography. The subscriptions created using the Hamming code are embedded in the cover image. Using this method, IWT significantly reduces the occurrence of common rounding errors. As a result, secret key extraction becomes very accurate and eliminates the need to access the original images. This design, using secret sharing based on counting, not only has simplicity and efficiency, but also features such as flexibility, scalability, and lack of central authority. In addition, high‐quality steganography was obtained for keys with lengths of 64, 256, 512, 1024, and 3072, showing an average PSNR=80.11 in different color images. This outstanding performance makes it a highly efficient alternative to previous designs, representing a groundbreaking contribution with significant public interest in the field.
A novel hybrid simulated annealing algorithm for colored bottleneck traveling salesman problem

In the fields, such as intelligent transport and multiple tasks cooperation, many real-world problems can be modeled by colored bottleneck traveling salesman problem (CBTSP). Since CBTSP has been proved belong to the NP-hard problems, the population-based algorithms, such as genetic algorithm (GA), hill climbing GA (HCGA) and simulated annealing GA (SAGA), have been used for solving it. However, in term of convergence speed and solution quality, their performances are limited. Moreover, some intelligent algorithms still have the problems such as "black box problem" or lacking theoretical support. Aiming at these problems, this paper proposes a new hybrid simulated annealing algorithm with generating neighborhood solution and crossover operator (GCSA) for CBTSP. In the iterative process of GCSA, the gene coding (dual-chromosome coding) is used to code feasible solutions, and the improved generating neighborhood solution and crossover operator by ITÖ lemma (Wiener process) are used to explore and exploit new unknown region. During the process, generating neighborhood solution and crossover operator are both controlled by the activity intensity based on Wiener process. The experiments show that GCSA can demonstrate an improvement over the state-of-the-art algorithms for this problem.
CoLa-Diff: Conditional Latent Diffusion Model for Multi-modal MRI Synthesis

MRI synthesis promises to mitigate the challenge of missing MRI modality in clinical practice. Diffusion model has emerged as an effective technique for image synthesis by modelling complex and variable data distributions. However, most diffusion-based MRI synthesis models are using a single modality. As they operate in the original image domain, they are memory-intensive and less feasible for multi-modal synthesis. Moreover, they often fail to preserve the anatomical structure in MRI. Further, balancing the multiple conditions from multi-modal MRI inputs is crucial for multi-modal synthesis. Here, we propose the first diffusion-based multi-modality MRI synthesis model, namely Conditioned Latent Diffusion Model (CoLa-Diff). To reduce memory consumption, we perform the diffusion process in the latent space. We propose a novel network architecture, e.g., similar cooperative filtering, to solve the possible compression and noise in latent space. To better maintain the anatomical structure, brain region masks are introduced as the priors of density distributions to guide diffusion process. We further present auto-weight adaptation to employ multi-modal information effectively. Our experiments demonstrate that CoLa-Diff outperforms other state-of-the-art MRI synthesis methods, promising to serve as an effective tool for multi-modal MRI synthesis.
MDViT: Multi-domain Vision Transformer for Small Medical Image Segmentation Datasets

Despite its clinical utility, medical image segmentation (MIS) remains a daunting task due to images' inherent complexity and variability. Vision transformers (ViTs) have recently emerged as a promising solution to improve MIS; however, they require larger training datasets than convolutional neural networks. To overcome this obstacle, data-efficient ViTs were proposed, but they are typically trained using a single source of data, which overlooks the valuable knowledge that could be leveraged from other available datasets. Naïvly combining datasets from different domains can result in negative knowledge transfer (NKT), i.e., a decrease in model performance on some domains with non-negligible inter-domain heterogeneity. In this paper, we propose MDViT, the first multi-domain ViT that includes domain adapters to mitigate data-hunger and combat NKT by adaptively exploiting knowledge in multiple small data resources (domains). Further, to enhance representation learning across domains, we integrate a mutual knowledge distillation paradigm that transfers knowledge between a universal network (spanning all the domains) and auxiliary domain-specific network branches. Experiments on 4 skin lesion segmentation datasets show that MDViT outperforms state-of-the-art algorithms, with superior segmentation performance and a fixed model size, at inference time, even as more domains are added. Our code is available at https://github.com/siyi-wind/MDViT .
Instance-Aware Diffusion Model for Gland Segmentation in Colon Histology Images

In pathological image analysis, determination of gland morphology in histology images of the colon is essential to determine the grade of colon cancer. However, manual segmentation of glands is extremely challenging and there is a need to develop automatic methods for segmenting gland instances. Recently, due to the powerful noise-to-image denoising pipeline, the diffusion model has become one of the hot spots in computer vision research and has been explored in the field of image segmentation. In this paper, we propose an instance segmentation method based on the diffusion model that can perform automatic gland instance segmentation. Firstly, we model the instance segmentation process for colon histology images as a denoising process based on the diffusion model. Secondly, to recover details lost during denoising, we use Instance Aware Filters and multi-scale Mask Branch to construct global mask instead of predicting only local masks. Thirdly, to improve the distinction between the object and the background, we apply Conditional Encoding to enhance the intermediate features with the original image encoding. To objectively validate the proposed method, we compared state-of-the-art deep learning model on the 2015 MICCAI Gland Segmentation challenge (GlaS) dataset and the Colorectal Adenocarcinoma Gland (CRAG) dataset. The experimental results show that our method improves the accuracy of segmentation and proves the efficacy of the method.
Prompt-MIL: Boosting Multi-instance Learning Schemes via Task-Specific Prompt Tuning

Whole slide image (WSI) classification is a critical task in computational pathology, requiring the processing of gigapixel-sized images, which is challenging for current deep-learning methods. Current state of the art methods are based on multi-instance learning schemes (MIL), which usually rely on pretrained features to represent the instances. Due to the lack of task-specific annotated data, these features are either obtained from well-established backbones on natural images, or, more recently from self-supervised models pretrained on histopathology. However, both approaches yield task-agnostic features, resulting in performance loss compared to the appropriate task-related supervision, if available. In this paper, we show that when task-specific annotations are limited, we can inject such supervision into downstream task training, to reduce the gap between fully task-tuned and task agnostic features. We propose Prompt-MIL, an MIL framework that integrates prompts into WSI classification. Prompt-MIL adopts a prompt tuning mechanism, where only a small fraction of parameters calibrates the pretrained features to encode task-specific information, rather than the conventional full fine-tuning approaches. Extensive experiments on three WSI datasets, TCGA-BRCA, TCGA-CRC, and BRIGHT, demonstrate the superiority of Prompt-MIL over conventional MIL methods, achieving a relative improvement of 1.49%–4.03% in accuracy and 0.25%–8.97% in AUROC while using fewer than 0.3% additional parameters. Compared to conventional full fine-tuning approaches, we fine-tune less than 1.3% of the parameters, yet achieve a relative improvement of 1.29%–13.61% in accuracy and 3.22%–27.18% in AUROC and reduce GPU memory consumption by 38%–45% while training 21%–27% faster.
Efficient YOLOv7-Drone: An Enhanced Object Detection Approach for Drone Aerial Imagery

In recent years, the rise of low-cost mini rotary-wing drone technology across diverse sectors has emphasized the crucial role of object detection within drone aerial imagery. Low-cost mini rotary-wing drones come with intrinsic limitations, especially in computational power. Drones come with intrinsic limitations, especially in resource availability. This context underscores an urgent need for solutions that synergize low latency, high precision, and computational efficiency. Previous methodologies have primarily depended on high-resolution images, leading to considerable computational burdens. To enhance the efficiency and accuracy of object detection in drone aerial images, and building on the YOLOv7, we propose the Efficient YOLOv7-Drone. Recognizing the common presence of small objects in aerial imagery, we eliminated the less efficient P5 detection head and incorporated the P2 detection head for increased precision in small object detection. To ensure efficient feature relay from the Backbone to the Neck, channels within the CBS module were optimized. To focus the model more on the foreground and reduce redundant computations, the TGM-CESC module was introduced, achieving the generation of pixel-level constrained sparse convolution masks. Furthermore, to mitigate potential data losses from sparse convolution, we embedded the head context-enhanced method (HCEM). Comprehensive evaluation using the VisDrone and UAVDT datasets demonstrated our model’s efficacy and practical applicability. The Efficient Yolov7-Drone achieved state-of-the-art scores while ensuring real-time detection performance.
unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network

Large-scale data sets on scholarly publications are the basis for a variety of bibliometric analyses and natural language processing (NLP) applications.Especially data sets derived from publication's full-text have recently gained attention.While several such data sets already exist, we see key shortcomings in terms of their domain and time coverage, citation network completeness, and representation of full-text content.To address these points, we propose a new version of the data set unarXive.We base our data processing pipeline and output format on two existing data sets, and improve on each of them.Our resulting data set comprises 1.9 M publications spanning multiple disciplines and 32 years.It furthermore has a more complete citation network than its predecessors and retains a richer representation of document structure as well as non-textual publication content such as mathematical notation.In addition to the data set, we provide ready-to-use training/test data for citation recommendation and IMRaD classification.All data and source code is publicly available at https://github.com/IllDepence/unarXive.
Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance

The pathology is decisive for disease diagnosis but relies heavily on experienced pathologists. In recent years, there has been growing interest in the use of artificial intelligence in pathology (AIP) to enhance diagnostic accuracy and efficiency. However, the impressive performance of deep learning-based AIP in laboratory settings often proves challenging to replicate in clinical practice. As the data preparation is important for AIP, the paper has reviewed AIP-related studies in the PubMed database published from January 2017 to February 2022, and 118 studies were included. An in-depth analysis of data preparation methods is conducted, encompassing the acquisition of pathological tissue slides, data cleaning, screening, and subsequent digitization. Expert review, image annotation, dataset division for model training and validation are also discussed. Furthermore, we delve into the reasons behind the challenges in reproducing the high performance of AIP in clinical settings and present effective strategies to enhance AIP's clinical performance. The robustness of AIP depends on a randomized collection of representative disease slides, incorporating rigorous quality control and screening, correction of digital discrepancies, reasonable annotation, and sufficient data volume. Digital pathology is fundamental in clinical-grade AIP, and the techniques of data standardization and weakly supervised learning methods based on whole slide image (WSI) are effective ways to overcome obstacles of performance reproduction. The key to performance reproducibility lies in having representative data, an adequate amount of labeling, and ensuring consistency across multiple centers. Digital pathology for clinical diagnosis, data standardization and the technique of WSI-based weakly supervised learning will hopefully build clinical-grade AIP.
An eXplainable Artificial Intelligence analysis of Raman spectra for thyroid cancer diagnosis

Raman spectroscopy shows great potential as a diagnostic tool for thyroid cancer due to its ability to detect biochemical changes during cancer development. This technique is particularly valuable because it is non-invasive and label/dye-free. Compared to molecular tests, Raman spectroscopy analyses can more effectively discriminate malignant features, thus reducing unnecessary surgeries. However, one major hurdle to using Raman spectroscopy as a diagnostic tool is the identification of significant patterns and peaks. In this study, we propose a Machine Learning procedure to discriminate healthy/benign versus malignant nodules that produces interpretable results. We collect Raman spectra obtained from histological samples, select a set of peaks with a data-driven and label independent approach and train the algorithms with the relative prominence of the peaks in the selected set. The performance of the considered models, quantified by area under the Receiver Operating Characteristic curve, exceeds 0.9. To enhance the interpretability of the results, we employ eXplainable Artificial Intelligence and compute the contribution of each feature to the prediction of each sample.
Digital Legacy: A Systematic Literature Review

To more effectively support the dying and bereaved in end-of-life contexts, over the past two decades HCI and social computing scholars have sought to understand digital legacy. In this paper, we argue that it is time to take stock of digital legacy scholarship, examining what we know, what gaps remain, and what areas are imperative for future work. Through a Grounded Theory Literature Review, we identify four foci in digital legacy research to date: how identity is navigated in the passing of digital legacy, how digital legacies are engaged with, how digital legacies are put to rest, and how technology interfaces with offline legacy technologies. Based on our analysis, we present a model depicting how digital legacy research examines a lifecycle of data as it is passed down. This model identifies that digital legacy data moves through three stages: encoding, accessing, and dispossessing. The model illustrates gaps in current research and charts possible inflection points for future social computing research. Specifically, we highlight the importance of multi-user and multi-generational networks of people in end-of-life scenarios. Additionally, the model exhibits emerging theoretical findings and major concepts in the nascent field of digital legacy research.