Reviewerly/exHarmony huggingface dataset:
Absolute versus relative forgetting.

Slamecka and McElree (1983) and Rivera-Lares et al. (2022), like others before them, factorially manipulated the number of learning trials and the retention interval. The results revealed two unsurprising main effects: (a) the more study trials, the higher the initial degree of learning, and (b) the longer the retention interval, the more items were forgotten. However, across many experiments, the interaction was not significant, a finding that is often interpreted to mean that the degree of learning is independent of the absolute rate of forgetting (i.e., the absolute number of items forgotten per unit time). Yet there is considerable tension between that interpretation and the fact that forgetting has long been characterized by a power law, according to which the absolute rate of forgetting is not a particularly meaningful measure. When the power function is fit to the same data, the results show that a higher degree of learning results in a lower relative (i.e., proportional) rate of forgetting. This raises an interesting question: which of the two definitions of "forgetting rate" (absolute vs. relative) is theoretically relevant? Here, I make the case that it is the relative rate of forgetting. Theoretically, the explanation of why a higher degree of learning is associated with a lower relative rate of forgetting may be related to why, as observed by Jost (1897) long ago, the passage of time itself is associated with a lower relative rate of forgetting. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
GTrans: Grouping and Fusing Transformer Layers for Neural Machine Translation

Transformer structure, stacked by a sequence of encoder and decoder network layers, achieves significant development in neural machine translation. However, vanilla Transformer mainly exploits the top-layer representation, assuming the lower layers provide trivial or redundant information and thus ignoring the bottom-layer feature that is potentially valuable. In this work, we propose the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> roup- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trans</b> former model ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>GTrans</b></small> ) that flexibly divides multi-layer representations of both encoder and decoder into different groups and then fuses these group features to generate target words. To corroborate the effectiveness of the proposed method, extensive experiments and analytic experiments are conducted on three bilingual translation benchmarks and three multilingual translation tasks, including the IWLST-14, IWLST-17, LDC, WMT-14, WMT-21 and OPUS-100 benchmark. Experimental and analytical results demonstrate that our model outperforms its Transformer counterparts by a consistent gain. Furthermore, it can be successfully scaled up to 60 encoder layers and 36 decoder layers.
Transformers and CNNs fusion network for salient object detection

Recently, Salient Object Detection (SOD) has achieved promising results thanks to the rapid evolution of CNN architectures. However, those CNN-based methods show limited capacity in modeling long-range interaction among pixels. In this paper, we propose to combine the merits from CNN and Transformer and design a unified model for RGB and RGB-D SOD. First, we represent the image with a sequence-to-sequence perspective and use a Transformer-based branch to express long-distance relationships of image tokens to obtain global semantic information and predict a coarse saliency map. Second, we employ a CNN-based branch to extract multi-scale local detail features to predict contour prediction for auxiliary supervision at each level. Finally, we propose the Bi-enhancement Fusion Module to fuse multi-scale cues from two branches to predict a more accurate saliency map. In addition, for RGB-D SOD, to obtain effective cross-modality features, we propose a Cross-modality Multi-Scale Transformer Module and a Depth-induced Enhancement Module to fuse RGB and depth cues in the Transformers branch and the CNNs branch, respectively. Experiments on both RGB and RGB-D SOD datasets demonstrate that our proposed model achieves satisfactory performance compared with state-of-the-art methods.
Landscape of e-Learning during Covid-19: Case Study of Economic Disciplines in Croatia

Abstract Background: The Covid-19 pandemic has changed the digitalisation level of education. Many institutions had some knowledge and practical background in delivering lectures online. Some countries apply a top-down digitalisation approach driven by policy or strategy and externally impacted by the government. Some other countries rather initiate digitalisation internally by teachers and universities. Objectives: The main goal is to investigate the digitalisation level of the higher education system in Croatia, exploring the digitalisation of economic disciplines compared to other scientific fields. Methods/Approach: We assess the digitalisation level of higher education based on the regulatory framework, applied approach of digitalisation, the agility of transition to online teaching and the existing number of courses and online study programs. Education digitalisation in different countries is compared with the Croatian experience. Results: Although the satisfactory level of higher education institutions’ agility to switch online exist, there hasn’t been a centralised project led by the government and supported by proper funds to increase digitalisation in Croatia. Even before the pandemic, many e-courses from economics and business had been offered to students, but online study programs were exceptions, covering mainly the economics and business field. Conclusions: The Covid-19 pandemic has improved the digitalisation process in the Croatian education system. A general framework for the digitalisation of education should be developed containing the detailed administrative processes and appropriate funds to be implemented.
Empirical Compression Features of Mobile Computing and Data Applications Using Deep Neural Networks

Due to the enormous data sizes involved in mobile computing and multimedia data transfer, it is possible that more data traffic may be generated, necessitating the use of data compression. As a result, this paper investigates how mobile computing data are compressed under all transmission scenarios. The suggested approach integrates deep neural networks (DNN) at high weighting functionalities for compression modes. The proposed method employs appropriate data loading and precise compression ratios for successful data compression. The accuracy of multimedia data that must be conveyed to various users is higher even though compression ratios are higher. The same data are transferred at significantly higher compression ratios, which save time while also minimizing data mistakes that may occur at the receiver. The DNN process also includes a visible parameter for handling high data-weight situations. The visible parameter optimizes the data results, allowing simulation tools to readily observe the compressed data. A comparison case study was created for five different scenarios in order to confirm the results, and it shows that the suggested strategy is significantly more effective than existing methods in roughly 63 percent of the cases.
On-Chip Reconfigurable and Ultracompact Silicon Waveguide Mode Converters Based on Nonvolatile Optical Phase Change Materials

Reconfigurable mode converters are essential components in efficient higher-order mode sources for on-chip multimode applications. We propose an on-chip reconfigurable silicon waveguide mode conversion scheme based on the nonvolatile and low-loss optical phase change material antimony triselenide (Sb2Se3). The key mode conversion region is formed by embedding a tapered Sb2Se3 layer into the silicon waveguide along the propagation direction and further cladding with graphene and aluminum oxide layers as the microheater. The proposed device can achieve the TE0-to-TE1 mode conversion and reconfigurable conversion (no mode conversion) depending on the phase state of embedded Sb2Se3 layer, whereas such function could not be realized according to previous reports. The proposed device length is only 2.3 μm with conversion efficiency (CE) = 97.5%, insertion loss (IL) = 0.2 dB, and mode crosstalk (CT) = -20.5 dB. Furthermore, the proposed device scheme can be extended to achieve other reconfigurable higher-order mode conversions. We believe the proposed reconfigurable mode conversion scheme and related devices could serve as the fundamental building blocks to provide higher-order mode sources for on-chip multimode photonics.
The Application of Deep Learning for the Evaluation of User Interfaces

In this study, we tested the ability of a machine-learning model (ML) to evaluate different user interface designs within the defined boundaries of some given software. Our approach used ML to automatically evaluate existing and new web application designs and provide developers and designers with a benchmark for choosing the most user-friendly and effective design. The model is also useful for any other software in which the user has different options to choose from or where choice depends on user knowledge, such as quizzes in e-learning. The model can rank accessible designs and evaluate the accessibility of new designs. We used an ensemble model with a custom multi-channel convolutional neural network (CNN) and an ensemble model with a standard architecture with multiple versions of down-sampled input images and compared the results. We also describe our data preparation process. The results of our research show that ML algorithms can estimate the future performance of completely new user interfaces within the given elements of user interface design, especially for color/contrast and font/layout.
The Impact of Open-Book Management on Customer Satisfaction: the Mediating Role of Trust

Purpose: the current study is to test the impact of open-book management on customer satisfaction through the mediating role of trust in the hotel and tourism sector in Jordan from the perspective of customers. Design/methodology/ approach: The study deployed the descriptive method to describe the study sample and the analytical approach to test the study hypotheses. Furthermore, the study population comprises the hotel and tourism sector in Jordan that is indexed on the Amman stock exchange (2022) which amounts to (9) companies. Furthermore, a questionnaire was developed from the previous studies. Besides, the study sample consisted of (410) customers who is selected by a convenience sample, and the number of questionnaires returned was 358 which shaped 81%. Moreover, the hypotheses were tested using Baron and Kenny's (1986) methodology using SPSS. Findings: The current study showed that open-book management has a positive and significant impact on customer satisfaction. Further, showed that there is a positive and significant impact of open-book management on trust. Furthermore, there is a positive and significant impact of trust on customer satisfaction. In addition, trust has a partial mediating role in the relationship between open-book management and customer satisfaction. Research limitations: The current study is not free from any limitations, in this study, the limitations are employees selected by a convenience sample, Therefore, the results can’t be generalized to other sectors. furthermore, there is a shortage of prior studies that addressed the same topic. Research, Managerial implications: Open-book management is considered one of the commonly used methods that assist employees to get their needs from the information and knowledge. precisely, the hotel and tourism sector in Jordan must be aware of what is making their customers satisfied by conducting studies regarding their needs and wants before introducing the products and services. Originality: Despite there being many studies conducted in the area of open-book management and customer satisfaction, few studies addressed the theme of trust as a mediating variable. Therefore, the study came up to fill the gap in the literature and provide a theoretical and practical contribution.
The Effectiveness of Implementation Learning Media Based on Augmented Reality in Elementary School in Improving Critical Thinking Skills in Solar System Course

Abstract This research aims to explore students’ critical thinking skills in Augmented reality-based learning media implementation on solar system lessons in elementary school. Critical thinking skills at the elementary school level are still relatively low due to the lack of application of learning media that support the improvement of critical thinking skills. Learning media is a very important part of learning. Research design compares modern classes (experimental classes) with control classes. Teaching using augmented reality-based learning media is carried out in experimental classrooms, while teaching is directly carried out in the control class. As a researcher using research and development methods with a qualitative approach to quasi-experimental research methods (pseudo-experiments) with pure experimental design, namely Pre-test Post-test Control Design. It can be known that the value of significance in this study is 39.5% or 0.395 to pre-test. The average pre-test value obtained for the control class was 46.60 with a standard deviation of 12.32 while the average pre-test value for the experimental class was 48.57 with a standard deviation of 11.20. The collection of post-test in the control class was 72.3214 with a standard deviation of 10.45291 while the average value of the experimental class was 90.1429 with a standard deviation of 6.89452. Based on the t-test it can be known that in the pre-test values of the two classes there is no significant or homogeneous difference. After being given the treatment of augmented reality learning media in solar system materials, the post-test values of both classes experienced a significant difference with a Sig.2-Tailed value of 0.000. The results of this study that the learning medium of Augmented Reality has an impact on students' critical thinking skills.
PatchMask: A Data Augmentation Strategy with Gaussian Noise in Hyperspectral Images

Data augmentation (DA) is an effective way to enrich the richness of data and improve a model’s generalization ability. It has been widely used in many advanced vision tasks (e.g., classification, recognition, etc.), while it can hardly be seen in hyperspectral image (HSI) tasks. In this paper, we analyze whether existing augmentation methods are suitable for the task of HSI denoising and find that the biggest challenge lies in neither losing the spatial information of the original image nor destroying the correlation between the various bands for HSI denoising. Based on this, a new data augmentation method named PatchMask is proposed, which makes the training samples as diverse as possible while preserving the spatial and spectral information. The training data augmented by this method are somewhere between clear and noisy, which can make the network learn more effectively and generalize. Experiments demonstrate that our method outperforms other data augmentation methods, such as the benchmark CutBlur, in enhancing HSI denoising. In addition, the given DA method was used on several popular denoising networks, such as QRNN3D, DnCNN, MPRnet, CBDNet, and HSID-CNN, to verify the effectiveness of the proposed method. The results show that the given DA could increase the value of the PSNR by 0.2∼0.5 dB in various examples.
Design of a Honey Badger Optimization Algorithm with a Deep Transfer Learning-Based Osteosarcoma Classification Model

Osteosarcoma is one of the aggressive bone tumors with numerous histologic patterns. Histopathological inspection is a crucial criterion in the medical diagnosis of Osteosarcoma. Due to the advancement of computing power and hardware technology, pathological image analysis system based on artificial intelligence (AI) were more commonly used. But classifying many intricate pathology images by hand will be challenging for pathologists. The lack of labeling data makes the system difficult to build and costly. This article designs a Honey Badger Optimization with Deep Learning based Automated Osteosarcoma Classification (HBODL-AOC) model. The HBODL-AOC technique’s goal is to identify osteosarcoma’s existence using medical images. In the presented HBODL-AOC technique, image preprocessing is initially performed by contrast enhancement technique. For feature extraction, the HBODL-AOC technique employs a deep convolutional neural network-based Mobile networks (MobileNet) model with an Adam optimizer for hyperparameter tuning. Finally, the adaptive neuro-fuzzy inference system (ANFIS) approach is implemented for the HBO (Honey Badger Optimization) algorithm can tune osteosarcoma classification and the membership function (MF). To demonstrate the enhanced classification performance of the HBODL-AOC approach, a sequence of simulations was performed. The extensive simulation analysis portrayed the improved performance of the HBODL-AOC technique over existing DL models.
When physics-informed data analytics outperforms black-box machine learning: A case study in thickness control for additive manufacturing

Aerosol jet printing (AJP) has emerged as a promising noncontact additive manufacturing method for high-resolution printing for a wide range of material systems. A key challenge limiting the broader adoption of AJP in the material science community is the lack of methods to precisely control thickness. Herein, we develop a model-based design of experiment (MBDoE) framework that integrates physics-informed models, nonlinear regression, and information criteria to postulate, select and calibrate the best model to describe and optimize the AJP manufacturing process. Starting with already available data from system commissioning (e.g., prior single variable sensitivity analysis), four candidate physics-informed models are postulated and trained. MBDoE identifies a single additional optimal experiment to validate these predictive models with quantified uncertainties, which are then used to determine the best experimental conditions to control printed film thickness. As a comparative benchmark, the analysis is repeated using the same dataset with nonparametric Gaussian process regression (GPR) model that does not incorporate physical information. Using MBDoE principles, we find that only five experiments are necessary to calibrate the nonlinear physics-informed parametric model, and with said limited data, this model outperforms the black-box machine learning GPR model. This key result underscores an emerging trend in the data science community: incorporating physical information into predictive models often drastically reduces the data requirements. Leveraging MBDoE further increased the data efficiency. By design, the proposed data science framework is general in nature and can be easily extended to other experimental and additive manufacturing systems beyond AJP.
Robust endoscopic image mosaicking via fusion of multimodal estimation

A B S T R A C TWe propose an endoscopic image mosaicking algorithm that is robust to light conditioning changes, specular reflections, and feature-less scenes.These conditions are especially common in minimally invasive surgery where the light source moves with the camera to dynamically illuminate close range scenes.This makes it difficult for a single image registration method to robustly track camera motion and then generate consistent mosaics of the expanded surgical scene across different and heterogeneous environments.Instead of relying on one specialised feature extractor or image registration method, we propose to fuse different image registration algorithms according to their uncertainties, formulating the problem as affine pose graph optimisation.This allows to combine landmarks, dense intensity registration, and learning-based approaches in a single framework.To demonstrate our application we consider deep learning-based optical flow, handcrafted features, and intensity-based registration, however, the framework is general and could take as input other sources of motion estimation, including other sensor modalities.We validate the performance of our approach on three datasets with very different characteristics to highlighting its generalisability, demonstrating the advantages of our proposed fusion framework.While each individual registration algorithm eventually fails drastically on certain surgical scenes, the fusion approach flexibly determines which algorithms to use and in which proportion to more robustly obtain consistent mosaics.
Diagnostic and Prognostic Deep Learning Applications for Histological Assessment of Cutaneous Melanoma

Melanoma is among the most devastating human malignancies. Accurate diagnosis and prognosis are essential to offer optimal treatment. Histopathology is the gold standard for establishing melanoma diagnosis and prognostic features. However, discrepancies often exist between pathologists, and analysis is costly and time-consuming. Deep-learning algorithms are deployed to improve melanoma diagnosis and prognostication from histological images of melanoma. In recent years, the development of these machine-learning tools has accelerated, and machine learning is poised to become a clinical tool to aid melanoma histology. Nevertheless, a review of the advances in machine learning in melanoma histology was lacking. We performed a comprehensive literature search to provide a complete overview of the recent advances in machine learning in the assessment of melanoma based on hematoxylin eosin digital pathology images. In our work, we review 37 recent publications, compare the methods and performance of the reviewed studies, and highlight the variety of promising machine-learning applications in melanoma histology.
5G-Enabled Distributed Intelligence Based on O-RAN for Distributed IoT Systems

Edge-based distributed intelligence techniques, such as federated learning (FL), have recently been used in many research fields thanks, in part, to their decentralized model training process and privacy-preserving features. However, because of the absence of effective deployment models for the radio access network (RAN), only a tiny number of FL apps have been created for the latest generation of public mobile networks (e.g., 5G and 6G). There is an attempt, in new RAN paradigms, to move toward disaggregation, hierarchical, and distributed network function processing designs. Open RAN (O-RAN), as a cutting-edge RAN technology, claims to meet 5G services with high quality. It includes integrated, intelligent controllers to provide RAN with the power to make smart decisions. This paper proposes a methodology for deploying and optimizing FL tasks in O-RAN to deliver distributed intelligence for 5G applications. To accomplish model training in each round, we first present reinforcement learning (RL) for client selection for each FL task and resource allocation using RAN intelligence controllers (RIC). Then, a slice is allotted for training depending on the clients chosen for the task. Our simulation results show that the proposed method outperforms state-of-art FL methods, such as the federated averaging algorithm (FedAvg), in terms of convergence and number of communication rounds.