Reviewerly/exHarmony huggingface dataset:
A Comparative Analysis of Design Principles for Integration in Wearable Persuasive Multimedia

Many studies, to varying degrees, have confirmed the importance of persuasive approaches in wearable technology. Meanwhile, there are also a growing number of studies in persuasive multimedia, particularly in promoting awareness. Also, many studies reported on wearable multimedia, especially in game-based and VR/AR applications. Given the increasing emergence of these technologies, there is a need to integrate existing diverse research endeavours and consolidate them for improved planned effects on human attitude and behaviour, including one's awareness. However, a similar attempt to incorporate a triad of persuasive, multimedia and wearable design principles toward improved controllability awareness lacks empirical evidence. Here, this study explores the design principles of persuasive, multimedia and wearable technologies that can be leveraged into an integrated design model, especially in promoting controllability awareness of mental health issues. Moreover, this study believes exploring the potential integration of the design principles would significantly impact the application's effectiveness. Therefore, this study conducted a comparative analysis which involved 20 relevant studies pertinent to wearable design principles, persuasive design principles, and multimedia design principles. Furthermore, all identified studies were reviewed regarding the domain, the technology used, target outcomes, and utilisation of the design principles. As a result, this study discovered that many studies were on integrating persuasive and multimedia design principles and persuasive and wearable technologies. Therefore, the outcome of this study could be leveraged to incorporate all three design principles (i.e., wearable, persuasive technology, multimedia) into a conceptual model. The conceptual model is expected to produce a more effective result, especially in enhancing controllability awareness in the mental health domain.
One-Shot Federated Learning on Medical Data Using Knowledge Distillation with Image Synthesis and Client Model Adaptation

One-shot federated learning (FL) has emerged as a promising solution in scenarios where multiple communication rounds are not practical. Notably, as feature distributions in medical data are less discriminative than those of natural images, robust global model training with FL is non-trivial and can lead to overfitting. To address this issue, we propose a novel one-shot FL framework leveraging Image Synthesis and Client model Adaptation (FedISCA) with knowledge distillation (KD). To prevent overfitting, we generate diverse synthetic images ranging from random noise to realistic images. This approach (i) alleviates data privacy concerns and (ii) facilitates robust global model training using KD with decentralized client models. To mitigate domain disparity in the early stages of synthesis, we design noise-adapted client models where batch normalization statistics on random noise (synthetic images) are updated to enhance KD. Lastly, the global model is trained with both the original and noise-adapted client models via KD and synthetic images. This process is repeated till global model convergence. Extensive evaluation of this design on five small- and three large-scale medical image classification datasets reveals superior accuracy over prior methods. Code is available at https://github.com/myeongkyunkang/FedISCA .
B-Cos Aligned Transformers Learn Human-Interpretable Features

Vision Transformers (ViTs) and Swin Transformers (Swin) are currently state-of-the-art in computational pathology. However, domain experts are still reluctant to use these models due to their lack of interpretability. This is not surprising, as critical decisions need to be transparent and understandable. The most common approach to understanding transformers is to visualize their attention. However, attention maps of ViTs are often fragmented, leading to unsatisfactory explanations. Here, we introduce a novel architecture called the B-cos Vision Transformer (BvT) that is designed to be more interpretable. It replaces all linear transformations with the B-cos transform to promote weight-input alignment. In a blinded study, medical experts clearly ranked BvTs above ViTs, suggesting that our network is better at capturing biomedically relevant structures. This is also true for the B-cos Swin Transformer (Bwin). Compared to the Swin Transformer, it even improves the F1-score by up to 4.7% on two public datasets.
Laplacian-Former: Overcoming the Limitations of Vision Transformers in Local Texture Detection

Vision Transformer (ViT) models have demonstrated a breakthrough in a wide range of computer vision tasks. However, compared to the Convolutional Neural Network (CNN) models, it has been observed that the ViT models struggle to capture high-frequency components of images, which can limit their ability to detect local textures and edge information. As abnormalities in human tissue, such as tumors and lesions, may greatly vary in structure, texture, and shape, high-frequency information such as texture is crucial for effective semantic segmentation tasks. To address this limitation in ViT models, we propose a new technique, Laplacian-Former, that enhances the self-attention map by adaptively re-calibrating the frequency information in a Laplacian pyramid. More specifically, our proposed method utilizes a dual attention mechanism via efficient attention and frequency attention while the efficient attention mechanism reduces the complexity of self-attention to linear while producing the same output, selectively intensifying the contribution of shape and texture features. Furthermore, we introduce a novel efficient enhancement multi-scale bridge that effectively transfers spatial information from the encoder to the decoder while preserving the fundamental features. We demonstrate the efficacy of Laplacian-former on multi-organ and skin lesion segmentation tasks with +1.87% and +0.76% dice scores compared to SOTA approaches, respectively. Our implementation is publically available at GitHub .
Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network

Capturing global contextual information plays a critical role in breast ultrasound (BUS) image classification. Although convolutional neural networks (CNNs) have demonstrated reliable performance in tumor classification, they have inherent limitations for modeling global and long-range dependencies due to the localized nature of convolution operations. Vision Transformers have an improved capability of capturing global contextual information but may distort the local image patterns due to the tokenization operations. In this study, we proposed a hybrid multitask deep neural network called Hybrid-MT-ESTAN, designed to perform BUS tumor classification and segmentation using a hybrid architecture composed of CNNs and Swin Transformer components. The proposed approach was compared to nine BUS classification methods and evaluated using seven quantitative metrics on a dataset of 3,320 BUS images. The results indicate that Hybrid-MT-ESTAN achieved the highest accuracy, sensitivity, and F1 score of 82.7%, 86.4%, and 86.0%, respectively.
A Client-Server Deep Federated Learning for Cross-Domain Surgical Image Segmentation

This paper presents a solution to the cross-domain adaptation problem for 2D surgical image segmentation, explicitly considering the privacy protection of distributed datasets belonging to different centers. Deep learning architectures in medical image analysis necessitate extensive training data for better generalization. However, obtaining sufficient diagnostic and surgical data is still challenging, mainly due to the inherent cost of data curation and the need of experts for data annotation. Moreover, increased privacy and legal compliance concerns can make data sharing across clinical sites or regions difficult. Another ubiquitous challenge the medical datasets face is inevitable domain shifts among the collected data at the different centers. To this end, we propose a Client-server deep federated architecture for cross-domain adaptation. A server hosts a set of immutable parameters common to both the source and target domains. The clients consist of the respective domain-specific parameters and make requests to the server while learning their parameters and inferencing. We evaluate our framework in two benchmark datasets, demonstrating applicability in computer-assisted interventions for endoscopic polyp segmentation and diagnostic skin lesion detection and analysis. Our extensive quantitative and qualitative experiments demonstrate the superiority of the proposed method compared to competitive baseline and state-of-the-art methods. We will make the code available upon the paper’s acceptance. Codes are available at: https://github.com/bhattarailab/federated-da .
Minimal-Supervised Medical Image Segmentation via Vector Quantization Memory

Medical imaging segmentation is a critical key task for computer-assisted diagnosis and disease monitoring. However, collecting a large-scale medical dataset with well-annotation is time-consuming and requires domain knowledge. Reducing the number of annotations poses two challenges: obtaining sufficient supervision and generating high-quality pseudo labels. To address these, we propose a universal framework for annotation-efficient medical segmentation, which is capable of handling both scribble-supervised and point-supervised segmentation. Our approach includes an auxiliary reconstruction branch that provides more supervision and backwards sufficient gradients for learning visual representations. Besides, a novel pseudo label generation branch utilizes the Vector Quantization (VQ) bank to store texture-oriented and global features for generating pseudo labels. To boost the model training, we generate the high-quality pseudo labels by mixing the segmentation prediction and pseudo labels from the VQ bank. The experimental results on the ACDC MRI segmentation dataset demonstrate effectiveness of our designed method. We obtain a comparable performance (0.86 vs. 0.87 DSC score) with a few points.
LSOR: Longitudinally-Consistent Self-Organized Representation Learning

Interpretability is a key issue when applying deep learning models to longitudinal brain MRIs. One way to address this issue is by visualizing the high-dimensional latent spaces generated by deep learning via self-organizing maps (SOM). SOM separates the latent space into clusters and then maps the cluster centers to a discrete (typically 2D) grid preserving the high-dimensional relationship between clusters. However, learning SOM in a high-dimensional latent space tends to be unstable, especially in a self-supervision setting. Furthermore, the learned SOM grid does not necessarily capture clinically interesting information, such as brain age. To resolve these issues, we propose the first self-supervised SOM approach that derives a high-dimensional, interpretable representation stratified by brain age solely based on longitudinal brain MRIs (i.e., without demographic or cognitive information). Called Longitudinally-consistent Self-Organized Representation learning (LSOR), the method is stable during training as it relies on soft clustering (vs. the hard cluster assignments used by existing SOM). Furthermore, our approach generates a latent space stratified according to brain age by aligning trajectories inferred from longitudinal MRIs to the reference vector associated with the corresponding SOM cluster. When applied to longitudinal MRIs of the Alzheimer's Disease Neuroimaging Initiative (ADNI, N=632), LSOR generates an interpretable latent space and achieves comparable or higher accuracy than the state-of-the-art representations with respect to the downstream tasks of classification (static vs. progressive mild cognitive impairment) and regression (determining ADAS-Cog score of all subjects). The code is available at https://github.com/ouyangjiahong/longitudinal-som-single-modality.
Adaptive Supervised PatchNCE Loss for Learning H&amp;E-to-IHC Stain Translation with Inconsistent Groundtruth Image Pairs

Immunohistochemical (IHC) staining highlights the molecular information critical to diagnostics in tissue samples. However, compared to H&E staining, IHC staining can be much more expensive in terms of both labor and the laboratory equipment required. This motivates recent research that demonstrates that the correlations between the morphological information present in the H&E-stained slides and the molecular information in the IHC-stained slides can be used for H&E-to-IHC stain translation. However, due to a lack of pixel-perfect H&E-IHC groundtruth pairs, most existing methods have resorted to relying on expert annotations. To remedy this situation, we present a new loss function, Adaptive Supervised PatchNCE (ASP), to directly deal with the input to target inconsistencies in a proposed H&E-to-IHC image-to-image translation framework. The ASP loss is built upon a patch-based contrastive learning criterion, named Supervised PatchNCE (SP), and augments it further with weight scheduling to mitigate the negative impact of noisy supervision. Lastly, we introduce the Multi-IHC Stain Translation (MIST) dataset, which contains aligned H&E-IHC patches for 4 different IHC stains critical to breast cancer diagnosis. In our experiment, we demonstrate that our proposed method outperforms existing image-to-image translation methods for stain translation to multiple IHC stains. All of our code and datasets are available at https://github.com/lifangda01/AdaptiveSupervisedPatchNCE .
Memory Replay for Continual Medical Image Segmentation Through Atypical Sample Selection

Medical image segmentation is critical for accurate diagnosis, treatment planning and disease monitoring. Existing deep learning-based segmentation models can suffer from catastrophic forgetting, especially when faced with varying patient populations and imaging protocols. Continual learning (CL) addresses this challenge by enabling the model to learn continuously from a stream of incoming data without the need to retrain from scratch. In this work, we propose a continual learning-based approach for medical image segmentation using a novel memory replay-based learning scheme. The approach uses a simple and effective algorithm for image selection to create the memory bank by ranking and selecting images based on their contribution to the learning process. We evaluate our proposed algorithm on three different problems and compare it with several baselines, showing significant improvements in performance. Our study highlights the potential of continual learning-based algorithms for medical image segmentation and underscores the importance of efficient sample selection in creating memory banks.
Co-assistant Networks for Label Correction

The presence of corrupted labels is a common problem in the medical image datasets due to the difficulty of annotation. Meanwhile, corrupted labels might significantly deteriorate the performance of deep neural networks (DNNs), which have been widely applied to medical image analysis. To alleviate this issue, in this paper, we propose a novel framework, namely Co-assistant Networks for Label Correction (CNLC), to simultaneously detect and correct corrupted labels. Specifically, the proposed framework consists of two modules, i.e., noise detector and noise cleaner. The noise detector designs a CNN-based model to distinguish corrupted labels from all samples, while the noise cleaner investigates class-based GCNs to correct the detected corrupted labels. Moreover, we design a new bi-level optimization algorithm to optimize our proposed objective function. Extensive experiments on three popular medical image datasets demonstrate the superior performance of our framework over recent state-of-the-art methods. Source codes of the proposed method are available on https://github.com/shannak-chen/CNLC .
Phishing Message Detection Based on Keyword Matching

This paper proposes to use the Naïve Bayes-based algorithm for phishing detection, specifically in spam emails. The paper compares probability-based and frequency-based approaches and investigates the impact of imbalanced datasets and the use of stemming as a natural language processing (NLP) technique. Results show that both algorithms perform similarly in spam detection, with the choice between them depending on factors such as efficiency and scalability. Accuracy is influenced by the dataset configuration and stemming. Imbalanced datasets lead to higher accuracy in detecting emails in the majority class, while they struggle to classify minority-class emails. In contrast, balanced datasets yield overall high accuracy for both spam and ham email identification. This study reveals that stemming has a minor impact on algorithm performance, occasionally decreasing in accuracy due to word grouping. Balancing the dataset is crucial for improving algorithm performance and achieving accurate spam email detection. Hence, both probability-based and frequency-based Naïve Bayes algorithms are effective for phishing detection using balanced datasets. The frequency-based approach, with a balanced dataset and stemming, achieves a balanced performance between recall and precision, while the probability-based method with a balanced dataset and no stemming prioritises overall accuracy.
PENGEMBANGAN PERMAINAN EDUKASI UNTUK PENYULUHAN KESEHATAN JAJANAN PADA ANAK SEKOLAH DASAR BERBASIS MOBILE

Snack is one of the food or fast food that is often encountered and liked one of them by the children. This is because the habit of snack that is very inherent, especially in elementary school children. Nutrition education should be given as early as possible by spreading messages, and information. One application of information may be a media game application. With the application of this game is expected to spread the message / information for elementary school children aged 6-12 years can better understand about the importance in choosing snacks. This game application is made using Construct 2 application with multimedia based software development method. Data collected from the questionnaire of 100 respondents in the can in 3 categories of questions with the calculation using Likert scale formula. The results of the questionnaire is the category to 1 with the application display get 88.6% Very good, the second category with the goal of getting 90.58% application is very good and in the 3rd category with ease of application get 86.25% very good.
LawCrypt: Secret Sharing for Attorney-Client Data in a Multi-Provider Cloud Architecture

The accelerated employment of cloud computing among law firms is due to multiple benefits, including wide accessibility and inter-organizational information sharing. Nevertheless, the project’s preliminary case study revealed lawyers’ mass neglect of standard precautionary measures and, consequently, a high proportion of security breaches, putting confidential and important documents at risk. The goal of the multi-provider cloud secret sharing architecture was to ensure confidentiality, availability, and integrity of attorney documents while maintaining greater efficiency than traditional encryption algorithms. After an exhaustive development phase involving considerable testing and optimizations, software assessments of the architecture indicate the low computational overhead of adding the secret-sharing approach to a multi-provider law firm sharing environment. The efficient combination of constructions satisfies the engineering criteria as ChaCha20-Poly1305 warrants authenticity and privacy, and secret sharing ensures availability and perfect privacy. Compared with AES in CFB mode, widely used for encrypting data in the Cloud today, the secret sharing implementation boasts almost a 40% improvement over all file sizes. The only possible way of compromising this system is if multiple cloud providers collude, which is still unlikely given that the documents are additionally encrypted.
DEVELOPING DIGITAL PICTURE STORY BOOK TEACHING MATERIALS BASED ON MALUKU REGIONAL FOLKTALES BASED ON SEA-ISLAND FOR GRADE VIII MIDDLE SCHOOL STUDENTS IN AMBON CITY

The purpose of developing teaching materials for books in English is essential to improve English language skills and maintain, study, introduce, and preserve regional folklore in the marine island environment as well as the culture and potential of the Maluku region. This research employed Research and Developmet (R and D) according Borg and Gal. Research data for English teaching materials are grouped into two types of data, quantitative data, and qualitative data. Data was obtained through the distribution of questionnaires amd interview). The results show that, the expert validation said that the initial product development of teaching materials was good but still needed to be revised before being implemented in small group and field trials. The initial product development of Diglot Picture Storybook teaching materials was good but need to insert listening activities in the Listening Skill section.
Design of New Stegano-Cryptosystem Based Non-Hiding Principle

Abstract: In this work, we'll develop a novel text message steganography concept. In the proposed system, in fact, there is no hiding process in the cover-image, but the image is very important in retrieving the plaintext after it has been encrypted. The suggested system depends on comparing the bits of message, want to be hidden, with bits of the cover image, if they are similar then the index of the similar bit is recoded. After finishing all the message text, we obtain a list of indices, these indices are encrypted. Lastly, the image and the encrypted indices are being send to the receiver part. This style means no hiding be done in the image cover. The cover-images are shown before and after the acts of the proposed stego-system to prove that’s no effects have done in the cover-images.
Ascertaining Whether an Intelligent Voice Assistant Can Meet Older Adults’ Health-Related Needs in the Context of a Geriatrics 5Ms Framework

The Geriatrics 5Ms: Medications, Mind, Mobility, what Matters most and Multicomplexity is a framework to address the complex needs of older adults. Intelligent Voice Assistants (IVAs) are increasingly popular and have potential to support health-related needs of older adults. We utilized previously collected qualitative data on older adults’ views of how an IVA may address their health-related needs and ascertained their fit into the Geriatrics 5Ms framework. The codes describing health challenges and potential IVA solutions fit the framework: (1) Medications: difficulty remembering medications. Solution: reminders. (2) Mind: isolation, anxiety, memory loss. Solution: companionship, memory aids. (3) Mobility: barriers to exercise. Solution: incentives, exercise ideas. (4) Matters most: eating healthy foods. Solution: suggest and order nutritious foods, (5) Multicomplexity; managing multimorbidity. Solution: symptom tracking and communicating with health care professionals. Incorporating the 5Ms framework into IVA design can aid in addressing health care priorities of older adults.