Reviewerly/exHarmony huggingface dataset:
Innovative strategies for continuous and effective learning during health emergencies in Ciudad del Este

The present research, centered on Ciudad del Este, addressed the articulation and implementation of innovative strategies to ensure the perpetuity and effectiveness of learning in contexts of health emergencies. The exploration focused on distance education modalities, intensively integrating digital technologies and online resources. Adopting an exploratory-descriptive methodology, data collection was carried out through surveys to examine the emergent educational practices during the health crisis meticulously. The findings underscore the pivotality of distance education and technologization to ensure educational continuity while simultaneously identifying preponderant challenges, such as equity in educational access and active student participation. The proffered results provide a wealth of cardinal information for educational entities, teaching professionals, and decision-makers, facilitating the understanding and optimizing educational strategies in crises. In conclusion, this study highlights the imperative need to deploy innovative pedagogical approaches in health emergencies, pointing to distance education and implementing digital technologies as preeminent solutions to ensure continuous and quality education in the studied region. Such strategies not only lean towards the resilience of the current educational system but also pave the way for robust preparation in the face of future challenges.
FM-Directories: Extending the Burrows-Wheeler Transform for String Labeled Vertex Graphs of (Almost) Arbitrary Topology

We introduce an extension of the Burrows-Wheeler transform supporting exact pattern matching on a string graph $G(V,\ E,\ N)$ and yielding polynomial time queries.
Nonlinear DIP-DiracVTV Model for Color Image Restoration

Variational models for inverse problems are mainly based on the choice of the regularizer, whose goal is to give the solutions some desirable property. Total Variation, one of the most popular regularizer for image restoration, is induced by the Euclidean gradient operator and promotes piece-wise constant solutions. In this paper, we present a new regularizer for color image restoration, which is induced by a generalization of the Dirac operator. This new regularizer also encourages the gradients of the three color components of the solutions to be aligned, which is actually a property of natural images. This property is also encoded when the regularizer is induced by a Riemannian gradient, for a well-chosen Riemannian metric, but with a different mathematical formulation. Then, we compare the different regularizers by combining them with the Deep Image Prior model, this latter assuming that the restored image is the output of a neural network. Experiments on denoising and deblurring show that the proposed Dirac operator provides better results than the Euclidean and Riemannian gradient operators.
BakedAvatar: Baking Neural Fields for Real-Time Head Avatar Synthesis

Synthesizing photorealistic 4D human head avatars from videos is essential for VR/AR, telepresence, and video game applications. Although existing Neural Radiance Fields (NeRF)-based methods achieve high-fidelity results, the computational expense limits their use in real-time applications. To overcome this limitation, we introduce BakedAvatar, a novel representation for real-time neural head avatar synthesis, deployable in a standard polygon rasterization pipeline. Our approach extracts deformable multi-layer meshes from learned isosurfaces of the head and computes expression-, pose-, and view-dependent appearances that can be baked into static textures for efficient rasterization. We thus propose a three-stage pipeline for neural head avatar synthesis, which includes learning continuous deformation, manifold, and radiance fields, extracting layered meshes and textures, and fine-tuning texture details with differential rasterization. Experimental results demonstrate that our representation generates synthesis results of comparable quality to other state-of-the-art methods while significantly reducing the inference time required. We further showcase various head avatar synthesis results from monocular videos, including view synthesis, face reenactment, expression editing, and pose editing, all at interactive frame rates.
On the Resilience of Authentication Schemes in IoT Networks With Different Structural Topologies

The concern of network security is one of the major factors preventing the development of IoT-based applications. Such security risks can be mitigated by adopting a proper authentication scheme. The lack of quantitative comparison increases the difficulty when choosing the authentication scheme. This paper presents an empirical study of the effectiveness of three authentication methods including the blockchain-based method, a newly joined competitor. This study does not solely focus on the scheme level security but also five common structural topologies in IoT networks. We conduct simulations of these topological networks under six attack strategies and evaluate their survival sizes and survival link ratios. The network vulnerability coefficient is also proposed to illustrate a network’s expected exploit rate providing quantitative results to compare among schemes. The blockchain-based method achieves a 57.68% to 225.28% improvement compared to a defenseless network and outperforms other traditional schemes in BA, NSW and GRID structured networks. The results indicate that the network topology can greatly affect the resilience of an authentication scheme.
Estimation of 24 h continuous cloud cover using a ground-based imager with a convolutional neural network

Abstract. In this study, we aimed to estimate cloud cover with high accuracy using images from a camera-based imager and a convolutional neural network (CNN) as a potential alternative to human-eye observation on the ground. Image data collected at 1 h intervals from 2019 to 2020 at a staffed weather station, where human-eye observations were performed, were used as input data. The 2019 dataset was used for training and validating the CNN model, whereas the 2020 dataset was used for testing the estimated cloud cover. Additionally, we compared satellite (SAT) and ceilometer (CEI) cloud cover to determine the method most suitable for cloud cover estimation at the ground level. The CNN model was optimized using a deep layer and detailed hyperparameter settings. Consequently, the model achieved an accuracy, bias, root mean square error (RMSE), and correlation coefficient (R) of 0.92, −0.13, 1.40 tenths, and 0.95, respectively, on the test dataset, and exhibited approximately 93 % high agreement at a difference within ±2 tenths of the observed cloud cover. This result demonstrates an improvement over previous studies that used threshold, machine learning, and deep learning methods. In addition, compared with the SAT (with an accuracy, bias, RMSE, R, and agreement of 0.89, 0.33 tenths, 2.31 tenths, 0.87, and 83 %, respectively) and CEI (with an accuracy, bias, RMSE, R, agreement of 0.86, −1.58 tenths, 3.34 tenths, 0.76, and 74 %, respectively), the camera-based imager with the CNN was found to be the most suitable method to replace ground cloud cover observation by humans.
PSAM: Progressive Spatial Adaptive Matching for Reference-Based Super Resolution

Reference-based super-resolution (RefSR), which aims to introduce an additional high-resolution (HR) reference (Ref) image to improve the reconstruction performance of low-resolution (LR) image, has achieved great success. Existing RefSR methods rely on the texture information of the reference image to compensate for the missing information. However, the differences of scale and orientation are unavoidable when obtaining useful information from the Ref image. In addition, it is difficult to achieve a good match due to the ill-posed between the LR image and Ref image. To address these challenges, we propose a new matching module, named progressive spatial adaptation module (PSAM). PSAM is a progressive alignment model to effectively overcome the ill-pose between the LR image and the Ref image. Further, we propose a spatial correction module (SCM) to correct for scale and orientation. Meanwhile, we introduce a gradient map to further correct the matched features. In addition, we propose a new loss function MC-Loss to ensure the success of correction. Experiments show that the matching method using PSAM to directly replace the existing RefSR is significantly better than the original matching method in terms of both quantitative and qualitative results.
Use of a digital contact tracing system in Singapore to mitigate COVID-19 spread

Abstract Background Contact tracing has been essential to reducing spread of COVID-19. Singapore leveraged technology to assist with contact tracing efforts using a Bluetooth-based app and token platform called ‘TraceTogether’. Methods We reviewed the impact of this system during the country’s Delta and Omicron waves (24 August 2021 to 17 February 2022) to identify differences in number of close contacts and time savings between full automation using TraceTogether alone as compared to manual contact tracing supplemented by TraceTogether. Characteristics of digital contact tracing app or token users were reviewed. Thereafter, the number of close contacts identified by manual and digital contact tracing methods, and the number of confirmed COVID-19 cases among contacts were analysed. The difference in time taken for identification of close contacts was also determined. Findings Adoption rate for TraceTogether was high, with 93.3% of cases having a registered device. There was a 9.8 h (34.9%) reduction in time savings for close contacts to be informed using TraceTogether alone compared to manual contact tracing supplemented by TraceTogether. The proportion of close contacts automatically identified through TraceTogether alone and turned positive was 3.6%. For those identified through manual contact tracing supplemented by TraceTogether, this proportion was 12.5% and 6.2% for those served quarantine orders and health risk warnings respectively. Interpretation The high adoption rate of ‘TraceTogether’ suggest that digital solutions remain a promising option to improve contact tracing in future epidemics. This may have been through its concurrent use with vaccine differentiated public health measures and policies which engender public trust. There is future potential for utilising such technology in managing communicable diseases to achieve good public health outcomes.
Rec-Def: A Recommendation-based Defence Mechanism for Privacy Preservation in Federated Learning Systems

An emergence of attention and regulations on consumer privacy can be observed over the recent years with the ubiquitous availability of IoT systems handling personal data. Federated Learning (FL) arises as a privacy-preserved Machine Learning (ML) technique where data can be kept private within these devices without transmitting to third parties. Yet, many privacy attacks against FL can still leak private and sensitive information. Though solutions are currently available, they may not fit into the scope of IoT devices and may lead to sub-optimal results for the FL process. To balance these trade-offs, we propose a consumer-first approach to privacy protection where local privacy preservation is done via a privacy recommendation system. To evaluate the level of vulnerability of the local FL models, we use existing attacks and propose a novel time-based inference attack to test the resilience of FL models. Based on the vulnerability assessment, privacy recommendations are applied to local FL models based on a new gradient-split mechanism that adds a perturbation mask to the updates. Our experiments demonstrate the attacks can be mitigated effectively via the proposed mechanisms, with enhanced privacy and minimum compromise to the FL model utility.
Hand Gesture Recognition and Control for Human-Robot Interaction Using Deep Learning

This paper introduces a real-time system for recognizing hand gestures using Python and OpenCV, centred on a Convolutional Neural Network (CNN) model. The primary objective of this study is to address the challenge of recognizing hand gestures in varied and complex environments. The proposed approach employs several image and video processing techniques, including data augmentation and feature extraction, to segment the hand region and extract relevant features. The system’s performance is significantly improved by adding to the original training dataset, resulting in 5,000 images with 500 images per gesture, as shown by the evaluation metrics indicating a substantial increase in accuracy from 96.9% to 99.2%. This paper aims to provide feasible and economical solutions for utilizing robots in industrial settings, while also proposing future research possibilities for enhancing human-robot interaction through methods such as incorporating hand gesture recognition.
Usefulness of machine learning and deep learning approaches in screening and early detection of breast cancer

Breast cancer (BC) is one of the most common types of cancer in women, and its prevalence is on the rise. The diagnosis of this disease in the first steps can be highly challenging. Hence, early and rapid diagnosis of this disease in its early stages increases the likelihood of a patient's recovery and survival. This study presents a systematic and detailed analysis of the various ML approaches and mechanisms employed during the BC diagnosis process. Further, this study provides a comprehensive and accurate overview of techniques, approaches, challenges, solutions, and important concepts related to this process in order to provide healthcare professionals and technologists with a deeper understanding of new screening and diagnostic tools and approaches, as well as identify new challenges and popular approaches in this field. Therefore, this study has attempted to provide a comprehensive taxonomy of applying ML techniques to BC diagnosis, focusing on the data obtained from the clinical methods diagnosis. The taxonomy presented in this study has two major components. Clinical diagnostic methods such as MRI, mammography, and hybrid methods are presented in the first part of the taxonomy. The second part involves implementing machine learning approaches such as neural networks (NN), deep learning (DL), and hybrid on the dataset in the first part. Then, the taxonomy will be analyzed based on implementing ML approaches in clinical diagnosis methods. The findings of the study demonstrated that the approaches based on NN and DL are the most accurate and widely used models for BC diagnosis compared to other diagnostic techniques, and accuracy (ACC), sensitivity (SEN), and specificity (SPE) are the most commonly used performance evaluation criteria. Additionally, factors such as the advantages and disadvantages of using machine learning techniques, as well as the objectives of each research, separately for ML technology and BC detection, as well as evaluation criteria, are discussed in this study. Lastly, this study provides an overview of open and unresolved issues related to using ML for BC diagnosis, along with a proposal to resolve each issue to assist researchers and healthcare professionals.
Enhancing pancreatic cancer classification through dynamic weighted ensemble: a game theory approach

AbstractThe significant research carried out on medical healthcare networks is giving computing innovations lots of space to produce the most recent innovations. Pancreatic cancer, which ranks among of the most common tumors that are thought to be fatal and unsuspected since it is positioned in the region of the abdomen beyond the stomach and can’t be adequately treated once diagnosed. In radiological imaging, such as MRI and CT, computer-aided diagnosis (CAD), quantitative evaluations, and automated pancreatic cancer classification approaches are routinely provided. This study provides a dynamic weighted ensemble framework for pancreatic cancer classification inspired by game theory. Grey Level Co-occurrence Matrix (GLCM) is utilized for feature extraction, together with Gaussian kernel-based fuzzy rough sets theory (GKFRST) for feature reduction and the Random Forest (RF) classifier for categorization. The ResNet50 and VGG16 are used in the transfer learning (TL) paradigm. The combination of the outcomes from the TL paradigm and the RF classifier paradigm is suggested using an innovative ensemble classifier that relies on the game theory method. When compared with the current models, the ensemble technique considerably increases the pancreatic cancer classification accuracy and yields exceptional performance. The study improves the categorization of pancreatic cancer by using game theory, a mathematical paradigm that simulates strategic interactions. Because game theory has been not frequently used in the discipline of cancer categorization, this research is distinctive in its methodology.Keywords: Pancreatic cancerensemble methodgame theoryVGG16transfer learningrandom forestResNet50 Data availability statementAll data analysed during this study are included in this article.Disclosure statementThe authors declare no conflict of interest.Additional informationFundingThe author(s) reported there is no funding associated with the work featured in this article.
Deep Convolutional Neural Network with Symbiotic Organism Search-Based Human Activity Recognition for Cognitive Health Assessment

Cognitive assessment plays a vital role in clinical care and research fields related to cognitive aging and cognitive health. Lately, researchers have worked towards providing resolutions to measure individual cognitive health; however, it is still difficult to use those resolutions from the real world, and therefore using deep neural networks to evaluate cognitive health is becoming a hot research topic. Deep learning and human activity recognition are two domains that have received attention for the past few years. The former is for its relevance in application fields like health monitoring or ambient assisted living, and the latter is due to their excellent performance and recent achievements in various fields of application, namely, speech and image recognition. This research develops a novel Symbiotic Organism Search with a Deep Convolutional Neural Network-based Human Activity Recognition (SOSDCNN-HAR) model for Cognitive Health Assessment. The goal of the SOSDCNN-HAR model is to recognize human activities in an end-to-end way. For the noise elimination process, the presented SOSDCNN-HAR model involves the Wiener filtering (WF) technique. In addition, the presented SOSDCNN-HAR model follows a RetinaNet-based feature extractor for automated extraction of features. Moreover, the SOS procedure is exploited as a hyperparameter optimizing tool to enhance recognition efficiency. Furthermore, a gated recurrent unit (GRU) prototype can be employed as a categorizer to allot proper class labels. The performance validation of the SOSDCNN-HAR prototype is examined using a set of benchmark datasets. A far-reaching experimental examination reported the betterment of the SOSDCNN-HAR prototype over current approaches with enhanced precision of 86.51% and 89.50% on Penn Action and NW-UCLA datasets, respectively.
Development of intelligent information analytical webportals based on semantic Wiki technologies: problems and challenges

We analyse the main functions of modern web portals that differ them from other types of sites. In this work we consider intelligent information-analytical web portals as a specific subset of intelligent information-analytical systems, and their features and development trends that have an influence on their semanticization are analysed. As a part of such analysis we considered knowledge models and technologies of their processing that can be used for this aim. We determine standards and tools of the Semantic Web that can be used for the semanticization of web portals and describe the ways of such use on practical examples. Some basic requirements for technologies and instrumental tools used for development of semantic portals are considered. We analyse and classify typical problems related to the processing, structuring, and analysis of large volumes of data that arise in the process of creating and deploying such portals, and the criteria for evaluating the development opportunities of existing web portals are considered. On example of web portal based on semantic Wiki technologies we investigate practical problems of implementation and scaling of portal knowledge processing tools and services and propose some ways of their prevention and solution.
ChatGPT

There is nothing new about chatbots. They have been around for decades. But they have often seemed lacking, as it can be extremely difficult to understand and communicate with natural language.
Research on lightweight pedestrian detection based on improved YOLOv5

Aiming at the problems of low detection accuracy and the large size of the pedestrian detection algorithm, to improve the edge intelligent recognition capability of the terminal, this paper proposes a lightweight pedestrian detection scheme based on the improved YOLOv5. In this paper, the algorithm first takes the original YOLOv5 as the basic framework and uses the Ghost Bottleneck module to replace the C3 module in the original YOLOv5 network to reduce the number of parameters, eliminate redundant features, and obtain a more lightweight model. Then the attention mechanism CBAM module is added to improve the feature extraction capability and detection accuracy of the algorithm. After experimental verification, the improved lightweight YOLOv5 algorithm significantly reduces the model size and computational cost while guaranteeing accuracy, which is suitable for deployment in edge devices.
Visual-gestural Interface for Auslan Virtual Assistant

For Deaf people to use natural language interfaces, technologies must understand sign language input and respond in the same language. We developed a prototype smart home assistant that utilises gesture-based controls, improving accessibility and convenience for Deaf users. The prototype features Zelda, an interactive signing avatar that provides responses in Auslan (Australian Sign Language), enabling effective two-way communication. Our live demonstration includes gesture recognition and sign production.
Sign Language Motion Generation from Sign Characteristics

This paper proposes, analyzes, and evaluates a deep learning architecture based on transformers for generating sign language motion from sign phonemes (represented using HamNoSys: a notation system developed at the University of Hamburg). The sign phonemes provide information about sign characteristics like hand configuration, localization, or movements. The use of sign phonemes is crucial for generating sign motion with a high level of details (including finger extensions and flexions). The transformer-based approach also includes a stop detection module for predicting the end of the generation process. Both aspects, motion generation and stop detection, are evaluated in detail. For motion generation, the dynamic time warping distance is used to compute the similarity between two landmarks sequences (ground truth and generated). The stop detection module is evaluated considering detection accuracy and ROC (receiver operating characteristic) curves. The paper proposes and evaluates several strategies to obtain the system configuration with the best performance. These strategies include different padding strategies, interpolation approaches, and data augmentation techniques. The best configuration of a fully automatic system obtains an average DTW distance per frame of 0.1057 and an area under the ROC curve (AUC) higher than 0.94.