Reviewerly/exHarmony huggingface dataset:
Development and Evaluation of a Mobile Application with Augmented Reality for Guiding Visitors on Hiking Trails

Tourism on the island of Santa Maria, Azores, has been increasing due to its characteristics in terms of biodiversity and geodiversity. This island has several hiking trails; the available information can be consulted in pamphlets and physical placards, whose maintenance and updating is difficult and expensive. Thus, the need to improve the visitors’ experience arises, in this case, by using the technological means currently available to everyone: a smartphone. This paper describes the development and evaluation of the user experience of a mobile application for guiding visitors on said hiking trails, as well as the design principles and main issues observed during this process. The application is based on an augmented reality interaction model providing visitors with an interactive and recreational experience through Augmented Reality in outdoor environments (without additional marks in the physical space and using georeferenced information), helping in navigation during the route and providing updated information with easy maintenance. For the design and evaluation of the application, two studies were carried out with users on-site (Santa Maria, Azores). The first had 77 participants, to analyze users and define the application’s characteristics, and the second had 10 participants to evaluate the user experience. The feedback from participants was obtained through questionnaires. In these questionnaires, an average SUS (System Usability Scale) score of 83 (excellent) and positive results in the UEQ (User Experience Questionnaire) were obtained.
Photovoltaic Power Prediction Using Analytical Models and Homer-Pro: Investigation of Results Reliability

This paper aims to develop an analytical model for the prediction of the electricity produced in a Photovoltaic Power Station (PVS). In this context, the developed mathematical model is implemented in a Simulink Model. The obtained simulation results are compared to the experimental data, the results obtained from the software Homer-Pro model, and the results given by the online PV calculator (Photovoltaic Geographical Information System), developed by the European commission. The comparison results show the reliability of the developed analytical model for specific months of the year. However, an error of 10% between simulations and experimental results is observed for July and August. This error is mainly due to the effects of humidity and dust that were not considered in the analytical model. Nevertheless, the monthly and yearly produced electricity values show the robustness of the proposed model to predict the PVS generated power. The developed model will be used as a powerful tool for data prediction and the optimization of electricity generation. This permits us to reduce the losses in power generation by optimizing the connected generating power stations to the power grid.
Embedded System Vehicle Based on Multi-Sensor Fusion

As intelligent driving vehicles came out of concept into people's life, the combination of safe driving and artificial intelligence becomes the new direction of future transportation development.Autonomous driving technology is developing based on control algorithms and model recognitions.In this paper, a cloud-based interconnected multi-sensor fusion autonomous vehicle system is proposed that uses deep learning (YOLOv4) and improved ORB algorithms to identify pedestrians, vehicles, and various traffic signs.A cloud-based interactive system is built to enable vehicle owners to master the situation of their vehicles at any time.In order to meet multiple application of automatic driving vehicles, the environment perception technology of multi-sensor fusion processing has broadened the uses of automatic driving vehicles by being equipped with automatic speech recognition (ASR), vehicle following mode and road patrol mode.These functions enable automatic driving to be used in applications such as agricultural irrigation, road firefighting and contactless delivery under new coronavirus outbreaks.Finally, using the embedded system equipment, an intelligent car was built for experimental verification, and the overall recognition accuracy of the system was over 96%.
What can the metaverse do for urology?

Everyone talks about the metaverse but few know what it really is. Augmented reality, virtual reality, internet of things (IoT), 5G, blockchain: these are just some of the technologies underlying the structure of the metaverse, a sort of parallel dimension in which the physical and virtual worlds merge together enabling users to interact by emerging technologies in order to enhance their actions and decisions. The healthcare scientific community is already looking at the metaverse as a new research frontier, a tool to improve medical knowledge and patient care. We reviewed the metaverse applications and services, looking for those that could best be developed in the urological field. Urology, due to its technological nature, is a privileged laboratory for experimenting and exploiting the applications of the metaverse both inside and outside the operating room. The revolution of the metaverse is already happening, which is why it is necessary that urologists face it as protagonists in order to lead it in the right direction.
Blind Image Quality Assessment for Pathological Microscopic Image Under Screen and Immersion Scenarios

The high-quality pathological microscopic images are essential for physicians or pathologists to make a correct diagnosis. Image quality assessment (IQA) can quantify the visual distortion degree of images and guide the imaging system to improve image quality, thus raising the quality of pathological microscopic images. Current IQA methods are not ideal for pathological microscopy images due to their specificity. In this paper, we present deep learning-based blind image quality assessment model with saliency block and patch block for pathological microscopic images. The saliency block and patch block can handle the local and global distortions, respectively. To better capture the area of interest of pathologists when viewing pathological images, the saliency block is fine-tuned by eye movement data of pathologists. The patch block can capture lots of global information strongly related to image quality via the interaction between different image patches from different positions. The performance of the developed model is validated by the home-made Pathological Microscopic Image Quality Database under Screen and Immersion Scenarios (PMIQD-SIS) and cross-validated by the five public datasets. The results of ablation experiments demonstrate the contribution of the added blocks. The dataset and the corresponding code are publicly available at: https://github.com/mikugyf/PMIQD-SIS.
Blockchain-based secure privacy-preserving vehicle accident and insurance registration

Insurance claims processing involves multiple entities and data sources, necessitating communication between human agents. Consequently, vehicle insurance claims have traditionally required significant human effort and time. Daily vehicle-related transactions, including those managed by transportation authorities, pose challenges for tracking. Centralised systems have been utilised for national solutions, but trust management, transparency, and access control issues arise. There is potential for further integration of vehicle-related transactions. This article proposes a blockchain framework for vehicle insurance to streamline the reporting of accidents and filing of insurance claims. Blockchain-based automation platforms can enhance the scale and response time of claims processing, providing users with control over additional transactions, inspection, and insurance. For experimental purposes, a blockchain was created using Hyperledger Fabric to store information about vehicles, owners, and insurance. Efficient querying of this blockchain requires specific participants, assets, and transactions. The consensus algorithm can identify invalid claims if a transaction request contains an error. By deploying blockchain technology and smart contracts, this architecture has the potential to address trust and security concerns associated with traditional insurance policies and claims.
A multi-image compression and encryption scheme based on fractional chaotic map

Abstract In this paper, a visual security encryption scheme for multi-color images based on BP neural network and fractional chaotic map is proposed, which disguises secret images as a meaningful visual image. Firstly, three color images are compressed based on BP neural network. Then, according to the pseudo-random sequence generated by fractional chaotic map, the merged compressed images are scrambled by spiral transformation and diffused by XOR, in which the direction and degree of spiral transformation can be adjusted. In order to ensure the visual effect of the camouflage image, the lifting wavelet transform (LWT) is used to decompose the carrier image to obtain the coefficient matrix, and the cipher images are adjusted to a narrow range and embedded into the coefficient matrixes based on the pseudo-random sequence. Finally, visually secure image can be generated by inverse lifting wavelet transform. The reverse algorithm can restore the images by extraction, decryption and decompression. Experimental results verify that the proposed scheme has feasibility, robustness, anti-noise and clipping capability, and the PSNR value is no less than 31.4 under various attacks.
Hyperspectral Image Compression via Cross-Channel Contrastive Learning

In recent years, advances in deep learning have greatly promoted the development of hyperspectral image (HSI) compression algorithms. However, most existing compression approaches directly rely on rate-distortion optimization without other guidance during model learning. Therefore, this brings challenges to distinguishing similar features or objects that are widely available in HSIs, especially in remote sensing scenes, since quantification in lossy compression can cause informative attribute (e.g., category) collapse and loss problems at high compression ratios. In this paper, we propose a novel hyperspectral compression network via contrastive learning (HCCNet) to help generate discriminative representations and preserve informative attributes as much as possible. Specifically, we design a contrastive informative feature encoding (CIFE) to extract and organize discriminative attributes from the original HSIs by enlarging the discrimination over the learned latents in different channel indexes to relieve attribute collapses. In the case of attribute losses, we define a contrastive invariant feature recovery (CIFR) to discover the lost attributes via contrastive feature refinement. Experiments on five different HSI datasets illustrate that the proposed HCCNet can achieve impressive compression performance, such as improvement of the peak signal-to-noise ratio (PSNR) from 28.86 dB (at 0.2284 bpppb) to 30.30 dB (at 0.1960 bpppb) on the Chikusei dataset.
Artificial Intelligence at the Service of Precision Medicine

Recently, the ability of artificial intelligence to transform data into information has affected the field of medicine. Indeed, artificial intelligence (AI) has been implemented in disease diagnosis and prognosis, treatment optimization and outcome prediction, drug development, and public health. This technological and especially medical advancement results from the ability of machine learning algorithms to process multidimensional data. This paper aims to study and evaluate recent literature dealing with the application of machine learning and deep learning algorithms in healthcare with a focus on precision medicine applications.
Artificial Intelligence for Stroke Prediction

This paper uses some artificial intelligence algorithms to predict cerebrovascular accident, according to the analysis of patients’ records. The goal of this work is to analyze the correlation between different risk factors using Support Vector Machine (SVM), Random Forest and Logistic Regression algorithms. We also compare the accuracy and roc curve for every predicted model to provide the most appropriate method. By analyzing the final results, we define the tendency of a potential case and help in early diagnosis and treatment that can minimize brain damage.
Triplet adversarial Learning-driven graph architecture search network augmented with Probsparse-attention mechanism for fault diagnosis under Few-shot &amp; Domain-shift

The consistent probability distribution between training & testing data is one of the prerequisites for valid intelligent diagnosis models. Nevertheless, the ineluctable distribution discrepancies produced by variable working conditions cause intense conflicts with the above. Besides, many existing models fail to explicitly characterize the correlation between data, which makes them difficult to mine discriminative features in limited samples. Fortunately, the proposal of graph neural networks provides a potential solution to overcome the above issues. Inspired by this, this paper creatively proposes a graph architecture search network (GASN) for cross-domain fault diagnosis. Its main framework consists of graph-feature extraction modules, graph-architecture search modules and differentiated classifiers, which can effectively reduce the computational complexity and accelerate the inferential efficiency of GASN, with two distinctive characteristics: (1) Specially designed search module utilizes multi-head probsparse-attention mechanism to search the optimal graph-architecture, thus suppressing the redundancy and exclusivity among candidate graph-data; (2) Triplet loss-driven domain adversarial training strategy is proposed to enhance the domain adaptability of GASN, which can assist itself to achieve fine-grained adaptation of distinguishable architectures in response to unknown domains. Comparative results on four case studies indicate that the GASN can achieve superior performance to existing state-of-art models even under imbalanced data.
Recurrent Network Solutions for Human Posture Recognition Based on Kinect Skeletal Data

Ambient Assisted Living (AAL) systems are designed to provide unobtrusive and user-friendly support in daily life and can be used for monitoring frail people based on various types of sensors, including wearables and cameras. Although cameras can be perceived as intrusive in terms of privacy, low-cost RGB-D devices (i.e., Kinect V2) that extract skeletal data can partially overcome these limits. In addition, deep learning-based algorithms, such as Recurrent Neural Networks (RNNs), can be trained on skeletal tracking data to automatically identify different human postures in the AAL domain. In this study, we investigate the performance of two RNN models (2BLSTM and 3BGRU) in identifying daily living postures and potentially dangerous situations in a home monitoring system, based on 3D skeletal data acquired with Kinect V2. We tested the RNN models with two different feature sets: one consisting of eight human-crafted kinematic features selected by a genetic algorithm, and another consisting of 52 ego-centric 3D coordinates of each considered skeleton joint, plus the subject's distance from the Kinect V2. To improve the generalization ability of the 3BGRU model, we also applied a data augmentation method to balance the training dataset. With this last solution we reached an accuracy of 88%, the best we achieved so far.
Digital quantum simulation of the BCS model with a central-spin-like quantum processor

The simulation of quantum systems is one of the most promising applications of quantum computers. In this paper we present a quantum algorithm to perform digital quantum simulations of the BCS model on a quantum register with a star shaped connectivity map, as it is, e.g., featured by color centers in diamond. We show how to effectively translate the problem onto the quantum hardware and implement the algorithm using only the native interactions between the qubits. Furthermore we discuss the complexity of the circuit. We use the algorithm to simulate the dynamics of the BCS model by subjecting its mean-field ground state to a time-dependent perturbation. The quantum simulation algorithm is studied using a classical simulation.
Dynamic scene deblurring with continuous cross-layer attention transmission

The deep convolutional neural networks (CNNs) using attention mechanism have achieved great success for dynamic scene deblurring. In most of these networks, only the features refined by the attention maps can be passed to the next layer and the attention maps of different layers are separated from each other, which does not make full use of the attention information from different layers in the CNN. To address this problem, we introduce a new continuous cross-layer attention transmission (CCLAT) mechanism that can exploit hierarchical attention information from all the convolutional layers. Based on the CCLAT mechanism, we use a very simple attention module to construct a novel residual dense attention fusion block (RDAFB). In RDAFB, the attention maps inferred from the outputs of the preceding RDAFB and each layer are directly connected to the subsequent ones, leading to a CCLAT mechanism. Taking RDAFB as the building block, we design an effective architecture for dynamic scene deblurring named RDAFNet. The experiments on benchmark datasets show that the proposed model outperforms the state-of-the-art deblurring approaches, and demonstrate the effectiveness of CCLAT mechanism. The source code is available on: https://github.com/xjmz6/RDAFNet.
Using the metaverse in training: lessons from real cases

Purpose The purpose of this study is to explore the potential contribution of the metaverse to improve training and development as a function of human resource development (HRD) perspective. The authors explore the benefits and challenges of the metaverse and introduce cases of companies using the metaverse in training. Design/methodology/approach A narrative literature review was conducted to collect information on the metaverse in training. The authors reviewed peer- and non-peer-reviewed articles, book chapters, white papers, corporate websites and blogs and business magazines. Findings A total of 75 articles were reviewed, including 14 cases, which were summarized to demonstrate how companies are applying metaverse technology in training contexts. For a more in-depth review, three cases were selected and summarized in terms of context, process and outcomes. Originality/value The metaverse is an emergent topic in HRD. It has the potential to revolutionize the functions of training and development through the combination of advanced technologies, including virtual reality, augmented reality and mixed reality. This article is the foundational attempt to provide a comprehensive summary of existing literature and case studies that highlight the potential of the metaverse in training within the context of HRD.
MemoriEase: An Interactive Lifelog Retrieval System for LSC’23

Lifelogging is an activity of recording all events that happen in the daily life of an individual. The events can contain images, audio, health index, etc which are collected through various devices such as wearable cameras, smartwatches, and other digital services. Exploiting lifelog data can bring significant benefits for lifeloggers from creating personalized healthcare plans to retrieving events in the past. In recent years, there has been a growing development of interactive lifelog retrieval systems, such as competitors at the annual Lifelog Search Challenge (LSC), to assist lifeloggers in finding events from the past. This paper introduces an interactive lifelog image retrieval called MemoriEase for the LSC'23 challenge. This system combines concept-based and embedding-based retrieval approaches to answer accurate images for LSC'23 queries. This system uses BLIP for the embedding-based retrieval approach to reduce the semantic gap between images and text queries. The concept-based retrieval approach uses full-text search in Elasticsearch to retrieve images having visual concepts similar to keywords in the query. Regarding the user interface, we make it as simple as possible to make novices users can use it with only a small effort. This is the first version of MemoriEase and we expect this can help users perform well in the LSC'23 competition.
Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction

Internet of Things (IoT) has made significant strides in energy management systems recently. Due to the continually increasing cost of energy, supply-demand disparities, and rising carbon footprints, the need for smart homes for monitoring, managing, and conserving energy has increased. In IoT-based systems, device data are delivered to the network edge before being stored in the fog or cloud for further transactions. This raises worries about the data's security, privacy, and veracity. It is vital to monitor who accesses and updates this information to protect IoT end-users linked to IoT devices. Smart meters are installed in smart homes and are susceptible to numerous cyber attacks. Access to IoT devices and related data must be secured to prevent misuse and protect IoT users' privacy. The purpose of this research was to design a blockchain-based edge computing method for securing the smart home system, in conjunction with machine learning techniques, in order to construct a secure smart home system with energy usage prediction and user profiling. The research proposes a blockchain-based smart home system that can continuously monitor IoT-enabled smart home appliances such as smart microwaves, dishwashers, furnaces, and refrigerators, among others. An approach based on machine learning was utilized to train the auto-regressive integrated moving average (ARIMA) model for energy usage prediction, which is provided in the user's wallet, to estimate energy consumption and maintain user profiles. The model was tested using the moving average statistical model, the ARIMA model, and the deep-learning-based long short-term memory (LSTM) model on a dataset of smart-home-based energy usage under changing weather conditions. The findings of the analysis reveal that the LSTM model accurately forecasts the energy usage of smart homes.
Economic Growth and Extreme Poverty in Sub-Saharan African Countries: Non-Linearity and Governance Threshold Effect

Over the last few decades, many economies in sub-Saharan Africa have experienced much faster economic growth than other parts of the world. However, many of these economies have not experienced significant poverty reduction. Several factors such as the quality of governance may limit the expected effects of economic growth on poverty. This paper examines the triangular relationship between extreme poverty, governance quality, and economic growth for the sub-Saharan African countries over the period 2010–2019. Compared to the work carried out until now, the novelty of this research lies in using the Panel Threshold Regression (PTR) and Panel Smooth Transition Regression (PSTR) models to determine the optimal level of governance index, which once attained, will make extreme poverty decrease with economic growth and governance quality. We found that the nexus between these three variables is nonlinear. Besides, results show that there exists a statistically negative relationship between governance and extreme poverty above the threshold level of 0.314 for the Global Governance Index (GGI) and 66.9 for the Ibrahim Index of African Governance (IIAG), above which governance quality decreases extreme poverty. The results showed that the economic growth would begin to reduce extreme poverty once governance reaches a threshold level of 0.367 for GGI and 63.2 for IIAG. Better performance of governance also appears to improve economic growth and reduces poverty.