Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence framework with traditional computer vision and deep learning approaches for optimal automatic segmentation of left ventricle with scar

Automatic segmentation of the cardiac left ventricle with scars remains a challenging and clinically significant task, as it is essential for patient diagnosis and treatment pathways. This study aimed to develop a novel framework and cost function to achieve optimal automatic segmentation of the left ventricle with scars using LGE-MRI images. To ensure the generalization of the framework, an unbiased validation protocol was established using out-of-distribution (OOD) internal and external validation cohorts, and intra-observation and inter-observer variability ground truths. The framework employs a combination of traditional computer vision techniques and deep learning, to achieve optimal segmentation results. The traditional approach uses multi-atlas techniques, active contours, and k-means methods, while the deep learning approach utilizes various deep learning techniques and networks. The study found that the traditional computer vision technique delivered more accurate results than deep learning, except in cases where there was breath misalignment error. The optimal solution of the framework achieved robust and generalized results with Dice scores of 82.8 ± 6.4% and 72.1 ± 4.6% in the internal and external OOD cohorts, respectively. The developed framework offers a high-performance solution for automatic segmentation of the left ventricle with scars using LGE-MRI. Unlike existing state-of-the-art approaches, it achieves unbiased results across different hospitals and vendors without the need for training or tuning in hospital cohorts. This framework offers a valuable tool for experts to accomplish the task of fully automatic segmentation of the left ventricle with scars based on a single-modality cardiac scan.
Design and feasibility study of a HCPS framework-based VR alpine skiing decision-making training system

Alpine skiing beginners are easily injured if they fail make the right decisions when faced with unexpected situations. Excellent decision-making requires extensive training, while alpine skiing training is limited by time and space. To address these issues, this paper combines VR and machine learning techniques to develop a high fidelity VR skiing decision-making training system based on HCPS framework. The aim is to train beginner skiers in decision-making by creating automated and repeatable virtual scenarios. Firstly, physical world (ski resort environment, and user motions) was mapped into the VR world using the HCPS framework. Afterwards, the user's decision motions were made based on the scenarios provided, while being identified through an improved SVM (94.5% accuracy). The motion recognition results were fed into the expert system to assess the feasibility of the decision-making. Finally, the feasibility of the system was assessed with a combination of subjective and objective approaches. The results showed that the VR system significantly improved the participants' skiing decision-making ability and speed compared to traditional 2D video training. The high fidelity environment reduced participants' motion sickness symptoms and resulted in a better sensory experience. In addition, participants were more focused in the high fidelity environment. Overall, these results demonstrated the construct validity of the virtual ski decision-making training system. This study changes the traditional training method to create an immersive training environment, and provides an effective avenue for the development of sports training.
Weakly supervised volumetric prostate registration for MRI-TRUS image driven by signed distance map

Image registration is a fundamental step for MRI-TRUS fusion targeted biopsy. Due to the inherent representational differences between these two image modalities, though, intensity-based similarity losses for registration tend to result in poor performance. To mitigate this, comparison of organ segmentations, functioning as a weak proxy measure of image similarity, has been proposed. Segmentations, though, are limited in their information encoding capabilities. Signed distance maps (SDMs), on the other hand, encode these segmentations into a higher dimensional space where shape and boundary information are implicitly captured, and which, in addition, yield high gradients even for slight mismatches, thus preventing vanishing gradients during deep-network training. Based on these advantages, this study proposes a weakly-supervised deep learning volumetric registration approach driven by a mixed loss that operates both on segmentations and their corresponding SDMs, and which is not only robust to outliers, but also encourages optimal global alignment. Our experimental results, performed on a public prostate MRI-TRUS biopsy dataset, demonstrate that our method outperforms other weakly-supervised registration approaches with a dice similarity coefficient (DSC), Hausdorff distance (HD) and mean surface distance (MSD) of 87.3 ± 11.3, 4.56 ± 1.95 mm, and 0.053 ± 0.026 mm, respectively. We also show that the proposed method effectively preserves the prostate gland's internal structure.
Open set classification of untranscribed handwritten text image documents

Content-based classification of manuscripts is an important task that is generally carried out by expert archivists. Nevertheless, many historical manuscript collections are so vast that in most cases this task is hardly feasible, even for large, well staffed archives. Nowadays, manuscripts are generally preserved in the form of sets of digital images. Therefore, the technical problem we are interested in is automatic classification of “‘image documents”, each consisting of a set of untranscribed handwritten text images, by the textual contents of the images. The traditional Pattern Recognition classification paradigm does provide the basic tools to deal with this problem. However, in practice, the set of relevant classes of a large documental series is seldom known in advance. Therefore, a classifier trained with a predefined set of classes will systematically fail when new image documents arrive which do not belong to any of the classes assumed in training. Here we adopt the “Open Set Classification” framework to extend and consolidate our previous work on image document classification in order to adequately handle new documents from unknown classes. The proposed approaches are based on a relatively novel technology for text image representation known as “probabilistic indexing”, which proves very effective to characterise the intrinsic word-level uncertainty exhibited by historical handwritten text images. We assess the performance of this approach on a moderately sized but representative dataset extracted from a huge series of complex notarial manuscripts from the Spanish Archivo Histórico Provincial de Cádiz, with good results.
Design methodology for reconfigurable multi-radio platform for future generation wireless systems

Owing to the continuous development of technology, the future generation of wireless communication systems is increasingly becoming more complex to support a spectrum of new and existing services and standards. Consequently, the need for flexible and adaptable radios has become more prominent. Radio signals can be modulated using various methods. Radio modulation recognition refers to a series of processes for acquiring information regarding modulation types of non-cooperative received signals. It plays an important role as a pre-demodulation process required to ensure accuracy, safety, and effectiveness in recovering information. This study proposed a reconfigurable multi-radio platform comprising a reconfigurable antenna and receiver board. The antenna was reconfigurable to facilitate operation in different frequency bands controlled via the receiver board using bias voltages. The receiver board was programmable using custom-built software. Moreover, it can detect the modulation scheme and accurately demodulate signals that are modulated in one of three common modulation schemes: amplitude modulation, frequency modulation, and phase modulation. This solution offers the advantages of reduced size, cost, and power consumption because it uses one hardware system as opposed to the deployment of several individual radios. Moreover, the platform presents an adaptable, cost-effective solution for future-generation wireless networks and a flexible and robust terminal that can operate under changing communication environments.
Performance and User Experience Studies of HILLES: Home-based Immersive Lower Limb Exergame System

Head-Mounted Devices (HMDs) have become popular for home-based immersive gaming. However, using lower limb motion in the immersive virtual environment is still restricted. This work introduces an RGB-D camera-based motion capture system alongside a standalone HMD for Home-based Immersive Lower Limbs Exergame Systems (HILLES) in a seated pose. With the advance of neural network models, camera-based 3D body tracking accuracy is increasing. Nevertheless, the high demand for computing resources on model inference may compromise the game engine's performance. Accordingly, HILLES applies a distributed architecture to leverage the resources effectively. The system performances, such as frames per second and latency, are compared with a centralized system. For an immersive exergame, a pet walking around could raise safety issues. Hence, we also showcase that the camera system can provide an additional safety feature by combining an object detection model. Besides, another challenge in games focusing on lower limb interactions is the safe reachability of different virtual objects from a seated pose. Accordingly, in the user study, a stomping game with two reachability enhancements, including leg extension and seated navigation, is implemented based on the HILLES to evaluate and explore the gaming experience. The result shows that the system motivates the leg exercise, and the added enhancements may adjust the game difficulty. However, the enhancements may also distract users from focusing on leg exertion. The derived insight could benefit the lower limb exergame design in the future.
Sparse Feature based Progressive Algorithm for Co-Saliency Detection

In order to locate the prominent objects which are featured among set of input photos, or to address the issue of co-saliency identification, a new approach is presented in this study. This research study intends to address the issue of co-saliency identification via early conscientious strategy, in contrast to the majority of prior approaches that call for correspondence matching. The proposed technique intends to detect co-saliency prior to the focused attentive response since it does not require to match the relationship between the two input images. The proposed approach also blocks the additional salient items detections, which only appears solo in single photos provided by the joint information of the image pair datasets. Finally, this research study demonstrates through various experimental results how well our algorithm locates the co-salient patterns in all paired input images.
Feature selection for domain adaptation using complexity measures and swarm intelligence

Particle Swarm Optimization is an optimization algorithm that mimics the behaviour of a flock of birds, setting multiple particles that explore the search space guided by a fitness function in order to find the best possible solution. We apply the Sticky Binary Particle Swarm Optimization algorithm to perform feature selection for domain adaptation, a specific type of transfer learning in which the source and the target domain have a common feature space, a common task, but different distributions. When applying Particle Swarm Optimization, classification error is usually employed in the fitness function to evaluate the goodness of subsets of features. In this paper, we aim to compare this approach with using complexity metrics instead, under the assumption that reducing the complexity of the problem will lead to results that are independent from the classifier used for testing while being less computationally demanding. Therefore, we carried out experiments to compare the performance of both approaches in terms of classification accuracy, speed and number of features selected. We found out that our proposal, although in some cases incurs in a slight degradation of classification performance, it is indeed faster and selects fewer features, making it a feasible trade-off.
A Comparison of Video Browsing Performance between Desktop and Virtual Reality Interfaces

Interactive retrieval with user-friendly and performant interfaces remains a necessity for video retrieval, even in light of significant gains in retrieval performance through multi-modal encoders. In recent years, novel interaction modalities such as virtual reality (VR) and augmented reality (AR) have gained popularity, but the best way to adapt paradigms from traditional retrieval interfaces, especially for result browsing and interaction, remains an open research question. In this paper, we compare two video retrieval interfaces in a controlled setting to gain insight into the differences in video browsing between VR and desktop interfaces. We formulate hypotheses explaining why there might be performance differences between the two interfaces, define metrics to test the hypotheses, and show results based on data gathered at an evaluation campaign. Our results show that VR interfaces can be competitive in browsing performance and indicate that there can even be an advantage when browsing larger result sets in VR.
An infrared image enhancement method via content and detail Two-Stream deep convolutional neural network

Aiming at the problems of low contrast and blurry details of infrared images, a novel two-stream deep full convolutional neural network is proposed for low-quality infrared image enhancement. An infrared detail enhancement sub-network and a global content-invariant sub-network are designed to achieve adaptive enhancement of infrared features. First, the detail enhancement network composed of mixed attention block (MAB) with multi-convolutions, residual learning (RL) and up-sampling unit is used to extract deep features from inputs, learn meaningful thermal radiation target information, restrain unnecessary background, and then perform the separation of target and background. Second, the content-invariant network mainly consisting of dilated convolutions and multi-scale convolutions captures rich contextual information to focus on the overall content, maintain the spatial structure, and avoid over-enhancement of local regions. Finally, the fine-tuning unit fuses the features extracted by the two-stream to complete the element complementation between different mappings and generate high-quality infrared images. Furthermore, experiments on public datasets and self-collected infrared datasets demonstrate that the proposed method outperforms other image enhancement methods not only for image quality on PSNR and SSIM, but also that has better visual quality with less artifact and noise.
Domain-adaptive graph neural network for few-shot learning

The task of few-shot classification (FSC) is to build a model to discriminate novel categories that do not present in training categories with limited labeled samples. However, in many applications, the training categories and novel categories are assumed to come from the same domain. Existing few-shot classification algorithms achieve promising performance in a single domain, but often fail to generalize to unseen domains because of the different feature distribution across domains. The main goal of this work is to propose an effective recognition model that can work on various image domains with domain shift. Specifically, we propose to construct a new domain-attention mechanism with some adapters based on the squeeze-and-excitation network architectures. The proposed network processes multiple domains simultaneously and all parameters are shared across domains. In addition, because of the large discrepancy in feature distributions among different domains, similarity transformation layers are used to reduce the differences in the feature distributions in the training stage. Extensive experiments were conducted to validate the domain generalization capability (GC) of this model on four FSC datasets: mini-ImageNet, Cars, CUB-200-2011, and Simpsons Characters Data. The results show that the presented method has excellent performances on various datasets across diverse domains.
Multicore Implementation of K-Means Clustering Algorithm

Multi-core processing is extensively used in every sector for its performance efficiency, with the advent of multi-core architecture have to modify the existing primitive algorithms. This study analyses the feasibility of K-mean data-mining technique, which is applied to a hybrid cluster with multi-core programming. The algorithm is developed using Message Passing Interface (MPI) and C programming languages for the parallel processing of the sets and uses the CPU to its maximum power for the hybrid sets. The heterogeneous clusters are confirmed by the usage of MPICH2 (High performance and portability implementation of MPI). examined the algorithm for the huge dataset. The dataset is split into a number of cores and each of the cores estimates the number of dusters on the same dataset interdependent to each other. By this, assert the core processor time for communication is significant for huge datasets. Hence, the same dataset for two different processors takes different times even with identical speed and memory and also with different speeds and access times.
Real-time planning and collision avoidance control method based on deep reinforcement learning

Real time planning and collision avoidance is an important part of the motion planning for USVs, its core is to design effective planning and collision avoidance methods. This paper proposes a real-time planning and collision avoidance method based on deep reinforcement learning for USVs in complex environments. Firstly, a novel reward function is designed, which transforms the collision avoidance problem into a deep reinforcement learning strategy solution problem that includes distance, direction, speed, and GOLREGs constraints, it ensures the smooth and safe operation of the USV. Secondly, inefficient exploration and exploitation methods make it difficult for intelligent agents to discover key information in the environment, high value experience is obtained through the designed layered sampling exploration mechanism in this article, the intelligent agent learns effective control strategies through this mechanism, which accelerates the convergence of the strategies and reduces the waste of computing resources. Finally, a real-time collision avoidance simulation platform for the USV is established, and experimental results of different exploration mechanisms are compared, the efficiency of the layered sampling exploration mechanism has been verified. The simulation scenarios from easy to difficult are designed, and the results indicate that the USV can complete real-time planning and collision avoidance tasks in complex environments.
Blockchain and NFTs-Based Trades of Second-Hand Vehicles

Recently, the automotive industry has been characterized by disruptive innovations, like selfdriving cars or hybrid/electric engines.Despite this fact, some operations, such as the trade of secondhand vehicles, still continue to be carried out in the ''traditional'' way, in which the buyer has to trust the seller about the state of the vehicle.Several studies highlighted that odometer fraud alone could cost around 8.9 billion euros per year.In order to overcome these limitations, which are related to information asymmetries between buyers and sellers, in this work we propose to exploit blockchain technology to store a previous vehicle's history in a transparent way.To further explore blockchain advantages, we also present how a decentralized second-hand vehicle market -enabling also automatic transfers of ownership upon monetary transfers -can be built, leveraging on Non-Fungible Tokens (NFTs).We propose an architecture and a practical implementation of a Decentralized Application (Dapp) and discuss the security of the proposed system, its costs, and future developments.
User perception of Context-Based Micro-Training – a method for cybersecurity training

User behavior is one of the biggest challenges to cybersecurity in modern organizations.Users are continuously targeted by attackers and required to have sufficient knowledge to spot and avoid such attacks.Different training methods are suggested and used in the industry to support users to behave securely.The challenge remains, and improved methods for end-user cybersecurity training are needed.This paper introduces and evaluates user perception of a method called Context-Based Micro-Training (CBMT).This approach suggests that training should be delivered in short sequences when the information is of direct relevance.The intention is to provide training directly related to the user's current situation while also providing an awareness-increasing effect.This notion is tested in a survey-based evaluation involving 1,452 respondents from Sweden, Italy, and the UK, comparing the perception of CBMT against the experience of traditional approaches.The results emphasize that current methods are not effective enough and show that CBMT is perceived positively by respondents in all sample groups.The study further evaluated how demographic aspects impact the perception of CBMT and found that a diverse group of users can appreciate it.
Towards a unified model for symbolic knowledge extraction with hypercube-based methods

The XAI community is currently studying and developing symbolic knowledge-extraction (SKE) algorithms as a means to produce human-intelligible explanations for black-box machine learning predictors, so as to achieve believability in human-machine interaction. However, many extraction procedures exist in the literature, and choosing the most adequate one is increasingly cumbersome, as novel methods keep on emerging. Challenges arise from the fact that SKE algorithms are commonly defined based on theoretical assumptions that typically hinder practical applicability. This paper focuses on hypercube-based SKE methods, a quite general class of extraction techniques mostly devoted to regression-specific tasks. We first show that hypercube-based methods are flexible enough to support classification problems as well, then we propose a general model for them, and discuss how they support SKE on datasets, predictors, or learning tasks of any sort. Empirical examples are reported as well –based upon the PSyKE framework –, showing the applicability of hypercube-based methods to actual classification tasks.
Image Defogging Method for Transmission Channel Inspection by UAV Based on Deep Multi-patch Layered Network

Unmanned aerial vehicle (UAV) checking transmission channel in the process, when the camera to collect images easily affected by fog particles, the images quality, affect the accuracy of transmission channel safety hazard identification. However, in the real environment, haze particles are not evenly distributed. Therefore, an image de-fogging algorithm based on deep multi-patch layered network (DMPHN) was proposed for transmission channel images taken by UAV. DMPHN is a multi-level structure. A single level consists of encoder and decoder pairs of multi-scale residuals. Among the three levels, the feature information of the upper level of the input image rain is fused to achieve a coarse to fine end to end fog removal. Experiments show that DMPHN is effective under both objective and subjective evaluation. The average processing time of 1600*1200 high-resolution single image is 0.035 seconds.