Reviewerly/exHarmony huggingface dataset:
The metaverse in medicine

The metaverse is an alternative digital world, accessed by means of dedicated audiovisual devices. In this parallel world, various forms of artificial intelligence meet, including individuals in the form of digital copies of real people (avatars), able to interact socially. Metaverse in medicine may be used in many different ways. The possibility to perform surgery at a distance of thousands of miles separating the patient from the surgeon, who could have also the possibility to visualize in real-time patient's clinical data, including diagnostic images, obviously is very appealing. It would be also possible to perform medical treatments and to adopt pharmacological protocols on human avatars clinically similar to the patients, thus observing treatment effects in advance and significantly reducing the clinical trials duration. Metaverse may reveal an exceptional educational tool, offering the possibility of interactive digital lessons, allowing to dissect and to study an anatomical apparatus in detail, to navigate within it, not only to study, but also to see the evolution of the pathological process, and to simulate in advance surgical or medical procedures on virtual patients. However, while artificial intelligence is now an established reality in the clinical practice, the metaverse is still in its initial stages, and to figure out its potential usefulness and reliability, further developments are expected.
Pairwise constraints-based semi-supervised fuzzy clustering with multi-manifold regularization

Introducing a handful of pairwise constraints into fuzzy clustering models to revise memberships has been proven beneficial to boosting clustering performance. However, current pairwise constraints-based semi-supervised fuzzy clustering methods suffer from common deficiencies, i.e., the insufficient and imprecise revisions of memberships, by which the further improvement of clustering performance may be encumbered. To yield more pleasurable results, this paper proposes a new pairwise constraints-based semi-supervised fuzzy clustering method with multi-manifold regularization (MMRFCM), which can overcome the above deficiencies simultaneously. Firstly, data are regarded as located in various manifolds, and the multi-manifold regularization is delicately designed to sufficiently revise memberships for all data objects to guarantee good overall clustering performance. Secondly, local structural information is incorporated into designed multi-manifold regularization to ensure the precision and stability of the revisions on memberships. Thirdly, the approximated non-linear similarities evolving from ensemble p-Laplacian are applied to discover implicit local structures more thoroughly to further strengthen the effect of the multi-manifold regularization. Based on these strategies, MMRFCM efficiently exploits pairwise constraints to sufficiently and precisely modify memberships during the clustering process and thus achieves excellent results. Like most fuzzy clustering methods, MMRFCM is solved by alternative updates and the solutions are locally optimal. In the comprehensive experiments conducted on different types of datasets, MMRFCM successfully outperforms several classical and state-of-the-art fuzzy clustering methods in terms of clustering accuracy (CA), normalized mutual information (NMI), and adjusted rand index (ARI). The excellent results demonstrate the superiority, stability, and reliability of the proposed method.
The elephant in the room: cybersecurity in healthcare

Cybersecurity has seen an increasing frequency and impact of cyberattacks and exposure of Protected Health Information (PHI). The uptake of an Electronic Medical Record (EMR), the exponential adoption of Internet of Things (IoT) devices, and the impact of the COVID-19 pandemic has increased the threat surface presented for cyberattack by the healthcare sector. Within healthcare generally and, more specifically, within anaesthesia and Intensive Care, there has been an explosion in wired and wireless devices used daily in the care of almost every patient—the Internet of Medical Things (IoMT); ventilators, anaesthetic machines, infusion pumps, pacing devices, organ support and a plethora of monitoring modalities. All of these devices, once connected to a hospital network, present another opportunity for a malevolent party to access the hospital systems, either to gain PHI for financial, political or other gain or to attack the systems directly to cause erroneous monitoring, altered settings of any device and even to access the EMR via this IoMT window. This exponential increase in the IoMT and the increasing wireless connectivity of anaesthesia and ICU devices as well as implantable devices presents a real and present danger to patient safety. There has, at the same time, been a chronic underfunding of cybersecurity in healthcare. This lack of cybersecurity investment has left the sector exposed, and with the monetisation of PHI, the introduction of technically unsecure IoT devices for monitoring and direct patient care, the healthcare sector is presenting itself for further devastating cyberattacks or breaches of PHI. Coupled with the immense strain that the COVID-19 pandemic has placed on healthcare and the changes in working patterns of many caregivers, this has further amplified the exposure of the sector to cyberattacks.
A framework for achieving a circular economy using the blockchain technology in a sustainable waste management system

In a circular economy, the use of blockchain applied to different models like the waste management process leads to sustainable solutions alternative to the non-efficient linear economy. This study proposes a new methodology capable of optimizing the waste management process through an enabling technology: the blockchain. An innovative method that can increase monitoring while reducing the general costs of waste management is proposed. A blockchain system is developed for the control of urban waste. In addition, a reward mechanism is established for the citizens in terms of the levels of sorted waste produced and the working procedures of the agency that manages waste collection are optimized. A mathematical model that formalizes the problem for an optimal solution to minimize the costs of all the stakeholders involved in the Waste Management process is also developed. The characteristics that the proposed system allows are very important for the entire process of solid waste management. In fact, the adoption of the blockchain enables full traceability of the waste, ascertainability of each action, and the possibility that every customer can directly verify the information that is stored in the blockchain, such as the quantity of disposed waste, the amount of his reward and several additional data. Last but not least, the integrity of all stored records is fundamental in order to enforce the trust between the citizen and the Municipality that is in charge of dealing with solid waste management.
Federated Learning for Medical Image Analysis with Deep Neural Networks

Medical image analysis using deep neural networks (DNN) has demonstrated state-of-the-art performance in image classification and segmentation tasks, aiding disease diagnosis. The accuracy of the DNN is largely governed by the quality and quantity of the data used to train the model. However, for the medical images, the critical security and privacy concerns regarding sharing of local medical data across medical establishments precludes exploiting the full DNN potential for clinical diagnosis. The federated learning (FL) approach enables the use of local model's parameters to train a global model, while ensuring data privacy and security. In this paper, we review the federated learning applications in medical image analysis with DNNs, highlight the security concerns, cover some efforts to improve FL model performance, and describe the challenges and future research directions.
Cross-domain recommendation via user interest alignment

Cross-domain recommendation aims to leverage knowledge from multiple domains to alleviate the data sparsity and cold-start problems in traditional recommender systems. One popular paradigm is to employ overlapping user representations to establish domain connections, thereby improving recommendation performance in all scenarios. Nevertheless, the general practice of this approach is to train user embeddings in each domain separately and then aggregate them in a plain manner, often ignoring potential cross-domain similarities between users and items. Furthermore, considering that their training objective is recommendation task-oriented without specific regularizations, the optimized embeddings disregard the interest alignment among user’s views, and even violate the user’s original interest distribution. To address these challenges, we propose a novel cross-domain recommendation framework, namely COAST, to improve recommendation performance on dual domains by perceiving the cross-domain similarity between entities and aligning user interests. Specifically, we first construct a unified cross-domain heterogeneous graph and redefine the message passing mechanism of graph convolutional networks to capture high-order similarity of users and items across domains. Targeted at user interest alignment, we develop deep insights from two more fine-grained perspectives of user-user and user-item interest invariance across domains by virtue of affluent unsupervised and semantic signals. We conduct intensive experiments on multiple tasks, constructed from two large recommendation data sets. Extensive results show COAST consistently and significantly outperforms state-of-the-art cross-domain recommendation algorithms as well as classic single-domain recommendation methods.
Augmented virtual reality and 360 spatial visualization for supporting user-engaged design

Abstract This paper discusses an approach to augmented virtual reality (AVR) and 360-degree spatial visualization. The approach involves locating stereoscopic three-dimensional virtual objects into a real off-site panorama, supporting spatial remodel design decision-making through realistic comparisons. Previous studies have shown that in the design process, end-user engagement promotes the quality and satisfaction of design solutions. Immersive media such as virtual reality (VR) and augmented reality (AR) have increasingly been used as communication tools for user engagement in design, as they provide intuitive and realistic user experiences, particularly in comparing design plans. However, the dichotomous affordance of current VR and AR devices is limited in satisfying both the sense of realism and immersion that are essential for user engagement. To overcome this shortcoming, we propose an AVR-based design visualization approach that integrates the advantages of both media technologies to provide a high sense of realism and immersion off-site, responding to location and environmental stimuli, such as lighting, material, and other factors. To achieve this goal, we used 360-degree panorama data of the target space as a design visualization background, with content immersion experienced through VR hardware. Additionally, we developed software to demonstrate the actual use of the AVR-based approach, and various visualization-purposed file formats can be exported automatically using this software. The software supports the authoring of 360-degree spatial visualization videos for realistic design comparisons, which can be easily accessed by end-users using a head-mounted display or smartphone, even in real-time. We performed a demonstration of this approach using an actual remodel design project for the university library lobby, and this paper shows the usability and applicability of the AVR-based approach for user engagement.
Deepfacelab: Integrated, flexible and extensible face-swapping framework

Face swapping has drawn a lot of attention for its compelling performance. However, current deepfake methods suffer the effects of obscure workflow and poor performance. To solve these problems, we present DeepFaceLab, the current dominant deepfake framework for practical face-swapping. It provides the necessary tools as well as an easy-to-use way to conduct high-quality face-swapping. It also offers a flexible and loose coupling structure for people who need to strengthen their pipeline with other features without writing complicated boilerplate code. We detail the principles that drive the implementation of DeepFaceLab and introduce its pipeline. DeepFaceLab could achieve cinema-level results with high fidelity as our supplemental video shows. We also demonstrate the advantage of our system by comparing our approach with other face-swapping methods. Deepfake defense not only requires the research of detection but also requires the efforts of generation methods. As for a popular and practical toolkit, we encourage users to promote harmless deepfake-entertainment content on social media, reminding the public of the existence of deepfake when they are looking for entertainment.
A Robust Framework for Traffic Object Detection using Intelligent Techniques

Object identirication is a method for pinpointing the exact position of particular items in still or moving visual media. The fields of machine learning and deep learning have resulted in a plethora of object recognition algorithms, such as SSD, SSP-net, SVM, CNN, R-CNN, Fast R-CNN, Faster R-CNN, HOG, R-FCN, YOLO, etc. While these models have been used for several object detection purposes, the difriculty of detecting tiny objects remains. Creating a lightweight and reliable object detection model that can accurately recognize little things is crucial. This research proposes an improved version of the You Only Look Once (YOLO) algorithm for identifying vehicles in surveillance footage. We replaced the YOLOv2’s original base network with DenseNet and reduced the number of parameters used. In our new and better model, we used the DenseNet-121 method to draw out the key characteristics of each picture. In addition, the compact nature of our suggested model is a result of the dense design of the underlying network. Since all of the nodes of DenseNet-121 are directly connected, we were able to use it as our foundational network and use its strength to glean insights from the lowest levels of the network and pass them on to the more advanced ones. We trained the proposed model using data obtained from Kaggle and KITTI, and we cross validated its results on the MS COCO and Pascal VOC datasets. We conducted extensive experiments to evaluate the suggested model, and the results show that our algorithm achieves higher accuracy (98.51%) than state-of-the-art methods for detecting vehicles.
CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning

Modern deep neural networks (DNNs) can easily overfit to biased training data containing corrupted labels or class imbalance. Sample re-weighting methods are popularly used to alleviate this data bias issue. Most current methods, however, require manually pre-specifying the weighting schemes as well as their additional hyper-parameters relying on the characteristics of the investigated problem and training data. This makes them fairly hard to be generally applied in practical scenarios, due to their significant complexities and inter-class variations of data bias situations. To address this issue, we propose a meta-model capable of adaptively learning an explicit weighting scheme directly from data. Specifically, by seeing each training class as a separate learning task, our method aims to extract an explicit weighting function with sample loss and task/class feature as input, and sample weight as output, expecting to impose adaptively varying weighting schemes to different sample classes based on their own intrinsic bias characteristics. Synthetic and real data experiments substantiate the capability of our method on achieving proper weighting schemes in various data bias cases, like the class imbalance, feature-independent and dependent label noise scenarios, and more complicated bias scenarios beyond conventional cases. Besides, the task-transferability of the learned weighting scheme is also substantiated, by readily deploying the weighting function learned on relatively smaller-scale CIFAR-10 dataset on much larger-scale full WebVision dataset. A performance gain can be readily achieved compared with previous state-of-the-art ones without additional hyper-parameter tuning and meta gradient descent step. The general availability of our method for multiple robust deep learning issues, including partial-label learning, semi-supervised learning and selective classification, has also been validated. Code for reproducing our experiments is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/xjtushujun/CMW-Net.</uri>
Two-stage reverse knowledge distillation incorporated and Self-Supervised Masking strategy for industrial anomaly detection

In recent years, unsupervised anomaly detection based on knowledge distillation has gained special attention and some promising results have been reported in the literature. However, there is still room to improve the sensitivity of the model to anomalies. To do so, in this paper, a novel two-stage training method in terms of reverse knowledge distillation is proposed for anomaly detection and localization. Firstly, self-supervised mask training is introduced after the initial training of reverse knowledge distillation, which contributes greatly to the model detection against random unknown anomalies by self-simulating anomalies and forcing repair so as to reinforce learning single-category prototype patterns. Then, with the aim to facilitate the anomaly localization, an anomaly feature diffusion module is employed, which strengthens the correlation between pixels and helps spread the anomaly information to the surrounding area by covering the central pixel and reconstructing the representation for features after diffused. Furthermore, inspired by the human memory mechanism, an innovative normalized embedding memory bank is adopted to regulate the low-dimensional representations after embedding the encoding, inhibit the flow of anomalous information to the student decoder, and encourage the high-quality reconstruction of the model. Finally, the contextual similarity loss is used to guide the student model to learn knowledge representations from a contextual perspective, capture higher-order similarities between teachers and students, and delicately evaluate the differences between teachers and students. The empirical experiments conducted on the MVTec dataset show that the proposed SSMRKD method can achieve the best performance compared to other state-of-the-art methods, meanwhile extensive experiments of the ablation study validate the contribution of each component of the model. In addition, the advanced performance achieved on four commonly used datasets verifies the generalizability of the model in the industrial domain. Overall, the proposed SSMRKD method has significant advantages over the state-of-the-art anomaly detection methods.
On The Detection of Synthetic Images Generated by Diffusion Models

Over the past decade, there has been tremendous progress in creating synthetic media, mainly thanks to the development of powerful methods based on generative adversarial networks (GAN). Very recently, methods based on diffusion models (DM) have been gaining the spotlight. In addition to providing an impressive level of photorealism, they enable the creation of text-based visual content, opening up new and exciting opportunities in many different application fields, from arts to video games. On the other hand, this property is an additional asset in the hands of malicious users, who can generate and distribute fake media perfectly adapted to their attacks, posing new challenges to the media forensic community. With this work, we seek to understand how difficult it is to distinguish synthetic images generated by diffusion models from pristine ones and whether current state-of-the-art detectors are suitable for the task. To this end, first we expose the forensics traces left by diffusion models, then study how current detectors, developed for GAN-generated images, perform on these new synthetic images, especially in challenging social-network scenarios involving image compression and resizing. Datasets and code are available at https:github.com/grip-unina/DMimageDetection.
Digital exploration of selected heavy metals using Random Forest and a set of environmental covariates at the watershed scale

The current study was established for predicting some selected heavy metals (HMs) including Zn, Mn, Fe, Co, Cr, Ni, and Cu, by applying random forest (RF) and a set of environmental covariates at watershed scale. The objectives were to find out the most effective combination of variables and controlling factors on the variability of HMs in a semiarid watershed in central Iran. One hundred locations were selected in the given watershed in the hypercube manner and soil samples from a surface 0–20 cm depth and concentration of HMs and some soil properties were measured in the laboratory. Three scenarios of input variables were defined for HMs prediction. The results revealed that the first scenario (remote sensing + topographic attributes) explained about 27–34% of the variability in HMs. Inclusion of a thematic map to the scenario I, improved the prediction accuracy for all HMs. Scenario III (remote sensing data+ topographic attributes + soil properties) was the most efficient scenario for prediction of HMs with R2 values ranging from 0.32 for Cu to 0.42 for Fe. Similarly, the lowest nRMSE was found for all HMs in scenario III, ranging from 0.271 for Fe to 0.351 for Cu. Among the soil properties, clay content and magnetic susceptibility were the most important variables, and also some remote sensing data (Carbonate index, Soil adjusted vegetation index, Band2, and Band7) and topographic attributes (mainly control soil redistribution along the landscape) were the most efficient variables for estimating HMs. We concluded that the RF model with a combination of remote sensing data, topographic attributes, and assisting of thematic maps such as land use in the studied watershed could reliably predict HMs content.