Reviewerly/exHarmony huggingface dataset:
Robust 3D breast reconstruction based on monocular images and artificial intelligence for robotic guided oncological interventions

Breast cancer is a global public health concern. For women with suspicious breast lesions, the current diagnosis requires a biopsy, which is usually guided by ultrasound (US). However, this process is challenging due to the low quality of the US image and the complexity of dealing with the US probe and the surgical needle simultaneously, making it largely reliant on the surgeon’s expertise. Some previous works employing collaborative robots emerged to improve the precision of biopsy interventions, providing an easier, safer, and more ergonomic procedure. However, for these equipment to be able to navigate around the breast autonomously, 3D breast reconstruction needs to be available. The accuracy of these systems still needs to improve, with the 3D reconstruction of the breast being one of the biggest focuses of errors. The main objective of this work is to develop a method to obtain a robust 3D reconstruction of the patient’s breast, based on RGB monocular images, which later can be used to compute the robot's trajectories for the biopsy. To this end, depth estimation techniques will be developed, based on a deep learning architecture constituted by a CNN, LSTM, and MLP, to generate depth maps capable of being converted into point clouds. After merging several from multiple points of view, it is possible to generate a real-time reconstruction of the breast as a mesh. The development and validation of our method was performed using a previously described synthetic dataset. Hence, this procedure takes RGB images and the cameras’ position and outputs the breasts’ meshes. It has a mean error of 3.9 mm and a standard deviation of 1.2 mm. The final results attest to the ability of this methodology to predict the breast’s shape and size using monocular images.Clinical Relevance— This work proposes a method based on artificial intelligence and monocular RGB images to obtain the breast’s volume during robotic guided breast biopsies, improving their execution and safety.
Optical vortices as massage-carriers in a reversible optical gate based on ternary logic

We developed a communication protocol that utilizes a reversible optical gate with optical vortices encoded through the digital micromirror device to determine the similarity between two signals using ternary logic.
Heterogeneous Signcryption Scheme From PKI to IBC With Multi-Ciphertext Equality Test in Internet of Vehicles

With the arrival of the era of self-driving technology, 5G and IoT technology, self-driving car internet technology is gradually changing the way people travel. The existing signcryption scheme test scheme with equality can only compare two ciphertexts, which is not suitable for the complex network of Internet of vehicles. To address this challenge, we propose heterogeneous signcryption scheme from PKI to IBC with multi-ciphertext equality test in the Internet of vehicles (HSCPI-MET). This scheme enables to communicate from PKI cryptosystem to IBC cryptosystem in Internet of vehicles, at the same time excuting the equality test of multi-ciphertext. Our scheme protects inter-vehicle communication data from being illegally accessed, tampered or forged by malicious vehicles. The proposed scheme satisfies confidentiality and unforgeability under the stochastic predictor model for the computational Diffie-Hellman assumption and the IDH assumption. In addition, we conduct a rigorous experimental evaluation of the scheme to prove that it is secure and effective in practical applications.
Single-sided cavity quantum electrodynamics effects on an optical nanofiber

We present the single-sided cavity-QED effects for single quantum emitters by combining an asymmetric defect mode grating with an optical nanofiber (ONF). We demonstrate significantly enhanced spontaneous emission into one side of the ONF.
Light Field Denoising with Adaptive Stochastic Gradient Langevin Dynamics

We propose a self-supervised learning method for light field denoising (LFDN). Numerical experiments on a simulated light field dataset verified that our LFDN model within the Adaptive Stochastic Gradient Langevin Dynamics framework is superior to the classical denoising methods.
Distributed and Joint Evidential <i>K</i>-Nearest Neighbor Classification

The performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -nearest neighbor (K-NN) classification depends significantly on the searched neighborhoods of test samples, namely, the neighborhood size <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> and the used distance metric. For these two issues, many methods either to acquire the adaptive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> or to learn a variant metric have been proposed and yielded appropriate performances. However, most of the existing methods ignore the fact that these two factors can be jointly learned. In this paper, we propose a Joint Evidential K-NN algorithm (JEKNN), which learns the adaptive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> of each sample and distance metric jointly based on the feedback of error function. To break the computational bottleneck of handling large datasets, a distributed version of JEKNN (JEKNN <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{\mathrm{{dis}}}$</tex-math></inline-formula> ) is implemented under Apache Spark, i.e., an optimization algorithm based on distributed gradient descent and data parallelism is proposed to accelerate the training stage. Ablation and comparison experiments on small-scale datasets shows the performance improvement from the joint learning and the state-of-the-art accuracy of JEKNN, respectively. Compared to other KNN-based methods designed for Big Data, experimental results on big datasets demonstrate that JEKNN <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{\mathrm{{dis}}}$</tex-math></inline-formula> achieves better scaling efficiency without significant loss of accuracy. Besides, the generalization error bound of the proposed algorithm is also analyzed theoretically.
Rotation Speed Identification of Multi-opening Objects Using Convolutional Neural Network

ResNet is utilized to identify eight different rotation speeds of objects with multiple openings. Image processing helps achieve an average identification accuracy of 96.66% across various classifications involving objects with up to 36 openings.
PAFormer: Anomaly Detection of Time Series With Parallel-Attention Transformer

Time-series anomaly detection is a critical task with significant impact as it serves a pivotal role in the field of data mining and quality management. Current anomaly detection methods are typically based on reconstruction or forecasting algorithms, as these methods have the capability to learn compressed data representations and model time dependencies. However, most methods rely on learning normal distribution patterns, which can be difficult to achieve in real-world engineering applications. Furthermore, real-world time-series data is highly imbalanced, with a severe lack of representative samples for anomalous data, which can lead to model learning failure. In this article, we propose a novel end-to-end unsupervised framework called the parallel-attention transformer (PAFormer), which discriminates anomalies by modeling both the global characteristics and local patterns of time series. Specifically, we construct parallel-attention (PA), which includes two core modules: the global enhanced representation module (GERM) and the local perception module (LPM). GERM consists of two pattern units and a normalization module, with attention weights that indicate the relationship of each data point to the whole series (global). Due to the rarity of anomalous points, they have strong associations with adjacent data points. LPM is composed of a learnable Laplace kernel function that learns the neighborhood relevancies through the distributional properties of the kernel function (local). We employ the PA to learn the global-local distributional differences for each data point, which enables us to discriminate anomalies. Finally, we propose a two-stage adversarial loss to optimize the model. We conduct experiments on five public benchmark datasets (real-world datasets) and one synthetic dataset. The results show that PAFormer outperforms state-of-the-art baselines.
Single-Photon Single-Atom Chiral Waveguide Quantum Electrodynamics (QED): A Time-Dependent Analysis

We investigate the interaction between a single photon and a two-level atom in a waveguide QED setup. Our goal is to calculate the time-dependent probabilities showing the evolution of the system in a transient manner.
Training about Drone Safety Challenges in Construction using a 360 Virtual Environment: Analyzing Learning Effectiveness

Utilizing drones on construction sites could put workers who are already in dangerous environments into even more risky situations. Therefore, researchers have explored the safety challenges and their countermeasures regarding drone integration into construction sites. This study proposes using a 360- degree virtual reality (360VR) training environment to educate construction professionals on the safety concerns that drones could pose and how to work safely on a drone-populated site. This study specifically focuses on the knowledge gap of how trainees’ backgrounds, such as construction experience, work experience with drones, and prior understanding of drones, could be associated with the effects of the training. This study created comprehensive pedagogical intervention content using 360VR, followed by a user-centered pre- and post-experiment. After training, participants’ knowledge levels improved by 41% on average. Furthermore, the results indicate that trainees’ construction experience and job experience with drones were not associated with their knowledge levels. Moreover, those with lower levels of understanding about drones significantly improved their knowledge scores after the training. The result shows that trainees with different knowledge about drones may be suitable for trainings with different levels of difficulty.
Efficient spatially resolved tomography of polarization entanglement using a fast camera

We demonstrate a highly efficient scheme for spatially resolving the polarization entanglement of photon pairs emitted into different paths. This scheme allows probing a two-photon entangled state with an effective dimensionality of over 1000. Our results pave the way for the practical implementations of quantum technologies based on high-dimensional entanglement.
Class-imbalanced Unsupervised and Semi-Supervised Domain Adaptation for Histopathology Images

In dealing with the lack of sufficient annotated data and in contrast to supervised learning, unsupervised, self-supervised, and semi-supervised domain adaptation methods are promising approaches, enabling us to transfer knowledge from rich labeled source domains to different (but related) unlabeled target domains, reducing distribution discrepancy between the source and target domains. However, most existing domain adaptation methods do not consider the imbalanced nature of the real-world data, affecting their performance in practice. We propose to overcome this limitation by proposing a novel domain adaptation approach that includes two modifications to the existing models. Firstly, we leverage the focal loss function in response to class-imbalanced labeled data in the source domain. Secondly, we introduce a novel co-training approach to involve pseudo-labeled target data points in the training process. Experiments show that the proposed model can be effective in transferring knowledge from source to target domain. As an example, we use the classification of prostate cancer images into low-cancerous and high-cancerous regions.
Temporal Shift Module (TSM) Based Automatic Fall Detection with Bounding Box Grounding

Fall detection is one of the important tenets of remote geriatric care operations. Fall is one of the main causes of injury in old individuals leading to fractures, concussions, and different issues that might lead to prompt demise. In a world increasingly making the elderly live in isolation, accurate and real-time detection of falls is very important to remote caregivers to be able to provide timely medical assistance. Recent advancements in vision-based technologies have got promising results; however, these models are often trained on acted datasets and their appropriateness for application in the wild is not well established. In this paper, we propose a vision-based fall detection mechanism that improves the accuracy of in-the-wild complex events. The proposed system is built leveraging Temporal Shift Module (TSM) with a bounding box grounding (BBG) approach for accurate Region Of Interest (ROI) sequence generation when sudden deformation in the shape is observed. Compared to the general 3D CNN based approaches, the proposed model achieves better accuracy while maintaining the level of computational complexity at that of the 2D CNN models. The proposed approach demonstrates promising performance on both acted and in-the-wild datasets.
Neural Activity and Information Processing Capacity of Neuronal Culture

Whether artificial or living, neural networks perform tremendously diverse kinds of information processing, such as visual feature extraction, speech recognition, motor control, and so on. Some studies have evaluated the computational ability of living neural networks based on the performances of specific tasks, yet could not comprehensively grasp the versatile ability. In this study, we investigated dissociated culture of neurons as a physical reservoir, which generates diverse outputs through linear regression, or readout, of the dynamical states. Based on the theory of reservoir computing, the potential computational capabilities of neuronal culture were evaluated by the information processing capacity (IPC), which indicates how a target function can be achieved from the given dynamics. As a result, we found that the neuronal culture exhibited significant IPC and that IPC varied with the inter-step interval (ISI), the time step of reservoir computing. The cultures exhibited a memory capacity of 10 time steps for computation, and this memory capacity decayed at an ISI of 5 ms or shorter. In addition, the IPC had a significant positive correlation with the intensity of the evoked response relative to spontaneous activity. The multiple regression model with evoked response and ISI showed the positive effect of evoked response and 30 ms as the best ISI for IPC. These results suggest that the distinct evoked response and the optimal time step to interact with the neuronal culture are key factors to uncover computational resources from the neuronal system.
Augmented Reality Holographic Visualization System for Surgery Auxiliary Visualization: Proof of Concept for Surgical Training*

An Augmented Reality (AR) system based on the holographic projection of the relevant anatomic structures is proposed for auxiliary visualization during surgeries. The current two-dimensional visualization systems require the surgeons to mentally extract the associated three-dimensional information during the interventions, which entails risks and complications. This work shows an AR holographic projection system for real-time three-dimensional representation of the relevant surgical information, thus overcoming this problem. As an initial proof of concept, the system is experimentally assessed as potential surgery training tool.Clinical Relevance— This work explores the potential of AR holographic projection systems for intraoperative assistance to the surgical team, starting from its possible use as surgery training and planning tool.
KL Divergence-based transfer learning for cross-subject eye movement recognition with EOG signals

Human-machine interfaces (HMIs) based on Electro-oculogram (EOG) signals have been widely explored. However, due to the individual variability, it is still challenging for an EOG-based eye movement recognition model to achieve favorable results among cross-subjects. The classical transfer learning methods such as CORrelation Alignment (CORAL), Transfer Component Analysis (TCA), and Joint Distribution Adaptation (JDA) are mainly based on feature transformation and distribution alignment, which do not consider similarities/dissimilarities between target subject and source subjects. In this paper, the Kullback-Leibler (KL) divergence of the log-Power Spectral Density (log-PSD) features of horizontal EOG (HEOG) between the target subject and each source subject is calculated for adaptively selecting partial subjects that suppose to have similar distribution with target subject for further training. It not only consider the similarity but also reduce computational consumption. The results show that the proposed approach is superior to the baseline and classical transfer learning methods, and significantly improves the performance of target subjects who have poor performance with the primary classifiers. The best improvement of Support Vector Machines (SVM) classifier has improved by 13.1% for subject 31 compared with baseline result. The preliminary results of this study demonstrate the effectiveness of the proposed transfer framework and provide a promising tool for implementing cross-subject eye movement recognition models in real-life scenarios.
Image Captioning for the Visually Impaired and Blind: A Recipe for Low-Resource Languages

Visually impaired and blind people often face a range of socioeconomic problems that can make it difficult for them to live independently and participate fully in society. Advances in machine learning pave new venues to implement assistive devices for the visually impaired and blind. In this work, we combined image captioning and text-to-speech technologies to create an assistive device for the visually impaired and blind. Our system can provide the user with descriptive auditory feedback in the Kazakh language on a scene acquired in real-time by a head-mounted camera. The image captioning model for the Kazakh language provided satisfactory results in both quantitative metrics and subjective evaluation. Finally, experiments with a visually unimpaired blindfolded participant demonstrated the feasibility of our approach.
Deep Learning for Channel Estimation in Physical Layer Wireless Communications: Fundamental, Methods, and Challenges

With the rapid development of wireless communication technology, intelligent communication has become one of the mainstream research directions after the fifth generation (5G). In particular, deep learning has emerged as a significant artificial intelligence technology widely applied in the physical layer of wireless communication for achieving intelligent receiving processing. Channel estimation, a crucial component of physical layer communication, is essential for further information recovery. As a motivation, this paper aims to review the relevant research on applying deep learning methods in channel estimation. Firstly, this paper briefly introduces the conventional channel estimation methods and then analyzes their respective merits and drawbacks. Subsequently, this paper introduces several common types of neural networks and describes the application of deep learning in channel estimation according to data-driven and model-driven approaches, respectively. Then, this paper extends to emerging communication scenarios and discusses the existing research on channel estimation based on deep learning for reconfigurable intelligent surface (RIS)-aided communication systems. Finally, to meet the demands of next-generation wireless communication, challenges and future research trends in deep-learning-based channel estimation are discussed.
All-optical data class-specific image encryption using diffractive neural networks

We present an all-optical data-class-specific transformation framework that encrypts image data using separate transformations uniquely assigned to each data class. The presented method was experimentally tested under terahertz and near-infrared wavelengths.
Automatic Generation of Labeled Data for Video-Based Human Pose Analysis via NLP applied to YouTube Subtitles

With recent advancements in computer vision as well as machine learning (ML), video-based at-home exercise evaluation systems have become a popular topic of current research. However, performance depends heavily on the amount of available training data. Since labeled datasets specific to exercising are rare, we propose a method that makes use of the abundance of fitness videos available online. Specifically, we utilize the advantage that videos often not only show the exercises, but also provide language as an additional source of information. With push-ups as an example, we show that through the analysis of subtitle data using natural language processing (NLP), it is possible to create a labeled (irrelevant, relevant correct, relevant incorrect) dataset containing relevant information for pose analysis. In particular, we show that irrelevant clips (n = 332) have significantly different joint visibility values compared to relevant clips (n = 298). Inspecting cluster centroids also show different poses for the different classes.
Towards building a Self-Sovereign Identity Framework for Healthcare

Drifted by the hype of new and efficient machine learning and artificial intelligence models aiming to unlock the information wealth hidden inside heterogeneous datasets across different markets and disciplines, healthcare data are in the center of novel technological advancements in predictive health diagnostics, remote healthcare, assistive leaving and wellbeing. Nevertheless, this emerging market has underlined the necessity of developing new methods and updating existing ones for preserving the privacy of the data and their owners, as well as, ensuring confidentiality and trust throughout the health care data processing pipelines. This paper presents one of the key innovations of a Horizon Europe funded project named "TRUSTEE", which focuses on building a trust and privacy framework for cross-European data exchange by employing a secure and private federated framework to empower companies, organizations, and individuals to securely access data across different disciplines, use and re-use data and metadata to extract knowledge with trust. In particular we present our work on implementing strong authentication and continuous authorization schemes based on the duality of eIDAS trust framework and Self Sovereign Identity (SSI) management to ensure security and trust over authentication, authorization and accounting processes for healthcare.