Reviewerly/exHarmony huggingface dataset:
Super-Resolution of Dental Panoramic Radiographs Using Deep Learning: A Pilot Study

Using super-resolution (SR) algorithms, an image with a low resolution can be converted into a high-quality image. Our objective was to compare deep learning-based SR models to a conventional approach for improving the resolution of dental panoramic radiographs. A total of 888 dental panoramic radiographs were obtained. Our study involved five state-of-the-art deep learning-based SR approaches, including SR convolutional neural networks (SRCNN), SR generative adversarial network (SRGAN), U-Net, Swin for image restoration (SwinIr), and local texture estimator (LTE). Their results were compared with one another and with conventional bicubic interpolation. The performance of each model was evaluated using the metrics of mean squared error (MSE), peak signal-to-noise ratio (PNSR), structural similarity index (SSIM), and mean opinion score by four experts (MOS). Among all the models evaluated, the LTE model presented the highest performance, with MSE, SSIM, PSNR, and MOS results of 7.42 ± 0.44, 39.74 ± 0.17, 0.919 ± 0.003, and 3.59 ± 0.54, respectively. Additionally, compared with low-resolution images, the output of all the used approaches showed significant improvements in MOS evaluation. A significant enhancement in the quality of panoramic radiographs can be achieved by SR. The LTE model outperformed the other models.
A Unified Framework for Quantifying Privacy Risk in Synthetic Data

Synthetic data is often presented as a method for sharing sensitive information in a privacy-preserving manner by reproducing the global statistical properties of the original data without dis closing sensitive information about any individual. In practice, as with other anonymization methods, synthetic data cannot entirely eliminate privacy risks. These residual privacy risks need instead to be ex-post uncovered and assessed. However, quantifying the actual privacy risks of any synthetic dataset is a hard task, given the multitude of facets of data privacy. We present Anonymeter, a statistical framework to jointly quantify different types of privacy risks in synthetic tabular datasets. We equip this framework with attack-based evaluations for the singling out, linkability, and inference risks, which are the three key indicators of factual anonymization according to data protection regulations, such as the European General Data Protection Regulation (GDPR). To the best of our knowledge, we are the first to introduce a coherent and legally aligned evaluation of these three privacy risks for synthetic data, as well as to design privacy attacks which model directly the singling out and linkability risks. We demonstrate the effectiveness of our methods by conducting an extensive set of experiments that measure the privacy risks of data with deliberately inserted privacy leakages, and of synthetic data generated with and without differential privacy. Our results highlight that the three privacy risks reported by our framework scale linearly with the amount of privacy leakage in the data. Furthermore, we observe that synthetic data exhibits the lowest vulnerability against linkability, indicating one-to-one relationships between real and synthetic data records are not preserved. Finally, with a quantitative comparison we demonstrate that Anonymeter outperforms existing synthetic data privacy evaluation frameworks both in terms of detecting privacy leaks, as well as computation speed. To contribute to a privacy-conscious usage of synthetic data, we publish Anonymeter as an open-source library (https://github.com/statice/anonymeter).
EHR foundation models improve robustness in the presence of temporal distribution shift

Temporal distribution shift negatively impacts the performance of clinical prediction models over time. Pretraining foundation models using self-supervised learning on electronic health records (EHR) may be effective in acquiring informative global patterns that can improve the robustness of task-specific models. The objective was to evaluate the utility of EHR foundation models in improving the in-distribution (ID) and out-of-distribution (OOD) performance of clinical prediction models. Transformer- and gated recurrent unit-based foundation models were pretrained on EHR of up to 1.8 M patients (382 M coded events) collected within pre-determined year groups (e.g., 2009-2012) and were subsequently used to construct patient representations for patients admitted to inpatient units. These representations were used to train logistic regression models to predict hospital mortality, long length of stay, 30-day readmission, and ICU admission. We compared our EHR foundation models with baseline logistic regression models learned on count-based representations (count-LR) in ID and OOD year groups. Performance was measured using area-under-the-receiver-operating-characteristic curve (AUROC), area-under-the-precision-recall curve, and absolute calibration error. Both transformer and recurrent-based foundation models generally showed better ID and OOD discrimination relative to count-LR and often exhibited less decay in tasks where there is observable degradation of discrimination performance (average AUROC decay of 3% for transformer-based foundation model vs. 7% for count-LR after 5-9 years). In addition, the performance and robustness of transformer-based foundation models continued to improve as pretraining set size increased. These results suggest that pretraining EHR foundation models at scale is a useful approach for developing clinical prediction models that perform well in the presence of temporal distribution shift.
Semi-Federated Learning for Collaborative Intelligence in Massive IoT Networks

Implementing existing federated learning in massive Internet of Things (IoT) networks faces critical challenges such as imbalanced and statistically heterogeneous data and device diversity. To this end, we propose a semi-federated learning (SemiFL) framework to provide a potential solution for the realization of intelligent IoT. By seamlessly integrating the centralized and federated paradigms, our SemiFL framework shows high scalability in terms of the number of IoT devices even in the presence of computing-limited sensors. Furthermore, compared to traditional learning approaches, the proposed SemiFL can make better use of distributed data and computing resources, due to the collaborative model training between the edge server and local devices. Simulation results show the effectiveness of our SemiFL framework for massive IoT networks. The code can be found at https://github.com/niwanli/SemiFL_IoT.
Sparse Personalized Federated Learning

Federated learning (FL) is a collaborative machine learning technique to train a global model (GM) without obtaining clients’ private data. The main challenges in FL are statistical diversity among clients, limited computing capability among clients’ equipment, and the excessive communication overhead between the server and clients. To address these challenges, we propose a novel sparse personalized FL scheme via maximizing correlation (FedMac). By incorporating an approximated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell_1$</tex-math> </inline-formula> -norm and the correlation between client models and GM into standard FL loss function, the performance on statistical diversity data is improved and the communicational and computational loads required in the network are reduced compared with nonsparse FL. Convergence analysis shows that the sparse constraints in FedMac do not affect the convergence rate of the GM, and theoretical results show that FedMac can achieve good sparse personalization, which is better than the personalized methods based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell_2$</tex-math> </inline-formula> -norm. Experimentally, we demonstrate the benefits of this sparse personalization architecture compared with the state-of-the-art personalization methods (e.g., FedMac, respectively, achieves 98.95%, 99.37%, 90.90%, 89.06%, and 73.52% accuracy on the MNIST, FMNIST, CIFAR-100, Synthetic, and CINIC-10 datasets under non-independent and identically distributed (i.i.d.) variants).
Quantum Error Correction with Quantum Autoencoders

Active quantum error correction is a central ingredient to achieve robust quantum processors. In this paper we investigate the potential of quantum machine learning for quantum error correction in a quantum memory. Specifically, we demonstrate how quantum neural networks, in the form of quantum autoencoders, can be trained to learn optimal strategies for active detection and correction of errors, including spatially correlated computational errors as well as qubit losses. We highlight that the denoising capabilities of quantum autoencoders are not limited to the protection of specific states but extend to the entire logical codespace. We also show that quantum neural networks can be used to discover new logical encodings that are optimally adapted to the underlying noise. Moreover, we find that, even in the presence of moderate noise in the quantum autoencoders themselves, they may still be successfully used to perform beneficial quantum error correction and thereby extend the lifetime of a logical qubit.
Improved YOLOv4-tiny based on attention mechanism for skin detection

An automatic bathing robot needs to identify the area to be bathed in order to perform visually-guided bathing tasks. Skin detection is the first step. The deep convolutional neural network (CNN)-based object detection algorithm shows excellent robustness to light and environmental changes when performing skin detection. The one-stage object detection algorithm has good real-time performance, and is widely used in practical projects.In our previous work, we performed skin detection using Faster R-CNN (ResNet50 as backbone), Faster R-CNN (MobileNetV2 as backbone), YOLOv3 (DarkNet53 as backbone), YOLOv4 (CSPDarknet53 as backbone), and CenterNet (Hourglass as backbone), and found that YOLOv4 had the best performance. In this study, we considered the convenience of practical deployment and used the lightweight version of YOLOv4, i.e., YOLOv4-tiny, for skin detection. Additionally, we added three kinds of attention mechanisms to strengthen feature extraction: SE, ECA, and CBAM. We added the attention module to the two feature layers of the backbone output. In the enhanced feature extraction network part, we applied the attention module to the up-sampled features. For full comparison, we used other lightweight methods that use MobileNetV1, MobileNetV2, and MobileNetV3 as the backbone of YOLOv4. We established a comprehensive evaluation index to evaluate the performance of the models that mainly reflected the balance between model size and mAP.The experimental results revealed that the weight file of YOLOv4-tiny without attention mechanisms was reduced to 9.2% of YOLOv4, but the mAP maintained 67.3% of YOLOv4. YOLOv4-tiny's performance improved after combining the CBAM and ECA modules, but the addition of SE deteriorated the performance of YOLOv4-tiny. MobileNetVX_YOLOv4 (X = 1, 2, 3), which used MobileNetV1, MobileNetV2, and MobileNetV3 as the backbone of YOLOv4, showed higher mAP than YOLOv4-tiny series (including YOLOv4-tiny and three improved YOLOv4-tiny based on the attention mechanism) but had a larger weight file. The network performance was evaluated using the comprehensive evaluation index. The model, which integrates the CBAM attention mechanism and YOLOv4-tiny, achieved a good balance between model size and detection accuracy.
Toward trustworthy human suspicious activity detection from surveillance videos using deep learning

Abstract In today’s world, suspicious or unusual activities express threats and danger to others. For the prevention of various security issues, an automatic video detection system is very important. It is difficult to consecutively monitor camera videos recorded in public places to detect any abnormal event, so an automated video detection system is needed. The study objective is to create an intelligent and trustworthy system that will take a video stream as input and detect what kind of suspicious activity is happening in that video to reduce the time that consumes watching the video. In this work, we use three models Convolutional Neural Network (CNN), GRU, and ConvLSTM model. These models are trained on the same dataset of 6 suspicious activities of humans that are: Running, Punching, Falling, Snatching, Kicking, and Shooting. The dataset consists of various videos related to each activity. Different deep learning techniques are applied in the proposed work: preprocessing, data annotation model training, and classification. The frames are extracted from the source video, and then features are calculated through the model in Inception v3, a Convolutional Neural Network variant. On the same dataset, the CNN model attains 91.55% accuracy, the ConvLSTM model attains 88.73% accuracy, and the GRU model attains 84.01% accuracy. The performance of the proposed models is evaluated using a confusion matrix, F1-Score, precision, and recall. The proposed model proved better than other models in terms of performance and accuracy. The findings of this study prove helpful unusual events by examining a person's abnormal behavior.
Empirical evaluation of the transfer of information resources in active learning

Active learning is an alternative to traditional learning, using the capabilities of the global network to access remote information resources. The purpose of the article is to analyse the sources and factors of delay in the implementation of network services and the possibilities of access to distributed information objects depending on their size and remoteness. The research was conducted by collecting empirical data (monitoring) on the transfer of resources between client and server. A research organization model was developed and an implementation application was created. Experiments were carried out, and the obtained results were summarized and presented through statistical evaluations and graphical interpretation.
Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering

There has been a growing effort to replace manual extraction of data from research papers with automated data extraction based on natural language processing, language models, and recently, large language models (LLMs). Although these methods enable efficient extraction of data from large sets of research papers, they require a significant amount of up-front effort, expertise, and coding. In this work we propose the ChatExtract method that can fully automate very accurate data extraction with minimal initial effort and background, using an advanced conversational LLM. ChatExtract consists of a set of engineered prompts applied to a conversational LLM that both identify sentences with data, extract that data, and assure the data's correctness through a series of follow-up questions. These follow-up questions largely overcome known issues with LLMs providing factually inaccurate responses. ChatExtract can be applied with any conversational LLMs and yields very high quality data extraction. In tests on materials data we find precision and recall both close to 90% from the best conversational LLMs, like ChatGPT-4. We demonstrate that the exceptional performance is enabled by the information retention in a conversational model combined with purposeful redundancy and introducing uncertainty through follow-up prompts. These results suggest that approaches similar to ChatExtract, due to their simplicity, transferability, and accuracy are likely to become powerful tools for data extraction in the near future. Finally, databases for critical cooling rates of metallic glasses and yield strengths of high entropy alloys are developed using ChatExtract.
Federated learning for 6G-enabled secure communication systems: a comprehensive survey

Machine learning (ML) and Deep learning (DL) models are popular in many areas, from business, medicine, industries, healthcare, transportation, smart cities, and many more. However, the conventional centralized training techniques may not apply to upcoming distributed applications, which require high accuracy and quick response time. It is mainly due to limited storage and performance bottleneck problems on the centralized servers during the execution of various ML and DL-based models. However, federated learning (FL) is a developing approach to training ML models in a collaborative and distributed manner. It allows the full potential exploitation of these models with unlimited data and distributed computing power. In FL, edge computing devices collaborate to train a global model on their private data and computational power without sharing their private data on the network, thereby offering privacy preservation by default. But the distributed nature of FL faces various challenges related to data heterogeneity, client mobility, scalability, and seamless data aggregation. Moreover, the communication channels, clients, and central servers are also vulnerable to attacks which may give various security threats. Thus, a structured vulnerability and risk assessment are needed to deploy FL successfully in real-life scenarios. Furthermore, the scope of FL is expanding in terms of its application areas, with each area facing different threats. In this paper, we analyze various vulnerabilities present in the FL environment and design a literature survey of possible threats from the perspective of different application areas. Also, we review the most recent defensive algorithms and strategies used to guard against security and privacy threats in those areas. For a systematic coverage of the topic, we considered various applications under four main categories: space, air, ground, and underwater communications. We also compared the proposed methodologies regarding the underlying approach, base model, datasets, evaluation matrices, and achievements. Lastly, various approaches’ future directions and existing drawbacks are discussed in detail.
Object Detection for UAV Aerial Scenarios Based on Vectorized IOU

Object detection in unmanned aerial vehicle (UAV) images is an extremely challenging task and involves problems such as multi-scale objects, a high proportion of small objects, and high overlap between objects. To address these issues, first, we design a Vectorized Intersection Over Union (VIOU) loss based on YOLOv5s. This loss uses the width and height of the bounding box as a vector to construct a cosine function that corresponds to the size of the box and the aspect ratio and directly compares the center point value of the box to improve the accuracy of the bounding box regression. Second, we propose a Progressive Feature Fusion Network (PFFN) that addresses the issue of insufficient semantic extraction of shallow features by Panet. This allows each node of the network to fuse semantic information from deep layers with features from the current layer, thus significantly improving the detection ability of small objects in multi-scale scenes. Finally, we propose an Asymmetric Decoupled (AD) head, which separates the classification network from the regression network and improves the classification and regression capabilities of the network. Our proposed method results in significant improvements on two benchmark datasets compared to YOLOv5s. On the VisDrone 2019 dataset, the performance increased by 9.7% from 34.9% to 44.6%, and on the DOTA dataset, the performance increased by 2.1%.
Statistically characterizing robustness and fidelity of quantum controls and quantum control algorithms

Robustness of quantum operations or controls is important to build reliable quantum devices. The robustness-infidelity measure (${\text{RIM}}_{p}$) is introduced to statistically quantify in a single measure the robustness and fidelity of a controller as the $p\mathrm{th}$ order Wasserstein distance between the fidelity distribution of the controller under any uncertainty and an ideal fidelity distribution. The ${\text{RIM}}_{p}$ is the $p\mathrm{th}$ root of the $p\mathrm{th}$ raw moment of the infidelity distribution. Using a metrization argument, we justify why ${\text{RIM}}_{1}$ (the average infidelity) is a good practical robustness measure. Based on the ${\text{RIM}}_{p}$, an algorithmic robustness-infidelity measure (ARIM) is developed to quantify the expected robustness and fidelity of controllers found by a control algorithm. The utility of the RIM and ARIM is demonstrated on energy landscape controllers of spin-$\frac{1}{2}$ networks subject to Hamiltonian uncertainty. The robustness and fidelity of individual controllers as well as the expected robustness and fidelity of controllers found by different popular quantum control algorithms are characterized. For algorithm comparisons, stochastic and nonstochastic optimization objectives are considered. Although high fidelity and robustness are often conflicting objectives, some high-fidelity, robust controllers can usually be found, irrespective of the choice of the quantum control algorithm. However, for noisy or stochastic optimization objectives, adaptive sequential decision-making approaches, such as reinforcement learning, have a cost advantage compared to standard control algorithms and, in contrast, the high infidelities obtained are more consistent with high RIM values for low noise levels.
Explainable AI identifies diagnostic cells of genetic AML subtypes

Explainable AI is deemed essential for clinical applications as it allows rationalizing model predictions, helping to build trust between clinicians and automated decision support tools. We developed an inherently explainable AI model for the classification of acute myeloid leukemia subtypes from blood smears and found that high-attention cells identified by the model coincide with those labeled as diagnostically relevant by human experts. Based on over 80,000 single white blood cell images from digitized blood smears of 129 patients diagnosed with one of four WHO-defined genetic AML subtypes and 60 healthy controls, we trained SCEMILA, a single-cell based explainable multiple instance learning algorithm. SCEMILA could perfectly discriminate between AML patients and healthy controls and detected the APL subtype with an F1 score of 0.86±0.05 (mean±s.d., 5-fold cross-validation). Analyzing a novel multi-attention module, we confirmed that our algorithm focused with high concordance on the same AML-specific cells as human experts do. Applied to classify single cells, it is able to highlight subtype specific cells and deconvolve the composition of a patient’s blood smear without the need of single-cell annotation of the training data. Our large AML genetic subtype dataset is publicly available, and an interactive online tool facilitates the exploration of data and predictions. SCEMILA enables a comparison of algorithmic and expert decision criteria and can present a detailed analysis of individual patient data, paving the way to deploy AI in the routine diagnostics for identifying hematopoietic neoplasms.