Reviewerly/exHarmony huggingface dataset:
Improving Performance of Breast Lesion Classification Using a ResNet50 Model Optimized with a Novel Attention Mechanism

Background: The accurate classification between malignant and benign breast lesions detected on mammograms is a crucial but difficult challenge for reducing false-positive recall rates and improving the efficacy of breast cancer screening. Objective: This study aims to optimize a new deep transfer learning model by implementing a novel attention mechanism in order to improve the accuracy of breast lesion classification. Methods: ResNet50 is selected as the base model to develop a new deep transfer learning model. To enhance the accuracy of breast lesion classification, we propose adding a convolutional block attention module (CBAM) to the standard ResNet50 model and optimizing a new model for this task. We assembled a large dataset with 4280 mammograms depicting suspicious soft-tissue mass-type lesions. A region of interest (ROI) is extracted from each image based on lesion center. Among them, 2480 and 1800 ROIs depict verified benign and malignant lesions, respectively. The image dataset is randomly split into two subsets with a ratio of 9:1 five times to train and test two ResNet50 models with and without using CBAM. Results: Using the area under ROC curve (AUC) as an evaluation index, the new CBAM-based ResNet50 model yields AUC = 0.866 ± 0.015, which is significantly higher than that obtained by the standard ResNet50 model (AUC = 0.772 ± 0.008) (p < 0.01). Conclusion: This study demonstrates that although deep transfer learning technology attracted broad research interest in medical-imaging informatic fields, adding a new attention mechanism to optimize deep transfer learning models for specific application tasks can play an important role in further improving model performances.
Evaluating unfairness of popularity bias in recommender systems: A comprehensive user-centric analysis

The popularity bias problem is one of the most prominent challenges of recommender systems, i.e., while a few heavily rated items receive much attention in presented recommendation lists, less popular ones are underrepresented even if they would be of close interest to the user. This structural tendency of recommendation algorithms causes several unfairness issues for most of the items in the catalog as they are having trouble finding a place in the top- N lists. In this study, we evaluate the popularity bias problem from users’ viewpoint and discuss how to alleviate it by considering users as one of the major stakeholders. We derive five critical discriminative features based on the following five essential attributes related to users’ rating behavior, (i) the interaction level of users with the system, (ii) the overall liking degree of users, (iii) the degree of anomalous rating behavior of users, (iv) the consistency of users, and (v) the informative level of the user profiles, and analyze their relationships to the original inclinations of users toward item popularity. More importantly, we investigate their associations with possible unfairness concerns for users, which the popularity bias in recommendations might induce. The analysis using ten well-known recommendation algorithms from different families on four real-world preference collections from different domains reveals that the popularity propensities of individuals are significantly correlated with almost all of the investigated features with varying trends, and algorithms are strongly biased towards popular items. Especially, highly interacting , selective , and hard-to-predict users face highly unfair, relatively inaccurate, and primarily unqualified recommendations in terms of beyond-accuracy aspects, although they are major stakeholders of the system. We also analyze how state-of-the-art popularity debiasing strategies act to remedy these problems. Although they are more effective for mistreated groups in alleviating unfairness and improving beyond-accuracy quality, they mostly fail to preserve ranking accuracy. • Five essential attributes are defined related to users’ rating profiles. • Five discriminative features are derived based on the correlations between user attributes. • Relationships of derived features to the users’ original inclinations on item popularity are examined. • Users are grouped and analyzed based on derived features. • How features-based groups are unfairly affected by the popularity bias is analyzed. • How traditional popularity-debiasing methods perform for features-based groups is examined.
CPAD-Net: Contextual parallel attention and dilated network for liver tumor segmentation

Liver cancer is one of the leading causes of cancer death. Accurate and automatic liver tumor segmentation methods are urgent needs in clinical practice. Currently, Fully Convolutional Network and U-Net framework have achieved good results in medical image segmentation tasks, but there is still room for improvement. The traditional U-Net extracted a large number of low-level features, and the detailed features cannot be transmitted to deeper layers, resulting in poor segmentation ability. Therefore, this paper proposed a novel liver tumor segmentation network with contextual parallel attention and dilated convolution, called CPAD-Net. The proposed network applies a subsampled module, which has the same dimensionality reduction function as max-pooling without losing detailed features. CPAD-Net employs a contextual parallel attention module at skip connection. The module fuses contextual multi-scale features and extracts channel-spatial features in parallel. These features are concatenated with deep features to narrow the semantic gap and increase detailed information. Hybrid dilated convolution and double-dilated convolution are used in the encoding and decoding stages to expand the network receptive field. Dropout is added after each hybrid dilated convolution block to prevent overfitting. The efficacy of the proposed network is proved by widespread experimentation on two public datasets (LiTS2017 and 3Dircadb-01) and a clinical dataset from the Affiliated Hospital of Hebei University. The proposed network achieved Dice scores of 74.2%, 73.7% and 73.26%. The experimental results show that the proposed network outperforms most segmentation networks.
Adaptive Physical Layer Authentication Using Machine Learning With Antenna Diversity

The heterogeneous characteristics of wireless mobile networks within the Internet of things (IoT) create authentication challenges due to the large number of devices with diverse requirements and capabilities. Physical layer authentication (PLA) can provide solutions for this heterogeneous environment using wireless channel attributes. In the paper, an adaptive lightweight physical layer authentication (ALPLA) scheme is proposed using machine learning (ML). Antenna diversity at the receiver is exploited to increase the number of features to achieve a high authentication rate (AR). A one-class classifier support vector machine (OCC-SVM) is used with the magnitude and real and imaginary parts of the received signal at each receive antenna as features. One-class classification is a ML technique for outlier and anomaly detection which uses only legitimate training data. The sounding reference signal (SRS) in the 5G uplink radio frame is employed to obtain the features. The proposed scheme is evaluated in an urban environment under different mobility conditions. Results are presented which show that this scheme provides a high AR with sufficient antenna diversity. Further it is superior to other approaches in the literature.
Comprehensive analyses of image forgery detection methods from traditional to deep learning approaches: an evaluation

The digital image proves critical evidence in the fields like forensic investigation, criminal investigation, intelligence systems, medical imaging, insurance claims, and journalism to name a few. Images are an authentic source of information on the internet and social media. But, using easily available software or editing tools such as Photoshop, Corel Paint Shop, PhotoScape, PhotoPlus, GIMP, Pixelmator, etc. images can be altered or utilized maliciously for personal benefits. Various active, passive and other new deep learning technology like GAN approaches have made photo-realistic images difficult to distinguish from real images. Digital image tamper detection now focuses on determining the authenticity and consistency of digital photos. The major research problems use generic solutions and strategies, such as standardized data sets, benchmarks, evaluation criteria and generalized approaches.This paper overviews the evaluation of various image tamper detection methods. A brief discussion of image datasets and a comparative study of image criminological (forensic) methods are included in this paper. Furthermore, recently developed deep learning techniques along with their limitations have also been addressed. This study aims to comprehensively analyze image forgery detection methods using conventional and advanced deep learning approaches.
Whole rock and mineral chemistry of the rare metals-bearing mylonitic rocks, Abu Rusheid borehole, south Eastern Desert, Egypt

Abu Rusheid area represents one of the typical rare metals-bearing rocks in the Northern Neoproterozoic Arabian-Nubian Shield. The current work reports a new detailed lithologic description, petrographic, and mineralogical studies integrated with a geochemical investigation of the borehole no.5 to discern the petrological features, rare metals enrichment and their origin. Abu Rusheid mylonitic rocks are composed mainly of quartz, K-feldspars (Or62-98), plagioclase (Ab98-99), primary muscovite, and neoformed biotite of annite type as well as some accessories such as zircon, columbite, tantalite, fluorite, allanite, xenotime, monazite, and kasolite. They have a noticeable high content of rare earth elements and a conspicuous enrichment of Zr (up to ∼ 4400 ppm), Nb (up to ∼ 2150 ppm), Ta (up to ∼ 270 ppm), Hf (up to ∼ 190 ppm), Zn (up to ∼ 5600 ppm), U (up to ∼ 175 ppm), Pb (up to ∼ 900 ppm) and Th (up to ∼ 1000 ppm) trace elements. REE patterns revel M-type tetrad effect (t1) with an absence of the W-type tetrad segment in the third tetrad (t3). Further constrains, lambda method (where τ2 can be detected) yield a positive values of τ1 (av. 0.534), τ2 (0.487), τ3 (0.498) and τ4 (0.28) for the examined rocks, reflecting M-type tetrads. The rare metals enrichment are related to magmatic as well as the extrusion of lamprophyre dykes yielding alteration processes that led to the remobilization and enrichment of these elements through joints, faulting and their deposition along contacts with the ophiolitic mélange.
Vehicle Detection in Traffic Monitoring Scenes Based on Improved YOLOV5s

Accurate vehicle detection plays an important role in road traffic monitoring. Aiming at the problem of false detection and missed detection caused by complex scenes and large differences in target scales, an improved vehicle detection algorithm based on YOLOv5s is proposed. Firstly, a detection layer is added to better learn the multi-level features of the vehicle, and then the Spatial Pyramid Pooling(SPP) module of the original YOLOv5s algorithm is replaced with the Atrous Spatial Pyramid Pooling(ASPP) module to increase the receptive field of images of different sizes and extract multi-scale context information. The experimental results on UA-DETRAC dataset show that the precision, recall and average accuracy of the proposed algorithm are improved compared with the original YOLOv5s algorithm, which achieves the purpose of improving the vehicle detection accuracy and reduces the phenomenon of missing and false detection of vehicles to a certain extent.
A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection

Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the application of the synthetic dataset in the actual vision system of intelligent vehicles. In this paper, we propose a novel synthetic-to-real domain adaptation method to settle the mismatch domain distribution from two aspects, i.e., data level and knowledge level. On the data level, a Style-Content Discriminated Data Recombination (SCD-DR) module is proposed, which decouples the style from content and recombines style and content from different domains to generate a hybrid domain as a transition between synthetic and real domains. On the knowledge level, a novel Iterative Cross-Domain Knowledge Transferring (ICD-KT) module including source knowledge learning, knowledge transferring and knowledge refining is designed, which achieves not only effective domain-invariant feature extraction, but also transfers the knowledge from labeled synthetic images to unlabeled actual images. Comprehensive experiments on public virtual and real dataset pairs demonstrate the effectiveness of our proposed synthetic-to-real domain adaptation approach in object detection of traffic scenes.
UDNet: Uncertainty-aware deep network for salient object detection

• We propose an uncertainty-aware SOD model with a high-level contrast feature extraction module and three decoding branches. • We design a feature interaction module to enhance the feature interaction among internal contour uncertainty features, saliency features and external contour uncertainty features. • Extensive experiments on four public benchmark datasets and the challenging SOC dataset demonstrate the superiority of the proposed model. Most of the existing deep learning based salient object detection (SOD) models adopt multi-level feature fusion strategies, and have achieved remarkable progress. However, current SOD models still suffer from the uncertainty dilemma in predicting salient probabilities of the pixels surrounding the contour of salient objects. To solve this issue, we propose a novel uncertainty-aware SOD model, where multiple supervision signals, i.e ., internal contour uncertainty map, saliency map and external contour uncertainty map, are used to guide the network to not only focus on the pixels in the salient object but also shift its partial attention to the pixels surrounding the contour of salient objects. Furthermore, we introduce a new feature interaction module to aggregate internal contour uncertainty features, saliency features and external contour uncertainty features in the decoding stage, aiming to enhance the model’s ability in dealing with the “uncertain” pixels. Extensive experiments on four public benchmark datasets demonstrate the superiority of the proposed method over the existing state-of-the-art SOD methods. Furthermore, the proposed method shows better attribute-based performance on the SOC dataset, suggesting that the proposed model can also handle challenging scenarios in SOD.
Multilayer dynamic ensemble model for intensive care unit mortality prediction of neonate patients

Robust and rabid mortality prediction is crucial in intensive care units because it is considered one of the critical steps for treating patients with serious conditions. Combining mortality prediction with the length of stay (LoS) prediction adds another level of importance to these models. No studies in the literature predict such tasks for neonates, especially using time-series data and dynamic ensemble techniques. Dynamic ensembles are novel techniques that dynamically select the base classifiers for each new case. Medically, implementing an accurate machine learning model is insufficient to gain the trust of physicians. The model must be able to justify its decisions. While explainable AI (XAI) techniques can be used to handle this challenge, no studies have been done in this regard for neonate monitoring in the neonatal intensive care unit (NICU). This study utilizes advanced machine learning approaches to predict mortality and LoS through data-driven learning. We propose a multilayer dynamic ensemble-based model to predict mortality as a classification task and LoS as a regression task for neonates admitted to the NICU. The model has been built based on the patient's time-series data of the first 24 h in the NICU. We utilized a cohort of 3,133 infants from the MIMIC-III real dataset to build and optimize the selected algorithms. It has shown that the dynamic ensemble models achieved better results than other classifiers, and static ensemble regressors achieved better results than classical machine learning regressors. The proposed optimized model is supported by three well-known explainability techniques of SHAP, decision tree visualization, and rule-based system. To provide online assistance to physicians in monitoring and managing neonates in the NICU, we implemented a web-based clinical decision support system based on the most accurate models and selected XAI techniques. The code of the proposed models is publicly available at https://github.com/InfoLab-SKKU/neonateMortalityPrediction.
Observation of the quantum Gouy phase

Controlling the evolution of a photonic quantum states is crucial for most quantum information processing and metrology tasks. Because of its importance, many mechanisms of quantum state evolution have been tested in detail and are well understood. However, the fundamental phase anomaly of evolving waves called the Gouy phase has not been studied in the context of elementary quantum states of light such as photon number states. Here we outline a simple method for calculating the quantum state evolution upon propagation and demonstrate experimentally how this quantum Gouy phase affects two-photon quantum states. Our results show that the increased phase sensitivity of multi-photon states also extends to this fundamental phase anomaly and has to be taken into account to fully understand the state evolution. We further demonstrate how the Gouy phase can be used as a tool for manipulating quantum states of any bosonic system in future quantum technologies, outline a possible application in quantum-enhanced sensing, and dispel a common misconception related to the nature of the increased phase sensitivity of multi-photon quantum states.
A model for CBDC audits based on blockchain technology: Learning from the DCEP

The rapid rise of central bank digital currency (CBDC) has sparked a heated discussion on its security, supervision and audit. To this end, this study designs a CBDC system based on the prototype of digital currency electronic payment (DCEP) that can provide real-time transaction verification, with an emphasis on comprehensive supervision, privacy-preserving transactions, verifiable audits, and a constant amount of CBDC. Meanwhile, we use blockchain technology and Pederson Commitment to design architecture to prove the records' veracity and the auditing's accuracy. The results of numerical experiments show that under the proposed CBDC system, (1) the central bank can audit 50 commercial banks in roughly one second; (2) the true value of the total bonds is comparable to the amount calculated by the Pedersen Committee; (3) the operation performance of the system is superior. Therefore, the security, effectiveness and practicability of our proposed CBDC system are demonstrated.
AFE-CNN: 3D Skeleton-based Action Recognition with Action Feature Enhancement

Existing 3D skeleton-based action recognition approaches reach impressive performance by encoding handcrafted action features to image format and decoding by CNNs. However, such methods are limited in two ways: a) the handcrafted action features are difficult to handle challenging actions, and b) they generally require complex CNN models to improve action recognition accuracy, which usually occur heavy computational burden. To overcome these limitations, we introduce a novel AFE-CNN, which devotes to enhance the features of 3D skeleton-based actions to adapt to challenging actions. We propose feature enhance modules from key joint, bone vector, key frame and temporal perspectives, thus the AFE-CNN is more robust to camera views and body sizes variation, and significantly improve the recognition accuracy on challenging actions. Moreover, our AFE-CNN adopts a light-weight CNN model to decode images with action feature enhanced, which ensures a much lower computational burden than the state-of-the-art methods. We evaluate the AFE-CNN on three benchmark skeleton-based action datasets: NTU RGB + D, NTU RGB + D 120, and UTKinect-Action3D, with extensive experimental results demonstrate our outstanding performance of AFE-CNN.
Retrieval-Augmented Transformer for Image Captioning

Image captioning models aim at connecting Vision and Language by providing natural language descriptions of input images. In the past few years, the task has been tackled by learning parametric models and proposing visual feature extraction advancements or by modeling better multi-modal connections. In this paper, we investigate the development of an image captioning approach with a kNN memory, with which knowledge can be retrieved from an external corpus to aid the generation process. Our architecture combines a knowledge retriever based on visual similarities, a differentiable encoder, and a kNN-augmented attention layer to predict tokens based on the past context and on text retrieved from the external memory. Experimental results, conducted on the COCO dataset, demonstrate that employing an explicit external memory can aid the generation process and increase caption quality. Our work opens up new avenues for improving image captioning models at larger scale.
Blockchain‐based asset storage and service mechanism to metaverse universe: Metarepo

Abstract Digital modeling of the real world with 3D modeling technologies has recently prepared the groundwork for Metaverse. Metaverse is an ecosystem that will allow social and cultural events in the real world to be carried out in the digital world as well. Inspired by this framework, this study presents an approach called MetaRepo, where users can securely store digital assets (cryptocurrency, avatars, clothes, tickets, etc.) and use them in various activities within the metaverse universe. The motivation for the realization of this study is the security problems in the exchange, buying and selling transactions that take place in the virtual universes that have become popular in recent days. Another source of motivation is the anxiety of the object owners having their possessions stolen, lost, and transferred to another universe in the Metaverse. Blockchain technology, which can store assets in MetaRepo, has been used to address these concerns. In the developed blockchain structure, New User Engine, Transaction Centre, Authenticator Engine (W eng ) and Repos models have been developed for user interaction, transaction processing and security mechanism. An exemplary metaverse universe including social activities has been designed for the testing and evaluation processes of the proposed MetaRepo approach. MetaRepo is communicated from the browser via APIs. Detailed performance analysis has been carried out for the proposed model. As a result, with MetaRepo, a mechanism is aimed at which users can communicate with different metaverse universes and platforms without the need for extra verification and security measures within the metaverse universe.