Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-level granularity adversarial contrast learning for prediction of axillary lymph node metastasis in breast cancer

Prediction of the axillary lymph node (ALN) metastasis is an essential factor to guide breast cancer therapy. MRI is the useful imaging tool for prediction of ALN metastasis. The fusion of multiple modalities of MRI images can improve the prediction accuracy of ALN metastasis in breast cancer. However, existing fusion methods often neglect the specific information across different modalities and do not fully exploit the specificity of medical data with respect to patients’ physiological conditions, result in the limitation of performance improvement. To address these issues, we propose a multi-level granularity adversarial contrast learning network (MACMH-net) for ALN metastasis prediction. First, a multi-modal adversarial contrastive learning module is developed with incorporation of adversarial contrast loss and orthogonal contrast loss. It has the ability to learn the shared information and modality-specific information, and combine more relevant information and complementary information in the fusion features. Second, a multi-level granularity contrast learning module is developed with incorporation of the multi-level granularity supervised contrast loss to make full use of specific physiological information of each patient, further improve the discriminative ability of features. Experimental results demonstrate the effectiveness of the proposed method.
Lifespan Face Age Progression using 3D-Aware Generative Adversarial Networks

Today, face recognition systems (FRS) are widely used in applications such as forensic and border control systems. Despite the increasing ability of deep neural networks to identify individuals based on their facial images, it remains challenging to recognize faces with long age gaps between reference and probe samples. To improve the robustness of FRS towards aging, training datasets can be enriched with synthetic data by simulating recurring aging effects. Typical aging signs include craniofacial changes during the child-to-adult age transition or textural changes that occur during adult-to-adult aging (e.g., wrinkles or furrows). Building upon the recent achievements of 3D-aware generative adversarial networks, we propose a geometry-aware face age modification algorithm (Age-EG3D) that enables lifespan face age simulation. We demonstrate the effectiveness of our approach by providing a comprehensive performance evaluation and comparison of Age-EG3D to prior works. All code and pre-trained models are available at https://github.com/johndoe133/eg3d-age.
Evolution of Digital Twin solutions in the manufacturing industry

Nowadays, the growing need to improve performance levels and reduce production costs is forcing the manufacturing industry to implement different monitoring, control, Machine Learning (ML) and optimization tools, frequently, without integrating them into an overall framework. Digital Twin (DT) emerges as a possible solution, being one of the most widely used concepts in the field of industrial digitalization. However, its architecture and applications are still under study. Some international institutions, such as the International Organization for Standardization or the International Academy for Production Engineering, are making progress in the search for a standard framework.The aim of this article is twofold. Firstly, to identify the main functionalities and architectures required by a DT, which, according to the progress made in regulations and academic publications, distinguish it from the concepts of Digital Model, Digital Shadow and Digital Clone. Secondly, to identify the industry requirements, the challenges of applying DT technology throughout the product lifecycle as well as the more accurate simulation tools and models for its successful implementation.
Radar Image Extrapolation with Conditional Generative Adversarial Network

Forecasting hazardous weather, especially severe convective weather, is critical to protecting the environment and human life. Radar image extrapolation is a widely adopted method for this purpose, including the traditional optical flow-based method and deep learning-based methods. However, the optical flow approach suffers from a significant decline in accuracy with time, and the predictions generated by ConvRNN and CNN-based models tend to be smooth and blurry. To solve these limitations, we propose a Radar Multi-scale Fusion Generative Prediction Network (Fugen-Net) for the task of radar image extrapolation. We use an enhanced U-Net as the generator of a conditional generative adversarial network (CGAN) and design a Multi-scale Feature Fusion and Transmission (MFT) structure to improve the feature extraction capacity and reduce the number of parameters. To evaluate the forecasting ability of Fugen-Net, we conduct experiments using 4 years of radar images collected in Beijing from 2014 to 2017 and compare its performance with optical flow and U-Net. The experimental results demonstrate that Fugen-Net outperforms the other two methods when both high threshold skill scores and image realism are considered.
A Scheme for Extracting Weak RFI Parameters Based on TJLS Matrix Decomposition

This letter proposes a scheme for automatically and accurately extracting the parameters of weak radio frequency interference (RFI) present in the signal within the time–frequency domain. This scheme utilizes short-time Fourier transform (STFT) to transform each frame of radar echo containing interference into the time–frequency domain. In the time domain, a joint low-rank sparse robust principal component analysis based on truncated nuclear norms (TJLS-RPCA) is employed to divide the time–frequency representation into two joint low-rank sparse matrices, from which the interference parameters are extracted. Subsequently, $K$ -means clustering is applied to determine the number of RFIs, and Gaussian fitting is applied to eliminate high-error items. This scheme can extract parameters such as the central frequency, bandwidth, and duration of the interference. The proposed scheme improves the accuracy of parameter extraction and processing speed, demonstrating notable effectiveness in handling weak interference. The Monte Carlo experimental results demonstrate that the scheme provides highly accurate parameter estimation when the signal-to-interference ratio (SIR) is less than 13 dB, with errors below 0.5%. Finally, the feasibility of the proposed scheme is validated using Sentinel-1A data, and the extracted results are consistent with the simulation results.
MDA: Masked Language Modeling Meets Data Augmentation for Multiple Choice Question Answering

Multiple Choice Question Answering (MCQA) is a well-established task in the field of Machine Reading Comprehension (MRC). Its objective is to identify the correct answer from a given set of options, based on the provided background passage and question. Recent advancements in large-scale Pre-trained Language Models (PLMs) have yielded impressive performance in MCQA. However, achieving such performance requires a significant number of training samples, leading to time-consuming and labor-intensive sample acquisition and annotation processes. To overcome the limitation posed by the availability of training samples, this paper explores the potential of leveraging the [Mask] token, which is commonly used in Masked Language Modeling (MLM) during the self-supervised training of PLMs. Specifically, the paper introduces a straightforward yet effective approach called [Mask] based Data Augmentation (MDA). The proposed method involves injecting [Mask] tokens into background passages to create masked versions of the original data. Moreover, a self-evaluator is introduced to regulate the process of masking production, with the objective of minimizing negative impact caused by argumentation noise. The effectiveness of the proposed method is empirically validated using various benchmark MCQA datasets. Experimental results demonstrate considerable improvements over state-of-the-arts.
A Preliminary Exploration of Market Supervision Informatization in the Context of Big Data Technology

The rapid development of network information processing technology and computer technology has pushed humanity into the era of big data. In recent years, with the increasing maturity of information processing technology, big data technology has gradually been applied to various fields. In order to improve the efficiency of market supervision, we integrate market supervision with big data. We first introduce the concept of big data technology, then analyze the current situation of market supervision informationization and the role of big data technology in guiding market supervision informationization. Finally, we propose several strategies for optimizing market supervision information management in the context of big data, and conduct preliminary exploration of market supervision supported by big data.
A Review on Smart Traffic Management System

This System holds significant potential to elevate traffic management efforts and bolster road safety within urban environments. The Smart Connected Traffic Management System is a web-based application developed using the Dash framework. The system offers real-time control and monitoring capabilities for traffic lights, along with a comprehensive suite of traffic statistics, aimed at improving the efficiency of traffic management. This implementation harnesses libraries like Dash, Dash Bootstrap Components, and Dash DAQ to construct a responsive and visually appealing user interface. Notably, the traffic control module enables seamless toggling between “Normal” and “Emergency” traffic modes. With its modular code structure and user-friendly design, the system remains poised for future expansion, including the integration of IoT devices.
Discriminative features-based trustworthiness prediction in IoT devices using machine learning models

In the realm of Internet of Things (IoT) devices, the trust management system (TMS) has been enhanced through the utilisation of diverse machine learning (ML) classifiers in recent times. The efficacy of training machine learning classifiers with pre-existing datasets for establishing trustworthiness in IoT devices is constrained by the inadequacy of selecting suitable features. The current study employes a subset of the UNSW-NB15 dataset to compute additional features such as throughput, goodput, packet loss. These features may be combined with the best discriminatory features to distinguish between trustworthy and non-trustworthy IoT networks. In addition, the transformed dataset undergoes filter-based and wrapper-based feature selection methods to mitigate the presence of irrelevant and redundant features. The evaluation of classifiers is performed utilising diverse metrics, including accuracy, precision, recall, F1-score, true positive rate (TPR), and false positive rate (FPR). The performance assessment is conducted both with and without the application of feature selection methodologies. Ultimately, a comparative analysis of the machine learning models is performed, and the findings of the analysis demonstrate that our model's efficacy surpasses that of the approaches utilised in the existing literature.
A Survey of PatchMatch-Based Multi-View Stereo

Multi-View Stereo (MVS) is a fundamental problem in computer vision, graphics, and machine learning communities, and is a crucial technique for image-based 3D reconstruction. Volume-based Plane-Sweeping (PS) and random-based Patch-Match (PM) are mainstream pipelines for depth-based MVS methods. Although the PS pipeline incorporated with deep networks has attracted generous attention, PM remains the dominant option for real-world applications. This article reviews current PS and PM pipelines and presents in-depth procedures for the latter. Furthermore, we compare and summarize the reconstruction performance of state-of-the-art PS and PM methods on three public benchmarks, providing enlightening observations and inspiring research directions.
KinfaceNet: A New Deep Transfer Learning based Kinship Feature Extraction Framework

Advances in vision and deep learning have revolutionized feature extraction for face recognition and verification systems, yet, performing kinship verification from such features is still challenging. Ongoing research attempts to imitate a human by identifying features for kinship verification. In this paper, we propose KinfaceNet, a deep learning based kinship feature extractor, capable of extracting kinship features from a single input image independently without requiring its kin pair image. The base model of the method is adopted from face recognition domain which is then transfer learned in the domain of kinship by learning a distance mapping from face images to a compact Euclidean space where distances directly correspond to a measure of kinship similarity. Thus, unlike most of the works in deep learning based kinship domain, the extracted features can be used in many other applications such as image generation and family based clustering, etc. Training is performed by rearranging the data into classes of kin pairs and using a state-of-the-art triplet mining algorithm to address the unbalanced kinship data problem which causes overfitting. Also, one of the major advantages of our framework is that training can be performed on any face feature extractor model pre-trained on large face recognition data, thereby reducing training time by a considerable amount. Comparable verification accuracy is obtained from simple MLP network at only 20th epoch with KinfaceNet features extracted from the Family-In-the-Wild dataset, the largest in the wild kinship dataset available, as well as KinfaceW-I and II datasets.
Enhancing IoT Security Through Resource-Efficient Feature Engineering

As a heterogeneous networks, the Internet of Things (loT) is vulnerable to attacks, which brings a threat to human life and property. Intrusion Detection Systems (IDS) based on machine learning, a powerful tool have been developed and applied in network security. However, the deployment of highly complex IDS should consider the resource cost. Data preprocessing and feature engineering are capable of enhancing the performance and resource cost with effectiveness and low cost. Thus, this paper focuses on feature engineering which is divided into 4 steps: elimination of spurious features, data splitting, labeling and normalization, and feature selection. Through the feature reduction, we generate a dataset with different dimensions on the TON IoT dataset. To evaluate the effectiveness and universality of the proposed method, four machine learning methods including gradient boosting machine (GBM), K-Nearest Neighbors (KNN), Random Forests (RF), and MultiLayer Perceptron (MLP) are implemented into the simulation in Python. The comparative results indicate that Random Forest performs optimally in terms of feature engineering, achieving accuracies of 99.96 % and 99.52 % in binary and multiclass experiments, respectively.
All about that BASE: Modeling Biometric Authentication Systems and their Evaluations to enable a more efficient Exchange of Research Results

If a researcher wants to contribute in the field of biometric authentication systems (BAS), much manual work is required to gather the current state-of-research (SoR) for a specific type of BAS. Due to loosely structured scientific papers as result reports, all relevant details need to be extracted, aggregated and collected individually. Once the SoR is determined, an exchange with other researchers is hardly possible due to a missing exchange format and they usually have to start from scratch repeating the same inefficient procedure often targeting the same papers. This work presents a domain model for biometric authentication systems and their evaluations, BASE, as a solution. It models and interconnects biometric characteristics, the target architecture, evaluation scenarios, and evaluation criteria grants to represent the most common parts of a BAS. For evaluation, we map 12 BAS approaches from related work into BASE and determine mainly low mapping efforts. In this context, we first identified diverse subsets of BASE being inferable from the analyzed papers. Secondly, most researchers still target rather broad evaluation categories like security or usability instead specific criteria within these categories where the latter would simplify the basic assessment of a BAS. Overall, we consider BASE a promising step to improve the efficiency in SoR determination.
Rich Features Aggregation For Hyperspectral Image Spectrum Reduction

Classification of hyperspectral images (HIC) plays an important role in remote sensing image studies. To address the issue of redundant spectral bands in hyperspectral photographs, both conventional and CNN-based solutions are proposed. However, present dimension reduction approaches frequently depend solely on spectral information and disregard the spatial information, so impairing classification accuracy. In addition, these methods lower spectral dimension without taking physical significance into account. In this study, we offer a new dimension reduction technique based on multi-scale spatial module. A physical-based feature extraction module and a rich spectrum feature selection module are included in this method. Experiments on three benchmark datasets demonstrate that our strategy can outstandingly improve performance on various HIC model.
An Improved LA-Transformer Machine Translation Model

As an important subtask in the field of natural language processing, machine translation can play a greater auxiliary role in the work of translators. Most current machine translation models are implemented using an end-to-end structure. However, regardless of the way of the absolute positional encoding method or the relative positional encoding method is adopted, it will lead to the problem of distraction of the attention mechanism when processing long sequences. To solve this problem, a local attention mechanism based on recurrent neural network is adopted, so that local position information and hidden information in sentences can be captured. At the same time, the local attention mechanism is introduced into the encoder part of Transformer, and experiments are carried out in Chinese-English and Japanese-English machine translation tasks. Compared with the baseline model, the experimental results show that the proposed model improves the BLEU score by 2.09 in the Chinese-English dataset, and increases the BLEU score by 1.34 in the Japanese-English dataset.
Text Data Augmentation Techniques for Word Embeddings in Fake News Classification

Contemporary language models heavily rely on large corpora for their training. The larger the corpus, the better a model can capture various semantic relationships. The issue at hand appears to be the limited scope of the corpora used. One potential solution to this problem is the application of data augmentation techniques to expand the existing corpus. Data augmentation encompasses several techniques for corpus augmentation. In this article, we delve deeper into the analysis of three techniques: Synonym Replacement, Back Translation, and Reduction of Function Words. Utilizing these three techniques, we prepared diverse versions of the corpus employed for training Word2Vec Skip-gram models. These techniques were validated through extrinsic evaluation, wherein Word2Vec Skip-gram models were used to generate word vectors for classifying fake news articles. Performance measures of the generated classifiers were analyzed. The study highlights significant statistical differences in classifier outcomes between augmented and original corpora. Specifically, Back Translation significantly enhances accuracy, notably with Support Vector and Bernoulli Naive Bayes models. Conversely, the Reduction of Function Words (FWD) improves Logistic Regression, while the original corpus excels in Random Forest classification. The article also includes an intrinsic evaluation involving lexical semantic relations between word pairs. The intrinsic evaluation highlights nuanced differences in semantic relations across augmented corpora. Notably, the Back Translation (BT) corpus better aligns with established lexical resources, showcasing promising improvements in understanding specific semantic relationships.
Enhancing Robot Self-Localization Accuracy and Robustness: A Variational Autoencoder-Based Approach

Self-localization is a crucial task for robots, demanding high accuracy. In this work, we propose a new robot localization method based on the Variational Autoencoder (VAE). In our method, the robot utilizes the captured image to generate robot localization in indoor environments. The utilization of VAE makes the system adaptive to varying environmental conditions. Our findings demonstrate that utilizing both the robot's coordinates and images as training data significantly enhances the accuracy of robot self-localization estimation and improves the robustness of the system due to sensor data noise.
Sclera Segmentation and Joint Recognition Benchmarking Competition: SSRBC 2023

This paper presents the summary of the Sclera Segmentation and Joint Recognition Benchmarking Competition (SSRBC 2023) held in conjunction with IEEE International Joint Conference on Biometrics (IJCB 2023). Different from the previous editions of the competition, SSRBC 2023 not only explored the performance of the latest and most advanced sclera segmentation models, but also studied the impact of segmentation quality on recognition performance. Five groups took part in SSRBC 2023 and submitted a total of six segmentation models and one recognition technique for scoring. The submitted solutions included a wide variety of conceptually diverse deep-learning models and were rigorously tested on three publicly available datasets, i.e., MASD, SBVPI and MOBIUS. Most of the segmentation models achieved encouraging segmentation and recognition performance. Most importantly, we observed that better segmentation results always translate into better verification performance.
Linking Ports’ Digital Twins to Those of Cities

Ports, often located on the outskirts of (smart) cities, are striving for innovative technological solutions to attain efficiency gains to cope with raising trade volumes and energy savings towards improving their environmental footprint. With two-thirds of the world’s major ports located in urban areas, these efforts do not take place in isolation, but in intersection with the multi-layered processes of the smart city. In this context, digital twinning can be a technology with the potential to significantly increase the efficiency of the multi-layered and interconnected ports and smart cities processes. While digital twins for the complex systems of the smart cities have gained momentum in recent years, digital twinning of ports is often narrowed to address only specific port assets. The aim of this article is to investigate how smart city digital twins tackle complex processes and how ports can draw from this experience. We discuss potential interfaces between a port digital twin and a city digital twin. It becomes evident that both these domains of application of digital twinning are complex systems having overlaps and functional interconnections. The paper presents how the port’s digital twin can contribute to threat detection, energy savings, cost reductions, performance gains, and improved stakeholder cooperation, based on insights from the city digital twin. Finally, the three overlap points maintenance of shared infrastructure, intelligent joint approaches to avoid congestion, and energy are identified as intersection points.