Reviewerly/exHarmony huggingface dataset:
Ensemble Transfer Learning on Augmented Domain Resources for Oncological Named Entity Recognition in Chinese Clinical Records

Biomedical Named Entity Recognition (NER) is a crucial task in Natural Language Processing (NLP) and can help mine knowledge from massive clinical and diagnostic records. However, the biomedical NER task often undergoes a low-resource training setting due to the high cost of human annotation, limiting the capability of traditional NER models. In this study, we propose a two-stage learning pipeline to tackle the oncological NER task in Chinese language, which is a typical task lacking training resources. In the first stage, two base models pre-trained by Word to Vector (Word2Vec) and Bidirectional Embeddings Representations from Transformer (BERT) are fine tuned to obtain domains-specific word embeddings that serve as the input for the downstream NER task. In the second stage, we feed the word embeddings into a neural network that consists of a Bidirectional Long and Short Time Memory Recurrent Neural Network (BiLSTM) and Linear-chain Conditional Random Field (CRF) for end task training. Meanwhile, we utilize a substitution-based generative model for data augmentation (DA), aiming to enhance the quantity and diversity of the training data. Experiments show that our proposed learning pipeline demonstrates superior performance compared to other model alternatives under a low-resource setting. Specifically, results show that the proposed fine-tuning strategy, when conducted on an augmented domain resource, can effectively incorporate rich domain knowledge into the final NER model, presenting a great potential in boosting a model’s predictive power with limited training data.
Making Generated Images Hard to Spot: A Transferable Attack on Synthetic Image Detectors

Visually realistic GAN-generated images have recently emerged as an important misinformation threat. Research has shown that these synthetic images contain forensic traces that are readily identifiable by forensic detectors. Unfortunately, these detectors are built upon neural networks, which are vulnerable to recently developed adversarial attacks. In this paper, we propose a new anti-forensic attack capable of fooling GAN-generated image detectors. Our attack uses an adversarially trained generator to synthesize traces that these detectors associate with real images. Furthermore, we propose a technique to train our attack so that it can achieve transferability, i.e. it can fool unknown CNNs that it was not explicitly trained against. We evaluate our attack through an extensive set of experiments, where we show that our attack can fool eight state-of-the-art detection CNNs with synthetic images created using seven different GANs, and outperform other alternative attacks.
Evidence for unexpected robustness of multipartite entanglement against sudden death from spontaneous emission

Evidence is found that sheds light on the origin of entanglement sudden death (ESD), a phenomenon characterized by anomalously abrupt decay of quantum entanglement. By identifying a trigger state that initiates ESD, this discovery suggests that systems involving multiple parties exhibit increased robustness against this disruptive effect.
Deep coded exposure: end-to-end co-optimization of flutter shutter and deblurring processing for general motion blur removal

Coded exposure photography is a promising computational imaging technique capable of addressing motion blur much better than using a conventional camera, via tailoring invertible blur kernels. However, existing methods suffer from restrictive assumptions, complicated preprocessing, and inferior performance. To address these issues, we proposed an end-to-end framework to handle general motion blurs with a unified deep neural network, and optimize the shutter’s encoding pattern together with the deblurring processing to achieve high-quality sharp images. The framework incorporates a learnable flutter shutter sequence to capture coded exposure snapshots and a learning-based deblurring network to restore the sharp images from the blurry inputs. By co-optimizing the encoding and the deblurring modules jointly, our approach avoids exhaustively searching for encoding sequences and achieves an optimal overall deblurring performance. Compared with existing coded exposure based motion deblurring methods, the proposed framework eliminates tedious preprocessing steps such as foreground segmentation and blur kernel estimation, and extends coded exposure deblurring to more general blind and nonuniform cases. Both simulation and real-data experiments demonstrate the superior performance and flexibility of the proposed method.
The inclusion degrees of skill maps and knowledge structures

Knowledge and learning assessment is a popular topic. In existing models for constructing the knowledge structure of an individual, it is often considered whether an individual has mastered the skills to solve the corresponding item. However, the relationship between the number of skills an individual has mastered and the item is ignored. It is not reasonable to explain the phenomenon that individuals solve the same item but have different knowledge structures behind it. This paper introduces the concept of skill inclusion degree and constructs the variable precision α-models to delineate knowledge structures. The skill inclusion degree takes into account an individual’s mastery of the number of skills assigned to each item. Firstly, the concept of the skill inclusion degree is given, and some of its properties are discussed. Then, the variable precision α-model is constructed. Moreover, the relationship between knowledge structures delineated via the variable precision α-models by a skill map is studied, and the algorithm of knowledge structures delineated via these models by a skill map is designed. Finally, the experimental results on a real dataset demonstrate the feasibility and effectiveness of the proposed algorithm.
Cryptanalysis of Two Privacy-Preserving Authentication Schemes for Smart Healthcare Applications

Ensuring the secure sharing of privacy-sensitive healthcare data is attracting considerable interest from researchers. Recently, Ogundoyin et al. designed a lightweight privacy-preserving authentication scheme named PAASH for smart health applications. Benil et al. proposed a public verification and auditing scheme named ECACS for securing e-health systems. Ogundoyin et al. and Benil et al. proposed an efficient certificateless aggregate signature (CLAS) scheme as their respective foundation signature schemes. They declared that their constructions were provably secure under the hardness assumption of cryptographic problems. In this work, we disprove their claim by analyzing the correctness and security of their underlying CLAS schemes. We first show that the batch verification process of n signatures for the CLAS scheme in PAASH is incorrect, and any public-key replacement attacker can easily break the scheme. We analyze the reasons for our attack and propose an improved scheme, named PAASH+. We then show that the CLAS scheme in ECACS fails to achieve correctness, an essential property that a cryptographic scheme should provide. As a result, it is impractical to deploy the designed PAASH and ECACS constructions in any real smart health applications.
XRANet: an extra-wide, residual and attention-based deep convolutional neural network for semantic segmentation

In this paper, we propose XRANet, a Deep Convolutional Neural Network (DNN) architecture for Semantic Segmentation. The recent advancements in deep learning and convolutional neural networks have greatly improved the accuracy of segmentation tasks. XRANet builds on the widely used U-Net architecture and adds several improvements to increase performance. The eXtra-wide mechanism in the encoder, combined with residual connections and an attention mechanism in both the encoder and decoder, enhances feature extraction and reduces the activation of pixels outside the regions of interest. The proposed architecture was evaluated on various public datasets, and the results were measured using the dice coefficient metric, obtaining promising quantitavive and qualitative results.
GLFFNet: A Global and Local Features Fusion Network with Biencoder for Remote Sensing Image Segmentation

In recent years, semantic segmentation of high-resolution remote sensing images has been gradually applied to many important scenes. However, with the rapid development of remote sensing data acquisition technology, the existing image data processing methods are facing major challenges. Especially in the accuracy of extraction and the integrity of the edges of objects, there are often problems such as small objects being assimilated by large objects. In order to solve the above problems, based on the excellent performance of Transformer, convolution and its variants, and feature pyramids in the field of deep learning image segmentation, we designed two encoders with excellent performance to extract global high-order interactive features and low-order local feature information. These encoders are then used as the backbone to construct a global and local feature fusion network with a dual encoder (GLFFNet) to effectively complete the segmentation of remote sensing images. Furthermore, a new auxiliary training module is proposed that uses the semantic attention layer to process the extracted feature maps separately, adjust the losses, and more specifically optimize each encoder of the backbone, thus optimizing the training process of the entire network. A large number of experiments show that our model achieves 87.96% mIoU on the Potsdam dataset and 80.42% mIoU on the GID dataset, and it has superior performance compared with some state-of-the-art methods on semantic segmentation tasks in the field of remote sensing.
Pointer Meter Recognition Method Based on Yolov7 and Hough Transform

The current manual reading of substation pointer meters wastes human resources, and existing algorithms have limitations in accuracy and robustness for detecting various pointer meters. This paper proposes a method for recognizing pointer meters based on Yolov7 and Hough transform to improve their automatic readability. The proposed method consists of three main contributions: (1) Using Yolov7 object detection technology, which is the latest Yolo technology, to enhance instrument recognition accuracy. (2) Providing a formula for calculating the angle of a square pointer meter after Hough transformation. (3) Applying OCR recognition to the instrument dial to obtain the model and scale value. This information helps differentiate between meter models and determine the measuring range. Test results demonstrate that the proposed algorithm achieves high accuracy and robustness in detecting different types and ranges of instruments. The map of the Yolov7 model on the instrument dataset is as high as 99.8%. Additionally, the accuracy of pointer readings obtained using this method exceeds 95%, indicating promising applications for a wide range of scenarios.
String Diagrams for Layered Explanations

We propose a categorical framework to reason about scientific explanations: descriptions of a phenomenon meant to translate it into simpler terms, or into a context that has been already understood. Our motivating examples come from systems biology, electrical circuit theory, and concurrency. We demonstrate how three explanatory models in these seemingly diverse areas can be all understood uniformly via a graphical calculus of layered props. Layered props allow for a compact visual presentation of the same phenomenon at different levels of precision, as well as the translation between these levels. Notably, our approach allows for partial explanations, that is, for translating just one part of a diagram while keeping the rest of the diagram untouched. Furthermore, our approach paves the way for formal reasoning about counterfactual models in systems biology.
The potential of robot eyes as predictive cues in HRI—an eye-tracking study

Robots currently provide only a limited amount of information about their future movements to human collaborators. In human interaction, communication through gaze can be helpful by intuitively directing attention to specific targets. Whether and how this mechanism could benefit the interaction with robots and how a design of predictive robot eyes in general should look like is not well understood. In a between-subjects design, four different types of eyes were therefore compared with regard to their attention directing potential: a pair of arrows, human eyes, and two anthropomorphic robot eye designs. For this purpose, 39 subjects performed a novel, screen-based gaze cueing task in the laboratory. Participants' attention was measured using manual responses and eye-tracking. Information on the perception of the tested cues was provided through additional subjective measures. All eye models were overall easy to read and were able to direct participants' attention. The anthropomorphic robot eyes were most efficient at shifting participants' attention which was revealed by faster manual and saccadic reaction times. In addition, a robot equipped with anthropomorphic eyes was perceived as being more competent. Abstract anthropomorphic robot eyes therefore seem to trigger a reflexive reallocation of attention. This points to a social and automatic processing of such artificial stimuli.
An Investigation of Transfer Learning Approaches to Overcome Limited Labeled Data in Medical Image Analysis

A significant amount of research has investigated automating medical diagnosis using deep learning. However, because medical data are collected through diagnostic tests, deep learning methods used in existing studies have had a disadvantage in that the number of training samples is insufficient and the labeling cost is high. Training approaches considering the common characteristics of medical images are needed. Therefore, in this study, we investigated approaches to overcome the lack of data for representative medical imaging tasks using transfer learning technologies. The tasks were divided into image classification, object detection, and segmentation, commonly needed functions in medical image analyses. We proposed transfer learning approaches suitable for each task that can be applied when there are little medical image data available. These approaches were experimentally validated in the following applications that share similar issues of lacking data: cervical cancer classification (image classification), skin lesion detection and classification (object detection and classification), and pressure ulcer segmentation (segmentation). We also proposed multi-task learning and ensemble learning that can be applied to these applications. Finally, the approaches were compared with state-of-the-art results. In cervical cancer analysis, the performance was improved by 5.4% in sensitivity. Skin lesion classification showed improvement in accuracy of 8.7%, precision of 28.3%, and sensitivity of 39.7%. Finally, pressure ulcer segmentation improved in accuracy by 1.2%, intersection over union by 16.9%, and Dice similarity coefficient by 3.5%.
A Method Based on an Autoencoder for Anomaly Detection in DC Motor Body Temperature

Anomaly detection has an important role in industrial systems. Abnormal situations occurring in a system cause anomalies, and the anomalies reduce system performance over time, and may also make the system malfunction. Therefore, the correct and timely detection of anomalies is of critical importance for predictive maintenance. In this study, an autoencoder-based method is proposed for anomaly detection in DC motor body temperature. The performance of the method was examined on a dataset that was created specifically for this study. In the experiments, the three-sigma outlier method was also applied on the same dataset for the same purpose and its performance results are used for comparison. The performance results of both methods are represented in terms of three measures, namely, accuracy, recall, and precision. The experimental study showed that the proposed method achieved over 96% ratios for all three measures, and it can be successfully used for anomaly detection in DC motor body temperature. Additionally, it can be concluded that the proposed system can be preferred for anomaly detection in time series data collected from different types of sensors when the performance results are taken into consideration.
Quadratic Curve Fitting-Based Image Edge Line Segment Detection: A Novel Methodology

In the field of computer vision, edge line segment detection in images is widely used in tasks such as 3D reconstruction and simultaneous localization and mapping. Currently, there are many algorithms that primarily focus on detecting straight line segments in undistorted images, but they do not perform well in detecting edge line segments in distorted images. To address this quandary, the present study introduces a novel method of line segment identification founded on the principles of quadratic fitting. The method proposed utilizes the inherent property of a linear projection in a three-dimensional space, whereby it appears as a quadratic curve in a distorted two-dimensional image. This approach applies an iterative estimation process to ascertain the optimal parameters of the quadratic form that aligns with the edge contour. This process is facilitated by implementing an assumption and validation mechanism. Upon deriving the optimal model, it is then employed to identify the line segments that are encompassed within the edge contour. The experimental assessment of this novel method incorporates its application to both distorted and distortion-free image datasets. The method eliminates the necessity for preliminary processing to discarding distortions, thereby making it universally applicable to both distorted and non-distorted images. In addition to this, the experimental results based on the dataset indicate that the proposed algorithm in this paper achieves an average computational efficiency that is 27 times faster than traditional ones. Thus, this research will contribute to line segment detection in computer vision.
Deep Image Prior Amplitude SAR Image Anonymization

This paper presents an extensive evaluation of the Deep Image Prior (DIP) technique for image inpainting on Synthetic Aperture Radar (SAR) images. SAR images are gaining popularity in various applications, but there may be a need to conceal certain regions of them. Image inpainting provides a solution for this. However, not all inpainting techniques are designed to work on SAR images. Some are intended for use on photographs, while others have to be specifically trained on top of a huge set of images. In this work, we evaluate the performance of the DIP technique that is capable of addressing these challenges: it can adapt to the image under analysis including SAR imagery; it does not require any training. Our results demonstrate that the DIP method achieves great performance in terms of objective and semantic metrics. This indicates that the DIP method is a promising approach for inpainting SAR images, and can provide high-quality results that meet the requirements of various applications.
Visual Static Hand Gesture Recognition Using Convolutional Neural Network

Hand gestures are an essential part of human-to-human communication and interaction and, therefore, of technical applications. The aim is increasingly to achieve interaction between humans and computers that is as natural as possible, for example, by means of natural language or hand gestures. In the context of human-machine interaction research, these methods are consequently being explored more and more. However, the realization of natural communication between humans and computers is a major challenge. In the field of hand gesture recognition, research approaches are being pursued that use additional hardware, such as special gloves, to classify gestures with high accuracy. Recently, deep learning techniques using artificial neural networks have been increasingly proposed for the problem of gesture recognition without using such tools. In this context, we explore the approach of convolutional neural network (CNN) in detail for the task of hand gesture recognition. CNN is a deep neural network that can be used in the fields of visual object processing and classification. The goal of this work is to recognize ten types of static hand gestures in front of complex backgrounds and different hand sizes based on raw images without the use of extra hardware. We achieved good results with a CNN network architecture consisting of seven layers. Through data augmentation and skin segmentation, a significant increase in the model’s accuracy was achieved. On public benchmarks, two challenging datasets have been classified almost perfectly, with testing accuracies of 96.5% and 96.57%.
Discrimination of Quartz Genesis Based on Explainable Machine Learning

Quartz is an important mineral in many metal deposits and can provide important indications about the deposit's origin through its chemical composition. However, traditional low-dimensional analysis methods are ineffective in utilizing quartz's chemical composition to reveal the deposit's origin type. In this study, 1140 quartz samples from eight geological environments were collected, and three machine learning (ML) models—random forest, eXtremely Greedy tree Boosting (XGBoost), and light gradient boosting machine (LightGBM) were used to classify quartz deposits. The application of the Shapley Additive Explanation (SHAP) algorithm and Spearman correlation analysis is utilized to interpret the predictive results of the model and analyze feature correlations, aiming to enhance the credibility of the classification results and discover underlying patterns. Finally, a visualization method based on XGBoost and t-SNE was proposed. By calculating SHAP values, the key geochemical indicators that differentiate each type of quartz deposit were determined. Furthermore, the impact of varying concentrations of different trace elements on the identification of quartz deposits was analyzed. This study demonstrated the effectiveness of using machine-learning algorithms based on trace elements to classify quartz and provided new insights into the relationships between trace elements and quartz genesis, as well as the effects of different trace element combinations and concentrations on quartz identification.
Quantum broadcast and multicast with non-maximally channels

Broadcast and multicast protocols are two branches of multi-party quantum communication. Implementing these protocols based on partial entanglement is of great practical importance since maximally entangled channels are not robust due to noise applied by the surrounding environment. Previous research has used global operators (i.e. operators which are applied on the qubits owned by different parties), which contradicts the concept of multi-party communication protocols. In this research, broadcast protocol and multicast protocol with two receivers are proposed using partially entangled channels. Then the protocols are extended to the case with an arbitrary number of receivers. In the proposed protocol, Alice shares a two-qubit maximally entangled state with each receiver, therefore the whole channel is a partially entangled state. Then, the proposed protocol is modified to be secure against eavesdroppers. Also, the effects of phase damping, bit flip, and phase damping noise on the broadcast protocol are examined and compared.