Reviewerly/exHarmony huggingface dataset:
The Early Japanese Books Text Line Segmentation base on Image Processing and Deep Learning

Early books record a lot of information such as politics, economy, culture, history at that time. Understanding the early books may help us know the history, becomes an important research recently. However, in Japan, a lot of early books are described by Kuzushi character, which is not used now and only few of specialists can understand it. Hence a large number of early Japanese books still do not be understood. Currently, researchers are trying to understand the early Japanese books with the assist of computer such as image processing and deep learning. However, these books are composed of articles and pictures, sometimes there are in the same page. This case increases the difficult of character recognition, and lets researchers have to segment the text line and separate the articles and pictures previously. This paper aims to segment the text line from the image of scanned early Japanese book by deep learning. For achieving better accuracy, this paper also proposes an image processing method which uses the projection profile for deleting the frame noise. The experimental results show that Precision, Recall and F Value achieve 95.2%, 98.3% and 96.6% respectively, and prove the effectiveness of our method.
Improving CNN Training using Disentanglement for Liver Lesion Classification in CT

Training data is the key component in designing algorithms for medical image analysis and in many cases it is the main bottleneck in achieving good results. Recent progress in image generation has enabled the training of neural network based solutions using synthetic data. A key factor in the generation of new samples is controlling the important appearance features and potentially being able to generate a new sample of a specific class with different variants. In this work we suggest the synthesis of new data by mixing the class specified and unspecified representation of different factors in the training data which are separated using a disentanglement based scheme. Our experiments on liver lesion classification in CT show an average improvement of 7.4% in accuracy over the baseline training scheme.
Fast and Effective Learning for Fuzzy Cognitive Maps: A Method Based on Solving Constrained Convex Optimization Problems

The learning of fuzzy cognitive maps (FCMs) is a timely issue pursued by numerous researchers. Many learning methods, such as population-based algorithms and some hybrid algorithms, have been developed and applied to many fields resulting in better performance. However, those learning algorithms also exhibit obvious limitations: some of them either are difficult to handle the learning problem of large-scale FCM or extremely time-consuming with high computational overload. Furthermore, the learning problem of FCM with noisy data is rarely considered in existing algorithms. In this article, a fast and efficient method for learning FCM is proposed. It first transforms the learning problem of the FCM into a convex optimization problem with constraints, and then, the classic interior-point methods are invoked to solve the optimization problem to obtain the optimized weight matrix of the FCM. A series of experiments involving synthetic data, noisy synthetic data, real data, and publicly available data with noise are considered to demonstrate that the proposed method can rapidly and efficiently learn small-scale and large-scale FCMs and deal with the problem of learning FCM from noisy historical data.
Swift Imbalance Data Classification using SMOTE and Extreme Learning Machine

Continuous expansion in the fields of science and technology has led to the immense availability and attainability of data in every field. Fundamentally understanding and analyzing this data is a critical job in the decision-making process. Although, great success has been achieved by the prevailing data engineering and mining techniques, the problem of swift classification of the imbalanced data still exists in academia and industry. A potential solution to the problem of skewness in data can be resolved by data upsampling or downsampling. There exists a few techniques that firstly remove skewness and then perform classification, however, these methods suffer from hurdles like abortive precision or slower learning rate. In this paper, a hybrid method to classify binary imbalanced data using Synthetic Minority Over-sampling Technique followed by Extreme Learning Machine is proposed. Our method along with swift learning rate is efficacious to predict the desired class. We verified our model using five standard imbalance dataset and obtained higher F-measure, G-mean and ROC score for all the dataset.
Deep Learning in Multimodal Medical Image Analysis

Various imaging modalities (CT, MRI, PET, etc.) encompass abundant information which is different and complementary to each other. It is reasonable to combine images from multiple modalities to make a more accurate assessment. Multimodal medical imaging has shown notable achievements in improving clinical accuracy. Deep learning has achieved great success in image recognition, and also shown huge potential for multimodal medical imaging analysis. This paper gives a review of deep learning in multimodal medical imaging analysis, aiming to provide a starting point for people interested in this field, and highlight gaps and challenges of this topic. Based on the introduction of basic ideas of deep learning and medical imaging, the state-of-the-art multimodal medical image analysis is given, with emphasis on the fusion technique and feature extraction deep models. Multimodal medical image applications, especially cross-modality related, are also summarized.
A Robust Visual Person-Following Approach for Mobile Robots in Disturbing Environments

This article proposes a robust visual following approach with a deep learning-based person detector, a Kalman filter (KF), and a reidentification module. The KF is introduced to predict the position of the target person, and its state is updated by the associated detection result. To deal with severe distractions and even full occlusion, the reidentification module with an identification model, a verification model, and an appearance gallery is employed in multi-person disturbing environments. Without any customized markers, the proposed approach can follow the target person steadily, and it is robust to occlusion and posture changes of the target person. Experiments results validate the effectiveness of the proposed approach.
Memory-assisted long-distance phase-matching quantum key distribution

We propose a scheme that generalizes the loss scaling properties of twin-field or phase-matching quantum key distribution (QKD) related to a channel of transmission $\eta_{total}$ from $\sqrt{\eta_{total}}$ to $\sqrt[2n]{\eta_{total}}$ by employing n-1 memory stations with spin qubits and n beam-splitter stations including optical detectors. Our scheme's resource states are similar to the coherent-state-based light-matter entangled states of a previous hybrid quantum repeater, but unlike the latter our scheme avoids the necessity of employing 2n-1 memory stations and writing the transmitted optical states into the matter memory qubits. The full scaling advantage of this memory-assisted phase-matching QKD (MA-PM QKD) is obtainable with threshold detectors in a scenario with only channel loss. We mainly focus on the obtainable secret-key rates per channel use for up to n=4 including memory dephasing and for n=2 (i.e., $\sqrt[4]{\eta_{total}}$-MA-PM QKD assisted by a single memory station) for error models including dark counts, memory dephasing and depolarization, and phase mismatch. By combining the twin-field concept of interfering phase-sensitive optical states with that of storing quantum states up to a cutoff memory time, distances well beyond 700 km with rates well above $\eta_{total}$ can be reached for realistic, high-quality quantum memories (up to 1s coherence time) and modest detector efficiencies. Similarly, the standard single-node quantum repeater, scaling as $\sqrt{\eta_{total}}$, can be beaten when approaching perfect detectors and exceeding spin coherence times of 5s; beating ideal twin-field QKD requires 1s. As for further experimental simplifications, our treatment includes the notion of weak nonlinearities for the light-matter states, a discussion on the possibility of using homodyne detectors, and a comparison between sequential and parallel entanglement distributions.
Structured subspace embedding on attributed networks

Attributed network embedding aims to learn low-dimensional node vector representations in the network. To date, the primary strategy of the existing approaches is to combine topological structure with attribute information based on the homophily assumption imposed on the whole attributed networks. However, this strategy ignores the formation of community structure driven by different subspaces of attributes. In fact, (1) not all attributes are relevant to the formation of clusters; (2) the generating mechanisms of different clusters may depend on distinct relevant attributes. Therefore, we propose a new network embedding method in this paper, called Structured Subspace Embedding (SSE), to learn a compact low-dimensional vector representation. We wish to consider the cluster structure in subspaces of attributes, which reflects the different generating mechanisms of cluster structure. To this end, the attribute matrix is first represented as a latent feature representation via matrix factorization. Afterward, the local homophily constraint between network topological structure and latent feature representation is imposed. Finally, the community-selection constraint is added to further ensure the latent feature representation has a subspace cluster structure via sparse representation. Extensive experimental results on real-world networks have demonstrated its superiority on both tasks of community detection and node classification over many state-of-the-art approaches.
PFA-ScanNet: Pyramidal Feature Aggregation with Synergistic Learning for Breast Cancer Metastasis Analysis

Automatic detection of cancer metastasis from whole slide images (WSIs) is a crucial step for following patient staging and prognosis. Recent convolutional neural network based approaches are struggling with the trade-off between accuracy and computational efficiency due to the difficulty in processing large-scale gigapixel WSIs. To meet this challenge, we propose a novel Pyramidal Feature Aggregation ScanNet (PFA-ScanNet) for robust and fast analysis of breast cancer metastasis. Our method mainly benefits from the aggregation of extracted local-to-global features with diverse receptive fields, as well as the proposed synergistic learning for training the main detector and extra decoder with semantic guidance. Furthermore, a high-efficiency inference mechanism is designed with dense pooling layers, which allows dense and fast scanning for gigapixel WSI analysis. As a result, the proposed PFA-ScanNet achieved the state-of-the-art FROC of 89.1% on the Camelyon16 dataset, as well as competitive kappa score of 0.905 on the Camelyon17 leaderboard without model ensemble. In addition, our method shows leading speed advantage over other methods, about 7.2 min per WSI with a single GPU, making automatic analysis of breast cancer metastasis more applicable in the clinical usage.
A Statistical and Deep Learning Approach for Human Activity Recognition

Evolution of sensor-based data accumulation process regulated the fact-finding stratum concerning the scrutiny of human-centered motion towards an exemplary appearance. Such evolution enriched the fabrication of a variety of functional datasets, which facilitates the exploration of facts of various research domain. The MHEALTH is such a secondary dataset, that was prepared so as to facilitate the exploration regarding Human Activity Recognition (HAR). This paper performs a comparative study on human activity recognition process in terms of employment of two different data preprocessing methods accompanied by five fashionable classifiers entitled as Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), Multilayer Perceptron (MLP) and Deep Convolutional Neural Network (CNN). We employed the MHEALTH dataset to realize the human activity recognition process. The dataset encompasses information regarding 12 human activities subjecting 10 volunteers. Sensors were employed for the data accumulation process namely Accelerometer, Gyroscope and Magnetometer. Data preprocessing methods, that were employed on the mentioned dataset are elimination of null label instances and uniformity of unbalanced classes. The goal of this study is to analyze performance of different classifiers in terms of the mentioned data preprocessing methods and also identifying the process for which the classifiers exhibit superior accuracy.
Deep representation learning for individualized treatment effect estimation using electronic health records

Utilizing clinical observational data to estimate individualized treatment effects (ITE) is a challenging task, as confounding inevitably exists in clinical data. Most of the existing models for ITE estimation tackle this problem by creating unbiased estimators of the treatment effects. Although valuable, learning a balanced representation is sometimes directly opposed to the objective of learning an effective and discriminative model for ITE estimation. We propose a novel hybrid model bridging multi-task deep learning and K-nearest neighbors (KNN) for ITE estimation. In detail, the proposed model firstly adopts multi-task deep learning to extract both outcome-predictive and treatment-specific latent representations from Electronic Health Records (EHR), by jointly performing the outcome prediction and treatment category classification. Thereafter, we estimate counterfactual outcomes by KNN based on the learned hidden representations. We validate the proposed model on a widely used semi-simulated dataset, i.e. IHDP, and a real-world clinical dataset consisting of 736 heart failure (HF) patients. The performance of our model remains robust and reaches 1.7 and 0.23 in terms of Precision in the estimation of heterogeneous effect (PEHE) and average treatment effect (ATE), respectively, on IHDP dataset, and 0.703 and 0.796 in terms of accuracy and F1 score respectively, on HF dataset. The results demonstrate that the proposed model achieves competitive performance over state-of-the-art models. In addition, the results reveal several findings which are consistent with existing medical domain knowledge, and discover certain suggestive hypotheses that could be validated through further investigations in the clinical domain.
Fuzzy based Pooling in Convolutional Neural Network for Image Classification

This paper introduces a novel pooling method namely fuzzy based pooling for image classification. Herein, a pooling method for bolstering the performance of conventional convolutional neural network (CNN) has been proposed. Conventional architecture of CNN uses pooling operation for dimension reduction which sometimes results in the loss of information. In this paper, a novel pooling method using fuzzy logic is introduced for dimension reduction. The proposed pooling method reduces the spatial size of convolved features in two steps. In the first step, the convolved features within a window to be pooled are processed using type-2 fuzzy logic for identifying the dominant features. Then, type-1 fuzzy logic with a weighted average of the dominant features within a window is used to reduce the spatial size. The proposed method is bench marked against conventional pooling techniques for MNIST dataset of handwritten digits recognition and CIFAR-10 dataset of RGB images. The accuracy shows that the proposed fuzzy based pooling performs better than the standard pooling techniques such as max and average pooling which helps to improve the performance of CNN.
Ship classification based on convolutional neural networks

Ship classification in optical images has been challenged by the complexity of various ships, different imaging conditions, and limited labelled images. Traditional methods focus on extracting handcrafted features for classification, but often fails to design well-performed features for complex images. Here, the authors propose a ship classification approach with CNN. It is capable of learning discriminative features itself by supervised learning and achieving good classification performance. They build two small datasets of optical ship images for training and validation, and conduct several experiments. The experimental results indicate that their approach is effective for ship classification.
Identification and simplification of T-S fuzzy neural networks based on incremental structure learning and similarity analysis

This paper proposes a novel identification method and a simplification scheme for T-S fuzzy neural networks, which consists of two steps. The first step refers to the structure design based on an incremental clustering approach whose basic ideas are that the structure identification of fuzzy neural networks is guided by the attenuation of output approximation error in each cluster and processed by a recursive refined clustering iteration with the input space clustering and sub-clustering as the main steps. Once the structure of a T-S fuzzy neural network is identified by the incremental clustering approach, its parameters are further learned and refined by the Levenberg-Marquardt optimization algorithm. The second step refers to the structure simplification including removing redundant fuzzy rules and merging highly similar fuzzy rules. Furthermore, the performances of several similarity calculating methods are analyzed and discussed, which provides a basis for the selection of the appropriate similarity analysis and effective calculating method for the merging of fuzzy rules and system simplification. That is, the given performance analysis provides the methodology basis and design guide for structure simplification based on similarity analysis and merger of fuzzy rules. Several experiments are implemented to illustrate the feasibility and effectiveness of the proposed approach.
A Novel Lattice-Based CP-ABPRE Scheme for Cloud Sharing

The ciphertext-policy attribute-based proxy re-encryption (CP-ABPRE) scheme supports access control and can transform a ciphertext under an access policy to a ciphertext under another access policy without decrypting the ciphertexts, which is flexible and efficient for cloud sharing. The existing CP-ABPRE schemes are constructed by bilinear pairing or multi-linear maps which are fragile when the post-quantum future comes. This paper presents an efficient unidirectional single-hop CP-ABPRE scheme with small public parameters from a lattice. For the transformation between two access structures, they are required to be disjoint. This paper uses the trapdoor sampling technique to generate the decryption key and the re-encryption key in constructing the scheme, and uses the decompose vectors technique to produce the re-encrypted ciphertexts in order to control their noise. Finally, we extended the scheme to a unidirectional single-hop CP-ABPRE scheme with keyword search for searching the encrypted data. Both schemes were proved secure under the learning with errors assumption, which is widely believed to be secure in quantum computer attacks. To the best of our knowledge, our scheme is the first CP-ABPRE scheme based on the learning with errors assumption.
Are quiz‐games an effective revision tool in Anatomical Sciences for Higher Education and what do students think of them?

Abstract Recently, there has been an increased volume of pedagogical research and practice of mobile learning (m‐learning) and gameplay in education. This paper presents the findings of a study that examined the effect on achievement and explored student perception towards quiz‐game play prior to an anatomy assessment in first year Higher Education students. Achievement data was collected over two academic years at all module assessment (A1–A4) points. A1 was used as a baseline, showing no difference between groups or years, A2 and A3 were comparable online assessments done on the lower and upper limbs that followed the same format; A4 was a viva–voce to assess the whole module learning. The optional 15‐minute quiz‐gameplay intervention (G) using a mobile application was initiated prior to A3, those students who chose not to participate performed their traditional study routine; no other changes to assessments were made resulting in a Gameplay and non‐gameplay (G, NG) group for each year. Students were invited to participate in an online focus group ( N = 84) and a sample undertook in‐depth interviews ( N = 9) to gain qualitative data on their perceptions of the intervention. Students who participated in the gameplay (G) group ( N = 87) demonstrated a significant improvement in A3 compared to A2 , and the non‐game play (NG) group ( N = 164) a significant decrease. A thematic analysis was undertaken on the focus group and interview data revealing key aspects of quiz‐gameplay as a learning tool. This paper offers insight into the potential benefit of encouraging m‐learning gameplay as part of revision or learning for anatomy students. This information could help educators and study support facilitate efficient revision methods as well as initiate further research into the use of anatomy gameplay to enhance the student learning experience. Practitioner Notes What is already known about this topic Repeated and formative quizzing can help memory or knowledge retention. Gameplay can help student engagement, learning and flow depending on the game in question. What this paper adds Using mobile app‐based quiz‐games prior to an anatomy assessment can improve student achievement. Students tend to play mobile app‐based quiz‐games whilst commuting on public transport, in their free time at home and at university. Elements of mobile app‐based quiz‐games students identified as important are that they are interactive, allow competition, are visually appealing, provide instant feedback, are user friendly and are facilitated by the teacher. Implications for practice Educators should encourage mobile app‐based quiz‐games to increase short‐term learning or revision. Educators should facilitate mobile learning quiz‐game opportunities to encourage learning outside of the classroom. Educators wanting to integrate mobile app‐based quiz‐games in their classes should use the game attributes identified in this study.
Select, Attend, and Transfer: Light, Learnable Skip Connections

Skip connections in deep networks have improved both segmentation and classification performance by facilitating the training of deeper network architectures and reducing the risks for vanishing gradients. The skip connections equip encoder-decoder like networks with richer feature representations, but at the cost of higher memory usage, computation, and possibly resulting in transferring non-discriminative feature maps. In this paper, we focus on improving the skip connections used in segmentation networks. We propose light, learnable skip connections which learn to first select the most discriminative channels, and then aggregate the selected ones as single channel attending to the most discriminative regions of input. We evaluate the proposed method on 3 different 2D and volumetric datasets and demonstrate that the proposed skip connections can outperform the traditional heavy skip connections of 4 different models in terms of segmentation accuracy (2% Dice), memory usage (at least 50%), and the number of network parameters (up to 70%).