Reviewerly/exHarmony huggingface dataset:
Structure-aware siamese graph neural networks for encounter-level patient similarity learning

Patient similarity learning has attracted great research interest in biomedical informatics. Correctly identifying the similarity between a given patient and patient records in the database could contribute to clinical references for diagnosis and medication. The sparsity of underlying relationships between patients poses difficulties for similarity learning, which becomes more challenging when considering real-world Electronic Health Records (EHRs) with a large number of missing values. In the paper, we organize EHRs as a graph and propose a novel deep learning framework, Structure-aware Siamese Graph neural Networks (SSGNet), to perform robust encounter-level patient similarity learning while capturing the intrinsic graph structure and mitigating the influence from missing values. The proposed SSGNet regards each patient encounter as a node, and learns the node embeddings and the similarity between nodes simultaneously via Graph Neural Networks (GNNs) with siamese architecture. Further, SSGNet employs a low-rank and contrastive objective to optimize the structure of the patient graph and enhance model capacity. The extensive experiments were conducted on two publicly available datasets and a real-world dataset regarding IgA nephropathy from Peking University First Hospital, in comparison with multiple baseline and state-of-the-art methods. The significant improvement in Accuracy, Precision, Recall and F1 score on the patient encounter pairwise similarity classification task demonstrates the superiority of SSGNet. The mean average precision (mAP) of SSGNet on the similar encounter retrieval task is also better than other competitors. Furthermore, SSGNet's stable similarity classification accuracies at different missing rates of data validate the effectiveness and robustness of our proposal.
Hierarchical Sliding Inference Generator for Question-driven Abstractive Answer Summarization

Text summarization on non-factoid question answering (NQA) aims at identifying the core information of redundant answer guidance using questions, which can dramatically improve answer readability and comprehensibility. Most existing approaches focus on extracting query-related sentences to construct a summary, where the logical connection of natural language and the hierarchical interpretable semantic association are often neglected, thus degrading performance. To address these issues, we propose a novel question-driven abstractive answer summarization model, called the H ierarchical S liding I nference G enerator (HSIG), to form inferable and interpretable summaries by explicitly introducing hierarchical information reasoning between questions and corresponding answers. Specifically, we first apply an elaborately designed hierarchical sliding fusion inference model to determine the most relevant question sentence-level representation that provides a deeper interpretable basis for sentence selection in summarization, which further increases computational performance on the premise of following the semantic inheritance structure. Additionally, to improve summary fluency, we construct a double-driven selective generator to integrate various semantic information from two mutual question-and-answer perspectives. Experimental results illustrate that compared with state-of-the-art baselines, our model achieves remarkable improvement on two benchmark datasets and specifically improves the 2.46 ROUGE-1 points on PubMedQA, which demonstrates the superiority of our model on abstractive summarization with hierarchical sequential reasoning.
Micro-Influencer Recommendation by Multi-Perspective Account Representation Learning

Influencer marketing is emerging as a new marketing method, changing the marketing strategies of brands profoundly. In order to help brands find suitable micro-influencers as marketing partners, the micro-influencer recommendation is regarded as an indispensable part of influencer marketing. However, previous works only focus on modeling the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">individual image</i> of brands/micro-influencers, which is insufficient to represent the characteristics of brands/micro-influencers over the marketing scenarios. In this case, we propose a micro-influencer ranking joint learning framework which models brands/micro-influencers from the perspective of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">individual image</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">target audiences</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cooperation preferences</i> . Specifically, to model accounts’ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">individual image</i> , we extract topics information and images semantic information from historical content information, and fuse them to learn the account content representation. We introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">target audiences</i> as a new kind of marketing role in the micro-influencer recommendation, in which audiences information of brand/micro-influencer is leveraged to learn the multi-modal account audiences representation. Afterward, we build the attribute co-occurrence graph network to mine <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cooperation preferences</i> from social media interaction information. Based on account attributes, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cooperation preferences</i> between brands and micro-influencers are refined to attributes’ co-occurrence information. The attribute node embeddings learned in the attribute co-occurrence graph network are further utilized to construct the account attribute representation. Finally, the global ranking function is designed to generate ranking scores for all brand-micro-influencer pairs from the three perspectives jointly. The extensive experiments on a publicly available dataset demonstrate the effectiveness of our proposed model over the state-of-the-art methods.
Domain Adaptation with Representation Learning and Nonlinear Relation for Time Series

In many real-world scenarios, machine learning models fall short in prediction performance due to data characteristics changing from training on one source domain to testing on a target domain. There has been extensive research to address this problem with Domain Adaptation (DA) for learning domain invariant features. However, when considering advances for time series, those methods remain limited to the use of hard parameter sharing (HPS) between source and target models, and the use of domain adaptation objective function. To address these challenges, we propose a soft parameter sharing (SPS) DA architecture with representation learning while modeling the relation as non-linear between parameters of source and target models and modeling the adaptation loss function as the squared Maximum Mean Discrepancy (MMD) . The proposed architecture advances the state-of-the-art for time series in the context of activity recognition and in fields with other modalities, where SPS has been limited to a linear relation. An additional contribution of our work is to provide a study that demonstrates the strengths and limitations of HPS versus SPS. Experiment results showed the success of the method in three domain adaptation cases of multivariate time series activity recognition with different users and sensors.
Bone Age Assessment Based on Deep Convolutional Features and Fast Extreme Learning Machine Algorithm

Bone age is an important metric to monitor children’s skeleton development in pediatrics. As the development of deep learning DL-based bone age prediction methods have achieved great success. However, it also faces the issue of huge computation overhead in deep features learning. Aiming at this problem, this paper proposes a new DL-based bone age assessment method based on the Tanner-Whitehouse method. This method extracts limited and useful regions for feature learning, then utilizes deep convolution layers to learn representative features in these interesting regions. Finally, to realize the fast computation speed and feature interaction, this paper proposes to use an extreme learning machine algorithm as the basic architecture in the final bone age assessment study. Experiments based on publicly available data validate the feasibility and effectiveness of the proposed method.
Attention-Based Bi-LSTM Network for Abusive Language Detection

Prevention of abusive content in social media platforms has received a lot of attention in recent years. The problem is still a challenge due to the nuances present in the content posted and the tactics of users to pass through the abusive detection algorithms employed by the social media giants. This paper presents a robust deep learning model that takes the advantage of both character and word representations, to address the challenges associated with the text content of a social media post for detecting abusive content. The proposed model uses character CNN to capture morphological information and learns the important features to distinguish abusive content with the help of an Attention-based Bi-LSTM network. In addition, it uses the pooling layer to avoid spatial translations. We confined our model to only the text content of the social media posts due to the limitations of collecting user characteristics for existing datasets and to honour the privacy concerns of users. Our model is dataset agnostic, as revealed by the empirical results on the data sets of multiple social media platforms. It outperformed all previous methods for abusive language detection with text-only features.
Double JPEG compression detection and localization based on convolutional auto-encoder for image content removal

Due to the widespread prevalence of JPEG images in daily life, their authenticity has often been investigated in detecting the traces caused by double JPEG (DJPEG) compression. This paper presents a novel method of DJPEG compression detection based on deep neural networks. After removing the image content using a convolutional auto-encoder (CAE), we separated the JPEG and DJPEG feature spaces into two aligned and non-aligned scenarios using a convolutional neural network (CNN) in the spatial domain. Besides detecting DJPEG compression for small-sized 64 × 64 image patches, the approach also lets us determine the manipulated regions. The proposed network was evaluated for different input images. Results on the standard raw images dataset (RAISE) demonstrated that the proposed methods outperform previous algorithms, especially in the non-aligned case. If an accurate determination of the first and second JPEG compression quality factors (QFs) is not possible, our algorithm is robust to their perturbations. Compared to one of the best existing methods, we achieved about 65% improvement in relative error reduction in some cases.
Research on the risk-evaluation model of power transmission and transformation projects based on multilevel fuzzy-thought weighting for cloud-model improvement

Abstract The risk evaluation of power transmission and transformation projects is a complex and comprehensive evaluation process influenced by many factors and involves many indicators. In order to solve the uncertainty and fuzziness problems in the process of the multilevel fuzzy risk evaluation of power transmission and transformation projects, this paper introduces the cloud theory, which is specialized in the study of uncertainty problems and constructs the multilevel fuzzy comprehensive risk-evaluation model of power transmission and transformation projects based on the improved multilevel fuzzy-thought weighting based on the cloud model. Finally, the risk of the Beijing 220-kV Tangyu power transmission and transformation project is evaluated and the feasibility of the evaluation model is verified. The results of the evaluation and the evaluation layer cloud model are combined with MATLAB simulation to show that the risk level of the project is between large risk and general risk.
Constructive image steganography using example-based weighted color transfer

Image steganography aims to hide secret information without arousing suspicion that an image has conveyed hidden messages. The majority of image steganographic algorithms usually embed secret messages by altering pixels of a cover image. Despite achieving some degree of success, such a cover modification approach leads to certain telltale signs detected by steganalytic tools, thus unveiling the very existence of secret messages. In this paper, we propose a novel constructive steganographic algorithm to disguise the hidden information without involving a pre-existing cover image. Our WCTCIS scheme transforms secret messages into a stego image, taking advantage of example-based weighted color transfer, which synthesizes a new image with different colors by referring to a reference image. Our scheme first encodes secret message to increase the security using a modified two-dimensional lag-complex logistic map. An optimal weight is then derived using one of four measure techniques we suggest. Next, we conduct an example-based weighted color transfer to produce a temporary image, where each pixel is represented by a floating-point format. Finally, we conceal secret messages when synthesizing a pixel using a predetermined embedding vector. An intensive testing based on a database containing 5,000 natural images confirms that our algorithm offers a large payload and produces a stego image with significantly high quality. The CIEDE2000 color difference metric demonstrates that the stego image has high-fidelity color appearance. A security analysis shows that our scheme can resist current state-of-the-art learning-based ECMV steganalysis as well as statistic-based RS steganalysis, and is robust to salt-and-pepper noise attacks. The probability of breaking down our scheme is insignificantly small. The contribution of our work is introducing a constructive steganographic algorithm which makes use of color changes to conceal secret messages, produces better performance than the current state-of-the-art algorithms, and extends a color processing technique to achieve constructive steganography.
Long Distance Ground Target Tracking with Aerial Image-to-Position Conversion and Improved Track Association

A small drone is capable of capturing distant objects at a low cost. In this paper, long distance (up to 1 km) ground target tracking with a small drone is addressed for oblique aerial images, and two novel approaches are developed. First, the coordinates of the image are converted to real-world based on the angular field of view, tilt angle, and altitude of the camera. Through the image-to-position conversion, the threshold of the actual object size and the center position of the detected object in real-world coordinates are obtained. Second, the track-to-track association is improved by adopting the nearest neighbor association rule to select the fittest track among multiple tracks in a dense track environment. Moving object detection consists of frame-to-frame subtraction and thresholding, morphological operation, and false alarm removal based on object size and shape properties. Tracks are initialized by differencing between the two nearest points in consecutive frames. The measurement statistically nearest to the state prediction updates the target’s state. With the improved track-to-track association, the fittest track is selected in the track validation region, and the direction of the displacement vector and velocity vectors of the two tracks are tested with an angular threshold. In the experiment, a drone hovered at an altitude of 400 m capturing video for about 10 s. The camera was tilted 30° downward from the horizontal. Total track life (TTL) and mean track life (MTL) were obtained for 86 targets within approximately 1 km of the drone. The interacting multiple mode (IMM)-CV and IMM-CA schemes were adopted with varying angular thresholds. The average TTL and MTL were obtained as 84.9–91.0% and 65.6–78.2%, respectively. The number of missing targets was 3–5; the average TTL and MTL were 89.2–94.3% and 69.7–81.0% excluding the missing targets.
Global Assists Local: Effective Aerial Representations for Field of View Constrained Image Geo-Localization

When we humans recognize places from images, we not only infer about the objects that are available but even think about landmarks that might be surrounding it. Current place recognition approaches lack the ability to go beyond objects that are available in the image and hence miss out on understanding the scene completely. In this paper, we take a step towards holistic scene understanding. We address the problem of image geo-localization by retrieving corresponding aerial views from a large database of geotagged aerial imagery. One of the main challenges in tackling this problem is the limited Field of View (FoV) nature of query images which needs to be matched to aerial views which contain 360°FoV details. State-of-the-art method DSM-Net [19] tackles this challenge by matching aerial images locally within fixed FoV sectors. We show that local matching limits complete scene understanding and is inadequate when partial buildings are visible in query images or when local sectors of aerial images are covered by dense trees. Our approach considers both local and global properties of aerial images and hence is robust to such conditions. Experiments on standard benchmarks demonstrates that the proposed approach improves top-1% image recall rate on the CVACT [9] data-set from 57.08% to 77.19% and from 61.20% to 75.21% on the CVUSA [28] data-set for 70°FoV. We also achieve state-of-the art results for 90°FoV on both CVACT [9] and CVUSA [28] data-sets demonstrating the effectiveness of our proposed method.
Depth Completion Auto-Encoder

This paper proposes a new approach to integrating image features for unsupervised depth completion. Instead of resorting to the image as input like existing works, we propose to employ the image to guide the learning process. Specifically, we regard dense depth as a reconstructed result of the sparse input, and formulate our model as an auto-encoder. To reduce structure inconsistency resulting from sparse depth, we employ the image to guide latent features by penalizing their difference in the training process. The image guidance loss enables our model to acquire more dense and structural cues that are beneficial for producing more accurate and consistent depth values. For inference, our model only takes sparse depth as input and no image is required. Our paradigm is new and pushes unsupervised depth completion further than existing works that require the image at test time. On the KITTI Depth Completion Benchmark, we validate its effectiveness through extensive experiments and achieve promising performance compared with other unsupervised works. The proposed method is also applicable to indoor scenes such as NYUv2.
Adaptive blood cell segmentation and hybrid Learning-based blood cell classification: A Meta-heuristic-based model

The fundamental goal of this paper is to develop a novel blood cell classification using a hybrid learning model. The proposed model encompasses different processing steps like "(a) pre-processing, (b) cell segmentation, (c) Feature extraction, and (d) classification". In the initial step, the blood smear images are pre-processed using Red Green Blue (RGB) scale to gray scale conversion and contrast enhancement. Then, the Adaptive Fuzzy C-Means (A-FCM) clustering with heuristic improvement is developed for blood cell classification. During testing, the feature extraction from the segmented cell image is performed by the Gray Level Co-occurrence Matrix1 (GLCM), Local Binary Pattern (LBP), geometric features, and color features. These features are subjected to the hybrid learning model with Neural Network (NN) and Long Short-Term Memory (LSTM) termed NLSTM. The modification of the A-FCM-based cell segmentation and hybrid learning-based cell classification is performed by a Best search-based Moth-Flame Optimization (BS-MFO) algorithm. The experimental analysis specifies that the suggested model has shown better efficiency on the identification of blood cell images, and attains high accuracy when compared over the competitive methods.
Development of Game-Based Learning Traffic Order For Android With The Game Development Live Cycle Method

With the lack of traffic law compliance in Indonesia, there is a need to spread awareness of traffic law. As such, this paper aims to analyze and design Android traffic violations game-based learning. The game will be developed using Unity according to the Game Development Life Cycle methodology. Evaluation will be tested using the Blackbox methods, the Whitebox methods, and questionnaires to the beta tester. According to the results of the questionnaires, this game can be a traffic education game.
DualDE

Knowledge Graph Embedding (KGE) is a popular method for KG reasoning and training KGEs with higher dimension are usually preferred since they have better reasoning capability. However, high-dimensional KGEs pose huge challenges to storage and computing resources and are not suitable for resource-limited or time-constrained applications, for which faster and cheaper reasoning is necessary. To address this problem, we propose DualDE, a knowledge distillation method to build low-dimensional student KGE from pre-trained high-dimensional teacher KGE. DualDE considers the dual-influence between the teacher and the student. In DualDE, we propose a soft label evaluation mechanism to adaptively assign different soft label and hard label weights to different triples, and a two-stage distillation approach to improve the student's acceptance of the teacher. Our DualDE is general enough to be applied to various KGEs. Experimental results show that our method can successfully reduce the embedding parameters of a high-dimensional KGE by 7× - 15× and increase the inference speed by 2× - 6× while retaining a high performance. We also experimentally prove the effectiveness of our soft label evaluation mechanism and two-stage distillation approach via ablation study.
A state-aware adaptive pathfinder for dynamic minimum dose path planning during an emergency in a complex radioactive environment

Optimal walking-path planning is one of the most effective measures to minimize radiation exposure of workers in radioactive environments. Several algorithms have been proposed to control workers' exposure especially during normal operations of a nuclear facility. However, when an emergency or accident occurs, the radioactive environment becomes dynamic and complex, rendering the conventional shortest path algorithms non-optimal and inefficient. This work presents a state-aware adaptive pathfinder (SAP), a method to solve the minimum dose path for an emergency worker in a dynamic nuclear environment. The algorithm dynamically tracks and updates the state of both the radiation source and the (human) target, and adaptively presents the minimum dose path based on the current state. The advantages of the proposed SAP approach are evaluated in two experiments with eight (8) different scenarios. In the first experiment, a minimum dose walking path is obtained for a worker in a static environment with three scenarios. In the second experiment, cumulative dose by the emergency worker along a path is dynamically computed in three scenarios that define both the worker and the radiation source moving in uniform and non-uniform speed. Compared with conventional methods, the results show that the proposed SAP is simpler, more flexible, and effective for path planning in a complex radioactive environment. The performance evaluation result of SAP in both static and dynamic environments, and during a nuclear emergency is discussed in this paper.