Reviewerly/exHarmony huggingface dataset:
Harmonized representation learning on dynamic EHR graphs

With the rise of deep learning, several recent studies on deep learning-based methods for electronic health records (EHR) successfully address real-world clinical challenges by utilizing effective representations of medical entities. However, existing EHR representation learning methods that focus on only diagnosis codes have limited clinical value, because such structured codes cannot concretely describe patients’ medical conditions, and furthermore, some of the codes assigned to patients contain errors and inconsistency; this is one of the well-known caveats in the EHR. To overcome this limitation, in this paper, we fuse more detailed and accurate information in the form of natural language provided by unstructured clinical data sources (i.e., clinical notes). We propose HORDE, a unified graph representation learning framework to embed heterogeneous medical entities into a harmonized space for further downstream analyses as well as robustness to inconsistency in structured codes. Our extensive experiments demonstrate that HORDE significantly improves the performances of conventional clinical tasks such as subsequent code prediction and patient severity classification compared to existing methods, and also show the promising results of a novel EHR analysis about the consistency of each diagnosis code assignment.
Active Domain Randomization

Domain randomization is a popular technique for improving domain transfer, often used in a zero-shot setting when the target domain is unknown or cannot easily be used for training. In this work, we empirically examine the effects of domain randomization on agent generalization. Our experiments show that domain randomization may lead to suboptimal, high-variance policies, which we attribute to the uniform sampling of environment parameters. We propose Active Domain Randomization, a novel algorithm that learns a parameter sampling strategy. Our method looks for the most informative environment variations within the given randomization ranges by leveraging the discrepancies of policy rollouts in randomized and reference environment instances. We find that training more frequently on these instances leads to better overall agent generalization. Our experiments across various physics-based simulated and real-robot tasks show that this enhancement leads to more robust, consistent policies.
Learning to detect community smells in open source software projects

Community smells are symptoms of organizational and social issues within the software development community that often lead to additional project costs. Recent studies identified a variety of community smells and defined them as sub-optimal patterns connected to organizational-social structures in the software development community. To early detect and discover existence of potential community smells in a software project, we introduce, in this paper, a novel machine learning-based detection approach, named csDetector, that learns from various existing bad community development practices to provide automated support in detecting such community smells. In particular, our approach learns from a set of organizational-social symptoms that characterize the existence of potential instances of community smells in a software project. We built a detection model using Decision Tree by adopting the C4.5 classifier to identify eight commonly occurring community smells in software projects. To evaluate the performance of our approach, we conduct an empirical study on a benchmark of 74 open source projects from Github. Our statistical results show a high performance of csDetector, achieving an average accuracy of 96% and AUC of 0.94. Moreover, our results indicate that the csDetector outperforms two recent state-of-the-art techniques in terms of detection accuracy. Finally, we investigate the most influential community-related metrics to identify each community smell type. We found that the number of commits and developers per time zone, the number of developers per community, and the social network betweenness and closeness centrality are the most influential community characteristics.
Enhancing Path Quality of Real-Time Path Planning Algorithms for Mobile Robots: A Sequential Linear Paths Approach

The traditional trade-off between execution speed and path quality has forced real-time robotic path planning algorithms to sacrifice path quality in order to execute in real-time.Producing a path planning algorithm that targets enhancing both, the path quality and swiftness is a challenging problem.However, this article proposes a novel path planning strategy that aims to break this traditional trade-off, by targeting both, increasing the swiftness, and enhancing the path quality represented by the path length and smoothness.The proposed strategy is based on the observation that most path planning algorithms waste the processing efforts in less critical areas of the map.Therefore, the proposed path planning strategy tends to focus on critical areas such as the areas around obstacles and areas around the goal point, and exhausts the processing power on these critical areas.This is done by neglecting all static obstacles that do not lie between the robot and the destination.For obstacles that intersect with the linear line from the robot to the destination, a basis traditional path planning algorithm such as A*, D* or the Probabilistic RoadMap (PRM) technique is only implemented around the obstacles in order to find a feasible path around each selected obstacle.This procedure would minimize the computational efforts compared to applying the basis algorithm on the whole map.Finally, the path quality is enhanced by finding any linear shortcuts between any two points in the path and fix these shortcuts as the final path from the starting point to the goal point.The proposed path planning strategy was tested on a P3-DX Pioneer mobile robot using a kinematic controller.The experimental results have proven that the path planning strategy was able to show a superior advantage over other path planning techniques in both aspects, computational time (reached up to 97.05% improvement) and path quality (reached up to 16.21% improvement for path length and 98.50% for smoothness).
Target Recognition Based on CNN with LeakyReLU and PReLU Activation Functions

The ReLU activation function accelerates the convergence of the training process in the classical framework of deep learning. ReLU causes a large part of the network neurons to die. When a very large gradient flows through a ReLU neuron and updates the parameters, it will not activate any data. This paper proposes target recognition based on CNN with LeakyReLU and PReLU activation functions. According to the advantages of ReLU, LeakyReLU function is used to fix a part of the parameters to cope with the gradient death. PReLU parameters combined with PReLU are trained to construct a new CNN framework. Experimental results show the method is effective and feasibile.
FDFtNet: Facing Off Fake Images Using Fake Detection Fine-Tuning Network

Creating fake images and videos such as "Deepfake" has become much easier these days due to the advancement in Generative Adversarial Networks (GANs). Moreover, recent research such as the few-shot learning can create highly realistic personalized fake images with only a few images. Therefore, the threat of Deepfake to be used for a variety of malicious intents such as propagating fake images and videos becomes prevalent. And detecting these machine-generated fake images has been more challenging than ever.
Event study methodology in business research: a bibliometric analysis

Purpose This study aims to provide an objective analysis of the state-of-the-art and intellectual development of publications related to event study methodology in business research. Design/methodology/approach The sample includes 1,219 papers related to event study methodology, covering all business disciplines and spanning 34 years from 1983 to 2016. Findings Through three stages of primary analysis, namely, initial sample, citation and co-citation analyses, the authors identified the publication trends, supplementary techniques, influential publications and intellectual clusters in the area of event study methodology in business. Research limitations/implications The findings serve as a benchmark for the extensive literature related to event study methodology in business and may facilitate the transference of the amassed useful techniques among disciplines and the identification of future research directions. Originality/value The current study represents as a pioneering effort to review event study-related publications using bibliometric analysis.
Clipped BagNet: Defending Against Sticker Attacks with Clipped Bag-of-features

Many works have demonstrated that neural networks are vulnerable to adversarial examples. We examine the adversarial sticker attack, where the attacker places a sticker somewhere on an image to induce it to be misclassified. We take a first step towards defending against such attacks using clipped BagNet, which bounds the influence that any limited-size sticker can have on the final classification. We evaluate our scheme on ImageNet and show that it provides strong security against targeted PGD attacks and gradient-free attacks, and yields certified security for a 95% of images against a targeted 20 × 20 pixel attack.
Two-Stream Encoder GAN With Progressive Training for Co-Saliency Detection

The recent end-to-end co-saliency models have good performance, however, they cannot express the semantic consistency among a group of images well and usually require many co-saliency labels. To this end, a two-stream encoder generative adversarial network (TSE-GAN) with progressive training is proposed in this paper. In the pre-training stage, the salient object detection generative adversarial networks (SOD-GAN) and classification network (CN) are separately trained by the salient object detection (SOD) datasets and co-saliency datasets with only category labels to learn the intra-saliency and preliminary inter-saliency cues and alleviate the problem of insufficient co-saliency labels. In the second training stage, the backbone of TSE-GAN is inherited from the trained SOD-GAN, the encoder of trained SOD-GAN (SOD-Encoder) is used to extract intra-saliency features, the group-wise semantic encoder (GS-Encoder) is constructed by the multi-level group-wise category features extracted from CN for extracting inter-saliency features with better semantic consistency, the TSE-GAN constructed by incorporating the GS-Encoder into SOD-GAN is trained on co-saliency datasets for co-saliency detection. The comprehensive comparisons with 13 state-of-the-art methods demonstrate the effectiveness of proposed method.
DS-PASS: Detail-Sensitive Panoramic Annular Semantic Segmentation through SwaftNet for Surrounding Sensing

Semantically interpreting the traffic scene is crucial for autonomous transportation and robotics systems. However, state-of-the-art semantic segmentation pipelines are dominantly designed to work with pinhole cameras and train with narrow Field-of-View (FoV) images. In this sense, the perception capacity is severely limited to offer higher-level confidence for upstream navigation tasks. In this paper, we propose a network adaptation framework to achieve Panoramic Annular Semantic Segmentation (PASS), which allows to re-use conventional pinhole-view image datasets, enabling modern segmentation networks to comfortably adapt to panoramic images. Specifically, we adapt our proposed SwaftNet to enhance the sensitivity to details by implementing attention-based lateral connections between the detail-critical encoder layers and the context-critical decoder layers. We benchmark the performance of efficient segmenters on panoramic segmentation with our extended PASS dataset, demonstrating that the proposed real-time SwaftNet outperforms state-of-the-art efficient networks. Furthermore, we assess real-world performance when deploying the Detail-Sensitive PASS (DS-PASS) system on a mobile robot and an instrumented vehicle, as well as the benefit of panoramic semantics for visual odometry, showing the robustness and potential to support diverse navigational applications.
Outlier Detection Based on Fuzzy Rough Granules in Mixed Attribute Data

Outlier detection is one of the most important research directions in data mining. However, most of the current research focuses on outlier detection for categorical or numerical attribute data. There are few studies on the outlier detection of mixed attribute data. In this article, we introduce fuzzy rough sets (FRSs) to deal with the problem of outlier detection in mixed attribute data. Since the outlier detection model of the classical rough set is only applicable to the categorical attribute data, we use FRS to generalize the outlier detection model and construct a generalized outlier detection model based on fuzzy rough granules. First, the granule outlier degree (GOD) is defined to characterize the outlier degree of fuzzy rough granules by employing the fuzzy approximation accuracy. Then, the outlier factor based on fuzzy rough granules is constructed by integrating the GOD and the corresponding weights to characterize the outlier degree of objects. Furthermore, the corresponding fuzzy rough granules-based outlier detection (FRGOD) algorithm is designed. The effectiveness of the FRGOD algorithm is evaluated through experiments on 16 real-world datasets. The experimental results show that the algorithm is more flexible for detecting outliers and is suitable for numerical, categorical, and mixed attribute data.
Red blood cell phenotyping from 3D confocal images using artificial neural networks

The investigation of cell shapes mostly relies on the manual classification of 2D images, causing a subjective and time consuming evaluation based on a portion of the cell surface. We present a dual-stage neural network architecture for analyzing fine shape details from confocal microscopy recordings in 3D. The system, tested on red blood cells, uses training data from both healthy donors and patients with a congenital blood disease, namely hereditary spherocytosis. Characteristic shape features are revealed from the spherical harmonics spectrum of each cell and are automatically processed to create a reproducible and unbiased shape recognition and classification. The results show the relation between the particular genetic mutation causing the disease and the shape profile. With the obtained 3D phenotypes, we suggest our method for diagnostics and theragnostics of blood diseases. Besides the application employed in this study, our algorithms can be easily adapted for the 3D shape phenotyping of other cell types and extend their use to other applications, such as industrial automated 3D quality control.
Point cloud transformers applied to collider physics

Methods for processing point cloud information have seen a great success in collider physics applications. One recent breakthrough in machine learning is the usage of Transformer networks to learn semantic relationships between sequences in language processing. In this work, we apply a modified Transformer network called Point Cloud Transformer as a method to incorporate the advantages of the Transformer architecture to an unordered set of particles resulting from collision events. To compare the performance with other strategies, we study jet-tagging applications for highly-boosted particles.
Removal of speckle noises from ultrasound images using five different deep learning networks

Image enhancement methods are applied to medical images to reduce the noise that they contain. There are many academic studies in the literature using classical image enhancement methods. Ultrasound imaging is a medical imaging method that is used for the diagnosis of diseases. In this study, speckle noises with Rayleigh distribution at four different noise levels (σ = 0.10, 0.25, 0.50, 0.75) are added to ultrasound images of the brachial plexus nerve region. Five different deep learning networks (Dilated Convolution Autoencoder Denoising Network/Di-Conv-AE-Net, Denoising U-Shaped Net/D-U-Net, BatchRenormalization U-Net/Br-U-Net, Generative Adversarial Denoising Network/DGan-Net, and CNN Residual Network/DeRNet) are used for reducing the speckle noises of the ultrasound images. The performances of the deep networks are compared with block-matching and 3D filtering (BM3D), which is one of the most preferred classical image enhancement algorithms; with classical filters including Bilateral, Frost, Kuan, Lee, Mean, and Median Filters; and with deep learning networks including Learning Pixel-Distribution Prior with Wider Convolution for Image Denoising (WIN5-RB), Denoising Prior Driven Deep Neural Network for Image Restoration (DPDNN), and Fingerprint Image Denoising and Inpainting Using M-Net Based Convolutional Neural Networks (FPD-M-Net). Network performance is evaluated according to peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and runtime criteria and the proposed deep learning networks are shown to outperform the other networks.
Accurate Machine-Learning-Based classification of Leukemia from Blood Smear Images

Background Conventional identification of blood disorders based on visual inspection of blood smears through microscope is time consuming, error-prone and is limited by hematologist's physical acuity. Therefore, an automated optical image processing system is required to support the clinical decision-making. Materials and Methods Blood smear slides (n = 250) were prepared from clinical samples, imaged and analyzed in Jimma Medical Center, Hematology department. Samples were collected, analyzed and preserved from out and in-patients. The system was able to categorize four common types of leukemia's such as acute and chronic myeloid leukemia; and acute and chronic lymphoblastic leukemia, through a robust image segmentation protocol, followed by classification using the support vector machine. Results The system was able to classify leukemia types with an accuracy, sensitivity, specificity of 97.69%, 97.86% and 100%, respectively for the test datasets, and 97.5%, 98.55% and 100%, respectively, for the validation datasets. In addition, the system also showed an accuracy of 94.75% for the WBC counts that include both lymphocytes and monocytes. The computer-assisted diagnosis system took less than one minute for processing and assigning the leukemia types, compared to an average period of 30 minutes by unassisted manual approaches. Moreover, the automated system complements the healthcare workers’ in their efforts, by improving the accuracy rates in diagnosis from ∼70% to over 97%. Conclusion Importantly, our module is designed to assist the healthcare facilities in the rural areas of sub-Saharan Africa, equipped with fewer experienced medical experts, especially in screening patients for blood associated diseases including leukemia.
As If by Magic: Self-Supervised Training of Deep Despeckling Networks With MERLIN

Speckle fluctuations seriously limit the interpretability of synthetic aperture radar (SAR) images. Speckle reduction has thus been the subject of numerous works spanning at least four decades. Techniques based on deep neural networks have recently achieved a new level of performance in terms of SAR image restoration quality. Beyond the design of suitable network architectures or the selection of adequate loss functions, the construction of training sets is of uttermost importance. So far, most approaches have considered a supervised training strategy: the networks are trained to produce outputs as close as possible to speckle-free reference images. Speckle-free images are generally not available, which requires resorting to natural or optical images or the selection of stable areas in long time series to circumvent the lack of ground truth. Self-supervision, on the other hand, avoids the use of speckle-free images. We introduce a self-supervised strategy based on the separation of the real and imaginary parts of single-look complex (SLC) SAR images, called coMplex sElf-supeRvised despeckLINg (MERLIN), and show that it offers a straightforward way to train all kinds of deep despeckling networks. Networks trained with MERLIN take into account the spatial correlations due to the SAR transfer function specific to a given sensor and imaging mode. By requiring only a single image, and possibly exploiting large archives, MERLIN opens the door to hassle-free as well as large-scale training of despeckling networks. The code of the trained models is made freely available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://gitlab.telecom-paris.fr/RING/MERLIN</uri> .
Towards a Rigorous Evaluation of Time-Series Anomaly Detection

In recent years, proposed studies on time-series anomaly detection (TAD) report high F1 scores on benchmark TAD datasets, giving the impression of clear improvements in TAD. However, most studies apply a peculiar evaluation protocol called point adjustment (PA) before scoring. In this paper, we theoretically and experimentally reveal that the PA protocol has a great possibility of overestimating the detection performance; even a random anomaly score can easily turn into a state-of-the-art TAD method. Therefore, the comparison of TAD methods after applying the PA protocol can lead to misguided rankings. Furthermore, we question the potential of existing TAD methods by showing that an untrained model obtains comparable detection performance to the existing methods even when PA is forbidden. Based on our findings, we propose a new baseline and an evaluation protocol. We expect that our study will help a rigorous evaluation of TAD and lead to further improvement in future researches.
Regularizing Action Policies for Smooth Control with Reinforcement Learning

A critical problem with the practical utility of controllers trained with deep Reinforcement Learning (RL) is the notable lack of smoothness in the actions learned by the RL policies. This trend often presents itself in the form of control signal oscillation and can result in poor control, high power consumption, and undue system wear. We introduce Conditioning for Action Policy Smoothness (CAPS), an effective yet intuitive regularization on action policies, which offers consistent improvement in the smoothness of the learned state-to-action mappings of neural network controllers, reflected in the elimination of high-frequency components in the control signal. Tested on a real system, improvements in controller smoothness on a quadrotor drone resulted in an almost 80% reduction in power consumption while consistently training flight-worthy controllers. Project website: http://ai.bu.edu/caps