Reviewerly/exHarmony huggingface dataset:
Deep computational pathology in breast cancer

Deep Learning (DL) algorithms are a set of techniques that exploit large and/or complex real-world datasets for cross-domain and cross-discipline prediction and classification tasks. DL architectures excel in computer vision tasks, and in particular image processing and interpretation. This has prompted a wave of disruptingly innovative applications in medical imaging, where DL strategies have the potential to vastly outperform human experts. This is particularly relevant in the context of histopathology, where whole slide imaging (WSI) of stained tissue in conjuction with DL algorithms for their interpretation, selection and cancer staging are beginning to play an ever increasing role in supporting human operators in visual assessments. This has the potential to reduce everyday workload as well as to increase precision and reproducibility across observers, centers, staining techniques and even pathologies. In this paper we introduce the most common DL architectures used in image analysis, with a focus on histopathological image analysis in general and in breast histology in particular. We briefly review how, state-of-art DL architectures compare to human performance on across a number of critical tasks such as mitotic count, tubules analysis and nuclear pleomorphism analysis. Also, the development of DL algorithms specialized to pathology images have been enormously fueled by a number of world-wide challenges based on large, multicentric image databases which are now publicly available. In turn, this has allowed most recent efforts to shift more and more towards semi-supervised learning methods, which provide greater flexibility and applicability. We also review all major repositories of manually labelled pathology images in breast cancer and provide an in-depth discussion of the challenges specific to training DL architectures to interpret WSI data, as well as a review of the state-of-the-art methods for interpretation of images generated from immunohistochemical analysis of breast lesions. We finally discuss the future challenges and opportunities which the adoption of DL paradigms is most likely to pose in the field of pathology for breast cancer detection, diagnosis, staging and prognosis. This review is intended as a comprehensive stepping stone into the field of modern computational pathology for a transdisciplinary readership across technical and medical disciplines.
Concepts of variation control systems

Version control systems are an integral part of today’s software engineering. They facilitate the collaborative management of revisions (sequential versions) and variants (concurrent versions) of software systems under development. Typical version control systems maintain revisions of files and variants of whole software systems. Variants are supported via branching or forking mechanisms that conceptually clone whole systems in a coarse-grained way. Unfortunately, such cloning leads to high maintenance efforts. To avoid these disadvantages and support fine-grained variation, developers need to employ custom configuration mechanisms, which leads to a misappropriation of tools and undesired context switches. Addressing this trade-off, a number of variation control systems has been conceived, providing a richer set of capabilities for handling variants. Variation control systems decompose a software system into finer-grained variable entities and offer high-level metaphors to automatically manage this variability. In this paper, we classify and compare variation control systems and illustrate their core concepts and characteristics. All investigated variation control systems offer an iterative (checkout–modify–commit) workflow, but there are essential differences affecting developers. We highlight challenges and discuss research perspectives for developing the next generation of version and variation control systems.
Multi-Scale Interaction for Real-Time LiDAR Data Segmentation on an Embedded Platform

Real-time semantic segmentation of LiDAR data is crucial for autonomously driving vehicles and robots, which are usually equipped with an embedded platform and have limited computational resources. Approaches that operate directly on the point cloud use complex spatial aggregation operations, which are very expensive and difficult to deploy on embedded platforms. As an alternative, projection-based methods are more efficient and can run on embedded hardware. However, current projection-based methods either have a low accuracy or require millions of parameters. In this letter, we therefore propose a projection-based method, called Multi-scale Interaction Network (MINet), which is very efficient and accurate. The network uses multiple paths with different scales and balances the computational resources between the scales. Additional dense interactions between the scales avoid redundant computations and make the network highly efficient. The proposed network outperforms point-based, image-based, and projection-based methods in terms of accuracy, number of parameters, and runtime. Moreover, the network processes more than 24 scans, captured by a high-resolution LiDAR sensor with 64 beams, per second on an embedded platform, which is higher than the framerate of the sensor. The network is therefore suitable for robotics applications.
Using Projection-Based Clustering to Find Distance- and Density-Based Clusters in High-Dimensional Data

Abstract For high-dimensional datasets in which clusters are formed by both distance and density structures (DDS), many clustering algorithms fail to identify these clusters correctly. This is demonstrated for 32 clustering algorithms using a suite of datasets which deliberately pose complex DDS challenges for clustering. In order to improve the structure finding and clustering in high-dimensional DDS datasets, projection-based clustering (PBC) is introduced. The coexistence of projection and clustering allows to explore DDS through a topographic map. This enables to estimate, first, if any cluster tendency exists and, second, the estimation of the number of clusters. A comparison showed that PBC is always able to find the correct cluster structure, while the performance of the best of the 32 clustering algorithms varies depending on the dataset.
A Dual Heterogeneous Graph Attention Network to Improve Long-Tail Performance for Shop Search in E-Commerce

Shop search has become an increasingly important service provided by Taobao, the China's largest e-commerce platform. By using shop search, a user can easily identify the desired shop that provides a full-scale of relevant items matching his information need. With the tremendous growth of users and shops, shop search faces several unique challenging problems: 1) many shop names do not fully express what they sell, i.e., the semantic gap between user query and shop name; 2) due to the lack of user interactions, it is difficult to deliver a good search result for the long-tail queries and retrieve long-tail shops that are highly relevant to a query.
DNA encoding for RGB image encryption with memristor based neuron model and chaos phenomenon

In this paper, a deoxyribonucleic acid (DNA) encoding algorithm with memristor based chaotic system is proposed. The study includes four different topics together which are neuron model, circuit design with memristor, chaos theory and DNA encoding based cryptology. Operational Transconductance Amplifier (OTA) based memristive Hindmarsh-Rose neuron model using three-dimensional differential equations is employed in chaotic circuit structure. Cryptographic keys obtained from suggested three-dimensional continuous time chaotic system are utilized in order to encrypt and decrypt the image. Diffusion and confusion methods are essential for cryptology. Circular shift algorithm is used as a diffusion method and logical XOR operation is preferred as a confusion method on image. Security analyses such as histogram, correlation coefficient of two adjacent pixels, information entropy, differential attack, the secret key sensitivity and the initial condition sensitivity analyses have been performed to show the performance of the encryption scheme. Analyses results reveal that the proposed encryption is robust against statistical and differential attacks and this chaotic system also exhibits the behaviour of sensitive dependence on the secret key and the initial condition. The chaotic behaviour of the suggested system has been given performing numerical analyses in MATLAB environment and doing the analog circuit analyses with LTspice simulation software.
Reimagining (Women’s) Health

An ever-increasing body of work within HCI investigates questions of around “Women’s Health” with the aim to disrupt the status quo of defaulting to an implicit norm of cis-male bodies. This laudable and feminist project has the potential to drastically improve the inclusivity and availability of health care. To explore how this research attends to gender, embodiment and identity, we conducted a critical discourse analysis of 17 publications explicitly positioning themselves as works concerned with “Women’s Health”. We find essentialised articulations of embodiment and gender, though little discussion on the intersections of race, class, sexuality and cultural contexts. Through two speculative designs, we illustrate potential responses to our analysis: The Shadow Zine, a reflection of self and the Compass, a token for community care. 1 Our work provides an opportunity to develop a broader frame of gender and health, one that centers (gendered) marginalised health by attending to the power structures of existing medical practices and norms.
MultiSage

Graph convolutional networks (GCNs) are a powerful class of graph neural networks. Trained in a semi-supervised end-to-end fashion, GCNs can learn to integrate node features and graph structures to generate high-quality embeddings that can be used for various downstream tasks like search and recommendation. However, existing GCNs mostly work on homogeneous graphs and consider a single embedding for each node, which do not sufficiently model the multi-facet nature and complex interaction of nodes in real-world networks. Here, we present a contextualized GCN engine by modeling the multipartite networks of target nodes and their intermediatecontext nodes that specify the contexts of their interactions. Towards the neighborhood aggregation process, we devise a contextual masking operation at the feature level and a contextual attention mechanism at the node level to achieve interaction contextualization by treating neighboring target nodes based on intermediate context nodes. Consequently, we compute multiple embeddings for target nodes that capture their diverse facets and different interactions during graph convolution, which is useful for fine-grained downstream applications. To enable efficient web-scale training, we build a parallel random walk engine to pre-sample contextualized neighbors, and a Hadoop2-based data provider pipeline to pre-join training data, dynamically reduce multi-GPU training time, and avoid high memory cost. Extensive experiments on the bipartite Pinterest graph and tripartite OAG graph corroborate the advantage of the proposed system.
HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units

Deep learning models have achieved expert-level performance in healthcare with an exclusive focus on training accurate models. However, in many clinical environments such as intensive care unit (ICU), real-time model serving is equally if not more important than accuracy, because in ICU patient care is simultaneously more urgent and more expensive. Clinical decisions and their timeliness, therefore, directly affect both the patient outcome and the cost of care. To make timely decisions, we argue the underlying serving system must be latency-aware. To compound the challenge, health analytic applications often require a combination of models instead of a single model, to better specialize individual models for different targets, multi-modal data, different prediction windows, and potentially personalized predictions. To address these challenges, we propose HOLMES---an online model ensemble serving framework for healthcare applications. HOLMES dynamically identifies the best performing set of models to ensemble for highest accuracy, while also satisfying sub-second latency constraints on end-to-end prediction. We demonstrate that HOLMES is able to navigate the accuracy/latency tradeoff efficiently, compose the ensemble, and serve the model ensemble pipeline, scaling to simultaneously streaming data from 100 patients, each producing waveform data at 250~Hz. HOLMES outperforms the conventional offline batch-processed inference for the same clinical task in terms of accuracy and latency (by order of magnitude). HOLMES is tested on risk prediction task on pediatric cardio ICU data with above 95% prediction accuracy and sub-second latency on 64-bed simulation.
Embodied Navigation in Immersive Abstract Data Visualization: Is Overview+Detail or Zooming Better for 3D Scatterplots?

Abstract data has no natural scale and so interactive data visualizations must provide techniques to allow the user to choose their viewpoint and scale. Such techniques are well established in desktop visualization tools. The two most common techniques are zoom+pan and overview+detail. However, how best to enable the analyst to navigate and view abstract data at different levels of scale in immersive environments has not previously been studied. We report the findings of the first systematic study of immersive navigation techniques for 3D scatterplots. We tested four conditions that represent our best attempt to adapt standard 2D navigation techniques to data visualization in an immersive environment while still providing standard immersive navigation techniques through physical movement and teleportation. We compared room-sized visualization versus a zooming interface, each with and without an overview. We find significant differences in participants' response times and accuracy for a number of standard visual analysis tasks. Both zoom and overview provide benefits over standard locomotion support alone (i.e., physical movement and pointer teleportation). However, which variation is superior, depends on the task. We obtain a more nuanced understanding of the results by analyzing them in terms of a time-cost model for the different components of navigation: way-finding, travel, number of travel steps, and context switching.
Online feature selection system for big data classification based on multi-objective automated negotiation

Feature Selection (FS) plays an important role in learning and classification tasks. Its objective is to select the relevant and non-redundant features. Considering the huge number of features in real-world applications, FS methods using batch learning technique cannot resolve big data problems especially when data arrive sequentially. In this paper, we proposed an online feature selection system which resolves this problem. The proposed OFS system called MOANOFS (Multi-Objective Automated Negotiation based Online Feature Selection) explore the recent advances of online machine learning techniques and a conflict resolution technique (Automated Negotiation) for the purpose of enhancing the classification performance of ultra-high dimensional databases. MOANOFS uses two decision levels. In the first level, we decided which k(s) among the learners (or OFS methods) are the trustful ones (with high confidence or trust value). These elected k learners would participate in the second level where we integrated our proposed Multilateral Automated Negotiation based OFS (MANOFS) method. This would enable us to finally decide which features are the most relevant. We showed that MOANOFS system achieves high accuracy with several real text classification datasets as 20Newsgroups, RCV1.
Optimal discriminative feature and dictionary learning for image set classification

Image set classification has drawn increasing attention and it has been widely applied to many real-life domains. Due to the existence of multiple images in a set, which contain various view appearance changes, image set classification is a rather challenging task. One potential solution is to learn powerful representations from multiple images to decrease the intra-class diversity and enlarge the inter-class separation. In this paper, we propose an optimal discriminative feature and dictionary learning (ODFDL) method, which attempts to learn a feature mapping matrix and a dictionary such that in the mapped feature space the inter-class sparse reconstruction error of data is maximized and the intra-class sparse reconstruction error is minimized. This learning strategy enforces the learned sparse representations from image sets have large inter-class separation and small intra-class scatter. Furthermore, to better exploit the non-linear information of data from different image sets, we also present two non-linear ODFDL methods, termed Kernel-ODFDL and Hierarchy-ODFDL to further improve the classification performance. Experiments on five commonly used image sets exhibit that our approaches are comparable with many state-of-the-arts.
A Survey on Human Activity Recognition and Classification

Activity Recognition and Classification is one of the most significant issues in the computer vision field. Identifying and recognizing actions or activities that are performed by a person is a primary key goal of intelligent video systems. Human activity is used in a variety of application areas, from human-computer interaction to surveillance, security, and health monitoring systems. Despite ongoing efforts in the field, activity recognition is still a difficult task in an unrestricted environment and faces many challenges. In this paper, we are focusing on some recent research papers on various methods of activity recognition. The work includes three popular methods of recognizing activity, namely vision-based (using pose estimation), wearable devices, and smartphone sensors. We will also discuss some pros and cons of the above technologies and take a view on a brief comparison between their accuracy. The findings will also show how the vision-based approach is becoming a popular approach for HAR research these days.
Improved Training Speed, Accuracy, and Data Utilization Through Loss Function Optimization

As the complexity of neural network models has grown, it has become increasingly important to optimize their design automatically through metalearning. Methods for discovering hyperparameters, topologies, and learning rate schedules have lead to significant increases in performance. This paper shows that loss functions can be optimized with metalearning as well, and result in similar improvements. The method, Genetic Lossfunction Optimization (GLO), discovers loss functions de novo, and optimizes them for a target task. Leveraging techniques from genetic programming, GLO builds loss functions hierarchically from a set of operators and leaf nodes. These functions are repeatedly recombined and mutated to find an optimal structure, and then a covariance-matrix adaptation evolutionary strategy (CMA-ES) is used to find optimal coefficients. Networks trained with GLO loss functions are found to outperform the standard cross-entropy loss on standard image classification tasks. Training with these new loss functions requires fewer steps, results in lower test error, and allows for smaller datasets to be used. Loss function optimization thus provides a new dimension of metalearning, and constitutes an important step towards AutoML.
Fuzzy-Based Fault-Tolerant Control for Omnidirectional Mobile Robot

The motion-planning problem is well known in robotics; it aims to find a free-obstacle path from a starting point to a destination. To make use of actuation generosity and the fuzzy fast response behavior compared to other non-linear controllers, a fuzzy-based fault-tolerant control for an omnidirectional mobile robot with four Mecanum wheels is proposed. The objective is to provide the robot with an online scheme to control the robot motion while moving toward the final destination with avoiding obstacles in its environment and providing an adaptive solution for a combination of one or combination of the wheel’s faults. The faults happen when the wheel does not receive the control command signal from the controller; in this case, the robot can rotate freely due to the interaction with the ground. The principle of fuzzy-based control proposed by Sugeno is used to develop the motion controller. The motion controller consists of two main controllers: the Run-To-Goal, and the obstacle-avoidance controller. The outputs of these two controllers are superposed to get the net potential force on the robot. By its simplicity, the fuzzy controller can be suitable for online applications (online path planning in our case). To the best of our knowledge, this is the first fuzzy-based fault-tolerant controller for an omnidirectional robot. The proposed controller is tested by a set of simulation scenarios to check the proposed fuzzy tolerant control. Kuka OmniRob is used as an example of the omnidirectional robot in these simulation runs. Matlab is used to build the fuzzy-based fault-tolerant control, and the 3D simulation is developed on the CoppeliaSim software. We examine five distinct scenarios, each one with a different fault state. In all scenarios, the proposed algorithm could control the robot to reach its final destination with the absence and presence of an obstacle in the workspace, despite actuator faults, without crossing the workspace boundaries.
Automated design for recognition of blood cells diseases from hematopathology using classical features selection and ELM

Abstract In the human immune system, the white blood cells (WBC) creates bone and lymphoid masses. These cells defend the human body toward several infections, such as fungi and bacteria. The popular WBC types are Eosinophils, Lymphocytes, Neutrophils, and Monocytes, which are manually diagnosis by the experts. The manual diagnosis process is complicated and time‐consuming; therefore, an automated system is required to classify these WBC. In this article, a new method is presented for WBC classification using feature selection and extreme learning machine (ELM). At the very first step, data augmentation is performed to increases the number of images and then implement a new contrast stretching technique name pixel stretch (PS). In the next step, color and gray level size zone matrix (GLSZM) features are calculated from PS images and fused in one vector based on the level of high similarity. However, few redundant features are also included that affect the classification performance. For handling this problem, a maximum relevance probability (MRP) based feature selection technique is implemented. The best‐selected features computed from a fitness function are ELM in this work. All maximum relevance features are put to ELM, and this process is continued until the error rate is minimized. In the end, the final selected features are classified through Cubic SVM. For validation of the proposed method, LISC and Dhruv datasets are used, and it achieved the highest accuracy of 96.60%. From the results, it is clearly shown that the proposed method results are improved as compared to other implemented techniques.