Reviewerly/exHarmony huggingface dataset:
Deep learning based digital cell profiles for risk stratification of urine cytology images

Abstract Urine cytology is a test for the detection of high‐grade bladder cancer. In clinical practice, the pathologist would manually scan the sample under the microscope to locate atypical and malignant cells. They would assess the morphology of these cells to make a diagnosis. Accurate identification of atypical and malignant cells in urine cytology is a challenging task and is an essential part of identifying different diagnosis with low‐risk and high‐risk malignancy. Computer‐assisted identification of malignancy in urine cytology can be complementary to the clinicians for treatment management and in providing advice for carrying out further tests. In this study, we presented a method for identifying atypical and malignant cells followed by their profiling to predict the risk of diagnosis automatically. For cell detection and classification, we employed two different deep learning‐based approaches. Based on the best performing network predictions at the cell level, we identified low‐risk and high‐risk cases using the count of atypical cells and the total count of atypical and malignant cells. The area under the receiver operating characteristic (ROC) curve shows that a total count of atypical and malignant cells is comparably better at diagnosis as compared to the count of malignant cells only. We obtained area under the ROC curve with the count of malignant cells and the total count of atypical and malignant cells as 0.81 and 0.83, respectively. Our experiments also demonstrate that the digital risk could be a better predictor of the final histopathology‐based diagnosis. We also analyzed the variability in annotations at both cell and whole slide image level and also explored the possible inherent rationales behind this variability.
Hold the Door! Fingerprinting Your Car Key to Prevent Keyless Entry Car Theft

Recently, the traditional way to unlock car doors has been replaced with a keyless entry system which proves more convenient for automobile owners. When a driver with a key fob is in the vicinity of the vehicle, doors automatically unlock on user command. However, unfortunately, it has been shown that these keyless entry systems are vulnerable to signal relaying attacks. While it is evident that automobile manufacturers incorporate preventative methods to secure these keyless entry systems, they continue to be vulnerable to a range of attacks. Relayed signals result in valid packets that are verified as legitimate, and this makes it is difficult to distinguish a legitimate door unlock request from a malicious signal. In response to this vulnerability, this paper presents an RF fingerprinting method (coined HOld the DOoR, HODOR) to detect attacks on keyless entry systems the first attempt to exploit the RF fingerprint technique in the automotive domain. HODOR is designed as a sub authentication method that supports existing authentication systems for keyless entry systems and does not require any modification of the main system to perform. Through a series of experiments, the results demonstrate that HODOR competently and reliably detects attacks on keyless entry systems. HODOR achieves both an average false positive rate (FPR) of 0.27 percent with a false negative rate (FNR) of 0 percent for the detection of simulated attacks, corresponding to current research on keyless entry car theft.
Lower bounds on circuit depth of the quantum approximate optimization algorithm

The quantum approximate optimization algorithm (QAOA) is a method of approximately solving combinatorial optimization problems. While QAOA is developed to solve a broad class of combinatorial optimization problems, it is not clear which classes of problems are best suited for it. One factor in demonstrating quantum advantage is the relationship between a problem instance and the circuit depth required to implement the QAOA method. As errors in noisy intermediate-scale quantum (NISQ) devices increase exponentially with circuit depth, identifying lower bounds on circuit depth can provide insights into when quantum advantage could be feasible. Here, we identify how the structure of problem instances can be used to identify lower bounds for circuit depth for each iteration of QAOA and examine the relationship between problem structure and the circuit depth for a variety of combinatorial optimization problems including MaxCut and MaxIndSet. Specifically, we show how to derive a graph, G, that describes a general combinatorial optimization problem and show that the depth of circuit is at least the chromatic index of G. By looking at the scaling of circuit depth, we argue that MaxCut, MaxIndSet, and some instances of vertex covering and Boolean satisfiability problems are suitable for QAOA approaches while knapsack and traveling salesperson problems are not.
A Survey of Crowdsourcing in Medical Image Analysis

Rapid advances in image processing capabilities have been seen across many domains, fostered by the application of machine learning algorithms to "big-data". However, within the realm of medical image analysis, advances have been curtailed, in part, due to the limited availability of large-scale, well-annotated datasets. One of the main reasons for this is the high cost often associated with producing large amounts of high-quality meta-data. Recently, there has been growing interest in the application of crowdsourcing for this purpose; a technique that has proven effective for creating large-scale datasets across a range of disciplines, from computer vision to astrophysics. Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis. In this survey, we review studies applying crowdsourcing to the analysis of medical images, published prior to July 2018. We identify common approaches, challenges and considerations, providing guidance of utility to researchers adopting this approach. Finally, we discuss future opportunities for development within this emerging domain.
Gaussian process learning via Fisher scoring of Vecchia’s approximation

We derive a single-pass algorithm for computing the gradient and Fisher information of Vecchia’s Gaussian process loglikelihood approximation, which provides a computationally efficient means for applying the Fisher scoring algorithm for maximizing the loglikelihood. The advantages of the optimization techniques are demonstrated in numerical examples and in an application to Argo ocean temperature data. The new methods find the maximum likelihood estimates much faster and more reliably than an optimization method that uses only function evaluations, especially when the covariance function has many parameters. This allows practitioners to fit nonstationary models to large spatial and spatial–temporal datasets.
A Surface Geometry Model for LiDAR Depth Completion

LiDAR depth completion is a task that predicts depth values for every pixel on the corresponding camera frame, although only sparse LiDAR points are available. Most of the existing state-of-the-art solutions are based on deep neural networks, which need a large amount of data and heavy computations for training the models. In this letter, a novel non-learning depth completion method is proposed by exploiting the local surface geometry that is enhanced by an outlier removal algorithm. The proposed surface geometry model is inspired by the observation that most pixels with unknown depth have a nearby LiDAR point. Therefore, it is assumed those pixels share the same surface with the nearest LiDAR point, and their respective depth can be estimated as the nearest LiDAR depth value plus a residual error. The residual error is calculated by using a derived equation with several physical parameters as input, including the known camera intrinsic parameters, estimated normal vector, and offset distance on the image plane. The proposed method is further enhanced by an outlier removal algorithm that is designed to remove incorrectly mapped LiDAR points from occluded regions. On KITTI dataset, the proposed solution achieves the best error performance among all existing non-learning methods and is comparable to the best self-supervised learning method and some supervised learning methods. Moreover, since outlier points from occluded regions is a commonly existing problem, the proposed outlier removal algorithm is a general preprocessing step that is applicable to many robotic systems with both camera and LiDAR sensors. The code has been published at https://github.com/placeforyiming/RAL_Non-Learning_DepthCompletion.
Sarcasm Detection Using Deep Learning With Contextual Features

Our work focuses on detecting sarcasm in tweets using deep learning extracted features combined with contextual handcrafted features. A feature set is extracted from a Convolutional Neural Network (CNN) architecture before it is combined with carefully handcrafted feature sets. These handcrafted feature sets are created based on their respective contextual explanations. Each feature sets are specifically designed for the sole task of sarcasm detection. The objective is to find the most optimal features. Some sets are good to go even when it is used in independence. Other sets are not really significant without any combination. The results of the experiments are positive in terms of Accuracy, Precision, Recall and F1-measure. The combination of features are classified using a few machine learning techniques for comparison purposes. Logistic Regression is found to be the best classification algorithm for this task. Furthermore, result comparison to recent works and the performance of each feature set are also shown as additional information.
ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating Prediction

Sentiment analysis has attracted increasing attention in e-commerce.The sentiment polarities underlying user reviews are of great value for business intelligence.Aspect category sentiment analysis (ACSA) and review rating prediction (RP) are two essential tasks to detect the fine-to-coarse sentiment polarities.ACSA and RP are highly correlated and usually employed jointly in real-world e-commerce scenarios.While most public datasets are constructed for ACSA and RP separately, which may limit the further exploitations of both tasks.To address the problem and advance related researches, we present a large-scale Chinese restaurant review dataset ASAP including 46, 730 genuine reviews from a leading online-to-offline (O2O) e-commerce platform in China.Besides a 5-star scale rating, each review is manually annotated according to its sentiment polarities towards 18 pre-defined aspect categories.We hope the release of the dataset could shed some light on the field of sentiment analysis.Moreover, we propose an intuitive yet effective joint model for ACSA and RP.Experimental results demonstrate that the joint model outperforms state-of-the-art baselines on both tasks.
Robust Extended Kalman Filtering for Systems With Measurement Outliers

Outliers can be caused by sensor errors, model uncertainties, changes in the ambient environment, data loss, or malicious cyberattacks to contaminate the measurement process of many nonlinear dynamic systems. When the extended Kalman filter (EKF) is applied to such systems for state estimation, the outliers can seriously reduce the estimation accuracy. This brief proposes an innovation saturation mechanism to make the EKF robust against outliers. This mechanism applies a saturation function to the innovation process that the EKF leverages to correct the state estimation. As such, when outliers occur, the distorted innovation is saturated, so as not to undermine the state estimation. The mechanism features an adaptive adjustment of the saturation bounds. The design leads to the development of robust EKF approaches for both continuous- and discrete-time systems. The stability of the proposed approaches when applied to linear systems is characterized, showing that they are capable of performing bounded-error estimation in the presence of bounded outlier disturbances in this case. A simulation study about mobile robot localization is presented to illustrate the efficacy of the proposed design. Compared to existing methods, the proposed approaches can effectively reject outliers of various magnitudes, types, and durations, at significant computational efficiency and without requiring additional measurement redundancy.
On the philosophical, cognitive and mathematical foundations of symbiotic autonomous systems

Symbiotic autonomous systems (SAS) are advanced intelligent and cognitive systems that exhibit autonomous collective intelligence enabled by coherent symbiosis of human–machine interactions in hybrid societies. Basic research in the emerging field of SAS has triggered advanced general-AI technologies that either function without human intervention or synergize humans and intelligent machines in coherent cognitive systems. This work presents a theoretical framework of SAS underpinned by the latest advances in intelligence, cognition, computer, and system sciences. SAS are characterized by the composition of autonomous and symbiotic systems that adopt bio-brain-social-inspired and heterogeneously synergized structures and autonomous behaviours. This paper explores the cognitive and mathematical foundations of SAS. The challenges to seamless human–machine interactions in a hybrid environment are addressed. SAS-based collective intelligence is explored in order to augment human capability by autonomous machine intelligence towards the next generation of general AI, cognitive computers, and trustworthy mission-critical intelligent systems. Emerging paradigms and engineering applications of SAS are elaborated via autonomous knowledge learning systems that symbiotically work between humans and cognitive robots. This article is part of the theme issue ‘Towards symbiotic autonomous systems'.
An Energy-Efficient Hybrid Scheduling Algorithm for Task Scheduling in the Cloud Computing Environments

The main intent of the cloud computing to provide utilities to the demands of the users that are booming day by day. To meet the requirements, existing scheduling algorithms focus on the improving the performance and neglecting the energy consumed to fulfill those demands. Hence, we propose a new Hybrid Scheduling Algorithm (HS) which is based on the Genetic Algorithm (GA) and Flower Pollination based Algorithm (FPA) for cloud environments. The proposed scheduling algorithm has surpassed in terms of performance across various parameters, i.e. completion time, resource utilization, cost of computation, and energy consumption for both cloud environments than the existing scheduling algorithms (GA and FPA). The simulation results revealed that HS has demonstrated maximum resource utilization with minimum energy consumption in less completion time for the execution of the tasks as compared to the existing scheduling algorithms in both environments. The simulation results have shown that HS has utilization of the resources, 36% better than GA and 16% better than FPA in homogeneous environment whereas in heterogenous environment, HS has performed 12% better than GA and 3.8% better than FPA. The performance of HS has an improvement of 2.6% from FPA and 6.9% from the GA for completion time in homogeneous environment whereas the completion time of the HS is reduced by 17.8% from FPA and 33.7% from GA in heterogeneous environment. For energy consumption, HS has improved 22% than FPA and 11% from GA in the homogeneous environment and HS is 4% better than FPA and 14% from GA in heterogeneous environment.
A new color image encryption technique using DNA computing and Chaos-based substitution box

Abstract In many cases, images contain sensitive information and patterns that require secure processing to avoid risk. It can be accessed by unauthorized users who can illegally exploit them to threaten the safety of people’s life and property. Protecting the privacies of the images has quickly become one of the biggest obstacles that prevent further exploration of image data. In this paper, we propose a novel privacy-preserving scheme to protect sensitive information within images. The proposed approach combines deoxyribonucleic acid (DNA) sequencing code, Arnold transformation (AT), and a chaotic dynamical system to construct an initial S-box. Various tests have been conducted to validate the randomness of this newly constructed S-box. These tests include National Institute of Standards and Technology (NIST) analysis, histogram analysis (HA), nonlinearity analysis (NL), strict avalanche criterion (SAC), bit independence criterion (BIC), bit independence criterion strict avalanche criterion (BIC-SAC), bit independence criterion nonlinearity (BIC-NL), equiprobable input/output XOR distribution, and linear approximation probability (LP). The proposed scheme possesses higher security wit NL = 103.75, SAC ≈ 0.5 and LP = 0.1560. Other tests such as BIC-SAC and BIC-NL calculated values are 0.4960 and 112.35, respectively. The results show that the proposed scheme has a strong ability to resist many attacks. Furthermore, the achieved results are compared to existing state-of-the-art methods. The comparison results further demonstrate the effectiveness of the proposed algorithm.
Feature Extraction of White Blood Cells Using CMYK-Moment Localization and Deep Learning in Acute Myeloid Leukemia Blood Smear Microscopic Images

Artificial intelligence has revolutionized medical diagnosis, particularly for cancers. Acute myeloid leukemia (AML) diagnosis is a tedious protocol that is prone to human and machine errors. In several instances, it is difficult to make an accurate final decision even after careful examination by an experienced pathologist. However, computer-aided diagnosis (CAD) can help reduce the errors and time associated with AML diagnosis. White Blood Cells (WBC) detection is a critical step in AML diagnosis, and deep learning is considered a state-of-the-art approach for WBC detection. However, the accuracy of WBC detection is strongly associated with the quality of the extracted features used in training the pixel-wise classification models. CAD depends on studying the different patterns of changes associated with WBC counts and features. In this study, a new hybrid feature extraction method was developed using image processing and deep learning methods. The proposed method consists of two steps: 1) a region of interest (ROI) is extracted using the CMYK-moment localization method and 2) deep learning-based features are extracted using a CNN-based feature fusion method. Several classification algorithms are used to evaluate the significance of the extracted features. The proposed feature extraction method was evaluated using an external dataset and benchmarked against other feature extraction methods. The proposed method achieved excellent performance, generalization, and stability using all the classifiers, with overall classification accuracies of 97.57% and 96.41% using the primary and secondary datasets, respectively. This method has opened a new alternative to improve the detection of WBCs, which could lead to a better diagnosis of AML.
Tensorized Bipartite Graph Learning for Multi-View Clustering

Despite the impressive clustering performance and efficiency in characterizing both the relationship between the data and cluster structure, most existing graph-based multi-view clustering methods still have the following drawbacks. They suffer from the expensive time burden due to both the construction of graphs and eigen-decomposition of Laplacian matrix. Moreover, none of them simultaneously considers the similarity of inter-view and similarity of intra-view. In this article, we propose a variance-based de-correlation anchor selection strategy for bipartite construction. The selected anchors not only cover the whole classes but also characterize the intrinsic structure of data. Following that, we present a <i>tensorized bipartite graph learning for multi-view clustering</i> (TBGL). Specifically, TBGL exploits the similarity of inter-view by minimizing the tensor Schatten <i>p</i>-norm, which well exploits both the spatial structure and complementary information embedded in the bipartite graphs of views. We exploit the similarity of intra-view by using the <inline-formula><tex-math notation="LaTeX">$\ell _{\textrm {1,2}}$</tex-math></inline-formula>-norm minimization regularization and connectivity constraint on each bipartite graph. So the learned graph not only well encodes discriminative information but also has the exact connected components which directly indicates the clusters of data. Moreover, we solve TBGL by an efficient algorithm which is time-economical and has good convergence. Extensive experimental results demonstrate that TBGL is superior to the state-of-the-art methods. Codes and datasets are available: <uri>https://github.com/xdweixia/TBGL-MVC</uri>.
One model is all you need: Multi-task learning enables simultaneous histology image segmentation and classification

The recent surge in performance for image analysis of digitised pathology slides can largely be attributed to the advances in deep learning. Deep models can be used to initially localise various structures in the tissue and hence facilitate the extraction of interpretable features for biomarker discovery. However, these models are typically trained for a single task and therefore scale poorly as we wish to adapt the model for an increasing number of different tasks. Also, supervised deep learning models are very data hungry and therefore rely on large amounts of training data to perform well. In this paper, we present a multi-task learning approach for segmentation and classification of nuclei, glands, lumina and different tissue regions that leverages data from multiple independent data sources. While ensuring that our tasks are aligned by the same tissue type and resolution, we enable meaningful simultaneous prediction with a single network. As a result of feature sharing, we also show that the learned representation can be used to improve the performance of additional tasks via transfer learning, including nuclear classification and signet ring cell detection. As part of this work, we train our developed Cerberus model on a huge amount of data, consisting of over 600 thousand objects for segmentation and 440 thousand patches for classification. We use our approach to process 599 colorectal whole-slide images from TCGA, where we localise 377 million, 900 thousand and 2.1 million nuclei, glands and lumina respectively. We make this resource available to remove a major barrier in the development of explainable models for computational pathology.
CAVER: Cross-Modal View-Mixed Transformer for Bi-Modal Salient Object Detection

Most of the existing bi-modal (RGB-D and RGB-T) salient object detection methods utilize the convolution operation and construct complex interweave fusion structures to achieve cross-modal information integration. The inherent local connectivity of the convolution operation constrains the performance of the convolution-based methods to a ceiling. In this work, we rethink these tasks from the perspective of global information alignment and transformation. Specifically, the proposed cross-modal view-mixed transformer (CAVER) cascades several cross-modal integration units to construct a top-down transformer-based information propagation path. CAVER treats the multi-scale and multi-modal feature integration as a sequence-to-sequence context propagation and update process built on a novel view-mixed attention mechanism. Besides, considering the quadratic complexity w.r.t. the number of input tokens, we design a parameter-free patch-wise token re-embedding strategy to simplify operations. Extensive experimental results on RGB-D and RGB-T SOD datasets demonstrate that such a simple two-stream encoder-decoder framework can surpass recent state-of-the-art methods when it is equipped with the proposed components.