Reviewerly/exHarmony huggingface dataset:
Multiclass classification of breast cancer histopathology images using multilevel features of deep convolutional neural network

Breast cancer is a common malignancy and a leading cause of cancer-related deaths in women worldwide. Its early diagnosis can significantly reduce the morbidity and mortality rates in women. To this end, histopathological diagnosis is usually followed as the gold standard approach. However, this process is tedious, labor-intensive, and may be subject to inter-reader variability. Accordingly, an automatic diagnostic system can assist to improve the quality of diagnosis. This paper presents a deep learning approach to automatically classify hematoxylin-eosin-stained breast cancer microscopy images into normal tissue, benign lesion, in situ carcinoma, and invasive carcinoma using our collected dataset. Our proposed model exploited six intermediate layers of the Xception (Extreme Inception) network to retrieve robust and abstract features from input images. First, we optimized the proposed model on the original (unnormalized) dataset using 5-fold cross-validation. Then, we investigated its performance on four normalized datasets resulting from Reinhard, Ruifrok, Macenko, and Vahadane stain normalization. For original images, our proposed framework yielded an accuracy of 98% along with a kappa score of 0.969. Also, it achieved an average AUC-ROC score of 0.998 as well as a mean AUC-PR value of 0.995. Specifically, for in situ carcinoma and invasive carcinoma, it offered sensitivity of 96% and 99%, respectively. For normalized images, the proposed architecture performed better for Makenko normalization compared to the other three techniques. In this case, the proposed model achieved an accuracy of 97.79% together with a kappa score of 0.965. Also, it attained an average AUC-ROC score of 0.997 and a mean AUC-PR value of 0.991. Especially, for in situ carcinoma and invasive carcinoma, it offered sensitivity of 96% and 99%, respectively. These results demonstrate that our proposed model outperformed the baseline AlexNet as well as state-of-the-art VGG16, VGG19, Inception-v3, and Xception models with their default settings. Furthermore, it can be inferred that although stain normalization techniques offered competitive performance, they could not surpass the results of the original dataset.
Multimodal data fusion for cancer biomarker discovery with deep learning

Technological advances have made it possible to study a patient from multiple angles with high-dimensional, high-throughput multiscale biomedical data. In oncology, massive amounts of data are being generated, ranging from molecular, histopathology, radiology to clinical records. The introduction of deep learning has greatly advanced the analysis of biomedical data. However, most approaches focus on single data modalities, leading to slow progress in methods to integrate complementary data types. Development of effective multimodal fusion approaches is becoming increasingly important as a single modality might not be consistent and sufficient to capture the heterogeneity of complex diseases to tailor medical care and improve personalized medicine. Many initiatives now focus on integrating these disparate modalities to unravel the biological processes involved in multifactorial diseases such as cancer. However, many obstacles remain, including lack of usable data as well as methods for clinical validation and interpretation. Here, we cover these current challenges and reflect on opportunities through deep learning to tackle data sparsity and scarcity, multimodal interpretability and standardization of datasets. Cancer diagnosis and treatment decisions often focus on one data source. Steyaert and colleagues discuss the current status and challenges of data fusion, including electronic health records, molecular data, digital pathology and radiographic images, in cancer research and translational development.
Exploring Tactile Movies: An Initial Tactile Glove Design and Concept Evaluation

Viewer expectations for rich, immersive interaction with multimedia are driving new technologies- such as high-definition 3D displays and multichannel audio systems-to greater levels of sophistication. While researchers continue to study ways to develop new capabilities for visual and audio sensory channels, improvements in haptic channels could lead to even more immersive experiences.
Quantum key distribution with setting-choice-independently correlated light sources

Despite the enormous theoretical and experimental progress made so far in quantum key distribution (QKD), the security of most existing QKD implementations is not rigorously established yet. A critical obstacle is that almost all existing security proofs make ideal assumptions on the QKD devices. Problematically, such assumptions are hard to satisfy in the experiments, and therefore it is not obvious how to apply such security proofs to practical QKD systems. Fortunately, any imperfections and security-loopholes in the measurement devices can be perfectly closed by measurement-device-independent QKD (MDI-QKD), and thus we only need to consider how to secure the source devices. Among imperfections in the source devices, correlations between the sending pulses are one of the principal problems. In this paper, we consider a setting-choice-independent correlation (SCIC) framework in which the sending pulses can present arbitrary correlations but they are independent of the previous setting choices such as the bit, the basis and the intensity settings. Within the framework of SCIC, we consider the dominant fluctuations of the sending states, such as the relative phases and the intensities, and provide a self-contained information theoretic security proof for the loss-tolerant QKD protocol in the finite-key regime. We demonstrate the feasibility of secure quantum communication within a reasonable number of pulses sent, and thus we are convinced that our work constitutes a crucial step toward guaranteeing implementation security of QKD.
Anchor Cascade for Efficient Face Detection

Face detection is essential to facial analysis tasks, such as facial reenactment and face recognition. Both cascade face detectors and anchor-based face detectors have translated shining demos into practice and received intensive attention from the community. However, cascade face detectors often suffer from a low detection accuracy, while anchor-based face detectors rely heavily on very large neural networks pre-trained on large-scale image classification datasets such as ImageNet, which is not computationally efficient for both training and deployment. In this paper, we devise an efficient anchor-based cascade framework called anchor cascade. To improve the detection accuracy by exploring contextual information, we further propose a context pyramid maxout mechanism for anchor cascade. As a result, anchor cascade can train very efficient face detection models with a high detection accuracy. Specifically, compared with a popular convolutional neural network (CNN)-based cascade face detector MTCNN, our anchor cascade face detector greatly improves the detection accuracy, e.g., from 0.9435 to 0.9704 at $1k$ false positives on FDDB, while it still runs in comparable speed. Experimental results on two widely used face detection benchmarks, FDDB and WIDER FACE, demonstrate the effectiveness of the proposed framework.
Feedback Convolutional Neural Network for Visual Localization and Segmentation

Feedback is a fundamental mechanism existing in the human visual system, but has not been explored deeply in designing computer vision algorithms. In this paper, we claim that feedback plays a critical role in understanding convolutional neural networks (CNNs), e.g., how a neuron in CNNs describes an object's pattern, and how a collection of neurons form comprehensive perception to an object. To model the feedback in CNNs, we propose a novel model named Feedback CNN and develop two new processing algorithms, i.e., neural pathway pruning and pattern recovering. We mathematically prove that the proposed method can reach local optimum. Note that Feedback CNN belongs to weakly supervised methods and can be trained only using category-level labels. But it possesses a powerful capability to accurately localize and segment category-specific objects. We conduct extensive visualization analysis, and the results reveal the close relationship between neurons and object parts in Feedback CNN. Finally, we evaluate the proposed Feedback CNN over the tasks of weakly supervised object localization and segmentation, and the experimental results on ImageNet and Pascal VOC show that our method remarkably outperforms the state-of-the-art ones.
Unsupervised Knowledge Transfer Using Similarity Embeddings

With the advent of deep neural networks, there is a growing interest in transferring the knowledge from a large and complex model to a smaller and faster one. In this brief, a method for unsupervised knowledge transfer (KT) between neural networks is proposed. To the best of our knowledge, the proposed method is the first method that utilizes similarity-induced embeddings to transfer the knowledge between any two layers of neural networks, regardless of the number of neurons in each of them. By this way, the knowledge is transferred without using any lossy dimensionality reduction transformations or requiring any information about the complex model, except for the activations of the layer used for KT. This is in contrast with most existing approaches that only generate soft-targets for training the smaller neural network or directly use the weights of the larger model. The proposed method is evaluated using six image data sets and it is demonstrated, through extensive experiments, that the knowledge of a neural network can be successfully transferred using different kinds of (synthetic or not) data, ranging from cross-domain data to just randomly generated data.
Robust visual tracking via scale-and-state-awareness

Convolutional neural networks (CNNs) have been applied to visual tracking with demonstrated success in recent years. However, the performance of CNN-based trackers can be further improved, because the predicted upright bounding box cannot tightly enclose the target due to factors such as deformations and rotations. Besides, many existing CNN-based trackers neglect to distinguish the occluded state of the target from non-occluded states, which causes the samples collected during occlusions wrongly update the tracker to focus on other objects. To address these problems, we propose to adaptively utilize the level set segmentation and bounding box regression techniques to obtain a tight enclosing box, and design a CNN to recognize whether the target is occluded. Extensive experimental results on a large benchmark dataset demonstrate the effectiveness of the proposed method compared to several state-of-the-art tracking algorithms.
Single Image Reflection Removal Using Convolutional Neural Networks

When people take a picture through glass, the scene behind the glass is often interfered by specular reflection. Due to relatively easy implementation, most studies have tried to recover the transmitted scene from multiple images rather than single image. However, the use of multiple images is not practical for common users in real situations due to the critical shooting conditions. In this paper, we propose single image reflection removal using convolutional neural networks. We provide a ghosting model that causes reflection effects in captured images. First, we synthesize multiple reflection images from the input single one based on ghosting model and relative intensity. Then, we construct an end-to-end network that consists of encoder and decoder. To optimize the network parameters, we use a joint training strategy to learn the layer separation knowledge from the synthesized reflection images. For the loss function, we utilize both internal and external losses in optimization. Finally, we apply the proposed network to single image reflection removal. Compared with the previous work, the proposed method does not need handcrafted features and specular filters for reflection removal. Experimental results show that the proposed method successfully removes reflection from both synthetic and real images as well as achieves the highest scores in PSNR, SSIM and FSIM.
A Study on the Security Implications of Information Leakages in Container Clouds

Container technology provides a lightweight operating system level virtual hosting environment. Its emergence profoundly changes the development and deployment paradigms of multi-tier distributed applications. However, due to the incomplete implementation of system resource isolation mechanisms in the Linux kernel, some security concerns still exist for multiple containers sharing an operating system kernel on a multi-tenancy container-based cloud service. In this paper, we first present the information leakage channels we discovered that are accessible within containers. Such channels expose a spectrum of system-wide host information to containers without proper resource partitioning. By exploiting such leaked host information, it becomes much easier for malicious adversaries (acting as tenants in a container cloud) to launch attacks that might impact the reliability of cloud services. We demonstrate that the information leakage channels could be exploited to infer private data, detect and verify co-residence, build covert channels, and launch more advanced cloud-based attacks. We discuss the root causes of the containers' information leakage and propose a two-stage defense approach. As demonstrated in the evaluation, our defense is effective and incurs trivial performance overhead.
Event-Based Gesture Recognition With Dynamic Background Suppression Using Smartphone Computational Capabilities

In this paper, we introduce a framework for dynamic gesture recognition with background suppression operating on the output of a moving event-based camera. The system is developed to operate in real-time using only the computational capabilities of a mobile phone. It introduces a new development around the concept of time-surfaces. It also presents a novel event-based methodology to dynamically remove backgrounds that uses the high temporal resolution properties of event-based cameras. To our knowledge, this is the first Android event-based framework for vision-based recognition of \textit{dynamic} gestures running on a smartphone without off-board processing. We assess the performances by considering several scenarios in both indoors and outdoors, for static and dynamic conditions, in uncontrolled lighting conditions. We also introduce a new event-based dataset for gesture recognition with static and dynamic backgrounds (made publicly available). The set of gestures has been selected following a clinical trial to allow human-machine interaction for the visually impaired and older adults. We finally report comparisons with prior work that addressed event-based gesture recognition reporting comparable results, without the use of advanced classification techniques nor power greedy hardware.
Deep Reconstruction of Least Significant Bits for Bit-Depth Expansion

Bit-depth expansion (BDE) is important for displaying a low bit-depth image in a high bit-depth monitor. Current BDE algorithms often utilize traditional methods to fill the missing least significant bits and suffer from multiple kinds of perceivable artifacts. In this paper, we present a deep residual network-based method for BDE. Based on the different properties of flat and non-flat areas, two channels are proposed to reconstruct these two kinds of areas, respectively. Moreover, a simple yet efficient local adaptive adjustment preprocessing is presented in the flat-area-channel. By combining the benefits of both the traditional debanding strategy and network-based reconstruction, the proposed method can further promote the subjective quality of the flat area. Experimental results on several image sets demonstrate that the proposed BDE network can obtain favorable visual quality and decent quantitative performance.
Solving Large Maximum Clique Problems on a Quantum Annealer

Commercial quantum annealers from D-Wave Systems can find high quality solutions of quadratic unconstrained binary optimization problems that can be embedded onto its hardware. However, even though such devices currently offer up to 2048 qubits, due to limitations on the connectivity of those qubits, the size of problems that can typically be solved is rather small (around 65 variables). This limitation poses a problem for using D-Wave machines to solve application-relevant problems, which can have thousands of variables. For the important Maximum Clique problem, this article investigates methods for decomposing larger problem instances into smaller ones, which can subsequently be solved on D-Wave. During the decomposition, we aim to prune as many generated subproblems that don’t contribute to the solution as possible, in order to reduce the computational complexity. The reduction methods presented in this article include upper and lower bound heuristics in conjunction with graph decomposition, vertex and edge extraction, and persistency analysis.
Joint sparse graph and flexible embedding for graph-based semi-supervised learning

This letter introduces a framework for graph-based semi-supervised learning by estimating a flexible non-linear projection and its linear regression model. Unlike existing works, the proposed framework jointly estimates the graph structure, the non-linear projection, and the linear regression model. By adopting this joint estimation an overall optimality can be reached. A series of experiments are conducted on five image datasets in order to compare the proposed method with some state-of-art semi-supervised methods. This evaluation demonstrates the effectiveness of the proposed embedding method. These experiments show the superiority of the proposed framework over the joint estimation of the graph and soft labels.
Solar Irradiance Forecast Using Naïve Bayes Classifier Based on Publicly Available Weather Forecasting Variables

This paper develops an approach for two-day-ahead global horizontal irradiance (GHI) forecast using the naïve Bayes classifier (NB). Based on publicly available weather forecasting information about temperature, relative humidity, dew point, and sky coverage, they are used as a training set in NB classification with hourly resolution. To reduce having two times with the same GHI affecting the classification in the proposed model, two characteristics of the GHI under different weather conditions are considered: The daylight variation and diurnal cycle. More importantly, NB’s independence assumption-based on simple Bayes’ theorem makes the process speed faster and less constrained than other classification algorithms. The forecast performance is verified with several error criteria from established analytical practices using relevant statistics. Moreover, commonly used forecasting error criteria are discussed. This NB model shows improved results regarding error criteria and a good agreement for a clear day that satisfies the guideline for the evaluation of two-days-ahead forecast, when compared with other recent techniques.
Instance-aware representation learning and association for online multi-person tracking

Multi-Person Tracking (MPT) is often addressed within the detection-to-association paradigm. In such approaches, human detections are first extracted in every frame and person trajectories are then recovered by a procedure of data association (usually offline). However, their performances usually degenerate in presence of detection errors, mutual interactions and occlusions. In this paper, we present a deep learning based MPT approach that learns instance-aware representations of tracked persons and robustly online infers states of the tracked persons. Specifically, we design a multi-branch neural network (MBN), which predicts the classification confidences and locations of all targets by taking a batch of candidate regions as input. In our MBN architecture, each branch (instance-subnet) corresponds to an individual to be tracked and new branches can be dynamically created for handling newly appearing persons. Then based on the output of MBN, we construct a joint association matrix that represents meaningful states of tracked persons (e.g., being tracked or disappearing from the scene) and solve it by using the efficient Hungarian algorithm. Moreover, we allow the instance-subnets to be updated during tracking by online mining hard examples, accounting to person appearance variations over time. We comprehensively evaluate our framework on a popular MPT benchmark, demonstrating its excellent performance in comparison with recent online MPT methods.
Automatic detection of Plasmodium parasites from microscopic blood images

Malaria is caused by Plasmodium parasite. It is transmitted by female Anopheles bite. Thick and thin blood smears of the patient are manually examined by an expert pathologist with the help of a microscope to diagnose the disease. Such expert pathologists may not be available in many parts of the world due to poor health facilities. Moreover, manual inspection requires full concentration of the pathologist and it is a tedious and time consuming way to detect the malaria. Therefore, development of automated systems is momentous for a quick and reliable detection of malaria. It can reduce the false negative rate and it can help in detecting the disease at early stages where it can be cured effectively. In this paper, we present a computer aided design to automatically detect malarial parasite from microscopic blood images. The proposed method uses bilateral filtering to remove the noise and enhance the image quality. Adaptive thresholding and morphological image processing algorithms are used to detect the malaria parasites inside individual cell. To measure the efficiency of the proposed algorithm, we have tested our method on a NIH Malaria dataset and also compared the results with existing similar methods. Our method achieved the detection accuracy of more than 91% outperforming the competing methods. The results show that the proposed algorithm is reliable and can be of great assistance to the pathologists and hematologists for accurate malaria parasite detection.
"Self-Organizing in Blockchain Infrastructures: Generativity Through Shifting Objectives and Forking"

Given the ubiquity of digital technologies, and increased use of autonomous algorithms, it is likely that many of today’s social and organizational processes will one day include autonomous elements. The Bitcoin blockchain is likely the first case of an increasingly generative and autonomous way of organizing, and the specific properties of blockchain infrastructures—distribution of control, openness to manipulation, and generativity of the underlying source code—make it an ideal case to study patterns of self-organizing. This paper investigates the phenomenon of self-organizing through a study of forking in the Bitcoin blockchain infrastructure between 2010 and 2016. It adds to the emerging body of research on digital infrastructures, and particularly blockchain infrastructures, by conceptualizing forking as a pattern of self-organizing in blockchain infrastructures that specifically involves the underlying infrastructure, the scale of code changes, individual objectives, and collective adoption, whether specific or general. Thus, this paper demonstrates how forking in blockchain infrastructures mediates between divergent organizing objectives and existing capabilities, on the one hand, and generates self-organizing on the other hand. In this paper, we further contextualize our findings in extant work on digital infrastructures, offer a guide for designers of blockchain infrastructures, and propose the concept of “generative mirroring” as a pattern through which blockchain infrastructures and organizing adaptively coevolve.