Reviewerly/exHarmony huggingface dataset:
Contextual Level-Set Method for Breast Tumor Segmentation

Breast ultrasound image segmentation is the foundation of the diagnosis and treatment of breast cancer. The level set method is widely used for medical image segmentation. However, it remained a challenge for traditional level set methods because they cannot fully understand the tumor regions with complex characteristics by only low-level features. Considering that contextual features can provide complementary discriminative information to low-level features, this paper proposed a contextual level set method for breast tumor segmentation. Firstly, an encoder-decoder architecture network such as UNet is developed to learn high-level contextual features with semantic information. After that, the contextual level set method has been proposed to incorporate the novel contextual energy term. The proposed term has the ability to embed the high-level contextual knowledge into the level set framework. The learned contextual features with semantic information can provide more discriminative information, which has been directly associated with category labels, instead of the original intensity. Therefore, it is robust to serious intensity inhomogeneity, which is helpful to improve segmentation performance. The experiments had taken place with the help of three databases, which indicates that the proposed method outperformed traditional methods.
Corporate contact tracing as a pandemic response

Since the start of the COVID-19 pandemic, a steady stream of propositions from tech giants and start-ups alike has furnished us with the idea that GPS- or Bluetooth-enabled contact tracing apps are a vital part of the pandemic response. This commentary considers these apps as 'corporate contact tracing', emphasizing the private-sector role that such developments imply. We first discuss corporate contact tracing's potential to de-center the power of public health authorities. Then, using the frames of surveillance capitalism and disaster capitalism, we suggest how corporate contact tracing might feed the rise of corporate power in the public sphere. We question its capacity to address structural inequalities and to foster a social justice vision of public health. And, we wonder whether corporate contact tracing might intensify the effects of discriminatory design and algorithmic oppression. We conclude by calling for a discussion of this technology beyond questions of privacy and efficacy.
Iterative quantum-assisted eigensolver

The task of estimating the ground state of Hamiltonians is an important problem in physics with numerous applications ranging from solid-state physics to combinatorial optimization. We provide a hybrid quantum-classical algorithm for approximating the ground state of a Hamiltonian that builds on the powerful Krylov subspace method in a way that is suitable for current quantum computers. Our algorithm systematically constructs the Ansatz using any given choice of the initial state and the unitaries describing the Hamiltonian. The only task of the quantum computer is to measure overlaps and no feedback loops are required. The measurements can be performed efficiently on current quantum hardware without requiring any complicated measurements such as the Hadamard test. Finally, a classical computer solves a well characterized quadratically constrained optimization program. Our algorithm can reuse previous measurements to calculate the ground state of a wide range of Hamiltonians without requiring additional quantum resources. Further, we demonstrate our algorithm for solving problems consisting of thousands of qubits. The algorithm works for almost every random choice of the initial state and circumvents the barren plateau problem.
Gender Slopes

Automated computer vision systems have been applied in many domains including security, law enforcement, and personal devices, but recent reports suggest that these systems may produce biased results, discriminating against people in certain demographic groups. Diagnosing and understanding the underlying true causes of model biases, however, are challenging tasks because modern computer vision systems rely on complex black-box models whose behaviors are hard to decode. We propose to use an encoder-decoder network developed for image attribute manipulation to synthesize facial images varying in the dimensions of gender and race while keeping other signals intact. We use these synthesized images to measure counterfactual fairness of commercial computer vision classifiers by examining the degree to which these classifiers are affected by gender and racial cues controlled in the images, e.g., feminine faces may elicit higher scores for the concept of nurse and lower scores for STEM-related concepts.
Auto-adaptive learning-based workload forecasting in dynamic cloud environment

To maintain elasticity and scalability of resources at cloud data centers, future workload prediction has become an indispensable requirement. However, there is high variance in resource demands, due to sudden peaks, drop of workload, noise and redundancy in user demands, that hinders accurate workload forecast. This article aims to present workload prediction adaptive neural network model to forecast average workload over consecutive prediction intervals in anticipation. The prediction model adaptively learns traces of workload for particular prediction interval from historical data by applying proposed novel Auto Adaptive Differential Evolution (AADE) algorithm. The two benchmark datasets viz. NASA and Saskatchewan HTTP traces are used for evaluating the performance of the proposed work. Experimental results reveal that AADE-trained neural network outperforms state-of-the-art schemes and achieves accuracy improvement upto 98.9%, 97.4% and 94.8% compared to Average, Backpropagation (BP) and Self adaptive Differential Evolution (SaDE) learning based workload prediction schemes respectively. In addition, the speed of convergence of AADE learning algorithm is observed to be 2-10 times faster than BP and SaDE based forecasting approaches for both datasets.
V2V: A Deep Learning Approach to Variable-to-Variable Selection and Translation for Multivariate Time-Varying Data

We present V2V, a novel deep learning framework, as a general-purpose solution to the variable-to-variable (V2V) selection and translation problem for multivariate time-varying data (MTVD) analysis and visualization. V2V leverages a representation learning algorithm to identify transferable variables and utilizes Kullback-Leibler divergence to determine the source and target variables. It then uses a generative adversarial network (GAN) to learn the mapping from the source variable to the target variable via the adversarial, volumetric, and feature losses. V2V takes the pairs of time steps of the source and target variable as input for training, Once trained, it can infer unseen time steps of the target variable given the corresponding time steps of the source variable. Several multivariate time-varying data sets of different characteristics are used to demonstrate the effectiveness of V2V, both quantitatively and qualitatively. We compare V2V against histogram matching and two other deep learning solutions (Pix2Pix and CycleGAN).
A new item similarity based on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si125.svg"><mml:mrow><mml:mi>α</mml:mi></mml:mrow></mml:math>-divergence for collaborative filtering in sparse data

In big data era, collaborative filtering as one of the most popular recommendation techniques plays an important role to promote the development of online trade. Similarity measurement is a core step in collaborative filtering as it not only determines the selection of neighbors but also has a decisive influence on the recommendation quality. However, most of existing similarity measures depend on the co-rated cases(i.e., cases where different users rated the same items or different items were rated by the same users), which usually leads to low data utilization and even poor recommendation results in a sparse dataset. To alleviate this problem, we proposed a new item similarity measure based on α-divergence, which does the computation according to the probability density distribution of ratings and greatly reduces the dependence on co-rated cases. Furthermore, the presented item similarity measure also considers the impact of the absolute number of ratings and the proportion of co-rated cases on the computation results, which effectively improves the accuracy of recommendation. Experiments on three open datasets suggest that the proposed scheme has high prediction accuracy and good adaptability to sparse data. Therefore, it has high potential to be applied in recommender systems.
Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patches

Zero-shot learning (ZSL) is commonly used to address the very pervasive problem of predicting unseen classes in fine-grained image classification and other tasks. One family of solutions is to learn synthesised unseen visual samples produced by generative models from auxiliary semantic information, such as natural language descriptions. However, for most of these models, performance suffers from noise in the form of irrelevant image backgrounds. Further, most methods do not allocate a calculated weight to each semantic patch. Yet, in the real world, the discriminative power of features can be quantified and directly leveraged to improve accuracy and reduce computational complexity. To address these issues, we propose a novel framework called multi-patch generative adversarial nets (MPGAN) that synthesises local patch features and labels unseen classes with a novel weighted voting strategy. The process begins by generating discriminative visual features from noisy text descriptions for a set of predefined local patches using multiple specialist generative models. The features synthesised from each patch for unseen classes are then used to construct an ensemble of diverse supervised classifiers, each corresponding to one local patch. A voting strategy averages the probability distributions output from the classifiers and, given that some patches are more discriminative than others, a discrimination-based attention mechanism helps to weight each patch accordingly. Extensive experiments show that MPGAN has significantly greater accuracy than state-of-the-art methods.
A descriptive framework for the field of deep learning applications in medical images

Deep learning in medical image analysis is a typical interdisciplinary application, which needs support and cooperation of computer techniques and medical experience, and has broad application prospects. Since 2017, the number of related published articles has increased exponentially, imposing a burden on the literature review in this field. In this survey, we clustered 2068 retrieved articles into 15 topics through Latent Dirichlet Allocation (LDA) and provided a rough overview on the application of deep learning in medical images. On this basis, we conducted a detailed review with 77 top representative articles. We built a descriptive review framework based on LDA and discussed classification, object detection, segmentation, and image generation applications in medical images for the field of deep learning from the perspective of image modalities. We ended with discussing current challenges and future research directions of deep learning in medical images analysis.
Trust in 5G Open RANs through Machine Learning: RF Fingerprinting on the POWDER PAWR Platform

5G and open radio access networks (Open RANs) will result in vendor-neutral hardware deployment that will require additional diligence towards managing security risks. This new paradigm will allow the same network infrastructure to support virtual network slices for transmit different waveforms, such as 5G New Radio, LTE, WiFi, at different times. In this multivendor, multi-protocol/waveform setting, we propose an additional physical layer authentication method that detects a specific emitter through a technique called as RF fingerprinting. Our deep learning approach uses convolutional neural networks augmented with triplet loss, where examples of similar/dissimilar signal samples are shown to the classifier over the training duration. We demonstrate the feasibility of RF fingerprinting base stations over the large-scale over-the-air experimental POWDER platform in Salt Lake City, Utah, USA. Using real world datasets, we show how our approach overcomes the challenges posed by changing channel conditions and protocol choices with 99.86% detection accuracy for different training and testing days.
A Blockchain-based Land Title Management System for Bangladesh

Bangladesh is a small country with a large population. Its increasingly developing economy further makes land a lucrative source of fixed capital. On the other hand, land titling is a cumbersome and lengthy process, where different government bodies process different sets of documents, and bureaucratic loopholes encourage fraudulent activities by organized people. As a result, the current model suffers from good governance. In this paper, we propose a Blockchain-based solution that offers data synchronization and transparency, ease of access, immutable records management, a faster and cheaper solution. Considering the technological knowledge and capacity of the people and the government, we introduced a phase by phase Blockchain adoption model that starts with a public Blockchain ledger and later gradually incorporates two levels of Hybrid Blockchain. We provide detailed smart contracts design of the public Blockchain and implement a prototype system using Ethereum. Our experimental setup uses local and live Ethereum test networks to demonstrate the efficacy of the proposed system. Our analysis shows that the proposed model reduces the number of required travels, the overall cost of information processing as well as provide easy access to vital information. As a result, Blockchain adoption can improve the land title digitization effort of Bangladesh.
S2SiamFC

To exploit rich information from unlabeled data, in this work, we propose a novel self-supervised framework for visual tracking which can easily adapt the state-of-the-art supervised Siamese-based trackers into unsupervised ones by utilizing the fact that an image and any cropped region of it can form a natural pair for self-training. Besides common geometric transformation-based data augmentation and hard negative mining, we also propose adversarial masking which helps the tracker to learn other context information by adaptively blacking out salient regions of the target. The proposed approach can be trained offline using images only without any requirement of manual annotations and temporal information from multiple consecutive frames. Thus, it can be used with any kind of unlabeled data, including images and video frames. For evaluation, we take SiamFC as the base tracker and name the proposed self-supervised method as S2SiamFC. Extensive experiments and ablation studies on the challenging VOT2016 and VOT2018 datasets are provided to demonstrate the effectiveness of the proposed method which not only achieves comparable performance to its supervised counterpart and other unsupervised methods requiring multiple frames.
Boundary-Assisted Region Proposal Networks for Nucleus Segmentation

Nucleus segmentation is an important task in medical image analysis. However, machine learning models cannot perform well because there are large amount of clusters of crowded nuclei. To handle this problem, existing approaches typically resort to sophisticated hand-crafted post-processing strategies; therefore, they are vulnerable to the variation of post-processing hyper-parameters. Accordingly, in this paper, we devise a Boundary-assisted Region Proposal Network (BRP-Net) that achieves robust instance-level nucleus segmentation. First, we propose a novel Task-aware Feature Encoding (TAFE) network that efficiently extracts respective high-quality features for semantic segmentation and instance boundary detection tasks. This is achieved by carefully considering the correlation and differences between the two tasks. Second, coarse nucleus proposals are generated based on the predictions of the above two tasks. Third, these proposals are fed into instance segmentation networks for more accurate prediction. Experimental results demonstrate that the performance of BRP-Net is robust to the variation of post-processing hyper-parameters. Furthermore, BRP-Net achieves state-of-the-art performances on both the Kumar and CPM17 datasets. The code of BRP-Net will be released at https://github.com/csccsccsccsc/brpnet.
Weakly Supervised Segmentation with Maximum Bipartite Graph Matching

In the weakly supervised segmentation task with only image-level labels, a common step in many existing algorithms is first to locate the image regions corresponding to each existing class with the Class Activation Maps (CAMs), and then generate the pseudo ground truth masks based on the CAMs to train a segmentation network in the fully supervised manner. The quality of the CAMs has a crucial impact on the performance of the segmentation model. We propose to improve the CAMs from a novel graph perspective. We model paired images containing common classes with a bipartite graph and use the maximum matching algorithm to locate corresponding areas in two images. The matching areas are then used to refine the predicted object regions in the CAMs. The experiments on Pascal VOC 2012 dataset show that our network can effectively boost the performance of the baseline model and achieves new state-of-the-art performance.
Collision-free trajectory planning of multi-rotor UAVs in a wind condition based on modified potential field

A multi-rotor unmanned aerial vehicle (UAV) is a rotorcraft with more than two rotors to enhance payload capability and endurance. A key feature required for the use of these vehicles under complex conditions is a technique to solve the problem of trajectory planning analytically. Hence, this paper proposes an indirect solution of the optimal control problem for path planning of the hexarotor system with different cost functions under different wind loads. First, the generalized Euler-Lagrange formulation is used to derive the dynamic equations of a hexarotor system. Hamiltonian function for a proper objective function is formed, then using the PMP optimality necessary conditions are obtained. Finally, in order to verify the effectiveness of the proposed approach, several simulation studies on a hexacopter are performed for finding the optimal paths at point-to-point motion with different objective functions like minimum effort, collision-free and windy environment. Also, a novel approach for obstacle avoidance of unmanned aerial vehicle is proposed by using a modified artificial potential field which overcomes the local minima issue and finds a practical trajectory for robot path planning. The results clearly show the effectiveness of the proposed approach on the multirotor systems.
Experimental Characterization, Modeling, and Analysis of Crosstalk in a Quantum Computer

In this article, we present the experimental characterization of crosstalk in quantum information processor using idle tomography and simultaneous randomized benchmarking. We quantify both “quantum” and “classical” crosstalk in the device and analyze quantum circuits considering crosstalk. We show that simulation considering only gate-error deviates from experimental results up to 27%, whereas simulation considering both gate error and crosstalk match the experiments more accurately with an average mismatch as low as 4.52%. This article enables simulation of quantum circuits with experimental crosstalk error rates.
Revisiting the Modifiable Areal Unit Problem in Deep Traffic Prediction with Visual Analytics

Deep learning methods are being increasingly used for urban traffic prediction where spatiotemporal traffic data is aggregated into sequentially organized matrices that are then fed into convolution-based residual neural networks. However, the widely known modifiable areal unit problem within such aggregation processes can lead to perturbations in the network inputs. This issue can significantly destabilize the feature embeddings and the predictions - rendering deep networks much less useful for the experts. This paper approaches this challenge by leveraging unit visualization techniques that enable the investigation of many-to-many relationships between dynamically varied multi-scalar aggregations of urban traffic data and neural network predictions. Through regular exchanges with a domain expert, we design and develop a visual analytics solution that integrates 1) a Bivariate Map equipped with an advanced bivariate colormap to simultaneously depict input traffic and prediction errors across space, 2) a Moran's I Scatterplot that provides local indicators of spatial association analysis, and 3) a Multi-scale Attribution View that arranges non-linear dot plots in a tree layout to promote model analysis and comparison across scales. We evaluate our approach through a series of case studies involving a real-world dataset of Shenzhen taxi trips, and through interviews with domain experts. We observe that geographical scale variations have important impact on prediction performances, and interactive visual exploration of dynamically varying inputs and outputs benefit experts in the development of deep traffic prediction models.
Adaptive feature fusion for visual object tracking

Recent advanced trackers, consisting of discriminative classification component and dedicated bounding box estimation, have achieved improved performance in the visual tracking community. The most essential factor for the development is the utilization of different Convolutional Neural Networks (CNNs), which significantly improves the model capacity via offline trained deep feature representations. Though powerful deep structures emphasize more semantic appearance through high dimensional latent variables, how to achieve effective feature adaptation in the online tracking stage has not been sufficiently considered yet. To this end, we argue the necessity of exploring hierarchical and complementary appearance descriptors from different convolutional layers to achieve online tracking adaptation. Therefore, in this paper, we propose an adaptive feature fusion mechanism, which can balance the detection granularities from shallow to deep convolutional layers. To be specific, the correlation between template and instance is employed to generate adaptive weights to achieve advanced saliency and discrimination. In addition, considering temporal appearance variation, the projection matrix for the multi-channel inputs is jointly updated with the correlation classifier to further enhance the robustness. The experimental results on four recent benchmarks, i.e., OTB-2015, VOT2018, LaSOT and TrackingNet, demonstrate the effectiveness and robustness of the proposed method, with superior performance compared to the state-of-the-art approaches.