Reviewerly/exHarmony huggingface dataset:
Deep Attentive Panoptic Model for Prostate Cancer Detection Using Biparametric MRI Scans

Multi-parametric MRI (mp-MRI) has recently been established in major guidelines as a first-line diagnostic test for men suspected of having prostate cancer (PCa) primarily to detect and classify clinically significant lesions. However, widespread utilization is still challenged by 1) the difficulty of interpretation specifically for radiologists less experienced in reading mp-MRI scans, and 2) decreased productivity associated with increased time spent per case for reading these complex scans. Deep learning based lesion detection and segmentation methods have been proposed for radiologists to perform their tasks more accurately and efficiently. In this work, we present a novel panoptic lesion detection and segmentation method with both semantic and instance branches as well as an attention module to optimally incorporate both local and global image features. In a free-response receiver operating characteristics (FROC) analysis for lesion sensitivity on an independent dataset with 243 patients, our method has achieved 89% sensitivity and 85% with 0.94 and 0.62 false positives per patient, respectively. Using the proposed method, we have achieved an unprecedented area under ROC curve (AUC) of 0.897 in identifying clinically significant cases.
Deep Program Structure Modeling Through Multi-Relational Graph-based Learning

Deep learning is emerging as a promising technique for building predictive models to support code-related tasks like performance optimization and code vulnerability detection. One of the critical aspects of building a successful predictive model is having the right representation to characterize the model input for the given task. Existing approaches in the area typically treat the program structure as a sequential sequence but fail to capitalize on the rich semantics of data and control flow information, for which graphs are a proven representation structure.
Virtual Learning and Educational Environment: New Opportunities and Challenges under the COVID-19 Pandemic

This paper analyzes the current changes in higher education due to the COVID-19 pandemic. As coronavirus spreads around the world, there is great concern about the problems of availability, accessibility and efficiency of higher education in different countries. The WHO and national 'stay-at-home' orders require professors and students should follow the rules of social-distancing and self-isolation. The present day situation in the sphere of education, including higher education, poses new challenges and opens up new perspectives for further development of virtual environment for both professors (educational environment) and students (learning environment). The research into the current problems of virtual learning and educational environment at the Russian State Social University and Moscow Aviation Institute (University) included 147 students (98 - from the Russian State Social University and 49 – from the Moscow Aviation Institute (University). They were asked to answer the following research questions: if the university virtual learning environment (UVLE) was capable of providing continuous process of education under pandemic, if the UVLE met the contemporary demands of acquiring knowledge and skills, if the UVLE was easy to operate in order to get proper level of knowledge and skills, if the UVLE was efficient to provide education for students with different social, cultural and educational backgrounds. It was also important to find out if the UVLE provided a reliable access to the university e-library and Internet resources. The researchers made an inquiry into the possibilities of the UNLE to provide a sufficient level of educational interaction between students and professors (teaching staff), between students and among teaching staff as well as to provide blended learning comprising elements of online and offline learning. The teachers and professors of the above mentioned universities were also engaged in the inquiry. They were to answer such major questions as: whether the university virtual learning environment (UVLE) was capable of providing continuous process of education under pandemic isolation conditions and was easily adaptable for modern requirements in order to transfer from the physical educational environment to virtual educational environment. The inquiry has demonstrated that the overwhelming majority of both students and teachers praise the UVLE and UVEE for such basic characteristics as continuity, usability and efficiency of educational process under the conditions of self-isolation during the pandemic. Though 99% of the interviewees spoke for working in the virtual environment while the COVID-19 was spreading, they also pointed out the necessity of constant upgrading of their technical knowledge. The results may help regulators, educators as well as IT experts develop strategies and tactics for effective online education.
Lower Bounds on the Degree of Block Ciphers

Only the method to estimate the upper bound of the algebraic degree on block ciphers is known so far, but it is not useful for the designer to guarantee the security. In this paper we provide meaningful lower bounds on the algebraic degree of modern block ciphers.
Caspar

A user's review of an app often describes the user's interactions with the app. These interactions, which we interpret as mini stories, are prominent in reviews with negative ratings. In general, a story in an app review would contain at least two types of events: user actions and associated app behaviors. Being able to identify such stories would enable an app's developer in better maintaining and improving the app's functionality and enhancing user experience.
R3T: Rapidly-exploring Random Reachable Set Tree for Optimal Kinodynamic Planning of Nonlinear Hybrid Systems

We introduce R3T, a reachability-based variant of the rapidly-exploring random tree (RRT) algorithm that is suitable for (optimal) kinodynamic planning in nonlinear and hybrid systems. We developed tools to approximate reachable sets using polytopes and perform sampling-based planning with them. This method has a unique advantage in hybrid systems: different dynamic modes in the reachable set can be explicitly represented using multiple polytopes. We prove that under mild assumptions, R3T is probabilistically complete in kinodynamic systems, and asymptotically optimal through rewiring. Moreover, R3T provides a formal verification method for reachability analysis of nonlinear systems. The advantages of R3T are demonstrated with case studies on nonlinear, hybrid, and contact-rich robotic systems.
A new dataset of dog breed images and a benchmark for finegrained classification

Abstract In this paper, we introduce an image dataset for fine-grained classification of dog breeds: the Tsinghua Dogs Dataset. It is currently the largest dataset for fine-grained classification of dogs, including 130 dog breeds and 70,428 real-world images. It has only one dog in each image and provides annotated bounding boxes for the whole body and head. In comparison to previous similar datasets, it contains more breeds and more carefully chosen images for each breed. The diversity within each breed is greater, with between 200 and 7000+ images for each breed. Annotation of the whole body and head makes the dataset not only suitable for the improvement of finegrained image classification models based on overall features, but also for those locating local informative parts. We show that dataset provides a tough challenge by benchmarking several state-of-the-art deep neural models. The dataset is available for academic purposes at https://cg.cs.tsinghua.edu.cn/ThuDogs/ .
Characterizing Label Errors: Confident Learning for Noisy-Labeled Image Segmentation

Convolutional neural networks (CNNs) have achieved remarkable performance in image processing for its mighty capability to fit huge amount of data. However, if the training data are corrupted by noisy labels, the resulting performance might be deteriorated. In the domain of medical image analysis, this dilemma becomes extremely severe. This is because the medical image annotation always requires medical expertise and clinical experience, which would inevitably introduce subjectivity. In this paper, we design a novel algorithm based on the teacher-student architecture for noisy-labeled medical image segmentation. Creatively, We introduce confident learning (CL) method to identify the corrupted labels and endow CNN an anti-interference ability to the noises. Specifically, the CL technique is introduced to the teacher model to characterize the suspected wrong-labeled pixels. Since the noise identification maps are a little away from sufficient precision, the spatial label smoothing regularization technique is utilized to generate soft-corrected masks for training the student model. Since our method identifies and revises the noisy labels of the training data in a pixel-level rather than simply assigns lower weights to the noisy masks, it outperforms the state-of-the-art method in the noisy-labeled image segmentation task on the JSRT dataset, especially when the training data are severely corrupted by noises.
Does transaction activity predict Bitcoin returns? Evidence from quantile-on-quantile analysis

This paper uses the quantile-on-quantile regression to examine the predictive power of transaction activity for Bitcoin returns over the period from January 2013 to December 2018. We measure the Bitcoin transaction activity using trading volumes, the number of unique Bitcoin transactions, and the number of unique Bitcoin addresses. Considering the onset of structural breaks, we identify considerable effects of the heterogeneity concerning the quantiles of transaction activity, which cannot be depicted fully by the traditional quantile regression method. The empirical results show that higher transaction activity tends to predict higher/lower Bitcoin returns when the market is in a bullish/bearish state. We find that the nexus is asymmetric across quantiles, depending on the sign and size of the transaction activity, and the predictive relationship intensifies in the upper or lower quantiles of the conditional distribution. In addition, this empirical evidence is in line with the volume-return association in the equity market due to private informative and noninformative trading actions. Overall, our findings suggest that transaction activity-based strategies should be made with respect to Bitcoin market performance, specifically during extreme conditions.
Learning a Formula of Interpretability to Learn Interpretable Formulas

Many risk-sensitive applications require Machine Learning (ML) models to be interpretable. Attempts to obtain interpretable models typically rely on tuning, by trial-and-error, hyper-parameters of model complexity that are only loosely related to interpretability. We show that it is instead possible to take a meta-learning approach: an ML model of non-trivial Proxies of Human Interpretability (PHIs) can be learned from human feedback, then this model can be incorporated within an ML training process to directly optimize for interpretability. We show this for evolutionary symbolic regression. We first design and distribute a survey finalized at finding a link between features of mathematical formulas and two established PHIs, simulatability and decomposability. Next, we use the resulting dataset to learn an ML model of interpretability. Lastly, we query this model to estimate the interpretability of evolving solutions within bi-objective genetic programming. We perform experiments on five synthetic and eight real-world symbolic regression problems, comparing to the traditional use of solution size minimization. The results show that the use of our model leads to formulas that are, for a same level of accuracy-interpretability trade-off, either significantly more or equally accurate. Moreover, the formulas are also arguably more interpretable. Given the very positive results, we believe that our approach represents an important stepping stone for the design of next-generation interpretable (evolutionary) ML algorithms.
One-step spectral rotation clustering for imbalanced high-dimensional data

The class distribution of imbalanced data sets is skewed in practical application. As traditional clustering methods mainly are designed for improving the overall learning performance, the majority class usually tends to be clustered and the minority class which is more valuable maybe ignored. Moreover, existing clustering methods can be limited for the performance of imbalanced and high-dimensional domains. In this paper, we present one-step spectral rotation clustering for imbalanced high-dimensional data (OSRCIH) by integrating self-paced learning and spectral rotation clustering in a unified learning framework, where sample selection and dimensionality reduction are simultaneously considered with mutual and iterative update. Specifically, the imbalance problem is considered by selecting the same number of training samples from each intrinsic group of the training data, where the sample-weight vector is obtained by self-paced learning. Moreover, dimensionality reduction is conducted by combining subspace learning and feature selection. Experimental analysis on synthetic datasets and real datasets showed that OSRCIH could recognize and enhance the weight of important samples and features so as to avoid the clustering method in favor of the majority class and to improve effectively the clustering performance.
Relative planar motion for vehicle-mounted cameras from a single affine correspondence

Two solvers are proposed for estimating the extrinsic camera parameters from a single affine correspondence assuming general planar motion. In this case, the camera movement is constrained to a plane and the image plane is orthogonal to the ground. The algorithms do not assume other constraints, e.g. the non-holonomic one, to hold. A new minimal solver is proposed for the semi-calibrated case, i.e. the camera parameters are known except a common focal length. Another method is proposed for the fully calibrated case. Due to requiring a single correspondence, robust estimation, e.g. histogram voting, leads to a fast and accurate procedure. The proposed methods are tested in our synthetic environment and on publicly available real datasets consisting of videos through tens of kilometers. They are superior to the state-of-the-art both in terms of accuracy and processing time.
The SmartSHARK ecosystem for software repository mining

Software repository mining is the foundation for many empirical software engineering studies. The collection and analysis of detailed data can be challenging, especially if data shall be shared to enable replicable research and open science practices. SmartSHARK is an ecosystem that supports replicable and reproducible research based on software repository mining.
Low-contrast image enhancement using spatial contextual similarity histogram computation and color reconstruction

This paper presents an adaptive enhancement framework for low contrast images to improve the deteriorated details and color information. The proposed algorithm is composed of two adaptive steps: Contrast enhancement and Color restoration. Firstly, according to the luminance map separated from the RGB image, a new spatial contextual similarity histogram (SCSH) is developed. The new SCSH considers the gray-level similarities among the adjacent pixels, following the standard deviation value of the luminance image. The contrast-enhanced image is obtained by applying the intensity transformation function derived from the cumulative distribution function obtained from the normalized values of SCSH. An adaptive weighted fusion is carried out between the luminance enhanced channel and original luminance channel with the weights computed from the enhanced luminance channel. Secondly, an adaptive color restoration technique is designed to reconstruct the more consistent color image using original chromatic information and modified luminance. The results are analyzed using the statistical paired t-test. Both objective and subjective experimentation prove the supremacy of the proposed algorithm with conventional and state-of-the-art algorithms.
FoT-Stream: A Fog platform for data stream analytics in IoT

The Internet of Things (IoT) has developed infrastructures and applications that generate large amounts of data. These data are usually yielded as streams, characterized for being continuous and infinite, and presenting the peculiarity of modifying their behavior over time. Due to the large capacity of storage, data processing, and provisioning of resources, such data are generally processed/analyzed in Cloud Computing environments. Although those environments provide IoT infrastructure with adequate scalability and resource-centric features, the distance between devices and the cloud can impose limitations to achieve low latency in data traffic. In order to maintain scalability, achieve low latency, and reduce data traffic between the IoT devices and the Cloud, the Fog Computing was proposed, providing resource availability at the edge of the network. Although Fog Computing establishes resource availability at the edge of the network, the technologies/techniques currently used for IoT data processing and analysis may not be sufficient to support the continuous and unlimited streams that IoT platforms and applications produce. In addition, data stream applications at Fog must be supported by the computationally limited devices. Therefore, aiming at taking advantage of Fog Computing and resolve the gap of the data stream in IoT environments, this work presents a new platform called FoT-Stream designed to process and analyze, in real-time, data stream from the Internet of Things in Fog. The main advantage of using our approach is the possibility of reducing the amount of data transmitted on the network infrastructure, which allows, as a consequence, to perform an online data modeling, by detecting changes in data behavior, and a reduction of the Internet usage. Our results in a real-world scenario emphasize FoT-Stream considerably reduces the latency and amount of data traffic in IoT environments without affecting the system throughput.
A Visual Analytics Approach for Exploratory Causal Analysis: Exploration, Validation, and Applications

Using causal relations to guide decision making has become an essential analytical task across various domains, from marketing and medicine to education and social science. While powerful statistical models have been developed for inferring causal relations from data, domain practitioners still lack effective visual interface for interpreting the causal relations and applying them in their decision-making process. Through interview studies with domain experts, we characterize their current decision-making workflows, challenges, and needs. Through an iterative design process, we developed a visualization tool that allows analysts to explore, validate, and apply causal relations in real-world decision-making scenarios. The tool provides an uncertainty-aware causal graph visualization for presenting a large set of causal relations inferred from high-dimensional data. On top of the causal graph, it supports a set of intuitive user controls for performing what-if analyses and making action plans. We report on two case studies in marketing and student advising to demonstrate that users can effectively explore causal relations and design action plans for reaching their goals.
A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes

Many statistical learning models hold an assumption that the training data and the future unlabeled data are drawn from the same distribution. However, this assumption is difficult to fulfill in real-world scenarios and creates barriers in reusing existing labels from similar application domains. Transfer Learning is intended to relax this assumption by modeling relationships between domains, and is often applied in deep learning applications to reduce the demand for labeled data and training time. Despite recent advances in exploring deep learning models with visual analytics tools, little work has explored the issue of explaining and diagnosing the knowledge transfer process between deep learning models. In this paper, we present a visual analytics framework for the multi-level exploration of the transfer learning processes when training deep neural networks. Our framework establishes a multi-aspect design to explain how the learned knowledge from the existing model is transferred into the new learning task when training deep neural networks. Based on a comprehensive requirement and task analysis, we employ descriptive visualization with performance measures and detailed inspections of model behaviors from the statistical, instance, feature, and model structure levels. We demonstrate our framework through two case studies on image classification by fine-tuning AlexNets to illustrate how analysts can utilize our framework.
Nonnegativity-enforced Gaussian process regression

Gaussian process (GP) regression is a flexible non-parametric approach to approximate complex models. In many cases, these models correspond to processes with bounded physical properties. Standard GP regression typically results in a proxy model which is unbounded for all temporal or spacial points, and thus leaves the possibility of taking on infeasible values. We propose an approach to enforce the physical constraints in a probabilistic way under the GP regression framework. In addition, this new approach reduces the variance in the resulting GP model.
Quantum algorithm for the nonlinear dimensionality reduction with arbitrary kernel

Abstract Dimensionality reduction (DR) techniques play an extremely critical role in the data mining and pattern recognition field. However, most DR approaches involve large-scale matrix computations, which cause too high running complexity to implement in the big data scenario efficiently. The recent developments in quantum information processing provide a novel path to alleviate this problem, where a potential quantum acceleration can be obtained comparing with the classical counterpart. Nevertheless, existing proposals for quantum DR methods faced the common dilemma of the nonlinear generalization owing to the intrinsic linear limitation of quantum computation. In this paper, an architecture to simulate the arbitrary nonlinear kernels on a universal quantum computer is illustrated and further propose the quantum kernel principal component analysis (QKPCA) algorithm. The key idea is employing the truncated Taylor expansion to approximate the arbitrary nonlinear kernel within the fixed error and then constructing the corresponding Hamiltonian simulation for the quantum phase estimation algorithm. It is demonstrated theoretically that the QKPCA is qualified for the nonlinear DR task while the exponential speedup is also maintained. In addition, this research has the potential ability to develop other quantum DR approaches and existing linear quantum machine learning models.