Reviewerly/exHarmony huggingface dataset:
(t, k, n) XOR-based visual cryptography scheme with essential shadows

Visual cryptography scheme (VCS) shares a binary secret image into multiple shadows, only qualified set of shadows can reveal the secret image by stacking operation. However, VCS suffers the problems of low visual quality of the revealed image and large shadow size. A (t, k, n) XOR-based visual cryptography scheme (XVCS) shares the secret image into n shadows including t essentials and n-t non-essentials. A qualified set of shadows contains any k shadows including t essentials. The revealing process is implemented by XOR operation on the involved shadows. In this paper, we propose a construction method for (t, k, n)-XVCS with essential shadows. The secret image can be revealed perfectly, and the shadow size is small compared with VCS. Theoretical analysis and experimental results show the security and effectiveness of the proposed scheme.
Anomaly Detection in Trajectory Data with Normalizing Flows

The task of detecting anomalous data patterns is as important in practical applications as challenging. In the context of spatial data, recognition of unexpected trajectories brings additional difficulties, such as high dimensionality and varying pattern lengths. We aim to tackle such a problem from a probability density estimation point of view, since it provides an unsupervised procedure to identify out of distribution samples. More specifically, we pursue an approach based on normalizing flows, a recent framework that enables complex density estimation from data with neural networks. Our proposal computes exact model likelihood values, an important feature of normalizing flows, for each segment of the trajectory. Then, we aggregate the segments' likelihoods into a single coherent trajectory anomaly score. Such a strategy enables handling possibly large sequences with different lengths. We evaluate our methodology, named aggregated anomaly detection with normalizing flows (GRADINGS), using real world trajectory data and compare it with more traditional anomaly detection techniques. The promising results obtained in the performed computational experiments indicate the feasibility of the GRADINGS, specially the variant that considers autoregressive normalizing flows.
Automatic security management of smart infrastructures using attack graph and risk analysis

Smart infrastructure integrates cyberphysical systems, self-organizing networks of devices, fog computing to enhance the quality and convenience of manufacturing and social life, providing efficient resource management and increasing productivity and availability of traditional services. New technologies are characterized with a high degree of heterogeneity, structural complexity, and dynamic topology that result in a large number of new information and cyber security threats. Given a high degree of integration of digital cyber space into the processes of management, monitoring, aggregation, and data transferring, an assessment of its security level is a necessary requirement to maintain and control a safe and reliable environment for handling data and performing physical processes. Risk analysis and assessment is an extremely important task at all stages of the life cycle of subsystems and components of the smart environment. It allows us to assess a level of protection, identify vulnerable assets, and counteract the most likely directions of security threats. The known methods for security risk assessment do not consider the features of modern smart technologies, and this forces us to search for new approaches. This paper proposes a comprehensive method that includes a calculation of security indicators, risk assessment and selection of protective measures, based on attack graphs and thus allows us to consider the dynamics of changes in the components of the smart infrastructure. The automated system for assessing the security risks in the smart infrastructure and choosing the protective measures has been implemented.
An Efficient Radio Frequency Interference (RFI) Recognition and Characterization Using End-to-End Transfer Learning

Radio Frequency Interference (RFI) detection and characterization play a critical role in ensuring the security of all wireless communication networks. Advances in Machine Learning (ML) have led to the deployment of many robust techniques dealing with various types of RFI. To sidestep an unavoidable complicated feature extraction step in ML, we propose an efficient Deep Learning (DL)-based methodology using transfer learning to determine both the type of received signals and their modulation type. To this end, the scalogram of the received signals is used as the input of the pretrained convolutional neural networks (CNN), followed by a fully-connected classifier. This study considers a digital video stream as the signal of interest (SoI), transmitted in a real-time satellite-to-ground communication using DVB-S2 standards. To create the RFI dataset, the SoI is combined with three well-known jammers namely, continuous-wave interference (CWI), multi- continuous-wave interference (MCWI), and chirp interference (CI). This study investigated four well-known pretrained CNN architectures, namely, AlexNet, VGG-16, GoogleNet, and ResNet-18, for the feature extraction to recognize the visual RFI patterns directly from pixel images with minimal preprocessing. Moreover, the robustness of the proposed classifiers is evaluated by the data generated at different signal to noise ratios (SNR).
Hybrid approach for Anomaly Detection in Time Series Data

Anomaly detection is an active research field which attracts the attention of many business and research actors. It has led to several research projects depending on the nature of the data, the availability of labels on normality, and domains of application that are diverse such as fraud detection, medical domains, cloud monitoring or network intrusions detection, etc. However, dealing with effective anomaly detection for complex and high-dimensional time series data remains a challenging task. In this work, we propose hybrid approach composed of an LSTM Autoencoder trained on normal records to learn efficient normal sequence representations combined with an SVM classifier for anomaly detection. Experimental results show that by encoding time series via a pretrained LSTM encoder allows efficient representation of data so that we can accurately detect abnormal records. In fact, the encoded representation reduces significantly the correlations between normal and abnormal records and allows us to have an efficient latent data representation that separates consistently the two classes. The proposed hybrid approach outperforms state-of-the art approaches [1], [2], [3], [4].
Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses

A typical task in bioinformatics consists of identifying which features are associated with a target outcome of interest and building a predictive model. Automated machine learning (AutoML) systems such as the Tree-based Pipeline Optimization Tool (TPOT) constitute an appealing approach to this end. However, in biomedical data, there are often baseline characteristics of the subjects in a study or batch effects that need to be adjusted for in order to better isolate the effects of the features of interest on the target. Thus, the ability to perform covariate adjustments becomes particularly important for applications of AutoML to biomedical big data analysis.We developed an approach to adjust for covariates affecting features and/or target in TPOT. Our approach is based on regressing out the covariates in a manner that avoids 'leakage' during the cross-validation training procedure. We describe applications of this approach to toxicogenomics and schizophrenia gene expression data sets. The TPOT extensions discussed in this work are available at https://github.com/EpistasisLab/tpot/tree/v0.11.1-resAdj .In this work, we address an important need in the context of AutoML, which is particularly crucial for applications to bioinformatics and medical informatics, namely covariate adjustments. To this end we present a substantial extension of TPOT, a genetic programming based AutoML approach. We show the utility of this extension by applications to large toxicogenomics and differential gene expression data. The method is generally applicable in many other scenarios from the biomedical field.
LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Imagery

An accurate depth map of the environment is critical to the safe operation of autonomous robots and vehicles. Currently, either light detection and ranging (LIDAR) or stereo matching algorithms are used to acquire such depth information. However, a high-resolution LIDAR is expensive and produces sparse depth map at large range; stereo matching algorithms are able to generate denser depth maps but are typically less accurate than LIDAR at long range. This paper combines these approaches together to generate high-quality dense depth maps. Unlike previous approaches that are trained using ground-truth labels, the proposed model adopts a self-supervised training process. Experiments show that the proposed method is able to generate high-quality dense depth maps and performs robustly even with low-resolution inputs. This shows the potential to reduce the cost by using LIDARs with lower resolution in concert with stereo systems while maintaining high resolution.
Deep Multi-view Stereo for Dense 3D Reconstruction from Monocular Endoscopic Video

3D reconstruction from monocular endoscopic images is a challenging task. State-of-the-art multi-view stereo (MVS) algorithms based on image patch similarity often fail to obtain a dense reconstruction from weakly-textured endoscopic images. In this paper, we present a novel deep-learning-based MVS algorithm that can produce a dense and accurate 3D reconstruction from a monocular endoscopic image sequence. Our method consists of three key steps. Firstly, a number of depth candidates are sampled around the depth prediction made by a pre-trained CNN. Secondly, each candidate is projected to the other images in the sequence, and the matching score is measured using a patch embedding network that maps each image patch into a compact embedding. Finally, the candidate with the highest score is selected for each pixel. Experiments on colonoscopy videos demonstrate that our patch embedding network outperforms zero-normalized cross-correlation and a state-of-the-art stereo matching network in terms of matching accuracy and that our MVS algorithm produces several degrees of magnitude denser reconstruction than the competing methods when same accuracy filtering is applied.
Design and analysis of improved high-speed adaptive filter architectures for ECG signal denoising

In this paper, improved high-speed adaptive filter-based denoising architectures are proposed and implemented using the Xilinx Virtex-6 series FPGA platform. The performance of the proposed DF-RDLMS, TDF-RDLMS and TF-RDLMS implementations is analyzed and compared with that of the existing adaptive filter architectures as well as state-of-the-art wavelet-based denoising architectures. The proposed adaptive filter implementations are found to perform better than existing adaptive filter architectures and wavelet-based architectures. As compared to the existing adaptive filter implementations, the proposed architectures facilitate design flexibility in choice of the step-size parameter and also allow processing of input signals in fixed-point format as well as floating-point format. Simulation results exhibit the effectiveness of the proposed architectures in efficiently denoising ECG signals in noisy environments and prove their high suitability for low-cost high-performance denoising applications in medical field. Moreover, it is observed that the proposed high-speed adaptive filter architectures require significantly less hardware as compared to state-of-the-art wavelet-based architectures.
Online Oversampling for Sparsely Labeled Imbalanced and Non-Stationary Data Streams

Learning from imbalanced data and data stream mining are among most popular areas in contemporary machine learning. There is a strong interplay between these domains, as data streams are frequently characterized by skewed distributions. However, most of existing works focus on binary problems, omitting significantly more challenging multi-class imbalanced data. In this paper, we propose a novel framework for learning from multi-class imbalanced data streams that simultaneously tackles three major problems in this area: (i) changing imbalance ratios among multiple classes; (ii) concept drift; and (iii) limited access to ground truth. We use active learning combined with streaming-based oversampling that uses both information about current class ratios and classifier errors on each class to create new instances in a meaningful way. Conducted experimental study shows that our single-classifier framework is capable of outperforming state-of-the-art ensembles dedicated to multi-class imbalanced data streams in both fully supervised and sparsely labeled learning scenarios.
An Interactive Mixed Reality Platform for Bedside Surgical Procedures

In many bedside procedures, surgeons must rely on their spatiotemporal reasoning to estimate the position of an internal target by manually measuring external anatomical landmarks. One particular example that is performed frequently in neurosurgery is ventriculostomy, where the surgeon inserts a catheter into the patient's skull to divert the cerebrospinal fluid and alleviate the intracranial pressure. However, about one-third of the insertions miss the target.
Towards the use of the readily available tests from the release pipeline as performance tests

Performance is one of the important aspects of software quality. Performance issues exist widely in software systems, and the process of fixing the performance issues is an essential step in the release cycle of software systems. Although performance testing is widely adopted in practice, it is still expensive and time-consuming. In particular, the performance testing is usually conducted after the system is built in a dedicated testing environment. The challenges of performance testing make it difficult to fit into the common DevOps process in software development. On the other hand, there exist a large number of tests readily available, that are executed regularly within the release pipeline during software development. In this paper, we perform an exploratory study to determine whether such readily available tests are capable of serving as performance tests. In particular, we would like to see whether the performance of these tests can demonstrate performance improvements obtained from fixing real-life performance issues. We collect 127 performance issues from Hadoop and Cassandra, and evaluate the performance of the readily available tests from the commits before and after the performance issue fixes. We find that most of the improvements from the fixes to performance issues can be demonstrated using the readily available tests in the release pipeline. However, only a very small portion of the tests can be used for demonstrating the improvements. By manually examining the tests, we identify eight reasons that a test cannot demonstrate performance improvements even though it covers the changed source code of the issue fix. Finally, we build random forest classifiers determining the important metrics influencing the readily available tests (not) being able to demonstrate performance improvements from issue fixes. We find that the test code itself and the source code covered by the test are important factors, while the factors related to the code changes in the performance issues fixes have a low importance. Practitioners may focus on designing and improving the tests, instead of fine-tuning tests for different performance issues fixes. Our findings can be used as a guideline for practitioners to reduce the amount of effort spent on leveraging and designing tests that run in the release pipeline for performance assurance activities.
Planning for untangling

Merge conflicts are inevitable in collaborative software development and are disruptive. When they occur, developers have to stop their current work, understand the conflict and the surrounding code, and plan an appropriate resolution. However, not all conflicts are equally problematic---some can be easily fixed, while others might be complicated enough to need multiple people. Currently, there is not much support to help developers plan their conflict resolution. In this work, we aim to predict the difficulty of a merge conflict so as to help developers plan their conflict resolution. The ability to predict the difficulty of a merge conflict and to identify the underlying factors for its difficulty can help tool builders improve their conflict detection tools to prioritize and warn developers of difficult conflicts. In this work, we investigate the characteristics of difficult merge conflicts, and automatically classify them. We analyzed 6,380 conflicts across 128 java projects and found that merge conflict difficulty can be accurately predicted (AUC of 0.76) through machine learning algorithms, such as bagging.
MLGuard: Mitigating Poisoning Attacks in Privacy Preserving Distributed Collaborative Learning

Distributed collaborative learning has enabled building machine learning models from distributed mobile users' data. It allows the server and users to collaboratively train a learning model where users only share model parameters with the server. To protect privacy, the server can use secure multiparty computation to learn the global model without revealing users' parameter updates in the clear. However this privacy preserving distributed learning opens the door to poisoning attacks, where malicious users poison their training data to maliciously influence the behavior of the global model. In this paper, we propose MLGuard, a privacy preserving distributed collaborative learning system with poisoning attack mitigation. MLGuard employs lightweight secret sharing scheme and a novel poisoning attack mitigation technique. We address several challenges such as preserving users' privacy, mitigating poisoning attacks, respecting resource constraints of mobile devices, and scaling to large number of users. Evaluation results demonstrate the effectiveness of MLGuard on building high accurate learning models with the existence of malicious users, while imposing minimal communication cost on mobile devices.
FarSee-Net: Real-Time Semantic Segmentation by Efficient Multi-scale Context Aggregation and Feature Space Super-resolution

Real-time semantic segmentation is desirable in many robotic applications with limited computation resources. One challenge of semantic segmentation is to deal with the object scale variations and leverage the context. How to perform multi-scale context aggregation within limited computation budget is important. In this paper, firstly, we introduce a novel and efficient module called Cascaded Factorized Atrous Spatial Pyramid Pooling (CF-ASPP). It is a lightweight cas-caded structure for Convolutional Neural Networks (CNNs) to efficiently leverage context information. On the other hand, for runtime efficiency, state-of-the-art methods will quickly decrease the spatial size of the inputs or feature maps in the early network stages. The final high-resolution result is usually obtained by non-parametric up-sampling operation (e.g. bilinear interpolation). Differently, we rethink this pipeline and treat it as a super-resolution process. We use optimized super-resolution operation in the up-sampling step and improve the accuracy, especially in sub-sampled input image scenario for real-time applications. By fusing the above two improvements, our methods provide better latency-accuracy trade-off than the other state-of-the-art methods. In particular, we achieve 68.4% mIoU at 84 fps on the Cityscapes test set with a single Nivida Titan X (Maxwell) GPU card. The proposed module can be plugged into any feature extraction CNN and benefits from the CNN structure development.
Scalable Multi-Task Imitation Learning with Autonomous Improvement

While robot learning has demonstrated promising results for enabling robots to automatically acquire new skills, a critical challenge in deploying learning-based systems is scale: acquiring enough data for the robot to effectively generalize broadly. Imitation learning, in particular, has remained a stable and powerful approach for robot learning, but critically relies on expert operators for data collection. In this work, we target this challenge, aiming to build an imitation learning system that can continuously improve through autonomous data collection, while simultaneously avoiding the explicit use of reinforcement learning, to maintain the stability, simplicity, and scalability of supervised imitation. To accomplish this, we cast the problem of imitation with autonomous improvement into a multi-task setting. We utilize the insight that, in a multi-task setting, a failed attempt at one task might represent a successful attempt at another task. This allows us to leverage the robot's own trials as demonstrations for tasks other than the one that the robot actually attempted. Using an initial dataset of multitask demonstration data, the robot autonomously collects trials which are only sparsely labeled with a binary indication of whether the trial accomplished any useful task or not. We then embed the trials into a learned latent space of tasks, trained using only the initial demonstration dataset, to draw similarities between various trials, enabling the robot to achieve one-shot generalization to new tasks. In contrast to prior imitation learning approaches, our method can autonomously collect data with sparse supervision for continuous improvement, and in contrast to reinforcement learning algorithms, our method can effectively improve from sparse, task-agnostic reward signals.
Cross-shard Transaction Processing in Sharding Blockchains

Sharding blockchains could improve the transaction throughput and achieve scalibility, making the application fields of the blockchain technology more extensive. Cross-shard transactions account for a large fraction of transactions in a sharding blockchain, so the processing method of cross-shard transactions is of vital importance to the system efficiency. In this paper, we focus on the study of cross-shard transaction processing methods. Firstly, a summary of cross-shard transaction processing methods for sharding blockchains is given. Secondly, we propose RSTBP, which is built on the basis of a two phase commit protocol. In RSTBP, an input shard runs an intra-shard consensus algorithm, i.e., a Byzantine fault tolerance (BFT) algorithm, to process multiple inputs of different transactions simultaneously. For each input, a corresponding proof of availability is generated and sent to the relevant shards. Compared with previous schemes, the number of BFT calls is reduced by hundreds of times when processing the same number of transactions. Thirdly, RSTSBP is designed by making some modifications to RSTBP. The proofs of availability are constructed according to different shards. The Merkel tree structure is different from that of RSTBP to cut down message complexity of the proofs. Both of the two schemes are proved to satisfy the consistency, liveness and responsiveness properties, and improve the cross-shard transaction processing efficiency.