Reviewerly/exHarmony huggingface dataset:
Black-box image deblurring and defiltering

Given an image filter, defiltering refers to the problem of recovering an original image from its filtered version, assuming that the internal structure of the filter is not known. In this paper, we propose five iterative image defiltering schemes and use them for a semi-blind image deblurring problem. Namely, given an image resulting from applying a blurring image filter corrupted by noise to a clean image, we use the proposed iterative schemes to achieve a restoration of the clean image. In particular, for the motion deblurring problem, we show that our defiltering schemes are competitive with modern non-blind image deconvolution methods while using less information. The schemes are inspired by classical methods solving inverse problems and consist of properly modified and extended versions of the Van Cittert iterations, Levenberg–Marquardt method, Wiener filter, Landweber iterations, and Richardson–Lucy algorithm. In addition to dealing with image deblurring problems, we show that the proposed schemes can be used for inverting non-linear filters, and show that they are competitive with state-of-the-art black-box defiltering methods for these problems.
Impacts of Data Anonymization on Semantic Segmentation

For the development of machine learning-based driver assistance systems and highly automated driving functions, training data play a significant role in ensuring machine learning algorithms generalize well on real driving scenarios. However, data protection regulations in Europe require that individuals’ data should be processed in such a way that the individual cannot be identified from the collected data. Therefore, before camera images taken from test vehicles save on a server, license plates and faces of individuals should be anonymized first. Nevertheless, the impact of using anonymized data on the performance of machine learning algorithms remains unclear. Our work aims to evaluate the impact of anonymization on the task of semantic segmentation using diverse neural network architectures, a range of input image resolutions, and different anonymization patterns. We observe statistically significant effects of anonymizing image data on model performance and investigate methods for mitigating segmentation precision loss.
Brain Tumor Segmentation Using U-net and U-net++ Networks

Segmentation of brain tumors helps with the diagnosis and treatment tasks. Due to the large number of patients and the high cost of manual segmentation, researchers proposed automatic segmentation methods. The most popular of these are methods based on deep learning and neural networks. In this paper, we implemented an automatic segmentation using U-net++, which is based on deep convolutional neural networks, on two publicly available data sets. Using the U-Net++architecture, the whole tumor (WT), is segmented with an accuracy of 90.37% based on the Dice similarity coefficient (DSC) for the Brats2018 dataset and 89.13% for the Brats2015 dataset. For comparison with the prior works, we implemented an alternative approach using UNet architecture, which segmented WT with an accuracy of 89.21% for the Brats2018 dataset and 89.12% for the Brats2015 dataset. As the results suggest, leveraging U-Net++ for tumor segmentation provides improvement in WT segmentation at a cost of modest increase in runtime.
Exploring the Impact of Virtualization on the Usability of Deep Learning Applications

Deep Learning-based (DL) applications are becoming increasingly popular and advancing at an unprecedented pace. While many research works are being undertaken to enhance Deep Neural Networks (DNN)-the centerpiece of DL applications-practical deployment challenges of these applications in the Cloud and Edge systems, and their impact on the usability of the applications have not been sufficiently investigated. In particular, the impact of deploying different virtualization platforms, offered by the Cloud and Edge, on the usability of DL applications (in terms of the End-to-End (E2E) inference time) has remained an open question. Importantly, resource elasticity (by means of scale-up), CPU pinning, and processor type (CPU vs GPU) configurations have shown to be influential on the virtualization overhead. Accordingly, the goal of this research is to study the impact of these potentially decisive deployment options on the E2E performance, thus, usability of the DL applications. To that end, we measure the impact of four popular execution platforms (namely, bare-metal, virtual machine (VM), container, and container in VM) on the E2E inference time of four types of DL applications, upon changing processor configuration (scale-up, CPU pinning) and processor types. This study reveals a set of interesting and sometimes counter-intuitive findings that can be used as best practices by Cloud solution architects to efficiently deploy DL applications in various systems. The notable finding is that the solution architects must be aware of the DL application characteristics, particularly, their pre- and post-processing requirements, to be able to optimally choose and configure an execution platform, determine the use of GPU, and decide the efficient scale-up range.
Prioritization of Compiled Quantum Circuits for Different Quantum Computers

Quantum computers can solve certain problems faster than classical computers. The number of quantum computers offered via the cloud increases such that a variety is accessible for the public. However, current quantum computers still suffer from a small number of qubits and high error rates. Selecting a quantum computer providing enough qubits and low error rates is tremendously important to receive stable execution results for a given quantum circuit. The execution results are also influenced by the compilation result of the selected quantum compiler for hardware mapping and optimization. For computing time, the access is regulated by vendors of quantum computers resulting in waiting times for the user. Thus, a deep and manifold analysis of quantum computers and compiled circuits of different quantum compilers is required to estimate in advance which compiled circuit will return stable execution results also in consideration of non-functional requirements such as waiting times. To address this, we present a framework that analyzes and prioritizes compiled circuits for different quantum computers using several compilers based on the requirements of the user. We show the practical feasibility of our framework by a prototype and two use cases.
Toward Understanding the Impact of Refactoring on Program Comprehension

Software refactoring is the activity associated with developers changing the internal structure of source code without modifying its external behavior. The literature argues that refactoring might have beneficial and harmful implications for software maintainability, primarily when performed without the support of automated tools. This paper continues the narrative on the effects of refactoring by exploring the dimension of program comprehension, namely the property that describes how easy it is for developers to understand source code. We start our investigation by assessing the basic unit of program comprehension, namely program readability. Next, we set up a large-scale empirical investigation &#x2013; conducted on 156 open-source projects &#x2013; to quantify the impact of refactoring on program readability. First, we mine refactoring data and, for each commit involving a refactoring, we compute (i) the amount and type(s) of refactoring actions performed and (ii) eight state-of-the-art program comprehension metrics. Afterwards, we build statistical models relating the various refactoring operations to each of the readability metrics considered to quantify the extent to which each refactoring impacts the metrics in either a positive or negative manner. The key results are that refactoring has a notable impact on most of the readability metrics considered.
Recommending Base Image for Docker Containers based on Deep Configuration Comprehension

Docker containers are being widely used in large-scale industrial environments. In practice, developers must manually specify the base image in the dockerfile in the process of container creation. However, finding the proper base image is a nontrivial task because manually searching is time-consuming and easily leads to the use of unsuitable base images, especially for newcomers. There is still a lack of automatic approaches for recommending related base image for developers through dockerfile configuration. To tackle this problem, this paper makes the first attempt to propose a neural network approach named DCCimagerec which is based on deep configuration comprehension. It aims to use the structural configuration features of dockerfile extracted by AST and path-attention model to recommend potentially suitable base image. The evaluation experiments based on about 83,000 dockerfiles show that DCCimagerec outperforms multiple baselines, improving Precision by 7.5&#x0025;-67.5&#x0025;, Recall by 6.2&#x0025;-106.6&#x0025;, and F1 by 7.5&#x0025;-150.2&#x0025;.
Online Handwriting Based Adult and Child Classification using Machine Learning Techniques

Different age group classification of handwriting such as child and adults is important in many different applications. In forensics, investigators obtain help from handwriting classification in the category of writers. There is little research conducted in this area. In this article, we proposed a novel approach to clarify changes in the development of handwritten words between adults and children and classify them based on their handwriting. In this work, we extracted various statistical and kinetic features from handwritten data and then applied sequential forward floating selection (SFFS) to select efficient features. Support vector machine (SVM) and random forest (RF) were employed to perform the classification. We developed a new dataset to evaluate our method where adult and child handwriting data was collected using a pen tablet. Each subject (child or adult) wrote the same 30 words (tasks) by using Japanese hiragana characters only. We performed classifications from three different views: i) perform classification using all tasks, ii) use information about the average of all tasks, and iii) individually each task. The results showed that the proposed approach substantially produced up to 93.0% accuracy in adults and child classification. We hope that this study provides evidence of the possibility of classifying children and adults based on handwriting data.
The Security of Blockchain-Based Medical Systems: Research Challenges and Opportunities

The current COVID-19 pandemic has, perhaps, expedited the move to electronic medical systems (e.g., telemedicine). However, in the digitalization of healthcare services, we have to ensure the security and privacy of (sensitive) healthcare data, often stored locally in the hospital’s server or remotely within a trusted cloud server. There have been many attempts to design blockchain-based approaches to support security and privacy in medical systems, and this is the focus of this article where we systematically review the existing literature on blockchain-based medical systems. We then categorize the existing security solutions into three categories, namely, 1) decentralized authentication, 2) access control, and 3) audit, and discuss the privacy protection technologies in blockchain-based healthcare systems. Based on our analysis, we identify a number of challenges, including performance limitations and inflexible audit, as well as future research opportunities (e.g., the need for lightweight security schemes for blockchain-based medical systems).
Neural Contourlet Network for Monocular 360° Depth Estimation

For a monocular 360 image, depth estimation is a challenging because the distortion increases along the latitude. To perceive the distortion, existing methods devote to designing a deep and complex network architecture. In this paper, we provide a new perspective that constructs an interpretable and sparse representation for a 360 image. Considering the importance of the geometric structure in depth estimation, we utilize the contourlet transform to capture an explicit geometric cue in the spectral domain and integrate it with an implicit cue in the spatial domain. Specifically, we propose a neural contourlet network consisting of a convolutional neural network and a contourlet transform branch. In the encoder stage, we design a spatial-spectral fusion module to effectively fuse two types of cues. Contrary to the encoder, we employ the inverse contourlet transform with learned low-pass subbands and band-pass directional subbands to compose the depth in the decoder. Experiments on the three popular panoramic image datasets demonstrate that the proposed approach outperforms the state-of-the-art schemes with faster convergence. Code is available at https://github.com/zhijieshen-bjtu/Neural-Contourlet-Network-for-MODE.
AC-SGD: Adaptively Compressed SGD for Communication-Efficient Distributed Learning

Gradient compression (e.g., gradient quantization and gradient sparsification) is a core technique in reducing communication costs in distributed learning systems. The recent trend of gradient compression is to use a varying number of bits across iterations, however, relying on empirical observations or engineering heuristics without a systematic treatment and analysis. To the best of our knowledge, a general dynamic gradient compression that leverages both quantization and sparsification techniques is still far from understanding. This paper proposes a novel Adaptively-Compressed Stochastic Gradient Descent (AC-SGD) strategy to adjust the number of quantization bits and the sparsification size with respect to the norm of gradients, the communication budget, and the remaining number of iterations. In particular, we derive an upper bound, tight in some cases, of the convergence error for arbitrary dynamic compression strategy. Then we consider communication budget constraints and propose an optimization formulation - denoted as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Adaptive Compression Problem (ACP)</i> - for minimizing the deep model’s convergence error under such constraints. By solving the ACP, we obtain an enhanced compression algorithm that significantly improves model accuracy under given communication budget constraints. Finally, through extensive experiments on computer vision and natural language processing tasks on MNIST, CIFAR-10, CIFAR-100 and AG-News datasets, respectively, we demonstrate that our compression scheme significantly outperforms the state-of-the-art gradient compression methods in terms of mitigating communication costs.
Visual Loop Closure Detection With Instance Segmentation and Image Inpainting in Dynamic Scenes Using Wearable Camera

Dynamic interference is an important factor that affects the accuracy of loop closure detection in visual SLAM. Traditional methods that remove the feature points in dynamic regions are prone to insufficient image features, which will decrease the reliability of loop closure detection. To overcome this problem, a novel loop closure detection approach based on image inpainting and feature selection is proposed. A dynamic object instance segmentation guided image inpainting network is utilized to segment and repair dynamic regions in scene images. To avoid the negative influence of image inpainting, an image quality evaluation network is used to confine the extraction of Superpoint features only in the properly inpainted patches. Therefore, valid Superpoint features only in the areas with high inpainting qualities are selected as the input of the Bag of Words model for loop-closure detection. We tested the proposed method on both the KITTI dataset and a wearable camera system. Experimental results indicate that the proposed method improves the accuracy of loop closure detection in dynamic scenes.
Aspect-Level Sentiment Analysis via a Syntax-Based Neural Network

Aspect-level sentiment classification (ALSC); a fine-grained task of sentiment analysis holds the promise of machines communicating knowledge of different aspects of a product/service with humans. Specifically, ALSC aims at inferring the sentiment expressed toward a specific aspect term in a user-generated textual content. It is a long-standing problem in sentiment analysis, and its usefulness cannot be underestimated. Recent studies employ the dependency tree and restructure it to model the syntactic relationship among words in text. However, the restructuring process destructs the structural information of the original dependency tree. As a solution, we first construct a syntax graph that preserves the structural information of the dependency tree. We then propose a reliable syntax-based neural network model that performs a thorough search on the syntax graph to effectively find the relevant contextual information with respect to the aspect term for the sentence encoding. Noting that dependency trees parsed from existing dependency parsers may contain incorrect syntactic dependencies due to grammatical errors in a sentence, we adopt a convolutional layer that takes into account the relations among features of words in a local neighborhood to mitigate the issues brought by incorrect syntactic dependencies. Our results on benchmark datasets demonstrate that our model outperforms the previous methods and achieves state-of-the-art results for the ALSC task.
Multimodal Fusion of Deeply Inferred Point Clouds for 3D Scene Reconstruction Using Cross-Entropy ICP

Depth estimation is a crucial step toward 3D scene understanding. Most traditional systems rely on direct sensing of this information by means of photogrammetry or on stereo imaging. As the scenes getting more complex, these modalities were impeded by, for instances, occlusion and imperfect lighting condition, etc. As a consequence, reconstructed surfaces are normally left with voids, due to missing data. Therefore, surface regularization is often required as post-processing. With the recent advances in deep learning, depth inference from a monocular image has attracted considerable interests. Many convolutional architectures have been proposed to infer depth information from a monocular image, with promising results. Thus far, visual cues learned and generalized by these networks may be ambiguous, resulting in inaccurate estimation. To address these issues, this paper presents an effective method for fusing point clouds extracted from depth values, directly measured by an infrared camera and estimated by a modified ResNet-50 from an RGB image, of the same scene. To ensure robustness and efficiency of finding the correspondence between and aligning these point clouds, an information theoretic alignment strategy, called CEICP, was proposed. The experimental results on a public dataset demonstrated that the proposed method outperformed its counterparts, while producing good quality surface renditions of the underlying scene.
iCEMIGE: Integration of CEll-morphometrics, MIcrobiome, and GEne biomarker signatures for risk stratification in breast cancers

The development of precision medicine is essential for personalized treatment and improved clinical outcome, whereas biomarkers are critical for the success of precision therapies.To investigate whether iCEMIGE (integration of CEll-morphometrics, MIcro biome, and GEne biomarker signatures) improves risk stratification of breast cancer (BC) patients.We used our recently developed machine learning technique to identify cellular morphometric biomarkers (CMBs) from the whole histological slide images in The Cancer Genome Atlas (TCGA) breast cancer (TCGA-BRCA) cohort. Multivariate Cox regression was used to assess whether cell-morphometrics prognosis score (CMPS) and our previously reported 12-gene expression prognosis score (GEPS) and 15-microbe abundance prognosis score (MAPS) were independent prognostic factors. iCEMIGE was built upon the sparse representation learning technique. The iCEMIGE scoring model performance was measured by the area under the receiver operating characteristic curve compared to CMPS, GEPS, or MAPS alone. Nomogram models were created to predict overall survival (OS) and progress-free survival (PFS) rates at 5- and 10-year in the TCGA-BRCA cohort.We identified 39 CMBs that were used to create a CMPS system in BCs. CMPS, GEPS, and MAPS were found to be significantly independently associated with OS. We then established an iCEMIGE scoring system for risk stratification of BC patients. The iGEMIGE score has a significant prognostic value for OS and PFS independent of clinical factors (age, stage, and estrogen and progesterone receptor status) and PAM50-based molecular subtype. Importantly, the iCEMIGE score significantly increased the power to predict OS and PFS compared to CMPS, GEPS, or MAPS alone.Our study demonstrates a novel and generic artificial intelligence framework for multimodal data integration toward improving prognosis risk stratification of BC patients, which can be extended to other types of cancer.
AdvU-Net: Generating Adversarial Example Based on Medical Image and Targeting U-Net Model

Deep neural networks (DNNs) provide excellent performance in image recognition, speech recognition, video recognition, and pattern analysis. These neural networks are often applied in the medical field to predict or classify patients’ illnesses. One such network, the U-Net model, has shown good performance in data segmentation and is an important technology in medical imaging. However, deep neural networks such as those applied in medicine are vulnerable to attack by adversarial examples. Adversarial examples are samples created by adding a small amount of noise to an original data sample that is difficult for a human to see but that induces misclassification by the targeted model. In this paper, we propose AdvU-Net, a method for generating an adversarial example targeting the U-Net model used in segmentation. Performance was analyzed according to epsilon, using the fast gradient sign method (FGSM) for generating adversarial examples. We used ISBI 2012 as the dataset and TensorFlow as the machine learning library. In the experiment, when an adversarial example was generated using an epsilon value of 0.3, the pixel error was 3.54 or greater while the pixel error of the original sample was maintained at 0.15 or less.
Investigating the role of central banks in the interconnection between financial markets and cryptoassets

Over the past two years, financial markets have been heavily affected by central banks’ unconventional monetary policies, in particular quantitative easing (QE). Accommodative monetary policies have also brought interest rates down to zero and/or negative territory as a mean to stimulate business investments. The recent rise of cryptocurrencies (The terms cryptocurrencies and crypto assets are used interchangeably.) poses, in turn, urgent challenges to central banks’ efforts to face the multiple pandemic-driven unprecedented consequences. Private currencies may lead to a relatively cashless society, thereby threatening central bank monopoly of issuance, a situation in which businesses, households and financial markets could lose access to risk-free central bank money. In addition, Decentralized Finance (DeFi) promises to transform the traditional financial system into an open, permission-less and autonomous financial system. The present paper discusses the rise of digital payments across the euro area, the spread of cryptocurrency trading and the intention of the European Central Bank (ECB) to issue a central bank digital currency (CBDC). We provide evidence on the existence of cointegration between selected cryptocurrencies, stablecoins and traditional financial assets. Both the rise of digital payments and the increased popularity of cryptocurrencies can be seen as the main drivers behind the prospective issuance of the digital euro, significantly affecting financial markets and business models alike.