Reviewerly/exHarmony huggingface dataset:
Realistic hair simulator for skin lesion images: A novel benchemarking tool

Automated skin lesion analysis is one of the trending fields that has gained attention among the dermatologists and health care practitioners. Skin lesion restoration is an essential pre-processing step for lesion enhancements for accurate automated analysis and diagnosis by both dermatologists and computer-aided diagnosis tools. Hair occlusion is one of the most popular artifacts in dermatoscopic images. It can negatively impact the skin lesions diagnosis by both dermatologists and automated computer diagnostic tools. Digital hair removal is a non-invasive method for image enhancement for decrease the hair-occlusion artifact in previously captured images. Several hair removal methods were proposed for skin delineation and removal without standardized benchmarking techniques. Manual annotation is one of the main challenges that hinder the validation of these proposed methods on a large number of images or against benchmarking datasets for comparison purposes. In the presented work, we propose a photo-realistic hair simulator based on context-aware image synthesis using image-to-image translation techniques via conditional adversarial generative networks for generation of different hair occlusions in skin images, along with ground-truth mask for hair location. Hair-occluded image is synthesized using the latent structure of any input hair-free image by deep encoding the input image into a latent vector of features. The locations of required hair are highlighted using white pixels on the input image. Then, these deep encoded features are used to reconstruct the synthetic highly realistic hair-occluded image. Besides, we explored using three loss functions including L1-norm, L2-norm and structural similarity index (SSIM) to maximize the image synthesis visual quality. For the evaluation of the generated samples, the t-SNE feature mapping and Bland–Altman test are used as visualization tools for the experimental results. The results show the superior performance of our proposed method compared to previous methods for hair synthesis with plausible colours and preserving the integrity of the lesion texture. The proposed method can be used to generate benchmarking datasets for comparing the performance of digital hair removal methods. The code is available online at: https://github.com/attiamohammed/realhair.
Binary Ostensibly‐Implicit Trees for Fast Collision Detection

Abstract We present a simple, efficient and low‐memory technique, targeting fast construction of bounding volume hierarchies (BVH) for broad‐phase collision detection. To achieve this, we devise a novel representation of BVH trees in memory. We develop a mapping of the implicit index representation to compact memory locations, based on simple bit‐shifts, to then construct and evaluate bounding volume test trees (BVTT) during collision detection with real‐time performance. We model the topology of the BVH tree implicitly as binary encodings which allows us to determine the nodes missing from a complete binary tree using the binary representation of the number of missing nodes. The simplicity of our technique allows for fast hierarchy construction achieving over 6× speedup over the state‐of‐the‐art. Making use of these characteristics, we show that not only it is feasible to rebuild the BVH at every frame, but that using our technique, it is actually faster than refitting and more memory efficient.
Multiview Spectral Clustering via Robust Subspace Segmentation

Multiview clustering refers to partition data according to its multiple views, where information from different perspectives can be jointly used in some certain complementary manner to produce more sensible clusters. It is believed that most of the existing multiview clustering methods technically suffer from possibly corrupted data, resulting in a dramatically decreased clustering performance. To overcome this challenge, we propose a multiview spectral clustering method based on robust subspace segmentation in this article. Our proposed algorithm is composed of three modules, that is: 1) the construction of multiple feature matrices from all views; 2) the formulation of a shared low-rank latent matrix by a low rank and sparse decomposition; and 3) the use of the Markov-chain-based spectral clustering method for producing the final clusters. To solve the optimization problem for a low rank and sparse decomposition, we develop an optimization procedure based on the scheme of the augmented Lagrangian method of multipliers. The experimental results on several benchmark datasets indicate that the proposed method outperforms favorably compared to several state-of-the-art multiview clustering techniques.
Automated classification of actions in bug reports of mobile apps

When users encounter problems with mobile apps, they may commit such problems to developers as bug reports. To facilitate the processing of bug reports, researchers proposed approaches to validate the reported issues automatically according to the steps to reproduce specified in bug reports. Although such approaches have achieved high success rate in reproducing the reported issues, they often rely on a predefined vocabulary to identify and classify actions in bug reports. However, such manually constructed vocabulary and classification have significant limitations. It is challenging for the vocabulary to cover all potential action words because users may describe the same action with different words. Besides that, classification of actions solely based on the action words could be inaccurate because the same action word, appearing in different contexts, may have different meaning and thus belongs to different action categories. To this end, in this paper we propose an automated approach, called MaCa, to identify and classify action words in Mobile apps’ bug reports. For a given bug report, it first identifies action words based on natural language processing. For each of the resulting action words, MaCa extracts its contexts, i.e., its enclosing segment, the associated UI target, and the type of its target element by both natural language processing and static analysis of the associated app. The action word and its contexts are then fed into a machine learning based classifier that predicts the category of the given action word in the given context. To train the classifier, we manually labelled 1,202 actions words from 525 bug reports that are associated with 207 apps. Our evaluation results on manually labelled data suggested that MaCa was accurate with high accuracy varying from 95% to 96.7%. We also investigated to what extent MaCa could further improve existing approaches (i.e., Yakusu and ReCDroid) in reproducing bug reports. Our evaluation results suggested that integrating MaCa into existing approaches significantly improved the success rates of ReCDroid and Yakusu by 22.7% = (69.2%-56.4%)/56.4% and 22.9%= (62.7%-51%)/51%, respectively.
Unsupervised Anomaly Detection for Self-flying Delivery Drones

We propose a novel anomaly detection framework for a fleet of hybrid aerial vehicles executing high-speed package pickup and delivery missions. The detection is based on machine learning models of normal flight profiles, trained on millions of flight log measurements of control inputs and sensor readings. We develop a new scalable algorithm for robust regression which can simultaneously fit predictive flight dynamics models while identifying and discarding abnormal flight missions from the training set. The resulting unsupervised estimator has a very high breakdown point and can withstand massive contamination of training data to uncover what normal flight patterns look like, without requiring any form of prior knowledge of aircraft aerodynamics or manual labeling of anomalies upfront. Across many different anomaly types, spanning simple 3sigma statistical thresholds to turbulence and other equipment anomalies, our models achieve high detection rates across the board. Our method consistently outperforms alternative robust detection methods on synthetic benchmark problems. To the best of our knowledge, dynamics modeling of hybrid delivery drones for anomaly detection at the scale of 100 million measurements from 5000 real flight missions in variable flight conditions is unprecedented.
The Power of ARM64 in Public Clouds

ARM processors, with their low power consumption and heat dissipation, have been highly successful in embedded systems. In the recent past, there have been attempts to adopt these energy-efficient processors for servers in data centers. However, a fundamental question remains open with ARM-based systems on server side is whether they are capable of handling compute-intensive workloads at scale. This paper gives our answer to this question with an empirical approach. We study the performance characteristics of the Amazon Graviton Processor - an ARM64 processor with the Cortex-A72 micro-architecture - using the A1 (Graviton) product family on AWS EC2, with comparisons to the I3 and M5 product families based on Intel Xeon processors. We use a combination of micro benchmark and performance counters to identify the lack of L3 cache and the slower memory access speed limit Graviton's capability in achieving higher performance. We confirm Graviton's capability in handling various large-scale horizontally scalable compute-intensive workloads, including multi-tier web service, video transcoding and terabyte scale sorting. In our large-scale evaluations, the test worker fleet has up to 1600 vCPU cores, which is by far the largest ARM64 cluster that has been reported. We observe that the A1 product family achieves the same price-performance in multi-tier web service, up to 37% cost saving in video transcoding, and up to 65% cost saving in terabyte scale sorting, as compared with the I3 and M5 product families.
Identification of Communication Signals Using Learning Approaches for Cognitive Radio Applications

Signal detection, identification, and characterization are among the major challenges in aerial communication systems.The ability to detect and recognize signals using cognitive technologies is still under active development when addressing uncertainties regarding signal parameters, such as blank spaces available within the transmitted signal and the utilized bandwidth.This paper proposes a learning-based identification framework for heterogeneous signals with orthogonal frequency division multiplexing (OFDM) modulation as generated in a simulated environment at an a priori unknown frequency.The implemented region-based signal identification method utilizes cyclostationary features for robust signal detection.Signal characterization is performed using a purposely-built, lightweight, region-based convolutional neural network (R-CNN).It is shown that the proposed framework is robust in the presence of additive white Gaussian noise (AWGN) and, despite its simplicity, shows better performance compared with conventional popular network architectures, such as GoogLeNet, AlexNet, and VGG 16.The signal characterization performance is validated under two degraded environments that are unknown to the system: Doppler shifted and small-scale fading.High performance is demonstrated under both degraded conditions over a wide range of signal to noise ratios (SNRs) and it is shown that the detection probability for the proposed approach is improved over those for conventional energy detectors.It is found that the signal characterization performance deteriorates under extreme conditions, such as lower SNRs and higher Doppler shifts.
Inside Out: Exploring the Emotional Side of Search Engines in the Classroom

In the classroom, children mainly use general search systems such as Google, Baidu or Bing. For many years and from different perspectives, a call has been made that it is necessary to provide children in an educational context with child-friendly search systems. Research responding to this call often focuses on the relevance, readability and reliability of the retrieved documents. Instead, inspired by a recent study based on adult users on the role emotions play in web search, we explore whether and how children searching in a school context react to the emotional content often part of Search Engine Result Pages. We do so by examining emotions inferred from queries and corresponding retrieved resources in query logs produced by children ages 9 to 11 in a classroom setting in 3 different countries. We also consider teachers' observations that contextualize this analysis.
Dynamic Double Classifiers Approximation for Cross-Domain Recognition

In general, existing cross-domain recognition methods mainly focus on changing the feature representation of data or modifying the classifier parameter and their efficiencies are indicated by the better performance. However, most existing methods do not simultaneously integrate them into a unified optimization objective for further improving the learning efficiency. In this article, we propose a novel cross-domain recognition algorithm framework by integrating both of them. Specifically, we reduce the discrepancies in both the conditional distribution and marginal distribution between different domains in order to learn a new feature representation which pulls the data from different domains closer on the whole. However, the data from different domains but the same class cannot interlace together enough and thus it is not reasonable to mix them for training a single classifier. To this end, we further propose to learn double classifiers on the respective domain and require that they dynamically approximate to each other during learning. This guarantees that we finally learn a suitable classifier from the double classifiers by using the strategy of classifier fusion. The experiments show that the proposed method outperforms over the state-of-the-art methods.
Classification of digital mammograms using information set features and Hanman Transform based classifiers

Several studies have been made in the literature for the early detection of breast cancer using mammograms. These studies mainly deal with methods that do not capture local information. To fill up this gap this paper presents an approach that extracts the local features called information set features representing the uncertainties in the distributions of grey levels in windows/sub-images of a mammogram using the Mamta-Hanman entropy function. The extracted features are used for classification into two-class (abnormal, normal) and three-class (normal, benign, malignant) modes. Two classifiers are used, the first is the Hanman transform classifier that represents the uncertainties in the error vectors between training feature vectors of a patient and the test feature vector of an unknown patient, and the second is hesitancy based Hanman transform classifier that not only represents the uncertainties in the error vectors but also the deficiencies in the modeling of membership and non-membership functions. Both classifiers outperform the methods considered for comparison on the same mini-MIAS database.
Using networks and partial differential equations to forecast bitcoin price movement

Over the past decade, the blockchain technology and its bitcoin cryptocurrency have received considerable attention. Bitcoin has experienced significant price swings in daily and long-term valuations. In this paper, we propose a partial differential equation (PDE) model on the bitcoin transaction network for forecasting the bitcoin price movement. Through analysis of bitcoin subgraphs or chainlets, the PDE model captures the influence of transaction patterns on the bitcoin price over time and combines the effect of all chainlet clusters. In addition, Google Trends index is incorporated to the PDE model to reflect the effect of the bitcoin market sentiment. The experiment results demonstrate that the PDE model is capable of forecasting the bitcoin price movement. The paper is the first attempt to apply a PDE model to the bitcoin transaction network for forecasting.
Cognitive Offloading and Assessment

Cognitive offloading refers to using tools like notes, calculators or spellcheckers to reduce the cognitive demands of a task. Assessment has a patchy history in attending to cognitive offloading. In some settings, such as exams, there are explicit rules that relate to cognitive offloading, such as the allowance or prohibition of textbooks and notes. However in other settings, particularly authentic open-ended tasks there is less clarity. This chapter proposes principles for incorporating cognitive offloading into assessment, with a focus on transparency, programmatic assessment, evaluative judgement and authentic assessment.
Extracting Structured Data from Physician-Patient Conversations by Predicting Noteworthy Utterances

Despite concerted efforts to mine various modalities of medical data, the conversations between physicians and patients at the time of care remain an untapped resource of insights. In this paper, we explore the possibility of leveraging this data to extract structured information that might assist physicians with post-visit documentation in electronic health records, potentially lightening the clerical burden. In this exploratory study, we describe a new dataset consisting of conversation transcripts, post-visit summaries, corresponding supporting evidence (in the transcript), and structured labels. We focus on the tasks of recognizing relevant diagnoses and abnormalities in the review of organ systems (RoS). One methodological challenge is that the conversations are long (around 1500 words) making it difficult for modern deep-learning models to use them as input. To address this challenge, we extract noteworthy utterances—parts of the conversation likely to be cited as evidence supporting some summary sentence. We find that by first filtering for (predicted) noteworthy utterances, we can significantly boost predictive performance for recognizing both diagnoses and RoS abnormalities.
A Survey on Sketch Based Content Creation: from the Desktop to Virtual and Augmented Reality

Abstract Sketching is one of the most natural ways for representing any object pictorially. It is however, challenging to convert sketches to 3D content that is suitable for various applications like movies, games and computer aided design. With the advent of more accessible Virtual Reality (VR) and Augmented Reality (AR) technologies, sketching can potentially become a more powerful yet easy‐to‐use modality for content creation. In this state‐of‐the‐art report, we aim to present a comprehensive overview of techniques related to sketch based content creation, both on the desktop and in VR/AR. We discuss various basic concepts related to static and dynamic content creation using sketches. We provide a structured review of various aspects of content creation including model generation, coloring and texturing, and finally animation. We try to motivate the advantages that VR/AR based sketching techniques and systems can offer into making sketch based content creation a more accessible and powerful mode of expression. We also discuss and highlight various unsolved challenges that current sketch based techniques face with the goal of encouraging future research in the domain.
MARBLE: A Multi-GPU Aware Job Scheduler for Deep Learning on HPC Systems

Deep learning (DL) has become a key tool for solving complex scientific problems. However, managing the multi-dimensional large-scale data associated with DL, especially atop extant multiple graphics processing units (GPUs) in modern supercomputers poses significant challenges. Moreover, the latest high-performance computing (HPC) architectures bring different performance trends in training throughput compared to the existing studies. Existing DL optimizations such as larger batch size and GPU locality-aware scheduling have little effect on improving DL training throughput performance due to fast CPU-to-GPU connections. Additionally, DL training on multiple GPUs scales sublinearly. Thus, simply adding more GPUs to a system is ineffective. To this end, we design MARBLE, a first-of-its-kind job scheduler, which considers the non-linear scalability of GPUs at the intra-node level to schedule an appropriate number of GPUs per node for a job. By sharing the GPU resources on a node with multiple DL jobs, MARBLE avoids low GPU utilization in current multi-GPU DL training on HPC systems. Our comprehensive evaluation in the Summit supercomputer shows that MARBLE is able to improve DL training performance by up to 48.3% compared to the popular Platform Load Sharing Facility (LSF) scheduler. Compared to the state-of-the-art of DL scheduler, Optimus, MARBLE reduces the job completion time by up to 47%.
Self-Attention for Cyberbullying Detection

In recent years, cyberbullying has grown out of proportion due to the increasing usage of social media platforms along with the benefit of user anonymization over the Internet. Affecting people across all demographics, the effect of cyberbullying has been more pronounced over adolescents and insecure individuals. Victims suffer from societal isolation, depression, degrading self-confidence and suicidal thoughts. Thus, prevention of cyberbullying becomes a necessity and requires timely detection. Recent advances in Deep learning and Natural Language Processing have provided suitable models to predict whether a text sample is an example of cyberbullying. In this context, we explore the adaptivity and efficiency of self-attention models in detecting cyberbullying. Though a few of the recent works in this context have employed models like deep neural networks, SVM, CNN, LSTM and other hybrid models, to the best of our knowledge, this is the first work exploring self-attention models which have achieved state-of-the-art accuracies in Machine Translation tasks since 2017. We experiment with the Wikipedia, Formspring and Twitter cyberbullying datasets and achieve more efficient results over existing cyberbullying detection models. We also propose new research directions within cyberbullying detection over recent forms of media like Internet memes which pose a variety of new and hybrid problems.
DLSAG: Non-interactive Refund Transactions for Interoperable Payment Channels in Monero

Monero has emerged as one of the leading cryptocurrencies with privacy by design. However, this comes at the price of reduced expressiveness and interoperability as well as severe scalability issues. First, Monero is restricted to coin exchanges among individual addresses and no further functionality is supported. Second, transactions are authorized by linkable ring signatures, a digital signature scheme used in Monero, hindering thereby the interoperability with virtually all the rest of cryptocurrencies that support different digital signature schemes. Third, Monero transactions require an on-chain footprint larger than other cryptocurrencies, leading to a rapid ledger growth and thus scalability issues. This work extends Monero expressiveness and interoperability while mitigating its scalability issues. We present Dual Linkable Spontaneous Anonymous Group Signature for Ad Hoc Groups (DLSAG), a linkable ring signature scheme that enables for the first time non-interactive refund transactions natively in Monero: DLSAG can seamlessly be implemented along with other cryptographic tools already available in Monero such as commitments and range proofs. We formally prove that DLSAG provides the same security and privacy notions introduced in the original linkable ring signature [29] namely, unforgeability, signer ambiguity, and linkability. We have evaluated DLSAG and showed that it imposes even slightly lower computation and similar communication overhead than the current digital signature scheme in Monero, demonstrating its practicality. We further show how to leverage DLSAG to enable off-chain scalability solutions in Monero such as payment channels and payment-channel networks as well as atomic swaps and interoperable payments with virtually all cryptocurrencies available today. DLSAG is currently being discussed within the Monero community as an option for adoption as a key building block for expressiveness, interoperability, and scalability.