Reviewerly/exHarmony huggingface dataset:
OfGAN: Realistic Rendition of Synthetic Colonoscopy Videos

Data-driven methods usually require a large amount of labelled data for training and generalization, especially in medical imaging. Targeting the colonoscopy field, we develop the Optical Flow Generative Adversarial Network (OfGAN) to transform simulated colonoscopy videos into realistic ones while preserving annotation. The advantages of our method are three-fold: the transformed videos are visually much more realistic; the annotation, such as optical flow of the source video is preserved in the transformed video, and it is robust to noise. The model uses a cycle-consistent structure and optical flow for both spatial and temporal consistency via adversarial training. We demonstrate that the performance of our OfGAN overwhelms the baseline method in relative tasks through both qualitative and quantitative evaluation.
Big Data Security and Privacy: Current Challenges and Future Research perspective in Cloud Environment

Cloud computing is an Internet-based technology that emerging rapidly in the last few years due to popular and demanded services required by various institutions, organizations, and individuals. structured, unstructured, semistructured data is transfer at a record pace on to the cloud server. These institutions, businesses, and organizations are shifting more and more increasing workloads on cloud server, due to high cost, space and maintenance issues from big data, cloud computing will become a potential choice for the storage of data. In Cloud Environment, It is obvious that data is not secure completely yet from inside and outside attacks and intrusions because cloud servers are under the control of a third party. The Security of data becomes an important aspect due to the storage of sensitive data in a cloud environment. In this paper, we give an overview of characteristics and state of art of big data and data security & privacy top threats, open issues and current challenges and their impact on business are discussed for future research perspective and review & analysis of previous and recent frameworks and architectures for data security that are continuously established against threats to enhance how to keep and store data in the cloud environment.
Is Implicit Authentication on Smartphones Really Popular? On Android Users’ Perception of “Smart Lock for Android”

Implicit authentication (IA) on smartphones has gained a lot of attention from the research community over the past decade. IA leverages behavioral and contextual data to identify users without requiring explicit input, and thus can alleviate the burden of smartphone unlocking. The reported studies on users’ perception of IA have painted a very positive picture, showing that more than 60% of their respective participants are interested in adopting IA, should it become available on their devices. These studies, however, have all been done either in lab environments, or with low- to medium-fidelity prototypes, which limits their generalizability and ecological validity. Therefore, the question of “how would smartphone users perceive a commercialized IA scheme in a realistic setting?” remains unanswered. To bridge this knowledge gap, we report on the findings of our qualitative user study (N = 26) and our online survey (N = 343) to understand how Android users perceive Smart Lock (SL). SL is the first and currently only widely-deployed IA scheme for smartphones. We found that SL is not a widely adopted technology, even among those who have an SL-enabled phone and are aware of the existence of the feature. Conversely, we found unclear usefulness, and perceived lack of security, among others, to be major adoption barriers that caused the SL adoption rate to be as low as 13%. To provide a theoretical framework for explaining SL adoption, we propose an extended version of the technology acceptance model (TAM), called SL-TAM, which sheds light on the importance of factors such as perceived security and utility on SL adoption.
A rotating machinery fault diagnosis method using composite multiscale fuzzy distribution entropy and minimal error of convex hull approximation

Abstract Rotating machinery plays an increasingly crucial role in mechanical systems. For its normal operation, a novel fault diagnosis method is proposed in this paper, using composite multiscale fuzzy distribution entropy (CMFDE) and minimal error of convex hull approximation (MECHA). In this paper, CMFDE is utilized to extract essential information and measure time series complexity for vibration signals. Results indicate the CMFDE has less information loss and better stability. Then, to fulfill the classification tasks, the first several main features obtained by principal components analysis are fed into the proposed MECHA-based classifier. Results show MECHA has better classification performance. Using the laboratory data, we validate the feasibility and superiority of the proposed fault diagnosis method through two cases consisting of different fault types or fault severity degrees.
Bicycle attacks considered harmful: Quantifying the damage of widespread password length leakage

This work examines the issue of password length leakage via encrypted traffic i.e., bicycle attacks. We aim to quantify both the prevalence of password length leakage bugs as well as the potential harm to users. We discuss several ways in which an eavesdropping attacker could link this password length with a particular user account e.g., a targeted campaign against a smaller group of users or via DNS hijacking for larger scale campaigns. We next use a decision-theoretic model to quantify the extent to which password length leakage might help an attacker to crack user passwords. In our analysis, we consider three different levels of password attackers: hacker, criminal and nation-state. In all cases, we find that such an attacker who knows the length of each user password gains a significant advantage over one without knowing the password length. As part of this analysis, we also release a new differentially private password frequency dataset from the 2016 LinkedIn breach using a differentially private algorithm of Blocki et al. (NDSS 2016) to protect user accounts. We advocate for a new W3C standard on how password fields are handled which would effectively eliminate most instances of password length leakage.
Blind Natural Image Quality Prediction Using Convolutional Neural Networks And Weighted Spatial Pooling

Typically, some regions of an image are more relevant for its perceived quality than the others. On the other hand, subjective image quality is also affected by low level characteristics, such as sensor noise and sharpness. This is why image rescaling, as often used in object recognition, is not a feasible approach for producing input images for convolutional neural networks (CNN) used for blind image quality prediction. Generally, convolution layer can accept images of arbitrary resolution as input, whereas fully connected (FC) layer only can accept a fixed length feature vector. To solve this problem, we propose weighted spatial pooling (WSP) to aggregate spatial information of any size of weight map, which can be used to replace global average pooling (GAP). In this paper, we present a blind image quality assessment (BIQA) method based on CNN and WSP. Our experimental results show that the prediction accuracy of the proposed method is competitive against the state-of-the-art image quality assessment methods.
Photometric Normalization of Chang’e-4 Visible and Near-Infrared Imaging Spectrometer Datasets: A Combined Study of In-Situ and Laboratory Spectral Measurements

Until 29 May 2020, the Visible and Near-Infrared Imaging Spectrometer (VNIS) onboard the Yutu-2 Rover of the Chang’e-4 (CE-4) has acquired 96 high-resolution surface in-situ imaging spectra. These spectra were acquired under different illumination conditions, thus photometric normalization should be conducted to correct the introduced albedo differences before deriving the quantitative mineralogy for accurate geologic interpretations. In this study, a Lommel–Seeliger (LS) model and Hapke radiative transfer (Hapke) model were used and empirical phase functions of the LS model were derived. The values of these derived phase functions exhibit declining trends with the increase in phase angles and the opposition effect and phase reddening effect were observed. Then, we discovered from in-situ and laboratory measurements that the shadows caused by surface roughness have significant impacts on reflectance spectra and proper corrections were introduced. The validations of different phase functions showed that the maximum discrepancy at 1500 nm of spectra corrected by the LS model was less (~3.7%) than that by the Hapke model (~7.4%). This is the first time that empirical phase functions have been derived for a wavelength from 450 to 2395 nm using in-situ visible and near-infrared spectral datasets. Generally, photometrically normalized spectra exhibit smaller spectral slopes, lower FeO contents and larger optical maturity parameter (OMAT) than spectra without correction. In addition, the band centers of the 1 and 2 μm absorption features of spectra after photometric normalization exhibit a more concentrated distribution, indicating the compositional homogeneity of soils at the CE-4 landing site.
On the Human Control of a Multiple Quadcopters with a Cable-suspended Payload System

A quadcopter is an under-actuated system with only four control inputs for six degrees of freedom, and yet the human control of a quadcopter is simple enough to be learned with some practice. In this work, we consider the problem of human control of a multiple quadcopters system to transport a cable-suspended payload. The coupled dynamics of the system, due to the inherent physical constraints, is used to develop a leader-follower architecture where the leader quadcopter is controlled directly by a human operator and the followers are controlled with the proposed Payload Attitude Controller and Cable Attitude Controller. Experiments, where a human operator flew a two quadcopters system to transport a cable-suspended payload, were conducted to study the performance of proposed controller. The results demonstrated successful implementation of human control in these systems. This work presents the possibility of enabling manual control for on-the-go maneuvering of the quadcopter-payload system which motivates aerial transportation in the unknown environments.
Proof-of-concept of a stair-climbing add-on device for wheelchairs

Motorized designs of stair-climbing wheelchairs available in western countries are heavy and expensive, and hence, unsuitable for developing countries. Manually operated solutions for stair-climbing wheelchairs also tend to be bulky and complex. As stair-climbing is an occasional activity for wheelchair users, having a built-in stair-climbing mechanism results in complexity and redundancy. In this work, an add-on device is envisaged, which requires the wheelchair to be mounted onto the add-on only when stair-climbing is needed. This work developed a Hex-wheel mechanism to address the demerits of the Y-wheel mechanism commonly used in load carriers, as well as to improve usability for stair-climbing. Furthermore, a suitably designed actuation mechanism was applied to the Hex-wheel to enable manual operation. Finally, a prototype of the stair-climbing add-on device was built to validate the developed mechanisms. The force required to operate the device was measured and found to be within 10% of the predicted theoretical value. The novel design provides a solution manually operable by an assistant, which is cost-effective and independent of wheelchair type to improve accessibility in low-resource settings.
Towards Human-Like Interpretable Object Detection Via Spatial Relation Encoding

The performance of recent deep neural networks in various computer vision areas such as object detection has increased significantly. Along with such advances, attempts to visualize and interpret the networks have been made in order to understand how a network predicts a certain result. However, there is a lack of research on ways to improve the interpretability of networks’ features. In this paper, we propose a spatial relation reasoning (SRR) framework to encode interpretable networks’ features, especially an object detector, by mimicking the human visual cognition system. The SRR consists of the spatial feature encoder (SFE) and the graph-based spatial relation encoder (GSRE) to consider spatial relationships between different parts of an object. So that, object detectors can encode spatially-related object features enabling humanlike visual interpretation. We verified the proposed framework with general object detectors on public datasets-PAS-CAL VOC and MS COCO.
Exploiting Cliques for Granular Computing-based Graph Classification

The most fascinating aspect of graphs is their ability to encode the information contained in the inner structural organization between its constituting elements. Learning from graphs belong to the so-called Structural Pattern Recognition, from which Graph Embedding emerged as a successful method for processing graphs by evaluating their dissimilarity in a suitable geometric space. In this paper, we investigate the possibility to perform the embedding into a geometric space by leveraging to peculiar constituent graph substructures extracted from training set, namely the maximal cliques, and providing the performances obtained under three main aspects concerning classification capabilities, running times and model complexity. Thanks to a Granular Computing approach, the employed methodology can be seen as a powerful framework able to synthesize models suitable to be interpreted by field-experts, pushing the boundary towards new frontiers in the field of explainable AI and knowledge discovery also in big data contexts.
Traceability Support for Multi-Lingual Software Projects

Software traceability establishes associations between diverse software artifacts such as requirements, design, code, and test cases. Due to the non-trivial costs of manually creating and maintaining links, many researchers have proposed automated approaches based on information retrieval techniques. However, many globally distributed software projects produce software artifacts written in two or more languages. The use of intermingled languages reduces the efficacy of automated tracing solutions. In this paper, we first analyze and discuss patterns of intermingled language use across multiple projects, and then evaluate several different tracing algorithms including the Vector Space Model (VSM), Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA), and various models that combine mono-and cross-lingual word embeddings with the Generative Vector Space Model (GVSM). Based on an analysis of 14 Chinese-English projects, our results show that best performance is achieved using mono-lingual word embeddings integrated into GVSM with machine translation as a preprocessing step.
3D Object Detection and Tracking Based on Streaming Data

Recent approaches for 3D object detection have made tremendous progresses due to the development of deep learning. However, previous researches are mostly based on individual frames, leading to limited exploitation of information between frames. In this paper, we attempt to leverage the temporal information in streaming data and explore 3D streaming based object detection as well as tracking. Toward this goal, we set up a dual-way network for 3D object detection based on keyframes, and then propagate predictions to non-key frames through a motion based interpolation algorithm guided by temporal information. Our framework is not only shown to have significant improvements on object detection compared with frame-by-frame paradigm, but also proven to produce competitive results on KITTI Object Tracking Benchmark, with 76.68% in MOTA and 81.65% in MOTP respectively.
Multilayer limited penetrable visibility graph for characterizing the gas-liquid flow behavior

The research of the dynamical flow behavior of gas-liquid two-phase flow remains a challenge of great importance. We first carry out the vertical upward gas-liquid two-phase flow experiment in a 50 mm inner-diameter pipe and capture flow information via four-sector distributed conductance sensor. Then, we develop multilayer limited penetrable visibility graph (MLPVG) to analyze multichannel measurements, aiming to characterize the complicated flow behavior. We calculate two projection networks of MLPVG from two different perspectives (i.e. the overlapping information and the whole information). The ratio between the global clustering coefficients of these two projection networks is calculated as a quantitative indicator to characterize the multilayer network. We find that the proposed multilayer network measure can effectively characterize the evolution of gas-liquid flow, from randomly dispersed bubbles to periodically arisen gas slugs. The results suggest that our method can effectively fuse multichannel measurements and reveal the evolution of the complicated flow behavior.
Bayesian analysis of longitudinal and multidimensional functional data

Multi-dimensional functional data arises in numerous modern scientific experimental and observational studies. In this article, we focus on longitudinal functional data, a structured form of multidimensional functional data. Operating within a longitudinal functional framework we aim to capture low dimensional interpretable features. We propose a computationally efficient nonparametric Bayesian method to simultaneously smooth observed data, estimate conditional functional means and functional covariance surfaces. Statistical inference is based on Monte Carlo samples from the posterior measure through adaptive blocked Gibbs sampling. Several operative characteristics associated with the proposed modeling framework are assessed comparatively in a simulated environment. We illustrate the application of our work in two case studies. The first case study involves age-specific fertility collected over time for various countries. The second case study is an implicit learning experiment in children with autism spectrum disorder.
Reliable Local Explanations for Machine Listening

One way to analyse the behaviour of machine learning models is through local explanations that highlight input features that maximally influence model predictions. Sensitivity analysis, which involves analysing the effect of input perturbations on model predictions, is one of the methods to generate local explanations. Meaningful input perturbations are essential for generating reliable explanations, but there exists limited work on what such perturbations are and how to perform them. This work investigates these questions in the context of machine listening models that analyse audio. Specifically, we use a state-of-the-art deep singing voice detection (SVD) model to analyse whether explanations from SoundLIME (a local explanation method) are sensitive to how the method perturbs model inputs. The results demonstrate that SoundLIME explanations are sensitive to the content in the occluded input regions. We further propose and demonstrate a novel method for quantitatively identifying suitable content type(s) for reliably occluding inputs of machine listening models. The results for the SVD model suggest that the average magnitude of input mel-spectrogram bins is the most suitable content type for temporal explanations.
Guidelines for clinical trials using artificial intelligence – <scp>SPIRIT‐AI</scp> and <scp>CONSORT‐AI</scp><sup>†</sup>

Abstract The rapidly growing use of artificial intelligence in pathology presents a challenge in terms of study reporting and methodology. The existing guidelines for the design (SPIRIT) and reporting (CONSORT) of clinical trials have been extended with the aim of ensuring production of the highest quality evidence in this field. We explore these new guidelines and their relevance and application to pathology as a specialty. © 2020 The Authors. The Journal of Pathology published by John Wiley &amp; Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.
Multi-Robot Cooperative Target Encirclement through Learning Distributed Transferable Policy

Making efficient motion decisions for a multi-robot system is a challenging problem in target encirclement with collision avoidance. Specifically, each robot with local communication has to consider cooperative target encirclement and collision avoidance simultaneously. In this paper, a distributed transferable policy network framework based on deep reinforcement learning is proposed to solve the problem of multi-robot cooperative target encirclement with collision avoidance. The proposed policy network framework is able to process the information of uncertain number of robots and obstacles, which is a desirable property for multi-robot systems. In particular, graph attention communication mechanism is adopted to model multi-robot interactions as a graph and extract cooperative information from the graph. Long short-term memory is used to accept the states of uncertain number of obstacles. In addition, a compound reward is designed to lead the training of the behavior of target encirclement with collision avoidance. Curriculum learning is implemented to speed up the process of this training. Simulation results validate the effectiveness of the proposed algorithm. Moreover, we further show that the learned policy can directly transfer to different scenarios along with good generalization.
A Comparative Study of Classifiers for Thumbnail Selection

As we move into the fourth industrial revolution video streaming platforms like Netflix are turning to machine learning techniques to maintain a competitive edge in the market. Various problems such as clip creation, network optimization, customer churn prediction, amongst others, have been solved for video streaming platforms using machine learning. This paper focuses on automatic thumbnail selection for movies and series. Classifiers are used to automate the thumbnail selection. The research firstly compares the performance of different convolutional neural networks (CNNs), namely, VGG-19, Inception-v3 and ResNet-50, for solving this problem. The performance of two classifiers, namely, the best performing convolutional neural network and a hybrid approach combining a CNN and genetic programming, are compared for thumbnail selection. The CNN is used for feature extraction and genetic programming for classification. The ResNet-50 CNN outperformed the other CNNs. Both classifiers were successful for thumbnail selection with the convolutional neural network outperforming the hybrid classifier.
Closing the Simulation-to-Reality Gap using Generative Neural Networks: Training Object Detectors for Soccer Robotics in Simulation as a Case Study

In order to address the simulation-to-reality-gap, in this paper a methodology for the real-time generation of realistic images in robotic simulation environments is proposed. The images rendered by the simulator are first segmented, and then a generative neural network transforms them into realistic images. This allows training object recognition methods in situations dynamically generated by the simulator, but using realistic images. The generative neural network is trained using a database obtained using an instance segmentation network (Mask R-CNN). The whole methodology is validated in the soccer robotics domain. The reported experiments show that CNN based object detectors trained in simulation, using the generated realistic images, can be directly transferred to reality, where state-of-the art results are obtained. Moreover, we show how the training process of these detectors is fast, easy, and does not require the repetitive use of robots, which is time consuming for humans.