Reviewerly/exHarmony huggingface dataset:
A Colorization Framework for Monochrome-Color Dual-Lens Systems Using a Deep Convolutional Network

In monochrome-color dual-lens systems, the monochrome camera can capture images with higher quality than the color camera. To obtain high quality color images, a better approach is to colorize the gray images from the monochrome camera with the color images from the color camera serving as a reference. In addition, the colorization may fail in some cases, which makes the estimation of the colorization quality a necessary step before outputting the colorization result. To solve these problems, we propose a deep convolutional network based framework. 1) In the colorization module, the proposed colorization CNN uses deep feature representations, attention operation, 3-D regulation and color correction to make use of colors of multiple pixels in the reference image for colorizing each pixel in the input gray image. 2) In the colorization quality estimation module, based on the symmetry property of colorization, we propose to utilize the colorization CNN again to colorize the gray map of the original reference color image using the first-time colorization result from the colorization module as reference. Then, the quality loss of the second-time colorization result can be used for estimating the colorization quality. Experimental results show that our method can largely outperform the state-of-the-art colorization methods and estimate the colorization quality accurately as well.
Linearly augmented real-time 4D expressional face capture

Personalised 3D face creation has always been a hot topic in the computer vision community. Many methods have been proposed including the statistic model, the non-rigid registration and high-end depth acquisition equipment. However, in practical applications, those existing methods still have their own limitations. For example, the performance of the statistic model-based methods highly depends on the generality of the pre-trained statistic model; the non-rigid registration based methods are sensitive to the quality of input data; the high-end equipment-based methods are less able to be popularised due to the expensive equipment costs; the deep learning-based methods can only perform well if proper training data provided for the target domain, and require GPU for better performance. To this end, this paper presents an adaptive template augmented method that can automatically obtain a personalised 4D facial modelling only using a consumer-grade device. The noisy data from such a cheap device are well handled. The whole process consists of a series of linear solutions and can be achieved in real-time for online processing only based on the CPU computation on a laptop. There is no constraint nor complex operation required by the proposed method. No additional time-consumptive pre- or post-processing for the personalisation is needed. Comparisons against several existing methods demonstrate the superiority of the proposed method.
A rules-based and Transfer Learning approach for deriving the Hubble type of a galaxy from the Galaxy Zoo data

The Galaxy Zoo project is a crowd-sourced astronomy galaxy classification endeavour whose results can have significant benefits to astronomers. The project has evolved into using crowd-sourced labelling together with machine learning to automate the classification of galaxies. If this process is to be automated using crowd-sourcing and machine learning, then understanding how these results will hold up against expert classifications on an academically accepted classification such as the Hubble tuning fork is timely. We propose a rules-based approach for deriving the Hubble type using the responses in the Galaxy Zoo as well as a Transfer Learning approach for solving this problem. The dataset we used to get the Hubble type for galaxies is the Revised Shapley-Ames catalogue of bright galaxies. Previous work in this field has mainly revolved around the Galaxy Zoo project with little to no attempt to map the Galaxy Zoo responses to a more robust method of classifying galaxies such as the Hubble tuning fork classification system. Previous research has tried to map the Galaxy Zoo responses to a set of classes like elliptical, spiral and irregular. Their work has shown promising results. Our experiments showed that by using the Galaxy Zoo response vectors, our rules-based approach was able to separate the elliptical and spiral shapes, however, it did not perform particularly well at separating the spiral shapes from one another. Our Transfer Learning model showed better potential for separating not only elliptical and spiral shapes but also for separating spiral shapes into exact Hubble types (e.g Sa, Sb and Sc).
Notice of Retraction: Using Deep Learning To Assign Rheumatoid Arthritis Scores

In this work, we report the performance of the deep learning model in automatically assigning joint scores and overall patients scores for Rheumatoid Arthritis patients' X-ray images. The dataset is from RA2 DREAM Challenge https://www.synapse.org/#!Synapse:syn20545111/wiki/594083. Overall, we achieve good predictive performance with an average accuracy of 0.908.
Data Pipeline Architecture for Serverless Platform

To provide cost effective cloud resources with high QoS, serverless platform is introduced that allows to pay for the exact amount of resource usage. On the other hand, a number of data management tools are developed to handle the data from a large number of IoT sensing devices. However, the modern data-intensive cloud applications require the power that comes from integrating data management tools with serverless platforms. This paper proposes a novel data pipeline architecture for serverless platform for providing an environment to develop applications that can be broken into independently deployable, schedulable, scalable, and re-usable modules and efficiently manage the flow of data between different environments.
Automated fiber placement path generation method based on prospective analysis of path performance under multiple constraints

A new path generation method is proposed to alleviate the contradiction between direction deviation and fiber wrinkles on complex surface. A critical radius index is first presented to characterize the fiber wrinkle constraint. Then, a mathematical model is established to describe the variation of the direction deviation and steering radius of the path. The existence criteria of the placement direction range that can satisfies the two constraints simultaneously is determined. And the placement direction at each path point is obtained considering the path performances at both the current point and the subsequent point. The comparative study with other two common path generation methods is carried out on an inlet surface. The evaluation results show that the steering radii of the paths generated by the proposed method are all larger than the critical radius, while the direction deviation is constrained within 10°.
Oracle Issues in Machine Learning and Where to Find Them

The rise in popularity of machine learning (ML), and deep learning in particular, has both led to optimism about achievements of artificial intelligence, as well as concerns about possible weaknesses and vulnerabilities of ML pipelines. Within the software engineering community, this has led to a considerable body of work on ML testing techniques, including white- and black-box testing for ML models. This means the oracle problem needs to be addressed. For supervised ML applications, oracle information is indeed available in the form of dataset 'ground truth', that encodes input data with corresponding desired output labels. However, while ground truth forms a gold standard, there still is no guarantee it is truly correct. Indeed, syntactic, semantic, and conceptual framing issues in the oracle may negatively affect the ML system's integrity. While syntactic issues may automatically be verified and corrected, the higher-level issues traditionally need human judgment and manual analysis. In this paper, we employ two heuristics based on information entropy and semantic analysis on well-known computer vision models and benchmark data from ImageNet. The heuristics are used to semi-automatically uncover potential higher-level issues in (i) the label taxonomy used to define the ground truth oracle (labels), and (ii) data encoding and representation. In doing this, beyond existing ML testing efforts, we illustrate the need for software engineering strategies that especially target and assess the oracle.
Extended Object Tracking on the Affine Group Aff (2)

With the rise of advanced driver assistance systems and highly automated driving, the research of highly reliable and accurate tracking algorithms received special attention within the last years. While most of the investigation treats the objects to be tracked as single points, the appropriate handling of uncertainties, when also the object's extent is of interest, has been ignored so far. Classical approaches do not capture the properties of the probability density function, which in fact resembles a banana-shaped distribution, when handling the object's state and the measurements in the Euclidean space. In this paper, we tackle this problem, called extended object tracking, by tracking the object's state on the affine group which belongs to the matrix Lie groups. By incorporating the second-order dynamics of the movement, we model the state space as the direct product Aff(2) ×\mathbbR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> and handle the uncertainties on the associated Lie algebra to establish a proper representation. We derive the necessary equations to use the extended Kalman filter on Lie groups including an appropriately modeled measurement space. We show the advantage of our proposed method by comparing it to a classical approach, which treats both the state and the measurement space in the Euclidean space. The results of the simulations prove that, by encapsulating the state and measurement space in the appropriate manifold, we obtain higher stability and realibility with smaller state estimation errors.
Effects of human land use and temperature on community dynamics in European forests

Climate change and human land use are thought to play a dominant role in the dynamics of European central-latitude forests in the Holocene. A wide range of mathematical and statistical models have been used to study the effects of these variables on forest dynamics, including physiologically-based simulations and phenomenological community models. However, for statistical analysis of pollen count data, compositional data analysis is particularly well suited, because pollen counts give only relative information. We studied the effects of changes in human land use and temperature on European central-latitude forest dynamics at 7 sites over most of the last 10ka, using a stochastic model for compositional dynamics of pollen count data. Our approach has a natural ecological interpretation in terms of relative proportional population growth rates, and does not require information on pollen production, dispersal, or deposition. We showed that the relative proportional population growth rates of Fagus and Picea were positively affected by intensified human land use, and that those of Tilia and Ulmus were negatively affected. Also, the relative proportional population growth rate of Fagus was negatively affected by increases in temperature above about 18∘C. Overall, the effects of temperature on the rate of change of forest composition were more important than those of human land use. Although there were aspects of dynamics, such as short-term oscillations, that our model did not capture, our approach is broadly applicable and founded on ecological principles, and gave results consistent with current thinking.
Security Issues With Functions as a Service

Functions as a Service (FaaS) is an ultimate expression of cloud computing. FaaS provides a significant business value proposition to users and offloads the security issues of the platform to the provider. The user is responsible for the security of their implementation. FaaS application implementations are typically subject to OWASP top 10 vulnerabilities and require corresponding security controls. For simple implementations this is straightforward. For large FaaS applications, strong process and operational controls combined with automation are necessary to provide reasonable assurance of application security during the development and deployment process. Without these controls, particularly in DevOps/continuous development environments, maintaining security can be problematic. The issue and control approaches are reviewed.
Investigating Near-Miss Micro-Clones in Evolving Software

Code clones are the same or nearly similar code fragments in a software system's code-base. While the existing studies have extensively studied regular code clones in software systems, micro-clones have been mostly ignored. Although an existing study investigated consistent changes in exact micro-clones, near-miss micro-clones have never been investigated. In our study, we investigate the importance of near-miss micro-clones in software evolution and maintenance by automatically detecting and analyzing the consistent updates that they experienced during the whole period of evolution of our subject systems. We compare the consistent co-change tendency of near-miss micro-clones with that of exact micro-clones and regular code clones. According to our investigation on thousands of revisions of six open-source subject systems written in two different programming languages, near-miss micro-clones have a significantly higher tendency of experiencing consistent updates compared to exact micro-clones and regular (both exact and near-miss) code clones. Consistent updates in near-miss micro-clones have a high tendency of being related with bug-fixes. Moreover, the percentage of commit operations where near-miss micro-clones experience consistent updates is considerably higher than that of regular clones and exact micro-clones. We finally observe that near-miss micro-clones staying in close proximity to each other have a high tendency of experiencing consistent updates. Our research implies that near-miss micro-clones should be considered equally important as of regular clones and exact micro-clones when making clone management decisions.
Research on Recommendation system based on user portrait

One potential solution to the problem of information overload is the recommendation system, and the main task of recommendation system based on user portrait is to combine user and product, by mapping user portrait and product portrait, combining association rule mining and reordering algorithm, a personalized product recommendation list is obtained. Not only can help users find valuable information for themselves, but also can make the information displayed in front of interested users, so as to achieve a win-win situation between information consumers and information producers.
Low Altitude Small UAV Detection Based on YOLO model

Low altitude small UAV recently becomes a hot technology with rapid development and comprehensive application which raising many risks in Low-altitude airspace. Considering the characteristics of low altitude small UAV, previous approaches like radar and radio are insufficient, this paper is devoted to the detection of small UAV by using images from low-cost cameras. Method of detecting low altitude small UAV based on YOLO model with two neural networks, ResNet and DenseNet, is designed and performed. One small dataset is established accordingly. Experiment on the dataset shows that the detection method with YOLO model can make contributions to the low altitude UAV detection in complicated environments.
Towards to Optimal Wavelet Denoising Scheme—A Novel Spatial and Volumetric Mapping of Wavelet-Based Biomedical Data Smoothing

Wavelet transformation is one of the most frequent procedures for data denoising, smoothing, decomposition, features extraction, and further related tasks. In order to perform such tasks, we need to select appropriate wavelet settings, including particular wavelet, decomposition level and other parameters, which form the wavelet transformation outputs. Selection of such parameters is a challenging area due to absence of versatile recommendation tools for suitable wavelet settings. In this paper, we propose a versatile recommendation system for prediction of suitable wavelet selection for data smoothing. The proposed system is aimed to generate spatial response matrix for selected wavelets and the decomposition levels. Such response enables the mapping of selected evaluation parameters, determining the efficacy of wavelet settings. The proposed system also enables tracking the dynamical noise influence in the context of Wavelet efficacy by using volumetric response. We provide testing on computed tomography (CT) and magnetic resonance (MR) image data and EMG signals mostly of musculoskeletal system to objectivise system usability for clinical data processing. The experimental testing is done by using evaluation parameters such is MSE (Mean Squared Error), ED (Euclidean distance) and Corr (Correlation index). We also provide the statistical analysis of the results based on Mann-Whitney test, which points out on statistically significant differences for individual Wavelets for the data corrupted with Salt and Pepper and Gaussian noise.
Path Planning Methodology for Multi-Layer Welding of Intersecting Pipes Considering Collision Avoidance

SUMMARY The V-groove joint of thick wall intersecting pipes must be filled by multi-layer weld. The welding path of intersecting pipes is complicated, and hence multi-layer welds increase the complexity of the problem. This paper proposes a methodology for path planning of multi-layer weld of thick wall intersecting pipes. The methodology is based on measuring the electrode pose located in both side and front views of intersecting pipes. In order to compensate for the path deviation around the pipe circumference, the measured values are used to interpolate the path of each pass between two views. The methodology has been applied in a case study. Simulation results approve that multi-layer weld appropriately fills the V-groove joint space around the pipe circumference. In addition, collision avoidance between welding torch and pipes is considered by introducing a safety ring. While the robot wrist moves inside the safety ring, no collision occurs. Simulation results show the robustness of the proposed path planning method, introduced for collision avoidance.
Fuzzy MLNs and QSTAGs for Activity Recognition and Modelling with RUSH

Event and activity modelling and recognition is at the centre of situational awareness (SA). Being able to detect and extract useful semantic information from diverse data sources in the form of events and activities, an SA system may assist human operators with cognitive analysis, situation monitoring and to inform decision making. Recognition of realworld activities with complex spatial and temporal interactions in the presence of uncertainty has been a major line of research. In this respect, Statistical Relational Learning methods such as Markov Logic Networks (MLNs) have been shown to provide a powerful and promising framework, and efficiently leveraging their representational power for event and activity recognition are active research and development activities. In this paper we propose a complementary method to existing approaches, FQSTAG-MLN, that combines Fuzzy MLNs with Fuzzy Qualitative Spatio-Temporal Activity Graphs (FQSTAGs) toward achieving efficient and explainable activity modelling and recognition. With FQSTAG-MLNs, the complexity of real-world activities is addressed by combining the representational richness of high-level activity specification with compact local spatio-temporal narratives for low-level events; while the uncertainty of activity and world models is calibrated using probabilistic methods and the vagueness of observations is calibrated using fuzzy set theory. We describe its current implementation within RUSH, which aims to be a flexible reasoning and learning platform for situational awareness. We present a use case illustration and early results.
Rule-based Word Equation Solving

We present a transformation-system-based technique in the frame-work of string solving, by reformulating a classical combinatorics on words result, the Lemma of Levi. We further enrich the induced rules by simplification steps based on results from the combinatorial theory of word equations, as well as by the addition of linear length constraints. This transformation-system approach cannot solve all equations efficiently by itself. To improve the efficiency of our transformation-system approach we integrate existing successful string solvers, which are called based on several heuristics. The experimental evaluation we performed shows that integrating our technique as an inprocessing step improves in general the performance of existing solvers.
Exquisite Analysis of Popular Machine Learning–Based Phishing Detection Techniques for Cyber Systems

Recent advances in data science have made available many URL-analysis–based detection and machine learning algorithms, which are being leveraged for phishing detection. It is necessary to measure the efficacy of each technique so as to select an efficient phishing detection technique for integrating into a system. There have been a number of studies stated so far in this related field to assist with this task. While most of the studies have covered all the approaches, only a few have concentrated their study on machine learning–based techniques. Additionally, the parameters such as true-positive, false-positive, true-negative, and false-negative rates have been analyzed in most of the studies in these related articles. In order to achieve new objectives in the field of security, the study on analysis of phishing detection techniques needs to be expanded. In contrast to state-of-the-art methods, our research scrutinizes the different mechanisms and taxonomy used in each machine learning–based detection technique succeeded by an upper bound computing time.
Digital Hadith authentication: Recent advances, open challenges, and future directions

Abstract The Holy Quran and Hadith are the two main sources of legislation and guidelines for Muslims to shape their lives. The daily activities, sayings, and deeds of the Holy Prophet Muhammad (PBUH) are called Hadiths. Hadiths are the optimal practical descriptions of the Holy Quran. Technological advancements of information and communication technologies (ICT) have revolutionized every field of daily life, including digitizing the Holy Quran and Hadith. Available online contents of Hadith are obtained from different sources. Thus, alterations and fabrications of fake Hadiths are feasible. Authentication of these online available Hadith contents is a complex and challenging task and a crucial area of study in Islam. Few Hadith authentication techniques and systems are proposed in the literature. In this study, we have surveyed all techniques and systems, which are proposed for Hadith authentication. Furthermore, classification, open challenges, and future research directions related to Hadith authentication are identified.