Reviewerly/exHarmony huggingface dataset:
Cascade Regression-Based Face Frontalization for Dynamic Facial Expression Analysis

Abstract Facial expression recognition has seen rapid development in recent years due to its wide range of applications such as human–computer interaction, health care, and social robots. Although significant progress has been made in this field, it is still challenging to recognize facial expressions with occlusions and large head-poses. To address these issues, this paper presents a cascade regression-based face frontalization (CRFF) method, which aims to immediately reconstruct a clean, frontal and expression-aware face given an in-the-wild facial image. In the first stage, a frontal facial shape is predicted by developing a cascade regression model to learn the pairwise spatial relation between non-frontal face-shape and its frontal counterpart. Unlike most existing shape prediction methods that used single-step regression, the cascade model is a multi-step regressor that gradually aligns non-frontal shape to its frontal view. We employ several different regressors and make a ensemble decision to boost prediction performance. For facial texture reconstruction, active appearance model instantiation is employed to warp the input face to the predicted frontal shape and generate a clean face. To remove occlusions, we train this generative model on manually selected clean-face sets, which ensures generating a clean face as output regardless of whether the input face involves occlusions or not. Unlike the existing face reconstruction methods that are computational expensive, the proposed method works in real time, so it is suitable for dynamic analysis of facial expression. The experimental validation shows that the ensembling cascade model has improved frontal shape prediction accuracy for an average of 5% and the proposed method has achieved superior performance on both static and dynamic recognition of facial expressions over the state-of-the-art approaches. The experimental results demonstrate that the proposed method has achieved expression-preserving frontalization, de-occlusion and has improved performance of facial expression recognition.
Cloud Computing: Analysis of Top 5 CSPs in SaaS, PaaS and IaaS Platforms

The Cloud computing refers to manipulating, configuring and accessing the applications as utilities over the internet. It involves online data computation, storation, infrastructure and application, and hence it is highly essential to make a smart decision, when and how computing, storage and network resources be distributed and allocated to users to utilize, manage and consume them. In this paper first architecture of cloud computing, characteristics of service models and services provided by CSPs are discussed, than after analyzed growth rate and market share of top five CSPs in cloud service models, i.e Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS).
Distributed Nonlinear Polynomial Graph Filter and Its Output Graph Spectrum: Filter Analysis and Design

While frequency-domain algorithms have been demonstrated to be powerful for conventional nonlinear signal processing, there is still not much progress in literature dedicated to nonlinear graph signal processing in frequency domain. The difficulties come from the irregular structure of graph signals and the lack of a straightforward description for the nonlinear relationship between input and output graph signals. In this paper, a distributed nonlinear polynomial graph filter (NPGF) is proposed to characterize the nonlinear input-output relationship by employing polynomial-type nonlinearity based on Hadamard product. Then, the nonlinear output graph signal is reformulated based on the multi-dimensional inverse graph Fourier transform (GFT), where the m-dimensional GFT coefficients are defined as explicit functions of input graph spectrum and filter structural factors (filter parameters, graph shift matrix, nonlinear degree, and graph shift order). The effects of filter structural factors on the output graph spectrum are further evaluated by proposing graph frequency response (GFR) coefficients, which inherently characterize the irregular structural behaviors of a distributed NPGF in frequency domain. The nonlinear output graph spectrum is then developed based on GFR vectors by mapping the GFR coefficients in the tensor formulation to the output GFT coefficients in the vector expression. This can greatly improve the efficiency in calculating the output graph signal if the input signal only involves a few frequency components. Finally, algorithms are proposed to design the filter parameters for the desired output signal. Numerical studies are presented to illustrate and validate the proposed frequency-domain framework for nonlinear graph signal processing.
Single-image deraining via a Recurrent Memory Unit Network

Single-image rain removal is a concern in the field of computer vision because rain streaks may reduce image quality. Images captured in rainy days may suffer from non-uniform rain consisting of different densities, shapes, and sizes. In this paper, we propose a novel single-image deraining method called a Recurrent Memory Unit Network (RMUN) to remove rain streaks from individual images. Unlike existing methods, the RMUN is a recurrent network, which can efficiently utilize the results of the current cycle for the next cycle. In addition, the RMUN employs a Residual Memory Unit Block (RMUB) to extract the features, which means that more attention can be paid to the channels of feature map. A Memory Unit block (MUB) is put in the transform path of the network to keep track of rain details. Different levels of features can be passed in the skip connections between the RMUB and MUB. The extensive experiments show that our proposed method performs better than the state-of-the-art methods on synthetic and real-world datasets.
Combining fuzzy analytic hierarchy process with concentration–area fractal for mineral prospectivity mapping: A case study involving Qinling orogenic belt in central China

We combined cluster analysis, the fuzzy analytic hierarchy process (fuzzy AHP), and the concentration–area (C-A) fractal for mineral prospectivity mapping. First, cluster analysis was used to determine the indicator elements for orogenic Au deposits (omitting redundant geochemical elements). Subsequently, according to pairwise comparisons of mineralization alternatives performed by three exploration experts, a series of fuzzy evaluation matrices were constructed, and the corresponding normalized weights were calculated. Furthermore, on the basis of the multifractal theory, two thresholds for each alternative were obtained (each alternative was divided into a background region, a general anomaly region, and a high anomaly region), and the fuzzy membership function was used to obtain the normalized score of each alternative. Finally, the weight of each alternative was multiplied by the normalized score, and fuzzy superposition was employed to generate a mineralization prediction map, which consisted of high, medium, low, and weak metallogenic potential areas, accounting for 7.05%, 19.67%, 40.99%, and 32.29% of the study area, which contained 4, 1, 1, and 0 of the six known mine occurrences, respectively. The prediction area (P-A) plot indicated that the result predicted 83.3% of Au deposits with an area of 16.7%, indicating that the combination of cluster analysis, the fuzzy AHP, and the concentration–area method is an efficient and economical forecasting method that has guiding significance for mineral prospectivity mapping (MPM).
Underwater Image Enhancement Via Learning Water Type Desensitized Representations

We present a novel underwater image enhancement method termed SCNet to improve the image quality meanwhile cope with the degradation diversity caused by the water. SCNet is based on normalization schemes across both spatial and channel dimensions with the key idea of learning water type desensitized features. Specifically, we apply whitening to de-correlate activations across spatial dimensions for each instance in a mini-batch. We also eliminate channel-wise correlation by standardizing and re-injecting the first two moments of the activations across channels. The normalization schemes of spatial and channel dimensions are performed at each scale of the U-Net to obtain multi-scale representations. With such water type irrelevant encodings, the decoder can easily reconstruct the clean signal and be unaffected by the distortion types. Experimental results on two real-world underwater image datasets show that our approach can successfully enhance images with diverse water types, and achieves competitive performance in visual quality improvement.
A theory of monitors

We develop a behavioural theory for monitors, computational entities that passively analyse the runtime behaviour of systems so as to infer properties about them. First, we present a monitor language and an instrumentation relation used for piCalculus process monitoring. We then identify contextual behavioural preorders that allow us to relate monitors according to criteria defined over monitored executions of piCalculus processes. Subsequently, we develop alternative monitor preorders that are compositional, since they allow us to relate monitors without resorting to their composite behaviour when they instrumented with systems. Importantly, we show that the latter alternative preorders are sound and complete with respect to the contextual preorders. Finally, we demonstrate how these preorders can assist the development of correct monitor synthesis tools.
Controlling Video Stimuli in Sign Language and Gesture Research: The OpenPoseR Package for Analyzing OpenPose Motion-Tracking Data in R

Researchers in the fields of sign language and gesture studies frequently present their participants with video stimuli showing actors performing linguistic signs or co-speech gestures. Up to now, such video stimuli have been mostly controlled only for some of the technical aspects of the video material (e.g., duration of clips, encoding, framerate, etc.), leaving open the possibility that systematic differences in video stimulus materials may be concealed in the actual motion properties of the actor’s movements. Computer vision methods such as OpenPose enable the fitting of body-pose models to the consecutive frames of a video clip and thereby make it possible to recover the movements performed by the actor in a particular video clip without the use of a point-based or markerless motion-tracking system during recording. The OpenPoseR package provides a straightforward and reproducible way of working with these body-pose model data extracted from video clips using OpenPose , allowing researchers in the fields of sign language and gesture studies to quantify the amount of motion (velocity and acceleration) pertaining only to the movements performed by the actor in a video clip. These quantitative measures can be used for controlling differences in the movements of an actor in stimulus video clips or, for example, between different conditions of an experiment. In addition, the package also provides a set of functions for generating plots for data visualization, as well as an easy-to-use way of automatically extracting metadata (e.g., duration, framerate, etc.) from large sets of video files.
Towards Deep Unsupervised Sar Despeckling with Blind-Spot Convolutional Neural Networks

SAR despeckling is a problem of paramount importance in remote sensing, since it represents the first step of many scene analysis algorithms. Recently, deep learning techniques have outperformed classical model-based despeckling algorithms. However, such methods require clean ground truth images for training, thus resorting to synthetically speckled optical images since clean SAR images cannot be acquired. In this paper, inspired by recent works on blind-spot denoising networks, we propose a self-supervised Bayesian despeckling method. The proposed method is trained employing only noisy images and can therefore learn features of real SAR images rather than synthetic data. We show that the performance of the proposed network is very close to the supervised training approach on synthetic data and competitive on real data.
Synthetic Data Augmentation Using Multiscale Attention CycleGAN for Aircraft Detection in Remote Sensing Images

Deep learning approaches require enough training samples to perform well, but it is a challenge to collect enough real training data and label them manually. In this letter, we propose a practical framework for automatically generating content-rich synthetic images with ground-truth annotations. By rendering 3-D CAD models, we generate two synthetic aircraft image data sets with wide distribution (Syn N and Syn U). For improving the quality of synthetic images, we propose a multiscale attention module which enhances the Cycle-Consistent Adversarial Network (CycleGAN) in spatial and channel dimensions. Then, we compare the synthetic images before and after translation qualitatively and quantitatively. Experiments on Northwestern Polytechnical University (NWPU) very high resolution (VHR)-10, University of Chinese Academy of Sciences, orientation robust object detection in aerial images (UCAS-AOD), and benchmark for object DetectIon in Optical Remote sensing images (DIOR) data sets demonstrate that synthetic data augmentation can improve the performance of aircraft detection in remote sensing images, especially when real data are insufficient. Synthetic data are available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://weix-liu.github.io/</uri> .
Robot path planning based on deep reinforcement learning

Q-learning algorithm based on Markov decision process as a reinforcement learning algorithm can achieve better path planning effect for mobile robot in continuous trial and error. However, Q-learning needs a huge Q-value table, which is easy to cause dimension disaster in decision-making, and it is difficult to get a good path in complex situations. By combining deep learning with reinforcement learning and using the perceptual advantages of deep learning to solve the decision-making problem of reinforcement learning, the deficiency of Q-learning algorithm can be improved. At the same time, the path planning of deep reinforcement learning is simulated by MATLAB, the simulation results show that the deep reinforcement learning can effectively realize the obstacle avoidance of the robot and plan a collision free optimal path for the robot from the starting point to the end point.
Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks

Social recommendation which aims to leverage social connections among users to enhance the recommendation performance. With the revival of deep learning techniques, many efforts have been devoted to developing various neural network-based social recommender systems, such as attention mechanisms and graph-based message passing frameworks. However, two important challenges have not been well addressed yet: (i) Most of existing social recommendation models fail to fully explore the multi-type user-item interactive behavior as well as the underlying cross-relational inter-dependencies. (ii) While the learned social state vector is able to model pair-wise user dependencies, it still has limited representation capacity in capturing the global social context across users. To tackle these limitations, we propose a new Social Recommendation framework with Hierarchical Graph Neural Networks (SR-HGNN). In particular, we first design a relation-aware reconstructed graph neural network to inject the cross-behavior collaborative semantics into the recommendation framework. In addition, we further augment SR-HGNN with a social relation encoder based on the mutual information learning paradigm between local subgraph-based user embeddings and high-level global representation, which endows SR-HGNN with the capability of capturing the global social contextual signals. Empirical results on three public benchmarks demonstrate that SR-HGNN significantly outperforms state-of-the-art methods. Source codes are available at: https://github.com/xhcdream/SR-HGNN.
ReMEMBeR: Ranking Metric Embedding-Based Multicontextual Behavior Profiling for Online Banking Fraud Detection

Anomaly detection relies on individuals' behavior profiling and works by detecting any deviation from the norm. When used for online banking fraud detection, however, it mainly suffers from three disadvantages. First, for an individual, the historical behavior data are often too limited to profile his/her behavior pattern. Second, due to the heterogeneous nature of transaction data, there lacks a uniform treatment of different kinds of attribute values, which becomes a potential barrier for model development and further usage. Third, the transaction data are highly skewed, and it becomes a challenge to utilize the label information effectively. The three disadvantages result in both poor generalization and high false positive rate of anomaly detection, and we propose a ranking metric embedding based multi-contextual behavior profiling (ReMEMBeR) model to battle them effectively. We solve the original fraud detection problem as a pseudo-recommender system problem, where an individual is treated as a pseudo-user, his/her behavior as a pseudo-item, and the label as the corresponding pseudo-rating. With the idea of collaborative filtering, for an individual, information from other similar individuals can be used to establish his/her behavior profile. In order to obtain a uniform treatment of heterogeneous attributes, we turn to an embedding based method to learn both attribute embedding and individuals' behavior profiles within a common latent space simultaneously. To utilize the label information better, our model is designed to fit pseudo-users' correct preference ranking for pseudo-items. By doing so, it explicitly learns to tell the fraudulent from the legitimate. Last but not least, we propose to identify and distinguish individuals under different contexts and further generalize the behavior profiling model to be a multi-contextual one. The proposed model can, thus, integrate the multi-contextual behavior patterns and allow transactions to be examined under the different contexts. Extensive experiments on a real-world online banking transaction dataset demonstrate that our model not only outperforms benchmarks on all metrics but also can be combined with them to achieve even better performance.
Being a Mentor in open source projects

Abstract Mentoring is a well-known way to help newcomers to Open Source Software (OSS) projects overcome initial contribution barriers. Through mentoring, newcomers learn to acquire essential technical, social, and organizational skills. Despite the importance of OSS mentors, they are understudied in the literature. Understanding who OSS project mentors are, the challenges they face, and the strategies they use can help OSS projects better support mentors’ work. In this paper, we employ a two-stage study to comprehensively investigate mentors in OSS. First, we identify the characteristics of mentors in the Apache Software Foundation, a large OSS community, using an online survey. We found that less experienced volunteer contributors are less likely to take on the mentorship role. Second, through interviews with OSS mentors (n=18), we identify the challenges that mentors face and how they mitigate them. In total, we identified 25 general mentorship challenges and 7 sub-categories of challenges regarding task recommendation. We also identified 13 strategies to overcome the challenges related to task recommendation. Our results provide insights for OSS communities, formal mentorship programs, and tool builders who design automated support for task assignment and internship.
A Hybrid Feature Location Technique for Re-engineeringSingle Systems into Software Product Lines

Software product lines (SPLs) are known for improving productivity and reducing time-to-market through the systematic reuse of assets. SPLs are adopted mainly by re-engineering existing system variants. Feature location techniques (FLTs) support the re-engineering process by mapping the variants’ features to their implementation. However, such FLTs do not perform well when applied to single systems. In this way, there is a lack of FLTs to aid the re-engineering process of a single system into an SPL. In this work, we present a hybrid technique that consists of two complementary types of analysis: i) a dynamic analysis by runtime monitoring traces of scenarios in which features of the system are exercised individually, and ii) a static analysis for refining overlapping traces. We evaluate our technique on three subject systems by computing the common metrics used in FL research. We thus computed Precision, Recall, and F-Score at the line- and method-level of source code. In addition to that, one of the systems has a ground truth available, which we also used for comparing results. Results show that our FLT reached an average of 68-78% precision and 72-81% recall on two systems at the line-level, and 67-65% precision and 68-48% recall at the method-level. In these systems, most of the implementation can be covered by the exercise of the features. For the largest system, our technique reached a precision of up to 99% at the line-level, 94% at the method-level, and 44% when comparing to traces. However, due to its size, it was difficult to reach high code coverage during execution, and thus the recall obtained was on average of 28% at the line-level, 25% at the method-level, and 30% when comparing to traces. The main contribution of this work is a hybrid FLT, its publicly available implementation, and a replication package for comparisons and future studies.
Experimenting with Latent Semantic Analysis and Latent Dirichlet Allocation on Automated Essay Grading

The demand of scoring natural language responses has created a need for new computational tools that can be applied to automatically grade student essays. Systems for automatic essay assessment have been commercially available since 1990's. However, the progress in the field was obstructed by a lack of qualitative information regarding the effectiveness of such systems. Most of the research in automatic essay grading has been associated with English writing due to its widespread use and the availability of more learner collection and language processing software for the language. In addition, there is large number of commercial software for grading programming assignments automatically. In this work, we investigate document semantic similarity based on Latent Semantic Analysis (LSA) and on Latent Dirichlet Allocation (LDA). We use an open-source Python software, Gensim, to develop and implement an essay grading system able to compare an essay to an answer-key and assign to it a grade based on semantic similarity between the two. We test our tool on variable-size essays and conduct experimental tests to compare the results obtained from human grader (professor) and those obtained from the automatic grading system. Results show high correlation between the professor grades and the grades assigned by both modeling techniques. However, LSA-based modeling showed more promising results than the LDA-based method.