Reviewerly/exHarmony huggingface dataset:
Understanding the Basis of Graph Signal Processing via an Intuitive Example-Driven Approach [Lecture Notes]

Graphs are irregular structures that naturally represent the multifaceted data attributes; however, traditional approaches have been established outside signal processing and largely focus on analyzing the underlying graphs rather than signals on graphs. Given the rapidly increasing availability of multisensor and multinode measurements, likely recorded on irregular or ad hoc grids, it would be extremely advantageous to analyze such structured data as "signals on graphs" and thus benefit from the ability of graphs to incorporate spatial sensing awareness, physical intuition, and sensor importance, together with the inherent "local versus global" sensor association. The aim of this lecture note is, therefore, to establish a common language between graph signals that are observed in irregular signal domains and some of the most fundamental paradigms in digital signal processing (DSP), such as spectral analysis, system transfer function, digital filter design, parameter estimation, and optimal denoising.
Non-Local ConvLSTM for Video Compression Artifact Reduction

Video compression artifact reduction aims to recover high-quality videos from low-quality compressed videos. Most existing approaches use a single neighboring frame or a pair of neighboring frames (preceding and/or following the target frame) for this task. Furthermore, as frames of high quality overall may contain low-quality patches, and high-quality patches may exist in frames of low quality overall, current methods focusing on nearby peak-quality frames (PQFs) may miss high-quality details in low-quality frames. To remedy these shortcomings, in this paper we propose a novel end-to-end deep neural network called non-local ConvLSTM (NL-ConvLSTM in short) that exploits multiple consecutive frames. An approximate non-local strategy is introduced in NL-ConvLSTM to capture global motion patterns and trace the spatiotemporal dependency in a video sequence. This approximate strategy makes the non-local module work in a fast and low space-cost way. Our method uses the preceding and following frames of the target frame to generate a residual, from which a higher quality frame is reconstructed. Experiments on two datasets show that NL-ConvLSTM outperforms the existing methods.
DesignAR: Immersive 3D-Modeling Combining Augmented Reality with Interactive Displays

We present DesignAR, an augmented design workstation for creating 3D models. Our approach seamlessly integrates an interactive surface displaying 2D views with head-mounted, stereoscopic Augmented Reality (AR). This creates a combined output space that expands the screen estate and enables placing 3D objects beyond display borders. For the effective combination of 2D and 3D views, we define different levels of proximity and alignment. Regarding input, multi-touch and pen mitigate issues of precision and ergonomics commonly found in mid-air VR/AR interaction. For creating and refining 3D models, we propose a set of pen and touch techniques with immediate AR feedback, including sketching of rotational solids or tracing physical objects on the surface. To further support a designer's modeling process, we additionally propose orthographic model views and UI offloading in AR as well as freely placeable model instances with real-world reference. Based on our DesignAR prototype, we report on challenges and insights regarding this novel type of display augmentation. The combination of high-resolution, high-precision interactive surfaces with carefully aligned AR views opens up exciting possibilities for future work and design environments, a vision we call Augmented Displays.
Application of breast cancer diagnosis based on a combination of convolutional neural networks, ridge regression and linear discriminant analysis using invasive breast cancer images processed with autoencoders

Invasive ductal carcinoma cancer, which invades the breast tissues by destroying the milk channels, is the most common type of breast cancer in women. Approximately, 80% of breast cancer patients have invasive ductal carcinoma and roughly 66.6% of these patients are older than 55 years. This situation points out a powerful relationship between the type of breast cancer and progressed woman age. In this study, the classification of invasive ductal carcinoma breast cancer is performed by using deep learning models, which is the sub-branch of artificial intelligence. In this scope, convolutional neural network models and the autoencoder network model are combined. In the experiment, the dataset was reconstructed by processing with the autoencoder model. The discriminative features obtained from convolutional neural network models were utilized. As a result, the most efficient features were determined by using the ridge regression method, and classification was performed using linear discriminant analysis. The best success rate of classification was achieved as 98.59%. Consequently, the proposed approach can be admitted as a successful model in the classification.
Interrogating Social Virtual Reality as a Communication Medium for Older Adults

A growing body of research is examining the way that virtual reality (VR) technology might enrich the lives of older adults. However, no studies have yet examined how this technology---combining head mounted displays, motion tracking, avatars, and virtual environments---might contribute to older adult wellbeing by facilitating greater social participation (social VR). To address this gap, we conducted three workshops in which 25 older adults aged 70 to 81 explored the utility of social VR as a medium for communicating with other older adults. Participants first created embodied avatars that were controlled through natural gestures, and subsequently used these avatars in two high-fidelity social VR prototypes. Findings from the workshops provide insight into older adults' design motivations when creating embodied avatars for social VR; their acceptance of social VR as a communication tool; and their views on how social VR might play a beneficial role in their lives. Outcomes from the workshops also illustrate the critical importance our participants placed onbehavioural anthropomorphism ---the embodied avatars' ability to speak, move, and act in a human-like manner--- alongsidetranslational factors, which encapsulate issues relating to the way physical movements are mapped to the embodied avatar and the way in which errors in these mappings may invoke ageing stereotypes. Findings demonstrate the critical role that these characteristics might play in the success of future social VR applications targeting older users. We translate our findings into a set of design considerations for developing social VR systems for older adults, and we reflect on how our participants' experiences can inform future research on social virtual reality.
Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors

To compare the accuracy and repeatability of emerging machine learning based (i.e. deep) automatic segmentation algorithms with those of well-established semi-automatic (interactive) methods for determining liver volume in living liver transplant donors at computerized tomography (CT) imaging.A total of 12 (6 semi-, 6 full-automatic) methods are evaluated. The semi-automatic segmentation algorithms are based on both traditional iterative models including watershed, fast marching, region growing, active contours and modern techniques including robust statistical segmenter and super-pixels. These methods entail some sort of interaction mechanism such as placing initialization seeds on images or determining a parameter range. The automatic methods are based on deep learning and they include three framework templates (DeepMedic, NiftyNet and U-Net) the first two of which are applied with default parameter sets and the last two involve adapted novel model designs. For 20 living donors (6 training and 12 test datasets), a group of imaging scientists and radiologists created ground truths by performing manual segmentations on contrast material-enhanced CT images. Each segmentation is evaluated using five metrics (i.e. volume overlap and relative volume errors, average/RMS/maximum symmetrical surface distances). The results are mapped to a scoring system and a final grade is calculated by taking their average. Accuracy and repeatability were evaluated using slice by slice comparisons and volumetric analysis. Diversity and complementarity are observed through heatmaps. Majority voting and Simultaneous Truth and Performance Level Estimation (STAPLE) algorithms are utilized to obtain the fusion of the individual results.The top four methods are determined to be automatic deep models having 79.63, 79.46 and 77.15 and 74.50 scores. Intra-user score is determined as 95.14. Overall, deep automatic segmentation outperformed interactive techniques on all metrics. The mean volume of liver of ground truth is found to be 1409.93 mL ± 271.28 mL, while it is calculated as 1342.21 mL ± 231.24 mL using automatic and 1201.26 mL ± 258.13 mL using interactive methods, showing higher accuracy and less variation on behalf of automatic methods. The qualitative analysis of segmentation results showed significant diversity and complementarity enabling the idea of using ensembles to obtain superior results. The fusion of automatic methods reached 83.87 with majority voting and 86.20 using STAPLE that are only slightly less than fusion of all methods that achieved 86.70 (majority voting) and 88.74 (STAPLE).Use of the new deep learning based automatic segmentation algorithms substantially increases the accuracy and repeatability for segmentation and volumetric measurements of liver. Fusion of automatic methods based on ensemble approaches exhibits best results almost without any additional time cost due to potential parallel execution of multiple models.
LAP-Net: Level-Aware Progressive Network for Image Dehazing

In this paper, we propose a level-aware progressive network (LAP-Net) for single image dehazing. Unlike previous multi-stage algorithms that generally learn in a coarse-to-fine fashion, each stage of LAP-Net learns different levels of haze with different supervision. Then the network can progressively learn the gradually aggravating haze. With this design, each stage can focus on a region with specific haze level and restore clear details. To effectively fuse the results of varying haze levels at different stages, we develop an adaptive integration strategy to yield the final dehazed image. This strategy is achieved by a hierarchical integration scheme, which is in cooperation with the memory network and the domain knowledge of dehazing to highlight the best-restored regions of each stage. Extensive experiments on both real-world images and two dehazing benchmarks validate the effectiveness of our proposed method.
CT image denoising using multivariate model and its method noise thresholding in non-subsampled shearlet domain

In today era, computed tomography (CT) is one of the exceptionally proficient crucial devices in medical science for the clinical reason. The consistent improvement and broad utilization of computed tomography in medical science has uplifted the harmfulness of higher dose to the patient. Low radiation dose may prompt expanded noise and artifacts, which can influence the radiologists' judgment. Therefore, we propose a method based on new shrinkage function in the nonsubsampled shearlet domain (NSST). In the proposed algorithm, method noise on multivariate shrinkage model is utilized viably by using stein's unbiased risk estimate and linear expansion of thresholds (SURE-LET) concept. To verify the execution of the proposed method, the qualitative and quantitative evaluations are performed. The results are evaluated over the both real noisy CT image and by adding Gaussian noise in real CT image and as well as on low complexity zoomed objects of noisy CT images. The results are also tested by some standard execution measurements, for example, PSNR, SSIM, ED, and DIV. The experimental results confirmed that proposed method is giving improved results in most cases.
Non-rigid object tracking via deep multi-scale spatial-temporal discriminative saliency maps

In this paper, we propose a novel effective non-rigid object tracking framework based on the spatial-temporal consistent saliency detection. In contrast to most existing trackers that utilize a bounding box to specify the tracked target, the proposed framework can extract accurate regions of the target as tracking outputs. It achieves a better description of the non-rigid objects and reduces the background pollution for the tracking model. Furthermore, our model has several unique characteristics. First, a tailored fully convolutional neural network (TFCN) is developed to model the local saliency prior for a given image region, which not only provides the pixel-wise outputs but also integrates the semantic information. Second, a novel multi-scale multi-region mechanism is proposed to generate local saliency maps that effectively consider visual perceptions with different spatial layouts and scale variations. Subsequently, the local saliency maps are fused via a weighted entropy method, resulting in a discriminative saliency map. Finally, we present a non-rigid object tracking algorithm based on the predicted saliency maps. By utilizing a spatial-temporal consistent saliency map (STCSM), we conduct the target-background classification and use an online fine-tuning scheme for model updating. Extensive experiments demonstrate that the proposed algorithm achieves competitive performance in both saliency detection and visual tracking, especially outperforming other related trackers on the non-rigid object tracking datasets. Source codes and compared results are released at https://github.com/Pchank/TFCNTracker.
Ticket Tagger: Machine Learning Driven Issue Classification

Software maintenance is crucial for software projects evolution and success: code should be kept up-to-date and error-free, this with little effort and continuous updates for the end-users. In this context, issue trackers are essential tools for creating, managing and addressing the several (often hundreds of) issues that occur in software systems. A critical aspect for handling and prioritizing issues involves the assignment of labels to them (e.g., for projects hosted on GitHub), in order to determine the type (e.g., bug report, feature request and so on) of each specific issue. Although this labeling process has a positive impact on the effectiveness of issue processing, the current labeling mechanism is scarcely used on GitHub. In this demo, we introduce a tool, called Ticket Tagger, which leverages machine learning strategies on issue titles and descriptions for automatically labeling GitHub issues. Ticket Tagger automatically predicts the labels to assign to issues, with the aim of stimulating the use of labeling mechanisms in software projects, this to facilitate the issue management and prioritization processes. Along with the presentation of the tool's architecture and usage, we also evaluate its effectiveness in performing the issue labeling/classification process, which is critical to help maintainers to keep control of their workloads by focusing on the most critical issue tickets.
Technical trading rules in the cryptocurrency market

This paper studies simple moving average trading strategies employing daily price data on the eleven most-traded cryptocurrencies in the 2016–2018 period. Our results indicate a variable moving average strategy is successful when using the 20 days moving average trading strategy. Specifically, excluding Bitcoin the technical trading rule generates an excess return of 8.76% p.a. after controlling for the average market return. Our results suggest that cryptocurrency markets are inefficient.
2-D Skeleton-Based Action Recognition via Two-Branch Stacked LSTM-RNNs

Action recognition in video sequences is an interesting field for many computer vision applications, including behavior analysis, event recognition, and video surveillance. In this article, a method based on 2D skeleton and two-branch stacked Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) cells is proposed. Unlike 3D skeletons, usually generated by RGB-D cameras, the 2D skeletons adopted in this article are reconstructed starting from RGB video streams, therefore allowing the use of the proposed approach in both indoor and outdoor environments. Moreover, any case of missing skeletal data is managed by exploiting 3D-Convolutional Neural Networks (3D-CNNs). Comparative experiments with several key works on KTH and Weizmann datasets show that the method described in this paper outperforms the current state-of-the-art. Additional experiments on UCF Sports and IXMAS datasets demonstrate the effectiveness of our method in the presence of noisy data and perspective changes, respectively. Further investigations on UCF Sports, HMDB51, UCF101, and Kinetics400 highlight how the combination between the proposed two-branch stacked LSTM and the 3D-CNN-based network can manage missing skeleton information, greatly improving the overall accuracy. Moreover, additional tests on KTH and UCF Sports datasets also show the robustness of our approach in the presence of partial body occlusions. Finally, comparisons on UT-Kinect and NTU-RGB+D datasets show that the accuracy of the proposed method is fully comparable to that of works based on 3D skeletons.
A copula-based Bayesian method for probabilistic solar power forecasting

With increased penetration of solar energy sources, solar power forecasting has become more crucial and challenging. This paper proposes a copula-based Bayesian approach to improve probabilistic solar power forecasting by capturing the joint distribution between solar power and ambient temperature. A prior forecast distribution is first obtained using different underlying point forecasting models. Parametric and empirical copulas of solar power and temperature are then developed to update the prior distribution to the posterior forecast distribution. A public solar power database is used to demonstrate effectiveness of the proposed method. Numerical results show that the copula-based Bayesian method outperforms the forecasting method that directly uses temperature as a feature. The Bayesian method is also compared with persistent models and show improved performance. This article includes supplementary material (data and code) for reproducibility.
Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records

Machine learning has become ubiquitous and a key technology on mining electronic health records (EHRs) for facilitating clinical research and practice. Unsupervised machine learning, as opposed to supervised learning, has shown promise in identifying novel patterns and relations from EHRs without using human created labels. In this paper, we investigate the application of unsupervised machine learning models in discovering latent disease clusters and patient subgroups based on EHRs. We utilized Latent Dirichlet Allocation (LDA), a generative probabilistic model, and proposed a novel model named Poisson Dirichlet Model (PDM), which extends the LDA approach using a Poisson distribution to model patients' disease diagnoses and to alleviate age and sex factors by considering both observed and expected observations. In the empirical experiments, we evaluated LDA and PDM on three patient cohorts, namely Osteoporosis, Delirium/Dementia, and Chronic Obstructive Pulmonary Disease (COPD)/Bronchiectasis Cohorts, with their EHR data retrieved from the Rochester Epidemiology Project (REP) medical records linkage system, for the discovery of latent disease clusters and patient subgroups. We compared the effectiveness of LDA and PDM in identifying disease clusters through the visualization of disease representations. We tested the performance of LDA and PDM in differentiating patient subgroups through survival analysis, as well as statistical analysis of demographics and Elixhauser Comorbidity Index (ECI) scores in those subgroups. The experimental results show that the proposed PDM could effectively identify distinguished disease clusters based on the latent patterns hidden in the EHR data by alleviating the impact of age and sex, and that LDA could stratify patients into differentiable subgroups with larger p-values than PDM. However, those subgroups identified by LDA are highly associated with patients' age and sex. The subgroups discovered by PDM might imply the underlying patterns of diseases of greater interest in epidemiology research due to the alleviation of age and sex. Both unsupervised machine learning approaches could be leveraged to discover patient subgroups using EHRs but with different foci.
Optimal Path Planning Using Generalized Voronoi Graph and Multiple Potential Functions

In this article, we present a novel nonuniform sampling technique, based on the pipeline of rapidly exploring random tree (RRT), for efficiently computing high-quality collision-free paths while maintaining fast asymptotic convergence to the optimal solution. Our nonuniform sampling method focuses on the area where the optimal path may exist so that the path planning process can be further accelerated. First, the environment map is initialized with the generalized Voronoi graph and a heuristic path is calculated. Second, the heuristic path is discretized to construct the multiple potential functions (MPF), where the MPF provides a nonuniform sampling distribution for the path planner. As a meta-algorithm, our method is applicable to other RRT methods and helps to improve the performance of the underlying algorithm dramatically. We also prove the probabilistic completeness and the asymptotic optimality of our algorithm. The time cost and path length are selected to demonstrate the effectiveness of our algorithm. Through a series of simulation and real-world experiments, it is confirmed that the proposed algorithm achieves convincing performance compared with the state-of-the-art path planning algorithms.
Empirical study of knowledge withholding in cyberspace: Integrating protection motivation theory and theory of reasoned behavior

This study integrates protection motivation theory and theory of reasoned action to investigate knowledge withholding in cyberspace, which is a highly prevalent counterproductive knowledge behavior but has received limited attention. The research model was tested with 386 valid online survey responses among Chinese Internet users. The results indicate that both threat appraisal (perceived severity, perceived susceptibility) and coping appraisal (response efficacy, self-efficacy) are positively associated with the attitude toward knowledge withholding, and that the attitude toward knowledge withholding and subjective norms about knowledge withholding are positively related to knowledge withholding intentions. In addition, the results also show that attitude toward knowledge withholding significantly mediates the relationships of threat appraisal and coping appraisal with knowledge withholding intentions. We believe that the findings of this study not only provide a new theoretical perspective on understanding knowledge withholding behavior but also offer valuable insights for reducing knowledge withholding behavior in cyberspace. Limitations and future research directions are also discussed.