Reviewerly/exHarmony huggingface dataset:
Learning A Cascaded Non-Local Residual Network for Super-resolving Blurry Images

Deblurring low-resolution images is quite challenging as blur exists in the images and the resolution of the images is low. Existing deblurring methods usually require high-resolution input while the super-resolution methods usually assume that the blur is known or small. Simply applying the deblurring and super-resolution does not solve this problem well. In this paper, we develop an effective cascaded non-local residual network which cascades the deblurring module and super-resolution module to estimate latent high-resolution images from blurry low-resolution ones. The network first uses the deblurring module to generate intermediate clear features and then develops a non-local residual network (NLRN) as the super-resolution module to generate clear high-resolution images from the intermediate clear features. To better constrain the network and reduce the training difficulty, we develop an effective constraint based on image gradients for edge preservation and adopt the progressive upsampling mechanism. We train the proposed network in an end-to-end manner. Both quantitative and qualitative results on the benchmarks demonstrate the effectiveness of the proposed method. Moreover, the proposed method achieves top-3 performance on the low-resolution track of the NTIRE 2021 Image Deblurring Challenge.
Decision support for comorbid conditions via execution-time integration of clinical guidelines using transaction-based semantics and temporal planning

In case of comorbidity, i.e., multiple medical conditions, Clinical Decision Support Systems (CDSS) should issue recommendations based on all relevant disease-related Clinical Practice Guidelines (CPG). However, treatments from multiple comorbid CPG often interact adversely (e.g., drug-drug interactions) or introduce operational inefficiencies (e.g., redundant scans). A common solution is the a-priori integration of computerized CPG, which involves integration decisions such as discarding, replacing or delaying clinical tasks (e.g., treatments) to avoid adverse interactions or inefficiencies. We argue this insufficiently deals with execution-time events: as the patient's health profile evolves, acute conditions occur, and real-time delays take place, new CPG integration decisions will often be needed, and prior ones may need to be reverted or undone. Any realistic CPG integration effort needs to further consider temporal aspects of clinical tasks—these are not only restricted by temporal constraints from CPGs (e.g., sequential relations, task durations) but also by CPG integration efforts (e.g., avoid treatment overlap). This poses a complex execution-time challenge and makes it difficult to determine an up-to-date, optimal comorbid care plan. We present a solution for dynamic integration of CPG in response to evolving health profiles and execution-time events. CPG integration policies are formulated by clinical experts for coping with comorbidity at execution-time, with clearly defined integration semantics that build on Description and Transaction Logics. A dynamic planning approach reconciles temporal constraints of CPG tasks at execution-time based on their importance, and continuously updates an optimal task schedule.
Visualization in Astrophysics: Developing New Methods, Discovering Our Universe, and Educating the Earth

Abstract We present a state‐of‐the‐art report on visualization in astrophysics. We survey representative papers from both astrophysics and visualization and provide a taxonomy of existing approaches based on data analysis tasks. The approaches are classified based on five categories: data wrangling, data exploration, feature identification, object reconstruction, as well as education and outreach. Our unique contribution is to combine the diverse viewpoints from both astronomers and visualization experts to identify challenges and opportunities for visualization in astrophysics. The main goal is to provide a reference point to bring modern data analysis and visualization techniques to the rich datasets in astrophysics.
Head Detection Based on DR Feature Extraction Network and Mixed Dilated Convolution Module

Pedestrian detection for complex scenes suffers from pedestrian occlusion issues, such as occlusions between pedestrians. As well-known, compared with the variability of the human body, the shape of a human head and their shoulders changes minimally and has high stability. Therefore, head detection is an important research area in the field of pedestrian detection. The translational invariance of neural network enables us to design a deep convolutional neural network, which means that, even if the appearance and location of the target changes, it can still be recognized effectively. However, the problems of scale invariance and high miss detection rates for small targets still exist. In this paper, a feature extraction network DR-Net based on Darknet-53 is proposed to improve the information transmission rate between convolutional layers and to extract more semantic information. In addition, the MDC (mixed dilated convolution) with different sampling rates of dilated convolution is embedded to improve the detection rate of small targets. We evaluated our method on three publicly available datasets and achieved excellent results. The AP (Average Precision) value on the Brainwash dataset, HollywoodHeads dataset, and SCUT-HEAD dataset reached 92.1%, 84.8%, and 90% respectively.
Lighting, Reflectance and Geometry Estimation from 360° Panoramic Stereo

We propose a method for estimating high-definition spatially-varying lighting, reflectance, and geometry of a scene from 360° stereo images. Our model takes advantage of the 360° input to observe the entire scene with geometric detail, then jointly estimates the scene’s properties with physical constraints. We first reconstruct a near-field environment light for predicting the lighting at any 3D location within the scene. Then we present a deep learning model that leverages the stereo information to infer the reflectance and surface normal. Lastly, we incorporate the physical constraints between lighting and geometry to refine the reflectance of the scene. Both quantitative and qualitative experiments show that our method, benefiting from the 360° observation of the scene, outperforms prior state-of-the-art methods and enables more augmented reality applications such as mirror-objects insertion.
Deep Learning Based Hand Gesture Translation System

Sign language plays an important role for the people who have the hearing and speech problems For the Non-verbal communication between the deaf-mute people. For the Kinetics gestures plays a crucial role in our day-to-day life. There are different sign languages that are available Hand Gesture Translation is one among them. The sign language is a collection of different Hand Gesture symbols and Each Hand Gesture symbol have special meaning. It is mostly used by the deaf-mute persons to express their views and thoughts very quickly with the normal people. Main problem with this system is very difficult to translate the symbols and required special training on sign language. To overcome this problem we have implemented a Hand Gesture Translation System it provides an ability to interact with the machine efficiently. It will help the deaf-mute to express their feelings and views more effectively with the normal people. In this paper implemented software prototype that will automatically recognize the hand gestures with an accuracy of 93.4% for gesture Translation which will help the deaf and dumb people to interact easily with the normal people. The main aim of the project is to provide the easy way of communication between the normal people and the deaf-mute through gestures.
Interactive web‐based data visualization with R, plotly, and shiny (Carson Sievert)

BiometricsVolume 77, Issue 2 p. 776-777 BOOK REVIEW Interactive web-based data visualization with R, plotly, and shiny (Carson Sievert) Ran Li, Ran Li Urban Health Collaborative, Drexel Dornsife School of Public Health, Philadelphia, Pennsylvania, USASearch for more papers by this authorUsama Bilal, Corresponding Author Usama Bilal ubilal@drexel.edu orcid.org/0000-0002-9868-7773 Urban Health Collaborative, Drexel Dornsife School of Public Health, Philadelphia, Pennsylvania, USA Department of Epidemiology and Biostatistics, Drexel Dornsife School of Public Health, Philadelphia, Pennsylvania, USA Correspondence Usama Bilal, MD MPH PhD, 3600 Market St. Suite 730, Philadelphia, PA 19104, USA. Email: ubilal@drexel.eduSearch for more papers by this author Ran Li, Ran Li Urban Health Collaborative, Drexel Dornsife School of Public Health, Philadelphia, Pennsylvania, USASearch for more papers by this authorUsama Bilal, Corresponding Author Usama Bilal ubilal@drexel.edu orcid.org/0000-0002-9868-7773 Urban Health Collaborative, Drexel Dornsife School of Public Health, Philadelphia, Pennsylvania, USA Department of Epidemiology and Biostatistics, Drexel Dornsife School of Public Health, Philadelphia, Pennsylvania, USA Correspondence Usama Bilal, MD MPH PhD, 3600 Market St. Suite 730, Philadelphia, PA 19104, USA. Email: ubilal@drexel.eduSearch for more papers by this author First published: 21 June 2021 https://doi.org/10.1111/biom.13474Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume77, Issue2June 2021Pages 776-777 RelatedInformation
Dihedral Group Codes Over Finite Fields

Bazzi and Mitter showed that binary dihedral group codes are asymptotically good. In this paper we prove that the dihedral group codes over any finite field with strong duality property are asymptotically good. If the characteristic of the field is even, self-dual dihedral group codes are asymptotically good. If the characteristic of the field is odd, maximal self-orthogonal dihedral group codes and LCD dihedral group codes are asymptotically good.
Sparse semi-supervised heterogeneous interbattery bayesian analysis

The Bayesian approach to feature extraction, known as factor analysis (FA), has been widely studied in machine learning to obtain a latent representation of the data. An adequate selection of the probabilities and priors of these bayesian models allows the model to better adapt to the data nature (i.e. heterogeneity, sparsity), obtaining a more representative latent space. The objective of this article is to propose a general FA framework capable of modelling any problem. To do so, we start from the Bayesian Inter-Battery Factor Analysis (BIBFA) model, enhancing it with new functionalities to be able to work with heterogeneous data, to include feature selection, and to handle missing values as well as semi-supervised problems. The performance of the proposed model, Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis (SSHIBA), has been tested on different scenarios to evaluate each one of its novelties, showing not only a great versatility and an interpretability gain, but also outperforming most of the state-of-the-art algorithms.
Example-guided synthesis of relational queries

Program synthesis tasks are commonly specified via input-output examples. Existing enumerative techniques for such tasks are primarily guided by program syntax and only make indirect use of the examples. We identify a class of synthesis algorithms for programming-by-examples, which we call Example-Guided Synthesis (EGS), that exploits latent structure in the provided examples while generating candidate programs. We present an instance of EGS for the synthesis of relational queries and evaluate it on 86 tasks from three application domains: knowledge discovery, program analysis, and database querying. Our evaluation shows that EGS outperforms state-of-the-art synthesizers based on enumerative search, constraint solving, and hybrid techniques in terms of synthesis time, quality of synthesized programs, and ability to prove unrealizability.
Privacy-preserving Biometric-driven Data for Student Identity Management: Challenges and Approaches

Biometric technologies are being considered lately for student identity management in Higher Education Institutions, as they provide several advantages over the traditional knowledge-based and token-based authentication methods, i.e., biometrics provide high security entropies, convenience and a sense of technological modernity to the end-users. While biometric technologies have many benefits from both a security and usability point of view, still there is a need for innovative user identity management solutions that continuously identify and authenticate students during academic and teaching activities. In addition, biometrics entail several threats and weaknesses with regards to the privacy of data stored about the user, which negatively affect the user acceptance and the wider adoption of biometrics due to regulatory and legal issues. In this paper, we refer to our ongoing research on intelligent and continuous online student identity management for improving security and trust in European Higher Education Institutions. We further highlight based on the literature, existing challenges, threats and state-of-the-art approaches with regards to preserving the privacy of biometric-driven data.
Gaussian process-based visual pursuit control with unknown target motion learning in three dimensions

In this paper, we propose an observer-based visual pursuit control integrating three-dimensional target motion learning by Gaussian Process Regression (GPR). We consider a situation where a visual sensor equipped rigid body pursuits a target rigid body whose velocity is unknown but dependent on the target's pose. We estimate the pose from visual information and propose a Gaussian Process (GP) model to predict the target velocity from the pose estimate. We analyse stability of the proposed control by showing that estimation and control errors are ultimately bounded with high probability. Finally, simulations illustrate the performance of the proposed control schemes even if the visual measurement is corrupted by noise.
Primary Teachers’ Principles for High-Quality Distance Teaching During COVID-19

School lockdowns in response to COVID-19 have forced teachers around the world to quickly and unexpectedly shift from face-to-face to distance teaching. The struggle with the forced transition has been even more severe in the lower levels of schooling than in higher education institutions, which had already established online course delivery before the crisis. The current paper investigates Finnish primary teachers' principles for high-quality distance teaching. Twenty primary teachers were interviewed in April 2020 during the fourth week of the two-month distance teaching period. The findings from phenomenographic analysis revealed that high-quality distance teaching at the primary level requires (1) relatedness, (2) design, (3) routine, and (4) control. This study illustrates the understanding of teachers' working in a basic education system ranked among the best in the world and thus provides valuable insights for developing instructional practices in distance education.
QoE Models in the Wild: Comparing Video QoE Models Using a Crowdsourced Data Set

Crowdsourced measurements solve the problem of being able to assess the performance of a communication network from an end-user perspective, but the new characteristics of the data pose new challenges for QoE modeling. In contrast to existing laboratory or network measurements, this type of measurement at the end user device primarily involves taking a large number of short sample measurements, which, however, are rich in measured parameters, including many user-, application-, and device-related parameters. To test the applicability and to facilitate the integration of such data, we applied four QoE models from the literature to 290k worldwide video streaming measurements from a commercial data set from August to October 2020. In this work, we will therefore first describe the crowdsourcing video streaming data set to provide insights into the properties of video streaming KPIs in the real world. Second, we run four popular QoE models using this data set, compare the resulting QoE scores, and derive the impact of individual KPIs for each model. We show that the models assess the QoE at least differently, but sometimes with contradicting statements. Reading this paper, it becomes evident that more work and subjective studies, based on real-world data like the one we have shown, are needed to extend the current QoE models.
Genetic adversarial training of decision trees

We put forward a novel learning methodology for ensembles of decision trees based on a genetic algorithm that is able to train a decision tree for maximizing both its accuracy and its robustness to adversarial perturbations. This learning algorithm internally leverages a complete formal verification technique for robustness properties of decision trees based on abstract interpretation, a well-known static program analysis technique. We implemented this genetic adversarial training algorithm in a tool called MetaSilvae and we experimentally evaluated it on some standard reference datasets used in adversarial training. The experimental results show that MetaSilvae is able to train robust models that compete with and often improve on the current state-of-the-art of adversarial training of decision trees while being much more compact and therefore interpretable and efficient tree models.
Neural Network Estimation Model to Optimize Timing and Schedule of Software Projects

Software projects have a probability of high failure rates that appear to linger around 60% for significant IT projects. Estimating time and project schedule are crucial tasks and extremely influence the project outcomes. Artificial Intelligence now can provide multiple solutions for most problems of software projects. This article aims to develop a Neural Network estimation model to manipulate the problem of timing for software projects. The model can predict the estimation value of project time which optimizes the scheduling process, the developed model achieved high accuracy after testing through the test datasets.
Overcoming Catastrophic Forgetting Using Sparse Coding and Meta Learning

Continuous learning occurs naturally in human beings.However, Deep Learning methods suffer from a problem known as Catastrophic Forgetting (CF) that consists of a model drastically decreasing its performance on previously learned tasks when it is sequentially trained on new tasks.This situation, known as task interference, occurs when a network modifies relevant weight values as it learns a new task.In this work, we propose two main strategies to face the problem of task interference in convolutional neural networks.First, we use a sparse coding technique to adaptively allocate model capacity to different tasks avoiding interference between them.Specifically, we use a strategy based on group sparse regularization to specialize groups of parameters to learn each task.Afterward, by adding binary masks, we can freeze these groups of parameters, using the rest of the network to learn new tasks.Second, we use a meta learning technique to foster knowledge transfer among tasks, encouraging weight reusability instead of overwriting.Specifically, we use an optimization strategy based on episodic training to foster learning weights that are expected to be useful to solve future tasks.Together, these two strategies help us to avoid interference by preserving compatibility with previous and future weight values.Using this approach, we achieve stateof-the-art results on popular benchmarks used to test techniques to avoid CF.In particular, we conduct an ablation study to identify the contribution of each component of the proposed method, demonstrating its ability to avoid retroactive interference with previous tasks and to promote knowledge transfer to future tasks.
Anomaly Detection of Spacecraft Telemetry Data Using Temporal Convolution Network

Spacecraft telemetry data is the only basis for the ground management and operation system to determine its on-orbit status. The rapid development of on-orbit spacecraft generates a large amount of telemetry data with high dimensionality and complex correlation between variables, which brings great challenges for current and future ground management and operation for these spacecrafts. As a result, its anomaly detection becomes an essential solution for enhancing the operation safely and reliably and realizing intelligent maintenance. The above issues make it a hot spot research topic for realization of high detection rate, low false detection rate, and strong interpretability of anomaly detection of telemetry data. In further, timely detection of abnormalities can reduce spacecraft unexpected accidents. Recently, Temporal Convolution Network (TCN) has arisen much attention for telemetry time series data modeling by its good parallel processing ability and temporal characterization. Thus, this paper proposes an anomaly detection model for spacecraft telemetry data based on TCN time series prediction. Firstly, TCN algorithm is used to extract features of telemetry data and make a prediction. Then, the potential anomalies are determined by the static threshold judgement method. Actual satellite telemetry data are used to implement performance evaluation and comparison. The proposed method is compared with Long Short Term Memory (LSTM) algorithm which is also a widely utilized algorithm in recent time series analysis. Four metrics are adopted for the experiments: the detection accuracy, detection rate, false detection rate and model inference time. TCN achieves high accuracy, high detection rate and low false detection rate. In addition, TCN save much more model inference time than LSTM with long timestep. Generally, TCN is superior to LSTM on operating efficiency.
Neural Side-By-Side: Predicting Human Preferences for No-Reference Super-Resolution Evaluation

Super-resolution based on deep convolutional networks is currently gaining much attention from both academia and industry. However, lack of proper evaluation measures makes it diﬃcult to compare approaches, hampering progress in the field. Traditional measures, such as PSNR or SSIM, are known to poorly correlate with the human perception of image quality. Therefore, in existing works common practice is also to report Mean-Opinion-Score (MOS) — the results of human evaluation of super-resolved images. Unfortunately, the MOS values from different papers are not directly comparable, due to the varying number of raters, their subjectivity, etc. By this paper, we introduce Neural Side-By-Side — a new measure that allows super-resolution models to be compared automatically, effectively approximating human preferences. Namely, we collect a large dataset of aligned image pairs, which were produced by different super-resolution models. Then each pair is annotated by several raters, who were instructed to choose a more visually appealing image. Given the dataset and the labels, we trained a CNN model that obtains a pair of images and for each image predicts a probability of being more preferable than its counterpart. In this work, we show that Neural Side-By-Side generalizes across both new models and new data. Hence, it can serve as a natural approximation of human preferences, which can be used to compare models or tune hyperparameters without raters’ assistance. We open-source the dataset and the pretrained model <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> and expect that it will become a handy tool for researchers and practitioners.