Reviewerly/exHarmony huggingface dataset:
Performance Analysis of Speckle Reduction Filtering algorithms in B-Mode Ultrasound Images

Medical field is enriched with various imaging modalities that help experts for analyzing and diagnosis of the diseases. Among them, Computer Tomography(CT), X-rays, Magnetic Resonance Imaging(MRI), Ultrasound(US) are popular practices that have great impact on the lives. Almost all the medical images are contaminated by noise while capturing the image and they pose huge difficulty for the interpretation of the details in the images. Automating various medical image tasks including prediction, classification, segmentation of certain region of interests require preprocessing of these images. Major task in preprocessing steps are noise removal and image enhancement. Ultrasonography is a non-invasive and inexpensive technique for obtaining cross sectional views of the different organs, visualizing blood vessels, tissues and detecting abnormal masses. Speckle noise intrinsically present in ultrasound images, are multiplicative unlike additive Gaussian noise. Images corrupted with noise degrades the image quality and reduces the accuracy in their interpretation. This paper is a comparative study on various denoising techniques that reduce the speckle noise present in B-mode US images. Different image quality metrics like Speckle suppression index(SSI), Peak signal to noise ratio(PSNR), and Structural similarity index measure(SSIM) are used to effectively measure the performance of the methods discussed.
RT-RMS: A Real-Time Resiliency Management System for Operational Decision Support

Power system resiliency has become a key priority for multiple stakeholders, given an observed increase in severe weather events in recent years and impact on the power distribution grid. Distribution network operators need to assess and analyze the resiliency of the system through carefully designed visualization driven by data and model based analytics. Operators require real-time data visualization of system states and resiliency indicators to make correct operational decisions and to control actions to minimize system impact. This paper describes a resilience-driven visualization tool, the Real-Time Resilience Management System Tool (RT-RMS), developed to assist operators in decision making and resilience assessment. RT-RMS utilizes multi-dimensional resilience metrics, geospatial visualization, and data monitors assessing resilience indicators and other key data points. This paper describes the basis of design, design considerations, open-source software components, and use cases demonstrating implementation and discusses the importance of the presented tool, especially in geographically isolated communities, where resilience is valued more than economic operation. The challenge of handling large amounts of data in a web-based application is analyzed by implementing a three-layered architecture and applying RESTful APIs in the back-end. Case studies are presented to highlight key RT-RMS features.
Teacher Training during Covid-19: A Case Study of the Virtual STEM Project in Africa

The Virtual STEM project was carried out in two countries - Nigeria and Kenya by Co-creation Hub (CcHub) Africa's largest innovation institute. The Virtual STEM training program was aimed at providing support to train and educate in-service and pre-service teachers with the right skills, tools and teaching methodology while integrating the IBL approach to foster the continuous learning for their students while at home and in turn support teachers' development. The goal of this research is to examine the impact of the programme and to evaluate the effectiveness of the project approach in delivering remote teaching training activities.
Bi-directional Long Short-Term Memory Model with Semantic Positional Attention for the Question Answering System

The intelligent question answering system aims to provide quick and concise feedback on the questions of users. Although the performance of phrase-level and numerous attention models have been improved, the sentence components and position information are not emphasized enough. This article combines Ci-Lin and word2vec to divide all of the words in the question-answer pairs into groups according to the semantics and select one kernel word in each group. The remaining words are common words and realize the semantic mapping mechanism between kernel words and common words. With this Chinese semantic mapping mechanism, the common words in all questions and answers are replaced by the semantic kernel words to realize the normalization of the semantic representation. Meanwhile, based on the bi-directional LSTM model, this article introduces a method of the combination of semantic role labeling and positional context, dividing the sentence into multiple semantic segments according to semantic logic. The weight is given to the neighboring words in the same semantic segment and propose semantic role labeling position attention based on the bi-directional LSTM model (BLSTM-SRLP). The good performance of the BLSTM-SRLP model has been demonstrated in comparative experiments on the food safety field dataset (FS-QA).
Current Trends in Research and Application of Digital Human Modeling

The paper reports an investigation conducted during the DHM2020 Symposium regarding current trends in research and application of DHM in academia, software development, and industry. The results show that virtual reality (VR), augmented reality (AR), and digital twin are major current trends. Furthermore, results show that human diversity is considered in DHM using established methods. Results also show a shift from the assessment of static postures to assessment of sequences of actions, combined with a focus mainly on human well-being and only partly on system performance. Motion capture and motion algorithms are alternative technologies introduced to facilitate and improve DHM simulations. Results from the DHM simulations are mainly presented through pictures or animations.
Improved Weighted Average-based Rhombus Predictor in Reversible Data Hiding Using Prediction Error Expansion

Rhombus predictor is a popular way to achieve prediction error expansion based reversible watermarking algorithm because of its superior performance. However, most of the detailed implementations were designed only for images with a majority of smooth regions leading to large prediction errors by using directly on texture regions. Despite the improvements, they failed to adequately consider correlations between pixels, moreover, they used inferior fluctuation-based sorting mechanisms. To cope with such problems, we designed a new weight calculation technique for the rhombus predictor. Specifically, based on the complexities of texture regions evaluated by correlations of four neighboring pixels, we ensure that texture regions have always smaller weights. Furthermore, to guarantee higher imperceptibility, we propose a new fluctuation-based sorting mechanism. Experimental results indicate that our predictor achieves lower distortion while obtaining high embedding capacity in comparison to the state-of-the-art.
Toto  Benchmarking the Efficiency of a Cloud Service

Microsoft aims to increase the efficiency of Azure SQL DB by maximizing the number of databases that can be hosted in a cluster. However, resource contention among customers increases when changing the configurations, policies, and features that control database co-location on cluster nodes. Tuning and evaluating the efficiency and customer impact of these variables in a scientific manner in production, with a dynamic system and customer workloads, is difficult or infeasible. Here, we present Toto, a benchmark framework for evaluating the efficiency of any cloud service that leverages orchestrators like Service Fabric or Kubernetes. Toto allows for reliable and repeatable specification of a benchmarking scenario of arbitrary scale, complexity, and time-length. An implementation of Toto is deployed in all SQL DB staging clusters and is used to evaluate system efficiency and behaviors. As an example of Toto's capabilities, we present a study to explore the balance between cluster database density and quality of service.
Blind restoration of solar images via the Channel Sharing Spatio-temporal Network

Context. Due to the presence of atmospheric turbulence, the quality of solar images tends to be significantly degraded when observed by ground-based telescopes. The adaptive optics (AO) system can achieve partial correction but stops short of reaching the diffraction limit. In order to further improve the imaging quality, post-processing for AO closed-loop images is still necessary. Methods based on deep learning (DL) have been proposed for AO image reconstruction, but the most of them are based on the assumption that the point spread function is spatially invariant. Aims. Our goal is to construct clear solar images by using a sophisticated spatially variant end-to-end blind restoration network. Methods. The proposed channel sharing spatio-temporal network (CSSTN) consists of three sub-networks: a feature extraction network, channel sharing spatio-temporal filter adaptive network (CSSTFAN), and a reconstruction network (RN). First, CSSTFAN generates two filters adaptively according to features generated from three inputs. Then these filters are delivered to the proposed channel sharing filter adaptive convolutional layer in CSSTFAN to convolve with the previous or current step features. Finally, the convolved features are concatenated as input of RN to restore a clear image. Ultimately, CSSTN and the other three supervised DL methods are trained on the binding real 705 nm photospheric and 656 nm chromospheric AO correction images as well as the corresponding speckle reconstructed images. Results. The results of CSSTN, the three DL methods, and one classic blind deconvolution method evaluated on four test sets are shown. The imaging condition of the first photospheric and second chromospheric set is the same as training set, except for the different time given in the same hour. The imaging condition of the third chromospheric and fourth photospheric set is the same as the first and second, except for the Sun region and time. Our method restores clearer images and performs best in both the peak signal-to-noise ratio and contrast among these methods.
Deep Learning Based Approach to Generate Realistic Data for ADAS Applications

Quantity, quality and diversity of datasets are prerequisites for training the deep-learning autonomous driving models. One major issue identified from the literature is the lack of realistic training data which eventually leads to a less robust model. Simulators can help in dealing with reducing reality gaps, however, commonly available simulators generate data that are far removed from the real world scenarios. The model proposed in this study is based on video-to-video synthesis and image synthesis methods using Generative Adversarial Networks (GANs). The results indicate improved realism. Kanade-Lucas-Tomasi (KLT) and Fr'echet Inception Distance (FID) based temporal coherence evaluation metrics have also been proposed as a possible alternative to human perception driven evaluations.
Attacks and Defenses for Single-Stage Residue Number System PRNGs

This paper explores the security of a single-stage residue number system (RNS) pseudorandom number generator (PRNG), which has previously been shown to provide extremely high-quality outputs when evaluated through available RNG statistical test suites or in using Shannon and single-stage Kolmogorov entropy metrics. In contrast, rather than blindly performing statistical analyses on the outputs of the single-stage RNS PRNG, this paper provides both white box and black box analyses that facilitate reverse engineering of the underlying RNS number generation algorithm to obtain the residues, or equivalently key, of the RNS algorithm. We develop and demonstrate a conditional entropy analysis that permits extraction of the key given a priori knowledge of state transitions as well as reverse engineering of the RNS PRNG algorithm and parameters (but not the key) in problems where the multiplicative RNS characteristic is too large to obtain a priori state transitions. We then discuss multiple defenses and perturbations for the RNS system that fool the original attack algorithm, including deliberate noise injection and code hopping. We present a modification to the algorithm that accounts for deliberate noise, but rapidly increases the search space and complexity. Lastly, we discuss memory requirements and time required for the attacker and defender to maintain these defenses.
Blind Non-Uniform Motion Deblurring using Atrous Spatial Pyramid Deformable Convolution and Deblurring-Reblurring Consistency

Many deep learning based methods are designed to remove non-uniform (spatially variant) motion blur caused by object motion and camera shake without knowing the blur kernel. Some methods directly output the latent sharp image in one stage, while others utilize a multi-stage strategy (e.g. multi-scale, multi-patch, or multi-temporal) to gradually restore the sharp image. However, these methods have the following two main issues: 1) The computational cost of multi-stage is high; 2) The same convolution kernel is applied in different regions, which is not an ideal choice for non-uniform blur. Hence, non-uniform motion deblurring is still a challenging and open problem. In this paper, we propose a new architecture which consists of multiple Atrous Spatial Pyramid Deformable Convolution (ASPDC) modules to deblur an image end-to-end with more flexibility. Multiple ASPDC modules implicitly learn the pixel-specific motion with different dilation rates in the same layer to handle movements of different magnitude. To improve the training, we also propose a reblurring network to map the deblurred output back to the blurred input, which constrains the solution space. Our experimental results show that the proposed method outperforms state-of-the-art methods on the benchmark datasets. The code is available at https://github.com/Dong-Huo/ASPDC.
Development of a BIM-VR application for e-learning engineering education

Building Information Modeling (BIM) is arguably the most important and promising ICT applied in the Architecture, Engineering, and Construction (AEC) industry. One of the most important dimensions of BIM is its implementation for Virtual Reality (VR) to simulate and interact inside virtual environments of construction projects. The benefits of using BIM and VR with educational purposes has been explored recently by separate, however there is no evidence of an approach that develops and implements both technologies together. The research in this study is the first one in developing the framework for the implementation of a user-friendly BIM-VR software application with specific pedagogical objectives for AEC education. This study demonstrates that the development of this high-tech educative tool aims to: 1) make more effective use of learning hours through playful interaction with BIM models, and 2) improve understanding of abstract complex concepts that are difficult to explain theoretically. The implementation of this technological tool is developed in the context of national AEC higher education programs in a university in Mexico. In this study, the effectiveness of using a collaborative BIM-VR tool as a didactic method for AEC is demonstrated. Also, this study demonstrates that the learning experience can be improved using BIM-VR.
Module learning with rounding based key agreement scheme with modified reconciliation

In this paper, we propose a probably secure key agreement scheme based on module learning with rounding (MLWR) problem by modifying the reconciliation mechanism to obtain a shared key. In addition, compared with other schemes with the same properties, the error probability is relatively small. The detailed security analysis is provided against a passive adversary. Then, a modified software package is developed to generate a parameter set for any security level. Primal and dual attack costs are also computed. The main contribution of this paper is to construct a modified reconciliation mechanism and obtain agreed values with low error probability and bandwidth.
Design Considerations for Operational Power System Simulation Scenarios

This paper describes the design of power grid operational scenarios that can enable students or professionals to experience operating the grid. The scenarios are created using complex and realistic grid systems of varying sizes, and range from normal daily operations to extreme events. They can involve one or multiple participants based on the objectives of the training exercise, complexity of the system and tasks, etc. The main objective is to use these scenarios towards effective grid operations training, and research applications. The scenarios created include, 1) a voltage control scenario to simulate normal operations, 2) a geomagnetic disturbance (GMD) simulation to experience operating a simple grid model under a GMD, and 3) a multi-user scenario to simulate normal operations while communicating with a team. The paper introduces the simulation environment used, and describes the scenario design process including the learning objective, system variation, and the user interface and controls.
Assessing exception handling testing practices in open-source libraries

Modern programming languages (e.g., Java and C#) provide features to separate error-handling code from regular code, seeking to enhance software comprehensibility and maintainability. Nevertheless, the way exception handling (EH) code is structured in such languages may lead to multiple, different, and complex control flows, which may affect the software testability. Previous studies have reported that EH code is typically neglected, not well tested, and its misuse can lead to reliability degradation and catastrophic failures. However, little is known about the relationship between testing practices and EH testing effectiveness. In this exploratory study, we (i) measured the adequacy degree of EH testing concerning code coverage (instruction, branch, and method) criteria; and (ii) evaluated the effectiveness of the EH testing by measuring its capability to detect artificially injected faults (i.e., mutants) using 7 EH mutation operators. Our study was performed using test suites of 27 long-lived Java libraries from open-source ecosystems. Our results show that instructions and branches within catch blocks and throw instructions are less covered, with statistical significance, than the overall instructions and branches. Nevertheless, most of the studied libraries presented test suites capable of detecting more than 70% of the injected faults. From a total of 12,331 mutants created in this study, the test suites were able to detect 68% of them.
Cyberbullying detection on Tweets

Nowadays the young generation spends a median of 3 hours a day on social media sites such as Twitter, Facebook, Instagram, Snapchat or TikTok. As a consequence, these platforms have gradually become part of their daily life as well as an influence on their attitude and behaviour. Although the social media platforms were originally designed to share information/news or connect families and friends, they also spread fake news, conspiracy theories, hate speech and cyberbullying. Cyberbullying is bullying with the use of digital technologies which is repeated, aimed at scaring, angering or shaming those who are targeted. Feeling vulnerable, powerless, humiliated, isolated, depressed or suicidal are examples of the negative effects from cyberbullying. Unfortunately, study has shown that one-third of the students have experienced cyberbullying in their lifetime. To mitigate this problem, we propose 1) a novel dataset of 67K tweets collected from Twitter, 2) automatic annotation methods for a large-scale dataset and 3) a detection system that to identify these toxic behaviours. The experimental results demonstrate that our method outperforms the baseline by 4%.
Feature Selection for Learning to Predict Outcomes of Compute Cluster Jobs with Application to Decision Support

We present a machine learning framework and a new test bed for data mining from the Slurm Workload Manager for high-performance computing (HPC) clusters. The focus was to find a method for selecting features to support decisions: helping users decide whether to resubmit failed jobs with boosted CPU and memory allocations or migrate them to a computing cloud. This task was cast as both supervised classification and regression learning, specifically, sequential problem solving suitable for reinforcement learning. Selecting relevant features can improve training accuracy, reduce training time, and produce a more comprehensible model, with an intelligent system that can explain predictions and inferences. We present a supervised learning model trained on a Simple Linux Utility for Resource Management (Slurm) data set of HPC jobs using three different techniques for selecting features: linear regression, lasso, and ridge regression. Our data set represented both HPC jobs that failed and those that succeeded, so our model was reliable, less likely to overfit, and generalizable. Our model achieved an R2 of 95% with 99% accuracy. We identified five predictors for both CPU and memory properties.
Locating Faulty Methods with a Mixed RNN and Attention Model

IR-based fault localization approaches achieves promising results when locating faulty files by comparing a bug report with source code. Unfortunately, they become less effective to locate faulty methods. We conduct a preliminary study to explore its challenges, and identify three problems: the semantic gap problem, the representation sparseness problem, and the single revision problem.To tackle these problems, we propose MRAM, a mixed RNN and attention model, which combines bug-fixing features and method structured features to explore both implicit and explicit relevance between methods and bug reports for method level fault localization task. The core ideas of our model are: (1) constructing code revision graphs from code, commits and past bug reports, which reveal the latent relations among methods to augment short methods and as well provide all revisions of code and past fixes to train more accurate models; (2) embedding three method structured features (token sequences, API invocation sequences, and comments) jointly with RNN and soft attention to represent source methods and obtain their implicit relevance with bug reports; and (3) integrating multi-revision bug-fixing features, which provide the explicit relevance between bug reports and methods, to improve the performance.We have implemented MRAM and conducted a controlled experiment on five open-source projects. Comparing with state-of-the-art approaches, our MRAM improves MRR values by 3.8-5.1% (3.7-5.4%) when the dataset contains (does not contain) localized bug reports. Our statistics test shows that our improvements are significant.
Emotion Distribution Profile for Movies Recommender Systems

Reviews, comments and feedbacks are user-generated content which comprises of insights regarding a given item or a thing and furthermore user’ emotions. Various highlights of user-created content incorporate feelings, opinions and survey helpfulness that shows a promising research in the field of recommender systems. Reviews contain various words and sentences that show their natural passionate substance. Emotions are a significant component of human conduct. They enable us for decision making by generating a liking or disliking toward a particular item. This paper harnesses reviews as the content generated from user to exploit, emotion as a basis for generating recommendations. Through experiments conducted on real dataset, our proposed approach compares the performance with traditional item-based collaborative filtering approach. Experimental results show 173% increase in prediction accuracy for top 25 recommendations as compared to prediction accuracy based on rating-based item similarity.