Reviewerly/exHarmony huggingface dataset:
SOLARNET Metadata Recommendations for Solar Observations

Prior to the EU Horizon 2020 and FP7 SOLARNET projects, metadata descriptions of Solar observations were only standardized for space-based observations, but the standards have been mostly within a single space mission at a time, at times with significant differences between different mission standards. In the context of ground-based Solar observations, data has typically not been made freely available to the general research community, resulting in an even greater lack of standards for metadata descriptions. This situation makes it difficult to construct multi-instrument archives/virtual observatories with anything more than the most basic metadata available for searching, as well as making it difficult to write generic software for instrument-agnostic data analysis. This document describes the metadata recommendations developed under the SOLARNET EU project, which aims foster more collaboration and data sharing between both ground-based and space-based Solar observatories. The recommendations will be followed by data pipelines developed under the SOLARNET project as well as e.g. the Solar Orbiter SPICE pipeline and the SST CHROMIS/CRISP common pipeline. These recommendations are meant to function as a common reference to which even existing diverse data sets may be related, for ingestion into solar virtual observatories and for analysis by generic software.
CNN-Derived Local Features for Condition-Invariant Robot Localization with a Single RGB Sensor

A conditio sine qua non for any autonomous system refers to its ability to localize itself into a known environment. Towards this end, camera sensors are typically deployed for measuring the relative transformation between the mobile platform and a pre-mapped scene due to their low cost and substantial accuracy. This paper deals with the task of robot localization under different environmental conditions using a single RGB sensor. To achieve this, we diverge from conventional approaches that detect local points of interest using hand-crafted sets of rules, and we utilize the cognitive properties of modern deep learning models for feature detection and description. The proposed architecture is evaluated and compared against other traditional techniques under a wide range of environmental conditions that significantly alter the view of a previously recorded area.
Finding Optimal Path Planning Method for Building Navigation in BIM

Building's spatial structure described in a BIM model can be used for retrieving the information required for determining the shortest path within the building.The matrix method and the visibility method are the two main ways of dividing space into prime factors.Both are widely used to find the shortest path.In order to compare the performance of both methods, several tests were carried out with various versions of the floorplan modification (room area, the surface of internal walls, distance between the entrance, and exit in a straight line and within the boundary marked by walls of the maze).The results revealed significant differences between the visibility graph and the matrix method.
Understanding the human in the design of cyber-human discovery systems for data-driven astronomy

High-quality, usable, and effective software is essential for supporting astronomers in the discovery-focused tasks of data analysis and visualisation. As the volume, and perhaps more crucially, the velocity of astronomical data grows, the role of the astronomer is changing. There is now an increased reliance on automated and autonomous discovery and decision-making workflows rather than visual inspection. We assert the need for an improved understanding of how astronomers (humans) currently make visual discoveries from data. This insight is a critical element for the future design, development and effective use of cyber-human discovery systems, where astronomers work in close collaboration with automated systems to gain understanding from continuous, real-time data streams. We discuss how relevant human performance data could be gathered, specifically targeting the domains of expertise and skill at visual discovery, and the identification and management of cognitive factors. By looking to other disciplines where human performance is assessed and measured, we propose four early-stage applications that would: (1) allow astronomers to evaluate, and potentially improve, their own visual discovery skills; (2) support just-in-time coaching; (3) enable talent identification; and (4) result in user interfaces that automatically respond to skill level and cognitive state. Throughout, we advocate for the importance of user studies and the incorporation of participatory design and co-design practices into the planning, implementation and evaluation of alternative user interfaces and visual discovery environments.
Optimal Approximations Made Easy

The fundamental result of Li, Long, and Srinivasan on approximations of set systems has become a key tool across several communities such as learning theory, algorithms, computational geometry, combinatorics and data analysis. The goal of this paper is to give a modular, self-contained, intuitive proof of this result for finite set systems. The only ingredient we assume is the standard Chernoff's concentration bound. This makes the proof accessible to a wider audience, readers not familiar with techniques from statistical learning theory, and makes it possible to be covered in a single self-contained lecture in a geometry, algorithms or combinatorics course.
Analyzing the Components of Distributed Coevolutionary GAN Training

Distributed coevolutionary Generative Adversarial Network (GAN) training has empirically shown success in overcoming GAN training pathologies. This is mainly due to diversity maintenance in the populations of generators and discriminators during the training process. The method studied here coevolves sub-populations on each cell of a spatial grid organized into overlapping Moore neighborhoods. We investigate the impact on the performance of two algorithm components that influence the diversity during coevolution: the performance-based selection/replacement inside each sub-population and the communication through migration of solutions (networks) among overlapping neighborhoods. In experiments on MNIST dataset, we find that the combination of these two components provides the best generative models. In addition, migrating solutions without applying selection in the sub-populations achieves competitive results, while selection without communication between cells reduces performance.
Towards Deep Clustering of Human Activities from Wearables

Our ability to exploit low-cost wearable sensing modalities for critical human behaviour and activity monitoring applications in health and wellness is reliant on supervised learning regimes; here, deep learning paradigms have proven extremely successful in learning activity representations from annotated data. However, the costly work of gathering and annotating sensory activity datasets is labor-intensive, time consuming and not scalable to large volumes of data. While existing unsupervised remedies of deep clustering leverage network architectures and optimization objectives that are tailored for static image datasets, deep architectures to uncover cluster structures from raw sequence data captured by on-body sensors remains largely unexplored. In this paper, we develop an unsupervised end-to-end learning strategy for the fundamental problem of human activity recognition (HAR) from wearables. Through extensive experiments, including comparisons with existing methods, we show the effectiveness of our approach to jointly learn unsupervised representations for sensory data and generate cluster assignments with strong semantic correspondence to distinct human activities.
The Road Not Taken: Re-thinking the Feasibility of Voice Calling Over Tor

Anonymous VoIP calls over the Internet holds great significance for privacy-conscious users, whistle-blowers and political activists alike. Prior research deems popular anonymization systems like Tor unsuitable for providing requisite performance guarantees that real-time applications like VoIP need. Their claims are backed by studies that may no longer be valid due to constant advancements in Tor. Moreover, we believe that these studies lacked the requisite diversity and comprehensiveness. Thus, conclusions from these studies led them to propose novel and tailored solutions. However, no such system is available for immediate use. Additionally, operating such new systems would incur significant costs for recruiting users and volunteered relays, to provide the necessary anonymity guarantees. It thus becomes imperative that the exact performance of VoIP over Tor be quantified and analyzed so that the potential performance bottlenecks can be amended. We thus conducted an extensive empirical study across various in-lab and real-world scenarios to shed light on VoIP performance over Tor. In over 0.5 million measurements spanning 12 months, across seven countries and covering about 6650 Tor relays, we observed that Tor supports good voice quality (Perceptual Evaluation of Speech Quality (PESQ) >3 and oneway delay <400ms) in more than 85% of cases. Further analysis indicates that in general for most Tor relays, the contentions due to cross-traffic were low enough to support VoIP calls, that are anyways transmitted at low rates (<120 Kbps). Our findings are supported by concordant measurements using iperf that show more than the adequate available bandwidth for most cases. Data published by the Tor Metrics also corroborates the same. Hence, unlike prior efforts, our research reveals that Tor is suitable for supporting anonymous VoIP calls.
Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm

It is well known that evolutionary algorithms (EAs) achieve peak performance only when their parameters are suitably tuned to the given problem. Even more, it is known that the best parameter values can change during the optimization process. Parameter control mechanisms are techniques developed to identify and to track these values. Recently, a series of rigorous theoretical works confirmed the superiority of several parameter control techniques over EAs with best possible static parameters. Among these results are examples for controlling the mutation rate of the $(1+\lambda)$~EA when optimizing the OneMax problem. However, it was shown in [Rodionova et al., GECCO'19] that the quality of these techniques strongly depends on the offspring population size $\lambda$. We introduce in this work a new hybrid parameter control technique, which combines the well-known one-fifth success rule with Q-learning. We demonstrate that our HQL mechanism achieves equal or superior performance to all techniques tested in [Rodionova et al., GECCO'19] and this -- in contrast to previous parameter control methods -- simultaneously for all offspring population sizes $\lambda$. We also show that the promising performance of HQL is not restricted to OneMax, but extends to several other benchmark problems.
A Two-Stage Multiple Instance Learning Framework for the Detection of Breast Cancer in Mammograms

Mammograms are commonly employed in the large scale screening of breast cancer which is primarily characterized by the presence of malignant masses. However, automated image-level detection of malignancy is a challenging task given the small size of the mass regions and difficulty in discriminating between malignant, benign mass and healthy dense fibro-glandular tissue. To address these issues, we explore a two-stage Multiple Instance Learning (MIL) framework. A Convolutional Neural Network (CNN) is trained in the first stage to extract local candidate patches in the mammograms that may contain either a benign or malignant mass. The second stage employs a MIL strategy for an image level benign vs. malignant classification. A global image-level feature is computed as a weighted average of patch-level features learned using a CNN. Our method performed well on the task of localization of masses with an average Precision/Recall of 0.76/0.80 and acheived an average AUC of 0.91 on the imagelevel classification task using a five-fold cross-validation on the INbreast dataset. Restricting the MIL only to the candidate patches extracted in Stage 1 led to a significant improvement in classification performance in comparison to a dense extraction of patches from the entire mammogram.
A Fortran-Keras Deep Learning Bridge for Scientific Computing

Implementing artificial neural networks is commonly achieved via high-level programming languages like Python and easy-to-use deep learning libraries like Keras. These software libraries come pre-loaded with a variety of network architectures, provide autodifferentiation, and support GPUs for fast and efficient computation. As a result, a deep learning practitioner will favor training a neural network model in Python, where these tools are readily available. However, many large-scale scientific computation projects are written in Fortran, making it difficult to integrate with modern deep learning methods. To alleviate this problem, we introduce a software library, the Fortran-Keras Bridge (FKB). This two-way bridge connects environments where deep learning resources are plentiful, with those where they are scarce. The paper describes several unique features offered by FKB, such as customizable layers, loss functions, and network ensembles. The paper concludes with a case study that applies FKB to address open questions about the robustness of an experimental approach to global climate simulation, in which subgrid physics are outsourced to deep neural network emulators. In this context, FKB enables a hyperparameter search of one hundred plus candidate models of subgrid cloud and radiation physics, initially implemented in Keras, to be transferred and used in Fortran. Such a process allows the model's emergent behavior to be assessed, i.e. when fit imperfections are coupled to explicit planetary-scale fluid dynamics. The results reveal a previously unrecognized strong relationship between offline validation error and online performance, in which the choice of optimizer proves unexpectedly critical. This reveals many neural network architectures that produce considerable improvements in stability including some with reduced error, for an especially challenging training dataset.
The Hessian Estimation Evolution Strategy

We present a novel black box optimization algorithm called Hessian Estimation Evolution Strategy. The algorithm updates the covariance matrix of its sampling distribution by directly estimating the curvature of the objective function. This algorithm design is targeted at twice continuously differentiable problems. For this, we extend the cumulative step-size adaptation algorithm of the CMA-ES to mirrored sampling. We demonstrate that our approach to covariance matrix adaptation is efficient by evaluation it on the BBOB/COCO testbed. We also show that the algorithm is surprisingly robust when its core assumption of a twice continuously differentiable objective function is violated. The approach yields a new evolution strategy with competitive performance, and at the same time it also offers an interesting alternative to the usual covariance matrix update mechanism.
Novel Language Resources for Hindi: An Aesthetics Text Corpus and a Comprehensive Stop Lemma List

This paper is an effort to complement the contributions made by researchers working toward the inclusion of non-English languages in natural language processing studies. Two novel Hindi language resources have been created and released for public consumption. The first resource is a corpus consisting of nearly thousand pre-processed fictional and nonfictional texts spanning over hundred years. The second resource is an exhaustive list of stop lemmas created from 12 corpora across multiple domains, consisting of over 13 million words, from which more than 200,000 lemmas were generated, and 11 publicly available stop word lists comprising over 1000 words, from which nearly 400 unique lemmas were generated. This research lays emphasis on the use of stop lemmas instead of stop words owing to the presence of various, but not all morphological forms of a word in stop word lists, as opposed to the presence of only the root form of the word, from which variations could be derived if required. It was also observed that stop lemmas were more consistent across multiple sources as compared to stop words. In order to generate a stop lemma list, the parts of speech of the lemmas were investigated but rejected as it was found that there was no significant correlation between the rank of a word in the frequency list and its part of speech. The stop lemma list was assessed using a comparative method. A formal evaluation method is suggested as future work arising from this study.
A Study on Multi-View Camera Casting Framework using Internet of Things Technology

With the advanced video streaming technology, smartphone users can share and stream real-time camera to any video subscriber with a high network transmission speed. On the other side, subscriber able to select their favorite video sources can create multiple screen (i.e., Multi-View features) display. In this paper, a video casting framework for displaying multiple video sources is proposed. This framework potentially leads to an object modelling when multiple cameras point to the same object with different angle of view. In addition, the multiview feature provides additional flexibility on a well-designed production line monitoring system in an Industrial IoT framework. With the existence of multiview content, earlier error detection and prevention can be performed to facilitate cyber-physical system, as an important element in Industrial IoT. Keywords: multi-view, Industry 4.0, Internet of Things, Real-Time Streaming Protocol, video casting
A Usability Evaluation of TARCApp Mobile Application

TARCApp mobile application is an application used by Tunku Abdul Rahman University College students on a daily basis to access necessary resources. Therefore, this study attempts to evaluate the usability of the application by comparing with the Concordia mobile application from the aspects of effectiveness, efficiency and satisfaction. Instruments such as Test Task, Single Ease Question (SEQ) and Software Usability Scale (SUS) questionnaires have been used to measure the usability of these mobile applications. Results indicate that the TARCApp mobile application had a high degree of effectiveness and efficiency. However, the satisfaction was poor, and some usability issues have been discovered during this study. Keywords: usability, mobile application, effectiveness, efficiency, satisfaction
Connected Augmented Assembly - Cloud based Augmented Reality applications in architecture

Current design practices rely on a set of computational tools to simulate and optimize the design in regards to questions concerning architecture, engineering, and construction.However, little progress has been made in tools related to the design and execution of a building assembly.This paper aims to present an integrated procedure that targets the assembly of complex structures.Two challenges are identified and addressed: first, the necessity of a connected design environment where multiple stakeholders can communicate, modify, and give feedback on the assembly sequence.Second, the instructions for the assembly of structures to untrained users.The suggested method is based on the Assembly Information Modeling framework, which provides a general approach to generate assembly information from CAD data and utilizes AEC cloud platforms as a base for communication and Augmented Reality devices as a Human Machine Interface.Ultimately, both cases are combined to constitute Connected Augmented Assembly, a bidirectional approach to assembly design, review, and execution.
Joint Face Super-Resolution and Deblurring Using a Generative Adversarial Network

Facial image super-resolution (SR) is an important preprocessing for facial image analysis, face recognition, and image-based 3D face reconstruction. Recent convolutional neural network (CNN) based method has shown excellent performance by learning mapping relation using pairs of low-resolution (LR) and high-resolution (HR) facial images. However, since the HR facial image reconstruction using CNN is conventionally aimed to increase the PSNR and SSIM metrics, the reconstructed HR image might not be realistic even with high scores. An adversarial framework is proposed in this study to reconstruct the HR facial image by simultaneously generating an HR image with and without blur. First, the spatial resolution of the LR facial image is increased by eight times using a five-layer CNN. Then, the encoder extracts the features of the up-scaled image. These features are finally sent to two branches (decoders) to generate an HR facial image with and without blur. In addition, local and global discriminators are combined to focus on the reconstruction of HR facial structures. Experiment results show that the proposed algorithm generates a realistic HR facial image. Furthermore, the proposed method can generate a variety of different facial images.
Information-geometric optimization with natural selection

Evolutionary algorithms, inspired by natural evolution, aim to optimize difficult objective functions without computing derivatives. Here we detail the relationship between population genetics and evolutionary optimization and formulate a new evolutionary algorithm. Optimization of a continuous objective function is analogous to searching for high fitness phenotypes on a fitness landscape. We summarize how natural selection moves a population along the non-euclidean gradient that is induced by the population on the fitness landscape (the natural gradient). Under normal approximations common in quantitative genetics, we show how selection is related to Newton's method in optimization. We find that intermediate selection is most informative of the fitness landscape. We describe the generation of new phenotypes and introduce an operator that recombines the whole population to generate variants that preserve normal statistics. Finally, we introduce a proof-of-principle algorithm that combines natural selection, our recombination operator, and an adaptive method to increase selection. Our algorithm is similar to covariance matrix adaptation and natural evolutionary strategies in optimization, and has similar performance. The algorithm is extremely simple in implementation with no matrix inversion or factorization, does not require storing a covariance matrix, and may form the basis of more general model-based optimization algorithms with natural gradient updates.
Continuous-Domain Assignment Flows

Assignment flows denote a class of dynamical models for contextual data labeling (classification) on graphs. We derive a novel parametrization of assignment flows that reveals how the underlying information geometry induces two processes for assignment regularization and for gradually enforcing unambiguous decisions, respectively, that seamlessly interact when solving for the flow. Our result enables to characterize the dominant part of the assignment flow as a Riemannian gradient flow with respect to the underlying information geometry. We consider a continuous-domain formulation of the corresponding potential and develop a novel algorithm in terms of solving a sequence of linear elliptic PDEs subject to a simple convex constraint. Our result provides a basis for addressing learning problems by controlling such PDEs in future work.