Reviewerly/exHarmony huggingface dataset:
Robust Bayesian Particle Filter for Space Object Tracking Under Severe Uncertainty

This paper introduces a robust Bayesian particle filter that can handle epistemic uncertainty in the measurements, dynamics, and initial conditions.The robust filter returns robust bounds on the output quantity of interest, rather than a crisp value.Particles are generated with an importance sampling technique and propagated only one time during the estimation process.The proposal distribution is constructed by running a parallel Unscented Kalman Filter to drive particles in regions of high expected likelihood and achieve a high effective sample size.The bounds are then computed by an inexpensive tuning of the importance weights via numerical optimisation.A Branch & Bound algorithm over simplexes with a Lipschitz bounding function is employed to achieve guaranteed convergence to the lower and upper bounds in a finite number of steps.The filter is applied to the robust computation of the collision probability of SENTINEL 2B with a FENGYUN 1C debris in different operational instances, all characterised by a mix of aleatory and epistemic uncertainty on initial conditions and observation likelihoods.
Adaptive Preferential Sampling in Phylodynamics With an Application to SARS-CoV-2

Longitudinal molecular data of rapidly evolving viruses and pathogens provide information about disease spread and complement traditional surveillance approaches based on case count data. The coalescent is used to model the genealogy that represents the sample ancestral relationships. The basic assumption is that coalescent events occur at a rate inversely proportional to the effective population size Ne (t), a time-varying measure of genetic diversity. When the sampling process (collection of samples over time) depends on Ne (t), the coalescent and the sampling processes can be jointly modeled to improve estimation of Ne (t). Failing to do so can lead to bias due to model misspecification. However, the way that the sampling process depends on the effective population size may vary over time. We introduce an approach where the sampling process is modeled as an inhomogeneous Poisson process with rate equal to the product of Ne (t) and a time-varying coefficient, making minimal assumptions on their functional shapes via Markov random field priors. We provide efficient algorithms for inference, show the model performance vis-a-vis alternative methods in a simulation study, and apply our model to SARS-CoV-2 sequences from Los Angeles and Santa Clara counties. The methodology is implemented and available in the R package adapref. Supplementary files for this article are available online.
Unsupervised Domain Adaptation for Semantic Segmentation via Self-Supervision

Recently, deep learning (DL) methods have been widely used for semantic segmentation of remote sensing and achieved significant progress. However, DL-based methods are time-consuming and labor intensive for the networks requiring abundant data with accurate labeling. To solve this issue, unsupervised domain adaption (UDA) has recently been used to transfer the information from labeled source domain to unlabeled target domain. In this paper, we propose a novel UDA approach based on the self-supervised theory for remote sensing image. Specifically, we firstly utilize the inter-domain adaptation to reduce the gap between the source and target domain. Secondly, based on our proposed spatial-frequency (SF) index, we detach the target domain into an easy and hard split. Ultimately, we adopt the intra-domain adaptation by self-supervised adaptation to improve the performance of hard split. Experimental results on ISPRS Vaihingen and Potsdam datasets demonstrate the effectiveness and rationality of our methods against the other state-of-the-art approaches.
Do We Really Need Frame-by-Frame Annotation Datasets for Object Tracking?

There has been an increasing emphasis on building large-scale datasets as the driver of deep learning-based trackers' success. However, accurately annotating tracking data is highly labor-intensive and expensive, making it infeasible in real-world applications. In this study, we investigate the necessity of large-scale training data to ensure tracking algorithms' performance. To this end, we introduce a FAT (Few-Annotation Tracking) benchmark constructed by sampling one or a few frames per video from some existing tracking datasets. The proposed dataset can be used to evaluate the effectiveness of tracking algorithms considering data efficiency and new data augmentation approaches for object tracking. We further present AMMC (Augmentation by Mimicking Motion Change), a data augmentation strategy that enables learning high-performing trackers using small-scale datasets. AMMC first cuts out the tracked targets and performs a sequence of transformations to simulate the possible change by object motion. Then the transformed targets are pasted on the inpainted background images and further conjointly augmented to mimic variability caused by camera motion. Compared with standard augmentation methods, AMMC explicitly considers tracking data characteristics, which synthesizes more valid data for object tracking. We extensively evaluate our approach with two popular trackers on the FAT datasets. Experiments show that our method allows these trackers to even trained on a dataset requiring much less annotation to achieve comparable or even better performance to those on the full-annotation dataset. The results imply complete video annotation might not be necessary for object tracking if leveraging motion-driven data augmentations during training.
Network-Based Contact Tracing for Detection of Covid-19 Contagions: A Privacy-Preserving Approach

The outbreak of coronavirus 2019 (Covid-19) has imposed a worldwide lockdown, changing the way people live and work, and pressuring the healthcare systems in many countries. Contact tracing based on smartphone apps (app-based contact tracing) has emerged as a possible solution to trace contagions and enforce more sustainable and selective quarantines. However, these apps require a very high adoption rate to reach the critical mass for effective contact tracing. As an alternative, network-based contact tracing, which exploits geo-localization in next generation networks (e.g., 5G) can be used by mobile operators (MOs) to passively trace users' mobility and contacts, provided that a targeted localization accuracy of down to one meter can be achieved. To effectively trace contagions, the identities of positive individuals, which are known by governmental authorities (GAs), are also required. Hence, in network-based contact tracing, MOs and GAs must exchange users' privacy-sensitive data, as geo-locations and infection status, to compute the likelihood that an individual has been infected. To address the privacy issues raised by network contact tracing, after presenting an overview of app-based vs. network-based contact tracing systems, we propose a protocol to make network-based systems compliant with stringent privacy requirements. From extensive simulations, we observe that the cost to guarantee privacy (evaluated in terms of data overhead introduced by the protocol) is acceptable. Finally, we elaborate on the advantages of the proposed system (e.g., more efficient monitoring of virus spread), as well as on the open issues that should be solved before its adoption (e.g., extensive research on potential privacy leakage is required).
SumPA: Efficient Pattern-Centric Graph Mining with Pattern Abstraction

Graph mining aims to explore interesting structural information of a graph. Pattern-centric systems typically transform a generic-purpose graph mining problem into a series of subgraph matching problems for high performance. Existing pattern-centric mining systems reduce the substantial search space towards a single pattern by exploring a highly-optimized matching order, but inherent computational redundancies of such a matching order itself still suffer severely, leading to significant performance degradation. The key innovation of this work lies in a general redundancy criterion that characterizes computational redundancies arising in not only handing a single pattern but also matching multiple patterns simultaneously. In this paper, we present SumPA, a high-performance pattern-centric graph mining system that can sufficiently remove redundant computations for any complex graph mining problems. SumPA features three key designs: (1) a pattern abstraction technique that can simplify numerous complex patterns into a few simple abstract patterns based on pattern similarity, (2) abstraction-guided pattern matching that completely eliminates (totally and partially) redundant computations during subgraph enumeration, and (3) a suite of system optimizations to maximize storage and computation efficiency. Our evaluation on a wide variety of real-world graphs shows that SumPA outperforms the two state-of-the-art systems Peregrine and GraphPi by up to 61.89× and 8.94×, respectively. For many mining problems on large graphs, Peregrine takes hours or even days while SumPA finishes in only a few minutes.
ElChemo: A cross-domain interoperability between chemical and electrical systems in a plant

The paper proposes a novel framework capable of establishing machine-to-machine (M2M) interactions between chemical and electrical systems in the industry. The framework termed as ElChemo addresses the challenges in M2M interaction of entities from different silos, such as differences in the domains' behaviour, the heterogeneities arising from different vocabularies and software. The OntoTwin ontology has been developed based on OntoPowSys and OntoEIP ontologies, which are parts of an intelligent platform called the "J-Park Simulator (JPS)". The ElChemo framework uses Description Logic (DL) and SPIN reasoning techniques to establish the interaction between the chemical and electrical systems in a plant. This paper presents a depropaniser section of a chemical plant and its corresponding electrical system as a use case scenario to demonstrate the interoperability between the two silos within the ElChemo framework. The results from the use case demonstrate, as a proof of concept, the potential of the proposed framework and can be considered as the first step towards the development of a knowledge graph based framework capable of increasing interoperability between cross-domain interactions.
Holistic data-driven requirements elicitation in the big data era

Abstract Digital transformation stimulates continuous generation of large amounts of digital data, both in organizations and in society at large. As a consequence, there have been growing efforts in the Requirements Engineering community to consider digital data as sources for requirements acquisition, in addition to human stakeholders. The volume, velocity and variety of the data make requirements discovery increasingly dynamic, but also unstructured and complex, which current elicitation methods are unable to consider and manage in a systematic and efficient manner. We propose a framework, in the form of a conceptual metamodel and a method, for continuous and automated acquisition, analysis and aggregation of heterogeneous digital sources that aims to support data-driven requirements elicitation and management. The usability of the framework is partially validated by an in-depth case study from the business sector of video game development.
BOAT: A Bayesian Optimization AutoML Time-series Framework for Industrial Applications

Driven by the increasing degree of automation, industrial production plants have become very data reliant, which poses a great potential for machine learning applications. AutoML is a fledgling research topic that has lately gained much attention in the industrial domain. However, existing applications of AutoML are limited, as industrial systems typically involve time-series data, while AutoML solutions for this type of data seem to be still underrepresented. On the contrary, existing AutoML libraries provide better solutions for, e.g., image, textual, tabular or categorical data. To close this gap to the data types and requirements that are typically found in the industrial domain, especially w.r.t. time-series data, a reusable framework is presented that provides native support for time-series models. The framework is equipped with 1) optimization support for a large number of model and hyperparameter configurations, 2) a warm starting module that performs meta-learning, 3) native support for time-series models, 4) an API for enabling user-defined custom models, and 5) a User Interface that provides a holistic view of the optimization results and deployment instructions. Experimental results show the framework’s competitive performance on time-series data and the effectiveness of the warm starting module in accelerating the optimization procedure. A qualitative analysis of the API is done to showcase the framework’s usability regarding defining custom models.
THQuAD: Turkish Historic Question Answering Dataset for Reading Comprehension

Question answering(QA) is a field in natural language processing and information retrieval, it aims to give answers to the questions using natural language. In this paper, we present the Turkish question answering dataset, which is THQuAD and baseline results with contextualized word embeddings. THQuAD consists of two different datasets one of them is TQuad on Turkish Islamic Science history within the scope of Teknofest 2018 “Artificial Intelligence competition”, the second dataset on Ottoman history within the scope of Teknofest 2020 “Doğal Dil İşleme Yarışması ” prepared by us. THQuAD is a reading comprehension dataset, consisting of questions, answers, and passages. Our objective is to give an answer to a specific question by understanding the passage and extracting the answer from this passage. We generate contextualized word embeddings from pre-trained Turkish Bert, Electra, Albert language models after fine-tuning on different hyperparameters with neural networks.
Accountability for the hidden codes toward a better user experience

Search engine optimization and web accessibility for private industry are the key components to our research methods and examination of user experience through hidden code. Hidden code herein refers to frontend code such as structural HTML5 including landmark elements, WAI-ARIA, and CSS styling. In highlighting the relevance of web accessibility to increased search engine exposure, better web development practices, and overall more holistic user experience, this research aims to advocate for the importance of accountability in design practices to business stakeholders and industry perspectives.
Exploring the Potential of Vertical Jump Training in Virtual Reality

In many athletic activities, jumping is a fundamental skill requiring strength, stability, and coordination. It is also a good indicator for general fitness and can predict the individual risk of injury. Therefore, most people profit from training to improve their jumping performance. However, amateur athletes might not have access to professional analysis equipment or jump trainers, and unsupervised training, such as online videos, cannot provide individual feedback. Therefore, we explore the potential of using virtual reality as a widely available platform for a personalized jump training application. Based on the current state of research, we discuss and identify possible use cases, such as instant replays of recorded jumps, an individual motion analysis, automatic exercise recommendations, and gamified workouts. We also demonstrate the feasibility of jumping interaction in virtual scenarios with an early prototype and explain future research directions.
Clinical Data Management in the United States Where We Have Been and Where We Are Going

In the United States, clinical data management (CDM) has been accelerating from a single focus on collecting and preparing clinical trial data for study analysis to a multi-pronged endeavor. This endeavor emphasizes quality data from inception or planning of study design to collection, coding, analysis, reporting, and sharing of all applicable and available data. In this article the influence of legislation and scientific expertise in the collection and analysis of valid, sound data is detailed. FDA requirements as well as academic and technical developments have led to better, more precise and reliable methods in CDM. As such, the perspectives of industry, academia, and non-profits are presented. The role of CDM, at the center of the clinical data explosion, is described, demonstrating how data managers coordinate and build on expertise from all stakeholders. The dependence of biomedical research on CDM and quality data for breakthroughs in diagnostics, drugs, biologics and vaccines is examined. The state of current educational programs is detailed and suggestions are made for new interdisciplinary programs that can provide technical support for all clinical research endeavors. In the article, CDM is viewed through the lens of providing leadership in clinical research and striving to overcome obstacles in communicating the latest technological changes to all stakeholders.&amp;nbsp;&amp;nbsp;
Modular Inverse for Integers using Fast Constant Time GCD Algorithm and its Applications

Modular inversion, the multiplicative inverse of an integer in the ring of integers modulo a prime number, is widely used in public-key cryptography. However, it is one of the most computationally intensive operations, thus, it remains the main performance bottleneck for many cryptographic algorithms.This paper presents to the best of the author’s knowledge, the first FPGA-based hardware design for computing the multiplicative inverse using the recently proposed fast constant-time Greatest Common Divisor (GCD) algorithm. This paper introduces two distinct design architectures targeting different applications: (a) a full-width design and (b) a sequential design. The presented designs are compact, parameterizable, and scalable in terms of area and speed. The evaluation shows the proposed designs, which are constant-time and protect against timing-based attacks, outperform existing software and hardware implementations that use other modular inversion techniques. As a specific example, this work presents an evaluation focusing on the use of the multiplicative inverse hardware module to accelerate the ElGamal cryptosystem. The proposed design achieves a speed-up of 90% in the modular inverse calculation and a speed-up of 45% in the overall ElGamal decryption algorithm using our sequential hardware design of fast constant-time GCD algorithm.In addition to developing the fast hardware implementation, this work potentially opens up a new direction for designing cryptosystems: the inverse operation is often avoided when designing algorithms, due to its complexity. With the new hardware module, using the inverse becomes more tractable, making it more appealing to use in the design of new cryptosystems.
Exploring Logical Reasoning for Referring Expression Comprehension

Referring expression comprehension aims to localize the target object in an image referred by a natural language expression. Most existing approaches neglect the implicit logical correlations among fine-grained cues, e.g., categories, attributes, which are beneficial for distinguishing objects. In this paper, we propose a logic-guided approach to explore logical knowledge for referring expression comprehension in a hierarchical modular-based framework. Specifically, we propose to extract fine-grained cues in visual and textual domains and perform logical reasoning over them with explicit logical expressions to regularize the matching process without extra parameters. Besides, we propose to improve existing modular-based methods by introducing context information of objects in the relationship module. Extensive experiments are conducted on three referring expression datasets, and the results demonstrate that our model can produce more consistent predictions and further achieve superior performance compared with previous methods.
Unsupervised cycle optimization learning for single-view depth and camera pose with Kalman filter

This paper presents a general cycle optimization framework with a Kalman filter (KF) module for single-view depth prediction and camera pose estimation. The framework designs a KF module based on measurement noise estimated from networks without supervision to reduce the noise of pose parameters and optimizes the DepthNet architecture to add a new upconvolutional module and a decoder structure to overcome the gradient locality and adjust the mode of multi-task coupling. All modules are integrated to construct a cycle optimization strategy as the core of this paper for overall performance improvement. Experimental results on the KITTI dataset show that the cycle optimization framework greatly improves the performance of the original framework and is better than other improvements on the same original framework; single-view depth prediction and camera pose estimation achieve state-of-the-art performance compared with existing methods under the same or comparable structure.
Joint Soft–Hard Attention for Self-Supervised Monocular Depth Estimation

In recent years, self-supervised monocular depth estimation has gained popularity among researchers because it uses only a single camera at a much lower cost than the direct use of laser sensors to acquire depth. Although monocular self-supervised methods can obtain dense depths, the estimation accuracy needs to be further improved for better applications in scenarios such as autonomous driving and robot perception. In this paper, we innovatively combine soft attention and hard attention with two new ideas to improve self-supervised monocular depth estimation: (1) a soft attention module and (2) a hard attention strategy. We integrate the soft attention module in the model architecture to enhance feature extraction in both spatial and channel dimensions, adding only a small number of parameters. Unlike traditional fusion approaches, we use the hard attention strategy to enhance the fusion of generated multi-scale depth predictions. Further experiments demonstrate that our method can achieve the best self-supervised performance both on the standard KITTI benchmark and the Make3D dataset.
AequeVox: Automated Fairness Testing of Speech Recognition Systems

Abstract Automatic Speech Recognition (ASR) systems have become ubiquitous. They can be found in a variety of form factors and are increasingly important in our daily lives. As such, ensuring that these systems are equitable to different subgroups of the population is crucial. In this paper, we introduce, AequeVox , an automated testing framework for evaluating the fairness of ASR systems. AequeVox simulates different environments to assess the effectiveness of ASR systems for different populations. In addition, we investigate whether the chosen simulations are comprehensible to humans. We further propose a fault localization technique capable of identifying words that are not robust to these varying environments. Both components of AequeVox are able to operate in the absence of ground truth data. We evaluate AequeVox on speech from four different datasets using three different commercial ASRs. Our experiments reveal that non-native English, female and Nigerian English speakers generate 109% , 528.5% and 156.9% more errors, on average than native English, male and UK Midlands speakers, respectively. Our user study also reveals that 82.9% of the simulations (employed through speech transformations) had a comprehensibility rating above seven (out of ten), with the lowest rating being 6.78. This further validates the fairness violations discovered by AequeVox . Finally, we show that the non-robust words, as predicted by the fault localization technique embodied in AequeVox , show 223.8% more errors than the predicted robust words across all ASRs.