Reviewerly/exHarmony huggingface dataset:
Real-Time Performance of the PLUM Earthquake Early Warning Method during the 2019 M 6.4 and 7.1 Ridgecrest, California, Earthquakes

ABSTRACT We evaluate the timeliness and accuracy of ground-motion-based earthquake early warning (EEW) during the July 2019 M 6.4 and 7.1 Ridgecrest earthquakes. In 2018, we began retrospective and internal real-time testing of the propagation of local undamped motion (PLUM) method for earthquake warning in California, Oregon, and Washington, with the potential that PLUM might one day be included in the ShakeAlert EEW system. A real-time version of PLUM was running on one of the ShakeAlert EEW system’s development servers at the time of the 2019 Ridgecrest sequence, allowing us to evaluate the timeliness and accuracy of PLUM’s warnings for the M 6.4 and 7.1 mainshocks in real time with the actual data availability and latencies of the operational ShakeAlert EEW system. The latter is especially important because high-data latencies during the M 7.1 earthquake degraded ShakeAlert’s performance. PLUM proved to be largely immune to these latencies. In this article, we present a retrospective analysis of PLUM performance and explore three potential regional alerting strategies ranging from spatially large regions (counties), to moderate-size regions (National Weather Service public forecast zones), to high-spatial specificity (50 km regular geographic grid). PLUM generated initial shaking forecasts for the two mainshocks 5 and 6 s after their respective origin times, and faster than the ShakeAlert system’s first alerts. PLUM was also able to accurately forecast shaking across southern California for all three alerting strategies studied. As would be expected, a cost-benefit analysis of each approach illustrates trade-offs between increasing warning time and minimizing the area receiving unneeded alerts. Choosing an optimal alerting strategy requires knowledge of users’ false alarm tolerance and minimum required warning time for taking protective action, as well as the time required to distribute alerts to users.
Understanding spatial language in radiology: Representation framework, annotation, and spatial relation extraction from chest X-ray reports using deep learning

Radiology reports contain a radiologist's interpretations of images, and these images frequently describe spatial relations. Important radiographic findings are mostly described in reference to an anatomical location through spatial prepositions. Such spatial relationships are also linked to various differential diagnoses and often described through uncertainty phrases. Structured representation of this clinically significant spatial information has the potential to be used in a variety of downstream clinical informatics applications. Our focus is to extract these spatial representations from the reports. For this, we first define a representation framework based on the Spatial Role Labeling (SpRL) scheme, which we refer to as Rad-SpRL. In Rad-SpRL, common radiological entities tied to spatial relations are encoded through four spatial roles: Trajector, Landmark, Diagnosis, and Hedge, all identified in relation to a spatial preposition (or Spatial Indicator). We annotated a total of 2,000 chest X-ray reports following Rad-SpRL. We then propose a deep learning-based natural language processing (NLP) method involving word and character-level encodings to first extract the Spatial Indicators followed by identifying the corresponding spatial roles. Specifically, we use a bidirectional long short-term memory (Bi-LSTM) conditional random field (CRF) neural network as the baseline model. Additionally, we incorporate contextualized word representations from pre-trained language models (BERT and XLNet) for extracting the spatial information. We evaluate both gold and predicted Spatial Indicators to extract the four types of spatial roles. The results are promising, with the highest average F1 measure for Spatial Indicator extraction being 91.29 (XLNet); the highest average overall F1 measure considering all the four spatial roles being 92.9 using gold Indicators (XLNet); and 85.6 using predicted Indicators (BERT pre-trained on MIMIC notes). The corpus is available in Mendeley at http://dx.doi.org/10.17632/yhb26hfz8n.1 and https://github.com/krobertslab/datasets/blob/master/Rad-SpRL.xml.
Regression Models for Compositional Data: General Log-Contrast Formulations, Proximal Optimization, and Microbiome Data Applications

Abstract Compositional data sets are ubiquitous in science, including geology, ecology, and microbiology. In microbiome research, compositional data primarily arise from high-throughput sequence-based profiling experiments. These data comprise microbial compositions in their natural habitat and are often paired with covariate measurements that characterize physicochemical habitat properties or the physiology of the host. Inferring parsimonious statistical associations between microbial compositions and habitat- or host-specific covariate data is an important step in exploratory data analysis. A standard statistical model linking compositional covariates to continuous outcomes is the linear log-contrast model. This model describes the response as a linear combination of log-ratios of the original compositions and has been extended to the high-dimensional setting via regularization. In this contribution, we propose a general convex optimization model for linear log-contrast regression which includes many previous proposals as special cases. We introduce a proximal algorithm that solves the resulting constrained optimization problem exactly with rigorous convergence guarantees. We illustrate the versatility of our approach by investigating the performance of several model instances on soil and gut microbiome data analysis tasks.
Mobile Computing Technologies for Health and Mobility Assessment: Research Design and Results of the Timed Up and Go Test in Older Adults

Due to the increasing age of the European population, there is a growing interest in performing research that will aid in the timely and unobtrusive detection of emerging diseases. For such tasks, mobile devices have several sensors, facilitating the acquisition of diverse data. This study focuses on the analysis of the data collected from the mobile devices sensors and a pressure sensor connected to a Bitalino device for the measurement of the Timed-Up and Go test. The data acquisition was performed within different environments from multiple individuals with distinct types of diseases. Then this data was analyzed to estimate the various parameters of the Timed-Up and Go test. Firstly, the pressure sensor is used to extract the reaction and total test time. Secondly, the magnetometer sensors are used to identify the total test time and different parameters related to turning around. Finally, the accelerometer sensor is used to extract the reaction time, total test time, duration of turning around, going time, return time, and many other derived metrics. Our experiments showed that these parameters could be automatically and reliably detected with a mobile device. Moreover, we identified that the time to perform the Timed-Up and Go test increases with age and the presence of diseases related to locomotion.
Real evaluation for designing sensor fusion in UAV platforms

Evaluation of UAV systems is mostly based on simulation tools that are manually configured to define the trajectory (ground truth trajectory) for comparing with the system output. In this work, the authors present an original method to evaluate the performance of UAV platform in real situations without considering simulations. The proposed evaluation methodology allows calculating the system accuracy and robustness with a considerable number of samples, accumulating the performance of different missions in the same conditions. The main innovation is an alternative evaluation for designing sensor fusion parameters using real performance indicators of navigation accuracy in UAVs based on a commercially available flight controller and peripherals. This methodology and selected performance indicators allow to select the best parameters for the fusion system of a determined configuration of sensors and a predefined real mission not requiring ground truth. The selected platform is described highlighting the available sensors and data processing software, and the experimental methodology is proposed to characterize the sensor data fusion output. The results show in detail the presented performance metrics for a set of trajectories in order to determine the best choice of parameters using quality measurements of navigation output.
Including intellectual disability in participatory design processes: Methodological adaptations and supports

People with intellectual disabilities are relevant actors in the design processes that aim to promote their independent living. We argue that it is necessary to extend and adapt existing codesign methods in order to incorporate this community into user-centred design processes fully. This paper presents a series of methods and adaptations carried out over three years, in the context of inclusive research. Throughout the codesign process, we have identified four different phases: preparation, fieldwork, ideation and validation. Within this framework, we present adaptations and supports, which may facilitate the participation of people with intellectual disability in research and design processes. Among the central adaptations and supports, we distinguish those of content, structure and operation. This initial but comprehensive effort aims to distil transferable knowledge for facilitating the participation of people with intellectual disabilities as valuable design team members.
Dynamic Pricing for Resource-Quota Sharing in Multi-Access Edge Computing

In this paper, we analyze resource allocation in Multi-Access Edge Computing (MEC) from the perspective of revenue and profit management. Current coarse-grained pricing and resource plans have not made full use of the extreme heterogeneity of computing usage levels across users in the Internet of Everything (IoE), leading to wastage. Some users would have excess un-utilized resource quota while others might have reserved insufficient resources. Therefore, we introduce a novel sharing economy-inspired business model, following the trend towards the decentralized provision and sharing of digital resources. In our model, a platform facilitates the sharing of excess resource quota among users, leading to a more efficient usage of resources. Based on our model, we design and analyse two dynamic pricing mechanisms for resource quota sharing, which maximizes the social welfare or profit of the platform respectively. In our dynamic pricing mechanisms, the platform coordinates the sharing of compute resource quota in real-time, without knowledge of users' payoffs or control over users' decisions. We prove the optimality and convergence of the proposed mechanisms, and provide simulations to illustrate the convergence and robustness of our mechanisms to changes in demand and supply, as well as provide insights under changes in exogenous conditions.
Light Field Compression Using Global Multiplane Representation and Two-Step Prediction

Due to its spatio-angular structure, light field image allows for a wealth of post-processing techniques like digital refocusing and depth estimation. In order to compress the data of the two domains, the current proposal intends to embed the disparity-based view synthesis method into the decoder. However, predicting each view separately or in local groups means bringing more computational burden to the decoder and destroying the light field structure. Since disparity contains the relationship between all light rays in the light field, the proposed solution is to predict a disparity-based global representation as the first step. In the second step, all the views can be predicted easily based on this representation. In this letter, we use the recently proposed multiplane as the form of this global representation. The experimental results show the effectiveness of the proposed solution, and the better RD performance compared to other schemes especially under low bitrates.
Secure Content-Based Image Retrieval in the Cloud With Key Confidentiality

Owing to the rapid development of cloud services and personal privacy demand, secure cloud storage services and search over encrypted datasets have become an important issue. Recently, the leaking of images such as identification and driver's licenses catches much attention. The trend towards secure computation has been widely discussed, especially asymmetric scalar-product-preserving encryption (ASPE) and homomorphic encryption (HE). Although ASPE have ability to encrypt and determine the similarity between ciphertexts efficiently, it is not a practical methodology due to its assumption that the users are fully trusted in real world and it also may have key leakage problem. Contrary to ASPE, HE can execute addition and multiplication in the encrypted domain and solve key leakage problem. Hence, in this paper, we combine the opinions of HE and ASPE to propose new privacy-preserving content-based image retrieval with key confidentiality scheme against the attacks from data owner, cloud server and users. Our privacy preserving image retrieval scheme is developed under strong threat model that is close to real world. Furthermore, to the best of our knowledge, our work is the first one that developing scheme under the assumption that all the entities involved in privacy-preserving image retrieval system are semi-trusted. Our scheme ensures the confidentiality of key and privacy of query information at the same time. In addition, we provide a lightweight verification to check whether the search query is fake or not. Finally, the experiment results show that the computation overheads and search precision are acceptable at the same time.
Counts: an outstanding challenge for log-ratio analysis of compositional data in the molecular biosciences

Abstract Thanks to sequencing technology, modern molecular bioscience datasets are often compositions of counts, e.g. counts of amplicons, mRNAs, etc. While there is growing appreciation that compositional data need special analysis and interpretation, less well understood is the discrete nature of these count compositions (or, as we call them, lattice compositions) and the impact this has on statistical analysis, particularly log-ratio analysis (LRA) of pairwise association. While LRA methods are scale-invariant, count compositional data are not; consequently, the conclusions we draw from LRA of lattice compositions depend on the scale of counts involved. We know that additive variation affects the relative abundance of small counts more than large counts; here we show that additive (quantization) variation comes from the discrete nature of count data itself, as well as (biological) variation in the system under study and (technical) variation from measurement and analysis processes. Variation due to quantization is inevitable, but its impact on conclusions depends on the underlying scale and distribution of counts. We illustrate the different distributions of real molecular bioscience data from different experimental settings to show why it is vital to understand the distributional characteristics of count data before applying and drawing conclusions from compositional data analysis methods.
Hyperspectral Image Denoising Based on Nonlocal Low-Rank and TV Regularization

Hyperspectral image (HSI) acquisitions are degraded by various noises, among which additive Gaussian noise may be the worst-case, as suggested by information theory. In this paper, we present a novel tensor-based HSI denoising approach by fully identifying the intrinsic structures of the clean HSI and the noise. Specifically, the HSI is first divided into local overlapping full-band patches (FBPs), then the nonlocal similar patches in each group are unfolded and stacked into a new third order tensor. As this tensor shows a stronger low-rank property than the original degraded HSI, the tensor weighted nuclear norm minimization (TWNNM) on the constructed tensor can effectively separate the low-rank clean HSI patches. In addition, a regularization strategy with spatial–spectral total variation (SSTV) is utilized to ensure the global spatial–spectral smoothness in both spatial and spectral domains. Our method is designed to model the spatial–spectral non-local self-similarity and global spatial–spectral smoothness simultaneously. Experiments conducted on simulated and real datasets show the superiority of the proposed method.
Sentiment Analysis of Facebook Users Reacting to Political Campaign Posts

A recent trend in political campaign studies is the use of sentiment analysis to understand users’ decisions. The scandal of Facebook and Cambridge Analytics is an example of efforts to use social media platforms to impact citizens’ will. This research aims to answer the question: Did the Facebook reactions of users in Mexico reflect the outcomes of the elections and possibly also the users’ emotions toward the political candidates of the State of Mexico in 2017? To answer the research question, we analyzed data collected from 4,128 Facebook posts and their reactions. The available reactions for Facebook users are: like, love, haha, wow, sad, and angry. Doing so revealed some kind of mood from the users in the Facebook comments section and opinions of the local government campaign in the central State of Mexico. The elections studied took place in June 2017. Our findings show that the winning political party had more negative sentiment and fewer posts and users’ discussions of the candidates in Facebook comments sections than the political party with the largest positive sentiment. This party was unsuccessful in winning the elections.
Assessing geochemical anomalies using geographically weighted lasso

Geochemical patterns in the surficial media are complex because of the various processes involved in their formation. These processes may or may not relate to mineralization. Therefore, geochemical anomalies identified are usually subject to uncertainty. Geographically weighted lasso (GWL) was adopted in this study to model the non-stationary relationships between the geological/geographical features and geochemical patterns in the surficial media. The sparsity and reliability of regression coefficients in the GWL modeling renders it suitable for quantifying the controls that geological/geographical features have on geochemical patterns. This information can improve current knowledge about the complexity of geochemical patterns. A case study in which the GWL model was established between geological/geographical variables and the geochemical patterns of Cu observed in stream sediments in the Southwestern Fujian province of China was presented. The following findings were obtained: (1) geological features show a major control on the geochemical patterns of Cu in the studied area. However, the geochemical patterns of Cu have been modified to some extent by secondary processes, resulting in the relationships between geochemical signatures and mineralization becoming more complex and uncertain, and (2) the GWL modeling can provide useful information for evaluating geochemical anomalies, which can help reduce the uncertainty and benefit geochemical exploration at a later stage.
An Identity-Preserved Model for Face Sketch-Photo Synthesis

Face sketch-photo synthesis can be regarded as an image-to-image translation problem. Although many generative models achieve good translations from sketches to photos, they still have limitations in preserving face identity due to the huge modality gap of the two domains. To this end, we propose an identity-preserved adversarial model (IPAM), which includes an extended U-Net to increase the weight of the original sketch in translation, two discriminators focusing on the real or fake image concatenation of two domains to learn more styles of the target domain, and an identity constraint to request the fakes and the real targets to have zero cosine distance in feature space. We evaluate our method on two face sketch databases with face recognition. The results demonstrate our translation method is superior to the existing methods in maintaining face identity information.
TLKC-Privacy Model for Process Mining

Process mining aims to provide insights into the actual processes based on event data. These data are widely available and often contain private information about individuals. Consider for example health-care information systems recording highly sensitive data related to diagnosis and treatment activities. Process mining should reveal insights in the form of annotated models, yet, at the same time, should not reveal sensitive information about individuals. In this paper, we discuss the challenges regarding directly applying existing well-known privacy-preserving techniques to event data. We introduce the TLKC-privacy model for process mining that provides privacy guarantees in terms of group-based anonymization. It extends and customizes the LKC-privacy model presented to deal with high-dimensional, sparse, and sequential trajectory data. Experiments on real-life event data demonstrate that our privacy model maintains a high utility for process discovery and performance analyses while preserving the privacy of the cases.
Effects of Facebook Personal News Sharing on Building Social Capital in Jordanian Universities

This study examines the relationship between types of social capital and the use of Facebook to share personal news. This relationship is significant because, as the research shows, there is a link between personal news sharing and personal self-esteem and life satisfaction. The quantitative study comprised a survey of 415 students at Yarmouk University and Amman University, Jordan, and was based on a theoretical model that employed structural equation modeling for measuring relations and correlations between variables and the effects of news sharing in promoting student social capital. The results showed a positive relationship between the intensity of news sharing using Facebook and social capital, bridging social capital, and self-esteem. The strongest relationship was with self-esteem; however, the results showed no significant relationship between social capital and the degree of personal life satisfaction.