Reviewerly/exHarmony huggingface dataset:
Tracking Proterozoic–Triassic sediment routing to western Laurentia via bivariate non-negative matrix factorization of detrital provenance data

When reconstructing sediment provenance, the challenge posed by multiple univariate (i.e., age-only) detrital geochronology data sets with similar one-dimensional distributions can be mitigated by incorporation of a second variable such as Hf isotopic data. However, there is no commonly applied method of sample intercomparison, much less forward or inverse modeling of these bivariate data sets. In this paper we explore application of non-negative matrix factorization (NMF) to bivariate data sets. Factorization of univariate mixed (a.k.a., sink or daughter) data sets has been demonstrated to successfully recover both the one-dimensional endmember (a.k.a., source or parent) distributions and their mixture weightings. We show that NMF can successfully recover both the two-dimensional distributions and mixing weights in synthetic data sets. Application of the method to 24 published Neoproterozoic–Triassic samples from western Laurentia yields six two-dimensional endmember distributions that are a close match to empirical sediment sources on the southern, eastern, and northern margin of Laurentia. The results are broadly consistent with previous interpretations and confirm that the method can characterize unknown sediment sources based on data from analyzed sink samples. Correlating between factorized endmembers and empirical sources indicates that the Transcontinental Arch was not a barrier to east–west sediment transport until the late Cambrian. Quantitative comparison also shows that the closest known match for a factorized endmember with highly evolved Permo-Triassic zircons is from northern South America and suggests northward transport of this detritus following assembly of Pangea. Recognizing these empirical sources and their distribution into strata that would later be incorporated into the North American Cordillera sets the stage for interpreting sediment provenance records in the Jurassic–Paleogene Cordilleran retroarc foreland basin. Supplementary material: The open-source software ( DZnmf2D ) and a user manual is available in public repositories: https://doi.org/10.5281/zenodo.4460336 . All other supplemental files are available at https://doi.org/10.6084/m9.figshare.c.5280320 Thematic collection: This article is part of the Fold-and-thrust belts and associated basins collection available at: https://www.lyellcollection.org/cc/fold-and-thrust-belts
ICEBIN: Image Contrast Enhancement Based on Induced Norm and Local Patch Approaches

Traditional histogram equalization may cause degraded results of over-enhanced images under uneven illuminations. In this paper, a simple and effective image contrast enhancement method is proposed to achieve high dynamic range imaging. First, the illumination of each pixel is estimated by using an induced norm of a patch of the image. Second, a pre-gamma correction is proposed to enhance the contrast of the illumination component appropriately. The parameters of gamma correction are set dynamically based on the local patch of the image. Third, an automatic Contrast-Limited Adaptive Histogram Equalization (CLAHE) whose clip point is automatically set is applied to the processed image for further image contrast enhancement. Fourth, a noise reduction algorithm based on the local patch is developed to reduce image noise and increase image quality. Finally, a post-gamma correction is applied to slightly enhance the dark regions of images and not affect the brighter areas. Experimental results show that the proposed method has its superiority over several state-of-the-art enhancement quality techniques by using qualitative and quantitative evaluations.
The accuracy of fully automated algorithms for surveillance of healthcare-associated urinary tract infections in hospitalized patients

BackgroundSurveillance for healthcare-associated infections such as healthcare-associated urinary tract infections (HA-UTI) is important for directing resources and evaluating interventions. However, traditional surveillance methods are resource-intensive and subject to bias.AimTo develop and validate a fully automated surveillance algorithm for HA-UTI using electronic health record (EHR) data.MethodsFive algorithms were developed using EHR data from 2979 admissions at Karolinska University Hospital from 2010 to 2011: (1) positive urine culture (UCx); (2) positive UCx + UTI codes (International Statistical Classification of Diseases and Related Health Problems, 10th revision); (3) positive UCx + UTI-specific antibiotics; (4) positive UCx + fever and/or UTI symptoms; (5) algorithm 4 with negation for fever without UTI symptoms. Natural language processing (NLP) was used for processing free-text medical notes. The algorithms were validated in 1258 potential UTI episodes from January to March 2012 and results extrapolated to all UTI episodes within this period (N = 16,712). The reference standard for HA-UTIs was manual record review according to the European Centre for Disease Prevention and Control (and US Centers for Disease Control and Prevention) definitions by trained healthcare personnel.FindingsOf the 1258 UTI episodes, 163 fulfilled the ECDC HA-UTI definition and the algorithms classified 391, 150, 189, 194, and 153 UTI episodes, respectively, as HA-UTI. Algorithms 1, 2, and 3 had insufficient performances. Algorithm 4 achieved better performance and algorithm 5 performed best for surveillance purposes with sensitivity 0.667 (95% confidence interval: 0.594–0.733), specificity 0.997 (0.996–0.998), positive predictive value 0.719 (0.624–0.807) and negative predictive value 0.997 (0.996–0.997).ConclusionA fully automated surveillance algorithm based on NLP to find UTI symptoms in free-text had acceptable performance to detect HA-UTI compared to manual record review. Algorithms based on administrative and microbiology data only were not sufficient.
Visual Saliency Detection guided by Neural Signals

Saliency detection is a fundamental process of human visual perception, since it allows us to identify the most important parts of a scene, directing our analysis and interpretation capabilities on a reduced set of information and reducing reaction times. However, current approaches for automatic saliency detection either attempt to mimic human capabilities by building attention maps from hand-crafted feature analysis, or employ convolutional neural networks trained as black boxes, without any architectural or information prior from human biology. In this paper, we present an approach for saliency detection that combines the success of deep learning in identifying representations for visual data with a training paradigm aimed at matching neural activity provided directly by brain signals recorded while subjects look at images. We show that our approach is able to capture correspondences between visual elements and neural activities, successfully generalizing to unseen images to identify their most salient regions.
Bayesian spatial homogeneity pursuit for survival data with an application to the SEER respiratory cancer data

In this work, we propose a new Bayesian spatial homogeneity pursuit method for survival data under the proportional hazards model to detect spatially clustered patterns in baseline hazard and regression coefficients. Specially, regression coefficients and baseline hazard are assumed to have spatial homogeneity pattern over space. To capture such homogeneity, we develop a geographically weighted Chinese restaurant process prior to simultaneously estimating coefficients and baseline hazards and their uncertainty measures. An efficient Markov chain Monte Carlo (MCMC) algorithm is designed for our proposed methods. Performance is evaluated using simulated data, and further applied to a real data analysis of respiratory cancer in the state of Louisiana.
Data Access Control in Cloud Computing: Flexible and Receiver Extendable

Broadcast encryption provides a promising technique of data access control for specified users in cloud computing. A data uploader can generate a ciphertext for a set of chosen users such that only the intended users are able to access the data. However, with the rapidly increasing of collaboration between users, it is desired to extend the receiver set to grant decryption right for more users. The existing broadcast encryption systems cannot support receiver extension. In this article, we for the first time take this problem into consideration and give a solution. We take the merits of identity-based cryptosystem and propose a notion of EIBBE: a flexible data access control with receiver extendable for cloud computing based on broadcast encryption. It allows the authorized user to extend the receiver set <inline-formula><tex-math notation="LaTeX">$S$</tex-math></inline-formula> stated in the IBBE ciphertext by adding a new receiver set <inline-formula><tex-math notation="LaTeX">$S^{\prime }$</tex-math></inline-formula> without re-encryption. Both the users in <inline-formula><tex-math notation="LaTeX">$S$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$S^{\prime }$</tex-math></inline-formula> can access the data successfully. Moreover, the data uploader determines the maximum number of extended receivers. We then give a concrete construction of EIBBE and provide a rigorous security analysis of our proposed scheme. Finally, we demonstrate the scheme's efficiency and feasibility.
A study of Human Resources Development through Chatbots using Artificial Intelligence

A chatbot, which could be a computer program outlined to recreate a discussion with a human, particularly over the web, will change essentially each trade work from marketing to deals to clients back within the close future. A chatbot can be very much characterized as Artificial Intelligence (AI) grounded PC program that mimics human visits. The examination work is finished with the chatbots. This article aims to recognize the ramifications of chatbots in the cutting edge period. This novel technique directs the current HR executives to examine the candidates by utilizing a graphic position. The assigned examination approach has been utilized in the exploration to dissect the advantages and disadvantages of AI and the result of the examination is certain. The chatbots can be reserved to test the utilization of information, aptitude advancement, and data maintenance in a basic, consistent, and non-meddlesome way. It can be utilized to gamify the learning experience by adopting dynamic levels and ceaselessly captivating workers with pioneer sheets and updates on their advancement. HR chatbots can likewise give quick criticism on the viability of preparing modules, along these lines, helping in ideal recognizing what's working and what isn't. This assists the managers with comprehension and examines the conduct of their representatives. Detailed applicable learning strategies are considered for better requirements.
Enhancing Artificial Intelligence with Indigenous Wisdom

The current "narrow" or "weak" form of artificial intelligence is, by itself, fundamentally a data analysis tool that does nothing more or less than its programming instructs it to do.It has no values or goals of its own, it simply follows the values and pursues the goals provided to it by its programmers.Artificial wisdom has the potential to make artificial intelligence a better tool and eventually perhaps more than a tool, but at least for now artificial wisdom must also be programmed and therefore similarly reflects only the wisdom of its programmers.Artificial intelligence, with its reductionistic ontology of data and its contrived epistemology of algorithms, is the quintessential product of the Western scientific worldview, and the development and application of artificial intelligence and discussions of artificial wisdom still largely reflect that one, narrow worldview.Artificial wisdom would greatly benefit from incorporating elements of non-Western worldviews, particularly the metaphysically inclusive Indigenous worldview.For example, the Navajo concept of hozho involves the normative values and goals of harmony, balance, interrelatedness, and connectedness.Hozho and other Indigenous concepts are potentially paradigm-shifting additions to artificial wisdom and could greatly enhance the usefulness of and overall benefit from applications of artificial intelligence.
A Flow Base Bi-path Network for Cross-Scene Video Crowd Understanding in Aerial View

Drones shooting can be applied in dynamic traffic monitoring, object detecting and tracking, and other vision tasks. The variability of the shooting location adds some intractable challenges to these missions, such as varying scale, unstable exposure, and scene migration. In this paper, we strive to tackle the above challenges and automatically understand the crowd from the visual data collected from drones. First, to alleviate the background noise generated in cross-scene testing, a double-stream crowd counting model is proposed, which extracts optical flow and frame difference information as an additional branch. Besides, to improve the model’s generalization ability at different scales and time, we randomly combine a variety of data transformation methods to simulate some unseen environments. To tackle the crowd density estimation problem under extreme dark environments, we introduce synthetic data generated by game Grand Theft Auto V(GTAV). Experiment results show the effectiveness of the virtual data. Our method wins the challenge with a mean absolute error (MAE) of 12.70 $$^1$$ . Moreover, a comprehensive ablation study is conducted to explore each component’s contribution.
ChannelAttention: Utilizing Attention Layers for Accurate Massive MIMO Channel Feedback

Recently the idea of using deep learning algorithms in massive multiple-input multiple-output channel state information feedback has been studied in great detail. To use the deep-learning-based feedback schemes over the air, they must be able to operate under extremely large antennas. These schemes also need to be verified under a realistic channel model and propagation environment. Thus, we propose ChannelAttention, a deep-learning-based channel state information feedback scheme that utilizes attention layers and residual blocks. Simulations for a 64-by-64 QuaDRiGa channel model based on the 3GPP 38.901 urban microcell scenario show that ChannelAttention surpasses the normalized mean square error and cosine similarity performance of the conventional CsiNet+ scheme across all compression ratio ranges.
Putting the Sec in DevSecOps: Using Social Practice Theory to Improve Secure Software Development

Practices such as open source development, agile, DevOps and DevSecOps mean that cyber security professionals need to find ways to blend cyber security with software development practices. One way of approaching this is as an awareness, education and training problem and many organisations are focusing on training software developers in cyber security. In this paper, however, we make the case for looking more broadly at group rather than individual behaviours, by examining the social practices of software developers. Changing software development practices are shaping the lived experience of software developers and we argue that understanding these practices will enable us to improve secure software development. We use social practice theory as a framework to develop recommendations for aligning and blending cyber security and software development. To achieve this, we carried out a rapid review of research on software development practices and supplemented this with data from ten key informant interviews to ascertain what we need to consider when developing an intervention for secure software development. Finally, we outline how our research could be used to develop a workshop that would facilitate the co-creation of security practices for software development. We conclude with suggestions for future research.
Convolutional Fully-Connected Capsule Network (CFC-CapsNet)

Capsule Networks (CapsNets) are the new generation of classifiers with several advantages over the previous ones. Such advantages include higher robustness to affine transformed datasets and detection of overlapping images. CapsNets, while obtaining state-of-the-art accuracy on the MNIST digit recognition dataset, fall behind Convolutional Neural Networks (CNNs) for other datasets. Moreover, CapsNets are slow compared to CNNs. In this work, we propose Convolutional Fully Connected (CFC) CapsNet as an alternative enhanced architecture to conventional CapsNet [8]. CFC-CapsNet is a more efficient network: training and testing are performed faster and a slightly higher accuracy is achieved compared to the conventional CapsNet. CFC-CapsNet includes fewer trainable weights (parameters) and therefore is more efficient in terms of memory usage. The code for CFC-CapsNet is available on Github 1.
Heterogeneous Sensor Data Fusion for Human Falling Detection

With the continuous improvement of human living standards, population aging has become a global development trend.At present, China has entered an aging society, the health and safety of the elderly have become the focus of social concern.Due to the aging of physiological structure and the decline of physical function, the probability and frequency of accidental falls in the elderly are very high.Under the above background, the purpose of this study is based on a heterogeneous sensor data fusion algorithm in an intelligent wearable sensor network.This article proposes a heterogeneous sensor data fusion algorithm based on wearable wireless body area network technology, and constructs a high-precision and stable wearable elderly activities of daily living (ADLs) and fall monitoring system.We first select the threeaxis acceleration sensor, three-axis magnetic sensor, and three-axis angular velocity sensor to monitor the activities of the elderly.Then, we use Bluetooth to transmit the data collected by heterogeneous sensors to smartphones, and communicate with service centers and users through the mobile phone communication network, Family members interact to form a wireless city network based on wearable technology.Our proposed data fusion approach is based on the Kalman filter algorithm, which can reduce the system noise and improve the stability of the system.The experimental results demonstrate that the fall detection system proposed and implemented in this study can well detect accidental falls in the daily activities of the elderly, the sensitivity and specificity of the fall detection system are 98.7% and 98.5% respectively.The study in this article has a high research value and practical application significance in protecting the healthy life of the elderly.
Local interpretation of supervised learning models based on high dimensional model representation

Machine learning models have been widely used to obtain prediction in various domains; however, most of such models are black boxes owing to the high complexity. The lack of transparency of machine learning models hampers their applications because the practitioners do not understand the internal mechanism of these models. This study proposes a model-agnostic method, based on the high dimensional model representation (HDMR), to interpret supervised learning models by determining the local feature contribution. Compared to the existing methods, which only assign a single value to the feature contribution and do not consider the feature dependence, the HDMR-based feature contribution can be decomposed into individual and combined contribution, and it can take feature dependence into account. Certain agnostic and specific methods to measure the HDMR-based feature contributions are developed and categorized as pertaining to either feature independence or dependence. Experiments are performed to demonstrate the effects of the HDMR-based feature contributions, and compare the performance of several estimation methods.
Single Image Dehazing Jointly Utilizing Dark Channel Prior and Guided Filtering in Dual-Tree Complex Wavelet Domain

In this paper, a new image haze removal algorithm is proposed by jointly utilizing dark channel prior and guided filtering in dual-tree complex wavelet transform domain. The algorithm firstly uses guided filtering to smooth the hazing image, then adopts dual-tree complex wavelet transform to decompose the hazing image into low-frequency and high-frequency sub-band components, and then uses the dark channel prior model to process the low-frequency sub-band of the image, finally adopts inverse dual-tree complex wavelet transform to get the dehazed image. The dark channel prior comes from the statistical law of the outdoor fog-free image datasets. Since dual-tree complex wavelet transform has approximate translation advantages such as degeneration, higher positioning accuracy, computational efficiency, and guided filtering can increase the scope of application of haze removal, which can greatly reduce the optimization time of initial transmittance and the complexity of the entire algorithm. Therefore, the new proposed algorithm outperforms traditional dark channel prior based defogging algorithm. The experimental results show that the proposed algorithm can effectively improve the contrast and clarity of the image.
A New Chaos-Based Cryptoystem for Voice Encryption

Since 1993, chaos-based cryptosystems were widely discussed in the literature for the encryption of different types of row data. In recent years, as the encryption methods have been considerably improved due to the notable development of communication equipment; a large amount of secure communication system-based researches has been published. In this paper, we present a new chaos-based secure communication system relying on three levels of cryptography that combines a conventional encryption algorithm and chaos masking technique. Furthermore, to enhance the security level, three different types of attractors that were generated from a unified hyperchaotic oscillator are used in the proposed communication system. This new chaotic secure communication scheme is implemented to transmit a voice signal. Finally, to demonstrate the efficiency of the suggested communication system, several testing methods are employed to prove the unpredictability of the signal carrier, by the attackers in both domains, time, and frequency.
The Influence of Autofocus Lenses in the Camera Calibration Process

Camera calibration is a crucial step in robotics and computer vision. Accurate camera parameters are necessary to achieve robust applications. Nowadays, camera calibration process consists of adjusting a set of data to a pin-hole model, assuming that with a reprojection error close to zero, camera parameters are correct. Since all camera parameters are unknown, computed results are considered true. However, the pin-hole model does not represent the camera behavior accurately if the autofocus is considered. Real cameras with autofocus lenses change the focal length slightly to obtain sharp objects in the image, and this feature skews the calibration result if a unique pin-hole model is computed with a constant focal length. In this article, a deep analysis of the camera calibration process is done to detect and strengthen its weaknesses when autofocus lenses are used. To demonstrate that significant errors exist in computed extrinsic parameters, the camera is mounted in a robot arm to know true extrinsic camera parameters with an accuracy under 1 mm. It is also demonstrated that errors in extrinsic camera parameters are compensated with bias in intrinsic camera parameters. Since significant errors exist with autofocus lenses, a modification of the widely accepted camera calibration method using images of a planar template is presented. A pin-hole model with distance-dependent focal length is proposed to improve the calibration process substantially.