Reviewerly/exHarmony huggingface dataset:
Performance of the ATMOS41 All-in-One Weather Station for Weather Monitoring

Affordable and accurate weather monitoring systems are essential in low-income and developing countries and, more recently, are needed in small-scale research such as precision agriculture and urban climate studies. A variety of low-cost solutions are available on the market, but the use of non-standard technologies raises concerns for data quality. Research-grade all-in-one weather stations could present a reliable, cost effective solution while being robust and easy to use. This study evaluates the performance of the commercially available ATMOS41 all-in-one weather station. Three stations were deployed next to a high-performance reference station over a three-month period. The ATMOS41 stations showed good performance compared to the reference, and close agreement among the three stations for most standard weather variables. However, measured atmospheric pressure showed uncertainties &gt;0.6 hPa and solar radiation was underestimated by 3%, which could be corrected with a locally obtained linear regression function. Furthermore, precipitation measurements showed considerable variability, with observed differences of ±7.5% compared to the reference gauge, which suggests relatively high susceptibility to wind-induced errors. Overall, the station is well suited for private user applications such as farming, while the use in research should consider the limitations of the station, especially regarding precise precipitation measurements.
<i>Mosaic</i>: Advancing User Quality of Experience in 360-Degree Video Streaming With Machine Learning

Conventional streaming solutions for streaming 360-degree panoramic videos are inefficient in that they download the entire 360-degree panoramic scene, while the user views only a small sub-part of the scene called the viewport. This can waste over 80% of the network bandwidth. We develop a comprehensive approach called Mosaic that combines a powerful neural network-based viewport prediction with a rate control mechanism that assigns rates to different tiles in the 360-degree frame such that the video quality of experience is optimized subject to a given network capacity. We model the optimization as a multi-choice knapsack problem and solve it using a greedy approach. We also develop an end-to-end testbed using standards-compliant components and provide a comprehensive performance evaluation of Mosaic along with five other streaming techniques - two for conventional adaptive video streaming and three for 360-degree tile-based video streaming. Mosaic outperforms the best of the competitions by as much as 47-191% in terms of average video quality of experience. Simulation-based evaluation as well as subjective user studies further confirm the superiority of the proposed approach.
Modeling multivariate clinical event time-series with recurrent temporal mechanisms

In this work, we propose a novel autoregressive event time-series model that can predict future occurrences of multivariate clinical events. Our model represents multivariate event time-series using different temporal mechanisms aimed to fit different temporal characteristics of the time-series. In particular, information about distant past is modeled through the hidden state space defined by an LSTM-based model, information on recently observed clinical events is modeled through discriminative projections, and information about periodic (repeated) events is modeled using a special recurrent mechanism based on probability distributions of inter-event gaps compiled from past data. We evaluate our proposed model on electronic health record (EHRs) data derived from MIMIC-III dataset. We show that our new model equipped with the above temporal mechanisms leads to improved prediction performance compared to multiple baselines.
IHC-Net: A fully convolutional neural network for automated nuclear segmentation and ensemble classification for Allred scoring in breast pathology

This paper presents a Deep Learning (DL)-based nuclear segmentation and ensemble classification scheme for quantitative evaluation of hormone status namely estrogen or progesterone on IHC specimen. This will mainly assist pathologists in automatic analysis and interpretation of breast cancer prognosis route. The proposed method consists of two major steps i.e., segmentation and classification. Since invasive breast cancer images are associated with numerous stained cells including artifacts like stromal and inflammatory particulars, it is crucial to develop a computerized method for segmenting them. A new segmentation method has been presented based on deep learning network for precisely segmenting out the stained nuclei region from breast tissue images. Morphological post-processing on segmented results shows the splitting of overlapped nuclei. Finally, to improve individual classifier’s results, the ensemble method is used, which integrates the decision of three machine learning (ML) models for final Allred cancer score. Statistical analysis reveals that all three classifiers perform adequately but proposed approach shows the best accuracy (98.24%), best correlation with the manual expert’s score (Pearson’s correlation coefficient = 0.908) and requires minimum computational time 44s/image (±2.33) compared to state-of-the-art methods. The proposed framework can be used as a reliable alternative to manual methods for automatic Allred scoring and in the prognostic assessment of breast cancer.
Knowledge Graph Semantic Enhancement of Input Data for Improving AI

Intelligent systems designed using machine learning algorithms require a large number of labeled data. Background knowledge provides complementary, real world factual information that can augment the limited labeled data to train a machine learning algorithm. The term Knowledge Graph (KG) is in vogue as for many practical applications, it is convenient and useful to organize this background knowledge in the form of a graph. Recent academic research and implemented industrial intelligent systems have shown promising performance for machine learning algorithms that combine training data with a knowledge graph. In this article, we discuss the use of relevant KGs to enhance input data for two applications that use machine learning -- recommendation and community detection. The KG improves both accuracy and explainability.
Locality Adaptive Discriminant Analysis Framework

Linear discriminant analysis (LDA) is a well-known technique for supervised dimensionality reduction and has been extensively applied in many real-world applications. LDA assumes that the samples are Gaussian distributed, and the local data distribution is consistent with the global distribution. However, real-world data seldom satisfy this assumption. To handle the data with complex distributions, some methods emphasize the local geometrical structure and perform discriminant analysis between neighbors. But the neighboring relationship tends to be affected by the noise in the input space. In this research, we propose a new supervised dimensionality reduction method, namely, locality adaptive discriminant analysis (LADA). In order to directly process the data with matrix representation, such as images, the 2-D LADA (2DLADA) is also developed. The proposed methods have the following salient properties: 1) they find the principle projection directions without imposing any assumption on the data distribution; 2) they explore the data relationship in the desired subspace, which contains less noise; and 3) they find the local data relationship automatically without the efforts for tuning parameters. The performance of dimensionality reduction shows the superiorities of the proposed methods over the state of the art.
An Arbitrarily Reconfigurable Extreme Learning Machine Inference Engine for Robust ECG Anomaly Detection

Extreme learning machine (ELM) has shown to be an effective and low-power approach for real-time electrocardiography (ECG) anomaly detection. However, prior ELM inference chips are noise-prone and lacking in reconfigurability. In this article, we present an arbitrarily reconfigurable ELM inference engine fabricated in 40-nm CMOS technology for robust ECG anomaly detection. By combining Adaptive boosting (Adaboost) and Eigenspace denoising with ELM (AE-ELM), robust classification under noisy conditions is achieved and saves the number of required multiplications by 95.9%. For chip implementation, a reconfigurable VLSI architecture is designed to support arbitrary complexity of AE-ELM, accounting for dynamic change in application requirements. On the other hand, we propose to construct the input weight matrix of ELM as a Bernoulli random matrix, which further reduces the number of multiplications by 55.2%. For real-time detection, parallel computing is exploited to reduce the latency by up to 86.8%. Overall, the 0.21-mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> AE-ELM inference engine shows its robustness against noisy signals and achieves $1.83\times$ AEE compared with the state-of-the-art ELM design.
SSIM Compliant Modeling Framework With Denoising and Deblurring Applications

In image processing, it is well known that mean square error criteria is perceptually inadequate. Consequently, image quality assessment (IQA) has emerged as a new branch to overcome this issue, and this has led to the discovery of one of the most popular perceptual measures, namely, the structural similarity index (SSIM). This measure is mathematically simple, yet powerful enough to express the quality of an image. Therefore, it is natural to deploy SSIM in model based applications, such as denoising, restoration, classification, etc. However, the non-convex nature of this measure makes this task difficult. Our attempt in this work is to discuss problems associated with its convex program and take remedial action in the process of obtaining a generalized convex framework. The obtained framework has been seen as a component of an alternative learning scheme for the case of a regularized linear model. Subsequently, we develop a relevant dictionary learning module as a part of alternative learning. This alternative learning scheme with sparsity prior is finally used in denoising and deblurring applications. To further boost the performance, an iterative scheme is developed based on the statistical nature of added noise. Experiments on image denoising and deblurring validate the effectiveness of the proposed scheme. Furthermore, it has been shown that the proposed framework achieves highly competitive performance with respect to other schemes in literature and performs better in natural images in terms of SSIM and visual inspection.
A holistic analysis towards understanding consumer perceptions of virtual reality devices in the post-adoption phase

Despite gaining consumer momentum and interest of Virtual Reality (VR) in the consumer marketplace, the literature has lagged in exploring the continuance usage behaviour and factors associated with the post-adoption. To build on this, the current research seeks to identify factors that support the continuance usage of current VR users. To examine this, we employ a mixed-method approach. In Study 1, we initially gathered a total of 3,205 actual purchasers (Amazon verified purchase) from the top 10 VR brands listed in Amazon.com, Through a nethnographic content analysis, the key determinants of post-adoption of VR devices emerged (i.e. perceived functional benefit, perceived discomfort, perceived focused immersion, temporal dissociation, perceived health risk, and task quality). In Study 2, hypotheses were tested using structural equation modelling from 119 current VR users. The results demonstrate temporal dissociation and task quality were found to be the most significant antecedents affecting continuance usage. Theoretical and managerial implications are debated, as well as suggestions for future research.
Permissionless and Permissioned, Technology-Focused and Business Needs-Driven: Understanding the Hybrid Opportunity in Blockchain Through a Case Study of Insolar

Blockchains can be public, permissionless networks implementing novel cryptocurrency-based technology features or permissioned, interorganizational networks championed by industry consortia. Some ventures operationalize a hybrid of these two network types to enhance adoption of their blockchain platforms by broadening their base of stakeholders or facilitating interoperability between heterogeneous blockchains. In this article, we synthesize literature and industry writings to identify four hybrid blockchain architectures: hybrid blockchain approach, connected hybrid blockchain, interoperable blockchain architecture, and hard-forked blockchain for enterprise use. We then analyze these architectures along dimensions of semantic modeling support between private and public networks, data connectivity between networks, syntactic interoperability support between networks with heterogeneous codebases, governance model, and technical features. We find that hybrid blockchain ventures make trade-offs: support API's, tools, and customized development so that a codebase is useful for private and public networks <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">or</i> provide such support for interoperation between heterogeneous codebases. We then conduct a case study of an exemplar for a hybrid blockchain approach, the startup Insolar. We identify characteristics that have led Insolar to be idiosyncratically agile and effective in its blockchain development, which together with our architecture analysis may be timely and prescriptive as enterprises grow interested in addressing blockchain hybridity and interoperability.
A noise-tolerant fast convergence ZNN for dynamic matrix inversion

In this paper, a noise-tolerant fast convergence zeroing neural network (NTFCZNN) adopting a new power-versatile activation function (PVAF) is proposed and analyzed for solving dynamic matrix inversion (DMI). The proposed NTFCZNN has not only the fixed-time convergence ability but also the strong noise-tolerant ability when it is used to solve DMI problems. The new NTFCZNN and the original ZNN (OZNN) activated by the recently reported universal AF (versatile AF) are simultaneously used in the matrix inverse problem under the context of all kinds of distractions. Then, through a comprehensive comparative analysis of the simulation results, the powerful anti-disturbance ability of NTFCZNN is better highlighted. Both the theoretical verification under various circumstances and the sharp contrast simulation experiments are sufficient to show that the NTFCZNN model has high reliability and noise resistance in the process of solving the DMI problems.
Application of Histogram Equalization for Image Enhancement in Corrosion Areas

In this paper, an image enhancement algorithm is presented for identification of corrosion areas and dealing with low contrast present in shadow areas of an image. This algorithm uses histogram equalization processing under the hue-saturation-intensity model. First of all, an etched image is transformed from red-green-blue color space to hue-saturation-intensity color space, and only the luminance component is enhanced. Then, part of the enhanced image is combined with the original tone component, followed by saturation and conversion to red-green-blue color space to obtain the enhanced corrosion image. Experimental results show that the proposed method significantly improves overall brightness, increases contrast details in shadow areas, and strengthens identification of corrosion areas in the image.
Underwater imaging enhancement based on a polarization filter and histogram attenuation prior

Abstract Underwater images always suffer from low contrast and inaccurate colors due to scattering and absorption by particles when the target light propagates through turbid water. In this paper, we first found that a lot of intensity space is occupied by fewer pixels, called ‘tails’, on both sides of the histograms for the red, green and blue channels of the image. Based on this histogram attenuation prior and taking account of the advantage of a polarization filter we proposed an effective polarimetric recovery method to enhance the underwater image quality, which includes a specially designed histogram processing method, named ‘cut-tail histogram stretching’. This processing overcomes the limitation of traditional histogram-based methods and can further improve the restoration performance. The experimental results corresponding to underwater scenes with different turbidities and colors show that the proposed method can simultaneously enhance the image contrast and reduce the color distortion to some extent, and thus realize clear underwater vision.
Verification and Validation of Digital Pathology (Whole Slide Imaging) for Primary Histopathological Diagnosis: All Wales Experience

The study is aimed to verify Aperio AT2 scanner for reporting on the digital pathology platform (DP) and to validate the cohort of pathologists in the interpretation of DP for routine diagnostic histopathological services in Wales, United Kingdom.This was a large multicenter study involving seven hospitals across Wales and unique with 22 (largest number) pathologists participating. 7491 slides from 3001 cases were scanned on Leica Aperio AT2 scanner and reported on digital workstations with Leica software of e-slide manager. A senior pathology fellow compared DP reports with authorized reports on glass slide (GS). A panel of expert pathologists reviewed the discrepant cases under multiheader microscope to establish ground truth. 2745 out of 3001 (91%) cases showed complete concordance between DP and GS reports. Two hundred and fifty-six cases showed discrepancies in diagnosis, of which 170 (5.6%) were deemed of no clinical significance by the review panel. There were 86 (2.9%) clinically significant discrepancies in the diagnosis between DP and GS. The concordance was raised to 97.1% after discounting clinically insignificant discrepancies. Ground truth lay with DP in 28 out of 86 clinically significant discrepancies and with GS in 58 cases. Sensitivity of DP was 98.07% (confidence interval [CI] 97.57-98.56%); for GS was 99.07% (CI 98.72-99.41%).We concluded that Leica Aperio AT2 scanner produces adequate quality of images for routine histopathologic diagnosis. Pathologists were able to diagnose in DP with good concordance as with GS.Strengths of this study - This was a prospective blind study. Different pathologists reported digital and glass arms at different times giving an ambience of real-time reporting. There was standardized use of software and hardware across Wales. A strong managerial support from efficiency through the technology group was a key factor for the implementation of the study.This study did not include Cytopathology and in situ hybridization slides. Difficulty in achieving surgical pathology practise standardization across the whole country contributed to intra-observer variations.
An Off-Policy Trust Region Policy Optimization Method With Monotonic Improvement Guarantee for Deep Reinforcement Learning

In deep reinforcement learning, off-policy data help reduce on-policy interaction with the environment, and the trust region policy optimization (TRPO) method is efficient to stabilize the policy optimization procedure. In this article, we propose an off-policy TRPO method, off-policy TRPO, which exploits both on- and off-policy data and guarantees the monotonic improvement of policies. A surrogate objective function is developed to use both on- and off-policy data and keep the monotonic improvement of policies. We then optimize this surrogate objective function by approximately solving a constrained optimization problem under arbitrary parameterization and finite samples. We conduct experiments on representative continuous control tasks from OpenAI Gym and MuJoCo. The results show that the proposed off-policy TRPO achieves better performance in the majority of continuous control tasks compared with other trust region policy-based methods using off-policy data.
An approach for a decision-making support system based on measuring the user satisfaction level on Twitter

Social networks are a very popular channel for people to communicate with, to find, to reference other users before making decisions, especially those concerning purchase. How can users’ opinions within social networks be used in making decisions cost-effective and reliable? In this paper, we propose an approach for supporting decision-making based on measuring the user satisfaction level by analyzing the sentiment of aspects and mining the fuzzy decision trees. Our proposal has been proved to overcome some of the disadvantages of previous methods. Specifically, we consider the fuzzy sentiments of users for aspects and the effects of user satisfaction, dissatisfaction, and hesitation for decision-making. The proposed method comprises four main stages. The first stage identifies a topic, which the user is interested. In the second stage, aspects of the topic and their sentiments within tweets are extracted. At the third stage, the user satisfaction level is calculated according to each kind of sentiment identified in the second step. Finally, a decision matrix is constructed, and the fuzzy decision tree is built to generate a set of rules for supporting users in decision-making. The experiments using tweets show that the proposed method achieves promising results regarding the accuracy and gained information.
Model-driven development platform selection: four industry case studies

Abstract Model-driven development platforms shift the focus of software development activity from coding to modeling for enterprises. A significant number of such platforms are available in the market. Selecting the best fitting platform is challenging, as domain experts are not typically model-driven deployment platform experts and have limited time for acquiring the needed knowledge. We model the problem as a multi-criteria decision-making problem and capture knowledge systematically about the features and qualities of 30 alternative platforms. Through four industry case studies, we confirm that the model supports decision-makers with the selection problem by reducing the time and cost of the decision-making process and by providing a richer list of options than the enterprises considered initially. We show that having decision knowledge readily available supports decision-makers in making more rational, efficient, and effective decisions. The study’s theoretical contribution is the observation that the decision framework provides a reliable approach for creating decision models in software production.
Multi-information-based convolutional neural network with attention mechanism for pedestrian trajectory prediction

Predicting pedestrian trajectory is useful in many applications, such as autonomous driving and unmanned vehicles. However, it is a challenging task because of the complexity of the interactions among pedestrians and the environment. Most existing works employ long short-term memory networks to learn pedestrian behaviors, but their prediction accuracy is not good, and their computing speed is relatively slow. To tackle this problem, we propose a multi-information-based convolutional neural network (MI-CNN) to incorporate the historical trajectory, depth map, pose, and 2D-3D size information to predict the future trajectory of the pedestrian subject. After training, we evaluate our model on crowded videos in the public datasets MOT16 and MOT20. Experiments demonstrate that the proposed method outperforms state-of-the-art approaches both in prediction accuracy and computing speed.