Reviewerly/exHarmony huggingface dataset:
Causal Inference in Generalizable Environments: Systematic Representative Design

Causal inference and generalizability both matter. Historically, systematic designs emphasize causal inference, while representative designs focus on generalizability. Here, we suggest a transformative synthesis - Systematic Representative Design (SRD) - concurrently enhancing both causal inference and "built-in" generalizability by leveraging today's intelligent agent, virtual environments, and other technologies. In SRD, a "default control group" (DCG) can be created in a virtual environment by representatively sampling from real-world situations. Experimental groups can be built with systematic manipulations onto the DCG base. Applying systematic design features (e.g., random assignment to DCG versus experimental groups) in SRD affords valid causal inferences. After explicating the proposed SRD synthesis, we delineate how the approach concurrently advances generalizability and robustness, cause-effect inference and precision science, a computationally-enabled cumulative psychological science supporting both "bigger theory" and concrete implementations grappling with tough questions (e.g., what is context?) and affording rapidly-scalable interventions for real-world problems.
Combining Contextualized Embeddings and Prior Knowledge for Clinical Named Entity Recognition: Evaluation Study

Named entity recognition (NER) is a key step in clinical natural language processing (NLP). Traditionally, rule-based systems leverage prior knowledge to define rules to identify named entities. Recently, deep learning-based NER systems have become more and more popular. Contextualized word embedding, as a new type of representation of the word, has been proposed to dynamically capture word sense using context information and has proven successful in many deep learning-based systems in either general domain or medical domain. However, there are very few studies that investigate the effects of combining multiple contextualized embeddings and prior knowledge on the clinical NER task.This study aims to improve the performance of NER in clinical text by combining multiple contextual embeddings and prior knowledge.In this study, we investigate the effects of combining multiple contextualized word embeddings with classic word embedding in deep neural networks to predict named entities in clinical text. We also investigate whether using a semantic lexicon could further improve the performance of the clinical NER system.By combining contextualized embeddings such as ELMo and Flair, our system achieves the F-1 score of 87.30% when only training based on a portion of the 2010 Informatics for Integrating Biology and the Bedside NER task dataset. After incorporating the medical lexicon into the word embedding, the F-1 score was further increased to 87.44%. Another finding was that our system still could achieve an F-1 score of 85.36% when the size of the training data was reduced to 40%.Combined contextualized embedding could be beneficial for the clinical NER task. Moreover, the semantic lexicon could be used to further improve the performance of the clinical NER system.
Squerall: Virtual Ontology-Based Access to Heterogeneous and Large Data Sources

The last two decades witnessed a remarkable evolution in terms of data formats, modalities, and storage capabilities. Instead of having to adapt one's application needs to the, earlier limited, available storage options, today there is a wide array of options to choose from to best meet an application's needs. This has resulted in vast amounts of data available in a variety of forms and formats which, if interlinked and jointly queried, can generate valuable knowledge and insights. In this article, we describe Squerall: a framework that builds on the principles of Ontology-Based Data Access (OBDA) to enable the querying of disparate heterogeneous sources using a unique query language, SPARQL. In Squerall, original data is queried on-the-fly without prior data materialization or transformation. In particular, Squerall allows the aggregation and joining of large data in a distributed manner. Squerall supports out-of-the-box five data sources and moreover, it can be programmatically extended to cover more sources and incorporate new query engines. The framework provides user interfaces for the creation of necessary inputs, as well as guiding non-SPARQL experts to write SPARQL queries. Squerall is integrated into the popular SANSA stack and available as open-source software via GitHub and as a Docker image. Software Framework. https://eis-bonn.github.io/Squerall .
Attribute-Aware Graph Recurrent Networks for Scholarly Friend Recommendation Based on Internet of Scholars in Scholarly Big Data

The academic society is stepping into the age of scholarly big data, where finding suitable scholars for collaboration has become ever difficult. Scholarly recommendation approaches are designed to overcome the information overload problems. However, previous methods mainly consider network topology without considering scholars' academic information and the manually designed similarity measurements may not have a good performance when applying to large-scale sparse networks. To this end, this article proposes to design a scholarly friend recommendation system by taking advantages of network embedding and scholar attributes. It is worth mentioning that different from traditional scientific collaborator recommendations, our goal is to recommend potential friends for scholars using academic social networks. We first construct an attributed social network by extracting scholars' academic attributes from digital libraries. Then, we perform an attributed random walk which can jointly model network structure and scholar attributes. Finally, a novel graph recurrent neural framework is adopted to embed attributed scholar interactions within the model for recommendations. Experimental results on two real-world scholarly datasets demonstrate the effectiveness of our proposed method.
A Circadian Rhythms Learning Network for Resisting Cognitive Periodic Noises of Time-Varying Dynamic System and Applications to Robots

Time-varying dynamic system contaminated by cognitive noises is universal in the fields of engineering and science. In this article, a circadian rhythms learning network (CRLN) is proposed and investigated for disposing the noise disturbed time-varying dynamic system. To do so, a vector-error function is first defined. Second, a neural dynamic model is formulated. Third, a co-state matrix is integrated into the model, of which the states are thPe linear combination of the previous periodic states and errors, which can effectively suppress periodic noises. Theoretical analysis and mathematical derivation prove the global exponential convergence performance of the proposed CRLN model. Finally, a practical noise disturbed time-varying dynamic system example with four different noises illustrates the accuracy and efficacy of the proposed CRLN model. Comparisons with traditional zeroing neural network further verify the advantages of the proposed CRLN model.
Unsupervised feature selection via adaptive hypergraph regularized latent representation learning

Due to the rapid development of multimedia technology, a large number of unlabelled data with high dimensionality need to be processed. The high dimensionality of data not only increases the computation burden of computer hardware, but also hinders algorithms to obtain optimal performance. Unsupervised feature selection, which is regarded as a means of dimensionality reduction, has been widely recognized as an important and challenging pre-step for many machine learning and data mining tasks. However, we observe that there are at least two issues in previous unsupervised feature selection methods. Firstly, traditional unsupervised feature selection algorithms usually assume that the data instances are identically distributed and there is no dependency between them. However, the data instances are not only associated with high dimensional features but also inherently interconnected with each other. Secondly, the traditional similarity graph used in previous methods can only describe the pair-wise relations of data, but cannot capture the high-order relations, so that the complex structures implied in the data cannot be sufficiently exploited. In this work, we propose a robust unsupervised feature selection method which embeds the latent representation learning into feature selection. Instead of measuring the feature importances in original data space, the feature selection is carried out in the learned latent representation space which is more robust to noises. In order to capture the local manifold geometrical structure of original data in a high-order manner, a hypergraph is adaptively learned and embedded into the resultant model. An efficient alternating algorithm is developed to optimize the problem. Experimental results on eight benchmark data sets demonstrate the effectiveness of the proposed method.
Cross-modal Neural Sign Language Translation

Sign Language is the primary means of communication for the majority of the Deaf and hard-of-hearing communities. Current computational approaches in this general research area have focused specifically on sign language recognition and the translation of sign language to text. However, the reverse problem of translating from spoken to sign language has so far not been widely explored. The goal of this doctoral research is to explore sign language translation in this generalized setting, i.e. translating from spoken language to sign language and vice versa. Towards that end, we propose a concrete methodology for tackling the problem of speech to sign language translation and introduce How2Sign, the first public, continuous American Sign Language dataset that enables such research. With a parallel corpus of almost 60 hours of sign language videos (collected with both RGB and depth sensor data) and the corresponding speech transcripts for over 2500 instructional videos, How2Sign is a public dataset of unprecedented scale that can be used to advance not only sign language translation, but also a wide range of sign language understanding tasks.
Camera Stabilization in 360° Videos and Its Impact on Cyber Sickness, Environmental Perceptions, and Psychophysiological Responses to a Simulated Nature Walk: A Single-Blinded Randomized Trial

Immersive virtual environments (IVEs) technology has emerged as a valuable tool to environmental psychology research in general, and specifically to studies of human-nature interactions. However, virtual reality is known to induce cyber sickness, which limits its application and highlights the need for scientific strategies to optimize virtual experiences. In this study, we assessed the impact of improved camera stability on cyber sickness, presence, and psychophysiological responses to a simulated nature walk. In a single-blinded trial, 50 participants were assigned to watch, using a head-mounted display, one of two 10-min 360° videos showing a first-person nature walk: one video contained small-magnitude scene oscillations associated with cameraman locomotion, while in the other video, the oscillations were drastically reduced thanks to an electric stabilizer and a dolly. Measurements of cyber sickness (in terms of both occurrence and severity of symptoms), perceptions of the IVE (presence and perceived environmental restorativeness), and indicators of psychophysiological responses [affect, enjoyment, and heart rate (HR)] were collected before and/or after the exposure. Compared to the low-stability (LS) condition, in the high-stability (HS) condition, participants reported lower severity of cyber sickness symptoms. The delta values for pre-post changes in affect for the LS video revealed a deterioration of participants' affect profile with a significant increase in ratings of negative affect and fatigue, and decrease in ratings of positive affect. In contrast, there were no pre-post changes in affect for the HS video. No differences were found between the HS and LS conditions with respect to presence, perceived environmental restorativeness, enjoyment, and HR. Cyber sickness was significantly correlated with all components of affect and enjoyment, but not with presence, perceived environmental restorativeness, or HR. These findings demonstrate that improved camera stability in 360° videos is crucial to reduce cyber sickness symptoms and negative affective responses in IVE users. The lack of associations between improved stability and presence, perceived environmental restorativeness, and HR suggests that other aspects of IVE technology must be taken into account in order to improve virtual experiences of nature.
Miniaturized Diffraction Grating Design and Processing for Deep Neural Network

Researchers of UCLA reported the fully connected optical neural network, the high computational rate, and low power consumption was realized, while the diffraction grating system based on the Terahertz source is expensive and bulky. In this letter, a long-wave infrared source with a wavelength of 10.6 um is used to establish an optical neural network transfer model using the Sommerfeld diffraction theory. Diffraction grating design, processing, and error analysis applied to deep neural networks are carried out. The MNIST handwritten database is used as the data set to train and optimize the phase parameters by forward propagation and backpropagation. The neuron size is 5um; the number of neurons is 200*200; the entire grating area is 1mm. Compared with the existing light diffraction neural network, the feature size of the deep learning neural network is reduced by 80 times. The Ge (Germanium)-based diffraction grating of 5 layers of neurons with four relative step heights is engraved by semiconductor standard processing technology. The surface-infrared high-efficiency anti-reflection film increased the grating transmission efficiency to over 90%.
Shape Reconstruction Using Differentiable Projections and Deep Priors

We investigate the problem of reconstructing shapes from noisy and incomplete projections in the presence of viewpoint uncertainities. The problem is cast as an optimization over the shape given measurements obtained by a projection operator and a prior. We present differentiable projection operators for a number of reconstruction problems which when combined with the deep image prior or shape prior allows efficient inference through gradient descent. We apply our method on a variety of reconstruction problems, such as tomographic reconstruction from a few samples, visual hull reconstruction incorporating view uncertainties, and 3D shape reconstruction from noisy depth maps. Experimental results show that our approach is effective for such shape reconstruction problems, without requiring any task-specific training.
A denoising model adapted for impulse and Gaussian noises using a constrained-PDE

In denoising problems, it is always hard to deal with a mixture of two noise densities. In this paper, we introduce a nonlinear constrained-PDE based on the fractional order tensor diffusion to approximate the clean image and also the impulse component in a mixture of Gaussian and impulse noise. Our model offers an ideal compromise between the edge preservation, staircasing creation and the loss of image contrasts. The proposed constrained-PDE is formulated using a variational model that features a L1 data discrepancy encoding the impulse noise and an L2 term of the clean image which is a solution of a high-order PDE. Then, a rigorous analysis of the existence of the solution to the proposed model is checked in a suitable functional framework. In addition, to solve the constrained-PDE, we consider an extension of the Primal-Dual algorithm to nonlinear operators with an accelerate Bregman iteration. Numerical experiments show that the proposed model produces pleasant results in terms of restoration quality and solution efficiency compared to some competitive regularizations.
Automatic First Arrival Picking via Deep Learning With Human Interactive Learning

First break picking is an inevitable process in land seismic data processing, which involves a huge amount of human labor to perform. Even after decades of investigation on the first break picking process, there are still enormous challenges in developing a robust automatic approach. Although many experts proposed techniques to solve the first break picking problems automatically, there are no solid solutions to avoid human labors during the picking process. In the late 20th century, the rise of the artificial intelligence and the advancement of computer hardware have overcome some challenges in first break picking but the level of their success is limited. In this article, we proposed a deep machine learning model to achieve automatic seismic first break picking. Our proposed model can find the underlying factors and determine the first break curve. In addition, the network is capable of updating itself through continuous learning. The system is able to identify labeling anomalies on-site and update the model through active learning. Unfortunately, training the machine learning model on a huge data set that contains unnecessary data points is an inefficient way for both model learning process and human labeling labors. Therefore, training the model with data selected by the experts can highly reduce the training time and the number of data that human has to label. In simulation, we show the advantage of our proposed deep semisupervised neural network, which uses both labeled and unlabeled data sets to achieve higher accuracy compared with the supervised neural networks.
Performance Evaluation of a Dense MEMS-Based Seismic Sensor Array Deployed in the Sichuan-Yunnan Border Region for Earthquake Early Warning

With the last decades of development, earthquake early warning (EEW) has proven to be one of the potential means for disaster mitigation. Usually, the density of the EEW network determines the performance of the EEW system. For reducing the cost of sensors and building a dense EEW network, an upgraded low-cost Micro Electro Mechanical System (MEMS)-based sensor named GL-P2B was developed in this research. This device uses a new high-performance CPU board and is built on a custom-tailored Linux 3.6.9 operating system integrating with seismological processing. Approximately 170 GL-P2Bs were installed and tested in the Sichuan-Yunnan border region from January 2017 to December 2018. We evaluated its performance on noise-level, dynamic range (DR), useful resolution (NU), collocated recording comparison, and shake map generation. The results proved that GL-P2B can be classified as a type of Class-B sensor. The records obtained are consistent with the data obtained by the collocated traditional force-balanced accelerometers even for stations with an epicenter distance of more than 150 km, and most of the relative percentage difference of peak ground acceleration (PGA) values is smaller than 10%. In addition, with the current density of the GL-P2B seismic network, near-real-time refined shake maps without using values derived for virtual stations could be directly generated, which will significantly improve the capability for earthquake emergency response. Overall, this MEMS-based sensor can meet the requirements of dense EEW purpose and lower the total investment of the National System for Fast Seismic Intensity Report and Earthquake Early Warning project.
A Secure and Scalable Data Source for Emergency Medical Care using Blockchain Technology

Emergency medical services universally get regarded as the essential part of the health care delivery system [1]. A relationship exists between the emergency patient death rate and factors such as the failure to access a patient's critical data and the time it takes to arrive at hospitals. Nearly thirty million Americans do not live within an hour of trauma care, so this poor access to trauma centers links to higher pre-hospital death rates in more than half of the United States [2]. So, we need to address the problem. In a patient care-cycle, loads of medical data items are born in different healthcare settings using a disparate system of records during patient visits. The ability for medical care providers to access a patient's complete picture of emergency-relevant medical data is critical and can significantly reduce the annual mortality rate. Today, the problem exists with a continuous recording system of the patient data between healthcare providers. In this paper, we've introduced a combination of secure file transfer methods/tools and blockchain technology as a solution to record patient Emergency relevant medical data as patient walk through from one clinic/medical facility to another, creating a continuous footprint of patient as a secure and scalable data source. So, ambulance crews can access and use it to provide high quality pre-hospital care. All concerns of medical record sharing and accessing like authentication, privacy, security, scalability and audibility, confidentiality has been considered in this approach.
Analyzing GDPR Compliance Through the Lens of Privacy Policy

With the arrival of the European Union's General Data Protection Regulation (GDPR), several companies are making significant changes to their systems to achieve compliance. The changes range from modifying privacy policies to redesigning systems which process personal data. Privacy policy is the main medium of information dissemination between the data controller and the users. This work analyzes the privacy policies of large-scaled cloud services which seek to be GDPR compliant. We show that many services that claim compliance today do not have clear and concise privacy policies. We identify several points in the privacy policies which potentially indicate non-compliance; we term these GDPR dark patterns. We identify GDPR dark patterns in ten large-scale cloud services. Based on our analysis, we propose seven best practices for crafting GDPR privacy policies.
Improving Password Guessing via Representation Learning

Learning useful representations from unstructured data is one of the core challenges, as well as a driving force, of modern data-driven approaches. Deep learning has demonstrated the broad advantages of learning and harnessing such representations.In this paper, we introduce a deep generative model representation learning approach for password guessing. We show that an abstract password representation naturally offers compelling and versatile properties that open new directions in the extensively studied, and yet presently active, password guessing field. These properties can establish novel password generation techniques that are neither feasible nor practical with the existing probabilistic and non-probabilistic approaches. Based on these properties, we introduce: (1) A general framework for conditional password guessing that can generate passwords with arbitrary biases; and (2) an Expectation Maximization-inspired framework that can dynamically adapt the estimated password distribution to match the distribution of the attacked password set.
Understanding Generalized Whitening and Coloring Transform for Universal Style Transfer

Style transfer is a task of rendering images in the styles of other images. In the past few years, neural style transfer has achieved a great success in this task, yet suffers from either the inability to generalize to unseen style images or fast style transfer. Recently, an universal style transfer technique that applies zero-phase component analysis (ZCA) for whitening and coloring image features realizes fast and arbitrary style transfer. However, using ZCA for style transfer is empirical and does not have any theoretical support. In addition, other whitening and coloring transforms (WCT) than ZCA have not been investigated. In this report, we generalize ZCA to the general form of WCT, provide an analytical performance analysis from the angle of neural style transfer, and show why ZCA is a good choice for style transfer among different WCTs and why some WCTs are not well applicable for style transfer.