Reviewerly/exHarmony huggingface dataset:
Cross-modal Language Generation using Pivot Stabilization for Web-scale Language Coverage

Cross-modal language generation tasks such as image captioning are directly hurt in their ability to support non-English languages by the trend of data-hungry models combined with the lack of non-English annotations.We investigate potential solutions for combining existing language-generation annotations in English with translation capabilities in order to create solutions at web-scale in both domain and language coverage.We describe an approach called Pivot-Language Generation Stabilization (PLuGS), which leverages directly at training time both existing English annotations (gold data) as well as their machinetranslated versions (silver data); at run-time, it generates first an English caption and then a corresponding target-language caption.We show that PLuGS models outperform other candidate solutions in evaluations performed over 5 different target languages, under a largedomain testset using images from the Open Images dataset.Furthermore, we find an interesting effect where the English captions generated by the PLuGS models are better than the captions generated by the original, monolingual English model.
Robust learning with the Hilbert-Schmidt independence criterion

We investigate the use of a non-parametric independence measure, the Hilbert-Schmidt Independence Criterion (HSIC), as a loss-function for learning robust regression and classification models. This loss-function encourages learning models where the distribution of the residuals between the label and the model prediction is statistically independent of the distribution of the instances themselves. This loss-function was first proposed by Mooij et al. (2009) in the context of learning causal graphs. We adapt it to the task of learning for unsupervised covariate shift: learning on a source domain without access to any instances or labels from the unknown target domain, but with the assumption that $p(y|x)$ (the conditional probability of labels given instances) remains the same in the target domain. We show that the proposed loss is expected to give rise to models that generalize well on a class of target domains characterised by the complexity of their description within a reproducing kernel Hilbert space. Experiments on unsupervised covariate shift tasks demonstrate that models learned with the proposed loss-function outperform models learned with standard loss functions, achieving state-of-the-art results on a challenging cell-microscopy unsupervised covariate shift task.
To Express or to End? Personality Traits Are Associated With the Reasons and Patterns for Using Emojis and Stickers

Emojis and stickers are becoming increasingly popular in computer mediated communications. The present study examined the associations between personality traits and people’s reasons and patterns for using both emojis and stickers. Participants (n= 312) completed three on-line questionnaires assessing shyness, the Big Five personality traits and why and how they used emojis and stickers. Results revealed that shyness, neuroticism, extraversion and agreeableness were correlated with different reasons of usage. Moreover, some participants exhibited a tendency to adjust frequency of usage depending on who the target person was and whether they were in a private or group chat. People who showed such tendencies were found to differ in personality with those who did not. Some differences in usage patterns were also observed between emojis and stickers. Together, the present study sheds more insights into how emojis and stickers can help people with different personality traits to achieve different purposes in their daily communication.
Dynamic Sampling Strategies for Multi-Task Reading Comprehension

Building general reading comprehension systems, capable of solving multiple datasets at the same time, is a recent aspirational goal in the research community.Prior work has focused on model architectures or generalization to held out datasets, and largely passed over the particulars of the multi-task learning set up.We show that a simple dynamic sampling strategy, selecting instances for training proportional to the multi-task model's current performance on a dataset relative to its singletask performance, gives substantive gains over prior multi-task sampling strategies, mitigating the catastrophic forgetting that is common in multi-task learning.We also demonstrate that allowing instances of different tasks to be interleaved as much as possible between each epoch and batch has a clear benefit in multitask performance over forcing task homogeneity at the epoch or batch level.Our final model shows greatly increased performance over the best model on ORB, a recently-released multitask reading comprehension benchmark.
Control Theoretical Modeling of Trust-Based Decision Making in Food-Energy-Water Management

We propose a hybrid Human-Machine decision making to manage Food-Energy-Water resources. In our system trust among human actors during decision making is measured and managed. Furthermore, such trust is used to pressure human actors to choose among the solutions generated by algorithms that satisfy the community's preferred trade-offs among various objectives. We model the trust-based loops in decision making by using control theory. In this system, the feedback information is the trust pressure that actors receive from peers. Using control theory, we studied the dynamics of the trust of an actor. Then, we presented the modeling of the change of solution distances. In both scenarios, we also calculated the settling times and the stability using the transfer functions and their Z-transforms as the number of rounds to show whether and when the decision making is finalized.
An Adaptive Data Placement Architecture in Multicloud Environments

Cloud service providers (CSPs) can offer infinite storage space with cheaper maintenance cost compared to the traditional storage mode. Users tend to store their data in geographical and diverse CSPs so as to avoid vendor lock-in. Static data placement has been widely studied in recent works. However, the data access pattern is often time-varying and users may pay more cost if static placement is adopted during the data lifetime. Therefore, it is a pending problem and challenge of how to dynamically store users’ data under time-varying data access pattern. To this end, we propose ADPA, an adaptive data placement architecture that can adjust the data placement scheme based on the time-varying data access pattern and subject for minimizing the total cost and maximizing the data availability. The proposed architecture includes two main components: data retrieval frequency prediction module based on LSTM and data placement optimization module based on Q-learning. The performance of ADPA is evaluated through several experimental scenarios using NASA-HTTP workload and cloud providers information.
Comparison of Instance Selection and Construction Methods with Various Classifiers

Instance selection and construction methods were originally designed to improve the performance of the k-nearest neighbors classifier by increasing its speed and improving the classification accuracy. These goals were achieved by eliminating redundant and noisy samples, thus reducing the size of the training set. In this paper, the performance of instance selection methods is investigated in terms of classification accuracy and reduction of training set size. The classification accuracy of the following classifiers is evaluated: decision trees, random forest, Naive Bayes, linear model, support vector machine and k-nearest neighbors. The obtained results indicate that for the most of the classifiers compressing the training set affects prediction performance and only a small group of instance selection methods can be recommended as a general purpose preprocessing step. These are learning vector quantization based algorithms, along with the Drop2 and Drop3. Other methods are less efficient or provide low compression ratio.
Scalable Nearest Neighbor Search for Optimal Transport

The Optimal Transport (a.k.a. Wasserstein) distance is an increasingly popular similarity measure for rich data domains, such as images or text documents. This raises the necessity for fast nearest neighbor search with respect to this distance, a problem that poses a substantial computational bottleneck for various tasks on massive datasets. In this work, we study fast tree-based approximation algorithms for searching nearest neighbors w.r.t. the Wasserstein-1 distance. A standard tree-based technique, known as Quadtree, has been previously shown to obtain good results. We introduce a variant of this algorithm, called Flowtree, and formally prove it achieves asymptotically better accuracy. Our extensive experiments, on real-world text and image datasets, show that Flowtree improves over various baselines and existing methods in either running time or accuracy. In particular, its quality of approximation is in line with previous high-accuracy methods, while its running time is much faster.
N-hidden layer artificial neural network architecture computer code: geophysical application example

<h2>Abstract</h2> We provide a MATLAB computer code for training artificial neural network (ANN) with <i>N+1</i> layer (<i>N-</i>hidden layer) architecture. Currently, the ANN application to solving geophysical problems have been confined to the 2-layer, i.e. 1-hidden layer, architecture because there are no open source software codes for higher numbered layer architecture. The restriction to the 2-layer architecture comes with the attendant model error due to insufficient hidden neurons to fully define the ANN machines. The <i>N</i>-hidden layer ANN has a general architecture whose sensitivity is the accumulation of the backpropagation of the error between the feedforward output and the target patterns. The trained ANN machine can be retrieved by the gradient optimization method namely: Levenberg-Marquardt, steepest descent or conjugate gradient methods. Our test results on the Poisson's ratio (as a function of compressional and shear wave velocities) machines with 2-, 3- and 4-layer ANN architectures reveal that the machines with higher number of layers outperform those with lower number of layers. Specifically, the 3- and 4-layer ANN machines have <mml:math><mml:mrow><mml:mo>≥</mml:mo><mml:mn>97</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math> accuracy, predicting the lithology and fluid identification in the oil and gas industry by means of the Poisson's ratio, whereas the 2-layer ANN machines poorly predict the results with as large error as <mml:math><mml:mrow><mml:mn>20</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math>. These results therefore reinforce our belief that this open source code will facilitate the training of accurate <i>N-</i>hidden layer ANN sophisticated machines with high performance and quality delivery of geophysical solutions. Moreover, the easy portability of the functions of the code into other software will enhance a versatile application and further research to improve its performance.
CoRiMaS—An Ontological Approach to Cooperative Risk Management in Seaports

For today’s global value chains, seaports and their operations are indispensable components. In many cases, the cargo handling takes place in close proximity to residential and/or environmentally sensitive areas. Furthermore, seaports are often not operated by a single organization, but need to be considered as communities of sometimes hundreds of internal and external stakeholders. Due to their close cooperation in the cargo handling process, risk management should be a common approach among the internal stakeholders as well in order to effectively mitigate and respond to emerging risks. However, empirical research has revealed that risk management is often limited to the organization itself, which indicates a clear lack of cooperation. Primary reasons in this regard are missing knowledge about the relations and responsibilities within the port and differing terminologies. Therefore, we propose an ontology (CoRiMaS) that implements a developed reference model for risk management that explicitly aims at seaports with a cooperative approach to risk management. CoRiMaS has been designed looking at the Semantic Web and at the Linked Data model to provide a common interoperable vocabulary in the target domain. The key concepts of our ontology comprise the hazard, stakeholder, seaport, cooperation aspect, and risk management process. We validated our ontology by applying it in a case study format to the Port of Hamburg (Germany). The CoRiMaS ontology can be widely applied to foster cooperation within and among seaports. We believe that such an ontological approach has the potential to improve current risk management practices and, thereby, to increase the resilience of operations, as well as the protection of sensitive surrounding areas.
Discriminative dual-stream deep hashing for large-scale image retrieval

Deep hashing has been an important research topic for using deep learning to boost performance of hash learning. Most existing deep supervised hashing methods mainly focus on how to effectively preserve the similarity in hash coding solely depending on pairwise supervision. However, such pairwise similarity-preserving strategy cannot fully explore the semantic information in most cases, which results in information loss. To address this problem, this paper proposes a discriminative dual-stream deep hashing (DDDH) method, which integrates the pairwise similarity loss and the classification loss into a unified framework to take full advantage of label information. Specifically, the pairwise similarity loss aims to preserve the similarity and structural information of high-dimensional original data. Meanwhile, the designed classification loss can enlarge the margin between different classes which improves the discrimination of learned binary codes. Moreover, an effective optimization algorithm is employed to train the hash code learning framework in an end-to-end manner. The results of extensive experiments on three image datasets demonstrate that our method is superior to several state-of-the-art deep and non-deep hashing methods. Ablation studies and analysis further show the effectiveness of introducing the classification loss in the overall hash learning framework.
Blockchain-Enabled Industrial Internet of Things: Advances, Applications, and Challenges

The articles in this special issue have solicited high-quality manuscripts that describe in-depth the breadth of real-world blockchain-based multi-disciplinary IIoT deployments and present actual experiences in resolving contextual blockchain-related challenges. In addition, these articles develop and share best practices, vision realizations and lessons learned in this integrated environment and establish guiding principles for technical, operational and business successes.
Identification of Acute Lymphoblastic Leukemia Subtypes in Touching Cells Based on Enhanced Edge Detection

Acute lymphoblastic leukemia (ALL) is a type of blood cancer that begins with immature lymphocytes in bone marrow.The challenge in automatic identification of ALL when there are touching cells in image.The previous studies related to the separation of touching cells are still constrained by oversegmentation, undersegmentation, and inaccurate in cell separation.To overcome this problem, this research proposed an algorithm for cell separation based on enhanced edge detection.The objective of this study is identification of L1, L2, and L3, in touching cell based on edge detection and edge linking.Edge detection was performed on grayscale images and Hue images for the area of White Blood Cell segmentation results then the image results were combined.The classification of Acute Lymphoblastic Leukemia (ALL) cells was carried out using the geometry and texture features of cells and nucleus with Support Vector Machine (SVM) as the classifier.The dataset for the training process amounted to 668 ALL single cell images and for the testing process was 301 multi-cell ALL images.The testing results of the identification of ALL subtypes showed that the proposed cell separation method gave accuracy of 75.42%.
Operational, gauge-free quantum tomography

As increasingly impressive quantum information processors are realized in laboratories around the world, robust and reliable characterization of these devices is now more urgent than ever. These diagnostics can take many forms, but one of the most popular categories is tomography, where an underlying parameterized model is proposed for a device and inferred by experiments. Here, we introduce and implement efficient operational tomography, which uses experimental observables as these model parameters. This addresses a problem of ambiguity in representation that arises in current tomographic approaches (the gauge problem). Solving the gauge problem enables us to efficiently implement operational tomography in a Bayesian framework computationally, and hence gives us a natural way to include prior information and discuss uncertainty in fit parameters. We demonstrate this new tomography in a variety of different experimentally-relevant scenarios, including standard process tomography, Ramsey interferometry, randomized benchmarking, and gate set tomography.
Real-Time Fuel Truck Detection Algorithm Based on Deep Convolutional Neural Network

This paper presents a new approach by training and improving a convolutional neural network (CNN) based on You Only Look Once version 2 (YOLOv2) to efficiently detect fuel trucks from images in embedded systems.The proposed method considers the entire image area for strong object detection compared with existing methods that only focus on the image area where the class object exists to predict its probability to be in a class.The loss function for CNN is improved to enhance effective learning, especially when only a limited amount of data is available for training.The class probability can be learned by improving the loss function although the anchor boxes are not in the center of the target object.The learning process of the model can be in a limited range and achieve rapid convergence although the sizes of the initial anchor and target boundary boxes are different.Experimental results of various fuel truck images show the efficiency of the proposed approach under different detection scenarios of real fuel trucks.The detection rate of the proposed method is approximately 4% higher than the YOLOv2 object detection method.The proposed method is suitable to monitor long country borders using unmanned drones.
The effect of entrepreneurial orientation and knowledge management on innovation performance: The mediation role of market orientation

This study attempts to conceptualize the direct implications of entrepreneurial orientation and knowledge management on innovation performance and seeks to determine the mediation effect of marketing orientation on these relationships.This is the first attempt to construct such a model.The study empirically examines the conceptual model using a sample of 221 CEOs and owners of Small and Medium Enterprises (SMEs) in Kuwait.The data was collected using a close ended self-administered questionnaire.The validity and reliability of the model is tested using a measurement model and the structural model is constructed to understand the significance of the conceptual relationship posited in the hypotheses of the study.The findings of the study indicate that entrepreneurial orientation and knowledge management had a considerable effect on innovation performance and that marketing orientation had a considerable influence on innovation performance.In addition, the findings indicate that marketing orientation mediated the effect of entrepreneurial orientation and knowledge management on innovation performance.This study has provided some empirical evidence for the importance of entrepreneurship and innovation as well as knowledge management in ensuring the success of SMEs; it also provides an explanation on the manner in which entrepreneurial orientation and knowledge management improve the innovation performance of SMEs.
Modelling Learners in Crowdsourcing Educational Systems

Traditionally, learner models estimate a student’s knowledge state solely based on their performance on attempting assessment items. This can be attributed to the fact that in many traditional educational systems, students are primarily involved in just answering assessment items. In recent years, the use of crowdsourcing to support learning at scale has received significant attention. In crowdsourcing educational systems, in addition to attempting assessment items, students are engaged with other various tasks such as creating resources, creating solutions, rating the quality of resources, and giving feedback. Past studies have demonstrated that engaging students in meaningful crowdsourcing tasks, also referred to as learningsourcing, has pedagogical benefits that can enhance student learning. In this paper, we present a learner model that leverages data from students’ learnersourcing contributions alongside attempting assessment items towards modelling of students’ knowledge state. Results from an empirical study suggest that indeed crowdsourced contributions from students can effectively be used in modelling learners.
Generalizable deep temporal models for predicting episodes of sudden hypotension in critically ill patients: a personalized approach

Abstract The vast quantities of data generated and collected in the Intensive Care Unit (ICU) have given rise to large retrospective datasets that are frequently used for observational studies. The temporal nature and fine granularity of much of the data collected in the ICU enable the pursuit of predictive modeling. In particular, forecasting acute hypotensive episodes (AHE) in intensive care patients has been of interest to researchers in critical care medicine. Given an advance warning of an AHE, care providers may be prompted to search for evolving disease processes and help mitigate negative clinical outcomes. However, the conventionally adopted definition of an AHE does not account for inter-patient variability and is restrictive. To reflect the wider trend of global clinical and research efforts in precision medicine, we introduce a patient-specific definition of AHE in this study and propose deep learning based models to predict this novel definition of AHE in data from multiple independent institutions. We provide extensive evaluation of the models by studying their accuracies in detecting patient-specific AHEs with lead-times ranging from 10 min to 1 hour before the onset of the event. The resulting models achieve AUROC values ranging from 0.57–0.87 depending on the lead time of the prediction. We demonstrate the generalizability and robustness of our approach through the use of independent multi-institutional data.