Reviewerly/exHarmony huggingface dataset:
On the Summarization of Consumer Health Questions

Question understanding is one of the main challenges in question answering.In real world applications, users often submit natural language questions that are longer than needed and include peripheral information that increases the complexity of the question, leading to substantially more false positives in answer retrieval.In this paper, we study neural abstractive models for medical question summarization.We introduce the MeQSum corpus of 1,000 summarized consumer health questions.We explore data augmentation methods and evaluate state-of-the-art neural abstractive models on this new task.In particular, we show that semantic augmentation from question datasets improves the overall performance, and that pointer-generator networks outperform sequence-to-sequence attentional models on this task, with a ROUGE-1 score of 44.16%.We also present a detailed error analysis and discuss directions for improvement that are specific to question summarization.
Energy-efficient video processing for virtual reality

Virtual reality (VR) has huge potential to enable radically new applications, behind which spherical panoramic video processing is one of the backbone techniques. However, current VR systems reuse the techniques designed for processing conventional planar videos, resulting in significant energy inefficiencies. Our characterizations show that operations that are unique to processing 360° VR content constitute 40% of the total processing energy consumption.
Multimedia Retrieval Through Unsupervised Hypergraph-Based Manifold Ranking

Accurately ranking images and multimedia objects are of paramount relevance in many retrieval and learning tasks. Manifold learning methods have been investigated for ranking mainly due to their capacity of taking into account the intrinsic global manifold structure. In this paper, a novel manifold ranking algorithm is proposed based on the hypergraphs for unsupervised multimedia retrieval tasks. Different from traditional graph-based approaches, which represent only pairwise relationships, hypergraphs are capable of modeling similarity relationships among a set of objects. The proposed approach uses the hyperedges for constructing a contextual representation of data samples and exploits the encoded information for deriving a more effective similarity function. An extensive experimental evaluation was conducted on nine public datasets including diverse retrieval scenarios and multimedia content. Experimental results demonstrate that high effectiveness gains can be obtained in comparison with the state-of-the-art methods.
Single-photon quantum router in the microwave regime utilizing double superconducting resonators with tunable coupling

We provide a proposal to achieve single-photon router in the microwave domain based on a waveguide quantum electrodynamics structure, which consists of a double-resonator side coupled to a superconducting transmission line. A superconducting quantum interference device is used to realize tunable and switchable coupling between the resonators; thereby the transport of a single photon in the transmission line can be effectively controlled. Our calculations show that the device has high on-off ratio and is experimentally feasible in currently available superconducting quantum circuits.
A Machine Learning Grading System Using Chatbots

The ability to provide students with timely and accurate feedback is critical to learning. However, grading written essays is demanding, and can be challenging to conduct in large classes. We explore an automated grading system involving the use of a Chatbot that asks students questions, requiring written responses. We implemented unsupervised machine learning techniques for the task of automated grading and conducted an experiment to assess the performance of the Chatbot as compared to human grading. The experiment involved posting questions to 15 students, requiring short written answers. To analyse the performance of the Chatbot, we used a combination of term-frequency inverse-document function (tfidf) with cosine Euclidean distance, and online semantic text analytics (Dandelion API), trained with neural networks on a large bank of questions and answer dataset. We then used Cohen’s kappa agreement. The result shows a good inter-rater agreement level between the automated grading and the human instructor. The work presented in the paper presents open up opportunities for using Chatbots in providing automated assessment and at the same time fosters engagement with students.
Construction of MDS Self-dual Codes over Finite Fields

In this paper, we obtain some new results on the existence of MDS self-dual codes utilizing (extended) generalized Reed-Solomon codes over finite fields of odd characteristic. For finite field with odd characteristic and square cardinality, our results can produce more classes of MDS self-dual codes than previous works.
Identification of multivariate geochemical anomalies using spatial autocorrelation analysis and robust statistics

Multivariate geochemical anomaly identification is one of the most important tasks in exploration geochemical data analysis. This paper proposes a new approach to identify multivariate geochemical anomalies which accounts for the degree of spatial autocorrelation among observations. This method is a combination of Robust Mahalanobis Distance (RMD), spatial autocorrelation analysis and robust statistics. The Minimum Covariance Determinant (MCD) estimator-based RMDs were first used to reduce the dimension of geochemical data sets. Spatial correlogram and local Moran's I statistics were then applied to determine the spatial variability (spatial scales) and to measure the degree of spatial autocorrelation at global and different local scales, respectively. Negative and positive anomalies were finally separated from a set of local Moran's I values using robust statistics, the MEDIAN ± 1.5*IQR (IQR: interquartile range) rule. We illustrated the proposed method by using a data set of 1842 stream sediment samples associated with Cu-Au-Mo and Sn mineralizations in the Jiurui ore district (Jiangxi province, southeastern China). The results showed that (i) the maximum spatial variability of RMDs were 12 km, in which strong positive spatial autocorrelation was detected at the first three spatial scales ranging from 0 to 6 km; (ii) both of negative and positive multivariate geochemical anomalies were strongly correlated with 13 known Cu-Au-Mo and Cu-Mo deposits, especially at spatial scales where high degrees of positive spatial autocorrelation were measured; (iii) weak anomalies associated with a magmatic-hydrothermal related Sn and polymetallic mineralization were successfully identified in the Zengiialong and Jianfengpo Sn deposits, which were not detected in previous studies. We demonstrate the combination of spatial autocorrelation analysis and robust statistics allows for improved identification of significant multivariate geochemical anomalies of the deposit-type sought from stream sediment geochemical data.
Example-dependent cost-sensitive adaptive boosting

Intelligent computer systems aim to help humans in making decisions. Many practical decision-making problems are classification problems in their nature, but standard classification algorithms often not applicable since they assume balanced distribution of classes and constant misclassification costs. From this point of view, algorithms that consider the cost of decisions are essential since they are more consistent with the requirements of real life. These algorithms generate decisions that directly optimize parameters valuable for business, for example, the costs savings. But despite on practical value of cost-sensitive algorithms, the little number of works study this problem concentrating mainly on the case when the cost of a classifier error is constant and does not depend on a specific example. However, many real-world classification tasks are example-dependent cost-sensitive (ECS), where the costs of misclassification vary between examples and not only within classes. Existing methods of ECS learning include just modifications of the simplest models of machine learning (naive Bayes, logistic regression, decision tree). These models produce promising results, but there is a need for further improvement in performance that can be achieved by using gradient-based ensemble methods. To break this gap, we present the ECS generalization of AdaBoost. We study three models which differ by the ways to introduce cost into the loss function: inside the exponent, outside the exponent, and both inside and outside the exponent. The results of the experiments on three synthetic and two real datasets (bank marketing and insurance fraud) show that example-dependent cost-sensitive modifications of AdaBoost outperform other known models. Empirical results also show that critical factors influencing the choice of the model are not only the distribution of features, which is typical for cost-insensitive and class-dependent cost-sensitive problems but also the distribution of costs. Next, since the outputs of AdaBoost are not well calibrated posterior probabilities, we check three approaches to calibration of classifier scores: Platt scaling, isotonic regression, and ROC modification. The results show that calibration not only significantly improves the performance of specific ECS models but allows making better capabilities of original AdaBoost. Obtained results provide new insight regarding the behavior of the cost-sensitive model from a theoretical point of view and prove that the presented approach can significantly improve the practical design of intelligent systems.
Unscented Kalman Filter-Based Unbiased Minimum-Variance Estimation for Nonlinear Systems With Unknown Inputs

This letter proposes an unscented Kalman filter (UKF)-based unbiased minimum-variance estimation (UMV) method for the nonlinear system with unknown inputs. By utilizing the statistical linearization, the nonlinear system and measurement functions are transformed into a “linear-like” regression form. The latter preserves the nonlinearity of the system and the measurement models. To this end, the unknown inputs can be estimated by the weighted least-squares. This “linear-like” regression form also allows us to resort to the UMV state estimation framework for the development of new nonlinear filter to handle unknown inputs. Specifically, two approaches have been developed: 1) given the estimated inputs, we derive a filter by minimizing the trace of the state error covariance matrix; 2) without input estimation, we derive the filter by minimizing the trace of the state error covariance matrix subject to a constraint imposed on the gain matrix. We prove that these two approaches provide the same results. Numerical results validate the effectiveness of the proposed method.
Deep Learning Based Classification of Malaria from Slide Images

As one of the most life-threatening disease in the tropical and warmer-climate countries, Malaria affects not only animals but also humans who can be infected by only a single bite from a mosquito. Although this disease is wiped out in high-income countries, as a result of traveling people, it can even emerge in all part of the world. World Health Organization announced that more than 400,000 people are expected to die due to this illness. However, it is a curable and preventable disease, if early detection is possible. Traditionally, Pathologists diagnosed this disease manually by using microscope which is a time-consuming process in our computerized world, and this model depends on the experience of the Pathologists, which is a critical problem in rural areas. Therefore, in recent years detection of Malaria using computerized image analysis which is trained using some dynamic learning mechanism has gained increasing importance. In this paper, we proposed an image processing-based Malaria detection system which is trained by deep learning. We used relatively big data for increasing the accuracy of the system, and the reached accuracy showed that the proposed system has an outstanding classification rate that can be used in real-world detection.
Enlightened Yoga

Designing technology to support instructed physical training is challenging, due to how instructions rely on complex interactional and situational social processes. To support in-the-moment instruction, we engaged in a co-creative Research through Design process with a Yoga instructor. Together, we designed and deployed Enlightened Yoga: a training class featuring wearable projecting lights that augment the instructor's and trainee's movements, and highlight the orientation and positioning of key body parts. We present insights from the design process and a study of the class. We show how the wearable lights enabled a new shared frame of reference between instructor and trainees, that became instructable through the way participants could reference and orient themselves to it. This allowed the instructor to extend his instructional strategies, and enabled trainees to better act upon cues. We discuss how this was made possible by jointly designing the technology, its coupling with the body, instructions and exercises.
Image Emotion Distribution Learning with Graph Convolutional Networks

Recently, with the rapid progress of techniques in visual analysis, a lot of attention has been paid to affective computing due to its wide potential applications. Traditional affective analysis mainly focus on single label image emotion classification. But a single image may invoke different emotions for different persons, even for one person. So emotion distribution learning is proposed to capture the underlying emotion distribution for images. Currently, state-of-the-art works model the distribution by deep convolutional networks equipped with distribution specific loss. However, the correlation among different emotions is ignored in these works. Some emotions usually co-appear, while some are hardly invoked at the same time. Properly modeling the correlation is important for image emotion distribution learning. Graph convolutional networks have shown great performance in capturing the underlying relationship in graph, and have been successfully applied in vision problems, such as zero-shot image classification. So, in this paper, we propose to apply graph convolutional networks for emotion distribution learning, termed EmotionGCN, which captures the correlation among emotions. The EmotionGCN can make use of correlation either mined from data, or directly from psychological models, such as Mikels' wheel. Extensive experiments are conducted on the FlickrLDL and TwitterLDL datasets, and the results on seven evaluation metrics demonstrate the superiority of the proposed method.
Axiomatic Interpretability for Multiclass Additive Models

Generalized additive models (GAMs) are favored in many regression and binary classification problems because they are able to fit complex, nonlinear functions while still remaining interpretable. In the first part of this paper, we generalize a state-of-the-art GAM learning algorithm based on boosted trees to the multiclass setting, showing that this multiclass algorithm outperforms existing GAM learning algorithms and sometimes matches the performance of full complexity models such as gradient boosted trees. In the second part, we turn our attention to the interpretability of GAMs in the multiclass setting. Surprisingly, the natural interpretability of GAMs breaks down when there are more than two classes. Naive interpretation of multiclass GAMs can lead to false conclusions. Inspired by binary GAMs, we identify two axioms that any additive model must satisfy in order to not be visually misleading. We then develop a technique called Additive Post-Processing for Interpretability (API) that provably transforms a pretrained additive model to satisfy the interpretability axioms without sacrificing accuracy. The technique works not just on models trained with our learning algorithm, but on any multiclass additive model, including multiclass linear and logistic regression. We demonstrate the effectiveness of API on a 12-class infant mortality dataset.
Brain tissue segmentation based on MP2RAGE multi-contrast images in 7 T MRI

We proposed a method for segmentation of brain tissues-gray matter, white matter, and cerebrospinal fluid-using multi-contrast images, including a T1 map and a uniform T1-weighted image, from a magnetization-prepared 2 rapid acquisition gradient echoes (MP2RAGE) sequence at 7 Tesla. The proposed method was evaluated with respect to the processing time and the similarity of the segmented masks of brain tissues with those obtained using FSL, FreeSurfer, and SPM12. The processing time of the proposed method (28 ± 0 s) was significantly shorter than those of FSL and SPM12 (444 ± 4 s and 159 ± 2 s for FSL and SPM12, respectively). In the similarity assessment, the tissue mask of the brain obtained by the proposed method showed higher consistency with those obtained using FSL than with those obtained using SPM12. The proposed method misclassified the subcortical structures and large vessels since it is based on the intensities of multi-contrast images obtained using MP2RAGE, which uses a similar segmentation approach as FSL but is not based on a template image or a parcellated brain atlas, which are used for FreeSurfer and SPM12, respectively. However, the proposed method showed good segmentation in the cerebellum and white matter in the medial part of the brain in comparison with the other methods. Thus, because the proposed method using different contrast images of MP2RAGE sequence showed the shortest processing time and similar segmentation ability as the other methods, it may be useful for both neuroimaging research and clinical diagnosis.
A new method to analyze rock joint roughness coefficient based on neutrosophic statistics

The joint roughness coefficient (JRC) is a key parameter evaluating the shear strength of a structural surface and the stability of engineering rock mass. In this paper, we present a new approach to determining roughness coefficient neutrosophic numbers (JRC-NNs) using the neutrosophic exponentially weighted moving average (NEWMA) in order to utilize the current and previous data information. The proposed method makes it more convenient to apply in simple and complex situations without fitting the functions. A new measure is called the neutrosophic coefficient of variation (NCV) based on NEWMA NNs. From the comparison, it is found that the proposed neutrosophic analysis is much better in reducing the range in neutrosophic standard deviation (NSD) and more consistent than the existing method.
High-accuracy 3D shape measurement of translucent objects by fringe projection profilometry

Acquiring complete and accurate 3D shape measurement results of translucent objects by fringe projection profilometry (FPP) is difficult because of the subsurface scattering effect. The phase offset introduces geometric errors, and the degraded image contrast leads to incomplete measurement results and random errors. In this research, a high-accuracy 3D shape measurement method for translucent objects based on phase-shifting FPP is proposed. The relationship between fringe period and phase error is investigated to determine the fringe periods. Random errors are suppressed by temporal noise reduction, and the robustness of multi-frequency heterodyne phase unwrapping is improved by increasing the interval of fringe periods along with temporal noise reduction. Geometric errors are compensated for by projecting multi-frequency fringe patterns to establish the relationship between fringe period and depth offset. Experimental results show that the proposed method can acquire complete measurement results and significantly reduce the overall error for measuring translucent objects.
A novel backtracking approach for two-axis solar PV tracking plants

Solar tracking is a technique required to increase energy production in multiple photovoltaic (PV) facilities. In these plants, during low-elevation solar angle hours, shadows appear between the collectors causing a dramatic decrease in production. This paper presents a novel optimal tracking strategy to prevent the creation of these shadows. The presented method determines whether or not there is shading between collectors. Thus, when the collectors are not shaded, a tracking trajectory for maximum irradiance on the collectors is suggested. However, when the collectors are shaded, backtracking is proposed. Therefore, energy production in plants with this novel tracking method can be 1.31% higher than that in PV installations with astronomical tracking. Moreover, this method allows the study of PV facilities for which there have been no published approaches, such as plants with non-rectangular collectors or those located on topographically heterogeneous surfaces.
Scalable Global Alignment Graph Kernel Using Random Features

Graph kernels are widely used for measuring the similarity between graphs. Many existing graph kernels, which focus on local patterns within graphs rather than their global properties, suffer from significant structure information loss when representing graphs. Some recent global graph kernels, which utilizes the alignment of geometric node embeddings of graphs, yield state-of-the-art performance. However, these graph kernels are not necessarily positive-definite. More importantly, computing the graph kernel matrix will have at least quadratic time complexity in terms of the number and the size of the graphs. In this paper, we propose a new family of global alignment graph kernels, which take into account the global properties of graphs by using geometric node embeddings and an associated node transportation based on earth mover's distance. Compared to existing global kernels, the proposed kernel is positive-definite. Our graph kernel is obtained by defining a distribution over random graphs, which can naturally yield random feature approximations. The random feature approximations lead to our graph embeddings, which is named as "random graph embeddings" (RGE). In particular, RGE is shown to achieve (quasi-)linear scalability with respect to the number and the size of the graphs. The experimental results on nine benchmark datasets demonstrate that RGE outperforms or matches twelve state-of-the-art graph classification algorithms.
An Intelligent Spam Detection Model Based on Artificial Immune System

Spam emails, also known as non-self, are unsolicited commercial or malicious emails, sent to affect either a single individual or a corporation or a group of people. Besides advertising, these may contain links to phishing or malware hosting websites set up to steal confidential information. In this paper, a study of the effectiveness of using a Negative Selection Algorithm (NSA) for anomaly detection applied to spam filtering is presented. NSA has a high performance and a low false detection rate. The designed framework intelligently works through three detection phases to finally determine an email’s legitimacy based on the knowledge gathered in the training phase. The system operates by elimination through Negative Selection similar to the functionality of T-cells’ in biological systems. It has been observed that with the inclusion of more datasets, the performance continues to improve, resulting in a 6% increase of True Positive and True Negative detection rate while achieving an actual detection rate of spam and ham of 98.5%. The model has been further compared against similar studies, and the result shows that the proposed system results in an increase of 2 to 15% in the correct detection rate of spam and ham.
Unifying logical and statistical AI with Markov logic

Markov logic can be used as a general framework for joining logical and statistical AI.