Reviewerly/exHarmony huggingface dataset:
Left ventricle quantification with sample-level confidence estimation via Bayesian neural network

Quantification of cardiac left ventricle has become a hot topic due to its great significance in clinical practice. Many efforts have been devoted to LV quantification and obtained promising performance with the help of various deep neural networks when validated on a group of samples. However, none of them can provide sample-level confidence of the results, i.e., how reliable is the prediction result for one single sample, which would help clinicians make decisions of whether or not to accept the automatic results. In this paper, we achieve this by introducing the uncertainty analysis theory into our LV quantification network. Two types of uncertainty, Model Uncertainty, and Data Uncertainty are analyzed for the quantification performance and contribute to the sample-level confidence. Experiments with data of 145 subjects validate that our method not only improved the quantification performance with an uncertainty-weighted regression loss but also is capable of providing for each sample the confidence level of the estimation results for clinicians' further consideration.
Multi-Feature Fusion Algorithm in VR Panoramic Image Detail Enhancement Processing

VR panoramic image is an image technology that covers a wide range of scenes. Its imaging range is much larger than that of traditional imaging systems, and it can fully reflect all the information of the imaging space. Although the multi-feature fusion method has been studied for a long time, the methods of multi-feature extraction, fusion and overall optimization have not been widely studied. In view of the shadow problem in VR panoramic images, this paper proposes a multi-feature fusion VR panoramic image shadow elimination algorithm, which uses HSV color features and LBP (Local Binary Pattern) / LSFP (Local Five Similarity Pattern) texture features to obtain shadow detection results and then obtains the final detection results by fusion. The experimental results prove that while ensuring a low missed detection rate, the false detection rate is greatly reduced. The comprehensive evaluation index Avg in this paper is improved by 3.4% compared with the shadow elimination algorithm based on a single feature. This paper proposes an image saliency detection algorithm and image detail enhancement algorithm based on multi-feature fusion. The final saliency map is obtained through linear fusion. Experiments prove that the image detail enhancement algorithm based on multi-feature fusion mentioned in this paper has achieved excellent results. In this paper, the performance of single feature fusion algorithm and multi-feature fusion algorithm are compared. The results show that the accuracy rate of multi-feature fusion algorithm based on HSV, LBP and LSFP is 93.39%, and the effect of multi-feature fusion is better than that of single feature.
Proposal Complementary Action Detection

Temporal action detection not only requires correct classification but also needs to detect the start and end times of each action accurately. However, traditional approaches always employ sliding windows or actionness to predict the actions, and it is different to train to model with sliding windows or actionness by end-to-end means. In this article, we attempt a different idea to detect the actions end-to-end, which can calculate the probabilities of actions directly through one network as one part of the results. We present PCAD, a novel proposal complementary action detector to deal with video streams under continuous, untrimmed conditions. Our approach first uses a simple fully 3D convolutional network to encode the video streams and then generates candidate temporal proposals for activities by using anchor segments. To generate more precise proposals, we also design a boundary proposal network to offer some complementary information for the candidate proposals. Finally, we learn an efficient classifier to classify the generated proposals into different activities and refine their temporal boundaries at the same time. Our model can achieve end-to-end training by jointly optimizing classification loss and regression loss. When evaluating on the THUMOS’14 detection benchmark, PCAD achieves state-of-the-art performance in high-speed models.
Quantum abstract detecting systems

In this paper, we study Quantum Abstract Detecting Systems (QADS), that generalize some key characteristics of the operators used in Grover’s algorithm, a wide variety of quantum walks and the quantum abstract search algorithm. A QADS is an algorithm that constructs a quantum state and a quantum operator that help testing whether a circuit-implemented boolean function f is identically zero. We also identify some relatively weak properties of QADS that lead to the construction of algorithms for the detection problem (i.e. determining whether there is a marked element in a given set). Our results provide not only a common framework to all the aforementioned search methods, and their transformation into algorithms for the detection problem, but also allow the development of new similar methods. As an example, we construct a modification of Grover’s algorithm (from the tensor product of controlled QADS) that shows improved detection probability.
Human body activity recognition using wearable inertial sensors integrated with a feature extraction–based machine-learning classification algorithm

This study employed wearable inertial sensors integrated with an activity-recognition algorithm to recognize six types of daily activities performed by humans, namely walking, ascending stairs, descending stairs, sitting, standing, and lying. The sensor system consisted of a microcontroller, a three-axis accelerometer, and a three-axis gyro; the algorithm involved collecting and normalizing the activity signals. To simplify the calculation process and to maximize the recognition accuracy, the data were preprocessed through linear discriminant analysis; this reduced their dimensionality and captured their features, thereby reducing the feature space of the accelerometer and gyro signals; they were then verified through the use of six classification algorithms. The new contribution is that after feature extraction, data classification results indicated that an artificial neural network was the most stable and effective of the six algorithms. In the experiment, 20 participants equipped the wearable sensors on their waists to record the aforementioned six types of daily activities and to verify the effectiveness of the sensors. According to the cross-validation results, the combination of linear discriminant analysis and an artificial neural network was the most stable classification algorithm for data generalization; its activity-recognition accuracy was 87.37% on the training data and 80.96% on the test data.
Application of Bayesian classifier to magnetic and gamma ray spectrometry data for targeting hydrocarbon microseepages

The search for new hydrocarbon resources is becoming increasingly complicated and expensive. Conventional geophysical methods, such as seismic, continue to focus on finding traps in deeper and more complex structures. To save significant amounts of both time and money, hydrocarbon microseepages may dramatically change the way we carry out hydrocarbon exploration by focusing more on searching petroleum deposits and by tracking their near-surface expressions. Hydrocarbon microseepages result in predominantly vertical long-term migration and it can be manifested with a number of near surface alterations such as formation of ferromagnetic minerals and modification of radioactive element concentrations in soil (potassium depletion and increase of the uranium concentration, in relation to the thorium content). In this study, airborne gamma ray spectrometry and magnetic data have been used in Hassi R'mel region (Algeria) to identify subtle variations in radioactive element concentrations and shallow magnetic anomalies induced by hydrocarbon microseepages. A Bayesian classification algorithm has been applied to characterize the residual magnetic and radiometric anomalies that occur above the known hydrocarbon reservoirs within the study area. Weights of evidence method was used to provide a quantitative measure of the spatial association between these anomalies and the mapped hydrocarbon accumulations. Most hydrocarbon areas were confirmed by the concurrent presence of the characteristic attributes of magnetic and radiometric anomalies, and those occurring outside the known hydrocarbon occurrences may point to yet unproven underlying hydrocarbon accumulations. The wells and drill core data, together with the blind wells available in the study area, validate the final results.
A comparison of preliminary test, Stein-type and penalty estimators in gamma regression model

Owing to the broad applicability of gamma regression, we propose some improved estimators based on the preliminary test and Stein-type strategies to estimate the unknown parameters in a gamma regression model. These estimators are considered when it is suspected that the parameters may be restricted to a subspace of the parameter space. Two penalty estimators such as LASSO and ridge regression are also presented. An asymptotic theory for the preliminary test and Stein-type estimators is developed, and asymptotic distributional bias and asymptotic quadratic risk of the proposed estimators are obtained. Comprehensive Monte-Carlo simulation experiments are conducted. Comparisons are then made based on simulated relative efficiency to clarify the performance of the proposed estimators. Practitioners are recommended to use the positive-part Stein-type estimator since its performance is robust irrespective of the reliability of the subspace information. A real data on prostate cancer is considered to illustrate the performance of the proposed estimators.
An Evaluation of Lower Facial Micro Expressions as an Implicit QoE Metric for an Augmented Reality Procedure Assistance Application

Augmented reality (AR) has been identified as a key technology to enhance worker utility in the context of increasing automation of repeatable procedures. AR can achieve this by assisting the user in performing complex and frequently changing procedures. Crucial to the success of procedure assistance AR applications is user acceptability, which can be measured by user quality of experience (QoE). An active research topic in QoE is the identification of implicit metrics that can be used to continuously infer user QoE during a multimedia experience. A user's QoE is linked to their affective state. Affective state is reflected in facial expressions. Emotions shown in micro facial expressions resemble those expressed in normal expressions but are distinguished from them by their brief duration. The novelty of this work lies in the evaluation of micro facial expressions as a continuous QoE metric by means of correlation analysis to the more traditional and accepted post-experience self-reporting. In this work, an optimal Rubik's Cube solver AR application was used as a proof of concept for complex procedure assistance. This was compared with a paper-based procedure assistance control. QoE expressed by affect in normal and micro facial expressions was evaluated through correlation analysis with post-experience reports. The results show that the AR application yielded higher task success rates and shorter task durations. Micro facial expressions reflecting disgust correlated moderately to the questionnaire responses for instruction disinterest in the AR application.
Towards Triclustering-Based Classification of Three-Way Clinical Data: A Case Study on Predicting Non-invasive Ventilation in ALS

The importance to learn disease progression patterns from longitudinal clinical data and use them effectively to improve prognosis, triggers the need for new approaches for three-way data analysis. In this context, triclustering has been widely researched for its potential in biomedical problems, showing promising results in the discovery of putative biological modules, patient profiles, and disease progression patterns. In this work, we propose a triclustering-based approach for three-way data classification, resulting from a combination of triclustering with random forests, and use it to predict the need for non-invasive ventilation in ALS patients. We analyse ALSFRS-R functional scores together with respiratory function tests collected from patient follow-up. The results are promising, enabling to understand the potential of triclustering and pinpointing improvements towards an effective triclustering-based classifier for clinical domains, taking advantage of the benefits of exploring disease progression patterns mined from three-way clinical data.
ERP systems and data security

Abstract Enterprise Resource Planning (ERP) integrates all business processes in the organization. Therefore, data security is the main point of security in ERP systems. Most of the biggest ERP developers have already implemented specialized hardware and software security solutions. New ITC technologies – cloud computing, IoT, blockchain are opportunities to make the ERP highly integrated, more intelligent, more flexible, cloud based. The fast growing of the e-business is making ERP systems more open and new decisions are needed to be granted information security. This paper introduces ERP technology regarding the data security point of view. New security solutions for ERP systems are present.
Emergent, situated and prospective ethics for child-computer interaction research

The increasing presence of interactive technologies in children's lives poses critical ethical questions for researchers and designers. Discourse specific to these intersecting topics is nascent, but is spread across communities and largely developed retrospectively. This workshop brings together those interested in ethical issues arising when researching, designing, and deploying technologies for children. The focus is on exploring approaches that are emergent and situated, arising during research or after deployment. Workshop activities will include: exploring ethical themes emerging in HCI research for children; synthesizing and adapting current applicable ethical guidance; identifying gaps; and developing preliminary methods and guidance to address these gaps. Outcomes will extend current best practices in ethics in ways that promote children's protection, empowerment and wellbeing.
Joint subspace and discriminative learning for self-paced domain adaptation

Unsupervised domain adaptation aims to address the problem in which the source data and target data are related but distributed differently. A widely-used two-stage strategy is to learn a domain-invariant subspace, and then train a cross-domain classifier on the resulting subspace. In this paper, we propose a single-stage domain adaption approach for joint subspace learning and discriminative learning. Specifically, a domain-invariant subspace and a cross-domain classifier are progressively learnt in a self-paced learning fashion. To avoid unlabeled target data dominating the overall loss and misleading model training, we progressively include more target data from "easy" to "complex" to optimize our model. Specifically, we propose an alternative optimization algorithm to efficiently find a reasonable solution for our task. Extensive experiments are conducted on multiple standard benchmarks to verify the effectiveness of the proposed approach. The results demonstrate that our model can outperform state-of-the-art non-deep domain adaptation methods.
Per-pixel classification of clouds from whole sky HDR images

Accurately identifying cloud types in images has multiple uses from meteorological science to computer graphics, especially as clouds are a major factor influencing atmospheric radiative transport. Understanding which cloud types are present in an image is typically performed on a coarse scale, where cloud types are identified per image, but do not permit a finer, per-pixel granularity of labelling cloud types. This paper presents a novel approach which solves this problem via a per-pixel classification method for identifying cloud types based on High Dynamic Range imagery of skies. The proposed method requires minimal labelling of the training data, and utilizes a hierarchical patch-based feature extraction technique which describes the statistical and structural features about regions of the image. This enables the extraction of representative feature vectors which are used for subsequent labelling. This approach is the first to produce a per-pixel classification of cloud types from a single image, with an accuracy of 84%. Additionally, when applied to whole sky cloud classification, our results produce a 98.3% accuracy, which is competitive with the state-of-the-art.
Learning Meta-Distance for Sequences by Learning a Ground Metric via Virtual Sequence Regression

Distance between sequences is structural by nature because it needs to establish the temporal alignments among the temporally correlated vectors in sequences with varying lengths. Generally, distances for sequences heavily depend on the ground metric between the vectors in sequences to infer the alignments and hence can be viewed as meta-distances upon the ground metric. Learning such meta-distance from multi-dimensional sequences is appealing but challenging. We propose to learn the meta-distance through learning a ground metric for the vectors in sequences. The learning samples are sequences of vectors for which how the ground metric between vectors induces the meta-distance is given. The objective is that the meta-distance induced by the learned ground metric produces large values for sequences from different classes and small values for those from the same class. We formulate the ground metric as a parameter of the meta-distance and regress each sequence to an associated pre-generated virtual sequence w.r.t. the meta-distance, where the virtual sequences for sequences of different classes are well-separated. We develop general iterative solutions to learn both the Mahalanobis metric and the deep metric induced by a neural network for any ground-metric-based sequence distance. Experiments on several sequence datasets demonstrate the effectiveness and efficiency of the proposed methods.
Review of Recent Eduinformatics Research

In 2018, we introduced “Eduinformatics,” a new field of education that combined both education and informatics. This article reviews recent eduinformatics research and considers the status and direction of future work in this particular field of education.
Evaluating the Effects of Non-Isomorphic Rotation on 3D Manipulation Tasks in Mixed Reality Simulation

As a hyper-natural interaction technique in 3D user interfaces, non-isomorphic rotation has been considered an effective approach for rotation tasks, where a static or dynamic control-display gain can be applied to amplify or attenuate a rotation. However, it is not clear whether non-isomorphic rotation can benefit 6-degree-of-freedom (6-DOF) manipulation tasks in AR and VR. In this article, we extended the usability studies of non-isomorphic rotation from rotation-only tasks to 6-DOF manipulation tasks and analyzed the collected data using a 2-component model. Using a mixed reality (MR) simulation approach, we also investigated whether environment (AR or VR) had an impact on 3D manipulation tasks. The results reveal that although both static and dynamic non-isomorphic rotation techniques could save time and effort in ballistic phases, only dynamic non-isomorphic rotation was significantly faster than isomorphic rotation. Interestingly, while environment had no significant impact on overall user performance, we found evidence that it could affect fine-tuning in correction phases. We also found that most participants preferred AR over VR, indicating that environmental visual realism could be helpful to improve user experience.
An Effective Approach for Selecting Cluster Centroids for the k-means Algorithm using IABC Approach

K-means is a popular grouping technique for unsupervised data. Though the technique is simple and potential it suffers from the ambiguity in the selection of k (number of clusters). The selection or initialization of k-cluster centroids significantly impacts the performance of the k-means. The distribution of data with the possibility of outliers may degrade the clustering strength. If the initialization of clustering is best fitted then the rest of the k-means process will save the execution time and can offer better clustering. A searching procedure is needed that learn the data under hand and decide the cluster initialization accordingly. ABC is a good technique for optimized search. In this paper, a study of different variants of ABC is undertaken in order to propose a new methodology named IABC. The methodology made use of bee intelligence for search optimization along with k-means and k-nearest neighborhood algorithms. The proposed approach adds the strength to the k-means approach in terms of optimum centroid initialization. Experiments are done on two popular datasets to compare the proposed approach with the existing techniques. The comparisons are made between the existing and proposed methods. Better and encouraging results are found. The proposed process is able to save a significant amount of time and can offer better and accurate clustering.
Efficient diabatic quantum algorithm in number factorization

Adiabatic quantum algorithm is developed to reduce the time complexity of a classical nondeterministic polynomial problem to a polynomial one. However, our numerical and analytical results reveal that the evolution time of the factorization with a standard adiabatic quantum algorithm increases exponentially with the system size. By introducing the expectation of practical computation time TE, we find the optimal TE on the single evolution time requires the quantum algorithm to work in the diabatic regime, contrast to the traditional adiabatic quantum algorithm.
Cloud and on-premises data center usage, expenditures, and approaches to return on investment: A survey of academic research computing organizations

The landscape of research in science and engineering is heavily reliant on computation and data processing. There is continued and expanded usage by disciplines that have historically used advanced computing resources, new usage by disciplines that have not traditionally used HPC, and new modalities of the usage in Data Science, Machine Learning, and other areas of AI. Along with these new patterns have come new advanced computing resource methods and approaches, including the availability of commercial cloud resources. The Coalition for Academic Scientific Computation (CASC) has long been an advocate representing the needs of academic researchers using computational resources, sharing best practices and offering advice to create a national cyberinfrastructure to meet US science, engineering, and other academic computing needs. CASC has completed the first of what we intend to be an annual survey of academic cloud and data center usage and practices in analyzing return on investment in cyberinfrastructure.