Miracle-dz/UniFAR-FTUs huggingface dataset:
A Critical Review of the Effectiveness of Narrative-Driven Digital Educational Games

Digital games are currently viewed, by many within the field of education, as a way to engage and motivate students, and to assist them in acquiring content knowledge and skills. Despite the growing interest in using digital games, including serious games, this is the first critical review of the literature on the effectiveness of digital narrative-driven educational games. Of 2550 articles initially screened, 130 were synthesised in the final review. The results suggest that such games can be more much more effective than traditional instruction for promoting attitude change, engagement, motivation, and skill acquisition; slightly more effective in promoting enjoyment and knowledge acquisition; and equal in fostering behaviour change. As such, they may be viewed as effective and versatile tools for teaching and learning.
General-to-Detailed GAN for Infrequent Class Medical Images

Deep learning has significant potential for medical imaging. However, since the incident rate of each disease varies widely, the frequency of classes in a medical image dataset is imbalanced, leading to poor accuracy for such infrequent classes. One possible solution is data augmentation of infrequent classes using synthesized images created by Generative Adversarial Networks (GANs), but conventional GANs also require certain amount of images to learn. To overcome this limitation, here we propose General-to-detailed GAN (GDGAN), serially connected two GANs, one for general labels and the other for detailed labels. GDGAN produced diverse medical images, and the network trained with an augmented dataset outperformed other networks using existing methods with respect to Area-Under-Curve (AUC) of Receiver Operating Characteristic (ROC) curve.
Human Mesenchymal Stem Cell Therapy Reverses Su5416/Hypoxia-Induced Pulmonary Arterial Hypertension in Mice

Aims: Pulmonary arterial hypertension (PAH) is a disease characterized by an increase in pulmonary vascular resistance and right ventricular (RV) failure. We aimed to determine the effects of human mesenchymal stem cell (hMSC) therapy in a SU5416/hypoxia (SuH) mice model of PAH. Methods and Results: C57BL/6 mice (20–25 g) were exposure to 4 weeks of hypoxia combined vascular endothelial growth factor receptor antagonism (20 mg/kg SU5416; weekly s.c. injections; PAH mice). Control mice were housed in room air. Following 2 weeks of SuH exposure, we injected 5 × 105 hMSCs cells suspended in 50 μL of vehicle (0.6 U/mL DNaseI in PBS) through intravenous injection in the caudal vein. PAH mice were treated only with vehicle. Ratio between pulmonary artery acceleration time and RV ejection time (PAAT/RVET), measure by echocardiography, was significantly reduced in the PAH mice, compared with controls, and therapy with hMSCs normalized this. Significant muscularization of the PA was observed in the PAH mice and hMSC reduced the number of fully muscularized vessels. RV free wall thickness was higher in PAH animals than in the controls, and a single injection of hMSCs reversed RV hypertrophy. Levels of markers of exacerbated apoptosis, tissue inflammation and damage, cell proliferation and oxidative stress were significantly greater in both lungs and RV tissues from PAH group, compared to controls. hMSC injection in PAH animals normalized the expression of these molecules which are involved with PAH and RV dysfunction development and the state of chronicity. Conclusion: These results indicate that hMSCs therapy represents a novel strategy for the treatment of PAH in the future.
Self-learning Local Supervision Encoding Framework to Constrict and Disperse Feature Distribution for Clustering

To obtain suitable feature distribution is a difficult task in machine learning, especially for unsupervised learning. In this paper, we propose a novel self-learning local supervision encoding framework based on RBMs, in which the self-learning local supervisions from visible layer are integrated into the contrastive divergence (CD) learning of RBMs to constrict and disperse the distribution of the hidden layer features for clustering tasks. In the framework, we use sigmoid transformation to obtain hidden layer and reconstructed hidden layer features from visible layer and reconstructed visible layer units during sampling procedure. The self-learning local supervisions contain local credible clusters which stem from different unsupervised learning and unanimous voting strategy. They are fused into hidden layer features and reconstructed hidden layer features. For the same local clusters, the hidden features and reconstructed hidden layer features of the framework tend to constrict together. Furthermore, the hidden layer features of different local clusters tend to disperse in the encoding process. Under such framework, we present two instantiation models with the reconstruction of two different visible layers. One is self-learning local supervision GRBM (slsGRBM) model with Gaussian linear visible units and binary hidden units using linear transformation for visible layer reconstruction. The other is self-learning local supervision RBM (slsRBM) model with binary visible and hidden units using sigmoid transformation for visible layer reconstruction.
Unified Analysis of HetNets Using Poisson Cluster Processes Under Max-Power Association

Owing to its flexibility in modeling real-world spatial configurations of users and base stations (BSs), the Poisson cluster process (PCP) has recently emerged as an appealing way to model and analyze heterogeneous cellular networks (HetNets). Despite its undisputed relevance to HetNets—corroborated by the models used in the industry—the PCP’s use in performance analysis has been limited. This is primarily because of the lack of analytical tools to characterize the performance metrics, such as the coverage probability of a user connected to the strongest BS. In this paper, we develop an analytical framework for the evaluation of the coverage probability, or equivalently the complementary cumulative density function (CCDF) of signal-to-interference-and-noise ratio (<italic>SINR</italic>), of a typical user in a <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-tier HetNet under a <inline-formula> <tex-math notation="LaTeX">$\max $ </tex-math></inline-formula> power-based association strategy, where the BS locations of each tier follow either a Poisson point process (PPP) or a PCP. The key enabling step involves conditioning on the parent PPPs of all the PCPs, which allows us to express the coverage probability as a product of sum-product and probability generating functionals (PGFLs) of the parent PPPs. In addition to several useful insights, our analysis provides a rigorous way to study the impact of the cluster size on the <inline-formula> <tex-math notation="LaTeX">${\it SINR}$ </tex-math></inline-formula> distribution, which was not possible using the existing PPP-based models.
Cooperative Multi-Agent Policy Gradients with Sub-optimal Demonstration

Many reality tasks such as robot coordination can be naturally modelled as multi-agent cooperative system where the rewards are sparse. This paper focuses on learning decentralized policies for such tasks using sub-optimal demonstration. To learn the multi-agent cooperation effectively and tackle the sub-optimality of demonstration, a self-improving learning method is proposed: On the one hand, the centralized state-action values are initialized by the demonstration and updated by the learned decentralized policy to improve the sub-optimality. On the other hand, the Nash Equilibrium are found by the current state-action value and are used as a guide to learn the policy. The proposed method is evaluated on the combat RTS games which requires a high level of multi-agent cooperation. Extensive experimental results on various combat scenarios demonstrate that the proposed method can learn multi-agent cooperation effectively. It significantly outperforms many state-of-the-art demonstration based approaches.
Unsupervised Generation of Optical Flow Datasets from Videos in the Wild

Dense optical flow ground truth of non-rigid motion for real-world images are not available due to the non-intuitive annotation. Aiming at training optical flow deep networks, we present an unsupervised algorithm to generate optical flow ground truth from real-world videos. The algorithm extracts and matches objects of interest from pairs of images in videos to find initial constraints, and applies as-rigid-as-possible deformation over the objects of interest to obtain dense flow fields. The ground truth correctness is enforced by warping the objects in the first frames using the flow fields. We apply the algorithm on the DAVIS dataset to obtain optical flow ground truths for non-rigid movement of real-world objects, using either ground truth or predicted segmentation. We discuss several methods to increase the optical flow variations in the dataset. Extensive experimental results show that training on non-rigid real motion is beneficial compared to training on rigid synthetic data. Moreover, we show that our pipeline generates training data suitable to train successfully FlowNet-S, PWC-Net, and LiteFlowNet deep networks.
Short‐term, low‐level nitrogen deposition dampens a trophic cascade between bears and plants

Abstract Human activities have substantially increased atmospheric nitrogen (N) deposition in ecosystems worldwide, often leading to higher plant quality for herbivores and greater herbivory. Predators frequently suppress herbivores and indirectly benefit plants via “trophic cascades”, and the strength of these interactions can also depend on N availability. However, the evidence for N deposition effects on cascades primarily comes from studies of high‐level N deposition. Most terrestrial ecosystems currently receive elevated, but low‐level N deposition, and it is unclear whether this subtle N enrichment has any effect on cascades. Here, I asked whether low‐level N deposition alters a trophic cascade from black bears to plants in Colorado. In this ecological network, bears indirectly benefit plants by consuming ants and suppressing positive effects of ants on herbivores. Using a three year N enrichment experiment, I assessed changes in this cascade by measuring plant and arthropod responses to simulated N deposition, bear damage to ant nests, and the presence of mutualist herbivores and ants. I found that low‐level N enrichment and bears had interacting effects on plant reproduction. In ambient N conditions, bears indirectly increased plant reproduction by causing ant nests to become inactive and suppressing positive ant effects on herbivores that were detrimental for plants. Yet, bear‐induced ant nest inactivity had no effect on plant reproduction in N‐enriched conditions. When N was added, ants had greater positive effects on herbivores, but herbivores had weak effects on plants, potentially because plants were more resistant to herbivores. Ultimately, the results indicate that N enrichment strengthened resource control of the community and weakened plant–herbivore interactions and the cascade from bears to plants. This study suggests that common rates of low‐level N deposition are changing the strength of trophic cascades and may have already altered resource versus consumer control of ecological community structure in many ecosystems.
Wireless Access to Ultimate Virtual Reality 360-Degree Video At Home

Virtual reality 360-degree videos will become the first prosperous online VR application. VR 360 videos are data-hungry and latency-sensitive that pose unique challenges to the networking infrastructure. In this paper, we focus on the ultimate VR 360 that satisfies human eye fidelity. The ultimate VR 360 requires downlink 1.5 Gbps for viewing and uplink 6.6 Gbps for live broadcasting, with round-trip time of less than 8.3 ms. On the other hand, wireless access to VR 360 services is preferred over wire-line transmission because of the better user experience and the safety concern (e.g., tripping hazard). We explore in this paper whether the most advanced wireless technologies from both cellular communications and WiFi communications support the ultimate VR 360. Specifically, we consider 5G in cellular communications, IEEE 802.11ac (operating in 5GHz) and IEEE 802.11ad (operating in 60GHz) in WiFi communications. According to their performance specified in their standards and/or empirical measurements, we have the following findings: (1) Only 5G has the potential to support both the the ultimate VR 360 viewing and live broadcasting. However, it is difficult for 5G to support multiple users of the ultimate VR live broadcasting at home; (2) IEEE 802.11ac supports the ultimate VR 360 viewing but fails to support the ultimate VR 360 live broadcasting because it does not meet the data rate requirement of the ultimate VR 360 live broadcasting; (3) IEEE 802.11ad fails to support the ultimate VR 360, because its current implementation incurs very high latency. Our preliminary results indicate that more advanced wireless technologies are needed to fully support multiple ultimate VR 360 users at home.
LEAP+: Efficient security mechanisms for large-scale distributed sensor networks

We describe LEAP+ (Localized Encryption and Authentication Protocol), a key management protocol for sensor networks that is designed to support in-network processing, while at the same time restricting the security impact of a node compromise to the immediate network neighborhood of the compromised node. The design of the protocol is motivated by the observation that different types of messages exchanged between sensor nodes have different security requirements, and that a single keying mechanism is not suitable for meeting these different security requirements. LEAP+ supports the establishment of four types of keys for each sensor node: an individual key shared with the base station, a pairwise key shared with another sensor node, a cluster key shared with multiple neighboring nodes, and a global key shared by all the nodes in the network. LEAP+ also supports (weak) local source authentication without precluding in-network processing. Our performance analysis shows that LEAP+ is very efficient in terms of computational, communication, and storage costs. We analyze the security of LEAP+ under various attack models and show that LEAP+ is very effective in defending against many sophisticated attacks, such as HELLO flood attacks, node cloning attacks, and wormhole attacks. A prototype implementation of LEAP+ on a sensor network testbed is also described.
Learning to generate filters for convolutional neural networks

Conventionally, convolutional neural networks (CNNs) process different images with the same set of filters. However, the variations in images pose a challenge to this fashion. In this paper, we propose to generate sample-specific filters for convolutional layers in the forward pass. Since the filters are generated on-the-fly, the model becomes more flexible and can better fit the training data compared to traditional CNNs. In order to obtain sample-specific features, we extract the intermediate feature maps from an autoencoder. As filters are usually high dimensional, we propose to learn a set of coefficients instead of a set of filters. These coefficients are used to linearly combine the base filters from a filter repository to generate the final filters for a CNN. The proposed method is evaluated on MNIST, MTFL and CIFAR10 datasets. Experiment results demonstrate that the classification accuracy of the baseline model can be improved by using the proposed filter generation method.
Listening through hearing aids affects spatial perception and speech intelligibility in normal-hearing listenersa)

Cubick and Dau [(2016). Acta Acust. Acust. 102, 547-557] showed that speech reception thresholds (SRTs) in noise, obtained with normal-hearing listeners, were significantly higher with hearing aids (HAs) than without. Some listeners reported a change in their spatial perception of the stimuli due to the HA processing, with auditory images often being broader and closer to the head or even internalized. The current study investigated whether worse speech intelligibility with HAs might be explained by distorted spatial perception and the resulting reduced ability to spatially segregate the target speech from the interferers. SRTs were measured in normal-hearing listeners with or without HAs in the presence of three interfering talkers or speech-shaped noises. Furthermore, listeners were asked to sketch their spatial perception of the acoustic scene. Consistent with the previous study, SRTs increased with HAs. Spatial release from masking was lower with HAs than without. The effects were similar for noise and speech maskers and appeared to be accounted for by changes to energetic masking. This interpretation was supported by results from a binaural speech intelligibility model. Even though the sketches indicated a change of spatial perception with HAs, no direct link between spatial perception and segregation of talkers could be shown.
Multiview Cross-supervision for Semantic Segmentation

This paper presents a semi-supervised learning framework for a customized semantic segmentation task using multiview image streams. A key challenge of the customized task lies in the limited accessibility of the labeled data due to the requirement of prohibitive manual annotation effort. We hypothesize that it is possible to leverage multiview image streams that are linked through the underlying 3D geometry, which can provide an additional supervisionary signal to train a segmentation model. We formulate a new cross-supervision method using a shape belief transfer---the segmentation belief in one image is used to predict that of the other image through epipolar geometry analogous to shape-from-silhouette. The shape belief transfer provides the upper and lower bounds of the segmentation for the unlabeled data where its gap approaches asymptotically to zero as the number of the labeled views increases. We integrate this theory to design a novel network that is agnostic to camera calibration, network model, and semantic category and bypasses the intermediate process of suboptimal 3D reconstruction. We validate this network by recognizing a customized semantic category per pixel from realworld visual data including non-human species and a subject of interest in social videos where attaining large-scale annotation data is infeasible.
Performance Assessment of Multiple Classifiers Based on Ensemble Feature Selection Scheme for Sentiment Analysis

Sentiment classification or sentiment analysis has been acknowledged as an open research domain. In recent years, an enormous research work is being performed in these fields by applying various numbers of methodologies. Feature generation and selection are consequent for text mining as the high-dimensional feature set can affect the performance of sentiment analysis. This paper investigates the inability or incompetency of the widely used feature selection methods (IG, Chi-square, and Gini Index) with unigram and bigram feature set on four machine learning classification algorithms (MNB, SVM, KNN, and ME). The proposed methods are evaluated on the basis of three standard datasets, namely, IMDb movie review and electronics and kitchen product review dataset. Initially, unigram and bigram features are extracted by applying n-gram method. In addition, we generate a composite features vector CompUniBi (unigram + bigram), which is sent to the feature selection methods Information Gain (IG), Gini Index (GI), and Chi-square (CHI) to get an optimal feature subset by assigning a score to each of the features. These methods offer a ranking to the features depending on their score; thus a prominent feature vector (CompIG, CompGI, and CompCHI) can be generated easily for classification. Finally, the machine learning classifiers SVM, MNB, KNN, and ME used prominent feature vector for classifying the review document into either positive or negative. The performance of the algorithm is measured by evaluation methods such as precision, recall, and F-measure. Experimental results show that the composite feature vector achieved a better performance than unigram feature, which is encouraging as well as comparable to the related research. The best results were obtained from the combination of Information Gain with SVM in terms of highest accuracy.
Trends and Drivers of Inpatient Antibiotic Consumption among 89 China Tertiary General Hospitals from 2011Q1 to 2015Q4

Antibacterial surveillance is an essential measure for strengthening the management of clinical antibiotic use. This study aimed to determine the trends and drivers of inpatient antibiotic consumption in China. A sample of 89 hospitals with complete data from 2011Q1 to 2015Q4 was included. Accumulative defined daily doses (DDDs), antibiotic use density (AUD), and drug variety were calculated to evaluate antibiotic consumption. From 2011Q1 to 2015Q4, the median values of DDDs, AUD, and drug variety dropped by 10.49%, 39.19%, and 27.96%, respectively. Panel regression results showed, for each additional quarter, DDDs reduced by 6.714 DDDs, AUD reduced by 0.013 DDDs per 100 inpatients per day, and drug variety reduced by 0.012 types (p < 0.001). National hospitals were more likely to use antibiotics, with the highest number of DDDs (106 709 DDDs) and AUD (60 DDDs per 100 inpatients per day) and a large number of drug variety (71 types of drug) all reported from national hospitals. Overall, a downward trend of inpatient antibiotic consumption was observed in competitive tertiary general hospitals in China. However, antibiotic use in China, especially in national hospitals, continues to exceed the guidelines set forth by the nationwide antibiotic stewardship program. China must continue to improve surveillance of antibiotic consumption by constructing a more comprehensive, continuous, and targeted stewardship program. Policy interventions in China should be made in consideration of unbalanced regional development and the consequences this may have on antibiotic consumption.
Point-To-Pose Voting Based Hand Pose Estimation Using Residual Permutation Equivariant Layer

Recently, 3D input data based hand pose estimation methods have shown state-of-the-art performance, because 3D data capture more spatial information than the depth image. Whereas 3D voxel-based methods need a large amount of memory, PointNet based methods need tedious preprocessing steps such as K-nearest neighbour search for each point. In this paper, we present a novel deep learning hand pose estimation method for an unordered point cloud. Our method takes 1024 3D points as input and does not require additional information. We use Permutation Equivariant Layer (PEL) as the basic element, where a residual network version of PEL is proposed for the hand pose estimation task. Furthermore, we propose a voting-based scheme to merge information from individual points to the final pose output. In addition to the pose estimation task, the voting-based scheme can also provide point cloud segmentation result without ground-truth for segmentation. We evaluate our method on both NYU dataset and the Hands2017Challenge dataset, where our method outperforms recent state-of-theart methods.
Toward Exploratory Search in Biomedicine: Evaluating Document Clusters by MeSH as a Semantic Anchor

The current mode of biomedical literature search is severely limited in effectively finding information relevant to specialists. A potential approach to solving this problem is exploratory search, which allows users to interactively navigate through a vast document collection. As the first step toward exploratory search for specialists in biomedicine, this paper develops a methodology to evaluate quality of document clusters. For this purpose, we incorporate human expertise into data set creation and evaluation framework by leveraging MeSH terms as semantic anchors. In addition, we investigate the benefit of full-text data for improving cluster quality.