Reviewerly/exHarmony huggingface dataset:
A framework of pre-service teachers’ conceptions about digital literacy: Comparing the United States and Sweden

We examine the conceptions of digital literacy of pre-service teachers in the United States (n = 188) and Sweden (n = 121). Pre-service teachers were asked to define digital literacy in an open-ended fashion and to select those skills that they considered to be essential for digital literacy from a list of 24 skills provided. Based on pre-service teachers' open-ended responses, four profiles of digital literacy conceptions, progressing in sophistication, were identified (i.e., technology focused, digital reading focused, goal directed, reflecting critical use). Moreover, pre-service teachers’ selections of skills or competencies essential for digital literacy were used in cluster analysis. Profiles of digital literacy conceptions were consistent across open-ended and selected-response forms of assessment. Important similarities and differences in conceptions of digital literacy across the United States and Sweden are discussed, as are implications for improving teacher education.
Dirichlet‐multinomial modelling outperforms alternatives for analysis of microbiome and other ecological count data

Molecular ecology regularly requires the analysis of count data that reflect the relative abundance of features of a composition (e.g., taxa in a community, gene transcripts in a tissue). The sampling process that generates these data can be modelled using the multinomial distribution. Replicate multinomial samples inform the relative abundances of features in an underlying Dirichlet distribution. These distributions together form a hierarchical model for relative abundances among replicates and sampling groups. This type of Dirichlet-multinomial modelling (DMM) has been described previously, but its benefits and limitations are largely untested. With simulated data, we quantified the ability of DMM to detect differences in proportions between treatment and control groups, and compared the efficacy of three computational methods to implement DMM-Hamiltonian Monte Carlo (HMC), variational inference (VI), and Gibbs Markov chain Monte Carlo. We report that DMM was better able to detect shifts in relative abundances than analogous analytical tools, while identifying an acceptably low number of false positives. Among methods for implementing DMM, HMC provided the most accurate estimates of relative abundances, and VI was the most computationally efficient. The sensitivity of DMM was exemplified through analysis of previously published data describing lung microbiomes. We report that DMM identified several potentially pathogenic, bacterial taxa as more abundant in the lungs of children who aspirated foreign material during swallowing; these differences went undetected with different statistical approaches. Our results suggest that DMM has strong potential as a statistical method to guide inference in molecular ecology.
Altered visual feedback from an embodied avatar unconsciously influences movement amplitude and muscle activity

Abstract Evidence suggests that the sense of the position of our body parts can be surreptitiously deceived, for instance through illusory visual inputs. However, whether altered visual feedback during limb movement can induce substantial unconscious motor and muscular adjustments is not known. To address this question, we covertly manipulated virtual body movements in immersive virtual reality. Participants were instructed to flex their elbow to 90° while tensing an elastic band, as their virtual arm reproduced the same, a reduced (75°), or an amplified (105°) movement. We recorded muscle activity using electromyography, and assessed body ownership, agency and proprioception of the arm. Our results not only show that participants compensated for the avatar’s manipulated arm movement while being completely unaware of it, but also that it is possible to induce unconscious motor adaptations requiring significant changes in muscular activity. Altered visual feedback through body ownership illusions can influence motor performance in a process that bypasses awareness.
Combining of Multiple Deep Networks via Ensemble Generalization Loss, Based on MRI Images, for Alzheimer's Disease Classification

This letter proposes a novel way of using an ensemble of multiple deep convolutional neural networks (DCNNs) for Alzheimer's disease classification, based on magnetic resonance imaging (MRI) images. To create this ensemble of DCNNs, we propose to combine the use of multiple MRI projections (as input) with that of different DCNN architectures to increase the deep ensemble diversity. In particular, to find the optimal fusion weights of the DCNN members, we designed a novel deep ensemble generalization loss, which accounts for interaction and cooperation during the optimal weight search. The optimization framework, equipped with our ensemble generalization loss, was formulated and solved using the sequential quadratic programming. Through this method, we achieved optimal DCNN fusion weights (i.e., a high generalization performance). The experimental results showed that our proposed DCNN ensemble outperforms current deep learning-based methods: it is able to produce state-of-the-art results on the Alzheimer's disease neuroimaging initiative (ADNI) dataset.
Research on Small Target Detection in Driving Scenarios Based on Improved Yolo Network

The obtainment of road condition information during driving is extremely important for a driver. However, drivers usually cannot notice multiple information at the same time, which definitely increases certain safety risks. Considering this problem, this paper designs a road information collection plus alarm system based on artificial intelligence to monitor road information. The underlying core algorithm of this system adopts the YOLO v3 network with the best comprehensive detection performance in the end-to-end network. We use this network’s advantage of fast detection speed to optimize on its original basis, and propose to “copy” part of the backbone network to build an auxiliary network, which enhances its feature extraction capability. Further, we apply the attention mechanism to the feature information fusion of the auxiliary network and the backbone network, suppress the invalid information channel, and improve the network processing efficiency. Besides, the training part of the network is optimized, and the mAP (mean Average Precision) is improved by setting the scale that meets the target to be detected. Through the test, the average test accuracy of the optimized network model reaches 84.76%, and the real-time detection speed on the 2080Ti reaches 41FPS. Compared with the previous network, the detection accuracy increases by 5.43% after optimization.
DCAD: Decentralized Collision Avoidance With Dynamics Constraints for Agile Quadrotor Swarms

We present DCAD, a novel, decentralized collision avoidance algorithm for navigating a swarm of quadrotors in dense environments populated with static and dynamic obstacles. Our algorithm relies on the concept of Optimal Reciprocal Collision Avoidance (ORCA) and utilizes a flatness-based Model Predictive Control (MPC) to generate local collision-free trajectories for each quadrotor. We feedforward linearize the non-linear dynamics of the quadrotor and subsequently use this linearized model in our MPC framework. Our approach tends to compute safe trajectories that avoid quadrotors from entering each other's downwash regions during close proximity maneuvers. In addition, we account for the uncertainty in the position and velocity sensor data using Kalman filter. We evaluate the performance of our algorithm with other state-of-the-art decentralized methods and demonstrate its superior performance in terms of smoothness of generated trajectories and lower probability of collision during high-velocity maneuvers.
PWStableNet: Learning Pixel-Wise Warping Maps for Video Stabilization

As the videos captured by hand-held cameras are often perturbed by high-frequency jitters, stabilization of these videos is an essential task. Many video stabilization methods have been proposed to stabilize shaky videos. However, most methods estimate one global homography or several homographies based on fixed meshes to warp the shaky frames into their stabilized views. Due to the existence of parallax, such single or a few homographies can not well handle the depth variation. In contrast to these traditional methods, we propose a novel video stabilization network, called PWStableNet, which comes up pixel-wise warping maps, i.e., potentially different warping for different pixels, and stabilizes each pixel to its stabilized view. To our best knowledge, this is the first deep learning based pixel-wise video stabilization. The proposed method is built upon a multi-stage cascade encoder-decoder architecture and learns pixel-wise warping maps from consecutive unstable frames. Inter-stage connections are also introduced to add feature maps of a former stage to the corresponding feature maps at a latter stage, which enables the latter stage to learn the residual from the feature maps of former stages. This cascade architecture can produce more precise warping maps at latter stages. To ensure the correct learning of pixel-wise warping maps, we use a well-designed loss function to guide the training procedure of the proposed PWStableNet. The proposed stabilization method achieves comparable performance with traditional methods, but stronger robustness and much faster processing speed. Moreover, the proposed stabilization method outperforms some typical CNN-based stabilization methods, especially in videos with strong parallax. Codes will be provided at https://github.com/mindazhao/pix-pix-warping-video-stabilization.
Machine learning explainability via microaggregation and shallow decision trees

Artificial intelligence (AI) is being deployed in missions that are increasingly critical for human life. To build trust in AI and avoid an algorithm-based authoritarian society, automated decisions should be explainable. This is not only a right of citizens, enshrined for example in the European General Data Protection Regulation, but a desirable goal for engineers, who want to know whether the decision algorithms are capturing the relevant features. For explainability to be scalable, it should be possible to derive explanations in a systematic way. A common approach is to use simpler, more intuitive decision algorithms to build a surrogate model of the black-box model (for example a deep learning algorithm) used to make a decision. Yet, there is a risk that the surrogate model is too large for it to be really comprehensible to humans. We focus on explaining black-box models by using decision trees of limited depth as a surrogate model. Specifically, we propose an approach based on microaggregation to achieve a trade-off between the comprehensibility and the representativeness of the surrogate model on the one side and the privacy of the subjects used for training the black-box model on the other side.
Touching and Being in Touch with the Menstruating Body

We describe a Research through Design project-Curious Cycles-a collection of objects and interactions which encourage people to be in close contact with their menstruating body. Throughout a full menstrual cycle, five participants used Curious Cycles to look at their bodies in unfamiliar ways and to touch their bodily fluids, specifically, menstrual blood, saliva, and cervical mucus. The act of touching and looking led to the construction of new knowledge about the self and to a nurturing appreciation for the changing body. Yet, participants encountered and reflected upon frictions within themselves, their home, and their social surroundings, which stem from societal stigma and preconceptions about menstruation and bodily fluids. We call for and show how interaction design can engage with technologies that mediate self-touch as a first step towards reconfiguring the way menstruating bodies are treated in society.
Nature-inspired metaheuristic techniques for automatic clustering: a survey and performance study

The application of several swarm intelligence and evolutionary metaheuristic algorithms in data clustering problems has in the past few decades gained wide popularity and acceptance due to their success in solving and finding good quality solutions to a variety of complex real-world optimization problems. Clustering is considered one of the most important data analysis techniques in the domain of data mining. A clustering problem refers to the partitioning of unlabeled data objects into a certain number of clusters based on their attribute values or features, with the objective of maximizing intra-clusters homogeneity and inter-cluster heterogeneity. This paper presents an up-to-date survey of major nature-inspired metaheuristic algorithms that have been employed to solve automatic clustering problems. Further, a comparative study of several modified well-known global metaheuristic algorithms is carried out to solve automatic clustering problems. Also, three hybrid swarm intelligence and evolutionary algorithms, namely, particle swarm differential evolution algorithm, firefly differential evolution algorithm and invasive weed optimization differential evolution algorithm, are proposed to deal with the task of automatic data clustering. In contrast to many of the existing traditional and evolutionary computational clustering techniques, the clustering algorithms presented in this paper do not require any predetermined information or prior-knowledge of the dataset that is to be classified, but rather they are capable of spontaneously identifying the optimal number of partitions of the data points during the course of program execution. Forty-one benchmarked datasets that comprise eleven artificial and thirty real world datasets are collated and utilized to evaluate the performances of the representative nature-inspired clustering algorithms. According to the extensive experimental results, comparisons and statistical significance, the firefly algorithm appeared to be more appropriate for better clustering of both low and high dimensional data objects than were other state-of-the-art algorithms. Further, an experimental study demonstrates the superiority of the three proposed hybrid algorithms over the standard state-of-the-art methods in finding meaningful clustering solutions to the problem at hand.
An Analysis of Phishing Blacklists: Google Safe Browsing, OpenPhish, and PhishTank

Blacklists play a vital role in protecting internet users against phishing attacks. The effectiveness of blacklists depends on their size, scope, update speed and frequency, and accuracy - among other characteristics. In this paper we present a measurement study that analyses 3 key phishing blacklists: Google Safe Browsing (GSB), OpenPhish (OP), and PhishTank (PT). We investigate the uptake, dropout, typical lifetimes, and overlap of URLs in these blacklists.
Streaming Random Patches for Evolving Data Stream Classification

Ensemble methods are a popular choice for learning from evolving data streams. This popularity is due to (i) the ability to simulate simple, yet, successful ensemble learning strategies, such as bagging and random forests; (ii) the possibility of incorporating drift detection and recovery in conjunction to the ensemble algorithm; (iii) the availability of efficient incremental base learners, such as Hoeffding Trees. In this work, we introduce the Streaming Random Patches (SRP) algorithm, an ensemble method specially adapted to stream classification which combines random subspaces and online bagging. We provide theoretical insights and empirical results illustrating different aspects of SRP. In particular, we explain how the widely adopted incremental Hoeffding trees are not, in fact, unstable learners, unlike their batch counterparts, and how this fact significantly influences ensemble methods design and performance. We compare SRP against state-of-the-art ensemble variants for streaming data in a multitude of datasets. The results show how SRP produce a high predictive performance for both real and synthetic datasets. Besides, we analyze the diversity over time and the average tree depth, which provides insights on the differences between local subspace randomization (as in random forest) and global subspace randomization (as in random subspaces).
Multi-View Mammographic Density Classification by Dilated and Attention-Guided Residual Learning

Breast density is widely adopted to reflect the likelihood of early breast cancer development. Existing methods of mammographic density classification either require steps of manual operations or achieve only moderate classification accuracy due to the limited model capacity. In this study, we present a radiomics approach based on dilated and attention-guided residual learning for the task of mammographic density classification. The proposed method was instantiated with two datasets, one clinical dataset and one publicly available dataset, and classification accuracies of 88.7 and 70.0 percent were obtained, respectively. Although the classification accuracy of the public dataset was lower than the clinical dataset, which was very likely related to the dataset size, our proposed model still achieved a better performance than the naive residual networks and several recently published deep learning-based approaches. Furthermore, we designed a multi-stream network architecture specifically targeting at analyzing the multi-view mammograms. Utilizing the clinical dataset, we validated that multi-view inputs were beneficial to the breast density classification task with an increase of at least 2.0 percent in accuracy and the different views lead to different model classification capacities. Our method has a great potential to be further developed and applied in computer-aided diagnosis systems. Our code is available at https://github.com/lich0031/Mammographic_Density_Classification.
Region-Enhanced Convolutional Neural Network for Object Detection in Remote Sensing Images

The convolutional neural networks (CNNs) have recently demonstrated to be a powerful tool for object detection. However, with the complex scenes in remote sensing images, feature extraction of the object in the CNN will be seriously affected by background information. To address this issue, in this article, a region-enhanced CNN (RECNN) is proposed for the object detection of remote sensing images. The RECNN introduces the saliency constraint and multilayer fusion strategy into the CNN model, which can effectively enhance the object regions for better detection. Specifically, the saliency map is extracted and utilized to guide the training of the proposed model to strengthen saliency regions in feature maps. In addition, since different layers can reflect the object regions in varied resolutions, a multilayer fusion strategy is introduced to connect different convolutional layers and explore the context, where the feature maps of object regions are further enhanced. Experimental results on a publicly available ten-class object detection data set demonstrate the superiority of the RECNN over several competitive object detection methods.
Traceability in Permissioned Blockchain

In this paper, we have achieved privacy protection and high transparency in a permissioned blockchain.There is a sidechain that connects the permissionless blockchain and the permissioned blockchain.The behavior in the permissioned blockchain is almost a black box from the perspective of the permissionless blockchain.While this fact is useful for privacy protection, there is room for improvement in terms of transparency.To improve the transparency of the permissioned blockchain under privacy protection, we consider traceability in the permissioned blockchain consisting of the following three properties: trade privacy (who trades with whom and at what asset amount), preservation (the total amount inside the permissioned blockchain, including deposits and withdrawals to the permissionless blockchain, is immutable), and noninvolvement (some members in the permissioned blockchain are not involved in some trades, and it is possible to prove that specified members performed the transaction).To the best of our knowledge, we are the first to achieve both preservation and noninvolvement while protecting the privacy of transactions.Our approach is as follows.We model traceability based on the hidden Markov model.Because the proof of traceability requires the calculation of more than quadratic degrees, we encrypt this model by homomorphic encryption.The number of participants in the permissioned blockchain corresponds to the number of additions in the model.Then, we can construct the encrypted model by employing somewhat homomorphic encryption.The establishment of the original model is verifiable by applying the noninteractive zero-knowledge proof of the knowledge that the plaintext is equal to zero.This is an adaptation of Benhamouda et al. (Asiacrypt 2014).
Performance modeling and analysis of a Hyperledger-based system using GSPN

As a highly scalable permissioned blockchain platform, Hyperledger Fabric supports a wide range of industry use cases ranging from governance to finance. In this paper, we propose a model to analyze the performance of a Hyperledger-based system by using Generalized Stochastic Petri Nets (GSPN). This model decomposes a transaction flow into multiple phases and provides a simulation-based approach to obtain the system latency and throughput with a specific arrival rate. Based on this model, we analyze the impact of different configurations of ordering service on system performance to find out the bottleneck. Moreover, a mathematical configuration selection approach is proposed to determine the best configuration which can maximize the system throughput. Finally, extensive experiments are performed on a real-time system to validate the proposed model and approaches.
Competitive Data Trading Model With Privacy Valuation for Multiple Stakeholders in IoT Data Markets

With the widespread of Internet of Things (IoT) environment, a big data concept has emerged to handle a large number of data generated by IoT devices.Moreover, since datadriven approaches now become important for business, IoT data markets have emerged, and IoT big data are exploited by major stakeholders such as data brokers and data service providers.Since many services and applications utilize data analytic methods with collected data from IoT devices, the conflict issues between privacy and data exploitation are raised, and the markets are mainly categorized as privacy protection markets and privacy valuation markets, respectively.Since these kinds of data value chains (which are mainly considered by business stakeholders) are revealed, data providers are interested in proper incentives in exchange for their privacy (i.e., privacy valuation) under their agreement.Therefore, this paper proposes a competitive data trading model that consists of data providers who weigh the value between privacy protection and valuation as well as other business stakeholders.Each data broker considers the willingness-to-sell of data providers, and a single data service provider considers the willingness-to-pay of service consumers.At the same time, multiple data brokers compete to sell their dataset to the data service provider as a non-cooperative game model.Based on the Nash Equilibrium analysis (NE) of the game, the feasibility is shown that the proposed model has the unique NE that maximizes the profits of business stakeholders while satisfying all market participants.