Reviewerly/exHarmony huggingface dataset:
Parental perception and English Learners' mobile-assisted language learning: An ethnographic case study from a technology-based Funds of Knowledge approach

The purpose of this ethnographic case study is to examine the parents' perceptions regarding young English Learners' (ELs) mobile-assisted language learning (MALL) experiences from a technology-based Funds of Knowledge (FoK) approach. Six immigrant families in the United States from four countries (including China, Mexico, Japan, and Cambodia) were home-visited with in-depth semi-structured interviews. Participant observations, field notes, analytical memos, and a descriptive survey were used to triangulate the data. By centering on ELs' technological households' cultural sources, parents' perceptions of ELs' MALL experiences were explored through motivation, physical and material access, digital skills, and usage frequency and diversity of mobile applications. Results demonstrated that the parents were motivated to support ELs' language learning using mobile technology. Analysis revealed differences in technology access and appropriation based on diverse cultural backgrounds and socioeconomic status. Findings also indicated the inequity in the current digital divide and informed future studies on creating FoK-featured curricula to contribute to ELs' language education through mobile technology. This study extends the current MALL horizon from the front line of specific student-centered learning experiences to further examine the facilitating factors and barriers that hide within a multicultural learning environment.
Dynamical Isometry is Achieved in Residual Networks in a Universal Way for any Activation Function

We demonstrate that in residual neural networks (ResNets) dynamical isometry is achievable irrespective of the activation function used. We do that by deriving, with the help of Free Probability and Random Matrix Theories, a universal formula for the spectral density of the input-output Jacobian at initialization, in the large network width and depth limit. The resulting singular value spectrum depends on a single parameter, which we calculate for a variety of popular activation functions, by analyzing the signal propagation in the artificial neural network. We corroborate our results with numerical simulations of both random matrices and ResNets applied to the CIFAR-10 classification problem. Moreover, we study consequences of this universal behavior for the initial and late phases of the learning processes. We conclude by drawing attention to the simple fact, that initialization acts as a confounding factor between the choice of activation function and the rate of learning. We propose that in ResNets this can be resolved based on our results by ensuring the same level of dynamical isometry at initialization.
Visualization of Large Molecular Trajectories

The analysis of protein-ligand interactions is a time-intensive task. Researchers have to analyze multiple physico-chemical properties of the protein at once and combine them to derive conclusions about the protein-ligand interplay. Typically, several charts are inspected, and 3D animations can be played side-by-side to obtain a deeper understanding of the data. With the advances in simulation techniques, larger and larger datasets are available, with up to hundreds of thousands of steps. Unfortunately, such large trajectories are very difficult to investigate with traditional approaches. Therefore, the need for special tools that facilitate inspection of these large trajectories becomes substantial. In this paper, we present a novel system for visual exploration of very large trajectories in an interactive and user-friendly way. Several visualization motifs are automatically derived from the data to give the user the information about interactions between protein and ligand. Our system offers specialized widgets to ease and accelerate data inspection and navigation to interesting parts of the simulation. The system is suitable also for simulations where multiple ligands are involved. We have tested the usefulness of our tool on a set of datasets obtained from protein engineers, and we describe the expert feedback.
Fuzzy classifier based on clustering with pairs of ε-hyperballs and its application to support fetal state assessment

Objective: In this study we propose a fuzzy classifier whose rule antecedents are determined based on the new approach to Clustering with Pairs of Prototypes (CPP). After demonstrating the high generalization ability of the classifier for six various benchmark datasets, a particular emphasis was placed on the application to support fetal state assessment based on the classification of cardiotocographic (CTG) signals. Methods: The CPP is a solution aimed at increasing the performance of fuzzy classifiers by introducing additional prototypes to those obtained using a given basal clustering method. The CPP improvement was achieved by applying the Fuzzy Clustering with ε-Hyperballs (FCεH) as basal clustering, as well as a new ant algorithm-based method of searching for pairs of prototypes. Results: The results were compared with three reference methods: the Lagrangian SVM with the Gaussian kernel function, and the same fuzzy classifier, but using the antecedents determined with the fuzzy c-means and the fuzzy (c+p)-means clustering. In case of five out of six benchmark datasets as well as for the CTG signals classification problem we achieved the highest generalization ability measured with the classification accuracy (benchmark data) and the classification quality index defined as geometric mean of sensitivity and specificity (CTG signals). Conclusions: The results of the numerical experiments showed high accuracy of the CPP-based fuzzy classifier when assessing various types of data. Moreover, the two-step classification of the CTG signals based on the proposed method allows for the efficient signal evaluation aiming to support the automated fetal state assessment. Significance and main impact: The most significant feature of the proposed method is the high generalization ability being the result of the ε-insensitive learning (FCεH clustering), while maintaining the possibility of interpreting the learning outcomes thanks to the linguistic representation of the knowledge in the form of fuzzy conditional (if-then) rules. Therefore, we believe that this solution will have a positive impact on other studies on intelligent systems.
Variational probabilistic generative framework for single image super-resolution

In this paper, a general variational probabilistic generative framework parameterized by deep networks is proposed for single image super-resolution, which assembles the advantages of coding-based methods and regression-based methods. We use probabilistic generative networks to model the joint full likelihood of a pair of low-resolution (LR) and high-resolution (HR) patches which are generated from a shared latent representation. An inference model is applied to infer the stochastic distribution of the latent representation. By jointly optimizing the generative and inference models, a regression process to the distribution of the HR patch is implied during the learning phase, which provides an efficient forward mapping to accomplish the super-resolution task. We use our framework as a guidance and develop a new model called PGM-CP, with the help of an informative conditional prior and a consistent recognition model. We likewise show how three existing popular example-based SR methods can be “reinvented” under our framework. The effectiveness and efficiency of the proposed method is examined based on three public datasets. Experimental results demonstrate that our model is competitive with state-of-the-art approaches, especially when the image is corrupted by noise.
Maximizing Cloud Revenue using Dynamic Pricing of Multiple Class Virtual Machines

The Infrastructure as a Service (IaaS) cloud industry that relies on leasing virtual machines (VMs) has significant portion of business values of finding the dynamic equilibrium between two conflicting phenomena: underutilization and surging congestion. Spot instance has been proposed as an elegant solution to overcome these challenges, with the ultimate goal to achieve greater profits. However, previous studies on recent spot pricing schemes reveal artificial pricing policies that do not comply with the dynamic nature of these phenomena. Motivated by these facts, this paper investigates dynamic pricing of stagnant resources in order to maximize cloud revenue. Specifically, our proposed approach manages multiple classes of virtual machines in order to achieve the maximum expected revenue within a finite discrete time horizon. For this sake, the proposed approach leverages the Markov decision processes with a number of properties under optimum controlling conditions that characterize a model's behaviour. Further, this approach applies approximate stochastic dynamic programming using linear programming to create a practical model. Experimental results confirm that this approach of dynamic pricing can scale up or down the price efficiently and effectively, according to the stagnant resources and the load thresholds. These results provide significant insights to maximizing the IaaS cloud revenue.
Randomized benchmarking under different gate sets

We provide a comprehensive analysis of the differences between two important standards for randomized benchmarking (RB): the Clifford-group RB protocol proposed originally in Emerson et al. [J. Opt. B 7, S347 (2005)] and Dankert et al. [Phys. Rev. A 80, 012304 (2009)], and a variant of that RB protocol proposed by the NIST group in Knill et al. [Phys. Rev. A 77, 012307 (2008)]. While these two protocols are frequently conflated or presumed equivalent, we prove that they produce distinct exponential fidelity decays leading to differences of up to a factor of 3 in the estimated error rates under experimentally realistic conditions. These differences arise because the NIST RB protocol does not satisfy the unitary two-design condition for the twirl in the Clifford-group protocol and thus the decay rate depends on noninvariant features of the error model. Our analysis provides an important first step toward developing definitive standards for benchmarking quantum gates and a more rigorous theoretical underpinning for the NIST protocol and other RB protocols lacking a group-structure. We conclude by discussing the potential impact of these differences for estimating fault-tolerant overheads.
COALA: A Neural Coverage-Based Approach for Long Answer Selection with Small Data

Current neural network based community question answering (cQA) systems fall short of (1) properly handling long answers which are common in cQA; (2) performing under small data conditions, where a large amount of training data is unavailable—i.e., for some domains in English and even more so for a huge number of datasets in other languages; and (3) benefiting from syntactic information in the model—e.g., to differentiate between identical lexemes with different syntactic roles. In this paper, we propose COALA, an answer selection approach that (a) selects appropriate long answers due to an effective comparison of all question-answer aspects, (b) has the ability to generalize from a small number of training examples, and (c) makes use of the information about syntactic roles of words. We show that our approach outperforms existing answer selection models by a large margin on six cQA datasets from different domains. Furthermore, we report the best results on the passage retrieval benchmark WikiPassageQA.
Hilbert-Schmidt distance and entanglement witnessing

Gilbert proposed an algorithm for bounding the distance between a given point and a convex set. We apply the Gilbert's algorithm to get an upper bound on the Hilbert-Schmidt distance between a given state and the set of separable states. While Hilbert-Schmidt distance does not form a proper entanglement measure, it can nevertheless be useful for witnessing entanglement. We provide a few methods based on the Gilbert's algorithm that can reliably qualify a given state as strongly entangled or practically separable, while being computationally efficient. The method also outputs successively improved approximations to the closest separable state for the given state. We demonstrate the efficacy of the method with examples.
Killing the Password and Preserving Privacy With Device-Centric and Attribute-Based Authentication

Current authentication methods on the Web have serious weaknesses. First, services heavily rely on the traditional password paradigm, which diminishes the end-users' security and usability. Second, the lack of attribute-based authentication does not allow anonymity-preserving access to services. Third, users have multiple online accounts that often reflect distinct identity aspects. This makes proving combinations of identity attributes hard on the users. In this paper, we address these weaknesses by proposing a privacy-preserving architecture for device-centric and attribute-based authentication based on: 1) the seamless integration between usable/strong device-centric authentication methods and federated login solutions; 2) the separation of the concerns for Authorization, Authentication, Behavioral Authentication and Identification to facilitate incremental deployability, wide adoption and compliance with NIST assurance levels; and 3) a novel centralized component that allows end-users to perform identity profile and consent management, to prove combinations of fragmented identity aspects, and to perform account recovery in case of device loss. To the best of our knowledge, this is the first effort towards fusing the aforementioned techniques under an integrated architecture. This architecture effectively deems the password paradigm obsolete with minimal modification on the service provider's software stack.
Distributed Compression of Correlated Classical-Quantum Sources or: The Price of Ignorance

We resume the investigation of the problem of independent local compression of correlated quantum sources, the classical case of which is covered by the celebrated Slepian-Wolf theorem. We focus specifically on classical-quantum (cq) sources, for which one edge of the rate region, corresponding to the compression of the classical part, using the quantum part as side information at the decoder, was previously determined by Devetak and Winter [Phys. Rev. A 68, 042301 (2003)]. Whereas the Devetak-Winter protocol attains a rate-sum equal to the von Neumann entropy of the joint source, here we show that the full rate region is much more complex, due to the partially quantum nature of the source. In particular, in the opposite case of compressing the quantum part of the source, using the classical part as side information at the decoder, typically the rate sum is strictly larger than the von Neumann entropy of the total source. We determine the full rate region in the generic case, showing that, apart from the Devetak-Winter point, all other points in the achievable region have a rate sum strictly larger than the joint entropy. We can interpret the difference as the price paid for the quantum encoder being ignorant of the classical side information. In the general case, we give an achievable rate region, via protocols that are built on the decoupling principle, and the protocols of quantum state merging and quantum state redistribution. Our achievable region is matched almost by a single-letter converse, which however still involves asymptotic errors and an unbounded auxiliary system.
Container Orchestration With Cost-Efficient Autoscaling in Cloud Computing Environments

Containers are widely used by organizations to deploy diverse workloads such as web services, big data, and IoT applications. Container orchestration platforms are designed to manage the deployment of containerized applications in large-scale clusters. The majority of these platforms optimize the scheduling of containers on a fixed-sized cluster and are not enabled to autoscale the size of the cluster nor to consider features specific to public cloud environments. This chapter presents a resource management approach with three objectives: 1) optimize the initial placement of containers by efficiently scheduling them on existing resources, 2) autoscale the number of resources at runtime based on the cluster's workload, and 3) consolidate applications into fewer VMs at runtime. The framework was implemented as a Kubernetes plugin and its efficiency was evaluated on an Australian cloud infrastructure. The experiments demonstrate that a reduction of 58% in cost can be achieved by dynamically managing the cluster size and placement of applications.
Acting and Planning Using Operational Models

The most common representation formalisms for planning are descriptive models. They abstractly describe what the actions do and are tailored for efficiently computing the next state(s) in a state transition system. But acting requires operational models that describe how to do things, with rich control structures for closed-loop online decision-making. Using descriptive representations for planning and operational representations for acting can lead to problems with developing and verifying consistency of the different models.We define and implement an integrated acting-and-planning system in which both planning and acting use the same operational models, which are written in a general-purpose hierarchical task-oriented language offering rich control structures. The acting component is inspired by the well-known PRS system, except that instead of being purely reactive, it can get advice from the planner. Our planning algorithm, RAEplan, plans by doing Monte Carlo rollout simulations of the actor’s operational models. Our experiments show significant benefits in the efficiency of the acting and planning system.
Certifying the True Error: Machine Learning in Coq with Verified Generalization Guarantees

We present MLCERT, a novel system for doing practical mechanized proof of the generalization of learning procedures, bounding expected error in terms of training or test error. MLCERT is mechanized in that we prove generalization bounds inside the theorem prover Coq; thus the bounds are machine checked by Coq’s proof checker. MLCERT is practical in that we extract learning procedures defined in Coq to executable code; thus procedures with proved generalization bounds can be trained and deployed in real systems. MLCERT is well documented and open source; thus we expect it to be usable even by those without Coq expertise. To validate MLCERT, which is compatible with external tools such as TensorFlow, we use it to prove generalization bounds on neural networks trained using TensorFlow on the extended MNIST data set.
Optimal Projection Guided Transfer Hashing for Image Retrieval

Recently, learning to hash has been widely studied for image retrieval thanks to the computation and storage efficiency of binary codes. For most existing learning to hash methods, sufficient training images are required and used to learn precise hashing codes. However, in some real-world applications, there are not always sufficient training images in the domain of interest. In addition, some existing supervised approaches need a amount of labeled data, which is an expensive process in terms of time, labor and human expertise. To handle such problems, inspired by transfer learning, we propose a simple yet effective unsupervised hashing method named Optimal Projection Guided Transfer Hashing (GTH) where we borrow the images of other different but related domain i.e., source domain to help learn precise hashing codes for the domain of interest i.e., target domain. Besides, we propose to seek for the maximum likelihood estimation (MLE) solution of the hashing functions of target and source domains due to the domain gap. Furthermore, an alternating optimization method is adopted to obtain the two projections of target and source domains such that the domain hashing disparity is reduced gradually. Extensive experiments on various benchmark databases verify that our method outperforms many state-of-the-art learning to hash methods. The implementation details are available at https://github.com/liuji93/GTH.
Complex Moment-Based Supervised Eigenmap for Dimensionality Reduction

Dimensionality reduction methods that project highdimensional data to a low-dimensional space by matrix trace optimization are widely used for clustering and classification. The matrix trace optimization problem leads to an eigenvalue problem for a low-dimensional subspace construction, preserving certain properties of the original data. However, most of the existing methods use only a few eigenvectors to construct the low-dimensional space, which may lead to a loss of useful information for achieving successful classification. Herein, to overcome the deficiency of the information loss, we propose a novel complex moment-based supervised eigenmap including multiple eigenvectors for dimensionality reduction. Furthermore, the proposed method provides a general formulation for matrix trace optimization methods to incorporate with ridge regression, which models the linear dependency between covariate variables and univariate labels. To reduce the computational complexity, we also propose an efficient and parallel implementation of the proposed method. Numerical experiments indicate that the proposed method is competitive compared with the existing dimensionality reduction methods for the recognition performance. Additionally, the proposed method exhibits high parallel efficiency.
Embedding-Based Complex Feature Value Coupling Learning for Detecting Outliers in Non-IID Categorical Data

Non-IID categorical data is ubiquitous and common in realworld applications. Learning various kinds of couplings has been proved to be a reliable measure when detecting outliers in such non-IID data. However, it is a critical yet challenging problem to model, represent, and utilise high-order complex value couplings. Existing outlier detection methods normally only focus on pairwise primary value couplings and fail to uncover real relations that hide in complex couplings, resulting in suboptimal and unstable performance. This paper introduces a novel unsupervised embedding-based complex value coupling learning framework EMAC and its instance SCAN to address these issues. SCAN first models primary value couplings. Then, coupling bias is defined to capture complex value couplings with different granularities and highlight the essence of outliers. An embedding method is performed on the value network constructed via biased value couplings, which further learns high-order complex value couplings and embeds these couplings into a value representation matrix. Bidirectional selective value coupling learning is proposed to show how to estimate value and object outlierness through value couplings. Substantial experiments show that SCAN (i) significantly outperforms five state-of-the-art outlier detection methods on thirteen real-world datasets; and (ii) has much better resilience to noise than its competitors.
Fuzzy-Classification Assisted Solution Preselection in Evolutionary Optimization

In evolutionary optimization, the preselection is an efficient operator to improve the search efficiency, which aims to filter unpromising candidate solutions before fitness evaluation. Most existing preselection operators rely on fitness values, surrogate models, or classification models. Basically, the classification based preselection regards the preselection as a classification procedure, i.e., differentiating promising and unpromising candidate solutions. However, the difference between promising and unpromising classes becomes fuzzy as the running process goes on, as all the left solutions are likely to be promising ones. Facing this challenge, this paper proposes a fuzzy classification based preselection (FCPS) scheme, which utilizes the membership function to measure the quality of candidate solutions. The proposed FCPS scheme is applied to two state-of-the-art evolutionary algorithms on a test suite. The experimental results show the potential of FCPS on improving algorithm performance.