Reviewerly/exHarmony huggingface dataset:
Statistical Model Checking-Based Evaluation and Optimization for Cloud Workflow Resource Allocation

Due to the existence of resource variations, it is very challenging for Cloud workflow resource allocation strategies to guarantee a reliable Quality of Service (QoS). Although dozens of resource allocation heuristics have been developed to improve the QoS of Cloud workflow, it is hard to predict their performance under variations because of the lack of accurate modeling and evaluation methods. So far, there is no comprehensive approach that can quantitatively reason the capability of resource allocation strategies or enable the tuning of parameters to optimize resource allocation solutions under variations. To address the above problems, this paper proposes a novel framework that can evaluate and optimize resource allocation strategies effectively and quantitatively. By using the statistical model checker UPPAAL-SMC and supervised learning approaches, our framework can: i) conduct complex QoS queries on resource allocation instances considering resource variations; ii) make quantitative and qualitative comparisons among resource allocation strategies; iii) enable the tuning of parameters to improve the overall QoS; and iv) support the quick optimization of overall workflow QoS under customer requirements and resource variations. The experimental results demonstrate that our automated framework can support both the Service Level Agreement (SLA) negotiation and workflow resource allocation optimization efficiently.
A Data Skew Oriented Reduce Placement Algorithm Based on Sampling

For frequent disk I/O and large data transmissions among different racks and physical nodes, intermediate data communication has become the most important performance bottle-neck in most running Hadoop systems. This paper proposes a reduce placement algorithm called CORP to schedule related map and reduce tasks on the near nodes of clusters or racks for data locality. Because the number of keys cannot be counted until the input data are processed by map tasks, this paper applies a reservoir algorithm for sampling the input data, which can bring the distribution of keys/values closer to the overall situation of original data. Based on the distribution matrix of the intermediate results in each partition, by calculating the distance and cost matrices among the cross node communication, the related map and reduce tasks can be scheduled to relatively nearby physical nodes for data locality. We implement CORP in Hadoop 2.4.0 and evaluate its performance using three widely used benchmarks: Sort, Grep, and Join. In these experiments, an evaluation model is proposed for selecting the appropriate sample rates, which can comprehensively consider the importance of cost, effect, and variance in sampling. Experimental results show that CORP can not only improve the balance of reduces tasks effectively but also decreases the job execution time for the lower inner data communication. Compared with some other reduce scheduling algorithms, the average data transmission of the entire system on the core switch has been reduced substantially.
QoS-Aware Service Selection Using an Incentive Mechanism

QoS-aware service selection seeks to find the optimal service providers to achieve the optimization goal of a service requester, such as the maximization of utility, while satisfying global QoS requirements. Service providers are usually self-interested and have some private information, such as minimum prices, that would significantly factor into the decision making of the service requester. Thus, service requesters face a decision making dilemma with incomplete information. Recent work has used iterative combinatorial auctions to address this problem. However, such studies do not sufficiently consider that the service requester can elicit the private information from service providers by observing their actions. This can help the service selection process achieve better outcomes. In this paper, we propose a type of incentive contract that can motivate the service providers to offer the QoS and prices that the service requester prefers. Based on the incentive contracts, we propose an incentive mechanism for effective service selection. In the mechanism, a service requester offers a set of incentive contracts to the service providers and then elicits their private information based on their responses to the incentive contracts. The process is iterated until the service requester finally obtains a solution that fulfills the global QoS requirements. Experimental results show that the incentive contracts have a positive impact on both service requesters and providers and that the incentive mechanism outperforms the existing combinatorial auction-based approaches in finding optimal solutions.
Learning Policies for Markov Decision Processes From Data

We consider the problem of learning a policy for a Markov decision process consistent with data captured on the state-action pairs followed by the policy. We parameterize the policy using features associated with the state-action pairs. The features can be handcrafted or defined using kernel functions in a reproducing kernel Hilbert space. In either case, the set of features can be large and only a small, unknown subset may need to be used to fit a specific policy to the data. The parameters of such a policy are recovered using 1-regularized logistic regression. We establish bounds on the difference between the average reward of the estimated and the unknown original policies (regret) in terms of the generalization error and the ergodic coefficient of the underlying Markov chain. To that end, we combine sample complexity theory and sensitivity analysis of the stationary distribution of Markov chains. Our analysis suggests that to achieve regret within order O(√ε), it suffices to use training sample size of the order of Ω(log n · poly(1/ε)), where n is the number of the features. We demonstrate the effectiveness of our method on a synthetic robot navigation example.
CD-CARS: Cross-domain context-aware recommender systems

In this paper, we address two research topics in Recommender Systems (RSs) which have been developed in parallel without a deeper integration: Cross-Domain RS (CDRS) and Context-Aware RS (CARS). CDRS have emerged to enhance the quality of recommendations in a target domain by leveraging sources of information in different domains. CDRS are especially useful to address cold-start, sparsity and diversity problems in target domains with scarce information. CARS, on its turn, have been proposed to consider contextual information for recommendations. Such systems are suitable when the users’ interests change according to factors like time, location, among others. By combining these two approaches, better RSs can be developed, considering both the availability of useful data from multiple domains and the use of contextual information. In this paper, we formalize the combination of CDRS and CARS, which represents a more systematic integration of these approaches compared to previous work. Based on this formulation, we developed novel RSs techniques, named CD-CARS. To evaluate the developed CD-CARS techniques, we performed extensive experimentation through real datasets taking into account several scenarios. The recommendations were evaluated in terms of predictive and ranking performance, respectively achieving up to 62.6% and 45%, depending on the scenario, in comparison to traditional cross-domain collaborative filtering techniques. Therefore, the experimental results have shown that the integration of techniques developed in isolation can be useful in a variety of situations, in which recommendations can be improved by information gathered from different sources and can be refined by considering specific contextual information.
Temporal Unknown Incremental Clustering Model for Analysis of Traffic Surveillance Videos

Optimized scene representation is an important characteristic of a framework for detecting abnormalities on live videos. One of the challenges for detecting abnormalities in live videos is real-time detection of objects in a non-parametric way. Another challenge is to efficiently represent the state of objects temporally across frames. In this paper, a Gibbs sampling-based heuristic model referred to as temporal unknown incremental clustering has been proposed to cluster pixels with motion. Pixel motion is first detected using optical flow and a Bayesian algorithm has been applied to associate pixels belonging to a similar cluster in subsequent frames. The algorithm is fast and produces accurate results in Θ(kn) time, where k is the number of clusters and n the number of pixels. Our experimental validation with publicly available data sets reveals that the proposed framework has good potential to open up new opportunities for real-time traffic analysis.
Kalman Filter-based Heuristic Ensemble (KFHE): A new perspective on multi-class ensemble classification using Kalman filters

This paper introduces a new perspective on multi-class ensemble classification that considers training an ensemble as a state estimation problem. The new perspective considers the final ensemble classifier model as a static state, which can be estimated using a Kalman filter that combines noisy estimates made by individual classifier models. A new algorithm based on this perspective, the Kalman Filter-based Heuristic Ensemble (KFHE), is also presented in this paper which shows the practical applicability of the new perspective. Experiments performed on 30 datasets compare KFHE with state-of-the-art multi-class ensemble classification algorithms and show the potential and effectiveness of the new perspective and algorithm. Existing ensemble approaches trade off classification accuracy against robustness to class label noise, but KFHE is shown to be significantly better or at least as good as the state-of-the-art algorithms for datasets both with and without class label noise.
Compression of Plenoptic Point Clouds

Point clouds have been recently used in applications involving real-time capture and rendering of 3D objects. In a point cloud, for practical reasons, each point or voxel is usually associated with one single color along with other attributes. The region-adaptive hierarchical transform (RAHT) coder has been proposed for single-color point clouds. The cloud is usually captured by many cameras and the colors are averaged in some fashion to yield the point color. This approach may not be very realistic since, in real world objects, the reflected light may significantly change with the viewing angle, especially if specular surfaces are present. For that, we are interested in a more complete representation, the plenoptic point cloud, wherein every point has associated colors in all directions. Here, we propose a compression method for such a representation. Instead of encoding a continuous function, since there is only a finite number of cameras, it makes sense to compress as many colors per voxel as cameras, and to leave any intermediary color rendering interpolation to the decoder. Hence, each voxel is associated with a vector of color values, for each color component. We have here developed and evaluated four methods to expand the RAHT coder to encompass the multiple colors case. Experiments with synthetic data helped us to correlate specularity with the compression, since object specularity, at a given point in space, directly affects color disparity among the cameras, impacting the coder performance. Simulations were carried out using natural (captured) data and results are presented as rate-distortion curves that show that a combination of Kahunen-Loève transform and RAHT achieves the best performance.
Framework for covert and secret key expansion over classical-quantum channels

Covert and secret quantum key distribution aims at generating information-theoretically secret bits between distant legitimate parties in a manner that remains provably undetectable by an adversary. We propose a framework in which to precisely define and analyze such an operation, and we show that covert and secret key expansion is possible. For fixed and known classical-quantum wiretap channels, we develop and analyze protocols based on forward and reverse reconciliation. The crux of our approach is the use of information reconciliation and privacy amplification techniques that are able to process the sparse signals required for covert operation and the Shannon entropy of which scales as the square root of their length. In particular, our results show that the coordination required between legitimate parties to achieve covert communication can be achieved with a negligible number of secret key bits.
Deep Attention-Guided Hashing

With the rapid growth of multimedia data (e.g., image, audio and video etc.) on the web, learning-based hashing techniques such as Deep Supervised Hashing (DSH) have proven to be very efficient for large-scale multimedia search. The recent successes seen in Learning-based hashing methods are largely due to the success of deep learning-based hashing methods. However, there are some limitations to previous learning-based hashing methods (e.g., the learned hash codes containing repetitive and highly correlated information). In this paper, we propose a novel learning-based hashing method, named Deep Attention-guided Hashing (DAgH). DAgH is implemented using two stream frameworks. The core idea is to use guided hash codes which are generated by the hashing network of the first stream framework (called first hashing network) to guide the training of the hashing network of the second stream framework (called second hashing network). Specifically, in the first network, it leverages an attention network and hashing network to generate the attention-guided hash codes from the original images. The loss function we propose contains two components: the semantic loss and the attention loss. The attention loss is used to punish the attention network to obtain the salient region from pairs of images; in the second network, these attention-guided hash codes are used to guide the training of the second hashing network (i.e., these codes are treated as supervised labels to train the second network). By doing this, DAgH can make full use of the most critical information contained in images to guide the second hashing network in order to learn efficient hash codes in a true end-to-end fashion. Results from our experiments demonstrate that DAgH can generate high quality hash codes and it outperforms current state-of-the-art methods on three benchmark datasets, CIFAR-10, NUS-WIDE, and ImageNet.
Spiking Echo State Convolutional Neural Network for Robust Time Series Classification

In this paper, a novel high-accuracy and robust computing framework for time series classification tasks is presented. The framework consists of a feature extraction module and a classification module, where the feature extraction is implemented by reservoir computing method of spiking neurons, and the classification result is obtained by the state-of-the-art analog convolutional neural networks (CNNs). The original time series input is first converted to multi-channel spike streams, then fed into the spiking reservoir layer to produce intermediate spike output, and subsequently, the spike output is transformed into a 2D mapping image, and deep CNN model is applied to classify the mapping image. The proposed model has the following three significant advantages: long-and-short term memory brought by the echo state of reservoir component, robustness to noise brought by the spiking encoding method, and high-accuracy performance brought by the deep CNN model. The experiments conducted on both synthetic time series data set and UCR time series data sets showed that our approach achieved highly competitive accuracy and robustness over other existing methods.
Efficient unitarity randomized benchmarking of few-qubit Clifford gates

Unitarity randomized benchmarking (URB) is an experimental procedure for estimating the coherence of implemented quantum gates independently of state preparation and measurement errors. These estimates of the coherence are measured by the unitarity. A central problem in this experiment is relating the number of data points to rigorous confidence intervals. In this work we provide a bound on the required number of data points for Clifford URB as a function of confidence and experimental parameters. This bound has favorable scaling in the regime of near-unitary noise and is asymptotically independent of the length of the gate sequences used. We also show that, in contrast to standard randomized benchmarking, a nontrivial number of data points is always required to overcome the randomness introduced by state preparation and measurement errors even in the limit of perfect gates. Our bound is sufficiently sharp to benchmark small-dimensional systems in realistic parameter regimes using a modest number of data points. For example, we show that the unitarity of single-qubit Clifford gates can be rigorously estimated using few hundred data points under the assumption of gate-independent noise. This is a reduction of orders of magnitude compared to previously known bounds.
Measurements of Nonlocal Variables and Demonstration of the Failure of the Product Rule for a Pre- and Postselected Pair of Photons

We report the first implementation of the von Neumann instantaneous measurements of nonlocal variables, which becomes possible due to technological achievements in creating hyperentangled photons. Tests of reliability and of the nondemolition property of the measurements have been performed with high precision, showing the suitability of the scheme as a basic ingredient of numerous quantum information protocols. The method allows us to demonstrate for the first time with strong measurements a special feature of pre- and postselected quantum systems: the failure of the product rule. It has been verified experimentally that for a particular pre- and postselected pair of particles, a single measurement on particle $A$ yields with certainty ${\ensuremath{\sigma}}_{x}^{A}=\ensuremath{-}1$, a single measurement on particle $B$ yields with certainty ${\ensuremath{\sigma}}_{y}^{B}=\ensuremath{-}1$, and a single nonlocal measurement on particles $A$ and $B$ yields with certainty ${\ensuremath{\sigma}}_{x}^{A}{\ensuremath{\sigma}}_{y}^{B}=\ensuremath{-}1$.
Learning to Make Analogies by Contrasting Abstract Relational Structure

Analogical reasoning has been a principal focus of various waves of AI research. Analogy is particularly challenging for machines because it requires relational structures to be represented such that they can be flexibly applied across diverse domains of experience. Here, we study how analogical reasoning can be induced in neural networks that learn to perceive and reason about raw visual data. We find that the critical factor for inducing such a capacity is not an elaborate architecture, but rather, careful attention to the choice of data and the manner in which it is presented to the model. The most robust capacity for analogical reasoning is induced when networks learn analogies by contrasting abstract relational structures in their input domains, a training method that uses only the input data to force models to learn about important abstract features. Using this technique we demonstrate capacities for complex, visual and symbolic analogy making and generalisation in even the simplest neural network architectures.
Data-Driven Vulnerability Exploration for Design Phase System Analysis

Applying security as a lifecycle practice is becoming increasingly important to combat targeted attacks in safety-critical systems. Among others, there are two significant challenges in this area: the need for models that can characterize a realistic system in the absence of an implementation and an automated way to associate attack vector information, that is, historical data, to such system models. We propose the cybersecurity body of knowledge (CYBOK), which takes in sufficiently characteristic models of systems and acts as a search engine for potential attack vectors. CYBOK is fundamentally an algorithmic approach to vulnerability exploration, which is a significant extension to the body of knowledge it builds upon. By using CYBOK, security analysts and system designers can work together to assess the overall security posture of systems early in their lifecycle, during major design decisions and before final product designs, consequently, assisting in applying security earlier and throughout the systems lifecycle.
Bayesian Estimation of Population Size Changes by Sampling Tajima’s Trees

Abstract The large state space of gene genealogies is a major hurdle for inference methods based on Kingman’s coalescent. Here, we present a new Bayesian approach for inferring past population sizes, which relies on a lower-resolution coalescent process that we refer to as “Tajima’s coalescent.” Tajima’s coalescent has a drastically smaller state space, and hence it is a computationally more efficient model, than the standard Kingman coalescent. We provide a new algorithm for efficient and exact likelihood calculations for data without recombination, which exploits a directed acyclic graph and a correspondingly tailored Markov Chain Monte Carlo method. We compare the performance of our Bayesian Estimation of population size changes by Sampling Tajima’s Trees (BESTT) with a popular implementation of coalescent-based inference in BEAST using simulated and human data. We empirically demonstrate that BESTT can accurately infer effective population sizes, and it further provides an efficient alternative to the Kingman’s coalescent. The algorithms described here are implemented in the R package phylodyn, which is available for download at https://github.com/JuliaPalacios/phylodyn.
Design and Analysis of New Zeroing Neural Network Models With Improved Finite-Time Convergence for Time-Varying Reciprocal of Complex Matrix

In this article, two improved finite-time convergent complex-valued zeroing neural network (IFTCVZNN) models are presented and investigated for real-time solution of time-varying reciprocal of complex matrices on account of two equivalent processing ways of complex calculations for nonlinear activation functions. Furthermore, a novel nonlinear activation function is explored to modify the comprehensive performance of such two IFTCVZNN models. Compared with existing complex-valued neural networks converging within the limited time, the proposed IFTCVZNN models with the new activation function have better finite-time convergence and less conservative upper bound. Numerical simulations verify that the maximum of convergence time estimated via Lyapunov stability is theoretically much closer to the actual convergence time.
Multi-Pose Facial Expression Recognition Based on Generative Adversarial Network

The recognition of human emotions from facial expression images is one of the most important topics in the machine vision and image processing fields. However, recognition becomes difficult when dealing with non-frontal faces. To alleviate the influence of poses, we propose an encoder-decoder generative adversarial network that can learn pose-invariant and expression-discriminative representations. Specifically, we assume that a facial image can be divided into an expressive component, an identity component, a head pose component and a remaining component. The encoder encodes each component into a feature representation space and the decoder recovers the original image from these encoded features. A classification loss on the components and an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> pixel-wise loss are applied to guarantee the rebuilt image quality and produce more constrained visual representations. Quantitative and qualitative evaluations on two multi-pose datasets demonstrate that the proposed algorithm performs favorably compared to state-of-the-art methods.