Reviewerly/exHarmony huggingface dataset:
Multilayered Autoscaling Performance Evaluation: Can Virtual Machines and Containers Co–Scale?

Abstract The wide adoption of cloud computing by businesses is due to several reasons, among which the elasticity of the cloud virtual infrastructure is the definite leader. Container technology allows increasing the flexibility of an application by adding another layer of virtualization. The containers can be dynamically created and terminated, and also moved from one host to another. A company can achieve a significant cost reduction and increase the manageability of its applications by allowing the running of containerized microservice applications in the cloud. Scaling for such solutions is conducted on both the virtual infrastructure layer and the container layer. Scaling on both layers needs to be synchronized so that, for example, the virtual machine is not terminated with containers still running on it. The synchronization between layers is enabled by multilayered cooperative scaling , implying that the autoscaling solution of the virtual infrastructure layers is aware of the decisions of the autoscaling solution on the container layer and vice versa. In this paper, we introduce the notion of cooperative multilayered scaling and the performance of multilayered autoscaling solutions evaluated using the approach implemented in ScaleX (previously known as Autoscaling Performance Measurement Tool, APMT). We provide the results of the experimental evaluation of multilayered autoscaling performance for the combination of virtual infrastructure autoscaling of AWS, Microsoft Azure and Google Compute Engine with pods horizontal autoscaling of Kubernetes by using ScaleX with four distinct load patterns. We also discuss the effect of the Docker container image size and its pulling policy on the scaling performance.
Bias-corrected support vector machine with Gaussian kernel in high-dimension, low-sample-size settings

In this paper, we study asymptotic properties of nonlinear support vector machines (SVM) in high-dimension, low-sample-size settings. We propose a bias-corrected SVM (BC-SVM) which is robust against imbalanced data in a general framework. In particular, we investigate asymptotic properties of the BC-SVM having the Gaussian kernel and compare them with the ones having the linear kernel. We show that the performance of the BC-SVM is influenced by the scale parameter involved in the Gaussian kernel. We discuss a choice of the scale parameter yielding a high performance and examine the validity of the choice by numerical simulations and actual data analyses.
Further study on the maximum number of bent components of vectorial functions

In 2018, Pott et al. have studied in (IEEE Trans Inf Theory 64(1):403-411, 2018) the maximum number of bent components of vectorial functions. They have presented many nice results and suggested several open problems in this context. This paper is in the continuation of their study in which we solve two open problems raised by Pott et al. and partially solve an open problem raised by the same authors. Firstly, we prove that for a vectorial function, the property of having the maximum number of bent components is invariant under the so-called CCZ equivalence. Secondly, we prove the non-existence of APN plateaued functions having the maximum number of bent components. In particular, quadratic APN functions cannot have the maximum number of bent components. Finally, we present some sufficient conditions that the vectorial function defined from $$\mathbb {F}_{2^{2k}}$$ to $$\mathbb {F}_{2^{2k}}$$ by its univariate representation: $$\begin{aligned} \alpha x^{2^i}\left( x+x^{2^k}+\sum \limits _{j=1}^{\rho }\gamma ^{(j)}x^{2^{t_j}} +\sum \limits _{j=1}^{\rho }\gamma ^{(j)}x^{2^{t_j+k}}\right) \end{aligned}$$ has the maximum number of bent components, where $$\rho \le k$$ . Further, we show that the differential spectrum of the function $$ x^{2^i}(x+x^{2^k}+x^{2^{t_1}}+x^{2^{t_1+k}}+x^{2^{t_2}}+x^{2^{t_2+k}})$$ (where $$i,t_1,t_2$$ satisfy some conditions) is different from the binomial function $$F^i(x)= x^{2^i}(x+x^{2^k})$$ presented in the article of Pott et al.
Discriminative Metric Learning with Deep Forest

A Discriminative Deep Forest (DisDF) as a metric learning algorithm is proposed in the paper. It is based on the Deep Forest or gcForest proposed by Zhou and Feng and can be viewed as a gcForest modification. The case of the fully supervised learning is studied when the class labels of individual training examples are known. The main idea underlying the algorithm is to assign weights to decision trees in random forest in order to reduce distances between objects from the same class and to increase them between objects from different classes. The weights are training parameters. A specific objective function which combines Euclidean and Manhattan distances and simplifies the optimization problem for training the DisDF is proposed. The numerical experiments illustrate the proposed distance metric algorithm.
Quantum Fast Algorithm Computational Intelligence PT I: SW / HW Smart Toolkit

A new approach to a circuit implementation design of quantum algorithm gates for quantum massive parallel fast computing implementation is presented. The main attention is focused on the development of design method of fast quantum algorithm operators as superposition, entanglement and interference which are in general time-consuming operations due to the number of products that have to be performed. SW &amp; HW support sophisticated smart toolkit of supercomputing accelerator of quantum algorithm simulation is described. The method for performing Grover’s interference without product operations as Benchmark introduced. The background of developed information technology is the "Quantum / Soft Computing Optimizer" (QSCOptKBTM) software based on soft and quantum computational intelligence toolkit. Quantum genetic and quantum fuzzy inference algorithm gate design considered. The quantum information technology of imperfect knowledge base self-organization design of fuzzy robust controllers for the guaranteed achievement of intelligent autonomous robot the control goal in unpredicted control situations is described.
Dependency Parsing with your Eyes: Dependency Structure Predicts Eye Regressions During Reading

Backward saccades during reading have been hypothesized to be involved in structural reanalysis, or to be related to the level of text difficulty. We test the hypothesis that backward saccades are involved in online syntactic analysis. If this is the case we expect that saccades will coincide, at least partially, with the edges of the relations computed by a dependency parser. In order to test this, we analyzed a large eye-tracking dataset collected while 102 participants read three short narrative texts. Our results show a relation between backward saccades and the syntactic structure of sentences.
Finding All Bayesian Network Structures within a Factor of Optimal

A Bayesian network is a widely used probabilistic graphical model with applications in knowledge discovery and prediction. Learning a Bayesian network (BN) from data can be cast as an optimization problem using the well-known score-andsearch approach. However, selecting a single model (i.e., the best scoring BN) can be misleading or may not achieve the best possible accuracy. An alternative to committing to a single model is to perform some form of Bayesian or frequentist model averaging, where the space of possible BNs is sampled or enumerated in some fashion. Unfortunately, existing approaches for model averaging either severely restrict the structure of the Bayesian network or have only been shown to scale to networks with fewer than 30 random variables. In this paper, we propose a novel approach to model averaging inspired by performance guarantees in approximation algorithms. Our approach has two primary advantages. First, our approach only considers credible models in that they are optimal or near-optimal in score. Second, our approach is more efficient and scales to significantly larger Bayesian networks than existing approaches.
Investigating Evaluation of Open-Domain Dialogue Systems With Human Generated Multiple References

The aim of this paper is to mitigate the shortcomings of automatic evaluation of open-domain dialog systems through multi-reference evaluation. Existing metrics have been shown to correlate poorly with human judgement, particularly in open-domain dialog. One alternative is to collect human annotations for evaluation, which can be expensive and time consuming. To demonstrate the effectiveness of multi-reference evaluation, we augment the test set of DailyDialog with multiple references. A series of experiments show that the use of multiple references results in improved correlation between several automatic metrics and human judgement for both the quality and the diversity of system output.
Probably approximately correct learning of Horn envelopes from queries

We propose an algorithm for learning the Horn envelope of an arbitrary domain using an expert, or an oracle, capable of answering certain types of queries about this domain. Attribute exploration from formal concept analysis is a procedure that solves this problem, but the number of queries it may ask is exponential in the size of the resulting Horn formula in the worst case. We recall a well-known polynomial-time algorithm for learning Horn formulas with membership and equivalence queries and modify it to obtain a polynomial-time probably approximately correct algorithm for learning the Horn envelope of an arbitrary domain.
Anonymously forecasting the number and nature of firefighting operations

Predicting the number and the type of operations by civil protection services is essential, both to optimize on-call firefighters in size and competence, to pre-position material and human resources... To accomplish this task, it is required to possess skills in artificial intelligence, which are not usually found in a medium-sized fire department. However, such a request may be mandated, for example from specialized companies or research laboratories. This mandate requires the transmission of potentially sensitive information relating to interventions which is not intended to be publicly available. The purpose of this article is to show that a machine learning tool can be deployed and provide accurate results, using a learning process based on anonymized data. Learning on real but anonymized data will be performed using extreme gradient boosting, and the performance of each anonymization will be compared on the number and of interventions per day, and their type.
1.2 Watt Classification of 3D Voxel Based Point-clouds using a CNN on a Neural Compute Stick

With the recent surge in popularity of Convolutional Neural Networks (CNNs), motivated by their significant performance in many classification and related tasks, a new challenge now needs to be addressed: how to accommodate CNNs in mobile devices, such as drones, smartphones, and similar low-power devices? In order to tackle this challenge we exploit the Vision Processing Unit (VPU) that combines dedicated CNN hardware blocks and very low power requirement. The lack of readily available training data and memory requirements are two of the factors hindering the training and accuracy performance of 3D CNNs. In this paper, we propose a method for generating synthetic 3D point-clouds from realistic CAD scene models to enrich the training process for volumetric CNNs. Furthermore, an efficient 3D volumetric object representation Volumetric Accelerator format (VOLA) is employed. VOLA is a sexaquaternary (power-of-four subdivision) tree-based representation which allows for significant memory saving for volumetric data. Multiple CNN models were trained and pruning techniques for the weights were applied to the trained 3D Volumetric Network in order to remove almost 70% of the parameters and outperform the existing state-of-the-art networks. The top performing and efficient model was ported to the Movidius™Neural Compute Stick (NCS). After deployment on the NCS, it takes 11 ms ( ∼ 90 frames per second) to perform inference on each input volume, with a reported power requirement of 1.2 W, which leads to 75.75 inferences per second per Watt.
Exact lower bounds for the agnostic probably-approximately-correct (PAC) machine learning model

We provide an exact nonasymptotic lower bound on the minimax expected excess risk (EER) in the agnostic probably-approximately-correct (PAC) machine learning classification model and identify minimax learning algorithms as certain maximally symmetric and minimally randomized “voting” procedures. Based on this result, an exact asymptotic lower bound on the minimax EER is provided. This bound is of the simple form $c_{\infty}/\sqrt{\nu}$ as $\nu\to\infty$, where $c_{\infty}=0.16997\dots$ is a universal constant, $\nu=m/d$, $m$ is the size of the training sample and $d$ is the Vapnik–Chervonenkis dimension of the hypothesis class. It is shown that the differences between these asymptotic and nonasymptotic bounds, as well as the differences between these two bounds and the maximum EER of any learning algorithms that minimize the empirical risk, are asymptotically negligible, and all these differences are due to ties in the mentioned “voting” procedures. A few easy to compute nonasymptotic lower bounds on the minimax EER are also obtained, which are shown to be close to the exact asymptotic lower bound $c_{\infty}/\sqrt{\nu}$ even for rather small values of the ratio $\nu=m/d$. As an application of these results, we substantially improve existing lower bounds on the tail probability of the excess risk. Among the tools used are Bayes estimation and apparently new identities and inequalities for binomial distributions.
Dual-Path in Dual-Path Network for Single Image Dehazing

Recently, deep learning-based single image dehazing method has been a popular approach to tackle dehazing. However, the existing dehazing approaches are performed directly on the original hazy image, which easily results in image blurring and noise amplifying. To address this issue, the paper proposes a DPDP-Net (Dual-Path in Dual-Path network) framework by employing a hierarchical dual path network. Specifically, the first-level dual-path network consists of a Dehazing Network and a Denoising Network, where the Dehazing Network is responsible for haze removal in the structural layer, and the Denoising Network deals with noise in the textural layer, respectively. And the second-level dual-path network lies in the Dehazing Network, which has an AL-Net (Atmospheric Light Network) and a TM-Net (Transmission Map Network), respectively. Concretely, the AL-Net aims to train the non-uniform atmospheric light, while the TM-Net aims to train the transmission map that reflects the visibility of the image. The final dehazing image is obtained by nonlinearly fusing the output of the Denoising Network and the Dehazing Network. Extensive experiments demonstrate that our proposed DPDP-Net achieves competitive performance against the state-of-the-art methods on both synthetic and real-world images.
AutoSense Model for Word Sense Induction

Word sense induction (WSI), or the task of automatically discovering multiple senses or meanings of a word, has three main challenges: domain adaptability, novel sense detection, and sense granularity flexibility. While current latent variable models are known to solve the first two challenges, they are not flexible to different word sense granularities, which differ very much among words, from aardvark with one sense, to play with over 50 senses. Current models either require hyperparameter tuning or nonparametric induction of the number of senses, which we find both to be ineffective. Thus, we aim to eliminate these requirements and solve the sense granularity problem by proposing AutoSense, a latent variable model based on two observations: (1) senses are represented as a distribution over topics, and (2) senses generate pairings between the target word and its neighboring word. These observations alleviate the problem by (a) throwing garbage senses and (b) additionally inducing fine-grained word senses. Results show great improvements over the stateof-the-art models on popular WSI datasets. We also show that AutoSense is able to learn the appropriate sense granularity of a word. Finally, we apply AutoSense to the unsupervised author name disambiguation task where the sense granularity problem is more evident and show that AutoSense is evidently better than competing models. We share our data and code here: https://github.com/rktamplayo/AutoSense.
FRESH: Fréchet Similarity with Hashing

This paper studies the r-range search problem for curves under the continuous Fréchet distance: given a dataset S of n polygonal curves and a threshold $$r>0$$ , construct a data structure that, for any query curve q, efficiently returns all entries in S with distance at most r from q. We propose FRESH, an approximate and randomized approach for r-range search, that leverages on a locality sensitive hashing scheme for detecting candidate near neighbors of the query curve, and on a subsequent pruning step based on a cascade of curve simplifications. We experimentally compare FRESH to exact and deterministic solutions, and we show that high performance can be reached by suitably relaxing precision and recall.
Hallucinating Optical Flow Features for Video Classification

Appearance and motion are two key components to depict and characterize the video content. Currently, the two-stream models have achieved state-of-the-art performances on video classification. However, extracting motion information, specifically in the form of optical flow features, is extremely computationally expensive, especially for large-scale video classification. In this paper, we propose a motion hallucination network, namely MoNet, to imagine the optical flow features from the appearance features, with no reliance on the optical flow computation. Specifically, MoNet models the temporal relationships of the appearance features and exploits the contextual relationships of the optical flow features with concurrent connections. Extensive experimental results demonstrate that the proposed MoNet can effectively and efficiently hallucinate the optical flow features, which together with the appearance features consistently improve the video classification performances. Moreover, MoNet can help cutting down almost a half of computational and data-storage burdens for the two-stream video classification. Our code is available at: https://github.com/YongyiTang92/MoNet-Features
Attention Bilinear Pooling for Fine-Grained Classification

Fine-grained image classification is a challenging problem because of its large intra-class differences and low inter-class variance. Bilinear pooling based models have been shown to be effective at fine-grained classification, while most previous approaches neglect the fact that distinctive features or modeling distinguishing regions usually have an important role in solving the fine-grained problem. In this paper, we propose a novel convolutional neural network framework, i.e., attention bilinear pooling, for fine-grained classification with attention. This framework can learn the distinctive feature information from the channel or spatial attention. Specifically, the channel and spatial attention allows the network to better focus on where the key targets are in the image. This paper embeds spatial attention and channel attention in the underlying network architecture to better represent image features. To further explore the differences between channels and spatial attention, we propose channel attention bilinear pooling (CAB), spatial attention bilinear pooling (SAB), channel spatial attention bilinear pooling (CSAB), and spatial channel attention bilinear pooling (SCAB) as four alternative frames. A variety of experiments on several datasets show that our proposed method has a very impressive performance compared to other methods based on bilinear pooling.
Neural Machine Translation With Noisy Lexical Constraints

Lexically constrained decoding for machine translation has shown to be beneficial in previous studies. Unfortunately, constraints provided by users may contain mistakes in real-world situations. It is still an open question that how to manipulate these noisy constraints in such practical scenarios. We present a novel framework that treats constraints as external memories. In this soft manner, a mistaken constraint can be corrected. Experiments demonstrate that our approach can achieve substantial BLEU gains in handling noisy constraints. These results motivate us to apply the proposed approach on a new scenario where constraints are generated without the help of users. Experiments show that our approach can indeed improve the translation quality with the automatically generated constraints.
Learn How to Cook a New Recipe in a New House: Using Map Familiarization, Curriculum Learning, and Common Sense to Learn Families of Text-Based Adventure Games

We consider the task of learning to play families of text-based computer adventure games, i.e., fully textual environments with a common theme (e.g. cooking) and goal (e.g. prepare a meal from a recipe) but with different specifics; new instances of such games are relatively straightforward for humans to master after a brief exposure to the genre but have been curiously difficult for computer agents to learn. We find that the deep Q-learning strategies that have been successfully leveraged for superhuman performance in single-instance action video games can be applied to learn families of text video games when adopting simple strategies that correlate with human-like learning behavior. Specifically, we build agents that learn to tackle simple scenarios before more complex ones using curriculum learning, that familiarize themselves in an unfamiliar environment by navigating before acting, and that explore uncertain environments more thoroughly using contextual multi-armed bandit decision policies. We demonstrate improved task completion rates over reasonable baselines when evaluating on never-before-seen games of that theme.
Federated Identity Management and Interoperability for Heterogeneous Cloud Platform Ecosystems

This paper describes an approach to overcome the interoperability challenges related to identity management systems supporting cross-collaboration between heterogeneous manufacturing platforms. Traditional identity management systems have shown many weaknesses when it comes to cloud platforms and their federations, from not being able to support a simplified login process, to information disclosure and complexity of implementation in practice. This paper discusses workflows to practically implement federated identity management across the heterogeneous manufacturing platforms and design interoperability at different levels, e.g. at the platform level and at the platform integration level. Our motivation to find the best federated identity management solution for heterogeneous cloud-based platforms is related to practical requirements coming from the ongoing European project eFactory.