Reviewerly/exHarmony huggingface dataset:
Set based particle swarm optimization for the feature selection problem

Selecting the correct features when training a classification algorithm, has a significant impact on the performance of the classifier. More features provide more information, but can lead to overfitting and utilizing features that are redundant, irrelevant or too noisy. The feature selection problem (FSP) is concerned with identifying those features, from the entire set of features, that lead to the best possible classification. This article evaluates the performance of the set based particle swarm optimization (SBPSO) algorithm on the FSP. SBPSO was specifically developed to solve discrete-valued optimization problems that can be formulated as set-based problems. A wrapper based SBPSO algorithm based on a k-nearest neighbor classifier is proposed in this paper. The SBPSO wrapper algorithm was compared to three other discrete PSO wrapper algorithms on a large number of datasets of different sizes and outperformed, with statistical significance, the other algorithms on the FSP. The SBPSO algorithm can thus be considered an effective tool for solving the FSP.
Cv-CapsNet: Complex-Valued Capsule Network

Capsule network (CapsNet) can recognize the objects by encoding the part-whole relationships in a way similar to our human perceptual system and has already shown its great potential in image classification tasks. However, it is limited to the real domain while the complex numbers having much richer representational capacity and facilitating the noise-robust memory retrieval mechanisms. Therefore, we propose two architectures: Complex-valued Dense CapsNet (Cv-CapsNet) and Complex-valued Diverse CapsNet (Cv-CapsNet++), each of them consists of three stages. In the first stage, multi-scale complex-valued features are obtained by the restricted dense complex-valued subnetwork. Particularly, Cv-CapsNet++ utilizes a three-level Cv-CapsNet hierarchical model to extract the multi-scale high-level complex-valued features in order to adapt to the complicated datasets. In the second stage, these complex-valued features are encoded into the complex-valued primary capsules, Particularly, Cv-CapsNet++ encodes the complex-valued features from different hierarchies into the multi-dimensional complex-valued primary capsules. In the third stage, we generalize the dynamic routing algorithm to the complex-valued domain and employ it to fuse the real- and imaginary-valued information of complex-valued primary capsules. The experimental results show that the proposed architectures lead to fewer trainable parameters, better performance, and fewer iterations during training than Real-valued CapsNets (Rv-CapsNets) with similar structure and original CapsNet on FashionMNIST and CIFAR10 datasets.
Solo or Ensemble? Choosing a CNN Architecture for Melanoma Classification

Convolutional neural networks (CNNs) deliver exceptional results for computer vision, including medical image analysis. With the growing number of available architectures, picking one over another is far from obvious. Existing art suggests that, when performing transfer learning, the performance of CNN architectures on ImageNet correlates strongly with their performance on target tasks. We evaluate that claim for melanoma classification, over 9 CNNs architectures, in 5 sets of splits created on the ISIC Challenge 2017 dataset, and 3 repeated measures, resulting in 135 models. The correlations we found were, to begin with, much smaller than those reported by existing art, and disappeared altogether when we considered only the top-performing networks: uncontrolled nuisances (i.e., splits and randomness) overcome any of the analyzed factors. Whenever possible, the best approach for melanoma classification is still to create ensembles of multiple models. We compared two choices for selecting which models to ensemble: picking them at random (among a pool of high-quality ones) vs. using the validation set to determine which ones to pick first. For small ensembles, we found a slight advantage on the second approach but found that random choice was also competitive. Although our aim in this paper was not to maximize performance, we easily reached AUCs comparable to the first place on the ISIC Challenge 2017.
QoS-aware cloud service composition: A systematic mapping study from the perspective of computational intelligence

Cloud service composition builds new value-added services by combining existing single services. However, because of the exuberant growth of cloud services and the varying quality of service (QoS), discovering required services and creating a service composition with certain quality guarantees becomes a significant technical issue and attracts much concern. Computational intelligence techniques are considered to be effective in solving such problems, and researchers have made substantial efforts in this area. Nevertheless, to the best of our knowledge, there is not any systematic research about this issue with a particular focus on computing intelligence. Thus, the current study aims to create a panoramic view of QoS-aware cloud service composition from the perspective of computational intelligence. The objectives of this paper are to (1) investigate the relevant studies on this field; (2) make a comprehensive examination of the literature from different aspects: active researchers, research motivations, QoS parameters, algorithms, datasets, optimization strategies and could layers; (3) identify the areas which need further research. For this, a search protocol has been well defined, and 105 articles from 2009 to 2018 were selected. This study classified these articles into three groups, including non-heuristic, heuristic and meta-heuristic, and then examined the research works from several aspects. The results indicate that reducing response time is the most important motivation for researchers, and meta-heuristic algorithms, especially genetic algorithms, are the most widely used computational intelligence techniques. Besides, the most widely used QoS attributes and datasets are also revealed. Additionally, the study points out there are still some research challenges in this area, such as QoS evaluation in a dynamic environment and inter-service correlations. In general, this study classified and compared the existing computational intelligence techniques; analyzed the research status and identified future research directions. It can provide a basis for both researchers and practitioners who are interested in this area. More significantly, in the field of expert and intelligent systems, this study can assist in the design and development of expert and intelligent systems in enterprises, it can efficiently assist enterprises in business decisions and risk reduction.
Methodological Principles for Reproducible Performance Evaluation in Cloud Computing

The rapid adoption and the diversification of cloud computing technology exacerbate the importance of a sound experimental methodology for this domain. This work investigates how to measure and report performance in the cloud, and how well the cloud research community is already doing it. We propose a set of eight important methodological principles that combine best-practices from nearby fields with concepts applicable only to clouds, and with new ideas about the time-accuracy trade-off. We show how these principles are applicable using a practical use-case experiment. To this end, we analyze the ability of the newly released SPEC Cloud IaaS benchmark to follow the principles, and showcase real-world experimental studies in common cloud environments that meet the principles. Last, we report on a systematic literature review including top conferences and journals in the field, from 2012 to 2017, analyzing if the practice of reporting cloud performance measurements follows the proposed eight principles. Worryingly, this systematic survey and the subsequent two-round human reviews, reveal that few of the published studies follow the eight experimental principles. We conclude that, although these important principles are simple and basic, the cloud community is yet to adopt them broadly to deliver sound measurement of cloud environments.
Evaluation and selection of clustering methods using a hybrid group MCDM

Due to the lack of objective measures, the evaluation and prioritization of clustering methods is inherently challenging. Since their evaluation generally involves numerous criteria, it can be designed as a multiple criteria decision making (MCDM) problem and using multiple data sets, the problem can be formulated as a group MCDM modeling. In this paper, a MCDM-based framework is proposed to evaluate and rank a number of clustering methods. The proposed approach employs three group MCDM algorithms and a Borda count method which leads to a comprehensive, robust framework capable of evaluating and ranking multiple clustering models on manifold data sets (cases). Moreover, we introduce a hybrid data clustering algorithm which combines a particle swarm optimization (PSO) algorithm with a K-means clustering algorithm. Finally, a clustering comparison with regard to both external and internal evaluation indicators is another contribution of this paper. Six clustering methods are compared based on five evaluation measures. The results of comparative experiments on ten data sets indicate the effectiveness of the proposed hybrid clustering method. More importantly, the experimental results vividly demonstrate the effectiveness of the group MCDM-based evaluation on clustering model selection.
On the Design of a Flexible Delegation Model for the Internet of Things Using Blockchain

The Internet of things (IoT) presents new opportunities and challenges due to its scale and dynamic nature. One significant challenge for the IoT is the need for security, in particular access control solutions, that are designed to meet the characteristics of these systems. Delegation of rights, from one entity to another, is a crucial component of an access control system. The IoT requires a secure, flexible, and fine-grained delegation model. While there has been considerable work in the area of delegation, much of it assumes a centralized, well-resourced system and these solutions have limited capacity in the context of the IoT. Where delegation models for the IoT have been proposed they typically provide only coarse-grained control over the delegation of rights. Moreover, many of them require a centralized trusted authority, which can suffer from a single-point failure and is not an ideal base for a large and dynamic system like the IoT. In this paper, we propose an identity-less, asynchronous, and decentralized delegation model for the IoT based on blockchain technology. We describe system components, architecture, and key aspects related to the security of the system. We use attributes to validate an entity rather than depending upon unique identities. We demonstrate the feasibility of our model through use-case examples and analyze the performance with a proof of concept testbed implementation using Ethereum private blockchain.
The Culture of Professional Self-Realization as a Fundamental Factor of Students’ Internet Communication in the Modern Educational Environment of Higher Education

This article deals with the problem of the culture of professional self-realization of Russian students in the process of university education at the present stage. The role of information and communications technologies is revealed, in particular the interactive technology of self-regulation developed by the authors, as a necessary pedagogical condition for the successful professional self-realization of students. The paper describes the interactive technology of self-regulation formation used in teaching a foreign language in order to improve the educational process. The paper provides an analysis of the current state of the classroom educational environment in teaching a foreign language in order to determine the possibilities of improving the educational process. The main goal of the interactive technology of self-regulation formation is to optimize and intensify the activities of students in the classroom and extracurricular activities. The interactive technology of self-regulation formation in a nonlinguistic university was applied in the proposed work. Special attention was paid to the problem of structuring and the algorithmization of independent work. Ways for increasing interactivity are revealed, and an algorithm for teaching and speech actions in interactive mode was developed. It was established that the introduction of structured algorithmization using the interactive technology of self-regulation formation could be effectively used for teaching poorly prepared students. It has been established that the culture of professional self-actualization for modern students is directly dependent on the quality of online interaction with a university teacher. It was revealed that it is the student’s awareness of the objectives of learning interaction and ways for its implementation that allow him to competently perform his academic work, which in turn contributes to the actualization of personal professional qualities that are required by a university graduate for successful implementation in the future.
RoemNet: Robust Meta Learning Based Channel Estimation in OFDM Systems

Recently, in order to achieve performance improvement in scenarios where the channel is either unknown, or too complex for an analytical description, Neural Network (NN) based channel estimation is introduced in Orthogonal Frequency Division Multiplexing (OFDM) systems. However, this kind of learning method is not reliable enough when the conditions of online deployment of the common NNs are not consistent with the channel models used in the training stage. Furthermore, common NNs need plenty of data as well as time to be trained, which is not suitable for the OFDM communication network with time varying channels. To tackle these challenges, we propose a novel meta learning based channel estimation approach called RoemNet. The most distinctive characteristic of RoemNet is that it involves a meta-learner that can learn from the environment of different channels. With the update of meta-learner, RoemNet is robust enough to solve new channel learning tasks using only a small number of pilots. Furthermore, RoemNet can alleviate the effect of Doppler spread and significantly improve the Bit Error Ratio (BER) performance under different channel environments. Experiment results demonstrate that the proposed RoemNet outperforms existing channel estimation methods including existing learning methods under various scenarios.
Superstrong coupling in circuit quantum electrodynamics

Abstract Vacuum fluctuations fundamentally affect an atom by inducing a finite excited state lifetime along with a Lamb shift of its transition frequency. Here we report the reverse effect: modification of vacuum modes by a single atom in circuit quantum electrodynamics. Our one-dimensional vacuum is a long section of a high wave impedance (comparable to resistance quantum) superconducting transmission line. It is directly wired to a transmon qubit circuit. Owing to the combination of high impedance and galvanic connection, the transmon’s spontaneous emission linewidth can greatly exceed the discrete transmission line modes spacing. This condition defines a previously unexplored superstrong coupling regime of quantum electrodynamics where many frequency-resolved vacuum modes hybridize with a single atom. We establish this regime by observing the spontaneous emission line of the transmon, revealed through the mode-by-mode measurement of the vacuum’s density of states. The linewidth as well as the atom-induced dispersive photon−photon interaction are accurately described by a physically transparent Caldeira−Leggett model, with the transmon’s quartic nonlinearity treated as a perturbation. Nonperturbative modification of vacuum, including inelastic scattering of single photons, can be enabled by the superstrong coupling regime upon replacing the transmon by more anharmonic qubits, with broad implications for simulating quantum impurity models of many-body physics.
Human Activity Recognition for Production and Logistics—A Systematic Literature Review

This contribution provides a systematic literature review of Human Activity Recognition for Production and Logistics. An initial list of 1243 publications that complies with predefined Inclusion Criteria was surveyed by three reviewers. Fifty-two publications that comply with the Content Criteria were analysed regarding the observed activities, sensor attachment, utilised datasets, sensor technology and the applied methods of HAR. This review is focused on applications that use marker-based Motion Capturing or Inertial Measurement Units. The analysed methods can be deployed in industrial application of Production and Logistics or transferred from related domains into this field. The findings provide an overview of the specifications of state-of-the-art HAR approaches, statistical pattern recognition and deep architectures and they outline a future road map for further research from a practitioner’s perspective.
On the Intrinsic Dimensionality of Image Representations

This paper addresses the following questions pertaining to the intrinsic dimensionality of any given image representation: (i) estimate its intrinsic dimensionality, (ii) develop a deep neural network based non-linear mapping, dubbed DeepMDS, that transforms the ambient representation to the minimal intrinsic space, and (iii) validate the veracity of the mapping through image matching in the intrinsic space. Experiments on benchmark image datasets (LFW, IJB-C and ImageNet-100) reveal that the intrinsic dimensionality of deep neural network representations is significantly lower than the dimensionality of the ambient features. For instance, SphereFace's 512-dim face representation and ResNet's 512-dim image representation have an intrinsic dimensionality of 16 and 19 respectively. Further, the DeepMDS mapping is able to obtain a representation of significantly lower dimensionality while maintaining discriminative ability to a large extent, 59.75% TAR @ 0.1% FAR in 16-dim vs 71.26% TAR in 512-dim on IJB-C and a Top-1 accuracy of 77.0% at 19-dim vs 83.4% at 512-dim on ImageNet-100.
Collaborative Educational Environments Incorporating Mixed Reality Technologies: A Systematic Mapping Study

In this paper, we report findings from a systematic mapping study, conducted to review the existing literature on collaborative educational environments incorporating mixed reality technologies. There is increasing interest in mixed reality technologies in education, especially with the introduction of new over head mounted displays (OHMDs), such as HoloLens, Oculus Rift, and HTC Vive. with the consideration of areas, such as education, dynamic technology, and complex environments, a research area is identified. We carried out an extensive review of the literature from 2007 to 2017 and conducted an analysis of the works on mixed reality technologies and its subcategories applied to collaborative education environments. Results highlighted the lack of research across the mixed reality spectrum, especially in the augmented virtuality subcategory, as well as technical limitations such as response time in the development of mixed reality technologies for collaborative environments. Furthermore, the difficulty of teaching professionals to replicate mixed reality experiments in real environments, due to the technical skills required, was identified. The main contribution of this paper is the discussion of the current works with visualization of the present state of the area, which is aimed to encourage educators to develop mixed reality artefacts and conduct further research to support collaborative educational environments.
DeepStyle: Multimodal Search Engine for Fashion and Interior Design

In this paper, we propose a multimodal search engine that combines visual and textual cues to retrieve items from a multimedia database aesthetically similar to the query. The goal of our engine is to enable intuitive retrieval of fashion merchandise such as clothes or furniture. Existing search engines treat textual input only as an additional source of information about the query image and do not correspond to the real-life scenario, where the user looks for “the same shirt but of denim”. Our novel method, dubbed DeepStyle, mitigates those shortcomings by using a joint neural network architecture to model contextual dependencies between features of different modalities. We prove the robustness of this approach on two different challenging datasets of fashion items and furniture where our DeepStyle engine outperforms baseline methods by more than 20% on tested datasets. Our search engine is commercially deployed and available through a Web-based application.
DIAVA: A Traffic-Based Framework for Detection of SQL Injection Attacks and Vulnerability Analysis of Leaked Data

SQL injection attack (SQLIA) is among the most common security threats to web-based services that are deployed on cloud. By exploiting web software vulnerabilities, SQL injection attackers can run arbitrary malicious code on target databases to acquire or compromise sensitive data. Although web application firewalls (WAFs) are offered by most cloud service providers, tenants are reluctant to pay for them, since there are few approaches that can report accurate SQLIA statistics for their deployed services. Traditional WAFs focus on blocking suspicious SQL requests. Few of them can accurately decide whether an attack is really harmful and quickly answer how severe the attack is. To raise the tenants' awareness of the seriousness of SQLIAs, in this paper, we introduce a novel traffic-based SQLIA detection and vulnerability analysis framework named DIAVA, which can proactively send warnings to tenants promptly. By analyzing the bidirectional network traffic of SQL operations and applying our proposed multilevel regular expression model, DIAVA can accurately identify successful SQLIAs among all the suspects. Meanwhile, the severity of such SQLIAs and the vulnerabilities of the corresponding leaked data can be quickly evaluated by DIAVA based on its GPU-based dictionary attack analysis engine. Experimental results show that DIAVA not only outperforms state-of-the-art WAFs in detecting SQLAs from the perspectives of precision and recall, but also enables real-time vulnerability evaluation of leaked data caused by SQL injection.
Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems

This paper is concerned with learning and stochastic control in physical systems that contain unknown input signals. These unknown signals are modeled as Gaussian processes (GP) with certain parameterized covariance structures. The resulting latent force models can be seen as hybrid models that contain a first-principle physical model part and a nonparametric GP model part. We briefly review the statistical inference and learning methods for this kind of models, introduce stochastic control methodology for these models, and provide new theoretical observability and controllability results for them.
Improving Conditional Sequence Generative Adversarial Networks by Stepwise Evaluation

Sequence generative adversarial networks (SeqGAN) have been used to improve conditional sequence generation tasks, for example, chit-chat dialogue generation. To stabilize the training of SeqGAN, Monte Carlo tree search (MCTS) or reward at every generation step (REGS) is used to evaluate the goodness of a generated subsequence. MCTS is computationally intensive, but the performance of REGS is worse than MCTS. In this paper, we propose stepwise GAN (StepGAN), in which the discriminator is modified to automatically assign scores quantifying the goodness of each subsequence at every generation step. StepGAN has significantly less computational costs than MCTS. We demonstrate that StepGAN outperforms previous GAN-based methods on both synthetic experiment and chit-chat dialogue generation.