Reviewerly/exHarmony huggingface dataset:
Workload Stability-Aware Virtual Machine Consolidation Using Adaptive Harmony Search in Cloud Datacenters

Owing to the increasing complexity of managing IT infrastructure caused by rapid technological advancements, organizations are transforming their datacenter management environments from on-premises to the cloud. Datacenters operating in the cloud environment have large amounts of IT infrastructure, such as servers, storage devices, and network equipment, and are operational on all days of the year, thus causing power overconsumption problems. However, efforts to reduce power consumption are not the first priority as datacenters seek stable operation to avoid violating their service level agreements. Therefore, a research model that reduces power consumption of the datacenter while enabling stable operation by utilizing virtual machine (VM) consolidation is proposed here. To obtain the optimal solution for the proposed VM consolidation model, an adaptive harmony search methodology is developed, which expends less effort to set the parameters of the model compared to existing harmony search methods. Comparative experiments were conducted to validate the accuracy and performance of the proposed model. As a result, Original harmony search (HS) showed better performance than the existing heuristic algorithm, and novel self-adaptive (NS)-HS showed the best result among Adaptive HS. In addition, when considering workload stability, it showed better results in terms of datacenters (DC) stability than otherwise.
Metaphoric perceptions of pre-service teachers about ‘LEGO Robotic Instructional Practices,’ ‘Augmented Reality’ and ‘Flipped Classroom’ concepts

The aim of this study is to reveal perceptions of pre-service teachers about ‘LEGO robotic instructional practices,’ ‘augmented reality’ and ‘flipped classroom’ concepts through metaphors. The study is descriptive research that was conducted as a survey model. The study group consists of 37 pre-service teachers studying the undergraduate programme of Science Teaching, Faculty of Education of a university in the Western Black Sea Region in the spring semester of the academic year 2016–2017. A form consisting of three open-ended questions was used as a data collection tool during the study. The data acquired were analyzed through content analysis method. Following the study, 15 different metaphors created in relation to the LEGO robotic instructional practices concept were categorized as ‘edutainment tool,’ ‘a technological tool’ and a ‘development tool,’ while 24 different metaphors created in relation to the augmented reality concept were categorized as ‘Perception Of Reality’, ‘A Technological Tool’, ‘Change/Transformation Tool’ and ‘Entertainment Tool’. Moreover, 25 different metaphors created in relation to the flipped classroom concept were classified in two categories as a ‘form of education’ and ‘an education shaping tool’. Moreover, almost all of the metaphors created were observed to have positive features.
The Hierarchical Classifier for COVID-19 Resistance Evaluation

Finding dependencies in the data requires the analysis of relations between dozens of parameters of the studied process and hundreds of possible sources of influence on this process. Dependencies are nondeterministic and therefore modeling requires the use of statistical methods for analyzing random processes. Part of the information is often hidden from observation or not monitored. That is why many difficulties have arisen in the process of analyzing the collected information. The paper aims to find frequent patterns and parameters affected by COVID-19. The novelty of the paper is hierarchical architecture comprises supervised and unsupervised methods. It allows the development of an ensemble of the methods based on k-means clustering and classification. The best classifiers from the ensemble are random forest with 500 trees and XGBoost. Classification for separated clusters gives us higher accuracy on 4% in comparison with dataset analysis. The proposed approach can be used also for personalized medicine decision support in other domains. The features selection allows us to analyze the following features with the highest impact on COVID-19: age, sex, blood group, had influenza.
Multisensory integration involved in the body perception of community-dwelling older adults

Abstract This study investigates how the multisensory integration in body perception changes with increasing age, and whether it is associated with older adults’ risk of falling. For this, the rubber hand illusion (RHI) and rubber foot illusion (RFI) were used. Twenty-eight community-dwelling older adults and 25 university students were recruited. They viewed a rubber hand or foot that was stimulated in synchrony or asynchrony with their own hidden hand or foot. The illusion was assessed by using a questionnaire, and measuring the proprioceptive drift and latency. The Timed Up and Go Test was used to classify the older adults into lower and higher fall-risk groups. No difference was observed in the RHI between the younger and older adults. However, several differences were observed in the RFI. Specifically, the older adults with a lower fall-risk hardly experienced the illusion, whereas those with a higher fall-risk experienced it with a shorter latency and no weaker than the younger adults. These results suggest that in older adults, the mechanism of multisensory integration for constructing body perception can change depending on the stimulated body parts, and that the risk of falling is associated with multisensory integration.
The Marriage of Univalence and Parametricity

Reasoning modulo equivalences is natural for everyone, including mathematicians. Unfortunately, in proof assistants based on type theory, which are frequently used to mechanize mathematical results and carry out program verification efforts, equality is appallingly syntactic, and as a result, exploiting equivalences is cumbersome at best. Parametricity and univalence are two major concepts that have been explored in the literature to transport programs and proofs across type equivalences, but they fall short of achieving seamless, automatic transport. This work first clarifies the limitations of these two concepts when considered in isolation and then devises a fruitful marriage between both. The resulting concept, called univalent parametricity , is an extension of parametricity strengthened with univalence that fully realizes programming and proving modulo equivalences. Our approach handles both type and term dependency, as well as type-level computation. In addition to the theory of univalent parametricity, we present a lightweight framework implemented in the Coq proof assistant that allows the user to transparently transfer definitions and theorems for a type to an equivalent one, as if they were equal. For instance, this makes it possible to conveniently switch between an easy-to-reason-about representation and a computationally efficient representation as soon as they are proven equivalent. The combination of parametricity and univalence supports transport à la carte : basic univalent transport, which stems from a type equivalence, can be complemented with additional proofs of equivalences between functions over these types, in order to be able to transport more programs and proofs, as well as to yield more efficient terms. We illustrate the use of univalent parametricity on several examples, including a recent integration of native integers in Coq. This work paves the way to easier-to-use proof assistants by supporting seamless programming and proving modulo equivalences.
Reinforced Transformer with Cross-Lingual Distillation for Cross-Lingual Aspect Sentiment Classification

Though great progress has been made in the Aspect-Based Sentiment Analysis(ABSA) task through research, most of the previous work focuses on English-based ABSA problems, and there are few efforts on other languages mainly due to the lack of training data. In this paper, we propose an approach for performing a Cross-Lingual Aspect Sentiment Classification (CLASC) task which leverages the rich resources in one language (source language) for aspect sentiment classification in a under-resourced language (target language). Specifically, we first build a bilingual lexicon for domain-specific training data to translate the aspect category annotated in the source-language corpus and then translate sentences from the source language to the target language via Machine Translation (MT) tools. However, most MT systems are general-purpose, it non-avoidably introduces translation ambiguities which would degrade the performance of CLASC. In this context, we propose a novel approach called Reinforced Transformer with Cross-Lingual Distillation (RTCLD) combined with target-sensitive adversarial learning to minimize the undesirable effects of translation ambiguities in sentence translation. We conduct experiments on different language combinations, treating English as the source language and Chinese, Russian, and Spanish as target languages. The experimental results show that our proposed approach outperforms the state-of-the-art methods on different target languages.
Weighted Schatten p-norm minimization for impulse noise removal with TV regularization and its application to medical images

Noise of impulse type was common in medical images. In this paper, we modeled the denoising problem for impulse noise by Weighted Schatten p-norm minimization (WSNM) with Robust Principal Component Analysis (RPCA). The anisotropic Total Variation (TV) regularization was incorporated to preserve edge information which was important for clinic detection and diagnosis. The alternating direction method of multipliers (ADMM) algorithm was adopted for solving the formulated nonconvex optimization problem. We tested the performance on both standard natural images and medical images with additive impulse noise in different levels. Experiment results implied its competitiveness compared to traditional denoising algorithms that validated to be state-of-the-art. The propose algorithm restored images with better structure information preservation outperformed the conventional techniques in terms of visual appearances. Quantitative metrics (PSNR, SSIM and FSIM) further objectively demonstrated the effectiveness of the proposed algorithm for impulse noise removal superior to the existing ones.
Cross Entropy Hyperparameter Optimization for Constrained Problem Hamiltonians Applied to QAOA

Hybrid quantum-classical algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) are considered as one of the most encouraging approaches for taking advantage of near-term quantum computers in practical applications. Such algorithms are usually implemented in a variational form, combining a classical optimization method with a quantum machine to find good solutions to an optimization problem. The solution quality of QAOA depends to a high degree on the parameters chosen by the classical optimizer at each iteration. However, the solution landscape of those parameters is highly multi-dimensional and contains many low-quality local optima. In this study we apply a Cross-Entropy method to shape this landscape, which allows the classical optimizer to find better parameter more easily and hence results in an improved performance. We empirically demonstrate that this approach can reach a significant better solution quality for the Knapsack Problem.
Minimum Relevant Features to Obtain Explainable Systems for Predicting Cardiovascular Disease Using the Statlog Data Set

Learning systems have been focused on creating models capable of obtaining the best results in error metrics. Recently, the focus has shifted to improvement in the interpretation and explanation of the results. The need for interpretation is greater when these models are used to support decision making. In some areas, this becomes an indispensable requirement, such as in medicine. The goal of this study was to define a simple process to construct a system that could be easily interpreted based on two principles: (1) reduction of attributes without degrading the performance of the prediction systems and (2) selecting a technique to interpret the final prediction system. To describe this process, we selected a problem, predicting cardiovascular disease, by analyzing the well-known Statlog (Heart) data set from the University of California’s Automated Learning Repository. We analyzed the cost of making predictions easier to interpret by reducing the number of features that explain the classification of health status versus the cost in accuracy. We performed an analysis on a large set of classification techniques and performance metrics, demonstrating that it is possible to construct explainable and reliable models that provide high quality predictive performance.
Reducing Confusion in Active Learning for Part-Of-Speech Tagging

Active learning (AL) uses a data selection algorithm to select useful training samples to minimize annotation cost. This is now an essential tool for building low-resource syntactic analyzers such as part-of-speech (POS) taggers. Existing AL heuristics are generally designed on the principle of selecting uncertain yet representative training instances, where annotating these instances may reduce a large number of errors. However, in an empirical study across six typologically diverse languages (German, Swedish, Galician, North Sami, Persian, and Ukrainian), we found the surprising result that even in an oracle scenario where we know the true uncertainty of predictions, these current heuristics are far from optimal. Based on this analysis, we pose the problem of AL as selecting instances that maximally reduce the confusion between particular pairs of output tags. Extensive experimentation on the aforementioned languages shows that our proposed AL strategy outperforms other AL strategies by a significant margin. We also present auxiliary results demonstrating the importance of proper calibration of models, which we ensure through cross-view training, and analysis demonstrating how our proposed strategy selects examples that more closely follow the oracle data distribution. The code is publicly released here. 1
A method of identifying allographs in undeciphered scripts and its application to the Indus Valley Script

Abstract This work describes a general method of testing for redundancies in the sign lists of ancient scripts by data mining the positions of the signs within the inscriptions. The redundant signs are allographs of the same grapheme. The method is applied to the undeciphered Indus Valley Script, which stands out from other ancient scripts by having a large proposed sign list that contains dozens of asymmetric signs that have mirrored pairs. By a statistical analysis of mirrored asymmetric signs, this paper shows that the Indus Valley Script was multi-directional and the mirroring of signs often denotes only the direction of writing without any difference in meaning. For this and five other specific reasons listed in the paper, 50 pairs of signs, 23 mirrored, and 27 non-mirrored, can be grouped together because each pair consists of only insignificant variations of the same original sign. The reduced sign list may make decipherment easier in the future.
Beyond the Limits of Shannon’s Information in Quantum Key Distribution

We present a new post-processing method for Quantum Key Distribution (QKD) that raises cubically the secret key rate in the number of double matching detection events. In Shannon’s communication model, information is prepared at Alice’s side, and it is then intended to pass it over a noisy channel. In our approach, secret bits do not rely in Alice’s transmitted quantum bits but in Bob’s basis measurement choices. Therefore, measured bits are publicly revealed, while bases selections remain secret. Our method implements sifting, reconciliation, and amplification in a unique process, and it just requires a round iteration; no redundancy bits are sent, and there is no limit in the correctable error percentage. Moreover, this method can be implemented as a post-processing software into QKD technologies already in use.
A virtual tourist counselor expressing intimacy behaviors: A new perspective to create emotion in visitors and offer them a better user experience?

Professional embodied conversational agents (ECAs) deployed in the market are dedicated to satisfying the digital customer relationship. However, ECAs still suffer from a lack of user adoption, in particular because most of them have few social skills. Inspired by social presence theories and based on the ergonomic components of user experience (CUE) model (Mahlke and Lindgaard, 2007), the paper focuses on one social skill, i.e., virtual intimacy, a 3-dimensional concept including honesty and genuineness, positivity and mutual comprehension. Virtual intimacy may be a way to reinforce the social dimension of human-agent interactions and to provide a better user experience. We therefore propose an interactive experiment that incorporates natural interactions between real tourists and an autonomously intimate virtual counselor who is an expert in tourism and able to express intimacy-related behaviors in verbal and nonverbal communication. The paper studies the impact of the agent’s expression of intimacy-related behaviors on the perception of virtual intimacy, social presence and the user experience. The results show that users adopt a social attitude toward the intimate counselor, and although they do not significantly perceive virtual intimacy, they clearly perceive the dimension of honesty and genuineness. Moreover, the agent’s expression of intimacy only enhances copresence, the first perceptive level of social presence. Except for user social status, which is enhanced, the user experience is also not significantly influenced by intimate expression. The study results further demonstrate that perceiving virtual intimacy is a good predictor of social presence and user experience, especially user emotional reactions. Perceiving virtual intimacy influences user experience independently of social presence, which thereby indicates that independent intimacy-related mechanisms, such as emotional contagion, may be involved. Mediation analyses also underline that perceiving virtual intimacy has a direct effect on user emotional reactions, but this finding is not supported by the CUE model. The findings in this study provide new evidence that perceiving virtual intimacy in human-agent interaction elicits emotions in users and enhances user experience. In line with this, we propose an outline for an ECA-adapted, user experience model based on the CUE model.
Power Transformers Thermal Modeling using an Enhanced Set-Membership Multivariable Gaussian Evolving Fuzzy System

In this work we present two new models based on Evolving Multivariable Gaussian approach and on the Set-Membership/Enhanced Set-Membership adaptive filtering framework to model the thermal behavior of power transformers. In these new models, adaptive filtering approaches work to adjust the learning rates of the evolutionary model, while the use of multivariable Gaussian membership functions, instead of single-variable as evolving models in general, makes information about interactions between input variables preserved and used in the training process. In addition, the evolving structure of the proposed models make these models more adaptable to changes in the operational conditions of power transformers (like insulation aging, environmental changes, load profile changes, among others) than its non-evolving counterparts. To evaluate the performance of the proposed models they were applied to two benchmark problems and to the thermal modeling of real power transformers problem under two load conditions: with and without an overload condition. The obtained results are compared with the performance of the original evolving Multivariable Gaussian and with other classical models suggested in the literature. Both proposed models obtained significantly higher performances than all the other tested models, suggesting that the models are flexible and efficient approaches in these scenarios representing a promising approach in the modeling of power transformers, especially for real-time applications.
Visual Interestingness Prediction: A Benchmark Framework and Literature Review

In this paper, we report on the creation of a publicly available, common evaluation framework for image and video visual interestingness prediction. We propose a robust data set, the Interestingness10k, with 9831 images and more than 4 h of video, interestigness scores determined based on more than 1M pair-wise annotations of 800 trusted annotators, some pre-computed multi-modal descriptors, and 192 system output results as baselines. The data were validated extensively during the 2016–2017 MediaEval benchmark campaigns. We provide an in-depth analysis of the crucial components of visual interestingness prediction algorithms by reviewing the capabilities and the evolution of the MediaEval benchmark systems, as well as of prominent systems from the literature. We discuss overall trends, influence of the employed features and techniques, generalization capabilities and the reliability of results. We also discuss the possibility of going beyond state-of-the-art performance via an automatic, ad-hoc system fusion, and propose a deep MLP-based architecture that outperforms the current state-of-the-art systems by a large margin. Finally, we provide the most important lessons learned and insights gained.
Reversible data hiding in encrypted images for coding channel based on adaptive steganography

In this study, a novel reversible data hiding (RDH) in encrypted domain scheme for coding channel based on sliding-block segmentation and adaptive steganography is proposed. The proposed scheme enriches the residual information with as little additional encryption information as possible to improve the testing error rate of a steganalyser by sliding-block segmentation with bit stream encryption. The specific encryption process effectively weakens the correlation between the adjacent pixels and minimises the size of key stream bits. The encryption key can be further embedded in the channel code stream before transmitted in the channel. Experimental analysis shows that the image encrypted by the proposed RDH scheme can achieve a peak-signal-to-noise ratio of >50 dB, as the payload is 0.5 bits per pixel (bpp). In terms of security performance, compared with the state-of-the-art methods, their method has a higher testing error rate when the steganalyser is utilised. Even if the payload is 0.5 bpp, the testing error rate is >0.25.
5G Intelligence Underpinning Railway Safety in the COVID-19 Era

OPINION article Front. Built Environ., 24 February 2021Sec. Transportation and Transit Systems Volume 7 - 2021 | https://doi.org/10.3389/fbuil.2021.639753
Audio-Visual Hybrid Approach for Filling Mass Estimation

Object handover is a fundamental and essential capability for robots interacting with humans in many applications such as household chores. In this challenge, we estimate the physical properties of a variety of containers with different fillings such as container capacity and the type and percentage of the content to achieve collaborative physical handover between humans and robots. We introduce multi-modal prediction models using audio-visual-datasets of people interacting with containers distributed by CORSMAL.