Reviewerly/exHarmony huggingface dataset:
Containers Placement and Migration on Cloud System

Currently, many businesses are using cloud computing to obtain an entire IT infrastructure remotely while delegating its management to a third party. The provider of this architecture ensures the operation and maintenance of the services while offering management capabilities via web consoles.These providers offer solutions based on bare metal or virtualization platforms (mainly virtual machines). Recently, a new type of virtualization-based on containerization technology has emerged. Containers can be deployed on bare metal servers or in virtual machines. The migration of virtual machines (VMs) and Containers in Dynamic Resource Management (DRM) is a crucial factor in minimizing the operating costs of data centers by reducing their energy consumption and subsequently limiting their impact on climate change.In this article, live migration for both types of virtualization will be studied. for that, container placement and migration algorithms are proposed, which takes into account the QoS requirements of different users in order to minimize energy consumption. Thus, a dynamic approach is suggested based on a threshold of RAM usage for host and virtual machines in the data center to avoid unnecessary power consumption. In this paper, the proposed work is compared with VM/Container placement and migration methods, the results of the experiment indicate that using container migration instead of VMs demonstrates a reduction in power consumption, and also reduces the migration time which impacts QoS and reduces SLA violation.
Towards Ethical Machines Via Logic Programming

Autonomous intelligent agents are playing increasingly important roles in our lives.They contain information about us and start to perform tasks on our behalves.Chatbots are an example of such agents that need to engage in a complex conversations with humans.Thus, we need to ensure that they behave ethically.In this work we propose a hybrid logic-based approach for ethical chatbots.
Twin-field quantum key distribution with passive-decoy state

Abstract Twin-Field quantum key distribution (TF-QKD) and its variants, e.g. phase-maching QKD, sending-or-not-sending QKD, and no phase post-selection TFQKD promise high key rates at long distance to beat the rate distance limit without a repeater. The security proof of these protocols are based on decoy-state method, which is usually performed by actively modulating a variable optical attenuator together with a random number generator in practical experiments, however, active-decoy schemes like this may lead to side channel and could open a security loophole. To enhance the source security of TF-QKD, in this paper, we propose passive-decoy based TF-QKD, in which we combine TF-QKD with the passive-decoy method. And we present a simulation comparing the key generation rate with that in active-decoy, the result shows our scheme performs as good as active decoy TF-QKD, and our scheme could reach satisfactory secret key rates with just a few photon detectors. This shows our work is meaningful in practice.
Visualization model validation via inline replication

Data visualizations typically show a representation of a data set with little to no focus on the repeatability or generalizability of the displayed trends and patterns. However, insights gleaned from these visualizations are often used as the basis for decisions about future events. Visualizations of retrospective data therefore often serve as “visual predictive models.” However, this visual predictive model approach can lead to invalid inferences. In this article, we describe an approach to visual model validation called Inline Replication. Inline Replication is closely related to the statistical techniques of bootstrap sampling and cross-validation and, like those methods, provides a non-parametric and broadly applicable technique for assessing the variance of findings from visualizations. This article describes the overall Inline Replication process and outlines how it can be integrated into both traditional and emerging “big data” visualization pipelines. It also provides examples of how Inline Replication can be integrated into common visualization techniques such as bar charts and linear regression lines. Results from an empirical evaluation of the technique and two prototype Inline Replication–based visual analysis systems are also described. The empirical evaluation demonstrates the impact of Inline Replication under different conditions, showing that both (1) the level of partitioning and (2) the approach to aggregation have a major influence over its behavior. The results highlight the trade-offs in choosing Inline Replication parameters but suggest that using [Formula: see text] partitions is a reasonable default.
Morphological Analysis Using a Sequence Decoder

We introduce Morse, a recurrent encoder-decoder model that produces morphological analyses of each word in a sentence. The encoder turns the relevant information about the word and its context into a fixed size vector representation and the decoder generates the sequence of characters for the lemma followed by a sequence of individual morphological features. We show that generating morphological features individually rather than as a combined tag allows the model to handle rare or unseen tags and to outperform whole-tag models. In addition, generating morphological features as a sequence rather than, for example, an unordered set allows our model to produce an arbitrary number of features that represent multiple inflectional groups in morphologically complex languages. We obtain state-of-the-art results in nine languages of different morphological complexity under low-resource, high-resource, and transfer learning settings. We also introduce TrMor2018, a new high-accuracy Turkish morphology data set. Our Morse implementation and the TrMor2018 data set are available online to support future research. 1 See https://github.com/ai-ku/Morse.jl for a Morse implementation in Julia/Knet (Yuret, 2016 ) and https://github.com/ai-ku/TrMor2018 for the new Turkish data set.
Online Evaluations for Everyone: Mr. DLib’s Living Lab for Scholarly Recommendations

We introduce the first 'living lab' for scholarly recommender systems. This lab allows recommender-system researchers to conduct online evaluations of their novel algorithms for scholarly recommendations, i.e., recommendations for research papers, citations, conferences, research grants, etc. Recommendations are delivered through the living lab's API to platforms such as reference management software and digital libraries. The living lab is built on top of the recommender-system as-a-service Mr. DLib. Current partners are the reference management software JabRef and the CORE research team. We present the architecture of Mr. DLib's living lab as well as usage statistics on the first sixteen months of operating it. During this time, 1,826,643 recommendations were delivered with an average click-through rate of 0.21%.
Reservoir computing using high order synchronization of coupled oscillators

We propose a concept for reservoir computing on oscillators using the high-order synchronization effect. The reservoir output is presented in the form of oscillator synchronization metrics: fractional high-order synchronization value and synchronization efficiency, expressed as a percentage. Using two coupled relaxation oscillators built on VO2 switches, we created an oscillator reservoir that allows simulating the XOR operation. The reservoir can operate as with static input data (power currents, coupling forces), as with dynamic data in the form of spike sequences. Having a small number of oscillators and significant non-linearity, the reservoir expresses a wide range of dynamic states. The proposed computing concept can be implemented on oscillators of diverse nature.
Void Probability Function of Simulated Surveys of High-redshift Ly<i>α</i> Emitters

We calculate the void probability function (VPF) in simulations of Lyman-$\alpha$ emitters (LAEs) across a wide redshift range ($z=3.1,\ 4.5,\ 5.7,\ 6.6$). The VPF measures the zero-point correlation function (i.e. places devoid of galaxies) and naturally connects to higher order correlation functions while being computationally simple to calculate. We explore the Poissonian and systematic errors on the VPF, specify its accuracy as a function of average source density and the volume probed, and provide the appropriate size scales to measure the VPF. At small radii the accuracy of the VPF is limited by galaxy density, while at large radii the VPF is limited by the number of independent volumes probed. We also offer guidelines for understanding and quantifying the error in the VPF. We approximate the error in the VPF by using independent sub-volumes of the catalogs, after finding that jackknife statistics underestimate the uncertainty. We use the VPF to probe the strength of higher order correlation functions by measuring and examining the hierarchical scaling between the correlation functions using count-in-cells. The negative binomial model (NBM) has been shown to best describe the scaling between the two point correlation function and VPF for low-redshift galaxy observations. We further test the fit of the NBM by directly deriving the volume averaged two-point correlation function from the VPF and vice versa. We find the NBM best describes the $z=3.1, 4.5, 5.7$ simulated LAEs, with a 1$\sigma$ deviation from the model in the $z=6.6$ catalog. This suggests that LAEs show higher order clustering terms similar to those of normal low redshift galaxies.
LOKI Vote: A Blockchain-Based Coercion Resistant E-Voting Protocol

Creating an online electronic voting system that ensures coercion-resistance and end-to-end verifiability at the same time, has constituted a real challenge for a long period of time. The notion of coercion-resistance was first introduced by Juels, Catalano, and Jakobsson (JCJ) in 2005. Since that time, several research papers have appeared to address the main issue of JCJ scheme (the quadratic complexity of verifying credentials). The majority of these systems have been based on the availability of a secure web bulletin board. Despite this widespread requirement, the notion of an append-only web bulletin board remains vague, and no method of constructing such a bulletin board has been proposed in those papers. Our paper fills the gap and proposes an end-to-end verifiable e-voting protocol based on Blockchain technology. In this research work, we propose a Blockchain-based online electronic voting protocol that ensures all the security requirements expected from secure and democratic elections. Our proposal is inspired from the scheme proposed by Araùjo and Traoré in 2013, which is based on the work of JCJ and has a linear complexity. Called LOKI Vote, our scheme is practical for large scale elections and ensures a strong privacy for voters by using a variety of cryptographic primitives. Additionally, our protocol enhance the complexity of the old coercion resistant systems by using a new mix network, called Low Latency Anonymous Routing Protocol, which is characterized by a lower complexity and a higher level of security. Finally, we formally prove the security of LOKI Vote using the automated verification tool, ProVerif, and the Applied Pi-Calculus modeling language.
Rapid Tests As an Aspect of Communication in the Digital Environment

The transition of the economy to a digital format requires universities to train graduates with special competencies. The most important of them, according to the government and employers, is the competence of communication and cooperation in the digital environment. For its sustainable formation, systematic work is required, covering the entire learning process. The article discusses the possibilities of developing communication competence in the framework of teaching mathematics to first-year undergraduate students of an economic university. The authors see the potential of online tests for building digital communication skills. Regular use of online response systems in the educational process makes it possible to prepare students for some of the features of digital communication: instant immersion on the necessary platform, communication under strict regulations and time constraints. The paper describes the experience of testing students using the Socrative and PollEverywhere resources. Methodical recommendations for the organization of the educational process are given. The effectiveness of using online tests is confirmed by a survey of students.
Overview of existing information on important closed (or in closing phase) and abandoned mining waste sites and related mines in Slovenia

The presented work provides a comprehensive inventory of data on closed (or in closing phase) and abandoned underground and surface mines and mining waste sites in Slovenia, collected within the framework of the Geological Survey of Slovenia (GeoZS) over the last two decades. Furthermore, a detailed overview of information on closed and abandoned mining waste sites in Slovenia is given. In order to establish the inventory, a definition of the work methodology, a comprehensive research of the archived and published literature, and the harmonisation and management of the collected data was carried out. Based on a selected methodology, the inventory contains information on 33 metal mines, 43 coal mines, 51 non-metallic mineral resource mines, 156 waste sites from metal mines and 18 waste sites from coal mines. The inventory is in the open access Google Earth file. It provides a basis for further research into the environmental impact of mining waste, which has been carried out since the EU Directive on the management of waste from extractive industries (Directive 2006/21/EC hereafter) was adopted into national law. In general, about 11,621,333 m3 of waste sites from metal mines and 76,188,000 m3 of waste sites from coal mines are covering about 678 hectares of Slovenian territory. More than half (64 %) of the waste from metal mines was produced in the Mežica lead and zinc mine, while the other two mines with a still significant share of produced waste were the Idrija mercury mine (18 %) and the Žirovski vrh uranium mine (15 %). Among the closed coal mines, the largest amount (46 %) of waste was generated by the coal mine Trbovlje-Hrastnik, while the other two coal mines with still significant share of waste produced were Kočevje (28 %) and Kanižarica (17 %).
Aerodynamic Statistics-Based Trajectory Estimation of Hypersonic Maneuvering Target

Trajectory tracking and estimation of hypersonic glide vehicles (HGVs) is a very challenging issue in the defense systems.Insufficient knowledge about the HGV and inaccurate dynamic models for the accelerating HGV are the main challenges in this regard.In the present study, an integrated nonlinear Markov acceleration model is established to formulate the nonlinear dynamic characteristics of HGVs.Since the aerodynamic accelerations of the HGV are dominant and the corresponding aerodynamic coefficients are unknown, a statistics-based aerodynamic model is proposed.The proposed aerodynamic model is capable of providing primary information of the aerodynamic characteristics even without knowing the configuration of the HGV.Then, considering the maneuver mode of the vehicle, the iterative extended Kalman filter (IEKF) is applied to track the trajectory of the HGV by using the proposed model.Obtained results from the numerical simulation for the equilibrium glide mode and skip maneuver mode indicate that the proposed model can effectively improve the velocity estimation accuracy by about 40%-50% and acceleration estimation accuracy by about 20%-50% in the given examples.
The improved mayfly optimization algorithm with opposition based learning rules

Abstract Literal researches proved that not only the best candidates or the best historical trajectories would perform well in guiding the individuals in swarms to find the best solution, the worst and the worst historical trajectories would also work well in doing so. Such situations could be directly treated as pairs of oppositions, and satisfied the ancient Chinese Yin-Yang philosophy, where the opposition based learning (OBL) rule was directly derived from. In this paper, the improved mayfly optimization (MO) algorithm with OBL rules were proposed, simulation experiments were carried out and results showed that the improved MO algorithm with OBL rules would perform better than usual.
A dataset of micro-scale tomograms of unidirectional glass fiber/epoxy and carbon fiber/epoxy composites acquired via synchrotron computed tomography during in-situ tensile loading

We have performed synchrotron computed tomography on two different fiber-reinforced composites while they were being continuously in-situ loaded in 0° tension. One material is a glass/epoxy laminate and the other is a carbon/epoxy laminate. The voxel size is 1.1 µm, which allows clear recognition of the glass fibers, but not distinct individual carbon fibers. For each material, four loading steps are selected with approximately 0, 40, 73, and 95% of the failure load, and the 3D images of the four volumes from each material are overlaid. A volume of interest in the middle 0° ply is chosen and located in the 3D image of each loading step (Fig. 1). The cropped volumes of interest for each material are presented in this publication and are publicly available on Mendeley Data[1]. As examples of two frequently-used type of unidirectional fiber-reinforced composites, the presented data can be used for different microstructural analyses, including investigation of the 3D variability in fiber distribution and orientation, and their evolution during tensile loading. For example, we have performed fiber orientation analysis on this data, using our digital image correlation-based technique, in [2]. Moreover, real-time formation of fiber breaks with tensile loading can be investigated in the data.
Belief Based on Inconsistent Information

A recent line of research has developed around logics of belief based on evidence [4, 6]. The approach of [6] understands belief as based on information confirmed by a reliable source. We propose a finer analysis of how belief can be based on information, where the confirmation comes from multiple possibly conflicting sources and is of a probabilistic nature. We use Belnap-Dunn logic and its probabilistic extensions to account for potentially contradictory information on which belief is grounded. We combine it with an extension of Łukasiewicz logic, or a bilattice logic, within a two-layer modal logical framework to account for belief.
On Statistical Efficiency in Learning

A central issue of many statistical learning problems is to select an appropriate model from a set of candidate models.Large models tend to inflate the variance (or overfitting), while small models tend to cause biases (or underfitting) for a given fixed dataset.In this work, we address the critical challenge of model selection to strike a balance between model fitting and model complexity, thus gaining reliable predictive power.We consider the task of approaching the theoretical limit of statistical learning, meaning that the selected model has the predictive performance that is as good as the best possible model given a class of potentially misspecified candidate models.We propose a generalized notion of Takeuchi's information criterion and prove that the proposed method can asymptotically achieve the optimal out-sample prediction loss under reasonable assumptions.It is the first proof of the asymptotic property of Takeuchi's information criterion to our best knowledge.Our proof applies to a wide variety of nonlinear models, loss functions, and high dimensionality (in the sense that the models' complexity can grow with sample size).The proposed method can be used as a computationally efficient surrogate for leave-one-out cross-validation.Moreover, for modeling streaming data, we propose an online algorithm that sequentially expands the model complexity to enhance selection stability and reduce computation cost.Experimental studies show that the proposed method has desirable predictive power and significantly less computational cost than some popular methods.
Explicit and implicit measures of emotions: Data-science might help to account for data complexity and heterogeneity

Measuring emotions is a real challenge for fundamental and applied research, especially in ecological contexts. de Wijk and Noldus propose combining two types of measures-explicit to characterize a specific food, and implicit-physiological-to capture the whole experience of a meal in real-life situations. This raises several challenges including development of new and miniaturized sensors and devices but also developing new ways of data analysis. We suggest a path to follow for future studies regarding data analysis: to include Data Science in the game. This field of research may enable developing predictive but also explicative models that link subjective experience of emotions and physiological responses in real-life contexts. We suggest that food scientists should go out of their comfort zone by collaborating with computer scientists and then be trained with the new tools of Data Science, which will undoubtedly enable them 1/ to better manage complex and heterogeneous data sets, 2/ to extract knowledge that will be essential to this field of research.
Characterization and identification of Au pathfinder minerals from an artisanal mine site using X-ray diffraction

Abstract Gold-associated pathfinder minerals have been investigated by identifying host minerals of Au for samples collected from an artisanal mining site near a potential gold mine (Kubi Gold Project) in Dunkwa-On-Offin in the central region of Ghana. We find that for each composition of Au powder (impure) and the residual black hematite/magnetite sand that remains after gold panning, there is a unique set of associated diverse indicator minerals. These indicator minerals are identified as SiO 2 (quartz), Fe 3 O 4 (magnetite) and Fe 2 O 3 (hematite), while contributions from pyrite, arsenopyrites, iridosmine, scheelite, tetradymite, garnet, gypsum and other sulfate materials are insignificant. This constitutes a confirmative identification of Au pathfinding minerals in this particular mineralogical area. The findings suggest that X-ray diffraction could also be applied in other mineralogical sites to aid in identifying indicator minerals of Au and the location of ore bodies at reduced environmental and exploration costs.
Statistical Benchmarking of Scalable Photonic Quantum Systems

Targeting at the realization of scalable photonic quantum technologies, the generation of many photons, their propagation in large optical networks, and a subsequent detection and analysis of sophisticated quantum correlations are essential for the understanding of macroscopic quantum systems. In this experimental contribution, we explore the joint operation of all mentioned ingredients. We benchmark our time-multiplexing framework that includes a high-performance source of multiphoton states and a large multiplexing network, together with unique detectors with high photon-number resolution, readily available for distributing quantum light and measuring complex quantum correlations. Using an adaptive approach that employs flexible time bins, rather than static ones, we successfully verify high-order nonclassical correlations of many photons distributed over many modes. By exploiting the symmetry of our system and using powerful analysis tools, we can analyze correlations that would be inaccessible by classical means otherwise. In particular, we produce on the order of ten photons and distribute them over 64 modes. Nonclassicality is verified with correlation functions up to the 128th order and statistical significances of up to 20 standard deviations.