Reviewerly/exHarmony huggingface dataset:
Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks

Recent advances in large-scale distributed learning algorithms have enabled communication-efficient training via SignSGD. Unfortunately, a major issue continues to plague distributed learning: namely, Byzantine failures may incur serious degradation in learning accuracy. This paper proposes Election Coding, a coding-theoretic framework to guarantee Byzantine-robustness for SignSGD with Majority Vote, which uses minimum worker-master communication in both directions. The suggested framework explores new information-theoretic limits of finding the majority opinion when some workers could be malicious, and paves the road to implement robust and efficient distributed learning algorithms. Under this framework, we construct two types of explicit codes, random Bernoulli codes and deterministic algebraic codes, that can tolerate Byzantine attacks with a controlled amount of computational redundancy. For the Bernoulli codes, we provide upper bounds on the error probability in estimating the majority opinion, which give useful insights into code design for tolerating Byzantine attacks. As for deterministic codes, we construct an explicit code which perfectly tolerates Byzantines, and provide tight upper/lower bounds on the minimum required computational redundancy. Finally, the Byzantine-tolerance of the suggested coding schemes is confirmed by deep learning experiments on Amazon EC2 using Python with MPI4py package.
Typical entanglement for Gaussian states

We consider ensembles of bipartite states resulting from a random passive Gaussian unitary applied to a fiducial pure Gaussian state. We show that the symplectic spectra of the reduced density operators concentrate around that of a thermal state with the same energy. This implies, in particular, the concentration of the entanglement entropy as well as other entropy measures. Our work extends earlier results on the typicality of entanglement beyond the two ensembles and the reduced purity measure considered in the work of Serafini et al. [J. Phys. A: Math. Theor. 40, 9551 (2007)].
Saliency difference based objective evaluation method for a superimposed screen of the HUD with various background

The head-up display (HUD) is an emerging device which can project information on a transparent screen. The HUD has been used in airplanes and vehicles, and it is usually placed in front of the operator's view. In the case of the vehicle, the driver can see not only various information on the HUD but also the backgrounds (driving environment) through the HUD. However, the projected information on the HUD may interfere with the colors in the background because the HUD is transparent. For example, a red message on the HUD will be less noticeable when there is an overlap between it and the red brake light from the front vehicle. As the first step to solve this issue, how to evaluate the mutual interference between the information on the HUD and backgrounds is important. Therefore, this paper proposes a method to evaluate the mutual interference based on saliency. It can be evaluated by comparing the HUD part cut from a saliency map of a measured image with the HUD image.
Analysis of Signals via Non-Maximally Decimated Non-Uniform Filter Banks

This paper addresses the important problem of reconstructing a signal from multiple multirate observations. The observations are modeled as the output of an analysis bank, and time-domain analysis is carried out to design an optimal FIR synthesis bank. We pose this as a minimizing the mean-square problem and prove that at least one optimal solution is always possible. A parametric form for all optimal solutions is obtained for a non-maximally decimated filter bank. The necessary and sufficient conditions for an optimal solution that results in perfect reconstruction (PR), are derived as time-domain pseudocirculant conditions. This represents a novel theoretical contribution in multirate filter bank theory. We explore PR in a more general setting. This results in the ability to design a synthesis bank with a particular delay in the reconstruction. Using this delay, one can achieve PR in cases where it might not have been possible otherwise. Further, we extend the design and analysis to non-uniform filter banks and carry out simulations to verify the derived results.
A verified packrat parser interpreter for parsing expression grammars

Parsing expression grammars (PEGs) offer a natural opportunity for building verified parser interpreters based on higher-order parsing combinators. PEGs are expressive, unambiguous, and efficient to parse in a top-down recursive descent style. We use the rich type system of the PVS specification language and verification system to formalize the metatheory of PEGs and define a reference implementation of a recursive parser interpreter for PEGs. In order to ensure termination of parsing, we define a notion of a well-formed grammar. Rather than relying on an inductive definition of parsing, we use abstract syntax trees that represent the computational trace of the parser to provide an effective proof certificate for correct parsing and ensure that parsing properties including soundness and completeness are maintained. The correctness properties are embedded in the types of the operations so that the proofs can be easily constructed from local proof obligations. Building on the reference parser interpreter, we define a packrat parser interpreter as well as an extension that is capable of semantic interpretation. Both these parser interpreters are proved equivalent to the reference one. All of the parsers are executable. The proofs are formalized in mathematical terms so that similar parser interpreters can be defined in any specification language with a type system similar to PVS.
Data-Driven Interaction Review of an Ed-Tech Application

Smile and Learn is an Ed-Tech company that runs a smart library with more that100 applications, games and interactive stories, aimed at children aged two to 10 and their families.The platform gathers thousands of data points from the interaction with the system to subsequentlyoffer reports and recommendations. Given the complexity of navigating all the content, the libraryimplements a recommender system. The purpose of this paper is to evaluate two aspects of such systemfocused on children: the influence of the order of recommendations on user exploratory behavior, andthe impact of the choice of the recommendation algorithm on engagement. The assessment, based ondata collected between 15 October 2018 and 1 December 2018, required the analysis of the number ofclicks performed on the recommendations depending on their ordering, and an A/B/C testing wheretwo standard recommendation algorithmswere comparedwith a randomrecommendation that servedas baseline. The results suggest a direct connection between the order of the recommendation and theinterest raised, and the superiority of recommendations based on popularity against other alternatives.
Higher dimensional entanglement without correlations

It has been demonstrated both theoretically and experimentally that genuine multipartite entanglement between qubits can exist even in the absence of multipartite correlations. Here, we provide first examples of this effect in higher dimensional systems – qudits. We construct states in which genuine N-partite entanglement between qudits is supported only by correlations involving strictly less than N particles. The construction differs in several aspects from the ones for qubits. The states introduced here are a natural test-bed for candidate quantifiers of genuinely multipartite quantum correlations.
Capacity of the covariance perceptron

The classical perceptron is a simple neural network that performs a binary classification by a linear mapping between static inputs and outputs and application of a threshold. For small inputs, neural networks in a stationary state also perform an effectively linear input-output transformation, but of an entire time series. Choosing the temporal mean of the time series as the feature for classification, the linear transformation of the network with subsequent thresholding is equivalent to the classical perceptron. Here we show that choosing covariances of time series as the feature for classification maps the neural network to what we call a 'covariance perceptron'; a mapping between covariances that is bilinear in terms of weights. By extending Gardner's theory of connections to this bilinear problem, using a replica symmetric mean-field theory, we compute the pattern and information capacities of the covariance perceptron in the infinite-size limit. Closed-form expressions reveal superior pattern capacity in the binary classification task compared to the classical perceptron in the case of a high-dimensional input and low-dimensional output. For less convergent networks, the mean perceptron classifies a larger number of stimuli. However, since covariances span a much larger input and output space than means, the amount of stored information in the covariance perceptron exceeds the classical counterpart. For strongly convergent connectivity it is superior by a factor equal to the number of input neurons. Theoretical calculations are validated numerically for finite size systems using a gradient-based optimization of a soft-margin, as well as numerical solvers for the NP hard quadratically constrained quadratic programming problem, to which training can be mapped.
Implementations of more general solid-state (SWAP) 1 / m and controlled-(swap) 1 / m gates

Abstract Universal quantum gates are the core elements in quantum information processing. We design two schemes to realize more general (SWAP) 1/ m and controlled-(swap) 1/ m gates (for integer <?CDATA $m\geqslant 1$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>m</mml:mi> <mml:mo>≥</mml:mo> <mml:mn>1</mml:mn> </mml:math> ) by directing flying single photons to solid-state quantum dots. The parameter m is easily controlled by adjusting two quarter-wave plates and one half-wave plate. Additional computational qubits are not required to construct the two gates. Evaluations of the gates indicate that our proposals are feasible with current experimental technology.
Machine Learning-based Anomaly Detection of Ganglia Monitoring Data in HEP Data Center

This paper introduces a generic and scalable anomaly detection framework. Anomaly detection can improve operation and maintenance eﬃciency and assure experiments can be carried out eﬀectively. The framework facilitates common tasks such as data sample building, retagging and visualization, deviation measurement and performance measurement for machine learning-based anomaly detection methods. The samples we used are sourced from Ganglia monitoring data. There are several anomaly detection methods to handle spatial and temporal anomalies within the framework. Finally, we show the rudimental application of the framework on Lustre distributed file systems in daily operation and maintenance.
Photonic Engineering for CV-QKD Over Earth-Satellite Channels

Quantum Key Distribution (QKD) via satellite offers up the possibility of unconditionally secure communications on a global scale. Increasing the secret key rate in such systems, via photonic engineering at the source, is a topic of much ongoing research. In this work we investigate the use of photon-added states and photon-subtracted states, derived from two mode squeezed vacuum states, as examples of such photonic engineering. Specifically, we determine which engineered-photonic state provides for better QKD performance when implemented over channels connecting terrestrial receivers with Low-Earth-Orbit satellites. We quantify the impact the number of photons that are added or subtracted has, and highlight the role played by the adopted model for atmospheric turbulence and loss on the predicted key rates. Our results are presented in terms of the complexity of deployment used, with the simplest deployments ignoring any estimate of the channel, and the more sophisticated deployments involving a feedback loop that is used to optimize the key rate for each channel estimation. The optimal quantum state is identified for each deployment scenario investigated.
SELF-SUPERVISED LEARNING FOR MONOCULAR DEPTH ESTIMATION FROM AERIAL IMAGERY

Abstract. Supervised learning based methods for monocular depth estimation usually require large amounts of extensively annotated training data. In the case of aerial imagery, this ground truth is particularly difficult to acquire. Therefore, in this paper, we present a method for self-supervised learning for monocular depth estimation from aerial imagery that does not require annotated training data. For this, we only use an image sequence from a single moving camera and learn to simultaneously estimate depth and pose information. By sharing the weights between pose and depth estimation, we achieve a relatively small model, which favors real-time application. We evaluate our approach on three diverse datasets and compare the results to conventional methods that estimate depth maps based on multi-view geometry. We achieve an accuracy δ1:25 of up to 93.5 %. In addition, we have paid particular attention to the generalization of a trained model to unknown data and the self-improving capabilities of our approach. We conclude that, even though the results of monocular depth estimation are inferior to those achieved by conventional methods, they are well suited to provide a good initialization for methods that rely on image matching or to provide estimates in regions where image matching fails, e.g. occluded or texture-less regions.
Multilevel Ensemble Kalman–Bucy Filters

In this article we consider the linear filtering problem in continuous time.We develop and apply multilevel Monte Carlo (MLMC) strategies for ensemble Kalman-Bucy filters (EnKBFs).These filters can be viewed as approximations of conditional McKean-Vlasov-type diffusion processes.They are also interpreted as the continuous-time analogue of the ensemble Kalman filter , which has proven to be successful due to its applicability and computational cost.We prove that an ideal version of our multilevel EnKBF can achieve a mean square error (MSE) of O( 2), > 0, with a cost of order O( -2 log( ) 2 ).In order to prove this result we provide a Monte Carlo convergence and approximation bounds associated to time-discretized EnKBFs.This implies a reduction in cost compared to the (single level) EnKBF which requires a cost of O( -3 ) to achieve an MSE of O( 2).We test our theory on a linear Ornstein-Uhlenbeck process, which we motivate through highdimensional examples of order ∼ O(10 4 ) and O(10 5 ), where we also numerically test an alternative deterministic counterpart of the EnKBF.
DENETİM ALANINDA BLOKZİNCİR UYGULAMALARININ HARİTALAMA YÖNTEMİ İLE ANALİZİ

Blockchain technology has a wide scope of applicability for accounting and auditing purposes, separate from Bitcoin or other cryptocurrencies. However, although blockchain technology is identified as a popular topic within the field, there currently exists a lack of consensus on how it will be realistically applied. This paper aims to map the presently existing international auditing literature and offer clarity to an otherwise heterogeneous field. The mapping analysis will aid in examining the underlying theme employed within studies. Through the use of the Bibliometrix R-Package "Biblioshiny", a sample of 112 studies were downloaded from the Web of Science Core Collection and analyzed. This study's results show that the topic has gained increased international popularity and separated into distinct research streams; Encrypted Private and Secure Information Sharing, Distributed Ledger, Smart Contracts, Continuous Audit, Audit Trail and Tokenization. However, there still exists a research gap that must be addressed to hasten the adoption of blockchain technology in auditing.
A reference dataset for the Neogene lithostratigraphy in Flanders, Belgium

Subsurface research often makes use of information from locations where subsurface investigations occurred or that had temporary outcrops. In time, data and knowledge of these locations increases, though compilation of the former information, and uniquely identifying these in subsequent publications is seldom done. Data quality control and documentation are therefore required, including tracing the data sources to their unique reference in governmental databases. In this paper, a five-step approach is described on how all relevant data can be uniquely combined into a reference dataset for the Neogene of Flanders. The dataset is made available in the online web portal for soil and subsoil in Flanders. The individual data points, as well as the reference dataset, can be consulted and are re-usable in an accessible format by scientists, professionals and citizens with an interest in the subsurface, and even so by machines. The reference dataset approach can be elaborated for other subsurface data collections and is proposed to evolve to a standard practice for open subsurface data in Flanders. It increases the visibility and the quality of the data and the research. Inclusion of a reference dataset URL in research or other portals further contributes to data and knowledge integration. Such an open data approach is pivotal for (governmental) data management institutes providing geological services that facilitate a more sustainable use and management of the subsurface.
Self-sovereign Identities for Fighting the Impact of COVID-19 Pandemic

Whereas part of the world can benefit from the wonders of digital technologies, undocumented migrants, asylum seekers, and refugees are facing the rough reality of being “invisible” in society and ignored by governments. During the COVID-19 pandemic, the economic impact of the closure of the service industry and the impossibility to keep most of the informal work they perform place them into a limbo without income and unemployment benefits. They are denied formal support because of the lack of a legally recognized identity. For this type of humanitarian support, contemporary identities can offer a way out. Self-sovereign identities (SSI) can provide individuals ownership over their data and the ability to share this with others. The essence is that the owner determines whom they share their identity data with. They have the sole ownership of the identity data. As such, an SSI ensures privacy for undocumented persons. This would allow to provide support to those who are left behind by a lack of formal identity. In this article, we explore the possibility of SSI to assist undocumented individuals throughout the pandemic times. SSI can be used to interact with undocumented workers and to share all kinds of identity data, such as sharing the attestation of COVID-19 tests or the ability to donate money to those who are illegal without sacrificing their identity.
Dynamic real-time risk analytics of uncontrollable states in complex internet of things systems: cyber risk at the edge

The Internet of Things (IoT) triggers new types of cyber risks. Therefore, the integration of new IoT devices and services requires a self-assessment of IoT cyber security posture. By security posture this article refers to the cybersecurity strength of an organisation to predict, prevent and respond to cyberthreats. At present, there is a gap in the state of the art, because there are no self-assessment methods for quantifying IoT cyber risk posture. To address this gap, an empirical analysis is performed of 12 cyber risk assessment approaches. The results and the main findings from the analysis is presented as the current and a target risk state for IoT systems, followed by conclusions and recommendations on a transformation roadmap, describing how IoT systems can achieve the target state with a new goal-oriented dependency model. By target state, we refer to the cyber security target that matches the generic security requirements of an organisation. The research paper studies and adapts four alternatives for IoT risk assessment and identifies the goal-oriented dependency modelling as a dominant approach among the risk assessment models studied. The new goal-oriented dependency model in this article enables the assessment of uncontrollable risk states in complex IoT systems and can be used for a quantitative self-assessment of IoT cyber risk posture.
RBI Distributed Ledger Technology and Blockchain - A Future of Decentralized India

Despite various countries getting hands-on technology such as blockchain for banking, transaction, and multiple benefits, a developing country such as India must use these technologies because of the advantages it provides in order to keep pace. In the age of digital currencies and new emerging technologies, central banking is a fast-growing topic in the monetary economy. Cryptocurrencies, blockchain, and distributed Ledger technologies appear to be feasible rivals to Fiat Currency central bank. Blockchain technology's influence behind Cryptocurrencies. Cryptocurrencies have the ability to boost payments and operations by central banks and serve as a forum from which central banks could Perhaps launch their own digital currencies. RBI Indian central bank is no less important when it comes to technology that would pave the way for the new economy, enriched with technology-centric growth momentum, by increasing support from India's reserve bank and the Indian government for innovation and integrating technologies through regulatory sandboxes and various other systems. This article illustrates distributed ledger technology in the Indian context. The secondary data were obtained from various scholarly journals and websites. We have analysed distributed ledger technology, India’s move towards learning new technologies, different central banks distributed ledger project and examined blockchain technology in the Indian market using the SWOC framework as a research case study.
Hybrid feature selection method based on particle swarm optimization and adaptive local search method

Machine learning has been expansively examined with data classification as the most popularly researched subject. The accurateness of prediction is impacted by the data provided to the classification algorithm. Meanwhile, utilizing a large amount of data may incur costs especially in data collection and preprocessing. Studies on feature selection were mainly to establish techniques that can decrease the number of utilized features (attributes) in classification, also using data that generate accurate prediction is important. Hence, a particle swarm optimization (PSO) algorithm is suggested in the current article for selecting the ideal set of features. PSO algorithm showed to be superior in different domains in exploring the search space and local search algorithms are good in exploiting the search regions. Thus, we propose the hybridized PSO algorithm with an adaptive local search technique which works based on the current PSO search state and used for accepting the candidate solution. Having this combination balances the local intensification as well as the global diversification of the searching process. Hence, the suggested algorithm surpasses the original PSO algorithm and other comparable approaches, in terms of performance.
Discrete statistical models with rational maximum likelihood estimator

A discrete statistical model is a subset of a probability simplex. Its maximum likelihood estimator (MLE) is a retraction from that simplex onto the model. We characterize all models for which this retraction is a rational function. This is a contribution via real algebraic geometry which rests on results on Horn uniformization due to Huh and Kapranov. We present an algorithm for constructing models with rational MLE, and we demonstrate it on a range of instances. Our focus lies on models familiar to statisticians, like Bayesian networks, decomposable graphical models and staged trees.