Reviewerly/exHarmony huggingface dataset:
Improved Summation from Shuffling

A protocol by Ishai et al.\ (FOCS 2006) showing how to implement distributed $n$-party summation from secure shuffling has regained relevance in the context of the recently proposed \emph{shuffle model} of differential privacy, as it allows to attain the accuracy levels of the curator model at a moderate communication cost. To achieve statistical security $2^{-σ}$, the protocol by Ishai et al.\ requires the number of messages sent by each party to {\em grow} logarithmically with $n$ as $O(\log n + σ)$. In this note we give an improved analysis achieving a dependency of the form $O(1+σ/\log n)$. Conceptually, this addresses the intuitive question left open by Ishai et al.\ of whether the shuffling step in their protocol provides a "hiding in the crowd" amplification effect as $n$ increases. From a practical perspective, our analysis provides explicit constants and shows, for example, that the method of Ishai et al.\ applied to summation of $32$-bit numbers from $n=10^4$ parties sending $12$ messages each provides statistical security $2^{-40}$.
Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis

Several data analysis techniques employ similarity relationships between data points to uncover the intrinsic dimension and geometric structure of the underlying data-generating mechanism. In this paper we work under the model assumption that the data is made of random perturbations of feature vectors lying on a low-dimensional manifold. We study two questions: how to define the similarity relationship over noisy data points, and what is the resulting impact of the choice of similarity in the extraction of global geometric information from the underlying manifold. We provide concrete mathematical evidence that using a local regularization of the noisy data to define the similarity improves the approximation of the hidden Euclidean distance between unperturbed points. Furthermore, graph-based objects constructed with the locally regularized similarity function satisfy better error bounds in their recovery of global geometric ones. Our theory is supported by numerical experiments that demonstrate that the gain in geometric understanding facilitated by local regularization translates into a gain in classification accuracy in simulated and real data.
Design and Implementation of Human-Computer Interaction Based on User Experience for Dynamic Mathematics Software

Human-computer interaction (HCI) serves as a critical mediator between users and dynamic mathematics software (DMS), which directly affects users in effective and emotional experience. To improve user experience, this paper puts forward eight HCI design principles for DMS based on user experience with the concept of clarity, reliability, efficiency and motivation. The principles were applied into NetPad, a web-based DMS, for the purpose of effectiveness validation. The experiment suggested that the eight principles are beneficial to improve user experience, making users feel friendly and raising the efficiency of DMS in teaching as well.
Approximate Bayesian inference for mixture cure models

Cure models in survival analysis deal with populations in which a part of the individuals cannot experience the event of interest. Mixture cure models consider the target population as a mixture of susceptible and non-susceptible individuals. The statistical analysis of these models focuses on examining the probability of cure (incidence model) and inferring on the time to event in the susceptible subpopulation (latency model). Bayesian inference for mixture cure models has typically relied upon Markov chain Monte Carlo (MCMC) methods. The integrated nested Laplace approximation (INLA) is a recent and attractive approach for doing Bayesian inference but in its natural definition cannot fit mixture models. This paper focuses on the implementation of a feasible INLA extension for fitting standard mixture cure models. Our proposal is based on an iterative algorithm which combines the use of INLA for estimating the process of interest in each of the subpopulations in the study, and Gibbs sampling for computing the posterior distribution of the cure latent indicator variable which classifies individuals to the susceptible or non-susceptible subpopulations. We illustrated our approach by means of the analysis of two paradigmatic datasets in the framework of clinical trials. Outputs provide closing estimates and a substantial reduction of computational time in relation to those using MCMC.
Statistical Sequential Analysis for Object-based Video Forgery Detection

Over the years, video surveillance systems have been used for indisputable evidence of a crime. Unfortunately, videos of the surveillance systems can be forged through adding (deleting) an object to (from) a video scene (i.e., object-based forgery) with invisible traces and little effort. In this paper, we propose a novel approach that uses spatial decomposition, temporal filtering, and sequential analysis to detect object-based video forgery and estimate a movement of removed objects. The results show that our approach not only outperforms a previous approach in detecting forged videos but it is also more robust against compressed and lower resolution videos. Also, our approach can effectively estimate a movement of different sizes of removed objects.
Static Analysis for Proactive Security

We reflect on current problems and practices in system security, distinguishing between reactive security – which deals with vulnerabilities as they are being exploited – and proactive security – which means to make vulnerabilities un-exploitable by removing them from a system entirely. Then we argue that static analysis is well poised to support approaches to proactive security, since it is sufficiently expressive to represent many vulnerabilities yet sufficiently efficient to detect vulnerabilities prior to system deployment. We further show that static analysis interacts well with both confidentiality and integrity aspects and discuss what security assurances it can attain. Next we argue that security models such as those for access control can also be statically analyzed to support proactive security of such models. Finally, we identify research problems in static analysis whose solutions would stand to improve the effectiveness and adoption of static analysis for proactive security in the practice of designing, implementing, and assuring future ICT systems.
A Sustainable and Open Access Knowledge Organization Model to Preserve Cultural Heritage and Language Diversity

This paper proposes a new collaborative and inclusive model for Knowledge Organization Systems (KOS) for sustaining cultural heritage and language diversity. It is based on contributions of end-users as well as scientific and scholarly communities from across borders, languages, nations, continents, and disciplines. It consists in collecting knowledge about all worldwide translations of one original work and sharing that data through a digital and interactive global knowledge map. Collected translations are processed in order to build multilingual parallel corpora for a large number of under-resourced languages as well as to highlight the transnational circulation of knowledge. Building such corpora is vital in preserving and expanding linguistic and traditional diversity. Our first experiment was conducted on the world-famous and well-traveled American novel Adventures of Huckleberry Finn by the American author Mark Twain. This paper reports on 10 parallel corpora that are now sentence-aligned pairs of English with Basque (an European under-resourced language), Bulgarian, Dutch, Finnish, German, Hungarian, Polish, Portuguese, Russian, and Ukrainian, processed out of 30 collected translations.
Discounted Reinforcement Learning Is Not an Optimization Problem

Discounted reinforcement learning is fundamentally incompatible with function approximation for control in continuing tasks. It is not an optimization problem in its usual formulation, so when using function approximation there is no optimal policy. We substantiate these claims, then go on to address some misconceptions about discounting and its connection to the average reward formulation. We encourage researchers to adopt rigorous optimization approaches, such as maximizing average reward, for reinforcement learning in continuing tasks.
A Gravitational-Double Layer Extreme Learning Machine and its Application in Powerlifting Analysis

Powerlifting is a strength sport that is quite popular in the world. Powerlifters have their power levels varied at different ages and body weights, and their power levels are closely related to their performance. Therefore, studying the impact of age and weight on the performance of powerlifters is an important work. The traditional method relies mainly on artificial experience to judge the performance, and often does not get the desired results. In recent years, machine learning has developed rapidly, and applying machine learning in sports is a very interesting topic. This study is based on a new machine learning algorithm to construct a prediction model for the best performance of powerlifters. We propose a double-layer extreme learning machine based on affine transformation and two-layer extreme learning machine theory (AF-DELM). Then use a dynamic weight-gravitational search algorithm to improve the AF-DELM networks. The results show that the algorithm can better predict the performance and provide an effective predictive aid for the powerlifting competition.
ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation

Transparent objects are a common part of everyday life, yet they possess unique visual properties that make them incredibly difficult for standard 3D sensors to produce accurate depth estimates for. In many cases, they often appear as noisy or distorted approximations of the surfaces that lie behind them. To address these challenges, we present ClearGrasp -- a deep learning approach for estimating accurate 3D geometry of transparent objects from a single RGB-D image for robotic manipulation. Given a single RGB-D image of transparent objects, ClearGrasp uses deep convolutional networks to infer surface normals, masks of transparent surfaces, and occlusion boundaries. It then uses these outputs to refine the initial depth estimates for all transparent surfaces in the scene. To train and test ClearGrasp, we construct a large-scale synthetic dataset of over 50,000 RGB-D images, as well as a real-world test benchmark with 286 RGB-D images of transparent objects and their ground truth geometries. The experiments demonstrate that ClearGrasp is substantially better than monocular depth estimation baselines and is capable of generalizing to real-world images and novel objects. We also demonstrate that ClearGrasp can be applied out-of-the-box to improve grasping algorithms' performance on transparent objects. Code, data, and benchmarks will be released. Supplementary materials available on the project website: https://sites.google.com/view/cleargrasp
Towards Deployment of Robust AI Agents for Human-Machine Partnerships

We study the problem of designing AI agents that can robustly cooperate with people in human-machine partnerships. Our work is inspired by real-life scenarios in which an AI agent, e.g., a virtual assistant, has to cooperate with new users after its deployment. We model this problem via a parametric MDP framework where the parameters correspond to a user's type and characterize her behavior. In the test phase, the AI agent has to interact with a user of unknown type. Our approach to designing a robust AI agent relies on observing the user's actions to make inferences about the user's type and adapting its policy to facilitate efficient cooperation. We show that without being adaptive, an AI agent can end up performing arbitrarily bad in the test phase. We develop two algorithms for computing policies that automatically adapt to the user in the test phase. We demonstrate the effectiveness of our approach in solving a two-agent collaborative task.
A Good-Turing estimator for feature allocation models

Feature allocation models generalize classical species sampling models by allowing every observation to belong to more than one species, now called features.Under the popular Bernoulli product model for feature allocation, we assume n observable samples and we consider the problem of estimating the expected number Mn of hitherto unseen features that would be observed if one additional individual was sampled.The interest in estimating Mn is motivated by numerous applied problems where the sampling procedure is expensive, in terms of time and/or financial resources allocated, and further samples can be only motivated by the possibility of recording new unobserved features.We consider a nonparametric estimator Mn of Mn which has the same analytic form of the popular Good-Turing estimator of the missing mass in the context of species sampling models.We show that Mn admits a natural interpretation both as a jackknife estimator and as a nonparametric empirical Bayes estimator.Furthermore, we give provable guarantees for the performance of Mn in terms of minimax rate optimality, and we provide with an interesting connection between Mn and the Good-Turing estimator for species sampling.Finally, we derive nonasymptotic confidence intervals for Mn, which are easily computable and do not rely on any asymptotic approximation.Our approach is illustrated with synthetic data and SNP data from the ENCODE sequencing genome project.
V.P. Adrianova-Peretts’s Memorial Library As an Artifact of History of Russian Philological Science Rising

The article characterizes V.P. Adrianova-Peretts’s memorial library that is currently divided into two parts stored in Rare Book and Manuscript Department of the SPSTL SD RAS (Novosibirsk) and the Library of the Institute of the Russian Literature (Pushkin House of the Russian Academy of Sciences) (Saint Petersburg). The collection is based on books purchased by V.N. Peretts (i.e., academician, V.P. Adrianova-Peretts’s husband). The library summarizes research interests because it includes books related to philology, history and literature search methodology, as well as ethnographic and folk books dedicated to various national cultures. The memorial library contains several rarities. &#x0D; Authors analysed the library composition and sources of several books. Several inscriptions and autographs observed on the library books were submitted and analysed. Records of famous scientists (historians, ethnographers, philologists etc.) were detected among them. Unique D.S. Likhachyov’s inscriptions designed as ornaments of ancient Russian manuscripts were particularly noted.
Development of a Directed Teleport Function for Immersive Training in Virtual Reality

Recent advances in Virtual Reality (VR) technology have contributed to the development of immersive applications for training and simulation. VR serious games can be utilized to support and supplement traditional training. In VR-based training environments, the player can make mistakes without serious consequences to gather experiences which help them to make better decisions in the future. However, it is difficult to guide the player's attention throughout the VR game, since the player has the freedom to look anywhere anytime. Thus, many game designers create linear and restrictive experiences. In this paper, we develop a dynamic story and guide the player's attention to the specific game elements. To this end, we propose a novel directed teleport function to show points of interest to the player. We evaluate the effect of the proposed function by conducting a user study among two groups of 20 participants: one group can use only the common teleport function, whereas the other group can additionally use the directed teleport function. The results of our study indicate that the directed teleport function is very effective, has a positive effect on the orientation, and is very easy to use. In particular, the directed teleport function not only helps the player to navigate through the virtual world but also reveals Interesting points.
Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: A Joint Gradient Estimation and Tracking Approach

Many modern large-scale machine learning problems benefit from decentralized and stochastic optimization. Recent works have shown that utilizing both decentralized computing and local stochastic gradient estimates can outperform state-of-the-art centralized algorithms, in applications involving highly non-convex problems, such as training deep neural networks. In this work, we propose a decentralized stochastic algorithm to deal with certain smooth non-convex problems where there are $m$ nodes in the system, and each node has a large number of samples (denoted as $n$). Differently from the majority of the existing decentralized learning algorithms for either stochastic or finite-sum problems, our focus is given to both reducing the total communication rounds among the nodes, while accessing the minimum number of local data samples. In particular, we propose an algorithm named D-GET (decentralized gradient estimation and tracking), which jointly performs decentralized gradient estimation (which estimates the local gradient using a subset of local samples) and gradient tracking (which tracks the global full gradient using local estimates). We show that, to achieve certain $\epsilon$ stationary solution of the deterministic finite sum problem, the proposed algorithm achieves an $\mathcal{O}(mn^{1/2}\epsilon^{-1})$ sample complexity and an $\mathcal{O}(\epsilon^{-1})$ communication complexity. These bounds significantly improve upon the best existing bounds of $\mathcal{O}(mn\epsilon^{-1})$ and $\mathcal{O}(\epsilon^{-1})$, respectively. Similarly, for online problems, the proposed method achieves an $\mathcal{O}(m \epsilon^{-3/2})$ sample complexity and an $\mathcal{O}(\epsilon^{-1})$ communication complexity, while the best existing bounds are $\mathcal{O}(m\epsilon^{-2})$ and $\mathcal{O}(\epsilon^{-2})$, respectively.
Noise-Resilient Quantum Dynamics Using Symmetry-Preserving Ansatzes

We describe and demonstrate a method for the computation of quantum dynamics on small, noisy universal quantum computers. This method relies on the idea of `restarting' the dynamics; at least one approximate time step is taken on the quantum computer and then a parameterized quantum circuit ansatz is optimized to produce a state that well approximates the time-stepped results. The simulation is then restarted from the optimized state. By encoding knowledge of the form of the solution in the ansatz, such as ensuring that the ansatz has the appropriate symmetries of the Hamiltonian, the optimized ansatz can recover from the effects of decoherence. This allows for the quantum dynamics to proceed far beyond the standard gate depth limits of the underlying hardware, albeit incurring some error from the optimization, the quality of the ansatz, and the typical time step error. We demonstrate this methods on the Aubry-André model with interactions at half-filling, which shows interesting many-body localization effects in the long time limit. Our method is capable of performing high-fidelity Hamiltonian simulation hundred of time steps longer than the standard Trotter approach. These results demonstrate a path towards using small, lossy devices to calculate quantum dynamics.
Evolution of $k$ -Mer Frequencies and Entropy in Duplication and Substitution Mutation Systems

Genomic evolution can be viewed as string-editing processes driven by mutations. An understanding of the statistical properties resulting from these mutation processes is of value in a variety of tasks related to biological sequence data, e.g., estimation of model parameters and compression. At the same time, due to the complexity of these processes, designing tractable stochastic models and analyzing them are challenging. In this paper, we study two kinds of systems, each representing a set of mutations. In the first system, tandem duplications and substitution mutations are allowed and in the other, interspersed duplications. We provide stochastic models and, via stochastic approximation, study the evolution of substring frequencies for these two systems separately. Specifically, we show that k-mer frequencies converge almost surely and determine the limit set. Furthermore, we present a method for finding upper bounds on entropy for such systems.
Human factor in industry of the future: Knowledge acquisition and motivation

Human Factor in Industry of the Future -Knowledge Acquisition and MotivationIndustry of the future bases on people knowledge, creativeness and motivations.Although, the number of workers needed in factories of the future decreases, the requiremenets concerning employees skills have been increasing.The knowledge of employees determines the factory system quality and efficiency.The motivation of people determines continuous improvement and development realized by problems identification and elimination.Hence, adequate learning methods are required to be implemented to achieve the following goals: empower and motivate people.This paper presents chosen methods such as learning by doing, computer simulations and virtual reality which support knowledge acquisition by people being prepared for work in factories of the future.The presented methods also increase employee awareness concerning possibilities of improvements.
Do Cryptocurrency Prices Camouflage Latent Economic Effects? A Bayesian Hidden Markov Approach

We study the Bitcoin and Ether price series under a financial perspective. Specifically, we use two econometric models to perform a two-layer analysis to study the correlation and prediction of Bitcoin and Ether price series with traditional assets. In the first part of this study, we model the probability of positive returns via a Bayesian logistic model. Even though the fitting performance of the logistic model is poor, we find that traditional assets can explain some of the variability of the price returns. Along with the fact that standard models fail to capture the statistic and econometric attributes—such as extreme variability and heteroskedasticity—of cryptocurrencies, this motivates us to apply a novel Non-Homogeneous Hidden Markov model to these series. In particular, we model Bitcoin and Ether prices via the non-homogeneous Pólya-Gamma Hidden Markov (NHPG) model, since it has been shown that it outperforms its counterparts in conventional financial data. The transition probabilities of the underlying hidden process are modeled via a logistic link whereas the observed series follow a mixture of normal regressions conditionally on the hidden process. Our results show that the NHPG algorithm has good in-sample performance and captures the heteroskedasticity of both series. It identifies frequent changes between the two states of the underlying Markov process. In what constitutes the most important implication of our study, we show that there exist linear correlations between the covariates and the ETH and BTC series. However, only the ETH series are affected non-linearly by a subset of the accounted covariates. Finally, we conclude that the large number of significant predictors along with the weak degree of predictability performance of the algorithm back up earlier findings that cryptocurrencies are unlike any other financial assets and predicting the cryptocurrency price series is still a challenging task. These findings can be useful to investors, policy makers, traders for portfolio allocation, risk management and trading strategies.