Reviewerly/exHarmony huggingface dataset:
Topological Properties of Multi-Party Blockchain Transactions

The cross-blockchain transaction remains one of the most challenging problems in blockchains. The root cause of the challenge lies in the nondeterministic nature of blockchains: A $n$-party transaction across multiple blockchains might be partially rolled back due to the potential forks in any of the participating blockchains---eventually, only one fork will survive in the competition among miners. While some effort has recently been made to developing hierarchically distributed commit protocols to make multi-party transactions progress, there is no systematic method to reason about the transaction outcome. This paper tackles this problem from a perspective of point-set topology. We construct multiple topological spaces for the transactions and blockchain forks, and show that these spaces are internally related through either homeomorphism or continuous functions. Combined together, these tools allow us to reason about the cross-blockchain transactions through the growing-fork topology, an intuitive representation of blockchains.
Inherent Dependency Displacement Bias of Transition-Based Algorithms

A wide variety of transition-based algorithms are currently used for dependency parsers. Empirical studies have shown that performance varies across different treebanks in such a way that one algorithm outperforms another on one treebank and the reverse is true for a different treebank. There is often no discernible reason for what causes one algorithm to be more suitable for a certain treebank and less so for another. In this paper we shed some light on this by introducing the concept of an algorithm's inherent dependency displacement distribution. This characterises the bias of the algorithm in terms of dependency displacement, which quantify both distance and direction of syntactic relations. We show that the similarity of an algorithm's inherent distribution to a treebank's displacement distribution is clearly correlated to the algorithm's parsing performance on that treebank, specifically with highly significant and substantial correlations for the predominant sentence lengths in Universal Dependency treebanks. We also obtain results which show a more discrete analysis of dependency displacement does not result in any meaningful correlations.
Planning with Brain-inspired AI

This article surveys engineering and neuroscientific models of planning as a cognitive function, which is regarded as a typical function of fluid intelligence in the discussion of general intelligence. It aims to present existing planning models as references for realizing the planning function in brain-inspired AI or artificial general intelligence (AGI). It also proposes themes for the research and development of brain-inspired AI from the viewpoint of tasks and architecture.
Cantor-solus and Cantor-multus Distributions

The Cantor distribution is obtained from bitstrings; the Cantor-solus distribution (a new name) admits only strings without adjacent 1 bits. We review moments and order statistics associated with these. The Cantor-multus distribution is introduced -- which instead admits only strings without isolated 1 bits -- and more complicated formulas emerge.
Vyper: A Security Comparison with Solidity Based on Common Vulnerabilities

Vyper has been proposed as a new high-level language for Ethereum smart contract development due to numerous security vulnerabilities and attacks witnessed on contracts written in Solidity since the system's inception. Vyper aims to address these vulnerabilities by providing a language that focuses on simplicity, auditability and security. We present a survey where we study how well-known and commonly-encountered vulnerabilities in Solidity feature in Vyper's development environment. We analyze all such vulnerabilities individually and classify them into five groups based on their status in Vyper. To the best of our knowledge, our survey is the first attempt to study security vulnerabilities in Vyper.
Error Bounds and Applications for Stochastic Approximation with  Non-Decaying Gain

This work analyzes the stochastic approximation algorithm with non-decaying gains as applied in time-varying problems. The setting is to minimize a sequence of scalar-valued loss functions $f_k(\cdot)$ at sampling times $\tau_k$ or to locate the root of a sequence of vector-valued functions $g_k(\cdot)$ at $\tau_k$ with respect to a parameter $\theta\in R^p$. The available information is the noise-corrupted observation(s) of either $f_k(\cdot)$ or $g_k(\cdot)$ evaluated at one or two design points only. Given the time-varying stochastic approximation setup, we apply stochastic approximation algorithms with non-decaying gains, so that the recursive estimate denoted as $\hat{\theta}_k$ can maintain its momentum in tracking the time-varying optimum denoted as $\theta_k^*$. Chapter 3 provides a bound for the root-mean-squared error $ \sqrt{E(\|\hat{\theta}_k-\theta_k^*\|^2})$. Overall, the bounds are applicable under a mild assumption on the time-varying drift and a modest restriction on the observation noise and the bias term. After establishing the tracking capability in Chapter 3, we also discuss the concentration behavior of $\hat{\theta}_k $ in Chapter 4. The weak convergence limit of the continuous interpolation of $\hat{\theta}_k$ is shown to follow the trajectory of a non-autonomous ordinary differential equation. Both Chapter 3 and Chapter 4 are probabilistic arguments and may not provide much guidance on the gain-tuning strategies useful for one single experiment run. Therefore, Chapter 5 discusses a data-dependent gain-tuning strategy based on estimating the Hessian information and the noise level. Overall, this work answers the questions "what is the estimate for the dynamical system $\theta_k^*$" and "how much we can trust $\hat{\theta}_k $ as an estimate for $\theta_k^*$."
REST vs GraphQL: A Controlled Experiment

GraphQL is a novel query language for implementing service-based software architectures. The language is gaining momentum and it is now used by major software companies, such as Facebook and GitHub. However, we still lack empirical evidence on the real gains achieved by GraphQL, particularly in terms of the effort required to implement queries in this language. Therefore, in this paper we describe a controlled experiment with 22 students (10 undergraduate and 12 graduate), who were asked to implement eight queries for accessing a web service, using GraphQL and REST. Our results show that GraphQL requires less effort to implement remote service queries when compared to REST (9 vs 6 minutes, median times). These gains increase when REST queries include more complex endpoints, with several parameters. Interestingly, GraphQL outperforms REST even among more experienced participants (as is the case of graduate students) and among participants with previous experience in REST, but no previous experience in GraphQL.
Evaluation Framework For Large-scale Federated Learning

Federated learning is proposed as a machine learning setting to enable distributed edge devices, such as mobile phones, to collaboratively learn a shared prediction model while keeping all the training data on device, which can not only take full advantage of data distributed across millions of nodes to train a good model but also protect data privacy. However, learning in scenario above poses new challenges. In fact, data across a massive number of unreliable devices is likely to be non-IID (identically and independently distributed), which may make the performance of models trained by federated learning unstable. In this paper, we introduce a framework designed for large-scale federated learning which consists of approaches to generating dataset and modular evaluation framework. Firstly, we construct a suite of open-source non-IID datasets by providing three respects including covariate shift, prior probability shift, and concept shift, which are grounded in real-world assumptions. In addition, we design several rigorous evaluation metrics including the number of network nodes, the size of datasets, the number of communication rounds and communication resources etc. Finally, we present an open-source benchmark for large-scale federated learning research.
Coaxiality Measurement Method for Micro Components based on Image Processing

The precise measurement of micro components is of great importance in the field of micro assembly. However, the innate characteristic of micro components poses huge challenges both in the efficiency and accuracy for precise measurement, due to its complex spatial structure. In this work, a HALCON-based coxiality measurement method for micro components is proposed. Firstly, the CCD camera is used to obtain the geometric vision image of micro components. Secondly, the precise positioning and parameter measurement for micro components can be achieved through HALCON with threshold selection, edge detection and feature extraction. Then, the contradiction between high precise measurement and low consumption time in traditional precision measurement is resolved., Finally, the efficiency and accuracy of the proposed method are verified by the experiment.
Social phobia of university students as a modern phenomenon of online learning

The purpose of the article is to study the formation of students’ detachment from group communication during distance learning and the reasons for prolonging this state after going offline. The relevance of this issue is justified by the experience of working with students during offline and online learning and the obvious difference in the psychological state of all subjects of the educational process after leaving the remote mode. The tasks include substantiating the importance and expediency of creating a psychological service of the university, analyzing and monitoring the behavior of students with pronounced social phobia and leveling such behavior in the course of purposeful and programmatic work both individually with a student and integrated in a group. In the course of the study, it is highlighted how much online learning allows you to expand or narrow the student's social consciousness, as well as to identify the causes of social phobia when continuing traditional education. The study was conducted in conversations on the “VK” social network for each group of students, by identifying the quality of online and offline learning, reactions to group relationships and changes in these relationships during long-term distance learning. The results were discussed in the working mode among colleagues of the Department of Marketing and Advertising of the Rostov State University of Economics (Rostov-on-Don, Russia).
STC-Flow: Spatio-temporal Context-aware Optical Flow Estimation

In this paper, we propose a spatio-temporal contextual network, STC-Flow, for optical flow estimation. Unlike previous optical flow estimation approaches with local pyramid feature extraction and multi-level correlation, we propose a contextual relation exploration architecture by capturing rich long-range dependencies in spatial and temporal dimensions. Specifically, STC-Flow contains three key context modules - pyramidal spatial context module, temporal context correlation module and recurrent residual contextual upsampling module, to build the relationship in each stage of feature extraction, correlation, and flow reconstruction, respectively. Experimental results indicate that the proposed scheme achieves the state-of-the-art performance of two-frame based methods on the Sintel dataset and the KITTI 2012/2015 datasets.
Learning Cross-domain Generalizable Features by Representation  Disentanglement

Deep learning models exhibit limited generalizability across different domains. Specifically, transferring knowledge from available entangled domain features(source/target domain) and categorical features to new unseen categorical features in a target domain is an interesting and difficult problem that is rarely discussed in the current literature. This problem is essential for many real-world applications such as improving diagnostic classification or prediction in medical imaging. To address this problem, we propose Mutual-Information-based Disentangled Neural Networks (MIDNet) to extract generalizable features that enable transferring knowledge to unseen categorical features in target domains. The proposed MIDNet is developed as a semi-supervised learning paradigm to alleviate the dependency on labeled data. This is important for practical applications where data annotation requires rare expertise as well as intense time and labor. We demonstrate our method on handwritten digits datasets and a fetal ultrasound dataset for image classification tasks. Experiments show that our method outperforms the state-of-the-art and achieve expected performance with sparsely labeled data.
Multiplicative Gaussian Particle Filter

We propose a new sampling-based approach for approximate inference in filtering problems. Instead of approximating conditional distributions with a finite set of states, as done in particle filters, our approach approximates the distribution with a weighted sum of functions from a set of continuous functions. Central to the approach is the use of sampling to approximate multiplications in the Bayes filter. We provide theoretical analysis, giving conditions for sampling to give good approximation. We next specialize to the case of weighted sums of Gaussians, and show how properties of Gaussians enable closed-form transition and efficient multiplication. Lastly, we conduct preliminary experiments on a robot localization problem and compare performance with the particle filter, to demonstrate the potential of the proposed method.
Infinitely Wide Graph Convolutional Networks: Semi-supervised Learning  via Gaussian Processes

Graph convolutional neural networks~(GCNs) have recently demonstrated promising results on graph-based semi-supervised classification, but little work has been done to explore their theoretical properties. Recently, several deep neural networks, e.g., fully connected and convolutional neural networks, with infinite hidden units have been proved to be equivalent to Gaussian processes~(GPs). To exploit both the powerful representational capacity of GCNs and the great expressive power of GPs, we investigate similar properties of infinitely wide GCNs. More specifically, we propose a GP regression model via GCNs~(GPGC) for graph-based semi-supervised learning. In the process, we formulate the kernel matrix computation of GPGC in an iterative analytical form. Finally, we derive a conditional distribution for the labels of unobserved nodes based on the graph structure, labels for the observed nodes, and the feature matrix of all the nodes. We conduct extensive experiments to evaluate the semi-supervised classification performance of GPGC and demonstrate that it outperforms other state-of-the-art methods by a clear margin on all the datasets while being efficient.
Incremental Sampling Without Replacement for Sequence Models

Sampling is a fundamental technique, and sampling without replacement is often desirable when duplicate samples are not beneficial. Within machine learning, sampling is useful for generating diverse outputs from a trained model. We present an elegant procedure for sampling without replacement from a broad class of randomized programs, including generative neural models that construct outputs sequentially. Our procedure is efficient even for exponentially-large output spaces. Unlike prior work, our approach is incremental, i.e., samples can be drawn one at a time, allowing for increased flexibility. We also present a new estimator for computing expectations from samples drawn without replacement. We show that incremental sampling without replacement is applicable to many domains, e.g., program synthesis and combinatorial optimization.
Denotational semantics as a foundation for cost recurrence extraction  for functional languages

A standard informal method for analyzing the asymptotic complexity of a program is to extract a recurrence that describes its cost in terms of the size of its input, and then to compute a closed-form upper bound on that recurrence. We give a formal account of that method for functional programs in a higher-order language with let-polymorphism. The method consists of two phases. In the first phase, a monadic translation is performed to extract a cost-annotated version of the original program. In the second phase, the extracted program is interpreted in a model. The key feature of this second phase is that different models describe different notions of size. This plays out in several ways. For example, when analyzing functions that take arguments of inductive types, different notions of size may be appropriate depending on the analysis. When analyzing polymorphic functions, our approach shows that one can formally describe the notion of size of an argument in terms of the data that is common to the notions of size for each type instance of the domain type. We give several examples of different models that formally justify various informal cost analyses to show the applicability of our approach.
On the complexity of finding tensor ranks

The purpose of this note is to give a linear algebra algorithm to find out if a rank of a given tensor over a field $\F$ is at most $k$ over the algebraic closure of $\F$, where $k$ is a given positive integer. We estimate the arithmetic complexity of our algorithm.
Interpretable Companions for Black-Box Models

We present an interpretable companion model for any pre-trained black-box classifiers. The idea is that for any input, a user can decide to either receive a prediction from the black-box model, with high accuracy but no explanations, or employ a companion rule to obtain an interpretable prediction with slightly lower accuracy. The companion model is trained from data and the predictions of the black-box model, with the objective combining area under the transparency--accuracy curve and model complexity. Our model provides flexible choices for practitioners who face the dilemma of choosing between always using interpretable models and always using black-box models for a predictive task, so users can, for any given input, take a step back to resort to an interpretable prediction if they find the predictive performance satisfying, or stick to the black-box model if the rules are unsatisfying. To show the value of companion models, we design a human evaluation on more than a hundred people to investigate the tolerable accuracy loss to gain interpretability for humans.
Unbalanced GANs: Pre-training the Generator of Generative Adversarial  Network using Variational Autoencoder

We propose Unbalanced GANs, which pre-trains the generator of the generative adversarial network (GAN) using variational autoencoder (VAE). We guarantee the stable training of the generator by preventing the faster convergence of the discriminator at early epochs. Furthermore, we balance between the generator and the discriminator at early epochs and thus maintain the stabilized training of GANs. We apply Unbalanced GANs to well known public datasets and find that Unbalanced GANs reduce mode collapses. We also show that Unbalanced GANs outperform ordinary GANs in terms of stabilized learning, faster convergence and better image quality at early epochs.
Solving Raven's Progressive Matrices with Neural Networks

Raven's Progressive Matrices (RPM) have been widely used for Intelligence Quotient (IQ) test of humans. In this paper, we aim to solve RPM with neural networks in both supervised and unsupervised manners. First, we investigate strategies to reduce over-fitting in supervised learning. We suggest the use of a neural network with deep layers and pre-training on large-scale datasets to improve model generalization. Experiments on the RAVEN dataset show that the overall accuracy of our supervised approach surpasses human-level performance. Second, as an intelligent agent requires to automatically learn new skills to solve new problems, we propose the first unsupervised method, Multilabel Classification with Pseudo Target (MCPT), for RPM problems. Based on the design of the pseudo target, MCPT converts the unsupervised learning problem to a supervised task. Experiments show that MCPT doubles the testing accuracy of random guessing e.g. 28.50% vs. 12.5%. Finally, we discuss the problem of solving RPM with unsupervised and explainable strategies in the future.
A lower bound on the number of inequivalent APN functions

In this paper, we establish a lower bound on the total number of inequivalent APN functions on the finite field with $2^{2m}$ elements, where $m$ is even. We obtain this result by proving that the APN functions introduced by Pott and the second author, that depend on three parameters $k$, $s$ and $\alpha$, are pairwise inequivalent for distinct choices of the parameters $k$ and $s$. Moreover, we determine the automorphism group of these APN functions.
The Non-parametric Regression Methods in Sequence Data Study

The regression analysis is the statistical methodology for creating related functions among response variables and explanatory variables for estimating response variables given explanatory variables. However, sometimes two variables may not be the regression analysis's assumption. The non-parametric regression method is proposed to avoid this problem. The estimating parameters construct the smoothing curve with the data or called the smoothing method in the form of non-linear relationship data. This research aims to study and compare the efficiency of six non-parametric regression methods for sequence data: kernel smoothing, smoothing spline, natural cubic spline, B-spline, penalized spline, and trend filtering methods. The efficiency of the non-parametric regression method determines by the lowest average mean squared error. The smoothing parameter is used to control the smoothing performance of the curves by using the cross-validation method and defining the number of knots for fitting the curve on the closet data. The response variable character is simulated in trend, non-linear, and cycle data, where the explanatory variable is defined as the sequence data. The R program sets the sample sizes as 50, 100, 150, and 200 and repeats 500 times in each situation, while the standard deviation of error is 1, 3, and 5. The results were founded that the natural cubic spline method played the lowest average mean squared error in all cases. When the data character was considered, the cycle data had the lowest mean squared error in all sample sizes. It can be concluded that the natural cubic spline method was the performance method for sequence data.