Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Multi-Domain Learning for Automatic Short Answer Grading

One of the fundamental challenges towards building any intelligent tutoring system is its ability to automatically grade short student answers. A typical automatic short answer grading system (ASAG) grades student answers across multiple domains (or subjects). Grading student answers requires building a supervised machine learning model that evaluates the similarity of the student answer with the reference answer(s). We observe that unlike typical textual similarity or entailment tasks, the notion of similarity is not universal here. On one hand, para-phrasal constructs of the language can indicate similarity independent of the domain. On the other hand, two words, or phrases, that are not strict synonyms of each other, might mean the same in certain domains. Building on this observation, we propose JMD-ASAG, the first joint multidomain deep learning architecture for automatic short answer grading that performs domain adaptation by learning generic and domain-specific aspects from the limited domain-wise training data. JMD-ASAG not only learns the domain-specific characteristics but also overcomes the dependence on a large corpus by learning the generic characteristics from the task-specific data itself. On a large-scale industry dataset and a benchmarking dataset, we show that our model performs significantly better than existing techniques which either learn domain-specific models or adapt a generic similarity scoring model from a large corpus. Further, on the benchmarking dataset, we report state-of-the-art results against all existing non-neural and neural models.
Real-Time detection, classification and DOA estimation of Unmanned Aerial Vehicle

The present work deals with a new passive system for real-time detection, classification and direction of arrival estimator of Unmanned Aerial Vehicles (UAVs). The proposed system composed of a very low cost hardware components, comprises two different arrays of three or six-microphones, non-linear amplification and filtering of the analog acoustic signal, avoiding also the saturation effect in case where the UAV is located nearby to the microphones. Advance array processing methods are used to detect and locate the wide-band sources in the near and far-field including array calibration and energy based beamforming techniques. Moreover, oversampling techniques are adopted to increase the acquired signals accuracy and to also decrease the quantization noise. The classifier is based on the nearest neighbor rule of a normalized Power Spectral Density, the acoustic signature of the UAV spectrum in short periods of time. The low-cost, low-power and high efficiency embedded processor STM32F405RG is used for system implementation. Preliminary experimental results have shown the effectiveness of the proposed approach.
Cooperative Learning of Disjoint Syntax and Semantics

There has been considerable attention devoted to models that learn to jointly infer an expression’s syntactic structure and its semantics. Yet, Nangia and Bowman (2018) has recently shown that the current best systems fail to learn the correct parsing strategy on mathematical expressions generated from a simple context-free grammar. In this work, we present a recursive model inspired by Choi et al. (2018) that reaches near perfect accuracy on this task. Our model is composed of two separated modules for syntax and semantics. They are cooperatively trained with standard continuous and discrete optimisation schemes. Our model does not require any linguistic structure for supervision, and its recursive nature allows for out-of-domain generalisation. Additionally, our approach performs competitively on several natural language tasks, such as Natural Language Inference and Sentiment Analysis.
An Automatic Speedup Theorem for Distributed Problems

Recently, Brandt et al.\ [STOC'16] proved a lower bound for the distributed Lovász Local Lemma, which has been conjectured to be tight for sufficiently relaxed LLL criteria by Chang and Pettie [FOCS'17]. At the heart of their result lies a speedup technique that, for graphs of girth at least 2t+2, transforms any t-round algorithm for one specific LLL problem into a (t-1)-round algorithm for the same problem. We substantially improve on this technique by showing that such a speedup exists for any locally checkable problem ¶i, with the difference that the problem ¶i_1 the inferred (t-1)-round algorithm solves is not (necessarily) the same problem as ¶i. Our speedup is automatic in the sense that there is a fixed procedure that transforms a description for ¶i into a description for ¶i_1 and reversible in the sense that any (t-1)-round algorithm for ¶i_1 can be transformed into a t-round algorithm for ¶i. In particular, for any locally checkable problem ¶i with exact deterministic time complexity T(n, Δ) łeq t on graphs with n nodes, maximum node degree Δ, and girth at least 2t+2, there is a sequence of problems ¶i_1, ¶i_2, \dots with time complexities T(n, Δ)-1, T(n, Δ)-2, \dots, that can be inferred from ¶i. As a first application of our generalized speedup, we solve a long-standing open problem of Naor and Stockmeyer [STOC'93]: we show that weak 2-coloring in odd-degree graphs cannot be solved in o(łog^* Δ) rounds, thereby providing a matching lower bound to their upper bound.
Similarity Measures based on Local Game Trees

We study strategic similarity of game positions in two-player extensive games of perfect information, by looking at the structure of their local game trees, with the aim of improving the performance of game playing agents in detecting forcing continuations. We present a range of measures over the induced game trees and compare them against benchmark problems in chess, observing a promising level of accuracy in matching up trap states.
Disentangled Representation Learning for 3D Face Shape

In this paper, we present a novel strategy to design disentangled 3D face shape representation. Specifically, a given 3D face shape is decomposed into identity part and expression part, which are both encoded and decoded in a nonlinear way. To solve this problem, we propose an attribute decomposition framework for 3D face mesh. To better represent face shapes which are usually nonlinear deformed between each other, the face shapes are represented by a vertex based deformation representation rather than Euclidean coordinates. The experimental results demonstrate that our method has better performance than existing methods on decomposing the identity and expression parts. Moreover, more natural expression transfer results can be achieved with our method than existing methods.
Optimizing Space of Parallel Processes

This paper is a contribution to exploring and analyzing space-improvements in concurrent programming languages, in particular in the functional process-calculus CHF. Space-improvements are defined as a generalization of the corresponding notion in deterministic pure functional languages. The main part of the paper is the O(n*log n) algorithm SpOptN for offline space optimization of several parallel independent processes. Applications of this algorithm are: (i) affirmation of space improving transformations for particular classes of program transformations; (ii) support of an interpreter-based method for refuting space-improvements; and (iii) as a stand-alone offline-optimizer for space (or similar resources) of parallel processes.
Backscatter Data Collection With Unmanned Ground Vehicle: Mobility Management and Power Allocation

Collecting data from the massive Internet of Things (IoT) devices is a challenging task since communication circuits are power-demanding while energy supply at IoT devices is limited. To overcome this challenge, backscatter communication emerges as a promising solution as it eliminates radio frequency components in the IoT devices. Unfortunately, the transmission range of backscatter communication is short. To facilitate backscatter communication, this paper proposes to integrate unmanned ground vehicle (UGV) with backscatter data collection. With such a scheme, the UGV could improve the communication quality by approaching various IoT devices. However, moving also costs energy consumption and a fundamental question is: what is the right balance between spending energy on moving versus on communication? To answer this question, this paper studies energy minimization under a joint graph mobility and backscatter communication model. With the joint model, the mobility management and power allocation problem, unfortunately, involves nonlinear coupling between discrete variables brought by mobility and continuous variables brought by communication. Despite the optimization challenges, an algorithm that theoretically achieves the minimum energy consumption is derived, and it leads to automatic trade-off between spending energy on moving versus on communication in the UGV backscatter system. The simulation results show that if the noise power is small (e.g., ≤−100 dBm), the UGV should collect the data with small movements. However, if the noise power is increased to a larger value (e.g., −60 dBm), the UGV should spend more motion energy to get closer to the IoT users.
A Review on the Application of Blockchain to the Next Generation of Cybersecure Industry 4.0 Smart Factories

Industry 4.0 is a concept devised for improving the way modern factories operate through the use of some of the latest technologies, like the ones used for creating the Industrial Internet of Things (IIoT), robotics, or Big Data applications. One of such technologies is blockchain, which is able to add trust, security, and decentralization to different industrial fields. This article focuses on analyzing the benefits and challenges that arise when using blockchain and smart contracts to develop Industry 4.0 applications. In addition, this paper presents a thorough review of the most relevant blockchain-based applications for Industry 4.0 technologies. Thus, its aim is to provide a detailed guide for the future Industry 4.0 developers that allows for determining how the blockchain can enhance the next generation of cybersecure industrial applications.
FEELVOS: Fast End-To-End Embedding Learning for Video Object Segmentation

Many of the recent successful methods for video object segmentation (VOS) are overly complicated, heavily rely on fine-tuning on the first frame, and/or are slow, and are hence of limited practical use. In this work, we propose FEELVOS as a simple and fast method which does not rely on fine-tuning. In order to segment a video, for each frame FEELVOS uses a semantic pixel-wise embedding together with a global and a local matching mechanism to transfer information from the first frame and from the previous frame of the video to the current frame. In contrast to previous work, our embedding is only used as an internal guidance of a convolutional network. Our novel dynamic segmentation head allows us to train the network, including the embedding, end-to-end for the multiple object segmentation task with a cross entropy loss. We achieve a new state of the art in video object segmentation without fine-tuning with a J&F measure of 71.5% on the DAVIS 2017 validation set. We make our code and models available at https://github.com/tensorflow/models/tree/master/research/feelvos.
DLedger: An IoT-Friendly Private Distributed Ledger System Based on DAG

With the ever growing Internet of Things (IoT) market, ledger systems are facing new challenges to efficiently store and secure enormous customer records collected by the IoT devices. The authenticity, availability, and integrity of these records are critically important for both business providers and customers. In this paper, we describe DLedger, a lightweight and resilient distributed ledger system. Instead of a single chain of blocks, DLedger builds the ledger over a directed acyclic graph (DAG), so that its operations can tolerate network partition and intermittent connectivity. Instead of compute-intensive Proof-of-Work (PoW), DLedger utilizes Proof-of-Authentication (PoA), whose light-weight operations are IoT-friendly, to achieve consensus. Furthermore, DLedger is built upon a data-centric network called Named Data Networking (NDN), which facilitates the peer-to-peer data dissemination in heterogeneous IoT networks.
Hand Pose Estimation: A Survey

The success of Deep Convolutional Neural Networks (CNNs) in recent years in almost all the Computer Vision tasks on one hand, and the popularity of low-cost consumer depth cameras on the other, has made Hand Pose Estimation a hot topic in computer vision field. In this report, we will first explain the hand pose estimation problem and will review major approaches solving this problem, especially the two different problems of using depth maps or RGB images. We will survey the most important papers in each field and will discuss the strengths and weaknesses of each. Finally, we will explain the biggest datasets in this field in detail and list 22 datasets with all their properties. To the best of our knowledge this is the most complete list of all the datasets in the hand pose estimation field.
Message Distortion in Information Cascades

Information diffusion is usually modeled as a process in which immutable pieces of information propagate over a network. In reality, however, messages are not immutable, but may be morphed with every step, potentially entailing large cumulative distortions. This process may lead to misinformation even in the absence of malevolent actors, and understanding it is crucial for modeling and improving online information systems. Here, we perform a controlled, crowdsourced experiment in which we simulate the propagation of information from medical research papers. Starting from the original abstracts, crowd workers iteratively shorten previously produced summaries to increasingly smaller lengths. We also collect control summaries where the original abstract is compressed directly to the final target length. Comparing cascades to controls allows us to separate the effect of the length constraint from that of accumulated distortion. Via careful manual coding, we annotate lexical and semantic units in the medical abstracts and track them along cascades. We find that iterative summarization has a negative impact due to the accumulation of error, but that high-quality intermediate summaries result in less distorted messages than in the control case. Different types of information behave differently; in particular, the conclusion of a medical abstract (i.e., its key message) is distorted most. Finally, we compare extractive with abstractive summaries, finding that the latter are less prone to semantic distortion. Overall, this work is a first step in studying information cascades without the assumption that disseminated content is immutable, with implications on our understanding of the role of word-of-mouth effects on the misreporting of science.
Using logical form encodings for unsupervised linguistic transformation: Theory and applications

We present a novel method to architect automatic linguistic transformations for a number of tasks, including controlled grammatical or lexical changes, style transfer, text generation, and machine translation. Our approach consists in creating an abstract representation of a sentence’s meaning and grammar, which we use as input to an encoder-decoder network trained to reproduce the original sentence. Manipulating the abstract representation allows the transformation of sentences according to user-provided parameters, both grammatically and lexically, in any combination. Additionally, the same architecture can be used for controlled text generation, and even unsupervised machine translation, where the network is used to translate between different languages using no parallel corpora outside of a lemma-level dictionary. This strategy holds the promise of enabling many tasks that were hitherto outside the scope of NLP techniques for want of sufficient training data. We provide empirical evidence for the effectiveness of our approach by reproducing and transforming English sentences, and evaluating the results both manually and automatically. A single unsupervised model is used for all tasks. We report BLEU scores between 55.29 and 81.82 for sentence reproduction as well as back-and-forth grammatical transformations between 14 class pairs.
clusterNOR: A NUMA-Optimized Clustering Framework

Clustering algorithms are iterative and have complex data access patterns that result in many small random memory accesses. The performance of parallel implementations suffer from synchronous barriers for each iteration and skewed workloads. We rethink the parallelization of clustering for modern non-uniform memory architectures (NUMA) to maximizes independent, asynchronous computation. We eliminate many barriers, reduce remote memory accesses, and maximize cache reuse. We implement the 'Clustering NUMA Optimized Routines' (clusterNOR) extensible parallel framework that provides algorithmic building blocks. The system is generic, we demonstrate nine modern clustering algorithms that have simple implementations. clusterNOR includes (i) in-memory, (ii) semi-external memory, and (iii) distributed memory execution, enabling computation for varying memory and hardware budgets. For algorithms that rely on Euclidean distance, clusterNOR defines an updated Elkan's triangle inequality pruning algorithm that uses asymptotically less memory so that it works on billion-point data sets. clusterNOR extends and expands the scope of the 'knor' library for k-means clustering by generalizing underlying principles, providing a uniform programming interface and expanding the scope to hierarchical and linear algebraic classes of algorithms. The compound effect of our optimizations is an order of magnitude improvement in speed over other state-of-the-art solutions, such as Spark's MLlib and Apple's Turi.
Block-Coordinate Minimization for Large SDPs with Block-Diagonal Constraints

The so-called Burer-Monteiro method is a well-studied technique for solving large-scale semidefinite programs (SDPs) via low-rank factorization. The main idea is to solve rank-restricted, albeit non-convex, surrogates instead of the SDP. Recent works have shown that, in an important class of SDPs with elegant geometric structure, one can find globally optimal solutions to the SDP by finding rank-deficient second-order critical points of an unconstrained Riemannian optimization problem. Hence, in such problems, the Burer-Monteiro approach can provide a scalable and reliable alternative to interior-point methods that scale poorly. Among various Riemannian optimization methods proposed, block-coordinate minimization (BCM) is of particular interest due to its simplicity. Erdogdu et al. in their recent work proposed BCM for problems over the Cartesian product of unit spheres and provided global convergence rate estimates for the algorithm. This report extends the BCM algorithm and the global convergence rate analysis of Erdogdu et al. from problems over the Cartesian product of unit spheres to the Cartesian product of Stiefel manifolds. The latter more general setting has important applications such as synchronization over the special orthogonal (SO) and special Euclidean (SE) groups.
Killing Four Birds with one Gaussian Process: Analyzing Test-Time Attack Vectors on Classification

The wide usage of Machine Learning (ML) leads to direct security threats, as ML algorithms are vulnerable to a plethora of attacks themselves. Different attack vectors are known, and target for example the training phase using manipulated data. Alternatively, they take place at test time and aim for miss-classification, the leakage of the training data or extraction of the model. Previous works studied different test time attacks individually. We show that using an ML model enabling formal analysis and allowing control over the decision surface curvature, interesting insights can be gained when attack vectors are not studied in isolation but in relation to each pother. We show for example how we can secure Gaussian Process Classification against empirical membership inference by properly configuring the algorithm. In this configuration, however, the model's parameters are leaked. This allows an analytic computation of the the training data, which is thus leaked, against the original intention of protecting the data. We extend our study to evasion attacks, and find that analogously, hardening the model against one attack boils down to enabling a different attacker.
A Dictionary-Based Generalization of Robust PCA Part I: Study of Theoretical Properties

We consider the decomposition of a data matrix assumed to be a superposition of a low-rank matrix and a component which is sparse in a known dictionary, using a convex demixing method. We consider two sparsity structures for the sparse factor of the dictionary sparse component, namely entry-wise and column-wise sparsity, and provide a unified analysis, encompassing both undercomplete and the overcomplete dictionary cases, to show that the constituent matrices can be successfully recovered under some relatively mild conditions on incoherence, sparsity, and rank. We corroborate our theoretical results by presenting empirical evaluations in terms of phase transitions in rank and sparsity, in comparison to related techniques. Investigation of a specific application in hyperspectral imaging is included in an accompanying paper.
Approximation Properties of Variational Bayes for Vector Autoregressions

Variational Bayes (VB) is a recent approximate method for Bayesian inference. It has the merit of being a fast and scalable alternative to Markov Chain Monte Carlo (MCMC) but its approximation error is often unknown. In this paper, we derive the approximation error of VB in terms of mean, mode, variance, predictive density and KL divergence for the linear Gaussian multi-equation regression. Our results indicate that VB approximates the posterior mean perfectly. Factors affecting the magnitude of underestimation in posterior variance and mode are revealed. Importantly, We demonstrate that VB estimates predictive densities accurately.
Disturbance Observer-Based Robust Control and Its Applications: 35th Anniversary Overview

Disturbance observer (DOB) has been one of the most widely used robust control tools since it was proposed by Ohnishi in 1983. This paper introduces the origins of DOB and presents a survey of the major results on DOB-based robust control in the last 35 years. Furthermore, it explains DOB's analysis and synthesis techniques for linear and nonlinear systems by using a unified framework. In final section, this paper presents concluding remarks on DOB-based robust control and its engineering applications.
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs

Stochastic differential equations are an important modeling class in many disciplines. Consequently, there exist many methods relying on various discretization and numerical integration schemes. In this paper, we propose a novel, probabilistic model for estimating the drift and diffusion given noisy observations of the underlying stochastic system. Using state-of-the-art adversarial and moment matching inference techniques, we avoid the discretization schemes of classical approaches. This leads to significant improvements in parameter accuracy and robustness given random initial guesses. On four established benchmark systems, we compare the performance of our algorithms to state-of-the-art solutions based on extended Kalman filtering and Gaussian processes.