OpenRaiser/Intern-Atlas huggingface dataset:
A Lower Bound on the Number of Boolean Functions with Median Correlation Immunity
Venue: arXiv cs.IT 2019
Year: 2019

The number of $n$-ary balanced correlation immune (resilient) Boolean functions of order $\frac{n}{2}$ is not less than $n^{2^{(n/2)-2}(1+o(1))}$ as $n\rightarrow\infty$. Keywords: resilient function, correlation immune function, orthogonal array
Thinging the Use Case Model
Venue: arXiv cs.SE 2019
Year: 2019

Use cases as textual visual modeling techniques have become a key construct and the foundation of the most popular de facto standard technique for performing software requirements analysis and specification. This paper describes use cases in terms of a recently proposed model, the thinging machine (TM) model. Such a link to TM strengthens the notion of use cases and clarifies related concepts. For example, the concept of events is utilized in an unconstrained way in use cases, and TM may contribute in this direction. Several selected use cases are remodeled using TM. This study provides many insights. For example, one result shows that use cases are an activation apparatus of mega-triggering (high-level events) wherein a group of submachines (processes) are actuated.
Riemannian Motion Policy Fusion through Learnable Lyapunov Function Reshaping
Venue: arXiv cs.RO 2019
Year: 2019

RMPflow is a recently proposed policy-fusion framework based on differential geometry. While RMPflow has demonstrated promising performance, it requires the user to provide sensible subtask policies as Riemannian motion policies (RMPs: a motion policy and an importance matrix function), which can be a difficult design problem in its own right. We propose RMPfusion, a variation of RMPflow, to address this issue. RMPfusion supplements RMPflow with weight functions that can hierarchically reshape the Lyapunov functions of the subtask RMPs according to the current configuration of the robot and environment. This extra flexibility can remedy imperfect subtask RMPs provided by the user, improving the combined policy's performance. These weight functions can be learned by back-propagation. Moreover, we prove that, under mild restrictions on the weight functions, RMPfusion always yields a globally Lyapunov-stable motion policy. This implies that we can treat RMPfusion as a structured policy class in policy optimization that is guaranteed to generate stable policies, even during the immature phase of learning. We demonstrate these properties of RMPfusion in imitation learning experiments both in simulation and on a real-world robot.
BERT for Evidence Retrieval and Claim Verification
Venue: arXiv cs.CL 2019
Year: 2019

Motivated by the promising performance of pre-trained language models, we investigate BERT in an evidence retrieval and claim verification pipeline for the FEVER fact extraction and verification challenge. To this end, we propose to use two BERT models, one for retrieving potential evidence sentences supporting or rejecting claims, and another for verifying claims based on the predicted evidence sets. To train the BERT retrieval system, we use pointwise and pairwise loss functions, and examine the effect of hard negative mining. A second BERT model is trained to classify the samples as supported, refuted, and not enough information. Our system achieves a new state of the art recall of 87.1 for retrieving top five sentences out of the FEVER documents consisting of 50K Wikipedia pages, and scores second in the official leaderboard with the FEVER score of 69.7.
Towards a Data Centric Approach for the Design and Verification of Cryptographic Protocols
Venue: arXiv cs.CR 2019
Year: 2019

We propose MetaCP, a Meta Cryptography Protocol verification tool, as an automated tool simplifying the design of security protocols through a graphical interface. The graphical interface can be seen as a modern editor of a non-relational database whose data are protocols. The information of protocols are stored in XML, enjoying a fixed format and syntax aiming to contain all required information to specify any kind of protocol. This XML can be seen as an almost semanticless language, where different plugins confer strict semantics modelling the protocol into a variety of back-end verification languages. In this paper, we showcase the effectiveness of this novel approach by demonstrating how easy MetaCP makes it to design and verify a protocol going from the graphical design to formally verified protocol using a Tamarin prover plugin. Whilst similar approaches have been proposed in the past, most famously the AVISPA Tool, no previous approach provides such as small learning curve and ease of use even for non security professionals, combined with the flexibility to integrate with the state of the art verification tools.
Interpretable Disentanglement of Neural Networks by Extracting Class-Specific Subnetwork
Venue: arXiv cs.LG 2019
Year: 2019

We propose a novel perspective to understand deep neural networks in an interpretable disentanglement form. For each semantic class, we extract a class-specific functional subnetwork from the original full model, with compressed structure while maintaining comparable prediction performance. The structure representations of extracted subnetworks display a resemblance to their corresponding class semantic similarities. We also apply extracted subnetworks in visual explanation and adversarial example detection tasks by merely replacing the original full model with class-specific subnetworks. Experiments demonstrate that this intuitive operation can effectively improve explanation saliency accuracy for gradient-based explanation methods, and increase the detection rate for confidence score-based adversarial example detection methods.
Automatic Testing and Improvement of Machine Translation
Venue: arXiv cs.SE 2019
Year: 2019

This paper presents TransRepair, a fully automatic approach for testing and repairing the consistency of machine translation systems. TransRepair combines mutation with metamorphic testing to detect inconsistency bugs (without access to human oracles). It then adopts probability-reference or cross-reference to post-process the translations, in a grey-box or black-box manner, to repair the inconsistencies. Our evaluation on two state-of-the-art translators, Google Translate and Transformer, indicates that TransRepair has a high precision (99%) on generating input pairs with consistent translations. With these tests, using automatic consistency metrics and manual assessment, we find that Google Translate and Transformer have approximately 36% and 40% inconsistency bugs. Black-box repair fixes 28% and 19% bugs on average for Google Translate and Transformer. Grey-box repair fixes 30% bugs on average for Transformer. Manual inspection indicates that the translations repaired by our approach improve consistency in 87% of cases (degrading it in 2%), and that our repairs have better translation acceptability in 27% of the cases (worse in 8%).
MaskParse@Deskin at SemEval-2019 Task 1: Cross-lingual UCCA Semantic Parsing using Recursive Masked Sequence Tagging
Venue: arXiv cs.CL 2019
Year: 2019

This paper describes our recursive system for SemEval-2019 \textit{ Task 1: Cross-lingual Semantic Parsing with UCCA}. Each recursive step consists of two parts. We first perform semantic parsing using a sequence tagger to estimate the probabilities of the UCCA categories in the sentence. Then, we apply a decoding policy which interprets these probabilities and builds the graph nodes. Parsing is done recursively, we perform a first inference on the sentence to extract the main scenes and links and then we recursively apply our model on the sentence using a masking feature that reflects the decisions made in previous steps. Process continues until the terminal nodes are reached. We choose a standard neural tagger and we focused on our recursive parsing strategy and on the cross lingual transfer problem to develop a robust model for the French language, using only few training samples.
An Interactive Control Approach to 3D Shape Reconstruction
Venue: arXiv cs.CV 2019
Year: 2019

The ability to accurately reconstruct the 3D facets of a scene is one of the key problems in robotic vision. However, even with recent advances with machine learning, there is no high-fidelity universal 3D reconstruction method for this optimization problem as schemes often cater to specific image modalities and are often biased by scene abnormalities. Simply put, there always remains an information gap due to the dynamic nature of real-world scenarios. To this end, we demonstrate a feedback control framework which invokes operator inputs (also prone to errors) in order to augment existing reconstruction schemes. For proof-of-concept, we choose a classical region-based stereoscopic reconstruction approach and show how an ill-posed model can be augmented with operator input to be much more robust to scene artifacts. We provide necessary conditions for stability via Lyapunov analysis and perhaps more importantly, we show that the stability depends on a notion of absolute curvature. Mathematically, this aligns with previous work that has shown Ricci curvature as proxy for functional robustness of dynamical networked systems. We conclude with results that show how our method can improve standalone reconstruction schemes.
Modeling and Analysis of Heterogeneous Traffic Networks with Anarchists and Socialist Traffic
Venue: arXiv cs.GT 2019
Year: 2019

In this paper, we consider a heterogeneous traffic network with multiple users classes which differ considerably in their path selection objective. In particular, we consider two classes of users: ones who seek to minimize social cost (socialists) and the ones with typical greedy objective (anarchists) which leads to a heterogeneous game termed as HetGame. The paper proposes an analytical framework to derive optimal/equilibrium flow in such a heterogeneous game along with a method for the same. The paper considers multiple examples of different networks to derive the optimal traffic assignment. We introduce two metrics: price of $α$ anarchy and price of good behavior to evaluate the impact of anarchists and implications of central directives. Finally, the proposed algorithm is implemented for a real traffic network to derive insights.
On Leveraging the Visual Modality for Neural Machine Translation
Venue: arXiv cs.CL 2019
Year: 2019

Leveraging the visual modality effectively for Neural Machine Translation (NMT) remains an open problem in computational linguistics. Recently, Caglayan et al. posit that the observed gains are limited mainly due to the very simple, short, repetitive sentences of the Multi30k dataset (the only multimodal MT dataset available at the time), which renders the source text sufficient for context. In this work, we further investigate this hypothesis on a new large scale multimodal Machine Translation (MMT) dataset, How2, which has 1.57 times longer mean sentence length than Multi30k and no repetition. We propose and evaluate three novel fusion techniques, each of which is designed to ensure the utilization of visual context at different stages of the Sequence-to-Sequence transduction pipeline, even under full linguistic context. However, we still obtain only marginal gains under full linguistic context and posit that visual embeddings extracted from deep vision models (ResNet for Multi30k, ResNext for How2) do not lend themselves to increasing the discriminativeness between the vocabulary elements at token level prediction in NMT. We demonstrate this qualitatively by analyzing attention distribution and quantitatively through Principal Component Analysis, arriving at the conclusion that it is the quality of the visual embeddings rather than the length of sentences, which need to be improved in existing MMT datasets.
Learning Navigation by Visual Localization and Trajectory Prediction
Venue: arXiv cs.CV 2019
Year: 2019

When driving, people make decisions based on current traffic as well as their desired route. They have a mental map of known routes and are often able to navigate without needing directions. Current self-driving models improve their performances when using additional GPS information. Here we aim to push forward self-driving research and perform route planning even in the absence of GPS. Our system learns to predict in real-time vehicle's current location and future trajectory, as a function of time, on a known map, given only the raw video stream and the intended destination. The GPS signal is available only at training time, with training data annotation being fully automatic. Different from other published models, we predict the vehicle's trajectory for up to seven seconds ahead, from which complete steering, speed and acceleration information can be derived for the entire time span. Trajectories capture navigational information on multiple levels, from instant steering commands that depend on present traffic and obstacles ahead, to longer-term navigation decisions, towards a specific destination. We collect our dataset with a regular car and a smartphone that records video and GPS streams. The GPS data is used to derive ground-truth supervision labels and create an analytical representation of the traversed map. In tests, our system outperforms published methods on visual localization and steering and gives accurate navigation assistance between any two known locations.
Open Set Medical Diagnosis
Venue: arXiv cs.LG 2019
Year: 2019

Machine-learned diagnosis models have shown promise as medical aides but are trained under a closed-set assumption, i.e. that models will only encounter conditions on which they have been trained. However, it is practically infeasible to obtain sufficient training data for every human condition, and once deployed such models will invariably face previously unseen conditions. We frame machine-learned diagnosis as an open-set learning problem, and study how state-of-the-art approaches compare. Further, we extend our study to a setting where training data is distributed across several healthcare sites that do not allow data pooling, and experiment with different strategies of building open-set diagnostic ensembles. Across both settings, we observe consistent gains from explicitly modeling unseen conditions, but find the optimal training strategy to vary across settings.
A Learnable Safety Measure
Venue: arXiv cs.LG 2019
Year: 2019

Failures are challenging for learning to control physical systems since they risk damage, time-consuming resets, and often provide little gradient information. Adding safety constraints to exploration typically requires a lot of prior knowledge and domain expertise. We present a safety measure which implicitly captures how the system dynamics relate to a set of failure states. Not only can this measure be used as a safety function, but also to directly compute the set of safe state-action pairs. Further, we show a model-free approach to learn this measure by active sampling using Gaussian processes. While safety can only be guaranteed after learning the safety measure, we show that failures can already be greatly reduced by using the estimated measure during learning.
The false myth of the rise in self-citations, and the impressively positive effect of bibliometric evaluations on the increase of the impact of Italian research
Venue: arXiv cs.DL 2019
Year: 2019

It has recently been claimed by Baccini and coauthors that due to ANVUR's bibliometric evaluations of individuals, departments, and universities, in Italy there has been a surge in self-citations in the last ten years, thus increasing the "inwardness" of Italian research more than has happened abroad. We have studied the database of Ioannidis et al. published on 12 August 2019 of the one hundred thousand most "highly cited" scientists, including about two thousand Italians, and we found that the problem of self-citations in relation to this scientific elite is not significant in Italy, while perhaps observing a small deviation in the low scores in the rankings. The effect indicated by Baccini et al. consequently, does not seem worrying for the scientific elite (we quantified it in 2 percent of the total of scientists of the "best" one hundred thousand), and is probably largely concentrated in the further less cited scientists. Evaluation agencies like ANVUR should probably exclude self-citations in future evaluations, for the noise introduced by the young researchers. The overall state of health of the Italian research system and the positive effect of the ANVUR assessments are demonstrated by the number of Italian researchers in the top one hundred thousand, which has increased by comparing the "career" databased of 22 years, with that of the "young" researchers in the "2017" database. Italy, looking at the elite researchers, not only is not the most indulgent in self-citations, but has shown the best improvements, proving that the introduction of ANVUR had a positive effect. Indeed, all countries apart from Italy have suffered a decline, even substantial (-20 percent on a national Japan scale), of the number of researchers present in the 2017 data sets compared to career data. Italy instead shows a +0.2 percent on a global basis and an impressive +11.53 percent on a national basis.
DNN-Aided Block Sparse Bayesian Learning for User Activity Detection and Channel Estimation in Grant-Free Non-Orthogonal Random Access
Venue: arXiv cs.IT 2019
Year: 2019

In the upcoming Internet-of-Things (IoT) era, the communication is often featured by massive connection, sporadic transmission, and small-sized data packets, which poses new requirements on the delay expectation and resource allocation efficiency of the Random Access (RA) mechanisms of the IoT communication stack. A grant-free non-orthogonal random access (NORA) system is considered in this paper, which could simultaneously reduce the access delay and support more Machine Type Communication (MTC) devices with limited resources. In order to address the joint user activity detection (UAD) and channel estimation (CE) problem in the grant-free NORA system, we propose a deep neural network-aided message passing-based block sparse Bayesian learning (DNN-MP-BSBL) algorithm. In the DNN-MP-BSBL algorithm, the iterative message passing process is transferred from a factor graph to a deep neural network (DNN). Weights are imposed on the messages in the DNN and trained to minimize the estimation error. It is shown that the trained weights could alleviate the convergence problem of the MP-BSBL algorithm, especially on crowded RA scenarios. Simulation results show that the proposed DNN-MP-BSBL algorithm could improve the UAD and CE accuracy with a smaller number of iterations, indicating its advantages for low-latency grant-free NORA systems.
Parallel computational optimization in operations research: A new integrative framework, literature review and research directions
Venue: arXiv cs.DC 2019
Year: 2019

Solving optimization problems with parallel algorithms has a long tradition in OR. Its future relevance for solving hard optimization problems in many fields, including finance, logistics, production and design, is leveraged through the increasing availability of powerful computing capabilities. Acknowledging the existence of several literature reviews on parallel optimization, we did not find reviews that cover the most recent literature on the parallelization of both exact and (meta)heuristic methods. However, in the past decade substantial advancements in parallel computing capabilities have been achieved and used by OR scholars so that an overview of modern parallel optimization in OR that accounts for these advancements is beneficial. Another issue from previous reviews results from their adoption of different foci so that concepts used to describe and structure prior literature differ. This heterogeneity is accompanied by a lack of unifying frameworks for parallel optimization across methodologies, application fields and problems, and it has finally led to an overall fragmented picture of what has been achieved and still needs to be done in parallel optimization in OR. This review addresses the aforementioned issues with three contributions: First, we suggest a new integrative framework of parallel computational optimization across optimization problems, algorithms and application domains. The framework integrates the perspectives of algorithmic design and computational implementation of parallel optimization. Second, we apply the framework to synthesize prior research on parallel optimization in OR, focusing on computational studies published in the period 2008-2017. Finally, we suggest research directions for parallel optimization in OR.
On the Interpretability and Evaluation of Graph Representation Learning
Venue: arXiv cs.LG 2019
Year: 2019

With the rising interest in graph representation learning, a variety of approaches have been proposed to effectively capture a graph's properties. While these approaches have improved performance in graph machine learning tasks compared to traditional graph techniques, they are still perceived as techniques with limited insight into the information encoded in these representations. In this work, we explore methods to interpret node embeddings and propose the creation of a robust evaluation framework for comparing graph representation learning algorithms and hyperparameters. We test our methods on graphs with different properties and investigate the relationship between embedding training parameters and the ability of the produced embedding to recover the structure of the original graph in a downstream task.
Force Field Generalization and the Internal Representation of Motor Learning
Venue: arXiv cs.RO 2019
Year: 2019

When learning a new motor behavior, e.g. reaching in a force field, the nervous system builds an internal representation. Examining how subsequent reaches in unpracticed directions generalize reveals this representation. Though it is the subject of frequent studies, it is not known how this representation changes across training directions, or how changes in reach direction and the corresponding changes in limb impedance, influence measurements of it. We ran a force field adaptation experiment using eight groups of subjects each trained on one of eight standard directions and then tested for generalization in the remaining seven directions. Generalization in all directions was local and asymmetric, providing limited and unequal transfer to the left and right side of the trained target. These asymmetries were not consistent in either magnitude or direction even after correcting for changes in limb impedance, at odds with previous explanations. Relying on a standard model for generalization the inferred representations inconsistently shifted to one side or the other of their respective training direction. A second model that accounted for limb impedance and variations in baseline trajectories explained more data and the inferred representations were centered on their respective training directions. Our results highlight the influence of limb mechanics and impedance on psychophysical measurements and their interpretations for motor learning.
End-to-End Resume Parsing and Finding Candidates for a Job Description using BERT
Venue: arXiv cs.IR 2019
Year: 2019

The ever-increasing number of applications to job positions presents a challenge for employers to find suitable candidates manually. We present an end-to-end solution for ranking candidates based on their suitability to a job description. We accomplish this in two stages. First, we build a resume parser which extracts complete information from candidate resumes. This parser is made available to the public in the form of a web application. Second, we use BERT sentence pair classification to perform ranking based on their suitability to the job description. To approximate the job description, we use the description of past job experiences by a candidate as mentioned in his resume. Our dataset comprises resumes in LinkedIn format and general non-LinkedIn formats. We parse the LinkedIn resumes with 100\% accuracy and establish a strong baseline of 73\% accuracy for candidate suitability.