OpenRaiser/Intern-Atlas huggingface dataset:
At-Most-One Constraints in Efficient Representations of Mutex Networks
Venue: arXiv cs.AI 2020
Year: 2020

The At-Most-One (AMO) constraint is a special case of cardinality constraint that requires at most one variable from a set of Boolean variables to be set to TRUE. AMO is important for modeling problems as Boolean satisfiability (SAT) from domains where decision variables represent spatial or temporal placements of some objects that cannot share the same spatial or temporal slot. The AMO constraint can be used for more efficient representation and problem solving in mutex networks consisting of pair-wise mutual exclusions forbidding pairs of Boolean variable to be simultaneously TRUE. An on-line method for automated detection of cliques for efficient representation of incremental mutex networks where new mutexes arrive using AMOs is presented. A comparison of SAT-based problem solving in mutex networks represented by AMO constraints using various encodings is shown.
Report from the NSF Future Directions Workshop, Toward User-Oriented Agents: Research Directions and Challenges
Venue: arXiv cs.CL 2020
Year: 2020

This USER Workshop was convened with the goal of defining future research directions for the burgeoning intelligent agent research community and to communicate them to the National Science Foundation. It took place in Pittsburgh Pennsylvania on October 24 and 25, 2019 and was sponsored by National Science Foundation Grant Number IIS-1934222. Any opinions, findings and conclusions or future directions expressed in this document are those of the authors and do not necessarily reflect the views of the National Science Foundation. The 27 participants presented their individual research interests and their personal research goals. In the breakout sessions that followed, the participants defined the main research areas within the domain of intelligent agents and they discussed the major future directions that the research in each area of this domain should take
Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies
Venue: arXiv cs.CV 2020
Year: 2020

Since DARPA Grand Challenges (rural) in 2004/05 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications. Almost at the same time, deep learning has made breakthrough by several pioneers, three of them (also called fathers of deep learning), Hinton, Bengio and LeCun, won ACM Turin Award in 2019. This is a survey of autonomous driving technologies with deep learning methods. We investigate the major fields of self-driving systems, such as perception, mapping and localization, prediction, planning and control, simulation, V2X and safety etc. Due to the limited space, we focus the analysis on several key areas, i.e. 2D and 3D object detection in perception, depth estimation from cameras, multiple sensor fusion on the data, feature and task level respectively, behavior modelling and prediction of vehicle driving and pedestrian trajectories.
0-MMS: Zero-Shot Multi-Motion Segmentation With A Monocular Event Camera
Venue: arXiv cs.CV 2020
Year: 2020

Segmentation of moving objects in dynamic scenes is a key process in scene understanding for navigation tasks. Classical cameras suffer from motion blur in such scenarios rendering them effete. On the contrary, event cameras, because of their high temporal resolution and lack of motion blur, are tailor-made for this problem. We present an approach for monocular multi-motion segmentation, which combines bottom-up feature tracking and top-down motion compensation into a unified pipeline, which is the first of its kind to our knowledge. Using the events within a time-interval, our method segments the scene into multiple motions by splitting and merging. We further speed up our method by using the concept of motion propagation and cluster keyslices. The approach was successfully evaluated on both challenging real-world and synthetic scenarios from the EV-IMO, EED, and MOD datasets and outperformed the state-of-the-art detection rate by 12\%, achieving a new state-of-the-art average detection rate of 81.06%, 94.2% and 82.35% on the aforementioned datasets. To enable further research and systematic evaluation of multi-motion segmentation, we present and open-source a new dataset/benchmark called MOD++, which includes challenging sequences and extensive data stratification in-terms of camera and object motion, velocity magnitudes, direction, and rotational speeds.
Model-Size Reduction for Reservoir Computing by Concatenating Internal States Through Time
Venue: arXiv cs.LG 2020
Year: 2020

Reservoir computing (RC) is a machine learning algorithm that can learn complex time series from data very rapidly based on the use of high-dimensional dynamical systems, such as random networks of neurons, called "reservoirs." To implement RC in edge computing, it is highly important to reduce the amount of computational resources that RC requires. In this study, we propose methods that reduce the size of the reservoir by inputting the past or drifting states of the reservoir to the output layer at the current time step. These proposed methods are analyzed based on information processing capacity, which is a performance measure of RC proposed by Dambre et al. (2012). In addition, we evaluate the effectiveness of the proposed methods on time-series prediction tasks: the generalized Henon-map and NARMA. On these tasks, we found that the proposed methods were able to reduce the size of the reservoir up to one tenth without a substantial increase in regression error. Because the applications of the proposed methods are not limited to a specific network structure of the reservoir, the proposed methods could further improve the energy efficiency of RC-based systems, such as FPGAs and photonic systems.
Performance in the Courtroom: Automated Processing and Visualization of Appeal Court Decisions in France
Venue: arXiv cs.CL 2020
Year: 2020

Artificial Intelligence techniques are already popular and important in the legal domain. We extract legal indicators from judicial judgment to decrease the asymmetry of information of the legal system and the access-to-justice gap. We use NLP methods to extract interesting entities/data from judgments to construct networks of lawyers and judgments. We propose metrics to rank lawyers based on their experience, wins/loss ratio and their importance in the network of lawyers. We also perform community detection in the network of judgments and propose metrics to represent the difficulty of cases capitalising on communities features.
A Novel Meta-Heuristic Optimization Algorithm Inspired by the Spread of Viruses
Venue: arXiv cs.NE 2020
Year: 2020

According to the no-free-lunch theorem, there is no single meta-heuristic algorithm that can optimally solve all optimization problems. This motivates many researchers to continuously develop new optimization algorithms. In this paper, a novel nature-inspired meta-heuristic optimization algorithm called virus spread optimization (VSO) is proposed. VSO loosely mimics the spread of viruses among hosts, and can be effectively applied to solving many challenging and continuous optimization problems. We devise a new representation scheme and viral operations that are radically different from previously proposed virus-based optimization algorithms. First, the viral RNA of each host in VSO denotes a potential solution for which different viral operations will help to diversify the searching strategies in order to largely enhance the solution quality. In addition, an imported infection mechanism, inheriting the searched optima from another colony, is introduced to possibly avoid the prematuration of any potential solution in solving complex problems. VSO has an excellent capability to conduct adaptive neighborhood searches around the discovered optima for achieving better solutions. Furthermore, with a flexible infection mechanism, VSO can quickly escape from local optima. To clearly demonstrate both its effectiveness and efficiency, VSO is critically evaluated on a series of well-known benchmark functions. Moreover, VSO is validated on its applicability through two real-world examples including the financial portfolio optimization and optimization of hyper-parameters of support vector machines for classification problems. The results show that VSO has attained superior performance in terms of solution fitness, convergence rate, scalability, reliability, and flexibility when compared to those results of the conventional as well as state-of-the-art meta-heuristic optimization algorithms.
The perturbation analysis of nonconvex low-rank matrix robust recovery
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we bring forward a completely perturbed nonconvex Schatten $p$-minimization to address a model of completely perturbed low-rank matrix recovery. The paper that based on the restricted isometry property generalizes the investigation to a complete perturbation model thinking over not only noise but also perturbation, gives the restricted isometry property condition that guarantees the recovery of low-rank matrix and the corresponding reconstruction error bound. In particular, the analysis of the result reveals that in the case that $p$ decreases $0$ and $a>1$ for the complete perturbation and low-rank matrix, the condition is the optimal sufficient condition $δ_{2r}<1$ \cite{Recht et al 2010}. The numerical experiments are conducted to show better performance, and provides outperformance of the nonconvex Schatten $p$-minimization method comparing with the convex nuclear norm minimization approach in the completely perturbed scenario.
Adaptive Reward-Free Exploration
Venue: arXiv cs.LG 2020
Year: 2020

Reward-free exploration is a reinforcement learning setting studied by Jin et al. (2020), who address it by running several algorithms with regret guarantees in parallel. In our work, we instead give a more natural adaptive approach for reward-free exploration which directly reduces upper bounds on the maximum MDP estimation error. We show that, interestingly, our reward-free UCRL algorithm can be seen as a variant of an algorithm of Fiechter from 1994, originally proposed for a different objective that we call best-policy identification. We prove that RF-UCRL needs of order $({SAH^4}/{\varepsilon^2})(\log(1/δ) + S)$ episodes to output, with probability $1-δ$, an $\varepsilon$-approximation of the optimal policy for any reward function. This bound improves over existing sample-complexity bounds in both the small $\varepsilon$ and the small $δ$ regimes. We further investigate the relative complexities of reward-free exploration and best-policy identification.
The Role of Modularity and Neuro-Regulation for the Production of Multiple Behaviors
Venue: arXiv cs.RO 2020
Year: 2020

This project investigates whether functional specialization or modularity can support the development of multiple behaviors. In principle, modular solutions of this type can facilitate the development of multiple behaviors since each module is responsible for the production of different behavior. Consequently, the interfaces that arise neural mechanisms supporting the production of different behaviors can be reduced. The project involves the implementation of regulatory networks of this type and the realization of experiments involving the production of different behaviors.
Geometric and Stiffness Modeling and Design of Calibration Experiments for the 7 dof Serial Manipulator KUKA iiwa 14 R820
Venue: arXiv cs.RO 2020
Year: 2020

The present project deals with the elastostatic modeling and calibration experiment of spacial industrial manipulators using an optimal selection of measurements pose, for the calibration procedure, the optimal pose selection aims to the efficiency improvement of identification procedure for serial manipulators which reduces noise impact on the parameters identification precision, it is usually used for planar manipulators, our work is mainly to extend the approach for a more complicated manipulator in 3D space using a wise decomposition of the spacial manipulator into a set of serial sub-chains, the optimal pose configuration is then used in the calibration procedure using the complete and irreducible model for the 7 DOF serial manipulator. The methodology is illustrated with the anthropomorphic industrial robot KUKA iiwa14 R820 for which, we performed the calibration and constructed the stiffness modeling using two different approaches namely VJM (Virtual Joint Modeling) and MSA (Matrix Structural Analysis).
See what I'm saying? Comparing Intelligent Personal Assistant use for Native and Non-Native Language Speakers
Venue: arXiv cs.HC 2020
Year: 2020

Limited linguistic coverage for Intelligent Personal Assistants (IPAs) means that many interact in a non-native language. Yet we know little about how IPAs currently support or hinder these users. Through native (L1) and non-native (L2) English speakers interacting with Google Assistant on a smartphone and smart speaker, we aim to understand this more deeply. Interviews revealed that L2 speakers prioritised utterance planning around perceived linguistic limitations, as opposed to L1 speakers prioritising succinctness because of system limitations. L2 speakers see IPAs as insensitive to linguistic needs resulting in failed interaction. L2 speakers clearly preferred using smartphones, as visual feedback supported diagnoses of communication breakdowns whilst allowing time to process query results. Conversely, L1 speakers preferred smart speakers, with audio feedback being seen as sufficient. We discuss the need to tailor the IPA experience for L2 users, emphasising visual feedback whilst reducing the burden of language production.
AoI-optimal Joint Sampling and Updating for Wireless Powered Communication Systems
Venue: arXiv cs.IT 2020
Year: 2020

This paper characterizes the structure of the Age of Information (AoI)-optimal policy in wireless powered communication systems while accounting for the time and energy costs of generating status updates at the source nodes. In particular, for a single source-destination pair in which a radio frequency (RF)-powered source sends status updates about some physical process to a destination node, we minimize the long-term average AoI at the destination node. The problem is modeled as an average cost Markov Decision Process (MDP) in which, the generation times of status updates at the source, the transmissions of status updates from the source to the destination, and the wireless energy transfer (WET) are jointly optimized. After proving the monotonicity property of the value function associated with the MDP, we analytically demonstrate that the AoI-optimal policy has a threshold-based structure w.r.t. the state variables. Our numerical results verify the analytical findings and reveal the impact of state variables on the structure of the AoI-optimal policy. Our results also demonstrate the impact of system design parameters on the optimal achievable average AoI as well as the superiority of our proposed joint sampling and updating policy w.r.t. the generate-at-will policy.
A framework for step-wise explaining how to solve constraint satisfaction problems
Venue: arXiv cs.LO 2020
Year: 2020

We explore the problem of step-wise explaining how to solve constraint satisfaction problems, with a use case on logic grid puzzles. More specifically, we study the problem of explaining the inference steps that one can take during propagation, in a way that is easy to interpret for a person. Thereby, we aim to give the constraint solver explainable agency, which can help in building trust in the solver by being able to understand and even learn from the explanations. The main challenge is that of finding a sequence of simple explanations, where each explanation should aim to be as cognitively easy as possible for a human to verify and understand. This contrasts with the arbitrary combination of facts and constraints that the solver may use when propagating. We propose the use of a cost function to quantify how simple an individual explanation of an inference step is, and identify the explanation-production problem of finding the best sequence of explanations of a CSP. Our approach is agnostic of the underlying constraint propagation mechanisms, and can provide explanations even for inference steps resulting from combinations of constraints. In case multiple constraints are involved, we also develop a mechanism that allows to break the most difficult steps up and thus gives the user the ability to zoom in on specific parts of the explanation. Our proposed algorithm iteratively constructs the explanation sequence by using an optimistic estimate of the cost function to guide the search for the best explanation at each step. Our experiments on logic grid puzzles show the feasibility of the approach in terms of the quality of the individual explanations and the resulting explanation sequences obtained.
Latent Transformations for Discrete-Data Normalising Flows
Venue: arXiv cs.LG 2020
Year: 2020

Normalising flows (NFs) for discrete data are challenging because parameterising bijective transformations of discrete variables requires predicting discrete/integer parameters. Having a neural network architecture predict discrete parameters takes a non-differentiable activation function (eg, the step function) which precludes gradient-based learning. To circumvent this non-differentiability, previous work has employed biased proxy gradients, such as the straight-through estimator. We present an unbiased alternative where rather than deterministically parameterising one transformation, we predict a distribution over latent transformations. With stochastic transformations, the marginal likelihood of the data is differentiable and gradient-based learning is possible via score function estimation. To test the viability of discrete-data NFs we investigate performance on binary MNIST. We observe great challenges with both deterministic proxy gradients and unbiased score function estimation. Whereas the former often fails to learn even a shallow transformation, the variance of the latter could not be sufficiently controlled to admit deeper NFs.
PAC Bounds for Imitation and Model-based Batch Learning of Contextual Markov Decision Processes
Venue: arXiv cs.LG 2020
Year: 2020

We consider the problem of batch multi-task reinforcement learning with observed context descriptors, motivated by its application to personalized medical treatment. In particular, we study two general classes of learning algorithms: direct policy learning (DPL), an imitation-learning based approach which learns from expert trajectories, and model-based learning. First, we derive sample complexity bounds for DPL, and then show that model-based learning from expert actions can, even with a finite model class, be impossible. After relaxing the conditions under which the model-based approach is expected to learn by allowing for greater coverage of state-action space, we provide sample complexity bounds for model-based learning with finite model classes, showing that there exist model classes with sample complexity exponential in their statistical complexity. We then derive a sample complexity upper bound for model-based learning based on a measure of concentration of the data distribution. Our results give formal justification for imitation learning over model-based learning in this setting.
DEPOSafe: Demystifying the Fake Deposit Vulnerability in Ethereum Smart Contracts
Venue: arXiv cs.CR 2020
Year: 2020

Cryptocurrency has seen an explosive growth in recent years, thanks to the evolvement of blockchain technology and its economic ecosystem. Besides Bitcoin, thousands of cryptocurrencies have been distributed on blockchains, while hundreds of cryptocurrency exchanges are emerging to facilitate the trading of digital assets. At the same time, it also attracts the attentions of attackers. Fake deposit, as one of the most representative attacks (vulnerabilities) related to exchanges and tokens, has been frequently observed in the blockchain ecosystem, causing large financial losses. However, besides a few security reports, our community lacks of the understanding of this vulnerability, for example its scale and the impacts. In this paper, we take the first step to demystify the fake deposit vulnerability. Based on the essential patterns we have summarized, we implement DEPOSafe, an automated tool to detect and verify (exploit) the fake deposit vulnerability in ERC-20 smart contracts. DEPOSafe incorporates several key techniques including symbolic execution based static analysis and behavior modeling based dynamic verification. By applying DEPOSafe to 176,000 ERC-20 smart contracts, we have identified over 7,000 vulnerable contracts that may suffer from two types of attacks. Our findings demonstrate the urgency to identify and prevent the fake deposit vulnerability.
A Probabilistic Model with Commonsense Constraints for Pattern-based Temporal Fact Extraction
Venue: arXiv cs.CL 2020
Year: 2020

Textual patterns (e.g., Country's president Person) are specified and/or generated for extracting factual information from unstructured data. Pattern-based information extraction methods have been recognized for their efficiency and transferability. However, not every pattern is reliable: A major challenge is to derive the most complete and accurate facts from diverse and sometimes conflicting extractions. In this work, we propose a probabilistic graphical model which formulates fact extraction in a generative process. It automatically infers true facts and pattern reliability without any supervision. It has two novel designs specially for temporal facts: (1) it models pattern reliability on two types of time signals, including temporal tag in text and text generation time; (2) it models commonsense constraints as observable variables. Experimental results demonstrate that our model significantly outperforms existing methods on extracting true temporal facts from news data.
DNF-Net: A Neural Architecture for Tabular Data
Venue: arXiv cs.LG 2020
Year: 2020

A challenging open question in deep learning is how to handle tabular data. Unlike domains such as image and natural language processing, where deep architectures prevail, there is still no widely accepted neural architecture that dominates tabular data. As a step toward bridging this gap, we present DNF-Net a novel generic architecture whose inductive bias elicits models whose structure corresponds to logical Boolean formulas in disjunctive normal form (DNF) over affine soft-threshold decision terms. In addition, DNF-Net promotes localized decisions that are taken over small subsets of the features. We present an extensive empirical study showing that DNF-Nets significantly and consistently outperform FCNs over tabular data. With relatively few hyperparameters, DNF-Nets open the door to practical end-to-end handling of tabular data using neural networks. We present ablation studies, which justify the design choices of DNF-Net including the three inductive bias elements, namely, Boolean formulation, locality, and feature selection.