OpenRaiser/Intern-Atlas huggingface dataset:
SAFARI: Safe and Active Robot Imitation Learning with Imagination
Venue: arXiv cs.RO 2020
Year: 2020

One of the main issues in Imitation Learning is the erroneous behavior of an agent when facing out-of-distribution situations, not covered by the set of demonstrations given by the expert. In this work, we tackle this problem by introducing a novel active learning and control algorithm, SAFARI. During training, it allows an agent to request further human demonstrations when these out-of-distribution situations are met. At deployment, it combines model-free acting using behavioural cloning with model-based planning to reduce state-distribution shift, using future state reconstruction as a test for state familiarity. We empirically demonstrate how this method increases the performance on a set of manipulation tasks with respect to passive Imitation Learning, by gathering more informative demonstrations and by minimizing state-distribution shift at test time. We also show how this method enables the agent to autonomously predict failure rapidly and safely.
Implementing BOLA-BASIC on Puffer: Lessons for the use of SSIM in ABR logic
Venue: arXiv cs.NI 2020
Year: 2020

One ABR algorithm implemented on Puffer is BOLA-BASIC, the simplest variant of BOLA. BOLA finds wide use in industry, notably in the MPEG-DASH reference player used as the basis for video players at Akamai, BBC, Orange, and CBS. The overall goal of BOLA is to maximize each encoded chunk's video quality while minimizing rebuffering. To measure video quality, Puffer uses the structural similarity metric SSIM, whereas BOLA and other ABR algorithms like BBA, MPC, and Pensieve are more commonly implemented using bitrate (or a variant of bitrate). While bitrate is frequently used, BOLA allows the video provider to define its own proxy of video quality as the algorithm's "utility" function. However, using SSIM as utility proved surprisingly complex for BOLA-BASIC, despite the algorithm's simplicity. Given the rising popularity of SSIM and related quality metrics, we anticipate that a growing number of Puffer-like systems will face similar challenges. We hope developers of such systems find our experiences informative as they implement algorithms designed with bitrate-based utility in mind.
To Terminate or Not to Terminate Secure Sockets Layer (SSL) Traffic at the Load Balancer
Venue: arXiv cs.NI 2020
Year: 2020

The concepts of terminating or not terminating Secure Sockets Layer (SSL) at the load balancer have always generated intriguing conversations. In this paper, the author explains the pros and cons of such concepts in a simplistic manner and also provides suggested recommendations to help organizations understand the security implications associated with unencrypted traffic flowing from the Load Balancer to the App Servers. Keywords: Secure Sockets Layer (SSL), Load Balancer, App Server, Man-in-the-Middle Attack (MITM),End to End Encryption (E2EE)
Are Pre-trained Language Models Knowledgeable to Ground Open Domain Dialogues?
Venue: arXiv cs.CL 2020
Year: 2020

We study knowledge-grounded dialogue generation with pre-trained language models. Instead of pursuing new state-of-the-art on benchmarks, we try to understand if the knowledge stored in parameters of the pre-trained models is already enough to ground open domain dialogues, and thus allows us to get rid of the dependency on external knowledge sources in generation. Through extensive experiments on benchmarks, we find that by fine-tuning with a few dialogues containing knowledge, the pre-trained language models can outperform the state-of-the-art model that requires external knowledge in automatic evaluation and human judgment, suggesting a positive answer to the question we raised.
Exploring Constraint Handling Techniques in Real-world Problems on MOEA/D with Limited Budget of Evaluations
Venue: arXiv cs.AI 2020
Year: 2020

Finding good solutions for Multi-objective Optimization (MOPs) Problems is considered a hard problem, especially when considering MOPs with constraints. Thus, most of the works in the context of MOPs do not explore in-depth how different constraints affect the performance of MOP solvers. Here, we focus on exploring the effects of different Constraint Handling Techniques (CHTs) on MOEA/D, a commonly used MOP solver when solving complex real-world MOPs. Moreover, we introduce a simple and effective CHT focusing on the exploration of the decision space, the Three Stage Penalty. We explore each of these CHTs in MOEA/D on two simulated MOPs and six analytic MOPs (eight in total). The results of this work indicate that while the best CHT is problem-dependent, our new proposed Three Stage Penalty achieves competitive results and remarkable performance in terms of hypervolume values in the hard simulated car design MOP.
Towards Spatio-Temporal Video Scene Text Detection via Temporal Clustering
Venue: arXiv cs.CV 2020
Year: 2020

With only bounding-box annotations in the spatial domain, existing video scene text detection (VSTD) benchmarks lack temporal relation of text instances among video frames, which hinders the development of video text-related applications. In this paper, we systematically introduce a new large-scale benchmark, named as STVText4, a well-designed spatial-temporal detection metric (STDM), and a novel clustering-based baseline method, referred to as Temporal Clustering (TC). STVText4 opens a challenging yet promising direction of VSTD, termed as ST-VSTD, which targets at simultaneously detecting video scene texts in both spatial and temporal domains. STVText4 contains more than 1.4 million text instances from 161,347 video frames of 106 videos, where each instance is annotated with not only spatial bounding box and temporal range but also four intrinsic attributes, including legibility, density, scale, and lifecycle, to facilitate the community. With continuous propagation of identical texts in the video sequence, TC can accurately output the spatial quadrilateral and temporal range of the texts, which sets a strong baseline for ST-VSTD. Experiments demonstrate the efficacy of our method and the great academic and practical value of the STVText4. The dataset and code will be available soon.
ReAssert: Deep Learning for Assert Generation
Venue: arXiv cs.SE 2020
Year: 2020

The automated generation of test code can reduce the time and effort required to build software while increasing its correctness and robustness. In this paper, we present RE-ASSERT, an approach for the automated generation of JUnit test asserts which produces more accurate asserts than previous work with fewer constraints. This is achieved by targeting projects individually, using precise code-to-test traceability for learning and by generating assert statements from the method-under-test directly without the need to write an assert-less test first. We also utilise Reformer, a state-of-the-art deep learning model, along with two models from previous work to evaluate ReAssert and an existing approach, known as ATLAS, using lexical accuracy,uniqueness, and dynamic analysis. Our evaluation of ReAssert shows up to 44% of generated asserts for a single project match exactly with the ground truth, increasing to 51% for generated asserts that compile. We also improve on the ATLAS results through our use of Reformer with 28% of generated asserts matching exactly with the ground truth. Reformer also produces the greatest proportion of unique asserts (71%), giving further evidence that Reformer produces the most useful asserts.
The Robot Household Marathon Experiment
Venue: arXiv cs.RO 2020
Year: 2020

In this paper, we present an experiment, designed to investigate and evaluate the scalability and the robustness aspects of mobile manipulation. The experiment involves performing variations of mobile pick and place actions and opening/closing environment containers in a human household. The robot is expected to act completely autonomously for extended periods of time. We discuss the scientific challenges raised by the experiment as well as present our robotic system that can address these challenges and successfully perform all the tasks of the experiment. We present empirical results and the lessons learned as well as discuss where we hit limitations.
Error Exponents in the Bee Identification Problem
Venue: arXiv cs.IT 2020
Year: 2020

We derive various error exponents in the bee identification problem under two different decoding rules. Under naïve decoding, which decodes each bee independently of the others, we analyze a general discrete memoryless channel and a relatively wide family of stochastic decoders. Upper and lower bounds to the random coding error exponent are derived and proved to be equal at relatively high coding rates. Then, we propose a lower bound on the error exponent of the typical random code, which improves upon the random coding exponent at low coding rates. We also derive a third bound, which is related to expurgated codes, which turns out to be strictly higher than the other bounds, also at relatively low rates. We show that the universal maximum mutual information decoder is optimal with respect to the typical random code and the expurgated code. Moving further, we derive error exponents under optimal decoding, the relatively wide family of symmetric channels, and the maximum likelihood decoder. We first propose a random coding lower bound, and then, an improved bound which stems from an expurgation process. We show numerically that our second bound strictly improves upon the random coding bound at an intermediate range of coding rates, where a bound derived in a previous work no longer holds.
Do We Need Online NLU Tools?
Venue: arXiv cs.CL 2020
Year: 2020

The intent recognition is an essential algorithm of any conversational AI application. It is responsible for the classification of an input message into meaningful classes. In many bot development platforms, we can configure the NLU pipeline. Several intent recognition services are currently available as an API, or we choose from many open-source alternatives. However, there is no comparison of intent recognition services and open-source algorithms. Many factors make the selection of the right approach to the intent recognition challenging in practice. In this paper, we suggest criteria to choose the best intent recognition algorithm for an application. We present a dataset for evaluation. Finally, we compare selected public NLU services with selected open-source algorithms for intent recognition.
Logics with Multiteam Semantics
Venue: arXiv cs.LO 2020
Year: 2020

Team semantics is the mathematical basis of modern logics of dependence and independence. In contrast to classical Tarski semantics, a formula is evaluated not for a single assignment of values to the free variables, but on a set of such assignments, called a team. Team semantics is appropriate for a purely logical understanding of dependency notions, where only the presence or absence of data matters, but based on sets, it does not take into account multiple occurrences of data values. It is therefore insufficient in scenarios where such multiplicities matter, in particular for reasoning about probabilities and statistical independencies. Therefore, an extension from teams to multiteams (i.e. multisets of assignments) has been proposed by several authors. In this paper we aim at a systematic development of logics of dependence and independence based on multiteam semantics. We study atomic dependency properties of finite multiteams and discuss the appropriate meaning of logical operators to extend the atomic dependencies to full-fledged logics for reasoning about dependence properties in a multiteam setting. We explore properties and expressive power of a wide spectrum of different multiteam logics and compare them to logics with team semantics. We also study the relationship of logics with multiteam semantics with existential second-order logic for a specific class of metafinite structures. It turns out that inclusion-exclusion logic can be characterised in a precise sense by the Presburger fragment of this logic, but for capturing independence, we need to go beyond it and add some form of multiplication. Finally we also consider multiteams with weights in the reals and study the expressive power of formulae by means of topological properties.
First Order-Rewritability and Containment of Conjunctive Queries in Horn Description Logics
Venue: arXiv cs.AI 2020
Year: 2020

We study FO-rewritability of conjunctive queries in the presence of ontologies formulated in a description logic between EL and Horn-SHIF, along with related query containment problems. Apart from providing characterizations, we establish complexity results ranging from ExpTime via NExpTime to 2ExpTime, pointing out several interesting effects. In particular, FO-rewriting is more complex for conjunctive queries than for atomic queries when inverse roles are present, but not otherwise.
Modular Multi Target Tracking Using LSTM Networks
Venue: arXiv cs.LG 2020
Year: 2020

The process of association and tracking of sensor detections is a key element in providing situational awareness. When the targets in the scenario are dense and exhibit high maneuverability, Multi-Target Tracking (MTT) becomes a challenging task. The conventional techniques to solve such NP-hard combinatorial optimization problem involves multiple complex models and requires tedious tuning of parameters, failing to provide an acceptable performance within the computational constraints. This paper proposes a model free end-to-end approach for airborne target tracking system using sensor measurements, integrating all the key elements of multi target tracking -- association, prediction and filtering using deep learning with memory. The challenging task of association is performed using the Bi-Directional Long short-term memory (LSTM) whereas filtering and prediction are done using LSTM models. The proposed modular blocks can be independently trained and used in multitude of tracking applications including non co-operative (e.g., radar) and co-operative sensors (e.g., AIS, IFF, ADS-B). Such modular blocks also enhances the interpretability of the deep learning application. It is shown that performance of the proposed technique outperforms conventional state of the art technique Joint Probabilistic Data Association with Interacting Multiple Model (JPDA-IMM) filter.
Contextual Stochastic Block Model: Sharp Thresholds and Contiguity
Venue: arXiv cs.SI 2020
Year: 2020

We study community detection in the contextual stochastic block model arXiv:1807.09596 [cs.SI], arXiv:1607.02675 [stat.ME]. In arXiv:1807.09596 [cs.SI], the second author studied this problem in the setting of sparse graphs with high-dimensional node-covariates. Using the non-rigorous cavity method from statistical physics, they conjectured the sharp limits for community detection in this setting. Further, the information theoretic threshold was verified, assuming that the average degree of the observed graph is large. It is expected that the conjecture holds as soon as the average degree exceeds one, so that the graph has a giant component. We establish this conjecture, and characterize the sharp threshold for detection and weak recovery.
The human quest for discovering mathematical beauty in the arts
Venue: arXiv cs.CY 2020
Year: 2020

In the words of the twentieth-century British mathematician G. H. Hardy, "the human function is to 'discover or observe' mathematics" (1). For centuries, starting from the ancient Greeks, mankind has hunted for beauty and order in arts and in nature. This quest for mathematical beauty has led to the discovery of recurrent mathematical structures, such as the golden ratio, Fibonacci, and Lucas numbers, whose ubiquitous presences have been tantalizing the minds of artists and scientists alike. The captivation for this quest comes with high stakes. In fact, art is the definitive expression of human creativity, and its mathematical understanding would deliver us the keys for decoding human culture and its evolution (2). However, it was not until fairly recently that the scope and the scale of the human quest for mathematical beauty was radically expanded by the simultaneous confluence of three separate innovations. The mass digitization of large art archives, the surge in computational power, and the development of robust statistical methods to capture hidden patterns in vast amounts of data have made it possible to reveal the---otherwise unnoticeable to the human eye---mathematics concealed in large artistic corpora. Starting from its inception, marked by the foundational work by Birkhoff (3), progress in the broad field of computational aesthetics has reached a scale that would have been unimaginable just a decade ago. The recent expansion is not limited to the visual arts (2) but includes music (4), stories (5), language phonology (6), humor in jokes (7), and even equations (8); for a comprehensive review, see ref. 9.
Imputation techniques on missing values in breast cancer treatment and fertility data
Venue: arXiv cs.LG 2020
Year: 2020

Clinical decision support using data mining techniques offers more intelligent way to reduce the decision error in the last few years. However, clinical datasets often suffer from high missingness, which adversely impacts the quality of modelling if handled improperly. Imputing missing values provides an opportunity to resolve the issue. Conventional imputation methods adopt simple statistical analysis, such as mean imputation or discarding missing cases, which have many limitations and thus degrade the performance of learning. This study examines a series of machine learning based imputation methods and suggests an efficient approach to in preparing a good quality breast cancer (BC) dataset, to find the relationship between BC treatment and chemotherapy-related amenorrhoea, where the performance is evaluated with the accuracy of the prediction.
Electric Vehicle Charging Infrastructure Planning: A Scalable Computational Framework
Venue: arXiv cs.AI 2020
Year: 2020

The optimal charging infrastructure planning problem over a large geospatial area is challenging due to the increasing network sizes of the transportation system and the electric grid. The coupling between the electric vehicle travel behaviors and charging events is therefore complex. This paper focuses on the demonstration of a scalable computational framework for the electric vehicle charging infrastructure planning over the tightly integrated transportation and electric grid networks. On the transportation side, a charging profile generation strategy is proposed leveraging the EV energy consumption model, trip routing, and charger selection methods. On the grid side, a genetic algorithm is utilized within the optimal power flow program to solve the optimal charger placement problem with integer variables by adaptively evaluating candidate solutions in the current iteration and generating new solutions for the next iterations.
Transfer learning of chaotic systems
Venue: arXiv cs.NE 2020
Year: 2020

Can a neural network trained by the time series of system A be used to predict the evolution of system B? This problem, knowing as transfer learning in a broad sense, is of great importance in machine learning and data mining, yet has not been addressed for chaotic systems. Here we investigate transfer learning of chaotic systems from the perspective of synchronization-based state inference, in which a reservoir computer trained by chaotic system A is used to infer the unmeasured variables of chaotic system B, while A is different from B in either parameter or dynamics. It is found that if systems A and B are different in parameter, the reservoir computer can be well synchronized to system B. However, if systems A and B are different in dynamics, the reservoir computer fails to synchronize with system B in general. Knowledge transfer along a chain of coupled reservoir computers is also studied, and it is found that, although the reservoir computers are trained by different systems, the unmeasured variables of the driving system can be successfully inferred by the remote reservoir computer. Finally, by an experiment of chaotic pendulum, we show that the knowledge learned from the modeling system can be used to predict the evolution of the experimental system.
Estimation of Shortest Path Covariance Matrices
Venue: arXiv cs.LG 2020
Year: 2020

We study the sample complexity of estimating the covariance matrix $\mathbfΣ \in \mathbb{R}^{d\times d}$ of a distribution $\mathcal D$ over $\mathbb{R}^d$ given independent samples, under the assumption that $\mathbfΣ$ is graph-structured. In particular, we focus on shortest path covariance matrices, where the covariance between any two measurements is determined by the shortest path distance in an underlying graph with $d$ nodes. Such matrices generalize Toeplitz and circulant covariance matrices and are widely applied in signal processing applications, where the covariance between two measurements depends on the (shortest path) distance between them in time or space. We focus on minimizing both the vector sample complexity: the number of samples drawn from $\mathcal{D}$ and the entry sample complexity: the number of entries read in each sample. The entry sample complexity corresponds to measurement equipment costs in signal processing applications. We give a very simple algorithm for estimating $\mathbfΣ$ up to spectral norm error $ε\left\|\mathbfΣ\right\|_2$ using just $O(\sqrt{D})$ entry sample complexity and $\tilde O(r^2/ε^2)$ vector sample complexity, where $D$ is the diameter of the underlying graph and $r \le d$ is the rank of $\mathbfΣ$. Our method is based on extending the widely applied idea of sparse rulers for Toeplitz covariance estimation to the graph setting. In the special case when $\mathbfΣ$ is a low-rank Toeplitz matrix, our result matches the state-of-the-art, with a far simpler proof. We also give an information theoretic lower bound matching our upper bound up to a factor $D$ and discuss some directions towards closing this gap.
Approximate Bipartite Vertex Cover in the CONGEST Model
Venue: arXiv cs.DS 2020
Year: 2020

We give efficient distributed algorithms for the minimum vertex cover problem in bipartite graphs in the CONGEST model. From Kőnig's theorem, it is well known that in bipartite graphs the size of a minimum vertex cover is equal to the size of a maximum matching. We first show that together with an existing $O(n\log n)$-round algorithm for computing a maximum matching, the constructive proof of Kőnig's theorem directly leads to a deterministic $O(n\log n)$-round CONGEST algorithm for computing a minimum vertex cover. We then show that by adapting the construction, we can also convert an \emph{approximate} maximum matching into an \emph{approximate} minimum vertex cover. Given a $(1-δ)$-approximate matching for some $δ>1$, we show that a $(1+O(δ))$-approximate vertex cover can be computed in time $O(D+\mathrm{poly}(\frac{\log n}δ))$, where $D$ is the diameter of the graph. When combining with known graph clustering techniques, for any $\varepsilon\in(0,1]$, this leads to a $\mathrm{poly}(\frac{\log n}{\varepsilon})$-time deterministic and also to a slightly faster and simpler randomized $O(\frac{\log n}{\varepsilon^3})$-round CONGEST algorithm for computing a $(1+\varepsilon)$-approximate vertex cover in bipartite graphs. For constant $\varepsilon$, the randomized time complexity matches the $Ω(\log n)$ lower bound for computing a $(1+\varepsilon)$-approximate vertex cover in bipartite graphs even in the LOCAL model. Our results are also in contrast to the situation in general graphs, where it is known that computing an optimal vertex cover requires $\tildeΩ(n^2)$ rounds in the CONGEST model and where it is not even known how to compute any $(2-\varepsilon)$-approximation in time $o(n^2)$.