OpenRaiser/Intern-Atlas huggingface dataset:
Alquist: The Alexa Prize Socialbot
Venue: arXiv cs.CL 2018
Year: 2018

This paper describes a new open domain dialogue system Alquist developed as part of the Alexa Prize competition for the Amazon Echo line of products. The Alquist dialogue system is designed to conduct a coherent and engaging conversation on popular topics. We are presenting a hybrid system combining several machine learning and rule based approaches. We discuss and describe the Alquist pipeline, data acquisition, and processing, dialogue manager, NLG, knowledge aggregation and hierarchy of sub-dialogs. We present some of the experimental results.
Demo of Sanskrit-Hindi SMT System
Venue: arXiv cs.CL 2018
Year: 2018

The demo proposal presents a Phrase-based Sanskrit-Hindi (SaHiT) Statistical Machine Translation system. The system has been developed on Moses. 43k sentences of Sanskrit-Hindi parallel corpus and 56k sentences of a monolingual corpus in the target language (Hindi) have been used. This system gives 57 BLEU score.
Forecasting the presence and intensity of hostility on Instagram using linguistic and social features
Venue: arXiv cs.CL 2018
Year: 2018

Online antisocial behavior, such as cyberbullying, harassment, and trolling, is a widespread problem that threatens free discussion and has negative physical and mental health consequences for victims and communities. While prior work has proposed automated methods to identify hostile comments in online discussions, these methods work retrospectively on comments that have already been posted, making it difficult to intervene before an interaction escalates. In this paper we instead consider the problem of forecasting future hostilities in online discussions, which we decompose into two tasks: (1) given an initial sequence of non-hostile comments in a discussion, predict whether some future comment will contain hostility; and (2) given the first hostile comment in a discussion, predict whether this will lead to an escalation of hostility in subsequent comments. Thus, we aim to forecast both the presence and intensity of hostile comments based on linguistic and social features from earlier comments. To evaluate our approach, we introduce a corpus of over 30K annotated Instagram comments from over 1,100 posts. Our approach is able to predict the appearance of a hostile comment on an Instagram post ten or more hours in the future with an AUC of .82 (task 1), and can furthermore distinguish between high and low levels of future hostility with an AUC of .91 (task 2).
On Abelian Longest Common Factor with and without RLE
Venue: arXiv cs.DS 2018
Year: 2018

We consider the Abelian longest common factor problem in two scenarios: when input strings are uncompressed and are of size $n$, and when the input strings are run-length encoded and their compressed representations have size at most $m$. The alphabet size is denoted by $σ$. For the uncompressed problem, we show an $o(n^2)$-time and $\Oh(n)$-space algorithm in the case of $σ=\Oh(1)$, making a non-trivial use of tabulation. For the RLE-compressed problem, we show two algorithms: one working in $\Oh(m^2σ^2 \log^3 m)$ time and $\Oh(m (σ^2+\log^2 m))$ space, which employs line sweep, and one that works in $\Oh(m^3)$ time and $\Oh(m)$ space that applies in a careful way a sliding-window-based approach. The latter improves upon the previously known $\Oh(nm^2)$-time and $\Oh(m^4)$-time algorithms that were recently developed by Sugimoto et al.\ (IWOCA 2017) and Grabowski (SPIRE 2017), respectively.
Approximation of Meta Distribution and Its Moments for Poisson Cellular Networks
Venue: arXiv cs.IT 2018
Year: 2018

The notion of meta distribution as the distribution of the conditional coverage probability (CCP) was introduced in \cite{Haenggi2015}. In this letter, we show how we can reconstruct the entire meta distribution only from its moments using Fourier-Jacobi expansion. As an example, we specifically consider Poisson cellular networks. We also provide a simple closed-form approximation for its moments, along with its error analysis. Lastly, we apply the approximation to obtain a power scaling law for downlink Poisson cellular networks.
Reducing Conservatism in Model-Invariant Safety-Preserving Control of Propofol Anesthesia Using Falsification
Venue: arXiv cs.SY 2018
Year: 2018

This work provides a formalized model-invariant safety system for closed-loop anesthesia that uses feedback from measured data for model falsification to reduce conservatism. The safety system maintains predicted propofol plasma concentrations, as well as the patient's blood pressure, within safety bounds despite uncertainty in patient responses to propofol. Model-invariant formal verification is used to formalize the safety system. This technique requires a multi-model description of model-uncertainty. Model-invariant verification considers all possible dynamics of an uncertain system, and the resulting safety system may be conservative for systems that do not exhibit the worst-case dynamical response. In this work, we employ model falsification to reduce conservatism of the model-invariant safety system. Members of a model set that characterizes model- uncertainty are falsified if discrepancy between predictions of those models and measured responses of the uncertain system is established, thereby reducing model uncertainty. We show that including falsification in a model-invariant safety system reduces conservatism of the safety system.
K-Nearest Oracles Borderline Dynamic Classifier Ensemble Selection
Venue: arXiv cs.LG 2018
Year: 2018

Dynamic Ensemble Selection (DES) techniques aim to select locally competent classifiers for the classification of each new test sample. Most DES techniques estimate the competence of classifiers using a given criterion over the region of competence of the test sample (its the nearest neighbors in the validation set). The K-Nearest Oracles Eliminate (KNORA-E) DES selects all classifiers that correctly classify all samples in the region of competence of the test sample, if such classifier exists, otherwise, it removes from the region of competence the sample that is furthest from the test sample, and the process repeats. When the region of competence has samples of different classes, KNORA-E can reduce the region of competence in such a way that only samples of a single class remain in the region of competence, leading to the selection of locally incompetent classifiers that classify all samples in the region of competence as being from the same class. In this paper, we propose two DES techniques: K-Nearest Oracles Borderline (KNORA-B) and K-Nearest Oracles Borderline Imbalanced (KNORA-BI). KNORA-B is a DES technique based on KNORA-E that reduces the region of competence but maintains at least one sample from each class that is in the original region of competence. KNORA-BI is a variation of KNORA-B for imbalance datasets that reduces the region of competence but maintains at least one minority class sample if there is any in the original region of competence. Experiments are conducted comparing the proposed techniques with 19 DES techniques from the literature using 40 datasets. The results show that the proposed techniques achieved interesting results, with KNORA-BI outperforming state-of-art techniques.
Volur: Concurrent Edge/Core Route Control in Data Center Networks
Venue: arXiv cs.NI 2018
Year: 2018

A perennial question in computer networks is where to place functionality among components of a distributed computer system. In data centers, one option is to move all intelligence to the edge, essentially relegating switches and middleboxes, regardless of their programmability, to simple static routing policies. Another is to add more intelligence to the middle of the network in the hopes that it can handle any issue that arises. This paper presents an architecture, called Volur, that provides a third option by facilitating the co-existence of an intelligent network with an intelligent edge. The key architectural principle of Volur is predictability of the network. We describe the key design requirements, and show through case studies how our approach facilitates more democratic innovation of all parts of the network. We also demonstrate the practicality of our architecture by describing how to implement the architecture on top of existing hardware and by deploying a prototype on top of a large production data center.
Entropy rates for Horton self-similar trees
Venue: arXiv cs.DM 2018
Year: 2018

In this paper we examine planted binary plane trees. First, we provide an exact formula for the number of planted binary trees with given Horton-Strahler orders. Then, using the notion of entropy, we examine the structural complexity of random planted binary trees with N vertices. Finally, we quantify the complexity of the tree's structural properties as tree grows in size, by evaluating the entropy rate for planted binary plane trees with N vertices and for planted binary plane trees that satisfy Horton Law with Horton exponent R.
Fast Identification of Wiener-Hammerstein Systems using Discrete Optimization
Venue: arXiv cs.SY 2018
Year: 2018

This letter proposes a fast identification algorithm for Wiener-Hammerstein systems. The computational cost of separating the front and the back linear time invariant block dynamics is significantly improved by using discrete optimization. The discrete optimization is implemented as a genetic algorithm. Numerical results confirm the efficiency and accuracy of the proposed approach.
Multi-Timescale Online Optimization of Network Function Virtualization for Service Chaining
Venue: arXiv cs.SY 2018
Year: 2018

Network Function Virtualization (NFV) can cost-efficiently provide network services by running different virtual network functions (VNFs) at different virtual machines (VMs) in a correct order. This can result in strong couplings between the decisions of the VMs on the placement and operations of VNFs. This paper presents a new fully decentralized online approach for optimal placement and operations of VNFs. Building on a new stochastic dual gradient method, our approach decouples the real-time decisions of VMs, asymptotically minimizes the time-average cost of NFV, and stabilizes the backlogs of network services with a cost-backlog tradeoff of $[ε,1/ε]$, for any $ε> 0$. Our approach can be relaxed into multiple timescales to have VNFs (re)placed at a larger timescale and hence alleviate service interruptions. While proved to preserve the asymptotic optimality, the larger timescale can slow down the optimal placement of VNFs. A learn-and-adapt strategy is further designed to speed the placement up with an improved tradeoff $[ε,\log^2(ε)/{\sqrtε}]$. Numerical results show that the proposed method is able to reduce the time-average cost of NFV by 30\% and reduce the queue length (or delay) by 83\%, as compared to existing benchmarks.
A Provenance Framework for Policy Analytics in Smart Cities
Venue: arXiv cs.CY 2018
Year: 2018

Sustainable urban environments based on Internet of Things (IoT) technologies require appropriate policy management. However, such policies are established as a result of underlying, potentially complex and long-term policy making processes. Consequently, better policies require improved and verifiable planning processes. In order to assess and evaluate the planning process, transparency of the system is pivotal which can be achieved by tracking the provenance of policy making process. However, at present no system is available that can track the complete cycle of urban planning and decision making. We propose to capture the complete process of policy making and to investigate the role of IoT provenance to support design-making for policy analytics and implementation. The environment in which this research will be demonstrated is that of Smart Cities whose requirements will drive the research process.
Homomorphisms and Minimality for Enrich-by-Need Security Analysis
Venue: arXiv cs.CR 2018
Year: 2018

Cryptographic protocols are used in different environments, but existing methods for protocol analysis focus only on the protocols, without being sensitive to assumptions about their environments. LPA is a tool which analyzes protocols in context. LPA uses two programs, cooperating with each other: CPSA, a well-known system for protocol analysis, and Razor, a model-finder based on SMT technology. Our analysis follows the enrich-by-need paradigm, in which models of protocol execution are generated and examined. The choice of which models to generate is important, and we motivate and evaluate LPA's strategy of building minimal models. "Minimality" can be defined with respect to either of two preorders, namely the homomorphism preorder and the embedding preorder (i.e. the preorder of injective homomorphisms); we discuss the merits of each. Our main technical contributions are algorithms for building homomorphism-minimal models and for generating a set-of-support for the models of a theory, in each case by scripting interactions with an SMT solver.
Efficient Interpolation for the Theory of Arrays
Venue: arXiv cs.LO 2018
Year: 2018

Existing techniques for Craig interpolation for the quantifier-free fragment of the theory of arrays are inefficient for computing sequence and tree interpolants: the solver needs to run for every partitioning $(A, B)$ of the interpolation problem to avoid creating $AB$-mixed terms. We present a new approach using Proof Tree Preserving Interpolation and an array solver based on Weak Equivalence on Arrays. We give an interpolation algorithm for the lemmas produced by the array solver. The computed interpolants have worst-case exponential size for extensionality lemmas and worst-case quadratic size otherwise. We show that these bounds are strict in the sense that there are lemmas with no smaller interpolants. We implemented the algorithm and show that the produced interpolants are useful to prove memory safety for C programs.
Beam Selection for MmWave Massive MIMO Systems Under Hybrid Transceiver Architecture
Venue: arXiv cs.IT 2018
Year: 2018

Hybrid analog/digital precoding is a promising technology to cut down hardware cost in massive multi-input multi-output (MIMO) systems at millimeter wave frequencies. In this letter, we propose a joint beam selection scheme for analog domain precoding under discrete lens array. By considering channel correlation among users, the proposed scheme is able to avoid inter-user interference and maximize system sum rate. Moreover, a bio-inspired ant colony optimization-based algorithm is proposed to obtain a near-optimal solution with dramatically reduced computational complexity. Finally, simulations show the advantages of the proposed scheme in improving system sum rate.
Do neutrons publish? A neutron publication survey 2005-2015
Venue: arXiv cs.DL 2018
Year: 2018

Publication in scientific journals is the main product of scientific research. The amount of papers published, their placement in high impact journals, and their citations are used as a measure of the productivity of individual scientists, institutes or fields of science. To give a profound basis on the publication record and the quality of the publication efforts in neutron scattering, a survey has been done following the approach to use bibliographic databases. Questions to be addressed by this survey are: Is the productivity of research with neutrons changing over the years? Which is the geographic distribution in this field of research? Which ones are leading facilities? Is the quality of publications changing? The main results found are presented.
Motion Fused Frames: Data Level Fusion Strategy for Hand Gesture Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Acquiring spatio-temporal states of an action is the most crucial step for action classification. In this paper, we propose a data level fusion strategy, Motion Fused Frames (MFFs), designed to fuse motion information into static images as better representatives of spatio-temporal states of an action. MFFs can be used as input to any deep learning architecture with very little modification on the network. We evaluate MFFs on hand gesture recognition tasks using three video datasets - Jester, ChaLearn LAP IsoGD and NVIDIA Dynamic Hand Gesture Datasets - which require capturing long-term temporal relations of hand movements. Our approach obtains very competitive performance on Jester and ChaLearn benchmarks with the classification accuracies of 96.28% and 57.4%, respectively, while achieving state-of-the-art performance with 84.7% accuracy on NVIDIA benchmark.
A Decomposition-based Approach towards the Control of Boolean Networks (Technical Report)
Venue: arXiv cs.SY 2018
Year: 2018

We study the problem of computing a minimal subset of nodes of a given asynchronous Boolean network that need to be controlled to drive its dynamics from an initial steady state (or attractor) to a target steady state. Due to the phenomenon of state-space explosion, a simple global approach that performs computations on the entire network, may not scale well for large networks. We believe that efficient algorithms for such networks must exploit the structure of the networks together with their dynamics. Taking such an approach, we derive a decomposition-based solution to the minimal control problem which can be significantly faster than the existing approaches on large networks. We apply our solution to both real-life biological networks and randomly generated networks, demonstrating promising results.
Parsimonious Volterra System Identification
Venue: arXiv cs.SY 2018
Year: 2018

In this short paper, we aim at developing algorithms for sparse Volterra system identification when the system to be identified has infinite impulse response. Assuming that the impulse response is represented as a sum of exponentials and given input-output data, the problem of interest is to find the "simplest" nonlinear Volterra model which is compatible with the a priori information and the collected data. By simplest, we mean the model whose impulse response has the least number of exponentials. The algorithms provided are able to handle both fragmented data and measurement noise. Academic examples at the end of paper show the efficacy of proposed approach.
A Survey on Graph Drawing Beyond Planarity
Venue: arXiv cs.CG 2018
Year: 2018

Graph Drawing Beyond Planarity is a rapidly growing research area that classifies and studies geometric representations of non-planar graphs in terms of forbidden crossing configurations. Aim of this survey is to describe the main research directions in this area, the most prominent known results, and some of the most challenging open problems.
The lost academic home: institutional affiliation links in Google Scholar Citations
Venue: arXiv cs.DL 2018
Year: 2018

This paper analyzes the new affiliation feature available in Google-Scholar Citations revealing that the affiliation-tool works well for most-institutions, it is unable to detect all existing institutions in database, and it is not always able to create unique-standardized entry for each-institution.
Socially Guided Intrinsic Motivation for Robot Learning of Motor Skills
Venue: arXiv cs.RO 2018
Year: 2018

This paper presents a technical approach to robot learning of motor skills which combines active intrinsically motivated learning with imitation learning. Our architecture, called SGIM-D, allows efficient learning of high-dimensional continuous sensorimotor inverse models in robots, and in particular learns distributions of parameterised motor policies that solve a corresponding distribution of parameterised goals/tasks. This is made possible by the technical integration of imitation learning techniques within an algorithm for learning inverse models that relies on active goal babbling. After reviewing social learning and intrinsic motivation approaches to action learning, we describe the general framework of our algorithm, before detailing its architecture. In an experiment where a robot arm has to learn to use a flexible fishing line , we illustrate that SGIM-D efficiently combines the advantages of social learning and intrinsic motivation and benefits from human demonstration properties to learn how to produce varied outcomes in the environment, while developing more precise control policies in large spaces.
A Practical Acyclicity Notion for Query Answering over Horn-SRIQ Ontologies
Venue: arXiv cs.LO 2018
Year: 2018

Conjunctive query answering over expressive Horn Description Logic ontologies is a relevant and challenging problem which, in some cases, can be addressed by application of the chase algorithm. In this paper, we define a novel acyclicity notion which provides a sufficient condition for termination of the restricted chase over Horn-SRIQ TBoxes. We show that this notion generalizes most of the existing acyclicity conditions (both theoretically and empirically). Furthermore, this new acyclicity notion gives rise to a very efficient reasoning procedure. We provide evidence for this by providing a materialization based reasoner for acyclic ontologies which outperforms other state-of-the-art systems.
Bar recursion is not computable via iteration
Venue: arXiv cs.LO 2018
Year: 2018

We show that the bar recursion operators of Spector and Kohlenbach, considered as third-order functionals acting on total arguments, are not computable in Goedel's System T plus minimization, which we show to be equivalent to a programming language with a higher-order iteration construct. The main result is formulated so as to imply the non-definability of bar recursion in T + min within a variety of partial and total models, for instance the Kleene-Kreisel continuous functionals. The paper thus supplies proofs of some results stated in the book by Longley and Normann. The proof of the main theorem makes serious use of the theory of nested sequential procedures (also known as PCF Boehm trees), and proceeds by showing that bar recursion cannot be represented by any sequential procedure within which the tree of nested function applications is well-founded.
Sampling-free Uncertainty Estimation in Gated Recurrent Units with Exponential Families
Venue: arXiv cs.LG 2018
Year: 2018

There has recently been a concerted effort to derive mechanisms in vision and machine learning systems to offer uncertainty estimates of the predictions they make. Clearly, there are enormous benefits to a system that is not only accurate but also has a sense for when it is not sure. Existing proposals center around Bayesian interpretations of modern deep architectures -- these are effective but can often be computationally demanding. We show how classical ideas in the literature on exponential families on probabilistic networks provide an excellent starting point to derive uncertainty estimates in Gated Recurrent Units (GRU). Our proposal directly quantifies uncertainty deterministically, without the need for costly sampling-based estimation. We demonstrate how our model can be used to quantitatively and qualitatively measure uncertainty in unsupervised image sequence prediction. To our knowledge, this is the first result describing sampling-free uncertainty estimation for powerful sequential models such as GRUs.
Challenges and pitfalls of partitioning blockchains
Venue: arXiv cs.DC 2018
Year: 2018

Blockchain has received much attention in recent years. This immense popularity has raised a number of concerns, scalability of blockchain systems being a common one. In this paper, we seek to understand how Ethereum, a well-established blockchain system, would respond to sharding. Sharding is a prevalent technique to increase the scalability of distributed systems. To understand how sharding would affect Ethereum, we model Ethereum blockchain as a graph and evaluate five methods to partition the graph. We analyze the results using three metrics: the balance among shards, the number of transactions that would involve multiple shards, and the amount of data that would be relocated across shards upon a repartitioning of the system.