OpenRaiser/Intern-Atlas huggingface dataset:
RDF2PT: Generating Brazilian Portuguese Texts from RDF Data
Venue: arXiv cs.CL 2018
Year: 2018

The generation of natural language from Resource Description Framework (RDF) data has recently gained significant attention due to the continuous growth of Linked Data. A number of these approaches generate natural language in languages other than English, however, no work has been proposed to generate Brazilian Portuguese texts out of RDF. We address this research gap by presenting RDF2PT, an approach that verbalizes RDF data to Brazilian Portuguese language. We evaluated RDF2PT in an open questionnaire with 44 native speakers divided into experts and non-experts. Our results suggest that RDF2PT is able to generate text which is similar to that generated by humans and can hence be easily understood.
What's the Over/Under? Probabilistic Bounds on Information Leakage
Venue: arXiv cs.PL 2018
Year: 2018

Quantitative information flow (QIF) is concerned with measuring how much of a secret is leaked to an adversary who observes the result of a computation that uses it. Prior work has shown that QIF techniques based on abstract interpretation with probabilistic polyhedra can be used to analyze the worst-case leakage of a query, on-line, to determine whether that query can be safely answered. While this approach can provide precise estimates, it does not scale well. This paper shows how to solve the scalability problem by augmenting the baseline technique with sampling and symbolic execution. We prove that our approach never underestimates a query's leakage (it is sound), and detailed experimental results show that we can match the precision of the baseline technique but with orders of magnitude better performance.
SeNA-CNN: Overcoming Catastrophic Forgetting in Convolutional Neural Networks by Selective Network Augmentation
Venue: arXiv cs.LG 2018
Year: 2018

Lifelong learning aims to develop machine learning systems that can learn new tasks while preserving the performance on previous learned tasks. In this paper we present a method to overcome catastrophic forgetting on convolutional neural networks, that learns new tasks and preserves the performance on old tasks without accessing the data of the original model, by selective network augmentation. The experiment results showed that SeNA-CNN, in some scenarios, outperforms the state-of-art Learning without Forgetting algorithm. Results also showed that in some situations it is better to use SeNA-CNN instead of training a neural network using isolated learning.
Learning to Make Predictions on Graphs with Autoencoders
Venue: arXiv cs.LG 2018
Year: 2018

We examine two fundamental tasks associated with graph representation learning: link prediction and semi-supervised node classification. We present a novel autoencoder architecture capable of learning a joint representation of both local graph structure and available node features for the multi-task learning of link prediction and node classification. Our autoencoder architecture is efficiently trained end-to-end in a single learning stage to simultaneously perform link prediction and node classification, whereas previous related methods require multiple training steps that are difficult to optimize. We provide a comprehensive empirical evaluation of our models on nine benchmark graph-structured datasets and demonstrate significant improvement over related methods for graph representation learning. Reference code and data are available at https://github.com/vuptran/graph-representation-learning
PIRT: A Runtime Framework to Enable Energy-Efficient Real-Time Robotic Applications on Heterogeneous Architectures
Venue: arXiv cs.RO 2018
Year: 2018

Enabling full robotic workloads with diverse behaviors on mobile systems with stringent resource and energy constraints remains a challenge. In recent years, attempts have been made to deploy single-accelerator-based computing platforms (such as GPU, DSP, or FPGA) to address this challenge, but with little success. The core problem is two-fold: firstly, different robotic tasks require different accelerators, and secondly, managing multiple accelerators simultaneously is overwhelming for developers. In this paper, we propose PIRT, the first robotic runtime framework to efficiently manage dynamic task executions on mobile systems with multiple accelerators as well as on the cloud to achieve better performance and energy savings. With PIRT, we enable a robot to simultaneously perform autonomous navigation with 25 FPS of localization, obstacle detection with 3 FPS, route planning, large map generation, and scene understanding, traveling at a max speed of 5 miles per hour, all within an 11W computing power envelope.
Novel Approaches to Accelerating the Convergence Rate of Markov Decision Process for Search Result Diversification
Venue: arXiv cs.IR 2018
Year: 2018

Recently, some studies have utilized the Markov Decision Process for diversifying (MDP-DIV) the search results in information retrieval. Though promising performances can be delivered, MDP-DIV suffers from a very slow convergence, which hinders its usability in real applications. In this paper, we aim to promote the performance of MDP-DIV by speeding up the convergence rate without much accuracy sacrifice. The slow convergence is incurred by two main reasons: the large action space and data scarcity. On the one hand, the sequential decision making at each position needs to evaluate the query-document relevance for all the candidate set, which results in a huge searching space for MDP; on the other hand, due to the data scarcity, the agent has to proceed more "trial and error" interactions with the environment. To tackle this problem, we propose MDP-DIV-kNN and MDP-DIV-NTN methods. The MDP-DIV-kNN method adopts a $k$ nearest neighbor strategy, i.e., discarding the $k$ nearest neighbors of the recently-selected action (document), to reduce the diversification searching space. The MDP-DIV-NTN employs a pre-trained diversification neural tensor network (NTN-DIV) as the evaluation model, and combines the results with MDP to produce the final ranking solution. The experiment results demonstrate that the two proposed methods indeed accelerate the convergence rate of the MDP-DIV, which is 3x faster, while the accuracies produced barely degrade, or even are better.
Multi-RATs Support to Improve V2X Communication
Venue: arXiv cs.NI 2018
Year: 2018

As the next generation of wireless system targets at providing a wider range of services with divergent QoS requirements, new applications will be enabled by the fifth generation (5G) network. Among the emerging applications, vehicle-to-everything (V2X) communication is an important use case targeted by 5G to enable an improved traffic safety and traffic efficiency. Since the V2X communication requires a low end-to-end (E2E) latency and an ultra-high reliability, the legacy cellular networks can not meet the service requirement. In this work, we inspect on the system performance of applying the LTE-Uu and PC5 interfaces to enable the V2X communication. With the LTE-Uu interface, one V2X data packet is transmitted through the cellular network infrastructure, while the PC5 interface facilitates the direct V2X communication without involving the network infrastructure in user-plane. In addition, due to the high reliability requirement, the application of a single V2X transmission technology can not meet the targets in some scenarios. Therefore, we also propose a multi-radio access technologies (multi-RATs) scheme where the data packet travels through both the LTE-Uu and PC5 interfaces to obtain a diversity gain. Last but not least, in order to derive the system performance, a system level simulator is implemented in this work. The numerical results provide us insights on how the different technologies will perform in different scenarios and also validate the proposed multi-RATs scheme.
Parameterized verification of synchronization in constrained reconfigurable broadcast networks
Venue: arXiv cs.LO 2018
Year: 2018

Reconfigurable broadcast networks provide a convenient formalism for modelling and reasoning about networks of mobile agents broadcasting messages to other agents following some (evolving) communication topology. The parameterized verification of such models aims at checking whether a given property holds irrespective of the initial configuration (number of agents, initial states and initial communication topology). We focus here on the synchronization property, asking whether all agents converge to a set of target states after some execution. This problem is known to be decidable in polynomial time when no constraints are imposed on the evolution of the communication topology (while it is undecidable for static broadcast networks). In this paper we investigate how various constraints on reconfigurations affect the decidability and complexity of the synchronization problem. In particular, we show that when bounding the number of reconfigured links between two communications steps by a constant, synchronization becomes undecidable; on the other hand, synchronization remains decidable in PTIME when the bound grows with the number of agents.
SimCommSys: Taking the errors out of error-correcting code simulations
Venue: arXiv cs.IT 2018
Year: 2018

In this paper we present SimCommSys, a Simulator of Communication Systems that we are releasing under an open source license. The core of the project is a set of C++ libraries defining communication system components and a distributed Monte Carlo simulator. Of principal interest is the error-control coding component, where various kinds of binary and non-binary codes are implemented, including turbo, LDPC, repeat-accumulate, and Reed-Solomon. The project also contains a number of ready-to-build binaries implementing various stages of the communication system (such as the encoder and decoder), a complete simulator, and a system benchmark. Finally, SimCommSys also provides a number of shell and python scripts to encapsulate routine use cases. As long as the required components are already available in SimCommSys, the user may simulate complete communication systems of their own design without any additional programming. The strict separation of development (needed only to implement new components) and use (to simulate specific constructions) encourages reproducibility of experimental work and reduces the likelihood of error. Following an overview of the framework, we provide some examples of how to use the framework, including the implementation of a simple codec, the specification of communication systems and their simulation.
Weighted Double Deep Multiagent Reinforcement Learning in Stochastic Cooperative Environments
Venue: arXiv cs.MA 2018
Year: 2018

Recently, multiagent deep reinforcement learning (DRL) has received increasingly wide attention. Existing multiagent DRL algorithms are inefficient when facing with the non-stationarity due to agents update their policies simultaneously in stochastic cooperative environments. This paper extends the recently proposed weighted double estimator to the multiagent domain and propose a multiagent DRL framework, named weighted double deep Q-network (WDDQN). By utilizing the weighted double estimator and the deep neural network, WDDQN can not only reduce the bias effectively but also be extended to scenarios with raw visual inputs. To achieve efficient cooperation in the multiagent domain, we introduce the lenient reward network and the scheduled replay strategy. Experiments show that the WDDQN outperforms the existing DRL and multiaent DRL algorithms, i.e., double DQN and lenient Q-learning, in terms of the average reward and the convergence rate in stochastic cooperative environments.
Semantic Vector Spaces for Broadening Consideration of Consequences
Venue: arXiv cs.AI 2018
Year: 2018

Reasoning systems with too simple a model of the world and human intent are unable to consider potential negative side effects of their actions and modify their plans to avoid them (e.g., avoiding potential errors). However, hand-encoding the enormous and subtle body of facts that constitutes common sense into a knowledge base has proved too difficult despite decades of work. Distributed semantic vector spaces learned from large text corpora, on the other hand, can learn representations that capture shades of meaning of common-sense concepts and perform analogical and associational reasoning in ways that knowledge bases are too rigid to perform, by encoding concepts and the relations between them as geometric structures. These have, however, the disadvantage of being unreliable, poorly understood, and biased in their view of the world by the source material. This chapter will discuss how these approaches may be combined in a way that combines the best properties of each for understanding the world and human intentions in a richer way.
Moore: Interval Arithmetic in C++20
Venue: arXiv cs.MS 2018
Year: 2018

This article presents the Moore library for interval arithmetic in C++20. It gives examples of how the library can be used, and explains the basic principles underlying its design.
Time-Varying Block Codes for Synchronization Errors: MAP Decoder and Practical Issues
Venue: arXiv cs.IT 2018
Year: 2018

\begin{abstract} In this paper we consider Time-Varying Block (TVB) codes, which generalize a number of previous synchronization error-correcting codes. We also consider various practical issues related to MAP decoding of these codes. Specifically, we give an expression for the expected distribution of drift between transmitter and receiver due to synchronization errors. We determine an appropriate choice for state space limits based on the drift probability distribution. In turn, we obtain an expression for the decoder complexity under given channel conditions in terms of the state space limits used. For a given state space, we also give a number of optimizations that reduce the algorithm complexity with no further loss of decoder performance. We also show how the MAP decoder can be used in the absence of known frame boundaries, and demonstrate that an appropriate choice of decoder parameters allows the decoder to approach the performance when frame boundaries are known, at the expense of some increase in complexity. Finally, we express some existing constructions as TVB codes, comparing performance with published results, and showing that improved performance is possible by taking advantage of the flexibility of TVB codes.
8T SRAM Cell as a Multi-bit Dot Product Engine for Beyond von-Neumann Computing
Venue: arXiv cs.ET 2018
Year: 2018

Large scale digital computing almost exclusively relies on the von-Neumann architecture which comprises of separate units for storage and computations. The energy expensive transfer of data from the memory units to the computing cores results in the well-known von-Neumann bottleneck. Various approaches aimed towards bypassing the von-Neumann bottleneck are being extensively explored in the literature. Emerging non-volatile memristive technologies have been shown to be very efficient in computing analog dot products in an in-situ fashion. The memristive analog computation of the dot product results in much faster operation as opposed to digital vector in-memory bit-wise Boolean computations. However, challenges with respect to large scale manufacturing coupled with the limited endurance of memristors have hindered rapid commercialization of memristive based computing solutions. In this work, we show that the standard 8 transistor (8T) digital SRAM array can be configured as an analog-like in-memory multi-bit dot product engine. By applying appropriate analog voltages to the read-ports of the 8T SRAM array, and sensing the output current, an approximate analog-digital dot-product engine can be implemented. We present two different configurations for enabling multi-bit dot product computations in the 8T SRAM cell array, without modifying the standard bit-cell structure. Since our proposal preserves the standard 8T-SRAM array structure, it can be used as a storage element with standard read-write instructions, and also as an on-demand analog-like dot product accelerator.
Towards Distributed OPF using ALADIN
Venue: arXiv cs.DC 2018
Year: 2018

The present paper discusses the application of the recently proposed Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) method to non-convex AC Optimal Power Flow Problems (OPF) in a distributed fashion. In contrast to the often used Alternating Direction of Multipliers Method (ADMM), ALADIN guarantees locally quadratic convergence for AC OPF. Numerical results for 5 to 300 bus test cases indicate that ALADIN is able to outperform ADMM and to reduce the number of iterations by about one order of magnitude. We compare ALADIN to numerical results for ADMM documented in the literature. The improved convergence speed comes at the cost of increasing the communication effort per iteration. Therefore, we propose a variant of ALADIN that uses inexact Hessians to reduce communication. Additionally, we provide a detailed comparison of these ALADIN variants to ADMM from an algorithmic and communication perspective. Moreover, we prove that ALADIN converges locally at quadratic rate even for the relevant case of suboptimally solved local NLPs.
Automatic Speech Recognition and Topic Identification for Almost-Zero-Resource Languages
Venue: arXiv cs.CL 2018
Year: 2018

Automatic speech recognition (ASR) systems often need to be developed for extremely low-resource languages to serve end-uses such as audio content categorization and search. While universal phone recognition is natural to consider when no transcribed speech is available to train an ASR system in a language, adapting universal phone models using very small amounts (minutes rather than hours) of transcribed speech also needs to be studied, particularly with state-of-the-art DNN-based acoustic models. The DARPA LORELEI program provides a framework for such very-low-resource ASR studies, and provides an extrinsic metric for evaluating ASR performance in a humanitarian assistance, disaster relief setting. This paper presents our Kaldi-based systems for the program, which employ a universal phone modeling approach to ASR, and describes recipes for very rapid adaptation of this universal ASR system. The results we obtain significantly outperform results obtained by many competing approaches on the NIST LoReHLT 2017 Evaluation datasets.
Conflict-Aware Replicated Data Types
Venue: arXiv cs.DC 2018
Year: 2018

We introduce Conflict-Aware Replicated Data Types (CARDs). CARDs are significantly more expressive than Conflict-free Replicated Data Types (CRDTs) as they support operations that can conflict with each other. Introducing conflicting operations typically brings the need to block an operation in at least some executions, leading to difficulties in programming and reasoning about correctness, as well as potential inefficiencies in implementation. The salient aspect of CARDs is that they allow ease of programming and reasoning about programs comparable to CRDTs, while enabling algorithmic inference of conflicts so that an operation is blocked only when necessary. The key idea is to have a language that allows associating with each operation a two-state predicate called {\em consistency guard} that relates the state of the replica on which the operation is executing to a global state (which is never computed). The consistency guards bring three advantages. First, a programmer developing an operation needs only to choose a consistency guard that states what the operation will rely on. In particular, they do not need to consider the operation conflicts with other operation. This allows purely {\em modular reasoning}. Second, we show that consistency guard allow reducing the complexity of reasoning needed to prove invariants that hold as CARD operations are executing. The reason is that consistency guard allow reducing the reasoning about concurrency among operations to purely {\em sequential reasoning}. Third, conflicts among operations can be algorithmically inferred by checking whether the effect of one operation preserves the consistency guard of another operation.
Superpixel based Class-Semantic Texton Occurrences for Natural Roadside Vegetation Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

Vegetation segmentation from roadside data is a field that has received relatively little attention in present studies, but can be of great potentials in a wide range of real-world applications, such as road safety assessment and vegetation condition monitoring. In this paper, we present a novel approach that generates class-semantic color-texture textons and aggregates superpixel based texton occurrences for vegetation segmentation in natural roadside images. Pixel-level class-semantic textons are first learnt by generating two individual sets of bag-of-word visual dictionaries from color and filter-bank texture features separately for each object class using manually cropped training data. For a testing image, it is first oversegmented into a set of homogeneous superpixels. The color and texture features of all pixels in each superpixel are extracted and further mapped to one of the learnt textons using the nearest distance metric, resulting in a color and a texture texton occurrence matrix. The color and texture texton occurrences are aggregated using a linear mixing method over each superpixel and the segmentation is finally achieved using a simple yet effective majority voting strategy. Evaluations on two public image datasets from videos collected by the Department of Transport and Main Roads (DTMR), Queensland, Australia, and a public roadside grass dataset show high accuracy of the proposed approach. We also demonstrate the effectiveness of the approach for vegetation segmentation in real-world scenarios.
Multispectral Image Intrinsic Decomposition via Low Rank Constraint
Venue: arXiv cs.CV 2018
Year: 2018

Multispectral images contain many clues of surface characteristics of the objects, thus can be widely used in many computer vision tasks, e.g., recolorization and segmentation. However, due to the complex illumination and the geometry structure of natural scenes, the spectra curves of a same surface can look very different. In this paper, a Low Rank Multispectral Image Intrinsic Decomposition model (LRIID) is presented to decompose the shading and reflectance from a single multispectral image. We extend the Retinex model, which is proposed for RGB image intrinsic decomposition, for multispectral domain. Based on this, a low rank constraint is proposed to reduce the ill-posedness of the problem and make the algorithm solvable. A dataset of 12 images is given with the ground truth of shadings and reflectance, so that the objective evaluations can be conducted. The experiments demonstrate the effectiveness of proposed method.
Constrained Image Generation Using Binarized Neural Networks with Decision Procedures
Venue: arXiv cs.CV 2018
Year: 2018

We consider the problem of binary image generation with given properties. This problem arises in a number of practical applications, including generation of artificial porous medium for an electrode of lithium-ion batteries, for composed materials, etc. A generated image represents a porous medium and, as such, it is subject to two sets of constraints: topological constraints on the structure and process constraints on the physical process over this structure. To perform image generation we need to define a mapping from a porous medium to its physical process parameters. For a given geometry of a porous medium, this mapping can be done by solving a partial differential equation (PDE). However, embedding a PDE solver into the search procedure is computationally expensive. We use a binarized neural network to approximate a PDE solver. This allows us to encode the entire problem as a logical formula. Our main contribution is that, for the first time, we show that this problem can be tackled using decision procedures. Our experiments show that our model is able to produce random constrained images that satisfy both topological and process constraints.