OpenRaiser/Intern-Atlas huggingface dataset:
Scalable Multiagent Coordination with Distributed Online Open Loop Planning
Venue: arXiv cs.MA 2017
Year: 2017

We propose distributed online open loop planning (DOOLP), a general framework for online multiagent coordination and decision making under uncertainty. DOOLP is based on online heuristic search in the space defined by a generative model of the domain dynamics, which is exploited by agents to simulate and evaluate the consequences of their potential choices. We also propose distributed online Thompson sampling (DOTS) as an effective instantiation of the DOOLP framework. DOTS models sequences of agent choices by concatenating a number of multiarmed bandits for each agent and uses Thompson sampling for dealing with action value uncertainty. The Bayesian approach underlying Thompson sampling allows to effectively model and estimate uncertainty about (a) own action values and (b) other agents' behavior. This approach yields a principled and statistically sound solution to the exploration-exploitation dilemma when exploring large search spaces with limited resources. We implemented DOTS in a smart factory case study with positive empirical results. We observed effective, robust and scalable planning and coordination capabilities even when only searching a fraction of the potential search space.
Analysis of video quality losses in the homogenous HEVC video transcoding
Venue: arXiv cs.MM 2017
Year: 2017

The paper presents quantitative analysis of the video quality losses in the homogenous HEVC video transcoder. With the use of HM15.0 reference software and a set of test video sequences, cascaded pixel domain video transcoder (CPDT) concept has been used to gather all the necessary data needed for the analysis. This experiment was done for wide range of source and target bitrates. The essential result of the work is extensive evaluation of CPDT, commonly used as a reference in works on effective video transcoding. Until now no such extensively performed study have been made available in the literature. Quality degradation between transcoded video and the video that would be result of direct compression of the original video at the same bitrate as the transcoded one have been reported. The dependency between quality degradation caused by transcoding and the bitrate changes of the transcoded data stream are clearly presented on graphs.
RNN Decoding of Linear Block Codes
Venue: arXiv cs.IT 2017
Year: 2017

Designing a practical, low complexity, close to optimal, channel decoder for powerful algebraic codes with short to moderate block length is an open research problem. Recently it has been shown that a feed-forward neural network architecture can improve on standard belief propagation decoding, despite the large example space. In this paper we introduce a recurrent neural network architecture for decoding linear block codes. Our method shows comparable bit error rate results compared to the feed-forward neural network with significantly less parameters. We also demonstrate improved performance over belief propagation on sparser Tanner graph representations of the codes. Furthermore, we demonstrate that the RNN decoder can be used to improve the performance or alternatively reduce the computational complexity of the mRRD algorithm for low complexity, close to optimal, decoding of short BCH codes.
How to specify Non-functional Requirements to support seamless modeling? A Study Design and Preliminary Results
Venue: arXiv cs.SE 2017
Year: 2017

Context: Seamless model-based development provides integrated chains of models, covering all software engineering phases. Non-functional requirements (NFRs), like reusability, further play a vital role in software and systems engineering, but are often neglected in research and practice. It is still unclear how to integrate NFRs in a seamless model-based development. Goal: Our long-term goal is to develop a theory on the specification of NFRs such that they can be integrated in seamless model-based development. Method: Our overall study design includes a multi-staged procedure to infer an empirically founded theory on specifying NFRs to support seamless modeling. In this short paper, we present the study design and provide a discussion of (i) preliminary results obtained from a sample, and (ii) current issues related to the design. Results: Our study already shows significant fields of improvement, e.g., the low agreement during the classification. However, the results indicate to interesting points; for example, many of commonly used NFR classes concern system modeling concepts in a way that shows how blurry the borders between functional and NFRs are. Conclusions: We conclude so far that our overall study design seems suitable to obtain the envisioned theory in the long run, but we could also show current issues that are worth discussing within the empirical software engineering community. The main goal of this contribution is not to present and discuss current results only, but to foster discussions on the issues related to the integration of NFRs in seamless modeling in general and, in particular, discussions on open methodological issues.
A recommender system to restore images with impulse noise
Venue: arXiv cs.CV 2017
Year: 2017

We build a collaborative filtering recommender system to restore images with impulse noise for which the noisy pixels have been previously identified. We define this recommender system in terms of a new color image representation using three matrices that depend on the noise-free pixels of the image to restore, and two parameters: $k$, the number of features; and $λ$, the regularization factor. We perform experiments on a well known image database to test our algorithm and we provide image quality statistics for the results obtained. We discuss the roles of bias and variance in the performance of our algorithm as determined by the values of $k$ and $λ$, and provide guidance on how to choose the values of these parameters. Finally, we discuss the possibility of using our collaborative filtering recommender system to perform image inpainting and super-resolution.
Unifying local and non-local signal processing with graph CNNs
Venue: arXiv cs.CV 2017
Year: 2017

This paper deals with the unification of local and non-local signal processing on graphs within a single convolutional neural network (CNN) framework. Building upon recent works on graph CNNs, we propose to use convolutional layers that take as inputs two variables, a signal and a graph, allowing the network to adapt to changes in the graph structure. In this article, we explain how this framework allows us to design a novel method to perform style transfer.
Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation
Venue: arXiv cs.CV 2017
Year: 2017

Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis and treatment. However, variations in MRI acquisition protocols result in different appearances of normal and diseased tissue in the images. Convolutional neural networks (CNNs), which have shown to be successful in many medical image analysis tasks, are typically sensitive to the variations in imaging protocols. Therefore, in many cases, networks trained on data acquired with one MRI protocol, do not perform satisfactorily on data acquired with different protocols. This limits the use of models trained with large annotated legacy datasets on a new dataset with a different domain which is often a recurring situation in clinical settings. In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, 1) How much data from the new domain is required for a decent adaptation of the original network?; and, 2) What portion of the pre-trained model parameters should be retrained given a certain number of the new domain training samples? To address these questions, we conducted extensive experiments in white matter hyperintensity segmentation task. We trained a CNN on legacy MR images of brain and evaluated the performance of the domain-adapted network on the same task with images from a different domain. We then compared the performance of the model to the surrogate scenarios where either the same trained network is used or a new network is trained from scratch on the new dataset.The domain-adapted network tuned only by two training examples achieved a Dice score of 0.63 substantially outperforming a similar network trained on the same set of examples from scratch.
Online Learning with Many Experts
Venue: arXiv cs.LG 2017
Year: 2017

We study the problem of prediction with expert advice when the number of experts in question may be extremely large or even infinite. We devise an algorithm that obtains a tight regret bound of $\widetilde{O}(εT + N + \sqrt{NT})$, where $N$ is the empirical $ε$-covering number of the sequence of loss functions generated by the environment. In addition, we present a hedging procedure that allows us to find the optimal $ε$ in hindsight. Finally, we discuss a few interesting applications of our algorithm. We show how our algorithm is applicable in the approximately low rank experts model of Hazan et al. (2016), and discuss the case of experts with bounded variation, in which there is a surprisingly large gap between the regret bounds obtained in the statistical and online settings.
On Chordal-$k$-Generalized Split Graphs
Venue: arXiv cs.DM 2017
Year: 2017

A graph $G$ is a {\em chordal-$k$-generalized split graph} if $G$ is chordal and there is a clique $Q$ in $G$ such that every connected component in $G[V \setminus Q]$ has at most $k$ vertices. Thus, chordal-$1$-generalized split graphs are exactly the split graphs. We characterize chordal-$k$-generalized split graphs by forbidden induced subgraphs. Moreover, we characterize a very special case of chordal-$2$-generalized split graphs for which the Efficient Domination problem is \NP-complete.
Customising Agent Based Analysis Towards Analysis of Disaster Management Knowledge
Venue: arXiv cs.SE 2017
Year: 2017

In developed countries such as Australia, for recurring disasters (e.g. floods), there are dedicated document repositories of Disaster Management Plans (DISPLANs), and supporting doctrine and processes that are used to prepare organisations and communities for disasters. They are maintained on an ongoing cyclical basis and form a key information source for community education, engagement and awareness programme in the preparation for and mitigation of disasters. DISPLANS, generally in semi-structured text document format, are then accessed and activated during the response and recovery to incidents to coordinate emergency service and community safety actions. However, accessing the appropriate plan and the specific knowledge within the text document from across its conceptual areas in a timely manner and sharing activities between stakeholders requires intimate domain knowledge of the plan contents and its development. This paper describes progress on an ongoing project with NSW State Emergency Service (NSW SES) to convert DISPLANs into a collection of knowledge units that can be stored in a unified repository with the goal to form the basis of a future knowledge sharing capability. All Australian emergency services covering a wide range of hazards develop DISPLANs of various structure and intent, in general the plans are created as instances of a template, for example those which are developed centrally by the NSW and Victorian SESs State planning policies. In this paper, we illustrate how by using selected templates as part of an elaborate agent-based process, we can apply agent-oriented analysis more efficiently to convert extant DISPLANs into a centralised repository. The repository is structured as a layered abstraction according to Meta Object Facility (MOF). The work is illustrated using DISPLANs along the flood-prone Murrumbidgee River in central NSW.
Detecting (Un)Important Content for Single-Document News Summarization
Venue: arXiv cs.CL 2017
Year: 2017

We present a robust approach for detecting intrinsic sentence importance in news, by training on two corpora of document-summary pairs. When used for single-document summarization, our approach, combined with the "beginning of document" heuristic, outperforms a state-of-the-art summarizer and the beginning-of-article baseline in both automatic and manual evaluations. These results represent an important advance because in the absence of cross-document repetition, single document summarizers for news have not been able to consistently outperform the strong beginning-of-article baseline.
Bayesian Nonparametric Feature and Policy Learning for Decision-Making
Venue: arXiv cs.LG 2017
Year: 2017

Learning from demonstrations has gained increasing interest in the recent past, enabling an agent to learn how to make decisions by observing an experienced teacher. While many approaches have been proposed to solve this problem, there is only little work that focuses on reasoning about the observed behavior. We assume that, in many practical problems, an agent makes its decision based on latent features, indicating a certain action. Therefore, we propose a generative model for the states and actions. Inference reveals the number of features, the features, and the policies, allowing us to learn and to analyze the underlying structure of the observed behavior. Further, our approach enables prediction of actions for new states. Simulations are used to assess the performance of the algorithm based upon this model. Moreover, the problem of learning a driver's behavior is investigated, demonstrating the performance of the proposed model in a real-world scenario.
CRSTIP - An Assessment Scheme for Security Assessment Processes
Venue: arXiv cs.CR 2017
Year: 2017

Complex networked systems are an integral part of today's support infrastructures. Due to their importance, these systems become more and more the target for cyber-attacks, suffering a notable number of security incidents. Also, they are subject to regulation by national and international legislation. An operator of such an infrastructure or system is responsible for ensuring its security and correct functioning in order to satisfy customers. In addition, the entire process of risk and quality control needs to be efficient and manageable. This short paper introduces the Compliance, Risk Assessment and Security Testing Improvement Profiling (CRSTIP) scheme. CRSTIP is an evaluation scheme that enables assessing the maturity of security assessment processes, taking into consideration systematic use of formalisms, integration and tool-support in the areas of compliance assessment, security risk assessment and security testing. The paper describes the elements of the scheme and their application to one of the case studies of the RASEN research project.
Euclidean and Hermitian LCD MDS codes
Venue: arXiv cs.IT 2017
Year: 2017

Linear codes with complementary duals (abbreviated LCD) are linear codes whose intersection with their dual is trivial. When they are binary, they play an important role in armoring implementations against side-channel attacks and fault injection attacks. Non-binary LCD codes in characteristic 2 can be transformed into binary LCD codes by expansion. On the other hand, being optimal codes, maximum distance separable codes (abbreviated MDS) have been of much interest from many researchers due to their theoretical significant and practical implications. However, little work has been done on LCD MDS codes. In particular, determining the existence of $q$-ary $[n,k]$ LCD MDS codes for various lengths $n$ and dimensions $k$ is a basic and interesting problem. In this paper, we firstly study the problem of the existence of $q$-ary $[n,k]$ LCD MDS codes and completely solve it for the Euclidean case. More specifically, we show that for $q>3$ there exists a $q$-ary $[n,k]$ Euclidean LCD MDS code, where $0\le k \le n\le q+1$, or, $q=2^{m}$, $n=q+2$ and $k= 3 \text{or} q-1$. Secondly, we investigate several constructions of new Euclidean and Hermitian LCD MDS codes. Our main techniques in constructing Euclidean and Hermitian LCD MDS codes use some linear codes with small dimension or codimension, self-orthogonal codes and generalized Reed-Solomon codes.
Benefits of Cache Assignment on Degraded Broadcast Channels
Venue: arXiv cs.IT 2017
Year: 2017

Degraded K-user broadcast channels (BC) are studied when receivers are facilitated with cache memories. Lower and upper bounds are derived on the capacity-memory tradeoff, i.e., on the largest rate of reliable communication over the BC as a function of the receivers' cache sizes, and the bounds are shown to match for some special cases. The lower bounds are achieved by two new coding schemes that benefit from non-uniform cache assignment. Lower and upper bounds are also established on the global capacity-memory tradeoff, i.e., on the largest capacity-memory tradeoff that can be attained by optimizing the receivers' cache sizes subject to a total cache memory budget. The bounds coincide when the total cache memory budget is sufficiently small or sufficiently large, characterized in terms of the BC statistics. For small cache memories, it is optimal to assign all the cache memory to the weakest receiver. In this regime, the global capacity-memory tradeoff grows as the total cache memory budget divided by the number of files in the system. In other words, a perfect global caching gain is achievable in this regime and the performance corresponds to a system where all cache contents in the network are available to all receivers. For large cache memories, it is optimal to assign a positive cache memory to every receiver such that the weaker receivers are assigned larger cache memories compared to the stronger receivers. In this regime, the growth rate of the global capacity-memory tradeoff is further divided by the number of users, which corresponds to a local caching gain. Numerical indicate suggest that a uniform cache-assignment of the total cache memory is suboptimal in all regimes unless the BC is completely symmetric. For erasure BCs, this claim is proved analytically in the regime of small cache-sizes.
A Novel Device-to-Device Discovery Scheme for Underlay Cellular Networks
Venue: arXiv cs.NI 2017
Year: 2017

Tremendous growing demand for high data rate services such as video, gaming and social networking in wireless cellular systems, attracted researchers' attention to focus on developing proximity services. In this regard, device-to-device (D2D) communications as a promising technology for future cellular systems, plays crucial rule. The key factor in D2D communication is providing efficient peer discovery mechanisms in ultra dense networks. In this paper, we propose a centralized D2D discovery scheme by employing a signaling algorithm to exchange D2D discovery messages between network entities. In this system, potential D2D pairs share uplink cellular users' resources with collision detection, to initiate a D2D links. Stochastic geometry is used to analyze system performance in terms of success probability of the transmitted signal and minimum required time slots for the proposed discovery scheme. Extensive simulations are used to evaluate the proposed system performance.
Knowledge Reuse for Customization: Metamodels in an Open Design Community for 3d Printing
Venue: arXiv cs.HC 2017
Year: 2017

Theories of knowledge reuse posit two distinct processes: reuse for replication and reuse for innovation. We identify another distinct process, reuse for customization. Reuse for customization is a process in which designers manipulate the parameters of metamodels to produce models that fulfill their personal needs. We test hypotheses about reuse for customization in Thingiverse, a community of designers that shares files for three-dimensional printing. 3D metamodels are reused more often than the 3D models they generate. The reuse of metamodels is amplified when the metamodels are created by designers with greater community experience. Metamodels make the community's design knowledge available for reuse for customization-or further extension of the metamodels, a kind of reuse for innovation.
Learning Control for Air Hockey Striking using Deep Reinforcement Learning
Venue: arXiv cs.LG 2017
Year: 2017

We consider the task of learning control policies for a robotic mechanism striking a puck in an air hockey game. The control signal is a direct command to the robot's motors. We employ a model free deep reinforcement learning framework to learn the motoric skills of striking the puck accurately in order to score. We propose certain improvements to the standard learning scheme which make the deep Q-learning algorithm feasible when it might otherwise fail. Our improvements include integrating prior knowledge into the learning scheme, and accounting for the changing distribution of samples in the experience replay buffer. Finally we present our simulation results for aimed striking which demonstrate the successful learning of this task, and the improvement in algorithm stability due to the proposed modifications.
On Algorithmic Statistics for space-bounded algorithms
Venue: arXiv cs.IT 2017
Year: 2017

Algorithmic statistics studies explanations of observed data that are good in the algorithmic sense: an explanation should be simple i.e. should have small Kolmogorov complexity and capture all the algorithmically discoverable regularities in the data. However this idea can not be used in practice because Kolmogorov complexity is not computable. In this paper we develop algorithmic statistics using space-bounded Kolmogorov complexity. We prove an analogue of one of the main result of `classic' algorithmic statistics (about the connection between optimality and randomness deficiences). The main tool of our proof is the Nisan-Wigderson generator.
Opportunities to Parallelize Path Planning Algorithms for Autonomous Underwater Vehicles
Venue: arXiv cs.RO 2017
Year: 2017

This paper discusses opportunities to parallelize graph based path planning algorithms in a time varying environment. Parallel architectures have become commonplace, requiring algorithm to be parallelized for efficient execution. An additional focal point of this paper is the inclusion of inaccuracies in path planning as a result of forecast error variance, accuracy of calculation in the cost functions and a different observed vehicle speed in the real mission than planned. In this context, robust path planning algorithms will be described. These algorithms are equally applicable to land based, aerial, or underwater mobile autonomous systems. The results presented here provide the basis for a future Research project in which the parallelized algorithms will be evaluated on multi and many core systems such as the dual core ARM Panda board and the 48 core Single-chip Cloud Computer (SCC). Modern multi and many core processors support a wide range of performance vs. energy tradeoffs that can be exploited in energyconstrained environments such as battery operated autonomous underwater vehicles. For this evaluation, the boards will be deployed within the Slocum glider, a commercially available, buoyancy driven autonomous underwater vehicle (AUV).