OpenRaiser/Intern-Atlas huggingface dataset:
Compositionality and String Diagrams for Game Theory
Venue: arXiv cs.GT 2016
Year: 2016

We introduce string diagrams as a formal mathematical, graphical language to represent, compose, program and reason about games. The language is well established in quantum physics, quantum computing and quantum linguistic with the semantics given by category theory. We apply this language to the game theoretical setting and show examples how to use it for some economic games where we highlight the compositional nature of our higher-order game theory.
Automatic Graphic Logo Detection via Fast Region-based Convolutional Networks
Venue: arXiv cs.CV 2016
Year: 2016

Brand recognition is a very challenging topic with many useful applications in localization recognition, advertisement and marketing. In this paper we present an automatic graphic logo detection system that robustly handles unconstrained imaging conditions. Our approach is based on Fast Region-based Convolutional Networks (FRCN) proposed by Ross Girshick, which have shown state-of-the-art performance in several generic object recognition tasks (PASCAL Visual Object Classes challenges). In particular, we use two CNN models pre-trained with the ILSVRC ImageNet dataset and we look at the selective search of windows `proposals' in the pre-processing stage and data augmentation to enhance the logo recognition rate. The novelty lies in the use of transfer learning to leverage powerful Convolutional Neural Network models trained with large-scale datasets and repurpose them in the context of graphic logo detection. Another benefit of this framework is that it allows for multiple detections of graphic logos using regions that are likely to have an object. Experimental results with the FlickrLogos-32 dataset show not only the promising performance of our developed models with respect to noise and other transformations a graphic logo can be subject to, but also its superiority over state-of-the-art systems with hand-crafted models and features.
Nonextensive information theoretical machine
Venue: arXiv cs.LG 2016
Year: 2016

In this paper, we propose a new discriminative model named \emph{nonextensive information theoretical machine (NITM)} based on nonextensive generalization of Shannon information theory. In NITM, weight parameters are treated as random variables. Tsallis divergence is used to regularize the distribution of weight parameters and maximum unnormalized Tsallis entropy distribution is used to evaluate fitting effect. On the one hand, it is showed that some well-known margin-based loss functions such as $\ell_{0/1}$ loss, hinge loss, squared hinge loss and exponential loss can be unified by unnormalized Tsallis entropy. On the other hand, Gaussian prior regularization is generalized to Student-t prior regularization with similar computational complexity. The model can be solved efficiently by gradient-based convex optimization and its performance is illustrated on standard datasets.
Social Play Spaces for Active Community Engagement
Venue: arXiv cs.HC 2016
Year: 2016

This paper puts forward the perspective that social play spaces are opportunities to utilise both technology and body for the benefit of community culture and engagement. Co-located social gaming coupled with tangible interfaces offer active participant engagement and the development of the local video game scene. This paper includes a descriptive account of Rabble Room Arcade, an experimental social event combining custom-built physical interface devices and multiplayer video games.
Task scheduling system for UAV operations in indoor environment
Venue: arXiv cs.AI 2016
Year: 2016

Application of UAV in indoor environment is emerging nowadays due to the advancements in technology. UAV brings more space-flexibility in an occupied or hardly-accessible indoor environment, e.g., shop floor of manufacturing industry, greenhouse, nuclear powerplant. UAV helps in creating an autonomous manufacturing system by executing tasks with less human intervention in time-efficient manner. Consequently, a scheduler is one essential component to be focused on; yet the number of reported studies on UAV scheduling has been minimal. This work proposes a methodology with a heuristic (based on Earliest Available Time algorithm) which assigns tasks to UAVs with an objective of minimizing the makespan. In addition, a quick response towards uncertain events and a quick creation of new high-quality feasible schedule are needed. Hence, the proposed heuristic is incorporated with Particle Swarm Optimization (PSO) algorithm to find a quick near optimal schedule. This proposed methodology is implemented into a scheduler and tested on a few scales of datasets generated based on a real flight demonstration. Performance evaluation of scheduler is discussed in detail and the best solution obtained from a selected set of parameters is reported.
A Revision of the Mool Language
Venue: arXiv cs.PL 2016
Year: 2016

We present here in a thorough analysis of the Mool language, covering not only its implementation but also the formalisation (syntax, operational semantics, and type system). The objective is to detect glitches in both the implementation and in the formal definitions, proposing as well new features and added expressiveness. To test our proposals we implemented the revision developed in the Racket platform.
Iterative Judgment Aggregation
Venue: arXiv cs.AI 2016
Year: 2016

Judgment aggregation problems form a class of collective decision-making problems represented in an abstract way, subsuming some well known problems such as voting. A collective decision can be reached in many ways, but a direct one-step aggregation of individual decisions is arguably most studied. Another way to reach collective decisions is by iterative consensus building -- allowing each decision-maker to change their individual decision in response to the choices of the other agents until a consensus is reached. Iterative consensus building has so far only been studied for voting problems. Here we propose an iterative judgment aggregation algorithm, based on movements in an undirected graph, and we study for which instances it terminates with a consensus. We also compare the computational complexity of our iterative procedure with that of related judgment aggregation operators.
Perfect-Information Stochastic Games with Generalized Mean-Payoff Objectives
Venue: arXiv cs.LO 2016
Year: 2016

Graph games provide the foundation for modeling and synthesizing reactive processes. In the synthesis of stochastic reactive processes, the traditional model is perfect-information stochastic games, where some transitions of the game graph are controlled by two adversarial players, and the other transitions are executed probabilistically. We consider such games where the objective is the conjunction of several quantitative objectives (specified as mean-payoff conditions), which we refer to as generalized mean-payoff objectives. The basic decision problem asks for the existence of a finite-memory strategy for a player that ensures the generalized mean-payoff objective be satisfied with a desired probability against all strategies of the opponent. A special case of the decision problem is the almost-sure problem where the desired probability is 1. Previous results presented a semi-decision procedure for epsilon-approximations of the almost-sure problem. In this work, we show that both the almost-sure problem as well as the general basic decision problem are coNP-complete, significantly improving the previous results. Moreover, we show that in the case of 1-player stochastic games, randomized memoryless strategies are sufficient and the problem can be solved in polynomial time. In contrast, in two-player stochastic games, we show that even with randomized strategies exponential memory is required in general, and present a matching exponential upper bound. We also study the basic decision problem with infinite-memory strategies and present computational complexity results for the problem. Our results are relevant in the synthesis of stochastic reactive systems with multiple quantitative requirements.
The Synergistic Gains of Coded Caching and Delayed Feedback
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we consider the $K$-user cache-aided wireless MISO broadcast channel (BC) with random fading and delayed CSIT, and identify the optimal cache-aided degrees-of-freedom (DoF) performance within a factor of 4. The achieved performance is due to a scheme that combines basic coded-caching with MAT-type schemes, and which efficiently exploits the prospective-hindsight similarities between these two methods. This delivers a powerful synergy between coded caching and delayed feedback, in the sense that the total synergistic DoF-gain can be much larger than the sum of the individual gains from delayed CSIT and from coded caching. The derived performance interestingly reveals --- for the first time --- substantial DoF gains from coded caching, even when the (normalized) cache size $γ$ (fraction of the library stored at each receiving device) is very small. Specifically, a microscopic $γ\approx e^{-G}$ can come within a factor of $G$ from the interference-free optimal. For example, storing at each device only a \emph{thousandth} of what is deemed as `popular' content ($γ\approx 10^{-3}$), we approach the interference-free optimal within a factor of $ln(10^3) \approx 7$ (per user DoF of $1/7$), for any number of users. This result carries an additional practical ramification as it reveals how to use coded caching to essentially buffer CSI, thus partially ameliorating the burden of having to acquire real-time CSIT.
Density Evolution for Deterministic Generalized Product Codes with Higher-Order Modulation
Venue: arXiv cs.IT 2016
Year: 2016

Generalized product codes (GPCs) are extensions of product codes (PCs) where coded bits are protected by two component codes but not necessarily arranged in a rectangular array. It has recently been shown that there exists a large class of deterministic GPCs (including, e.g., irregular PCs, half-product codes, staircase codes, and certain braided codes) for which the asymptotic performance under iterative bounded-distance decoding over the binary erasure channel (BEC) can be rigorously characterized in terms of a density evolution analysis. In this paper, the analysis is extended to the case where transmission takes place over parallel BECs with different erasure probabilities. We use this model to predict the code performance in a coded modulation setup with higher-order signal constellations. We also discuss the design of the bit mapper that determines the allocation of the coded bits to the modulation bits of the signal constellation.
Kernelized Covariance for Action Recognition
Venue: arXiv cs.CV 2016
Year: 2016

In this paper we aim at increasing the descriptive power of the covariance matrix, limited in capturing linear mutual dependencies between variables only. We present a rigorous and principled mathematical pipeline to recover the kernel trick for computing the covariance matrix, enhancing it to model more complex, non-linear relationships conveyed by the raw data. To this end, we propose Kernelized-COV, which generalizes the original covariance representation without compromising the efficiency of the computation. In the experiments, we validate the proposed framework against many previous approaches in the literature, scoring on par or superior with respect to the state of the art on benchmark datasets for 3D action recognition.
K-Bit-Swap: A New Operator For Real-Coded Evolutionary Algorithms
Venue: arXiv cs.NE 2016
Year: 2016

There has been a variety of crossover operators proposed for Real-Coded Genetic Algorithms (RCGAs), which recombine values from the same location in pairs of strings. In this article we present a recombination operator for RC- GAs that selects the locations randomly in both parents, and compare it to mainstream crossover operators in a set of experiments on a range of standard multidimensional optimization problems and a clustering problem. We present two variants of the operator, either selecting both bits uniformly at random in the strings, or sampling the second bit from a normal distribution centered at the selected location in the first string. While the operator is biased towards exploitation of fitness space, the random selection of the second bit for swap- ping makes it slightly less exploitation-biased. Extensive statistical analysis using a non-parametric test shows the advantage of the new recombination operators on a range of test functions.
Forman-Ricci flow for change detection in large dynamic data sets
Venue: arXiv cs.SI 2016
Year: 2016

We present a viable solution to the challenging question of change detection in complex networks inferred from large dynamic data sets. Building on Forman's discretization of the classical notion of Ricci curvature, we introduce a novel geometric method to characterize different types of real-world networks with an emphasis on peer-to-peer networks. Furthermore we adapt the classical Ricci flow that already proved to be a powerful tool in image processing and graphics, to the case of undirected and weighted networks. The application of the proposed method on peer-to-peer networks yields insights into topological properties and the structure of their underlying data.
Study on Neural Immune PD Type Tracking Control for DC Actuating Mechanism
Venue: arXiv cs.SY 2016
Year: 2016

Artificial Immune Systems(AIS) have been widely used in different fields, such as control, robotics, computer science and multi-agent systems. In this paper is proposed a new approach of neural immune PD type tracking control combining artificial immune control with neural network. It is assumed that the output of the helper T-cell is concerned with not only the error of system but also its changing rate, while the output of suppressor T-cell is unknown nonlinear function with respect to the amount and changing rate of antigens and the changing rate of antibodies, which is approximated by the output of neural network. From this, we derive neural immune PD type control law and apply it to the trajectory tracking of DC actuating mechanism. The validity of the proposed method is verified by simulation and the simulation results show that this method can follow the desired trajectory more rapidly and more accurately compared to the previous method.
Causal Linearizability
Venue: arXiv cs.DC 2016
Year: 2016

Most work on the verification of concurrent objects for shared memory assumes sequential consistency, but most multicore processors support only weak memory models that do not provide sequential consistency. Furthermore, most verification efforts focus on the linearizability of concurrent objects, but there are existing implementations optimized to run on weak memory models that are not linearizable. In this paper, we address these problems by introducing causal linearizability, a correctness condition for concurrent objects running on weak memory models. Like linearizability itself, causal linearizability enables concurrent objects to be composed, under weak constraints on the client's behaviour. We specify these constraints by introducing a notion of operation-race freedom, where programs that satisfy this property are guaranteed to behave as if their shared objects were in fact linearizable. We apply these ideas to objects from the Linux kernel, optimized to run on TSO, the memory model of the x86 processor family.
A Computational Model for Situated Task Learning with Interactive Instruction
Venue: arXiv cs.AI 2016
Year: 2016

Learning novel tasks is a complex cognitive activity requiring the learner to acquire diverse declarative and procedural knowledge. Prior ACT-R models of acquiring task knowledge from instruction focused on learning procedural knowledge from declarative instructions encoded in semantic memory. In this paper, we identify the requirements for designing compu- tational models that learn task knowledge from situated task- oriented interactions with an expert and then describe and evaluate a model of learning from situated interactive instruc- tion that is implemented in the Soar cognitive architecture.
Adaptive Content-based Routing using Subscription Subgrouping in Structured Overlays
Venue: arXiv cs.DC 2016
Year: 2016

Cyclic or general overlays may provide multiple paths between publishers and subscribers. However, an advertisement tree and a matching subscription activates only one path for notifications routing in publish/subscribe systems. This poses serious challenges in handling network conditions like congestion, and link or broker failures. Further, content-based dynamic routing of notifications requires instantaneous updates in routing paths, which is not a scalable option. This paper introduces a clustering approach with a bit-vector technique for inter-cluster dynamic routing of notifications in a structured cyclic topology that provides multiple paths between publishers and interested subscribers. The advertisement forwarding process exploits the structured nature of the overlay topology to generate advertisement trees of length 1 without generating duplicate messages in the advertisement forwarding process. Issued subscriptions are divided into multiple disjoint subgropus, where each subscription is broadcast to a cluster, which is a limited part of the structured cyclic overlay network. We implemented novel static and intra-cluster dynamic routing algorithms in the proposed overlay topology for our advertisement-based publish/subscribe system, called OctopiA. We also performed a pragmatic comparison of our two algorithms with the state-of-the-art. Experiments on a cluster testbed show that our approach generates fewer inter-broker messages, and is scalable.
Agnostic Estimation of Mean and Covariance
Venue: arXiv cs.DS 2016
Year: 2016

We consider the problem of estimating the mean and covariance of a distribution from iid samples in $\mathbb{R}^n$, in the presence of an $η$ fraction of malicious noise; this is in contrast to much recent work where the noise itself is assumed to be from a distribution of known type. The agnostic problem includes many interesting special cases, e.g., learning the parameters of a single Gaussian (or finding the best-fit Gaussian) when $η$ fraction of data is adversarially corrupted, agnostically learning a mixture of Gaussians, agnostic ICA, etc. We present polynomial-time algorithms to estimate the mean and covariance with error guarantees in terms of information-theoretic lower bounds. As a corollary, we also obtain an agnostic algorithm for Singular Value Decomposition.
Better Analysis of GREEDY Binary Search Tree on Decomposable Sequences
Venue: arXiv cs.DS 2016
Year: 2016

In their seminal paper [Sleator and Tarjan, J.ACM, 1985], the authors conjectured that the splay tree is dynamically optimal binary search tree (BST). In spite of decades of intensive research, the problem remains open. Perhaps a more basic question, which has also attracted much attention, is if there exists any dynamically optimal BST algorithm. One such candidate is GREEDY which is a simple and intuitive BST algorithm [Lucas, Rutgers Tech. Report, 1988; Munro, ESA, 2000; Demaine, Harmon, Iacono, Kane and Patrascu, SODA, 2009]. [Demaine et al., SODA, 2009] showed a novel connection between a geometric problem. Since dynamic optimality conjecture in its most general form remains elusive despite much effort, researchers have studied this problem on special sequences. Recently, [Chalermsook, Goswami, Kozma, Mehlhorn and Saranurak, FOCS, 2015] studied a type of sequences known as $k$-{\em decomposable sequences} in this context, where $k$ parametrizes easiness of the sequence. Using tools from forbidden submatrix theory, they showed that GREEDY takes $n2^{O(k^2)}$ time on this sequence and explicitly raised the question of improving this bound. In this paper, we show that GREEDY takes $O(n \log{k})$ time on $k$-decomposable sequences. In contrast to the previous approach, ours is based on first principles. One of the main ingredients of our result is a new construction of a lower bound certificate on the performance of any algorithm. This certificate is constructed using the execution of GREEDY, and is more nuanced and possibly more flexible than the previous independent set certificate of Demaine et al. This result, which is applicable to all sequences, may be of independent interest and may lead to further progress in analyzing GREEDY on $k$-decomposable as well as general sequences.
AUV Rendezvous Online Path Planning in a Highly Cluttered Undersea Environment Using Evolutionary Algorithms
Venue: arXiv cs.RO 2016
Year: 2016

In this study, a single autonomous underwater vehicle (AUV) aims to rendezvous with a submerged leader recovery vehicle through a cluttered and variable operating field. The rendezvous problem is transformed into a nonlinear optimal control problem (NOCP) and then numerical solutions are provided. A penalty function method is utilized to combine the boundary conditions, vehicular and environmental constraints with the performance index that is final rendezvous time.Four evolutionary based path planning methods namely particle swarm optimization (PSO), biogeography-based optimization (BBO), differential evolution (DE) and Firefly algorithm (FA) are employed to establish a reactive planner module and provide a numerical solution for the proposed NOCP. The objective is to synthesize and analysis the performance and capability of the mentioned methods for guiding an AUV from loitering point toward the rendezvous place through a comprehensive simulation study.The proposed planner module entails a heuristic for refining the path considering situational awareness of underlying environment, encompassing static and dynamic obstacles overwhelmed in spatiotemporal current vectors.This leads to accommodate the unforeseen changes in the operating field like emergence of unpredicted obstacles or variability of current vector filed and turbulent regions. The simulation results demonstrate the inherent robustness and significant efficiency of the proposed planner in enhancement of the vehicle's autonomy in terms of using current force, coping undesired current disturbance for the desired rendezvous purpose. Advantages and shortcoming of all utilized methods are also presented based on the obtained results.
Rotation-Invariant Restricted Boltzmann Machine Using Shared Gradient Filters
Venue: arXiv cs.CV 2016
Year: 2016

Finding suitable features has been an essential problem in computer vision. We focus on Restricted Boltzmann Machines (RBMs), which, despite their versatility, cannot accommodate transformations that may occur in the scene. As a result, several approaches have been proposed that consider a set of transformations, which are used to either augment the training set or transform the actual learned filters. In this paper, we propose the Explicit Rotation-Invariant Restricted Boltzmann Machine, which exploits prior information coming from the dominant orientation of images. Our model extends the standard RBM, by adding a suitable number of weight matrices, associated with each dominant gradient. We show that our approach is able to learn rotation-invariant features, comparing it with the classic formulation of RBM on the MNIST benchmark dataset. Overall, requiring less hidden units, our method learns compact features, which are robust to rotations.