OpenRaiser/Intern-Atlas huggingface dataset:
Energy Efficient Scheduling and Groupping for Machine-TYpe Communications over Cellular Networks
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, energy-efficient scheduling for grouped machine-type devices deployed in cellular networks is investigated. We introduce a scheduling-based cooperation incentive scheme which enables machine nodes to organize themselves locally, create machine groups, and communicate through group representatives to the base station. This scheme benefits from a novel scheduler design which takes into account the cooperation level of each node, reimburses the extra energy consumptions of group representatives, and maximizes the network lifetime. As reusing cellular uplink resources for communications inside the groups degrades the Quality of Service (QoS) of the primary users, analytical results are provided which present a tradeoff between maximum allowable number of simultaneously active machine groups in a given cell and QoS of the primary users. Furthermore, we extend our derived solutions for the existing cellular networks, propose a cooperation-incentive LTE scheduler, and present our simulation results in the context of LTE. The simulation results show that the proposed solutions significantly prolong the network lifetime. Also, it is shown that under certain circumstances, reusing uplink resource by machine devices can degrade the outage performance of the primary users significantly, and hence, coexistence management of machine devices and cellular users is of paramount importance for next generations of cellular networks in order to enable group-based machine-type communications while guaranteeing QoS for the primary users.
The complexity of approximations for epistemic synthesis (extended abstract)
Venue: arXiv cs.LO 2016
Year: 2016

Epistemic protocol specifications allow programs, for settings in which multiple agents act with incomplete information, to be described in terms of how actions are related to what the agents know. They are a variant of the knowledge-based programs of Fagin et al [Distributed Computing, 1997], motivated by the complexity of synthesizing implementations in that framework. The paper proposes an approach to the synthesis of implementations of epistemic protocol specifications, that reduces the problem of finding an implementation to a sequence of model checking problems in approximations of the ultimate system being synthesized. A number of ways to construct such approximations is considered, and these are studied for the complexity of the associated model checking problems. The outcome of the study is the identification of the best approximations with the property of being PTIME implementable.
On the Accuracy of Interference Models in Wireless Communications
Venue: arXiv cs.IT 2016
Year: 2016

We develop a new framework for measuring and comparing the accuracy of any wireless interference models used in the analysis and design of wireless networks. Our approach is based on a new index that assesses the ability of the interference model to correctly predict harmful interference events, i.e., link outages. We use this new index to quantify the accuracy of various interference models used in the literature, under various scenarios such as Rayleigh fading wireless channels, directional antennas, and blockage (impenetrable obstacles) in the network. Our analysis reveals that in highly directional antenna settings with obstructions, even simple interference models (e.g., the classical protocol model) are accurate, while with omnidirectional antennas, more sophisticated and complex interference models (e.g., the classical physical model) are necessary. Our new approach makes it possible to adopt the appropriate interference model of adequate accuracy and simplicity in different settings.
Learning scale-variant and scale-invariant features for deep image classification
Venue: arXiv cs.CV 2016
Year: 2016

Convolutional Neural Networks (CNNs) require large image corpora to be trained on classification tasks. The variation in image resolutions, sizes of objects and patterns depicted, and image scales, hampers CNN training and performance, because the task-relevant information varies over spatial scales. Previous work attempting to deal with such scale variations focused on encouraging scale-invariant CNN representations. However, scale-invariant representations are incomplete representations of images, because images contain scale-variant information as well. This paper addresses the combined development of scale-invariant and scale-variant representations. We propose a multi- scale CNN method to encourage the recognition of both types of features and evaluate it on a challenging image classification task involving task-relevant characteristics at multiple scales. The results show that our multi-scale CNN outperforms single-scale CNN. This leads to the conclusion that encouraging the combined development of a scale-invariant and scale-variant representation in CNNs is beneficial to image recognition performance.
Inverse Inequality Estimates with Symbolic Computation
Venue: arXiv cs.SC 2016
Year: 2016

In the convergence analysis of numerical methods for solving partial differential equations (such as finite element methods) one arrives at certain generalized eigenvalue problems, whose maximal eigenvalues need to be estimated as accurately as possible. We apply symbolic computation methods to the situation of square elements and are able to improve the previously known upper bound, given in "p- and hp-finite element methods" (Schwab, 1998), by a factor of 8. More precisely, we try to evaluate the corresponding determinant using the holonomic ansatz, which is a powerful tool for dealing with determinants, proposed by Zeilberger in 2007. However, it turns out that this method does not succeed on the problem at hand. As a solution we present a variation of the original holonomic ansatz that is applicable to a larger class of determinants, including the one we are dealing with here. We obtain an explicit closed form for the determinant, whose special form enables us to derive new and tight upper resp. lower bounds on the maximal eigenvalue, as well as its asymptotic behaviour.
A Framework for Accelerating Bottlenecks in GPU Execution with Assist Warps
Venue: arXiv cs.AR 2016
Year: 2016

Modern Graphics Processing Units (GPUs) are well provisioned to support the concurrent execution of thousands of threads. Unfortunately, different bottlenecks during execution and heterogeneous application requirements create imbalances in utilization of resources in the cores. For example, when a GPU is bottlenecked by the available off-chip memory bandwidth, its computational resources are often overwhelmingly idle, waiting for data from memory to arrive. This work describes the Core-Assisted Bottleneck Acceleration (CABA) framework that employs idle on-chip resources to alleviate different bottlenecks in GPU execution. CABA provides flexible mechanisms to automatically generate "assist warps" that execute on GPU cores to perform specific tasks that can improve GPU performance and efficiency. CABA enables the use of idle computational units and pipelines to alleviate the memory bandwidth bottleneck, e.g., by using assist warps to perform data compression to transfer less data from memory. Conversely, the same framework can be employed to handle cases where the GPU is bottlenecked by the available computational units, in which case the memory pipelines are idle and can be used by CABA to speed up computation, e.g., by performing memoization using assist warps. We provide a comprehensive design and evaluation of CABA to perform effective and flexible data compression in the GPU memory hierarchy to alleviate the memory bandwidth bottleneck. Our extensive evaluations show that CABA, when used to implement data compression, provides an average performance improvement of 41.7% (as high as 2.6X) across a variety of memory-bandwidth-sensitive GPGPU applications.
Unrestricted State Complexity of Binary Operations on Regular Languages
Venue: arXiv cs.FL 2016
Year: 2016

I study the state complexity of binary operations on regular languages over different alphabets. It is well known that if $L'_m$ and $L_n$ are languages restricted to be over the same alphabet, with $m$ and $n$ quotients, respectively, the state complexity of any binary boolean operation on $L'_m$ and $L_n$ is $mn$, and that of the product (concatenation) is $(m-1)2^n +2^{n-1}$. In contrast to this, I show that if $L'_m$ and $L_n$ are over their own different alphabets, the state complexity of union and symmetric difference is $mn+m+n+1$, that of intersection is $mn$, that of difference is $mn+m$, and that of the product is $m2^n+2^{n-1}$.
Unsupervised Regenerative Learning of Hierarchical Features in Spiking Deep Networks for Object Recognition
Venue: arXiv cs.NE 2016
Year: 2016

We present a spike-based unsupervised regenerative learning scheme to train Spiking Deep Networks (SpikeCNN) for object recognition problems using biologically realistic leaky integrate-and-fire neurons. The training methodology is based on the Auto-Encoder learning model wherein the hierarchical network is trained layer wise using the encoder-decoder principle. Regenerative learning uses spike-timing information and inherent latencies to update the weights and learn representative levels for each convolutional layer in an unsupervised manner. The features learnt from the final layer in the hierarchy are then fed to an output layer. The output layer is trained with supervision by showing a fraction of the labeled training dataset and performs the overall classification of the input. Our proposed methodology yields 0.92%/29.84% classification error on MNIST/CIFAR10 datasets which is comparable with state-of-the-art results. The proposed methodology also introduces sparsity in the hierarchical feature representations on account of event-based coding resulting in computationally efficient learning.
Linear codes with a few weights from inhomogeneous quadratic functions
Venue: arXiv cs.IT 2016
Year: 2016

Linear codes with few weights have been an interesting subject of study for many years, as these codes have applications in secrete sharing, authentication codes, association schemes, and strongly regular graphs. In this paper, linear codes with a few weights are constructed from inhomogeneous quadratic functions over the finite field $\gf(p)$, where $p$ is an odd prime. They include some earlier linear codes as special cases. The weight distributions of these linear codes are also determined.
Market-based Microgrid Optimal Scheduling
Venue: arXiv cs.SY 2016
Year: 2016

This paper presents an optimal scheduling model for a microgrid participating in the electricity distribution market in interaction with the Distribution Market Operator (DMO). The DMO is a concept proposed here, which administers the established electricity market in the distribution level, i.e., similar to the role of Independent System Operator (ISO) in the wholesale electricity market, sets electricity prices, determines the amounts of the power exchange between market participators, and interacts with the ISO. Considering a predetermined main grid power transfer to the microgrid, the microgrid scheduling problem will aim at balancing the power supply and demand while taking financial objectives into account. A stochastic programming method is employed to model prevailing uncertainties in the microgrid grid-connected and islanded operations. Numerical simulations exhibit the application and the effectiveness of the proposed market-based microgrid scheduling model.
Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification
Venue: arXiv cs.CV 2016
Year: 2016

We present an analysis of three possible strategies for exploiting the power of existing convolutional neural networks (ConvNets) in different scenarios from the ones they were trained: full training, fine tuning, and using ConvNets as feature extractors. In many applications, especially including remote sensing, it is not feasible to fully design and train a new ConvNet, as this usually requires a considerable amount of labeled data and demands high computational costs. Therefore, it is important to understand how to obtain the best profit from existing ConvNets. We perform experiments with six popular ConvNets using three remote sensing datasets. We also compare ConvNets in each strategy with existing descriptors and with state-of-the-art baselines. Results point that fine tuning tends to be the best performing strategy. In fact, using the features from the fine-tuned ConvNet with linear SVM obtains the best results. We also achieved state-of-the-art results for the three datasets used.
Under a cloud of uncertainty: Legal questions affecting Internet storage and transmission of copyright-protected video content
Venue: arXiv cs.NI 2016
Year: 2016

The rapid growth of multimedia consumption has triggered technical, economic, and business innovations that improve the quality and accessibility of content. It has also opened new markets, promising large revenues for industry players. However, new technologies also pose new questions regarding the legal aspects of content delivery, which are often resolved through litigation between copyright owners and content distributors. The precedents set by these cases will act as a game changer in the content delivery industry and will shape the existing offerings in the market in terms of how new technologies can be deployed and what kind of pricing strategies can be associated with them. In this paper, we offer a tutorial on key copyright and communications laws and decisions related to storage and transmission of video content over the Internet. We summarize legal limitations on the deployment of new technologies and pricing mechanisms, and explain the implications of recent lawsuits. Understanding these concerns is essential for engineers engaged in designing the technical and economic aspects of video delivery systems.
A Factorized Recurrent Neural Network based architecture for medium to large vocabulary Language Modelling
Venue: arXiv cs.CL 2016
Year: 2016

Statistical language models are central to many applications that use semantics. Recurrent Neural Networks (RNN) are known to produce state of the art results for language modelling, outperforming their traditional n-gram counterparts in many cases. To generate a probability distribution across a vocabulary, these models require a softmax output layer that linearly increases in size with the size of the vocabulary. Large vocabularies need a commensurately large softmax layer and training them on typical laptops/PCs requires significant time and machine resources. In this paper we present a new technique for implementing RNN based large vocabulary language models that substantially speeds up computation while optimally using the limited memory resources. Our technique, while building on the notion of factorizing the output layer by having multiple output layers, improves on the earlier work by substantially optimizing on the individual output layer size and also eliminating the need for a multistep prediction process.
Random Feature Maps via a Layered Random Projection (LaRP) Framework for Object Classification
Venue: arXiv cs.CV 2016
Year: 2016

The approximation of nonlinear kernels via linear feature maps has recently gained interest due to their applications in reducing the training and testing time of kernel-based learning algorithms. Current random projection methods avoid the curse of dimensionality by embedding the nonlinear feature space into a low dimensional Euclidean space to create nonlinear kernels. We introduce a Layered Random Projection (LaRP) framework, where we model the linear kernels and nonlinearity separately for increased training efficiency. The proposed LaRP framework was assessed using the MNIST hand-written digits database and the COIL-100 object database, and showed notable improvement in object classification performance relative to other state-of-the-art random projection methods.
Scheduling of unit-length jobs with bipartite incompatibility graphs on four uniform machines
Venue: arXiv cs.DS 2016
Year: 2016

In the paper we consider the problem of scheduling $n$ identical jobs on 4 uniform machines with speeds $s_1 \geq s_2 \geq s_3 \geq s_4,$ respectively. Our aim is to find a schedule with a minimum possible length. We assume that jobs are subject to some kind of mutual exclusion constraints modeled by a bipartite incompatibility graph of degree $Δ$, where two incompatible jobs cannot be processed on the same machine. We show that the problem is NP-hard even if $s_1=s_2=s_3$. If, however, $Δ\leq 4$ and $s_1 \geq 12 s_2$, $s_2=s_3=s_4$, then the problem can be solved to optimality in time $O(n^{1.5})$. The same algorithm returns a solution of value at most 2 times optimal provided that $s_1 \geq 2s_2$. Finally, we study the case $s_1 \geq s_2 \geq s_3=s_4$ and give an $O(n^{1.5})$-time $32/15$-approximation algorithm in all such situations.
Generate Image Descriptions based on Deep RNN and Memory Cells for Images Features
Venue: arXiv cs.CV 2016
Year: 2016

Generating natural language descriptions for images is a challenging task. The traditional way is to use the convolutional neural network (CNN) to extract image features, followed by recurrent neural network (RNN) to generate sentences. In this paper, we present a new model that added memory cells to gate the feeding of image features to the deep neural network. The intuition is enabling our model to memorize how much information from images should be fed at each stage of the RNN. Experiments on Flickr8K and Flickr30K datasets showed that our model outperforms other state-of-the-art models with higher BLEU scores.
Catcher-Evader Games
Venue: arXiv cs.GT 2016
Year: 2016

Algorithms for computing game-theoretic solutions have recently been applied to a number of security domains. However, many of the techniques developed for compact representations of security games do not extend to {\em Bayesian} security games, which allow us to model uncertainty about the attacker's type. In this paper, we introduce a general framework of {\em catcher-evader} games that can capture Bayesian security games as well as other game families of interest. We show that computing Stackelberg strategies is NP-hard, but give an algorithm for computing a Nash equilibrium that performs well in experiments. We also prove that the Nash equilibria of these games satisfy the {\em interchangeability} property, so that equilibrium selection is not an issue.
Fantastic 4 system for NIST 2015 Language Recognition Evaluation
Venue: arXiv cs.CL 2016
Year: 2016

This article describes the systems jointly submitted by Institute for Infocomm (I$^2$R), the Laboratoire d'Informatique de l'Université du Maine (LIUM), Nanyang Technology University (NTU) and the University of Eastern Finland (UEF) for 2015 NIST Language Recognition Evaluation (LRE). The submitted system is a fusion of nine sub-systems based on i-vectors extracted from different types of features. Given the i-vectors, several classifiers are adopted for the language detection task including support vector machines (SVM), multi-class logistic regression (MCLR), Probabilistic Linear Discriminant Analysis (PLDA) and Deep Neural Networks (DNN).
Automatic and Quantitative evaluation of attribute discovery methods
Venue: arXiv cs.CV 2016
Year: 2016

Many automatic attribute discovery methods have been developed to extract a set of visual attributes from images for various tasks. However, despite good performance in some image classification tasks, it is difficult to evaluate whether these methods discover meaningful attributes and which one is the best to find the attributes for image descriptions. An intuitive way to evaluate this is to manually verify whether consistent identifiable visual concepts exist to distinguish between positive and negative images of an attribute. This manual checking is tedious, labor intensive and expensive and it is very hard to get quantitative comparisons between different methods. In this work, we tackle this problem by proposing an attribute meaningfulness metric, that can perform automatic evaluation on the meaningfulness of attribute sets as well as achieving quantitative comparisons. We apply our proposed metric to recent automatic attribute discovery methods and popular hashing methods on three attribute datasets. A user study is also conducted to validate the effectiveness of the metric. In our evaluation, we gleaned some insights that could be beneficial in developing automatic attribute discovery methods to generate meaningful attributes. To the best of our knowledge, this is the first work to quantitatively measure the semantic content of automatically discovered attributes.
Efficient distribution and improved security for reliable cloud storage system
Venue: arXiv cs.IT 2016
Year: 2016

The distributed data storage systems are constructed by large number of nodes which are interconnected over a network. Each node in such peer-to-peer network is vulnerable and at a potential risk for attack. The attackers can eavesdrop the nodes and possibly modify their data. Hence distributed storage systems should be secure apart from satisfying the reconstruction and repair requirements. We constructed a distributed storage system, Twin MDS code framework which is more efficient than the regenerating codes based storage systems. We prove that this Twin MDS code framework gives better performance than MBR codes and equal with MSR codes in the distribution process and investigate its security performance comparing with the security of the MBR and MSR codes. Such Twin MDS code framework is examined in an eavesdropper model where passive attackers can access to the stored data or/and downloaded data during the repair process. We demonstrate that the Twin MDS code framework manages better results than MBR and MSR codes regarding the security in the system.
A fast, deterministic algorithm for computing a Hermite Normal Form of a polynomial matrix
Venue: arXiv cs.SC 2016
Year: 2016

Given a square, nonsingular matrix of univariate polynomials $\mathbf{F} \in \mathbb{K}[x]^{n \times n}$ over a field $\mathbb{K}$, we give a fast, deterministic algorithm for finding the Hermite normal form of $\mathbf{F}$ with complexity $O^{\sim}\left(n^ωd\right)$ where $d$ is the degree of $\mathbf{F}$. Here soft-$O$ notation is Big-$O$ with log factors removed and $ω$ is the exponent of matrix multiplication. The method relies of a fast algorithm for determining the diagonal entries of its Hermite normal form, having as cost $O^{\sim}\left(n^ωs\right)$ operations with $s$ the average of the column degrees of $\mathbf{F}$.
Region Based Approximation for High Dimensional Bayesian Network Models
Venue: arXiv cs.AI 2016
Year: 2016

Performing efficient inference on Bayesian Networks (BNs), with large numbers of densely connected variables is challenging. With exact inference methods, such as the Junction Tree algorithm, clustering complexity can grow exponentially with the number of nodes and so computation becomes intractable. This paper presents a general purpose approximate inference algorithm called Triplet Region Construction (TRC) that reduces the clustering complexity for factorized models from worst case exponential to polynomial. We employ graph factorization to reduce connection complexity and produce clusters of limited size. Unlike MCMC algorithms TRC is guaranteed to converge and we present experiments that show that TRC achieves accurate results when compared with exact solutions.