OpenRaiser/Intern-Atlas huggingface dataset:
Content Caching and Distribution in Smart Grid Enabled Wireless Networks
Venue: arXiv cs.NI 2016
Year: 2016

To facilitate wireless transmission of multimedia content to mobile users, we propose a content caching and distribution framework for smart grid enabled OFDM networks, where each popular multimedia file is coded and distributively stored in multiple energy harvesting enabled serving nodes (SNs), and the green energy distributively harvested by SNs can be shared with each other through the smart grid. The distributive caching, green energy sharing and the on-grid energy backup have improved the reliability and performance of the wireless multimedia downloading process. To minimize the total on-grid power consumption of the whole network, while guaranteeing that each user can retrieve the whole content, the user association scheme is jointly designed with consideration of resource allocation, including subchannel assignment, power allocation and power flow among nodes, where the user association scheme decides which SN serves which user. First, the optimal power allocated to each subchannel and the power flows among nodes are derived analytically. Then, the user association problem is decoupled from the subchannel assignment problem, and the proposed user association scheme has leveraged the multicasting nature of the content download process. To decide which subchannel is assigned to which node, the number of subchannels assigned to each node is first determined, according to the green energy generating rate and the associated traffic offload, and then the corresponding subchannels are selected from the total assigned system spectrum. Simulation results demonstrate that bringing content, green energy and serving node closer to the end user can notably reduce the on-grid energy consumption.
Information Flow for Security in Control Systems
Venue: arXiv cs.SY 2016
Year: 2016

This paper considers the development of information flow analyses to support resilient design and active detection of adversaries in cyber physical systems (CPS). The area of CPS security, though well studied, suffers from fragmentation. In this paper, we consider control systems as an abstraction of CPS. Here, we extend the notion of information flow analysis, a well established set of methods developed in software security, to obtain a unified framework that captures and extends system theoretic results in control system security. In particular, we propose the Kullback Liebler (KL) divergence as a causal measure of information flow, which quantifies the effect of adversarial inputs on sensor outputs. We show that the proposed measure characterizes the resilience of control systems to specific attack strategies by relating the KL divergence to optimal detection techniques. We then relate information flows to stealthy attack scenarios where an adversary can bypass detection. Finally, this article examines active detection mechanisms where a defender intelligently manipulates control inputs or the system itself in order to elicit information flows from an attacker's malicious behavior. In all previous cases, we demonstrate an ability to investigate and extend existing results by utilizing the proposed information flow analyses.
Tight Bounds for Single-Pass Streaming Complexity of the Set Cover Problem
Venue: arXiv cs.DS 2016
Year: 2016

We resolve the space complexity of single-pass streaming algorithms for approximating the classic set cover problem. For finding an $α$-approximate set cover (for any $α= o(\sqrt{n})$) using a single-pass streaming algorithm, we show that $Θ(mn/α)$ space is both sufficient and necessary (up to an $O(\log{n})$ factor); here $m$ denotes number of the sets and $n$ denotes size of the universe. This provides a strong negative answer to the open question posed by Indyk et al. (2015) regarding the possibility of having a single-pass algorithm with a small approximation factor that uses sub-linear space. We further study the problem of estimating the size of a minimum set cover (as opposed to finding the actual sets), and establish that an additional factor of $α$ saving in the space is achievable in this case and that this is the best possible. In other words, we show that $Θ(mn/α^2)$ space is both sufficient and necessary (up to logarithmic factors) for estimating the size of a minimum set cover to within a factor of $α$. Our algorithm in fact works for the more general problem of estimating the optimal value of a covering integer program. On the other hand, our lower bound holds even for set cover instances where the sets are presented in a random order.
Extending Demand Response to Tenants in Cloud Data Centers via Non-intrusive Workload Flexibility Pricing
Venue: arXiv cs.DC 2016
Year: 2016

Participating in demand response programs is a promising tool for reducing energy costs in data centers by modulating energy consumption. Towards this end, data centers can employ a rich set of resource management knobs, such as workload shifting and dynamic server provisioning. Nonetheless, these knobs may not be readily available in a cloud data center (CDC) that serves cloud tenants/users, because workloads in CDCs are managed by tenants themselves who are typically charged based on a usage-based or flat-rate pricing and often have no incentive to cooperate with the CDC operator for demand response and cost saving. Towards breaking such "split incentive" hurdle, a few recent studies have tried market-based mechanisms, such as dynamic pricing, inside CDCs. However, such mechanisms often rely on complex designs that are hard to implement and difficult to cope with by tenants. To address this limitation, we propose a novel incentive mechanism that is not dynamic, i.e., it keeps pricing for cloud resources unchanged for a long period. While it charges tenants based on a Usage-based Pricing (UP) as used by today's major cloud operators, it rewards tenants proportionally based on the time length that tenants set as deadlines for completing their workloads. This new mechanism is called Usage-based Pricing with Monetary Reward (UPMR). We demonstrate the effectiveness of UPMR both analytically and empirically. We show that UPMR can reduce the CDC operator's energy cost by 12.9% while increasing its profit by 4.9%, compared to the state-of-the-art approaches used by today's CDC operators to charge their tenants.
Learning to Navigate the Energy Landscape
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we present a novel and efficient architecture for addressing computer vision problems that use `Analysis by Synthesis'. Analysis by synthesis involves the minimization of the reconstruction error which is typically a non-convex function of the latent target variables. State-of-the-art methods adopt a hybrid scheme where discriminatively trained predictors like Random Forests or Convolutional Neural Networks are used to initialize local search algorithms. While these methods have been shown to produce promising results, they often get stuck in local optima. Our method goes beyond the conventional hybrid architecture by not only proposing multiple accurate initial solutions but by also defining a navigational structure over the solution space that can be used for extremely efficient gradient-free local search. We demonstrate the efficacy of our approach on the challenging problem of RGB Camera Relocalization. To make the RGB camera relocalization problem particularly challenging, we introduce a new dataset of 3D environments which are significantly larger than those found in other publicly-available datasets. Our experiments reveal that the proposed method is able to achieve state-of-the-art camera relocalization results. We also demonstrate the generalizability of our approach on Hand Pose Estimation and Image Retrieval tasks.
Stuttering equivalence is too slow!
Venue: arXiv cs.LO 2016
Year: 2016

Groote and Wijs recently described an algorithm for deciding stuttering equivalence and branching bisimulation equivalence, acclaimed to run in $\mathcal{O}(m \log n)$ time. Unfortunately, the algorithm does not always meet the acclaimed running time. In this paper, we present two counterexamples where the algorithms uses $Ω(md)$ time. A third example shows that the correction is not trivial. In order to analyse the problem we present pseudocode of the algorithm, and indicate the time that can be spent on each part of the algorithm in order to meet the desired bound. We also propose fixes to the algorithm such that it indeed runs in $\mathcal{O}(m \log n)$ time.
A Connection Between Locally Repairable Codes and Exact Regenerating Codes
Venue: arXiv cs.IT 2016
Year: 2016

Typically, locally repairable codes (LRCs) and regenerating codes have been studied independently of each other, and it has not been clear how the parameters of one relate to those of the other. In this paper, a novel connection between locally repairable codes and exact regenerating codes is established. Via this connection, locally repairable codes are interpreted as exact regenerating codes. Further, some of these codes are shown to perform better than time-sharing codes between minimum bandwidth regenerating and minimum storage regenerating codes.
Approximated Robust Principal Component Analysis for Improved General Scene Background Subtraction
Venue: arXiv cs.CV 2016
Year: 2016

The research reported in this paper addresses the fundamental task of separation of locally moving or deforming image areas from a static or globally moving background. It builds on the latest developments in the field of robust principal component analysis, specifically, the recently reported practical solutions for the long-standing problem of recovering the low-rank and sparse parts of a large matrix made up of the sum of these two components. This article addresses a few critical issues including: embedding global motion parameters in the matrix decomposition model, i.e., estimation of global motion parameters simultaneously with the foreground/background separation task, considering matrix block-sparsity rather than generic matrix sparsity as natural feature in video processing applications, attenuating background ghosting effects when foreground is subtracted, and more critically providing an extremely efficient algorithm to solve the low-rank/sparse matrix decomposition task. The first aspect is important for background/foreground separation in generic video sequences where the background usually obeys global displacements originated by the camera motion in the capturing process. The second aspect exploits the fact that in video processing applications the sparse matrix has a very particular structure, where the non-zero matrix entries are not randomly distributed but they build small blocks within the sparse matrix. The next feature of the proposed approach addresses removal of ghosting effects originated from foreground silhouettes and the lack of information in the occluded background regions of the image. Finally, the proposed model also tackles algorithmic complexity by introducing an extremely efficient "SVD-free" technique that can be applied in most background/foreground separation tasks for conventional video processing.
Document Neural Autoregressive Distribution Estimation
Venue: arXiv cs.LG 2016
Year: 2016

We present an approach based on feed-forward neural networks for learning the distribution of textual documents. This approach is inspired by the Neural Autoregressive Distribution Estimator(NADE) model, which has been shown to be a good estimator of the distribution of discrete-valued igh-dimensional vectors. In this paper, we present how NADE can successfully be adapted to the case of textual data, retaining from NADE the property that sampling or computing the probability of observations can be done exactly and efficiently. The approach can also be used to learn deep representations of documents that are competitive to those learned by the alternative topic modeling approaches. Finally, we describe how the approach can be combined with a regular neural network N-gram model and substantially improve its performance, by making its learned representation sensitive to the larger, document-specific context.
A Survey of Stealth Malware: Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions
Venue: arXiv cs.CR 2016
Year: 2016

As our professional, social, and financial existences become increasingly digitized and as our government, healthcare, and military infrastructures rely more on computer technologies, they present larger and more lucrative targets for malware. Stealth malware in particular poses an increased threat because it is specifically designed to evade detection mechanisms, spreading dormant, in the wild for extended periods of time, gathering sensitive information or positioning itself for a high-impact zero-day attack. Policing the growing attack surface requires the development of efficient anti-malware solutions with improved generalization to detect novel types of malware and resolve these occurrences with as little burden on human experts as possible. In this paper, we survey malicious stealth technologies as well as existing solutions for detecting and categorizing these countermeasures autonomously. While machine learning offers promising potential for increasingly autonomous solutions with improved generalization to new malware types, both at the network level and at the host level, our findings suggest that several flawed assumptions inherent to most recognition algorithms prevent a direct mapping between the stealth malware recognition problem and a machine learning solution. The most notable of these flawed assumptions is the closed world assumption: that no sample belonging to a class outside of a static training set will appear at query time. We present a formalized adaptive open world framework for stealth malware recognition and relate it mathematically to research from other machine learning domains.
Towards Coordinated Bandwidth Adaptations for Hundred-Scale 3D Tele-Immersive Systems
Venue: arXiv cs.MM 2016
Year: 2016

3D tele-immersion improves the state of collaboration among geographically distributed participants. Unlike the traditional 2D videos, a 3D tele-immersive system employs multiple 3D cameras based in each physical site to cover a much larger field of view, generating a very large amount of stream data. One of the major challenges is how to efficiently transmit these bulky 3D streaming data to bandwidth-constrained sites. In this paper, we study an adaptive Human Visual System (HVS) -compliant bandwidth management framework for efficient delivery of hundred-scale streams produced from distributed 3D tele-immersive sites to a receiver site with limited bandwidth budget. Our adaptation framework exploits the semantics link of HVS with multiple 3D streams in the 3D tele-immersive environment. We developed TELEVIS, a visual simulation tool to showcase a HVS-aware tele-immersive system for realistic cases. Our evaluation results show that the proposed adaptation can improve the total quality per unit of bandwidth used to deliver streams in 3D tele-immersive systems.
Better bounds for coalescing-branching random walks
Venue: arXiv cs.DS 2016
Year: 2016

Coalescing-branching random walks, or {\em cobra walks} for short, are a natural variant of random walks on graphs that can model the spread of disease through contacts or the spread of information in networks. In a $k$-cobra walk, at each time step a subset of the vertices are active; each active vertex chooses $k$ random neighbors (sampled independently and uniformly with replacement) that become active at the next step, and these are the only active vertices at the next step. A natural quantity to study for cobra walks is the cover time, which corresponds to the expected time when all nodes have become infected or received the disseminated information. In this work, we extend previous results for cobra walks in multiple ways. We show that the cover time for the 2-cobra walk on $[0,n]^d$ is $O(n)$ (where the order notation hides constant factors that depend on $d$); previous work had shown the cover time was $O(n \cdot polylog(n))$. We show that the cover time for a 2-cobra walk on an $n$-vertex $d$-regular graph with conductance $ϕ_G$ is $O(ϕ_G^{-2} \log^2 n)$, significantly generalizing a previous result that held only for expander graphs with sufficiently high expansion. And finally we show that the cover time for a 2-cobra walk on a graph with $n$ vertices is always $O(n^{11/4} \log n)$; this is the first result showing that the bound of $Θ(n^3)$ for the worst-case cover time for random walks can be beaten using 2-cobra walks.
Inter-Paradigm Translation of Process Models using Simulation and Mining
Venue: arXiv cs.SE 2016
Year: 2016

Process modeling is usually done using imperative modeling languages like BPMN or EPCs. In order to cope with the complexity of human-centric and flexible business processes several declarative process modeling languages (DPMLs) have been developed during the last years. DPMLs allow for the specification of constraints that restrict execution flows. They differ widely in terms of their level of expressiveness and tool support. Furthermore, research has shown that the understandability of declarative process models is rather low. Since there are applications for both classes of process modeling languages, there arises a need for an automatic translation of process models from one language into another. Our approach is based upon well-established methodologies in process management for process model simulation and process mining without requiring the specification of model transformation rules. In this paper, we present the technique in principle and evaluate it by transforming process models between two exemplary process modeling languages.
Sentence Pair Scoring: Towards Unified Framework for Text Comprehension
Venue: arXiv cs.CL 2016
Year: 2016

We review the task of Sentence Pair Scoring, popular in the literature in various forms - viewed as Answer Sentence Selection, Semantic Text Scoring, Next Utterance Ranking, Recognizing Textual Entailment, Paraphrasing or e.g. a component of Memory Networks. We argue that all such tasks are similar from the model perspective and propose new baselines by comparing the performance of common IR metrics and popular convolutional, recurrent and attention-based neural models across many Sentence Pair Scoring tasks and datasets. We discuss the problem of evaluating randomized models, propose a statistically grounded methodology, and attempt to improve comparisons by releasing new datasets that are much harder than some of the currently used well explored benchmarks. We introduce a unified open source software framework with easily pluggable models and tasks, which enables us to experiment with multi-task reusability of trained sentence model. We set a new state-of-art in performance on the Ubuntu Dialogue dataset.
Adaptive coherence estimator (ACE) for explosive hazard detection using wideband electromagnetic induction (WEMI)
Venue: arXiv cs.CV 2016
Year: 2016

The adaptive coherence estimator (ACE) estimates the squared cosine of the angle between a known target vector and a sample vector in a whitened coordinate space. The space is whitened according to an estimation of the background statistics, which directly effects the performance of the statistic as a target detector. In this paper, the ACE detection statistic is used to detect buried explosive hazards with data from a Wideband Electromagnetic Induction (WEMI) sensor. Target signatures are based on a dictionary defined using a Discrete Spectrum of Relaxation Frequencies (DSRF) model. Results are summarized as a receiver operator curve (ROC) and compared to other leading methods.
Towards Automatic Wild Animal Monitoring: Identification of Animal Species in Camera-trap Images using Very Deep Convolutional Neural Networks
Venue: arXiv cs.CV 2016
Year: 2016

Non intrusive monitoring of animals in the wild is possible using camera trapping framework, which uses cameras triggered by sensors to take a burst of images of animals in their habitat. However camera trapping framework produces a high volume of data (in the order on thousands or millions of images), which must be analyzed by a human expert. In this work, a method for animal species identification in the wild using very deep convolutional neural networks is presented. Multiple versions of the Snapshot Serengeti dataset were used in order to probe the ability of the method to cope with different challenges that camera-trap images demand. The method reached 88.9% of accuracy in Top-1 and 98.1% in Top-5 in the evaluation set using a residual network topology. Also, the results show that the proposed method outperforms previous approximations and proves that recognition in camera-trap images can be automated.
A Survey on Object Detection in Optical Remote Sensing Images
Venue: arXiv cs.CV 2016
Year: 2016

Object detection in optical remote sensing images, being a fundamental but challenging problem in the field of aerial and satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. While enormous methods exist, a deep review of the literature concerning generic object detection is still lacking. This paper aims to provide a review of the recent progress in this field. Different from several previously published surveys that focus on a specific object class such as building and road, we concentrate on more generic object categories including, but are not limited to, road, building, tree, vehicle, ship, airport, urban-area. Covering about 270 publications we survey 1) template matching-based object detection methods, 2) knowledge-based object detection methods, 3) object-based image analysis (OBIA)-based object detection methods, 4) machine learning-based object detection methods, and 5) five publicly available datasets and three standard evaluation metrics. We also discuss the challenges of current studies and propose two promising research directions, namely deep learning-based feature representation and weakly supervised learning-based geospatial object detection. It is our hope that this survey will be beneficial for the researchers to have better understanding of this research field.
An Approximation Approach for Solving the Subpath Planning Problem
Venue: arXiv cs.RO 2016
Year: 2016

The subpath planning problem is a branch of the path planning problem, which has widespread applications in automated manufacturing process as well as vehicle and robot navigation. This problem is to find the shortest path or tour subject for travelling a set of given subpaths. The current approaches for dealing with the subpath planning problem are all based on meta-heuristic approaches. It is well-known that meta-heuristic based approaches have several deficiencies. To address them, we propose a novel approximation algorithm in the O(n^3) time complexity class, which guarantees to solve any subpath planning problem instance with the fixed ratio bound of 2. Also, the formal proofs of the claims, our empirical evaluation shows that our approximation method acts much better than a state-of-the-art method, both in result and execution time.
Multi-Task Cross-Lingual Sequence Tagging from Scratch
Venue: arXiv cs.CL 2016
Year: 2016

We present a deep hierarchical recurrent neural network for sequence tagging. Given a sequence of words, our model employs deep gated recurrent units on both character and word levels to encode morphology and context information, and applies a conditional random field layer to predict the tags. Our model is task independent, language independent, and feature engineering free. We further extend our model to multi-task and cross-lingual joint training by sharing the architecture and parameters. Our model achieves state-of-the-art results in multiple languages on several benchmark tasks including POS tagging, chunking, and NER. We also demonstrate that multi-task and cross-lingual joint training can improve the performance in various cases.