OpenRaiser/Intern-Atlas huggingface dataset:
Sequential Thresholds: Context Sensitive Default Extensions
Venue: arXiv cs.AI 2013
Year: 2013

Default logic encounters some conceptual difficulties in representing common sense reasoning tasks. We argue that we should not try to formulate modular default rules that are presumed to work in all or most circumstances. We need to take into account the importance of the context which is continuously evolving during the reasoning process. Sequential thresholding is a quantitative counterpart of default logic which makes explicit the role context plays in the construction of a non-monotonic extension. We present a semantic characterization of generic non-monotonic reasoning, as well as the instantiations pertaining to default logic and sequential thresholding. This provides a link between the two mechanisms as well as a way to integrate the two that can be beneficial to both.
Automatically Mining Program Build Information via Signature Matching
Venue: arXiv cs.SE 2013
Year: 2013

Program build information, such as compilers and libraries used, is vitally important in an auditing and benchmarking framework for HPC systems. We have developed a tool to automatically extract this information using signature-based detection, a common strategy employed by anti-virus software to search for known patterns of data within the program binaries. We formulate the patterns from various "features" embedded in the program binaries, and the experiment shows that our tool can successfully identify many different compilers, libraries, and their versions.
Discovery of Maximal Frequent Item Sets using Subset Creation
Venue: arXiv cs.DB 2013
Year: 2013

Data mining is the practice to search large amount of data to discover data patterns. Data mining uses mathematical algorithms to group the data and evaluate the future events. Association rule is a research area in the field of knowledge discovery. Many data mining researchers had improved upon the quality of association rule for business development by incorporating influential factors like utility, number of items sold and for the mining of association data patterns. In this paper, we propose an efficient algorithm to find maximal frequent itemset first. Most of the association rule algorithms used to find minimal frequent item first, then with the help of minimal frequent itemsets derive the maximal frequent itemsets, these methods consume more time to find maximal frequent itemsets. To overcome this problem, we propose a new approach to find maximal frequent itemset directly using the concepts of subsets. The proposed method is found to be efficient in finding maximal frequent itemsets.
Fairness in Collision-Free WLANs
Venue: arXiv cs.NI 2013
Year: 2013

CSMA/ECA is a contention protocol that makes it possible to construct a collision-free schedule by using a deterministic backoff after successful transmissions. In this paper, we further enhance the CSMA/ECA protocol with two properties that allows to fairly accommodate a large number of contenders in a collision-free schedule. The first property, called hysteresis, instructs the contenders not to reset their contention window after successful transmissions. Thanks to hysteresis, the protocol sustains a high throughput regardless of the number of contenders. The second property, called fair-share, preserves fairness when different nodes use different contention windows. We present simulations results that evidence how these properties account for performance gains that go even further beyond CSMA/CA.
Discrete Distributions in the Tardos Scheme, Revisited
Venue: arXiv cs.CR 2013
Year: 2013

The Tardos scheme is a well-known traitor tracing scheme to protect copyrighted content against collusion attacks. The original scheme contained some suboptimal design choices, such as the score function and the distribution function used for generating the biases. Skoric et al. previously showed that a symbol-symmetric score function leads to shorter codes, while Nuida et al. obtained the optimal distribution functions for arbitrary coalition sizes. Later, Nuida et al. showed that combining these results leads to even shorter codes when the coalition size is small. We extend their analysis to the case of large coalitions and prove that these optimal distributions converge to the arcsine distribution, thus showing that the arcsine distribution is asymptotically optimal in the symmetric Tardos scheme. We also present a new, practical alternative to the discrete distributions of Nuida et al. and give a comparison of the estimated lengths of the fingerprinting codes for each of these distributions.
Energy Minimization for Parallel Real-Time Systems with Malleable Jobs and Homogeneous Frequencies
Venue: arXiv cs.OS 2013
Year: 2013

In this work, we investigate the potential utility of parallelization for meeting real-time constraints and minimizing energy. We consider malleable Gang scheduling of implicit-deadline sporadic tasks upon multiprocessors. We first show the non-necessity of dynamic voltage/frequency regarding optimality of our scheduling problem. We adapt the canonical schedule for DVFS multiprocessor platforms and propose a polynomial-time optimal processor/frequency-selection algorithm. We evaluate the performance of our algorithm via simulations using parameters obtained from a hardware testbed implementation. Our algorithm has up to a 60 watt decrease in power consumption over the optimal non-parallel approach.
Efficient time synchronized one-time password scheme to provide secure wake-up authentication on wireless sensor networks
Venue: arXiv cs.NI 2013
Year: 2013

In this paper we propose Time Synchronized One-Time-Password scheme to provide secure wake up authentication. The main constraint of wireless sensor networks is their limited power resource that prevents us from using radio transmission over the network to transfer the passwords. On the other hand computation power consumption is insignificant when compared to the costs associated with the power needed for transmitting the right set of keys. In addition to prevent adversaries from reading and following the timeline of the network, we propose to encrypt the tokens using symmetric encryption to prevent replay attacks.
Wideband Spectrum Sensing for Cognitive Radio Networks: A Survey
Venue: arXiv cs.IT 2013
Year: 2013

Cognitive radio has emerged as one of the most promising candidate solutions to improve spectrum utilization in next generation cellular networks. A crucial requirement for future cognitive radio networks is wideband spectrum sensing: secondary users reliably detect spectral opportunities across a wide frequency range. In this article, various wideband spectrum sensing algorithms are presented, together with a discussion of the pros and cons of each algorithm and the challenging issues. Special attention is paid to the use of sub-Nyquist techniques, including compressive sensing and multi-channel sub-Nyquist sampling techniques.
H Index of History journals published in Spain according to Google Scholar Metrics (2007-2011)
Venue: arXiv cs.DL 2013
Year: 2013

Google Scholar Metrics (GSM), which was recently launched in April 2012, features new bibliometric systems for gauging scientific journals by counting the number of citations obtained in Google Scholar. This way, it opens new possibilities for measuring journal impacts in the field of Humanities. The present article intends to evaluate the scope of this tool through analysing GSM searches, from the 5th through 6th of December 2012, of History journals published in Spain. In sum, 69 journals were identified, accounting for only 24% of the History journals published in Spain. The ranges of H index values for this field are so small that the ranking can no longer be said to show a discriminating potential. In the light of this, we would like to propose a change in the way Google Scholar Metrics is designed so that it could also accommodate production and citation patterns in the particular field of History, and, in a broader scope, in the area of Humanities as well.
Testing and Evaluation of Service Oriented Systems
Venue: arXiv cs.SE 2013
Year: 2013

Evaluation of service oriented system has been a challenge, though there are large number of evaluation metrics exist but none of them is efficient to evaluate these systems effectively.This paper discusses the different testing tools and evaluation methods available for SOA and summarizes their limitation and support in context of service oriented architectures.
Towards a Taxonomy of Performance Evaluation of Commercial Cloud Services
Venue: arXiv cs.DC 2013
Year: 2013

Cloud Computing, as one of the most promising computing paradigms, has become increasingly accepted in industry. Numerous commercial providers have started to supply public Cloud services, and corresponding performance evaluation is then inevitably required for Cloud provider selection or cost-benefit analysis. Unfortunately, inaccurate and confusing evaluation implementations can be often seen in the context of commercial Cloud Computing, which could severely interfere and spoil evaluation-related comprehension and communication. This paper introduces a taxonomy to help profile and standardize the details of performance evaluation of commercial Cloud services. Through a systematic literature review, we constructed the taxonomy along two dimensions by arranging the atomic elements of Cloud-related performance evaluation. As such, this proposed taxonomy can be employed both to analyze existing evaluation practices through decomposition into elements and to design new experiments through composing elements for evaluating performance of commercial Cloud services. Moreover, through smooth expansion, we can continually adapt this taxonomy to the more general area of evaluation of Cloud Computing.
User Interface for Volume Rendering in Virtual Reality Environments
Venue: arXiv cs.GR 2013
Year: 2013

Volume Rendering applications require sophisticated user interaction for the definition and refinement of transfer functions. Traditional 2D desktop user interface elements have been developed to solve this task, but such concepts do not map well to the interaction devices available in Virtual Reality environments. In this paper, we propose an intuitive user interface for Volume Rendering specifically designed for Virtual Reality environments. The proposed interface allows transfer function design and refinement based on intuitive two-handed operation of Wand-like controllers. Additional interaction modes such as navigation and clip plane manipulation are supported as well. The system is implemented using the Sony PlayStation Move controller system. This choice is based on controller device capabilities as well as application and environment constraints. Initial results document the potential of our approach.
Content-based data leakage detection using extended fingerprinting
Venue: arXiv cs.CR 2013
Year: 2013

Protecting sensitive information from unauthorized disclosure is a major concern of every organization. As an organizations employees need to access such information in order to carry out their daily work, data leakage detection is both an essential and challenging task. Whether caused by malicious intent or an inadvertent mistake, data loss can result in significant damage to the organization. Fingerprinting is a content-based method used for detecting data leakage. In fingerprinting, signatures of known confidential content are extracted and matched with outgoing content in order to detect leakage of sensitive content. Existing fingerprinting methods, however, suffer from two major limitations. First, fingerprinting can be bypassed by rephrasing (or minor modification) of the confidential content, and second, usually the whole content of document is fingerprinted (including non-confidential parts), resulting in false alarms. In this paper we propose an extension to the fingerprinting approach that is based on sorted k-skip-n-grams. The proposed method is able to produce a fingerprint of the core confidential content which ignores non-relevant (non-confidential) sections. In addition, the proposed fingerprint method is more robust to rephrasing and can also be used to detect a previously unseen confidential document and therefore provide better detection of intentional leakage incidents.
Complexity distribution of agent policies
Venue: arXiv cs.AI 2013
Year: 2013

We analyse the complexity of environments according to the policies that need to be used to achieve high performance. The performance results for a population of policies leads to a distribution that is examined in terms of policy complexity and analysed through several diagrams and indicators. The notion of environment response curve is also introduced, by inverting the performance results into an ability scale. We apply all these concepts, diagrams and indicators to a minimalistic environment class, agent-populated elementary cellular automata, showing how the difficulty, discriminating power and ranges (previous to normalisation) may vary for several environments.
pROST : A Smoothed Lp-norm Robust Online Subspace Tracking Method for Realtime Background Subtraction in Video
Venue: arXiv cs.CV 2013
Year: 2013

An increasing number of methods for background subtraction use Robust PCA to identify sparse foreground objects. While many algorithms use the L1-norm as a convex relaxation of the ideal sparsifying function, we approach the problem with a smoothed Lp-norm and present pROST, a method for robust online subspace tracking. The algorithm is based on alternating minimization on manifolds. Implemented on a graphics processing unit it achieves realtime performance. Experimental results on a state-of-the-art benchmark for background subtraction on real-world video data indicate that the method succeeds at a broad variety of background subtraction scenarios, and it outperforms competing approaches when video quality is deteriorated by camera jitter.
Passive Self-Interference Suppression for Full-Duplex Infrastructure Nodes
Venue: arXiv cs.IT 2013
Year: 2013

Recent research results have demonstrated the feasibility of full-duplex wireless communication for short-range links. Although the focus of the previous works has been active cancellation of the self-interference signal, a majority of the overall self-interference suppression is often due to passive suppression, i.e., isolation of the transmit and receive antennas. We present a measurement-based study of the capabilities and limitations of three key mechanisms for passive self-interference suppression: directional isolation, absorptive shielding, and cross-polarization. The study demonstrates that more than 70 dB of passive suppression can be achieved in certain environments, but also establishes two results on the limitations of passive suppression: (1) environmental reflections limit the amount of passive suppression that can be achieved, and (2) passive suppression, in general, increases the frequency selectivity of the residual self-interference signal. These results suggest two design implications: (1) deployments of full-duplex infrastructure nodes should minimize near-antenna reflectors, and (2) active cancellation in concatenation with passive suppression should employ higher-order filters or per-subcarrier cancellation.
Security and Privacy Challenges in Cognitive Wireless Sensor Networks
Venue: arXiv cs.CR 2013
Year: 2013

Wireless sensor networks (WSNs) have attracted a lot of interest in the research community due to their potential applicability in a wide range of real-world practical applications. However, due to the distributed nature and their deployments in critical applications without human interventions and sensitivity and criticality of data communicated, these networks are vulnerable to numerous security and privacy threats that can adversely affect their performance. These issues become even more critical in cognitive wireless sensor networks (CWSNs) in which the sensor nodes have the capabilities of changing their transmission and reception parameters according to the radio environment under which they operate in order to achieve reliable and efficient communication and optimum utilization of the network resources. This chapter presents a comprehensive discussion on the security and privacy issues in CWSNs by identifying various security threats in these networks and various defense mechanisms to counter these vulnerabilities. Various types of attacks on CWSNs are categorized under different classes based on their natures and targets, and corresponding to each attack class, appropriate security mechanisms are also discussed. Some critical research issues on security and privacy in CWSNs are also identified.
Learning Universally Quantified Invariants of Linear Data Structures
Venue: arXiv cs.PL 2013
Year: 2013

We propose a new automaton model, called quantified data automata over words, that can model quantified invariants over linear data structures, and build poly-time active learning algorithms for them, where the learner is allowed to query the teacher with membership and equivalence queries. In order to express invariants in decidable logics, we invent a decidable subclass of QDAs, called elastic QDAs, and prove that every QDA has a unique minimally-over-approximating elastic QDA. We then give an application of these theoretically sound and efficient active learning algorithms in a passive learning framework and show that we can efficiently learn quantified linear data structure invariants from samples obtained from dynamic runs for a large class of programs.
Power Allocation and Time-Domain Artificial Noise Design for Wiretap OFDM with Discrete Inputs
Venue: arXiv cs.IT 2013
Year: 2013

Optimal power allocation for orthogonal frequency division multiplexing (OFDM) wiretap channels with Gaussian channel inputs has already been studied in some previous works from an information theoretical viewpoint. However, these results are not sufficient for practical system design. One reason is that discrete channel inputs, such as quadrature amplitude modulation (QAM) signals, instead of Gaussian channel inputs, are deployed in current practical wireless systems to maintain moderate peak transmission power and receiver complexity. In this paper, we investigate the power allocation and artificial noise design for OFDM wiretap channels with discrete channel inputs. We first prove that the secrecy rate function for discrete channel inputs is nonconcave with respect to the transmission power. To resolve the corresponding nonconvex secrecy rate maximization problem, we develop a low-complexity power allocation algorithm, which yields a duality gap diminishing in the order of O(1/\sqrt{N}), where N is the number of subcarriers of OFDM. We then show that independent frequency-domain artificial noise cannot improve the secrecy rate of single-antenna wiretap channels. Towards this end, we propose a novel time-domain artificial noise design which exploits temporal degrees of freedom provided by the cyclic prefix of OFDM systems {to jam the eavesdropper and boost the secrecy rate even with a single antenna at the transmitter}. Numerical results are provided to illustrate the performance of the proposed design schemes.
Robust Low-Rank LCMV Beamforming Algorithms Based on Joint Iterative Optimization Strategies
Venue: arXiv cs.IT 2013
Year: 2013

This chapter presents reduced-rank linearly constrained minimum variance (LCMV) algorithms based on the concept of joint iterative optimization of parameters. The proposed reduced-rank scheme is based on a constrained robust joint iterative optimization (RJIO) of parameters according to the minimum variance criterion. The robust optimization procedure adjusts the parameters of a rank-reduction matrix, a reduced-rank beamformer and the diagonal loading in an alternating manner. LCMV expressions are developed for the design of the rank-reduction matrix and the reduced-rank beamformer. Stochastic gradient and recursive least-squares adaptive algorithms are then devised for an efficient implementation of the RJIO robust beamforming technique. Simulations for a application in the presence of uncertainties show that the RJIO scheme and algorithms outperform in convergence and tracking performances existing algorithms while requiring a comparable complexity.
Extendability of continuous maps is undecidable
Venue: arXiv cs.CG 2013
Year: 2013

We consider two basic problems of algebraic topology, the extension problem and the computation of higher homotopy groups, from the point of view of computability and computational complexity. The extension problem is the following: Given topological spaces X and Y, a subspace A\subseteq X, and a (continuous) map f:A->Y, decide whether f can be extended to a continuous map \bar{f}:X->Y. All spaces are given as finite simplicial complexes and the map f is simplicial. Recent positive algorithmic results, proved in a series of companion papers, show that for (k-1)-connected Y, k>=2, the extension problem is algorithmically solvable if the dimension of X is at most 2k-1, and even in polynomial time when k is fixed. Here we show that the condition \dim X<=2k-1 cannot be relaxed: for \dim X=2k, the extension problem with (k-1)-connected Y becomes undecidable. Moreover, either the target space Y or the pair (X,A) can be fixed in such a way that the problem remains undecidable. Our second result, a strengthening of a result of Anick, says that the computation of π_k(Y) of a 1-connected simplicial complex Y is #P-hard when k is considered as a part of the input.
A Lower Bound on the Noncoherent Capacity Pre-log for the MIMO Channel with Temporally Correlated Fading
Venue: arXiv cs.IT 2013
Year: 2013

We derive a lower bound on the capacity pre-log of a temporally correlated Rayleigh block-fading multiple-input multiple-output (MIMO) channel with T transmit antennas and R receive antennas in the noncoherent setting (no a priori channel knowledge at the transmitter and the receiver). In this model, the fading process changes independently across blocks of length L and is temporally correlated within each block for each transmit-receive antenna pair, with a given rank Q of the corresponding correlation matrix. Our result implies that for almost all choices of the coloring matrix that models the temporal correlation, the pre-log can be lower-bounded by T(1-1/L) for T <= (L-1)/Q provided that R is sufficiently large. The widely used constant block-fading model is equivalent to the temporally correlated block-fading model with Q = 1 for the special case when the temporal correlation for each transmit-receive antenna pair is the same, which is unlikely to be observed in practice. For the constant block-fading model, the capacity pre-log is given by T(1-T/L), which is smaller than our lower bound for the case Q = 1. Thus, our result suggests that the assumptions underlying the constant block- fading model lead to a pessimistic result for the capacity pre-log.
No-Wait Flowshop Scheduling is as Hard as Asymmetric Traveling Salesman Problem
Venue: arXiv cs.DS 2013
Year: 2013

In this paper we study the classical no-wait flowshop scheduling problem with makespan objective (F|no-wait|C_max in the standard three-field notation). This problem is well-known to be a special case of the asymmetric traveling salesman problem (ATSP) and as such has an approximation algorithm with logarithmic performance guarantee. In this work we show a reverse connection, we show that any polynomial time α-approximation algorithm for the no-wait flowshop scheduling problem with makespan objective implies the existence of a polynomial-time α(1+ε)-approximation algorithm for the ATSP, for any ε>0. This in turn implies that all non-approximability results for the ATSP (current or future) will carry over to its special case. In particular, it follows that no-wait flowshop problem is APX-hard, which is the first non-approximability result for this problem.