OpenRaiser/Intern-Atlas huggingface dataset:
Perceptual quality comparison between single-layer and scalable videos at the same spatial, temporal and amplitude resolutions
Venue: arXiv cs.MM 2012
Year: 2012

In this paper, the perceptual quality difference between scalable and single-layer videos coded at the same spatial, temporal and amplitude resolution (STAR) is investigated through a subjective test using a mobile platform. Three source videos are considered and for each source video single-layer and scalable video are compared at 9 different STARs. We utilize paired comparison methods with and without tie option. Results collected from 10 subjects in the without "tie" option and 6 subjects in the with "tie" option show that there is no significant quality difference between scalable and singlelayer video when coded at the same STAR. An analysis of variance (ANOVA) test is also performed to further confirm the finding.
Routing Protocols for Mobile and Vehicular Ad-Hoc Networks: A Comparative Analysis
Venue: arXiv cs.NI 2012
Year: 2012

We present comparative analysis of MANET (Mobile Ad-Hoc Network) and VANET (Vehicular Ad-Hoc Network) routing protocols, in this paper. The analysis is based on various design factors. The traditional routing protocols of AODV (Ad hoc On-Demand Distance Vector), DSR (Dynamic Source Routing), and DSDV (Destination-Sequenced Distance-Vector) of MANET are utilizing node centric routing which leads to frequent breaking of routes, causing instability in routing. Usage of these protocols in high mobility environment like VANET may eventually cause many packets to drop. Route repairs and failures notification overheads increase significantly leading to low throughput and long delays. Such phenomenon is not suitable for Vehicular Ad hoc Networks (VANET) due to high mobility of nodes where network can be dense or sparse. Researchers have proposed various routing algorithms or mechanism for MANET and VANET. This paper describes the relevant protocols, associated algorithm and the strength and weakness of these routing protocols.
Completeness for Two Left-Sequential Logics
Venue: arXiv cs.LO 2012
Year: 2012

Left-sequential logics provide a means for reasoning about (closed) propositional terms with atomic propositions that may have side effects and that are evaluated sequentially from left to right. Such propositional terms are commonly used in programming languages to direct the flow of a program. In this thesis we explore two such left-sequential logics. First we discuss Fully Evaluated Left-Sequential Logic, which employs a full evaluation strategy, i.e., to evaluate a term every one of its atomic propositions is evaluated causing its possible side effects to occur. We then turn to Short-Circuit (Left-Sequential) Logic as presented in [BP10b], where the evaluation may be 'short-circuited', thus preventing some, if not all, of the atomic propositions in a term being evaluated. We propose evaluation trees as a natural semantics for both logics and provide axiomatizations for the least identifying variant of each. From this, we define a logic with connectives that prescribe a full evaluation strategy as well as connectives that prescribe a short-circuit evaluation strategy.
MediaWise - Designing a Smart Media Cloud
Venue: arXiv cs.DC 2012
Year: 2012

The MediaWise project aims to expand the scope of existing media delivery systems with novel cloud, personalization and collaboration capabilities that can serve the needs of more users, communities, and businesses. The project develops a MediaWise Cloud platform that supports do-it-yourself creation, search, management, and consumption of multimedia content. The MediaWise Cloud supports pay-as-you-go models and elasticity that are similar to those offered by commercially available cloud services. However, unlike existing commercial CDN services providers such as Limelight Networks and Akamai the MediaWise Cloud require no ownerships of computing infrastructure and instead rely on the public Internet and public cloud services (e.g., commercial cloud storage to store its content). In addition to integrating such public cloud services into a public cloud-based Content Delivery Network, the MediaWise Cloud also provides advanced Quality of Service (QoS) management as required for the delivery of streamed and interactive high resolution multimedia content. In this paper, we give a brief overview of MediaWise Cloud architecture and present a comprehensive discussion on research objectives related to its service components. Finally, we also compare the features supported by the existing CDN services against the envisioned objectives of MediaWise Cloud.
A novel 2.5D approach for interfacing with web applications
Venue: arXiv cs.HC 2012
Year: 2012

Web applications need better user interface to be interactive and attractive. A new approach/concept of dimensional enhancement - 2.5D "a 2D display of a virtual 3D environment", which can be implemented in social networking sites and further in other system applications.
Improving Retrieval Results with discipline-specific Query Expansion
Venue: arXiv cs.IR 2012
Year: 2012

Choosing the right terms to describe an information need is becoming more difficult as the amount of available information increases. Search-Term-Recommendation (STR) systems can help to overcome these problems. This paper evaluates the benefits that may be gained from the use of STRs in Query Expansion (QE). We create 17 STRs, 16 based on specific disciplines and one giving general recommendations, and compare the retrieval performance of these STRs. The main findings are: (1) QE with specific STRs leads to significantly better results than QE with a general STR, (2) QE with specific STRs selected by a heuristic mechanism of topic classification leads to better results than the general STR, however (3) selecting the best matching specific STR in an automatic way is a major challenge of this process.
Comparative Analysis of Peak Correlation Characteristics of Non-Orthogonal Spreading Codes for Wireless Systems
Venue: arXiv cs.IT 2012
Year: 2012

The performance of a CDMA based wireless system is largely dependent on the characteristics of pseudo-random spreading codes. The spreading codes should be carefully chosen to ensure highest possible peak value of auto-correlation function and lower correlation peaks (side-lobes) at non-zero time-shifts. Simultaneously, zero cross-correlation value at all time shifts is required in order to eliminate the effect of multiple access interference at the receiver. But no such code family exists which possess both characteristics simultaneously. That's why an exhaustive effort has been made in this paper to evaluate the peak correlation characteristics of various non-orthogonal spreading codes and suggest a suitable solution.
An Intercell Interference Model based on Scheduling for Future Generation Wireless Networks (Part 1 and Part 2)
Venue: arXiv cs.IT 2012
Year: 2012

This technical report is divided into two parts. The first part of the technical report presents a novel framework for modeling the uplink and downlink intercell interference (ICI) in a multiuser cellular network. The proposed framework assists in quantifying the impact of various fading channel models and multiuser scheduling schemes on the uplink and downlink ICI. Firstly, we derive a semi-analytical expression for the distribution of the location of the scheduled user in a given cell considering a wide range of scheduling schemes. Based on this, we derive the distribution and moment generating function (MGF) of the ICI considering a single interfering cell. Consequently, we determine the MGF of the cumulative ICI observed from all interfering cells and derive explicit MGF expressions for three typical fading models. Finally, we utilize the obtained expressions to evaluate important network performance metrics such as the outage probability, ergodic capacity and average fairness numerically. Monte-Carlo simulation results are provided to demonstrate the efficacy of the derived analytical expressions {\bf The first part of the technical report is currently submitted to IEEE Transactions on Wireless Communications}. The second part of the technical report deals with the statistical modeling of uplink inter-cell interference (ICI) considering greedy scheduling with power adaptation based on channel conditions. The derived model is utilized to evaluate important network performance metrics such as ergodic capacity, average fairness and average power preservation numerically. In parallel to the literature, we have shown that greedy scheduling with power adaptation reduces the ICI, average power consumption of users, and enhances the average fairness among users, compared to the case without power adaptation.
Q-STAR:A Perceptual Video Quality Model Considering Impact of Spatial, Temporal, and Amplitude Resolutions
Venue: arXiv cs.MM 2012
Year: 2012

In this paper, we investigate the impact of spatial, temporal and amplitude resolution (STAR) on the perceptual quality of a compressed video. Subjective quality tests were carried out on a mobile device. Seven source sequences are included in the tests and for each source sequence we have 27 test configurations generated by JSVM encoder (3 QP levels, 3 spatial resolutions, and 3 temporal resolutions), resulting a total of 189 processed video sequences (PVSs). Videos coded at different spatial resolutions are displayed at the full screen size of the mobile platform. Subjective data reveal that the impact of spatial resolution (SR), temporal resolution (TR) and quantization stepsize (QS) can each be captured by a function with a single content-dependent parameter. The joint impact of SR, TR and QS can be accurately modeled by the product of these three functions with only three parameters. We further find that the quality decay rates with SR and QS, respectively are independent of TR, and likewise, the decay rate with TR is independent of SR and QS, respectively. However, there is a significant interaction between the effects of SR and QS. The overall quality model is further validated on five other datasets with very high accuracy. The complete model correlates well with the subjective ratings with a Pearson Correlation Coefficient (PCC) of 0.991.
On the Exact BER of Bit-Wise Demodulators for One-Dimensional Constellations
Venue: arXiv cs.IT 2012
Year: 2012

The optimal bit-wise demodulator for M-ary pulse amplitude modulation (PAM) over the additive white Gaussian noise channel is analyzed in terms of uncoded bit-error rate (BER). New closed-form BER expressions for 4-PAM with any labeling are developed. Moreover, closed-form BER expressions for 11 out of 23 possible bit patterns for 8-PAM are presented, which enable us to obtain the BER for 8-PAM with some of the most popular labelings, including the binary reflected Gray code and the natural binary code. Numerical results show that, regardless of the labeling, there is no difference between the optimal demodulator and the symbol-wise demodulator for any BER of practical interest (below 0.1).
The cost of continuity: performance of iterative solvers on isogeometric finite elements
Venue: arXiv cs.NA 2012
Year: 2012

In this paper we study how the use of a more continuous set of basis functions affects the cost of solving systems of linear equations resulting from a discretized Galerkin weak form. Specifically, we compare performance of linear solvers when discretizing using $C^0$ B-splines, which span traditional finite element spaces, and $C^{p-1}$ B-splines, which represent maximum continuity. We provide theoretical estimates for the increase in cost of the matrix-vector product as well as for the construction and application of black-box preconditioners. We accompany these estimates with numerical results and study their sensitivity to various grid parameters such as element size $h$ and polynomial order of approximation $p$. Finally, we present timing results for a range of preconditioning options for the Laplace problem. We conclude that the matrix-vector product operation is at most $\slfrac{33p^2}{8}$ times more expensive for the more continuous space, although for moderately low $p$, this number is significantly reduced. Moreover, if static condensation is not employed, this number further reduces to at most a value of 8, even for high $p$. Preconditioning options can be up to $p^3$ times more expensive to setup, although this difference significantly decreases for some popular preconditioners such as Incomplete LU factorization.
Mechanisms for Risk Averse Agents, Without Loss
Venue: arXiv cs.GT 2012
Year: 2012

Auctions in which agents' payoffs are random variables have received increased attention in recent years. In particular, recent work in algorithmic mechanism design has produced mechanisms employing internal randomization, partly in response to limitations on deterministic mechanisms imposed by computational complexity. For many of these mechanisms, which are often referred to as truthful-in-expectation, incentive compatibility is contingent on the assumption that agents are risk-neutral. These mechanisms have been criticized on the grounds that this assumption is too strong, because "real" agents are typically risk averse, and moreover their precise attitude towards risk is typically unknown a-priori. In response, researchers in algorithmic mechanism design have sought the design of universally-truthful mechanisms --- mechanisms for which incentive-compatibility makes no assumptions regarding agents' attitudes towards risk. We show that any truthful-in-expectation mechanism can be generically transformed into a mechanism that is incentive compatible even when agents are risk averse, without modifying the mechanism's allocation rule. The transformed mechanism does not require reporting of agents' risk profiles. Equivalently, our result can be stated as follows: Every (randomized) allocation rule that is implementable in dominant strategies when players are risk neutral is also implementable when players are endowed with an arbitrary and unknown concave utility function for money.
Social Networks, Functional Differentiation of Society, and Data Protection
Venue: arXiv cs.CY 2012
Year: 2012

Most scholars, politicians, and activists are following individualistic theories of privacy and data protection. In contrast, some of the pioneers of the data protection legislation in Germany like Adalbert Podlech, Paul J. Müller, and Ulrich Dammann used a systems theory approach. Following Niklas Luhmann, the aim of data protection is (1) maintaining the functional differentiation of society against the threats posed by the possibilities of modern information processing, and (2) countering undue information power by organized social players. It could be, therefore, no surprise that the first data protection law in the German state of Hesse contained rules to protect the individual as well as the balance of power between the legislative and the executive body of the state. Social networks like Facebook or Google+ do not only endanger their users by exposing them to other users or the public. They constitute, first and foremost, a threat to society as a whole by collecting information about individuals, groups, and organizations from different social systems and combining them in a centralized data bank. They transgress the boundaries between social systems that act as a shield against total visibility and transparency of the individual and protect the freedom and the autonomy of the people. Without enforcing structural limitations on the organizational use of collected data by the social network itself or the company behind it, social networks pose the worst totalitarian peril for western societies since the fall of the Soviet Union.
Statistical Consistency of Finite-dimensional Unregularized Linear Classification
Venue: arXiv cs.LG 2012
Year: 2012

This manuscript studies statistical properties of linear classifiers obtained through minimization of an unregularized convex risk over a finite sample. Although the results are explicitly finite-dimensional, inputs may be passed through feature maps; in this way, in addition to treating the consistency of logistic regression, this analysis also handles boosting over a finite weak learning class with, for instance, the exponential, logistic, and hinge losses. In this finite-dimensional setting, it is still possible to fit arbitrary decision boundaries: scaling the complexity of the weak learning class with the sample size leads to the optimal classification risk almost surely.
Speeding Up Planning in Markov Decision Processes via Automatically Constructed Abstractions
Venue: arXiv cs.AI 2012
Year: 2012

In this paper, we consider planning in stochastic shortest path (SSP) problems, a subclass of Markov Decision Problems (MDP). We focus on medium-size problems whose state space can be fully enumerated. This problem has numerous important applications, such as navigation and planning under uncertainty. We propose a new approach for constructing a multi-level hierarchy of progressively simpler abstractions of the original problem. Once computed, the hierarchy can be used to speed up planning by first finding a policy for the most abstract level and then recursively refining it into a solution to the original problem. This approach is fully automated and delivers a speed-up of two orders of magnitude over a state-of-the-art MDP solver on sample problems while returning near-optimal solutions. We also prove theoretical bounds on the loss of solution optimality resulting from the use of abstractions.
Greedy Block Coordinate Descent for Large Scale Gaussian Process Regression
Venue: arXiv cs.LG 2012
Year: 2012

We propose a variable decomposition algorithm -greedy block coordinate descent (GBCD)- in order to make dense Gaussian process regression practical for large scale problems. GBCD breaks a large scale optimization into a series of small sub-problems. The challenge in variable decomposition algorithms is the identification of a subproblem (the active set of variables) that yields the largest improvement. We analyze the limitations of existing methods and cast the active set selection into a zero-norm constrained optimization problem that we solve using greedy methods. By directly estimating the decrease in the objective function, we obtain not only efficient approximate solutions for GBCD, but we are also able to demonstrate that the method is globally convergent. Empirical comparisons against competing dense methods like Conjugate Gradient or SMO show that GBCD is an order of magnitude faster. Comparisons against sparse GP methods show that GBCD is both accurate and capable of handling datasets of 100,000 samples or more.
Bounds on the Bethe Free Energy for Gaussian Networks
Venue: arXiv cs.LG 2012
Year: 2012

We address the problem of computing approximate marginals in Gaussian probabilistic models by using mean field and fractional Bethe approximations. As an extension of Welling and Teh (2001), we define the Gaussian fractional Bethe free energy in terms of the moment parameters of the approximate marginals and derive an upper and lower bound for it. We give necessary conditions for the Gaussian fractional Bethe free energies to be bounded from below. It turns out that the bounding condition is the same as the pairwise normalizability condition derived by Malioutov et al. (2006) as a sufficient condition for the convergence of the message passing algorithm. By giving a counterexample, we disprove the conjecture in Welling and Teh (2001): even when the Bethe free energy is not bounded from below, it can possess a local minimum to which the minimization algorithms can converge.
Bayesian network learning by compiling to weighted MAX-SAT
Venue: arXiv cs.AI 2012
Year: 2012

The problem of learning discrete Bayesian networks from data is encoded as a weighted MAX-SAT problem and the MaxWalkSat local search algorithm is used to address it. For each dataset, the per-variable summands of the (BDeu) marginal likelihood for different choices of parents ('family scores') are computed prior to applying MaxWalkSat. Each permissible choice of parents for each variable is encoded as a distinct propositional atom and the associated family score encoded as a 'soft' weighted single-literal clause. Two approaches to enforcing acyclicity are considered: either by encoding the ancestor relation or by attaching a total order to each graph and encoding that. The latter approach gives better results. Learning experiments have been conducted on 21 synthetic datasets sampled from 7 BNs. The largest dataset has 10,000 datapoints and 60 variables producing (for the 'ancestor' encoding) a weighted CNF input file with 19,932 atoms and 269,367 clauses. For most datasets, MaxWalkSat quickly finds BNs with higher BDeu score than the 'true' BN. The effect of adding prior information is assessed. It is further shown that Bayesian model averaging can be effected by collecting BNs generated during the search.
Learning When to Take Advice: A Statistical Test for Achieving A Correlated Equilibrium
Venue: arXiv cs.GT 2012
Year: 2012

We study a multiagent learning problem where agents can either learn via repeated interactions, or can follow the advice of a mediator who suggests possible actions to take. We present an algorithmthat each agent can use so that, with high probability, they can verify whether or not the mediator's advice is useful. In particular, if the mediator's advice is useful then agents will reach a correlated equilibrium, but if the mediator's advice is not useful, then agents are not harmed by using our test, and can fall back to their original learning algorithm. We then generalize our algorithm and show that in the limit it always correctly verifies the mediator's advice.
Improving Gradient Estimation by Incorporating Sensor Data
Venue: arXiv cs.AI 2012
Year: 2012

An efficient policy search algorithm should estimate the local gradient of the objective function, with respect to the policy parameters, from as few trials as possible. Whereas most policy search methods estimate this gradient by observing the rewards obtained during policy trials, we show, both theoretically and empirically, that taking into account the sensor data as well gives better gradient estimates and hence faster learning. The reason is that rewards obtained during policy execution vary from trial to trial due to noise in the environment; sensor data, which correlates with the noise, can be used to partially correct for this variation, resulting in an estimatorwith lower variance.
Learning Hidden Markov Models for Regression using Path Aggregation
Venue: arXiv cs.LG 2012
Year: 2012

We consider the task of learning mappings from sequential data to real-valued responses. We present and evaluate an approach to learning a type of hidden Markov model (HMM) for regression. The learning process involves inferring the structure and parameters of a conventional HMM, while simultaneously learning a regression model that maps features that characterize paths through the model to continuous responses. Our results, in both synthetic and biological domains, demonstrate the value of jointly learning the two components of our approach.
Observation Subset Selection as Local Compilation of Performance Profiles
Venue: arXiv cs.AI 2012
Year: 2012

Deciding what to sense is a crucial task, made harder by dependencies and by a nonadditive utility function. We develop approximation algorithms for selecting an optimal set of measurements, under a dependency structure modeled by a tree-shaped Bayesian network (BN). Our approach is a generalization of composing anytime algorithm represented by conditional performance profiles. This is done by relaxing the input monotonicity assumption, and extending the local compilation technique to more general classes of performance profiles (PPs). We apply the extended scheme to selecting a subset of measurements for choosing a maximum expectation variable in a binary valued BN, and for minimizing the worst variance in a Gaussian BN.