OpenRaiser/Intern-Atlas huggingface dataset:
On Variable Density Compressive Sampling
Venue: arXiv cs.IT 2011
Year: 2011

We advocate an optimization procedure for variable density sampling in the context of compressed sensing. In this perspective, we introduce a minimization problem for the coherence between the sparsity and sensing bases, whose solution provides an optimized sampling profile. This minimization problem is solved with the use of convex optimization algorithms. We also propose a refinement of our technique when prior information is available on the signal support in the sparsity basis. The effectiveness of the method is confirmed by numerical experiments. Our results also provide a theoretical underpinning to state-of-the-art variable density Fourier sampling procedures used in magnetic resonance imaging.
Using Stereoscopic 3D Technologies for the Diagnosis and Treatment of Amblyopia in Children
Venue: arXiv cs.HC 2011
Year: 2011

The 3D4Amb project aims at developing a system based on the stereoscopic 3D techonlogy, like the NVIDIA 3D Vision, for the diagnosis and treatment of amblyopia in young children. It exploits the active shutter technology to provide binocular vision, i.e. to show different images to the amblyotic (or lazy) and the normal eye. It would allow easy diagnosis of amblyopia and its treatment by means of interactive games or other entertainment activities. It should not suffer from the compliance problems of the classical treatment, it is suitable to domestic use, and it could at least partially substitute occlusion or patching of the normal eye.
Admissible and Restrained Revision
Venue: arXiv cs.AI 2011
Year: 2011

As partial justification of their framework for iterated belief revision Darwiche and Pearl convincingly argued against Boutiliers natural revision and provided a prototypical revision operator that fits into their scheme. We show that the Darwiche-Pearl arguments lead naturally to the acceptance of a smaller class of operators which we refer to as admissible. Admissible revision ensures that the penultimate input is not ignored completely, thereby eliminating natural revision, but includes the Darwiche-Pearl operator, Nayaks lexicographic revision operator, and a newly introduced operator called restrained revision. We demonstrate that restrained revision is the most conservative of admissible revision operators, effecting as few changes as possible, while lexicographic revision is the least conservative, and point out that restrained revision can also be viewed as a composite operator, consisting of natural revision preceded by an application of a "backwards revision" operator previously studied by Papini. Finally, we propose the establishment of a principled approach for choosing an appropriate revision operator in different contexts and discuss future work.
Incremental Versus Optimized Network Design
Venue: arXiv cs.NI 2011
Year: 2011

Even though the problem of network topology design is often studied as a "clean-slate" optimization, in practice most service-provider and enterprise networks are designed incrementally over time. This evolutionary process is driven by changes in the underlying parameters and constraints (the "environment") and it aims to minimize the modification cost after each change in the environment. In this paper, we first formulate the incremental design approach (in three variations), and compare that with the more traditional optimized design approach in which the objective is to minimize the total network cost. We evaluate the cost overhead and evolvability of incremental design under two network expansion models (random and gradual), comparing incremental and optimized networks in terms of cost, topological similarity, delay and robustness. We find that even though incremental design has some cost overhead, that overhead does not increase as the network grows. Also, it is less costly to evolve an existing network than to design it "from scratch" as long as the network expansion factor is less than a critical value.
Fault Tolerant Boolean Satisfiability
Venue: arXiv cs.AI 2011
Year: 2011

A delta-model is a satisfying assignment of a Boolean formula for which any small alteration, such as a single bit flip, can be repaired by flips to some small number of other bits, yielding a new satisfying assignment. These satisfying assignments represent robust solutions to optimization problems (e.g., scheduling) where it is possible to recover from unforeseen events (e.g., a resource becoming unavailable). The concept of delta-models was introduced by Ginsberg, Parkes and Roy (AAAI 1998), where it was proved that finding delta-models for general Boolean formulas is NP-complete. In this paper, we extend that result by studying the complexity of finding delta-models for classes of Boolean formulas which are known to have polynomial time satisfiability solvers. In particular, we examine 2-SAT, Horn-SAT, Affine-SAT, dual-Horn-SAT, 0-valid and 1-valid SAT. We see a wide variation in the complexity of finding delta-models, e.g., while 2-SAT and Affine-SAT have polynomial time tests for delta-models, testing whether a Horn-SAT formula has one is NP-complete.
Cognitive Principles in Robust Multimodal Interpretation
Venue: arXiv cs.AI 2011
Year: 2011

Multimodal conversational interfaces provide a natural means for users to communicate with computer systems through multiple modalities such as speech and gesture. To build effective multimodal interfaces, automated interpretation of user multimodal inputs is important. Inspired by the previous investigation on cognitive status in multimodal human machine interaction, we have developed a greedy algorithm for interpreting user referring expressions (i.e., multimodal reference resolution). This algorithm incorporates the cognitive principles of Conversational Implicature and Givenness Hierarchy and applies constraints from various sources (e.g., temporal, semantic, and contextual) to resolve references. Our empirical results have shown the advantage of this algorithm in efficiently resolving a variety of user references. Because of its simplicity and generality, this approach has the potential to improve the robustness of multimodal input interpretation.
Distributed Algorithms for Consensus and Coordination in the Presence of Packet-Dropping Communication Links - Part II: Coefficients of Ergodicity Analysis Approach
Venue: arXiv cs.SY 2011
Year: 2011

In this two-part paper, we consider multicomponent systems in which each component can iteratively exchange information with other components in its neighborhood in order to compute, in a distributed fashion, the average of the components' initial values or some other quantity of interest (i.e., some function of these initial values). In particular, we study an iterative algorithm for computing the average of the initial values of the nodes. In this algorithm, each component maintains two sets of variables that are updated via two identical linear iterations. The average of the initial values of the nodes can be asymptotically computed by each node as the ratio of two of the variables it maintains. In the first part of this paper, we show how the update rules for the two sets of variables can be enhanced so that the algorithm becomes tolerant to communication links that may drop packets, independently among them and independently between different transmission times. In this second part, by rewriting the collective dynamics of both iterations, we show that the resulting system is mathematically equivalent to a finite inhomogenous Markov chain whose transition matrix takes one of finitely many values at each step. Then, by using e a coefficients of ergodicity approach, a method commonly used for convergence analysis of Markov chains, we prove convergence of the robustified consensus scheme. The analysis suggests that similar convergence should hold under more general conditions as well.
The Statistical Inefficiency of Sparse Coding for Images (or, One Gabor to Rule them All)
Venue: arXiv cs.CV 2011
Year: 2011

Sparse coding is a proven principle for learning compact representations of images. However, sparse coding by itself often leads to very redundant dictionaries. With images, this often takes the form of similar edge detectors which are replicated many times at various positions, scales and orientations. An immediate consequence of this observation is that the estimation of the dictionary components is not statistically efficient. We propose a factored model in which factors of variation (e.g. position, scale and orientation) are untangled from the underlying Gabor-like filters. There is so much redundancy in sparse codes for natural images that our model requires only a single dictionary element (a Gabor-like edge detector) to outperform standard sparse coding. Our model scales naturally to arbitrary-sized images while achieving much greater statistical efficiency during learning. We validate this claim with a number of experiments showing, in part, superior compression of out-of-sample data using a sparse coding dictionary learned with only a single image.
A Novel Two-stage Entropy-based Robust Cooperative Spectrum Sensing Scheme with Two-bit Decision in Cognitive Radio
Venue: arXiv cs.IT 2011
Year: 2011

Spectrum sensing is a key problem in cognitive radio. However, traditional detectors become ineffective when noise uncertainty is severe. It is shown that the entropy of Gauss white noise is constant in the frequency domain, and a robust detector based on the entropy of spectrum amplitude was proposed. In this paper a novel detector is proposed based on the entropy of spectrum power density, and its performance is better than the previous scheme with less computational complexity. Furthermore, to improve the reliability of the detection, a two-stage entropy-based cooperative spectrum sensing scheme using two-bit decision is proposed, and simulation results show its superior performance with relatively low computational complexity.
A Fuzzy Co-Clustering approach for Clickstream Data Pattern
Venue: arXiv cs.IR 2011
Year: 2011

Web Usage mining is a very important tool to extract the hidden business intelligence data from large databases. The extracted information provides the organizations with the ability to produce results more effectively to improve their businesses and increasing of sales. Co-clustering is a powerful bipartition technique which identifies group of users associated to group of web pages. These associations are quantified to reveal the users' interest in the different web pages' clusters. In this paper, Fuzzy Co-Clustering algorithm is proposed for clickstream data to identify the subset of users of similar navigational behavior /interest over a subset of web pages of a website. Targeting the users group for various promotional activities is an important aspect of marketing practices. Experiments are conducted on real dataset to prove the efficiency of proposed algorithm. The results and findings of this algorithm could be used to enhance the marketing strategy for directing marketing, advertisements for web based businesses and so on.
An overview of metrics-based approaches to support software components reusability assessment
Venue: arXiv cs.SE 2011
Year: 2011

Objective: To present an overview on the current state of the art concerning metrics-based quality evaluation of software components and component assemblies. Method: Comparison of several approaches available in the literature, using a framework comprising several aspects, such as scope, intent, definition technique, and maturity. Results: The identification of common shortcomings of current approaches, such as ambiguity in definition, lack of adequacy of the specifying formalisms and insufficient validation of current quality models and metrics for software components. Conclusions: Quality evaluation of components and component-based infrastructures presents new challenges to the Experimental Software Engineering community.
Explanation-Based Auditing
Venue: arXiv cs.DB 2011
Year: 2011

To comply with emerging privacy laws and regulations, it has become common for applications like electronic health records systems (EHRs) to collect access logs, which record each time a user (e.g., a hospital employee) accesses a piece of sensitive data (e.g., a patient record). Using the access log, it is easy to answer simple queries (e.g., Who accessed Alice's medical record?), but this often does not provide enough information. In addition to learning who accessed their medical records, patients will likely want to understand why each access occurred. In this paper, we introduce the problem of generating explanations for individual records in an access log. The problem is motivated by user-centric auditing applications, and it also provides a novel approach to misuse detection. We develop a framework for modeling explanations which is based on a fundamental observation: For certain classes of databases, including EHRs, the reason for most data accesses can be inferred from data stored elsewhere in the database. For example, if Alice has an appointment with Dr. Dave, this information is stored in the database, and it explains why Dr. Dave looked at Alice's record. Large numbers of data accesses can be explained using general forms called explanation templates. Rather than requiring an administrator to manually specify explanation templates, we propose a set of algorithms for automatically discovering frequent templates from the database (i.e., those that explain a large number of accesses). We also propose techniques for inferring collaborative user groups, which can be used to enhance the quality of the discovered explanations. Finally, we have evaluated our proposed techniques using an access log and data from the University of Michigan Health System. Our results demonstrate that in practice we can provide explanations for over 94% of data accesses in the log.
Human-powered Sorts and Joins
Venue: arXiv cs.DB 2011
Year: 2011

Crowdsourcing markets like Amazon's Mechanical Turk (MTurk) make it possible to task people with small jobs, such as labeling images or looking up phone numbers, via a programmatic interface. MTurk tasks for processing datasets with humans are currently designed with significant reimplementation of common workflows and ad-hoc selection of parameters such as price to pay per task. We describe how we have integrated crowds into a declarative workflow engine called Qurk to reduce the burden on workflow designers. In this paper, we focus on how to use humans to compare items for sorting and joining data, two of the most common operations in DBMSs. We describe our basic query interface and the user interface of the tasks we post to MTurk. We also propose a number of optimizations, including task batching, replacing pairwise comparisons with numerical ratings, and pre-filtering tables before joining them, which dramatically reduce the overall cost of running sorts and joins on the crowd. In an experiment joining two sets of images, we reduce the overall cost from $67 in a naive implementation to about $3, without substantially affecting accuracy or latency. In an end-to-end experiment, we reduced cost by a factor of 14.5.
Distributed Selfish Load Balancing with Weights and Speeds
Venue: arXiv cs.DC 2011
Year: 2011

In this paper we consider neighborhood load balancing in the context of selfish clients. We assume that a network of n processors and m tasks is given. The processors may have different speeds and the tasks may have different weights. Every task is controlled by a selfish user. The objective of the user is to allocate his/her task to a processor with minimum load. We revisit the concurrent probabilistic protocol introduced in [6], which works in sequential rounds. In each round every task is allowed to query the load of one randomly chosen neighboring processor. If that load is smaller the task will migrate to that processor with a suitably chosen probability. Using techniques from spectral graph theory we obtain upper bounds on the expected convergence time towards approximate and exact Nash equilibria that are significantly better than the previous results in [6]. We show results for uniform tasks on non-uniform processors and the general case where the tasks have different weights and the machines have speeds. To the best of our knowledge, these are the first results for this general setting.
Feature-Aware Verification
Venue: arXiv cs.SE 2011
Year: 2011

A software product line is a set of software products that are distinguished in terms of features (i.e., end-user--visible units of behavior). Feature interactions ---situations in which the combination of features leads to emergent and possibly critical behavior--- are a major source of failures in software product lines. We explore how feature-aware verification can improve the automatic detection of feature interactions in software product lines. Feature-aware verification uses product-line verification techniques and supports the specification of feature properties along with the features in separate and composable units. It integrates the technique of variability encoding to verify a product line without generating and checking a possibly exponential number of feature combinations. We developed the tool suite SPLverifier for feature-aware verification, which is based on standard model-checking technology. We applied it to an e-mail system that incorporates domain knowledge of AT&T. We found that feature interactions can be detected automatically based on specifications that have only feature-local knowledge, and that variability encoding significantly improves the verification performance when proving the absence of interactions.
Wireless Bidirectional Relaying and Latin Squares
Venue: arXiv cs.IT 2011
Year: 2011

The design of modulation schemes for the physical layer network-coded two way relaying scenario is considered with the protocol which employs two phases: Multiple access (MA) Phase and Broadcast (BC) Phase. It was observed by Koike-Akino et al. that adaptively changing the network coding map used at the relay according to the channel conditions greatly reduces the impact of multiple access interference which occurs at the relay during the MA Phase and all these network coding maps should satisfy a requirement called the {\it exclusive law}. We highlight the issues associated with the scheme proposed by Koike-Akino et al. and propose a scheme which solves these issues. We show that every network coding map that satisfies the exclusive law is representable by a Latin Square and conversely, and this relationship can be used to get the network coding maps satisfying the exclusive law. Using the structural properties of the Latin Squares for a given set of parameters, the problem of finding all the required maps is reduced to finding a small set of maps for $M-$PSK constellations. This is achieved using the notions of isotopic and transposed Latin Squares. Even though, the completability of partially filled $M \times M$ Latin Square using $M$ symbols is an open problem, two specific cases where such a completion is always possible are identified and explicit construction procedures are provided. The Latin Squares constructed using the first procedure, helps towards reducing the total number of network coding maps used. The second procedure helps in the construction of certain Latin Squares for $M$-PSK signal set from the Latin squares obtained for $M/2$-PSK signal set.
A Nuclear Magnetic Resonance Implementation of a Classical Deutsch-Jozsa Algorithm
Venue: arXiv cs.ET 2011
Year: 2011

Nuclear magnetic resonance (NMR) has been widely used as a demonstrative medium for showcasing the ability for quantum computations to outperform classical ones. A large number of such experiments performed have been implementations of the Deutsch-Jozsa algorithm. It is known, however, that in some cases the Deutsch-Jozsa problem can be solved classically using as many queries to the black-box as in the quantum solution. In this paper we describe experiments in which we take the contrasting approach of using NMR as a classical computing medium, treating the nuclear spin vectors classically and utilising an alternative embedding of bits into the physical medium. This allows us to determine the actual Boolean function computed by the black-box for the n=1,2 cases, as opposed to only the nature (balanced or constant) as conventional quantum algorithms do. Discussion of these experiments leads to some clarification of the complications surrounding the comparison of different quantum algorithms, particularly black-box type algorithms.
Dealing with large schema sets in mobile SOS-based applications
Venue: arXiv cs.DB 2011
Year: 2011

Although the adoption of OGC Web Services for server, desktop and web applications has been successful, its penetration in mobile devices has been slow. One of the main reasons is the performance problems associated with XML processing as it consumes a lot of memory and processing time, which are scarce resources in a mobile device. In this paper we propose an algorithm to generate efficient code for XML data binding for mobile SOS-based applications. The algorithm take advantage of the fact that individual implementations use only some portions of the standards' schemas, which allows the simplification of large XML schema sets in an application-specific manner by using a subset of XML instance files conforming to these schemas.
Universal Codes for the Gaussian MAC via Spatial Coupling
Venue: arXiv cs.IT 2011
Year: 2011

We consider transmission of two independent and separately encoded sources over a two-user binary-input Gaussian multiple-access channel. The channel gains are assumed to be unknown at the transmitter and the goal is to design an encoder-decoder pair that achieves reliable communication for all channel gains where this is theoretically possible. We call such a system \emph{universal} with respect to the channel gains. Kudekar et al. recently showed that terminated low-density parity-check convolutional codes (a.k.a. spatially-coupled low-density parity-check ensembles) have belief-propagation thresholds that approach their maximum a-posteriori thresholds. This was proven for binary erasure channels and shown empirically for binary memoryless symmetric channels. It was conjectured that the principle of spatial coupling is very general and the phenomenon of threshold saturation applies to a very broad class of graphical models. In this work, we derive an area theorem for the joint decoder and empirically show that threshold saturation occurs for this problem. As a result, we demonstrate near-universal performance for this problem using the proposed spatially-coupled coding system.
Joint Routing, Scheduling And Power Control For Multihop Wireless Networks With Multiple Antennas
Venue: arXiv cs.NI 2011
Year: 2011

We consider the problem of Joint Routing, Scheduling and Power-control (JRSP) problem for multihop wireless networks (MHWN) with multiple antennas. We extend the problem and a (sub-optimal) heuristic solution method for JRSP in MHWN with single antennas. We present an iterative scheme to calculate link capacities(achievable rates) in the interference environment of the network using SINR model. We then present the algorithm for solving the JRSP problem. This completes a feasible system model for MHWN when nodes have multiple antennas. We show that the gain we achieve by using multiple antennas in the network is linear both in optimal performance as well as heuristic algorithmic performance.