OpenRaiser/Intern-Atlas huggingface dataset:
Maximum Margin Bayesian Networks
Venue: arXiv cs.LG 2012
Year: 2012

We consider the problem of learning Bayesian network classifiers that maximize the marginover a set of classification variables. We find that this problem is harder for Bayesian networks than for undirected graphical models like maximum margin Markov networks. The main difficulty is that the parameters in a Bayesian network must satisfy additional normalization constraints that an undirected graphical model need not respect. These additional constraints complicate the optimization task. Nevertheless, we derive an effective training algorithm that solves the maximum margin training problem for a range of Bayesian network topologies, and converges to an approximate solution for arbitrary network topologies. Experimental results show that the method can demonstrate improved generalization performance over Markov networks when the directed graphical structure encodes relevant knowledge. In practice, the training technique allows one to combine prior knowledge expressed as a directed (causal) model with state of the art discriminative learning methods.
Unstructuring User Preferences: Efficient Non-Parametric Utility Revelation
Venue: arXiv cs.AI 2012
Year: 2012

Tackling the problem of ordinal preference revelation and reasoning, we propose a novel methodology for generating an ordinal utility function from a set of qualitative preference statements. To the best of our knowledge, our proposal constitutes the first nonparametric solution for this problem that is both efficient and semantically sound. Our initial experiments provide strong evidence for practical effectiveness of our approach.
On the optimality of tree-reweighted max-product message-passing
Venue: arXiv cs.AI 2012
Year: 2012

Tree-reweighted max-product (TRW) message passing is a modified form of the ordinary max-product algorithm for attempting to find minimal energy configurations in Markov random field with cycles. For a TRW fixed point satisfying the strong tree agreement condition, the algorithm outputs a configuration that is provably optimal. In this paper, we focus on the case of binary variables with pairwise couplings, and establish stronger properties of TRW fixed points that satisfy only the milder condition of weak tree agreement (WTA). First, we demonstrate how it is possible to identify part of the optimal solution|i.e., a provably optimal solution for a subset of nodes| without knowing a complete solution. Second, we show that for submodular functions, a WTA fixed point always yields a globally optimal solution. We establish that for binary variables, any WTA fixed point always achieves the global maximum of the linear programming relaxation underlying the TRW method.
A Revision-Based Approach to Resolving Conflicting Information
Venue: arXiv cs.AI 2012
Year: 2012

In this paper, we propose a revision-based approach for conflict resolution by generalizing the Disjunctive Maxi-Adjustment (DMA) approach (Benferhat et al. 2004). Revision operators can be classified into two different families: the model-based ones and the formula-based ones. So the revision-based approach has two different versions according to which family of revision operators is chosen. Two particular revision operators are considered, one is the Dalal's revision operator, which is a model-based revision operator, and the other is the cardinality-maximal based revision operator, which is a formulabased revision operator. When the Dalal's revision operator is chosen, the revision-based approach is independent of the syntactic form in each stratum and it captures some notion of minimal change. When the cardinalitymaximal based revision operator is chosen, the revision-based approach is equivalent to the DMA approach. We also show that both approaches are computationally easier than the DMA approach.
Two-Way Latent Grouping Model for User Preference Prediction
Venue: arXiv cs.IR 2012
Year: 2012

We introduce a novel latent grouping model for predicting the relevance of a new document to a user. The model assumes a latent group structure for both users and documents. We compared the model against a state-of-the-art method, the User Rating Profile model, where only users have a latent group structure. We estimate both models by Gibbs sampling. The new method predicts relevance more accurately for new documents that have few known ratings. The reason is that generalization over documents then becomes necessary and hence the twoway grouping is profitable.
Efficient Test Selection in Active Diagnosis via Entropy Approximation
Venue: arXiv cs.AI 2012
Year: 2012

We consider the problem of diagnosing faults in a system represented by a Bayesian network, where diagnosis corresponds to recovering the most likely state of unobserved nodes given the outcomes of tests (observed nodes). Finding an optimal subset of tests in this setting is intractable in general. We show that it is difficult even to compute the next most-informative test using greedy test selection, as it involves several entropy terms whose exact computation is intractable. We propose an approximate approach that utilizes the loopy belief propagation infrastructure to simultaneously compute approximations of marginal and conditional entropies on multiple subsets of nodes. We apply our method to fault diagnosis in computer networks, and show the algorithm to be very effective on realistic Internet-like topologies. We also provide theoretical justification for the greedy test selection approach, along with some performance guarantees.
Algorithms for High-Performance Networking in the Presence of Obstacles
Venue: arXiv cs.NI 2012
Year: 2012

This work develops novel algorithms for high-performance networking in the presence of obstacles based on a method for communicating via ultrasonic rays reflected at the obstacles. The rays are curves determined by the variable speed of sound and initial conditions and we develop ultrasonic ray models based on a system of differential equations. We present new parallel algorithms and software for shape and trajectory reconstruction of moving obstacles and show how the reconstructed reflection point of a ray at an obstacle is a natural router for messages between the ray's transmitter and receiver and discuss the advantages of the new architecture. We discuss how the new algorithms and software improve the performance and properties of the network architecture.
Object Recognition with Multi-Scale Pyramidal Pooling Networks
Venue: arXiv cs.CV 2012
Year: 2012

We present a Multi-Scale Pyramidal Pooling Network, featuring a novel pyramidal pooling layer at multiple scales and a novel encoding layer. Thanks to the former the network does not require all images of a given classification task to be of equal size. The encoding layer improves generalisation performance in comparison to similar neural network architectures, especially when training data is scarce. We evaluate and compare our system to convolutional neural networks and state-of-the-art computer vision methods on various benchmark datasets. We also present results on industrial steel defect classification, where existing architectures are not applicable because of the constraint on equally sized input images. The proposed architecture can be seen as a fully supervised hierarchical bag-of-features extension that is trained online and can be fine-tuned for any given task.
A Novel Ergodic Capacity Analysis of Diversity Combining and Multihop Transmission Systems over Generalized Composite Fading Channels
Venue: arXiv cs.IT 2012
Year: 2012

Ergodic capacity is an important performance measure associated with reliable communication at the highest rate at which information can be sent over the channel with a negligible probability of error. In the shadow of this definition, diversity receivers (such as selection combining, equal-gain combining and maximal-ratio combining) and transmission techniques (such as cascaded fading channels, amplify-and-forward multihop transmission) are deployed in mitigating various performance impairing effects such as fading and shadowing in digital radio communication links. However, the exact analysis of ergodic capacity is in general not always possible for all of these forms of diversity receivers and transmission techniques over generalized composite fading environments due to it's mathematical intractability. In the literature, published papers concerning the exact analysis of ergodic capacity have been therefore scarce (i.e., only [1] and [2]) when compared to those concerning the exact analysis of average symbol error probability. In addition, they are essentially targeting to the ergodic capacity of the maximal ratio combining diversity receivers and are not readily applicable to the capacity analysis of the other diversity combiners / transmission techniques. In this paper, we propose a novel moment generating function-based approach for the exact ergodic capacity analysis of both diversity receivers and transmission techniques over generalized composite fading environments. As such, we demonstrate how to simultaneously treat the ergodic capacity analysis of all forms of both diversity receivers and multihop transmission techniques.
Introspective Pushdown Analysis of Higher-Order Programs
Venue: arXiv cs.PL 2012
Year: 2012

In the static analysis of functional programs, pushdown flow analysis and abstract garbage collection skirt just inside the boundaries of soundness and decidability. Alone, each method reduces analysis times and boosts precision by orders of magnitude. This work illuminates and conquers the theoretical challenges that stand in the way of combining the power of these techniques. The challenge in marrying these techniques is not subtle: computing the reachable control states of a pushdown system relies on limiting access during transition to the top of the stack; abstract garbage collection, on the other hand, needs full access to the entire stack to compute a root set, just as concrete collection does. \emph{Introspective} pushdown systems resolve this conflict. Introspective pushdown systems provide enough access to the stack to allow abstract garbage collection, but they remain restricted enough to compute control-state reachability, thereby enabling the sound and precise product of pushdown analysis and abstract garbage collection. Experiments reveal synergistic interplay between the techniques, and the fusion demonstrates "better-than-both-worlds" precision.
Differential Spectrum of Some Power Functions With Low Differential Uniformity
Venue: arXiv cs.CR 2012
Year: 2012

In this paper, for an odd prime $p$, the differential spectrum of the power function $x^{\frac{p^k+1}{2}}$ in $\mathbb{F}_{p^n}$ is calculated. For an odd prime $p$ such that $p\equiv 3\bmod 4$ and odd $n$ with $k|n$, the differential spectrum of the power function $x^{\frac{p^n+1}{p^k+1}+\frac{p^n-1}{2}}$ in $\mathbb{F}_{p^n}$ is also derived. From their differential spectrums, the differential uniformities of these two power functions are determined. We also find some new power functions having low differential uniformity.
Reconstruction Conjecture for Graphs Isomorphic to Cube of a Tree
Venue: arXiv cs.DM 2012
Year: 2012

This paper proves the reconstruction conjecture for graphs which are isomorphic to the cube of a tree. The proof uses the reconstructibility of trees from their peripheral vertex deleted subgraphs. The main result follows from (i) characterization of the cube of a tree (ii) recognizability of the cube of a tree (iii) uniqueness of tree as a cube root of a graph G, except when G is a complete graph (iv) reconstructibility of trees from their peripheral vertex deleted subgraphs.
Spreadsheets and Long Term Corporate Survival
Venue: arXiv cs.CY 2012
Year: 2012

We have conducted an empirical investigation into the long term survival rates of some small but representative samples of the 30,000 largest UK limited companies. These companies were either a control or known to have used, or been interested in the use of, spreadsheets, spreadsheet based monte carlo simulation software, other spreadsheet and decision analysis software and/or related management training. We show that there is a material and statistically significant increase in the long term survival rate of all of these groups of companies compared to the control.
Spatial And Spectral Quality Evaluation Based On Edges Regions Of Satellite Image Fusion
Venue: arXiv cs.CV 2012
Year: 2012

The Quality of image fusion is an essential determinant of the value of processing images fusion for many applications. Spatial and spectral qualities are the two important indexes that used to evaluate the quality of any fused image. However, the jury is still out of fused image's benefits if it compared with its original images. In addition, there is a lack of measures for assessing the objective quality of the spatial resolution for the fusion methods. Therefore, an objective quality of the spatial resolution assessment for fusion images is required. Most important details of the image are in edges regions, but most standards of image estimation do not depend upon specifying the edges in the image and measuring their edges. However, they depend upon the general estimation or estimating the uniform region, so this study deals with new method proposed to estimate the spatial resolution by Contrast Statistical Analysis (CSA) depending upon calculating the contrast of the edge, non edge regions and the rate for the edges regions. Specifying the edges in the image is made by using Soble operator with different threshold values. In addition, estimating the color distortion added by image fusion based on Histogram Analysis of the edge brightness values of all RGB-color bands and Lcomponent.
New Parameters of Linear Codes Expressing Security Performance of Universal Secure Network Coding
Venue: arXiv cs.IT 2012
Year: 2012

The universal secure network coding presented by Silva et al. realizes secure and reliable transmission of a secret message over any underlying network code, by using maximum rank distance codes. Inspired by their result, this paper considers the secure network coding based on arbitrary linear codes, and investigates its security performance and error correction capability that are guaranteed independently of the underlying network code. The security performance and error correction capability are said to be universal when they are independent of underlying network codes. This paper introduces new code parameters, the relative dimension/intersection profile (RDIP) and the relative generalized rank weight (RGRW) of linear codes. We reveal that the universal security performance and universal error correction capability of secure network coding are expressed in terms of the RDIP and RGRW of linear codes. The security and error correction of existing schemes are also analyzed as applications of the RDIP and RGRW.
dup -- Explicit un-sharing in Haskell
Venue: arXiv cs.PL 2012
Year: 2012

We propose two operations to prevent sharing in Haskell that do not require modifying the data generating code, demonstrate their use and usefulness, and compare them to other approaches to preventing sharing. Our claims are supported by a formal semantics and a prototype implementation.
Design of PIC12F675 Microcontroller Based Data Acquisition System for Slowly Varying Signals
Venue: arXiv cs.AR 2012
Year: 2012

The present paper describes the design of a cost effective, better resolution data acquisition system (DAS) which is compatible to most of the PC and laptops. A low cost DAS has been designed using PIC12F675 having 4-channel analog input with 10-bit resolution for the monitoring of slowly varying signals. The DAS so designed is interfaced to the serial port of the PC. Firmware is written in Basic using Oshonsoft PIC IDE and burn to the microcontroller by using PICkit2 programmer. An application program is also developed using Visual Basic 6 which allows to display the waveform of the signal(s) and simultaneously the data also can be saved into the hard disk of the computer for future use and analysis.
Constellation Shaping for Bit-Interleaved LDPC Coded APSK
Venue: arXiv cs.IT 2012
Year: 2012

An energy-efficient approach is presented for shaping a bit-interleaved low-density parity-check (LDPC) coded amplitude phase-shift keying (APSK) system. A subset of the interleaved bits output by a binary LDPC encoder are passed through a nonlinear shaping encoder whose output is more likely to be a zero than a one. The "shaping" bits are used to select from among a plurality of subconstellations, while the unshaped bits are used to select the symbol within the subconstellation. Because the shaping bits are biased, symbols from lower-energy subconstellations are selected more frequently than those from higher-energy subconstellations. An iterative decoder shares information among the LDPC decoder, APSK demapper, and shaping decoder. Information rates are computed for a discrete set of APSK ring radii and shaping bit probabilities, and the optimal combination of these parameters is identified for the additive white Gaussian noise (AWGN) channel. With the assistance of extrinsic-information transfer (EXIT) charts, the degree distributions of the LDPC code are optimized for use with the shaped APSK constellation. Simulation results show that the combination of shaping, degree-distribution optimization, and iterative decoding can achieve a gain in excess of 1 dB in AWGN at a rate of 3 bits/symbol compared with a system that does not use shaping, uses an unoptimized code from the DVB-S2 standard, and does not iterate between decoder and demodulator.
NAAS: Negotiation Automation Architecture with Buyer's Behavior Pattern Prediction Component
Venue: arXiv cs.MA 2012
Year: 2012

In this era of "Services" everywhere, with the explosive growth of E-Commerce and B2B transactions, there is a pressing need for the development of intelligent negotiation systems which consists of feasible architecture, a reliable framework and flexible multi agent based protocols developed in specialized negotiation languages with complete semantics and support for message passing between the buyers and sellers. This is possible using web services on the internet. The key issue is negotiation and its automation. In this paper we review the classical negotiation methods and some of the existing architectures and frameworks. We are proposing here a new combinatory framework and architecture, NAAS. The key feature in this framework is a component for prediction or probabilistic behavior pattern recognition of a buyer, along with the other classical approaches of negotiation frameworks and architectures. Negotiation is practically very complex activity to automate without human intervention so in the future we also intend to develop a new protocol which will facilitate automation of all the types of negotiation strategies like bargaining, bidding, auctions, under our NAAS framework.
A Hybrid Forecast of Exchange Rate based on Discrete Grey-Markov and Grey Neural Network Model
Venue: arXiv cs.CE 2012
Year: 2012

We propose a hybrid forecast model based on discrete grey-fuzzy Markov and grey neural network model and show that our hybrid model can improve much more the performance of forecast than traditional grey-Markov model and neural network models. Our simulation results are shown that our hybrid forecast method with the combinational weight based on optimal grey relation degree method is better than the hybrid model with combinational weight based minimization of error-squared criterion.
Improvement of ISOM by using filter
Venue: arXiv cs.MM 2012
Year: 2012

Image compression helps in storing the transmitted data in proficient way by decreasing its redundancy. This technique helps in transferring more digital or multimedia data over internet as it increases the storage space. It is important to maintain the image quality even if it is compressed to certain extent. Depend upon this the image compression is classified into two categories : lossy and lossless image compression. There are many lossy digital image compression techniques exists. Among this Incremental Self Organizing Map is a familiar one. The good pictures quality can be retrieved if image denoising technique is used for compression and also provides better compression ratio. Image denoising is an important pre-processing step for many image analysis and computer vision system. It refers to the task of recovering a good estimate of the true image from a degraded observation without altering and changing useful structure in the image such as discontinuities and edges. Many approaches have been proposed to remove the noise effectively while preserving the original image details and features as much as possible. This paper proposes a technique for image compression using Incremental Self Organizing Map (ISOM) with Discret Wavelet Transform (DWT) by applying filtering techniques which play a crucial role in enhancing the quality of a reconstructed image. The experimental result shows that the proposed technique obtained better compression ratio value.
Comparative Study for Inference of Hidden Classes in Stochastic Block Models
Venue: arXiv cs.LG 2012
Year: 2012

Inference of hidden classes in stochastic block model is a classical problem with important applications. Most commonly used methods for this problem involve na\"ıve mean field approaches or heuristic spectral methods. Recently, belief propagation was proposed for this problem. In this contribution we perform a comparative study between the three methods on synthetically created networks. We show that belief propagation shows much better performance when compared to na\"ıve mean field and spectral approaches. This applies to accuracy, computational efficiency and the tendency to overfit the data.
Optimal Multi-Robot Path Planning with LTL Constraints: Guaranteeing Correctness Through Synchronization
Venue: arXiv cs.RO 2012
Year: 2012

In this paper, we consider the automated planning of optimal paths for a robotic team satisfying a high level mission specification. Each robot in the team is modeled as a weighted transition system where the weights have associated deviation values that capture the non-determinism in the traveling times of the robot during its deployment. The mission is given as a Linear Temporal Logic (LTL) formula over a set of propositions satisfied at the regions of the environment. Additionally, we have an optimizing proposition capturing some particular task that must be repeatedly completed by the team. The goal is to minimize the maximum time between successive satisfying instances of the optimizing proposition while guaranteeing that the mission is satisfied even under non-deterministic traveling times. Our method relies on the communication capabilities of the robots to guarantee correctness and maintain performance during deployment. After computing a set of optimal satisfying paths for the members of the team, we also compute a set of synchronization sequences for each robot to ensure that the LTL formula is never violated during deployment. We implement and experimentally evaluate our method considering a persistent monitoring task in a road network environment.