OpenRaiser/Intern-Atlas huggingface dataset:
Extraction of Facial Feature Points Using Cumulative Histogram
Venue: arXiv cs.CV 2012
Year: 2012

This paper proposes a novel adaptive algorithm to extract facial feature points automatically such as eyebrows corners, eyes corners, nostrils, nose tip, and mouth corners in frontal view faces, which is based on cumulative histogram approach by varying different threshold values. At first, the method adopts the Viola-Jones face detector to detect the location of face and also crops the face region in an image. From the concept of the human face structure, the six relevant regions such as right eyebrow, left eyebrow, right eye, left eye, nose, and mouth areas are cropped in a face image. Then the histogram of each cropped relevant region is computed and its cumulative histogram value is employed by varying different threshold values to create a new filtering image in an adaptive way. The connected component of interested area for each relevant filtering image is indicated our respective feature region. A simple linear search algorithm for eyebrows, eyes and mouth filtering images and contour algorithm for nose filtering image are applied to extract our desired corner points automatically. The method was tested on a large BioID frontal face database in different illuminations, expressions and lighting conditions and the experimental results have achieved average success rates of 95.27%.
Synchronizing Automata on Quasi Eulerian Digraph
Venue: arXiv cs.FL 2012
Year: 2012

In 1964 Černý conjectured that each $n$-state synchronizing automaton posesses a reset word of length at most $(n-1)^2$. From the other side the best known upper bound on the reset length (minimum length of reset words) is cubic in $n$. Thus the main problem here is to prove quadratic (in $n$) upper bounds. Since 1964, this problem has been solved for few special classes of \sa. One of this result is due to Kari \cite{Ka03} for automata with Eulerian digraphs. In this paper we introduce a new approach to prove quadratic upper bounds and explain it in terms of Markov chains and Perron-Frobenius theories. Using this approach we obtain a quadratic upper bound for a generalization of Eulerian automata.
On the Impact of Information Technologies on Society: an Historical Perspective through the Game of Chess
Venue: arXiv cs.OH 2012
Year: 2012

The game of chess as always been viewed as an iconic representation of intellectual prowess. Since the very beginning of computer science, the challenge of being able to program a computer capable of playing chess and beating humans has been alive and used both as a mark to measure hardware/software progresses and as an ongoing programming challenge leading to numerous discoveries. In the early days of computer science it was a topic for specialists. But as computers were democratized, and the strength of chess engines began to increase, chess players started to appropriate to themselves these new tools. We show how these interactions between the world of chess and information technologies have been herald of broader social impacts of information technologies. The game of chess, and more broadly the world of chess (chess players, literature, computer softwares and websites dedicated to chess, etc.), turns out to be a surprisingly and particularly sharp indicator of the changes induced in our everyday life by the information technologies. Moreover, in the same way that chess is a modelization of war that captures the raw features of strategic thinking, chess world can be seen as small society making the study of the information technologies impact easier to analyze and to grasp.
Three new sensitivity analysis methods for influence diagrams
Venue: arXiv cs.AI 2012
Year: 2012

Performing sensitivity analysis for influence diagrams using the decision circuit framework is particularly convenient, since the partial derivatives with respect to every parameter are readily available [Bhattacharjya and Shachter, 2007; 2008]. In this paper we present three non-linear sensitivity analysis methods that utilize this partial derivative information and therefore do not require re-evaluating the decision situation multiple times. Specifically, we show how to efficiently compare strategies in decision situations, perform sensitivity to risk aversion and compute the value of perfect hedging [Seyller, 2008].
Bayesian Rose Trees
Venue: arXiv cs.LG 2012
Year: 2012

Hierarchical structure is ubiquitous in data across many domains. There are many hierarchical clustering methods, frequently used by domain experts, which strive to discover this structure. However, most of these methods limit discoverable hierarchies to those with binary branching structure. This limitation, while computationally convenient, is often undesirable. In this paper we explore a Bayesian hierarchical clustering algorithm that can produce trees with arbitrary branching structure at each node, known as rose trees. We interpret these trees as mixtures over partitions of a data set, and use a computationally efficient, greedy agglomerative algorithm to find the rose trees which have high marginal likelihood given the data. Lastly, we perform experiments which demonstrate that rose trees are better models of data than the typical binary trees returned by other hierarchical clustering algorithms.
ALARMS: Alerting and Reasoning Management System for Next Generation Aircraft Hazards
Venue: arXiv cs.AI 2012
Year: 2012

The Next Generation Air Transportation System will introduce new, advanced sensor technologies into the cockpit. With the introduction of such systems, the responsibilities of the pilot are expected to dramatically increase. In the ALARMS (ALerting And Reasoning Management System) project for NASA, we focus on a key challenge of this environment, the quick and efficient handling of aircraft sensor alerts. It is infeasible to alert the pilot on the state of all subsystems at all times. Furthermore, there is uncertainty as to the true hazard state despite the evidence of the alerts, and there is uncertainty as to the effect and duration of actions taken to address these alerts. This paper reports on the first steps in the construction of an application designed to handle Next Generation alerts. In ALARMS, we have identified 60 different aircraft subsystems and 20 different underlying hazards. In this paper, we show how a Bayesian network can be used to derive the state of the underlying hazards, based on the sensor input. Then, we propose a framework whereby an automated system can plan to address these hazards in cooperation with the pilot, using a Time-Dependent Markov Process (TMDP). Different hazards and pilot states will call for different alerting automation plans. We demonstrate this emerging application of Bayesian networks and TMDPs to cockpit automation, for a use case where a small number of hazards are present, and analyze the resulting alerting automation policies.
Super-Samples from Kernel Herding
Venue: arXiv cs.LG 2012
Year: 2012

We extend the herding algorithm to continuous spaces by using the kernel trick. The resulting "kernel herding" algorithm is an infinite memory deterministic process that learns to approximate a PDF with a collection of samples. We show that kernel herding decreases the error of expectations of functions in the Hilbert space at a rate O(1/T) which is much faster than the usual O(1/pT) for iid random samples. We illustrate kernel herding by approximating Bayesian predictive distributions.
Playing games against nature: optimal policies for renewable resource allocation
Venue: arXiv cs.AI 2012
Year: 2012

In this paper we introduce a class of Markov decision processes that arise as a natural model for many renewable resource allocation problems. Upon extending results from the inventory control literature, we prove that they admit a closed form solution and we show how to exploit this structure to speed up its computation. We consider the application of the proposed framework to several problems arising in very different domains, and as part of the ongoing effort in the emerging field of Computational Sustainability we discuss in detail its application to the Northern Pacific Halibut marine fishery. Our approach is applied to a model based on real world data, obtaining a policy with a guaranteed lower bound on the utility function that is structurally very different from the one currently employed.
Learning Game Representations from Data Using Rationality Constraints
Venue: arXiv cs.GT 2012
Year: 2012

While game theory is widely used to model strategic interactions, a natural question is where do the game representations come from? One answer is to learn the representations from data. If one wants to learn both the payoffs and the players' strategies, a naive approach is to learn them both directly from the data. This approach ignores the fact the players might be playing reasonably good strategies, so there is a connection between the strategies and the data. The main contribution of this paper is to make this connection while learning. We formulate the learning problem as a weighted constraint satisfaction problem, including constraints both for the fit of the payoffs and strategies to the data and the fit of the strategies to the payoffs. We use quantal response equilibrium as our notion of rationality for quantifying the latter fit. Our results show that incorporating rationality constraints can improve learning when the amount of data is limited.
Formula-Based Probabilistic Inference
Venue: arXiv cs.AI 2012
Year: 2012

Computing the probability of a formula given the probabilities or weights associated with other formulas is a natural extension of logical inference to the probabilistic setting. Surprisingly, this problem has received little attention in the literature to date, particularly considering that it includes many standard inference problems as special cases. In this paper, we propose two algorithms for this problem: formula decomposition and conditioning, which is an exact method, and formula importance sampling, which is an approximate method. The latter is, to our knowledge, the first application of model counting to approximate probabilistic inference. Unlike conventional variable-based algorithms, our algorithms work in the dual realm of logical formulas. Theoretically, we show that our algorithms can greatly improve efficiency by exploiting the structural information in the formulas. Empirically, we show that they are indeed quite powerful, often achieving substantial performance gains over state-of-the-art schemes.
The Hierarchical Dirichlet Process Hidden Semi-Markov Model
Venue: arXiv cs.LG 2012
Year: 2012

There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) as a natural Bayesian nonparametric extension of the traditional HMM. However, in many settings the HDP-HMM's strict Markovian constraints are undesirable, particularly if we wish to learn or encode non-geometric state durations. We can extend the HDP-HMM to capture such structure by drawing upon explicit-duration semi-Markovianity, which has been developed in the parametric setting to allow construction of highly interpretable models that admit natural prior information on state durations. In this paper we introduce the explicitduration HDP-HSMM and develop posterior sampling algorithms for efficient inference in both the direct-assignment and weak-limit approximation settings. We demonstrate the utility of the model and our inference methods on synthetic data as well as experiments on a speaker diarization problem and an example of learning the patterns in Morse code.
BEEM : Bucket Elimination with External Memory
Venue: arXiv cs.AI 2012
Year: 2012

A major limitation of exact inference algorithms for probabilistic graphical models is their extensive memory usage, which often puts real-world problems out of their reach. In this paper we show how we can extend inference algorithms, particularly Bucket Elimination, a special case of cluster (join) tree decomposition, to utilize disk memory. We provide the underlying ideas and show promising empirical results of exactly solving large problems not solvable before.
Approximating Higher-Order Distances Using Random Projections
Venue: arXiv cs.LG 2012
Year: 2012

We provide a simple method and relevant theoretical analysis for efficiently estimating higher-order lp distances. While the analysis mainly focuses on l4, our methodology extends naturally to p = 6,8,10..., (i.e., when p is even). Distance-based methods are popular in machine learning. In large-scale applications, storing, computing, and retrieving the distances can be both space and time prohibitive. Efficient algorithms exist for estimating lp distances if 0 < p <= 2. The task for p > 2 is known to be difficult. Our work partially fills this gap.
Negative Tree Reweighted Belief Propagation
Venue: arXiv cs.LG 2012
Year: 2012

We introduce a new class of lower bounds on the log partition function of a Markov random field which makes use of a reversed Jensen's inequality. In particular, our method approximates the intractable distribution using a linear combination of spanning trees with negative weights. This technique is a lower-bound counterpart to the tree-reweighted belief propagation algorithm, which uses a convex combination of spanning trees with positive weights to provide corresponding upper bounds. We develop algorithms to optimize and tighten the lower bounds over the non-convex set of valid parameter values. Our algorithm generalizes mean field approaches (including naive and structured mean field approximations), which it includes as a limiting case.
The Cost of Troubleshooting Cost Clusters with Inside Information
Venue: arXiv cs.AI 2012
Year: 2012

Decision theoretical troubleshooting is about minimizing the expected cost of solving a certain problem like repairing a complicated man-made device. In this paper we consider situations where you have to take apart some of the device to get access to certain clusters and actions. Specifically, we investigate troubleshooting with independent actions in a tree of clusters where actions inside a cluster cannot be performed before the cluster is opened. The problem is non-trivial because there is a cost associated with opening and closing a cluster. Troubleshooting with independent actions and no clusters can be solved in O(n lg n) time (n being the number of actions) by the well-known "P-over-C" algorithm due to Kadane and Simon, but an efficient and optimal algorithm for a tree cluster model has not yet been found. In this paper we describe a "bottom-up P-over-C" O(n lg n) time algorithm and show that it is optimal when the clusters do not need to be closed to test whether the actions solved the problem.
Learning networks determined by the ratio of prior and data
Venue: arXiv cs.LG 2012
Year: 2012

Recent reports have described that the equivalent sample size (ESS) in a Dirichlet prior plays an important role in learning Bayesian networks. This paper provides an asymptotic analysis of the marginal likelihood score for a Bayesian network. Results show that the ratio of the ESS and sample size determine the penalty of adding arcs in learning Bayesian networks. The number of arcs increases monotonically as the ESS increases; the number of arcs monotonically decreases as the ESS decreases. Furthermore, the marginal likelihood score provides a unified expression of various score metrics by changing prior knowledge.
Truthful Feedback for Sanctioning Reputation Mechanisms
Venue: arXiv cs.GT 2012
Year: 2012

For product rating environments, similar to that of Amazon Reviews, it has been shown that the truthful elicitation of feedback is possible through mechanisms which pay buyer reports contingent on the reports of other buyers. We study whether similar mechanisms can be designed for reputation mechanisms at online auction sites where the buyers' experiences are partially determined by a strategic seller. We show that this is impossible for the basic setting. However, introducing a small prior belief that the seller is a cooperative commitment player leads to a payment scheme with a truthful perfect Bayesian equilibrium.
Investigations on a Pedagogical Calculus of Constructions
Venue: arXiv cs.LO 2012
Year: 2012

In the last few years appeared pedagogical propositional natural deduction systems. In these systems, one must satisfy the pedagogical constraint: the user must give an example of any introduced notion. First we expose the reasons of such a constraint and properties of these "pedagogical" calculi: the absence of negation at logical side, and the "usefulness" feature of terms at computational side (through the Curry-Howard correspondence). Then we construct a simple pedagogical restriction of the calculus of constructions (CC) called CCr. We establish logical limitations of this system, and compare its computational expressiveness to Godel system T. Finally, guided by the logical limitations of CCr, we propose a formal and general definition of what a pedagogical calculus of constructions should be.
Morpes: A Model for Personalized Rendering of Web Content on Mobile Devices
Venue: arXiv cs.HC 2012
Year: 2012

With the tremendous growth in the information communication sector, the mobile phones have become the prime information communication devices. The convergence of traditional telephony with the modern web enabled communication in the mobile devices has made the communication much effective and simpler. As mobile phones are becoming the crucial source of accessing the contents of the World Wide Web which was originally designed for personal computers, has opened up a new challenge of accommodating the web contents in to the smaller mobile devices. This paper proposes an approach towards building a model for rendering the web pages in mobile devices. The proposed model is based on a multi-dimensional web page segment evaluation model. The incorporation of personalization in the proposed model makes the rendering user-centric. The proposed model is validated with a prototype implementation.
Comparative Analysis of Congestion Control Algorithms Using ns-2
Venue: arXiv cs.NI 2012
Year: 2012

In order to curtail the escalating packet loss rates caused by an exponential increase in network traffic, active queue management techniques such as Random Early Detection (RED) have come into picture. Flow Random Early Drop (FRED) keeps state based on instantaneous queue occupancy of a given flow. FRED protects fragile flows by deterministically accepting flows from low bandwidth connections and fixes several shortcomings of RED by computing queue length during both arrival and departure of the packet. Stochastic Fair Queuing (SFQ) ensures fair access to network resources and prevents a busty flow from consuming more than its fair share. In case of (Random Exponential Marking) REM, the key idea is to decouple congestion measure from performance measure (loss, queue length or delay). Stabilized RED (SRED) is another approach of detecting nonresponsive flows. In this paper, we have shown a comparative analysis of throughput, delay and queue length for the various congestion control algorithms RED, SFQ and REM. We also included the comparative analysis of loss rate having different bandwidth for these algorithms.
From Strong Amalgamability to Modularity of Quantifier-Free Interpolation
Venue: arXiv cs.LO 2012
Year: 2012

The use of interpolants in verification is gaining more and more importance. Since theories used in applications are usually obtained as (disjoint) combinations of simpler theories, it is important to modularly re-use interpolation algorithms for the component theories. We show that a sufficient and necessary condition to do this for quantifier-free interpolation is that the component theories have the 'strong (sub-)amalgamation' property. Then, we provide an equivalent syntactic characterization, identify a sufficient condition, and design a combined quantifier-free interpolation algorithm capable of handling both convex and non-convex theories, that subsumes and extends most existing work on combined interpolation.
A Study on the Behavior of a Neural Network for Grouping the Data
Venue: arXiv cs.NE 2012
Year: 2012

One of the frequently stated advantages of neural networks is that they can work effectively with non-normally distributed data. But optimal results are possible with normalized data.In this paper, how normality of the input affects the behaviour of a K-means fast learning artificial neural network(KFLANN) for grouping the data is presented. Basically, the grouping of high dimensional input data is controlled by additional neural network input parameters namely vigilance and tolerance.Neural networks learn faster and give better performance if the input variables are pre-processed before being fed to the input units of the neural network. A common way of dealing with data that is not normally distributed is to perform some form of mathematical transformation on the data that shifts it towards a normal distribution.In a neural network, data preprocessing transforms the data into a format that will be more easily and effectively processed for the purpose of the user. Among various methods, Normalization is one which organizes data for more efficient access. Experimental results on several artificial and synthetic data sets indicate that the groups formed in the data vary with non-normally distributed data and normalized data and also depends on the normalization method used.
Personal data disclosure and data breaches: the customer's viewpoint
Venue: arXiv cs.CR 2012
Year: 2012

Every time the customer (individual or company) has to release personal information to its service provider (e.g., an online store or a cloud computing provider), it faces a trade-off between the benefits gained (enhanced or cheaper services) and the risks it incurs (identity theft and fraudulent uses). The amount of personal information released is the major decision variable in that trade-off problem, and has a proxy in the maximum loss the customer may incur. We find the conditions for a unique optimal solution to exist for that problem as that maximizing the customer's surplus. We also show that the optimal amount of personal information is influenced most by the immediate benefits the customer gets, i.e., the price and the quantity of service offered by the service provider, rather than by maximum loss it may incur. Easy spenders take larger risks with respect to low-spenders, but an increase in price drives customers towards a more careful risk-taking attitude anyway. A major role is also played by the privacy level, which the service provider employs to regulate the benefits released to the customers. We also provide a closed form solution for the limit case of a perfectly secure provider, showing that the results do not differ significantly from those obtained in the general case. The trade-off analysis may be employed by the customer to determine its level of exposure in the relationship with its service provider.
A Fully Distributed Algorithm for Throughput Performance in Wireless Networks
Venue: arXiv cs.NI 2012
Year: 2012

We study link scheduling in wireless networks under stochastic arrival processes of packets, and give an algorithm that achieves stability in the physical (SINR) interference model. The efficiency of such an algorithm is the fraction of the maximum feasible traffic that the algorithm can handle without queues growing indefinitely. Our algorithm achieves two important goals: (i) efficiency is independent of the size of the network, and (ii) the algorithm is fully distributed, i.e., individual nodes need no information about the overall network topology, not even local information.