OpenRaiser/Intern-Atlas huggingface dataset:
An Approach to Select Cost-Effective Risk Countermeasures Exemplified in CORAS
Venue: arXiv cs.OH 2013
Year: 2013

Risk is unavoidable in business and risk management is needed amongst others to set up good security policies. Once the risks are evaluated, the next step is to decide how they should be treated. This involves managers making decisions on proper countermeasures to be implemented to mitigate the risks. The countermeasure expenditure, together with its ability to mitigate risks, is factors that affect the selection. While many approaches have been proposed to perform risk analysis, there has been less focus on delivering the prescriptive and specific information that managers require to select cost-effective countermeasures. This paper proposes a generic approach to integrate the cost assessment into risk analysis to aid such decision making. The approach makes use of a risk model which has been annotated with potential countermeasures, estimates for their cost and effect. A calculus is then employed to reason about this model in order to support decision in terms of decision diagrams. We exemplify the instantiation of the generic approach in the CORAS method for security risk analysis.
Curves on Flat Tori and Analog Source-Channel Codes
Venue: arXiv cs.IT 2013
Year: 2013

In this paper we consider the problem of transmitting a continuous alphabet discrete-time source over an AWGN channel. We propose a constructive scheme based on a set of curves on the surface of a N-dimensional sphere. Our approach shows that the design of good codes for this communication problem is related to geometrical properties of spherical codes and projections of N-dimensional rectangular lattices. Theoretical comparisons with some previous works in terms of the mean square error as a function of the channel SNR as well as simulations are provided.
Low-power Secret-key Agreement over OFDM
Venue: arXiv cs.IT 2013
Year: 2013

Information-theoretic secret-key agreement is perhaps the most practically feasible mechanism that provides unconditional security at the physical layer to date. In this paper, we consider the problem of secret-key agreement by sharing randomness at low power over an orthogonal frequency division multiplexing (OFDM) link, in the presence of an eavesdropper. The low power assumption greatly simplifies the design of the randomness sharing scheme, even in a fading channel scenario. We assess the performance of the proposed system in terms of secrecy key rate and show that a practical approach to key sharing is obtained by using low-density parity check (LDPC) codes for information reconciliation. Numerical results confirm the merits of the proposed approach as a feasible and practical solution. Moreover, the outage formulation allows to implement secret-key agreement even when only statistical knowledge of the eavesdropper channel is available.
Failure Data Analysis of HPC Systems
Venue: arXiv cs.DC 2013
Year: 2013

Continuous availability of HPC systems built from commodity components have become a primary concern as system size grows to thousands of processors. In this paper, we present the analysis of 8-24 months of real failure data collected from three HPC systems at the National Center for Supercomputing Applications (NCSA) during 2001-2004. The results show that the availability is 98.7-99.8% and most outages are due to software halts. On the other hand, the downtime are mostly contributed by hardware halts or scheduled maintenance. We also used failure clustering analysis to identify several correlated failures.
An Optical Watermarking Solution for Color Personal Identification Pictures
Venue: arXiv cs.MM 2013
Year: 2013

This paper presents a new approach for embedding authentication information into image on printed materials based on optical projection technique. Our experimental setup consists of two parts, one is a common camera, and the other is a LCD projector, which project a pattern on personnel's body (especially on the face). The pattern, generated by a computer, act as the illumination light source with sinusoidal distribution and it is also the watermark signal. For a color image, the watermark is embedded into the blue channel. While we take pictures (256 *256 and 512*512, 567*390 pixels, respectively), an invisible mark is embedded directly into magnitude oefficients of Discrete Fourier transform (DFT) at exposure moment. Both optical an d digital correlation is suitable for detection of this type of watermark. The decoded watermark is a set of concentric circles or sectors in the DFT domain (middle frequencies region) which is robust to photographing, printing and scanning. The unlawful people modify or replace the original photograph, and make fake passport (drivers' license and so on). Experiments show, it is difficult to forge certificates in which a watermark was embedded by our projector-camera combination based on analogue watermark method rather than classical digital method.
Opportunistic Wireless Energy Harvesting in Cognitive Radio Networks
Venue: arXiv cs.NI 2013
Year: 2013

Wireless networks can be self-sustaining by harvesting energy from ambient radio-frequency (RF) signals. Recently, researchers have made progress on designing efficient circuits and devices for RF energy harvesting suitable for low-power wireless applications. Motivated by this and building upon the classic cognitive radio (CR) network model, this paper proposes a novel method for wireless networks coexisting where low-power mobiles in a secondary network, called secondary transmitters (STs), harvest ambient RF energy from transmissions by nearby active transmitters in a primary network, called primary transmitters (PTs), while opportunistically accessing the spectrum licensed to the primary network. We consider a stochastic-geometry model in which PTs and STs are distributed as independent homogeneous Poisson point processes (HPPPs) and communicate with their intended receivers at fixed distances. Each PT is associated with a guard zone to protect its intended receiver from ST's interference, and at the same time delivers RF energy to STs located in its harvesting zone. Based on the proposed model, we analyze the transmission probability of STs and the resulting spatial throughput of the secondary network. The optimal transmission power and density of STs are derived for maximizing the secondary network throughput under the given outage-probability constraints in the two coexisting networks, which reveal key insights to the optimal network design. Finally, we show that our analytical result can be generally applied to a non-CR setup, where distributed wireless power chargers are deployed to power coexisting wireless transmitters in a sensor network.
Towards a Semantic-based Approach for Modeling Regulatory Documents in Building Industry
Venue: arXiv cs.CL 2013
Year: 2013

Regulations in the Building Industry are becoming increasingly complex and involve more than one technical area. They cover products, components and project implementation. They also play an important role to ensure the quality of a building, and to minimize its environmental impact. In this paper, we are particularly interested in the modeling of the regulatory constraints derived from the Technical Guides issued by CSTB and used to validate Technical Assessments. We first describe our approach for modeling regulatory constraints in the SBVR language, and formalizing them in the SPARQL language. Second, we describe how we model the processes of compliance checking described in the CSTB Technical Guides. Third, we show how we implement these processes to assist industrials in drafting Technical Documents in order to acquire a Technical Assessment; a compliance report is automatically generated to explain the compliance or noncompliance of this Technical Documents.
Probabilistic Frame Induction
Venue: arXiv cs.CL 2013
Year: 2013

In natural-language discourse, related events tend to appear near each other to describe a larger scenario. Such structures can be formalized by the notion of a frame (a.k.a. template), which comprises a set of related events and prototypical participants and event transitions. Identifying frames is a prerequisite for information extraction and natural language generation, and is usually done manually. Methods for inducing frames have been proposed recently, but they typically use ad hoc procedures and are difficult to diagnose or extend. In this paper, we propose the first probabilistic approach to frame induction, which incorporates frames, events, participants as latent topics and learns those frame and event transitions that best explain the text. The number of frames is inferred by a novel application of a split-merge method from syntactic parsing. In end-to-end evaluations from text to induced frames and extracted facts, our method produced state-of-the-art results while substantially reducing engineering effort.
NLP and CALL: integration is working
Venue: arXiv cs.CL 2013
Year: 2013

In the first part of this article, we explore the background of computer-assisted learning from its beginnings in the early XIXth century and the first teaching machines, founded on theories of learning, at the start of the XXth century. With the arrival of the computer, it became possible to offer language learners different types of language activities such as comprehension tasks, simulations, etc. However, these have limits that cannot be overcome without some contribution from the field of natural language processing (NLP). In what follows, we examine the challenges faced and the issues raised by integrating NLP into CALL. We hope to demonstrate that the key to success in integrating NLP into CALL is to be found in multidisciplinary work between computer experts, linguists, language teachers, didacticians and NLP specialists.
Graphical Models for Preference and Utility
Venue: arXiv cs.AI 2013
Year: 2013

Probabilistic independence can dramatically simplify the task of eliciting, representing, and computing with probabilities in large domains. A key technique in achieving these benefits is the idea of graphical modeling. We survey existing notions of independence for utility functions in a multi-attribute space, and suggest that these can be used to achieve similar advantages. Our new results concern conditional additive independence, which we show always has a perfect representation as separation in an undirected graph (a Markov network). Conditional additive independencies entail a particular functional for the utility function that is analogous to a product decomposition of a probability function, and confers analogous benefits. This functional form has been utilized in the Bayesian network and influence diagram literature, but generally without an explanation in terms of independence. The functional form yields a decomposition of the utility function that can greatly speed up expected utility calculations, particularly when the utility graph has a similar topology to the probabilistic network being used.
Automating Computer Bottleneck Detection with Belief Nets
Venue: arXiv cs.AI 2013
Year: 2013

We describe an application of belief networks to the diagnosis of bottlenecks in computer systems. The technique relies on a high-level functional model of the interaction between application workloads, the Windows NT operating system, and system hardware. Given a workload description, the model predicts the values of observable system counters available from the Windows NT performance monitoring tool. Uncertainty in workloads, predictions, and counter values are characterized with Gaussian distributions. During diagnostic inference, we use observed performance monitor values to find the most probable assignment to the workload parameters. In this paper we provide some background on automated bottleneck detection, describe the structure of the system model, and discuss empirical procedures for model calibration and verification. Part of the calibration process includes generating a dataset to estimate a multivariate Gaussian error model. Initial results in diagnosing bottlenecks are presented.
Practical Model-Based Diagnosis with Qualitative Possibilistic Uncertainty
Venue: arXiv cs.AI 2013
Year: 2013

An approach to fault isolation that exploits vastly incomplete models is presented. It relies on separate descriptions of each component behavior, together with the links between them, which enables focusing of the reasoning to the relevant part of the system. As normal observations do not need explanation, the behavior of the components is limited to anomaly propagation. Diagnostic solutions are disorders (fault modes or abnormal signatures) that are consistent with the observations, as well as abductive explanations. An ordinal representation of uncertainty based on possibility theory provides a simple exception-tolerant description of the component behaviors. We can for instance distinguish between effects that are more or less certainly present (or absent) and effects that are more or less certainly present (or absent) when a given anomaly is present. A realistic example illustrates the benefits of this approach.
Decision Flexibility
Venue: arXiv cs.AI 2013
Year: 2013

The development of new methods and representations for temporal decision-making requires a principled basis for characterizing and measuring the flexibility of decision strategies in the face of uncertainty. Our goal in this paper is to provide a framework - not a theory - for observing how decision policies behave in the face of informational perturbations, to gain clues as to how they might behave in the face of unanticipated, possibly unarticulated uncertainties. To this end, we find it beneficial to distinguish between two types of uncertainty: "Small World" and "Large World" uncertainty. The first type can be resolved by posing an unambiguous question to a "clairvoyant," and is anchored on some well-defined aspect of a decision frame. The second type is more troublesome, yet it is often of greater interest when we address the issue of flexibility; this type of uncertainty can be resolved only by consulting a "psychic." We next observe that one approach to flexibility used in the economics literature is already implicitly accounted for in the Maximum Expected Utility (MEU) principle from decision theory. Though simple, the observation establishes the context for a more illuminating notion of flexibility, what we term flexibility with respect to information revelation. We show how to perform flexibility analysis of a static (i.e., single period) decision problem using a simple example, and we observe that the most flexible alternative thus identified is not necessarily the MEU alternative. We extend our analysis for a dynamic (i.e., multi-period) model, and we demonstrate how to calculate the value of flexibility for decision strategies that allow downstream revision of an upstream commitment decision.
A Constraint Satisfaction Approach to Decision under Uncertainty
Venue: arXiv cs.AI 2013
Year: 2013

The Constraint Satisfaction Problem (CSP) framework offers a simple and sound basis for representing and solving simple decision problems, without uncertainty. This paper is devoted to an extension of the CSP framework enabling us to deal with some decisions problems under uncertainty. This extension relies on a differentiation between the agent-controllable decision variables and the uncontrollable parameters whose values depend on the occurrence of uncertain events. The uncertainty on the values of the parameters is assumed to be given under the form of a probability distribution. Two algorithms are given, for computing respectively decisions solving the problem with a maximal probability, and conditional decisions mapping the largest possible amount of possible cases to actual decisions.
Efficient Decision-Theoretic Planning: Techniques and Empirical Analysis
Venue: arXiv cs.AI 2013
Year: 2013

This paper discusses techniques for performing efficient decision-theoretic planning. We give an overview of the DRIPS decision-theoretic refinement planning system, which uses abstraction to efficiently identify optimal plans. We present techniques for automatically generating search control information, which can significantly improve the planner's performance. We evaluate the efficiency of DRIPS both with and without the search control rules on a complex medical planning problem and compare its performance to that of a branch-and-bound decision tree algorithm.
Fuzzy Logic and Probability
Venue: arXiv cs.AI 2013
Year: 2013

In this paper we deal with a new approach to probabilistic reasoning in a logical framework. Nearly almost all logics of probability that have been proposed in the literature are based on classical two-valued logic. After making clear the differences between fuzzy logic and probability theory, here we propose a {em fuzzy} logic of probability for which completeness results (in a probabilistic sense) are provided. The main idea behind this approach is that probability values of crisp propositions can be understood as truth-values of some suitable fuzzy propositions associated to the crisp ones. Moreover, suggestions and examples of how to extend the formalism to cope with conditional probabilities and with other uncertainty formalisms are also provided.
Reasoning, Metareasoning, and Mathematical Truth: Studies of Theorem Proving under Limited Resources
Venue: arXiv cs.AI 2013
Year: 2013

In earlier work, we introduced flexible inference and decision-theoretic metareasoning to address the intractability of normative inference. Here, rather than pursuing the task of computing beliefs and actions with decision models composed of distinctions about uncertain events, we examine methods for inferring beliefs about mathematical truth before an automated theorem prover completes a proof. We employ a Bayesian analysis to update belief in truth, given theorem-proving progress, and show how decision-theoretic methods can be used to determine the value of continuing to deliberate versus taking immediate action in time-critical situations.
Cautious Propagation in Bayesian Networks
Venue: arXiv cs.AI 2013
Year: 2013

Consider the situation where some evidence e has been entered to a Bayesian network. When performing conflict analysis, sensitivity analysis, or when answering questions like "What if the finding on X had been y instead of x?" you need probabilities P (e'| h), where e' is a subset of e, and h is a configuration of a (possibly empty) set of variables. Cautious propagation is a modification of HUGIN propagation into a Shafer-Shenoy-like architecture. It is less efficient than HUGIN propagation; however, it provides easy access to P (e'| h) for a great deal of relevant subsets e'.
Probabilistic Exploration in Planning while Learning
Venue: arXiv cs.AI 2013
Year: 2013

Sequential decision tasks with incomplete information are characterized by the exploration problem; namely the trade-off between further exploration for learning more about the environment and immediate exploitation of the accrued information for decision-making. Within artificial intelligence, there has been an increasing interest in studying planning-while-learning algorithms for these decision tasks. In this paper we focus on the exploration problem in reinforcement learning and Q-learning in particular. The existing exploration strategies for Q-learning are of a heuristic nature and they exhibit limited scaleability in tasks with large (or infinite) state and action spaces. Efficient experimentation is needed for resolving uncertainties when possible plans are compared (i.e. exploration). The experimentation should be sufficient for selecting with statistical significance a locally optimal plan (i.e. exploitation). For this purpose, we develop a probabilistic hill-climbing algorithm that uses a statistical selection procedure to decide how much exploration is needed for selecting a plan which is, with arbitrarily high probability, arbitrarily close to a locally optimal one. Due to its generality the algorithm can be employed for the exploration strategy of robust Q-learning. An experiment on a relatively complex control task shows that the proposed exploration strategy performs better than a typical exploration strategy.
A New Pruning Method for Solving Decision Trees and Game Trees
Venue: arXiv cs.AI 2013
Year: 2013

The main goal of this paper is to describe a new pruning method for solving decision trees and game trees. The pruning method for decision trees suggests a slight variant of decision trees that we call scenario trees. In scenario trees, we do not need a conditional probability for each edge emanating from a chance node. Instead, we require a joint probability for each path from the root node to a leaf node. We compare the pruning method to the traditional rollback method for decision trees and game trees. For problems that require Bayesian revision of probabilities, a scenario tree representation with the pruning method is more efficient than a decision tree representation with the rollback method. For game trees, the pruning method is more efficient than the rollback method.
Causal Inference in the Presence of Latent Variables and Selection Bias
Venue: arXiv cs.AI 2013
Year: 2013

We show that there is a general, informative and reliable procedure for discovering causal relations when, for all the investigator knows, both latent variables and selection bias may be at work. Given information about conditional independence and dependence relations between measured variables, even when latent variables and selection bias may be present, there are sufficient conditions for reliably concluding that there is a causal path from one variable to another, and sufficient conditions for reliably concluding when no such causal path exists.
A Polynomial Algorithm for Computing the Optimal Repair Strategy in a System with Independent Component Failures
Venue: arXiv cs.AI 2013
Year: 2013

The goal of diagnosis is to compute good repair strategies in response to anomalous system behavior. In a decision theoretic framework, a good repair strategy has low expected cost. In a general formulation of the problem, the computation of the optimal (lowest expected cost) repair strategy for a system with multiple faults is intractable. In this paper, we consider an interesting and natural restriction on the behavior of the system being diagnosed: (a) the system exhibits faulty behavior if and only if one or more components is malfunctioning. (b) The failures of the system components are independent. Given this restriction on system behavior, we develop a polynomial time algorithm for computing the optimal repair strategy. We then go on to introduce a system hierarchy and the notion of inspecting (testing) components before repair. We develop a linear time algorithm for computing an optimal repair strategy for the hierarchical system which includes both repair and inspection.
Generating Explanations for Evidential Reasoning
Venue: arXiv cs.AI 2013
Year: 2013

In this paper, we present two methods to provide explanations for reasoning with belief functions in the valuation-based systems. One approach, inspired by Strat's method, is based on sensitivity analysis, but its computation is simpler thus easier to implement than Strat's. The other one is to examine the impact of evidence on the conclusion based on the measure of the information content in the evidence. We show the property of additivity for the pieces of evidence that are conditional independent within the context of the valuation-based systems. We will give an example to show how these approaches are applied in an evidential network.