OpenRaiser/Intern-Atlas huggingface dataset:
Propagation of Belief Functions: A Distributed Approach
Venue: arXiv cs.AI 2013
Year: 2013

In this paper, we describe a scheme for propagating belief functions in certain kinds of trees using only local computations. This scheme generalizes the computational scheme proposed by Shafer and Logan1 for diagnostic trees of the type studied by Gordon and Shortliffe, and the slightly more general scheme given by Shafer for hierarchical evidence. It also generalizes the scheme proposed by Pearl for Bayesian causal trees (see Shenoy and Shafer). Pearl's causal trees and Gordon and Shortliffe's diagnostic trees are both ways of breaking the evidence that bears on a large problem down into smaller items of evidence that bear on smaller parts of the problem so that these smaller problems can be dealt with one at a time. This localization of effort is often essential in order to make the process of probability judgment feasible, both for the person who is making probability judgments and for the machine that is combining them. The basic structure for our scheme is a type of tree that generalizes both Pearl's and Gordon and Shortliffe's trees. Trees of this general type permit localized computation in Pearl's sense. They are based on qualitative judgments of conditional independence. We believe that the scheme we describe here will prove useful in expert systems. It is now clear that the successful propagation of probabilities or certainty factors in expert systems requires much more structure than can be provided in a pure production-system framework. Bayesian schemes, on the other hand, often make unrealistic demands for structure. The propagation of belief functions in trees and more general networks stands on a middle ground where some sensible and useful things can be done. We would like to emphasize that the basic idea of local computation for propagating probabilities is due to Judea Pearl. It is a very innovative idea; we do not believe that it can be found in the Bayesian literature prior to Pearl's work. We see our contribution as extending the usefulness of Pearl's idea by generalizing it from Bayesian probabilities to belief functions. In the next section, we give a brief introduction to belief functions. The notions of qualitative independence for partitions and a qualitative Markov tree are introduced in Section III. Finally, in Section IV, we describe a scheme for propagating belief functions in qualitative Markov trees.
TCLOUD: Challenges and Best Practices for Cloud Computing
Venue: arXiv cs.DC 2013
Year: 2013

Cloud computing has achieved an unbelievable adoption response rate but still its infancy stage is not over. It is an emerging paradigm and amazingly gaining popularity. The size of the market shared of the applications provided by cloud computing is still not much behind the expectations. It provides the organizations with great potential to minimize the cost and maximizes the overall operating effectiveness of computing required by an organization. Despite its growing popularity, still it is faced with security, privacy, and portability issues, which in one or the other way create hurdles in the fast acceptance of this new technology for the computing community. This paper provides a concise all around analysis of the challenges faced by cloud computing community and also presents the solutions available to these challenges.
Maximizing Matching in Double-sided Auctions
Venue: arXiv cs.GT 2013
Year: 2013

In this paper, we introduce a novel, non-recursive, maximal matching algorithm for double auctions, which aims to maximize the amount of commodities to be traded. It differs from the usual equilibrium matching, which clears a market at the equilibrium price. We compare the two algorithms through experimental analyses, showing that the maximal matching algorithm is favored in scenarios where trading volume is a priority and that it may possibly improve allocative efficiency over equilibrium matching as well. A parameterized algorithm that incorporates both maximal matching and equilibrium matching as special cases is also presented to allow flexible control on how much to trade in a double auction.
Buffer Management of Multi-Queue QoS Switches with Class Segregation
Venue: arXiv cs.DS 2013
Year: 2013

In this paper, we focus on buffer management of multi-queue QoS switches in which packets of different values are segregated in different queues. Our model consists of $m$ queues and $m$ packet values $0 < v_{1} < v_{2} < ... < v_{m}$. Recently, Al-Bawani and Souza [IPL 113(4), pp.145-150, 2013] presented an online algorithm GREEDY for buffer management of multi-queue QoS switches with class segregation and showed thatif $m$ queues have the same size, then the competitive ratio of GREEDY is $1+r$, where $r=\max_{1 \leq i \leq m-1} v_{i}/v_{i+1}$. In this paper, we precisely analyze the behavior of GREEDY and show that it is $(1+r)$-competitive for the case that $m$ queues do not necessarily have the same size.
Cloud Computing Research Challenges
Venue: arXiv cs.DC 2013
Year: 2013

In recent times cloud computing has appeared as a new model for hosting and conveying services over the Internet. This model is striking to business vendors as it eradicates the requirement for users to plan in advance, and it permits the organization to start from low level and then add more resources only if there is an increase in the service demand. Even though cloud computing presents greater opportunities not only to information technology industry, but every organization involved in utilizing the computing in one way or the other, it is still in infancy with many problems to be fixed. The paper discusses research challenges in cloud computing.
A Secure Color Image Steganography In Transform Domain
Venue: arXiv cs.CR 2013
Year: 2013

Steganography is the art and science of covert communication. The secret information can be concealed in content such as image, audio, or video. This paper provides a novel image steganography technique to hide both image and key in color cover image using Discrete Wavelet Transform (DWT) and Integer Wavelet Transform (IWT). There is no visual difference between the stego image and the cover image. The extracted image is also similar to the secret image. This is proved by the high PSNR (Peak Signal to Noise Ratio), value for both stego and extracted secret image. The results are compared with the results of similar techniques and it is found that the proposed technique is simple and gives better PSNR values than others.
Performance of Vehicle-to-Vehicle Communication using IEEE 802.11p in Vehicular Ad-hoc Network Environment
Venue: arXiv cs.NI 2013
Year: 2013

Traffic safety applications using vehicle-to-vehicle (V2V) communication is an emerging and promising area within the ITS environment. Many of these applications require real-time communication with high reliability. To meet a real-time deadline, timely and predictable access to the channel is paramount. The medium access method used in 802.11p, CSMA with collision avoidance, does not guarantee channel access before a finite deadline. The well-known property of CSMA is undesirable for critical communications scenarios. The simulation results reveal that a specific vehicle is forced to drop over 80% of its packets because no channel access was possible before the next message was generated. To overcome this problem, we propose to use STDMA for real-time data traffic between vehicles. The real- time properties of STDMA are investigated by means of the highway road simulation scenario, with promising results.
An Inequality Paradigm for Probabilistic Knowledge
Venue: arXiv cs.AI 2013
Year: 2013

We propose an inequality paradigm for probabilistic reasoning based on a logic of upper and lower bounds on conditional probabilities. We investigate a family of probabilistic logics, generalizing the work of Nilsson [14]. We develop a variety of logical notions for probabilistic reasoning, including soundness, completeness justification; and convergence: reduction of a theory to a simpler logical class. We argue that a bound view is especially useful for describing the semantics of probabilistic knowledge representation and for describing intermediate states of probabilistic inference and updating. We show that the Dempster-Shafer theory of evidence is formally identical to a special case of our generalized probabilistic logic. Our paradigm thus incorporates both Bayesian "rule-based" approaches and avowedly non-Bayesian "evidential" approaches such as MYCIN and DempsterShafer. We suggest how to integrate the two "schools", and explore some possibilities for novel synthesis of a variety of ideas in probabilistic reasoning.
Probabilistic Interpretations for MYCIN's Certainty Factors
Venue: arXiv cs.AI 2013
Year: 2013

This paper examines the quantities used by MYCIN to reason with uncertainty, called certainty factors. It is shown that the original definition of certainty factors is inconsistent with the functions used in MYCIN to combine the quantities. This inconsistency is used to argue for a redefinition of certainty factors in terms of the intuitively appealing desiderata associated with the combining functions. It is shown that this redefinition accommodates an unlimited number of probabilistic interpretations. These interpretations are shown to be monotonic transformations of the likelihood ratio p(EIH)/p(El H). The construction of these interpretations provides insight into the assumptions implicit in the certainty factor model. In particular, it is shown that if uncertainty is to be propagated through an inference network in accordance with the desiderata, evidence must be conditionally independent given the hypothesis and its negation and the inference network must have a tree structure. It is emphasized that assumptions implicit in the model are rarely true in practical applications. Methods for relaxing the assumptions are suggested.
Uncertain Reasoning Using Maximum Entropy Inference
Venue: arXiv cs.AI 2013
Year: 2013

The use of maximum entropy inference in reasoning with uncertain information is commonly justified by an information-theoretic argument. This paper discusses a possible objection to this information-theoretic justification and shows how it can be met. I then compare maximum entropy inference with certain other currently popular methods for uncertain reasoning. In making such a comparison, one must distinguish between static and dynamic theories of degrees of belief: a static theory concerns the consistency conditions for degrees of belief at a given time; whereas a dynamic theory concerns how one's degrees of belief should change in the light of new information. It is argued that maximum entropy is a dynamic theory and that a complete theory of uncertain reasoning can be gotten by combining maximum entropy inference with probability theory, which is a static theory. This total theory, I argue, is much better grounded than are other theories of uncertain reasoning.
Selecting Uncertainty Calculi and Granularity: An Experiment in Trading-Off Precision and Complexity
Venue: arXiv cs.AI 2013
Year: 2013

The management of uncertainty in expert systems has usually been left to ad hoc representations and rules of combinations lacking either a sound theory or clear semantics. The objective of this paper is to establish a theoretical basis for defining the syntax and semantics of a small subset of calculi of uncertainty operating on a given term set of linguistic statements of likelihood. Each calculus is defined by specifying a negation, a conjunction and a disjunction operator. Families of Triangular norms and conorms constitute the most general representations of conjunction and disjunction operators. These families provide us with a formalism for defining an infinite number of different calculi of uncertainty. The term set will define the uncertainty granularity, i.e. the finest level of distinction among different quantifications of uncertainty. This granularity will limit the ability to differentiate between two similar operators. Therefore, only a small finite subset of the infinite number of calculi will produce notably different results. This result is illustrated by two experiments where nine and eleven different calculi of uncertainty are used with three term sets containing five, nine, and thirteen elements, respectively. Finally, the use of context dependent rule set is proposed to select the most appropriate calculus for any given situation. Such a rule set will be relatively small since it must only describe the selection policies for a small number of calculi (resulting from the analyzed trade-off between complexity and precision).
A Framework for Non-Monotonic Reasoning About Probabilistic Assumptions
Venue: arXiv cs.AI 2013
Year: 2013

Attempts to replicate probabilistic reasoning in expert systems have typically overlooked a critical ingredient of that process. Probabilistic analysis typically requires extensive judgments regarding interdependencies among hypotheses and data, and regarding the appropriateness of various alternative models. The application of such models is often an iterative process, in which the plausibility of the results confirms or disconfirms the validity of assumptions made in building the model. In current expert systems, by contrast, probabilistic information is encapsulated within modular rules (involving, for example, "certainty factors"), and there is no mechanism for reviewing the overall form of the probability argument or the validity of the judgments entering into it.
Induction, of and by Probability
Venue: arXiv cs.AI 2013
Year: 2013

This paper examines some methods and ideas underlying the author's successful probabilistic learning systems(PLS), which have proven uniquely effective and efficient in generalization learning or induction. While the emerging principles are generally applicable, this paper illustrates them in heuristic search, which demands noise management and incremental learning. In our approach, both task performance and learning are guided by probability. Probabilities are incrementally normalized and revised, and their errors are located and corrected.
Combining Uncertain Estimates
Venue: arXiv cs.AI 2013
Year: 2013

In a real expert system, one may have unreliable, unconfident, conflicting estimates of the value for a particular parameter. It is important for decision making that the information present in this aggregate somehow find its way into use. We cast the problem of representing and combining uncertain estimates as selection of two kinds of functions, one to determine an estimate, the other its uncertainty. The paper includes a long list of properties that such functions should satisfy, and it presents one method that satisfies them.
Evidential Confirmation as Transformed Probability
Venue: arXiv cs.AI 2013
Year: 2013

A considerable body of work in AI has been concerned with aggregating measures of confirmatory and disconfirmatory evidence for a common set of propositions. Claiming classical probability to be inadequate or inappropriate, several researchers have gone so far as to invent new formalisms and methods. We show how to represent two major such alternative approaches to evidential confirmation not only in terms of transformed (Bayesian) probability, but also in terms of each other. This unifies two of the leading approaches to confirmation theory, by showing that a revised MYCIN Certainty Factor method [12] is equivalent to a special case of Dempster-Shafer theory. It yields a well-understood axiomatic basis, i.e. conditional independence, to interpret previous work on quantitative confirmation theory. It substantially resolves the "taxe-them-or-leave-them" problem of priors: MYCIN had to leave them out, while PROSPECTOR had to have them in. It recasts some of confirmation theory's advantages in terms of the psychological accessibility of probabilistic information in different (transformed) formats. Finally, it helps to unify the representation of uncertain reasoning (see also [11]).
Probabilistic Conflict Resolution in Hierarchical Hypothesis Spaces
Venue: arXiv cs.AI 2013
Year: 2013

Artificial intelligence applications such as industrial robotics, military surveillance, and hazardous environment clean-up, require situation understanding based on partial, uncertain, and ambiguous or erroneous evidence. It is necessary to evaluate the relative likelihood of multiple possible hypotheses of the (current) situation faced by the decision making program. Often, the evidence and hypotheses are hierarchical in nature. In image understanding tasks, for example, evidence begins with raw imagery, from which ambiguous features are extracted which have multiple possible aggregations providing evidential support for the presence of multiple hypothesis of objects and terrain, which in turn aggregate in multiple ways to provide partial evidence for different interpretations of the ambient scene. Information fusion for military situation understanding has a similar evidence/hypothesis hierarchy from multiple sensor through message level interpretations, and also provides evidence at multiple levels of the doctrinal hierarchy of military forces.
Compressed Sensing and Affine Rank Minimization under Restricted Isometry
Venue: arXiv cs.IT 2013
Year: 2013

This paper establishes new restricted isometry conditions for compressed sensing and affine rank minimization. It is shown for compressed sensing that $δ_{k}^A+θ_{k,k}^A < 1$ guarantees the exact recovery of all $k$ sparse signals in the noiseless case through the constrained $\ell_1$ minimization. Furthermore, the upper bound 1 is sharp in the sense that for any $ε> 0$, the condition $δ_k^A + θ_{k, k}^A < 1+ε$ is not sufficient to guarantee such exact recovery using any recovery method. Similarly, for affine rank minimization, if $δ_{r}^\mathcal{M}+θ_{r,r}^\mathcal{M}< 1$ then all matrices with rank at most $r$ can be reconstructed exactly in the noiseless case via the constrained nuclear norm minimization; and for any $ε> 0$, $δ_r^\mathcal{M} +θ_{r,r}^\mathcal{M} < 1+ε$ does not ensure such exact recovery using any method. Moreover, in the noisy case the conditions $δ_{k}^A+θ_{k,k}^A < 1$ and $δ_{r}^\mathcal{M}+θ_{r,r}^\mathcal{M}< 1$ are also sufficient for the stable recovery of sparse signals and low-rank matrices respectively. Applications and extensions are also discussed.
A modified dna computing approach to tackle the exponential solution space of the graph coloring problem
Venue: arXiv cs.CC 2013
Year: 2013

Although it has been evidenced that DNA computing is able to solve the graph coloring problem in a polynomial time complexity, but the exponential solution space is still a restrictive factor in applying this technique for solving really large problems. In this paper a modified DNA computing approach based on Adleman-Lipton model is proposed which tackles the mentioned restriction by coloring the vertices one by one. In each step, it expands the DNA strands encoding promising solutions and discards those which encode infeasible ones. A sample graph is colored by simulating the proposed approach and shows a notable reduction in the number of DNA strands used.
Data, text and web mining for business intelligence: a survey
Venue: arXiv cs.IR 2013
Year: 2013

The Information and Communication Technologies revolution brought a digital world with huge amounts of data available. Enterprises use mining technologies to search vast amounts of data for vital insight and knowledge. Mining tools such as data mining, text mining, and web mining are used to find hidden knowledge in large databases or the Internet.
SCAF An effective approach to Classify Subspace Clustering algorithms
Venue: arXiv cs.DB 2013
Year: 2013

Subspace clustering discovers the clusters embedded in multiple, overlapping subspaces of high dimensional data. Many significant subspace clustering algorithms exist, each having different characteristics caused by the use of different techniques, assumptions, heuristics used etc. A comprehensive classification scheme is essential which will consider all such characteristics to divide subspace clustering approaches in various families. The algorithms belonging to same family will satisfy common characteristics. Such a categorization will help future developers to better understand the quality criteria to be used and similar algorithms to be used to compare results with their proposed clustering algorithms. In this paper, we first proposed the concept of SCAF (Subspace Clustering Algorithms Family). Characteristics of SCAF will be based on the classes such as cluster orientation, overlap of dimensions etc. As an illustration, we further provided a comprehensive, systematic description and comparison of few significant algorithms belonging to 'Axis parallel, overlapping, density based' SCAF.
The Social Maintenance of Cooperation through Hypocrisy
Venue: arXiv cs.SI 2013
Year: 2013

Cooperation is widespread in human societies, but its maintenance at the group level remains puzzling if individuals benefit from not cooperating. Explanations of the maintenance of cooperation generally assume that cooperative and non-cooperative behavior in others can be assessed and copied accurately. However, humans have a well known capacity to deceive and thus to manipulate how others assess their behavior. Here, we show that hypocrisy - claiming to be acting cooperatively while acting selfishly - can maintain social cooperation because it prevents the spread of selfish behavior. We demonstrate this effect both theoretically and experimentally. Hypocrisy allows the cooperative strategy to spread by taking credit for the success of the non-cooperative strategy.
Polar Coding for Fading Channels
Venue: arXiv cs.IT 2013
Year: 2013

A polar coding scheme for fading channels is proposed in this paper. More specifically, the focus is Gaussian fading channel with a BPSK modulation technique, where the equivalent channel could be modeled as a binary symmetric channel with varying cross-over probabilities. To deal with variable channel states, a coding scheme of hierarchically utilizing polar codes is proposed. In particular, by observing the polarization of different binary symmetric channels over different fading blocks, each channel use corresponding to a different polarization is modeled as a binary erasure channel such that polar codes could be adopted to encode over blocks. It is shown that the proposed coding scheme, without instantaneous channel state information at the transmitter, achieves the capacity of the corresponding fading binary symmetric channel, which is constructed from the underlying fading AWGN channel through the modulation scheme.
Automatic case acquisition from texts for process-oriented case-based reasoning
Venue: arXiv cs.AI 2013
Year: 2013

This paper introduces a method for the automatic acquisition of a rich case representation from free text for process-oriented case-based reasoning. Case engineering is among the most complicated and costly tasks in implementing a case-based reasoning system. This is especially so for process-oriented case-based reasoning, where more expressive case representations are generally used and, in our opinion, actually required for satisfactory case adaptation. In this context, the ability to acquire cases automatically from procedural texts is a major step forward in order to reason on processes. We therefore detail a methodology that makes case acquisition from processes described as free text possible, with special attention given to assembly instruction texts. This methodology extends the techniques we used to extract actions from cooking recipes. We argue that techniques taken from natural language processing are required for this task, and that they give satisfactory results. An evaluation based on our implemented prototype extracting workflows from recipe texts is provided.