OpenRaiser/Intern-Atlas huggingface dataset:
Automatic Structuring Of Semantic Web Services An Approach
Venue: arXiv cs.IR 2013
Year: 2013

Ontologies have become the effective modeling for various applications and significantly in the semantic web. The difficulty of extracting information from the web, which was created mainly for visualising information, has driven the birth of the semantic web, which will contain much more resources than the web and will attach machine-readable semantic information to these resources. Ontological bootstrapping on a set of predefined sources, such as web services, must address the problem of multiple, largely unrelated concepts. The web services consist of basically two components, Web Services Description Language (WSDL) descriptors and free text descriptors. The WSDL descriptor is evaluated using two methods, namely Term Frequency/Inverse Document Frequency (TF/IDF) and web context generation. The proposed bootstrapping ontological process integrates TF/IDF and web context generation and applies validation using the free text descriptor service, so that, it offers more accurate definition of ontologies. This paper uses ranking adaption model which predicts the rank for a collection of web service documents which leads to the automatic construction, enrichment and adaptation of ontologies.
On Appropriate Selection of Fuzzy Aggregation Operators in Medical Decision Support System
Venue: arXiv cs.AI 2013
Year: 2013

The Decision Support System (DSS) contains more than one antecedent and the degrees of strength of the antecedents need to be combined to determine the overall strength of the rule consequent. The membership values of the linguistic variables in Fuzzy have to be combined using an aggregation operator. But it is not feasible to predefine the form of aggregation operators in decision making. Instead, each rule should be found based on the feeling of the experts and on their actual decision pattern over the set of typical examples. Thus this work illustrates how the choice of aggregation operators is intended to mimic human decision making and can be selected and adjusted to fit empirical data, a series of test cases. Both parametrized and nonparametrized aggregation operators are adapted to fit empirical data. Moreover, they provided compensatory properties and, therefore, seemed to produce a better decision support system. To solve the problem, a threshold point from the output of the aggregation operators is chosen as the separation point between two classes. The best achieved accuracy is chosen as the appropriate aggregation operator. Thus a medical decision can be generated which is very close to a practitioner's guideline.
A New Distributed Evolutionary Computation Technique for Multi-Objective Optimization
Venue: arXiv cs.NE 2013
Year: 2013

Now-a-days, it is important to find out solutions of Multi-Objective Optimization Problems (MOPs). Evolutionary Strategy helps to solve such real world problems efficiently and quickly. But sequential Evolutionary Algorithms (EAs) require an enormous computation power to solve such problems and it takes much time to solve large problems. To enhance the performance for solving this type of problems, this paper presents a new Distributed Novel Evolutionary Strategy Algorithm (DNESA) for Multi-Objective Optimization. The proposed DNESA applies the divide-and-conquer approach to decompose population into smaller sub-population and involves multiple solutions in the form of cooperative sub-populations. In DNESA, the server distributes the total computation load to all associate clients and simulation results show that the time for solving large problems is much less than sequential EAs. Also DNESA shows better performance in convergence test when compared with other three well-known EAs.
Higher Order Probabilities
Venue: arXiv cs.AI 2013
Year: 2013

A number of writers have supposed that for the full specification of belief, higher order probabilities are required. Some have even supposed that there may be an unending sequence of higher order probabilities of probabilities of probabilities.... In the present paper we show that higher order probabilities can always be replaced by the marginal distributions of joint probability distributions. We consider both the case in which higher order probabilities are of the same sort as lower order probabilities and that in which higher order probabilities are distinct in character, as when lower order probabilities are construed as frequencies and higher order probabilities are construed as subjective degrees of belief. In neither case do higher order probabilities appear to offer any advantages, either conceptually or computationally.
Do We Need Higher-Order Probabilities and, If So, What Do They Mean?
Venue: arXiv cs.AI 2013
Year: 2013

The apparent failure of individual probabilistic expressions to distinguish uncertainty about truths from uncertainty about probabilistic assessments have prompted researchers to seek formalisms where the two types of uncertainties are given notational distinction. This paper demonstrates that the desired distinction is already a built-in feature of classical probabilistic models, thus, specialized notations are unnecessary.
A Perspective on Confidence and Its Use in Focusing Attention During Knowledge Acquisition
Venue: arXiv cs.AI 2013
Year: 2013

We present a representation of partial confidence in belief and preference that is consistent with the tenets of decision-theory. The fundamental insight underlying the representation is that if a person is not completely confident in a probability or utility assessment, additional modeling of the assessment may improve decisions to which it is relevant. We show how a traditional decision-analytic approach can be used to balance the benefits of additional modeling with associated costs. The approach can be used during knowledge acquisition to focus the attention of a knowledge engineer or expert on parts of a decision model that deserve additional refinement.
NAIVE: A Method for Representing Uncertainty and Temporal Relationships in an Automated Reasoner
Venue: arXiv cs.AI 2013
Year: 2013

This paper describes NAIVE, a low-level knowledge representation language and inferencing process. NAIVE has been designed for reasoning about nondeterministic dynamic systems like those found in medicine. Knowledge is represented in a graph structure consisting of nodes, which correspond to the variables describing the system of interest, and arcs, which correspond to the procedures used to infer the value of a variable from the values of other variables. The value of a variable can be determined at an instant in time, over a time interval or for a series of times. Information about the value of a variable is expressed as a probability density function which quantifies the likelihood of each possible value. The inferencing process uses these probability density functions to propagate uncertainty. NAIVE has been used to develop medical knowledge bases including over 100 variables.
Using T-Norm Based Uncertainty Calculi in a Naval Situation Assessment Application
Venue: arXiv cs.AI 2013
Year: 2013

RUM (Reasoning with Uncertainty Module), is an integrated software tool based on a KEE, a frame system implemented in an object oriented language. RUM's architecture is composed of three layers: representation, inference, and control. The representation layer is based on frame-like data structures that capture the uncertainty information used in the inference layer and the uncertainty meta-information used in the control layer. The inference layer provides a selection of five T-norm based uncertainty calculi with which to perform the intersection, detachment, union, and pooling of information. The control layer uses the meta-information to select the appropriate calculus for each context and to resolve eventual ignorance or conflict in the information. This layer also provides a context mechanism that allows the system to focus on the relevant portion of the knowledge base, and an uncertain-belief revision system that incrementally updates the certainty values of well-formed formulae (wffs) in an acyclic directed deduction graph. RUM has been tested and validated in a sequence of experiments in both naval and aerial situation assessment (SA), consisting of correlating reports and tracks, locating and classifying platforms, and identifying intents and threats. An example of naval situation assessment is illustrated. The testbed environment for developing these experiments has been provided by LOTTA, a symbolic simulator implemented in Flavors. This simulator maintains time-varying situations in a multi-player antagonistic game where players must make decisions in light of uncertain and incomplete data. RUM has been used to assist one of the LOTTA players to perform the SA task.
A Knowledge Engineer's Comparison of Three Evidence Aggregation Methods
Venue: arXiv cs.AI 2013
Year: 2013

The comparisons of uncertainty calculi from the last two Uncertainty Workshops have all used theoretical probabilistic accuracy as the sole metric. While mathematical correctness is important, there are other factors which should be considered when developing reasoning systems. These other factors include, among other things, the error in uncertainty measures obtainable for the problem and the effect of this error on the performance of the resulting system.
Combining Symbolic and Numeric Approaches to Uncertainty Management
Venue: arXiv cs.AI 2013
Year: 2013

A complete approach to reasoning under uncertainty requires support for incremental and interactive formulation and revision of, as well as reasoning with, models of the problem domain capable of representing our uncertainty. We present a hybrid reasoning scheme which combines symbolic and numeric methods for uncertainty management to provide efficient and effective support for each of these tasks. The hybrid is based on symbolic techniques adapted from Assumption-based Truth Maintenance systems (ATMS), combined with numeric methods adapted from the Dempster/Shafer theory of evidence, as extended in Baldwin's Support Logic Programming system. The hybridization is achieved by viewing an ATMS as a symbolic algebra system for uncertainty calculations. This technique has several major advantages over conventional methods for performing inference with numeric certainty estimates in addition to the ability to dynamically determine hypothesis spaces, including improved management of dependent and partially independent evidence, faster run-time evaluation of propositional certainties, the ability to query the certainty value of a proposition from multiple perspectives, and the ability to incrementally extend or revise domain models.
Advantages and a Limitation of Using LEG Nets in a Real-TIme Problem
Venue: arXiv cs.AI 2013
Year: 2013

After experimenting with a number of non-probabilistic methods for dealing with uncertainty many researchers reaffirm a preference for probability methods [1] [2], although this remains controversial. The importance of being able to form decisions from incomplete data in diagnostic problems has highlighted probabilistic methods [5] which compute posterior probabilities from prior distributions in a way similar to Bayes Rule, and thus are called Bayesian methods. This paper documents the use of a Bayesian method in a real time problem which is similar to medical diagnosis in that there is a need to form decisions and take some action without complete knowledge of conditions in the problem domain. This particular method has a limitation which is discussed.
On partial sparse recovery
Venue: arXiv cs.IT 2013
Year: 2013

We consider the problem of recovering a partially sparse solution of an underdetermined system of linear equations by minimizing the $\ell_1$-norm of the part of the solution vector which is known to be sparse. Such a problem is closely related to a classical problem in Compressed Sensing where the $\ell_1$-norm of the whole solution vector is minimized. We introduce analogues of restricted isometry and null space properties for the recovery of partially sparse vectors and show that these new properties are implied by their original counterparts. We show also how to extend recovery under noisy measurements to the partially sparse case.
Roborobo! a Fast Robot Simulator for Swarm and Collective Robotics
Venue: arXiv cs.RO 2013
Year: 2013

Roborobo! is a multi-platform, highly portable, robot simulator for large-scale collective robotics experiments. Roborobo! is coded in C++, and follows the KISS guideline ("Keep it simple"). Therefore, its external dependency is solely limited to the widely available SDL library for fast 2D Graphics. Roborobo! is based on a Khepera/ePuck model. It is targeted for fast single and multi-robots simulation, and has already been used in more than a dozen published research mainly concerned with evolutionary swarm robotics, including environment-driven self-adaptation and distributed evolutionary optimization, as well as online onboard embodied evolution and embodied morphogenesis.
Inferring Fences in a Concurrent Program Using SC proof of Correctness
Venue: arXiv cs.LO 2013
Year: 2013

Most proof systems for concurrent programs assume the underlying memory model to be sequentially consistent (SC), an assumption which does not hold for modern multicore processors. These processors, for performance reasons, implement relaxed memory models. As a result of this relaxation a program, proved correct on the SC memory model, might execute incorrectly. To ensure its correctness under relaxation, fence instructions are inserted in the code. In this paper we show that the SC proof of correctness of an algorithm, carried out in the proof system of [Sou84], identifies per-thread instruction orderings sufficient for this SC proof. Further, to correctly execute this algorithm on an underlying relaxed memory model it is sufficient to respect only these orderings by inserting fence instructions.
Centrality of Trees for Capacitated k-Center
Venue: arXiv cs.DS 2013
Year: 2013

There is a large discrepancy in our understanding of uncapacitated and capacitated versions of network location problems. This is perhaps best illustrated by the classical k-center problem: there is a simple tight 2-approximation algorithm for the uncapacitated version whereas the first constant factor approximation algorithm for the general version with capacities was only recently obtained by using an intricate rounding algorithm that achieves an approximation guarantee in the hundreds. Our paper aims to bridge this discrepancy. For the capacitated k-center problem, we give a simple algorithm with a clean analysis that allows us to prove an approximation guarantee of 9. It uses the standard LP relaxation and comes close to settling the integrality gap (after necessary preprocessing), which is narrowed down to either 7, 8 or 9. The algorithm proceeds by first reducing to special tree instances, and then solves such instances optimally. Our concept of tree instances is quite versatile, and applies to natural variants of the capacitated k-center problem for which we also obtain improved algorithms. Finally, we give evidence to show that more powerful preprocessing could lead to better algorithms, by giving an approximation algorithm that beats the integrality gap for instances where all non-zero capacities are uniform.
Anticipatory Buffer Control and Resource Allocation for Wireless Video Streaming
Venue: arXiv cs.MM 2013
Year: 2013

This paper describes a new approach for allocating resources to video streaming traffic. Assuming that the future channel state can be predicted for a certain time, we minimize the fraction of the bandwidth consumed for smooth streaming by jointly allocating wireless channel resources and play-out buffer size. To formalize this idea, we introduce a new model to capture the dynamic of a video streaming buffer and the allocated spectrum in an optimization problem. The result is a Linear Program that allows to trade off buffer size and allocated bandwidth. Based on this tractable model, our simulation results show that anticipating poor channel states and pre-loading the buffer accordingly allows to serve more users at perfect video quality.
Knowledge Engineering Within A Generalized Bayesian Framework
Venue: arXiv cs.AI 2013
Year: 2013

During the ongoing debate over the representation of uncertainty in Artificial Intelligence, Cheeseman, Lemmer, Pearl, and others have argued that probability theory, and in particular the Bayesian theory, should be used as the basis for the inference mechanisms of Expert Systems dealing with uncertainty. In order to pursue the issue in a practical setting, sophisticated tools for knowledge engineering are needed that allow flexible and understandable interaction with the underlying knowledge representation schemes. This paper describes a Generalized Bayesian framework for building expert systems which function in uncertain domains, using algorithms proposed by Lemmer. It is neither rule-based nor frame-based, and requires a new system of knowledge engineering tools. The framework we describe provides a knowledge-based system architecture with an inference engine, explanation capability, and a unique aid for building consistent knowledge bases.
Reasoning With Uncertain Knowledge
Venue: arXiv cs.AI 2013
Year: 2013

A model of knowledge representation is described in which propositional facts and the relationships among them can be supported by other facts. The set of knowledge which can be supported is called the set of cognitive units, each having associated descriptions of their explicit and implicit support structures, summarizing belief and reliability of belief. This summary is precise enough to be useful in a computational model while remaining descriptive of the underlying symbolic support structure. When a fact supports another supportive relationship between facts we call this meta-support. This facilitates reasoning about both the propositional knowledge. and the support structures underlying it.
An Uncertainty Management Calculus for Ordering Searches in Distributed Dynamic Databases
Venue: arXiv cs.AI 2013
Year: 2013

MINDS is a distributed system of cooperating query engines that customize, document retrieval for each user in a dynamic environment. It improves its performance and adapts to changing patterns of document distribution by observing system-user interactions and modifying the appropriate certainty factors, which act as search control parameters. It argued here that the uncertainty management calculus must account for temporal precedence, reliability of evidence, degree of support for a proposition, and saturation effects. The calculus presented here possesses these features. Some results obtained with this scheme are discussed.
Learning Link-Probabilities in Causal Trees
Venue: arXiv cs.AI 2013
Year: 2013

A learning algorithm is presented which given the structure of a causal tree, will estimate its link probabilities by sequential measurements on the leaves only. Internal nodes of the tree represent conceptual (hidden) variables inaccessible to observation. The method described is incremental, local, efficient, and remains robust to measurement imprecisions.
Estimating Uncertain Spatial Relationships in Robotics
Venue: arXiv cs.AI 2013
Year: 2013

In this paper, we describe a representation for spatial information, called the stochastic map, and associated procedures for building it, reading information from it, and revising it incrementally as new information is obtained. The map contains the estimates of relationships among objects in the map, and their uncertainties, given all the available information. The procedures provide a general solution to the problem of estimating uncertain relative spatial relationships. The estimates are probabilistic in nature, an advance over the previous, very conservative, worst-case approaches to the problem. Finally, the procedures are developed in the context of state-estimation and filtering theory, which provides a solid basis for numerous extensions.
Qualitative Probabilistic Networks for Planning Under Uncertainty
Venue: arXiv cs.AI 2013
Year: 2013

Bayesian networks provide a probabilistic semantics for qualitative assertions about likelihood. A qualitative reasoner based on an algebra over these assertions can derive further conclusions about the influence of actions. While the conclusions are much weaker than those computed from complete probability distributions, they are still valuable for suggesting potential actions, eliminating obviously inferior plans, identifying important tradeoffs, and explaining probabilistic models.
Exact Algorithms for Weighted and Unweighted Borda Manipulation Problems
Venue: arXiv cs.DS 2013
Year: 2013

Both weighted and unweighted Borda manipulation problems have been proved $\mathcal{NP}$-hard. However, there is no exact combinatorial algorithm known for these problems. In this paper, we initiate the study of exact combinatorial algorithms for both weighted and unweighted Borda manipulation problems. More precisely, we propose $O^*((m\cdot 2^m)^{t+1})$time and $O^*(t^{2m})$time\footnote{$O^*()$ is the $O()$ notation with suppressed factors polynomial in the size of the input.} combinatorial algorithms for weighted and unweighted Borda manipulation problems, respectively, where $t$ is the number of manipulators and $m$ is the number of candidates. Thus, for $t=2$ we solve one of the open problems posted by Betzler et al. [IJCAI 2011]. As a byproduct of our results, we show that the {unweighted Borda manipulation} problem admits an algorithm of running time $O^*(2^{9m^2\log{m}})$, based on an integer linear programming technique. Finally, we study the {unweighted Borda manipulation} problem under single-peaked elections and present polynomial-time algorithms for the problem in the case of two manipulators, in contrast to the $\mathcal{NP}$-hardness of this case in general settings.
An Approach to Solve Linear Equations Using a Time-Variant Adaptation Based Hybrid Evolutionary Algorithm
Venue: arXiv cs.NE 2013
Year: 2013

For small number of equations, systems of linear (and sometimes nonlinear) equations can be solved by simple classical techniques. However, for large number of systems of linear (or nonlinear) equations, solutions using classical method become arduous. On the other hand evolutionary algorithms have mostly been used to solve various optimization and learning problems. Recently, hybridization of evolutionary algorithm with classical Gauss-Seidel based Successive Over Relaxation (SOR) method has successfully been used to solve large number of linear equations; where a uniform adaptation (UA) technique of relaxation factor is used. In this paper, a new hybrid algorithm is proposed in which a time-variant adaptation (TVA) technique of relaxation factor is used instead of uniform adaptation technique to solve large number of linear equations. The convergence theorems of the proposed algorithms are proved theoretically. And the performance of the proposed TVA-based algorithm is compared with the UA-based hybrid algorithm in the experimental domain. The proposed algorithm outperforms the hybrid one in terms of efficiency.