Probabilistic Logic Research Papers - Academia.edu (original) (raw)

Abstract—The current proliferation of mobile devices has resulted in a large diversity of hardware specifications, each designed for different services and applications (e.g. cell phones, smart phones, PDAs). At the same time, e-mail message delivery has become a vital part of everyday communications. This article provides a cost-aware study of an RSA-based e-mail protocol executed upon the widely used Apple iPhone1,2 with ARM1176JZF-S, operating in an High Speed Downlink Packet Access (HSDPA) mobile environment. The proposed study employs formal analysis techniques, such as probabilistic model checking, and proceeds to a quantitative analysis of the e-mail protocol, taking into account computational parameters derived by the devices ’ specifications. The value of this study is to form a computer-aided framework which balances the tradeoff between gaining in security, using high-length RSA keys, and conserving CPU resources, due to hardware limitations of mobile devices. To the best...

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- Model Checking, Protocol Design, Mobile Computing, Formal Analysis
- by Brian Nielsen
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- Model Checking, Embedded Systems, Probabilistic Logic, Computer Model
- by Ruben Morales-Menendez
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- Data Analysis, Power System, Neural Networks, Fault Detection

The features are considered the cornerstone of text summarization. The most important issue is what feature to be considered in a text summarization process. Including all the features in the summarization process may not be considered as an optimal solution. Therefore, other methods need to be deployed. In this paper, random five features used and investigated using a (pseudo) Genetic concept as an optimized trainable features selection mechanism. The Document Understanding Conference (DUC2002) used to train our proposed model; hence the objective of this paper is to learn the weight (importance) of each used feature. For each input document using the genetic concept, the size of the generation is defined and the chromosome dimension (genes) is equal to number of features used. Each gene is represents a feature and in binary format. A chromosome with high fitness value is selected to be enrolled in the final round. The average of each gene is computed for all best chromosomes and c...

- by Ameer Hamza
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- Genetics, Computer Science, Information Retrieval, Statistical Analysis
- by Ioannis Gerontidis
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- Finite Element Methods, Fuzzy set theory, Fuzzy Sets, Markov Processes
- by Femi Aderohunmu and +1
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- Approximation Theory, Wireless Communications, Computer Networks, Wireless Sensor Networks

The integration of probabilistic reasoning with logic programming has become one of the challenges in Artificial Intelligence. Lately, a lot of Probabilistic Logic Programming (PLP) formalisms have surfaced. Given that PLP is the combination of logic programming and probabilities which are two very different fields, it is expected that researchers from several fields come up with different approaches to tackle the presented challenges. This has resulted in a new discipline called Probabilistic Logic Learning (PLL) or Statistical Relational Learning (SRL) and a very active research community. Within this community, ProbLog a probabilistic extension of Prolog, has appeared. ProbLog was motivated by the task of mining links in large probabilistic graphs. The simple but powerful ProbLog formulation was extended in order to support inference on several different models. Soon ProbLog evolved into a general purpose probabilistic programming language that provides infrastructure for many PL...

In this work, Classical Turkish Music songs are classified into six makams. Makam is a modal framework for melodic development in Classical Turkish Music. The effect of the sound clip length on the system performance was also evaluated. The Mel Frequency Cepstral Coefficients (MFCC) were used as features. Obtained data were classified by using Probabilistic Neural Network. The best correct recognition ratio was obtained as 89,4% by using a clip length of 6 s.

- by Nilambar Sethi
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- Probability Theory, Computer Science, Artificial Intelligence, Fuzzy set theory
- by Ajay Mishra
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- Information Systems, Algorithms, Computer Vision, Visualization
- by Marco A Assfalk de Oliveira
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- Cognitive Science, Fuzzy Logic, Intelligent Systems, Control system
- by Anibal Matos
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- Engineering, Robotics, Education, Engineering Education
- by Max Henrion
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- Probabilistic Logic, Bayesian Network, UAI
- by Dragan Doder
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- Applied Mathematics, Evidence, Probabilistic Logic, Decidability
- by USMAN KHALID
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- Markov Processes, Probability, Probabilistic Logic, Intelligent
- by Minsu Cho and +2
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- Computer Vision, Machine Learning, Graph Theory, Pattern Recognition

The main goal of this work is connection between theory of probability and logic (expecially logical deduction). Probabilistic logic are combining the capacity of probability theory with the capacity of deductive logic to exploit the structure. The result is a richer and more expressive formalism with a broad range of possible application areas. In probabilistic logic statements have no value of truth and false, but a series of truths between 0 and 1, where 0 represents an impossible situation, and 1 complete security. Difficulties arising from the probabilistic logic is that they multiply computing complexity and allow non-intuitive results as supporting the theory of probability. Probabilistic logic is divided into two forms : 1. expressions that have the probability associated with tem, and 2. evidential probability that are based on the frequency interpretation of probability. In first chapter I describe some historical connections between logic and theory of probability. The second part is devoted to the problems of probabilistic logic, semantic and syntax, probabilistic argumentation and Bayesian epistemology, as well. In the third part, I solve problem of probabilistic logical networks, expecially on Markov logic netwok (MLN). In coclusion, there will be presented some problems of other philosophical disciplines, such as epistemology, which problems can be solved by probabilistic calculus.

- by Pedro Forero
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- Mathematics, Set Theory, Computer Science, Machine Learning
- by Sandeep K Shukla
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- Vlsi Design, Manufacturing, Computer Aided Design, Nanotechnology
- by horacio paggi
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- Psychology, Artificial Intelligence, Multiagent Systems, Fuzzy Logic
- by Urbano Nunes
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- Remote Sensing, Support Vector Machines, Probability, Probabilistic Logic

In this paper, we address the issue of handling uncertainty and information fusion for an efficient WSN deployment. We present a flexible framework for collaborative target detection within the transferable belief model. Using the developed framework, we propose an uncertainty-aware deployment algorithm that is able to determine the minimum number of sensors and their locations such that full area coverage is achieved. The issues of connectivity, obstacles, preferential coverage, challenging environments and sensor reliability are also discussed. Experimental results are provided to demonstrate the ability of our approach to achieve an efficient sensor deployment by exploiting a collaborative target detection scheme.

- by latifa Oukhellou
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- Wireless Sensor Networks, Probabilistic Logic, Information Fusion, IEEE

In 2009 we have celebrated 30 years since the first live maintenance techniques (LMT) have been carried out in Romania. This paper, in acknowledgement of the Romanian Live Maintenance Association's valuable activity in this field, is tackling a major issue for the Romanian Power Grid Company Transelectrica S.A.: the increase of the overhead line transmission capacity applying reconductoring with high temperature low sag (HTLS) conductors. The authors are proposing the use of live maintenance techniques, which considerably reduce the overhead line unavailability.

- by Marius Oltean
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- Power System, Probabilistic Logic, Case Study, Load Flow
- by vipin Kumar
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- Bioinformatics, Machine Learning, Data Mining, Computational Biology
- by Habibah Lateh
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- Logistic Regression, Probabilistic Logic, Prediction Model, Hazard Mapping
- by Kathryn Laskey
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- Cognitive Science, Stochastic Process, Computer Architecture, Quantum Physics
- by Gregory Wheeler
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- Probabilistic Logic, Statistical Inference, Bayesian statistics, Bayesian Network

In the present study, the Probabilistic Seismic Hazard Analysis (PSHA) has been carried out for a powerplant site in Chittagong using the latest available information on seismicity in the region. The latest updated earthquake catalog homogenized in uniform moment magnitude scale was prepared. Seismicity analysis was carried out, and the seismicity parameters for the region were estimated from the frequency magnitude distribution plot. The peak ground acceleration (PGA) and the spectral acceleration at the bedrock for 10% and 2% probability of exceedance within 50 years were evaluated using different source models and attenuation relations in a logic tree framework. The site was characterized based on the soil investigation data from 31 boreholes and amplification factors considering the local geology (SPT-N values) were obtained through ground response analysis. Subsequently, the surface level peak ground acceleration was estimated and depicted in the form of hazard contour maps.

- by Soumyadeep Sengupta
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- Seismic Hazard, Probabilistic Logic
- by Timothy Havens
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- Fuzzy set theory, Clustering Algorithms, Fuzzy Clustering, Probabilistic Logic
- by Gour Karmakar
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- Approximation Theory, Wireless Sensor Networks, Probability, Probabilistic Logic

The 2001 workshop on information security system rating and ranking 4] discussed different aspects of security metrics. What should we count and what do the numbers mean as pertaining to software security metrics was one of the challenge problems discussed by security experts at the 2003 UW-MSR Summer Institute 7]. The development of meaningful security metrics was chosen as a grand challenge at the onference on rand Research hallenges consecutively in 2002 5] and 2003 8]. This exemplifies the immediate need for ...

- by Eugene H Spafford
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- Computer Science, Probabilistic Logic
- by Pedro Forero
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- Mathematics, Set Theory, Computer Science, Machine Learning
- by Sharon Alpert
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- Information Systems, Mathematics, Computer Science, Algorithms

The consideration of uncertainties in the future
system operation is a key aspect in current planning methodologies.
In this context, load flow studies based on probabilistic theory,
fuzzy numbers, and Monte Carlo simulations have been proposed
in the literature. This work analyzes in a novel way the application
of fuzzy number arithmetic for the DC load flow problem. In this
sense, fuzzy sets theory is reviewed and is showed that there are
two alternative and valid procedures to perform a subtraction.
A key aspect for the right selection of one of these procedures is
the independency or dependency among the involved variables.
Assuming input data as independent variables, this work is focused
on analyzing the fuzzy subtraction between dependent state
variables of the system, such as voltage phase angle. Accordingly,
two new valid alternative methodologies are proposed and applied
to case studies. Results are compared to previous related works
showing the consistency of the proposed methodologies. Future
research will be focused on the consideration of ohmic losses and
AC network modeling in the field of expansion planning studies.

We investigate the discrete (finite) case of the Popper–Renyi theory of conditional probability, introducing discrete conditional probabilistic models for knowledge and conditional belief, and comparing them with the more standard plau-sibility models. We also consider a related notion, that of safe belief, which is a weak (non-negatively introspective) type of " knowledge ". We develop a probabilistic version of this concept (" degree of safety ") and we analyze its role in games. We completely axiomatize the logic of conditional belief, knowledge and safe belief over conditional probabilistic models. We develop a theory of probabilistic dynamic belief revision, introducing probabilistic " action models " and proposing a notion of probabilistic update product, that comes together with appropriate reduction laws.

- by Sonja Smets and +1
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- Belief Revision (Computer Science), Formal Epistemology, Probabilistic Logic, Doxastic Logic

Situational awareness and prediction are essential elements of information fusion. Both involve various types of uncertainty and require a sound automated inferential process. Probabilistic ontologies support uncertainty management in se-mantically aware systems, and facilitate modular, interoperable systems. This paper describes the process of developing a probabilistic ontology for a Maritime Domain Awareness application. The ontology was created to support identification of ships behaving suspiciously enough to be declared ships of interest. The original model was expanded in two ways: to provide reasons for declaring a ship as being of interest, and to include individual crew member associations. The latter is achieved by supporting inferences about a person's close relations, group associations, communications, and background influences to assess his likelihood of having terrorist links.

- by Richard Haberlin
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- Computer Science, Terrorism, Situation awareness, Web Services
- by Budianto Tandianus
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- Probabilistic Logic, Energy Saving, Error Analysis, Ray Tracing
- by Zoran Markovic
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- Statistics, Possible Worlds, Probabilistic Logic, Conditional probability
- by Saikat Chakrabarti
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- Electronics, Distributed System, Genetic Algorithm, Probabilistic Logic
- by ali amiri
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- Linear Programming, Nonlinear Programming, Integer Programming, Randomization
- by horacio paggi
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- Multiagent Systems, Fuzzy Logic, Beliefs, Probabilistic Logic

In wireless heterogeneous networks, one of the most challenging problems is Radio Access Technology (RAT) selection that must be designed to avoid resource wastage. In this paper we adopt a hybrid model for RAT selection where the system allocates the downlink traffic between two different technologies in order to enhance global performance. We study the case of an integrated hybrid Wireless Local Area Network environment where the challenge we face is the high computational complexity necessary to obtain the global optimal solution. Therefore, we propose four distributed heuristic algorithms for RAT selection, where two of them are based on the distance between the user and the access points (APs), namely, distance based and probabilistic distance based algorithms. While the two others schemes are based on the peak rate that each user receives from these APs (peak rate based and probabilistic peak rate based algorithms). Results show that the proposed algorithms give efficient results compared to the optimal one depending on the spatial users distribution. Moreover these algorithms have a low computational complexity which makes them more advantageous compared to the optimal scheme in presence of a large number of users.

- by Marc Ibrahim and +1
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- Computational Complexity, Distributed Algorithms, Mobile Communication, Probability
- by Farhan Aadil
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- RFID, Computer Networks, Topology, Routing protocols
- by Olivier Rosec
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- Engineering, Speech Processing, Speech, Probabilistic Logic