Deduction Algorithm Research Articles

Contradiction separation based dynamic multi-clause synergized automated deduction

Resolution as a famous rule of inference has played a key role in automated reasoning for over five decades. A number of variants and refinements of resolution have been also studied, essentially, they are all based on binary resolution, that is, the cutting rule of the complementary pair while every deduction involves only two clauses. In the present work, we consider an extension of binary resolution rule, which is proposed as a novel contradiction separation based inference rule for automated deduction, targeted for dynamic and multiple (two or more) clauses handling in a synergized way, while binary resolution is its special case. This contradiction separation based dynamic multi-clause synergized automated deduction theory is then proved to be sound and complete. The development of this new extension is motivated not only by our view to show that such a new rule of inference can be generic, but also by our wish that this inference rule could provide a basis for more efficient automated deduction algorithms and systems.

Substation Reliability Evaluation Considering the Failure Events

In this paper, bus and breaker structures of typical two substations were modelled by graph circuit to evaluate each reliability considering the various failure events and compare them each other. Passive failure event, active failure events, breaker stuck conditions and overlapping failure events in substation were conceptually explained and the reliability equations were presented to calculate the failure rate, repair rate and unavailability of each failure events. Also, deduction algorithms were implemented as a software programming to find the minimal cut sets for passive failure events and active minimal cut sets for active failure events. In the case study, reliability indices of each failure events and entire reliability indices (failure rate, annual outage time and MTTR) of typical two substations were calculated and quantitatively compared.

Spam analysis of big reviews dataset using Fuzzy Ranking Evaluation Algorithm and Hadoop

Online reviews are the most easily available free information sources used by both organizations and customers to make decisions. Establishments are utilizing significance of opinions to earn undue profit by hiring professionals known as spammers, giving positive comments on their products and negative opinions on their competitor’s product. This activity is known as opinion spamming and should be identified to give genuine results containing sentiments towards a product. So far, opinion spam detection has been considered as a discrete classification problem, generally as spam and non-spam. However, it involves uncertainty as suspicious behavior of a user might be due to coincidence. As, fuzzy logic handles real world uncertainty very well, we propose a novel fuzzy modeling based solution to the problem. We have proposed four fuzzy input linguistic variable and considered suspicious level of a spammer group to be one of—Ultra, Mega, Immense, Highly, Moderate, Slightly and Feebly. We have defined novel FSL Deduction Algorithm generating 81 fuzzy rules and Fuzzy Ranking Evaluation Algorithm (FREA) to determine the extent to which a group is suspicious. As reviews dataset satisfy the three V’s of big data (Volume, Velocity and Variety), we have considered this problem as a big data problem and used Hadoop for storage and analyzation. We have further demonstrated our proposed algorithm using a sample reviews dataset and Amazon reviews dataset achieving an accuracy of 80.77% which unlike other approaches remains steady for large number of groups and deals well with uncertainty involved in opinion spam detection.

Deduction Algorithms for Some Extensions of Allen’s Interval Logic

This paper considers Allen’s interval logic language and the languages whose statements are Boolean combinations of Allen’s logic statements, possibly, with metric information. Some deduction algorithms for the languages are presented. The algorithms are based on the analytic tableaux method.

FACTORS OF INTEGRATION AND CONFLICT IN HUMAN DEVELOPMENT OF THE SOUTH OF RUSSIA: RESEARCH METHODOLOGY

The article is devoted to theoretical-methodological analysis of the factors of integration and conflict in the development of human potential of the South of Russia. Presents an analysis of methodological approaches to the study of human potential, problems of human development and factors influencing this process. Specifies the contents of the structural components of human potential, indicators of its measurement and assessment in Southern Russia. The approaches to understanding the conflict category in modern political science are revealed, and a theoretical and empirical interpretation of the concept of «human potential» is given. The possibilities of the deductive algorithms of the search scenario forecast for the study of integral factors that influence the development of human potential taking into account the specifics of the South of Russia are described. Detailed scenarios of human potential development in the South of Russia (intensive development, extensive, stagnation, regression, degradation) are described in detail. Particular emphasis is placed on the possibilities of measuring the ethnic and ideological components of human development as specific features of the Southern macroregion. Some results of the pilot study (Krasnodar Territory) are presented, which reflect the dependence of the innovative potential as an integral indicator of human development on social parameters, such as protest potential, migration settings, living standards, etc.

Software Reliability Analysis Using Weakest Preconditions in Linear Assignment Programs

Weakest preconditions derived from triple axiomatic semantics have been widely used to prove the correctness of programs. They can also be applied to evaluate the reliability of software. However, deducing a weakest precondition, as well as determining its propagation path, encounters challenges such as unknown constraint conditions, symbol computation and means of representation. To address these challenges, in this paper, we utilize the disjunctive normal form of if-else branch structure to capture reasonable propagation paths of the weakest precondition. Meanwhile, by removing the sequential dependencies, we demonstrate how to get the weakest precondition of loop-structure by leveraging program function. Moreover, we extensively explore three modeling characteristics (i.e., path extension, innermost connection and condition leap) for deducing the weakest precondition of structured programs. Finally, taking the definition of program node and storage structure of weakest precondition as bases, we design a serial of modeling algorithms. Based on symbol computation and recursive call technology with Depth-First Search (DFS), our algorithms can not only be used to deduce the weakest precondition, but also to capture the propagate path of the weakest precondition. Experiments illustrate the efficacy and effectiveness of our proposed models and designed deductive algorithms.

Collaborative process cartography deduction based on collaborative ontology and model transformation

With an increasing background in inter-enterprise collaboration and interoperability, the automatic deduction of collaborative business processes is increasingly being viewed as a crucial research subject. The common solution is believed to involve either semantic ontologies or metamodeling, which can be combined with semantic algorithms or transformation rules. However, from the viewpoint of enterprises, the modeling of collaborative processes should be software integrated and can be changed to workflow. The design of the targeted collaborative process model does not fit the need. This has led to a lack of concepts and transformation rules for the ontology or metamodel. In this paper, a new collaborative process model called collaborative process cartography is designed. Related collaborative ontology and its knowledge-expanding rules have also been updated. Collaborative ontology contains essential concepts for the collaborative situation and includes the rules and algorithms for process deduction. A prototype for the supporting tools is also provided.

Modelação proativa de metas e elaboração de soluções informatizadas

Computerized solutions for solving concrete everyday problems are structured through deductive algorithms. However, even before designing algorithms, there is an abductive step guided by the goal to be achieved, which is usually neglected. Following Rauen’s (2014) Goal Conciliation Theory, I intend in this study to describe and explain the process of formulation, execution and checking of ante-factual abductive hypotheses in developing a computerized solution.

The use of Prolog language family for image recognition

The paper is devoted to the investigation of Prolog language family possibility for its use in the solving of the display image recognition. Diculti appeared in the Prolog implementation of the approach are marked out . It is shown the way in which the number of deduction algorithm steps allows to overcome the appeared diculties. Examples of the Prolog programs implementation to the separation of an etalon image from a complex one are done. Peculiarities of the use of dierent recognizable image formats are analized.

CLUSTER BASED DUPLICATE DETECTION

We propose a clustering technique for entropy based text dis-similarity calculation of de-duplication system. Improve the quality of grouping; in this st udy we propose a Multi-Level Group Detection (MLGD) algorithm which produces a most accurate group with most closely related object using Alternative Decision Tree (ADT) technique. Our propose a two new algorithm; first one is Multi-Level Group Detection (MLGD) formation using Alternative Decision Tree (AD Tree), which will split the bunch of record into self-sized cluster to reduce the volume of data for text comparisons. Second one is calculating the dis-similarity percentage using ent ropy and Information Gain (IG). We show experimentally our proposed technique achieves high er average accuracy than existing traditional deduplication system. Further, our technique not requ ired any manual tuning for clustering formations as well as dis-similarity calculation for any kind of business data. In this study, we have presented a n ew efficient method is introduced for clustering forma tion using ADTree algorithm for duplicate deduction. The new method offers more accuracy dis-similarity measure for each cluster data without manual intervention at the time of duplicate deduct ion.

Алгоритмы обработки запросов к дедуктивным базам данных и реализация алгоритма QSQ

Алгоритмы обработки запросов к дедуктивным базам данных и реализация алгоритма QSQ

IFC-Based Graph Data Model for Topological Queries on Building Elements

AbstractThe adoption of building information modeling (BIM) in construction has led to greater integration of architecture, engineering, construction/facility management (AEC/FM) stakeholders at the project design stage; the result being the incorporation of new complex tasks into construction applications. However, conventional two-dimensional (2D) and nonsemantic three-dimensional (3D) models cannot handle the topological analysis of 3D objects that is required by BIM, especially with regard to building elements. This article describes a new schema, termed the graph data model (GDM) that can be used to employ semantic information, to extract, analyze, and present the topological relationships among 3D objects in 3D space, and to perform topological queries faster. This GDM uses weighted graph principles for simplicity and incorporates an industry foundation classes (IFC)-based algorithm for automatic deduction of topological relationships. A prototype of GDM is implemented in a C# platform and verified ...

The Dynamic Modeling of the Project Management Process

This paper suggests the method of modeling of project breakdown structures (WBS, OBS, TRM, network model etc.) and its relationships on the basis of ER-methodology. This data model could be considered as a semantic network and application of the logical deduction algorithm for the determination of the project breakdown structures solving sequence is also proposed. The derived sequence defines the specific project management process taking into account the restrictions and priorities of the particular project. This algorithm could be applied for project planning and change management.

Deductive inference for the interiors and exteriors of horn theories

In this article, we investigate deductive inference for interiors and exteriors of Horn knowledge bases, where interiors and exteriors were introduced by Makino and Ibaraki [1996] to study stability properties of knowledge bases. We present a linear time algorithm for deduction for interiors and show that deduction is coNP-complete for exteriors. Under model-based representation, we show that the deduction problem for interiors is NP-complete while the one for exteriors is coNP-complete. As for Horn envelopes of exteriors, we show that it is linearly solvable under model-based representation, while it is coNP-complete under formula-based representation. We also discuss polynomially solvable cases for all the intractable problems.

MilQuant: A free, generic software tool for isobaric tagging-based quantitation

Isobaric tagging techniques such as iTRAQ and TMT are widely used in quantitative proteomics and especially useful for samples that demand in vitro labeling. Due to diversity in choices of MS acquisition approaches, identification algorithms, and relative abundance deduction strategies, researchers are faced with a plethora of possibilities when it comes to data analysis. However, the lack of generic and flexible software tool often makes it cumbersome for researchers to perform the analysis entirely as desired. In this paper, we present MilQuant, mzXML-based isobaric labeling quantitator, a pipeline of freely available programs that supports native acquisition files produced by all mass spectrometer types and collection approaches currently used in isobaric tagging based MS data collection. Moreover, aside from effective normalization and abundance ratio deduction algorithms, MilQuant exports various intermediate results along each step of the pipeline, making it easy for researchers to customize the analysis. The functionality of MilQuant was demonstrated by four distinct datasets from different laboratories. The compatibility and extendibility of MilQuant makes it a generic and flexible tool that can serve as a full solution to data analysis of isobaric tagging-based quantitation.

Quality Verification of a Riveting Process by Statistical Analysis and Neural Network

The manufacturing integrity and quality of an aircraft structural joint largely rely on an experienced technician with consistent riveting operation. Highly sophisticated airframe structural geometries have made the training cost of an experienced technician very high. Moreover, manufacturing defects are still unavoidable due to human errors. A quality system, consisting of a riveting control device, data acquisition system, and a programmable monitoring laptop, has been established by analyzing impact signals from a riveting process statistically. The purpose of the current study is to verify the riveting process through a neural network (NN) algorithm. Through proper input data deduction, pattern recognition, and learning algorithm, the NN provides an alternative methodology to verify the riveting quality with reassuring confidence.

Solving flexible flow-shop problem with a hybrid genetic algorithm and data mining: A fuzzy approach

In this paper, an efficient algorithm is presented to solve flexible flow-shop problems using fuzzy approach. The goal is to minimize the total job tardiness. We assume parallel machines with different operation times. In this algorithm, parameters like “due date” and “operation time” follow a triangular fuzzy number. We used data mining technique as a facilitator to help in finding a better solution in such combined optimization problems. Therefore, using a combination of genetic algorithm and an attribute-deductive tool such as data mining, a near optimal solution can be achieved. According to the structure of the presented algorithm, all of the feasible solutions for the flexible flow-shop problem are considered as a database. Via data mining and attribute-driven deduction algorithm, hidden relationships among reserved solutions in the database are extracted. Then, genetic algorithm can use them to seek an optimum solution. Since there are inherited properties in the solutions provided by genetic algorithm, future generation should have the same behavioral models more than preliminary ones. Data mining can significantly improve the performance of the genetic algorithm through analysis of near-optimal scheduling programs and exploration of hidden relationships among pre-reached solutions.

Reliability Analysis Methods for Mixed Uncertainties in Structural System with Multiple Modes

For structural system with multiple modes simultaneously involving random variables, interval variables and fuzzy variables, a logical deduction algorithm is proposed to deal with propagation of uncertainties. Corresponding to assumed membership level in the membership level interval [0,1], the membership interval of fuzzy variables can be obtained. Firstly the fuzzy variables and the corresponding design points that optimize the respective mode will be calculated by iteration. Secondly, with the logical deduction, the interval of upper bound and lower bound of the system reliability with multiple modes can be solved by the fourth moment algorithm based on the point estimate, then the average of the interval will be used to approximate the true value of the upper bound and lower bound. Several numerical examples are presented to demonstrate the proposed method’s advantages both in efficiency and accuracy.

A simple algorithm for deduction

It is shown that a simple deduction engine can be developed for a propositional logic that follows the normal rules of classical logic in symbolic form, but the description of what is known about a proposition uses a two numeric state variables that conveniently describe unknown and inconsistent, as well as true and false. Partly true and partly false can be included in deductions. The multi-valued logic is easily understood as the proposition values relate directly to true and false. The deduction engine provides a convenient standard method for handling multiple or complicated logical relations. It is particularly convenient when the deduction can start with different propositions being given initial values of true or false. It extends Horn clause based deduction for propositional logic to arbitrary clauses. The logic system used has potential applications in many areas. A comparison with propositional logic makes the paper self contained. doi:10.1017/S1446181109000352

Automatic Verification for Secrecy of Cryptographic Protocols in First-Order Logic

In this article, we present a new formal approach for specification and automatic verification of cryptographic protocols. First, we use the first-order theory to formalize cryptographic protocols and intruders. Assuming that messages transmitted by each principal can be received by the intruder, and messages received by each principal can be known by the intruder, we test the security of cryptographic protocols at the standpoint of the intruder. The protocol representation includes three parts: initial knowledge of the intruder, message exchange of the protocol itself, and computation abilities of the intruder. After defining the term and its typeset, we give a first-order frame to formalizing the protocol based on roles, using axioms to depict the communicating actions of each role. The predicate corresponding to the role's message receiving can be affiliated to the axiom by logical connective ^, and the predicate corresponding to the role's message transmitting can be the conclusion of the implication relation. The universal quantification ∀ is used to eliminate the limitation for protocol runs, and existential quantification ∃ is used to denote generating of the key or the nonce, and through renaming and consistency check, ensure the refreshness and boundlessness of the new value. In order to implement the automatic verification, we propose a normal approach to transform the axioms into Horn clauses. Following the Dolev-Yao Model, the computation abilities of the intruder are modeled in Horn clauses too. We adopt the deductive reasoning method to verify the secrecy property of cryptographic protocols. The secrecy property is considered as a goal, and based on a deductive algorithm we can check whether the goal can be inferred from the known rules. The known rules form a rule base B containing the Horn clauses of protocol description and the intruder's abilities and initial knowledge. If the goal can be inferred from the base, the sequence of rules applied will lead to the description of an attack scenario. This approach is fully automatic and terminable. The main contributions of the paper are: a general framework of formalizing cryptographic protocol and abilities of the intruder, a practical solving algorithm based on automatic reasoning, and a simple method to find the attack scenarios.