The First Logic Programming Language - A Retrospective and a Commentary . Abstract In the research literature , logic programming , as a procedural interpretation of SLD resolution , has largely been associated with developments arising from the interaction of Colmerauer and Kowalski and their colleagues in the early seventies . Around 1967 the Group for Computing Research at the University of Aberdeen designed and implemented a programming system called Absys . This paper presents a Prolog interface to the MiniSat satisfiability solver . Logic programming with satisfiability combines the strengths of the two paradigms : logic programming for encoding search problems into satisfiability on the one hand and efficient SAT solving on the other . This synergy between these two exposes a programming paradigm that we propose here as a logic programming pearl . Results We present the Miscoto tool to facilitate the selection of a community optimizing a desired function in a microbiome by reporting several possibilities which can be then sorted according to biological criteria . Communities are exhaustively identified using logical programming and by combining the non‐compartmentalized and the compartmentalized frameworks . The benchmarking of 4.9 million metabolic functions associated with the Human Microbiome Project , shows that Miscoto is suited to screen and classify metabolic producibility in terms of feasibility , functional redundancy and cooperation processes involved . Hence , the presence of a Personal Assistance Service can become a cornerstone in achieving independence and quality of life . Therefore , the objective of this work is to present an intelligent system aimed at an endless individuals monitoring and alerting system , based on a Logical Programming approach to Knowledge Representation and Reasoning , and centre on RapidMiner , a software platform that provides an integrated environment for machine learning , predictive analysis or application development and deployment . It undergoes a Case Based approach to computing that tracks patient ’s performance , learn and deliver content when it is needed , and assures that patient ’s key information is changed into the indispensable ongoing knowledge . In this chapter , the authors present a methodology based on a hybrid approach that adopts a graph-based multidimensional model . In order to automate the whole design process , the methodology has been implemented using logical programming . Francesco Di Tria Università degli Studi di Bari Aldo Moro , Italy Ezio Lefons Università degli Studi di Bari Aldo Moro , Italy Filippo Tangorra Università degli Studi di Bari Aldo Moro , Italy On health policy terms , LOS remains a measurable index of efficiency , and most of the studies that have been undertaken show that QoC or health outcomes do not appear to be compromised by reductions in LOS times . Therefore , and in order to assess this statement , a Logic Programming based methodology to Knowledge Representation and Reasoning , allowing the modeling of the universe of discourse in terms of defective data , information and knowledge is used , being complemented with an Artificial Neural Networks based approach to computing , allowing one to predict for how long a patient should remain in a hospital or at home , during his/her illness experience . The solver module engine starts by producing a First-Order Logic expression representing the question and a logic facts list representing the texts information . There is a logic-programming based module that looks for answers within the facts list that unify and validate the question logic form . For cases where the answer can be directly detected in the text there is an ad-hoc module . We describe practical experiences of using a logic programming based approach to model and reason about concurrent systems . We argue that logic programming is a good foundation for developing , prototyping , and animating new specification languages . Current studies have rarely explored the plentiful uncertain data populating the semantic web . This algorithm handles uncertain data in an inductive logic programming framework by modifying the performance evaluation criteria . A pseudo-log-likelihood based measure is used to evaluate the performance of different literals under uncertainties . Inductive Logic Programming — ILP — is a new paradigm in the field of Machine Learning , which adopts the language of logic programs as a description language for the expression of instances , hypothesis and domain theory . Learning logical definitions requires the exploration of a very large space of hypothesis descriptions and , consequently , restrictions should be imposed on the hypothesis space to make learning a feasible task . structure can efficiently exploited by different item set extraction as well as this novel index structure is implemented by using FPGrowth and LCM V.2 algorithms . Again in this paper we have focused that how the MRDM techniques are used in different approaches like classification , clustering ILP ( Inductive Logic Program ) etc . Thus , an automated mechanism to convert textual data into ontology instances Ontology Population may be crucial . In this context , this paper presents an inductive logic programming-based method that induces rules for extracting instances of various entity classes . This method uses two sources of evidence : domain-independent linguistic patterns for identifying candidates of class instances , and a WordNet semantic similarity measure . As yet , such rules mostly have to be designed by human experts . This article describes a novel approach to machine learning of attribute grammar rules based on the Inductive Logic Programming paradigm . Apart from syntactic differences , logic programs and attribute grammars are basically the same language . So the logic provide easy loop construction and allow to obtain totally correct program schemes . In Nepejvoda calculi Ω actions are ivestigated inside propositional logic framework . In this paper we elaborate a notion of well-founded action and prove useful results allowing investigate actions inside framework of any logic with compactness property . In practice , the decision of specifying a constraint is very important and extremely challenging . We show how techniques from propositional logic can be utilised to offer decision support for specifying Boolean and multivalued dependencies between properties of entities and relationships in conceptual databases . In particular , we use a search version of SAT-solvers to semi-automatically generate sample databases for this class of dependencies in Entity-Relationship models . In this paper , we introduce methods of encoding propositional logic programs in vector spaces . Interpretations are represented by vectors and programs are represented by matrices . The rules associated with propositional logic programs and the stable model semantics are not expressive enough to let one write concise programs . This problem is alleviated by introducing some new types of propositional rules . Semantics of constraint logic programs with bounded quantifiers.- Translating a modal language with embedded implication into Horn clause logic.- Pruning the search space of logic programs.- A significant extension of logic programming by adapting model building rules.- Efficient resource management for linear logic proof search.- A logic language based on GAMMA-like multiset rewriting.- Default negated conclusions : Why not?.- Handling equality in logic programming via basic folding.- An abstract machine for reasoning about situations , actions , and causality.- On the computational complexity of propositional logic programs with nested implications.- Proof-theory for extensions of Logic Programming.- A fibrational semantics for logic programs.- Higher Order Babel : Language and implementation.- A new framework for declarative programming : Categorical perspectives.- Extending constructive negation for partial functions in lazy functional-logic languages.- Super logic programs and negation as belief.- Total correctness of logic programs : A formal approach.- A declarative semantics for the Prolog cut operator.- A definitional approach to functional logic programming.- Soundness and completeness of non-classical extended SLD-resolution.- Some postulates for nonmonotonic theory revision applied to logic programming . This article surveys various complexity and expressiveness results on different forms of logic programming . The main focus is on decidable forms of logic programming , in particular , propositional logic programming and datalog , but we also mention general logic programming with function symbols . Next to classical results on plain logic programming ( pure Horn clause programs ) , more recent results on various important extensions of logic programming are surveyed . This paper surveys various complexity results on different forms of logic programming . The main focus is on decidable forms of logic programming , in particular propositional logic programming and datalog , but we also mention general logic programming with function symbols . Next to classical results on plain logic programming ( pure Horn clause programs ) , more recent results on various important extensions of logic programming are surveyed . The first paper is “ A Logic-Based Approach to Polymer Sequence Analysis ” by R. Bruni . This paper presents an improved propositional logic based approach for the estimation of protein sequences from mass spectroscopy results . The correspondence Ontology matching is the process of discovering similarities between ontology and determines the relationships holding two sets of entities that belong to different discrete ontology . The traditional methodologies of Ontology matching used the intersection of Natural Language Processing techniques and ordinary propositional-logic based approaches , mainly focusing on presence of dominant words in the documents . In this paper a positive attempt has been taken to analyze the presence of some of the non-dominant categorization of words like determiners , time clauses and modal verbs occurring very often in the text documents . In this paper we relate two logical similarity-based approaches to approximate reasoning . One approach extends the framework of ( propositional ) classical logic programming by introducing a similarity relation in the alphabet of the language that allows for an extended unification procedure . The second approach is a many-valued modal logic approach where 3p is understood as approximately p. This survey covers recent contributions from the artificial intelligence research literature about logic-based information fusion . First , the main current approaches dealing with the standard propositional logic formalism are presented and compared . Then , techniques for fusing weighted belief bases are discussed . Current approaches for modelling multi-agent systems with epistemic logic use Kripke semantics where the knowledge base of an agent is represented as atomic propositions , but intelligent agents need to be equipped with formulas to derive implicit information . In this paper , we propose a metamodelling approach where agents ’ state of affairs are separated in different scopes , and the knowledge base of an agent is represented by a propositional logic language restricted to Horn clauses . We propose to use a model driven approach for the diagrammatic representation of multi-agent systems knowledge ( and nested knowledge ) . In this paper we present a propositional logic programming language for reasoning under possibilistic uncertainty and representing vague knowledge . Formulas are represented by pairs ( ϕ , α ) , where ϕ is a many valued proposition and α ∈ [ 0 , 1 ] is a lower bound on the belief on ϕ in terms of necessity measures . Many approaches have been used to detect race conditions , but there still is no effective formal model to visualize the race conditions and program interactions . In this paper , we present a formal graph , named race condition graph ( RCG ) , to represent race conditions in concurrent programs . Some characteristics of RCG are presented and the potential RCG ( PRCG ) as well as the data structure is discussed . The analyzing system directly manipulating graphs is useful for knowledge discovery . Formal Graph System ( FGS ) is a kind of logic programming system which directly deals with graphs just like first order terms . We have designed and implemented a knowledge discovery system KD-FGS , which receives the graph data and produces a hypothesis by using FGS as a knowledge representation language . We describe aggregating as an internal sequencing of the content units derived from a GLO . Our contribution is a formal graph-based model to specify the problem when the variability of LO units is large . First we formulate the problem and consider properties of the proposed model ; and then we analyze a case study , implementation capabilities , and evaluate the approach for e-learning . In previous studies , attempts were made to build specific models for multilingual websites but without differentiating between the different possible patterns . In this paper , we present a new formal graph based model , composed of the link structure and of the translation path between the different language versions of a multilingual website . We define some characteristic properties based on this model and derive four localisation patterns . After preprocessing , a mail piece consists of a set of bounding boxes for each region . In this paper a formal graph representation for mail pieces will be shown , in which relative constellations of bounding boxes are represented in a qualitative way . From a labeled set of such graphs , a model graph of the domain represented by the training set can be derived automatically . The new definition includes detailed information about trust itself . It defines trust of a component as the relationship of the expected behaviors and the trusted prerequisites , and puts forward a formal directed-graph model to express it . With the directed-graph model , it expands trust chain to trust tree and trust forest , use them to give a formal description of trust and security of information system , it also discusses trusted module , and brings forward a multi-layer trusted structure to design trusted module . The complexity of such propositionalisation methods is now in the size of the reformulated problem which is exponential when tackling determinate relational problems . We propose a method that selectively propositionalises the FOL training set by interleaving attribute-value reformulation and algebraic resolution . It avoids , as much as possible , the generation of reformulated examples which are not relevant wrt the discrimination task , and still ensures that explicit correct and complete definitions are learned . The complexity of such propositionalization methods is now in the size of the reformulated problem which can be exponential . We propose a method that selectively propositionalizes the FOL training set by interleaving boolean reformulation and algebraic resolution . It avoids , as much as possible , the generation of redundant boolean examples , and still ensures that explicit correct and complete definitions are learned . The invention is a resolution compensating method for video frequency image convergent-divergent process . In the system , a resolution compensating unit is arranged together with the image convergent-divergent unit parallel . The resolution compensating unit generates a high frequency information component into the result pixel generated by the image convergent-divergent unit when the image is magnified by the resolution compensating unit ; when the image is reduced , it generates a high frequency information component subtracted from the result image pixels by the image reducing unit . The invention is adaptive , and the operation is flexile , and it is convenient . The major objective is to integrate techniques of micro-gripper design , microscopic imaging and high precision motion control to build a prototype system for industrial applications . The workcell consists of a multiple-view imaging system , a 4-DOF micromanipulator with high resolution rotation control , a large working space 4-DOF precision positioning system , a flexible micro-gripper , and a control software system . A piezoelectric force sensing unit is developed to be integrated with the manipulator system to enhance pickup reliability . This paper reports our ongoing work on a microassembly workstation developed for efficient and robust 3D automated assembly of microobjects . The workstation consists of multiple view imaging system , two 3-DOF high precision micromanipulators , and a 3-DOF positioning stage with high resolution rotation control , force sensing probe and gripper , and the control software system . A hybrid control scheme using both vision and force sensory information is proposed for precise and dexterous manipulation of microobjects . Taking photographic description as one of way of image description in internet and camera , including camera tied up with high picture element and high resolution ratio system popular in today has been loved by so many photographers , just because of its easy , fast operation . Photographic description is not simply combined with photos , while its order list shows a kind of " self-practice and remember " of readers in inner closed logical relationship . High‐resolution matched‐field processing requires rather exact specification of the environment through which the acoustic field has propagated in order to perform satisfactorily . Measuring the environment to this level of precision presents a severe challenge to the implementation of high‐resolution matched‐field algorithms . Hence , techniques that can extract environmental information from the ambient acoustic field make a desirable complement to the high‐resolution source localization techniques that require such a well‐specified environment . We also demonstrate applications of our algorithm to quantified-temporal access control ( using V and 3 quantifications ) and partial-order tree-based authorization models . We describe two implementations of our key management protocols on two diverse platforms : a broadcast service operating on top of a publish/subscribe infrastructure and an extension to the Google Maps API to support quality (resolution)-based access control . We analytically and experimentally show the performance and scalability benefits of our approach over traditional key management approaches . Their work however , deals only with an encoder/decoder construction . In this work we introduce a more general framework which we call the precision-resolution ( PR ) constrained system . In PR systems , the encoder has precision constraints , while the decoder has resolution constraints . We implemented our design with three well-known localization algorithms and tested it in two types of outdoor test-bed experiments : an 850-foot-long linear network with 54 MICAz motes , and a regular 2D network covering an area of 10000 square feet with 49 motes . Results show that our design helps eliminate estimation ambiguity with sub-hop resolution , and reduces localization errors by as much as 35 % . In addition , extensive simulations reveal an interesting feature of robustness for our design under unevenly distributed radio propagation path loss , and confirm its effectiveness for large-scale networks . We then go on to introduce the models used and to present the results of our neural-network training . Transcoding Configurations and Parameters We explore resolution-dependent ABR in which each representation is at a different resolution and a different bitrate . In this formulation when the client player switches representations , the stream changes resolution and bitrate . By employing this network , the performance of change detection algorithms can be improved with a smaller number of layers for U-Net with a low computational complexity . Furthermore , the proposed algorithm detects small changes by taking benefits of both geometrical and spectral resolution enhancement and adopting an intensity-hue-saturation ( IHS ) pan-sharpening method . A modified of IHS pan-sharpening algorithm is introduced to solve spectral distortion problem by applying mean filtering in high frequency . In addition , some approaches aim to be scale - invariant . Park et al . adopted a multi - resolution model that acts as a deformable part - based model when scoring large instances and a rigid template when scoring small instances . Yan et al . proposed to map the pedestrians in different resolutions to a common subspace to reduce the differences of local features . Coupled with use of deeper trees and a denser sampling of the data , the improvement obtained using our locally decorrelated channel features ( LDCF ) is substantial . While in the past year the use of deep learning [ 25 ] , motion features [ 27 ] , and multi - resolution models [ 36 ] has brought down log - average miss rate ( MR ) to under 40 % on the Caltech Pedestrian Dataset [ 10 ] , LDCF reduces MR to under 25 % . This translates to a nearly tenfold reduction in false positives over the ( very recent ) state - of - the - art . Note that we do not input the second image directly . All inputs are at full image resolution , flow field estimates from previous networks are upsampled with nearest neighbor upsampling . section : Evaluation However this would an unfair comparison as we would are comparing a 16 times cheaper model on a more difficult task . Also these results of table [ reference ] suggest , one might consider using dedicated high - cost low resolution networks for smaller objects in the R - CNN context . section : Experimental Results and Comparisons Furthermore , we applied JMPF to image super - resolution , because the transformed , compact features are more discriminative to the clustering - regression scheme . Experiment results on several public benchmark datasets also showed that the JMPF - based image super - resolution scheme is consistently superior to recent state - of - the - art image super - resolution algorithms . section : The input segments to the second pathway are extracted from the images down - sampled by a factor of three . Thus , the network is capable of capturing context in a area of the original image through the receptive field of the lower - resolution pathway , while only doubling the computational and memory requirements over the single pathway CNN . In comparison , the most recent 2D CNN systems proposed for lesion segmentation ( ) have a receptive field limited to voxels . We use this generator design to effectively aggregate global and local information for the image synthesis task . We note that such a multi - resolution pipeline is a well - established practice in computer vision and two - scale is often enough . Similar ideas but different architectures could be found in recent unconditional GANs and conditional image generation . Green and red bounding boxes represent the valid and invalid ground - truth objects corresponding to the scale of the chip . As can be seen , in this example , SNIPER efficiently processes all ground - truth objects in an appropriate scale by forming low - resolution chips . subsection : Negative Chip Selection
The First Logic Programming Language - A Retrospective and a Commentary . Abstract In the research literature , logic programming , as a procedural interpretation of SLD resolution , has largely been associated with developments arising from the interaction of Colmerauer and Kowalski and their colleagues in the early seventies . Around 1967 the Group for Computing Research at the University of Aberdeen designed and implemented a programming system called Absys .
This paper presents a Prolog interface to the MiniSat satisfiability solver . Logic programming with satisfiability combines the strengths of the two paradigms : logic programming for encoding search problems into satisfiability on the one hand and efficient SAT solving on the other . This synergy between these two exposes a programming paradigm that we propose here as a logic programming pearl .
Results We present the Miscoto tool to facilitate the selection of a community optimizing a desired function in a microbiome by reporting several possibilities which can be then sorted according to biological criteria . Communities are exhaustively identified using logical programming and by combining the non‐compartmentalized and the compartmentalized frameworks . The benchmarking of 4.9 million metabolic functions associated with the Human Microbiome Project , shows that Miscoto is suited to screen and classify metabolic producibility in terms of feasibility , functional redundancy and cooperation processes involved .
Hence , the presence of a Personal Assistance Service can become a cornerstone in achieving independence and quality of life . Therefore , the objective of this work is to present an intelligent system aimed at an endless individuals monitoring and alerting system , based on a Logical Programming approach to Knowledge Representation and Reasoning , and centre on RapidMiner , a software platform that provides an integrated environment for machine learning , predictive analysis or application development and deployment . It undergoes a Case Based approach to computing that tracks patient ’s performance , learn and deliver content when it is needed , and assures that patient ’s key information is changed into the indispensable ongoing knowledge .
In this chapter , the authors present a methodology based on a hybrid approach that adopts a graph-based multidimensional model . In order to automate the whole design process , the methodology has been implemented using logical programming . Francesco Di Tria Università degli Studi di Bari Aldo Moro , Italy Ezio Lefons Università degli Studi di Bari Aldo Moro , Italy Filippo Tangorra Università degli Studi di Bari Aldo Moro , Italy
On health policy terms , LOS remains a measurable index of efficiency , and most of the studies that have been undertaken show that QoC or health outcomes do not appear to be compromised by reductions in LOS times . Therefore , and in order to assess this statement , a Logic Programming based methodology to Knowledge Representation and Reasoning , allowing the modeling of the universe of discourse in terms of defective data , information and knowledge is used , being complemented with an Artificial Neural Networks based approach to computing , allowing one to predict for how long a patient should remain in a hospital or at home , during his/her illness experience .
The solver module engine starts by producing a First-Order Logic expression representing the question and a logic facts list representing the texts information . There is a logic-programming based module that looks for answers within the facts list that unify and validate the question logic form . For cases where the answer can be directly detected in the text there is an ad-hoc module .
We describe practical experiences of using a logic programming based approach to model and reason about concurrent systems . We argue that logic programming is a good foundation for developing , prototyping , and animating new specification languages .
Current studies have rarely explored the plentiful uncertain data populating the semantic web . This algorithm handles uncertain data in an inductive logic programming framework by modifying the performance evaluation criteria . A pseudo-log-likelihood based measure is used to evaluate the performance of different literals under uncertainties .
Inductive Logic Programming — ILP — is a new paradigm in the field of Machine Learning , which adopts the language of logic programs as a description language for the expression of instances , hypothesis and domain theory . Learning logical definitions requires the exploration of a very large space of hypothesis descriptions and , consequently , restrictions should be imposed on the hypothesis space to make learning a feasible task .
structure can efficiently exploited by different item set extraction as well as this novel index structure is implemented by using FPGrowth and LCM V.2 algorithms . Again in this paper we have focused that how the MRDM techniques are used in different approaches like classification , clustering ILP ( Inductive Logic Program ) etc .
Thus , an automated mechanism to convert textual data into ontology instances Ontology Population may be crucial . In this context , this paper presents an inductive logic programming-based method that induces rules for extracting instances of various entity classes . This method uses two sources of evidence : domain-independent linguistic patterns for identifying candidates of class instances , and a WordNet semantic similarity measure .
As yet , such rules mostly have to be designed by human experts . This article describes a novel approach to machine learning of attribute grammar rules based on the Inductive Logic Programming paradigm . Apart from syntactic differences , logic programs and attribute grammars are basically the same language .
So the logic provide easy loop construction and allow to obtain totally correct program schemes . In Nepejvoda calculi Ω actions are ivestigated inside propositional logic framework . In this paper we elaborate a notion of well-founded action and prove useful results allowing investigate actions inside framework of any logic with compactness property .
In practice , the decision of specifying a constraint is very important and extremely challenging . We show how techniques from propositional logic can be utilised to offer decision support for specifying Boolean and multivalued dependencies between properties of entities and relationships in conceptual databases . In particular , we use a search version of SAT-solvers to semi-automatically generate sample databases for this class of dependencies in Entity-Relationship models .
In this paper , we introduce methods of encoding propositional logic programs in vector spaces . Interpretations are represented by vectors and programs are represented by matrices .
The rules associated with propositional logic programs and the stable model semantics are not expressive enough to let one write concise programs . This problem is alleviated by introducing some new types of propositional rules .
Semantics of constraint logic programs with bounded quantifiers.- Translating a modal language with embedded implication into Horn clause logic.- Pruning the search space of logic programs.- A significant extension of logic programming by adapting model building rules.- Efficient resource management for linear logic proof search.- A logic language based on GAMMA-like multiset rewriting.- Default negated conclusions : Why not?.- Handling equality in logic programming via basic folding.- An abstract machine for reasoning about situations , actions , and causality.- On the computational complexity of propositional logic programs with nested implications.- Proof-theory for extensions of Logic Programming.- A fibrational semantics for logic programs.- Higher Order Babel : Language and implementation.- A new framework for declarative programming : Categorical perspectives.- Extending constructive negation for partial functions in lazy functional-logic languages.- Super logic programs and negation as belief.- Total correctness of logic programs : A formal approach.- A declarative semantics for the Prolog cut operator.- A definitional approach to functional logic programming.- Soundness and completeness of non-classical extended SLD-resolution.- Some postulates for nonmonotonic theory revision applied to logic programming .
This article surveys various complexity and expressiveness results on different forms of logic programming . The main focus is on decidable forms of logic programming , in particular , propositional logic programming and datalog , but we also mention general logic programming with function symbols . Next to classical results on plain logic programming ( pure Horn clause programs ) , more recent results on various important extensions of logic programming are surveyed .
This paper surveys various complexity results on different forms of logic programming . The main focus is on decidable forms of logic programming , in particular propositional logic programming and datalog , but we also mention general logic programming with function symbols . Next to classical results on plain logic programming ( pure Horn clause programs ) , more recent results on various important extensions of logic programming are surveyed .
The first paper is “ A Logic-Based Approach to Polymer Sequence Analysis ” by R. Bruni . This paper presents an improved propositional logic based approach for the estimation of protein sequences from mass spectroscopy results . The correspondence
Ontology matching is the process of discovering similarities between ontology and determines the relationships holding two sets of entities that belong to different discrete ontology . The traditional methodologies of Ontology matching used the intersection of Natural Language Processing techniques and ordinary propositional-logic based approaches , mainly focusing on presence of dominant words in the documents . In this paper a positive attempt has been taken to analyze the presence of some of the non-dominant categorization of words like determiners , time clauses and modal verbs occurring very often in the text documents .
In this paper we relate two logical similarity-based approaches to approximate reasoning . One approach extends the framework of ( propositional ) classical logic programming by introducing a similarity relation in the alphabet of the language that allows for an extended unification procedure . The second approach is a many-valued modal logic approach where 3p is understood as approximately p.
This survey covers recent contributions from the artificial intelligence research literature about logic-based information fusion . First , the main current approaches dealing with the standard propositional logic formalism are presented and compared . Then , techniques for fusing weighted belief bases are discussed .
Current approaches for modelling multi-agent systems with epistemic logic use Kripke semantics where the knowledge base of an agent is represented as atomic propositions , but intelligent agents need to be equipped with formulas to derive implicit information . In this paper , we propose a metamodelling approach where agents ’ state of affairs are separated in different scopes , and the knowledge base of an agent is represented by a propositional logic language restricted to Horn clauses . We propose to use a model driven approach for the diagrammatic representation of multi-agent systems knowledge ( and nested knowledge ) .
In this paper we present a propositional logic programming language for reasoning under possibilistic uncertainty and representing vague knowledge . Formulas are represented by pairs ( ϕ , α ) , where ϕ is a many valued proposition and α ∈ [ 0 , 1 ] is a lower bound on the belief on ϕ in terms of necessity measures .
Many approaches have been used to detect race conditions , but there still is no effective formal model to visualize the race conditions and program interactions . In this paper , we present a formal graph , named race condition graph ( RCG ) , to represent race conditions in concurrent programs . Some characteristics of RCG are presented and the potential RCG ( PRCG ) as well as the data structure is discussed .
The analyzing system directly manipulating graphs is useful for knowledge discovery . Formal Graph System ( FGS ) is a kind of logic programming system which directly deals with graphs just like first order terms . We have designed and implemented a knowledge discovery system KD-FGS , which receives the graph data and produces a hypothesis by using FGS as a knowledge representation language .
We describe aggregating as an internal sequencing of the content units derived from a GLO . Our contribution is a formal graph-based model to specify the problem when the variability of LO units is large . First we formulate the problem and consider properties of the proposed model ; and then we analyze a case study , implementation capabilities , and evaluate the approach for e-learning .
In previous studies , attempts were made to build specific models for multilingual websites but without differentiating between the different possible patterns . In this paper , we present a new formal graph based model , composed of the link structure and of the translation path between the different language versions of a multilingual website . We define some characteristic properties based on this model and derive four localisation patterns .
After preprocessing , a mail piece consists of a set of bounding boxes for each region . In this paper a formal graph representation for mail pieces will be shown , in which relative constellations of bounding boxes are represented in a qualitative way . From a labeled set of such graphs , a model graph of the domain represented by the training set can be derived automatically .
The new definition includes detailed information about trust itself . It defines trust of a component as the relationship of the expected behaviors and the trusted prerequisites , and puts forward a formal directed-graph model to express it . With the directed-graph model , it expands trust chain to trust tree and trust forest , use them to give a formal description of trust and security of information system , it also discusses trusted module , and brings forward a multi-layer trusted structure to design trusted module .
The complexity of such propositionalisation methods is now in the size of the reformulated problem which is exponential when tackling determinate relational problems . We propose a method that selectively propositionalises the FOL training set by interleaving attribute-value reformulation and algebraic resolution . It avoids , as much as possible , the generation of reformulated examples which are not relevant wrt the discrimination task , and still ensures that explicit correct and complete definitions are learned .
The complexity of such propositionalization methods is now in the size of the reformulated problem which can be exponential . We propose a method that selectively propositionalizes the FOL training set by interleaving boolean reformulation and algebraic resolution . It avoids , as much as possible , the generation of redundant boolean examples , and still ensures that explicit correct and complete definitions are learned .
The invention is a resolution compensating method for video frequency image convergent-divergent process . In the system , a resolution compensating unit is arranged together with the image convergent-divergent unit parallel . The resolution compensating unit generates a high frequency information component into the result pixel generated by the image convergent-divergent unit when the image is magnified by the resolution compensating unit ; when the image is reduced , it generates a high frequency information component subtracted from the result image pixels by the image reducing unit . The invention is adaptive , and the operation is flexile , and it is convenient .
The major objective is to integrate techniques of micro-gripper design , microscopic imaging and high precision motion control to build a prototype system for industrial applications . The workcell consists of a multiple-view imaging system , a 4-DOF micromanipulator with high resolution rotation control , a large working space 4-DOF precision positioning system , a flexible micro-gripper , and a control software system . A piezoelectric force sensing unit is developed to be integrated with the manipulator system to enhance pickup reliability .
This paper reports our ongoing work on a microassembly workstation developed for efficient and robust 3D automated assembly of microobjects . The workstation consists of multiple view imaging system , two 3-DOF high precision micromanipulators , and a 3-DOF positioning stage with high resolution rotation control , force sensing probe and gripper , and the control software system . A hybrid control scheme using both vision and force sensory information is proposed for precise and dexterous manipulation of microobjects .
Taking photographic description as one of way of image description in internet and camera , including camera tied up with high picture element and high resolution ratio system popular in today has been loved by so many photographers , just because of its easy , fast operation . Photographic description is not simply combined with photos , while its order list shows a kind of " self-practice and remember " of readers in inner closed logical relationship .
High‐resolution matched‐field processing requires rather exact specification of the environment through which the acoustic field has propagated in order to perform satisfactorily . Measuring the environment to this level of precision presents a severe challenge to the implementation of high‐resolution matched‐field algorithms . Hence , techniques that can extract environmental information from the ambient acoustic field make a desirable complement to the high‐resolution source localization techniques that require such a well‐specified environment .
We also demonstrate applications of our algorithm to quantified-temporal access control ( using V and 3 quantifications ) and partial-order tree-based authorization models . We describe two implementations of our key management protocols on two diverse platforms : a broadcast service operating on top of a publish/subscribe infrastructure and an extension to the Google Maps API to support quality (resolution)-based access control . We analytically and experimentally show the performance and scalability benefits of our approach over traditional key management approaches .
Their work however , deals only with an encoder/decoder construction . In this work we introduce a more general framework which we call the precision-resolution ( PR ) constrained system . In PR systems , the encoder has precision constraints , while the decoder has resolution constraints .
We implemented our design with three well-known localization algorithms and tested it in two types of outdoor test-bed experiments : an 850-foot-long linear network with 54 MICAz motes , and a regular 2D network covering an area of 10000 square feet with 49 motes . Results show that our design helps eliminate estimation ambiguity with sub-hop resolution , and reduces localization errors by as much as 35 % . In addition , extensive simulations reveal an interesting feature of robustness for our design under unevenly distributed radio propagation path loss , and confirm its effectiveness for large-scale networks .
We then go on to introduce the models used and to present the results of our neural-network training . Transcoding Configurations and Parameters We explore resolution-dependent ABR in which each representation is at a different resolution and a different bitrate . In this formulation when the client player switches representations , the stream changes resolution and bitrate .
By employing this network , the performance of change detection algorithms can be improved with a smaller number of layers for U-Net with a low computational complexity . Furthermore , the proposed algorithm detects small changes by taking benefits of both geometrical and spectral resolution enhancement and adopting an intensity-hue-saturation ( IHS ) pan-sharpening method . A modified of IHS pan-sharpening algorithm is introduced to solve spectral distortion problem by applying mean filtering in high frequency .
In addition , some approaches aim to be scale - invariant . Park et al . adopted a multi - resolution model that acts as a deformable part - based model when scoring large instances and a rigid template when scoring small instances . Yan et al . proposed to map the pedestrians in different resolutions to a common subspace to reduce the differences of local features .
Coupled with use of deeper trees and a denser sampling of the data , the improvement obtained using our locally decorrelated channel features ( LDCF ) is substantial . While in the past year the use of deep learning [ 25 ] , motion features [ 27 ] , and multi - resolution models [ 36 ] has brought down log - average miss rate ( MR ) to under 40 % on the Caltech Pedestrian Dataset [ 10 ] , LDCF reduces MR to under 25 % . This translates to a nearly tenfold reduction in false positives over the ( very recent ) state - of - the - art .
Note that we do not input the second image directly . All inputs are at full image resolution , flow field estimates from previous networks are upsampled with nearest neighbor upsampling . section : Evaluation
However this would an unfair comparison as we would are comparing a 16 times cheaper model on a more difficult task . Also these results of table [ reference ] suggest , one might consider using dedicated high - cost low resolution networks for smaller objects in the R - CNN context . section : Experimental Results and Comparisons
Furthermore , we applied JMPF to image super - resolution , because the transformed , compact features are more discriminative to the clustering - regression scheme . Experiment results on several public benchmark datasets also showed that the JMPF - based image super - resolution scheme is consistently superior to recent state - of - the - art image super - resolution algorithms . section :
The input segments to the second pathway are extracted from the images down - sampled by a factor of three . Thus , the network is capable of capturing context in a area of the original image through the receptive field of the lower - resolution pathway , while only doubling the computational and memory requirements over the single pathway CNN . In comparison , the most recent 2D CNN systems proposed for lesion segmentation ( ) have a receptive field limited to voxels .
We use this generator design to effectively aggregate global and local information for the image synthesis task . We note that such a multi - resolution pipeline is a well - established practice in computer vision and two - scale is often enough . Similar ideas but different architectures could be found in recent unconditional GANs and conditional image generation .
Green and red bounding boxes represent the valid and invalid ground - truth objects corresponding to the scale of the chip . As can be seen , in this example , SNIPER efficiently processes all ground - truth objects in an appropriate scale by forming low - resolution chips . subsection : Negative Chip Selection
Absys: The First Logic Programming Language - A Retrospective and a Commentary Logic programming with satisfiability Scalable and exhaustive screening of metabolic functions carried out by microbial consortia Kidney Care - A Personal Assistant Assessment Big Data Warehouse Automatic Design Methodology Length of Hospital Stay and Quality of Care The Senso Question Answering Approach to Portuguese QA@CLEF-2007 Design and Implementation of the High-Level Specification Language CSP(LP) in Prolog DLP Learning from Uncertain Data Constructing the Extensional Representation of an Intentional Domain Theory in Inductive Logic Programming State of Art of Multi Relational Data Mining Approaches: A Rule Mining Algorithm An Inductive Logic Programming-Based Approach for Ontology Population from the Web Learning grammar rules of building parts from precise models and noisy observations A Predicate Logic of Well-Founded Actions Constraint acquisition for Entity-Relationship models Partial Evaluation of Logic Programs in Vector Spaces Extending the Stable Model Semantics with More Expressive Rules Extensions of Logic Programming: 5th International Workshop, Elp '96, Leipzig, Germany, March 28-30, 1996 : Proceedings Complexity and expressive power of logic programming Complexity and expressive power of logic programming Operations Research Models for Computational Biology, Bioinformatics and Medicine Deontic Based Ontology Matching for Conflict Resolution between Text Documents A connection between Similarity Logic Programming and Gödel Modal Logic Logic-based approaches to information fusion An MDE Approach for Modelling and Reasoning About Multi-agent Systems A Complete Calcultis for Possibilistic Logic Programming with Fuzzy Propositional Variables A race condition graph for concurrent program behavior KD-FGS: A Knowledge Discovery System from Graph Data Using Formal Graph System Aggregating of Learning Object Units Derived from a Generative Learning Object A Graph Based Model for Understanding Localisation Patterns in Multilingual Websites Learning regions of interest in postal automation A Formal Model of Trust and Security for Task-Oriented Information System Lazy Propositionalisation for Relational Learning Selective Propositionalization for Relational Learning A method of definition compensation during video image zoom A flexible experimental workcell for efficient and reliable wafer-level 3D micro-assembly Micromanipulation Using a Microassembly Workstation with Vision and Force Sensing A Research on Photographic Description and Its Application in Wuyi Folk Custom A scheme for measuring environmental parameters from the ambient acoustic field with a vertical array of hydrophones Scalable Key Management Algorithms for Location-Based Services On the Capacity of Precision-Resolution Constrained Systems Achieving range-free localization beyond connectivity Optimizing Transcoder Quality Targets Using a Neural Network with an Embedded Bitrate Model Change Detection on Multi-Spectral Images Based on Feature-level U-Net Scale-Aware Fast R-CNN for Pedestrian Detection Local Decorrelation For Improved Pedestrian Detection FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks Rethinking the Inception Architecture for Computer Vision Joint maximum purity forest with application to image super-resolution Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs SNIPER: Efficient Multi-Scale Training
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Personal Assistants CLEF (Working Notes) PADL SBIA DEXA (1) LFCS LPNMR IEEE Conference on Computational Complexity EUSFLAT Conf. PAKDD Culture and Computing ICDAR ISECS ECAI PKDD ICRA ICIC (1) ISIT SenSys Visual Information Processing and Communication IEEE Transactions on Multimedia NIPS 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) J. Electronic Imaging Medical Image Anal. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition NeurIPS
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