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Image authentication is an important research topic of maintaining the integrity of digital image contents. Fragile image authentication is the technique for achieving the goal of image content integrity maintenance. This article presents a fragile image authentication scheme based on the concept of wet paper codes. The proposed scheme modifies dry pixels on an image to conceal an image signature. The proposed authentication scheme can exactly detect the tampered area on a tampered image. For saving computation cost of signature embedding, an exclusive-or operation is used in the proposed authentication scheme. The experimental results show that the proposedmethod not only has good visual quality of an authorized image but also successfully detects tampered areas on a tampered image.

This paper studies the cluster ensemble selection problem for unsupervised learning. Given a large ensemble of clustering solutions, our goal is to select a subset of solutions to form a smaller yet better performing cluster ensemble than using all available solutions. The common way of aggregating the chosen solutions is accumulating the information of the selected results to a similarity matrix. This paper suggests transforming the similarity matrix to a modularity matrix and then applying a new consensus function which optimizes modularity measure in it. We represent the modularity maximization problem as a 0-1 quadratic program which can be exactly solved for small datasets. We also established a new greedy algorithm, namely sum linkage, to optimize the objective function specially for large scale datasets in a very short time. We show that the proposed consensus partition gets much closer to the actual cluster structure than the partitions obtained from the direct application of common cluster ensemble methods. The promising results compared with other most cited consensus functions show the excellent efficiency of the proposed method.

Issues that are related to decision making that is based on dispersed knowledge are discussed in the paper. A system, that was proposed in the article [12], is used in this paper. In the system the process of combining classifiers in coalitions is very important and negotiation is applied in the clustering process. The main aim of the article is to compare the results obtained using five different methods of conflict analysis in the system. All these methods are used if the individual classifiers generate probability vectors over decision classes. The most popular methods are considered: a sum, a product, a median, a maximum and a minimum rules. In the paper, tests, which were performed on data from the UCI repository, are presented. The best methods in a particular situation are indicated. It was found out that some methods do not generate satisfactory results when there are dummy agents in a dispersed data set. That is there are undecided agents who assign the same probability value to many different decision values.

Data used in machine learning applications is prone to contain both vague and incom- plete information. Many authors have proposed to use fuzzy rough set theory in the development of new techniques tackling these characteristics. Fuzzy sets deal with vague data, while rough sets allow to model incomplete information. As such, the hybrid setting of the two paradigms is an ideal candidate tool to confront the separate challenges. In this paper, we present a thorough review on the use of fuzzy rough sets in machine learning applications. We recall their integration in preprocess- ing methods and consider learning algorithms in the supervised, unsupervised and semi-supervised domains and outline future challenges. Throughout the paper, we highlight the interaction between theoretical advances on fuzzy rough sets and practical machine learning tools that take advantage of them.

In our previous paper (10) we proposed new variants of the Discrete Algebraic Recon- struction Technique with combined filtering technique and p erformed experiments on binary soft- ware phantoms within a new test framework to investigate the effect of the filters. Continuing our work, in this paper we extend our study to multivalued phantoms for a deeper investigation in this field. We create a new test phantom set with different intensi ty levels and perform a comprehensive experimental study. The results are evaluated with Relative Mean Error which is extended to multi- valued discrete phantoms. We use a ranking system to create different views to our quantitative data. Finally, the achievements are discussed.

International audience; This paper proposes an approach to solve cost-optimal factored planning problems. Planning consists in organizing actions in order to reach some predefined goal. In factored planning one considers several interacting planning problems and has to design an action plan for each of them. But one must also guarantee that all these local plans are compatible: actions shared among several problems must be jointly performed or jointly rejected. We enrich the problem with the extra requirement that the global plan computed in this modular manner must also minimize the sum of all action costs. A solution is provided to this problem, based on classical message passing algorithms, known as belief propagation in the setting of Bayesian networks. Here, messages carry complex information under the form of weighted (or (min; +)) automata, and all computations are performed with these objects. At the time our first paper on this topic was published, this method was the only one to solve cost-optimal factored planning problems in a modular way. Since then, new approaches were proposed. Experiments on classical benchmarks show that it is a valuable alternative to existing methods.

A Web service is defined as an autonomous unit of application logic that provides either some business functionality or information to other applications through an Internet connection. Web services are based on a set of eXtensible Markup Language XML standards such as Universal Description, Discovery and Integration UDDI, Web Services Description Language WSDL, and Simple Object Access Protocol SOAP. Nowadays Web services are becoming more and more popular for supporting different social applications, thus there are also increasing demands and discussions about Web services privacy protection in information. In general, privacy policies describe an organization's data practices on what information they collect from individuals e.g., consumers and what e.g., purposes they do with it. To enable privacy protection for Web service consumers across multiple domains and services, the World Wide Web Consortium W3C published a document called “Web Services Architecture WSA Requirements” that defines some specific privacy requirements for Web services as a future research topic. This paper presents a mathematical model to construct the privacy policies in SOAP Message Exchange Patterns MEP for social services. Further, this paper also presents the privacy policies in security tokens with SOAP messages.

In this paper we define a new class of contextual grammars and study how the languages generated by such grammars can be accepted by go-through automata. The newly introduced class of grammars is a generalization of the formalism previously used to describe the linguistic process of syllabification. Go-through automata which are used here to recognize, and also parse, the languages generated by this new class of grammars are generalizations of push-down automata in the area of context-sensitivity; they have been proved to be an efficient tool for the recognition of languages generated by contextual grammars. The main results of the paper show how the newly introduced generative model is related with other classes of Marcus contextual languages, and how syllabic languages are recognized and parsed using go-through automata.

This paper presents a new tool for the study of relationships between the total path length or the average depth and the number of misclassifications for decision trees. In addition to algorithm, the paper also presents the results of experiments with datasets from UCI ML Repository [9] and datasets representing Boolean functions with 10 variables.

The majority of today's systems increasingly require sophisticated data management as they need to store and to query large amounts of data for analysis and reporting purposes. In order to keep more “detailed” data available for longer periods, “old” data has to be reduced gradually to save space and improve query performance, especially on resource-constrained systems with limited storage and query processing capabilities. A number of data reduction solutions have been developed, however an effective solution particularly based on gradual data reduction is missing. This paper presents an effective solution for data reduction based on gradual granular data aggregation. With the gradual granular data aggregation mechanism, older data can be made coarse-grained while keeping the newest data fine-grained. For instance, when data is 3 months old aggregate to 1 minute level from 1 second level, when data is 6 months old aggregate to 2 minutes level from 1 minute level and so on. The proposed solution introduces a time granularity based data structure, namely a relational time granularity table that enables long term storage of old data by maintaining it at different levels of granularity and effective query processing due to a reduction in data volume. In addition, the paper describes the implementation strategy derived from a farming case study using standard database technologies.

The paper includes a discussion of issues related to the process of global decision-making on the basis of information stored in several local knowledge bases. The local knowledge bases contain information on the same subject, but are defined on different sets of conditional attributes that are not necessarily disjoint. A decision-making system, which uses a number of knowledge bases, makes global decisions on the basis of a set of conditional attributes specified for all of the local knowledge bases used. The paper contains a description of a multi-agent decision-making system with a hierarchical structure. Additionally, it briefly overviews methods of inference that enable global decision-making in this system and that were proposed in our earlier works. The paper also describes the application of the conditional attributes reduction technique to local knowledge bases. Our main aim was to investigate the effect of attribute reduction on the efficiency of inference in such a system. For a measure of the efficiency of inference, we mean mainly an error rate of classification, for which a definition is given later in this paper. Therefore, our goal was to reduce the error rate of classification.

In this paper we review Lattice Machine, a learning paradigm that “learns” by generalising data in a consistent, conservative and parsimonious way, and has the advantage of being able to provide additional reliability information for any classification. More specifically, we review the related concepts such as hyper tuple and hyper relation, the three generalising criteria equilabelledness, maximality, and supportedness as well as the modelling and classifying algorithms. In an attempt to find a better method for classification in Lattice Machine, we consider the contextual probability which was originally proposed as a measure for approximate reasoning when there is insufficient data. It was later found to be a probability function that has the same classification ability as the data generating probability called primary probability. It was also found to be an alternative way of estimating the primary probability without much model assumption. Consequently, a contextual probability based Bayes classifier can be designed. In this paper we present a new classifier that utilises the Lattice Machine model and generalises the contextual probability based Bayes classifier. We interpret the model as a dense set of data points in the data space and then apply the contextual probability based Bayes classifier. A theorem is presented that allows efficient estimation of the contextual probability based on this interpretation. The proposed classifier is illustrated by examples.

The software architecture is typically defined as the fundamental organization of the system embodied in its components, their relationships to one another and to the system's environment. It also encompases principles governing the system's design and evolution. In order to manage the architecture of a large software system the architect needs a holistic model that supports continuous integration and verification for all system artifacts. In earlier papers we proposed a unified graph-based approach to the problem of managing knowledge about the architecture of a software system. In this paper we demonstrate that this approach facilitates convenient and efficient project measurement. First, we show how existing software metrics can be translated into our model in a way that is independent of the programming language. Second, we introduce new metrics that cross the programming language boundaries and are easily implementable using our approach. We conclude by demonstrating how the new model can be implemented using existing tools. In particular, graph databases are a convenient implementation of an architectural repository. Graph query languages and graph algorithms are an effective way to define metrics and specialized graph views.

This paper introduces descriptive set patterns that originated from our visits with Zdzislaw Pawlak and Andrzej Skowron at Banacha and environs in Warsaw. This paper also celebrates the generosity and caring manner of Andrzej Skowron, who made our visits to Warsaw memorable events. The inspiration for the recent discovery of descriptive set patterns can be traced back to our meetings at Banacha. Descriptive set patterns are collections of near sets that arise rather naturally in the context of an extension of Solomon Leader's uniform topology, which serves as a base topology for compact Hausdorff spaces that are proximity spaces. The particular form of proximity space called EF-proximity reported here is an extension of the proximity space introduced by V. Efremovic during the first half of the 1930s. Proximally continuous functions introduced by Yu.V. Smirnov in 1952 lead to pattern generation of comparable set patterns. Set patterns themselves were first considered by T. Pavlidis in 1968 and led to U. Grenander's introduction of pattern generators during the 1990s. This article considers descriptive set patterns in EF-proximity spaces and their application in digital image classification. Images belong to the same class, provided each image in the class contains set patterns that resemble each other. Image classification then reduces to determining if a set pattern in a test image is near a set pattern in a query image.

In this paper we study the efficiency of information flooding protocols in various communication networks, and in the presence of random faults. We show big differences between the flooding performance of networks with a seemingly similar structure. Since real-life systems usually consist of a moderate number of devices, the analysis presented in this paper is not limited to the asymptotic behavior of the flooding protocol. Instead, exact formulas are provided whenever possible. The presented results can be useful building blocks for the analysis of other, more sophisticated protocols. In particular, they may be used for planning and analyzing sensors network deployed in an environment subject to communication failures.

In this paper we develop a negotiation and contracting framework for inter-organisational workflows. The overall aim is to compute a group-plan from a given set of individual plans, where plans are formulated in the context of a given inter-organisational workflow between the agents. A general problem is that the individual plans are not consistent, i.e. the intersection of all individual plans does not contain a complete process, which leads from the initial state of the workflow to its final state. Therefore, negotiation is needed to obtain a compromise. In this paper we develop a generic negotiation protocol and use branching processes as the elementary data structure.The generic protocol is adapted within the specifics of our SONAR-framework. SONAR is a specification framework that defines the organisational structure of multi-agent systems. SONAR has a formal notion of teams and team-formation which is used here to instantiate the strategy parameters.

The paper presents the clustering algorithm for data with missing values. In this approach both marginalisation and imputation are applied. The result of the clustering is the type-2 fuzzy set / rough fuzzy set. This approach enables the distinction between original and imputed data. The method can be applied to the data sets with all attributes lacking some values. The paper is accompanied by the numerical examples of clustering of synthetic and real-life data sets.

This paper discusses the issues related to the conflict analysis method and the rough set theory, process of global decision-making on the basis of knowledge which is stored in several local knowledge bases. The value of the rough set theory and conflict analysis applied in practical decision support systems with complex domain knowledge are expressed. The furthermore examples of decision support systems with complex domain knowledge are presented in this article. The paper proposes a new approach to the organizational structure of a multi-agent decision-making system, which operates on the basis of dispersed knowledge. In the presented system, the local knowledge bases will be combined into groups in a dynamic way. We will seek to designate groups of local bases on which the test object is classified to the decision classes in a similar manner. Then, a process of knowledge inconsistencies elimination will be implemented for created groups. Global decisions will be made using one of the methods for analysis of conflicts.

In the paper, a computer tool called ROSECON, used for modeling and analyzing systems of concurrent processes, is described. A special attention is focused on synthesis and verification of concurrent systems specified by information systems. Two kinds of models, synchronous and asynchronous, are considered. In the first approach, all processes included in the modeled system are synchronized globally whereas in the second one, each process is synchronized individually. The presented tool allows generating automatically an appropriate model of a system of concurrent processes, in the form of colored Petri nets, from the specification given by an information system. Analysis of the model behaviors enables users to verify the correctness and/or optimality of the obtained models and to provide some modification procedures to get correct and/or more optimal solutions. Examples of selected well known problems in concurrency, in the paper, emphasize usefulness of the tool in the designing systems of concurrent processes.

The classification of texture images, especially those with spatial rotation and region shift, is a challenge and important problem in image analysis and classification. This paper proposes a novel algorithm design, an ellipse invariant algorithm, to improve the capability of texture classification for spatial rotation and region shift. The principle of an ellipse invariant algorithm is to use a minimum ellipse to enclose specific representative pixels extracted by the subtracting clustering method. After translating the coordinates, the ellipse in the rotated texture would be formulated as the ellipse in original texture. Also in this paper a hybrid texture filter is proposed. In the hybrid texture filter the scheme of texture feature extraction include Gabor wavelet, neighboring grey level dependence matrix and the ellipse invariant algorithm. Support vector machines SVMs are introduced as the classifier. The proposed hybrid texture filter can classify both the stochastic textures and structural textures. Experimental results reveal that this proposed algorithm outperforms existing design algorithms.

It is well known that Modal Epistemic logic (MEL) suffers from the problem of logical omniscience. In this paper, we will argue that in order to solve the problem, the temporal dimension of knowledge has to be revealed and following this analysis, we present a general epistemic framework, timed Modal Epistemic Logic (tMEL), modified from MEL, such that the time at which a formula is known by an agent based on his reasoning procedure is explicitly stated. With the help of the additional temporal devices, we are able to determine what is actually known by the agent within a reasonable time of reasoning. The discussions will focus on tS4, the tMEL counterpart of S4, but the method can be uniformly generalized to the study of other tMEL logics. Both the semantics and axiomatic proof systems will be provided, accompanied by soundness and completeness results, and other technical features of tMEL are also examined. This work originates from the study of Justification Logic, which shapes many aspects of this paper, and is also a direct response to the request to utilize the use of awareness functions such that time can be added to the picture. A generalized awareness function is employed in the semantics to trace when a formula is deduced.

The ability to grasp objects of different size and shape is one of the most important skills of a humanoid robot. There are a lot of different approaches tackling this problem; however, there is no general solution. The complexity and the skill of a possible grasping motion depend hardly on a particular robot. In this paper we analyze the kinematic and sensory grasping abilities of the humanoid robot Nao. Its kinematic constraints and hand's mechanical structure represent an interesting case of study due to lack of actuators for fingers and the limited computation power. After describing the platform and studying its capabilities, we propose some simple controllers and we present a benchmark based on some experimental data. (The authors are grateful to the members of the Nao Team Humboldt for technical support and also to Manfred Hild and Philipp Hahnel for inspiring discussions.) (Both authors contributed equally to this paper.)

The aim of the paper is to propose how to enrich a formal model of persuasion with a specification for actions which are typical for persuasion process. First, since these actions are verbal, they influence a receiver but do not change the agent’s environment. In a formal framework, we represent them as actions that change not the particular state of a model, but the whole model. Second, effects of those actions depend on how much the receiver trusts the persuader. To formally model this phenomenon, we use a trust function. Finally, we want to represent uncertainty in terms of probability. Thus far, our model did not allow to express those properties of the persuasion process. Therefore, in this paper we extend Multimodal Logic of Actions and Graded Beliefs (AG n) with Probabilistic Dynamic Epistemic Logic (PDEL) and elements of Reputation Management framework (RM). Incorporation of PDEL into the model of persuasion requires some modifications of PDEL. Such extended model is then used to enrich Perseus - our software tool that enables to examine persuasive multi-agent systems. New components of the tool allow us to execute parametric verification of the different properties related to updating probabilistic beliefs in persuasion.

We consider the inductive inference model of Gold [15]. Suppose we are given a set of functions that are learnable with certain number of mind changes and errors. What properties of these functions are learnable if we allow fewer number of mind changes or errors? In order to answer this question this paper extends the Inductive Inference model introduced by Gold [15]. Another motivation for this extension is to understand and characterize properties that are learnable for a given set of functions. Our extension considers a wide range of properties of function based on their input-output relationship. Two specific properties of functions are studied in this paper. The first property, which we call modality, explores how the output of a function fluctuates. For example, consider a function that predicts the price of a stock. A brokerage company buys and sells stocks very often in a day for its clients with the intent of maximizing their profit. If the company is able predict the trend of the stock market "reasonably" accurately then it is bound to be very successful. Identification criterion for this property of a function f is called PREX which predicts if f(x) is equal to, less than or greater than f(x+1) for each x. Next, as opposed to a constant tracking by a brokerage company, an individual investor does not often track dynamic changes in stock values. Instead, the investor would like to move the investment to a less risky option when the investment exceeds or falls below certain threshold. We capture this notion using an identification criterion called TREX that essentially predicts if a function value is at, above, or below a threshold value. Conceptually,modality prediction (i.e., PREX) and threshold prediction (i.e., TREX) are "easier" than EX learning. We show that neither the number of errors nor the number of mind-changes can be reduced when we ease the learning criterion from exact learning to learning modality or threshold. We also prove that PREX and TREX are totally different properties to predict. That is, the strategy for a brokerage company may not be a good strategy for individual investor and vice versa.

This paper presents a novel image encryption scheme based on Toeplitz matrix and Hankel matrix. According to the definition and the expression of Toeplitz matrix and Hankel matrix, we permutate the positions of image pixels to confuse the relationship between the plain-image and cipher-image, and construct a new key. To enhance security further, hyper-chaos system of Lu and Chen is taken to change the grey distribution of image pixels simultaneously. Numerical experimental results demonstrate that the key space is large enough, and the key is sensitive to initial conditions to resist the brute-force attack in the proposed scheme. Additionally, the distribution of grey values in encrypted image has a random-like behavior to resist statistical analysis. The proposed encryption scheme in this paper can suggest a high secure and efficient way for real-time image encryption and transmission in practice.

In this paper, a novel point symmetry based pattern classifier (PSC) is proposed. A recently developed point symmetry based distance is utilized to determine the amount of point symmetry of a particular test pattern with respect to a class prototype. Kd-tree based nearest neighbor search is used for reducing the complexity of point symmetry distance computation. The proposed point symmetry based classifier is well-suited for classifying data sets having point symmetric classes, irrespective of any convexity, overlap or size. In order to classify data sets having line symmetry property, a line symmetry based classifier (LSC) along the lines of PSC is thereafter proposed in this paper. To measure the total amount of line symmetry of a particular point in a class, a new definition of line symmetry based distance is also provided. Proposed LSC preserves the advantages of PSC. The performance of PSC and LSC are demonstrated in classifying fourteen artificial and real-life data sets of varying complexities. For the purpose of comparison, k-NN classifier and the well-known support vector machine (SVM) based classifiers are executed on the data sets used here for the experiments. Statistical analysis, ANOVA, is also performed to compare the performance of these classification techniques.

This paper considers distributed systems, defined as a collection of components interacting through interfaces. Components, interfaces and distributed systems are modeled as Petri nets. It is well known that the unfolding of such a distributed system factorises, in the sense that it can be expressed as the composition of unfoldings of its components. This factorised form of the unfolding generally provides a more compact representation of the system runs, because each component does not need to represent the possible choices (conflicts) appearing in the other components. Moreover, the unfolding factorisation makes it possible to analyse the system by parts. The paper focuses on the derivation of a finite and complete prefix (FCP) in the unfolding of a distributed system. Specifically, one would like to directly obtain such a FCP in factorised form, without computing first a FCP of the global distributed system and then factorising it. The construction of such a "modular FCP" is based on deriving summaries of component behaviours w.r.t. their interfaces, that are then communicated to the neighbouring components. The latter combine these summaries with their local behaviours, and prepare interface summaries for the next components. This globally takes the form of a message passing algorithm, where the global system is never considered.

This paper introduces enhancements to the synthesis of circuits that involve write-afterread (WAR) operations and use the four-phase handshake protocol. The paper demonstrates that the use of edge-triggering makes possible many useful trade-offs among speed, area and powerdelay product. Significant increases in speed are possible as a result of increased concurrency in the circuit's operation, which compensates for much of the penalty associated with the down phase of the four-phase protocol. It is also shown that concurrency can be increased by the judicious insertion of T-elements in the system's control structure. Simulation results for a 16-bit accumulator showed a speed increase of 50% and a reduction of 23% in the power-delay product. The speed of a radix-4 Booth multiplier increased by over 27% and its area and energy consumption were reduced by 5%. These results were derived using test circuits synthesized by Balsa and implemented in Synopsys using 180-nm technology.

In this paper, an evolutionary technique for the semi-supervised clustering is proposed. The proposed technique uses a point symmetry based distance measure. Semi-supervised classification uses aspects of both unsupervised and supervised learning to improve upon the performance of traditional classification methods. In this paper the existing point symmetry based genetic clustering technique, GAPS-clustering, is extended in two different ways to handle the semi-supervised classi- fication problem. The proposed semi-GAPS clustering algorithmis able to detect any type of clusters irrespective of shape, size and convexity as long as they possess the point symmetry property. Kdtree based nearest neighbor search is used to reduce the complexity of finding the closest symmetric point. Adaptive mutation and crossover probabilities are used. Experimental results demonstrate practical performance benefits of the methodology in detecting classes having symmetrical shapes in case of semi-supervised clustering.

Automatic Image Annotation is an important research topic in pattern recognition area. There are many different approaches to Automatic Image Annotation. In many of these approaches a key problem is determining correct word distributions in the generated annotations. Incorrect word distributions (word frequencies) results in large reduction of annotation quality. The paper presents a generic approach to find correct word frequencies. The approach may be used with many automatic image annotators, based on various machine learning paradigms. Proposed method is an improved version of our already presented method, called Greedy Resulted Word Counts Optimization. The key differences and novelties in the paper are: the concept of border support values and a method of optimal step selection in the optimization routine. Optimal step selection allows to reduce the number of computations during the optimization procedure, comparing to old Greedy Resulted Word Counts Optimization method.

Data mining methods in medicine is a very important tool for developing automated decision support systems. However, since information granularity of disease codes used in hospital information system is coarser than that of real clinical definitions of diseases and their treatment, automated data curation is needed to extract knowledge useful for clinical decision making. This paper proposes automated construction of clinical process plan from nursing order histories and discharge summaries stored in hospital information system with curation of disease codes as follows. First, the system applies EM clustering to estimate subgrouping of a given disease code from clinical cases. Second, it decomposes the original datasets into datasets of subgroups by using granular homogenization. Thirdly, clinical pathway generation method is applied to the datasets. Fourthly, classification models of subgroups are constructed by using the analysis of discharge summaries to capture the meaning of each subgroup. Finally, the clinical pathway of a given disease code is output as the combination of the classifiers of subgroups and the the pathways of the corresponding subgroups. The proposed method was evaluated on the datasets extracted hospital information system in Shimane University Hosptial. The obtained results show that more plausible clinical pathways were obtained, compared with previously introduced methods.

A Triangulated Irregular Network (TIN) is a data structure that is usually used for representing and storing monotone geographic surfaces, approximately. In this representation, the surface is approximated by a set of triangular faces whose projection on the XY-plane is a triangulation. The visibility graph of a TIN is a graph whose vertices correspond to the vertices of the TIN and there is an edge between two vertices if their corresponding vertices on TIN see each other, i.e. the segment that connects these vertices completely lies above the TIN. Computing the visibility graph of a TIN and its properties have been considered thoroughly in the literature. In this paper, we consider this problem in reverse: Given a graph G, is there a TIN with the same visibility graph as G? We show that this problem is ∃ℝ-Complete.

Epilepsy is a neurological condition of human being, mostly treated based on the patients’ seizure symptoms, often recorded over multiple visits to a health-care facility. The lengthy time-consuming process of obtaining multiple recordings creates an obstacle in detecting epileptic patients in real time. An epileptic signature validated over EEG data of multiple similar kinds of epilepsy cases will haste the decision-making process of clinicians. In this paper, we have identified EEG data derived signatures for differentiating epileptic patients from normal individuals. Here we define the signatures with the help of various machine learning techniques, viz., feature selection and classification, pattern mining, and fuzzy rule mining. These signatures will add confidence to the decision-making process for detecting epileptic patients. Moreover, we define separate signatures by incorporating few demographic features like gender and age. Such signatures may aid the clinicians with the generalized epileptic signature in case of complex decisions.

This paper is a cryptographically motivated study of the knapsack, its subset sum function and its inverse relation, the decipherment function. The novelty is that the subset sum function is not assumed to be injective. Instead, various forms of “jectivity” are introduced, distinguished by the amount of subsets that are allowed to have the same sum. Besides charting several general properties of knapsacks and the functions the paper reports on experiments that looked for dense knapsacks. Also a concrete construction of a relatively dense easily decipherable knapsack is presented.

This paper proposes a framework for developing a broad variety of soft clustering and learning vector quantization (LVQ) algorithms based on gradient descent minimization of a reformulation function. According to the proposed axiomatic approach to learning vector quantization, the development of specific algorithms reduces to the selection of a generator function. A linear generator function leads to the fuzzy c-means (FCM) and fuzzy LVQ (FLVQ) algorithms while an exponential generator function leads to entropy constrained fuzzy clustering (ECFC) and entropy constrained LVQ (ECLVQ) algorithms. The reformulation of clustering and LVQ algorithms is also extended to supervised learning models through an axiomatic approach proposed for reformulating radial basis function (RBF) neural networks. This approach results in a broad variety of admissible RBF models, while the form of the radial basis functions is determined by a generator function. This paper shows that gradient descent learning makes reformulated RBF neural networks an attractive alternative to conventional feed-forward neural networks.

In critical infrastructures like airports, much care has to be devoted in protecting radio communication networks from external electromagnetic interference. Protection of such mission-critical radio communication networks is usually tackled by exploiting radiogoniometers: at least three suitably deployed radiogoniometers, and a gateway gathering information from them, permit to monitor and localise sources of electromagnetic emissions that are not supposed to be present in the monitored area. Typically, radiogoniometers are connected to the gateway through relay nodes. As a result, some degree of fault-tolerance for the network of relay nodes is essential in order to offer a reliable monitoring. On the other hand, deployment of relay nodes is typically quite expensive. As a result, we have two conflicting requirements: minimise costs while guaranteeing a given fault-tolerance. In this paper, we address the problem of computing a deployment for relay nodes that minimises the relay node network cost while at the same time guaranteeing proper working of the network even when some of the relay nodes (up to a given maximum number) become faulty (fault-tolerance). We show that, by means of a computation-intensive pre-processing on a HPC infrastructure, the above optimisation problem can be encoded as a 0/1 Linear Program, becoming suitable to be approached with standard Artificial Intelligence reasoners like MILP, PB-SAT, and SMT/OMT solvers. Our problem formulation enables us to present experimental results comparing the performance of these three solving technologies on a real case study of a relay node network deployment in areas of the Leonardo da Vinci Airport in Rome, Italy., 33 pages, 11 figures

A Distributed Knowledge-Based System (DKBS) is a collection of autonomous knowledge-based systems called agents which are capable of interacting with each other. A query can be submitted to one agent or a group of agents. An agent when contacted by the user acts as a master agent. If he is unable to answer the query, he looks for help from other agents which act as his slaves. In this paper, an agent is represented by an information system (either complete or incomplete), a collection of rules called a knowledge base and the Query Approximate Answering System (QAAS). Rules are interpreted as descriptions of some attribute values in terms of other attribute values. These descriptions are usually not precise and they only provide a lower approximation of attribute values. We say that an attribute value is reachable by an agent if either it belongs to the domain of one of the attributes in his information system or it is a decision part of one of the rules in his knowledge base. In the second case, we assume that all attribute values from the classification part of a rule have to be reachable. When rules are discovered by one site of DKBS which currently acts as a slave, they are sent to the master agent of that slave. The QAAS of the master agent will use these rules to answer a query submitted by the user and next it will store these rules in the agent's knowledge base. So, the set of reachable attribute values at any site of DKBS is constantly changing. Knowledge bases built that way might easily become inconsistent because rules they contain are created independently at different sites of DKBS. The problem of repairing inconsistent rules was investigated in [19]. In this paper, we propose a strategy for discovering rules in incomplete information systems and give a formal system for handling queries in DKBS where each site contains either an incomplete or a complete information system.

Alternative approaches to data processing, e.g., fuzzy set theory, rough set theory and neural networks, known collectively as ‘soft computing,’ have drawn significant interest from the scientific community. They have already found numerous applications to real-world problems and will certainly become even more important in the future. This paper concentrates on two important classes of soft computing methods, i.e., fuzzy sets and rough sets. In particular, knowledge acquisition and representation in fuzzy and rough controllers is discussed and compared. The paper is concluded with an illustrative example, showing the application of fuzzy and rough set theory to the control of the ‘inverted pendulum.’

The theory introduced, presented and developed in this paper, is concerned with Rough Concept Analysis. This theory is a synthesis of the theory of Rough Sets pioneered by Zdzislaw Pawlak [10] with the theory of Formal Concept Analysis pioneered by Rudolf Wille [11]. The central notion in this paper of a rough formal concept combines in a natural fashion the two notions of rough set and formal concept — to use a slogan: “rough set + formal concept = rough formal concept”. This paper is an extension of the paper [5] presented at the international workshop on Rough Sets and Knowledge Discovery (RSKD'93). A related paper [8] using distributed constraints provides a synthesis of the two important data modeling techniques: conceptual scaling of Formal Concept Analysis, and Entity-Relationship database modeling. A follow-up paper [9] will extend rough concept analysis from formal contexts to distributed constraints.

In order to acquire knowledge from databases, there have been proposed several methods of inductive learning, such as ID3 family and AQ family. These methods are applied to discover meaningful knowledge from large databases, and their usefulness is ensured. However, since there has been no formal approach proposed to treat these methods, efficiency of each method is only compared empirically. In this paper, we introduce matroid theory and rough sets to construct a common framework for empirical machine learning methods which induce the combination of attribute-value pairs from databases. Combination of the concepts of rough sets and matroid theory gives us an excellent set-theoretical framework and enables us to understand the differences and the similarities between these methods clearly. In this paper, we compare three classical methods, AQ, Pawlak's Consistent Rules and ID3. The results show that there exist the differences in algebraic structure between the former two and the latter and that this causes the differences between AQ and ID3.

Selman and Kautz's paper on the logic of model-preference defaults introduced a new language and tractability results. This paper assesses the contributions of that work, including the properties of the logic itself, its relationship to some previous logics, and its applicability to some known tractable problems.

In this paper we investigate the type inference problem for a large class of type assignment systems for the λ-calculus. This is the problem of determining if a term has a type in a given system. We discuss, in particular, a collection of type assignment systems which correspond to the typed systems of Barendregt’s “cube”. Type dependencies being shown redundant, we focus on the strongest of all, Fω, the type assignment version of the system Fω of Girard. In order to manipulate uniformly type inferences we give a syntax directed presentation of Fω and introduce the notions of scheme and of principal type scheme. Making essential use of them, we succeed in reducing the type inference problem for Fω to a restriction of the higher order semi-unification problem and in showing that the conditional type inference problem for Fω is undecidable. Throughout the paper we call attention to open problems and formulate some conjectures.

The relation between a structure and the function running on that structure is of central interest in many fields, including computer science, biology (organ vs. function), psychology (body vs. mind), architecture (designs vs. functionality), etc. Our paper addresses this question with reference to computer science recent hardware and software advances, particularly in areas as robotics, AI-hardware, self-adaptive systems, IoT, CPS, etc. At the modelling, conceptual level, our main contribution is the introduction of the concept of "virtual organism" (VO), to populate the intermediary level between rigid, slightly reconfigurable, hardware agents and abstract, intelligent, adaptive software agents. A virtual organism has a structure, resembling the hardware capabilities, and it runs low-level functions, implementing the software requirements. The model is compositional in space (allowing the virtual organisms to aggregate into larger organisms) and in time (allowing the virtual organisms to get composed functionalities). Technically, the virtual organisms studied here are in 2D and their structures are described by regular 2D pattens; adding the time dimension, we conclude the VO model is in 3D. By reconfiguration, an organism may change its structure to another structure belonging to the same 2D pattern. We illustrate the VO concept with three increasingly more complex VOs: (1) a tree collector; (2) a feeding cell; and (3) a collection of connected feeding cells. We implemented a simulator for tree collector organisms and the quantitative results show reconfigurable structures are better suited than fixed structures in dynamically changing environments. We briefly show how Agapia - a structured parallel, interactive programming language where dataflow and control flow structures can be freely mixed - may be used for getting quick implementations for VO's simulation.

In this paper we define two new inference classes Team Density and Team Uniform Density. The team density class turns out to be the “most powerful” of all the inference classes that have been defined and studied so far. We compare these classes to previously defined classes. We obtain necessary and sufficient conditions for one team density class to be a subset of another team density class. Similar results are obtained for team uniform density class. We also have compare team density and team uniform density classes and obtained necessary and sufficient conditions for one class to be a subset of another. Most importantly we show that team density, the “most powerfull” class, cannot cover the BC class. From this result we obtain several theorems as corollaries that have been proved in earlier papers.

In this paper we investigate similarity measures based on minimal absent words, introduced by Chairungsee and Crochemore in [1]. They make use of a length-weighted index on a sample set corresponding to the symmetric difference M(x)ΔM(y) of the minimal absent words M(x) and M(y) of two sequences x and y, respectively. We first propose a variant of this measure by choosing as a sample set a proper subset (x, y) of M(x)ΔM(y), which appears to be more appropriate for distinguishing x and y. From the algebraic point of view, we prove that (x, y) is the base of the ideal generated by M(x)ΔM(y). We then remark that such measures are able to recognize whether the sequences x and y share a common structure, but they are not able to detect the difference on the number of occurrences of such a structure in the two sequences. In order to take into account such a multiplicity, we introduce the notion of multifactor, and define a new measure that uses both absent words and multifactors. Surprisingly, we prove that this similarity measure coincides with a distance on sequences introduced by Ehrenfeucht and Haussler in [2], in the context of block-moves strategies. In this way, our result creates a non trivial bridge between similarity measures based on absent words and those based on the block-moves approach.

DNA strand replacement technology has the advantages of simple operation which makes it becomes a common method of DNA computing. A four bit binary number Complementer based on two-domain DNA strand displacement is proposed in this paper. It implements the function of converting binary code into complement code. Simulation experiment based on Visual DSD software is carried out. The simulation results show the correctness and feasibility of the logic model of the Complementer, and it makes useful exploration for further expanding the application of molecular logic circuit.

International audience; In conjunction with the rapid growth and adoption of social media, people are more and more willing to share their personal experiences and opinions about products and/or services with the community. Opinions could be the basis of developing systems that would advise future users on how to proceed with any purchase without risking any disappointment. Unfortunately, opinions are not always genuine due to for instance, biased users as well as mixed feedback coming from the same users (i.e., multi-identity). This paper presents an approach for opinion analysis using credibility as a decisive criterion for supporting future users make sound decisions. The effectiveness of this approach has been tested using opinions posted on Twitter.

This paper is a review of the formal definitions of a description language of functional models of Constraint-Based Lexicalized Grammar (CBLG), with a notion of well-formedness, models of linguisti ...

In the paper, an application of dynamic programming approach for optimization of association rules from the point of view of knowledge representation is considered. Experimental results present cardinality of the set of association rules constructed for information system and lower bound on minimum possible cardinality of rule set based on the information obtained during algorithm work.

Moments and moment invariants have become a powerful tool in image processing ow- ing to their image description capability and invariance property. But, conventional methods are mainly introduced to deal with the binary or gray-scale images, and the only approaches for color image always have poor color image description capability. Based on Exponent moments (EMs) and quaternion, we introduced the quaternion Exponent moments (QEMs) for describing color images in this paper, which can be seen as the generalization of EMs for gray-level images. It is shown that the QEMs can be obtained from the EMs of each color channel. We derived and analyzed the rotation, scaling, and translation (RST) invariant property of QEMs. We also discussed the problem of color image retrieval using QEMs. Experimental results are provided to illustrate the efficiency of the proposed color image descriptors.