bluuebunny/crossref_metadata_2025 huggingface dataset:
An Efficient Computation of the Equation K-automaton of a Regular	K-expression
Authors: Champarnaud Jean-Marc , Ouardi Faissal , Ziadi Djelloul
Published: December 2019

The aim of this paper is to describe a quadratic algorithm for computing the equation K-automaton of a regular K-expression as defined by Lombardy and Sakarovitch. Our construction is based on an extension to regular K-expressions of the notion of c-continuation that we introduced to compute the equation automaton of a regular expression as a quotient of its position automaton.
A Fragile Digital Image Authentication Scheme Inspired by Wet	Paper Codes
Authors: Chang Chin-Chen , Chou Yung-Chen
Published: December 2019

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.
Approximate Sorting
Authors: Giesen Joachim , Schuberth Eva , Stojaković Miloš
Published: December 2019

We show that any comparison based, randomized algorithm to approximate any given ranking of n items within expected Spearman's footrule distance n ^{2} /ν(n) needs at least

n (min{log ν(n), log n} − 6)

comparisons in the worst case. This bound is tight up to a constant factor since there exists a deterministic algorithm that shows that 6n log (n) comparisons are always sufficient.
Indexing of Spatio-Temporal Telemetric Data Based on Adaptive	Multi-Dimensional Bucket Index
Authors: Gorawski Marcin , Dyga Adam
Published: December 2019

This paper describes the idea of a multi-dimensional bucket index designed for efficient indexing of telemetric readings. This data structure answers spatio-temporal range queries concerning utility usage within user selected region and time. In addition, it has a capability to adjust to incoming data and therefore is suitable to process data of highly dynamic nature. The paper also presents a stochastic prediction method to estimate utility usage in the near future.
Nondeterministic State Complexity of Basic Operations for	Prefix-Free Regular Languages
Authors: Han Yo-Sub , Salomaa Kai , Wood Derick
Published: December 2019

We investigate the nondeterministic state complexity of basic operations for prefix-free regular languages. The nondeterministic state complexity of an operation is the number of states that are necessary and sufficient in the worst-case for a minimal nondeterministic finite-state automaton that accepts the language obtained from the operation. We establish the precise state complexity of catenation, union, intersection, Kleene star, reversal and complementation for prefix-free regular languages.
A Computational Model of Language Acquisition: the Emergence of	Words
Authors: ten Bosch Louis , Boves Lou , Van Hamme Hugo , Moore Roger K.
Published: December 2019

In this paper, we discuss a computational model that is able to detect and build word-like representations on the basis of sensory input. The model is designed and tested with a further aim to investigate how infants may learn to communicate by means of spoken language. The computational model makes use of a memory, a perception module, and the concept of 'learning drive'. Learning takes place within a communicative loop between a 'caregiver' and the 'learner'.

Experiments carried out on three European languages with different genetic background (Finnish, Swedish, and Dutch) show that a robust word representation can be learned in using less than 100 acoustic tokens (examples) of that word. The model is inspired by the memory structure that is assumed functional for human cognitive processing.
A Digital Image Encryption Algorithm Based on Hyper-chaotic	Cellular Neural Network
Authors: Peng Jun , Zhang Du , Liao Xiaofeng
Published: December 2019

Using Chaotic characteristics of dynamic system is a promising direction to design cryptosystems that play a pivotal role in a very important engineering application of cognitive informatics, i.e., information assurance and security. However, encryption algorithms based on the lowdimensional chaotic maps face a potential risk of the keystream being reconstructed via return map technique or neural network method. In this paper, we propose a new digital image encryption algorithm that employs a hyper-chaotic cellular neural network. To substantiate its security characteristics, we conduct the following security analyses of the proposed algorithm: key space analysis, sensitivity analysis, information entropy analysis and correlation coefficients analysis of adjacent pixels. The results demonstrate that the proposed encryption algorithm has desirable security properties and can be deployed as a cornerstone in a sound security cryptosystem. The comparison of the proposed algorithm with five other chaos-based image encryption algorithms indicates that our algorithm has a better security performance.
Paradigms of Denotational Mathematics for Cognitive Informatics	and Cognitive Computing
Authors: Wang Yingxu
Published: December 2019

The abstract, rigorous, and expressive needs in cognitive informatics, intelligence science, software science, and knowledge science lead to new forms of mathematics collectively known as denotational mathematics. Denotational mathematics is a category of expressive mathematical structures that deals with high level mathematical entities beyond numbers and sets, such as abstract objects, complex relations, behavioral information, concepts, knowledge, processes, and systems. Denotational mathematics is usually in the form of abstract algebra that is a branch of mathematics in which a system of abstract notations is adopted to denote relations of abstract mathematical entities and their algebraic operations based on given axioms and laws. Four paradigms of denotational mathematics, known as concept algebra, system algebra, Real-Time Process Algebra (RTPA), and Visual Semantic Algebra (VSA), are introduced in this paper. Applications of denotational mathematics in cognitive informatics and computational intelligence are elaborated. Denotational mathematics is widely applicable to model and manipulate complex architectures and behaviors of both humans and intelligent systems, as well as long chains of inference processes.
Preface: Cognitive Informatics, Cognitive Computing, and Their	Denotational Mathematical Foundations (II)
Authors: Wang Yingxu , Zhang Du , Tsumoto Shusaku
Published: December 2019

Cognitive Informatics is a cutting-edge and multidisciplinary research area that tackles the fundamental problems shared by modern informatics, computing, software engineering, AI, cybernetics, cognitive science, neuropsychology, medical science, systems science, philosophy, linguistics, economics, management science, and life sciences. This editorial introduces the emerging field of cognitive informatics and its applications in cognitive computing, abstract intelligence, computational mathematics, and computational intelligence. The themes and structure of this special issue on cognitive informatics are described, and then, focuses of the selected papers in Part II of this special issue are highlighted.
A Formal Syntax of Natural Languages and the Deductive	Grammar
Authors: Wang Yingxu
Published: December 2019

This paper presents a formal syntax framework of natural languages for computational linguistics. The abstract syntax of natural languages, particularly English, and their formal manipulations are described. On the basis of the abstract syntax, a universal language processing model and the deductive grammar of English are developed toward the formalization of Chomsky's universal grammar in linguistics. Comparative analyses of natural and programming languages, as well as the linguistic perception on software engineering, are discussed. A wide range of applications of the deductive grammar of English have been explored in language acquisition, comprehension, generation, and processing in cognitive informatics, computational intelligence, and cognitive computing.
Investigating Accuracies of Classifications for Randomized	Imbalanced Class Distributions
Authors: Abe Hidenao , Tsumoto Shusaku
Published: December 2019

In datamining post-processing, rule selection with objective rule evaluation indices is one of useful methods for extracting valuable knowledge from mined patterns. However, the relationship between an index value and experts' criteria has never been clarified. In order to determine the relationship, we have developed a method to obtain learning models from a dataset consisting of objective rule evaluation indices and evaluation labels for rules. In this study, we have compared accuracies of classification learning algorithms for datasets with randomized class labels. Then, the result shows that accuracies of classification learning algorithms without any criterion of a human expert can not outperform each percentage of majority class on both of the balanced and imbalanced class distribution datasets. With regarding to this result, we can determine whether or not a labeled rule set contains some criteria based on the dataset consisting the objective rule evaluation indices.
Behaviour Preservation of a Biological Regulatory Network when	Embedded into a Larger Network
Authors: Bernot Gilles , Tahi Fariza
Published: December 2019

The main contribution of this work is a mathematical theorem which establishes a necessary and sufficient condition to preserve the behaviour of a genetic regulatory network when it is embedded into a larger network. We adopt the modelling approach of René Thomas, which provides a discrete representation of biological regulatory networks. This framework is entirely formalized using labelled graphs with semantics defined in terms of state graphs with transitions. Our theorem offers the possibility to automatically verify whether a subnetwork has autonomous behaviour. It will allow biologists to better identify relevant sets of genes which should be studied together.
Handling Inconsistency In Distributed Software Requirements	Specifications Based On Prioritized Merging
Authors: Mu Kedian , Liu Weiru , Jin Zhi , Lu Ruqian , Yue Anbu , Bell David
Published: December 2019

Developing a desirable framework for handling inconsistencies in software requirements specifications is a challenging problem. It has been widely recognized that the relative priority of requirements can help developers to make some necessary trade-off decisions for resolving con- flicts. However, for most distributed development such as viewpoints-based approaches, different stakeholders may assign different levels of priority to the same shared requirements statement from their own perspectives. The disagreement in the local levels of priority assigned to the same shared requirements statement often puts developers into a dilemma during the inconsistency handling process. The main contribution of this paper is to present a prioritized merging-based framework for handling inconsistency in distributed software requirements specifications. Given a set of distributed inconsistent requirements collections with the local prioritization, we first construct a requirements specification with a prioritization from an overall perspective. We provide two approaches to constructing a requirements specification with the global prioritization, including a merging-based construction and a priority vector-based construction. Following this, we derive proposals for handling inconsistencies from the globally prioritized requirements specification in terms of prioritized merging. Moreover, from the overall perspective, these proposals may be viewed as the most appropriate to modifying the given inconsistent requirements specification in the sense of the ordering relation over all the consistent subsets of the requirements specification. Finally, we consider applying negotiation-based techniques to viewpoints so as to identify an acceptable common proposal from these proposals.
Preventing Cheating in Computational Visual Cryptography
Authors: Chang Chin-Chen , Chen Tzung-Her , Liu Li-Jen
Published: December 2019

Visual Cryptography (VC) has drawn much attention for providing the service of secret communication. Basically, VC is the process of encoding a secret into several meaningless shares and later decoding the secret by superimposing all or some of the shares without any computation involved. VC has been adopted to support some practical applications, such as image authentication, visual authentication, image hiding, and digital watermarking. Unfortunately, in many applications, VC has been shown to suffer from the "cheating problem" in which the disclosed secret image may be altered by malicious insiders who are called "cheaters." While ubiquitous computing has been well developed, it has recently occurred to people in both academia and industry that research could benefit more from computational VC by introducing light-weight computation costs in the decoding phase. In this paper, a simple scheme is proposed to conquer the cheating problem by facilitating the capability of share authentication. It is worthwhile to note that the proposed scheme can identify for certain whether cheating attacks have occurred or not, while other schemes that have the same objective frequently provide a vague answer. In addition, the proposed scheme effectively addresses the two main problems of VC, i.e., the inconvenience of meaningless share management and the challenge of achieving difficult alignment.
Greedy Algorithm for Construction of Partial Association	Rules
Authors: Moshkov Mikhail Ju. , Piliszczuk Marcin , Zielosko Beata
Published: December 2019

Partial association rules can be used for representation of knowledge, for inference in expert systems, for construction of classifiers, and for filling missing values of attributes. This paper is devoted to the study of approximate algorithms for minimization of partial association rule length. It is shown that under some natural assumptions on the class NP, a greedy algorithm is close to the best polynomial approximate algorithms for solving of this NP-hard problem. The paper contains various bounds on precision of the greedy algorithm, bounds on minimal length of rules based on an information obtained during the greedy algorithm work, and results of theoretical and experimental study of association rules for the most part of binary information systems.
AC is Equivalent to the Coherence Principle. Corrigendum to my	Paper "Induction Principles for Sets"
Authors: Czelakowski Janusz
Published: December 2019

Theorem 3.7 of [1] is corrected. Two coherence principles and the ultrafilter property for partial functions contained in a relation are formulated. The equivalence of the coherent principles with AC and the equivalence of the ultrafilter property with BPI is shown.
Timed P Automata
Authors: Barbuti Roberto , Maggiolo-Schettini Andrea , Milazzo Paolo , Tesei Luca
Published: December 2019

To study systems whose dynamics changes with time, an extension of timed P systems is introduced in which evolution rules may vary with time. The proposed model is a timed automaton with a discrete time domain and in which each state is a timed P system. A result on expressive power and on features of the formalism sufficient for full expressiveness is proved and, as an application example, the model of an ecological system is given.
First-order Generalization of the MPMA Belief Update Operator
Authors: Madalińska-Bugaj Ewa , Łukaszewicz Witold
Published: December 2019

In this paper we generalize the MPMA belief update operator by admitting first-order knowledge bases and restricted first-order update formulae. This allows us to consider scenarios which cannot be properly formalized either in the originalMPMA or in other existing approaches to belief update.
Greedy Algorithms withWeights for Construction of Partial	Association Rules
Authors: Ju. Moshkov Mikhail , Piliszczu Marcin , Zielosko Beata
Published: December 2019

This paper is devoted to the study of approximate algorithms for minimization of the total weight of attributes occurring in partial association rules. We consider mainly greedy algorithms with weights for construction of rules. The paper contains bounds on precision of these algorithms and bounds on the minimal weight of partial association rules based on an information obtained during the greedy algorithm run.
QoS-Driven Service Selection and Composition Using Quantitative	Constraint Automata
Authors: Meng Sun , Arbab Farhad
Published: December 2019

Assuring Quality of Service (QoS) properties is critical in Service-Oriented Application (SOA) development. In this paper, we present an approach for specifying the QoS properties of services along multiple dimensions and selecting services for their composition in a way that optimizes the QoS of the result. We apply the integration of concerns paradigm to allow combined specification of QoS and functional properties by using Quantitative Constraint Automata, which integrate QoS aspects into service-oriented application development processes, mainly for service selection and composition.
Time-Bounded Model Checking of Infinite-State Continuous-Time	Markov Chains
Authors: Hahn E. Moritz , Hermanns Holger , Wachter Björn , Zhang Lijun
Published: December 2019

The design of complex concurrent systems often involves intricate performance and dependability considerations. Continuous-time Markov chains (CTMCs) are a widely used modeling formalism that captures such performance and dependability properties, and makes them analyzable by model checking. In this paper, we focus on time-bounded probabilistic properties of infinite-state CTMCs, expressible in a subset of continuous stochastic logic (CSL). This comprises important dependability measures, such as time-bounded probabilistic reachability, performability, survivability, and various availability measures like instantaneous, conditional instantaneous and interval availabilities. Conventional model checkers explore the given model exhaustively, which is often costly, due to state explosion, and sometimes impossible because the model is infinite. This paper presents a method that only explores the model up to a finite depth. The required depth is determined on the fly by an algorithm that is configurable in order to adapt to the characteristics of different classes of models. We provide experimental evidence showing that our method is effective.
Performance Evaluation of Distributed Systems Based on a Discrete	Real- and Stochastic-Time Process Algebra
Authors: Markovski Jasen , de Vink Erik P.
Published: December 2019

We present a process-algebraic framework for performance evaluation of discrete-time discrete-event systems. The modeling of the system builds on a process algebra with conditionallydistributed discrete-time delays and generally-distributed stochastic delays. In the general case, the performance analysis is done with the toolset of the modeling language χ by means of discrete-event simulation. The process-algebraic setting allows for expansion laws for the parallel composition and the maximal progress operator, so one can directly manipulate the process terms and transform the specification in a required form. This approach is illustrated by specifying and solving the recursive specification of the G/G/1/∞ queue, as well as by specifying a variant of the concurrent alternating bit protocol with generally-distributed unreliable channels. In a specific situation when all delays are assumed deterministic, we turn to performance analysis of probabilistic timed systems. This work employs discrete-time probabilistic reward graphs, which comprise deterministic delays and immediate probabilistic choices. Here, we extend previous investigations on the topic, which only touched long-run analysis, to tackle transient analysis as well. The theoretical results obtained allow us to extend the χ-toolset. For illustrative purposes, we analyze the concurrent alternating bit protocol in the extended environment of the χ-toolset using discrete-event simulation for generallydistributed channels, the developed analytical method for deterministic channels, and Markovian analysis for exponentially-distributed delays.
Modular Construction of Finite and Complete Prefixes of Petri net	Unfoldings
Authors: Madalinski. Agnes , Fabre Eric
Published: December 2019

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.
On Recognizable Tree Languages Beyond the Borel Hierarchy
Authors: Finkel Olivier , Simonnet Pierre
Published: December 2019

We investigate the topological complexity of non Borel recognizable tree languages with regard to the difference hierarchy of analytic sets. We show that, for each integer n ⩾ 1, there is a D _{ω^n} (Σ ^1_1 )-complete tree language L _n accepted by a (non deterministic) Muller tree automaton. On the other hand, we prove that a tree language accepted by an unambiguous Büchi tree automaton must be Borel. Then we consider the game tree languages W _{(ı,κ)} , for Mostowski-Rabin indices (ıκ). We prove that the D _{ω^n} (Σ ^1_1 )-complete tree languages L _n are Wadge reducible to the game tree language W _{(ı,κ)} for κ−ı⩾ 2. In particular these languages W _{(ı,κ)} are not in any class D _{α} (Σ ^1_1 ) for α<ω ^{ω} .
Verifying a Class: combining Testing and Proving
Authors: Mirkowska Grażyna , Salwicki Andrzej , Świda Oskar
Published: December 2019

The problem of correctness of a class C w.r.t. a specification S is discussed. A formal counterpart of the problem is the question well known in metamathematics, whether an algebraic structure is a model of a given theory. Now, this metamathematical problem has to be adapted to the context of software engineering. As a theory we consider the (algorithmic) specification S. The algebraic structure A _C induced by the class C is our candidate for a model of S. Remark, that this problem differs from the correctness' problem of an algorithm w.r.t. a pre- and a post-conditions. In the paper we consider the specification ATPQ of priority queues and the class PQS, and we verify the correctness of this class with respect to the specification ATPQ.

Programmers and software companies prefer to test software instead of proving it. Surely, proving is more difficult, testing is easier. In this article we combine these two approaches. Hence, the following actions appear in our method of verification: experiment, observe, formulate hypotheses, prove. It is our hope that this method is of general use and adapts well to many practical cases of verification of object-oriented software.
Mining the Largest Dense Vertexlet in a Weighted Scale-free	Graph
Authors: Bandyopadhyay Sanghamitra , Bhattacharyya Malay
Published: December 2019

An important problem of knowledge discovery that has recently evolved in various reallife networks is identifying the largest set of vertices that are functionally associated. The topology of many real-life networks shows scale-freeness, where the vertices of the underlying graph follow a power-law degree distribution. Moreover, the graphs corresponding to most of the real-life networks are weighted in nature. In this article, the problem of finding the largest group or association of vertices that are dense (denoted as dense vertexlet) in a weighted scale-free graph is addressed. Density quantifies the degree of similarity within a group of vertices in a graph. The density of a vertexlet is defined in a novel way that ensures significant participation of all the vertices within the vertexlet. It is established that the problem is NP-complete in nature. An upper bound on the order of the largest dense vertexlet of a weighted graph, with respect to certain density threshold value, is also derived. Finally, an O(n $^2$ log n) (n denotes the number of vertices in the graph) heuristic graph mining algorithm that produces an approximate solution for the problem is presented.
Structured Occurrence Nets: A Formalism for Aiding System Failure	Prevention and Analysis Techniques
Authors: Koutny Maciej , Randell Brian
Published: December 2019

This paper introduces the concept of a 'structured occurrence net', which as its name indicates is based on that of an 'occurrence net', a well-established formalism for an abstract record that represents causality and concurrency information concerning a single execution of a system. Structured occurrence nets consist of multiple occurrence nets, associated together by means of various types of relationship, and are intended for recording or predicting, either the actual behaviour of complex systems as they communicate and evolve, or evidence that is being gathered and analysed concerning their alleged past behaviour. We provide a formal basis for the new formalism and show how it can be used to gain better understanding of complex fault-error-failure chains (i) among co-existing communicating systems, (ii) between systems and their sub-systems, and (iii) involving systems that are controlling, creating ormodifying other systems. We then go on to discuss how, with appropriate tools support, perhaps using extended versions of existing tools, structured occurrence nets could form a basis for improved techniques of system failure prevention and analysis.
Algebraic Semantics of Similarity-Based Bitten Rough Set Theory
Authors: Mani A.
Published: December 2019

We develop two algebraic semantics for bitten rough set theory ([19]) over similarity spaces and their abstract granular versions. Connections with choice based generalized rough semantics developed in [15] by the present author and general cover based rough set theories are also considered.
MR Brain Image Segmentation Using A Multi-seed Based Automatic	Clustering Technique
Authors: Saha Sriparna , Bandyopadhyay Sanghamitra
Published: December 2019

In this paper, the automatic segmentation of multispectral magnetic resonance image of the brain is posed as a clustering problem in the intensity space. Thereafter an automatic clustering technique is proposed to solve this problem. The proposed real-coded variable string length genetic clustering technique (MCVGAPS clustering) is able to evolve the number of clusters present in the data set automatically. Each cluster is divided into several small hyperspherical subclusters and the centers of all these small sub-clusters are encoded in a string to represent the whole clustering. For assigning points to different clusters, these local sub-clusters are considered individually. For the purpose of objective function evaluation, these sub-clusters are merged appropriately to form a variable number of global clusters. A recently developed point symmetry distance based cluster validity index, Sym-index, is optimized to automatically evolve the appropriate number of clusters present in an MR brain image. The proposed method is applied on several simulated T1-weighted, T2- weighted and proton density normal and MS lesion magnetic resonance brain images. Superiority of the proposed method over Fuzzy C-means, Expectation Maximization clustering algorithms are demonstrated quantitatively. The automatic segmentation obtained by multiseed based multiobjective clustering technique (MCVGAPS) is also compared with the available ground truth information.
Tensor Framework and Combined Symmetry for Hypertext Mining
Authors: Saha Suman , Murthy C.A. , Pal Sankar K.
Published: December 2019

We have made a case here for utilizing tensor framework for hypertext mining. Tensor is a generalization of vector and tensor framework discussed here is a generalization of vector space model which is widely used in the information retrieval and web mining literature. Most hypertext documents have an inherent internal tag structure and external link structure that render the desirable use of multidimensional representations such as those offered by tensor objects. We have focused on the advantages of Tensor Space Model, in which documents are represented using sixth-order tensors. We have exploited the local-structure and neighborhood recommendation encapsulated by the proposed representation. We have defined a similarity measure for tensor objects corresponding to hypertext documents, and evaluated the proposed measure for mining tasks. The superior performance of the proposed methodology for clustering and classification tasks of hypertext documents have been demonstrated here. The experiment using different types of similarity measure in the different components of hypertext documents provides the main advantage of the proposed model. It has been shown theoretically that, the computational complexity of an algorithm performing on tensor framework using tensor similarity measure as distance is at most the computational complexity of the same algorithmperforming on vector space model using vector similarity measure as distance.
An Algebraic Framework for Defining Behaviours of Concurrent	Systems. Part 1: The Constructive Presentation
Authors: Winkowski Józef
Published: December 2019

The paper is the first part of a two-part paper that contributes with a concept of a process viewed as a model of a run of a phenomenon (discrete, continuous, or of a mixed type), with operations allowing to define complex processes in terms of their components, with the respective algebras, and with the idea of using the formal tools thus obtained to describe the behaviours of concurrent systems.

A process may have an initial state (a source), a final state (a target), or both. A process can be represented by a partially ordered multiset. Processes of which one can be a continuation of the other can be composed sequentially. Independent processes, i.e. processes which do not disturb each other, can be composed in parallel. Processes may be prefixes, i.e. independent components of initial segments of other processes.

Processes in a universe of objects and operations on such processes form a partial algebra, called algebra of processes. Algebras of processes are partial categories with respect to the sequential composition, and partial monoids with respect to the parallel composition.

Algebras of processes can be used to define behaviours of concurrent systems. The behaviour of a system can be defined as the set of possible processes of this system with a structure on this set. Such a set is prefix-closed. The structure on this set reflects the prefix order and, possibly, specific features of the behaviour like observability, the relation to flow of real time, etc.

Algebras of processes can also be used to define behaviours, to define operations on behaviours similar to those in the existing calculi of behaviours, and to define random behaviours.

The first part of the whole paper investigates algebras of processes and their applications to describing behaviours of systems. In the second part the properties of algebras of processes described in the first part are regarded as axioms defining a class of abstract partial algebras, called behaviouroriented algebras, and they initiate a theory of such algebras.
An Image Authentication Based on Discrete Fourier Transform
Authors: Kieu The Duc , Chang Chin-Chen
Published: December 2019

The advances of network technologies and digital devices facilitate users to exchange multimedia data over the public networks. However, this also raises significant concerns about how to protect sensitive multimedia data from being illegally copied and unauthorized modifications. Thus, this paper proposes a fragile watermarking method to detect illegitimate alterations of the watermarked data. The proposed method embeds a grayscale watermark image into a grayscale cover image in a block-by-block manner by using discrete Fourier transform. Experimental results show that the proposedmethod can successfully and exactly detect and localize any tampered regions of the watermarked image.
Data Clustering Using Multi-objective Differential Evolution	Algorithms
Authors: Suresh Kaushik , Kundu Debarati , Ghosh Sayan , Das Swagatam , Abraham Ajith
Published: December 2019

The article considers the task of fuzzy clustering in a multi-objective optimization (MO) framework. It compares the relative performance of four recently developedmulti-objective variants of Differential Evolution (DE) on over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized. The resultant Pareto optimal set of solutions from each algorithm consists of a number of non-dominated solutions, from which the user can choose the most promising ones according to the problem specifications. A real-coded representation for the candidates is used for DE. A comparative study of four DE variants with two most well-known MO clustering techniques, namely the NSGA II (Non Dominated Sorting GA) and MOCK (Multi- Objective Clustering with an unknown number of clusters K) is also undertaken. Experimental results reported for six artificial and four real life datasets (including a microarray dataset of budding yeast) of varying range of complexities indicates that DE can serve as a promising algorithm for devising MO clustering techniques.
An Interval-Valued Intuitionistic Fuzzy Rough Set Model
Authors: Zhang Zhiming
Published: December 2019

Given a widespread interest in rough sets as being applied to various tasks of data analysis it is not surprising at all that we have witnessed a wave of further generalizations and algorithmic enhancements of this original concept. This paper proposes an interval-valued intuitionistic fuzzy rough model by means of integrating the classical Pawlak rough set theory with the interval-valued intuitionistic fuzzy set theory. Firstly, some concepts and properties of interval-valued intuitionistic fuzzy set and interval-valued intuitionistic fuzzy relation are introduced. Secondly, a pair of lower and upper interval-valued intuitionistic fuzzy rough approximation operators induced from an interval-valued intuitionistic fuzzy relation is defined, and some properties of approximation operators are investigated in detail. Furthermore, by introducing cut sets of interval-valued intuitionistic fuzzy sets, classical representations of interval-valued intuitionistic fuzzy rough approximation operators are presented. Finally, the connections between special interval-valued intuitionistic fuzzy relations and interval-valued intuitionistic fuzzy rough approximation operators are constructed, and the relationships of this model and the others rough set models are also examined.
Adaptive Rough Entropy Clustering Algorithms in Image Segmentation
Authors: Małyszko Dariusz , Stepaniuk Jarosław
Published: December 2019

High quality performance of image segmentation methods presents one leading priority in design and implementation of image analysis systems. Incorporating the most important image data information into segmentation process has resulted in development of innovative frameworks such as fuzzy systems, rough systems and recently rough – fuzzy systems. Data analysis based on rough and fuzzy systems is designed to apprehend internal data structure in case of incomplete or uncertain information. Rough entropy framework proposed in [12,13] has been dedicated for application in clustering systems, especially for image segmentation systems. We extend that framework into eight distinct rough entropy measures and related clustering algorithms. The introduced solutions are capable of adaptive incorporation of the most important factors that contribute to the relation between data objects and makes possible better understanding of the image structure. In order to prove the relevance of the proposed rough entropy measures, the evaluation of rough entropy segmentations based on the comparison with human segmentations from Berkeley and Weizmann image databases has been presented. At the same time, rough entropy based measures applied in the domain of image segmentation quality evaluation have been compared with standard image segmentation indices. Additionally, rough entropy measures seem to comprehend properly properties validated by different image segmentation quality indices.
Design of a Hybrid Quantizer with Variable Length Code
Authors: Perić Zoran H. , Dinčić Milan R. , Petković Marko D.
Published: December 2019

In this paper a new model for compression of Laplacian source is given. This model consists of hybrid quantizer whose output levels are coded with Golomb-Rice code. Hybrid quantizer is combination of uniform and nonuniform quantizer, and it can be considered as generalized quantizer, whose special cases are uniform and nonuniformquantizers. We propose new generalized optimal compression function for companding quantizers. Hybrid quantizer has better performances (smaller bit-rate and complexity for the same quality) than both uniform and nonuniformquantizers, because it joins their good characteristics. Also, hybrid quantizer allows great flexibility, because there are many combinations of number of levels in uniform part and in nonuniformpart, which give similar quality. Each of these combinations has different bit-rate and complexity, so we have freedom to choose combination which is the most appropriate for our application, in regard to quality, bit-rate and complexity. We do not have such freedom of choice when we use uniform or nonuniform quantizers. Until now, it has been thought that uniform quantizer is the most appropriate to use with lossless code, but in this paper we show that combination of hybrid quantizer and lossless code gives better performances. As lossless code we use Golomb-Rice code because it is especially suitable for Laplacian source since it gives average bit-rate very close to the entropy and it is easier for implementation than Huffman code. Golomb-Rice code is used in many modern compression standards. Our model can be used for compression of all signals with Laplacian distribution.
Tree Structure Based Data Hiding for Progressive Transmission Images
Authors: Tsai Piyu
Published: December 2019

Progressive image transmission (PIT) is supported by several encoders such as SPIHT, JPEG2000 and so on. However, few of data hiding scheme for progressive transmission images is designed. In this paper, tree-structure-based data hiding for set partitioning in hierarchical trees (SPIHT) images is proposed. The bit stream of SPIHT multi-stage encoded was structured of tree. The secret image can be progressively embedded into SPIHT bit trees.

Experimental results showed the progressive image data hiding is achieved. The secret image is progressively embedded/extracted in SPIHT encoded images. Furthermore, a higher hiding capacity was provided in an earlier encoding which helped the secret image to be identified earlier. Also, an adaptive hiding capacity could be developed by using different tree structures. In comparison with Tsai et al.'s scheme, the proposed scheme had a higher hiding capacity and a better secret image quality.
Tree-width in Algebraic Complexity
Authors: Meer Klaus
Published: December 2019

The paper surveys some of the author's work studying the algorithmic importance of the tree-width notion in algebraic frameworks. Two approaches are described. The first gives an algorithmicmeta-theoremfor certain logically characterized propertieswithin the Blum-Shub-Smale BSS model of computation over the reals. The second reports on recent joint work with P. Koiran relating Boolean complexity and Valiant's approach to study families of polynomial systems over infinite fields and their complexity. We define particular families of polynomials via bounding the tree-width of suitably attached graphs and study the expressive power of the resulting families. The work described here is partially co-authoredwith and partially verymuch influenced by previous work of Janos A. Makowsky.
Grill Determined L-Approach Merotopological Spaces
Authors: Khare Mona , Tiwari Surabhi
Published: December 2019

The present paper is devoted to the study of grill determined L-approachmerotopological spaces. The category having such spaces as objects is shown to be a topological construct (its initial and final structures are provided explicitly). The lattice structure of the family of all these spaces is also discussed. In the classical theory, this category (that is, when L={0, 1}) is a supercategory of the category of pseudo metric spaces and nonexpansive maps.
Matrix Graph Grammars with Application Conditions
Authors: Velasco Pedro Pablo Pérez , de Lara Juan
Published: December 2019

In the Matrix approach to graph transformation we represent simple digraphs and rules with Boolean matrices and vectors, and the rewriting is expressed using Boolean operators only. In previous works, we developed analysis techniques enabling the study of the applicability of rule sequences, their independence, state reachability and the minimal graph able to fire a sequence.

In the present paper we improve our framework in two ways. First, we make explicit (in the form of a Boolean matrix) some negative implicit information in rules. This matrix (called nihilation matrix) contains the elements that, if present, forbid the application of the rule (i.e. potential dangling edges, or newly added edges, which cannot be already present in the simple digraph). Second, we introduce a novel notion of application condition, which combines graph diagrams together with monadic second order logic. This allows for more flexibility and expressivity than previous approaches, as well as more concise conditions in certain cases. We demonstrate that these application conditions can be embedded into rules (i.e. in the left hand side and the nihilation matrix), and show that the applicability of a rule with arbitrary application conditions is equivalent to the applicability of a sequence of plain rules without application conditions. Therefore, the analysis of the former is equivalent to the analysis of the latter, showing that in our framework no additional results are needed for the study of application conditions. Moreover, all analysis techniques of [21,22] for the study of sequences can be applied to application conditions.
A Generic Approach to Connector Architectures Part II: Instantiation to Petri Nets and CSP
Authors: Orejas Fernando , Ehrig Hartmut , Klein Markus , Padberg Julia , Pino Elvira , Pérez Sonia
Published: December 2019

The aim of this paper is to show how the generic approach to connector architectures, presented in the first part of this work, can be applied to a given modeling formalism to define architectural component and connector notions associated to that formalism. Starting with a review of the generic approach, in this second part of the paper we consider two modeling formalisms: elementary Petri nets and CSP. As main results we show that both cases satisfy the axioms of our component framework, so that the results concerning the semantics of architectures can be applied. Moreover, a small case study in terms of Petri Nets is presented in order to show how the results can be applied to a connector architecture based on Petri nets.
On the State Complexity of Scattered Substrings and Superstrings
Authors: Okhotin Alexander
Published: December 2019

It is proved that the set of scattered substrings of a language recognized by an n-state DFA requires a DFA with at least 2 $^{n/2-2}$ states (the known upper bound is 2 $^n$ ), with witness languages given over an exponentially growing alphabet. For a 3-letter alphabet, scattered substrings are shown to require at least 2 $^{sqrt{2n+30}-6}$ states. A similar state complexity function for scattered superstrings is determined to be exactly 2 $^{n-2}$ + 1 for an alphabet of at least n − 2 letters, and strictly less for any smaller alphabet. For a 3-letter alphabet, the state complexity of scattered superstrings is at least 1/5 4sqrt{n/2}n-3/4.
Algebraic Ordinals
Authors: Bloom Stephen L. , Ésik Zoltan
Published: December 2019

An algebraic tree T is one determined by a finite system of fixed point equations. The frontier Fr(T ) of an algebraic tree T is linearly ordered by the lexicographic order <ℓ If (Fr(T) <ℓ) is well-ordered, its order type is an algebraic ordinal. We prove that the algebraic ordinals are exactly the ordinals less than ω $^{ω}$ $^{ω}$ .
Bit-Parallel Algorithm for the Constrained Longest Common	Subsequence Problem
Authors: Deorowicz Sebastian
Published: December 2019

The problem of finding a constrained longest common subsequence (CLCS) for the sequences A and B with respect to the sequence P was introduced recently. Its goal is to find a longest subsequence C of A and B such that P is a subsequence of C. Most of the algorithms solving the CLCS problem are based on dynamic programming. Bit-parallelism is a technique of using single bits in a machine word for concurrent computation. We propose the first bit-parallel algorithm computing a CLCS and/or its length which outperforms the other known algorithms in terms of speed.
A Robust Multiple Classifier System for Pixel Classification of Remote Sensing Images
Authors: Maulik Ujjwal , Chakraborty Debasis
Published: December 2019

Satellite image classification is a complex process that may be affected by many factors. This article addresses the problem of pixel classification of satellite images by a robust multiple classifier system that combines k-NN, support vector machine (SVM) and incremental learning algorithm (IL). The effectiveness of this combination is investigated for satellite imagery which usually have overlapping class boundaries. These classifiers are initially designed using a small set of labeled points. Combination of these algorithms has been done based on majority voting rule. The effectiveness of the proposed technique is first demonstrated for a numeric remote sensing data described in terms of feature vectors and then identifying different land cover regions in remote sensing imagery. Experimental results on numeric data as well as two remote sensing data show that employing combination of classifiers can effectively increase the accuracy label. Comparison is made with each of these single classifiers in terms of kappa value, accuracy, cluster quality indices and visual quality of the classified images.