bluuebunny/crossref_metadata_2025 huggingface dataset:
Processing Deductive Databases under the Disjunctive Stable Model Semantics
Authors: Johnson C.A.
Published: December 2019

Cyclic covers are shown to characterise disjunctive stable models of unstratified deductive databases, and to facilitate top-down query processing, query compilation and view updating under the disjunctive stable model semantics. Such processing is shown to be more complex than comparable processing of stratified databases.
Rough Sets and Association Rule Generation
Authors: Nguyen Hung Son , Nguyen Sinh Hoa
Published: December 2019

ASSOCIATION RULE (see [1]) extraction methods have been developed as the main methods for mining of real life data, in particular in Basket Data Analysis. In this paper we present a novel approach to generation of association rules, based on Rough Set and Boolean reasoning methods. Some results presented in this paper has been mentioned in [13, 17]. We will explain them precisely (with full proofs of theorems) in this paper. We show the relationship between the problems of association rule extraction for transaction data and relative reducts (or α-reducts generation) for a decision table. Moreover, the present approach can be used to extract association rules in general form. The experimental results show that the presented methods are quite efficient. Large number of association rules with given support and confidence can be extracted in a short time.
Generalized Mutual Exclusion with Semaphores Only
Authors: Ordman E.T. , Eberbach E. , Anwar A.
Published: December 2019

The paper deals with a generic solution of the Mutual Exclusion Problem using semaphores only. We use in the solution weakly fair binary semaphores and (not necessarily fair) semaphores with initial value k. All semaphores are simple in that the P and V operations on them are paired in the natural way avoiding split semaphores. No auxiliary shared variables are used. We define a general form of the mutual exclusion problem. We claim: (1) All mutual exclusion problems can be solved using only simple semaphores. (2) All mutual exclusion problems can be solved fairly (with bounded waiting) using simple semaphores (weakly fair binary and initially–k; semaphores). We give some bounds on how many semaphores are needed for standard problems. The solutions given may be inefficient: a mutual exclusion problem which can be solved in linear time and space with shared variables may require exponentially many semaphores.
On Algorithm for Constructing of Decision Trees with Minimal Depth
Authors: Moshkov Mikhail , Chikalov Igor
Published: December 2019

An algorithm is considered which for a given decision table constructs a decision tree with minimal depth. The class of all information systems (finite and infinite) is described for which this algorithm has polynomial time complexity depending on the number of columns (attributes) in decision tables.
Building Knowledge Scouts Using KGL Metalanguage
Authors: Michalski Ryszard S. , Kaufman Kenneth A.
Published: December 2019

Knowledge scouts are software agents that autonomously synthesize user-oriented knowledge (target knowledge) from information present in local or distributed databases. A knowledge generation metalanguage, KGL, is used to creating scripts defining such knowledge scouts. Knowledge scouts operate in an inductive database, by which we mean a database system in which conventional data and knowledge management operators are integrated with a wide range of data mining and inductive inference operators. Discovered knowledge is represented in two forms: (1) attributional rules, which are rules in attributional calculus—a logic-based language between prepositional and predicate calculus, and (2) association graphs, which graphically and abstractly represent relations expressed by the rules. These graphs can depict multi-argument relationships among different concepts, with a visual indication of the relative strength of each dependency. Presented ideas are illustrated by two simple knowledge scouts, one that seeks relations among lifestyles, environmental conditions, symptoms and diseases in a large medical database, and another that searches for patterns of children's behavior in the National Youth Survey database. The preliminary results indicate a high potential utility of this methodology for deriving knowledge from databases.
A Formalization of Autonomous Dynamic Reconfiguration in Distributed Constraint Satisfaction
Authors: Hannebauer Markus
Published: December 2019

Several interesting practical problems in process control, planning and scheduling can be expressed and solved using the model of constraint satisfaction problems. At least four drawbacks of this classical model directly relate to areas of distribution: complexity, scalability, privacy and robustness. Hence, research on distributed constraint satisfaction problems is a new direction in the area of multi-agent systems. A typical engineering task in distributed constraint satisfaction is the design of the distribution itself. A careful look at this task reveals that the design of distribution is critical to the quality and efficiency of the problem solving process and is itself an optimization problem. In this article we formalize different variants of this configuration problem and prove them to be all at least NP-complete. For solving these problems, we present two local operators, agent melting and agent splitting, that can be combined to allow for an autonomous and dynamic reconfiguration of the organizational structure of the problem-solving agents. We prove sequences of these operators to be sufficient for solving any given configuration problem. We also briefly describe what practical steps are necessary to exploit the rather theoretical result of the proof in realistic applications.
Coping With Strong Fairness
Authors: Latvala Timo , Heljanko Keijo
Published: December 2019

We consider the verification of linear temporal logic (LTL) properties of Petri nets, where the transitions can have both weak and strong fairness constraints. Allowing the transitions to have weak or strong fairness constraints simplifies the modeling of systems in many cases. We use the automata theoretic approach to model checking. To cope with the strong fairness constraints efficiently we employ Streett automata where appropriate. We present memory efficient algorithms for both the emptiness checking and counterexample generation problems for Streett automata.
Stable Models for Stubborn Sets
Authors: Varpaaniemi Kimmo
Published: December 2019

The stubborn set method is one of the methods that try to relieve the state space explosion problem that occurs in state space generation. Spending some time in looking for “good” stubborn sets can pay off in the total time spent in generating a reduced state space. This article shows how the method can exploit tools that solve certain problems of logic programs. The restriction of a definition of stubbornness to a given state can be translated into a variable-free logic program. When a stubborn set satisfying additional constraints is wanted, the additional constraints should be translated, too. It is easy to make the translation in such a way that each acceptable stubborn set of the state is represented by at least one stable model of the program, each stable model of the program represents at least one acceptable stubborn set of the state, and for each pair in the representation relation, the number of certain atoms in the stable model is equal to the number of enabled transitions of the represented stubborn set. So, in order to find a stubborn set which is good w.r.t. the number of enabled transitions, it suffices to find a stable model which is good w.r.t. the number of certain atoms. The article also presents a new NP-completeness result concerning stubborn sets.
Some Symmetry Based Classifiers
Authors: Saha Sriparna , Bandyopadhyay Sanghamitra
Published: December 2019

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.
NovelWatermarking Authentication Schemes for Binary Images Based	on Dual-Pair Block Pixel Patterns
Authors: Wang Chung-Chuan , Chang Chin-Chen , Jan Jinn-Ke
Published: December 2019

In this paper, watermarking authentication schemes for binary images are investigated. Dual-Pair Blocks (DPBs), 2 × 2 blocks with two black and two white pixels, are employed as the basic units of information embedding. With 6 distinctive patterns obtained by pixel patterns, a DPB can be represented by a senary value as the embedding information. Variable length coding is adopted to enhance the information embedding capacity of the DPB. The secret key of the watermarking is a pseudo random function with a seed value, which selects DPBs for information embedding. Based on the aforementioned framework, two novel watermarking schemes are presented. The first one, named dual-pair block authentication (DPBA), is an irreversible one featuring high information embedding capacity, and well visual quality. The second scheme, named senary Huffman compression authentication (SHCA) scheme, supports reversible watermarking unique in the literature. Matching-pair technique is further employed both schemes to reduce the number of altered bits after watermarking for better visual quality. Experimental results show that, the DPBA scheme has the highest information embedding capacity on all comparing schemes. The average compression ratio of the reversible SHCA scheme is up to 38.96%. The average distortion rates per accessed DPB for the proposed DPBA and SHCA schemes are 0.485 and 0.46, respectively.
Concept Lattices of Subcontexts of a Context
Authors: Wang Xia , Zhang Wenxiu
Published: December 2019

As an effective tool for data analysis and knowledge processing, the theory of concept lattices has been studied extensively and applied to various fields. In order to discover useful knowledge, one often ignores some attributes according to a particular purpose and merely considers the subcontexts of a rather complex context. In this paper, we make a deep investigation on the theory of concept lattices of subcontexts. An approach to construct the concept lattice of a context is first presented by means of the concept lattices of its subcontexts. Then the concept lattices induced by all subcontexts of the context are considered as a set, and an order relation is introduced into the set. It is proved that the set together with the order relation is a complete lattice. Finally, the top element and the bottom element of the complete lattice are also obtained.
Extractive Support Vector Algorithm on Support Vector Machines for	Image Restoration
Authors: Yao Chih-Chia , Yu Pao-Ta , Hung Ruo-Wei
Published: December 2019

The major problem of SVMs is the dependence of the nonlinear separating surface on the entire dataset which creates unwieldy storage problems. This paper proposes a novel design algorithm, called the extractive support vector algorithm, which provides improved learning speed and a vastly improved performance. Instead of learning and training with all input patterns, the proposed algorithm selects support vectors from the input patterns and uses these support vectors as the training patterns. Experimental results reveal that our proposed algorithmprovides near optimal solutions and outperforms the existing design algorithms. In addition, a significant framework which is based on extractive support vector algorithm is proposed for image restoration. In the framework, input patterns are classified by three filters: weighted order statistics filter, alpha-trimmed mean filter and identity filter. Our proposed filter can achieve three objectives: noise attenuation, chromaticity retention, and preservation of edges and details. Extensive simulation results illustrate that our proposed filter not only achieves these three objectives but also possesses robust and adaptive capabilities, and outperforms other proposed filtering techniques.
New Construction of Short Hierarchical ID-Based Signature in the	Standard Model
Authors: Zhang Leyou , Hu Yupu , Wu Qing
Published: December 2019

In this paper, a new construction of hierarchical ID-Based signature (HIBS) scheme is proposed. The new scheme has some advantages over the available schemes: the private keys size shrinks as the identity depth increases and the signature size is a constant as it consists of three group elements. Furthermore, under the h-CDH assumption, our scheme is provably secure in the standard model. This assumption is more natural than many of the hardness assumptions recently introduced to HIBS in the standard model.
Preface: Cognitive Informatics, Cognitive Computing, and Their	Denotational Mathematical Foundations (I)
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 this special issue are highlighted.
Testing Semantics for RTPA
Authors: Llana Luis , Núuñez Manuel
Published: December 2019

The language RTPA, Real Time Process Algebra, has been created to enable rigorous treatment of knowledge representation and manipulation in terms of to be I to have / to do in a formal and coherent framework. This language has been designed to cope with the three dimensions involved in the problem of software specification: (i) mathematical operations, (ii) event/process timing, and (iii) memory manipulation. In this paper we focus on giving a testing semantics to the second dimension: Process timing dimension. First, we will provide a SOS like operational semantics for the process relations of RTPA. Next, we will define what a test is and we will introduce a relation based on which tests are passed by processes. Finally, we will obtain an operational characterization that can be used as a first step to define a denotational sematics sound and complete with respect the testing semantics.
3DM: Domain-oriented Data-driven Data Mining
Authors: Wang Guoyin , Wang Yan
Published: December 2019

Recent developments in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for knowledge discovery large-scale database. Data mining technology is a useful tool for this task. It is an emerging area of computational intelligence that offers new theories, techniques, and tools for processing large volumes of data, such as data analysis, decision making, etc. There are countless researchers working on designing efficient data mining techniques, methods, and algorithms. Unfortunately,most data mining researchers pay much attention to technique problems for developing data mining models and methods, while little to basic issues of data mining. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? What is the rule we would obey in a data mining process? What is the relationship between the prior knowledge of domain experts and the knowledgemind from data? In this paper, we will address these basic issues of data mining from the viewpoint of informatics [1]. Data is taken as a manmade format for encoding knowledge about the natural world. We take data mining as a process of knowledge transformation. A domain-oriented data-driven data mining (3DM) model based on a conceptual data mining model is proposed. Some data-driven data mining algorithms are also proposed to show the validity of this model, e.g., the data-driven default rule generation algorithm, data-driven decision tree pre-pruning algorithm and data-driven knowledge acquisition from concept lattice.
Contingency Matrix Theory II: Degree of Dependence as	Granularity
Authors: Tsumoto Shusaku , Hirano Shoji
Published: December 2019

The degree of granularity of a contingency table is closely related with that of dependence of contingency tables. We investigate these relations from the viewpoints of determinantal devisors and determinants. From the results of determinantal divisors, it seems that the devisors provide information on the degree of dependencies between the matrix of the whole elements and its submatrices and the increase of the degree of granularity may lead to that of dependence. However, the other approach shows that a constraint on the sample size of a contingency table is very strong, which leads to the evaluation formula where the increase of degree of granularity gives the decrease of dependency.
Toward Formal Models of the Theoretical Framework of Fundamental	Economics
Authors: Wang Yingxu
Published: December 2019

Many fundamental theories and doctrines of micro- and macro-economics are not formally studied in economics. This paper presents a rigorous treatment and explanation of a set of fundamental empirical theories of economics. It is recognized that the adaptive equilibrium of market systems is rooted in the negative feedbackmechanisms of demands and supplies. A mathematical model of economic equilibrium developed in this paper provides a formal proof of Adam Smith's hypothesis of the 'invisible hand'. An economic equilibrium theorem is derived to deal with more complicated multivariable equilibrium problems that could not be handled in conventional economic theories. Then, a set of mathematical models is developed for dynamic cost analyses and the estimation of economical outcomes of engineering projects.
Real Polygonal Covers of Digital Discs – Some Theories and	Experiments
Authors: Bhowmick Partha , Bhattacharya Bhargab B.
Published: December 2019

There are several algorithms for digitization of a real disc (circle) to derive a digital disc, and also for finding the real disc corresponding to a digital disc. However, the correspondence of a digital disc with a regular polygon in the real plane is not well studied. This paper presents some theories and related experiments on setting the correspondence from a digital disc to its polygonal cover in the real plane. For an ideal regular polygon covering a digital disc, all the grid points of the digital disc should lie on and inside the polygon, and vice versa. That an ideal regular polygon corresponding to a digital disc is possible for some of the digital discs, especially for the ones having smaller radii, is shown. Further, for a disc whose ideal regular polygon is not possible, an approximate polygon, tending to the ideal one, is possible, in which the error of approximation can be controlled by the number of vertices of the approximate polygon. These (ideal or approximate) polygonal covers of digital discs have several applications in many problems of point set pattern matching. We have reported the conditions under which an ideal regular polygon always exists corresponding to a digital disc, and the conditions under which the existence of an ideal regular polygon becomes uncertain. Experimental results have been given to demonstrate the possibilities of approximation and the trade-off in terms of error versus the number of vertices in the approximate polygon.
On Inferences ofWeak Multivalued Dependencies
Authors: Hartmann Sven , Link Sebastian
Published: December 2019

Nesting is a useful technique in many areas of database practice. For instance, nesting is a fundamental operation for the nested relational data model, it can be applied to reduce the level of data redundancy in a database instance, to improve query processing or to convert data from one model to another.

We further address the question when nesting operations commute with one another, i.e., when the final nested database relation is independent of the order in which the nesting operations are applied. In fact, it has been shown that the satisfaction of weakmultivalued dependencies provides a sufficient and necessary condition for the commutativity of nesting operations.

We study inference systems for different notions of implication for weak multivalued dependencies. First, we establish an axiomatisation with the property that every weak multivalued dependency can be inferred either without any application of the complementation rule or by a single application of the complementation rule necessary only in the very last step of the inference. Consequently, the complementation rule is a mere means to achieve a decomposition of the database. Secondly, we drop the assumption of having a fixed underlying schema, and establish an axiomatisation of weak multivalued dependencies for the notion of implication in this context.
On Domain Theory over Girard Quantales
Authors: Waszkiewicz Paweł
Published: December 2019

This paper is about a generalization of Scott's domain theory in such a way that its definitions and theorems become meaningful in quasimetric spaces. The generalization is achieved by a change of logic: the fundamental concepts of original domain theory (order, way-below relation, Scott-open sets, continuous maps, etc.) are interpreted as predicates that are valued in an arbitrary completely distributive Girard quantale (a CDG quantale). Girard quantales are known to provide a sound and complete semantics for commutative linear logic, and complete distributivity adds a notion of approximation to our setup. Consequently, in this paper we speak about domain theory based on commutative linear logic with some additional reasoning principles following from approximation between truth values.

Concretely, we: (1) show how to define continuous Q-domains, i.e. continuous domains over a CDG quantale Q; (2) study their way-below relation, and (3) study the rounded ideal completion of Q-abstract bases. As a case study, we (4) demonstrate that the domain-theoretic construction of the Hoare, Smyth and Plotkin powerdomains of a continuous dcpo can be straightforwardly adapted to yield corresponding constructions for continuous Q-domains.
On Insertion Grammars with Maximum Parallel Derivation
Authors: Dinu Liviu P.
Published: December 2019

In this paper we investigate insertion grammars and explore their capacity to generate words parallelly by introducing parallel derivation (where more than one rule can be applied to the string in parallel) and maximum parallel derivation (where as many rules as possible are applied to the string in parallel). We compare the generative power of these grammars with context sensitive and context free grammars and with different variants of contextual grammars. We apply these grammars to syllabification in Romanian and provide arguments that they can also be used in a cognitive perspective.
Branching Bisimilarity with Explicit Divergence
Authors: van Glabbeek Rob , Luttik Bas , Trčka Nikola
Published: December 2019

We consider the relational characterisation of branching bisimilarity with explicit divergence. We prove that it is an equivalence and that it coincides with the original definition of branching bisimilarity with explicit divergence in terms of coloured traces. We also establish a correspondence with several variants of an action-based modal logic with until- and divergence modalities.
Clausal Tableaux for Multimodal Logics of Belief
Authors: Goré Rajeev , Nguyen Linh Anh
Published: December 2019

We develop clausal tableau calculi for six multimodal logics variously designed for reasoning about multi-degree belief, reasoning about distributed systems of belief and for reasoning about epistemic states of agents in multi-agent systems. Our tableau calculi are sound, complete, cut-free and have the analytic superformula property, thereby giving decision procedures for all of these logics. We also use our calculi to obtain complexity results for five of these logics. The complexity of the remaining logic was known.
Algebraic Properties of Generalized Rough Sets
Authors: Kondo Michiro
Published: December 2019

In this paper we consider the problem whether for a map φ : A(B) → B there is a relation R on A(B) such that properties of a map φ: B → B are reflected to R. This is a converse problem that J. Järvinen proved in [5]. We give an affirmative answer to the problem. Moreover we give equational bases which characterize properties of the map φ. This means that the classes of some types of algebras form varieties. Hence, there are full correspondence between properties of relations and varieties of modal algebras.
Application of Concurrency in the Asynchronous Design of	Write-after-read Operations
Authors: Toosizadeh Navid , Zaky Safwat G.
Published: December 2019

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.
A Token-Managed Admission Control System for QoS Provision on a	Best-Effort GALS Interconnect
Authors: Yang Shufan , Furber Steve B. , Shi Yebin , Plana Luis A.
Published: December 2019

A Token-ManagedAdmission Control (TMAC) mechanism is introduced in order to provide efficient Quality-of-Service (QoS) support for different types of application on a best-effort Globally-Asynchronous Locally-Synchronous (GALS) interconnect fabric. The mechanism is applied at the ingress edges of the fabric using tokens to allocate dynamic network resources and prevent network congestion. The degree of fairness is controllable, in order to balance the desired throughput and data transfer resource allocation appropriately for a particular application. The simulation and analysis presented here shows efficient QoS provision. Our detailed implementation and analysis show that TMAC provides service guarantees on the network while using a modest physical area because of the simplicity of the control logic.
Synthesis of Petri Nets from Term Based Representations of	Infinite Partial Languages
Authors: Bergenthum Robin , Desel Jörg , Mauser Sebastian , Lorenz. Robert
Published: December 2019

In this paper we present an algorithm to synthesize a finite unlabelled place/transition Petri net (p/t-net) from a possibly infinite partial language, which is given by a term over a finite set of labelled partial orders using operators for union, iteration, parallel composition and sequential composition. The synthesis algorithm is based on the theory of regions for partial languages presented in [17] and produces a p/t-net having minimal net behaviour including the given partial language. The algorithm uses linear programming techniques that were already successfully applied in [22] for the synthesis of p/t-nets from finite partial languages. Also, an equality test algorithm to check whether the behaviour of the synthesized p/t-net equals the given partial language is shown. Moreover, we present an implementation of the developed synthesis algorithm together with an example case study. Finally, a possible generalization of the presented term based representation of infinite partial languages is discussed.
Stochastic Model of Evolutionary and Immunological Multi-Agent	Systems: Mutually Exclusive Actions
Authors: Byrski Aleksander , Schaefer Robert
Published: December 2019

The mathematical model of the biologically inspired, memetic, agent-based computation systems EMAS and iEMAS conformed to BDI standard is presented. The state of the systems and their dynamics are expressed as stationary Markov chains. Such an approach allows to better understand their complex behavior as well as their limitations.
Stochastic Model of Evolutionary and Immunological Multi-Agent	Systems: Parallel Execution of Local Actions
Authors: Schaefer Robert , Byrski Aleksander , Smołka Maciej
Published: December 2019

The refined model for the biologically inspired agent-based computation systems EMAS and iEMAS conforming to the BDI standard is presented. Moreover, their evolution is expressed in the form of the stationary Markov chains. This paper generalizes the results obtained by Byrski and Schaefer [7] to a strongly desired case in which some agents' actions can be executed in parallel. In order to find the Markov transition rule, the precise synchronization scheme was introduced, which allows to establish the stepwise stochastic evolution of the system. The crucial feature which allows to compute the probability transition function in case of parallel execution of local actions is the commutativity of their transition operators. Some abstract conditions expressing such a commutativity which allow to classify the agents' actions as local or global are formulated and verified in a very simple way. The above-mentioned Markov model constitutes the basis of the asymptotic analysis of EMAS and iEMAS necessary to evaluate their search possibilities and efficiency.
Properties of Quantum Particles in Multi-Swarms for Dynamic	Optimization
Authors: Trojanowski Krzysztof
Published: December 2019

This paper studies properties of a multi-swarm system based on a concept of physical quantum particles (mQSO). Quantum particles differ from the classic ones in the way they move. As opposed to the classic view of particle movement, where motion is controlled by linear kinematic laws, quantum particles change their location according to random distributions. The procedure for generating a new location for the quantum particle is similar to mutation operators widely used in evolutionary computation with real-valued representation. In this paper we study a set of new distributions of candidates for quantum particle location, and we show different features of these distributions. The distributions considered in this paper are divided into two classes: those with a limited range of the new location coordinates and those without such limitations. They are tested on different types of dynamic optimization problems. Experimental verification has been based on a number of testing environments and two main versions of the algorithm: with and without mechanisms protecting against stagnation caused by convergence of sub-swarms during the search process. The experimental results show the advantages of the distribution class, in which the candidates are spread out in the entire search space, and indicate the positive and negative aspects of application of anti-convergencemechanisms.
Congruences Generated by Extended Ground Term Rewrite	Systems
Authors: Vágvölgyi Sándor
Published: December 2019

We show that it is decidable for any extended ground term rewrite system R whether there is a ground term rewrite system S such that the congrunce ↔ ^*_R generated by R is equal to the congruence ↔ ^*_S generated by S. If the answer is yes, then we can effectively construct such a ground term rewrite system S. We characterize congruences generated by extended ground term rewrite systems.
On Using LSB Matching Function for Data Hiding in Pixels
Authors: Chan Chi-Shiang
Published: December 2019

The proposedmethod in this paper is based on the LSB matching function ofMielikainen for Steganography. However, there is a significant change with respect to Mielikainen's method in the use of the LSB matching function to compute secret bits. Melikainen extracts secret bits in pixel pairs: the first one is the LSB of the first pixel in a pixel pair and the other is computed by a LSB matching function applied to both pixels. The proposed method does not make any partition, but makes the sequential image processing: the current secret bit is always computed by a LSB matching function where the LSB matching function is modified using only an XOR operation. To make the data extraction compatible with data embedding, the proposed method modifies sparse pixels by adding/subtracting their value to/from one. In the experimental results, the values of embedding efficiency increase while the involved bits increase. Taking test images in the experiments, the numbers of modified pixels in the proposed method are always lower than those in Mielikainen's method.
On a Modal Epistemic Axiom Emerging from McDermott-Doyle	Logics
Authors: Koutras Costas D. , Zikos Yorgos
Published: December 2019

An important question in modal nonmonotonic logics concerns the limits of propositional definability for logics of the McDermott-Doyle family. Inspired by this technical question we define a variant of autoepistemic logic which provably corresponds to the logic of the McDermott-Doyle family that is based on themodal axiom p5: ◊φ⊃ (¬□φ⊃□¬□φ). This axiomis a naturalweakening of classical negative introspection restricting its scope to possible facts. It closely resembles the axiom w5: φ⊃ (¬□φ⊃□¬□φ) which restricts the effect of negative introspection to true facts. We examine p5 in the context of classical possible-worlds Kripke models, providing results for correspondence, completeness and the finite model property. We also identify the corresponding condition for p5 in the context of neighbourhood semantics. Although rather natural epistemically, this axiom has not been investigated in classical modal epistemic reasoning, probably because its addition to S4 gives the well-known strong modal system S5.
Hypergraph Cuts & Unsupervised Representation for Image	Segmentation
Authors: Rital Soufiane
Published: December 2019

This paper presents a novel approach to image segmentation based on hypergraph cut techniques. Natural images contain more components: Edge, homogeneous region, noise. So, to facilitate the natural image analysis, we introduce an Image Neighborhood Hypergraph representation (INH). This representation extracts all features and their consistencies in the image data and its mode of use is close to the perceptual grouping. Then, we formulate an image segmentation problem as a hypergraph partitioning problem and we use the recent k-way hypergraph techniques to find the partitions of the image into regions of coherent brightness/color. Experimental results of image segmentation on a wide range of images from Berkeley Database show that the proposed method provides a significant performance improvement compared with the stat-of-the-art graph partitioning strategy based on Normalized Cut (Ncut) criteria.
Quantization Step Parity-based Steganography for MP3 Audio
Authors: Diqun Yan , Rangding Wang , Liguang Zhang
Published: December 2019

Petitcolas has proposed a steganographic technique called MP3Stego which can hide secret messages in a MP3 audio. This technique is well-known because of its high capacity. However, in rare cases, the normal audio encoding process will be terminated due to the endless loop problem caused by embedding operation. In addition, the statistical undetectability of MP3Stego can be further improved. Inspired by MP3Stego, a new steganographic method for MP3 audio is proposed in this paper. The parity bit of quantization step rather than the parity bit of block size in MP3Stego is employed to embed secret messages. Compared with MP3Stego, the proposed method can avoid the endless loop problem and achieve better imperceptibility and higher security.
Expressiveness of Petri Nets with Stopwatches. Dense-time	Part
Authors: Magnin. Morgan , Molinaro Pierre , Roux Olivier (H.)
Published: December 2019

With this contribution, we aim to draw a comprehensive classification of Petri nets with stopwatches w.r.t. expressiveness and decidability issues. This topic is too ambitious to be summarized in a single paper. That is why we present our results in two different parts. The scope of this first paper is to address the general results that apply for both dense-time and discrete-time semantics. We study the class of bounded Petri nets with stopwatches and reset arcs (rSwPNs), which is an extension of T-time Petri nets (TPNs) where time is associated with transitions. Stopwatches can be reset, stopped and started. We give the formal dense-time and discrete-time semantics of these models in terms of Transition Systems. We study the expressiveness of rSwPNs and its subclasses w.r.t. (weak) bisimilarity (behavioral semantics). The main results are following: 1) bounded rSw- PNs and 1-safe rSwPNs are equally expressive; 2) For all models, reset arcs add expressiveness. 3) The resulting partial classification of models is given by a set of relations explained in Fig. 7: in the forthcoming paper, we will complete these results by covering expressiveness and decidability issues when discrete-time nets are considered. For the sake of simplicity, our results are explained on a model such that the stopwatches behaviors are expressed using inhibitor arcs. Our conclusions can however be easily extended to the general class of Stopwatch Petri nets.
Expressiveness of Petri Nets with Stopwatches. Discrete-time	Part
Authors: Magnin Morgan , Molinaro Pierre , Roux Olivier (H.)
Published: December 2019

With this contribution, we aim to draw a comprehensive classification of Petri nets with stopwatches w.r.t. expressiveness and decidability issues. This topic is too ambitious to be summarized in a single paper. That is why we present our results in two different parts. In the first part of our work, we established new results regarding to both dense-time and discrete-time semantics. We now focus on the discrete-time specificities. We address the class of bounded Petri nets with stopwatches and reset arcs (rSwPNs), which is an extension of T-time Petri nets (TPNs) where time is associated with transitions. Stopwatches can be reset, stopped and started. We recall the formal dense-time and discrete-time semantics of these models in terms of Transition Systems. We study the expressiveness of rSwPNs and its subclasses w.r.t. (weak) bisimilarity (behavioral semantics). The main results are following: 1) Discrete-time bounded TPNs, discrete-time bounded rSwPNs and untimed Petri nets are equally expressive; 2) The resulting (final) classification of models is given by a set of relations explained in Fig. 7. While investigating expressiveness, we exhibit proofs that can be easily extended to the resolution of decidability issues. Among other results, we prove that, for bounded rSwPNs, the state and marking reachability problems - undecidable with dense-time semantics - are decidable when discrete-time is considered. Table 1 gives a synthesis of the main decidability results for these models. For the sake of simplicity, our results are explained on a model such that the stopwatches behaviors are expressed using inhibitor arcs. Our conclusions can however be easily extended to the general class of Stopwatch Petri nets.
Homogeneous Spiking Neural P Systems
Authors: Zeng Xiangxiang , Zhang Xingyi , Pan Linqiang
Published: December 2019

Spiking neural P systems are a class of distributed parallel computing models inspired from the way the neurons communicate with each other by means of electrical impulses (called "spikes"). In this paper, we consider a restricted variant of spiking neural P systems, called homogeneous spiking neural P systems, where each neuron has the same set of rules. The universality of homogeneous spiking neural P systems is investigated. One of universality results is that it is sufficient for homogeneous spiking neural P system to have only one neuron that behaves nondeterministically in order to achieve Turing completeness.
Modeling and Reasoning with Paraconsistent Rough Sets
Authors: Vitória Aida , Małuszyński Jan , Szałas Andrzej
Published: December 2019

We present a language for defining paraconsistent rough sets and reasoning about them. Our framework relates and brings together two major fields: rough sets [23] and paraconsistent logic programming [9]. To model inconsistent and incomplete information we use a four-valued logic.

The language discussed in this paper is based on ideas of our previous work [21,32,22] developing a four-valued framework for rough sets. In this approach membership function, set containment and set operations are four-valued, where logical values are t (true), f (false), i (inconsistent) and u (unknown).

We investigate properties of paraconsistent rough sets as well as develop a paraconsistent rule language, providing basic computational machinery for our approach.
An Algebraic Framework for Defining Behaviours of Concurrent	Systems. Part 2: The Axiomatic Presentation
Authors: Winkowski Józef
Published: December 2019

The paper is the second part of two-part paper that contributeswith a concept of a process, 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.

In the first part a universal model of a process have been introduced and operations on processes have been defined.

A process is viewed as a model of a run of a system (discrete, continuous, or of a mixed type). 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 is a continuation of the other can be composed sequentially. Independent processes, can be composed in parallel. Processes may be prefixes, i.e. independent components of initial segments of other processes.

It has been shown that processes in a universe of objects and operations on such processes form a partial algebra, called algebra of processes, that is a specific partial category with respect to the sequential composition, and a specific partial monoid with respect to the parallel composition.

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 behaviour-oriented algebras, and properties of such algebras are investigated. In particular, it is shown how some of the behaviour-oriented algebras can be represented as algebras of processes, and how to use them to describe phenomena with states and processes provided with specific structures.
Deterministic and Unambiguous Families within Recognizable Two-dimensional Languages
Authors: Anselmo Marcella , Giammarresi Dora , Madonia Maria
Published: December 2019

Recognizable two-dimensional languages (REC) are defined by tiling systems that generalize to two dimensions non-deterministic finite automata for strings. We introduce the notion of deterministic tiling system and the corresponding family of languages (DREC) and study its structural and closure properties. Furthermore we show that, in contrast with the one-dimensional case, there exist other classes between deterministic and non-deterministic families that we separate by means of examples and decidability properties.
Feature Selection via Maximizing Fuzzy Dependency
Authors: Hu Qinghua , Zhu Pengfei , Liu Jinfu , Yang Yongbin , Yu Daren
Published: December 2019

Feature selection is an important preprocessing step in pattern analysis and machine learning. The key issue in feature selection is to evaluate quality of candidate features. In this work, we introduce a weighted distance learning algorithm for feature selection via maximizing fuzzy dependency. We maximize fuzzy dependency between features and decision by distance learning and then evaluate the quality of features with the learned weight vector. The features deriving great weights are considered to be useful for classification learning. We test the proposed technique with some classical methods and the experimental results show the proposed algorithm is effective.
Learning Behaviors of Functions
Authors: Kalyanasundaram Bala , Velauthapillai Mahe
Published: December 2019

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.