This work introduces an innovative approach from modeling to simulations, integrating quantum fuzzy interpretations, focusing on the expression of membership degrees in quantum circuits, interpreted as a unitary quantum transformation. Our research contrasts Quantum Fuzzy Computing with Classical Computing, particularly in the context of the prisoner's dilemma and parent-child relationships. Through the Qiskit framework, we conduct simulations, demonstrating the differences in results obtained by both approaches. Well-known fuzzy connectives, such as “exclusive or” and “arithmetic means”, provide an algebraic description for the corresponding composition of quantum operators and circuit representations. Thus, such algorithms can easily extend to model multiple agents' relationships, described by multidimensional quantum registers. The Qiskit simulations provide the structure for the algorithms' computation on the actual quantum platforms, indicating a promising direction for future research in this hybrid research area.

This study introduces an innovative quantum fuzzy approach for modeling and simulating complex decision-making processes, utilizing quantum circuits to express membership degrees as unitary transformations. We contrast Quantum Fuzzy Computing with Classical Computing, highlighting advancements in modeling the prisoner's dilemma. Using the Qiskit framework, we demonstrate how quantum fuzzy algorithms featuring connectives such as ‘exclusive or’ and ‘arithmetic mean’ enable detailed modeling of multi-agent relationships via multidimensional quantum registers, showcasing potential advancements in this hybrid research area.

This study is given at the intersection of three important areas: Modal Logic (ML), Fuzzy Logic (FL), and Quantum Computing (QC), leveraging from their main features. On the one hand, we have the QC ability to handle complex data more efficiently, taking advantage of the quantum mechanics concepts. On the other hand, ML and FL allow us to express uncertainties by mathematically modeling the imprecision of the natural language. Therefore, we first provide an algebraic model to interpret modal operators on fuzzy logic, and then we represent them in a quantum computing environment. Besides, we present some case studies simulating the fuzzy modal connectives in the Qiskit platform, aiming to better understand the evolution of quantum circuits.

Firstly, this work discusses the main conditions guarantying that general overlap (grouping) functions can be obtained from n-dimensional overlap (grouping) functions. Focusing on QL-implications, which are usually generated by strong negations together with t-norms and t-conorms, we consider a non-restrictive construction, by relaxing not only the associativity and the corresponding neutral elements (NE) but also the reverse construction of other properties. Thus, the main properties of the QL-implication class are studied, considering a tuple (G,N,O) generated from grouping and overlap functions together with the greatest fuzzy negation. In addition, in order to provide more flexibility, we define a subclass of QL-implications generated from general overlap and general grouping functions. Some examples are introduced, illustrating the constructive methods to generate such operators.

This paper contributes to classifying video streaming traffic by exploring concepts of Interval-Valued Fuzzy Logic. This approach extends related works by considering the uncertainties generated by variations in network conditions and the parameter imprecision affecting the behavior of network flow, which increases the complexity to reach high accuracy in identifying the network traffic. Some evaluations using the interval-valued logic approach for video streaming traffic classification are presented using application tools and datasets to validate the proposal.

This paper considers the consensual analysis in decision-making (CDM) processes based on fuzzy logic (FL) and interval-valued fuzzy logic (IVFL), providing a CDM-strategy, by exploring the axiomatic properties of fuzzy consensus measures (FCM) via penalty functions. Thus, two models are formalized, FS-FCM and IVFS-FCM. In the former, the fuzzy-valued lattice enables the analysis of fuzzy information for linguistic variables (LV), which is obtained by the aggregation of penalty functions. And, in the latter, the consensus measures of fuzzy sets are aggregated to build a new consensual analysis modeling. Thus, e.g., the cohesion of several terms related to the same LV can be analyzed, and also the coherence between fuzzy sets referring to the lowest and highest projections. Such models decide based on relevance criteria and qualitative assessments, via the selection of alternatives, supporting the corresponding algorithmic strategies: FS-FCM strategy, applied to fuzzy values, and IVFS-FCM strategy, covering fuzzy sets. The Intf-HybridMem approach explores the access patterns to volatile and non-volatile memories related to decision-making in two steps: (i) the FS-FCM strategy explores consensus measures of fuzzy values from membership functions; and (ii) the IVFS-FCM strategy, modeling inaccuracy inherent in input variables, as read/write frequency and access recency, also including the migration recommendation as output, which is validated by evaluations carried out in both proposed strategies.

This paper explores fuzzy logic-based consensus analysis as support to the decision making problem related to page migrations in hybrid memories. The proposed interval fuzzy approach considers the uncertainties on data access profiles in hybrid memories and its main characteristics. The fuzzy set consensus analysis, in its turn, is proposed using arithmetic and exponential extended aggregations. Aiming to evaluate the proposal, a case study was developed in the Intf-HybridMem memory management architecture.

In the Hybrid Memory research approach, the development of new storing data strategies combines the traditional volatile memories (DRAM) with the emerging non-volatile memory (NVM) to take advantage of their potential and best features. One question in the hybrid memory research is the decision of which memory technology to use in order to store data of distinct applications. These decision processes have to examine the behavior of memory operations, such as read/write frequency, and also their memory characteristics. Also, considering the large volume of parameters and the uncertainties inherent in the data migration, the decision to store data in hybrid memories is not a trivial task. This paper presents the f-HybridMem component, a fuzzy-based system to support the uncertainty in data management for hybrid memory architectures. Thus, this proposal contributes to determine a correct selection between memory modules by improving the data management, in a page level organization. The memory management takes into account the behavior of memory operations and the memory characteristics. Such architecture is conceived based on the following modules: (i) Access Updater, a hardware module to identify the pages access patterns, and (ii) f-HybridMem component, a fuzzy-based software module supporting the migration decision processes. In addition, tests are conducted attempt evaluating the accuracy of the fuzzy-based system. Moreover, the proposed evaluations aim to estimate the influence of the following parameters: Buffer Size, Counters Size, Frequency of Migration, Promote Value, Demote Value, providing a correct recommend for data migrations.

The increasing offer of Internet-connected resources, providing more than one service, confers complexity to the classification procedures and selection of adequate resources to meet customer demands. In this context, this article aims to present a proposal for the classification of resources in IoT, considering non-functional parameters of QoS. The proposal explores two complementary approaches: (i) use of type-2 fuzzy logic; and (ii) dynamic adequacy of the fuzzy rules employed. The results obtained in the classification of the QWS dataset are presented and are satisfactory, adjusting the selection of the most relevant resources according to the preferences specified by the client.

This work extends the study of properties related to the interval extension of the generalized Atanassov’s intuitionistic fuzzy index (A-GIvIFIx) which is defined as a composition between an interval-valued aggregation and fuzzy implication w.r.t partial/total order on $\mathbb{U}$. The relationship between Atanassov’s intuitionistic fuzzy index and its interval extension is discussed. Aggregation and conjugate operators provide two methods to obtain new classes of A-IvIFIx.

Dynamic consolidation of virtual machines (VMs) is an effective way to improve resource utilization and power efficiency in cloud computing, directly affecting Quality of Service aspects. This paper presents Int-FLBCC, a new proposal with exploring a Type-2 Fuzzy Logic approach to address the uncertainties and inaccuracies in determining resource usage, aiming at energy savings with minimal performance degradation. Validation results in a simulated cloud computing environment showed improvements in energy efficiency of 8.83% with IQR_XY, 22.43% with MAD_XY. For fulfillment of Service Level Agreements (SLAs), the best values achieved were 9.06% with MAD_XY and 25% of THR_Lex1.

Computer systems based on intuitionistic fuzzy logic are capable of generating a reliable output even when handling inaccurate input data by applying a rule based system, even with rules that are generated with imprecision. The main contribution of this paper is to show that quantum computing can be used to extend the class of intuitionistic fuzzy sets with respect to representing intuitionistic fuzzy Xor operators. This paper describes a multi-dimensional quantum register using aggregations operators such as t-(co)norms based on quantum gates allowing the modeling and interpretation of intuitionistic fuzzy Xor operations.

With the technological advances that permeate the computer systems, the technologies are increasingly integrated into people's daily lives. Unfortunately, all the facilities offered by these advances are becoming targets for malicious attacks. Although different technologies are used to provide information security, they do not present a view of the environment as a whole. Thus, this paper presents an approach to provide situational awareness for security in computational environments, producing a holistic view. This approach explores different features since the acquisition of event, a hybrid processing, hybrid data storage, and the resulting actuation. The proposed approach was evaluated through of a prototype, which was applied in three use cases, showing to be stable and flexible in the provision of security aspects in computational environments.

Currently, billions of resources are connected to the Internet, many providing services. There is forecast to be a significant increase in the number of resources in the coming years. The adequate selection of resources that best meet the demands of users among a large number of options has been a relevant and current research challenge in the autonomic IoT management. This paper specifies and evaluates the pre-classification of new resources of the EXEHDA middleware based on the non-functional parameters of QoS. Fuzzy logic is used in the treatment of uncertainties in defining the importance weights of QoS attributes. The results obtained in the evaluation of the accuracy of the pre-classification through fuzzy logic and machine learning are presented.

The incidence of false alerts in a hospital environment undermines the health professionals' tasks, since: (i) they stressful the teams, their caregivers and the patients themselves; (ii) promote additional costs of time and attention; (iii) may cause risky situations, some of which have severe health implications in patients. Considering this scenario, the objectives of this work are to discuss the ocurrency of alerts in intravenous systems and to contribute to the reduction of the emission of false alerts in electromedical equipments, in particular in infusion pumps. To this purpose, the developed proposal, called BIRB, explores the use of Bayesian Networks to minimize the occurrence of false alerts when occurs a change in the flow rate caused by occlusions in infusion pumps. The occlusion is the procedure with the highest index of false alerts in this type of equipment. The achieved results with the BIRB proposal are promising, reaching 85% of accuracy, based on data from actual infusion pumps.

Advances in Internet of Things (IoT) have enabled interoperation between various intelligent devices, collecting and distributing contextual information, thus allowing more sophisticated models to provide context awareness. The use of middlewares is highlighted in the literature as an alternative to device management in distributed IoT infrastructure, as well as to make contextual processing more transparent to applications. Regarding this scenario, the objective of this work is to present the approach designed to provide Fog Computing to the EXEHDA middleware, providing Context Awareness for the computational edge devices present in the monitored environments. A study case was carried out in the area of Precision Viticulture (PV) evaluating the proposed approach.

The typical infrastructures of Ubiquitous Computing assume characteristics of flexibility regarding the connectivity in the environments. Aiming security in these scenarios, several solutions are deployed in its own syntax languages, providing events in different formats. In this sense, Situation Awareness, as a strategy capable of integrating events from different sources, becomes a requirement for the controls implementation. This work proposes an ontological approach to SA applied to the information security domain, called EXEHDA-SO. The proposal was evaluated based on a real infrastructure, showing itself capable of handling heterogeneous events from different contextual source.

Among the research challenges, in Grid Computing we need to make the system robust as much as the uncertainties of the different measures extracted from the computational infrastructure, as for the imprecision of the computations related to the decision making. In particular, the appropriation of information relevant to task scheduling from a set of heterogeneous sources and dynamic behavior has been the focus of a significant number of studies. Scheduling tasks is a known NP-Hard problem. This paper provides an approach with Fuzzy Type-2 logics to treat uncertainties and dynamic behavior for scheduling tasks in grid environments, named Int-fGrid. The scheduler was validated through simulations in the SimGrid framework with a model of the GridRS architecture. Our results show that the Fuzzy Type-2 approach provides makespans up to 17.60% better than the best alternative tested named scheduler XSufferage.

The exponential increase in the temporal and spatial complexities is one of the main challenges in the widespread use of quantum algorithm simulation, especially in dense quantum transformations (QTs) such as the Hadamard transformation (H), which has found wide applications in computer and communication science and also comprising the simplest quantum universal set of QTs. The main reason for these costs is the expansion of QTs by using tensor product in multi-dimension quantum applications. In this work, new optimizations for the execution of reduction and decomposition based on the Identity operator are introduced in the Distributed Geometric Machine framework (D-GM). Instead of executing the quantum transformation in a single step, they are divided in sub-quantum transformations and only the values different from Identity transformations are stored. Mixed Partial Processes provide control over the increase in the size of read/write memory states in the calculation of a QT, thus contributing to increase the scalability of applications regarding hardware-GPUs memory limit. In the evaluation of this D-GM extension, Hadamard Transformations were simulated up to 28 qubits applications over a single GPU. Our new simulator is 10, 829χ faster and allows for the simulation of more qubits when compared to our previous implementation running on the same GPU.

The necessity of making a system robust to the uncertainties of the different measures taken from the computing infrastructure is one of the Grid Computing research challenges. In particular, taking relevant information to tasks scheduling from a set of heterogeneous sources and dynamic behavior has been the focus of a significant number of studies. Considering this background, this paper aims to approach a module for task scheduling in Grid Computing, using a taken decision system under uncertainty regime. The proposed module uses the fuzzy logic, called fGrid, to treat uncertainties. Simulations were carried out using the SimGrid framework and modeling GridRS architecture. In addition to the presented results, the a standard scale use with linguistic variation makes both the manipulation of the priority list as the scheduling easier, which contributes to maintainability.

The recent advances in the Internet of Things (IoT) area, which has provided an increasing availability of networked sensors and actuators, has given a new perspective to research in the sensor data processing in IoT. In this sense, the main contribution of this paper is the proposition of COIOT (COntext + IOT), a rule-based architecture proposal for distributed sensing in IoT designed with the aim of providing the proactive management of the EXEHDA Middleware interactions with the physical environment. To evaluate the functionalities of the proposed architecture we implemented a case study in the agricultural area. The achieved results are very promising.

The applications in Ubiquitous Computing (UbiComp) environments must be aware of their contexts of interest and adapt to changes in them. Thus, a major research challenge in the area of UbiComp is related to context awareness. Considering the high distribution, heterogeneity, dynamism, and mobility of ubiquitous environments, this paper presents an architecture for context awareness, named UbiC (Ubiquitous Context awareness), which is integrated to EXEHDA middleware (Execution Environment for Highly Distributed Applications). We consider that the main contribution of this work is an architecture that enables the acquisition, storage, and processing of context data, in a distributed way, independently of the application, in a rule-based autonomic perspective, and with support to mobility. To assess the functionalities of the UbiC architecture, we present a case study, highlighting prototypes developed, technologies employed, and tests realized.

A number of ubiquitous systems are considering how to deal with situation-awareness to act in a personalized manner according to user's needs. Indeed, most of the research efforts in situation-awareness are generally focused on the implementation of common characteristics, differing basically on the relevant context observed and how this contextual information is managed. Consequently, little attention has been paid to an abstract and general view for situation-awareness. The goal of this work is to provide a shift of the attention focus from the implementation to the modeling phase. Our objectives are (1) to define the conceptual modeling of situation-awareness for adaptive and ubiquitous learning systems, (2) to identify its relevant concepts, relationships, and structural, dynamic and representational elements, (3) to propose a method for situation detection. The domain concepts are explored by an application scenario that allows the understanding of the situations configured in u-learning systems and their dynamic factors. The conceptual model was implemented in the CONIC module, which was evaluated in an e-learning system specifying the application and constructing the improvements necessaries to deploy the system in a real application for tests.

This paper presents the fuzzy (S, N)- QL- and D-subimplication classes, which are obtained by OWA operators performed over the families of triangular subnorms and sub-conorms along with fuzzy negations. Since these classes of subim-plications are explicitly represented by such connectives, the corresponding (S, N)- QL- and D-subimplicatios are characterized by the generalized associativity and distributive properties together with extensions of the exchange and neutrality principles. As the main results, these families of subimplications extend related implications by preserving their corresponding properties.

In this paper we present a view of EXEHDA middleware and a novel service created for dynamic adaptation. EXEHDA is service-oriented, adaptive and was conceived to support the execution of ubiquitous applications. The main concept in the proposed design for the middleware and for the application is context awareness expressed in an adaptive behavior. The middleware manages and implements the follow-me semantics for ubiquitous applications. This is also a key to provide functionality adapted to the constraints and unpredictability of the large-scale environment. EXEHDA provides services for distributed adaptive execution, context recognition, ubiquitous storage and access, and anonymous and asynchronous communications. To evaluate the proposed service we developed a case study, implementing an application in medical area. Analyzing the results we can see that the users found the application easy to use and usefulness for health workers at a hospital.

Ubiquitous computing (Ubicomp) environments are characterized by high distribution, heterogeneity and dynamism. In these environments, applications must be aware of their contexts and adapt to changes in them. A major research challenge in the area of Ubicomp is related to context awareness. Considering the characteristics of ubiquitous environments, this paper presents an architecture for context awareness, called DynamiCC (Dynamic Context Composition), that includes elements to support: context modeling, contextual data collection, actuation on the environment, and composition and interpretation of contextual information. We consider that the main contributions of this work are the employ of a hybrid approach for context modeling, and the proposal of an architecture that supports the interpretation and the composition of dynamic contexts, which enables the construction of complex contexts, in runtime of applications. To assess the functionality of the DynamiCC, we did a discussion of usage scenarios, highlighting the prototypes and tests performed.

The simulation of quantum algorithms in classic computers is a task which requires high processing and storing capabilities, limiting the size of quantum systems supported by the simulators. However, optimizations for reduction of temporal and spatial complexities are promising, expanding the capabilities of some simulators. The main contribution of this work consists in designing optimizations resulting from the description of quantum transformations using Quantum Processes and Partial Quantum Processes conceived in the qGM theoretical model. These processes, when computed on the VPE-qGM execution environment, require low execution time and result in the improvement of the performance, allowing the simulation of more complex quantum algorithms. The performance evaluation of this proposal was performed by benchmarks used in similar works and included the sequential simulation of quantum algorithms up to 24 qubits. The results are promising when compared to the state-of-art, indicating the possibilities of advances in this research.

In Ubiquitous Computing the systems must provide mechanisms to promote the cooperation among users through interoperability of their applications. With this motivation, this paper presents the EXEHDA-TS, which is a distributed mechanism for coordination of applications in Ubiquitous Computing. The proposed software architecture has proactive behavior and allows a dynamic control of communication costs, promoting scalability. The EXEHDA-TS was modeled as an integrated service to the EXEHDA middleware. The prototype was evaluated through case studies in the medical field, considering the challenges inherent in the PERTMED design, satisfactorily fulfilling the requirements of the focused scenarios.

The latest technological advances, which introduced innovative and more affordable devices, have contributed to boost the practical application of research in the field of ubiquitous computing (ubicomp). For the development of applications in this area, we need an adequate software infrastructure. In order to do so, we have proposed Continuum, an infrastructure based on service-oriented architecture (SOA), making use of framework and middleware, and employing a redefinition of follow-me semantics. In this redefined vision, users can go anywhere carrying the data and application they want, which they can use in a seamlessly integrated fashion with the real world. In this article, we focus on the description of the service-based architecture proposed for Continuum. The proposal widens the Web services standards to support the mobility of services, allowing them to be deployed, copied, or moved. Besides, the abstraction provided enables the adaptation of legacy applications as Continuum pluggable services.

Abstract The concept of pervasiveness provides the future of computing with an attractive perspective. However, software support for physical and logical mobility raises a set of new requirements and challenges for software production, creating demand for new types of applications, the so‐called pervasive applications, which express follow‐me semantics. The core of these challenges is a dynamic operating environment, which originates with the users' movement in different terminals and locations, and determines different execution contexts. For this envision to become a reality, developers must build applications that constantly adapt to a highly dynamic computing environment. Research works in pervasive computing have already addressed important issues, but they do not approach the problem of how to program general‐purpose pervasive systems. Pervasive applications are distributed, mobile, adaptive and consider context as a first‐order concept. To make the developers' task easier, we have introduced the software architecture called ISAM, which provides an integrated environment aimed at building pervasive applications composed of a development environment and an execution middleware. As part of our study within the ISAM project, we have been investigating how context‐awareness can be expressed at the programming language level with a basis on four main abstractions: context, adapters, adaptation commands, and adaptive behavior management policies. This paper introduces such abstractions, and presents some development and management tools anchored on an example application, which is under development. Copyright © 2006 John Wiley & Sons, Ltd.

The ISAM architecture presents a platform to the development and to the execution of pervasive applications. DIMI (Disseminador Multicast de Informacoes - information multicast disseminator) is an information dissemination service designed for the ISAM architecture. DIMI seeks mainly to obtain scalability, avoiding bottlenecks formation while accepting new consumers on a channel. Besides the search for scalability, this service outfits other necessary characteristics of the computational environment proposed by the ISAM architecture, as planned disconnection support and user mobility support.

The multiparadigm approach integrates programming language paradigms. We have proposed the Holoparadigm (Holo) as a multiparadigm model oriented to the development of distributed systems. Holo uses a logic blackboard (called history) to implement a coordination mechanism. The programs are organized in levels using abstract entities called beings. First, we describe the principal concepts of the Holoparadigm. After, we propose the Distributed Holo (DHolo), a model to support the distributed execution of programs developed in Holo. DHolo is based on object mobility and blackboards. This distributed model can be fully implemented on the Java platform. Experiments were done using Voyager and Horb to implement mobility. Blackboards were implemented using Java and JavaSpaces.

Mobile computing has emerged as a new field, distinct from conventional distributed computing by its focus on mobility and its consequence. The physical and logical mobility needs of applications with new requirements: built-in mobility, adaptability and flexibility. Providing a system architecture that simplifies the task of implementing the mobile applications with adaptive behavior is the objective of the ongoing ISAM project. In order to achieve its goal, ISAM uses, as strategies, an integrated environment that: (a) provides a programming paradigm and its execution environment; (b) treats of adaptation process through multilevel collaborative model, in which both the system and the application contribute for that. We present the ISAM architecture and introduce the relative issues to the treatment of adaptive mobile applications.

ISAM is a proposal directed to resource management in heterogeneous networks, supporting physical and logical mobility, dynamic adaptation and the execution of distributed applications based on components. In order to achieve its goals, ISAM uses, as strategy, an integrated environment that: (a) provides a programming paradigm and its execution environment; (b) handles the adaptation process through a multilevel collaborative model, in which both the system and the application contribute. In this paper we discuss the main mechanisms used to implement the ISAM features, and we also present a parallel application that explores some of this features.