KLAIM: A Kernel Language for Agents Interaction and Mobility

We investigate the issue of designing a kernel programming language for mobile computing and describe KLAIM, a language that supports a programming paradigm where processes, like data, can be moved from one computing environment to another. The language consists of a core Linda with multiple tuple spaces and of a set of operators for building processes. KLAIM naturally supports programming with explicit localities. Localities are first-class data (they can be manipulated like any other data), but the language provides coordination mechanisms to control the interaction protocols among located processes. The formal operational semantics is useful for discussing the design of the language and provides guidelines for implementations. KLAIM is equipped with a type system that statically checks access rights violations of mobile agents. Types are used to describe the intentions (read, write, execute, etc.) of processes in relation to the various localities. The type system is used to determine the operations that processes want to perform at each locality, and to check whether they comply with the declared intentions and whether they have the necessary rights to perform the intended operations at the specific localities. Via a series of examples, we show that many mobile code programming paradigms can be naturally implemented in our kernel language. We also present a prototype implementaton of KLAIM in Java

Temporal fuzzy association rule mining with 2-tuple linguistic representation

This paper reports on an approach that contributes towards the problem of discovering fuzzy association rules that exhibit a temporal pattern. The novel application of the 2-tuple linguistic representation identifies fuzzy association rules in a temporal context, whilst maintaining the interpretability of linguistic terms. Iterative Rule Learning (IRL) with a Genetic Algorithm (GA) simultaneously induces rules and tunes the membership functions. The discovered rules were compared with those from a traditional method of discovering fuzzy association rules and results demonstrate how the traditional method can loose information because rules occur at the intersection of membership function boundaries. New information can be mined from the proposed approach by improving upon rules discovered with the traditional method and by discovering new rules

On a Formal and User-friendly Linguistic Approach to Access Control of Electronic Health Data

The importance of the exchange of Electronic Health Records (EHRs) between hospitals has been recognized by governments and institutions. Due to the sensitivity of data exchanged, only mature standards and implementations can be chosen to operate. This exchange process is of course under the control of the patient, who decides who has the rights to access her personal healthcare data and who has not, by giving her personal privacy consent. Patients’ privacy consent is regulated by local legislations, which can vary frequently from region to region. The technology implementing such privacy aspects must be highly adaptable, often resulting in complex security scenarios that cannot be easily managed by patients and software designers. To overcome such security problems, we advocate the use of a linguistic approach that relies on languages for expressing policies with solid mathematical foundations. Our approach bases on FACPL, a policy language we have intentionally designed by taking inspiration from OASIS XACML, the de-facto standard used in all projects covering secure EHRs transmission protected by patients’ privacy consent. FACPL can express policies similar to those expressible by XACML but, differently from XACML, it has an intuitive syntax, a formal semantics and easy to use software tools supporting policy development and enforcement. In this paper, we present the potentialities of our approach and outline ongoing work

Architecture value mapping: using fuzzy cognitive maps as a reasoning mechanism for multi-criteria conceptual design evaluation

The conceptual design phase is the most critical phase in the systems engineering life cycle. The design concept chosen during this phase determines the structure and behavior of the system, and consequently, its ability to fulfill its intended function. A good conceptual design is the first step in the development of a successful artifact. However, decision-making during conceptual design is inherently challenging and often unreliable. The conceptual design phase is marked by an ambiguous and imprecise set of requirements, and ill-defined system boundaries. A lack of usable data for design evaluation makes the problem worse. In order to assess a system accurately, it is necessary to capture the relationships between its physical attributes and the stakeholders\u27 value objectives. This research presents a novel conceptual architecture evaluation approach that utilizes attribute-value networks, designated as \u27Architecture Value Maps\u27, to replicate the decision makers\u27 cogitative processes. Ambiguity in the system\u27s overall objectives is reduced hierarchically to reveal a network of criteria that range from the abstract value measures to the design-specific performance measures. A symbolic representation scheme, the 2-Tuple Linguistic Representation is used to integrate different types of information into a common computational format, and Fuzzy Cognitive Maps are utilized as the reasoning engine to quantitatively evaluate potential design concepts. A Linguistic Ordered Weighted Average aggregation operator is used to rank the final alternatives based on the decision makers\u27 risk preferences. The proposed methodology provides systems architects with the capability to exploit the interrelationships between a system\u27s design attributes and the value that stakeholders associate with these attributes, in order to design robust, flexible, and affordable systems --Abstract, page iii

Study on Evaluating Wireless Sensor Network Security Based on Uncertain Linguistic Information

Wireless sensor network (WSN), as an integrated network which can perform information gathering, processing and delivering, can connect the real world and logistic information world. It is greatly changing the interaction between people and nature. There are wide potential applications for wireless sensor network, such as industry, agriculture, military affairs, environment monitoring, biomedicine, city managing and disaster succoring. The problem of evaluating security of Wireless Sensor Network (WSN) with uncertain linguistic information is the multiple attribute group decision making (MAGDM). In this paper, we investigate the multiple attribute group decision making (MAGDM) problems for evaluating the wireless sensor network (WSN) security with uncertain linguistic information. We utilize the uncertain linguistic weighted averaging (ULWA) operator to aggregate the uncertain linguistic information corresponding to each alternative and get the overall value of the alternatives, then rank the alternatives and select the most desirable one(s) by using the formula of the degree of possibility for the comparison between two uncertain linguistic variables. Finally, an illustrative example is given

Dynamic Federated Learning Model for Identifying Adversarial Clients

Federated learning, as a distributed learning that conducts the training on the local devices without accessing to the training data, is vulnerable to dirty-label data poisoning adversarial attacks. We claim that the federated learning model has to avoid those kind of adversarial attacks through filtering out the clients that manipulate the local data. We propose a dynamic federated learning model that dynamically discards those adversarial clients, which allows to prevent the corruption of the global learning model. We evaluate the dynamic discarding of adversarial clients deploying a deep learning classification model in a federated learning setting, and using the EMNIST Digits and Fashion MNIST image classification datasets. Likewise, we analyse the capacity of detecting clients with poor data distribution and reducing the number of rounds of learning by selecting the clients to aggregate. The results show that the dynamic selection of the clients to aggregate enhances the performance of the global learning model, discards the adversarial and poor clients and reduces the rounds of learning.Comment: 11 pages, 6 figure