Interval-valued intuitionistic fuzzy ordered precise weighted aggregation operator and its application in group decision making

An important research topic related to the theory and application of the interval-valued intuitionistic fuzzy weighted aggregation operators is how to determine their associated weights. In this paper, we propose a precise weight-determined (PWD) method of the monotonicity and scale-invariance, just based on the new score and accuracy functions of interval-valued intuitionistic fuzzy number (IIFN). Since the monotonicity and scale-invariance, the PWD method may be a precise and objective approach to calculate the weights of IIFN and interval-valued intuitionistic fuzzy aggregation operator, and a more suitable approach to distinguish different decision makers (DMs) and experts in group decision making. Based on the PWD method, we develop two new interval-valued intuitionistic fuzzy aggregation operators, i.e. interval-valued intuitionistic fuzzy ordered precise weighted averaging (IIFOPWA) operator and interval-valued intuitionistic fuzzy ordered precise weighted geometric (IIFOPWG) operator, and study their desirable properties in detail. Finally, we provide an illustrative example. First published online: 24 Jan 201

Automating Intensity Modulated Radiation Therapy Treatment Planning by using Hierarchical Optimization

The intensity modulated radiation therapy (IMRT) optimizes the beam’s intensity to deliver the prescribed dose to the target while minimizing the radiation exposure to normal structures. The IMRT optimization is a complex optimization problem because of the multiple conflicting objectives in it. Due to the complexity of the optimization, the IMRT treatment planning is still a trial and error process. Hierarchical optimization was proposed to automate the treatment planning process, but its potential has not been demonstrated in a clinical setting. Moreover, hierarchical optimization is slower than the traditional optimization. The dissertation studied a sampling algorithm to reduce the hierarchical optimization time, customized an open source optimization solver to solve the nonlinear optimization formulation and demonstrated the potential of hierarchical optimization to automate the treatment planning process in a clinical setting. We generated the treatment plans of 31 prostate patients by hierarchical optimization using the same criteria as used by planners to prepare the treatment plans at Memorial Sloan Kettering Cancer Center. We found that hierarchical optimization produced the same or better treatment plans than that produced by a planner using the Eclipse treatment planning system. Therefore, the dissertation demonstrated that hierarchical optimization could automate the treatment planning process and shift the paradigm of the treatment planning from manual trial and error to an ideal automated process

Decision Support Elements and Enabling Techniques to Achieve a Cyber Defence Situational Awareness Capability

[ES] La presente tesis doctoral realiza un análisis en detalle de los elementos de decisión necesarios para mejorar la comprensión de la situación en ciberdefensa con especial énfasis en la percepción y comprensión del analista de un centro de operaciones de ciberseguridad (SOC). Se proponen dos arquitecturas diferentes basadas en el análisis forense de flujos de datos (NF3). La primera arquitectura emplea técnicas de Ensemble Machine Learning mientras que la segunda es una variante de Machine Learning de mayor complejidad algorítmica (lambda-NF3) que ofrece un marco de defensa de mayor robustez frente a ataques adversarios. Ambas propuestas buscan automatizar de forma efectiva la detección de malware y su posterior gestión de incidentes mostrando unos resultados satisfactorios en aproximar lo que se ha denominado un SOC de próxima generación y de computación cognitiva (NGC2SOC). La supervisión y monitorización de eventos para la protección de las redes informáticas de una organización debe ir acompañada de técnicas de visualización. En este caso, la tesis aborda la generación de representaciones tridimensionales basadas en métricas orientadas a la misión y procedimientos que usan un sistema experto basado en lógica difusa. Precisamente, el estado del arte muestra serias deficiencias a la hora de implementar soluciones de ciberdefensa que reflejen la relevancia de la misión, los recursos y cometidos de una organización para una decisión mejor informada. El trabajo de investigación proporciona finalmente dos áreas claves para mejorar la toma de decisiones en ciberdefensa: un marco sólido y completo de verificación y validación para evaluar parámetros de soluciones y la elaboración de un conjunto de datos sintéticos que referencian unívocamente las fases de un ciberataque con los estándares Cyber Kill Chain y MITRE ATT & CK.[CA] La present tesi doctoral realitza una anàlisi detalladament dels elements de decisió necessaris per a millorar la comprensió de la situació en ciberdefensa amb especial èmfasi en la percepció i comprensió de l'analista d'un centre d'operacions de ciberseguretat (SOC). Es proposen dues arquitectures diferents basades en l'anàlisi forense de fluxos de dades (NF3). La primera arquitectura empra tècniques de Ensemble Machine Learning mentre que la segona és una variant de Machine Learning de major complexitat algorítmica (lambda-NF3) que ofereix un marc de defensa de major robustesa enfront d'atacs adversaris. Totes dues propostes busquen automatitzar de manera efectiva la detecció de malware i la seua posterior gestió d'incidents mostrant uns resultats satisfactoris a aproximar el que s'ha denominat un SOC de pròxima generació i de computació cognitiva (NGC2SOC). La supervisió i monitoratge d'esdeveniments per a la protecció de les xarxes informàtiques d'una organització ha d'anar acompanyada de tècniques de visualització. En aquest cas, la tesi aborda la generació de representacions tridimensionals basades en mètriques orientades a la missió i procediments que usen un sistema expert basat en lògica difusa. Precisament, l'estat de l'art mostra serioses deficiències a l'hora d'implementar solucions de ciberdefensa que reflectisquen la rellevància de la missió, els recursos i comeses d'una organització per a una decisió més ben informada. El treball de recerca proporciona finalment dues àrees claus per a millorar la presa de decisions en ciberdefensa: un marc sòlid i complet de verificació i validació per a avaluar paràmetres de solucions i l'elaboració d'un conjunt de dades sintètiques que referencien unívocament les fases d'un ciberatac amb els estàndards Cyber Kill Chain i MITRE ATT & CK.[EN] This doctoral thesis performs a detailed analysis of the decision elements necessary to improve the cyber defence situation awareness with a special emphasis on the perception and understanding of the analyst of a cybersecurity operations center (SOC). Two different architectures based on the network flow forensics of data streams (NF3) are proposed. The first architecture uses Ensemble Machine Learning techniques while the second is a variant of Machine Learning with greater algorithmic complexity (lambda-NF3) that offers a more robust defense framework against adversarial attacks. Both proposals seek to effectively automate the detection of malware and its subsequent incident management, showing satisfactory results in approximating what has been called a next generation cognitive computing SOC (NGC2SOC). The supervision and monitoring of events for the protection of an organisation's computer networks must be accompanied by visualisation techniques. In this case, the thesis addresses the representation of three-dimensional pictures based on mission oriented metrics and procedures that use an expert system based on fuzzy logic. Precisely, the state-of-the-art evidences serious deficiencies when it comes to implementing cyber defence solutions that consider the relevance of the mission, resources and tasks of an organisation for a better-informed decision. The research work finally provides two key areas to improve decision-making in cyber defence: a solid and complete verification and validation framework to evaluate solution parameters and the development of a synthetic dataset that univocally references the phases of a cyber-attack with the Cyber Kill Chain and MITRE ATT & CK standards.Llopis Sánchez, S. (2023). Decision Support Elements and Enabling Techniques to Achieve a Cyber Defence Situational Awareness Capability [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19424

Multiple Criteria Decision Support; Proceedings of an International Workshop, Helsinki, Finland, August 7-11, 1989

Multiple Criteria Decision Making has been an important and active research area for some 20 years. In the 1970's, research focused on the theory of multiple objective mathematical programming and on procedures for solving multiple objective mathematical programming problems. During the 1980's, a shift in emphasis towards multiple criteria decision support was observed. Accordingly, much research has focused on the user interface, the behavioral foundations of decision making, and on supporting the entire decision-making process from problem structuring to solution implementation. Because of the shift in research emphasis the authors decided to make "Multiple Criteria Decision Support" the theme for the International Workshop, which was held at Suomen Saeaestoepankkiopisto in Espoo, Finland. The Workshop was organized by the Helsinki School of Economics, and sponsored by the Helsinki School of Economics and IIASA, Austria. This volume provides an up-to-date coverage of the theory and practice of multiple criteria decision support. The authors trust that it will serve the research community as well as the previously published Conference Proceedings based on IIASA Workshops