17 research outputs found
Explanation plug-in for stream-based collaborative filtering
Collaborative filtering is a widely used recommendation technique, which often relies on rating information shared by users, i.e., crowdsourced data. These filters rely on predictive algorithms, such as, memory or model based predictors, to build direct or latent user and item profiles from crowdsourced data. To predict unknown ratings, memory-based approaches rely on the similarity between users or items, whereas model-based mechanisms explore user and item latent profiles. However, many of these filters are opaque by design, leaving users with unexplained recommendations. To overcome this drawback, this paper introduces Explug, a local model-agnostic plug-in that works alongside stream-based collaborative filters to reorder and explain recommendations. The explanations are based on incremental user Trust & Reputation profiling and co-rater relationships. Experiments performed with crowdsourced data from TripAdvisor show that Explug explains and improves the quality of stream-based collaborative filter recommendations.Xunta de Galicia | Ref. ED481B-2021-118Fundação para a Ciência e a Tecnologia | Ref. UIDB/50014/202
Perceptions of Interactive, Real-Time Persuasive Technology for Managing Online Gambling
Background: Interactive persuasive techniques, supported by the ability to retrieve real-time behaviour and other contextual data, offer an unprecedented opportunity to manage online activity. An example is Responsible Gambling (RG) tools. Currently, despite vast potential, they do not make use of real time gambling behaviour data, whether captured by operators (device, location, bets, limits set) or self-reported (finance, emotion, online browsing history). To design useful interactive persuasive tools, it is important to understand users’ perceptions to ensure maximum acceptance. Aims: Explore gamblers’ perceptions of the potential of future online platforms in providing data-driven, real-time, persuasive interventions for supporting responsible online gambling. Method: Qualitative semi-structured interviews conducted with 22 gamblers (80% men; 15 ex-problem, 7 current), regarding perceptions of the potential of persuasive techniques. Results: Thematic analysis showed participants were positive about data-driven, real-time, interactive technology for (i) providing information (educational, personal and comparative), (ii) limiting gambling (time and money spent, access to gambling operators) and (iii) providing support to gamblers (advice, feedback and context sensing). The technology was identified as most appropriate for low to moderate gamblers. Conclusions: Participants were positive about the new data access, techniques and modalities of interactions for supporting responsible online gambling. To ensure maximum reach and acceptability, such technology should be customised to fit individual profiles. Personalisation and tailoring of content, interactivity, framing and timing are necessary to enhance acceptance of such technology and avoid reactance, unintended harm, inconvenience, and information overload
A flow-based intrusion detection framework for internet of things networks
The application of the Internet of Things concept in domains such as industrial control, building automation, human health,
and environmental monitoring, introduces new privacy and security challenges. Consequently, traditional implementation
of monitoring and security mechanisms cannot always be presently feasible and adequate due to the number of IoT devices,
their heterogeneity and the typical limitations of their technical specifications. In this paper, we propose an IP flow-based
Intrusion Detection System (IDS) framework to monitor and protect IoT networks from external and internal threats in
real-time. The proposed framework collects IP flows from an IoT network and analyses them in order to monitor and detect
attacks, intrusions, and other types of anomalies at different IoT architecture layers based on some flow features instead of
using packet headers fields and their payload. The proposed framework was designed to consider both the IoT network
architecture and other IoT contextual characteristics such as scalability, heterogeneity, interoperability, and the minimization
of the use of IoT networks resources. The proposed IDS framework is network-based and relies on a hybrid
architecture, as it involves both centralized analysis and distributed data collection components. In terms of detection
method, the framework uses a specification-based approach drawn on normal traffic specifications. The experimental
results show that this framework can achieve & 100% success and 0% of false positives in detection of intrusions and
anomalies. In terms of performance and scalability in the operation of the IDS components, we study and compare it with
three different conventional IDS (Snort, Suricata, and Zeek) and the results demonstrate that the proposed solution can
consume fewer computational resources (CPU, RAM, and persistent memory) when compared to those conventional IDS.This work was supported by Portuguese national
funds through the FCT—Foundation for Science and Technology,
I.P., under the project UID/CEC/04524/2019info:eu-repo/semantics/publishedVersio
Automatic generation of software interfaces for supporting decisionmaking processes. An application of domain engineering & machine learning
[EN] Data analysis is a key process to foster knowledge generation in particular domains
or fields of study. With a strong informative foundation derived from the analysis of
collected data, decision-makers can make strategic choices with the aim of obtaining
valuable benefits in their specific areas of action. However, given the steady growth
of data volumes, data analysis needs to rely on powerful tools to enable knowledge
extraction.
Information dashboards offer a software solution to analyze large volumes of
data visually to identify patterns and relations and make decisions according to the
presented information. But decision-makers may have different goals and,
consequently, different necessities regarding their dashboards. Moreover, the variety
of data sources, structures, and domains can hamper the design and implementation
of these tools.
This Ph.D. Thesis tackles the challenge of improving the development process of
information dashboards and data visualizations while enhancing their quality and
features in terms of personalization, usability, and flexibility, among others.
Several research activities have been carried out to support this thesis. First, a
systematic literature mapping and review was performed to analyze different
methodologies and solutions related to the automatic generation of tailored
information dashboards. The outcomes of the review led to the selection of a modeldriven
approach in combination with the software product line paradigm to deal with
the automatic generation of information dashboards.
In this context, a meta-model was developed following a domain engineering
approach. This meta-model represents the skeleton of information dashboards and
data visualizations through the abstraction of their components and features and has
been the backbone of the subsequent generative pipeline of these tools.
The meta-model and generative pipeline have been tested through their
integration in different scenarios, both theoretical and practical. Regarding the theoretical dimension of the research, the meta-model has been successfully
integrated with other meta-model to support knowledge generation in learning
ecosystems, and as a framework to conceptualize and instantiate information
dashboards in different domains.
In terms of the practical applications, the focus has been put on how to transform
the meta-model into an instance adapted to a specific context, and how to finally
transform this later model into code, i.e., the final, functional product. These practical
scenarios involved the automatic generation of dashboards in the context of a Ph.D.
Programme, the application of Artificial Intelligence algorithms in the process, and
the development of a graphical instantiation platform that combines the meta-model
and the generative pipeline into a visual generation system.
Finally, different case studies have been conducted in the employment and
employability, health, and education domains. The number of applications of the
meta-model in theoretical and practical dimensions and domains is also a result itself.
Every outcome associated to this thesis is driven by the dashboard meta-model, which
also proves its versatility and flexibility when it comes to conceptualize, generate, and
capture knowledge related to dashboards and data visualizations
Ensembles of choice-based models for recommender systems
In this thesis, we focused on three main paradigms: Recommender
Systems, Decision Making, and Ensembles. The work is structured as follows. First, the thesis analyzes the
potential of choice-based models. The motivation behind this was based on the idea of applying sound decisionmaking
paradigms, such as choice and utility theory, in the field of Recommender Systems. Second, this research
analyzes the cognitive process underlying choice behavior. On the one hand, neural and gaze activity were
recorded experimentally from different subjects performing a choice task in a Web Interface. On the other hand,
cognitive were fitted using rational, emotional, and attentional features. Finally, the work explores the hybridization
of choice-based models with ensembles. The goal is to take the best of the two worlds: transparency and
performance. Two main methods were analyzed to build optimal choice-based ensembles: uninformed and
informed. First one, two strategies were evaluated: 1-Learner and N-Learners ensembles. Second one, we relied
on three types of prior information: (1) High diversity, (2) Low error prediction (MSE), (3) and Low crowd error
Problematic Attachment to Social Media: Five Behavioural Archetypes
Today, social media play an important role in people’s daily lives. Many people use
social media to satisfy their personal and social needs, such as enhancing self-image, acquiring
self-esteem, and gaining popularity. However, when social media are used obsessively and excessively,
behavioural addiction symptoms can occur, leading to negative impacts on one’s life, which is defined
as a problematic attachment to social media. Research suggests that tools can be provided to assist
the change of problematic attachment behaviour, but it remains unclear how such tools should
be designed and personalised to meet individual needs and profiles. This study makes the first
attempt to tackle this problem by developing five behavioural archetypes, characterising how social
media users differ in their problematic attachments to them. The archetypes are meant to facilitate
effective ideation, creativity, and communication during the design process and helping the elicitation
and customisation of the variability in the requirements and design of behaviour change tools for
combatting problematic usage of social media. This was achieved by using a four-phase qualitative
study where the diary study method was considered at the initial stage, and also the refinement and
confirmation stage, to enhance ecological validity
Argumentation dialogues in web-based GDSS: an approach using machine learning techniques
Tese de doutoramento em InformaticsA tomada de decisão está presente no dia a dia de qualquer pessoa, mesmo que muitas vezes ela
não tenha consciência disso. As decisões podem estar relacionadas com problemas quotidianos, ou
podem estar relacionadas com questões mais complexas, como é o caso das questões organizacionais.
Normalmente, no contexto organizacional, as decisões são tomadas em grupo.
Os Sistemas de Apoio à Decisão em Grupo têm sido estudados ao longo das últimas décadas com o
objetivo de melhorar o apoio prestado aos decisores nas mais diversas situações e/ou problemas a resolver.
Existem duas abordagens principais à implementação de Sistemas de Apoio à Decisão em Grupo:
a abordagem clássica, baseada na agregação matemática das preferências dos diferentes elementos do
grupo e as abordagens baseadas na negociação automática (e.g. Teoria dos Jogos, Argumentação, entre
outras).
Os atuais Sistemas de Apoio à Decisão em Grupo baseados em argumentação podem gerar uma
enorme quantidade de dados. O objetivo deste trabalho de investigação é estudar e desenvolver modelos
utilizando técnicas de aprendizagem automática para extrair conhecimento dos diálogos argumentativos
realizados pelos decisores, mais concretamente, pretende-se criar modelos para analisar, classificar e
processar esses dados, potencializando a geração de novo conhecimento que será utilizado tanto por
agentes inteligentes, como por decisiores reais. Promovendo desta forma a obtenção de consenso entre
os membros do grupo. Com base no estudo da literatura e nos desafios em aberto neste domÃnio,
formulou-se a seguinte hipótese de investigação - É possÃvel usar técnicas de aprendizagem automática
para apoiar diálogos argumentativos em Sistemas de Apoio à Decisão em Grupo baseados na web.
No âmbito dos trabalhos desenvolvidos, foram aplicados algoritmos de classificação supervisionados
a um conjunto de dados contendo argumentos extraÃdos de debates online, criando um classificador
de frases argumentativas que pode classificar automaticamente (A favor/Contra) frases argumentativas
trocadas no contexto da tomada de decisão. Foi desenvolvido um modelo de clustering dinâmico para
organizar as conversas com base nos argumentos utilizados. Além disso, foi proposto um Sistema de
Apoio à Decisão em Grupo baseado na web que possibilita apoiar grupos de decisores independentemente
de sua localização geográfica. O sistema permite a criação de problemas multicritério e a configuração
das preferências, intenções e interesses de cada decisor. Este sistema de apoio à decisão baseado na
web inclui os dashboards de relatórios inteligentes que são gerados através dos resultados dos trabalhos
alcançados pelos modelos anteriores já referidos. A concretização de cada um dos objetivos permitiu
validar as questões de investigação identificadas e assim responder positivamente à hipótese definida.Decision-making is present in anyone’s daily life, even if they are often unaware of it. Decisions can be
related to everyday problems, or they can be related to more complex issues, such as organizational
issues. Normally, in the organizational context, decisions are made in groups.
Group Decision Support Systems have been studied over the past decades with the aim of improving
the support provided to decision-makers in the most diverse situations and/or problems to be solved.
There are two main approaches to implementing Group Decision Support Systems: the classical approach,
based on the mathematical aggregation of the preferences of the different elements of the group, and the
approaches based on automatic negotiation (e.g. Game Theory, Argumentation, among others).
Current argumentation-based Group Decision Support Systems can generate an enormous amount
of data. The objective of this research work is to study and develop models using automatic learning techniques
to extract knowledge from argumentative dialogues carried out by decision-makers, more specifically,
it is intended to create models to analyze, classify and process these data, enhancing the generation
of new knowledge that will be used both by intelligent agents and by real decision-makers. Promoting in
this way the achievement of consensus among the members of the group. Based on the literature study
and the open challenges in this domain, the following research hypothesis was formulated - It is possible
to use machine learning techniques to support argumentative dialogues in web-based Group Decision
Support Systems.
As part of the work developed, supervised classification algorithms were applied to a data set containing
arguments extracted from online debates, creating an argumentative sentence classifier that can
automatically classify (For/Against) argumentative sentences exchanged in the context of decision-making.
A dynamic clustering model was developed to organize conversations based on the arguments used. In
addition, a web-based Group Decision Support System was proposed that makes it possible to support
groups of decision-makers regardless of their geographic location. The system allows the creation of multicriteria
problems and the configuration of preferences, intentions, and interests of each decision-maker.
This web-based decision support system includes dashboards of intelligent reports that are generated
through the results of the work achieved by the previous models already mentioned. The achievement of
each objective allowed validation of the identified research questions and thus responded positively to the
defined hypothesis.I also thank to Fundação para a Ciência e a Tecnologia, for the Ph.D. grant funding with the reference: SFRH/BD/137150/2018
Context-aware Plan Repair in Environments shared by Multiple Agents
[ES] La monitorización de la ejecución de un plan es crucial para un agente autónomo que realiza su labor en un entorno dinámico, pues influye en su capacidad de reaccionar ante los cambios. Mientras ejecuta su plan puede sufrir un fallo y, en su esfuerzo por solucionarlo, puede interferir sin saberlo con otros agentes que operan en su mismo entorno. Por otra parte, para actuar racionalmente es necesario que el agente sea consciente del contexto y pueda recopilar y ampliar su información a partir de lo que percibe para poder compensar su conocimiento previo parcial o incorrecto del problema y lograr el mejor resultado posible ante las nuevas situaciones que aparecen.
El trabajo realizado en esta tesis permite a los agentes autónomos ejecutar sus planes en un entorno dinámico y adaptarse a eventos inesperados y circunstancias desconocidas. Pueden utilizar su percepción del contexto para proporcionar respuestas deliberativas conscientes y ser capaces asà de aprovechar las oportunidades que surgen o reparar los fallos sin perturbar a otros agentes. Este trabajo se centra en el desarrollo de una arquitectura independiente del dominio capaz de manejar las necesidades de agentes con este tipo de comportamiento autónomo. Los tres pilares de la arquitectura propuesta los forman el sistema inteligente para la simulación de la ejecución en entornos dinámicos, la adquisición de conocimiento consciente del contexto para ampliar la base de datos del agente y la reparación de planes ante fallos u oportunidades tratando de interferir lo mÃnimo con los planes de otros agentes.
El sistema inteligente de simulación de la ejecución permite al agente representar el plan en una lÃnea de tiempo, actualizar periódicamente su estado interno con información del mundo real y disparar nuevos eventos en momentos concretos. Los eventos se procesan en el contexto del plan; si se detecta un error, el simulador reformula el problema de planificación, invoca de nuevo al planificador y reanuda la ejecución. El simulador es una aplicación de consola y ofrece una interfaz gráfica diseñada especÃficamente para una aplicación inteligente de turismo.
El módulo de adquisición de conocimiento sensible al contexto utiliza operaciones semánticas para aumentar dinámicamente la lista predefinida de tipos de objetos de la tarea de planificación con nuevos tipos relevantes. Esto permite que el agente sea consciente de su entorno, enriquezca el modelo de su tarea y pueda razonar a partir de un conocimiento incompleto. Con todo esto se consigue potenciar la autonomÃa del sistema y la conciencia del contexto.
La novedosa estrategia de reparación de planes le permite a un agente reparar su plan al detectar un fallo de manera responsable con el resto de agentes que comparten su mismo entorno de ejecución. El agente utiliza una nueva métrica, el compromiso del plan, como función heurÃstica para guiar la búsqueda hacia un plan solución comprometido con el plan original, en el sentido de que se trata de respetar los compromisos adquiridos con otros agentes al mismo tiempo que se alcanzan los objetivos originales. En consecuencia, la comunidad de agentes sufrirá menos fallos por cambios bruscos en el entorno o requerirá menos tiempo para ejecutar las acciones correctoras si el fallo es inevitable.
Estos tres módulos han sido desarrollados y evaluados en varias aplicaciones como un asistente turÃstico, una agencia de reparación de electrodomésticos y un asistente del hogar.[CA] El monitoratge de l'execució d'un pla és crucial per a un agent autònom que realitza la seua labor en un entorn dinà mic, perquè influeix en la seua capacitat de reaccionar davant els canvis. Mentre executa el seu pla pot patir una fallada i, en el seu esforç per solucionar-lo, pot interferir sense saber-ho amb altres agents que operen en el seu mateix entorn. D'altra banda, per a actuar racionalment és necessari que l'agent siga conscient del context i puga recopilar i ampliar la seua informació a partir del que percep per a poder compensar el seu coneixement previ parcial o incorrecte del problema i aconseguir el millor resultat possible davant les noves situacions que apareixen.
El treball realitzat en aquesta tesi permet als agents autònoms executar els seus plans en un entorn dinà mic i adaptar-se a esdeveniments inesperats i circumstà ncies desconegudes. Poden utilitzar la seua percepció del context per a proporcionar respostes deliberatives conscients i ser capaces aixà d'aprofitar les oportunitats que sorgeixen o reparar les fallades sense pertorbar a altres agents. Aquest treball se centra en el desenvolupament d'una arquitectura independent del domini capaç de manejar les necessitats d'agents amb aquesta mena de comportament autònom. Els tres pilars de l'arquitectura proposada els formen el sistema intel·ligent per a la simulació de l'execució en entorns dinà mics, l'adquisició de coneixement conscient del context per a ampliar la base de dades de l'agent i la reparació de plans davant fallades o oportunitats tractant d'interferir el mÃnim amb els plans d'altres agents.
El sistema intel·ligent de simulació de l'execució permet a l'agent representar el pla en una lÃnia de temps, actualitzar periòdicament el seu estat intern amb informació del món real i disparar nous esdeveniments en moments concrets. Els esdeveniments es processen en el context del pla; si es detecta un error, el simulador reformula el problema de planificació, invoca de nou al planificador i reprén l'execució. El simulador és una aplicació de consola i ofereix una interfÃcie grà fica dissenyada especÃficament per a una aplicació intel·ligent de turisme.
El mòdul d'adquisició de coneixement sensible al context utilitza operacions semà ntiques per a augmentar dinà micament la llista predefinida de tipus d'objectes de la tasca de planificació amb nous tipus rellevants. Això permet que l'agent siga conscient del seu entorn, enriquisca el model de la seua tasca i puga raonar a partir d'un coneixement incomplet. Amb tot això s'aconsegueix potenciar l'autonomia del sistema i la consciència del context.
La nova estratègia de reparació de plans li permet a un agent reparar el seu pla en detectar una fallada de manera responsable amb la resta d'agents que comparteixen el seu mateix entorn d'execució. L'agent utilitza una nova mètrica, el compromÃs del pla, com a funció heurÃstica per a guiar la cerca cap a un pla solució compromés amb el pla original, en el sentit que es tracta de respectar els compromisos adquirits amb altres agents al mateix temps que s'aconsegueixen els objectius originals. En conseqüència, la comunitat d'agents patirà menys fallades per canvis bruscos en l'entorn o requerirà menys temps per a executar les accions correctores si la fallada és inevitable.
Aquests tres mòduls han sigut desenvolupats i avaluats en diverses aplicacions com un assistent turÃstic, una agència de reparació d'electrodomèstics i un assistent de la llar.[EN] Execution Monitoring is crucial for the success of an autonomous agent executing a plan in a dynamic environment as it influences its ability to react to changes. While executing its plan in a dynamic world, it may suffer a failure and, in its endeavour to fix the problem, it may unknowingly disrupt other agents operating in the same environment. Additionally, being rational requires the agent to be context-aware, gather information and extend what is known from what is perceived to compensate for partial or incorrect prior knowledge and achieve the best possible outcome in various novel situations.
The work carried out in this PhD thesis allows the autonomous agents executing a plan in a dynamic environment to adapt to unexpected events and unfamiliar circumstances, utilise their perception of context and provide context-aware deliberative responses for seizing an opportunity or repairing a failure without disrupting other agents. This work is focused on developing a domain-independent architecture capable of handling the requirements of such autonomous behaviour. The architecture pillars are the intelligent system for execution simulation in a dynamic environment, the context-aware knowledge acquisition for planning applications and the plan commitment repair.
The intelligent system for execution simulation in a dynamic environment allows the agent to transform the plan into a timeline, periodically update its internal state with real-world information and create timed events. Events are processed in the context of the plan; if a failure occurs, the simulator reformulates the planning problem, reinvokes a planner and resumes the execution. The simulator is a console application and has a GUI designed specifically for smart tourism.
The context-aware knowledge acquisition module utilises semantic operations to dynamically augment the predefined list of object types of the planning task with relevant new object types. This allows the agent to be context-aware of the environment and the task and reason with incomplete knowledge, boosting the system's autonomy and context-awareness.
The novel plan commitment repair strategy among multiple agents sharing the same execution environment allows the agent to repair its plan responsibly when a failure is detected. The agent utilises a new metric, plan commitment, as a heuristic to guide the search for the most committed repair plan to the original plan from the perspective of commitments made to other agents whilst achieving the original goals. Consequently, the community of agents will suffer fewer failures due to the sudden changes or will have less lost time if the failure is inevitable.
All these developed modules were investigated and evaluated in several applications, such as a tourist assistant, a kitchen appliance repair agency and a living home assistant.Babli, M. (2023). Context-aware Plan Repair in Environments shared by Multiple Agents [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19868
Ciguatoxins
Ciguatoxins (CTXs), which are responsible for Ciguatera fish poisoning (CFP), are liposoluble toxins produced by microalgae of the genera Gambierdiscus and Fukuyoa. This book presents 18 scientific papers that offer new information and scientific evidence on: (i) CTX occurrence in aquatic environments, with an emphasis on edible aquatic organisms; (ii) analysis methods for the determination of CTXs; (iii) advances in research on CTX-producing organisms; (iv) environmental factors involved in the presence of CTXs; and (v) the assessment of public health risks related to the presence of CTXs, as well as risk management and mitigation strategies
An Elicitation Method for Technology-Assisted Goal Setting: Combating Problematic Social Networks Use as a Case Study.
Now that digital media has become an integral part of our everyday lives, people spend significant time using it for various purposes, including social networking and gaming. There is increasing acceptance in the literature of the link between obsessive, compulsive, and excessive usage of social media, e.g. social networks, and the wellbeing of users, whether personal, economic, or social. Despite the research on the negative experiences linked to problematic social networking usage, the work on how to regulate such an effect is at a preliminary stage. In the literature on behavioural change, technology-assisted solutions that utilise the concept of behavioural goals have started to appear, such as gamification and persuasive technology, mainly to increase motivation for change. Also, the literature has revealed that social networks can be augmented with functionalities to assist those seeking to regulate their problematic usage.
When technology is used to assist behavioural change, e.g. apps for diet and smoking cessation, requirements become behavioural. While there are established methods for capturing business requirements in organisational information systems, characterised mainly by being a desired and declared state of the system, capturing behavioural requirements, such as goals, requires a different approach to the entire engineering lifecycle. Behavioural requirements gathering and validation would require dealing with issues of unreliability and denial present in problematic behaviours. Therefore, this thesis aims to provide a method expressly tailored to the elicitation of behavioural requirements. The method will be supported by the goal setting strategy and its associated elements.
In order to attain this aim, this thesis followed a qualitative research approach with experts, practitioners, and end-users who self-declared having problematic social networking usage and seeking help. This process includes literature reviews, focus group sessions, experts' and practitioners' interviews, user interviews, and analysis of extended survey comments. Research conducted resulted in reference checklists for common goal setting elements, a taxonomy of the negative life experiences associated with problematic usage, and users' perceptions of the use of technology to assist goal setting. The results of the studies helped to propose a method to support users in specifying their goal-setting design requirements. The thesis then evaluated the proposed method with representative users who self-declared having problematic social network usage. The evaluation aimed to investigate the method’s effectiveness, whether it covers all the goal-setting elements, and how communication should work between study participants