Modeling of Phenomena and Dynamic Logic of Phenomena

Modeling of complex phenomena such as the mind presents tremendous computational complexity challenges. Modeling field theory (MFT) addresses these challenges in a non-traditional way. The main idea behind MFT is to match levels of uncertainty of the model (also, problem or theory) with levels of uncertainty of the evaluation criterion used to identify that model. When a model becomes more certain, then the evaluation criterion is adjusted dynamically to match that change to the model. This process is called the Dynamic Logic of Phenomena (DLP) for model construction and it mimics processes of the mind and natural evolution. This paper provides a formal description of DLP by specifying its syntax, semantics, and reasoning system. We also outline links between DLP and other logical approaches. Computational complexity issues that motivate this work are presented using an example of polynomial models

Synergies between machine learning and reasoning - An introduction by the Kay R. Amel group

This paper proposes a tentative and original survey of meeting points between Knowledge Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have been developed quite separately in the last four decades. First, some common concerns are identified and discussed such as the types of representation used, the roles of knowledge and data, the lack or the excess of information, or the need for explanations and causal understanding. Then, the survey is organised in seven sections covering most of the territory where KRR and ML meet. We start with a section dealing with prototypical approaches from the literature on learning and reasoning: Inductive Logic Programming, Statistical Relational Learning, and Neurosymbolic AI, where ideas from rule-based reasoning are combined with ML. Then we focus on the use of various forms of background knowledge in learning, ranging from additional regularisation terms in loss functions, to the problem of aligning symbolic and vector space representations, or the use of knowledge graphs for learning. Then, the next section describes how KRR notions may benefit to learning tasks. For instance, constraints can be used as in declarative data mining for influencing the learned patterns; or semantic features are exploited in low-shot learning to compensate for the lack of data; or yet we can take advantage of analogies for learning purposes. Conversely, another section investigates how ML methods may serve KRR goals. For instance, one may learn special kinds of rules such as default rules, fuzzy rules or threshold rules, or special types of information such as constraints, or preferences. The section also covers formal concept analysis and rough sets-based methods. Yet another section reviews various interactions between Automated Reasoning and ML, such as the use of ML methods in SAT solving to make reasoning faster. Then a section deals with works related to model accountability, including explainability and interpretability, fairness and robustness. Finally, a section covers works on handling imperfect or incomplete data, including the problem of learning from uncertain or coarse data, the use of belief functions for regression, a revision-based view of the EM algorithm, the use of possibility theory in statistics, or the learning of imprecise models. This paper thus aims at a better mutual understanding of research in KRR and ML, and how they can cooperate. The paper is completed by an abundant bibliography

Ethnographic Causality

This book explores the problem of causal inference when a sufficient number of comparative cases cannot be found, which would permit the application of frequency based models formulated in terms of explanatory causal generalizations

Uncertainty-Aware Personal Assistant for Making Personalized Privacy Decisions

Many software systems, such as online social networks, enable users to share information about themselves. Although the action of sharing is simple, it requires an elaborate thought process on privacy: what to share, with whom to share, and for what purposes. Thinking about these for each piece of content to be shared is tedious. Recent approaches to tackle this problem build personal assistants that can help users by learning what is private over time and recommending privacy labels such as private or public to individual content that a user considers sharing. However, privacy is inherently ambiguous and highly personal. Existing approaches to recommend privacy decisions do not address these aspects of privacy sufficiently. Ideally, a personal assistant should be able to adjust its recommendation based on a given user, considering that user's privacy understanding. Moreover, the personal assistant should be able to assess when its recommendation would be uncertain and let the user make the decision on her own. Accordingly, this article proposes a personal assistant that uses evidential deep learning to classify content based on its privacy label. An important characteristic of the personal assistant is that it can model its uncertainty in its decisions explicitly, determine that it does not know the answer, and delegate from making a recommendation when its uncertainty is high. By factoring in the user's own understanding of privacy, such as risk factors or own labels, the personal assistant can personalize its recommendations per user. We evaluate our proposed personal assistant using a well-known dataset. Our results show that our personal assistant can accurately identify uncertain cases, personalize them to its user's needs, and thus helps users preserve their privacy well

Bayesian probability encoding in medical decision analysis

Ph.DDOCTOR OF PHILOSOPH

A diagnostic expert system as a tool for technology improvement support

This dissertation focuses on design, modelling and development of a diagnostic expert system, which was implemented as a tool (named Capability Diagnostic) in FutureSME project (and web portal) as one of the tools for self-diagnostic of SMEs, where according to analysis of current state the output data were generated. These data were used for creation of an action plan, which serves as a list of improvements that need to be done to solve crucial processes of the company. After improvements in company processes were completed, a new diagnostic process was initiated and a comparison with previous results was performed. This tool was evaluated by companies partnering to the project as one of the most contributive results of the project. Design, modelling and development of the system were focused on general use of the diagnostic system for company processes and improvement support.Tato disertační práce se zabývá návrhem, modelováním, vývojem a realizací obecného diagnostického expertního systému, který byl jako nástroj (nazván jako Capability Diagnostic) nasazen v rámci projektu (a portálu) FutureSME jako jeden z nástrojů pro diagnostiku malých a středních podniků, kde na základě analýzy aktuálního stavu generoval výstupní data, která byla použita pro vytvoření akčního plánu, na základě kterého byly provedeny zásahy do chodu firmy, které měly napomoci k řešení klíčových procesů jejího fungování. Po zavedení opatření byla opakovaně provedena diagnostika a výsledek byl porovnán s původním či předchozím stavem. Tento nástroj byl partnery projektu (majiteli či řediteli firem) hodnocen jako jeden z nejpřínosnějších v tomto projektu. Návrh, model i vývoj systému byl zaměřen na obecné využití i v jiných oblastech (nejen průmyslových) pro zlepšování firemních procesů.352 - Katedra automatizační techniky a řízenívyhově

Inferring Complex Activities for Context-aware Systems within Smart Environments

The rising ageing population worldwide and the prevalence of age-related conditions such as physical fragility, mental impairments and chronic diseases have significantly impacted the quality of life and caused a shortage of health and care services. Over-stretched healthcare providers are leading to a paradigm shift in public healthcare provisioning. Thus, Ambient Assisted Living (AAL) using Smart Homes (SH) technologies has been rigorously investigated to help address the aforementioned problems. Human Activity Recognition (HAR) is a critical component in AAL systems which enables applications such as just-in-time assistance, behaviour analysis, anomalies detection and emergency notifications. This thesis is aimed at investigating challenges faced in accurately recognising Activities of Daily Living (ADLs) performed by single or multiple inhabitants within smart environments. Specifically, this thesis explores five complementary research challenges in HAR. The first study contributes to knowledge by developing a semantic-enabled data segmentation approach with user-preferences. The second study takes the segmented set of sensor data to investigate and recognise human ADLs at multi-granular action level; coarse- and fine-grained action level. At the coarse-grained actions level, semantic relationships between the sensor, object and ADLs are deduced, whereas, at fine-grained action level, object usage at the satisfactory threshold with the evidence fused from multimodal sensor data is leveraged to verify the intended actions. Moreover, due to imprecise/vague interpretations of multimodal sensors and data fusion challenges, fuzzy set theory and fuzzy web ontology language (fuzzy-OWL) are leveraged. The third study focuses on incorporating uncertainties caused in HAR due to factors such as technological failure, object malfunction, and human errors. Hence, existing studies uncertainty theories and approaches are analysed and based on the findings, probabilistic ontology (PR-OWL) based HAR approach is proposed. The fourth study extends the first three studies to distinguish activities conducted by more than one inhabitant in a shared smart environment with the use of discriminative sensor-based techniques and time-series pattern analysis. The final study investigates in a suitable system architecture with a real-time smart environment tailored to AAL system and proposes microservices architecture with sensor-based off-the-shelf and bespoke sensing methods. The initial semantic-enabled data segmentation study was evaluated with 100% and 97.8% accuracy to segment sensor events under single and mixed activities scenarios. However, the average classification time taken to segment each sensor events have suffered from 3971ms and 62183ms for single and mixed activities scenarios, respectively. The second study to detect fine-grained-level user actions was evaluated with 30 and 153 fuzzy rules to detect two fine-grained movements with a pre-collected dataset from the real-time smart environment. The result of the second study indicate good average accuracy of 83.33% and 100% but with the high average duration of 24648ms and 105318ms, and posing further challenges for the scalability of fusion rule creations. The third study was evaluated by incorporating PR-OWL ontology with ADL ontologies and Semantic-Sensor-Network (SSN) ontology to define four types of uncertainties presented in the kitchen-based activity. The fourth study illustrated a case study to extended single-user AR to multi-user AR by combining RFID tags and fingerprint sensors discriminative sensors to identify and associate user actions with the aid of time-series analysis. The last study responds to the computations and performance requirements for the four studies by analysing and proposing microservices-based system architecture for AAL system. A future research investigation towards adopting fog/edge computing paradigms from cloud computing is discussed for higher availability, reduced network traffic/energy, cost, and creating a decentralised system. As a result of the five studies, this thesis develops a knowledge-driven framework to estimate and recognise multi-user activities at fine-grained level user actions. This framework integrates three complementary ontologies to conceptualise factual, fuzzy and uncertainties in the environment/ADLs, time-series analysis and discriminative sensing environment. Moreover, a distributed software architecture, multimodal sensor-based hardware prototypes, and other supportive utility tools such as simulator and synthetic ADL data generator for the experimentation were developed to support the evaluation of the proposed approaches. The distributed system is platform-independent and currently supported by an Android mobile application and web-browser based client interfaces for retrieving information such as live sensor events and HAR results

Evidential Reasoning & Analytical Techniques In Criminal Pre-Trial Fact Investigation

This thesis is the work of the author and is concerned with the development of a neo-Wigmorean approach to evidential reasoning in police investigation. The thesis evolved out of dissatisfaction with cardinal aspects of traditional approaches to police investigation, practice and training. Five main weaknesses were identified: Firstly, a lack of a theoretical foundation for police training and practice in the investigation of crime and evidence management; secondly, evidence was treated on the basis of its source rather than it's inherent capacity for generating questions; thirdly, the role of inductive elimination was underused and misunderstood; fourthly, concentration on single, isolated cases rather than on the investigation of multiple cases and, fifthly, the credentials of evidence were often assumed rather than considered, assessed and reasoned within the context of argumentation. Inspiration from three sources were used to develop the work: Firstly, John Henry Wigmore provided new insights into the nature of evidential reasoning and formal methods for the construction of arguments; secondly, developments in biochemistry provided new insights into natural methods of storing and using information; thirdly, the science of complexity provided new insights into the complex nature of collections of data that could be developed into complex systems of information and evidence. This thesis is an application of a general methodology supported by new diagnostic and analytical techniques. The methodology was embodied in a software system called Forensic Led Intelligence System: FLINTS. My standpoint is that of a forensic investigator with an interest in how evidential reasoning can improve the operation we call investigation. New areas of evidential reasoning are in progress and these are discussed including a new application in software designed by the author: MAVERICK. There are three main themes; Firstly, how a broadened conception of evidential reasoning supported by new diagnostic and analytical techniques can improve the investigation and discovery process. Secondly, an explanation of how a greater understanding of the roles and effects of different styles of reasoning can assist the user; and thirdly; a range of concepts and tools are presented for the combination, comparison, construction and presentation of evidence in imaginative ways. Taken together these are intended to provide examples of a new approach to the science of evidential reasoning. Originality will be in four key areas; 1. Extending and developing Wigmorean techniques to police investigation and evidence management. 2. Developing existing approaches in single case analysis and introducing an intellectual model for multi case analysis. 3. Introducing a new model for police training in investigative evidential reasoning. 4. Introducing a new software system to manage evidence in multi case approaches using forensic scientific evidence. FLINTS