Search results for "formas"
showing 10 items of 979 documents
Deep Q-Learning With Q-Matrix Transfer Learning for Novel Fire Evacuation Environment
2021
We focus on the important problem of emergency evacuation, which clearly could benefit from reinforcement learning that has been largely unaddressed. Emergency evacuation is a complex task which is difficult to solve with reinforcement learning, since an emergency situation is highly dynamic, with a lot of changing variables and complex constraints that makes it difficult to train on. In this paper, we propose the first fire evacuation environment to train reinforcement learning agents for evacuation planning. The environment is modelled as a graph capturing the building structure. It consists of realistic features like fire spread, uncertainty and bottlenecks. We have implemented the envir…
Towards Responsible AI for Financial Transactions
2020
Author's accepted manuscript. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The application of AI in finance is increasingly dependent on the principles of responsible AI. These principles-explainability, fairness, privacy, accountability, transparency and soundness form the basis for trust in future AI systems. In this empirical study, we address the first p…
Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization With Medical Applications
2019
Medical applications challenge today's text categorization techniques by demanding both high accuracy and ease-of-interpretation. Although deep learning has provided a leap ahead in accuracy, this leap comes at the sacrifice of interpretability. To address this accuracy-interpretability challenge, we here introduce, for the first time, a text categorization approach that leverages the recently introduced Tsetlin Machine. In all brevity, we represent the terms of a text as propositional variables. From these, we capture categories using simple propositional formulae, such as: if "rash" and "reaction" and "penicillin" then Allergy. The Tsetlin Machine learns these formulae from a labelled tex…
Environment Sound Classification using Multiple Feature Channels and Attention based Deep Convolutional Neural Network
2020
In this paper, we propose a model for the Environment Sound Classification Task (ESC) that consists of multiple feature channels given as input to a Deep Convolutional Neural Network (CNN) with Attention mechanism. The novelty of the paper lies in using multiple feature channels consisting of Mel-Frequency Cepstral Coefficients (MFCC), Gammatone Frequency Cepstral Coefficients (GFCC), the Constant Q-transform (CQT) and Chromagram. Such multiple features have never been used before for signal or audio processing. And, we employ a deeper CNN (DCNN) compared to previous models, consisting of spatially separable convolutions working on time and feature domain separately. Alongside, we use atten…
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation
2019
Our understanding and ability to effectively monitor and manage coastal ecosystems are severely limited by observation methods. Automatic recognition of species in natural environment is a promising tool which would revolutionize video and image analysis for a wide range of applications in marine ecology. However, classifying fish from images captured by underwater cameras is in general very challenging due to noise and illumination variations in water. Previous classification methods in the literature relies on filtering the images to separate the fish from the background or sharpening the images by removing background noise. This pre-filtering process may negatively impact the classificat…
Word-level human interpretable scoring mechanism for novel text detection using Tsetlin Machines
2021
Recent research in novelty detection focuses mainly on document-level classification, employing deep neural networks (DNN). However, the black-box nature of DNNs makes it difficult to extract an exact explanation of why a document is considered novel. In addition, dealing with novelty at the word-level is crucial to provide a more fine-grained analysis than what is available at the document level. In this work, we propose a Tsetlin machine (TM)-based architecture for scoring individual words according to their contribution to novelty. Our approach encodes a description of the novel documents using the linguistic patterns captured by TM clauses. We then adopt this description to measure how …
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples
2022
In this article, we propose a novel algorithm for deep reinforcement learning named Expert Q-learning. Expert Q-learning is inspired by Dueling Q-learning and aims at incorporating semi-supervised learning into reinforcement learning through splitting Q-values into state values and action advantages. We require that an offline expert assesses the value of a state in a coarse manner using three discrete values. An expert network is designed in addition to the Q-network, which updates each time following the regular offline minibatch update whenever the expert example buffer is not empty. Using the board game Othello, we compare our algorithm with the baseline Q-learning algorithm, which is a…
Dynamics of inertial pair coupled via frictional interface
2022
Understanding the dynamics of two inertial bodies coupled via a friction interface is essential for a wide range of systems and motion control applications. Coupling terms within the dynamics of an inertial pair connected via a passive frictional contact are non-trivial and have long remained understudied in system communities. This problem is particularly challenging from a point of view of modeling the interaction forces and motion state variables. This paper deals with a generalized motion problem in systems with a free (of additional constraints) friction interface, assuming the classical Coulomb friction with discontinuity at the velocity zero crossing. We formulate the dynamics of mot…
Innføring av CRM i offentlig sektor
2007
Masteroppgave i informasjonssystemer 2007 - Høgskolen i Agder, Kristiansand Modernisering av offentlig sektor basert på informasjons- og kommunikasjonsteknologi (IKT) er i økende grad et fokusområde under begrepet e-forvaltning. Dette har ført til nye teknologiske muligheter og arbeidsmåter internt og eksternt mellom offentlige organisasjoner, næringslivet og innbyggere. Integrering av isolerte systemer til standardiserte og virksomhetsomfattende informasjonssystemer har potensiale til å samle informasjonsflyten i en organisasjon. Dette kan skape radikale endringer både fra et organisatorisk og et teknologisk perspektiv. Customer Relationship Management (CRM) er et konsept fra privat sektor…
Latvijas un Nīderlandes veselības aprūpes sistēmas finansēšanas modeļu salīdzinājums.Iespējamība ievest Nīderlandes veselības aprūpes sistēmas modeli…
2015
Veselības aprūpe ir nozare, kas skar un ietekmē katru valsts iedzīvotāju. Diemžēl Latvijā kopējais sabiedrības noskaņojums par veselības aprūpes sistēmu ir ļoti negatīvs. Kā galvenie neapmierinātības faktori tiek minēti sekojoši punkti: veselības aprūpe ir pārāk dārga, pēc veselības pakalpojumiem ir pārāk tālu jābrauc, lai saņemtu veselības aprūpi, pacientiem ir pārāk ilgi jāgaida uz pieņemšanu. Visi šie punkti spēlē nozīmīgu lomu, kopējā sabiedrības viedoklī par veselības pakalpojumu kvalitāti. Diplomdarbs dot priekšstatu par Latvijā pastāvošo veselības aprūpes modeli un tā finansēšanas mehānismiem. Tā kā pastāv augsta sabiedrības neapmierinātība ar pastāvošo veselības aprūpes modeli, ir n…