МЕТОД МОЖЛИВІСНОГО ОЦІНЮВАННЯ ПОЯСНЕННЯ В СИСТЕМІ ШТУЧНОГО ІНТЕЛЕКТУ

Предметом дослідження є процеси формування пояснень щодо рішення системи штучного інтелекту. Пояснення використовуються для того, щоб користувач зрозумів процес отримання результату і міг більш ефективно застосовувати інтелектуальну інформаційну систему для формування практично прийнятих для нього рішень. Мета роботи полягає у розробці методу оцінки пояснень з урахуванням відмінностей у вхідних даних  та відповідному рішенні системи штучного інтелекту. Вирішення цієї задачі дає можливість оцінити відповідність пояснення щодо внутрішньому механізму прийняття рішення в інтелектуальній інформаційній системі незалежно від рівня знань користувача щодо особливостей формування та використання такого рішення. Для досягнення мети вирішуються такі задачі: структуризація оцінки пояснень в залежності від рівня їх деталізації з урахуванням їх відповідності процесу прийняття рішення в інтелектуальній системі та рівню сприйняття користувача такої системи; розробка методу оцінки пояснень на основі їх відповідності процесу прийняття рішення в інтелектуальній системі. Висновки. Виконано структуризацію оцінки пояснень в залежності від рівня їх деталізації. Виділено рівні асоціативних залежностей, прецедентів, каузальних залежностей та інтерактивний, що визначають різний ступінь деталізації пояснень. Показано, що асоціативний та каузальний рівні деталізації пояснень можуть бути оцінені з використанням числових, ймовірнісних або можливісних показників. Прецедентний та інтерактивний рівні потребують суб’єктивної оцінки на основі опитування користувачів системи штучного інтелекту. Розроблено метод можливісного оцінювання відповідності пояснень процесу прийняття рішень в інтелектуальній системі з урахуванням залежностей між вхідними даними та рішенням інтелектуальної системи. Метод містить етапи оцінювання чутливості, коректності та складності пояснення на основі порівняння значень та кількості використаних у поясненні вхідних даних. Метод дає можливість комплексно оцінити пояснення з позицій стійкості до несуттєвих змін у вхідних даних, відповідності пояснення отриманому результату, а також складності обчислення пояснення. У аспекті практичного застосування метод дає можливість мінімізувати кількість вхідних змінних для пояснення при задоволенні обмеження на чутливість пояснення, що створює умови для більш ефективного формування тлумачення на основі використання підмножини ключових вхідних змінних, які мають суттєвий вплив на отримане в інтелектуальній системі рішення

Towards explainable face aging with Generative Adversarial Networks

Generative Adversarial Networks (GAN) are being increasingly used to perform face aging due to their capabilities of automatically generating highly-realistic synthetic images by using an adversarial model often based on Convolutional Neural Networks (CNN). However, GANs currently represent black box models since it is not known how the CNNs store and process the information learned from data. In this paper, we propose the \ufb01rst method that deals with explaining GANs, by introducing a novel qualitative and quantitative analysis of the inner structure of the model. Similarly to analyzing the common genes in two DNA sequences, we analyze the common \ufb01lters in two CNNs. We show that the GANs for face aging partially share their parameters with GANs trained for heterogeneous applications and that the aging transformation can be learned using general purpose image databases and a \ufb01ne-tuning step. Results on public databases con\ufb01rm the validity of our approach, also enabling future studies on similar models

Review and analysis of research on video games and artificial intelligence: a look back and a step forward

This article shows the intimate relationship between Artificial Intelligence (AI) and video games research in 13 categories of analysis based on a bibliometric survey carried out in the Scopus database. We first briefly reviewed the relation between video games and AI. Then, we introduced the methodology of literature collection, presented and discussed the query, as well the flow of data treatment in the applications and plugins used. Since the article is concerned with a historical point of view of the relationship between digital games and AI the results were many and, therefore, we focused on the top 10 of each ranking, and discussed these results separately. Finally, we discuss the limitations of our review, proposing future research directions for scholars.info:eu-repo/semantics/publishedVersio

Contextualizing support vector machine predictions

Classification in artificial intelligence is usually understood as a process whereby several objects are evaluated to predict the class(es) those objects belong to. Aiming to improve the interpretability of predictions resulting from a support vector machine classification process, we explore the use of augmented appraisal degrees to put those predictions in context. A use case, in which the classes of handwritten digits are predicted, illustrates how the interpretability of such predictions is benefitted from their contextualization

A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

Alzheimer’s disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provides physicians with accurate decisions along with a set of explanations for every decision. Specifically, the model integrates 11 modalities of 1048 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) real-world dataset: 294 cognitively normal, 254 stable mild cognitive impairment (MCI), 232 progressive MCI, and 268 AD. It is actually a two-layer model with random forest (RF) as classifier algorithm. In the first layer, the model carries out a multi-class classification for the early diagnosis of AD patients. In the second layer, the model applies binary classification to detect possible MCI-to-AD progression within three years from a baseline diagnosis. The performance of the model is optimized with key markers selected from a large set of biological and clinical measures. Regarding explainability, we provide, for each layer, global and instance-based explanations of the RF classifier by using the SHapley Additive exPlanations (SHAP) feature attribution framework. In addition, we implement 22 explainers based on decision trees and fuzzy rule-based systems to provide complementary justifications for every RF decision in each layer. Furthermore, these explanations are represented in natural language form to help physicians understand the predictions. The designed model achieves a cross-validation accuracy of 93.95% and an F1-score of 93.94% in the first layer, while it achieves a cross-validation accuracy of 87.08% and an F1-Score of 87.09% in the second layer. The resulting system is not only accurate, but also trustworthy, accountable, and medically applicable, thanks to the provided explanations which are broadly consistent with each other and with the AD medical literature. The proposed system can help to enhance the clinical understanding of AD diagnosis and progression processes by providing detailed insights into the effect of different modalities on the disease riskThis work was supported by National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science and ICT)-NRF-2020R1A2B5B02002478). In addition, Dr. Jose M. Alonso is Ramon y Cajal Researcher (RYC-2016-19802), and its research is supported by the Spanish Ministry of Science, Innovation and Universities (grants RTI2018-099646-B-I00, TIN2017-84796-C2-1-R, TIN2017-90773-REDT, and RED2018-102641-T) and the Galician Ministry of Education, University and Professional Training (grants ED431F 2018/02, ED431C 2018/29, ED431G/08, and ED431G2019/04), with all grants co-funded by the European Regional Development Fund (ERDF/FEDER program)S

Visão baseada na atenção: : passado, presente e futuro

Objective: To present a review and prognosis of academic research focused on the field of Attention-Based View (ABV). Methodology: Bibliometric analysis using co-word analysis and co-citation techniques of 165 relevant articles published between 2004 and 2021 in journals included in the Web of Science and Scopus databases, considering conceptual and intellectual structures, trends, and possible paths for the field of ABV. The retrieved articles were selected based on the key terms present in the title, abstract, and keywords. Relevance: Identifies the field development, research network, documents the most relevant journals and articles, concepts, and the intellectual framework of prominent authors. Findings: The seminal author, Ocasio, is the most expressive and the center of all networks in ABV research. The leading journal, according to the number of articles published, is the Strategic Management Journal. The conceptual structure presents three groups: the central roots of ABV, the attentional dynamics within organizations, and managerial cognition. Relevant topics are strategic change and communication channels, strategy as practice and studies in multinational companies (MNCs), organizational design, senior management team and governance. Theoretical implications: To present the state of the art, to integrate ABV knowledge and identify gaps for future research showing new trends, such as interfaces with metacognition and governance. Practical implications: Top management team decision-making process is ABV dependent so organizational architecture and its dynamics plays a crucial role in strategy for business success.Objetivo: Presentar una revisión y pronóstico de la investigación académica enfocada en el campo de la Visión Basada en Atención (ABV en inglés). Metodología: Análisis bibliométrico mediante técnicas de análisis de co-palabras y co-citación de 165 artículos relevantes publicados entre 2004 y 2021 en revistas incluidas en las bases de datos Web of Science y Scopus, considerando estructuras conceptuales e intelectuales, tendencias y posibles caminos para el campo de la ABV. Los artículos recuperados fueron seleccionados en base a los términos clave presentes en el título, resumen y palabras clave.   Relevancia: Identifica el desarrollo y evolución; documenta las revistas y artículos más relevantes, los conceptos y el marco intelectual de autores destacados. Resultados: El autor seminal, Ocasio, es el más expresivo y el centro de todas las redes en la investigación ABV. La revista líder, según el número de artículos publicados, es Strategic Management Journal. El marco conceptual presenta tres grupos: las raíces centrales de ABV, las dinámicas atencionales dentro de las organizaciones y la cognición gerencial. Los temas destacados son el cambio estratégico y los canales de comunicación, la estrategia como práctica y estudios en corporaciones multinacionales (MNC), diseño organizacional, equipo de alta gerencia y gobernanza. Implicaciones teóricas: presentar el estado del arte, integrar el conocimiento de ABV e identificar brechas para futuras investigaciones que muestran nuevas tendencias, como interfaces con metacognición y gobernanza. Implicaciones prácticas: El proceso de toma de decisiones del equipo de alta dirección depende de ABV; por lo tanto, la arquitectura organizacional y su dinámica juegan un papel crucial en la estrategia para el éxito empresarial.Objetivo: Apresentar uma revisão e prognóstico da pesquisa acadêmica voltada para o campo da Visão Baseada na Atenção (ABV). Metodologia: Análise bibliométrica utilizando técnicas de co-word analysis e cocitação de 165 artigos relevantes publicados entre 2004 e 2021 em periódicos incluídos nas bases de dados Web of Science e Scopus, considerando estruturas conceituais e intelectuais, tendências e possíveis caminhos para o campo da ABV. Os artigos recuperados foram selecionados com base nos termos-chave presentes no título, resumo e palavras-chave. Relevância: Identifica o desenvolvimento do campo, a rede de pesquisa, documenta os periódicos e artigos mais relevantes, os conceitos e o arcabouço intelectual de autores de destaque. Resultados: O autor seminal, Ocasio, é o mais expressivo e o centro de todas as redes na pesquisa ABV. A revista líder, de acordo com o número de artigos publicados, é a Revista de Gestão Estratégica. A estrutura conceitual apresenta três grupos: as raízes centrais da ABV, a dinâmica atencional dentro das organizações e a cognição gerencial. Os temas relevantes são mudança estratégica e canais de comunicação, estratégia como prática e estudos em empresas multinacionais (MNCs), desenho organizacional, equipe de alta gestão e governança. Implicações teóricas: Apresentar o estado da arte, integrar o conhecimento ABV e identificar lacunas para pesquisas futuras que mostrem novas tendências, como interfaces com metacognição e governança. Implicações práticas: O processo decisório da equipe de alta administração é dependente do ABV; portanto, a arquitetura organizacional e sua dinâmica desempenham um papel crucial na estratégia para o sucesso do negócio

Explainable pattern modelling and summarization in sensor equipped smart homes of elderly

In the next several decades, the proportion of the elderly population is expected to increase significantly. This has led to various efforts to help live them independently for longer periods of time. Smart homes equipped with sensors provide a potential solution by capturing various behavioral and physiological patterns of the residents. In this work, we develop techniques to model and detect changes in these patterns. The focus is on methods that are explainable in nature and allow for generating natural language descriptions. We propose a comprehensive change description framework that can detect unusual changes in the sensor parameters and describe the data leading to those changes in natural language. An approach that models and detects variations in physiological and behavioral routines of the elderly forms one part of the change description framework. The second part comes from a natural language generation system in which we identify important health-relevant features from the sensor parameters. Throughout this dissertation, we validate the developed techniques using both synthetic and real data obtained from the homes of the elderly living in sensor-equipped facilities. Using multiple real data retrospective case studies, we show that our methods are able to detect variations in the sensor data that are correlated with important health events in the elderly as recorded in their Electronic Health Records.Includes bibliographical reference

Visualising the intellectual and social structures of digital humanities using an invisible college model

This thesis explores the intellectual and social structures of an emerging field, Digital Humanities (DH). After around 70 years of development, DH claims to differentiate itself from the traditional Humanities for its inclusiveness, diversity, and collaboration. However, the ‘big tent’ concept not only limits our understandings of its research structure, but also results in a lack of empirical review and sustainable support. Under this umbrella, whether there are merely fragmented topics, or a consolidated knowledge system is still unknown. This study seeks to answer three research questions: a) Subject: What research topics is the DH subject composed of? b) Scholar: Who has contributed to the development of DH? c) Environment: How diverse are the backgrounds of DH scholars? The Invisible College research model is refined and applied as the methodological framework that produces four visualised networks. As the results show, DH currently contributes more towards the general historical literacy and information science, while longitudinally, it was heavily involved in computational linguistics. Humanistic topics are more popular and central, while technical topics are relatively peripheral and have stronger connections with non-Anglophone communities. DH social networks are at the early stages of development, and the formation is heavily influenced by non-academic and non-intellectual factors, e.g., language, working country, and informal relationships. Although male scholars have dominated the field, female scholars have encouraged more communication and built more collaborations. Despite the growing appeals for more diversity, the level of international collaboration in DH is more extensive than in many other disciplines. These findings can help us gain new understandings on the central and critical questions about DH. To the best of the candidate’s knowledge, this study is the first to investigate the formal and informal structures in DH with a well-grounded research model