This paper proposes an innovative way to address real cases of production prediction. This approach consists in the decomposition of original time series into time sub-series according to a group of factors in order to generate a predictive model from the partial predictive models of the sub-series. The adjustment of the models is carried out by means of a set of statistic techniques and Automatic Learning. This method was compared to an intuitive method consisting of a direct prediction of time series. The results show that this approach achieves better predictive performance than the direct way, so applying a decomposition method is more appropriate for this problem than non-decomposition.

Künstliche Intelligenz (KI) verändert die Arbeitswelt und führt zu einer dynamischen Neugestaltung der Arbeitsteilung zwischen Mensch und Technik in Unternehmen. Dies betrifft nicht zuletzt auch den Bereich der Führung. KI-Systeme können Führungskräfte bei der Ausübung ihrer Aufgaben unterstützen, etwa indem sie vor allem administrative Koordinations- und Kontrollaufgaben und -entscheidungen übernehmen. Dadurch bleibt den Führungskräften mehr Zeit, sich der Personalführung oder Innovationsprozessen zu widmen. Führungskräfte nehmen künftig eine zentrale Rolle ein, den KI-Transformationsprozess erfolgreich mitzugestalten und dabei im Rahmen ihrer Fürsorgepflichten besonders auf die menschengerechte Gestaltung der KI-Systeme mit den und für die Beschäftigten hinzuwirken. Expertinnen und Experten der Arbeitsgruppe Arbeit/Qualifikation und Mensch-Maschine-Interaktion der Plattform Lernende Systeme wollen mit diesem Whitepaper aufzeigen, welche neuen Möglichkeiten und Chancen, aber auch welche Herausforderungen durch den Einsatz von KI-Systemen in Führungsaufgaben entstehen können. Dazu skizzieren sie zunächst die Herausforderungen, mit denen Führungskräfte schon heute durch digitale Technologien konfrontiert sind (Kapitel 2). Darauf aufbauend wird dargelegt, welche spezifischen Auswirkungen der Einsatz von Lernenden Systemen auf unterschiedliche Führungsaufgaben haben kann, die anhand von vier Aufgabenclustern vorgestellt werden: Strategische Führung, Organisationale Führung, Personalführung und Selbstführung (Kapitel 3). Entlang dieser Systematik wird aufgezeigt, welche Beiträge Lernende Systeme dabei als „unterstützender Akteur“ leisten können. Führung unterliegt einem Wandel. Bedingt durch den digitalen, strategischen Transformationsprozess in Unternehmen werden traditionelle, hierarchische Führungsmodelle sukzessive durch kooperative, netzwerkdynamische und werteorientierte Führungsstile abgelöst. Dies verändert zunehmend die Rolle der Führungskraft hin zu einem vermittelnden ,Übersetzer‘, Vorbild und Coach und verlangt nach einem stärkeren partizipativen Führungsstil (Kapitel 2). Lernende Systeme in der Arbeitswelt werden nicht nur die Rolle von Führungskräften weiter verändern, sondern zunehmend auch deren Aufgaben. Als technologisches Hilfsmittel unterstützen sie bei Aufgaben, die eine hohe Strukturierung und Regelmäßigkeiten aufweisen, sodass mehr Zeit für strategische Aufgaben und Entwicklungen oder mitarbeiterbezogene Führung bleibt. Eigenständig lernende (KI-)Systeme kommen zudem neben der Führungsperson als weiterer unterstützender und möglichst entlastender ,Akteur‘ zum Führungsprozess hinzu. Mit dem Einsatz von Lernenden Systemen bekommen zentrale Werte wie Datenschutz, Transparenz oder Fairness in der Führung für die Beschäftigten eine neue Bedeutung: Daher darf es nicht verwundern, wenn die Einführung von KI in der Führung zunächst von Skepsis begleitet wird. Entscheidend wird es deshalb sein, das Vertrauen und die Akzeptanz der Beschäftigten sowohl in die Technologie als auch in die eigene Führung für eine gelingende Zusammenarbeit zu fördern. Dies setzt eine frühzeitige Einbindung der Beschäftigten sowie der Interessensvertretungen in Planung und Gestaltung der KI-Systeme voraus: Für ein gelingendes KI-Change-Management in Unternehmen wird eine passende Führungs- und Unternehmenskultur notwendig sein, die auf Partizipation, Offenheit und Transparenz beruht. KI und Führung lassen sich gut ergänzen und können für eine moderne, menschenzentrierte Führung einen wichtigen Beitrag leisten. Dafür sind notwendige Rahmenbedingungen (in den Unternehmen) zu schaffen, damit das volle Potenzial der KI-Systeme auch für Führungskräfte nutzbar werden kann. Welche effektiven Maßnahmen dabei helfen, KI-Systeme für Führungsaufgaben einzusetzen, formulieren die Autorinnen und Autoren in passenden Gestaltungsoptionen (Kapitel 4). Zu diesen Gestaltungsoptionen zählen unter anderem eine menschenzentrierte Aufgabenzuteilung zwischen KI und Beschäftigten durch die Führungskräfte, das Aufbauen von notwendigen KI-Kompetenzen der Mitarbeitenden oder das Vorleben einer Feedbackkultur, die die Perspektiven der Beschäftigten und ihrer Interessensvertretungen offen einbindet.

Technological innovations have been shown to improve the quality of health information and improve safety in health care systems. The purpose of this project was to offer hospital nurses a more flexible and practical alternative to education and training than the traditional face-to-face method, supporting nurse educators in overcoming many of the obstacles in responding to nurses' needs in the clinical areas. This project used a randomized, 2-group posttest-only experimental design to measure the effect of treatment at a targeted hospital. The experimental group received a new instructional approach using an Electronic Learning Management System (ELMS) and the control group used the site's traditional standard method; both groups completed the Posttest Knowledge Assessment. The study population consisted of registered nurses who had attended the project site's Safe Blood Transfusion Practice program over a period of 1 month. There were no significant differences between the 2 groups' members' gender, age, level of education, or nursing experience. Data analysis showed a significant (p < .00) difference between the 2 groups' posttest scores, indicating that the participants who used the ELMS attained a higher median knowledge (M = 89.39, SD = 9.26) than did participants who received traditional, face-to-face instruction (M = 76.85, SD = 10.628). These results suggest that ELMS-based learning is a more effective method of instructional delivery that could effectively replace many of the traditional face-to-face education programs. Implementing this innovative system will create positive social change on the targeted hospital by improving health care delivery. The application of the finding would support clinical educators to improve educational delivery to their clients at the clinical areas.

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[EN] The stupefying success of Artificial Intelligence (AI) for specific problems, from recommender systems to self-driving cars, has not yet been matched with a similar progress in general AI systems, coping with a variety of problems. This dissertation deals with the long-standing problem of creating more general AI systems, through the analysis of their development and the evaluation of their cognitive abilities. Firstly, this thesis contributes with a general-purpose learning system that meets several desirable characteristics in terms of expressiveness, comprehensibility and versatility. The system works with approaches that are inherently general: inductive programming and reinforcement learning. The system does not rely on a fixed library of learning operators, but can be endowed with new ones, so being able to operate in a wide variety of contexts. This flexibility, jointly with its declarative character, makes it possible to use the system as an instrument for better understanding the role (and difficulty) of the constructs that each task requires. The learning process is also overhauled with a new developmental and lifelong approach for knowledge acquisition, consolidation and forgetting, which is necessary when bounded resources (memory and time) are considered. Secondly, this thesis analyses whether the use of intelligence tests for AI evaluation is a much better alternative to most task-oriented evaluation approaches in AI. Accordingly, we make a review of what has been done when AI systems have been confronted against tasks taken from intelligence tests. In this regard, we scrutinise what intelligence tests measure in machines, whether they are useful to evaluate AI systems, whether they are really challenging problems, and whether they are useful to understand (human) intelligence. Finally, the analysis of the concepts of development and incremental learning in AI systems is done at the conceptual level but also through several of these intelligence tests, providing further insight for the understanding and construction of general-purpose developing AI systems. / [ES] El éxito abrumador de la Inteligencia Artificial (IA) en la resolución de tareas específicas (desde sistemas de recomendación hasta vehículos de conducción autónoma) no ha sido aún igualado con un avance similar en sistemas de IA de carácter más general enfocados en la resolución de una mayor variedad de tareas. Esta tesis aborda la creación de sistemas de IA de propósito general así como el análisis y evaluación tanto de su desarrollo como de sus capacidades cognitivas. En primer lugar, esta tesis contribuye con un sistema de aprendizaje de propósito general que reúne distintas ventajas como expresividad, comprensibilidad y versatilidad. El sistema está basado en aproximaciones de carácter inherentemente general: programación inductiva y aprendizaje por refuerzo. Además, dicho sistema se basa en una biblioteca dinámica de operadores de aprendizaje por lo que es capaz de operar en una amplia variedad de contextos. Esta flexibilidad, junto con su carácter declarativo, hace que sea posible utilizar el sistema de forma instrumental con el objetivo de facilitar la comprensión de las distintas construcciones que cada tarea requiere para ser resuelta. Por último, el proceso de aprendizaje también se revisa por medio de un enfoque evolutivo e incremental de adquisición, consolidación y olvido de conocimiento, necesario cuando se trabaja con recursos limitados (memoria y tiempo). En segundo lugar, esta tesis analiza el uso de tests de inteligencia humana para la evaluación de sistemas de IA y plantea si su uso puede constituir una alternativa válida a los enfoques actuales de evaluación de IA (más orientados a tareas). Para ello se realiza una exhaustiva revisión bibliográfica de aquellos sistemas de IA que han sido utilizados para la resolución de este tipo de problemas. Esto ha permitido analizar qué miden realmente los tests de inteligencia en los sistemas de IA, si son significativos para su evaluación, si realmente constituyen problemas complejos y, por último, si son útiles para entender la inteligencia (humana). Finalmente se analizan los conceptos de desarrollo cognitivo y aprendizaje incremental en sistemas de IA no solo a nivel conceptual, sino también por medio de estos problemas mejorando por tanto la comprensión y construcción de sistemas de propósito general evolutivos. / [CA] L'èxit aclaparant de la Intel·ligència Artificial (IA) en la resolució de tasques específiques (des de sistemes de recomanació fins a vehicles de conducció autònoma) no ha sigut encara igualat amb un avanç similar en sistemes de IA de caràcter més general enfocats en la resolució d'una major varietat de tasques. Aquesta tesi aborda la creació de sistemes de IA de propòsit general així com l'anàlisi i avaluació tant del seu desenvolupament com de les seues capacitats cognitives. En primer lloc, aquesta tesi contribueix amb un sistema d'aprenentatge de propòsit general que reuneix diferents avantatges com ara expressivitat, comprensibilitat i versatilitat. El sistema està basat en aproximacions de caràcter inherentment general: programació inductiva i aprenentatge per reforç. A més, el sistema utilitza una biblioteca dinàmica d'operadors d'aprenentatge pel que és capaç d'operar en una àmplia varietat de contextos. Aquesta flexibilitat, juntament amb el seu caràcter declaratiu, fa que siga possible utilitzar el sistema de forma instrumental amb l'objectiu de facilitar la comprensió de les diferents construccions que cada tasca requereix per a ser resolta. Finalment, el procés d'aprenentatge també és revisat mitjançant un enfocament evolutiu i incremental d'adquisició, consolidació i oblit de coneixement, necessari quan es treballa amb recursos limitats (memòria i temps). En segon lloc, aquesta tesi analitza l'ús de tests d'intel·ligència humana per a l'avaluació de sistemes de IA i planteja si el seu ús pot constituir una alternativa vàlida als enfocaments actuals d'avaluació de IA (més orientats a tasques). Amb aquesta finalitat, es realitza una exhaustiva revisió bibliogràfica d'aquells sistemes de IA que han sigut utilitzats per a la resolució d'aquest tipus de problemes. Açò ha permès analitzar què mesuren realment els tests d'intel·ligència en els sistemes de IA, si són significatius per a la seua avaluació, si realment constitueixen problemes complexos i, finalment, si són útils per a entendre la intel·ligència (humana). Finalment s'analitzen els conceptes de desenvolupament cognitiu i aprenentatge incremental en sistemes de IA no solament a nivell conceptual, sinó també per mitjà d'aquests problemes millorant per tant la comprensió i construcció de sistemes de propòsit general evolutius. / Martínez Plumed, F. (2016). Incremental and developmental perspectives for general-purpose learning systems [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/67269

Le raisonnement, la capacité de tirer des conclusions logiques à partir de connaissances existantes, est une caractéristique marquante de l’être humain. Avec la perception, ils constituent les deux thèmes majeurs de l’intelligence artificielle. Alors que l’apprentissage profond a repoussé les limites de la perception au-delà des performances humaines en vision par ordinateur et en traitement du langage naturel, les progrès dans les domaines du raisonnement sont loin derrière. L’une des raisons fondamentales est que les problèmes de raisonnement ont généralement des structures flexibles à la fois pour les connaissances (par exemple, les graphes de connaissances) et les requêtes (par exemple, les requêtes en plusieurs étapes), et de nombreux modèles existants ne fonctionnent bien que sur les structures vues pendant l’entraînement. Dans cette thèse, nous visons à repousser les limites des modèles de raisonnement en concevant des algorithmes qui généralisent à travers les structures de connaissances et de requêtes, ainsi que des systèmes qui accélèrent le développement sur des données structurées. Cette thèse est composée de trois parties. Dans la partie I, nous étudions des modèles qui peuvent généraliser de manière inductive à des graphes de connaissances invisibles, qui impliquent de nouveaux vocabulaires d’entités et de relations. Pour les nouvelles entités, nous proposons un nouveau cadre qui apprend les opérateurs neuronaux dans un algorithme de programmation dynamique calculant des représentations de chemin. Ce cadre peut être étendu à des graphes de connaissances à l’échelle d’un million en apprenant une fonction de priorité. Pour les relations, nous construisons un graphe de relations pour capturer les interactions entre les relations, convertissant ainsi les nouvelles relations en nouvelles entités. Cela nous permet de développer un modèle pré-entraîné unique pour des graphes de connaissances arbitraires. Dans la partie II, nous proposons deux solutions pour généraliser les requêtes en plusieurs étapes sur les graphes de connaissances et sur le texte respectivement. Pour les graphes de connaissances, nous montrons que les requêtes en plusieurs étapes peuvent être résolues par plusieurs appels de réseaux neuronaux graphes et d’opérations de logique floue. Cette conception permet la généralisation à de nouvelles entités, et peut être intégrée à notre modèle pré-entraîné pour prendre en charge des graphes de connaissances arbitraires. Pour le texte, nous concevons un nouvel algorithme pour apprendre des connaissances explicites sous forme de règles textuelles afin d’améliorer les grands modèles de langage sur les requêtes en plusieurs étapes. Dans la partie III, nous proposons deux systèmes pour faciliter le développement de l’apprentissage automatique sur des données structurées. Notre bibliothèque open source traite les données structurées comme des citoyens de première classe et supprime la barrière au développement d’algorithmes d’apprentissage automatique sur des données structurées, y compris des graphes, des molécules et des protéines. Notre système d’intégration de noeuds résout le goulot d’étranglement de la mémoire GPU des matrices d’intégration et s’adapte aux graphes avec des milliards de noeuds. / Reasoning, the ability to logically draw conclusions from existing knowledge, is a hallmark of human. Together with perception, they constitute the two major themes of artificial intelligence. While deep learning has pushed the limit of perception beyond human-level performance in computer vision and natural language processing, the progress in reasoning domains is way behind. One fundamental reason is that reasoning problems usually have flexible structures for both knowledge (e.g. knowledge graphs) and queries (e.g. multi-step queries), and many existing models only perform well on structures seen during training. In this thesis, we aim to push the boundary of reasoning models by devising algorithms that generalize across knowledge and query structures, as well as systems that accelerate development on structured data. This thesis is composed of three parts. In Part I, we study models that can inductively generalize to unseen knowledge graphs, which involve new entity and relation vocabularies. For new entities, we propose a novel framework that learns neural operators in a dynamic programming algorithm computing path representations. This framework can be further scaled to million-scale knowledge graphs by learning a priority function. For relations, we construct a relation graph to capture the interactions between relations, thereby converting new relations into new entities. This enables us to develop a single pre-trained model for arbitrary knowledge graphs. In Part II, we propose two solutions for generalizing across multi-step queries on knowledge graphs and text respectively. For knowledge graphs, we show multi-step queries can be solved by multiple calls of graph neural networks and fuzzy logic operations. This design enables generalization to new entities, and can be integrated with our pre-trained model to accommodate arbitrary knowledge graphs. For text, we devise a new algorithm to learn explicit knowledge as textual rules to improve large language models on multi-step queries. In Part III, we propose two systems to facilitate machine learning development on structured data. Our open-source library treats structured data as first-class citizens and removes the barrier for developing machine learning algorithms on structured data, including graphs, molecules and proteins. Our node embedding system solves the GPU memory bottleneck of embedding matrices and scales to graphs with billion nodes.

Machine learning (ML) is transforming various aspects of our lives, driving the need for computing systems that efficiently support large-scale ML workloads. As models grow in size and complexity, existing systems struggle to adapt, limiting both performance and flexibility. Additionally, ML techniques can enhance traditional computing tasks, but current systems lack the adaptability to integrate these advancements effectively. Building systems for running machine learning workloads, and running workloads using machine learning - both require a careful understanding of the nature of the systems and ML models. In this dissertation we design and develop a series of novel storage and scheduling solutions for ML systems by bringing attention to the unique characteristics of workloads and the underlying system. We find that by designing ML systems that are finely tuned to workload characteristics and underlying infrastructure, we can significantly enhance application performance and maximize resource utilization. In the first part of this dissertation (Ch- 3), we analyze popular ML models and datasets, uncovering insights that inspired SHADE, a data-importance-aware caching solution for ML. The second part of this dissertation (Ch- 4) proposes to leverage system characteristics of hundreds of client devices along with the characteristics of the samples within the clients to design novel sampling, caching and client scheduling mechanisms to tackle the data and system heterogeneity among client devices and thereby fundamentally improve the performance of federated learning using edge devices in the cloud. The third part of this dissertation (Ch- 5) proposes to leverage multi-agent LLM application and user request characteristics to design an efficient request scheduling mechanism that can serve clients in multi-tenant environments in a fair and efficient manner while preventing abuse. My dissertation demonstrates that leveraging workload-aware strategies can significantly enhance the efficiency (e.g., reduced training time, increased throughput, lower latency) and flexibility (e.g., improved ease of use, deployment, and programmability) of ma- chine learning systems. By accounting for workload dynamicity and heterogeneity, these principles can guide the design of next-generation ML systems, ensuring adaptability to emerging models and evolving hardware technologies. / Doctor of Philosophy / Machine learning (ML) has become an integral part of our daily lives, powering applications from virtual assistants to medical diagnostics. As ML models grow larger and more complex, the systems that run them must evolve to keep pace. This dissertation explores how we can build more efficient and adaptable computing systems to support large-scale ML workloads. Traditional computing systems often struggle to accommodate the ever-changing demands of ML applications. Similarly, ML techniques can be leveraged to improve the performance of non-ML workloads, but existing systems lack the flexibility to integrate these advancements seamlessly. This research tackles both challenges: designing systems optimized for ML workloads and enhancing traditional systems using ML-driven insights. By designing intelligent, workload-aware strategies, this research demonstrates substantial improvements in the speed, efficiency, and flexibility of ML systems. These principles will help shape the next generation of computing infrastructure, ensuring that future ML models and applications can be deployed smoothly, regardless of scale or complexity.

Web-based learning has become an important component of education. Higher education institutions in Thailand have become increasingly aware of the widespread use and effectiveness of web-based learning systems. However, the adoption of such learning systems is growing at a slow pace in Thailand. The purpose of this study was to test the Unified Theory of Acceptance and Use of Technology (UTAUT), that performance expectancy, effort expectancy, social influence, and facilitating conditions have a positive effect on usage intention and adoption of web-based learning systems by instructors, in the Departments of Education at the Rajabhat Universities, Thailand; and to test whether experience of use, age, and gender have moderating effects in the adoption of web-based learning systems there. The research design used in this study was a cross-sectional survey design. Data were collected by means of a self-administered paper questionnaire. The study was conducted among the instructors in the departments of education at the Rajabhat Universities in Thailand. A total of 725 surveys were sent out, 454 questionnaires were returned by the respondents, and 14 were eliminated as outliers; thus, the final data set for the study was 440 samples. The two-step approach of SEM was used to test the model and the study's hypotheses; first, the measurement model was measured to examine the validity and reliability of the data; next, the structural model was measured to test the hypotheses of the study and the fitness of the data to the model. The results of this study revealed several factors that can affect instructors’ adoption of a web-based learning system and which can enhance the web-based learning performance of instructors in the Rajabhat Universities and throughout higher education in Thailand.

Studies have revealed that securing Information Systems (IS) from intentional misuse is a concern among organizations today. The use of Web-based systems has grown dramatically across industries including e-commerce, e-banking, e-government, and e learning to name a few. Web-based systems provide e-services through a number of diverse activities. The demand for e-learning systems in both academic and non-academic organizations has increased the need to improve security against impersonation fraud. Although there are a number of studies focused on securing Web-based systems from Information Systems (IS) misuse, research has recognized the importance of identifying suitable levels of authenticating strength for various activities. In e-learning systems, it is evident that due to the variation in authentication strength among controls, a ‘one size fits all’ solution is not suitable for securing diverse e-learning activities against impersonation fraud. The main goal of this study was to use the framework of the Task-Technology Fit (TTF) theory to conduct an exploratory research design to empirically investigate what levels of authentication strength users perceive to be most suitable for activities in e-learning systems against impersonation fraud. This study aimed to assess if the ‘one size fits all’ approach mainly used nowadays is valid when it comes to securing e-learning activities from impersonation fraud. Following the development of an initial survey instrument (Phase 1), expert panel feedback was gathered for instrument validity using the Delphi methodology. The initial survey instrument was adjusted according to feedback (Phase 2). The finalized Web-based survey was used to collect quantitative data for final analyses (Phase 3). This study reported on data collected from 1,070 e-learners enrolled at a university. Descriptive statistics was used to identify what e-learning activities perceived by users and what users perceived that their peers would identify to have a high potential for impersonation. The findings determined there are a specific set of e-learning activities that high have potential for impersonation fraud and need a moderate to high level of authentication strength to reduce the threat. Principal Component Analysis was used to identify significant components of authentication strength to be suitable against the threats of impersonation for e-learning activities.

Throughout life, we continuously accumulate data, information and knowledge. The ability to recall much of this accumulated knowledge commonly deteriorates with time, though some forms part of what is referred to as tacit knowledge. In the context of education, students access and interact with a teacher’s knowledge in order to create their own, and may have their own data, information and knowledge that could be added to teacher’s knowledge for everyone’s benefit. The realization that students can contribute to enhancing personal knowledge is an important cornerstone in developing a mentor (teacher, tutor and facilitator) focused knowledge system. The research presented in this thesis discusses an integrated framework that manages an individual’s personal data, information and knowledge and enables it to be enhanced by others, in the context of a blended teaching and learning environment. Existing related models, structures, systems and current practices are discussed. The core outcomes of this thesis include: • the virtualMe framework that can be utilized when developing Web based teaching and learning systems; • the sniplet content model that can be used as the basis for sharing information and knowledge; • an annotation framework used to manage knowledge acquisition; and • a multimedia object (MMO) model that: o allows for related media artefacts to be intuitively grouped in a logical collection; o includes a meta-data schema that encompasses other metadata structures, and manages context and referencing; and o includes a model allowing component parts to be reaggregated if they are separated. The virtualMe framework provides the ability to retain context while transferring the content from one person to another and from one place to another. The framework retains the content’s original context and then allows the receiver to customise the content and metadata so that the content becomes that person’s knowledge. A mechanism has been created for such contextual transfer of content (context retained by the metadata).