23,058 research outputs found
Using Cognitive Computing for Learning Parallel Programming: An IBM Watson Solution
While modern parallel computing systems provide high performance resources,
utilizing them to the highest extent requires advanced programming expertise.
Programming for parallel computing systems is much more difficult than
programming for sequential systems. OpenMP is an extension of C++ programming
language that enables to express parallelism using compiler directives. While
OpenMP alleviates parallel programming by reducing the lines of code that the
programmer needs to write, deciding how and when to use these compiler
directives is up to the programmer. Novice programmers may make mistakes that
may lead to performance degradation or unexpected program behavior. Cognitive
computing has shown impressive results in various domains, such as health or
marketing. In this paper, we describe the use of IBM Watson cognitive system
for education of novice parallel programmers. Using the dialogue service of the
IBM Watson we have developed a solution that assists the programmer in avoiding
common OpenMP mistakes. To evaluate our approach we have conducted a survey
with a number of novice parallel programmers at the Linnaeus University, and
obtained encouraging results with respect to usefulness of our approach
Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education
This paper presents a novel framework, Artificial Intelligence-Enabled
Intelligent Assistant (AIIA), for personalized and adaptive learning in higher
education. The AIIA system leverages advanced AI and Natural Language
Processing (NLP) techniques to create an interactive and engaging learning
platform. This platform is engineered to reduce cognitive load on learners by
providing easy access to information, facilitating knowledge assessment, and
delivering personalized learning support tailored to individual needs and
learning styles. The AIIA's capabilities include understanding and responding
to student inquiries, generating quizzes and flashcards, and offering
personalized learning pathways. The research findings have the potential to
significantly impact the design, implementation, and evaluation of AI-enabled
Virtual Teaching Assistants (VTAs) in higher education, informing the
development of innovative educational tools that can enhance student learning
outcomes, engagement, and satisfaction. The paper presents the methodology,
system architecture, intelligent services, and integration with Learning
Management Systems (LMSs) while discussing the challenges, limitations, and
future directions for the development of AI-enabled intelligent assistants in
education.Comment: 29 pages, 10 figures, 9659 word
FGPE+: the mobile FGPE environment and the Pareto-optimized gamified programming exercise selection model - an empirical evaluation
This paper is poised to inform educators, policy makers and software developers about the untapped potential of PWAs in creating engaging, effective, and personalized learning experiences in the field of programming education. We aim to address a significant gap in the current understanding of the potential advantages and underutilisation of Progressive Web Applications (PWAs) within the education sector, specifically for programming education. Despite the evident lack of recognition of PWAs in this arena, we present an innovative approach through the Framework for Gamification in Programming Education (FGPE). This framework takes advantage of the ubiquity and ease of use of PWAs, integrating it with a Pareto optimized gamified programming exercise selection model ensuring personalized adaptive learning experiences by dynamically adjusting the complexity, content, and feedback of gamified exercises in response to the learners’ ongoing progress and performance. This study examines the mobile user experience of the FGPE PLE in different countries, namely Poland and Lithuania, providing novel insights into its applicability and efficiency. Our results demonstrate that combining advanced adaptive algorithms with the convenience of mobile technology has the potential to revolutionize programming education. The FGPE+ course group outperformed the Moodle group in terms of the average perceived knowledge (M = 4.11, SD = 0.51).info:eu-repo/semantics/publishedVersio
Opportunities and Challenges of Applying Large Language Models in Building Energy Efficiency and Decarbonization Studies: An Exploratory Overview
In recent years, the rapid advancement and impressive capabilities of Large
Language Models (LLMs) have been evident across various domains. This paper
explores the application, implications, and potential of LLMs in building
energy efficiency and decarbonization studies. The wide-ranging capabilities of
LLMs are examined in the context of the building energy field, including
intelligent control systems, code generation, data infrastructure, knowledge
extraction, and education. Despite the promising potential of LLMs, challenges
including complex and expensive computation, data privacy, security and
copyright, complexity in fine-tuned LLMs, and self-consistency are discussed.
The paper concludes with a call for future research focused on the enhancement
of LLMs for domain-specific tasks, multi-modal LLMs, and collaborative research
between AI and energy experts
Advancing Building Energy Modeling with Large Language Models: Exploration and Case Studies
The rapid progression in artificial intelligence has facilitated the
emergence of large language models like ChatGPT, offering potential
applications extending into specialized engineering modeling, especially
physics-based building energy modeling. This paper investigates the innovative
integration of large language models with building energy modeling software,
focusing specifically on the fusion of ChatGPT with EnergyPlus. A literature
review is first conducted to reveal a growing trend of incorporating of large
language models in engineering modeling, albeit limited research on their
application in building energy modeling. We underscore the potential of large
language models in addressing building energy modeling challenges and outline
potential applications including 1) simulation input generation, 2) simulation
output analysis and visualization, 3) conducting error analysis, 4)
co-simulation, 5) simulation knowledge extraction and training, and 6)
simulation optimization. Three case studies reveal the transformative potential
of large language models in automating and optimizing building energy modeling
tasks, underscoring the pivotal role of artificial intelligence in advancing
sustainable building practices and energy efficiency. The case studies
demonstrate that selecting the right large language model techniques is
essential to enhance performance and reduce engineering efforts. Besides direct
use of large language models, three specific techniques were utilized: 1)
prompt engineering, 2) retrieval-augmented generation, and 3) multi-agent large
language models. The findings advocate a multidisciplinary approach in future
artificial intelligence research, with implications extending beyond building
energy modeling to other specialized engineering modeling
An introduction to learning technology in tertiary education in the UK.
Contents: 1. The Learning Technology Arena
2. The Learning Technology Community
3. Learning Technology Tools
4. Key issues and developments in the Learning Technology Field
5. Implementing Learning Technologies
6. Further Resource
BIM-GPT: a Prompt-Based Virtual Assistant Framework for BIM Information Retrieval
Efficient information retrieval (IR) from building information models (BIMs)
poses significant challenges due to the necessity for deep BIM knowledge or
extensive engineering efforts for automation. We introduce BIM-GPT, a
prompt-based virtual assistant (VA) framework integrating BIM and generative
pre-trained transformer (GPT) technologies to support NL-based IR. A prompt
manager and dynamic template generate prompts for GPT models, enabling
interpretation of NL queries, summarization of retrieved information, and
answering BIM-related questions. In tests on a BIM IR dataset, our approach
achieved 83.5% and 99.5% accuracy rates for classifying NL queries with no data
and 2% data incorporated in prompts, respectively. Additionally, we validated
the functionality of BIM-GPT through a VA prototype for a hospital building.
This research contributes to the development of effective and versatile VAs for
BIM IR in the construction industry, significantly enhancing BIM accessibility
and reducing engineering efforts and training data requirements for processing
NL queries.Comment: 35 pages, 15 figure
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Engineering Adaptive Model-Driven User Interfaces for Enterprise Applications
Enterprise applications such as enterprise resource planning systems have numerous complex user interfaces (UIs). Usability problems plague these UIs because they are offered as a generic off-the-shelf solution to end-users with diverse needs in terms of their required features and layout preferences. Adaptive UIs can help in improving usability by tailoring the features and layout based on the context-of-use. The model-driven UI development approach offers the possibility of applying different types of adaptations on the various UI levels of abstraction. This approach forms the basis for many works researching the development of adaptive UIs. Yet, several gaps were identified in the state-of-the-art adaptive model-driven UI development systems. To fill these gaps, this thesis presents an approach that offers the following novel contributions:
- The Cedar Architecture serves as a reference for developing adaptive model-driven enterprise application user interfaces.
- Role-Based User Interface Simplification (RBUIS) is a mechanism for improving usability through adaptive behavior, by providing end-users with a minimal feature-set and an optimal layout based on the context-of-use.
- Cedar Studio is an integrated development environment, which provides tool support for building adaptive model-driven enterprise application UIs using RBUIS based on the Cedar Architecture.
The contributions were evaluated from the technical and human perspectives. Several metrics were established and applied to measure the technical characteristics of the proposed approach after integrating it into an open-source enterprise application. Additional insights about the approach were obtained through the opinions of industry experts and data from real-life projects. Usability studies showed the approach’s ability to significantly improve usability in terms of end-user efficiency, effectiveness and satisfaction
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