21,801 research outputs found
Enhancing Undergraduate AI Courses through Machine Learning Projects
It is generally recognized that an undergraduate introductory Artificial Intelligence course is challenging to teach. This is, in part, due to the diverse and seemingly disconnected core topics that are typically covered. The paper presents work funded by the National Science Foundation to address this problem and to enhance the student learning experience in the course. Our work involves the development of an adaptable framework for the presentation of core AI topics through a unifying theme of machine learning. A suite of hands-on semester-long projects are developed, each involving the design and implementation of a learning system that enhances a commonly-deployed application. The projects use machine learning as a unifying theme to tie together the core AI topics. In this paper, we will first provide an overview of our model and the projects being developed and will then present in some detail our experiences with one of the projects – Web User Profiling which we have used in our AI class
Semantic data mining and linked data for a recommender system in the AEC industry
Even though it can provide design teams with valuable performance insights and enhance decision-making, monitored building data is rarely reused in an effective feedback loop from operation to design. Data mining allows users to obtain such insights from the large datasets generated throughout the building life cycle. Furthermore, semantic web technologies allow to formally represent the built environment and retrieve knowledge in response to domain-specific requirements. Both approaches have independently established themselves as powerful aids in decision-making. Combining them can enrich data mining processes with domain knowledge and facilitate knowledge discovery, representation and reuse. In this article, we look into the available data mining techniques and investigate to what extent they can be fused with semantic web technologies to provide recommendations to the end user in performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations
Who are Like-minded: Mining User Interest Similarity in Online Social Networks
In this paper, we mine and learn to predict how similar a pair of users'
interests towards videos are, based on demographic (age, gender and location)
and social (friendship, interaction and group membership) information of these
users. We use the video access patterns of active users as ground truth (a form
of benchmark). We adopt tag-based user profiling to establish this ground
truth, and justify why it is used instead of video-based methods, or many
latent topic models such as LDA and Collaborative Filtering approaches. We then
show the effectiveness of the different demographic and social features, and
their combinations and derivatives, in predicting user interest similarity,
based on different machine-learning methods for combining multiple features. We
propose a hybrid tree-encoded linear model for combining the features, and show
that it out-performs other linear and treebased models. Our methods can be used
to predict user interest similarity when the ground-truth is not available,
e.g. for new users, or inactive users whose interests may have changed from old
access data, and is useful for video recommendation. Our study is based on a
rich dataset from Tencent, a popular service provider of social networks, video
services, and various other services in China
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
Species-level functional profiling of metagenomes and metatranscriptomes.
Functional profiles of microbial communities are typically generated using comprehensive metagenomic or metatranscriptomic sequence read searches, which are time-consuming, prone to spurious mapping, and often limited to community-level quantification. We developed HUMAnN2, a tiered search strategy that enables fast, accurate, and species-resolved functional profiling of host-associated and environmental communities. HUMAnN2 identifies a community's known species, aligns reads to their pangenomes, performs translated search on unclassified reads, and finally quantifies gene families and pathways. Relative to pure translated search, HUMAnN2 is faster and produces more accurate gene family profiles. We applied HUMAnN2 to study clinal variation in marine metabolism, ecological contribution patterns among human microbiome pathways, variation in species' genomic versus transcriptional contributions, and strain profiling. Further, we introduce 'contributional diversity' to explain patterns of ecological assembly across different microbial community types
User Modeling and User Profiling: A Comprehensive Survey
The integration of artificial intelligence (AI) into daily life, particularly
through information retrieval and recommender systems, has necessitated
advanced user modeling and profiling techniques to deliver personalized
experiences. These techniques aim to construct accurate user representations
based on the rich amounts of data generated through interactions with these
systems. This paper presents a comprehensive survey of the current state,
evolution, and future directions of user modeling and profiling research. We
provide a historical overview, tracing the development from early stereotype
models to the latest deep learning techniques, and propose a novel taxonomy
that encompasses all active topics in this research area, including recent
trends. Our survey highlights the paradigm shifts towards more sophisticated
user profiling methods, emphasizing implicit data collection, multi-behavior
modeling, and the integration of graph data structures. We also address the
critical need for privacy-preserving techniques and the push towards
explainability and fairness in user modeling approaches. By examining the
definitions of core terminology, we aim to clarify ambiguities and foster a
clearer understanding of the field by proposing two novel encyclopedic
definitions of the main terms. Furthermore, we explore the application of user
modeling in various domains, such as fake news detection, cybersecurity, and
personalized education. This survey serves as a comprehensive resource for
researchers and practitioners, offering insights into the evolution of user
modeling and profiling and guiding the development of more personalized,
ethical, and effective AI systems.Comment: 71 page
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