3,305 research outputs found
AI in marketing, consumer research and psychology: A systematic literature review and research agenda
This study is the first to provide an integrated view on the body of knowledge of artificial intelligence (AI) published in the marketing, consumer research, and psychology literature. By leveraging a systematic literature review using a data-driven approach and quantitative methodology (including bibliographic coupling), this study provides an overview of the emerging intellectual structure of AI research in the three bodies of literature examined. We identified eight topical clusters: (1) memory and computational logic; (2) decision making and cognitive processes; (3) neural networks; (4) machine learning and linguistic analysis; (5) social media and text mining; (6) social media content analytics; (7) technology acceptance and adoption; and (8) big data and robots. Furthermore, we identified a total of 412 theoretical lenses used in these studies with the most frequently used being: (1) the unified theory of acceptance and use of technology; (2) game theory; (3) theory of mind; (4) theory of planned behavior; (5) computational theories; (6) behavioral reasoning theory; (7) decision theories; and (8) evolutionary theory. Finally, we propose a research agenda to advance the scholarly debate on AI in the three literatures studied with an emphasis on cross-fertilization of theories used across fields, and neglected research topics
A machine learning taxonomic classifier for science publications
Dissertação de mestrado integrado em Engineering and Management of Information SystemsThe evolution in scientific production, associated with the growing interdomain collaboration of knowledge and the increasing co-authorship of scientific works remains supported by processes of manual, highly subjective classification, subject to misinterpretation. The very taxonomy on which this same classification process is based is not consensual, with governmental organizations resorting to taxonomies that do not keep up with changes in scientific areas, and indexers / repositories that seek to keep up with those changes.
We find a reality distinct from what is expected and that the domains where scientific work is recorded can easily be misrepresentative of the work itself.
The taxonomy applied today by governmental bodies, such as the one that regulates scientific production in Portugal, is not enough, is limiting, and promotes classification in areas close to the desired, therefore with great potential for error.
An automatic classification process based on machine learning algorithms presents itself as a possible solution to the subjectivity problem in classification, and while it does not solve the issue of taxonomy mismatch this work shows this possibility with proved results.
In this work, we propose a classification taxonomy, as well as we develop a process based on machine learning algorithms to solve the classification problem. We also present a set of directions for future work for an increasingly representative classification of evolution in science, which is not intended as airtight, but flexible and perhaps increasingly based on phenomena and not just disciplines.A evolução na produção de ciência, associada à crescente colaboração interdomínios do conhecimento e à também crescente coautoria de trabalhos permanece suportada por processos de classificação manual, subjetiva e sujeita a interpretações erradas. A própria taxonomia na qual assenta esse mesmo processo de classificação não é consensual, com organismos estatais a recorrerem a taxonomias que não acompanham as alterações nas áreas científicas, e indexadores/repositórios que procuram acompanhar essas mesmas alterações.
Verificamos uma realidade distinta do espectável e que os domínios onde são registados os trabalhos científicos podem facilmente estar desenquadrados.
A taxonomia hoje aplicada pelos organismos governamentais, como o caso do organismo que regulamenta a produção científica em Portugal, não é suficiente, é limitadora, e promove a classificação em domínios aproximados do desejado, logo com grande potencial para erro.
Um processo de classificação automática com base em algoritmos de machine learning apresenta-se como uma possível solução para o problema da subjetividade na classificação, e embora não resolva a questão do desenquadramento da taxonomia utilizada, é apresentada neste trabalho como uma possibilidade comprovada.
Neste trabalho propomos uma taxonomia de classificação, bem como nós desenvolvemos um processo baseado em machine learning algoritmos para resolver o problema de classificação. Apresentamos ainda um conjunto de direções para trabalhos futuros para uma classificação cada vez mais representativa da evolução nas ciências, que não pretende ser hermética, mas flexível e talvez cada vez mais baseada em fenómenos e não apenas em disciplinas
The strategic impacts of Intelligent Automation for knowledge and service work : An interdisciplinary review
We would like to thank Professor Jarvenpaa and the review team for all the constructive comments and suggestions that were most helpful in revising the paper and in offering a stronger contribution. We would also like to thank Professor Guy Fitzgerald for his constructive comments on earlier versions of the paper. This study was funded by the Chartered Institute of Professional Development (CIPD). The views expressed are those of the authors and not necessarily those of the CIPD.Peer reviewedPublisher PD
TSTEM: A Cognitive Platform for Collecting Cyber Threat Intelligence in the Wild
The extraction of cyber threat intelligence (CTI) from open sources is a
rapidly expanding defensive strategy that enhances the resilience of both
Information Technology (IT) and Operational Technology (OT) environments
against large-scale cyber-attacks. While previous research has focused on
improving individual components of the extraction process, the community lacks
open-source platforms for deploying streaming CTI data pipelines in the wild.
To address this gap, the study describes the implementation of an efficient and
well-performing platform capable of processing compute-intensive data pipelines
based on the cloud computing paradigm for real-time detection, collecting, and
sharing CTI from different online sources. We developed a prototype platform
(TSTEM), a containerized microservice architecture that uses Tweepy, Scrapy,
Terraform, ELK, Kafka, and MLOps to autonomously search, extract, and index
IOCs in the wild. Moreover, the provisioning, monitoring, and management of the
TSTEM platform are achieved through infrastructure as a code (IaC). Custom
focus crawlers collect web content, which is then processed by a first-level
classifier to identify potential indicators of compromise (IOCs). If deemed
relevant, the content advances to a second level of extraction for further
examination. Throughout this process, state-of-the-art NLP models are utilized
for classification and entity extraction, enhancing the overall IOC extraction
methodology. Our experimental results indicate that these models exhibit high
accuracy (exceeding 98%) in the classification and extraction tasks, achieving
this performance within a time frame of less than a minute. The effectiveness
of our system can be attributed to a finely-tuned IOC extraction method that
operates at multiple stages, ensuring precise identification of relevant
information with low false positives
Autonomous Threat Hunting: A Future Paradigm for AI-Driven Threat Intelligence
The evolution of cybersecurity has spurred the emergence of autonomous threat
hunting as a pivotal paradigm in the realm of AI-driven threat intelligence.
This review navigates through the intricate landscape of autonomous threat
hunting, exploring its significance and pivotal role in fortifying cyber
defense mechanisms. Delving into the amalgamation of artificial intelligence
(AI) and traditional threat intelligence methodologies, this paper delineates
the necessity and evolution of autonomous approaches in combating contemporary
cyber threats. Through a comprehensive exploration of foundational AI-driven
threat intelligence, the review accentuates the transformative influence of AI
and machine learning on conventional threat intelligence practices. It
elucidates the conceptual framework underpinning autonomous threat hunting,
spotlighting its components, and the seamless integration of AI algorithms
within threat hunting processes.. Insightful discussions on challenges
encompassing scalability, interpretability, and ethical considerations in
AI-driven models enrich the discourse. Moreover, through illuminating case
studies and evaluations, this paper showcases real-world implementations,
underscoring success stories and lessons learned by organizations adopting
AI-driven threat intelligence. In conclusion, this review consolidates key
insights, emphasizing the substantial implications of autonomous threat hunting
for the future of cybersecurity. It underscores the significance of continual
research and collaborative efforts in harnessing the potential of AI-driven
approaches to fortify cyber defenses against evolving threats
Recommended from our members
AI in Marketing, Consumer Research & Psychology: A Systematic Literature Review and Research Agenda
This study is the first to provide an integrated view on the body of knowledge on artificial intelligence (AI) published in the marketing, consumer research, and psychology literature. By leveraging a systematic literature review (SLR) using a data-driven approach and quantitative methodology (including bibliographic coupling), this study provides an overview of the emerging intellectual structure of AI research in the three bodies of literature examined. We identified eight topical clusters: (1) memory and computational logic; (2) decision making and cognitive processes; (3) neural networks; (4) machine learning and linguistic analysis; (5) social media and text mining; (6) social media content analytics; (7) technology acceptance and adoption; and (8) big data and robots. Furthermore, we identified a total of 412 theoretical lenses used in these studies with the most frequently used being: (1) the unified theory of acceptance and use of technology; (2) game theory; (3) theory of mind; (4) theory of planned behavior; (5) computational theories; (6) behavioral reasoning theory; (7) decision theories; and (8) evolutionary theory. Finally, we propose a research agenda to advance the scholarly debate on AI in the three literatures studied
- …