1,400 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
GPT models in construction industry: Opportunities, limitations, and a use case validation
Large Language Models (LLMs) trained on large data sets came into prominence in 2018 after Google introduced BERT. Subsequently, different LLMs such as GPT models from OpenAI have been released. These models perform well on diverse tasks and have been gaining widespread applications in fields such as business and education. However, little is known about the opportunities and challenges of using LLMs in the construction industry. Thus, this study aims to assess GPT models in the construction industry. A critical review, expert discussion and case study validation are employed to achieve the study's objectives. The findings revealed opportunities for GPT models throughout the project lifecycle. The challenges of leveraging GPT models are highlighted and a use case prototype is developed for materials selection and optimization. The findings of the study would be of benefit to researchers, practitioners and stakeholders, as it presents research vistas for LLMs in the construction industry
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
UMSL Bulletin 2022-2023
The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp
NEMISA Digital Skills Conference (Colloquium) 2023
The purpose of the colloquium and events centred around the central role that data plays
today as a desirable commodity that must become an important part of massifying digital
skilling efforts. Governments amass even more critical data that, if leveraged, could
change the way public services are delivered, and even change the social and economic
fortunes of any country. Therefore, smart governments and organisations increasingly
require data skills to gain insights and foresight, to secure themselves, and for improved
decision making and efficiency. However, data skills are scarce, and even more
challenging is the inconsistency of the associated training programs with most curated for
the Science, Technology, Engineering, and Mathematics (STEM) disciplines.
Nonetheless, the interdisciplinary yet agnostic nature of data means that there is
opportunity to expand data skills into the non-STEM disciplines as well.College of Engineering, Science and Technolog
LEAP: Efficient and Automated Test Method for NLP Software
The widespread adoption of DNNs in NLP software has highlighted the need for
robustness. Researchers proposed various automatic testing techniques for
adversarial test cases. However, existing methods suffer from two limitations:
weak error-discovering capabilities, with success rates ranging from 0% to
24.6% for BERT-based NLP software, and time inefficiency, taking 177.8s to
205.28s per test case, making them challenging for time-constrained scenarios.
To address these issues, this paper proposes LEAP, an automated test method
that uses LEvy flight-based Adaptive Particle swarm optimization integrated
with textual features to generate adversarial test cases. Specifically, we
adopt Levy flight for population initialization to increase the diversity of
generated test cases. We also design an inertial weight adaptive update
operator to improve the efficiency of LEAP's global optimization of
high-dimensional text examples and a mutation operator based on the greedy
strategy to reduce the search time. We conducted a series of experiments to
validate LEAP's ability to test NLP software and found that the average success
rate of LEAP in generating adversarial test cases is 79.1%, which is 6.1%
higher than the next best approach (PSOattack). While ensuring high success
rates, LEAP significantly reduces time overhead by up to 147.6s compared to
other heuristic-based methods. Additionally, the experimental results
demonstrate that LEAP can generate more transferable test cases and
significantly enhance the robustness of DNN-based systems.Comment: Accepted at ASE 202
Exploring Text Mining and Analytics for Applications in Public Security: An in-depth dive into a systematic literature review
Text mining and related analytics emerge as a technological approach to support human activities in extracting useful knowledge through texts in several formats. From a managerial point of view, it can help organizations in planning and decision-making processes, providing information that was not previously evident through textual materials produced internally or even externally. In this context, within the public/governmental scope, public security agencies are great beneficiaries of the tools associated with text mining, in several aspects, from applications in the criminal area to the collection of people's opinions and sentiments about the actions taken to promote their welfare. This article reports details of a systematic literature review focused on identifying the main areas of text mining application in public security, the most recurrent technological tools, and future research directions. The searches covered four major article bases (Scopus, Web of Science, IEEE Xplore, and ACM Digital Library), selecting 194 materials published between 2014 and the first half of 2021, among journals, conferences, and book chapters. There were several findings concerning the targets of the literature review, as presented in the results of this article
Adaptive neuro-fuzzy inference system and particle swarm optimization: A modern paradigm for securing VANETs
Vehicular Adhoc Networks (VANET) facilitate inter-vehicle communication using their dedicated connection infrastructure. Numerous advantages and applications exist associated with this technology, with road safety particularly noteworthy. Ensuring the transportation and security of information is crucial in the majority of networks, similar to other contexts. The security of VANETs poses a significant challenge due to the presence of various types of attacks that threaten the communication infrastructure of mobile vehicles. This research paper introduces a new security scheme known as the Soft Computing-based Secure Protocol for VANET Environment (SC-SPVE) method, which aims to tackle security challenges. The SC-SPVE technique integrates an adaptive neuro-fuzzy inference system and particle swarm optimisation to identify different attacks in VANETs efficiently. The proposed SC-SPVE method yielded the following average outcomes: a throughput of 148.71 kilobits per second, a delay of 23.60 ms, a packet delivery ratio of 95.62%, a precision of 92.80%, an accuracy of 99.55%, a sensitivity of 98.25%, a specificity of 99.65%, and a detection time of 6.76 ms using the Network Simulator NS2
Sensing Collectives: Aesthetic and Political Practices Intertwined
Are aesthetics and politics really two different things? The book takes a new look at how they intertwine, by turning from theory to practice. Case studies trace how sensory experiences are created and how collective interests are shaped. They investigate how aesthetics and politics are entangled, both in building and disrupting collective orders, in governance and innovation. This ranges from populist rallies and artistic activism over alternative lifestyles and consumer culture to corporate PR and governmental policies. Authors are academics and artists. The result is a new mapping of the intermingling and co-constitution of aesthetics and politics in engagements with collective orders
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