170 research outputs found
Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination
The hallucination issue is recognized as a fundamental deficiency of large
language models (LLMs), especially when applied to fields such as finance,
education, and law. Despite the growing concerns, there has been a lack of
empirical investigation. In this paper, we provide an empirical examination of
LLMs' hallucination behaviors in financial tasks. First, we empirically
investigate LLM model's ability of explaining financial concepts and
terminologies. Second, we assess LLM models' capacity of querying historical
stock prices. Third, to alleviate the hallucination issue, we evaluate the
efficacy of four practical methods, including few-shot learning, Decoding by
Contrasting Layers (DoLa), the Retrieval Augmentation Generation (RAG) method
and the prompt-based tool learning method for a function to generate a query
command. Finally, our major finding is that off-the-shelf LLMs experience
serious hallucination behaviors in financial tasks. Therefore, there is an
urgent need to call for research efforts in mitigating LLMs' hallucination
Artificial intelligence in construction asset management: a review of present status, challenges and future opportunities
The built environment is responsible for roughly 40% of global greenhouse emissions, making the sector a crucial factor for climate change and sustainability. Meanwhile, other sectors (like manufacturing) adopted Artificial Intelligence (AI) to solve complex, non-linear problems to reduce waste, inefficiency, and pollution. Therefore, many research efforts in the Architecture, Engineering, and Construction community have recently tried introducing AI into building asset management (AM) processes. Since AM encompasses a broad set of disciplines, an overview of several AI applications, current research gaps, and trends is needed. In this context, this study conducted the first state-of-the-art research on AI for building asset management. A total of 578 papers were analyzed with bibliometric tools to identify prominent institutions, topics, and journals. The quantitative analysis helped determine the most researched areas of AM and which AI techniques are applied. The areas were furtherly investigated by reading in-depth the 83 most relevant studies selected by screening the articles’ abstracts identified in the bibliometric analysis. The results reveal many applications for Energy Management, Condition assessment, Risk management, and Project management areas. Finally, the literature review identified three main trends that can be a reference point for future studies made by practitioners or researchers: Digital Twin, Generative Adversarial Networks (with synthetic images) for data augmentation, and Deep Reinforcement Learning
A Survey on Interpretable Cross-modal Reasoning
In recent years, cross-modal reasoning (CMR), the process of understanding
and reasoning across different modalities, has emerged as a pivotal area with
applications spanning from multimedia analysis to healthcare diagnostics. As
the deployment of AI systems becomes more ubiquitous, the demand for
transparency and comprehensibility in these systems' decision-making processes
has intensified. This survey delves into the realm of interpretable cross-modal
reasoning (I-CMR), where the objective is not only to achieve high predictive
performance but also to provide human-understandable explanations for the
results. This survey presents a comprehensive overview of the typical methods
with a three-level taxonomy for I-CMR. Furthermore, this survey reviews the
existing CMR datasets with annotations for explanations. Finally, this survey
summarizes the challenges for I-CMR and discusses potential future directions.
In conclusion, this survey aims to catalyze the progress of this emerging
research area by providing researchers with a panoramic and comprehensive
perspective, illuminating the state of the art and discerning the
opportunities
Text Classification: A Review, Empirical, and Experimental Evaluation
The explosive and widespread growth of data necessitates the use of text
classification to extract crucial information from vast amounts of data.
Consequently, there has been a surge of research in both classical and deep
learning text classification methods. Despite the numerous methods proposed in
the literature, there is still a pressing need for a comprehensive and
up-to-date survey. Existing survey papers categorize algorithms for text
classification into broad classes, which can lead to the misclassification of
unrelated algorithms and incorrect assessments of their qualities and behaviors
using the same metrics. To address these limitations, our paper introduces a
novel methodological taxonomy that classifies algorithms hierarchically into
fine-grained classes and specific techniques. The taxonomy includes methodology
categories, methodology techniques, and methodology sub-techniques. Our study
is the first survey to utilize this methodological taxonomy for classifying
algorithms for text classification. Furthermore, our study also conducts
empirical evaluation and experimental comparisons and rankings of different
algorithms that employ the same specific sub-technique, different
sub-techniques within the same technique, different techniques within the same
category, and categorie
A Review of Deep Learning Techniques for Speech Processing
The field of speech processing has undergone a transformative shift with the
advent of deep learning. The use of multiple processing layers has enabled the
creation of models capable of extracting intricate features from speech data.
This development has paved the way for unparalleled advancements in speech
recognition, text-to-speech synthesis, automatic speech recognition, and
emotion recognition, propelling the performance of these tasks to unprecedented
heights. The power of deep learning techniques has opened up new avenues for
research and innovation in the field of speech processing, with far-reaching
implications for a range of industries and applications. This review paper
provides a comprehensive overview of the key deep learning models and their
applications in speech-processing tasks. We begin by tracing the evolution of
speech processing research, from early approaches, such as MFCC and HMM, to
more recent advances in deep learning architectures, such as CNNs, RNNs,
transformers, conformers, and diffusion models. We categorize the approaches
and compare their strengths and weaknesses for solving speech-processing tasks.
Furthermore, we extensively cover various speech-processing tasks, datasets,
and benchmarks used in the literature and describe how different deep-learning
networks have been utilized to tackle these tasks. Additionally, we discuss the
challenges and future directions of deep learning in speech processing,
including the need for more parameter-efficient, interpretable models and the
potential of deep learning for multimodal speech processing. By examining the
field's evolution, comparing and contrasting different approaches, and
highlighting future directions and challenges, we hope to inspire further
research in this exciting and rapidly advancing field
Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents
Large language models (LLMs) have dramatically enhanced the field of language
intelligence, as demonstrably evidenced by their formidable empirical
performance across a spectrum of complex reasoning tasks. Additionally,
theoretical proofs have illuminated their emergent reasoning capabilities,
providing a compelling showcase of their advanced cognitive abilities in
linguistic contexts. Critical to their remarkable efficacy in handling complex
reasoning tasks, LLMs leverage the intriguing chain-of-thought (CoT) reasoning
techniques, obliging them to formulate intermediate steps en route to deriving
an answer. The CoT reasoning approach has not only exhibited proficiency in
amplifying reasoning performance but also in enhancing interpretability,
controllability, and flexibility. In light of these merits, recent research
endeavors have extended CoT reasoning methodologies to nurture the development
of autonomous language agents, which adeptly adhere to language instructions
and execute actions within varied environments. This survey paper orchestrates
a thorough discourse, penetrating vital research dimensions, encompassing: (i)
the foundational mechanics of CoT techniques, with a focus on elucidating the
circumstances and justification behind its efficacy; (ii) the paradigm shift in
CoT; and (iii) the burgeoning of language agents fortified by CoT approaches.
Prospective research avenues envelop explorations into generalization,
efficiency, customization, scaling, and safety. This paper caters to a wide
audience, including beginners seeking comprehensive knowledge of CoT reasoning
and language agents, as well as experienced researchers interested in
foundational mechanics and engaging in cutting-edge discussions on these
topics. A repository for the related papers is available at
https://github.com/Zoeyyao27/CoT-Igniting-Agent
Bridging Cross-Modal Alignment for OCR-Free Content Retrieval in Scanned Historical Documents
In this work, we address the limitations of current approaches to document retrieval by incorporating vision-based topic extraction. While previous methods have primarily focused on visual elements or relied on optical character recognition (OCR) for text extraction, we propose a paradigm shift by directly incorporating vision into the topic space. We demonstrate that recognizing all visual elements within a document is unnecessary for identifying its underlying topic. Visual cues such as icons, writing style, and font can serve as sufficient indicators. By leveraging ranking loss functions and convolutional neural networks (CNNs), we learn complex topological representations that mimic the behavior of text representations. Our approach aims to eliminate the need for OCR and its associated challenges, including efficiency, performance, data-hunger, and expensive annotation. Furthermore, we highlight the significance of incorporating vision in historical documentation, where visually antiquated documents contain valuable cues. Our research contributes to the understanding of topic extraction from a vision perspective and offers insights into annotation-cheap document retrieval system
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The promise of nanofluids: A bibliometric journey through advanced heat transfer fluids in heat exchanger tubes
© 2024 The Author(s). Published by Elsevier B.V. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Thermal management is a critical challenge in advanced systems such as electric vehicles (EVs), electronic components, and photoelectric modules. Thermal alleviation is carried out through the cooling systems in which the coolant and the heat exchangers are the key components. The study examines recent literature on nanofluids and heat exchanger tubes along with state-of-the-art concepts being tested for heat transfer intensification. The performance of nanofluids in several common heat transfer tubes’ geometries/configurations and the effectiveness of novel heat transfer augmentation mechanisms are presented. Promising results have been reported, showing improved heat transfer parameters with the use of nanofluids and intensification mechanisms like turbulators, fins, grooves, and variations in temperature and flow velocity. These mechanisms enhance dispersion stability, achieve a more uniform temperature distribution, and reduce the boundary layer thickness, resulting in lower tube wall temperatures. Moreover, introducing flow pulsations and magnetic effects further enhances particle mobility and heat exchange. However, there are limitations, such as increased frictional losses and pressure drop due to magnetic effects. The combination of nanofluids, novel heat exchanger tube geometries, and turbulators holds great promise for highly efficient cooling systems in the future. The study also presents a bibliometric analysis that offers valuable insights into the impact and visibility of research in the integration of nanofluids into heat transfer systems. These insights aid in identifying emerging trends and advancing the field towards more efficient and compact systems, paving the way for future advancements.Peer reviewe
Blockchain and Internet of Things in smart cities and drug supply management: Open issues, opportunities, and future directions
Blockchain-based drug supply management (DSM) requires powerful security and privacy procedures for high-level authentication, interoperability, and medical record sharing. Researchers have shown a surprising interest in Internet of Things (IoT)-based smart cities in recent years. By providing a variety of intelligent applications, such as intelligent transportation, industry 4.0, and smart financing, smart cities (SC) can improve the quality of life for their residents. Blockchain technology (BCT) can allow SC to offer a higher standard of security by keeping track of transactions in an immutable, secure, decentralized, and transparent distributed ledger. The goal of this study is to systematically explore the current state of research surrounding cutting-edge technologies, particularly the deployment of BCT and the IoT in DSM and SC. In this study, the defined keywords “blockchain”, “IoT”, drug supply management”, “healthcare”, and “smart cities” as well as their variations were used to conduct a systematic search of all relevant research articles that were collected from several databases such as Science Direct, JStor, Taylor & Francis, Sage, Emerald insight, IEEE, INFORMS, MDPI, ACM, Web of Science, and Google Scholar. The final collection of papers on the use of BCT and IoT in DSM and SC is organized into three categories. The first category contains articles about the development and design of DSM and SC applications that incorporate BCT and IoT, such as new architecture, system designs, frameworks, models, and algorithms. Studies that investigated the use of BCT and IoT in the DSM and SC make up the second category of research. The third category is comprised of review articles regarding the incorporation of BCT and IoT into DSM and SC-based applications. Furthermore, this paper identifies various motives for using BCT and IoT in DSM and SC, as well as open problems and makes recommendations. The current study contributes to the existing body of knowledge by offering a complete review of potential alternatives and finding areas where further research is needed. As a consequence of this, researchers are presented with intriguing potential to further create decentralized DSM and SC apps as a result of a comprehensive discussion of the relevance of BCT and its implementation.© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed
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