Artificial Intelligence (AI) with its highly cognitive features has been increasingly adopted by FinTech firms. With increasing market and economic fluctuations during the unprecedented times of Covid-19, AI offers hi...
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ISBN:
(数字)9783031153426
ISBN:
(纸本)9783031153426;9783031153419
Artificial Intelligence (AI) with its highly cognitive features has been increasingly adopted by FinTech firms. With increasing market and economic fluctuations during the unprecedented times of Covid-19, AI offers high computational and easily accessible personalized financial solutions. During the Covid-19 pandemic, customers showed keen interest in AI-assisted financial services. AI in the FinTech industry is now gaining a lot of traction in terms of customer engagement and business prosperity. But with the benefits of availability of consumer data and automation for offering customized and personalized services, the black box effect of AI has a potential dark side affecting both consumers and employees. Lack of human intervention has questioned the accountability and transparency of these financial wealth management solutions that are susceptible to security threats and biased decisions. The purpose of this empirical study is to better understand the adoption of AI in the disruption of the FinTech ecosystem. A mixed approach of focus group and interviews for the purpose of data collection, and qualitative content analysis using naturallanguageprocessing (NLP) for data analysis have been used to conduct this exploratory study. The findings of the study help to develop an understanding of the social, ethical, and economic consequences of strategic AI adoption for both consumers and businesses.
The characters in the text image or video is having more information for recovery and indexing applications. The recognition of characters from the video text images is a difficult task due to the complex backgrounds,...
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Multi-label text classification is a challenging task because it requires capturing label dependencies. It becomes even more challenging when class distribution is long-tailed. Re-sampling and re-weighting are common ...
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Objectives/Scope: Wood aims to introduce a novel approach for rapidly and effectively benchmarking maintenance and reliability in the upstream and midstream oil and gas sectors. The proposed method will provide valuab...
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ISBN:
(纸本)9781959025078
Objectives/Scope: Wood aims to introduce a novel approach for rapidly and effectively benchmarking maintenance and reliability in the upstream and midstream oil and gas sectors. The proposed method will provide valuable insights at both macro (overall build) and micro (individual maintenance items) levels, with the goal of improving asset performance management and setting new industry standards. This approach promotes collaboration among industry peers and sharing of best practices, driving overall industry progress. methods, Procedures, Process: Our approach uses advanced naturallanguageprocessing (NLP) algorithms to match short text descriptions from multiple maintenance builds, utilizing techniques ranging from Jaccard similarity to state-of-the-art language model embeddings. In-house developed denoising algorithms remove unnecessary information to clarify the text, addressing potential issues of data quality and consistency. This process allows for the identification of similar tasks, which then serve as key columns to match the entire build, facilitating effective benchmarking and enabling more accurate comparisons. Results, Observations, Conclusions: The proposed method enables swift benchmarking among maintenance builds and allows for frequent comparison at both micro and macro levels. This overcomes the limitations of traditional manual matching processes, which are time-consuming, costly, and error prone. By determining the optimal frequency of maintenance intervention to minimize cost and carbon footprint while maximizing reliability, our approach highlights the power of data- driven decisions to enhance asset reliability and streamline maintenance processes. The method fosters a culture of collaboration among industry peers, promoting the sharing of best practices and driving overall industry progress. Moreover, it has been shown to provide valuable insights that can be leveraged for continuous improvement and to address common challenges in asset perf
Named Entity Recognition (NER) is widely used for naturallanguageprocessing (NLP) but most of the current work focus on analyzing English-based text. This paper compares different NER models in extracting key conten...
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In recent years, the breakthrough of Large language Models (LLMs) offers new ideas for achieving universal methods on graph data. The common practice of converting graphs into naturallanguage for LLMs, which refers t...
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ISBN:
(数字)9798350392098
ISBN:
(纸本)9798350392104
In recent years, the breakthrough of Large language Models (LLMs) offers new ideas for achieving universal methods on graph data. The common practice of converting graphs into naturallanguage for LLMs, which refers to graph flattening, exhibits good generalizability and interpretability. However, the poor organization of the textual format results in poor performance in long-distance scenario understanding. Inspired by human cognitive reasoning habits, we propose a novel method for graph flattening to fit LLMs, termed as End-to-End DAG-Path prompting (EEDP). Experiments on real-world datasets show that EEDP enhances the reasoning performance of LLMs in long-distance scenarios while maintaining excellent performance in short-distance scenarios, demonstrating good robustness in the face of distance variations.
In the factory's information processing system, there is a need to use voice input instead of buttons as input, which requires understanding the user's voice input. Unlike general naturallanguage understandin...
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On the brink of the one-year anniversary since the public release of ChatGPT, scholarly research has directed their interest toward detection methodologies for machine-generated text. Different models have been propos...
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In recent years, some studies showed that deep neural networks (DNNs) are vulnerable to being attacked by small perturbated examples. To satisfy the lexical, grammatical, and semantic constrain, some works proposed us...
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ISBN:
(纸本)9781450392686
In recent years, some studies showed that deep neural networks (DNNs) are vulnerable to being attacked by small perturbated examples. To satisfy the lexical, grammatical, and semantic constrain, some works proposed using black-box population-based optimization algorithms to attack neural networks in naturallanguageprocessing. However, they are inefficient enough because they do not consider the characteristics of the text itself. Also, they are slow to close to the decision boundary. In this paper, we propose a more efficient attention based genetic algorithm adversarial attack method, called AGA. We use attention mechanism to pay more attention to the important tokens and utilize the multi-membered strategy to accelerate the search procedure. The result shows that our attack achieves a higher success rate with less than 136% of the number of queries than the existing methods.
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