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Impact of Artificial Intelligence on Decision-Making in Information systems 5

Impact of Artificial Intelligence on Decision-Making in Info...

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5th International Conference on Advanced Research in computing, ICARC 2025

作者： Rajasinghe, Rwmr Dharani Faculty of Computing Nsbm Green University Dept. of Software Engineering & Information Systems Homagama Sri Lanka

ISBN: (纸本)9798331530983

The study examines how artificial intelligence (AI) affects information systems (IS) decision-making, emphasizing how AI improves precision, speed, and flexibility in a range of industries. AI presents potential and problems, including ethical, legal, and societal ramifications, as it revolutionizes organizational decision-making. In order to demonstrate AI's function in intricate IS systems, this paper examines a variety of AI strategies used in decision-making, assesses their efficacy using accepted metrics, and examines real-world case studies. Key findings address issues including data bias, lack of transparency, and job displacement while highlighting the advantages of AI in processing massive information and producing useful insights. The recommendations highlight the necessity of worker reskilling, sector-specific adaptability, explainable AI (XAI) models, and regulatory frameworks. In order to facilitate sustainable AI integration in IS for improved organizational and social results, this research emphasizes the significance of striking a balance between AI-driven innovation and ethical issues. © 2025 IEEE.

关键词： Risk perception

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COCOTEROS: A Spanish Corpus with Contextual Knowledge for Natural Language Generation 40

COCOTEROS: A Spanish Corpus with Contextual Knowledge for Na...

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40th Annual Conference of the Spanish Association for Natural Language Processing, SEPLN-P 2024

作者： Maestre, María Miró Martínez-Murillo, Iván Lloret, Elena Moreda, Paloma Cueto, Armando Suárez Dept. of Software and Computing Systems University of Alicante Apdo. de Correos 99 AlicanteE-03080 Spain

Contextual information is one of the key elements when automatically generating language with a more semantic-pragmatic perspective. To contribute to the study of this linguistic aspect, we present COCOTEROS, a COrpus of COnTextual TExt geneRatiOn in Spanish. COCOTEROS is available at https://***/datasets/gplsi/cocoteros. The corpus is composed of sentences and automatically generated context pairs. For creating it, a semi-automatic weakly supervised methodology is implemented. Taking as a reference the Spanish section of the Tatoeba dataset, we filtered the sentences according to our research purpose. Then, we determined several linguistic parameters that the generated contexts need to fulfil considering their reference sentence. Finally, contexts were automatically generated using prompt engineering with Google’s large language model Bard. Furthermore, we performed two types of evaluation to check both the linguistic quality and the presence of gender bias in the corpus: the former by manually measuring the magnitude estimation metric and the latter thanks to the GenBit automatic metric. The results show that COCOTEROS is an appropriate language resource to approach Natural Language Generation tasks from a semantic-pragmatic perspective for Spanish. For instance, the NLG task of concept-to-text generation could benefit from contextual information by generating sentences according to the information provided in the context and a set of given concepts. Additionally, regarding the task of question-answering, the inclusion of linguistic context can enhance the generation of more appropriate answers by serving as a guide on what information to include in the automatically generated answer. © 2024 Copyright for this paper by its authors.

关键词： Semantics

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Analysing Linguistic Markers on Fake News to Enhance the Explainability of Deception Detection systems 7

Analysing Linguistic Markers on Fake News to Enhance the Exp...

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7th Doctoral Symposium on Natural Language Processing, NLP-DS 2024

作者： Pérez-Montero, Alba Dept. of Software and Computing Systems University of Alicante Apdo. de Correos 99 AlicanteE-03080 Spain

The massive use of social media has increased the ease of dissemination of information. Unfortunately, every type of information can be massively disseminated (even deceptive information deliberately created to mislead). In this research we introduce a deep analysis of linguistic cues (i.e., adjectives, pronouns, complex syntax, emotion words, etc.) that can lead to distinguish which texts can be deceptive. The research focuses on extracting a combination of features: content-based, context-based, readability, virality and information richness. The objective is to test if current NLP tools for deception detection can extract in a satisfactory way these features and to examine the grade of explainability that these systems offer. The methodology starts from a multidisciplinary point of view, focusing in elaborate an integrative research. To pursue the objective of this research, we also combine both analytical and empirical methodologies. The expected impact is to enhance the ability to distinguish misinformation by improving the accuracy and transparency of deception detection systems for everyone. © 2024 Copyright for this paper by its authors.

关键词： Fake detection

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An Approach to a Cost-Effective and Controllable Text Generation Architecture 7

An Approach to a Cost-Effective and Controllable Text Genera...

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7th Doctoral Symposium on Natural Language Processing, NLP-DS 2024

作者： Martínez-Murillo, Iván Dept. of Software and Computing Systems University of Alicante Apdo. de Correos 99 AlicanteE-03080 Spain

Large Language Models (LLMs), which take advantage of the Transformers architecture, have obtained remarkable outcomes in the Generative Artificial Intelligence (AI) field. Specifically, these models have boosted the Natural Language Generation (NLG) field to new dimensions. Nonetheless, state-of-the-art NLG models also entail some challenges. Firstly, these models can generate text which may contain biased or hallucinated information, that can be used in an unethical way. Secondly, these models sometimes fail at generating commonsense knowledge, a fundamental factor in the human language. Thirdly, the expense of training LLMs is excessively high. Finally, most of the NLG research proposing more efficient architectures is focused on the English language. Thus, other languages such as Spanish still lack resources and models to address the NLG. Given the challenges mentioned above, the main objective of this paper is to provide a detailed research line that aims to propose an efficient and effective text generation architecture that could generate high-quality text in Spanish. © 2024 Copyright for this paper by its authors.

关键词： Personnel training

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Progress in the Modeling of Violent Messages in Spanish Social Networks 6

Progress in the Modeling of Violent Messages in Spanish Soci...

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6th Doctoral Symposium on Natural Language Processing from the Proyecto ILENIA, PLN-DS 2023

作者： Botella-Gil, Beatriz Dept. of Software and Computing Systems University of Alicante Apdo. de Correos 99 AlicanteE-03080 Spain

Society advances loaded with new and highly accessible knowledge, that is published in the virtual world. It is a reality that ICTs have brought many benefits for our lives but we also see how year after year the use of violence in platforms increase. The application of PLN is fundamental in this type of research given the large volume of existing data, which facilitates a breakthrough in the investigation of the detection of this type of messageThe doctoral work focuses on the detection of violent messages in the social network twitter from different perspectives. © 2023 Copyright for this paper by its authors.

关键词： Modeling languages

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Commonsense Knowledge and Controllable Techniques for an Effective and Efficient Approach to Text Generation 6

Commonsense Knowledge and Controllable Techniques for an Eff...

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6th Doctoral Symposium on Natural Language Processing from the Proyecto ILENIA, PLN-DS 2023

作者： Martínez-Murillo, Iván Dept. of Software and Computing Systems University of Alicante Apdo. de Correos 99 AlicanteE-03080 Spain

The Natural Language Generation (NLG) field has advanced at a breakneck speed, favoured by the development of Large Language Models (LLMs). Notwithstanding, these models also have some drawbacks. On the one hand, these models can introduce some risks such as hallucination or bias which can be used in an unethical way to potentially generate dis- and mis-information. On the other hand, the expense of time and cost of training these models is too high. In account of this, the purpose of this paper is to propose a new research line for my PhD thesis. During the research, I will propose an efficient architecture, that could generate quality text in a controllable way, while integrating external commonsense knowledge. The objective is that this proposed architecture could achieve similar performance to state-of-the-art models while being more efficient. © 2023 Copyright for this paper by its authors.

关键词： Natural language processing systems

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Requirements Modeling and Elicitation for Explainable Artificial Intelligence Based on i 17

Requirements Modeling and Elicitation for Explainable Artifi...

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17th International iStar Workshop, iStar 2024

作者： Navarro, Álvaro Lavalle, Ana Maté, Alejandro Trujillo, Juan Lucentia Research Group Dept. of Software and Computing Systems University of Alicante Carretera San Vicente del Raspeig s/n San Vicente del Raspeig03690 Spain

EXplainable Artificial Intelligence (XAI) has risen as one of the prominent and complex topics to be addressed to foster the adoption of Artificial Intelligence (AI) solutions. With independence of the domain, explainability is key in gaining user trust and understanding the rationale behind the AI systems' outputs. Multiple XAI techniques exist, each with its own level of confidence and technical level required to understand the results among other characteristics. While an adequate modeling of XAI requirements would aid in selecting the correct XAI techniques for the relevant stakeholders, it has been mostly overlooked so far in the XAI field. We claim that a more formal representation of user requirements would help developers with the design of their systems. Therefore, we provide: a requirements language that aids in XAI requirements elicitation and modeling, covering the key XAI concepts and elements. Our requirements language is based on the i* framework, a technique that can facilitate the communication across diverse stakeholders. The benefits of our proposal include ensuring the fulfillment of multiple stakeholders' needs as well as the generation of combined explanations. Furthermore, our proposal also makes it easier for non-expert users to select the XAI techniques applicable to the context. © 2024 Copyright for this paper by its authors.

关键词： Requirements engineering

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*** Enhancing the Modernization Public Sector Organizations by Deploying Natural Language Processing to Make Their Digital Content CLEARER to Those with Cognitive Disabilities

*** Enhancing the Modernization Public Sector Organizations ...

引用

2023 Annual Conference of the Spanish Association for Natural Language Processing: Projects and System Demonstrations, SEPLN-PD 2023

作者： Moreda, Paloma Botella, Beatriz Espinosa-Zaragoza, Isabel Lloret, Elena Martin, Tania J. Martínez, Patricio Suárez, Armando Palomar, Manuel GPLSI Research Group Dept. of Software and Computing Systems Universidad de Alicante CENID Centro de Inteligencia Digital San Vicente Alicante03690 Spain

The *** project (TED2021-130707B-I00) researches how natural language processing technology can support the authoring of accessible content in Spanish for people with cognitive disabilities. Our main objective is to research, implement, deploy, evaluate, and ultimately provide robust technologies for natural language processing to support the authoring of accessible Spanish content for public sector organisations (at local, regional and national level) that is intelligible to people with cognitive disability, thereby widening their inclusion and empowerment in Europe. It is expected to impact positively the quality of life of people with cognitive disabilities, facilitating their access to educational, vocational, cultural, and social opportunities in public sector organisations © 2023 Copyright for this paper by its authors.

关键词： Natural language processing systems

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Conscious Natural Text Generation

Conscious Natural Text Generation

引用

2023 Annual Conference of the Spanish Association for Natural Language Processing: Projects and System Demonstrations, SEPLN-PD 2023

作者： LLoret, Elena Suárez, Armando Ferrández, Antonio Martin, Tania Martínez-Murillo, Iván Maestre, María Miró Moreda, Paloma Navarro-Colorado, Borja Palomar, Manuel GPLSI Research Group Dept. of Software and Computing Systems University of Alicante Ctra. San Vicente s/n 03690 San Vicente Alicante Spain

The "Conscious Natural Text Generation"project (CORTEX, grant ref. PID2021-123956OB-I00) investigates how Natural Language Generation (NLG) architectures can be improved so that they can exploit external and commonsense knowledge, and integrate it in a controllable manner (i.e., determining what knowledge to include and how to include it) when generating new text. Our main scientific objective is, therefore, to investigate and propose novel NLG architectures that integrate different types of knowledge to automatically produce reliable, truthful and quality texts, whose information complies with the principles of objectivity and plurality, thereby minimizing bias or manipulated content. The new generation of NLG systems obtained from CORTEX will significantly improve the semantic quality of the generated text, preventing, among other phenomena, the inclusion of invented facts that do not match reality (i.e., hallucination). © 2023 Copyright for this paper by its authors.

关键词： Semantics

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An Image Analysis-Based Automated Method using Deep Learning for Grain Counting 17

An Image Analysis-Based Automated Method using Deep Learning...

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17th IEEE International Conference on Industrial and Information systems, ICIIS 2023

作者： Ajikaran, Ramesh Hewarathna, Ashen Iranga Palanisamy, Vigneshwaran Joseph, Charles Thuseethan, Selvarajah Sabaragamuwa University of Sri Lanka Dept. of Computing and Infromation Systems Sri Lanka Sabaragamuwa University of Sri Lanka Dept. of Software Engineering Sri Lanka Fau Dept. of Computer Science Germany School of Information Technology Deakin University Australia

ISBN: (纸本)9798350323634

In the process of quality analysis of grains, number counting is one of the key steps for agricultural production. Traditional manual grain counting methods are time-consuming and subject to human error, and automated methods have the potential to improve accuracy and save time. In this study, we aimed to develop an image analysis-based method to automatically quantify the number of grains in a quicker manner. The 576 grain images were collected manually, and labelImg tagging tool used to annotate to generate a text file with their respective positions by drawing bounding boxes manually. The datasets consist were separated into three groups: training, validation, and test. For object detection, the YOLOv5, YOLOv4, and YOLOv3 algorithms represent cutting-edge deep learning frameworks. They replace the tedious and error-prone manual counting process by precisely identifying and counting grains in images obtained from agricultural fields. This technique helps to increase grain counting's precision and effectiveness. We believe this method will be extremely beneficial in guiding the development of high throughput systems for counting the number of grains in other crops as it performs well with a wide range of backgrounds, picture sizes, grain sizes, as well as various quantities of grain crowding. When compared to the other two approaches, YOLOv4 performed well in terms of accuracy, speed, and robustness (97.65%), demonstrating that the suggested strategy is competitive with other cutting-edge deep networkst. © 2023 IEEE.

关键词： Grain (agricultural product)

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