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Full-Text Articles in Entire DC Network
Developing Policies For Digital Twin Data Quality And Security Controls, Ahmad Abdelbaset Hassan
Developing Policies For Digital Twin Data Quality And Security Controls, Ahmad Abdelbaset Hassan
Theses
This thesis is concerned with the data quality and security of the digital twin and how it is going to impact its adoption, trustworthiness, and potential for real-world applications. By addressing the potential vulnerabilities and ensuring the integrity of data, this research aims to contribute to the development of robust and trustworthy digital twin policies that to complement the existing international standards across different domains. Moreover, it underscores the important need to establish robust policies to ensure the successful and secure deployment of digital twins across industries. Previous research, while valuable, may not have fully addressed the critical interplay between …
Experience As Source For Anticipation And Planning : Experiential Policy Learning For Target-Driven Recommendation Dialogues, Quang Huy Dao, Yang Deng, Khanh-Huyen Bui, Dung D. Le, Lizi Liao
Experience As Source For Anticipation And Planning : Experiential Policy Learning For Target-Driven Recommendation Dialogues, Quang Huy Dao, Yang Deng, Khanh-Huyen Bui, Dung D. Le, Lizi Liao
Research Collection School Of Computing and Information Systems
Target-driven recommendation dialogues present unique challenges in dialogue management due to the necessity of anticipating user interactions for successful conversations. Current methods face significant limitations: (I) inadequate capabilities for conversation anticipation, (II) computational inefficiencies due to costly simulations, and (III) neglect of valuable past dialogue experiences. To address these limitations, we propose a new framework, Experiential Policy Learning (EPL), for enhancing such dialogues. EPL embodies the principle of Learning From Experience, facilitating anticipation with an experiential scoring function that estimates dialogue state potential using similar past interactions stored in long-term memory. To demonstrate its flexibility, we introduce Tree-structured EPL (T-EPL) …
Context-Aware Adapter Tuning For Few-Shot Relation Learning In Knowledge Graphs, Ran Liu, Zhongzhou Liu, Xiaoli Li, Yuan Fang
Context-Aware Adapter Tuning For Few-Shot Relation Learning In Knowledge Graphs, Ran Liu, Zhongzhou Liu, Xiaoli Li, Yuan Fang
Research Collection School Of Computing and Information Systems
Knowledge graphs (KGs) are instrumental in various real-world applications, yet they often suffer from incompleteness due to missing relations. To predict instances for novel relations with limited training examples, few-shot relation learning approaches have emerged, utilizing techniques such as meta-learning. However, the assumption is that novel relations in meta-testing and base relations in meta-training are independently and identically distributed, which may not hold in practice. To address the limitation, we propose RelAdapter, a context-aware adapter for few-shot relation learning in KGs designed to enhance the adaptation process in meta-learning. First, RelAdapter is equipped with a lightweight adapter module that facilitates …
Dc-Instruct : An Effective Framework For Generative Multi-Intent Spoken Language Understanding, Bowen Xing, Lizi Liao, Minlie Huang
Dc-Instruct : An Effective Framework For Generative Multi-Intent Spoken Language Understanding, Bowen Xing, Lizi Liao, Minlie Huang
Research Collection School Of Computing and Information Systems
In the realm of multi-intent spoken language understanding, recent advancements have leveraged the potential of prompt learning frameworks. However, critical gaps exist in these frameworks: the lack of explicit modeling of dual-task dependencies and the oversight of task-specific semantic differences among utterances. To address these shortcomings, we propose DC-Instruct, a novel generative framework based on Dual-task Inter-dependent Instructions (DII) and Supervised Contrastive Instructions (SCI). Specifically, DII guides large language models (LLMs) to generate labels for one task based on the other task’s labels, thereby explicitly capturing dual-task inter-dependencies. Moreover, SCI leverages utterance semantics differences by guiding LLMs to determine whether …
Multimodal Misinformation Detection By Learning From Synthetic Data With Multimodal Llms, Fengzhu Zeng, Wenqian Li, Wei Gao, Yan Pang
Multimodal Misinformation Detection By Learning From Synthetic Data With Multimodal Llms, Fengzhu Zeng, Wenqian Li, Wei Gao, Yan Pang
Research Collection School Of Computing and Information Systems
Detecting multimodal misinformation, especially in the form of image-text pairs, is crucial. Obtaining large-scale, high-quality real-world fact-checking datasets for training detectors is costly, leading researchers to use synthetic datasets generated by AI technologies. However, the generalizability of detectors trained on synthetic data to real-world scenarios remains unclear due to the distribution gap. To address this, we propose learning from synthetic data for detecting real-world multimodal misinformation through two model-agnostic data selection methods that match synthetic and real-world data distributions. Experiments show that our method enhances the performance of a small MLLM (13B) on real-world fact-checking datasets, enabling it to even …
A Comprehensive Survey On Relation Extraction: Recent Advances And New Frontiers, Xiaoyan Zhao, Yang Deng, Min Yang, Lingzhi Wang, Rui Zhang, Hong Cheng, Wai Lam, Ying Shen, Ruifeng Xu
A Comprehensive Survey On Relation Extraction: Recent Advances And New Frontiers, Xiaoyan Zhao, Yang Deng, Min Yang, Lingzhi Wang, Rui Zhang, Hong Cheng, Wai Lam, Ying Shen, Ruifeng Xu
Research Collection School Of Computing and Information Systems
Relation extraction (RE) involves identifying the relations between entities from underlying content. RE serves as the foundation for many natural language processing (NLP) and information retrieval applications, such as knowledge graph completion and question answering. In recent years, deep neural networks have dominated the field of RE and made noticeable progress. Subsequently, the large pre-trained language models (PLMs) have taken the state-of-the-art RE to a new level. This survey provides a comprehensive review of existing deep learning techniques for RE. First, we introduce RE resources, including datasets and evaluation metrics. Second, we propose a new taxonomy to categorize existing works …
Learning Peer Support Interactions Via Bi-Lstm Graph Neural Networks For Suicide Risk Prediction, Harikrishna Marampelly
Learning Peer Support Interactions Via Bi-Lstm Graph Neural Networks For Suicide Risk Prediction, Harikrishna Marampelly
USF Tampa Graduate Theses and Dissertations
Suicide prevention through early detection using social media data has been widely studied. However, the critical role of peer support interactions among individuals with similar mental disorders has not been deeply investigated or explored. In this study, we explore peer interactions in online communities for individuals with bipolar disorder and leverage this information to predict suicide risk levels. We propose a model that uses contextualized posts and comments along with their sentiment features. By embedding these features into a peer support network, our model captures peer interactions and predicts suicide risk levels using the bidirectional LSTM Graph Neural Networks (Bi-LSTM …
Leveraging Large Language Models For Enhancing Literature-Based Discovery, Ikbal Taleb, Alramzana Nujum Navaz, Mohamed Adel Serhani
Leveraging Large Language Models For Enhancing Literature-Based Discovery, Ikbal Taleb, Alramzana Nujum Navaz, Mohamed Adel Serhani
All Works
The exponential growth of biomedical literature necessitates advanced methods for Literature-Based Discovery (LBD) to uncover hidden, meaningful relationships and generate novel hypotheses. This research integrates Large Language Models (LLMs), particularly transformer-based models, to enhance LBD processes. Leveraging LLMs’ capabilities in natural language understanding, information extraction, and hypothesis generation, we propose a framework that improves the scalability and precision of traditional LBD methods. Our approach integrates LLMs with semantic enhancement tools, continuous learning, domain-specific fine-tuning, and robust data cleansing processes, enabling automated analysis of vast text and identification of subtle patterns. Empirical validations, including scenarios on the effects of garlic on …
Review Of Current Trends In Information Technology Concerning Phonetic Similarity”, Zaid Rajih Mohammed, Ahmed H. Aliwy
Review Of Current Trends In Information Technology Concerning Phonetic Similarity”, Zaid Rajih Mohammed, Ahmed H. Aliwy
Al-Bahir
With the increasing availability of textual information in various languages via the Internet in homes and companies through Internet and intranet services, there is an urgent need for the technologies and tools necessary to process this information, phonetic representation, and voice interaction. For example voice to voice machine translation need to phonetic mapping and similarity among the languages especially for names and foreign words. This one example of the importance of phonetic mapping and similarity. This article aims to describe, in detail, the recent surge in interest and advancements in phonetic similarity (PS), phonetic representation, and phonetic mapping researches. PS …
Enhancing Cybersecurity Awareness Through Gamification: Design An Interactive Cybersecurity Learning Platform For Multimedia University Students, Adlil Khaliq Abdul Razack, Mohamad Firdaus Mat Saad
Enhancing Cybersecurity Awareness Through Gamification: Design An Interactive Cybersecurity Learning Platform For Multimedia University Students, Adlil Khaliq Abdul Razack, Mohamad Firdaus Mat Saad
Journal of Informatics and Web Engineering
Cybersecurity has emerged as a critical imperative in contemporary digital landscapes, necessitating heightened awareness and proficiency across all demographic segments. Accordingly, this research has been meticulously crafted to delve into the complexities of cybersecurity awareness, with a specific focus on university students. The study embarks on an exhaustive analysis encompassing the evaluation of cybersecurity awareness levels within the targeted groups, the identification of prevailing issues and practices, and an exploration of novel methodologies, notably gamification, to fortify cybersecurity knowledge and skills among diverse user cohorts. Central to this investigation is the efficacy of gamified learning environments tailored expressly for augmenting …
Investigation On Understanding The Numeracy Capacity Of Intellectual Disabled Students Using Enabling Technology Tools: Web Application, Ar And Ui/Ux, Vishvjit Thakar, Romany Thakar, Pratik Vyas
Investigation On Understanding The Numeracy Capacity Of Intellectual Disabled Students Using Enabling Technology Tools: Web Application, Ar And Ui/Ux, Vishvjit Thakar, Romany Thakar, Pratik Vyas
Journal of Informatics and Web Engineering
The population of individuals with intellectual disabilities (ID) is increasing, necessitating assistance with a wide range of daily activities. Acquiring and assessing numeracy and communication skills are critical for this demographic, requiring tools and techniques tailored to their specific needs. Effective educational tools must employ multi-modal and multi-sensory approaches to cater to diverse learning styles and incorporate assistive technological solutions. Despite the availability of numerous tools, there is a need to enhance their utility and effectiveness. This study aims to identify and refine the requirements for an innovative educational tool that employs Two-dimensional (2D) and Augmented Reality (AR) technologies. To …
Editorial: Artificial Intelligence And Cybersecurity In Pervasive Computing, Ji-Jian Chin
Editorial: Artificial Intelligence And Cybersecurity In Pervasive Computing, Ji-Jian Chin
Journal of Informatics and Web Engineering
Pervasive computing, or ubiquitous computing, is rapidly increasing in capacity and capabilities. With the Internet of Things (IoT) becoming an integral part of daily life and the growing availability of edge computing resources, automation guided by data is advancing applications in healthcare, manufacturing, automotive, and other areas. It's natural that pervasive computing will intersect with artificial intelligence (AI) and cybersecurity. AI can improve detection, prediction, and anticipative responses to human needs, while cybersecurity addresses topics like misuse prevention, ethics, policies, and governance. This issue features seven articles on these intersections, including four AI articles exploring natural language processing and computer …
Treatment Recommendation Using Bert Personalization, J Jayapradha, Yukta Kulkarni, Lakshmi Vadhanie G, Palanichamy Naveen, Elham Abdulwahab Anaam
Treatment Recommendation Using Bert Personalization, J Jayapradha, Yukta Kulkarni, Lakshmi Vadhanie G, Palanichamy Naveen, Elham Abdulwahab Anaam
Journal of Informatics and Web Engineering
This research work develops a new framework that combines patient feedback with evidence-based best practices across disease states to improve drug recommendations. It employs BERT as its free-text processing engine to deal with sentiment judgment and classification. The functionality of the system, named `PharmaBERT`, includes acceptance of drug review data as a comprehensive input, drug categorization when dealing with a wide range of treatments and fine-tuning the BERT-based model for gaining positive or negative sentiment towards specific medications. PharmaBERT categorizes various drugs and fine-tunes the BERT structure to perceive lots of possible sentiments for specific medications. Consequently, PharmaBERT brings all …
Intelligent Abstractive Summarization Of Scholarly Publications With Transfer Learning, Farooq Zaman, Munaza Afzal, Pin Shen Teh, Raheem Sarwar, Faisal Kamiran, Naif R. Aljohani, Raheel Nawaz, Muhammad Umair Hassan, Fahad Sabah
Intelligent Abstractive Summarization Of Scholarly Publications With Transfer Learning, Farooq Zaman, Munaza Afzal, Pin Shen Teh, Raheem Sarwar, Faisal Kamiran, Naif R. Aljohani, Raheel Nawaz, Muhammad Umair Hassan, Fahad Sabah
Journal of Informatics and Web Engineering
Intelligent abstractive text summarization of scholarly publications refers to machine-generated summaries that capture the essential ideas of an article while maintaining semantic coherence and grammatical accuracy. As information continues to grow at an overwhelming rate, text summarization has emerged as a critical area of research. In the past, summarization of scientific publications predominantly relied on extractive methods. These approaches involve selecting key sentences or phrases directly from the original document to create a summary or generate a suitable title. Although extractive methods preserve the original wording, they often lack the ability to produce a coherent, concise, and fluent summary, especially …
Intelligrader: A Framework For Automatic Short Answer Grading, Inconsistency Check And Feedback In Educational Context - Conception, Implementation And Evaluation, Paradesi Sree Lakshmi, Jay B. Simha, Rajeev Ranjan
Intelligrader: A Framework For Automatic Short Answer Grading, Inconsistency Check And Feedback In Educational Context - Conception, Implementation And Evaluation, Paradesi Sree Lakshmi, Jay B. Simha, Rajeev Ranjan
Karbala International Journal of Modern Science
Automatic Short Answer Grading (ASAG), an escalating realm in natural language understanding, constitutes a focal point of research within the broader field of learning analytics. Over time, many ASAG solutions have been proposed to address the difficulties in teaching. However, no work addressed three crucial aspects of evaluation together, i.e., i) automatic evaluation of brief subjective/descriptive answers written in English, ii) identifying the evaluation inconsistency, and iii) provision of providing feedback about inconsistent evaluation to the evaluator. The current work proposes IntelliGrader, a comprehensive ASAG system that addresses the above-mentioned issues. Automated grading is accomplished through a model answer-based approach. …
Historical Review Of Variants Of Informal Semantics For Logic Programs Under Answer Set Semantics: Gl’88, Gl’91, Gk’14, D-V’12, Yuliya Lierler
Historical Review Of Variants Of Informal Semantics For Logic Programs Under Answer Set Semantics: Gl’88, Gl’91, Gk’14, D-V’12, Yuliya Lierler
Computer Science Faculty Publications
This note presents a historical survey of informal semantics that are associated with logic programming under answer set semantics. We review these in uniform terms and align them with two paradigms: Answer Set Programming and ASP-Prolog — two prominent Knowledge Representation and Reasoning Paradigms in Artificial Intelligence.
Ai And Future-Making: Design, Biases, And Human-Plant Interactions, Maliheh Ghajargar
Ai And Future-Making: Design, Biases, And Human-Plant Interactions, Maliheh Ghajargar
Art Faculty Articles and Research
Design researchers and practitioners are turning to generative AI (genAI) to support activities such as ideation and concept development in pursuit of preferred futures. At the same time, genAI is known to have biases, which prompts questions about how these biases might adversely affect design practices. In the domain of sustainable HCI, with its recent trends in human-nature interactions and more-than-human design, the question can be further refined into whether and how genAI biases might perpetuate anthropocentric biases that these practices are increasingly seeking to confront. In the present research, we conducted three workshops, focusing on genAI for human-plant interactions; …
Bi-Directional Transformers Vs. Word2vec: Discovering Vulnerabilities In Lifted Compiled Code, Gary Mccully, John Hastings, Shengjie Xu, Adam Fortier
Bi-Directional Transformers Vs. Word2vec: Discovering Vulnerabilities In Lifted Compiled Code, Gary Mccully, John Hastings, Shengjie Xu, Adam Fortier
Research & Publications
Detecting vulnerabilities within compiled binaries is challenging due to lost high-level code structures and other factors such as architectural dependencies, compilers, and optimization options. To address these obstacles, this research explores vulnerability detection using natural language processing (NLP) embedding techniques with word2vec, BERT, and RoBERTa to learn semantics from intermediate representation (LLVM IR) code. Long short-term memory (LSTM) neural networks were trained on embeddings from encoders created using approximately 48k LLVM functions from the Juliet dataset. This study is pioneering in its comparison of word2vec models with multiple bidirectional transformers (BERT, RoBERTa) embeddings built using LLVM code to train neural …
Investigating Public Acceptance Of Responses To Public Emergencies: Based On Text Transparency And Empathy Sentiment Analysis, Xuefeng Zhang, Yelin Huang
Investigating Public Acceptance Of Responses To Public Emergencies: Based On Text Transparency And Empathy Sentiment Analysis, Xuefeng Zhang, Yelin Huang
Journal of Scientific Information Research
[Purpose/significance]Regarding the responses to public emergencies published by official agencies on social media, this study aims to measure information transparency and empathy in the response text, and further to investigate their influence on public acceptance of responses. [Method/process]This study used public emergency responses published on Sina Weibo, a Chinese popular social media, as data source. Through carefully collecting and filtering, we finally acquired 170 public emergency responses released from 2021 to 2023. Furthermore, by using methods of content analysis, manual coding, and natural language processing, we measured information transparency of responses from three aspects: information disclosure, information accuracy, and information …
A Survey Of Unikernel Security: Insights And Trends From A Quantitative Analysis, Alex Wollman, John Hastings
A Survey Of Unikernel Security: Insights And Trends From A Quantitative Analysis, Alex Wollman, John Hastings
Research & Publications
Unikernels, an evolution of LibOSs, are emerging as a virtualization technology to rival those currently used by cloud providers. Unikernels combine the user and kernel space into one ``uni''fied memory space and omit functionality that is not necessary for its application to run, thus drastically reducing the required resources. The removed functionality is significant however, and includes components that have become common security technologies such as Address Space Layout Randomization (ASLR), Data Execution Prevention (DEP), and Non-executable bits (NX bits). This raises questions about the security of unikernels. This research presents a quantitative methodology using TF-IDF to analyze the focus …
Self-Adaptive Fine-Grained Multi-Modal Data Augmentation For Semi-Supervised Muti-Modal Coreference Resolution, Li Zheng, Boyu Chen, Hao Fei, Fei Li, Shengqiong Wu, Lizi Liao, Donghong Ji
Self-Adaptive Fine-Grained Multi-Modal Data Augmentation For Semi-Supervised Muti-Modal Coreference Resolution, Li Zheng, Boyu Chen, Hao Fei, Fei Li, Shengqiong Wu, Lizi Liao, Donghong Ji
Research Collection School Of Computing and Information Systems
Coreference resolution, an essential task in natural language processing, is particularly challenging in multi-modal scenarios where data comes in various forms and modalities. Despite advancements, limitations due to scarce labeled data and underleveraged unlabeled data persist. We address these issues with a self-adaptive fine-grained multi-modal data augmentation framework for semi-supervised MCR, focusing on enriching training data from labeled datasets and tapping into the untapped potential of unlabeled data. Regarding the former issue, we first leverage text coreference resolution datasets and diffusion models,to perform fine-grained text-to-image generation with aligned text entities and image bounding boxes. We then introduce a self-adaptive selection …
Does Ceo Agreeableness Personality Mitigate Real Earnings Management?, Shan Liu, Xingying Wu, Nan Hu
Does Ceo Agreeableness Personality Mitigate Real Earnings Management?, Shan Liu, Xingying Wu, Nan Hu
Research Collection School Of Computing and Information Systems
Despite efforts to mitigate aggressive financial reporting, earnings management remains challenging to parties interested in inhibiting its dysfunctional effects. Using linguistic algorithms to assess CEO agreeableness personality from their unscripted texts in conference calls, we find that it is a determinant that mitigates a firm's real earnings management. Furthermore, such an effect is more pronounced when firms confront intensive market competition and financial distress and have weaker managerial entrenchment or when CEOs face stronger internal governance. Our findings persist even after we utilize several alternative real earnings management metrics and control other confounding personalities in prior earnings management studies. The …
Collaborative Cross-Modal Fusion With Large Language Model For Recommendation, Zhongzhou Liu, Hao Zhang, Kuicai Dong, Yuan Fang
Collaborative Cross-Modal Fusion With Large Language Model For Recommendation, Zhongzhou Liu, Hao Zhang, Kuicai Dong, Yuan Fang
Research Collection School Of Computing and Information Systems
Despite the success of conventional collaborative filtering (CF) approaches for recommendation systems, they exhibit limitations in leveraging semantic knowledge within the textual attributes of users and items. Recent focus on the application of large language models for recommendation (LLM4Rec) has highlighted their capability for effective semantic knowledge capture. However, these methods often overlook the collaborative signals in user behaviors. Some simply instruct-tune a language model, while others directly inject the embeddings of a CF-based model, lacking a synergistic fusion of different modalities. To address these issues, we propose a framework of Collaborative Cross-modal Fusion with Large Language Models, termed CCF-LLM, …
Joint Weakly Supervised Image Emotion Analysis Based On Interclass Discrimination And Intraclass Correlation, Xinyue Zhang, Zhaoxia Wang, Guitao Cao, Seng-Beng Ho
Joint Weakly Supervised Image Emotion Analysis Based On Interclass Discrimination And Intraclass Correlation, Xinyue Zhang, Zhaoxia Wang, Guitao Cao, Seng-Beng Ho
Research Collection School Of Computing and Information Systems
Regional information-based image emotion analysis has recently garnered significant attention. However, existing methods often focus on identifying region proposals through layered steps or merely rely on visual saliency. These approaches may lead to an underestimation of emotional categories and a lack of comprehensive interclass discrimination perception and emotional intraclass contextual mining. To address these limitations, we propose a novel approach named InterIntraIEA, which combines interclass discrimination and intraclass correlation joint learning capabilities for image emotion analysis. The proposed method not only employs category-specific dictionary learning for class adaptation, but also models intraclass contextual relationships and perceives correlations at the channel …
Temporal Relational Graph Convolutional Network Approach To Financial Performance Prediction, Jeyaraman Brindha Priyadarshini, Bing Tian Dai, Yuan Fang
Temporal Relational Graph Convolutional Network Approach To Financial Performance Prediction, Jeyaraman Brindha Priyadarshini, Bing Tian Dai, Yuan Fang
Research Collection School Of Computing and Information Systems
Accurately predicting financial entity performance remains a challenge due to the dynamic nature of financial markets and vast unstructured textual data. Financial knowledge graphs (FKGs) offer a structured representation for tackling this problem by representing complex financial relationships and concepts. However, constructing a comprehensive and accurate financial knowledge graph that captures the temporal dynamics of financial entities is non-trivial. We introduce FintechKG, a comprehensive financial knowledge graph developed through a three-dimensional information extraction process that incorporates commercial entities and temporal dimensions and uses a financial concept taxonomy that ensures financial domain entity and relationship extraction. We propose a temporal and …
Exploring Conversations Between A Practitioner And A Person With Dementia, Kotaro Hara, Rosiana Natalie, Wei Soon Cheong, Jingjing Gu, Qianli Xu
Exploring Conversations Between A Practitioner And A Person With Dementia, Kotaro Hara, Rosiana Natalie, Wei Soon Cheong, Jingjing Gu, Qianli Xu
Research Collection School Of Computing and Information Systems
In social service centers, practitioners engage in conversations with clients with dementia to facilitate their daily activities and provide support when they are distressed. However, the nature of the care demands the practitioner’s active engagement, which becomes difficult to deliver as the number of people who need care expands. Researchers have been investigating the efficacy of developing agents that assume conversational tasks to alleviate this work. To contribute to the future design of agents for caregiving, we collected and analyzed ten conversations between clients with mild dementia and practitioners who provide care. Our analyses of turn-taking dynamics and dialogue acts …
Unveiling Similarities In The Code Of Life: A Detailed Exploration Of Dna Sequence Matching Algorithm, Mahmoud Y. Shams, Romany M. Farag, Dalia A. Aldawody, Huda E. Khalid, Ahmed K. Essa, Hazem M. El-Bakry, A. A. Salama
Unveiling Similarities In The Code Of Life: A Detailed Exploration Of Dna Sequence Matching Algorithm, Mahmoud Y. Shams, Romany M. Farag, Dalia A. Aldawody, Huda E. Khalid, Ahmed K. Essa, Hazem M. El-Bakry, A. A. Salama
Neutrosophic Systems with Applications
Identifying similar DNA sequences is crucial in various biological research endeavors. This paper delves into the intricate workings of a specific algorithm designed for this purpose. We provide a systematic explanation, exploring how the algorithm handles user input, reads stored DNA sequences, utilizes the Word2Vec model for vector representation, and calculates sequence similarity using diverse metrics like Cosine Similarity and Neutrosophic Distance. Additionally, the paper explores the incorporation of neutrosophic values to account for uncertainty in the comparisons. Finally, we discuss the extraction of results, including matched sequences, similarity scores, and accuracy measures. This in-depth exploration provides a clear understanding …
An Approach To Multi-Attribute Decision-Making Based On Single-Valued Neutrosophic Hesitant Fuzzy Aczel-Alsina Aggregation Operator, Raiha Imran, Kifayat Ullah, Zeeshan Ali, Maria Akram
An Approach To Multi-Attribute Decision-Making Based On Single-Valued Neutrosophic Hesitant Fuzzy Aczel-Alsina Aggregation Operator, Raiha Imran, Kifayat Ullah, Zeeshan Ali, Maria Akram
Neutrosophic Systems with Applications
A single-valued Neutrosophic hesitant fuzzy set (SVNHFS) is a combination of a single-valued neutrosophic set (SVNS) and hesitant fuzzy set (HFS) that has been developed to address insufficient, unreliable, and vague environments in which each element has several possible options determined by the truthiness, indeterminacy and falsity value. By considering this, in this paper, we have proposed the Aczel-Alsina aggregation operator (AAAO) for SVNHFS, which is more flexible t-norm and t-conorm than the other and due to the flexible nature of parameters to solve Multi-Attribute decision making (MADM) problems. Further, the score function, accuracy function, and certainty function of SVNHFS …
Evaluation Of Renewable Energy Sources For A Sustainable Future: A Multi-Criteria Decision-Making Approach, Mai Mohamed, Asmaa Elsayed
Evaluation Of Renewable Energy Sources For A Sustainable Future: A Multi-Criteria Decision-Making Approach, Mai Mohamed, Asmaa Elsayed
Neutrosophic Systems with Applications
The urgent global challenges of climate change, energy security, and environmental degradation highlight the need for sustainable energy solutions. Renewable energy sources (RES) present a viable pathway towards sustainability by mitigating greenhouse gas emissions, reducing reliance on fossil fuels, and fostering economic resilience. Purpose: The purpose of this paper is to propose an advanced Multi-Criteria Decision-Making (MCDM) approach to evaluate various RES by integrating environmental, economic, technological, social acceptance, and resource availability criteria, to identify the most suitable RES for sustainable energy solutions. Methodology: The study employs a hybrid method combining Type-2 Neutrosophic Numbers (T2NN) with LOPCOW (Logarithmic Percentage Change …
Nigerian Software Engineer Or American Data Scientist? Github Profile Recruitment Bias In Large Language Models, Takashi Nakano, Kazumasa Shimari, Raula Gaikovina Kula, Christoph Treude, Marc Cheong, Kenichi Matsumoto
Nigerian Software Engineer Or American Data Scientist? Github Profile Recruitment Bias In Large Language Models, Takashi Nakano, Kazumasa Shimari, Raula Gaikovina Kula, Christoph Treude, Marc Cheong, Kenichi Matsumoto
Research Collection School Of Computing and Information Systems
Large Language Models (LLMs) have taken the world by storm, demonstrating their ability not only to automate tedious tasks, but also to show some degree of proficiency in completing software engineering tasks. A key concern with LLMs is their “black-box” nature, which obscures their internal workings and could lead to societal biases in their outputs. In the software engineering context, in this early results paper, we empirically explore how well LLMs can automate recruitment tasks for a geographically diverse software team. We use OpenAI's ChatGPT to conduct an initial set of experiments using GitHub User Profiles from four regions to …