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The Role of AI in Modern Language Translation and Its Societal Applications: A Systematic Literature Review 5th

The Role of AI in Modern Language Translation and Its Societ...

引用

5th Southern African Conference for Artificial Intelligence Research

作者： Ssemugabi, Samuel Univ South Africa Pretoria South Africa

ISBN: (纸本)9783031782541;9783031782558

Artificial intelligence (AI) has greatly transformed language translation, especially over the past five years. The main aim of this research was to explore the role AI has played in enhancing language translation technologies and its societal implications to provide an understanding of its current state, usage and future prospects. A systematic review was performed using the PRISMA protocol and the Scopus database, resulting in the identification of 351 publications. After applying strict exclusion and inclusion criteria, 52 articles were closely examined, and 16 were included in the final review. The findings demonstrate that AI, mainly through the adoption of neural machine translation (NMT) and transformer-based models, has greatly improved the accuracy and efficiency of translations. These advancements have led to innovations in fields such as education, healthcare and business that facilitate communication in different languages. However, the review also identified important societal implications, including ethical concerns, possible job displacement and the risk of cultural homogenisation. The study identified the need for future AI language translation technologies to focus on culturally sensitive models, universal language frameworks and ethical AI systems that prioritise transparency and fairness. This research provides valuable information for researchers, industry professionals and policymakers on how to harness AI to maximise social benefits while addressing the challenges associated with language translation.

关键词： Artificial intelligence Language translation Systematic literature review Society Applications Language Translation AI SLR

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Approximating Temporal Katz Centrality with Monte Carlo Methods 7th

Approximating Temporal Katz Centrality with Monte Carlo Meth...

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8th Asia Pacific Web and Web-Age information Management Joint International Conference on Web and Big Data

作者： Yan, Haonan Yang, Zhengyi Zhang, Tianming Wen, Dong Luo, Qi Ukey, Nimish Univ New South Wales Sydney NSW Australia Zhejiang Univ Technol Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9789819600540;9789819600557

Graphs have long served as fundamental data models across various disciplines such as data mining, social media analysis, and knowledge management. In real-world applications, interactions between nodes often evolve over time, necessitating the use of temporal graphs. Centrality measures are pivotal in graph analysis for identifying key nodes. Specifically, Temporal Katz Centrality (TKC) has garnered significant attention in recent years for its ability tomeasure node influence in temporal graphs. TKC evaluates the influence of nodes by considering all walks originating from a node, where contributions are weighted based on a user-specified time decay factor. This approach captures temporal dynamics by incorporating both the timing of interactions and the intervals between them, offering a comprehensive assessment of node importance over time. However, computing TKC in large temporal graphs is computationally intensive due to the requirement to traverse all edges and update centrality values along temporal paths. To address this challenge, this paper proposes an efficient Monte Carlo approximation method for TKC. This approach employs random sampling via Simple Random Walk and Alpha Walk to estimate TKC values. The paper rigorously proves the asymptotic consistency of the method using the Law of Large Numbers and Central Limit Theorem, ensuring that estimated TKC values converge to true TKC values as the sample size increases. Experiments conducted on six real-world temporal graph datasets demonstrate the effectiveness of the proposed approximation methods. Under the same running time, the Alpha walk outperforms other methods on large temporal graphs, exhibiting the lowest mean relative error and highest precision.

关键词： Temproal Graphs Katz Centrality Monte Carlo Sampling

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Digital Literacy: Challenges and Opportunities 53rd

Digital Literacy: Challenges and Opportunities

引用

53rd Southern African computer Lecturers Association Meeting

作者： Zepe, Xolile Petratos, Sue Nelson Mandela Univ Port Elizabeth South Africa

ISBN: (纸本)9783031824777;9783031824784

When the #FeesMustFall student movement, successfully swayed the South African Government to introduce free Higher Education tuition in 2017, the biggest obstacle to further education for the majority of matriculants, was removed. Due to the fast-paced world of technology, Higher Education Institutions have significantly changed how learning and teaching are conducted. This was further advanced with the onset of the COVID-19 pandemic when classes were forced to go online. Students and lecturers battled to find a balance between the computer skills needed, and how to use them in a digital classroom. The Institute of Race Relations reported in 2018 that the South African Education system is in crisis and that the learners leaving school are poorly prepared for the world after school. computer skills are among the essential skills required to be successful in any environment, yet many students find themselves encountering a computer for the first time at university. The objective of this study is to reflect on the experience of Nelson Mandela University lecturers, in computer Skills classes over the last seven years. An overview of how the offering of the module has changed will be presented. The lecturers' perceptions of the successes and failures over the same period will be captured;including the observed challenges that students experienced. A possible solution, to the challenges identified, such as lack of digital skills for classroom readiness, time management, and student numbers, which would be suitable at all HEIs, to provide a better learning and teaching experience in computer Skills classes will be presented.

关键词： computer Skills Digital Skills Classroom Readiness End User Skills Online Learning Blended Learning

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Cognitive Ad Hoc Trust Routing for Enhanced Quality of Service 5th

Cognitive Ad Hoc Trust Routing for Enhanced Quality of Servi...

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5th International Conference on Computing science, Communication and Security

作者： Neelima, N. Pratap, P. Syam Kiran, P. Satya JNTUK Velagapudi Ramakrishna Siddhartha Engn Coll Vijayawada Andhra Pradesh India

ISBN: (纸本)9783031751691;9783031751707

Because of the dynamic nature and lack of infrastructure of MANETs, routing in such networks is vulnerable to several attacks, and standard fixed policy routing algorithms are inefficient in tackling them. Reinforcement learning methods and appropriate models based on trust are assuring for dealing with the issues and variable behaviour of rogue network nodes. In this research, we present a cognition layer that interacts with the network layer in parallel and consists of two phases: pathfinding (routing) and trust *** first phase uses ML techniques, while the second is focused on trust assessment. Regarding three performance indicators, CTR, our technique is evaluated against a well-known protocol, TQR. The simulation findings demonstrate improved end-to-end latency and communication.

关键词： MANETS CTR TQR Routing

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The Benefits and Challenges of Using Datathons as a Method of Learning Data Analytics 4th

The Benefits and Challenges of Using Datathons as a Method o...

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4th International Conference on Society 5.0

作者： Msweli, Nkosikhona Theoren Mawela, Tendani Univ Pretoria Dept Informat ZA-0028 Pretoria South Africa

ISBN: (纸本)9783031714115;9783031714122

The acquisition of data analytics skills is becoming increasingly crucial in today's data-driven world. At the same time datathons are gaining traction in the field of data science since these events enable participants to collaboratively solve practical problems and make decisions using real-world data sets. This study explored how datathons may support the learning of data analytics. The study, which was exploratory and qualitative, collected data via five focus group sessions that were conducted with datathon participants. The aim was to investigate participants' views on the perceived benefits, and challenges of using datathons to learn data analytics. The study results indicate that datathons provide a productive starting point for learning data analytics for individuals with no knowledge of data science. However, further learning opportunities and research are needed for an individual to fully grasp data analytics. For students in the field of technology, participating in datathons provides a unique opportunity to hone their data analytical skills in a competitive and collaborative environment. By working on real-world datasets and solving complex problems, participants indicated that they gained practical experience and learned valuable techniques for extracting insights from data. While datathons offer enriching experiences, there are some challenges with the intense, time-limited and competitive nature of these events. This study contributes to the understanding of the role of datathons in facilitating learning and skill development in data analytics and provides recommendations towards leveraging the potential of datathons as a learning tool in the field of data science.

关键词： datathon data analytics data science pedagogies transdisciplinary

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Digitalising the Supply Chain for Enhanced Efficiency and Customer Satisfaction 5th

Digitalising the Supply Chain for Enhanced Efficiency and Cu...

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5th 5th Olympus - International Conference on Supply Chain

作者： Balaska, Vasiliki Symeonidis, Symeon Antoniou, Sarantis Fotiadis, Thomas Chatzoglou, Prodromos Gasteratos, Antonios Democritus Univ Thrace Dept Prod & Management Engn Xanthi Greece

ISBN: (纸本)9783031693502;9783031693519

In the rapidly evolving landscape of global supply chains, integrating digital technologies is a key factor in enhancing operational efficiency, ensuring product traceability, and capturing consumer sentiment. This research delves into the transformative potential of blockchain, Non-Fungible Tokens (NFTs), and advanced sentiment analysis in redefining supply chain management practices. By harnessing these technologies, the study proposes a novel framework to achieve real-time visibility, end-to-end traceability, and a deeper understanding of consumer preferences. The methodology encompasses the deployment of digital passports and NFTs for securing product information, alongside utilising natural language processing techniques to analyse customer feedback across various platforms. Through case studies in food safety, pharmaceuticals, and retail, the paper shows how integrating digital strategies mitigates risks, boosts transparency, and enhances customer engagement. The findings reveal significant improvements in supply chain resilience, operational transparency, and customer satisfaction. The research outlines challenges such as data security, technological complexity, and workforce upskilling for adopting digital tools. Conclusively, this paper underscores the critical importance of digital innovation in supply chains, advocating for further research and development to overcome existing barriers. The future focuses on digitalised supply chains enhancing efficiency and value creation via customer insights and sustainable methods.

关键词： supply chain digitalisation blockchain traceability NFT digital passports sentiment analysis

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Dual Model Dynamics: Enhancing Depression Prediction Through the Integrated Use of Convolutional Neural Networks and Support Vector Machines in Data-Driven Methods 4th

Dual Model Dynamics: Enhancing Depression Prediction Through...

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4th International Conference on Artificial Intelligence and Knowledge Processing

作者： Jose, Alphonsa Baniya, Achal George, Angelina Kumar, K. Dinesh Amrita Vishwa Vidyapeetham Amrita Sch Comp Dept Comp Sci & Engn Bangalore Karnataka India

ISBN: (纸本)9783031734762;9783031734779

Depression being a prevalent mental health issue that significantly affects and impacts society, it calls for effective treatments that offer precise and timely prediction approaches. This paper introduces a unique hybrid model that combines Support Vector Machine (SVM) classification with Convolutional Neural Networks (CNNs). Due to potential risks associated with generating synthetic samples for medical datasets, the study evaluates the models both with and without the Synthetic Minority Over-sampling Technique (SMOTE). The study assessed K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), and SVM as baseline models, with SVM achieving the highest test accuracy among the three, recording 92.4% with SMOTE and 90% without SMOTE. The hybrid model, combining CNN and SVM with SMOTE augmentation, achieved a remarkable test accuracy of 93%, compared to 88.4% without SMOTE. These findings highlight the hybrid model's efficacy and stress how crucial it is to resolve class imbalance in medical datasets in order to improve forecast accuracy.

关键词： Deep Learning Feature Extraction Hybrid Methodology Machine Learning Mental Health

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Skeleton-Based Point Cloud Sampling and Its Facilitation to Classification 5th

Skeleton-Based Point Cloud Sampling and Its Facilitation to ...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Guo, Yugang Tang, Xue-song Hao, Kuangrong Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

Advances in 3D computer vision have increased the amount of point cloud data, highlighting the growing importance of point cloud sampling. Commonly used sampling methods like random sampling and farthest point sampling often fail to capture the geometric topology information of an object. We propose a skeleton-based point cloud sampling method to preserve the geometric topology information of the input point cloud. Skeletons have properties such as the extraction of global topology and the preservation of morphological features. Our method fully leverages these features to enhance the preservation of geometric topological information during the sampling stage. We utilize the variability of the local neighborhood normals as a prior information for sampling. Analyzing changes in skeleton normals facilitates the capture of salient points, enhancing the accuracy of point cloud sampling. To evaluate the effectiveness of our method, we conducted extensive comparative experiments using various sampling methods and scales, and found that it outperforms existing techniques.

关键词： Skeleton Extraction Point Cloud Sampling Shape Classification

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Privacy Protection and Standardization of Electronic Medical Records Using Large Language Model

Privacy Protection and Standardization of Electronic Medical...

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2024 International Workshop on Deidentification of Electronic Medical Record Notes

作者： Huang, Chao-Long Rianto, Babam Sun, Jun-Teng Fu, Zheng-Xin Lee, Chung-Hong Natl Kaohsiung Univ Sci & Technol Kaohsiung 807618 Taiwan

ISBN: (纸本)9789819779659;9789819779666

Recently, the widespread application of electronic medical records (EMRs) has made protecting patients' personal privacy information crucial and highly important. However, the sources of these EMRs are different, resulting in various time formats, and this inconsistency makes historical data challenging to read. Therefore, the key to solving the problem lies in two major issues in medical information: de-identification of sensitive health information (SHI) as defined by the Health Insurance Portability and Accountability Act (HIPAA) and normalization of time according to the ISO 8601 standard. To identify the items in case reports, we used the powerful capabilities of large language models (LLMs) as the foundation. We developed a set of algorithms based on In Context Learning (ICL), which ensures that the model inference process is always in the correct order while allowing the features contained in invalid labels to be utilized. The efficacy of our method has been validated through its exceptional performance in a real-world competition, attaining an macro-F-measure of 93.51% on the de-identification task and remarkably achieving over 92% accuracy for time normalization.

关键词： De-identification Time Normalization Large Language Models Electronic Medical Records Sensitive Health information

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User Perception of Ontology-Based Explanations of AI Models 8th

User Perception of Ontology-Based Explanations of AI Models

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8th International Conference on computer-Human Interaction Research and Applications-CHIRA-Annual

作者： Agafonov, Anton Ponomarev, Andrew Smirnov, Alexander Russian Acad Sci St Petersburg Fed Res Ctr 14th Line 39 St Petersburg 199178 Russia

ISBN: (纸本)9783031838446;9783031838453

For using AI models in high-stake applications it is crucial for a decision maker to understand why the model came to a certain conclusion. Ontology-based explanation techniques of artificial neural networks aim to provide explanations adapted to domain vocabulary (which is encoded using ontology) in order to make them easier to interpret and reason about. However, few studies actually explore the perception of ontology-based explanations and their effectiveness with respect to more common explanation techniques for neural networks (e.g., LIME, GradCAM, etc.). The paper proposes two benchmark datasets with different task representations (tabular and graph) and a methodology to compare users' effectiveness of processing explanations, employing both objective (decision time, accuracy) and subjective metrics. The methodology and datasets were then used in a user study to compare several explanation representations (non-ontology-based and three ontology-based ones-textual representation of ontological inference, inference graph, and attributive). It was found that according to subjective evaluation, graph and textual explanations caused the least difficulty for the participants. Objective metrics vary with the size of the ontology, but inference graphs show good results in all the examined cases. Surprisingly, non-ontology-based explanations have almost the same positive effect on decision-making than ontology-based (although, a bit harder subjectively).

关键词： XAI Explainable AI Ontology Ontology-Based explanations User study Machine learning Neural networks

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