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CareCompanion: A Personalized Virtual Assistant for Enhancing Support and Independence in ADRD Patients and Older Adults

CareCompanion: A Personalized Virtual Assistant for Enhancin...

引用

international conference on Smart Applications, Communications and Networking (SmartNets)

作者： Ul Hasan, Wordh Zaman, Kimia Tuz Shalan, Mohammed Wang, Xin Li, Juan Xie, Bo Tao, Cui North Dakota State Univ Dept Comp Sci Fargo ND 58105 USA Univ Texas Austin Sch Informat Sch Nursing Austin TX USA Mayo Clin Dept Artificial Intelligence & Informat Rochester MN USA

ISBN: (纸本)9798350385328;9798350385335

Alzheimer's Disease and Related Dementias (ADRD) patients and older adults face challenges related to memory loss, navigation difficulties, and social isolation, impacting their daily tasks, appointments, and social connections. To address these multifaceted challenges, this paper presents the design, development, and preliminary evaluation of "CareCompanion," a virtual assistant tailored specifically for this population. Leveraging advanced AI technologies such as natural language processing, machine learning, and knowledge graphs, CareCompanion provides personalized reminders, navigation assistance, and social connectivity features. Preliminary evaluation results demonstrate the potential of CareCompanion in improving the quality of life, independence, and social engagement for ADRD patients and older adults. Further research and development can enhance its effectiveness, usability, and customization, catering to the unique needs of this population, while fostering connections and mitigating the impact of memory loss, navigation difficulties, and social isolation.

关键词： Alzheimer's Disease and Related Dementias knowledge graph natural language processing virtual assistant memory loss

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Large language Model for Humanoid Cognition in Proactive Human-Robot Collaboration 20

Large Language Model for Humanoid Cognition in Proactive Hum...

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ieee 20th international conference on Automation Science and engineering (CASE)

作者： Li, Shufei Wang, Zuoxu Ya, Zhijie Gaos, Yiping Jiang, Han Zhengl, Pai Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Beihang Univ Sch Mech Engn & Automat Beijing Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan Peoples R China Koester Digital Intelligent Technol Shenzhen Co L Shenzhen Peoples R China

ISBN: (纸本)9798350358513;9798350358520

Proactive Human-Robot Collaboration (HRC), which aims to achieve mutual-cognitive, predictable, and self-organizing collaboration between multiple humans and robots, is crucial for today's human-centric smart manufacturing. To enable Proactive HRC, various methods have been explored, including deep neural networks for visual detection, scene graph for decision-making, and reinforcement learning for robot execution. However, these methods often require re-training with domain-specific datasets in different scenarios, lacking generalizability and transferability for diverse manufacturing activities. The advent of Large language Model (LLM) technology offers a promising solution for comprehending diverse tasks, modelling human intentions, and planning robot operations using natural vision-language instructions. This ability closely resembles human intelligence, specifically humanoid cognition, which allows flexible knowledge acquisition of the surrounding environment and exerting physical influence on tasks. Therefore, this paper delves into the concept of humanoid cognition in Proactive HRC and evaluates relevant LLM methods from the perspectives of task explainability, human-centricity, and robot executability. Based on the testing results, the authors provide discussions and future prospects for successfully integrating LLM approaches into Proactive HRC in the manufacturing domain.

关键词： Smart manufacturing

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Navigating Data Abundance: Generative Conversational AI Agents in Information Analysis 3

Navigating Data Abundance: Generative Conversational AI Agen...

引用

3rd Odisha international conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Sahu, Barnali Das, Sanjog Pattnaik, Ayush Puri, Abhishek Mohanty, Kasmik Computer Science & Engineering Department Bhubaneswar India

ISBN: (纸本)9798350354379

In response to the escalating challenges posed by information overload, particularly in today's data-rich environment, we propose the development of a sophisticated Generative Conversational AI Agent (GCAIA) tailored specifically for information retrieval tasks. This innovative system integrates cutting-edge techniques in natural language processing, machine learning, and generative modeling to provide users with an intuitive platform for accessing, analyzing, and retrieving information across diverse domains. Key features include robust natural language understanding facilitating seamless interaction, efficient retrieval mechanisms enabling quick access to relevant data, contextual adaptation for delivering personalized responses, generative capabilities fostering natural and engaging conversations, multimodal integration supporting various data formats, and utilization of knowledge graphs for enhanced organization and semantic understanding. Through rigorous research, development, and evaluation processes, our goal is to demonstrate the effectiveness, reliability, and scalability of the GCAIA in significantly improving information retrieval processes across diverse domains. Ultimately, our aim is to empower users to navigate the ever-expanding landscape of knowledge and data effectively, thus mitigating the challenges posed by information overload in the digital age. © 2024 ieee.

关键词： Generative adversarial networks

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Efficiency & Innovation in Recruitment usingAI & Android

Efficiency & Innovation in Recruitment usingAI & Android

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2024 international conference on knowledge engineering and Communication Systems, ICKECS 2024

作者： Chalana, Kanika Gautam, Ashwin Ravi Raj, Anand Daivik, J.S. Amrutha, H.R. Dayananda Sagar College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

The goal of screening resumes is to determine the top applicants for a position and to inform users of their resume score and areas for improvement. The literature on existing approaches has been analyzed, and it has been discovered that the traditional systems like manual screening may result in false assumptions and the wasting of human potential, but they lackrobustness Regarding processing, accuracy and efficiency. To acquire accurate results, software must utilized to process natural language and machine learning techniques to match and rate the candidates in real-time by ranking their resumes. The input would be the resumes and output would be a ranked candidate's resumes on the admin side and recommendations for the user side. Instantaneous real-time output results are acquired by employing Object character recognition. In The system that is being suggested the authors used OCR model and android principles along with python scripts. This system has the many benefits including security, interpretability, high accuracy, lightweight model, and quick processing. © 2024 ieee.

关键词： Adversarial machine learning

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Investigating the Efficacy of Large language Models for Code Clone Detection 32

Investigating the Efficacy of Large Language Models for Code...

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32nd ieee/ACM international conference on Program Comprehension (ICPC)

作者： Khajezade, Mohamad Wu, Jie J. W. Fard, Fatemeh Hendijani Rodriguez-Perez, Gema Shehata, Mohamed Sami Univ British Columbia Kelowna BC Canada

ISBN: (纸本)9798400705861

Large language Models (LLMs) have demonstrated remarkable success in various natural language processing and software engineering tasks, such as code generation. The LLMs are mainly utilized in the prompt-based zero/few-shot paradigm to guide the model in accomplishing the task. GPT-based models are one of the popular ones studied for tasks such as code comment generation or test generation. These tasks are 'generative' tasks. However, there is limited research on the usage of LLMs for 'non-generative' tasks such as classification using the prompt-based paradigm. In this preliminary exploratory study, we investigated the applicability of LLMs for Code Clone Detection (CCD), a non-generative task. By building a mono-lingual and cross-lingual CCD dataset derived from CodeNet, we first investigated two different prompts using ChatGPT to detect Type-4 code clones in Java-Java and Java-Ruby pairs in a zero-shot setting. We then conducted an analysis to understand the strengths and weaknesses of ChatGPT in CCD. ChatGPT surpasses the baselines in cross-language CCD attaining an F1-score of 0.877 and achieves comparable performance to fully fine-tuned models for mono-lingual CCD, with an F1-score of 0.878. Also, the prompt and the difficulty level of the problems has an impact on the performance of ChatGPT. Finally, we provide insights and future directions based on our initial analysis(1).

关键词： Large language Models Code Clone Detection Zero-shot Learning Few-shot Learning.

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A Four-Dimension Gold Standard Dataset for Opinion Mining in Software engineering 21

A Four-Dimension Gold Standard Dataset for Opinion Mining in...

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ieee/ACM 21st international conference on Mining Software Repositories (MSR)

作者： Islam, Md Rakibul Rabbi, Md Fazle Jo, Youngeun Champa, Arifa Young, Ethan Wilson, Camden Scott, Gavin Zibran, Minhaz Lamar Univ Beaumont TX 77705 USA Idaho State Univ Pocatello ID USA

ISBN: (纸本)9798350363982;9798400705878

We present the first four-dimension gold standard dataset to advance opinion mining focused on the software engineering domain. Through a well-defined sampling and annotation strategy leveraging multiple coders, we construct a corpus of 2,000 Stack Overflow posts labeled with four dimensions/tuples, including sentiments, polar facts, aspects, and named entities. This multidimensional ground truth dataset opens up new research opportunities for opinion mining in domain-adapted NLP tools for software engineering by capturing existing relationships between extracted elements at a more granular level. It also facilitates investigating the effects of sentiments in the developers' social forums.

关键词： Sentiment Analysis Opinion Mining Aspects Named Entity Software engineering natural language processing

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Enhancing natural language processing: A Comprehensive Review of Retrieval Augmented Generation 4

Enhancing Natural Language Processing: A Comprehensive Revie...

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4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Shahade, Aniket K. Deshmukh, Priyanka V. Pune Campus Pune India

ISBN: (纸本)9798331540364

Retrieval augmented generation (RAG) has emerged as a promising approach in natural language processing, combining retrieval and generation techniques to produce high-quality text. By incorporating external knowledge into the generation process, RAG addresses the limitations of generative models that rely solely on learned information. This review explores the foundational principles and architecture of RAG, which typically involves a two-step process: retrieving relevant information based on the input query and generating text informed by both the query and the retrieved knowledge. The review discusses key considerations for the retriever and generator components, as well as the integration of retrieved knowledge into the generative model. By understanding the underlying principles and architecture of RAG, researchers and practitioners can leverage this powerful technique to improve the quality and contextual relevance of text generation tasks. © 2024 ieee.

关键词： Nearest neighbor search

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natural language processing Algorithms for Academic Content Generation 4

Natural Language Processing Algorithms for Academic Content ...

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4th ieee international conference on Advanced Learning Technologies for Education and Research, ICALTER 2024

作者： Paredes-Vargas, Alfredo Salinas-Llorca, Piero Santisteban, Jose Information Systems Engineering Universidad Peruana de ciencias Aplicadas UPC Lima Peru Information Systems Engineering Universidad Peruana de Ciencias Aplicadas Lima Peru

ISBN: (纸本)9798331510244

This study has conducted several activities to analyze and compare the performance of five natural language processing (NLP) models: GPT-3, T5, ERNIE, BERT, and XLNet for generating academic content in universities located in Lima, Peru. Some NLP models, including GPT and BERT, were assessed based on accuracy, quality, speed, and customization capabilities. The research examined the use of various algorithms for academic tasks like essay and summary creation. The findings show that all models perform adequately;however, some excel in generating coherent content tailored to different academic styles, thereby optimizing the time of both students and teachers. In conclusion, the ERNIE model emerges as one of the best, as it enables the generation of diverse and high-quality content efficiently. © 2024 ieee.

关键词： natural language processing systems

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Automated Disease Diagnosis Using natural language processing and Deep Learning 4

Automated Disease Diagnosis Using Natural Language Processin...

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4th international conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Garg, Devesh Mani, Chandra Srivastava, Sanjay Jayant, Khel Prakash Kumar Saraswat, Birendra Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India

ISBN: (纸本)9798350364729

Current advances in natural language processing (NLP) and deep studying have made it possible to increase automatic structures for ailment analysis. By leveraging NLP strategies, such systems can procedure unfastened-form text, spotting precious patterns to extract capabilities from scientific information that may be used to appropriately identify disease. Deep learning algorithms can take those features and learn to recognize the presence of a sickness and appropriately perceive the most possibly diagnosis. Such mechanically generated diagnoses can then be used to guide clinicians in making appropriate decisions. This paper gives a top level view of automatic disorder diagnosis systems that use NLP and deep learning, highlighting key additives of those structures, and discussing present challenges and destiny guidelines of such systems. Computerized sickness analysis the usage of natural language processing and Deep studying is an rising field that leverages advances in herbal language processing (NLP) and deep getting to know to discover diagnosable diseases from patient-provided information. Moreover, such models can discover ways to interpret clinical information from herbal language sources which includes scientific records and different scientific files. Via applying NLP and deep gaining knowledge of techniques to patient facts, an automated diagnosis set of rules may want to discover affected person statistics this is indicative of a diagnosable disorder. In addition, such algorithms have capacity to similarly enhance the accuracy and speed of analysis so one can make sure patient health outcomes. Moreover, these algorithms will be included with current healthcare systems to be able to mechanically recommend remedies and in addition save charges related to healthcare shipping. © 2024 ieee.

关键词： Diseases

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knowledge Graphs for Textbooks: Extraction and Completion Techniques 6

Knowledge Graphs for Textbooks: Extraction and Completion Te...

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6th ieee international conference on Artificial Intelligence and knowledge engineering, AIKE 2023

作者： Yao, Yutong Potikas, Petros Potika, Katerina Department of Computer Science San José State University San JoseCA United States School of Electrical and Computer Engineering National Technical University of Athens Greece

ISBN: (纸本)9798350331288

This paper aims to apply knowledge graph construction techniques to textbooks, explicitly focusing on the challenge of the absence of domain-specific schema for each textbook. Various entity and relation extraction models are utilized to capture logical and semantic information related to the textbook s topic. These models include a Text-Encoding-Initiative (TEI) model to extract hierarchical concepts, spaCy natural language processing (NLP), and Google Cloud natural language to extract semantic information from the main textual content. The study includes a case study on a cloud computing textbook, where each approach is evaluated and analyzed. Ultimately, the goal is to create knowledge graphs of textbooks, enabling the completion task of predicting missing entities or relations in a low-dimensional space. © 2023 ieee.

关键词： knowledge graph

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