... IEEE Global Communications Conference. IEEE Global Communications Conference最新文献

Cog-TiPRO: Iterative Prompt Refinement with LLMs to Detect Cognitive Decline via Longitudinal Voice Assistant Commands. Cog-TiPRO：迭代提示细化与llm检测认知衰退通过纵向语音助理命令。

... IEEE Global Communications Conference. IEEE Global Communications Conference Pub Date : 2025-12-01 Epub Date: 2026-03-19 DOI: 10.1109/globecom59602.2025.11432582

Kristin Qi, Youxiang Zhu, Caroline Summerour, John A Batsis, Xiaohui Liang

{"title":"Cog-TiPRO: Iterative Prompt Refinement with LLMs to Detect Cognitive Decline via Longitudinal Voice Assistant Commands.","authors":"Kristin Qi, Youxiang Zhu, Caroline Summerour, John A Batsis, Xiaohui Liang","doi":"10.1109/globecom59602.2025.11432582","DOIUrl":"10.1109/globecom59602.2025.11432582","url":null,"abstract":"Early detection of cognitive decline is crucial for enabling interventions that can slow neurodegenerative disease progression. Traditional diagnostic approaches rely on labor-intensive clinical assessments, which are impractical for frequent monitoring. Our pilot study investigates voice assistant systems (VAS) as non-invasive tools for detecting cognitive decline through longitudinal analysis of speech patterns in short and unstructured voice commands. Over an 18-month period, we collected voice commands from 35 older adults, with 15 participants providing daily at-home VAS interactions. To address the challenges of analyzing these short, unstructured and noisy commands, we propose Cog-TiPRO, a framework that combines (1) LLM-driven iterative prompt refinement for linguistic feature extraction, (2) HuBERT-based acoustic feature extraction, and (3) transformer-based temporal modeling. Using iTransformer, our approach achieves 73.80% accuracy and 72.67% F1-score in detecting MCI, outperforming its baseline by 27.13%. Through our LLM approach, we identify linguistic features that uniquely characterize everyday command usage patterns in individuals experiencing cognitive decline.","PeriodicalId":72021,"journal":{"name":"... IEEE Global Communications Conference. IEEE Global Communications Conference","volume":"2025 ","pages":"356-362"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13004481/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147500596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unveil Multi-Picture Descriptions for Multilingual Mild Cognitive Impairment Detection via Contrastive Learning. 通过对比学习揭示多语言轻度认知障碍检测的多图像描述。

... IEEE Global Communications Conference. IEEE Global Communications Conference Pub Date : 2025-12-01 Epub Date: 2026-03-19 DOI: 10.1109/globecom59602.2025.11431883

Kristin Qi, Jiali Cheng, Youxiang Zhu, Hadi Amiri, Xiaohui Liang

{"title":"Unveil Multi-Picture Descriptions for Multilingual Mild Cognitive Impairment Detection via Contrastive Learning.","authors":"Kristin Qi, Jiali Cheng, Youxiang Zhu, Hadi Amiri, Xiaohui Liang","doi":"10.1109/globecom59602.2025.11431883","DOIUrl":"10.1109/globecom59602.2025.11431883","url":null,"abstract":"Detecting Mild Cognitive Impairment from picture descriptions is critical yet challenging, especially in multilingual and multiple picture settings. Prior work has primarily focused on English speakers describing a single picture (e.g., the 'Cookie Theft'). The TAUKDIAL-2024 challenge expands this scope by introducing multilingual speakers and multiple pictures, which presents new challenges in analyzing picture-dependent content. To address these challenges, we propose a framework with three components: (1) enhancing discriminative representation learning via supervised contrastive learning, (2) involving image modality rather than relying solely on speech and text modalities, and (3) applying a Product of Experts (PoE) strategy to mitigate spurious correlations and overfitting. Our framework improves MCI detection performance, achieving a +7.1% increase in Unweighted Average Recall (UAR) (from 68.1% to 75.2%) and a +2.9% increase in F1 score (from 80.6% to 83.5%) compared to the text unimodal baseline. Notably, the contrastive learning component yields greater gains for the text modality compared to speech. These results highlight our framework's effectiveness in multilingual and multi-picture MCI detection.","PeriodicalId":72021,"journal":{"name":"... IEEE Global Communications Conference. IEEE Global Communications Conference","volume":"2025 ","pages":"4879-4884"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13003545/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147500590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploiting Privacy Preserving Prompt Techniques for Online Large Language Model Usage. 利用在线大型语言模型使用的隐私保护提示技术。

... IEEE Global Communications Conference. IEEE Global Communications Conference Pub Date : 2024-12-01 Epub Date: 2025-03-11 DOI: 10.1109/globecom52923.2024.10901426

Youxiang Zhu, Ning Gao, Xiaohui Liang, Honggang Zhang

{"title":"Exploiting Privacy Preserving Prompt Techniques for Online Large Language Model Usage.","authors":"Youxiang Zhu, Ning Gao, Xiaohui Liang, Honggang Zhang","doi":"10.1109/globecom52923.2024.10901426","DOIUrl":"10.1109/globecom52923.2024.10901426","url":null,"abstract":"Online Large Language Models (LLMs) are widely employed across various tasks, including privacy-sensitive ones like financial advice or paragraph rewriting. Presently, users directly submit prompts to online LLM servers, inadvertently revealing sensitive keywords and facilitating server tracking to build user profiles. In this paper, we propose a local privacy-preserving prompt assistant (LPPA) that provides users with a usable method to balance privacy in the prompts and the utility of the LLM output. The LPPA will analyze the users' prompts, suggest modifying the prompts to protect the sensitive keywords, and provide an inference of the potential utility impact of the online LLM output. Specifically, we first propose a privacy module to identify the sensitive keywords in the prompt and adopt four privacy techniques, including remove, mask, replace, and rewrite to hide the keywords. While these techniques affect the utility of online LLM output, we measure such impact using the LLM output of the original prompt and modified prompts and discuss the cases with high, median, and low impact. In addition, we propose a utility inference model to infer the utility impact locally without disclosing the prompts to the online LLM. We evaluated LPPA on the real-world users' prompts and showed that the remove technique achieves the best performance, and it empowers users with meaningful ways to adjust their prompts to safeguard their privacy while still maintaining a satisfactory level of utility in online LLM usage.","PeriodicalId":72021,"journal":{"name":"... IEEE Global Communications Conference. IEEE Global Communications Conference","volume":"2024 ","pages":"4304-4309"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12439101/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145082520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE GLOBECOM 2022 General Chair Welcome Message IEEE GLOBECOM 2022主席欢迎辞

... IEEE Global Communications Conference. IEEE Global Communications Conference Pub Date : 2022-12-04 DOI: 10.1109/gcwkshps56602.2022.10008686

N. Fonseca, José Roberto B. da Marca, S. Bregni, L. Granville

引用次数: 0

Message from the Technical Program Committee Chair 来自技术计划委员会主席的信息

... IEEE Global Communications Conference. IEEE Global Communications Conference Pub Date : 2022-12-04 DOI: 10.1109/globecom48099.2022.10000680

F. Granelli

引用次数: 0

Operations Committee 行动委员会

... IEEE Global Communications Conference. IEEE Global Communications Conference Pub Date : 2022-12-04 DOI: 10.1109/globecom48099.2022.10001734

Faissal El Bouanani

引用次数: 0

Adaptive LLR-based APs selection for Grant-Free Random Access in Cell-Free Massive MIMO 基于自适应llr的大规模MIMO无授权随机接入ap选择

... IEEE Global Communications Conference. IEEE Global Communications Conference Pub Date : 2022-01-01 DOI: 10.1109/GCWkshps56602.2022.10008736

R. Renna, R. D. Lamare

引用次数: 0

Lifelong Autonomous Botnet Detection 终身自主僵尸网络检测

... IEEE Global Communications Conference. IEEE Global Communications Conference Pub Date : 2022-01-01 DOI: 10.1109/GLOBECOM48099.2022.10001436

Alex Medeiros de Araújo, Anderson Bergamini de Neira, M. N. Lima

引用次数: 0

Design of fog computing infrastructures with rotary-wing UAVs 旋翼无人机雾计算基础设施设计

... IEEE Global Communications Conference. IEEE Global Communications Conference Pub Date : 2022-01-01 DOI: 10.1109/GLOBECOM48099.2022.10001523

Rodrigo A. C. da Silva, N. Fonseca

引用次数: 0

The Image of Africa in Ghana’s Press 加纳新闻界的非洲形象

... IEEE Global Communications Conference. IEEE Global Communications Conference Pub Date : 2021-01-01 DOI: 10.11647/obp.0227

M. Serwornoo

引用次数: 1