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检索条件"任意字段=Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing"

共 334 条记录，以下是11-20 订阅

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LonelyTracker: An IoT Solution with Multimodal Data and Split computing for Secure Loneliness Detection 25

LonelyTracker: An IoT Solution with Multimodal Data and Spli...

引用

proceedings of the 40th acm/sigapp symposium on applied computing

作者： Tarannum Ara Bivas Mitra Computer Science & Engineering Indian Institute of Technology Kharagpur Kharagpur West Bengal India

ISBN: (纸本)9798400706295

Loneliness is a critical public health issue, linked to increased morbidity and cognitive decline. While previous studies have explored loneliness detection using smartphones and wearables, they often rely on phone calls, SMS, and app usage, which may not fully capture loneliness's complexity. Others focus on limited physiological signals, but both approaches raise privacy concerns. In this paper, we introduce LonelyTracker, a privacy-aware device designed to detect loneliness through continuous monitoring of physiological signals and daily activity patterns. Our system integrates wearable data, such as heart rate variability (HRV), electrodermal activity (EDA), and mobility data from IMU sensors, to develop a comprehensive loneliness detection model. We use a deep neural network (DNN) with a split computing framework to ensure data privacy, processing sensor data locally and sending anonymized feature representations to the cloud for model training. Our 10-week study with 20 participants shows that combining multimodal data improves accuracy, achieving results between 81.1% and 96.5%. Furthermore, LonelyTracker outperforms smartphone-based solutions in energy efficiency and real-time performance, demonstrating its potential for improved mental health monitoring.

关键词： loneliness detection

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Language Flavors in the Lusophone World: A BERT-Based Social Media Study of Portuguese in Brazil, Portugal, and Mozambique 25

Language Flavors in the Lusophone World: A BERT-Based Social...

引用

proceedings of the 40th acm/sigapp symposium on applied computing

作者： Annie Amorim Gabriel Assis Laura Ribeiro Daniel de Oliveira Aline Paes Institute of Computing Universidade Federal Fluminense Niterói RJ Brazil

ISBN: (纸本)9798400706295

Social networks have become invaluable data sources, enabling many studies about human behavior, language, and social prediction. Users commonly propose new expressions to make communication faster, more fluid, and more original. Moreover, although they follow a specific idiom, the employed language is also mixed-coded, making even the same idiom go beyond the usual variations. this way, texts in Portuguese from different regions that already have linguistic, syntactic, lexical, and orthographic differences go to a new level when written in social media posts. this paper proposes relying on Portuguese language models to analyze the variations of Portuguese in social media posts in Brazil, Portugal, and Mozambique. Our methodology involves pre-trained BERT-based models tuned for the task of text classification and the analysis of the attribution value of the models in the tokens. Moreover, we propose inspecting the ability of the models to predict masked terms specific to regional variants. the analysis reveals that models trained on one variation of Portuguese can perform well on more than one variation. However, there are still challenges in distinguishing between the Portuguese and Mozambican variants.

关键词： NLP

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WMN-CDA: Contrastive Domain Adaptation for Wireless Mesh Network Configuration 25

WMN-CDA: Contrastive Domain Adaptation for Wireless Mesh Net...

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proceedings of the 40th acm/sigapp symposium on applied computing

作者： Aitian Ma Mo Sha Knight Foundation School of Computing and Information Sciences Florida International University Miami FL USA

ISBN: (纸本)9798400706295

Wireless Mesh Networks (WMNs) have become essential for a wide range of applications, such as industrial automation, environmental monitoring, and smart cities. Network operators encounter significant challenges when selecting WMN parameters to ensure good network performance under various ambient operating conditions. Current domain adaptation methods designed for WMN configurations transfer the network configuration knowledge learned from simulations (source domain) to the physical deployment (target domain) at the domain level and fail to consider the simulation-to-reality gap variance under different network configurations, which causes misalignment and overfitting issues. To address such issues, we introduce the WMN Contrastive Domain Adaptation (WMN-CDA) framework, which leverages contrastive learning to transfer the network configuration knowledge learned from simulations to the physical deployment in a discriminative way at a granular scale. WMN-CDA employs the Network Configuration and Simulation-to-Reality contrastive losses to align feature representations and provide good network configuration predictions for physical deployments. We have implemented WMN-CDA and evaluated it with the data collected from a physical testbed with 50 devices and four wireless simulators. Experimental results show significant improvements over the baseline.

关键词： cyber-physical systems

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Energy-Aware Vision Model Partitioning for Edge AI 25

Energy-Aware Vision Model Partitioning for Edge AI

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proceedings of the 40th acm/sigapp symposium on applied computing

作者： Dewant Katare Mengying Zhou Yang Chen Marijn Janssen Aaron Yi Ding Delft University of Technology Delft Netherlands Fudan University Shanghai China

ISBN: (纸本)9798400706295

Deploying scalable Vision Transformer applications on mobile and edge devices is constrained by limited memory and computational resources. Existing model development and deployment strategies include distributed computing and inference methods such as federated learning, split computing, collaborative inference and edge-cloud offloading mechanisms. While these strategies have deployment advantages, they fail to optimize memory usage and processing efficiency, resulting in increased energy consumption. this paper optimizes energy consumption by introducing adaptive model partitioning mechanisms and dynamic scaling methods for ViTs such as EfficientViT and TinyViT, adjusting model complexity based on the available computational resources and operating conditions. We implement energy-efficient strategies that minimize inter-layer communication for distributed machine learning across edge devices, thereby reducing energy consumption from data flow and computation. Our evaluations on a series of benchmark models show improvements, including up to a 32.6% reduction in latency and 16.6% energy savings, while maintaining mean average precision sacrifices within 2.5 to 4.5% of baseline models. these results show that our proposal is a practical approach for improving edge AI sustainability and efficiency.

关键词： energy-aware computing

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Unsupervised Training Framework for 3D Point Cloud Object Detection Model 25

Unsupervised Training Framework for 3D Point Cloud Object De...

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proceedings of the 40th acm/sigapp symposium on applied computing

作者： Min-Jyun Wu Hsiang-Jui Lin Shih-Hao Hung Chi-Sheng Shih Dept. of Computer Science and Information Engineering National Taiwan University Taipei Taiwan High Performance and Scientific Computing (HPSC) Center National Taiwan University Taipei Taiwan Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan Center of Artificial Intelligence and Advanced Robotics National Taiwan University Taipei Taiwan

ISBN: (纸本)9798400706295

Point cloud data frames are critical, if not indispensable, for precise robot navigation and localization, but training the object detection models for them remains challenging. Many models require labeled 3D objects to train the model. However, the sparse and occluded 3D point cloud data make it difficult, if not impossible, to automate the labeling process. this work proposes a training framework to generate 3D labels on point clouds to tackle the aforementioned challenges. the proposed method takes advantage of the consecutive presence of the same object on different frames to automate the labeling process. the experimental results show that the unsupervised framework trains a robust model for 3D object detection. On the roadside data, the model archives 90.27% AP for scooters and 91.33% AP for cars. On nuScenes dataset, the framework demonstrates the detection precision doubles on IoU 0.25 and IoU 0.5 when the recalls remain similar, compared to the model trained by the MODEST framework.

关键词： computer systems organization

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Advanced Knowledge Transfer: Refined Feature Distillation for Zero-Shot Quantization in Edge computing 25

Advanced Knowledge Transfer: Refined Feature Distillation fo...

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proceedings of the 40th acm/sigapp symposium on applied computing

作者： Inpyo Hong Youngwan Jo Hyojeong Lee Sunghyun Ahn Sanghyun Park Department of Computer Science Yonsei University Seoul Republic of Korea Department of Artificial Intelligence Yonsei University Seoul Republic of Korea

ISBN: (纸本)9798400706295

We introduce AKT (Advanced Knowledge Transfer), a novel method to enhance the training ability of low-bit quantized (Q) models in the field of zero-shot quantization (ZSQ). Existing research in ZSQ has focused on generating high-quality data from full-precision (FP) models. However, these approaches struggle with reduced learning ability in low-bit quantization due to its limited information capacity. To overcome this limitation, we propose an effective training strategy compared to data generation. Particularly, we analyzed that refining feature maps in the feature distillation process is an effective way to transfer knowledge to the Q model. Based on this analysis, AKT efficiently transfers core information from the FP model to the Q model. AKT is the first approach to utilize both spatial and channel attention information in feature distillation in ZSQ. Our method addresses the fundamental gradient exploding problem in low-bit Q models. Experiments on CIFAR-10 and CIFAR-100 datasets demonstrated the effectiveness of the AKT. Our method led to significant performance enhancement in existing generative models. Notably, AKT achieved significant accuracy improvements in low-bit Q models, achieving state-of-the-art in the 3-bit and 5-bit scenarios on CIFAR-10. the code is available at https://***/Inpyo-Hong/AKT-Advanced-knowledge-Transfer.

关键词： AI for edge computing

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PreMatch: A Prediction-based Scheduler for Dataflow Asynchronous Processing on the computing Continuum 25

PreMatch: A Prediction-based Scheduler for Dataflow Asynchro...

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proceedings of the 40th acm/sigapp symposium on applied computing

作者： Narges Mehran Zahra Najafabadi Samani Samira Afzal Radu Prodan Frank Pallas Peter Dorfinger Intelligent Connectivity Group Salzburg Research Forschungsgesellschaft mbH Salzburg Salzburg Austria Faculty of Digital and Analytical Sciences Paris Lodron University of Salzburg Salzburg Salzburg Austria Distributed and Parallel Systems Group University of Innsbruck Innsbruck Tyrol Austria Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Overijssel Netherlands Institute of Information Technology Alpen-Adria-University of Klagenfurt Klagenfurt Carinthia Austria

ISBN: (纸本)9798400706295

the popularity of asynchronous data exchange patterns has recently increased, as evidenced by 23% of the communication between microservices in an Alibaba trace analysis. Such workloads necessitate methods for reducing dataflow processing and completion time by forecasting the future requirements of their microservices and (re-)scheduling them. therefore, we investigate a prediction-based scheduling method of asynchronous dataflow processing applications by considering the stochastic changes due to dynamic user requirements. We present a microservice scaling and scheduling method named PreMatch combining a machine learning (ML) prediction strategy based on gradient boosting with ranking and game theory matching scheduling principles. Firstly, PreMatch predicts the number of microservice replicas, and then, the ranking method orders the microservice replicas and devices based on microservice and transmission times. thereafter, the PreMatch schedules microservice replicas requiring dataflow processing on computing devices. Experimental analysis of the PreMatch method shows lower completion times on average 13% compared to a related prediction-based scheduling method.

关键词： microservice

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proceedings of the 37th acm/sigapp symposium on applied computing, SAC 2022

Proceedings of the 37th ACM/SIGAPP Symposium on Applied Comp...

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37th acm/sigapp symposium on applied computing, SAC 2022

ISBN: (纸本)9781450387132

the proceedings contain 272 papers. the topics discussed include: residual size is not enough for anomaly detection: improving detection performance using residual similarity in multivariate time series;CSP specification and verification of relay-based railway interlocking systems;on enduring more data through enabling page rewrite capability on multi-level-cell flash memory;RAM: exploiting restrained and approximate management for enabling neural network training on NVM-based systems;CIIA: critical infrastructure impact assessment;ontology-based user privacy management in the smart grid;efficient and scalable geographical peer matching for P2P energy sharing communities;planetary digital twin: a case study in aquaculture;a framework for representing internet of things security and privacy policies and detecting potential problems;and energy-efficient fog computing-enabled data transmission protocol in tactile internet-based applications.

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Collaborative filtering through weighted similarities of user and item embeddings 25

Collaborative filtering through weighted similarities of use...

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proceedings of the 40th acm/sigapp symposium on applied computing

作者： Pedro R. Pires Rafael T. Sereicikas Gregorio F. Azevedo Tiago A. Almeida Department of Computing (DComp) Federal University of São Carlos (UFSCar) Sorocaba SP Brazil

ISBN: (纸本)9798400706295

In recent years, neural networks and other complex models have dominated recommender systems, often setting new benchmarks for state-of-the-art performance. Yet, despite these advancements, award-winning research has demonstrated that traditional matrix factorization methods can remain competitive, offering simplicity and reduced computational overhead. Hybrid models, which combine matrix factorization with newer techniques, are increasingly employed to harness the strengths of multiple approaches. this paper proposes a novel ensemble method that unifies user-item and item-item recommendations through a weighted similarity framework to deliver top-N recommendations. Our approach is distinctive in its use of shared user and item embeddings for both recommendation strategies, simplifying the architecture and enhancing computational efficiency. Extensive experiments across multiple datasets show that our method achieves competitive performance and is robust in varying scenarios that favor either user-item or item-item recommendations. Additionally, by eliminating the need for embedding-specific fine-tuning, our model allows for the seamless reuse of hyperparameters from the base algorithm without sacrificing performance. this results in a method that is both efficient and easy to implement. Our open-source implementation is available at https://***/UFSCar-LaSID/weightedsims-recommender.

关键词： recommender systems

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proceedings of the 38th acm/sigapp symposium on applied computing, SAC 2023

Proceedings of the 38th ACM/SIGAPP Symposium on Applied Comp...

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38th Annual acm symposium on applied computing, SAC 2023

ISBN: (纸本)9781450395175

the proceedings contain 259 papers. the topics discussed include: EdGCon: auto-assigner of iconicity ratings grounded by lexical properties to aid in generation of technical gestures;a semantic evidence-based approach to continuous cloud service certification;digital forgetting using key decay;on the measurement of performance metrics for virtualization-enhanced architectures;COSTA: a cost-driven solution for migrating applications in multi-cloud environments;towards a high-interaction physics-aware honeynet for industrial control systems;enhancing polar codes efficiency on 3D flash memory by exploiting multiple error variations;traffic intersections as agents: a model checking approach for analyzing communicating agents;an extensible framework for implementing byzantine fault-tolerant protocols;detection of adversarial attacks by observing deep features with structured data algorithms;and proof of swarm based ensemble learning for federated learning applications.

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