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Evaluating Performance of SMOTE and ADASYN to Classify Falls and Activities of Daily Living 12th

Evaluating Performance of SMOTE and ADASYN to Classify Falls...

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12th international conference on Soft computing for Problem Solving, SocProS 2023

作者： Kaur, Rajbinder Sharma, Rohini Dhaliwal, Manpreet Kaur Department of Computer Science and Applications Panjab University Chandigarh India

ISBN: (纸本)9789819732913

Class real-life applications generate skewed data that leads to degraded performance of machine learning models. these methods are biased towards the majority class and cause overfitting problems. Oversampling techniques are used by researchers to balance the dataset by increasing minority class samples which increases the performance of the classifier. Detecting falls and activities of daily living accurately improves the quality of life, particularly for elderly people. In this study, real-time data pertaining to falls and activities are collected using a working prototype. Experiments are performed to balance the data using SMOTE and ADASYN oversampling techniques. three machine learning models, KNN, RF and CART, with and without parameter tuning, are used for the classification of different types of activities and falls. the results prove that ADASYN and SMOTE improve the accuracy of classification and ADASYN performs better than SMOTE. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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SentiGCN: Graph Contrastive Learning for Recommendation Based on Sentiment Analysis 12

SentiGCN: Graph Contrastive Learning for Recommendation Base...

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12th international conference on Information systems and computing Technology

作者： Lei, Fei Cao, Qian Wang, Xiaofeng Ao, Dun Beijing Univ Technol Coll Artificial Intelligence & Automat Beijing Peoples R China

ISBN: (纸本)9798350379860;9798350379877

Recommendation systems play a critical role in web applications (e.g.e-commerce sites, etc.), so as to alleviate data overload and suggest preferred items for users. Due to the limited interactions between users and items in real-life scenarios, contrastive learning has recently been adopted to enhance recommendation systems. However, most existing CL(Contrastive Learning)-based methods typically construct contrastive views through node or edge perturbation, which may disrupt the intrinsic semantic structure in user-item interaction graphs. And, these methods also neglect user preferences and emotional tendencies in reviews. therefore, this paper proposes a novel sentiment-aware graph contrastive learning method (SentiGCN), which can comprehensively explore information from both review content and user-item graphs. Firstly, sentiment information is extracted from reviews using Natural Language Processing (NLP) tools. then, the two graph structures integrating this information are constructed. Moreover, two contrastive strategies are designed to incorporate self-supervised signals. For the user-item interaction graph with sentiment features, a scheme involving the addition of random noise in the embedding space is applied for graph augmentation. For the item-item neighbor graph based on sentiment similarity, a cross-layer contrastive scheme is implemented to further explore the potential associations between items. Finally, extensive experiments on three datasets confirm the efficacy of SentiGCN.

关键词： Recommendation systems Sentiment Analysis Contrastive Learning Graph Neural Networks

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Safety-Aware Implementation of Control Tasks via Scheduling with Period Boosting and Compressing 29

Safety-Aware Implementation of Control Tasks via Scheduling ...

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29th IEEE international conference on embedded and real-time computing systems and applications, RTCSA 2023

作者： Xu, Shengjie Ghosh, Bineet Hobbs, Clara thiagarajan, P.S. Joshi, Prachi Chakraborty, Samarjit The University of North Carolina at Chapel Hill United States Chennai Mathematical Institute India General Motors United States

ISBN: (纸本)9798350337860

A crucial requirement for control tasks in safety-critical systems like automotive is that all deadlines be met. this is becoming increasingly difficult when several tasks share common resources. One main reason for this lies in obtaining tight WCET estimations, especially as software and processor architectures continue to become more complex. Using safe but not necessarily tight WCET estimates and meeting all deadlines come at the expense of very pessimistic and inefficient implementations. In this paper, we show that by focusing on 'higher-level' properties like control safety, instead of trying to meet all deadlines, it is possible to achieve more efficient implementations of control tasks on shared resources. this has considerable benefits in cost-sensitive domains like automotive. the core of our technique follows the AUTOSAR paradigm where groups of control computations with the same period constitute units of scheduling. Towards this, we suitably increase (boost) or decrease (compress) the sampling periods of control tasks and schedule them in a manner that is cognizant of their high-level safety constraints, but does not necessarily meet all deadlines. Our results for several standard controllers from the automotive domain illustrate the benefits of our approach. © 2023 IEEE.

关键词： real time systems

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EXTENDING POWER OF NATURE FROM BINARY TO real-VALUED GRAPH LEARNING IN real WORLD 12

EXTENDING POWER OF NATURE FROM BINARY TO REAL-VALUED GRAPH L...

引用

12th international conference on Learning Representations, ICLR 2024

作者： Wu, Chunshu Song, Ruibing Liu, Chuan Yang, Yunan Li, Ang Huang, Michael Geng, Tong Department of Electrical and Computer Engineering University of Rochester United States Department of Mathematics Cornell University United States Physical & Computational Sciences Directorate Pacific Northwestern National Laboratory United States

Nature performs complex computations constantly at clearly lower cost and higher performance than digital computers. It is crucial to understand how to harness the unique computational power of nature in Machine Learning (ML). In the past decade, besides the development of Neural Networks (NNs), the community has also relentlessly explored nature-powered ML paradigms. Although most of them are still predominantly theoretical, a new practical paradigm enabled by the recent advent of CMOS-compatible room-temperature nature-based computers has emerged. By harnessing a dynamical system's intrinsic behavior of chasing the lowest energy state, this paradigm can solve some simple binary problems delivering considerable speedup and energy savings compared with NNs, while maintaining comparable accuracy. Regrettably, its values to the real world are highly constrained by its binary nature. A clear pathway to its extension to real-valued problems remains elusive. this paper aims to unleash this pathway by proposing a novel end-to-end Nature-Powered Graph Learning (NP-GL) framework. Specifically, through a three-dimensional co-design, NP-GL can leverage the spontaneous energy decrease in nature to efficiently solve real-valued graph learning problems. Experimental results across 4 real-world applications with 6 datasets demonstrate that NP-GL delivers, on average, 6.97 × 103 speedup and 105 energy consumption reduction with comparable or even higher accuracy than Graph Neural Networks (GNNs). © 2024 12th international conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Dynamical systems

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SSE-YOLO: A Lightweight Model for Efficient Fire Detection in real-time applications 5

SSE-YOLO: A Lightweight Model for Efficient Fire Detection i...

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5th international conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Qiu, Zhike Qin, Yuhao Southwest University Business College Chongqing402460 China

ISBN: (纸本)9798331506612

the SSE-YOLO model, optimized for object detection, has exhibited exceptional effectiveness in fire detection tasks, particularly within environments where computational resources are limited, such as embedded devices and edge computing platforms. While the standard YOLOv8 model achieves commendable accuracy, its substantial computational load and memory requirements present obstacles for its adoption in embedded systems. In response to these limitations, this paper introduces the SSE-YOLO model, which integrates the SimAM module, a Slim-neck architecture, and a Shared Convolutional Detection head to enhance performance. the SimAM module is incorporated into the backbone network to improve feature extraction efficiency without increasing parameter counts. Additionally, the Slim-neck architecture is applied to streamline the model, reducing both computational burden and parameter complexity. Lastly, the Shared Convolutional Detection head is employed to further boost the model's computational efficiency, thereby lowering resource usage during inference. Experimental evaluations on a fire detection dataset reveal that SSE-YOLO reduces model complexity and parameters by 45% and 31%, respectively, compared to YOLOv8n, while achieving improvements of 1.8% in mAP@0.5 and 3.8% in mAP@0.5:0.95. these findings demonstrate that SSE-YOLO delivers significant reductions in computational requirements while sustaining high detection performance, making it an ideal candidate for real-time fire detection on mobile and embedded systems. © 2024 IEEE.

关键词： Fire detectors

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ADVANCING EARLY CHILDHOOD EDUCATION: A CONCEPTUAL FRAMEWORK FOR ARTIFICIAL INTELLIGENCE applications IN EARLY CHILDHOOD EDUCATION 18

ADVANCING EARLY CHILDHOOD EDUCATION: A CONCEPTUAL FRAMEWORK ...

引用

18th international conference on e-Learning and Digital Learning, ELDL 2024 and 12th international conference on Sustainability, Technology and Education, STE 2024, Part of the 18th Multi conference on Computer Science and Information systems 2024, MCCSIS 2024

作者： Alomyan, Hesham University of Petra Jordan

ISBN: (纸本)9789898704573

Artificial intelligence (henceforth AI) is one of the latest innovations in education, which provides new ways of improving learning in different educational settings. In particular, owning to their ability, the adoption of AI technologies at the preschool level can enhance and transform the teaching-learning process in as far as young students are concerned. the present paper introduces a conceptual framework of integrating AI into early childhood education. It is based on analyzing recent studies and literature that have discussed various components and considerations that should be taken into account when AI tools are used in education. the framework encompasses a wide range of practical applications such as AI-based adaptive learning systems, real-time assessment tools, tutoring systems, interactive activities, language learning applications, assessment and progress monitoring tools, assistive technologies, professional development resources, recommendation systems, and communication platforms. the suggested framework herein seeks to help instructional designers and teachers create inclusive and personalized learning experiences for young children. © 2024 e-Learning and Digital Learning. All Rights Reserved.

关键词： Federated learning

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Multiobject Detection and Distance Estimation System for Enhanced Spatial Awareness in the Visually Impaired People 12th

Multiobject Detection and Distance Estimation System for Enh...

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12th international conference on Frontiers of Intelligent computing: theory and applications, FICTA 2024

作者： Reddy Muley, Rajesh BalaChowdeswari, M. Reshma, Sk. Siva Prakash, P. Jaya Prakash, Budeti Desai, Smita Department of Information Technology Lakireddy Bali Reddy College of Engineering Andhra Pradesh Mylavaram521230 India Electronics and Telecommunication Department Dr.D.Y Patil Institute of Technology Pune India

ISBN: (纸本)9789819601424

this research study introduces cutting-edge assistive technology that provides precise distance calculation and real-time object identification to enable people with vision impairments. With the help of the MobileNetV3 trained model, the system can recognize up to 91 distinct items at once, providing a thorough awareness of the user's surroundings. In addition to identifying particular items, the system evaluates the distances between various things in the field of vision and between the user and those objects. It utilizes sophisticated computer vision algorithms to provide reliable object detection and depth estimates, therefore mitigating the difficulties encountered by those with visual impairments. By using a well-crafted aural interface to provide distance information, the audio feedback function improves the user experience. through the provision of accurate and timely spatial layout information, our technology seeks to enhance the ability of visually impaired people to navigate and to become independently mobile. In order to guarantee successful integration into the everyday life of the visually handicapped, the research also addresses the difficulties in designing such systems, such as the requirement for real-time processing, environmental adaptation, and user-friendly aural feedback. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： real time systems

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Performance Acceleration of Secure Machine Learning Computations for Edge applications 28

Performance Acceleration of Secure Machine Learning Computat...

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28th IEEE international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Lin, Zi-Jie Wang, Chuan-Chi Tu, Chia-Heng Hung, Shih-Hao Natl Taiwan Univ Taipei Taiwan Natl Cheng Kung Univ Tainan Taiwan

ISBN: (数字)9781665453448

ISBN: (纸本)9781665453448

Edge appliances built with machine learning applications have been gradually adopted in a wide variety of application fields, such as intelligent transportation, the banking industry, and medical diagnosis. Privacy-preserving computation approaches can be used on smart appliances in order to secure the privacy of sensitive data, including application data and the parameters of machine learning models. Nevertheless, the data privacy is achieved at the cost of execution time. that is, the execution speed of a secure machine learning application is several orders of magnitude slower than that of the application in plaintext. Especially, the performance gap is enlarged for edge appliances. In this work, in order to improve the execution efficiency of secure applications, an open-source software framework CrypTen is targeted, which is widely used for building secure machine learning applications using the Secure Multi-Party Computation (SMPC) based privacy-preserving computation approach. We analyze the performance characteristics of the secure machine learning applications built with CrypTen, and the analysis reveals that the communication overhead hinders the execution of the secure applications. To tackle the issue, a communication library, OpenMPI, is added to the CrypTen framework as a new communication backend to boost the application performance by up to 50%. We further develop a hybrid communication scheme by combining the OpenMPI backend with the original communication backend with the CrypTen framework. the experimental results show that the enhanced CrypTen framework is able to provide better performance for the small-size data (LeNet5 on MNIST dataset by up to 50% of speedup) and maintain similar performance for large-size data (AlexNet on CIFAR-10), compared to the original CrypTen framework.

关键词： Edge appliance communication performance communication library OpenMPI library secure multi-party computation deep learning model inference machine learning applications CrypTen framework

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Applicability of Deep Learning Model Trainings on embedded GPU Devices: An Empirical Study

Applicability of Deep Learning Model Trainings on Embedded G...

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12th Mediterranean conference on embedded computing, MECO 2023

作者： Chou, Po-Hsuan Wang, Chao Mei, Chih-Shuo National Taiwan Normal University Dept. of Computer Science and Information Engineering Taipei City Taiwan

ISBN: (纸本)9798350322910

the wide applications of deep learning techniques have motivated the inclusion of both embedded GPU devices and workstation GPU cards into contemporary Industrial Internet-of-things (IIoT) systems. Due to substantial differences between the two types of GPUs, deep-learning model training in its current practice is run on GPU cards, and embedded GPU devices are used for inferences or partial model training at best. To supply with empirical evidence and aid the decision of deep learning workload placement, this paper reports a set of experiments on the timeliness and energy efficiency of each GPU type, running both Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model training. the results suggest that embedded GPUs did save the total energy cost despite the longer response time, but the amount of energy saving might not be significant in a practical sense. Further in this paper we report a case study for prognostics applications using LSTM. the results suggest that, by comparison, an embedded GPU may save about 90 percent of energy consumption at the cost of doubling the application response time. But neither the save in energy cost nor the increase in response time is significant enough to impact the application. these findings suggest that it may be feasible to place model training workload on either workstation GPU or embedded GPU. © 2023 IEEE.

关键词： embedded systems

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real-time Anomaly Detection Using Distributed Tracing in Microservice Cloud applications 12

Real-Time Anomaly Detection Using Distributed Tracing in Mic...

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12th international conference on Cloud Networking (CloudNet)

作者： Raeiszadeh, Mahsa Ebrahimzadeh, Amin Saleem, Ahsan Glitho, Roch H. Eker, Johan Mini, Raquel A. F. Concordia Univ CIISE Montreal PQ Canada Ericsson Res Lund Sweden Univ Western Cape Comp Sci Programme Cape Town South Africa

ISBN: (纸本)9798350313062;9798350313079

Distributed tracing plays a vital role in microservice infrastructure, and learning-based trace analysis has been utilized to detect anomalies within such systems. However, existing approaches for learning-based trace-based anomaly detection face certain limitations. Some assume that trace patterns can be learned solely from normal executions, while others depend on anomaly injection to generate labeled traces categorized as normal or anomalous. However, in practical scenarios, anomalies may also happen during the normal execution. Moreover, a wide variety of anomalies may occur in practice, which cannot be captured solely through anomaly injection. To address these issues, we propose a Trace-Driven Anomaly Detection (TDAD) approach based on a Span Causal Graph (SCG) representation, which trains a model using a Graph Neural Network (GNN) and Positive and Unlabeled (PU) learning. this technique allows the model parameters to be optimized by estimating the underlying data distribution. As a result, TDAD can be effectively trained using a small number of labeled anomalous traces along with a relatively large number of unlabeled traces. Our evaluation reveals that TDAD outperforms not only the existing unsupervised trace-based anomaly detection methods by 11.9% in terms of F-1-score but also a supervised learning-based benchmark by 12x in terms of detection time.

关键词： Anomaly Detection Distributed Tracing Microservice Positive and Unlabeled Learning

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