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41st International Conference on Machine Learning, ICML 2024

作者： Bai, Xiaofan He, Chaoxiang Ma, Xiaojing Zhu, Bin Benjamin Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China Microsoft United States School of Computer Science and Technology Huazhong University of Science and Technology China Cluster and Grid Computing Lab China

Cloud-based AI services offer numerous benefits but also introduce vulnerabilities, allowing for tampering with deployed DNN models, ranging from injecting malicious behaviors to reducing computing resources. Fingerprint samples are generated to query models to detect such tampering. In this paper, we present Intersecting-Boundary-Sensitive Fingerprinting (IBSF), a novel method for black-box integrity verification of DNN models using only top-1 labels. Recognizing that tampering with a model alters its decision boundary, IBSF crafts fingerprint samples from normal samples by maximizing the partial Shannon entropy of a selected subset of categories to position the fingerprint samples near decision boundaries where the categories in the subset intersect. These fingerprint samples are almost indistinguishable from their source samples. We theoretically establish and confirm experimentally that these fingerprint samples' expected sensitivity to tampering increases with the cardinality of the subset. Extensive evaluation demonstrates that IBSF surpasses existing state-of-the-art fingerprinting methods, particularly with larger subset cardinality, establishing its state-of-the-art performance in black-box tampering detection using only top-1 labels. The IBSF code is available at: https://***/CGCL-codes/IBSF. Copyright 2024 by the author(s)

关键词： HTTP

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Real-Time Traffic Flow Optimization using Adaptive IoT and data Analytics: A Novel DeepStreamNet Model 4

Real-Time Traffic Flow Optimization using Adaptive IoT and D...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Saranya, V.S. Subbarao, Garimidi Balakotaiah, Dega Bhavsingh, Maloth Babu, K. Suresh Dhanikonda, Srinivasa Rao School of Computing Srm Institute of Science and Technology Department of Computing Technologies Chennai603 203 India India Department of Computer Science and Engineering India Ashok: Womens Engineering College Department of Computer Science and Engineering Andhra Pradesh Kurnool India Department of Computer Science and Engineering A.P Narasaraopet India Department of Artificial Intelligence and Data Science Telangana Hyderabad India

ISBN: (纸本)9798331540364

Thisstudy introduces the DeepStreamNet model, an advanced framework for enhancing real-time traffic management in urban environments using adaptive IoT and sophisticated big data analytics. Central to our approach is the integration of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) units, which synergize with IoT devices to facilitate dynamic adaptation to fluctuating traffic conditions. The model excels in operational performance, achieving a notable accuracy of 94.7% in congestion prediction with an exceptionally low latency of only 45 ms per decision cycle and the capability to process up to 50,000 data points per second. These technical achievements have marked significant improvements over traditional traffic management systems, enhancing traffic flow efficiency and reducing congestion effectively. The results underscore the transformative potential of the DeepStreamNet model in urban traffic management and public safety, offering actionable insights for intelligent transportation systems and setting the stage for more informed urban planning. Looking forward, we suggest extending the application of our model to areas such as logistics and broader smart city initiatives, highlighting its adaptability and broad utility in various urban contexts © 2024 IEEE.

关键词： Convolutional neural networks

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An Adjusted Gray Map Technique for Constructing Large Four-Level Uniform Designs

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Journal of Systems science & Complexity 2023年第1期36卷 433-456页

作者： ELSAWAH A M VISHWAKARMA G K MOHAMED H S FANG Kai-Tai Department of Statistics and Data Science Faculty of Science and TechnologyBeijing Normal University-Hong Kong Baptist University United International CollegeZhuhai 519087China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International CollegeZhuhai 519087China Department of Mathematics Faculty of ScienceZagazig UniversityZagazig 44519Egypt Department of Mathematics&Computing Indian Institute of Technology DhanbadDhanbad 826004India College of Transportation and Civil Engineering Fujian Agriculture and Forestry UniversityFuzhou 350002China The Key Lab of Random Complex Structures and Data Analysis The Chinese Academy of SciencesBeijing 100190China

A uniform experimental design(UED)is an extremely used powerful and efficient methodology for designing experiments with high-dimensional inputs,limited resources and unknown underlying models.A UED enjoys the following two significant advantages:(i)It is a robust design,since it does not require to specify a model before experimenters conduct their experiments;and(ii)it provides uniformly scatter design points in the experimental domain,thus it gives a good representation of this domain with fewer experimental trials(runs).Many real-life experiments involve hundreds or thousands of active factors and thus large UEDs are *** large UEDs using the existing techniques is an NP-hard problem,an extremely time-consuming heuristic search process and a satisfactory result is not *** paper presents a new effective and easy technique,adjusted Gray map technique(AGMT),for constructing(nearly)UEDs with large numbers of four-level factors and runs by converting designs with s two-level factors and n runs to(nearly)UEDs with 2^(t−1)s four-level factors and 2tn runs for any t≥0 using two simple transformation *** justifications for the uniformity of the resulting four-level designs are given,which provide some necessary and/or sufficient conditions for obtaining(nearly)uniform four-level *** results show that the AGMT is much easier and better than the existing widely used techniques and it can be effectively used to simply generate new recommended large(nearly)UEDs with four-level factors.

关键词： Aberration adjusted Gray map technique Gray map technique Hamming distance moment aberration threshold accepting algorithm uniform design

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Text Guided Road Marking Point Cloud Completion 6

Text Guided Road Marking Point Cloud Completion

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6th International Conference on Next Generation data-Driven Networks, NGDN 2024

作者： He, Xu Xu, Qihong Luo, Huan Fuzhou University College of Computer and Data Science Fuzhou China Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China

ISBN: (纸本)9798350388374

With the continuous development of intelligent transportation technologies such as autonomous driving and navigation, accurate perception of road markings becomes crucial. However, due to limitations in sensor perspectives and obstacles blocking the view, the scanned point cloud of road markings is often incomplete, potentially leading to erroneous decisions in intelligent transportation systems. Therefore, it becomes imperative to recover complete road markings from these incomplete point clouds. This paper proposes a text-guided road marking completion method, which integrates text information with point cloud data using attention mechanisms. By leveraging text information to guide the network in completing road marking point clouds, the proposed method aims to enhance the perception accuracy and completeness of road markings. Experimental validation on road marking datasets demonstrates the effectiveness and feasibility of the proposed approach. © 2024 IEEE.

关键词： Road and street markings

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Smart Home Based on IoT by Using Machine Learning 1

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6th International Conference on Microelectronics and Telecommunication Engineering, ICMETE 2022

作者： Lavanya, K. Archana, K.S. CSE Vels Institute of Science Technology & Advanced Studies Chennai India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9789811995125

ISBN: (纸本)9789811995118

Smart homes have surfaced as the coming wave of technological advancement. It is a collection of smaller systems connected to home network and comprising a variety of communication technologies which can assist inhabitants’ convenience, security, safety, and comfort. This paper examines several IoT based smart house approaches, which were divided as 2 categories depending on application: smart home security system and smart home automation. We explored several sensors, doors, and communication protocols utilized in IoT-based smart homes in this paper. We also discussed about research problems and also open research objectives. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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GPU Cluster RDMA communication technology and congestion control 23

GPU Cluster RDMA communication technology and congestion con...

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4th International Conference on computing, Networks and Internet of Things, CNIOT 2023

作者： Liang, Guicai Daud, Siti Norbaya Ismail, Nor Alina Binti Built Environment & Information Technology SEGi University Damansara Malaysia Faculty of Data Science and Computing University Malaysia Kelantan Kelantan Malaysia

ISBN: (纸本)9798400700705

This paper discusses Remote Direct Memory Access(RDMA) communication technology and the congestion control methods for Graphics Processing Unit(GPU) clusters. The implementation methods of RDMA networks widely used in GPU clusters are studied. Three implementation modes including InfiniBand, iWARP, and RoCE are analysed with comparison of their performance and applicable environments. Then, based on the analysis of a new congestion controls algorithm, DBCC & CBFC algorithm, is proposed. This algorithm based on delay feedback control and credit flow control prevents network congestion or increased latency in GPU cluster RDMA networks. The working principles of the algorithm are introduced including calculating the adjustment amount of the sending rate, initializing the sender and receiver and mechanisms to handle packet loss and timeout. Experimental results show that the algorithm optimizes network performance with RDMA communication in GPU clusters, while avoiding congestion and minimizing packet loss. However, due to the limitation of experimental conditions, it is not possible to conduct more environmental tests. In practical application, the applicability of the algorithm needs to be carefully evaluated and adjusted according to the specific situations. © 2023 ACM.

关键词： Graphics processing unit

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MusCare+: Muscle Monitoring for Anomalies

MusCare+: Muscle Monitoring for Anomalies

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2022 IEEE World AI IoT Congress, AIIoT 2022

作者： Foley, Nicholas Yu, Chen-Hsiang School of Computing and Data Science Wentworth Institute of Technology BostonMA United States

ISBN: (纸本)9781665484534

Muscles are an essential part of everyday life and any damage or illness that affects them can cause massive problems. Patients who are diagnosed with muscle injuries and illnesses largely remain unmonitored, even though a few appointments they have with doctors annually. Moving from unmonitored to constant monitoring can not only paint a better picture of how a muscle condition is progressing, but it also can inform medical professionals if their treatment regimen is actually working. In this paper, we propose a new system that can monitor muscle health of a patient and predict the muscle conditions. This system mainly focuses on the shoulder but could be expanded to other areas of the body. By utilizing the strength of machine learning and the Android platform, we created a platform that can monitor muscle health quickly and easily. The current prototype system is not only able to display live data gathered from an EMG sensor, but it can also predict whether the muscle is currently flexed or relaxed. Although there is a limitation in current prototype system, a more robust machine learning algorithm could be trained to give a wide array of muscle health predictions. copy;2022 IEEE. © 2022 IEEE.

关键词： Forecasting

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ECLAIR: A Centralized Al-Powered Recommendations System in a Multi-Node EXAIT System 31

ECLAIR: A Centralized Al-Powered Recommendations System in a...

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31st International Conference on Computers in Education, ICCE 2023

作者： Wijerathne, Isanka Flanagan, Brendan Dai, Yiling Ogata, Hiroaki Academic Center for Computing Media Studies Kyoto University Japan Center for Innovative Research and Education in Data Science Kyoto University Japan

ISBN: (纸本)9786269689019

Educational recommender systems are increasingly becoming a core feature of modern educational systems. Often the recommender component of a system is tightly integrated, or might be remotely located without accessing data from other local systems This paper proposes a framework called ECLAIR in which local educational systems can work and share data with a global recommender system that spans multiple educational institutions. In particular an Al-driven recommendation system is intricately integrated within a multi-node learning management system. Situated at the intersection of large-scale data analysis and personalized education, ECLAIR efficiently processes heterogeneous data from diverse LMS, while ensuring data security and privacy. The proposed ECLAIR's architecture, data pipeline, and processing mechanisms are explored in detail, focusing on its seamless integration with the existing infrastructure. By leveraging MongoDB change streams and relational databases, ECLAIR guarantees real-time data synchronization, secure storage, and efficient processing. Its unique Ingestor tool transforms selected xAPI data points into a relational table format, bolstering system functionality. The successful launch of ECLAIR serves as a testament to the potential of Al in enhancing personalized learning experiences, improving data security, and bolstering system efficiency. Nevertheless the paper emphasizes the need for ongoing research, specifically concerning privacy-preserving mechanisms and efficient management of data heterogeneity. It demonstrates ECLAIRs pivotal role in the rapidly evolving landscape of eLearning and its potential for future advancements, scalability, and adaptability, setting a new precedent for Al-powered eLearning recommendation systems. © 2023 Asia-Pacific Society for Computers in Education.

关键词： Recommender systems

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Improving VTE Identification through Adaptive NLP Model Selection and Clinical Expert Rule-based Classifier from Radiology Reports

Improving VTE Identification through Adaptive NLP Model Sele...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Deng, Jamie Wu, Yusen Hayssen, Hilary Englum, Brain Kankaria, Aman Mayorga-Carlin, Minerva Sahoo, Shalini Sorkin, John Lal, Brajesh Yesha, Yelena Nguyen, Phuong University of Miami Frost Institute for Data Science & Computing FL United States University of Maryland School of Medicine MD United States

ISBN: (纸本)9798350337488

Rapid and accurate identification of Venous thromboembolism (VTE), a severe cardiovascular condition including deep vein thrombosis (DVT) and pulmonary embolism (PE), is important for effective treatment. Leveraging Natural Language Processing (NLP) on radiology reports, automated methods have shown promising advancements in identifying VTE events from retrospective data cohorts or aiding clinical experts in identifying VTE events from radiology reports. However, effectively training Deep Learning (DL) and the NLP models is challenging due to limited labeled medical text data, the complexity and heterogeneity of radiology reports, and data imbalance. This study proposes novel method combinations of DL methods, along with data augmentation, adaptive pre-trained NLP model selection, and a clinical expert NLP rule-based classifier, to improve the accuracy of VTE identification in unstructured (free-text) radiology reports. Our experimental results demonstrate the model's efficacy, achieving an impressive 97% accuracy and 97% F1 score in predicting DVT, and an outstanding 98.3% accuracy and 98.4% F1 score in predicting PE. These findings emphasize the model's robustness and its potential to significantly contribute to VTE research. © 2023 IEEE.

关键词： BERT Bi-LSTM Deep Learning NLP Transfer Learning VTE

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DAHA: Accelerating GNN Training with data and Hardware Aware Execution Planning 50th

DAHA: Accelerating GNN Training with Data and Hardware Aware...

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50th International Conference on Very Large data Bases, VLDB 2024

作者： Li, Zhiyuan Jian, Xun Wang, Yue Shao, Yingxia Chen, Lei The Hong Kong University of Science and Technology Hong Kong Shenzhen Institute of Computing Sciences China Beijing University of Posts and Telecommunications China Data Science and Analytics Thrust The Hong Kong University of Science and Technology Guangzhou Hong Kong

Graph neural networks (GNNs) have been gaining a reputation for effective modeling of graph data. Yet, it is challenging to train GNNs efficiently. Many frameworks have been proposed but most of them suffer from high batch preparation cost and data transfer cost for mini-batch training. In addition, existing works have limitations on the device utilization pattern, which results in fewer opportunities for pipeline parallelism. In this paper, we present DAHA, a GNN training framework with data and hardware aware execution planning to accelerate end-to-end GNN training. We first propose a data and hardware aware cost model that is lightweight and gives accurate estimates on per-operation time cost for arbitrary input and hardware settings. Based on the cost model, we further explore the optimal execution plan for the data and hardware with three optimization strategies with pipeline parallelism: (1) group-based in-turn pipelining of batch preparation neural training to explore more optimization opportunities and prevent batch preparation bottlenecks;(2) data and hardware aware rewriting for intra-batch execution planning to improve computation efficiency and create more opportunities for pipeline parallelism;and (3) inter-batch scheduling to further boost the training efficiency. Extensive experiments demonstrate that DAHA can consistently and significantly accelerate end-to-end GNN training and generalize to different message-passing GNN models. © 2024, VLDB Endowment. All rights reserved.

关键词： Cost benefit analysis

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