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Towards Disaggregated NDP Architectures for Large-scale Graph Analytics

Towards Disaggregated NDP Architectures for Large-scale Grap...

引用

2024 Workshops of the international conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Lee, Suyeon Rao, Vishal Gavrilovska, Ada Georgia Institute of Technology Atlanta United States

ISBN: (纸本)9798350355543

The performance of large-scale graph analytics is limited by the capacity and performance of the memory subsystem on the platforms on which they execute. In this paper, we first discuss the limitations of existing approaches to scaling graph processing, and describe how they can be addressed via the use of disaggregated solutions with near-data processing (NDP) capabilities. Using observations from experimental analysis of the trade-offs for different types of graphs and analytics kernels, we identify the systems-level mechanisms that will be required by future graph analytics frameworks for disaggregated NDP architectures. © 2024 ieee.

关键词： computational memory disaggregated systems graph analytics near-data processing

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Research on the Trajectory Prediction Method Basing on Partially Observable Driving Scenario data 7

Research on the Trajectory Prediction Method Basing on Parti...

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7th international conference on Automation Electronics and Electrical Engineering

作者： Lai, Siqi Xu, Shujie Sun, Bohua Xi, Defang Chen, Yu Peng, Dechuang Zhang, Yitong Jilin Univ Natl Key Lab Automot Chassis Integrat & Bion Coll Automot Engn Changchun Peoples R China Connected Technol Co Ltd CATARC Intelligent Tianjin Peoples R China

ISBN: (纸本)9798350377040;9798350377033

Trajectory prediction is a critical component of automated driving systems within the broader context of intelligent transportation. This paper proposes a vehicle trajectory prediction model that operates on partially observable information. By utilizing intersection node data, the model facilitates vehicle re-identification and estimates driving states. It incorporates temporal state sequences of vehicle targets to visualize driving trajectories, thereby contributing to the development of a comprehensive vehicle perception system for digital road networks. The model demonstrates robust stability and reliability, effectively filtering noise during data acquisition and enhancing prediction accuracy. Using real-world road section data, the model predicts blind-spot trajectories within simulation software, with results showing minimal deviation from field-collected data.

关键词： Automated Vehicle Trajectory Prediction Kalman Filter

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Urban Work Zone Detection and Sizing: A data-Centric Training and Topology-Based Inference Approach 26

Urban Work Zone Detection and Sizing: A Data-Centric Trainin...

引用

ieee 26th international conference on intelligent Transportation systems (ITSC)

作者： Zuo, Fan Gao, Jingqin Ozbay, Kaan Bian, Zilin Zhang, Daniel NYU Dept Civil & Urban Engn C2SMART Ctr New York NY 10003 USA

ISBN: (纸本)9798350399462

This study aims to address the challenges of automatically recognizing and sizing work zones in complex urban environments. We developed a deep-learning based work zone object detection model with a data-centric approach to iteratively enhance the model's performance by augmenting a custom training dataset collected from multiple sources, thereby overcoming the sparsity of annotated real-world work zone images. The training data is acquired from traffic cameras, mined from the web, and 3D-simulated work zone images. An innovative topology-based inference method is introduced, using XGBoost, for distinguishing true work zones from nonwork operational zones with some work zone features. We also developed a reference-free work area size estimation method, which utilizes the standard heights of common construction equipment to provide a generalized real-pixel distance approximation. Our model's efficacy is demonstrated with an average mAP of 74.1% across all work zone classes, an accuracy of 98.4% for scene identification, and an accuracy of up to 89.52% for size estimation. Overall, our proposed approach significantly advances the capabilities of automated urban work zone detection and sizing, offering a cost-effective method to fill in the gap for the acquisition of work zone data in real-time by leveraging existing camera infrastructure.

关键词： Object detection

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Securing Healthcare data: Leveraging Blockchain for Electronic Health Records

Securing Healthcare Data: Leveraging Blockchain for Electron...

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2024 ieee international conference on Electronic systems and intelligent computing, ICESIC 2024

作者： Pethuraj, M. Vatchala, L. Gayathri, S. SIMATS Saveetha School of Engineering Department of Mechanical Engineering Tamil Nadu Chennai India SIMATS Saveetha School of Engineering Department of Information Technology Tamil Nadu Chennai India SIMATS Saveetha School of Engineering Department of BioInformatics Tamil Nadu Chennai India

ISBN: (纸本)9798331522988

The expanding use of Electronic Health Records (EHRs) has been a game-changer in healthcare data accessibility and patient care. The privacy, data security, and interoperability problems in traditional EHR systems are also major concerns. The integration of blockchain and mobile cloud computing presents a great potential solution. In combination with blockchain's decentralized and immutable properties, data security can be enhanced through cryptographic techniques and can ensure the availability of a patient's health records on demand from any place using mobile cloud computing. This paper reviews the existing research on blockchain-based EHR sharing platforms and their capabilities of addressing healthcare security, privacy, and efficiency challenges. The research explores the use of smart contracts for automatic access control, and privacy-preserving encryption, and integrates decentralized storage solutions like Interplanetary File System (IPFS). Despite the advantages of blockchain technology, challenges remain, including scalability issues and the lack of compatibility between artificial intelligence (AI) and blockchain systems. Future research should shift its focus toward overcoming these barriers and implementing solutions that better reflect real-world practices. This paper proposes that blockchain-powered mobile cloud computing has the potential to revolutionize EHR management by enabling secure, patient-centered, peer-to-peer data sharing, although numerous challenges still need to be addressed for widespread adoption. © 2024 ieee.

关键词： Electronic health record

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Power data Federated Learning Privacy Policy Based on Optimized Homomorphic Encryption 6

Power Data Federated Learning Privacy Policy Based on Optimi...

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6th ieee international conference on Power, intelligent computing and systems, ICPICS 2024

作者： Zhang, Yiying Lu, Wenjie Yu, Qianao Zhang, Suxiang Li, Wenjing Liang, Kun School of Artificial Intelligence Tianjin University of Science and Technology Tianjin China State Grid Beijing Electric Power Company Information and Communication Department Beijing China State Grid Information & Telecommunication Co. Ltd. Information and Communication Department Beijing China State Grid Information & Communication Industry Group Co. Ltd. Information and Communication Department Beijing China

ISBN: (纸本)9798350374315

With the advent of big data in electricity power, concerns about privacy and security have led most power stations to avoid sharing their electricity data, resulting in 'data islands.' Federated learning emerged to address this, but it still poses privacy risks during training. Balancing privacy protection and data utilization is a current challenge. Federated learning involves exchanging intermediate results, and sending plaintext results can risk privacy breaches. Encryption during training increases computational and communication overhead, affecting efficiency, and is challenging in resource-limited environments. To address these issues, a federated learning model combining differential privacy with optimized Paillier homomorphic encryption has been proposed, ensuring data protection and improving training accuracy. © 2024 ieee.

关键词： Federated learning

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EXPRESS 2.0: An intelligent Service Management Framework for AIoT systems in the Edge 38

EXPRESS 2.0: An Intelligent Service Management Framework for...

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38th ieee/ACM international conference on Automated Software Engineering (ASE)

作者： Xu, Jia Liu, Xiao Pan, Wuzhen Li, Xuejun Yao, Aiting Yang, Yun Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China Deakin Univ Sch Informat Technol Geelong Vic Australia Swinburne Univ Technol Dept Comp Technol Melbourne Vic Australia

ISBN: (纸本)9798350329964

AIoT (Artificial Intelligence of Things) which integrates AI and IoT has received rapidly growing interest from the software engineering community in recent years. It is crucial to design scalable, efficient, and reliable software solutions for large-scale AIoT systems in edge computing environments. However, the lack of effective service management including the support for service collaboration, AI application, and data security in the edge, has seriously limited the development of AIoT systems. To seal this gap, we propose EXPRESS 2.0 which is an intelligent service management framework for AIoT in the edge. Specifically, on top of the existing EXPRESS platform, EXPRESS 2.0 includes the intelligent service collaboration management module, AI application management module, and data security management module. To demonstrate the effectiveness of the framework, we design and implement a last-mile delivery system using both UAVs (Unmanned Aerial Vehicles) and UGVs (Unmanned Ground Vehicles). The EXPRESS 2.0 is open-sourced at https://***/ISEC-AHU/EXPRESS2.0. A video demonstration of EXPRESS 2.0 is at https://***/GHKD_VvJD88.

关键词： AIoT System Edge computing Service Collaboration AI Application data Security

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Next-Generation Intrusion Detection systems with Machine Learning and Deep Learning-Based Firewalls 3

Next-Generation Intrusion Detection Systems with Machine Lea...

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3rd international conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Rani, Ritu Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331515911

The growing complexity of cyber-attacks has underlined the requirement of strong Intrusion Detection systems (IDS) able to instantly recognize a variety of hazards. This work offers a strategy for improving Next-Generation Intrusion Detection systems (NGIDs) combined with firewalls by use of machine learning (ML) and deep learning (DL) approaches. Using the NSL-KDD dataset - which includes both normal and attack network traffic - the work creates a system able to classify traffic patterns into "Normal"and "Attack"categories. The proposed method consists in four phases: data collecting, data preprocessing, feature selection and extraction, and classifier application. In the preprocessing stage, the dataset is cleaned and normalized;hence, suitable feature selection follows to increase classification accuracy. Trained and tested for classification using performance criteria including accuracy, precision, recall, and F1-score, naïve bayes and support vector machine (SVM) models are then compared in efficacy. The results reveal that the SVM classifier has low misclassification and great accuracy (97.29%), therefore proving its prospective use in advanced IDS. The article underlines how several models could be merged inside a firewall system to offer real-time network protection. Future studies will look at using deep learning techniques and ensemble methods to raise detection accuracy even more, therefore allowing NGIDS to be more adaptable and efficient in countering evolving cyber threats. © 2025 ieee.

关键词： Network intrusion

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Envisioning the interactive convergence of Generative AI and Facial Expression Recognition 9

Envisioning the interactive convergence of Generative AI and...

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9th ieee international conference for Convergence in Technology, I2CT 2024

作者： Florance, G. Nirmala, M. Khan, Jumainah Saifudeen Hisham, K.M. New Horizon College of Engineering Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

This paper explores and envisions a collaboration between Facial Expression Recognition (FER) and Generative Artificial Intelligence (GenAI) to create an advanced human-computer interaction (HCI) experience. Deep learning techniques are harnessed to interpret human emotions from facial expressions, using a structured approach that includes pre-processing, feature extraction, and classification. The data acquired from the FER stage informs GenAI systems - such as Generative Pre-trained Transformers (GPTs) - to produce contextually relevant and emotionally resonant responses. The proposed system leverages a sophisticated camera setup to capture facial data with high accuracy under diverse lighting conditions, ensuring robust and reliable emotion inference. Virtual avatars complete the multimodal experience by generating corresponding nonverbal cues. Special attention is given to creating a dynamic feedback loop for continuous learning and adaptation, leading to increasingly effective human-computer dialogues. Ethical implications, such as user privacy, data security, and bias mitigation, are central to the design and deployment of this system, underscoring the commitment to ethical standards and user empowerment. This paper aims to demonstrate how the convergence of FER and GenAI can revolutionize HCI, paving the way for enhanced empathetic and intelligent computing interfaces. © 2024 ieee.

关键词： intelligent systems

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Smart Elevator System with intelligent Floor Level Prioritization using Object Detection

Smart Elevator System with Intelligent Floor Level Prioritiz...

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2024 international conference on Advances in data Engineering and intelligent computing systems, ADICS 2024

作者： Suraparaju, Rohith Varma Anjemeti, Venkata Sai Revathy, S. Hindustan Institute Of Technology And Science Department Of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350364828

The increasing need for effective vertical transit systems in present-day areas has led to the creation of clever elevator system solutions. In order to improve floor-level prioritizing, this study presents a Smart Elevator System (SES) that incorporates innovative object-detecting technologies. The proposed approach automatically alters elevator priority in realtime by using strong computer vision algorithms to identify and assess the number of passengers waiting on each floor. Computer vision algorithms identify and track passengers in realtime, allowing for the dynamic adjustment of elevator priorities based on the object detection count. Through the use of machine learning models, the system can detect user counts and make intelligent adjustments to reduce waiting times and energy usage. The integration of Internet of Things (IoT) devices, which improves system responsiveness overall, Our proposed work shows notable gains in waiting time and energy savings through intelligent floor-level prioritization. © 2024 ieee.

关键词： Object detection

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Design and Simulation of data acquisition system using Ethernet for Zynq 7000 SoC FPGA 16

Design and Simulation of Data Acquisition system using Ether...

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16th international conference on Electronics, Computers and Artificial Intelligence, ECAI 2024

作者： Angadi, Prajwal Satwika, S. Shreya, E. Jamuna, S. Dayananda College of Engineering Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350371154

data acquisition system using Ethernet for Zynq 7000 SoC FPGA, In autonomous vehicles, data transmission is essential for being reliable and for the efficient operation of the vehicle. In this project we are trying to implement the 10G Ethernet MAC for the data transmission. The advanced Driver Assistance System (ADAS) in autonomous vehicles now uses CAN networks, which are not very reliable in today's faster environment. The Ethernet is much faster than the CAN. Therefore, Ethernet is the protocol we're employing to achieve this fast data transmission. Analog data is transformed into digital data and stored in FIFO in this data acquisition system. Ethernet is used to transfer the data that is stored for analysis and visualization. The simulation is the primary emphasis of this project. This simulation's output is organized according to the ieee 802.3 Ethernet standard. This Ethernet output will be of use in ADAS system for increasing the rate of transmission and reliability. © 2024 ieee.

关键词： advanced driver assistance systems

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