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检索条件"任意字段=23rd International Conference on Computer Systems and Technologies, CompSysTech 2022"

共 798 条记录，以下是41-50 订阅

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Two Point Correlation Analysis of Cancer Cell Structure Formation 12

Two Point Correlation Analysis of Cancer Cell Structure Form...

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Joint 12th international conference on Soft Computing and Intelligent systems / 23rd international Symposium on Advanced Intelligent systems (SCIS and ISIS)

作者： Tsuchiya, Tomoka Nakano, Tadashi Tang, William C. Osaka Metropolitan Univ Grad Sch Informat Osaka Japan Univ Calif Irvine Dept Biomed Engn Irvine CA USA

ISBN: (纸本)9781665499248

Recent studies in cancer biology show that cancer cell populations form complex vascular-like fluid-conducting networks for their growth. Quantitative assessment of such complex multicellular structures helps advance cancer biology. The work in this paper uses the two-point correlation function to analyze multicellular structures originated from cancer cell populations. The data collected from experiments and simulations shows that the type of structure that a cell population forms eventually depends on its cell density and spatial distribution at an early stage. Based on this finding, we postulate an alternative cancer therapy that aims at controlling the spatial distribution of cancer cells at an early stage and to suppress formation of vascular-like fluid-conducting networks.

关键词： Cancer structure formation vasculogenic mimicry two point correlation analysis computer simulation

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Application of On-Road Surveillance Cameras for Traffic Light Optimization 12

Application of On-Road Surveillance Cameras for Traffic Ligh...

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Joint 12th international conference on Soft Computing and Intelligent systems / 23rd international Symposium on Advanced Intelligent systems (SCIS and ISIS)

作者： Hassan, Mohammad Mehedi Karungaru, Stephen Terada, Kenji Tokushima Univ Grad Sch Sci & Technol Innovat Tokushima Japan

ISBN: (纸本)9781665499248

In this work, we propose an application of on-road surveillance cameras for traffic light optimization. The experiments consist of two parts one is data collection and another is traffic light optimization. In the data collection part, data were collected from the main road with one conjunction. Vehicles and Pedestrians were detected using the Yolov3 method. For lane detection, the Hough transform was used. After collecting the data traffic lights were optimized by some rules. Moreover, due to lack of data and sensitivity, the Simulation of Urban MObility (SUMO) simulator was also used for experiments. We achieved traffic light optimization by 15 seconds in real-time experiments while in the simulator, the time difference between the normal and optimized situation was 129 seconds to clear all the vehicles.

关键词： computer vision Road-safety Vehicle detection Pedestrian's detection Traffic Light Optimization Hough transform

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A CAN Bus anomaly Detection Based on FLXGBoost Algorithm 23

A CAN Bus anomaly Detection Based on FLXGBoost Algorithm

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on Data Science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Wang, Jie Mo, Xiuliang School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9781665494571

Along with the networking and automation of smart vehicles and the accelerated application of information and communication technologies, Controller Area Network (CAN) has become the most commonly used protocol in automotive bus networks, but it cannot protect the in-vehicle network from attacks because it does not provide a security system against network and physical attacks, so it is necessary to develop anomaly detection countermeasures to secure the CAN bus. This article proposes a CAN bus anomaly detection model for masquerade attacks, mining the distribution characteristics of each byte of Data messages of all IDs without the bus message identification function, combining the information entropy of message IDs to compose a new data set;introducing a focal loss function to improve the XGBoost algorithm for the case of sample imbalance, and realizing the FLXGBoost-based algorithm. The experimental results show that the anomaly detection model has a high accuracy rate in resisting tampered data domain messages. © 2021 IEEE.

关键词： Protocols Smart cities Computational modeling Memory management Feature extraction Data models Information and communication technology

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23rd international conference on Next Generation Wired/Wireless Networking, NEW2AN 2023, and 16th conference on Internet of Things and Smart Spaces, ruSMART 2023

23rd International Conference on Next Generation Wired/Wirel...

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23rd international conference on Next Generation Wired/Wireless Networking, NEW2AN 2023, and 16th conference on Internet of Things and Smart Spaces, ruSMART 2023

ISBN: (纸本)9783031609961

The proceedings contain 67 papers. The special focus in this conference is on Next Generation Wired/Wireless Networking. The topics include: Detecting PII Leakage Using DPI and Machine Learning in an Enterprise Environment;deep Learning for Preventing Botnet Attacks on IoT;Design of Multi-polarisation MIMO Antenna for Heterogeneous Smartphone Applications;potential Possibilities of Voice Pattern Recognition by a Distributed Fiber Optic Sensor;Enhancing Wireless Connectivity Through Bayesian-Optimized UAV-BS Positioning and Charging;comparison of Centralized and Federated Machine Learning Techniques for Beamtracking in 5G/6G systems;exploring the Efficiencies and Vulnerabilities of Smart Door Control systems: A Systematic Review;System-Level Model for SINR and HPBW Evaluation in 5G mmWave UDN with Location-Aware Beamforming;reinforcement Learning Based Power Allocation for 6G Heterogenous Networks;The Impact of Capacity Averaging in Packet-Level Modeling of 5G NR with Blockage and Micromobility;Interference Mitigation for Reconfigurable Intelligent Surface (RIS)-Aided Non-terrestrial Base Station (NTBS) in NOMA Downlink HetNets;modeling a Digital Avatar of a Car Drivers Based on the Quantification of the Information Environment;A New Blockage Detection Approach for 6G THz systems;real-Time Anomaly Detection in Network Traffic Using Graph Neural Networks and Random Forest;addressing Security and Privacy Issues in a Smart Environment by Using Blockchain as a Preemptive Technique;the Role of Cryptocurrencies in the Development of Countries and the Fight Against Financial Problems;Designing the UzBCS Lending Platform Network Based on Blockchain Technology and Ensure Transaction Security;decentralized Blockchain Networks and Economic Security: Balancing Scalability and Security Tradeoffs;study of Machine Learning Models for IoT Based Efficient Classroom Usage;transforming Higher Education: A Comprehensive Analysis of Blockchain technologies and Digitalization.

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A Dynamic Algorithm for Submodular Maximization with a Knapsack Constraint 23

A Dynamic Algorithm for Submodular Maximization with a Knaps...

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作者： Zhu, Tianshuai Han, Kai Zhang, Enpei Wu, Benwei School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9781665494571

Submodular maximization is a fundamental problem in computer science theory, as well as occupies a significant place in machine learning and data mining applications, including influence maximization in social networks, recommendation systems, exemplar-based clustering, viral marketing, and data summarization. In this paper, we study this classic problem under a knapsack constraint in a fully dynamic setting. Given a stream of arbitrary interleaved element insertions and deletions, the dynamic setting requires a solution quickly after each update. By adopting repeated sampling, threshold greedy and filtering technique, our algorithm achieves an approximation ratio of 1/3 for monotone submodular objective function, with polylogarithmic amortized update time. We extensively evaluate the performance of our algorithm against the existing work in two applications including maximum coverage and movie recommendation. The experimental results demonstrate the superiority of our algorithm. © 2021 IEEE.

关键词： Machine learning algorithms Social networking (online) Smart cities Filtering Heuristic algorithms Machine learning Approximation algorithms

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23rd international conference on Next Generation Wired/Wireless Networking, NEW2AN 2023, and 16th conference on Internet of Things and Smart Spaces, ruSMART 2023

23rd International Conference on Next Generation Wired/Wirel...

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23rd international conference on Next Generation Wired/Wireless Networking, NEW2AN 2023, and 16th conference on Internet of Things and Smart Spaces, ruSMART 2023

ISBN: (纸本)9783031609930

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Resource-Efficient DNN Training and Inference for Heterogeneous Edge Intelligence in 6G 23

Resource-Efficient DNN Training and Inference for Heterogene...

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作者： Cui, Enfang Zhang, Weiting Yang, Dong Wu, Wen Lyu, Feng School of Electronic and Information Engineering Beijing Jiaotong University Beijing China University of Waterloo Department of Electrical and Computer Engineering Waterloo Canada School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9781665494571

Edge intelligence is expected to be a key enabler of the future sixth generation (6G) mobile network. However, the heterogeneous characteristics of edge intelligence, such as heterogeneous edge data, resources, and service requirements, pose challenges to deep neural network (DNN) training and inference at the edge. To fully tap the potential of DNN for 6G heterogeneous edge intelligence, a variety of solutions have been proposed to tackle the above challenges, such as federated learning and transfer learning, for DNN training;and DNN partitioning and early exiting, for DNN inference. In this paper, we provide a comprehensive survey about resource-efficient DNN training and inference in heterogeneous edge intelligence. We first discuss the challenges of DNN training and inference in heterogeneous edge intelligence. Then, we give a detailed review of the recent advances of DNN training and inference technologies in heterogeneous edge intelligence, including the principles and state-of-the-art solutions of these technologies. We further conduct a taxonomy and summary of the reviewed solutions to clarify their applicable scenarios and the limitations. Finally, we point out some potential future research opportunities on heterogeneous edge intelligence. © 2021 IEEE.

关键词： Training 6G mobile communication Deep learning Smart cities Taxonomy Transfer learning Neural networks

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Efficient Hardware Redo Logging for Secure Persistent Memory 23

Efficient Hardware Redo Logging for Secure Persistent Memory

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作者： Zhang, Zhan Yue, Jianhui Liao, Xiaofei Jin, Hai School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China Michigan Technological University Computer Science Department HoughtonMI United States

ISBN: (纸本)9781665494571

Write-ahead log and data encryption technologies are employed to ensure both crash consistency and data security for persistent memory (PM). The encryption/decryption of both data and log requests increase the memory request latency, degrading the system performance. To tackle this issue, this paper proposes a novel log-aware memory encryption scheme to reduce encryption/decryption operations, without compromising data security. Specifically, redo log blocks are stored as ciphertext for their corresponding data blocks to avoid encryption/decryption of log data blocks when in-place update operations are applied to them. We further design a compact log record layout with fewer encryption metadata in log records to reduce logging traffic. Our simulation results show that the transaction throughput of the proposed design outperforms the baseline design and the state-of-the-art design by 87.3% and 55.3% on average, respectively. © 2021 IEEE.

关键词： Smart cities System performance Simulation Memory management Layout Memory architecture Metadata

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Models for Network Traffic Behavioral Analysis in IoT systems 23rd

Models for Network Traffic Behavioral Analysis in IoT System...

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23rd international conference on Informatics in Economy, IE 2024

作者： Zamfiroiu, Alin Floroiu, Iustin Savu, Daniel Bătăgan, Lorena Ghai, Anupriya Sharma National Institute for Research & Development in Informatics Bucharest Romania Bucharest University of Economic Studies Bucharest Romania School of Computing Graphic Era Hill University Dehradun India

ISBN: (纸本)9789819601608

In today's interconnected world, the proliferation of Internet of Things (IoT) devices has revolutionized the way we interact with technology. From smart homes and wearable devices to industrial sensors and autonomous vehicles, IoT systems have permeated nearly every aspect of our daily lives. However, with this exponential growth in IoT adoption comes a host of challenges, particularly concerning the security and performance of these interconnected networks. One of the key methodologies employed to address these challenges is network traffic behavioral analysis. Moreover, network traffic behavioral analysis plays a crucial role in threat intelligence and incident response. By aggregating and analyzing network data over time, organizations can identify recurring attack patterns and indicators of compromise. This rich contextual information enhances their ability to anticipate and preempt future cyber threats. Additionally, in the event of a security incident, network traffic analysis provides invaluable forensic evidence, allowing investigators to reconstruct the sequence of events, attribute responsibility, and mitigate the impact of the breach. In this paper, we propose some models for the network traffic generated by sensors and actuators in IoT systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computer forensics

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Towards High-throughput and Low-latency Billion-scale Vector Search via CPU/GPU Collaborative Filtering and Re-ranking 23

Towards High-throughput and Low-latency Billion-scale Vector...

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23rd USENIX conference on File and Storage technologies, FAST 2025

作者： Tian, Bing Liu, Haikun Tang, Yuhang Xiao, Shihai Duan, Zhuohui Liao, Xiaofei Jin, Hai Zhang, Xuecang Zhu, Junhua Zhang, Yu National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Huawei Technologies Co. Ltd. China

ISBN: (纸本)9781939133458

Approximate nearest neighbor search (ANNS) has emerged as a crucial component of database and AI infrastructure. Ever-increasing vector datasets pose significant challenges in terms of performance, cost, and accuracy for ANNS services. None of modern ANNS systems can address these issues simultaneously. In this paper, we present FusionANNS, a high-throughput, low-latency, cost-efficient, and high-accuracy ANNS system for billion-scale datasets using SSDs and only one entry-level GPU. The key idea of FusionANNS lies in CPU/GPU collaborative filtering and re-ranking mechanisms, which significantly reduce I/O operations across CPUs, GPU, and SSDs to break through the I/O performance bottleneck. Specifically, we propose three novel designs: (1) multi-tiered indexing to avoid data swapping between CPUs and GPU, (2) heuristic re-ranking to eliminate unnecessary I/Os and computations while guaranteeing high accuracy, and (3) redundant-aware I/O deduplication to further improve I/O efficiency. We implement FusionANNS and compare it with the state-of-the-art SSD-based ANNS system-SPANN and GPU-accelerated in-memory ANNS system-RUMMY. Experimental results show that FusionANNS achieves 1) 9.4-13.1× higher query per second (QPS) and 5.7-8.8× higher cost efficiency compared with SPANN;2) and 2-4.9× higher QPS and 2.3-6.8× higher cost efficiency compared with RUMMY, while guaranteeing low latency and high accuracy. © 2025 FAST. All Rights Reserved.

关键词： Collaborative filtering

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