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检索条件"任意字段=International Conference on Machine Learning and Computing（ICMLC 2009）"

共 1949 条记录，以下是51-60 订阅

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A Survey of Novel Framework of Anomaly-Based Intrusion Detection System in Computer Networks Using Ensemble Feature Integration with Deep learning Techniques 24

A Survey of Novel Framework of Anomaly-Based Intrusion Detec...

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16th international conference on machine learning and computing (icmlc)

作者： Srinivas, Akkepalli Sagar, K. Osmania Univ Hyderabad 500007 India JNTUH Sreyas Inst Engn & Technol Hyderabad 68 India

ISBN: (纸本)9798400709234

Today's internet is made up of nearly half a million different networks. In any network connection, identifying the attacks by their types is a difficult task as different attacks may have various connections, and their number may vary from a few to hundreds of network connections. To solve this problem, IDS-based on machine learning (ML) has been developed to monitor and analyze data packets to detect abnormal behaviors and new attacks. Security in cyberspace is currently receiving more and more attention. Making efficient defenses against many sorts of network attacks, as well as making sure that network equipment is safe and information is secure, has grown to be a major issue. Network intrusion detection systems (NIDSs) are critical components of the architecture for network security protection because they detect harmful attack behaviors by examining the network traffic of key nodes in a network [1, 2]. Intrusion detection systems (IDSs) can be classified as signature-based, and anomaly-based methods [3]. Signature-based IDS (SIDS), often referred to as Rule-based or Misuse IDS, perform ongoing network traffic monitoring and look for sequences or patterns of incoming network data that resemble an attack signature. By keeping error rates low, they are able to identify potential invasions with high accuracy rates. One of the weaknesses of these systems is the demand for an updated database that contains the attack signatures. Anomaly-based intrusion detection systems (AIDS), also known as behavior-based detection, examine the typical behavior of networks by keeping an eye on network traffic for signs of anomalous activity. AIDS can be taught using anomaly detection algorithms or self-taught using self-learning algorithms, allowing them to recognize new kinds of intrusions. Anomaly-based approach distinguishes itself significantly from the signature-based approach when it comes to recognizing new attacks. Essence to develop an efficient NIDS The majority of the mo

关键词： SIDS AIDS NIDS

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Dual Part Siamese Attention Convolution Network for Change detection in Bi-temporal High Resolution Remote Sensing Images 24

Dual Part Siamese Attention Convolution Network for Change d...

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16th international conference on machine learning and computing (icmlc)

作者： Li Zongpu Bao Tengfei Xiao Zhiyun Inner Mongolia Univ Technol Hohhot Peoples R China Shanghai Jiao Tong Univ Inner Mongolia Univ Technol Shanghai Peoples R China

ISBN: (纸本)9798400709234

The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. However, due to the noise interference and redundancy in remote sensing multispectral images, it is valuable to transform the available spectral channels into a suitable feature space to alleviate noise and reduce redundancy. In this paper, we propose a supervised change detection method for optical aerial images based on deep Siam convolution network, which is mainly composed of two branch network, heuristic feature aggregation module and feature attention module. This method achieves better performance in experiments on remote sensing aerial public datasets. We show that our network can extract feature from two images at a same time, and depend on feature fusion strategy together with attention mechanism to further enrich the difference information. Subsequently, a decoder structure of semantic segmentation is developed to harvest binary change detection result. Experimental results show that our algorithm achieves a comprehensive F1 score of 83.9% on different data sets, which is superior to the traditional algorithm and other deep learning networks, and has a certain degree of effectiveness and robustness.

关键词： Change detection deep learning Siamese network feature fusion attention module

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Analyze Hand Motor Capabilities for Potential Autistic Intervention: A Feasibility Study

Analyze Hand Motor Capabilities for Potential Autistic Inter...

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2023 international conference on machine learning and Cybernetics, icmlc 2023

作者： Toptan, Carrie M. Zhang, Dinghuang Liu, Honghai School of Computing University of Portsmouth Portsmouth United Kingdom

ISBN: (纸本)9798350303780

This paper aims to explore differences in hand motor capabilities with a focus for potential autistic intervention. In order to achieve that, a predefined drawing scenario is introduced with a metric of three complexity levels and predefined motion protocols. Then, a vision-based acquisition system for hand motor capability is proposed. It is evident that detecting differences in hand motor capabilities has a huge potential to detect hand motion difficulties in Autism. Hence, this study paves the way for the use of technology for early diagnosis and potential intervention in Autism. © 2023 IEEE.

关键词： Diagnosis

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Proceedings of 2023 international conference on machine learning and Cybernetics, icmlc 2023

Proceedings of 2023 International Conference on Machine Lear...

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2023 international conference on machine learning and Cybernetics, icmlc 2023

ISBN: (纸本)9798350303780

The proceedings contain 91 papers. The topics discussed include: a cross-time zone transfer consumption model for base station computing tasks based on photovoltaic prediction;numerical embedding of categorical features in tabular data: a survey;SAPD: self-attention progressive seismic data denoising;heart rate measurement in low-light conditions and human natural sleeping positions based on video;on reinforcement learning in stabilizability of probabilistic Boolean control networks;anomaly detection in dynamic graphs via long short-term temporal attention network;prediction of laser drilling qualities for optical grade PMMA using artificial neural network;and samples on thin ice: re-evaluating adversarial pruning of neural networks.

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Action Quality Assessment for ASD Behaviour Evaluation

Action Quality Assessment for ASD Behaviour Evaluation

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2023 international conference on machine learning and Cybernetics, icmlc 2023

作者： Zhang, Dinghuang Zhou, Dalin Liu, Honghai School of Computing University of Portsmouth PortsmouthP01 3HE United Kingdom

ISBN: (纸本)9798350303780

Given the current increasing prevalence of autism, expensive and time-consuming manual diagnosis is highly detrimental to the management of the condition. With the development of computer-based methods of human behavioural analysis, these methods are expected to provide more accurate, objective and reproducible methods of early screening and diagnosis of autism. To advance the field of behavioural quantification in autism research, this study utilises human skeletal behavioural data from publicly available autism datasets and ADOS scores from clinical professionals in a first attempt to build deep neural networks that can predict ADOS scores from behavioural data using the AQA approach. This paper finds a moderately correlated between the ground truth ADOS score and the predicted ADOS score, it reveals the potential use of the AQA method in ASD diagnoses. © 2023 IEEE.

关键词： Quality control

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A Cross-Time Zone Transfer Consumption Model for Base Station computing Tasks Based on Photovoltaic Prediction

A Cross-Time Zone Transfer Consumption Model for Base Statio...

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2023 international conference on machine learning and Cybernetics, icmlc 2023

作者： Lin, Gui-Dong Xu, Jun-Hua Tan, Jing-Yu Xue, Ji-Zhong Wang, Yu School of Automation Guangdong University of Technology Department of Electrical Guangzhou510000 China

ISBN: (纸本)9798350303780

The rapid development of artificial intelligence is inseparable from 5G technology. However, the energy consumption of 5G base station also affects the construction of 5G to a great extent. In recent years, 5G base stations have also further reduced power consumption by combining with new energy consumption technology. However, the uncertainty of photovoltaic power generation also brings trouble to the energy planning of communication operators. This paper proposes a cross-time zone transfer consumption model for base station group computing tasks based on photovoltaic prediction. The photovoltaic prediction model based on LSTM is used to predict the photo-voltaic power. Aiming at the problem of different photovoltaic power generation across time zones, energy planning is carried out according to the energy consumption of local BBU. Through experimental simulation, it is verified that the model can reduce the power cost of communication operators and improve the consumption of new energy. © 2023 IEEE.

关键词： Base stations

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A Review of Systems for Educational User Data Storage and Security Through Decentralised Blockchain Storage Systems

A Review of Systems for Educational User Data Storage and Se...

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2023 international conference on machine learning and Cybernetics, icmlc 2023

作者： Warman, Dylan Tien, David Kabir, Muhammad Ashad School of Computing and Mathematics Charles Sturt University BathurstNSW2795 Australia

ISBN: (纸本)9798350303780

This paper provides a comprehensive review of the significant literature in the area of user data security with a specific focus on how this integrates with the area of blockchain technologies and zero-knowledge proofs within the context of educational data storage. The overall objective of this review is to provide an in-sight into what has already been made available, with regards to implementation, and how these form an overall direction for the field. The purpose of which is to assist in the efforts to provide a solution that unifies the existing solutions and frameworks and provides real world benefits by targeting the highlighted research gaps. © 2023 IEEE.

关键词： Blockchain

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Baileys: An Efficient Distributed machine learning Framework by Dynamic Grouping 23

Baileys: An Efficient Distributed Machine Learning Framework...

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15th international conference on machine learning and computing, icmlc 2023

作者： Ni, Chengdong Du, Haizhou Shanghai University of Electric Power Shanghai China

ISBN: (纸本)9781450398411

Many machine-learning applications rely on distributed machine learning (DML) systems to train models from massive datasets using massive computing resources (e.g., GPUs and TPUs). However, given a DML system in most applications, its parameter synchronization mechanism is fixed and independent from the types and amounts of resources available to the system. In this paper, we argue that given an application, the synchronization mechanism in its DML system should be co-designed with the available resources in the heterogeneous cluster. To this end, we design an efficient parameter synchronization framework called Baileys. First, Baileys retrieves resource information from the heterogeneous clusters and uses such information to partition heterogeneous workers into multiple groups dynamically. Second, Baileys develops efficient group-based parameter synchronization mechanisms to converge model parameters quickly and accurately. We implement a prototype of Baileys and demonstrate its efficiency and efficacy through experiments. Results show that Baileys can reduce block time up to 33.2% and decrease the test error up to 39.7%. © 2023 ACM.

关键词： Synchronization

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A Training Rate and Survival Heuristic for Inference and Robustness Evaluation (Trashfire) 23

A Training Rate and Survival Heuristic for Inference and Rob...

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23rd international conference on machine learning and Cybernetics, icmlc 2024

作者： Meyers, Charles Sedghpour, Mohammad Reza Saleh Lofstedt, Tommy Elmroth, Erik Umeå University Department of Computing Science Umeå Sweden Elastisys AB Sweden

ISBN: (纸本)9798331528041

machine learning models - deep neural networks in particular - have performed remarkably well on benchmark datasets across a wide variety of domains. However, the ease of finding adversarial counter-examples remains a persistent problem when training times are measured in hours or days and the time needed to find a successful adversarial counter-example is measured in seconds. Much work has gone into generating and defending against these adversarial counter-examples, however the relative costs of attacks and defences are rarely discussed. Additionally, machine learning research is almost entirely guided by test/train metrics, but these would require billions of samples to meet industry standards. The present work addresses the problem of understanding and predicting how particular model hyper-parameters influence the performance of a model in the presence of an adversary. The proposed approach uses survival models, worst-case examples, and a cost-aware analysis to precisely and accurately reject a particular model change during routine model training procedures rather than relying on real-world deployment, expensive formal verification methods, or accurate simulations of very complicated systems (e.g., digitally recreating every part of a car or a plane). Through an evaluation of many pre-processing techniques, adversarial counter-examples, and neural network configurations, the conclusion is that deeper models do offer marginal gains in survival times compared to more shallow counterparts. However, we show that those gains are driven more by the model inference time than inherent robustness properties. Using the proposed methodology, we show that ResNet is hopelessly insecure against even the simplest of white box attacks. © 2024 IEEE.

关键词： Adversarial machine learning

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Unexpectedly Useful: Convergence Bounds and Real-World Distributed learning 23

Unexpectedly Useful: Convergence Bounds and Real-World Distr...

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15th international conference on machine learning and computing, icmlc 2023

作者： Malandrino, Francesco Chiasserini, Carla Fabiana CNR-IEIIT CNIT Torino Italy Politecnico di Torino CNR-IEIIT CNIT Torino Italy

ISBN: (纸本)9781450398411

Convergence bounds are one of the main tools to obtain information on the performance of a distributed machine learning task, before running the task itself. In this work, we perform a set of experiments to assess to which extent, and in which way, such bounds can predict and improve the performance of real-world distributed (namely, federated) learning tasks. We find that, as can be expected given the way they are obtained, bounds are quite loose and their relative magnitude reflects the training rather than the testing loss. More unexpectedly, we find that some of the quantities appearing in the bounds turn out to be very useful to identify the clients that are most likely to contribute to the learning process, without requiring the disclosure of any information about the quality or size of their datasets. This suggests that further research is warranted on the ways - often counter-intuitive - in which convergence bounds can be exploited to improve the performance of real-world distributed learning tasks. © 2023 ACM.

关键词： learning systems

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