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检索条件"任意字段=Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition"

共 696 条记录，以下是681-690 订阅

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Dual Mixed Style Augmentation for Generalized Medical Image Segmentation 23

Dual Mixed Style Augmentation for Generalized Medical Image ...

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proceedings of the 2023 6th international conference on Artificial Intelligence and pattern recognition

作者： Jiaxi Li Yuntong Tian Liang Wan Academy of Medical Engineering and Translational Medicine Tianjin University China College of Intelligence and Computing Tianjin University China

ISBN: (纸本)9798400707674

Domain generalization aims to generalize models trained on source domains to unseen target domains, which usually have domain shift from the source domains. this challenging setting is common in clinical scenarios due to differences in scanning protocols, device manufacturers, and image modalities. In this paper, a novel style augmentation framework is proposed to improve out-of-domain performance for the model in the context of medical image segmentation. the proposed framework first investigates the style distribution between samples, for which a style clustering-based resampling is developed to generate evenly diversified styles, and then leverages a cross-structure style augmentation, which generates new styles by shuffling regional style information among structure within one sample. this structure style shuffling can greatly expand the style space while well respecting the anatomical structure of medical images. the method is validated through extensive experiments on 2 datasets, i.e., cross-modality abdominal multi-organ dataset, and cross-site cardiac dataset, with 2 settings, i.e., single-domain and multi-domain generalization. Experimental results show that our method yields significant improvement and outperforms state-of-the-art methods with significant margins.

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Graph Convolutional Network Representation Learning Method with Community-enhanced Temporal Features for Dynamic Graphs 23

Graph Convolutional Network Representation Learning Method w...

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proceedings of the 2023 6th international conference on Artificial Intelligence and pattern recognition

作者： Qi Bi Jiayuan Liu Jiexing Tang school of computing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

Dynamic graph representation learning aims at learning the low-dimensional vector representation of nodes in dynamic networks, and the learned network representation can be used for various dynamic network analysis *** dynamic networks, it is very important to accurately model time information, but most embedding methods completely ignore it, or use a series of static snapshots to approximate dynamic networks, which will lose a lot of important time information. Besides, community structure is an important feature of complex networks, and capturing network community structure during dynamic network representation learning has a positive impact on learning outcomes. In this paper, we propose a dynamic graph neural network representation learning method with Community-enhanced temporal features, the TCGCN. TCGCN introduces the community structure information of the dynamic network into the extracted temporal features, and then integrates the temporal features into the method based on graph convolution network to learn the representation of the nodes, so that the learning results can keep time dependence, and can maintain the community structure and global network structure features, comprehensive learning to the change of the dynamic network rule. the experimental results show that TCGCN can greatly improve the quality of dynamic network representation learning.

关键词： Community structure Dynamic Graph embedding Temporal features

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Long-short term anomaly detection in wireless sensor networks based on spatio-temporal correlation in IoT systems 23

Long-short term anomaly detection in wireless sensor network...

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proceedings of the 2023 6th international conference on Artificial Intelligence and pattern recognition

作者： Zhihao Wei Meng Li Jianghao Wei Industrial Big Data Laboratory Xi'an University of Posts and Telecommunications China Nanjing University of Science and Technology China

ISBN: (纸本)9798400707674

For the large number of different information sources collected by heterogeneous wireless sensor networks, traditional anomaly detection frameworks usually focus on the data analysis itself and lack a focus on unexpected sensor data, leading to unnecessary waste of resources. To address this problem, this paper proposes a method for automatic anomaly detection in heterogeneous sensor networks that combines edge data analysis and cloud data analysis. the former utilizes a completely unsupervised Isolation forest algorithm, while the cloud data analysis utilizes a Long-short term memory neural network algorithm. Experimental evaluation is performed by discussing the analysis of the proposed method using discrete and event outlier data. the experimental results show that the proposed method can reasonably cope with discrete outliers and event outliers, and the detection accuracy of event outliers is improved from 93.60% to 98.91%. At the same time, the Edge-Cloud combination alleviates to a certain extent the drawback of high energy consumption and latency of the traditional machine learning algorithm local combination model.

关键词： Anomaly detection Edge computing Isolation forests Spatio-temporal correlation

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Activity Attribute-Based User Behavior Model for Continuous User Authentication 22

Activity Attribute-Based User Behavior Model for Continuous ...

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proceedings of the 2022 12th international conference on Communication and Network Security

作者： Luthfi Azhari Ari Moesriami Barmawi School of Computing Telkom University INA

ISBN: (纸本)9781450397520

In general, authentication using a username and password has been used to manage access to the system. this way, it can be arranged so that only authorized people can access a system. However, it does not rule out the possibility of several spoofing attacks. User behavior can be used instead of a password to solve this problem. Given that user behavior is unique, the behavior can be used for the authentication process. One of the authentication processes is to continuously use weblogs, as proposed by Leslie et al. However, the method used by Leslie et al. still has a drawback with the Leslie method. Only the user's role is identified. So, the attacker's identity cannot be known if an attack occurs. A continuous authentication method using weblogs and user attributes such as user id, DateTime, event, category, and products is used to overcome this problem. Using this method, the user's identity can be identified immediately after the role is known. In addition, using the proposed method, the accuracy of user identity authentication obtained is, on average, 95.08%, which means it is greater than the accuracy of user role authentication using the Leslie method, which is 46%..

关键词： identity weblog authentication attribute behavior

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Attributes and Dimensions of Trust in Secure Systems 22

Attributes and Dimensions of Trust in Secure Systems

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proceedings of the 12th international conference on the Internet of things

作者： Matthew Bradbury Daniel Prince Victoria Marcinkiewicz Tim Watson School of Computing and Communications Lancaster University UK The Centre for Artificial Intelligence Robotics and Human-Machine Systems School of Psychology Cardiff University UK The Alan Turing Institute UK

ISBN: (纸本)9781450396653

What is it to be trusted? this is an important question as trust is increasingly placed in a system and the degree to which a system is trusted is increasingly being assessed. However, there are issues with how related terms are used. Many definitions focus on one attribute of trust (typically behaviour) preventing that definition from being used for other attributes (e.g., identity). this is confused further by conflating what trustors measure about a trustee and what conclusions a trustor reaches about a trustee. therefore, in this paper we present definitions of measures (trustiness and trustworthiness) and conclusions (trusted and trustworthy). these definitions are general and do not refer to a specific attribute allowing them to be used with arbitrary attributes which are being assessed (e.g., identity, behaviour, limitation, execution, correctness, data, environment). In addition, in order to demonstrate the complexities of describing if a trustee is designated as trusted or trustworthy, a set of dimensions are defined to describe attributes (time, scale, proactive/reactive, strength, scope, source). Finally, an example system is classified using these attributes and their dimensions in order to highlight the complexities of describing a system as holistically trusted or trustworthy.

关键词： limitations Trust dimensions attributes trustworthy

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Employing Frequency Independent Structure for Ultra-Wide Band Antenna 23

Employing Frequency Independent Structure for Ultra-Wide Ban...

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proceedings of the 2023 12th international conference on Software and Information Engineering

作者： Mostafa Mohamed Salah Hassan Aly Ragheb Electrical Engineering Department The British University in EgyptEl Sherouk City Misr-Ismaillia Road Egypt. Egypt and Electrical Engineering Department German International University German International University Administrative Capital Regional Ring Rd Cairo Governorate Desert Cairo Governorate Egypt Egypt Electrical Engineering Department The British University in EgyptEl Sherouk City Misr-Ismaillia Road Egypt. Egypt

ISBN: (纸本)9798400708107

the paper presents a novel and compact design for an ultra-wideband antenna based on frequency independent ethos. It consists of a Log-Periodic monopole structure with specific angle for the radiating patch with a defected ground plane. the antenna is fed using microstrip line feed and is designed on an FR4 substrate with a thickness of 1.6 mm, a relative permittivity of 4.4, and a loss tangent of 0.02. the Finite Element Method (FEM) of the Ansoft HFSS software is used during the design process. the proposed antenna covers the frequency range from 5.3 GHz up to 14 GHz for multiple frequency bands such as, WLAN, IEEE802.11a band (5.15-5.825GHz), C-band (4-8GHz), and X-band (8 to 12GHz). the antenna provides an omnidirectional radiation pattern, directivity and the efficiency of the antenna is about 70%. the dimensions of the antenna were optimized to realize compactness, high band width, and good matching impedance. Our design was fabricated , test and the measured results found to be in an excellent agreement with the simulation results. Proposed antenna will be useful for 5G applications, military applications, Healthcare applications, Smart homes, IoT and industrial automation.

关键词： Frequency Independent Keywords Ultra-Wide Band Log-Periodic Microstrip

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Task Allocation in Industrial Edge Networks with Particle Swarm Optimization and Deep Reinforcement Learning 22

Task Allocation in Industrial Edge Networks with Particle Sw...

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proceedings of the 12th international conference on the Internet of things

作者： Philippe Buschmann Mostafa H. M. Shorim Max Helm Arne Bröring Georg Carle Siemens AG Germany and School of Computation Information and Technology Technical University of Munich Germany School of Computation Information and Technology Technical University of Munich Germany Siemens AG Germany

ISBN: (纸本)9781450396653

To avoid the disadvantages of a cloud-centric infrastructure, next-generation industrial scenarios focus on using distributed edge networks. Task allocation in distributed edge networks with regards to minimizing the energy consumption is NP-hard and requires considerable computational effort to obtain optimal results with conventional algorithms like Integer Linear Programming (ILP). We extend an existing ILP problem including an ILP heuristic for multi-workflow allocation and propose a Particle Swarm Optimization (PSO) and a Deep Reinforcement Learning (DRL) algorithm. PSO and DRL outperform the ILP heuristic with a median optimality gap of and against . DRL has the lowest upper bound for the optimality gap. It performs better than PSO for problem sizes of more than 25 tasks and PSO fails to find a feasible solution for more than 60 tasks. the execution time of DRL is significantly faster with a maximum of 1 s in comparison to PSO with a maximum of 361 s. In conclusion, our experiments indicate that PSO is more suitable for smaller and DRL for larger sized task allocation problems.

关键词： Task Allocation Edge computing Deep Reinforcement Learning Internet of things (IoT) Particle Swarm Optimization Integer Linear Programming

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Accelerating Command Injection Vulnerability Discovery in Embedded Firmware with Static Backtracking Analysis 22

Accelerating Command Injection Vulnerability Discovery in Em...

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proceedings of the 12th international conference on the Internet of things

作者： Xiaokang Yin Ruijie Cai Yizheng Zhang Lukai Li Qichao Yang Shengli Liu State Key Laboratory of Mathematical Engineering and Advanced Computing China

ISBN: (纸本)9781450396653

Command injection vulnerability is a severe threat to the embedded device. Most methods detect command injection vulnerability with taint analysis and symbolic execution and achieve promising results. However, they waste too much time analyzing secure sink call-sites, resulting in less efficient vulnerability detection. To tackle the above problem, we propose a novel sink call-site classification method named CINDY to accelerate the command injection vulnerability discovery in embedded firmware with static backtracking analysis. CINDY first performs sink call-sites detection in the binary executables and constructs the data flow for function call parameters. then, CINDY analyzes whether the parameters passed to sink functions are derived from constant string or not and labels them “secure" or “risky". According to the labels, CINDY classifies the sink call-sites into risky and secure sink call-sites. Finally, CINDY performs taint analysis with symbolic execution to check whether a risky sink call-site is vulnerable. To demonstrate the efficacy of CINDY, we compare CINDY with the state-of-the-art method SaTC, using the dataset published by SaTC. Compared with SaTC, CINDY can filter out more of the secure sink call-sites, with a 35% decrease, and the efficiency is improved by 17% than SaTC.

关键词： Backtracking Analysis Taint Analysis Command Injection Vulnerability Firmware

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Instruments for sound therapy vs. music therapy for teenager's emotional development 23

Instruments for sound therapy vs. music therapy for teenager...

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proceedings of the 2023 12th international conference on Software and Information Engineering

作者： Valentina Mihaela Pomazan Equipment and systems/ Faculty of Mechanical Engineering Ovidius University Romania

ISBN: (纸本)9798400708107

Sound and music therapy has been used to cope with changes during this period of life. this study was conducted to determine and compare the effect of two types of therapeutic interventions. It aimed to compare the efficiency of sound therapy (passive listening), and active music therapy, for emotional state improvement in teenagers. A prospective, comparative, qualitative randomized-controlled study was carried out between September 2022 and December 2022. the study sample consisted of 28 teenagers, enrolled in public secondary education (10 in the sound group, 8 in the active music therapy group and 10 in the control group). Sound was played by the researcher to the subjects in the intervention group in a total of 12 sessions of 20 minutes, for eight weeks. Group active music therapy sessions were conducted for the music therapy group, for the same number of sessions and time duration. the data were collected using an information form and scored against the subject's scale. the posttest scores of the teenagers in the music group were higher than those in the sound group, without a statistically significant difference between the groups (P > 0.05). Comparison of the post-test scores of the participants in the music and control groups revealed a significant decrease in the aggression scores among the members in the music group (P = 0.035). Comparison of the pretest and posttest differences between the participants showed a statistically significant decrease in the aggression/ verbal violence and sadness scores among the teenagers in the music group after the intervention.

关键词： adolescents emotions music therapy sound pattern

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Environment-aware dynamic graph learning for out-of-distribution generalization 23

Environment-aware dynamic graph learning for out-of-distribu...

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proceedings of the 37th international conference on Neural Information Processing Systems

作者： Haonan Yuan Qingyun Sun Xingcheng Fu Ziwei Zhang Cheng Ji Hao Peng Jianxin Li Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University and School of Computer Science and Engineering Beihang University Key Lab of Education Blockchain and Intelligent Technology Guangxi Normal University Department of Computer Science and Technology Tsinghua University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-temporal patterns on dynamic graphs. However, existing works fail to generalize under distribution shifts, which are common in real-world scenarios. As the generation of dynamic graphs is heavily influenced by latent environments, investigating their impacts on the out-of-distribution (OOD) generalization is critical. However, it remains unexplored with the following two major challenges: (1) How to properly model and infer the complex environments on dynamic graphs with distribution shifts? (2) How to discover invariant patterns given inferred spatio-temporal environments? To solve these challenges, we propose a novel Environment-Aware dynamic Graph LEarning (EAGLE) framework for OOD generalization by modeling complex coupled environments and exploiting spatiotemporal invariant patterns. Specifically, we first design the environment-aware EADGNN to model environments by multi-channel environments disentangling. then, we propose an environment instantiation mechanism for environment diversification with inferred distributions. Finally, we discriminate spatio-temporal invariant patterns for out-of-distribution prediction by the invariant pattern recognition mechanism and perform fine-grained causal interventions node-wisely with a mixture of instantiated environment samples. Experiments on real-world and synthetic dynamic graph datasets demonstrate the superiority of our method against state-of-the-art baselines under distribution shifts. To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective.

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