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OWETC: Open world encrypted traffic classification based on semi-supervised class incremental learning 21

OWETC: Open world encrypted traffic classification based on ...

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21st IEEE international symposium on Parallel and Distributed Processing with Applications, 13th IEEE international Conference on Big Data and Cloud computing, 16th IEEE international Conference on Social computing and Networking and 13th international Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhao, Meng Di, Zhu Xia, Zhe Tian, Jing Xiang, Jianwen Jun, Lin China Electronic Product Reliability and Environmental Testing Research Institute Guangdong China Wuhan University of Technology Hubei Key Laboratory of Transportation of Internet of Things School of Computer Science and Artifical Intelligence Wuhan China Wuhan University of Technology Chongqing Research Institute Chongqing China

ISBN: (纸本)9798350329223

To address the dynamic changes in network environments and effectively tackle the open-world traffic classification challenge, we introduce a novel approach. Conventional traffic classification methods are constrained by their reliance on closed traffic sets and offline training scenarios, limiting their ability to recognize a fixed number of traffic classes and identify unknown traffic types. Our solution involves a traffic classification method founded on a combination of semi-supervised class-incremental learning and open-set recognition. To mitigate the issue of catastrophic forgetting in class-incremental learning, we leverage a substantial amount of external unlabeled traffic data. Specifically, we employ a class anchor clustering loss classifier with a discriminator to select external unlabeled traffic data as replay samples. This approach effectively mitigates the problem of catastrophic forgetting in class-incremental learning. Furthermore, the class anchor clustering loss is employed for incremental model training, enabling the model to identify unknown traffic data. We validate the performance of the model on encrypted traffic classical datasets, and the experimental results show that our method significantly outperforms traffic classification models based on other incremental learning methods. In particular, OWETC has a 3.28% performance improvement in average accuracy over multiple incremental sessions compared to the best baseline. Additionally, our enhanced open-set sampler displays a notable 17% reduction in false alarm rates with only a minor 1% accuracy degradation, surpassing the capabilities of existing open-set recognition methods. © 2023 IEEE.

关键词： Classification (of information)

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Reinforcement Learning Based Control Domain Division in LEO Satellite Networks 25

Reinforcement Learning Based Control Domain Division in LEO ...

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25th IEEE international Conferences on high performance computing and Communications, 9th international Conference on Data Science and Systems, 21st IEEE international Conference on Smart City and 9th IEEE international Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Tang, Feilong Li, Xue Chen, Long Liu, Jiacheng Gao, Ming Yang, Yanqin Xu, Wenchao Zhang, Heteng School of Data Science and Engineering East China Normal University Shanghai China School of Computing Science Simon Fraser University Vancouver Canada The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong School of Communication and Electric Engineering East China Normal University Shanghai China Suzhou All-Space-Time Information Technology Co. Ltd. Suzhou China

ISBN: (纸本)9798350330014

The performance of SDN-based LEO satellite networks significantly depends on the control domain division approaches. The dynamical topology in LEO networks results in the time-varying delay for network management. Therefore, the control domain division is very challenging and should be dynamical. In this paper, we propose a control domain division approach based on reinforcement learning (RL), which aims at reducing the average management delay of controllers during the control period, and as well as minimizing the number of control domain handovers. Firstly, we analyze the factors that need to be paid attention to in controller deployment, such as delay, load and controller switching cost, and formulate the controller deployment. By relaxing the constraints, we prove that the problem is NP-hard. Secondly, we propose an approach that considers future motion trajectory based on reinforcement learning (FMT _ RL) to deploy controllers. Since the dimension of the neural network in reinforcement learning is fixed, we use the improved K-Means algorithm to select the set of mechanisms to determine the dimension of the neural network. Particularly, in order to obtain the node composition control domain, the observation including both future motion trajectory and history controller selection information are input into the neural network to calculate the control domain division results. The experimental results demonstrate that our approach significantly outperforms related approaches, with better stability and average control cost. © 2023 IEEE.

关键词： Reinforcement learning

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A Case study for Exploring the Impact of Optimized K - Means Algorithm on the Energy Efficiency of Multicore Systems 25

A Case Study for Exploring the Impact of Optimized K - Means...

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作者： Hajiamini, Shervin Blanchard, Jeffrey D. Lin, Davin Shirazi, Behrooz Vanderbilt University Department of Computer Science Nashville United States Grinnell College Department of Mathematics and Statistics Grinnell United States Grinnell College Department of Computer Science Grinnell United States School of Eecs Washington State University Pullman United States

ISBN: (纸本)9798350330014

Cluster-based manycore system is a practical design for manufacturing the multicore systems. In this design, cores are divided into multiple clusters, where cores within a cluster share the same VoltagelFrequency (V/F) level. Each of clusters may have distinct V/F level compared to the others. Because all cores of a cluster share the same V/F level, it is critical that the size of a cluster would not be much bigger or smaller compared to the other clusters. This is because the uneven distribution of cores among clusters negatively impacts the energy efficiency: speeding up underloaded clusters wastes energy and slowing down overloaded clusters delays runtime. In this paper, we present a case study to optimize the functionality of the K - Means algorithm for improving the performance of a multi -core system. Among the existing clustering algorithms, we chose K-Means because of its simple design and broad use in various disciplines. The standard K - Means clusters cores with similar workloads that differ in computation from one phase to another. However, clusters formed by K-Means vary in size, putting too many cores inside some clusters while leaving others with very few cores. To address the balance of clusters, we use an existing variation of K - Means (constrained K-Means) that defines a lower bound for the number of cores in each of clusters. Constraining the size of each cluster of the multicore system creates the balanced clusters, where the sizes of clusters are almost identical. To measure the energy efficiency of the applied clustering, this paper suggests a Dynamic Voltage and Frequency Scaling (DVFS) methodology that adjusts the V/F levels of clusters at each phase of an application. For several multi-threaded applications, results show that at the best case the constrained K - Means improves the Energy-Delay Product (EDP) up to 40% compared to the standard version. The results also show that on average across all applications the constrained K-Means impro

关键词： Energy efficiency

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Exploring Efficiency of Vision Transformers for Self-Supervised Monocular Depth Estimation 21

Exploring Efficiency of Vision Transformers for Self-Supervi...

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21st IEEE international symposium on Mixed and Augmented Reality (ISMAR)

作者： Karpov, Aleksei Makarov, Ilya AIRI Moscow Russia HSE Univ Moscow Russia Docet TI Tel Aviv Israel

ISBN: (数字)9781665453257

ISBN: (纸本)9781665453257

Depth estimation is a crucial task for the creation of depth maps, one of the most important components for augmented reality (AR) and other applications. However, the most widely used hardware for AR and smartphones has only sparse depth sensors with different ground truth depth acquisition methods. Thus, depth estimation models that are robust for downstream AR tasks performance can only be trained reliably using self-supervised learning based on camera information. Previous works in the field mostly focus on self-supervised models with pure convolutional architectures, without taking global spatial context into account. In this paper, we utilize vision transformer architectures for self-supervised monocular depth estimation and propose VTDepth, a vision transformer-based model, which provides a solution to the problem of the global spatial context. We compare various combinations of convolutional and transformer architectures for self-supervised depth estimation and show that the best combination of models is an encoder with a transformer basis and convolutional decoder. Our experiments demonstrate the efficiency of VTDepth for self-supervised depth estimation. Our set of models achieves state-of-the-art performance for self-supervised learning on NYUv2 and KITTI datasets. Our code is available at https://***/ahbpp/VTDepth.

关键词： Human-centered computing Human computer interaction (HCI) Interaction paradigms Mixed / augmented reality Artificial intelligence computer vision Localization, spatial registration and tracking 3D reconstruction

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Construction of Anti-Terrorism Intelligence Graph Based on DIKW Framework 25

Construction of Anti-Terrorism Intelligence Graph Based on D...

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作者： Gao, Peng Yuan, Haoran Liu, Yanfei Mao, Bowen Duan, Yucong Wang, Zhenhua Gansu Police Vocational College Gansu China Chongqing City Science and Technology College Artificial Intelligence and Big Data Academy Chongqing China Tianjin University Chongqing Police College Department of Intelligence and Computing Department of Infor Security Chongqing China Tianjin University Department of Intelligence and Computing Tianjin China School of Computer Science and Technology Hainan University Haikou China Chongqing Police College Department of Tactics Chongqing China

ISBN: (纸本)9798350330014

'Construction of anti-Terrorism Intelligence Graph Based on the DIKW Framework' simplifies the process of handling decentralized and diverse sources of intelligence in counter-terrorism. It uses the DIKW model to consolidate and structure data for better efficiency. It creates a data graph to illustrate relationships, making data more accessible. This data is then used to form an information graph, connecting data with relevant knowledge. From this, intelligent models are extracted, aiding in counter-terrorism prevention and prediction. This approach emphasizes transforming data into intelligence and knowledge hierarchically to improve counter-terrorism efforts. © 2023 IEEE.

关键词： Terrorism

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Gait Differences in the Real World and Virtual Reality: The Effect of Prior Virtual Reality Experience 21

Gait Differences in the Real World and Virtual Reality: The ...

引用

21st IEEE international symposium on Mixed and Augmented Reality (ISMAR)

作者： Nasiri, Moloud Anaraky, Reza Ghaiumy Babu, Sabarish V. Robb, Andrew Clemson Univ Sch Comp Clemson SC 29631 USA

ISBN: (数字)9781665453257

ISBN: (纸本)9781665453257

Walking through immersive virtual environments is one of the important parts of Virtual Reality (VR) applications. Prior research has established that users' gait in virtual and real environments differs;however, little research has evaluated how users' gait differs as users gain more experience with VR. We conducted experiments measuring novice and experienced subjects' gait parameters in VR and real environments. Results showed that subjects' performance in VR and Real World was more similar in the last trials than in the first trials;their walking dissimilarity in the start trials diminished by walking more trials. We found trial as a significant variable affecting the walking speed, step length, and trunk angle for both groups of users. While the main effect of expertise was not observed, an interaction effect between expertise and the trial number was shown. Trunk angle increased over time for novices but decreased for experts.

关键词： Human-centered computing Human computer interaction (HCI) HCI design and evaluation methods User studies

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Joint Request Offloading and Resource Allocation for Energy Efficient D2D Enabled Multi-type Inference Services 21

Joint Request Offloading and Resource Allocation for Energy ...

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作者： Zeng, Ziyu Huang, Jiale Wu, Jiaxin Wu, Jigang Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China

ISBN: (纸本)9798350329223

Tremendous research efforts have devoted in providing multi-type inference services for users by deploying heterogeneous models in edge servers (ESs). But, resource contention in ES can degrade the quality-of-service (QoS) of users. To obtain the services with QoS guarantee, users can leverage device-to-device (D2D) collaboration to share the models and resources in mobile devices (MDs) with each other. However, it brings non-trivial energy consumption to MDs, and thus dramatically decreases their battery life. To fill this gap, this paper formulates an energy efficiency optimization problem for D2D enabled multi-type inference services. The goal is to minimize the total energy consumption of MDs, by jointly optimizing the decisions for request offloading and resource allocation under the constraints of resources and QoS. The formulated problem is NP-hard, as it is a mixed integer nonlinear programming problem. To solve the problem, this paper transforms it into an equivalent master request offloading (RO) problem with a low-complexity resource allocation (RA) subproblem. A two-level optimization algorithm problem is proposed for the transformed problem. In the outer level, a low-complexity heuristic algorithm is proposed to iteratively seek the optimal request offloading decision for RO problem. In the inner level, a subgradient based algorithm is proposed to derive the optimal resource allocation decision for RA subproblem. Experimental results show that, the proposed algorithms achieve the best performance, in terms of energy efficiency for all cases, in comparison to the baseline algorithms. © 2023 IEEE.

关键词： Resource allocation

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EEG classification algorithm of motor imagery based on CNN-Transformer fusion network 21

EEG classification algorithm of motor imagery based on CNN-T...

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21st IEEE international Conference on Trust, Security and Privacy in computing and Communications (IEEE TrustCom)

作者： Liu, Haofeng Liu, Yuefeng Wang, Yue Liu, Bo Bao, Xiang Inner Mongolia Univ Sci Technol Sch Informat Engn Baotou Peoples R China

ISBN: (纸本)9781665494250

In recent years, with the development of social economy and technology, the brain-computer interface based on motor imagery(MI-BCI) has gradually become the focus content of many re-searchers. However, the motor imagery EEG signal (MI-EEG) itself has the characteristics of non-linearity and low signal-to-noise ratio, and because the characteristics of different domains of MI-EEG cannot be effectively combined, the recognition rate of MI-EEG is unsatisfactory. To overcome the above problems, this paper proposes a Transformer-based onedimensional convolutional neural network model(CNNTransformer) for the classification and recognition of four types of motor imagery EEG signals. Firstly, the artifacts of the original EEG are removed and new time-space-frequency features are constructed by preprocessing such as bandpass filtering and PCA dimensionality;then, the local features in the temporal dimension are extracted through the convolution and pooling operations of 1D-CNN, while reducing the dimension of the time feature;next, the Transformer based on the attention mechanism is used to extract more abstract and high-level temporal features from multiple perspectives;finally, the classification results are integrated and output through the fully connected layer. The performance of the CNN-Transformer model is evaluated using the competition dataset 2008 BCICompetition 2A. The results show that the average accuracy and kappa value of the CNN-Transformer model are as high as 99.29%(+/- 0.07%) and 98.43%(+/- 0.21), respectively, which are 3.72% and 7.68% higher than the classical architecture (CNNLstM). This model provides a design idea for improving the accuracy of MI-EEG classification and recognition, and also lays a foundation for the wide application of MI-BCI.

关键词： motor imagery brain-computer interface deep learning classification and recognition convolutional neural network transformer

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Monoscale and Polyscale Analyses of Physical Signals for Compressive Detection of Relevant Complexity Features 21

Monoscale and Polyscale Analyses of Physical Signals for Com...

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21st IEEE international Conference on Cognitive Informatics and Cognitive computing, ICCI*CC 2022

作者： Hosseinpour, Soleiman Kinsner, Witold Sepehri, Nariman University of Manitoba Department of Mechanical Engineering Winnipeg Canada University of Manitoba Department of Electrical and Computer Engineering Winnipeg Canada

ISBN: (纸本)9781665490849

Dealing with the large volumes of data generated in the digital world requires efficient data interpretation techniques. Some compressive feature detection techniques are proposed to efficiently minimize the data size. Most of these algorithms in the signal processing area are based on monoscale or multi-scale methods. These methods are not sensitive to the reshuffling of the data, and critical features like correlation and covariance are lost. An alternative approach is to use polyscale algorithms. This paper provides a polyscale approach using the variance fractal dimension to be developed for compressive detection. Some critical aspects of using these measures are discussed, including robustness to noise and simplicity concerning other existing methods. These measures are applied to some speech utterances and experimental data measured from an electro hydrostatic actuator test rig to show the efficiency of this method. This work can provide insight into practical compressive detections to explore this promising area for future developments. © 2022 IEEE.

关键词： Fractal dimension

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Neural 3D Gaze: 3D Pupil Localization and Gaze Tracking based on Anatomical Eye Model and Neural Refraction Correction 21

Neural 3D Gaze: 3D Pupil Localization and Gaze Tracking base...

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21st IEEE international symposium on Mixed and Augmented Reality (ISMAR)

作者： Lu, Conny Chakravarthula, Praneeth Liu, Kaihao Liu, Xixiang Li, Siyuan Fuchs, Henry Univ N Carolina Chapel Hill NC 27599 USA Princeton Univ Princeton NJ 08544 USA

ISBN: (数字)9781665453257

ISBN: (纸本)9781665453257

Eye tracking has already made its way to current commercial wearable display devices, and is becoming increasingly important for virtual and augmented reality applications. However, the existing model-based eye tracking solutions are not capable of conducting very accurate gaze angle measurements, and may not be sufficient to solve challenging display problems such as pupil steering or eye-box expansion. In this paper, we argue that accurate detection and localization of pupil in 3D space is a necessary intermediate step in model-based eye tracking. Existing methods and datasets either ignore evaluating the accuracy of 3D pupil localization or evaluate it only on synthetic data. To this end, we capture the first 3D pupil-gaze-measurement dataset using a high precision setup with head stabilization and release it as the first benchmark dataset to evaluate both 3D pupil localization and gaze tracking methods. Furthermore, we utilize an advanced eye model to replace the commonly used oversimplified eye model. Leveraging the eye model, we propose a novel 3D pupil localization method with a deep learning-based corneal refraction correction. We demonstrate that our method outperforms the state-of-the-art works by reducing the 3D pupil localization error by 47.5% and the gaze estimation error by 18.7%. Our dataset and codes can be found here: link.

关键词： computing methodologies computer graphics Graphics systems and interfaces Mixed / augmented reality computing methodologies Artificial intelligence computer vision computer vision problems

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