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IEEE International Conference on Systems, Man and Cybernetics

作者： Jing Bi Ziqi Wang Haitao Yuan Jia Zhang Faculty of Information Technology Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Dept. of Computer Science in Lyle School of Engineering Southern Methodist University Dallas TX USA

Nowadays, smart mobile devices (SMDs) support various computation-intensive and delay-sensitive applications, e.g., online games, and figure compression. However, SMDs have limited computing resources and battery energy and cannot execute all tasks of the above applications in a real-time manner. Cloud computing provides enormous computing resources and energy that can easily execute tasks offloaded from SMDs. However, could data centers (CDCs) are often located in remote sites, which leads to long transmission time. Small base stations (SBSs) offer high-bandwidth and low-latency services for SMDs, which solves the problem of cloud computing. However, it becomes a challenge to achieve the lowest cost in such a heterogeneous architecture including multiple SMDs, SBSs, and the CDC while meeting delay requirements of tasks. This work proposes a cost-minimized computation offloading strategy to minimize the total cost of the system. A constrained optimization problem is first formulated based on the hybrid architecture. Afterward, a two-stage optimization algorithm called a Lévy flights and Simulated Annealing-based Grey wolf optimizer (LSAG) is developed to optimize the total cost of the system. In the first stage, the optimal edge selection policy is determined given multiple available SBSs. In the second stage, task offloading and resource allocation among SMDs, SBSs, and the cloud are determined. Experiments with real-life tasks prove that LSAG significantly achieves lower cost with faster convergence speed than state-of-the-art peers.

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Web Traffic Anomaly Detection Using a Hybrid Spatio-temporal Neural Network

Web Traffic Anomaly Detection Using a Hybrid Spatio-temporal...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jing Bi Lifeng Xu Haitao Yuan Jia Zhang Faculty of Information Technology Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Dept. of Computer Science in Lyle School of Engineering Southern Methodist University Dallas TX USA

Nowadays, rapid development of Internet has brought a sharp increase in traffic data. Abnormal traffic haS serious impact on network security. Traffic anomaly detection can be achieved by extracting characteristics of network traffic to detect anomalous intrusions, and therefore, anomaly detection algorithms are of great significance to maintenance of network security. This work proposes a hybrid spatio-temporal neural network with attention named CTGA to effectively identify anomalous traffic. CTGA combines a Convolutional neural network (CNN), a Temporal convolutional network (TCN), a bidirectional Gated recurrent unit network (BiGRU), and a self-Attention mechanism. It automatically extracts temporal and spatial features of sequences from raw data by sliding window preprocessing followed by CNN, TCN, BiGRU, and the self-attention mechanism to detect anomalous data. CNN is used to extract spatial features of time sequences and reduce the loss of spatial information. In the sequence, TCN obtains short-term features. Long-term dependencies in the data are captured by BiGRU, and the self-attention mechanism obtains important information in the sequence. Finally, experiments with the real-life Yahoo S5 dataset prove that CTGA outperforms other approaches substantially.

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Smart room for patient monitoring based on IoT technologies 23

Smart room for patient monitoring based on IoT technologies

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Proceedings of the 2023 6th Artificial Intelligence and Cloud Computing Conference

作者： Nataly Zhukova Anna Motienko Dmitriy Levonevskiy Alexey Subbotin Pham Tuan Anh Laboratory of big data technologies in socio-cyberphysical systems St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS) Russia Faculty of Computer Technologies and Informatics St. Petersburg State Electrotechnical University Russia Faculty of Software Engineering and Computer Systems ITMO University Russia

ISBN: (纸本)9798400716225

This article presents a method for building a hospital room with extended intelligent features based on Internet of Things (IoT) technologies. We begin by reviewing existing vision-based health care systems and identifying important and useful functions that they should have. We then present a method for building a smart room that allows increase the level of the intelligence of the existing hospital rooms due to extensive use of the up-to-date machine learning methods. The proposed solution assumes using readily available hardware devices to reduce the cost of smart room building. Compared to commercially available assistive technologies, the proposed smart room turn out to have high level of intelligence and to be very cost effective. The system's non-intrusive nature makes it easy to use it both in hospitals and at home for patient care.

关键词： Internet of Things IoT automation in medicine intellectual room machine learning smart room smart ward

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Energy-Optimized with Multi-Population Differential Annealed Optimization in Mobile Edge Computing

Energy-Optimized with Multi-Population Differential Annealed...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ruixuan Wu Yuliang Shi Haitao Yuan Jing Bi Jia Zhang Faculty of Information Technology Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Dept. of Computer Science Lyle School of Engineering Southern Methodist University Dallas TX USA

Mobile devices (MDs) cannot fully run all computation/delay-sensitive tasks due to their limited computing resources. Mobile edge computing (MEC) meets the demand by providing massive resources for MDs and offloading task partitions to MEC servers. However, task offloading also brings communication delay and energy consumption. Therefore, it is challenging to associate resource-constrained MDs with appropriate MEC servers to minimize power consumption. To address this problem, a constrained mixed integer nonlinear program is formulated to optimize the total energy consumption of the system including MDs and MEC servers. To solve this problem, this work designs an improved meta-heuristic optimization algorithm called Self-adaptive and Multi-population Differential Annealed Optimization (SMDAO). Experimental results demonstrate that compared with its two state-of-the-art peers, the proposed SMDAO yields the best solution with the smallest total energy consumption in the least time.

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Iris-Based Biometric Identification Using a Combination of the Right - Left Iris Statistical Features 12

Iris-Based Biometric Identification Using a Combination of t...

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12th International Conference on Mathematical Modeling in Physical Sciences, IC-MSQUARE 2023

作者： Dincǎ Lǎzǎrescu, A.M. Moldovanu, S. Moraru, L. The Modelling & Simulation Laboratory Dunarea de Jos University of Galati 47 Domneasca Street Galati800008 Romania Mihail Kogǎlniceanu High School 161B Brǎilei St. Galati800320 Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati 47 Domneasca Street Galati800008 Romania Department of Chemistry Physics & Environment Faculty of Sciences and Environment Dunarea de Jos University of Galati 47 Domneasca Street Galati800008 Romania

A combination between the information extracted for both right iris and left iris could increase the efficacy of the biometric recognition systems. In this paper, we propose a biometric identification method based on density of image patterns extracted from human iris images and the combination and comparison of the right iris and the left iris characteristics. The density of the patters approach for processed images can be a new biometric feature used to implement a biometric recognition system with high performance when a small feature dimension is used. In this way, we can maximize the retention of the effective biometric information. The experiments were conducted on the MMU Iris Database containing 225 images of the left eye and 225 images of the right eye. Two morphological Top-hat and Hit or Miss transforms were implemented to find out the particular pattern of pixels. They allow for the enhancement of detail in images. Then, a statistical feature extraction technique is employed to derive the density of the patterns in morphological transformed images. To assess the density of the patterns differences between the right and left iris data groups, the Pearson's correlation coefficient (PCC) is computed. We reported very good results with a PCC of 0.6164 (strong and positive correlation) for Top-hat morphological operation whilst the Hit or Miss transform returns a PCC of 0.0127 so there is no relationship between the density of the patterns in the right and left irises. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Biometrics

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Deep and Spatio-Temporal Detection for Abnormal Traffic in Cloud Data Centers

Deep and Spatio-Temporal Detection for Abnormal Traffic in C...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Haitao Yuan Shen Wang Jing Bi Jia Zhang School of Automation Science and Electrical Engineering Beihang University Beijing China Faculty of Information Technology Beijing University of Technology Beijing China Dept. of Computer Science in Lyle School of Engineering at Southern Methodist University Dallas TX USA

Current interactions of network traffic through cloud data centers have become an important process of network services. Precise and real-time detection and prediction of network traffic can assist system operators in effectively allocating resources, and assessing network performance based on actual service requirements, and analyzing network health. However, sources and distribution of network traffic are different, which makes accurate warnings of network attack traffic become a difficult problem. In recent years, neural networks have been proven to be effective in predicting time series data, particularly long short-term memory networks for capturing temporal features and convolutional methods for capturing spatial features. This work proposes a Deep Hybrid Spatio-Temporal (DHST) network method for abnormal traffic detection in cloud data centers, which combines a cooperative temporal convolutional network, an attention mechanism and a random inactivation method to capture the network traffic data's spatio-temporal features. It improves accuracy of abnormal traffic detection, and realizes classification of normal traffic and abnormal one. It achieves higher accuracy than typical detection methods when applied to a real-life dataset collected from Yahoo Webscope S5.

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Validity Matters: Uncertainty-Guided Testing of Deep Neural Networks

Validity Matters: Uncertainty-Guided Testing of Deep Neural ...

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作者： Jiang, Zhouxian Li, Honghui Wang, Rui Tian, Xuetao Liang, Ci Yan, Fei Zhang, Junwen Liu, Zhen School of Computer Science and Technology Beijing Jiaotong University Beijing China Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing China Faculty of Psychology Beijing Normal University Beijing China School of Transportation Science and Engineering Harbin Institute of Technology Harbin China School of Automation and Intelligence Beijing Jiaotong University Beijing China

Despite numerous applications of deep learning technologies on critical tasks in various domains, advanced deep neural networks (DNNs) face persistent safety and security challenges, such as the overconfidence in predicting out-of-distribution samples and susceptibility to adversarial examples. Thorough testing by exploring the input space serves as a key strategy to ensure their robustness and trustworthiness of these networks. However, existing testing methods focus on disclosing more erroneous model behaviours, overlooking the validity of the generated test inputs. To mitigate this issue, we investigate devising valid test input generation method for DNNs from a predictive uncertainty perspective. Through a large-scale empirical study across 11 predictive uncertainty metrics for DNNs, we explore the correlation between validity and uncertainty of test inputs. Our findings reveal that the predictive entropy-based and ensemble-based uncertainty metrics effectively characterize the input validity demonstration. Building on these insights, we introduce UCTest, an uncertainty-guided deep learning testing approach, to efficiently generate valid and authentic test inputs. We formulate a joint optimization objective: to uncover the model's misbehaviours by maximizing the loss function and concurrently generate valid test input by minimizing uncertainty. Extensive experiments demonstrate that our approach outperforms the current testing methods in generating valid test inputs. Furthermore, incorporating natural variation through data augmentation techniques into UCTest effectively boosts the diversity of generated test inputs. © 2024 John Wiley & Sons Ltd.

关键词： Adversarial machine learning

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Data-Driven DLT3 Federated Deep Reinforcement Learning for Secure and Efficient Autonomous Driving

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IEEE Transactions on Consumer Electronics 2025年

作者： Fei, Rong Li, Qianxi Wang, Kan Khan, Fazlullah Alturki, Ryan Lan, Dapeng Xi’an University of Technology School of Computer and Engineering Xi’an710000 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Ningbo315104 China Umm Al-Qura University College of Computing Department of Software Engineering Makkah21955 Saudi Arabia Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110003 China

As autonomous vehicles (AVs) become increasingly widespread, the intelligent driving control and safety concerns have emerged. Recent advents in Internet of Things (IoTs) and 6G technologies have vastly boosted AVs’ sensing and communication capabilities, thereby enabling the high-speed and real-time data exchange. However, efficiently utilizing data while safeguarding the privacy remains challenges. Thus, we propose a federated learning (FL) framework with the privacy protection to facilitate the deep reinforcement learning (DRL) for longitudinal vehicle safety tracking control. First, the proposed framework enables the efficient information sharing and policy updates via encrypted policy gradient transmission. Then, the twin delayed deep deterministic policy gradient (TD3) method makes the velocity decision in the local model, while the dual long short-term memory (LSTM) network captures the temporal trajectory information. Finally, stacked denoising autoencoders (SDAE) is used to extract high-level environmental features, and the dual-LSTM-TD3 (DLT3)-SDAE model is introduced to enhance the policy decision efficiency and accuracy. Experimental results demonstrate that within the FL framework supported by homomorphic encryption, the DLT3-SDAE-based AV agent surpasses other DRL methods in terms of collision avoidance and average rewards, in the LAF environment. The safety, efficiency and comfort of the DLT3-SDAE AV agent are also evaluated and demonstrated. © 1975-2011 IEEE.

关键词： Deep reinforcement learning

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Anomaly Detection Based on Principle of Justifiable Granularity and Probability Density Estimation

Anomaly Detection Based on Principle of Justifiable Granular...

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Chinese automation Congress (CAC)

作者： Sheng Du Xian Ma Xiang Li Min Wu Weihua Cao Witold Pedrycz School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Sariyer/Istanbul Turkiye

Anomaly detection is essential to ensure the safety of industrial processes. This paper presents an anomaly detection approach based on the probability density estimation and principle of justifiable granularity. First, time series data are transformed into a two-dimensional information granule by the principle of justifiable granularity. Then, the test statistic is constructed, and the probability density and cumulative distribution functions of the test statistic are calculated. Next, the confidence level determines the test threshold. Finally, the time series data of a key parameter in the sintering process is used as a case study. The experimental result demonstrates that the proposed approach can detect abnormal time series data effectively, providing an accurate and effective solution for detecting time series anomalies in industrial processes.

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Multi-Expert Adaptive Selection: Task-Balancing for All-in-One Image Restoration

arXiv

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arXiv 2024年

作者： Yu, Xiaoyan Zhou, Shen Li, Huafeng Zhu, Liehuang School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China

The use of a single image restoration framework to achieve multi-task image restoration has garnered significant attention from researchers. However, several practical challenges remain, including meeting the specific and simultaneous demands of different tasks, balancing relationships between tasks, and effectively utilizing task correlations in model design. To address these challenges, this paper explores a multi-expert adaptive selection mechanism. We begin by designing a feature representation method that accounts for both the pixel channel level and the global level, encompassing low-frequency and high-frequency components of the image. Based on this method, we construct a multi-expert selection and ensemble scheme. This scheme adaptively selects the most suitable expert from the expert library according to the content of the input image and the prompts of the current task. It not only meets the individualized needs of different tasks but also achieves balance and optimization across tasks. By sharing experts, our design promotes interconnections between different tasks, thereby enhancing overall performance and resource utilization. Additionally, the multi-expert mechanism effectively eliminates irrelevant experts, reducing interference from them and further improving the effectiveness and accuracy of image restoration. Experimental results demonstrate that our proposed method is both effective and superior to existing approaches, highlighting its potential for practical applications in multi-task image restoration. The source code of the proposed method is available at https://***/zhoushen1/MEASNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Restoration

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