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MDIot: IoT device identification method based on traffic characteristics 1

19th Chinese Intelligent Systems Conference, CISC 2023

作者： Zheng, Hanxi Liu, Ruijun Yin, Huanpu Li, Haisheng School of Computer and Engineering Beijing Technology and Business University Beijing100048 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing100048 China

ISBN: (数字)9789819968824

ISBN: (纸本)9789819968817

In recent years, the rapid growth of the Internet of Things (IoT) devices has brought with it security risks, highlighting the need for management for IoT devices. Identification and classification are key components of devices management. However, existing work on IoT device identification is based on prior knowledge to manually extract features, resulting in too many redundant features and reducing devices identification accuracy. In this paper, we propose amodel called MDIoT. Our model use the multi-votingmethod for feature selection, and is combined with Multiobjective Genetic Algorithm to reduce redundancy features and noise. Experimental results show that our feature selection method is more efficient in feature selection and the classification accuracy is over 95%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Internet of things

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Deployment optimization for target perpetual coverage in energy harvesting wireless sensor network

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Digital Communications and Networks 2024年第2期10卷 498-508页

作者： Zhenkun Jin Yixuan Geng Chenlu Zhu Yunzhi Xia Xianjun Deng Lingzhi Yi Xianlan Wang Wuhan Business University 430056China Wuhan Research Institute of Post and Telecommunication Wuhan430074China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data SecuritySchool of Cyber Science and EngineeringHuazhong University of Science and TechnologyWuhan430074China Hubei Chutian Expressway Digital Technology Co.Ltd Wuhan430074China School of Information and Safety Engineering Zhongnan University of Economics and LawWuhan430073China

Energy limitation of traditional Wireless Sensor Networks(WSNs)greatly confines the network lifetime due to generating and processing massive sensing data with a limited *** energy harvesting WSN is a novel network architecture to address the limitation of traditional ***,existing coverage and deployment schemes neglect the environmental correlation of sensor nodes and external energy with respect to physical *** considering the spatial correlation of the environment and the uneven distribution of energy in energy harvesting WSN,we investigate how to deploy a collection of sensor nodes to save the deployment cost while ensuring the target perpetual *** Confident Information Coverage(CIC)model is adopted to formulate the CIC Minimum Deployment Cost Target Perpetual Coverage(CICMTP)problem to minimize the deployed sensor *** the CICMTP is NP-hard,we devise two approximation algorithms named Local Greedy Threshold Algorithm based on CIC(LGTA-CIC)and Overall Greedy Search Algorithm based on CIC(OGSA-CIC).The LGTA-CIC has a low time complexity and the OGSA-CIC has a better approximation *** simulation results demonstrate that the OGSA-CIC is able to achieve lower deployment cost and the performance of the proposed algorithms outperforms GRNP,TPNP and EENP algorithms.

关键词： Energy harvesting WSN Deployment optimization Confident information coverage(CIC) Target perpetual coverage

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Understanding and improving fairness in cognitive diagnosis

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Science China(Information Sciences) 2024年第5期67卷 99-114页

作者： Zheng ZHANG Le WU Qi LIU Jiayu LIU Zhenya HUANG Yu YIN Yan ZHUANG Weibo GAO Enhong CHEN Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology& School of Data Science University of Science and Technology of China State Key Laboratory of Cognitive Intelligence School of Computer Science and Information Engineering Hefei University of Technology

Intelligent education is a significant application of artificial intelligence. One of the key research topics in intelligence education is cognitive diagnosis, which aims to gauge the level of proficiency among students on specific knowledge concepts(e.g., Geometry). To the best of our knowledge, most of the existing cognitive models primarily focus on improving diagnostic accuracy while rarely considering fairness issues; for instance, the diagnosis of students may be affected by various sensitive attributes(e.g., region). In this paper,we aim to explore fairness in cognitive diagnosis and answer two questions:(1) Are the results of existing cognitive diagnosis models affected by sensitive attributes?(2) If yes, how can we mitigate the impact of sensitive attributes to ensure fair diagnosis results? To this end, we first empirically reveal that several wellknown cognitive diagnosis methods usually lead to unfair performances, and the trend of unfairness varies among different cognitive diagnosis models. Then, we make a theoretical analysis to explain the reasons behind this phenomenon. To resolve the unfairness problem in existing cognitive diagnosis models, we propose a general fairness-aware cognitive diagnosis framework, FairCD. Our fundamental principle involves eliminating the effect of sensitive attributes on student proficiency. To achieve this, we divide student proficiency in existing cognitive diagnosis models into two components: bias proficiency and fair *** design two orthogonal tasks for each of them to ensure that fairness in proficiency remains independent of sensitive attributes and take it as the final diagnosed result. Extensive experiments on the Program for International Student Assessment(PISA) dataset clearly show the effectiveness of our framework.

关键词： fairness intelligent education cognitive diagnosis psychometrics adversarial learning

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An Actor-centric Causality Graph for Asynchronous Temporal Inference in Group Activity

An Actor-centric Causality Graph for Asynchronous Temporal I...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhao Xie Tian Gao Kewei Wu Jiao Chang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology

The causality relation modeling remains a challenging task for group activity recognition. The causality relations describe the influence on the centric actor (effect actor) from its correlative actors (cause actors). Most existing graph models focus on learning the actor relation with synchronous temporal features, which is insufficient to deal with the causality relation with asynchronous temporal features. In this paper, we propose an Actor-Centric Causality Graph Model, which learns the asynchronous temporal causality relation with three modules, i.e., an asynchronous temporal causality relation detection module, a causality feature fusion module, and a causality relation graph inference module. First, given a centric actor and its correlative actor, we analyze their influences to detect causality relation. We estimate the self influence of the centric actor with self regression. We estimate the correlative influence from the correlative actor to the centric actor with correlative regression, which uses asynchronous features at different timestamps. Second, we synchronize the two action features by estimating the temporal delay between the cause action and the effect action. The synchronized features are used to enhance the feature of the effect action with a channel-wise fusion. Third, we describe the nodes (actors) with causality features and learn the edges by fusing the causality relation with the appearance relation and distance relation. The causality relation graph inference provides crucial features of effect action, which are complementary to the base model using synchronous relation inference. Experiments show that our method achieves state-of-the-art performance on the Volleyball dataset and Collective Activity dataset.

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DSM: Question Generation over knowledge Base via Modeling Diverse Subgraphs with Meta-learner

DSM: Question Generation over Knowledge Base via Modeling Di...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Guo, Shasha Zhang, Jing Wang, Yanling Zhang, Qianyi Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education China

Existing methods on knowledge base question generation (KBQG) learn a one-size-fits-all model by training together all subgraphs without distinguishing the diverse semantics of subgraphs. In this work, we show that making use of the past experience on semantically similar subgraphs can reduce the learning difficulty and promote the performance of KBQG models. To achieve this, we propose a novel approach to model diverse subgraphs with meta-learner (DSM). Specifically, we devise a graph contrastive learning-based retriever to identify semantically similar subgraphs, so that we can construct the semantics-aware learning tasks for the meta-learner to learn semantics-specific and semantics-agnostic knowledge on and across these tasks. Extensive experiments on two widely-adopted benchmarks for KBQG show that DSM derives new state-of-the-art performance and benefits the question answering tasks as a means of data augmentation. Codes and datasets are available online. © 2022 Association for Computational Linguistics.

关键词： Semantics

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A two-lane lattice hydrodynamic model with mixed traffic of connected and non-connected vehicles 6

A two-lane lattice hydrodynamic model with mixed traffic of ...

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6th International Conference on Traffic engineering and Transportation System, ICTETS 2022

作者： Bai, Yingqiang Zhang, Ying School of Information and Engineering Zhejiang Ocean University Zhoushan316000 China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan316022 China

ISBN: (纸本)9781510663053

The development of intelligent transportation is of great significance to improving the effectiveness and safety of traffic. A new two-lane lattice hydrodynamics model (LHM) is proposed by simultaneously considering the effects of lane-changing and mixed traffic of connected and non-connected vehicles (CNVs). Using linear analysis, the stability conditions are derived, which indicates that lane-changing has a positive impact on traffic stability. In addition, the numerical simulation results provide support for theoretical analysis and confirm the correctness. © 2023 SPIE.

关键词： Hydrodynamics

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Remote Image Acquisition System Based on STM32 for Spodoptera frugiperda 5

Remote Image Acquisition System Based on STM32 for Spodopter...

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5th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2022

作者： Chang, Jumei Xu, Quanyuan Lu, Ying Zhang, Hanrui College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China Southwest Forestry University Key Laboratory of Forestry and Ecological Big Data State Forestry Administration China

ISBN: (纸本)9781665479677

To enable remote image acquisition and wireless transmission for field sex lure monitoring of Spodoptera frugiperda, the design consists of a CPU processor module, power supply module, image acquisition module, 4G wireless network transmission module, and external SRAM module. The system employs the STM32F407 control core, the OV2640 camera for image acquisition, the JPEG algorithm to compress and decode the image data collected by the camera, and automatically restore the image collected at the monitoring point, the EC20 as a 4G wireless communication network for image data transmission, and the transmission of real-time image data to the server side of the upper computer to achieve human-computer interaction and real-time interaction of system daemons. The experimental results show that the system achieves remote image acquisition of grassland moths and reliably transmits the collected image data to the server side of the upper computer via a 4G wireless network, allowing the monitor to view images of grassland moth monitoring points collected by the system in real-time and save the collected image data locally. This system accomplishes the automation and intelligence of the grassland moth monitoring system, prepares the data for subsequent grassland moth research, and has a broad application promise for grassland moth population monitoring. © 2022 IEEE.

关键词： Cameras

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Assisted diagnosis of breast cancer based on federated learning and improved DenseNet 5

Assisted diagnosis of breast cancer based on federated learn...

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5th International Conference on Computer Vision and data Mining, ICCVDM 2024

作者： Huang, Shan Xiang, Hexia Li, Jingjie Sun, Yongkai Duan, Xiaodong College of Computer Science and Engineering Dalian Minzu University Dalian116600 China State Ethnic Affairs Commission Key Laboratory of Big Data Applied Technology Dalian Minzu University Dalian116600 China

ISBN: (纸本)9781510682863

Breast cancer is one of the common cancers worldwide, and how to identify breast cancer as early as possible plays an important role in its later diagnosis and treatment. However, relying only on data from a single organisation can limit the ability of trained neural networks to meet the performance demands of medical diagnostic services, and centralised learning that collects data from decentralised organisations can lead to privacy leakage. To address the above challenges, this paper proposes an assisted diagnosis model for breast cancer combining federated learning and improved DenseNet. Specifically, in contrast to mainstream centralised learning, this paper introduces federated learning to integrate multiparty data and effectively prevent privacy leakage. Each client sends the local model to the central server for aggregation, and then the global model obtained from the aggregation is sent to each client for training, thus achieving collaborative training of neural network models. In addition, in order to enhance the ability to capture feature information, this paper incorporates a Dynamic Convolution module into the densely connected network to improve the diagnostic accuracy without significantly increasing the size of the network model. The experimental results show that the proposed method has better diagnostic performance in breast cancer diagnosis compared with other methods. © 2024 SPIE.

关键词： Information leakage

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Federated Intelligence for Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 1-5页

作者： Zhang, Weishan Zhang, Baoyu Jia, Xiaofeng Qi, Hongwei Qin, Rui Li, Juanjuan Tian, Yonglin Liang, Xiaolong Wang, Fei-Yue School of China University of Petroleum Qingdao China Department of Data Management Big Data Centre Beijing Beijing China Technology Company Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macao China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

This letter is a brief summary of a series of IEEE TIV's decentralized and hybrid workshops (DHWs) on Federated Intelligence for Intelligent Vehicles. The discussed results are: 1) Different scales of large models (LMs) can be federated and deployed on IVs, and three types of federated collaboration between large and small models can be adopted for IVs. 2) Federated fine-tuning of LMs is beneficial for IVs data security. 3) The sustainability of IVs can be improved through optimizing existing models and continuous learning using federated intelligence. 4) LM-enhanced knowledge can make IVs smarter. IEEE

关键词： Intelligent vehicle highway systems

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An Ultra-Wideband Cascaded Power Amplifier for Military Individual Radio 7

An Ultra-Wideband Cascaded Power Amplifier for Military Indi...

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7th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2024

作者： Gao, Fei Nan, Jingchang Fan, Hongji Wang, Cong Tang, Panpan Li, Zhaoqi Liaoning Technical University Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Huludao China Southeast University Nanjing China Harbin Institute of Technology Department of Microwave Engineering Harbin China

ISBN: (纸本)9798350384437

In recent years, there has been a tremendous interest in military individual radio in wireless communication. To obtain sufficient operating distance and long standby time, the output power and efficiency of power amplifiers have become the research focus by some of the research teams. GaN has higher electron mobility and forbidden bandwidth than other materials. Nowadays, GaN has become the mainstream key technology for radio frequency (RF) power amplifiers in 5G communication systems. In this work, a 500MHz~2500MHz GaN-based HEMT wideband cascaded power amplifier was designed. The output power of the driver amplifier is 36.6dBm ± 0.5dB, the input return loss is 20%, the DC power consumption is 10 dB, input voltage standing wave ratio © 2024 IEEE.

关键词： Gallium nitride

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