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Trust and Risk Based on Access Control Model in Social Internet of Things

International Journal of network Security 2023年第5期25卷 736-744页

作者： Zhang, Hongbin Liu, Jian Zhao, Dongmei Liu, Bin Wang, Yanmei Fan, Fan School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050000 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Scienc and Technology Shijiazhuang050000 China Hebei Geological Worker’s University Shijiazhuang050000 China

Integrating social networks and the Internet of Things has formed an emerging field of Social Internet of Things (IoT). Aiming to address the shortcomings of traditional TBAC (Trust based on Access Control) and ABAC (Attribute based on Access Control) in dynamic authorization and security, this paper proposes a hybrid access control model based on trust and risk for SIoT. The model supports social relationship-based trust assessment and risk prediction between SIoT nodes and integrates trust and risk values into SIoT security values through reasonable weight allocation. Furthermore, by simulating the safety requirements of different scenarios, it was verified that the model was efficient and more secure than the current models. © (2023). All Rights Reserved.

关键词： Authorization

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A Very-Short-Term Online PV Power Prediction Model Based on RAN With Secondary Dynamic Adjustment

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1214-1224页

作者： Zhang, Tengfei Ma, Fumin Peng, Chen Yu, Yang Yue, Dong Dou, Chunxia O'Hare, Gregory M. P. Nanjing University of Posts and Telecommunications College of Automation College of Artificial Intelligence The Jiangsu Engineering Laboratory of Big Data Analysis and Control for Active Distribution Network Nanjing210023 China Nanjing University of Finance and Economics College of Information Engineering Nanjing210023 China Shanghai University School of Mechatronic Engineering and Automation Shanghai200072 China Nanjing University of Posts and Telecommunications Institute of Advanced Technology Nanjing210023 China Trinity College Dublin School of Computer Science and Statistics Dublin 2 Ireland

Photovoltaic (PV) power is progressively being subsumed into power grids. As a consequence, reliable PV power forecasting has become essential in order to ensure the optimal functioning of the power grid. Neural networks remain the dominant prediction model utilized. Conventional neural network forecasting models are wholly dependent upon offline data. Subsequent to offline training, no further structural adjustments can be made during the forecasting process, which therefore they fail to cater for PV power supply fluctuations that are fundamentally dynamic. To address this failing, this article proposes a very-short-term online prediction model based on a resource-allocating network (RAN) incorporating a secondary dynamic adjustment. The RAN is initially trained offline to obtain a basic forecasting model. Thereafter, in an online prediction process, those samples with large prediction errors that exceed a preset value are recorded in a specific buffer. When the set conditions are triggered, the secondary dynamic adjustment strategy is employed, which enables the online prediction model to effectively relearn previously unmodeled samples while shielding external interference. Experimental results obtained from actual testing demonstrate the validity of the secondary dynamic adjustment strategy for online learning while also providing higher accuracy levels from the prediction model. © 2020 IEEE.

关键词： Forecasting

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Each Fake News is Fake in its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection 39

Each Fake News is Fake in its Own Way: An Attribution Multi-...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Guo, Hao Ma, Zihan Zeng, Zhi Luo, Minnan Zeng, Weixin Tang, Jiuyang Zhao, Xiang Laboratory for Big Data and Decision Nation University of Defense Technology China School of Computer Science and Technology Xi’an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China

ISBN: (纸本)157735897X

Social platforms, while facilitating access to information, have also become saturated with a plethora of fake news, resulting in negative consequences. Automatic multimodal fake news detection is a worthwhile pursuit. Existing multimodal fake news datasets only provide binary labels of real or fake. However, real news is alike, while each fake news is fake in its own way. These datasets fail to reflect the mixed nature of various types of multimodal fake news. To bridge the gap, we construct an attributing multi-granularity multimodal fake news detection dataset AMG, revealing the inherent fake pattern. Furthermore, we propose a multi-granularity clue alignment model MGCA to achieve multimodal fake news detection and attribution. Experimental results demonstrate that AMG is a challenging dataset, and its attribution setting opens up new avenues for future research. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Shuo Gu, Qiliang Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets. © 2024 IEEE.

关键词： Semantics

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A feature extraction with pose-auxiliary method for Person Reidentification 2

A feature extraction with pose-auxiliary method for Person R...

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2nd International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2022

作者： Ma, ZePing Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu610054 China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610054 China

ISBN: (纸本)9781510660571

Person re-identification is widely considered as a sub problem of image retrieval. It is a technology that uses computer vision technology to determine whether there is a specific pedestrian in an image or video, given a monitored pedestrian image, the pedestrian image under the cross device is retrieved. Many researches have been made to study how to match one people from two picture, but how to locating proper body parts has become a problem which could lead to misalign and wrongly divided sample. To solve this problem, we introduce a pose-auxiliary network to extract the position feature of pedestrian. We use a post estimation network to estimate the person's body location, then we decide whether it's a body part or a static obstacle by using a separated trained classifier. Then we compare these features from two picture and calculate their loss. Meanwhile, we use a graph-matching strategy to compare two pedestrians pose structure, which is also used into jugging the match problem. By using a multi-verification strategy, our method has some improvement than some previous study using Market-1501 and DukeMTMC-Reid datasets, but still remains room for improvement. © 2022 SPIE.

关键词： Image retrieval

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FERI: Feature Enhancement and Relational Interaction for Image-text Matching 30

FERI: Feature Enhancement and Relational Interaction for Ima...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Yu Zhang, Jianqiang Song, Gongpeng Lu, Qin Zhao, Shuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9798331515966

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： cross-modal retrieval feature enhancement image-text matching relationship interaction

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Recent advances in ethanol gas sensors based on metal oxide semiconductor heterojunctions

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Rare Metals 2022年第6期41卷 1818-1842页

作者： Ling-Yun Gai Run-Ping Lai Xian-Hui Dong Xing Wu Qiao-Tong Luan Jue Wang Hao-Feng Lin Wen-Hao Ding Guang-Lei Wu Wan-Feng Xie Big Data and Network Management Center Qingdao Agricultural UniversityQingdao 266109China School of Mechanical Electronic and Information Engineering China University of Mining and Technology-BeijingBeijing 10083China School of Material Science and Engineering State Key Laboratory of Bio-Fibers and Eco-TextilesQingdao UniversityQingdao 266071China School of Electronics and Information State Key Laboratory of Bio-Fibers and Eco-TextilesQingdao UniversityQingdao 266071China Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic EngineeringEast China Normal UniversityShanghai 200241China

Metal oxide semiconductor heterojunctions(MOSHs)can enhance the performance of ethanol gas sen-sors *** gas sensors based on MOSHs are cost-effective and have excellent sensing response,good selectivity,fast response and recovery,long-term stability or repeatability,a low operating temperature,a facile fabrica-tion process,and versatile *** paper reviews the recent advances in gas sensors that are based on MOSHs and the advantages of using them to detect ethanol *** to the literature,compared with ethanol gas sen-sors that use single-component sensing materials,the MOSHs exhibit superior performance due to the synergy between the different components,which can amplify the reception and transduction components of the sensor *** the best of our knowledge,heterojunctions can be grouped into four main categories as metal oxide/metal oxide,metal oxide/metal sulfide,metal oxide/noble metal,and metal oxide/other materials,including rare-earth metals,g-C_(3)N_(4),and graphene,*** future trends and challenges that would be faced in the development of ethanol gas sensors based on MOSHs are discussed in ***,critical ideas and thinking regarding the future progress of MOSH-based gas sensors are presented.

关键词： Metal oxides Heterojunction Chemiresistori Ethanol sensor Synergistic behavior

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Ultrasound-Guided Registration Pseudo-Labels for Semi-Supervised Brachial Plexus Segmentation

Ultrasound-Guided Registration Pseudo-Labels for Semi-Superv...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Luo, Jia Zhang, Ze Ding, Yi Wang, Yiqian Zhang, Jian Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Yibin GREAT Technology Co. Ltd. Yibin644000 China Sichuan Provincial Hospital for Women and Children/Affilated Women and Children's Hospital of Chengdu Medical College Chengdu610054 China

ISBN: (纸本)9798350368741

In semi-supervised medical image segmentation, two main challenges arise. First, the quality of pseudo-labels generated by segmentation networks in data-limited scenarios is often poor, reducing segmentation accuracy. Second, many methods fail to effectively utilize the temporal context in video data. Moreover, the intricate relationships among multiple targets make the conventional teacher-student network consistency loss unsuitable for multi-target tasks, leading to inaccurate feature capture and degraded performance. To address these issues, we propose a registration-based pseudo-label generation strategy that produces pseudo-labels more closely aligned with actual labels. Additionally, we introduce multiple guidance mechanisms into the teacher-student network to ensure correct optimization. Experimental results show that our approach significantly outperforms leading semi-supervised segmentation methods. Specifically, at a 50% label rate, our method improves the Dice coefficient by 6.9% over existing methods. © 2025 IEEE.

关键词： Multi-Object Registration Semi-Supervised Medical Image Segmentation

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Research on long and short-term social recommendation based on convolutional and gated recurrent units 29

Research on long and short-term social recommendation based ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Jia, Zihe Xue, Peng Dai, Zhiqiang Gao, Qian Zhang, Xiaomeng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China

ISBN: (纸本)9798350330717

The development of the Internet has made people more closely related and has put forward higher requirements for recommendation models. Most recommendation models are studied only for the long-term interests of users. In this paper, the interaction time between the user and the item is introduced as auxiliary information in the model construction. Interaction time is used to determine users' long-term preferences and short-term preferences. In this paper, temporal features are extracted by building a convolutional gated recurrent unit with attention neural network (CNN-GRU-Attention). Firstly, for the problem of accurate feature extraction, CNN are constructed to extract higher-level and more abstract features of themselves and transform high-dimensional data into low-dimensional data;secondly, for the problem of social temporality, GRU are used to not only extract temporal information, but also effectively reduce gradient dispersion, making model convergence and training easier;finally, Graph Attention networks are used to aggregate the social relationship information of users and items respectively, which constitute the final feature representation of users and items respectively. In particular, a modified cosine similarity is used to reduce the error caused by data insensitivity when constructing the social information of the item. In this study, simulation experiments are conducted on two publicly available datasets (Epinions and Ciao), and the experimental results show that the proposed recommended model performs better than other social recommendation models, improving the evaluation metrics of MAE and RMSE by 1.06%-1.33% and 1.19%-1.37%, respectively. The effectiveness of the model innovation is proved. © 2023 IEEE.

关键词： convolutional gated recurrent unit graph attention networks neighborhood recommendation systems social networks

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Diff-SFCT: A Diffusion Model with Spatial-Frequency Cross Transformer for Medical Image Segmentation

Diff-SFCT: A Diffusion Model with Spatial-Frequency Cross Tr...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Jiang, Yuxuan Ding, Yi Zhu, Guobin Qin, Zhen Pang, Minghui Cao, Mingsheng University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering Chengdu China Yibin Great Technology Co. Ltd. Yibin644000 China Sichuan Provincial People's Hospital Department of Geriatric General Surgery Chengdu610054 China

ISBN: (纸本)9798350337488

Most existing semantic segmentation methods primarily employ supervised learning with discriminative models. Although these methods are straightforward, they overlook the modeling of underlying data distributions. In this paper, we propose a novel medical image segmentation framework called Diff-SFCT based on Diffusion Model. We formulate semantic segmentation as a generative problem for segmentation masks, replacing the conventional pixel-wise discriminative learning with a latent prior learning process to produce more accurate segmentation results. Diff-SFCT employs a backbone network combining Convolutional Neural network (CNN) and Transformer, and utilizes the local perception of CNN and the global information modeling capability of Transformer. In Diff-SFCT, we design a Semantic Encoder that effectively extracts fine-grained semantic features from real images. Meanwhile, we propose a novel Spatial-Frequency Cross Transformer (SFCT) framework, which can effectively model and interact the global features of the diffuse noise mask and the real semantic features, reducing the domain gap between the two and enhancing the model's representational capacity. Additionally, to preserve spatial and frequency information in the diffusion model, we design a Spatial-Frequency Attention Module (SFAM) as part of the Convolutional Block. This module improves the model's spatial and frequency perception abilities while incurring negligible computational overhead. Experimental results evince that our DiffSFCT substantially outperforms other segmentation methods, exhibiting remarkable performance across various medical image segmentation datasets. © 2023 IEEE.

关键词： Attention mechanism CNN Diffusion Model Medical Image Segmentation Transformer

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