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Vector Dominance with Threshold Searchable Encryption (VDTSE) for the Internet of Things

computers, Materials & Continua 2024年第6期79卷 4763-4779页

作者： Jingjing Nie Zhenhua Chen College of Safety Science and Engineering Xi’an University of Science and TechnologyXi’an710054China College of Computer Science and Technology Xi’an University of Science and TechnologyXi’an710054China

The Internet of Medical Things(IoMT)is an application of the Internet of Things(IoT)in the medical *** is a cutting-edge technique that connects medical sensors and their applications to healthcare systems,which is essential in smart ***,Personal Health Records(PHRs)are normally kept in public cloud servers controlled by IoMT service providers,so privacy and security incidents may be ***,Searchable Encryption(SE),which can be used to execute queries on encrypted data,can address the issue ***,most existing SE schemes cannot solve the vector dominance threshold *** response to this,we present a SE scheme called Vector Dominance with Threshold Searchable Encryption(VDTSE)in this *** use a Lagrangian polynomial technique and convert the vector dominance threshold problem into a constraint that the number of two equal-length vectors’corresponding bits excluding wildcards is not less than a threshold ***,we solve the problem using the proposed technique modified in Hidden Vector Encryption(HVE).This technique makes the trapdoor size linear to the number of attributes and thus much smaller than that of other similar SE schemes.A rigorous experimental analysis of a specific application for privacy-preserving diabetes demonstrates the feasibility of the proposed VDTSE scheme.

关键词： Internet of Things(IoT) Internet of Medical Things(IoMT) vector dominance with threshold searchable encryption(VDTSE) threshold comparison electronic healthcare

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A Base Station Deployment Algorithm for Wireless Positioning Considering Dynamic Obstacles

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computers, Materials & Continua 2025年第3期82卷 4573-4591页

作者： Aiguo Li Yunfei Jia Department of Network Engineering Xi’an University of Science and TechnologyXi’an710600China College of Computer Science and Technology Xi’an University of Science and TechnologyXi’an710600China

In the context of security systems,adequate signal coverage is paramount for the communication between security personnel and the accurate positioning of *** studies focus on optimizing base station deployment under the assumption of static obstacles,aiming to maximize the perception coverage of wireless RF(Radio Frequency)signals and reduce positioning blind ***,in practical security systems,obstacles are subject to change,necessitating the consideration of base station deployment in dynamic ***,research in this area still needs to be *** paper proposes a Dynamic Indoor Environment Beacon Deployment Algorithm(DIE-BDA)to address this *** algorithm considers the dynamic alterations in obstacle locations within the designated *** determines the requisite number of base stations,the requisite time,and the area’s practical and overall signal coverage *** experimental results demonstrate that the algorithm can calculate the deployment strategy in 0.12 s following a change in obstacle *** results show that the algorithm in this paper requires 0.12 s to compute the deployment strategy after the positions of obstacles *** 13 base stations,it achieves an effective coverage rate of 93.5%and an overall coverage rate of 97.75%.The algorithm can rapidly compute a revised deployment strategy in response to changes in obstacle positions within security systems,thereby ensuring the efficacy of signal coverage.

关键词： Wireless positioning base station deployment dynamic obstacles dynamic obstacle wireless positioning

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Local Content-Aware Enhancement for Low-Light Images with Non-Uniform Illumination

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computers, Materials & Continua 2025年第3期82卷 4669-4690页

作者： Qi Mu Yuanjie Guo xiangfu Ge xinyue Wang Zhanli Li College of Computer Science and Technology Xi’an University of Science and TechnologyXi’an710054China

In low-light image enhancement,prevailing Retinex-based methods often struggle with precise illumina-tion estimation and brightness *** can result in issues such as halo artifacts,blurred edges,and diminished details in bright regions,particularly under non-uniform illumination *** propose an innovative approach that refines low-light images by leveraging an in-depth awareness of local content within the *** introducing multi-scale effective guided filtering,our method surpasses the limitations of traditional isotropic filters,such as Gaussian filters,in handling non-uniform *** dynamically adjusts regularization parameters in response to local image characteristics and significantly integrates edge perception across different *** balanced approach achieves a harmonious blend of smoothing and detail preservation,enabling more accurate illumination ***,we have designed an adaptive gamma correction function that dynamically adjusts the brightness value based on local pixel intensity,further balancing enhancement effects across different brightness levels in the *** results demonstrate the effectiveness of our proposed method for non-uniform illumination images across various *** exhibits superior quality and objective evaluation scores compared to existing *** method effectively addresses potential issues that existing methods encounter when processing non-uniform illumination images,producing enhanced images with precise details and natural,vivid colors.

关键词： Retinex non-uniform low illumination local content-aware effective guided image filtering

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引用

computers, Materials & Continua 2025年第3期82卷 5135-5151页

作者： Jian Feng Yifan Guo Cailing Du College of Computer Science & Technology Xi’an University of Science and TechnologyXi’an710054China

Graph similarity learning aims to calculate the similarity between pairs of *** unsupervised graph similarity learning methods based on contrastive learning encounter challenges related to random graph augmentation strategies,which can harm the semantic and structural information of graphs and overlook the rich structural information present in *** address these issues,we propose a graph similarity learning model based on learnable augmentation and multi-level contrastive ***,to tackle the problem of random augmentation disrupting the semantics and structure of the graph,we design a learnable augmentation method to selectively choose nodes and edges within the *** enhance contrastive levels,we employ a biased random walk method to generate corresponding subgraphs,enriching the contrastive ***,to solve the issue of previous work not considering multi-level contrastive learning,we utilize graph convolutional networks to learn node representations of augmented views and the original graph and calculate the interaction information between the attribute-augmented and structure-augmented views and the original *** goal is to maximize node consistency between different views and learn node matching between different graphs,resulting in node-level representations for each *** representations are then obtained through pooling operations,and we conduct contrastive learning utilizing both node and subgraph ***,the graph similarity score is computed according to different downstream *** conducted three sets of experiments across eight datasets,and the results demonstrate that the proposed model effectively mitigates the issues of random augmentation damaging the original graph’s semantics and structure,as well as the insufficiency of contrastive ***,the model achieves the best overall performance.

关键词： Graph similarity learning contrastive learning attributes structure

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A Video Captioning Method by Semantic Topic-Guided Generation

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computers, Materials & Continua 2024年第1期78卷 1071-1093页

作者： Ou Ye xinli Wei Zhenhua Yu Yan Fu Ying Yang College of Computer Science and Technology Xi’an University of Science and TechnologyXi’an710054China

In the video captioning methods based on an encoder-decoder,limited visual features are extracted by an encoder,and a natural sentence of the video content is generated using a ***,this kind ofmethod is dependent on a single video input source and few visual labels,and there is a problem with semantic alignment between video contents and generated natural sentences,which are not suitable for accurately comprehending and describing the video *** address this issue,this paper proposes a video captioning method by semantic topic-guided ***,a 3D convolutional neural network is utilized to extract the spatiotemporal features of videos during the ***,the semantic topics of video data are extracted using the visual labels retrieved from similar video *** the decoding,a decoder is constructed by combining a novel Enhance-TopK sampling algorithm with a Generative Pre-trained Transformer-2 deep neural network,which decreases the influence of“deviation”in the semantic mapping process between videos and texts by jointly decoding a baseline and semantic topics of video *** this process,the designed Enhance-TopK sampling algorithm can alleviate a long-tail problem by dynamically adjusting the probability distribution of the predicted ***,the experiments are conducted on two publicly used Microsoft Research Video Description andMicrosoft Research-Video to Text *** experimental results demonstrate that the proposed method outperforms several state-of-art ***,the performance indicators Bilingual Evaluation Understudy,Metric for Evaluation of Translation with Explicit Ordering,Recall Oriented Understudy for Gisting Evaluation-longest common subsequence,and Consensus-based Image Description Evaluation of the proposed method are improved by 1.2%,0.1%,0.3%,and 2.4% on the Microsoft Research Video Description dataset,and 0.1%,1.0%,0.1%,and 2.8% on the Microsoft Research-Video to Text dataset

关键词： Video captioning encoder-decoder semantic topic jointly decoding Enhance-TopK sampling

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Position-Aware and Subgraph Enhanced Dynamic Graph Contrastive Learning on Discrete-Time Dynamic Graph

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computers, Materials & Continua 2024年第11期81卷 2895-2909页

作者： Jian Feng Tian Liu Cailing Du College of Computer Science&Technology Xi’an University of Science and TechnologyXi’an710054China

Unsupervised learning methods such as graph contrastive learning have been used for dynamic graph represen-tation learning to eliminate the dependence of ***,existing studies neglect positional information when learning discrete snapshots,resulting in insufficient network topology *** the same time,due to the lack of appropriate data augmentation methods,it is difficult to capture the evolving patterns of the network *** address the above problems,a position-aware and subgraph enhanced dynamic graph contrastive learning method is proposed for discrete-time dynamic ***,the global snapshot is built based on the historical snapshots to express the stable pattern of the dynamic graph,and the random walk is used to obtain the position representation by learning the positional information of the ***,a new data augmentation method is carried out from the perspectives of short-term changes and long-term stable structures of dynamic ***,subgraph sampling based on snapshots and global snapshots is used to obtain two structural augmentation views,and node structures and evolving patterns are learned by combining graph neural network,gated recurrent unit,and attention ***,the quality of node representation is improved by combining the contrastive learning between different structural augmentation views and between the two representations of structure and *** results on four real datasets show that the performance of the proposed method is better than the existing unsupervised methods,and it is more competitive than the supervised learning method under a semi-supervised setting.

关键词： Dynamic graph representation learning graph contrastive learning structure representation position representation evolving pattern

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A Sentence Retrieval Generation Network Guided Video Captioning

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computers, Materials & Continua 2023年第6期75卷 5675-5696页

作者： Ou Ye Mimi Wang Zhenhua Yu Yan Fu Shun Yi Jun Deng College of Computer Science and Technology Xi’an University of Science and TechnologyXi’an710054China College of Safety and Engineering Xi’an University of Science and TechnologyXi’an710054China

Currently,the video captioning models based on an encoder-decoder mainly rely on a single video input *** contents of video captioning are limited since few studies employed external corpus information to guide the generation of video captioning,which is not conducive to the accurate descrip-tion and understanding of video *** address this issue,a novel video captioning method guided by a sentence retrieval generation network(ED-SRG)is proposed in this ***,a ResNeXt network model,an efficient convolutional network for online video understanding(ECO)model,and a long short-term memory(LSTM)network model are integrated to construct an encoder-decoder,which is utilized to extract the 2D features,3D features,and object features of video data *** features are decoded to generate textual sentences that conform to video content for sentence ***,a sentence-transformer network model is employed to retrieve different sentences in an external corpus that are semantically similar to the above textual *** candidate sentences are screened out through similarity ***,a novel GPT-2 network model is constructed based on GPT-2 network *** model introduces a designed random selector to randomly select predicted words with a high probability in the corpus,which is used to guide and generate textual sentences that are more in line with human natural language *** proposed method in this paper is compared with several existing works by *** results show that the indicators BLEU-4,CIDEr,ROUGE_L,and METEOR are improved by 3.1%,1.3%,0.3%,and 1.5%on a public dataset MSVD and 1.3%,0.5%,0.2%,1.9%on a public dataset MSR-VTT *** can be seen that the proposed method in this paper can generate video captioning with richer semantics than several state-of-the-art approaches.

关键词： Video captioning encoder-decoder sentence retrieval external corpus RS GPT-2 network model

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Research on gesture recognition algorithm based on MME-P3D

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Mathematical Biosciences and engineering 2024年第3期21卷 3594-3617页

作者： Jin, Hongmei He, Ning Liu, Boyu Li, Zhanli College of Computer Science and Technology Xi’an University of Science and Technology Xi’an710054 China

A Multiscale-Motion Embedding Pseudo-3D (MME-P3D) gesture recognition algorithm has been proposed to tackle the issues of excessive parameters and high computational complexity encountered by existing gesture recognition algorithms deployed in mobile and embedded devices. The algorithm initially takes into account the characteristics of gesture motion information, integrating the channel attention (CE) mechanism into the pseudo-3D (P3D) module, thereby constructing a P3D-C feature extraction network that can efficiently extract spatio-temporal feature information while reducing the complexity of the algorithmic model. To further enhance the understanding and learning of the global gesture movement’s dynamic information, a Multiscale Motion Embedding (MME) mechanism is subsequently designed. The experimental findings reveal that the MME-P3D model achieves recognition accuracies reaching up to 91.12% and 83.06% on the self-constructed conference gesture dataset and the publicly available Chalearn 2013 dataset, respectively. In comparison with the conventional 3D convolutional neural network, the MME-P3D model demonstrates a significant advantage in terms of parameter count and computational requirements, which are reduced by as much as 82% and 83%, respectively. This effectively addresses the limitations of the original algorithms, making them more suitable for deployment on embedded and mobile devices and providing a more effective means for the practical application of hand gesture recognition technology. © 2024 the Author(s), licensee AIMS Press.

关键词： Convolution

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Ab initio study of phase stability,elastic anisotropy,and minimum thermal conductivity of MnB2 in different crystal structures

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中国物理B（英文版） 2024年第12期33卷 411-420页

作者：王小凡王乙先王卓张宇轩顾建兵 College of Science Xi'an University of Science and Technology Xi'an 710054 School of Materials and Chemical Engineering Zhongyuan University of Technology Zhengzhou 450007

The phase stability,elastic anisotropy,and minimum thermal conductivity of MnB2 in different crystal structures have been investigated by first-principles calculations based on density functional *** results found that P63/mmc(hP6-MnB2),P6/mmm(hrP3-MnB2),Pmmn(oP6-MnB2),R(3)m(hR3-MnB2),Pnma(oP12-MnB2),and Immm(oI18-MnB2)all exhibit mechanical and dynamic stability under environmental conditions,and the sequence of phase stability was hP6＞hR3＞oP6＞oI18＞oP12＞*** addition,Vickers hardness calculations indicated that hP6,hR3,oP6,and oI18 of MnB2 have potential as hard materials,while hP3 and oP 12 are not suitable as hard ***,the elastic anisotropy of different MnB2 phases were also comprehensively *** is found that the anisotropic order of bulk modulus is oP12＞hP3＞hP6＞hR3＞oI18＞oP6,while that of Young's modulus is oP12＞hR3＞hP6＞oP6＞hP3＞***,the minimum thermal conductivity of different MnB2 phases was evaluated by means of Clarke's and Cahill's *** results suggested that these MnB2 diborides are all not suitable as thermal barrier coating materials.

关键词： transition metal boride mechanical properties density functional theory

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Enhancing flotation recovery of residual carbon from gasification waste by mixing hydrophobic powder with diesel as collector

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Particuology 2024年第6期89卷 211-217页

作者： Rui Han Ningning Zhang Anning Zhou Zhen Li Jinzhou Qu Hong Wang College of Chemistry and Chemical Engineering Xi’an University of Science and TechnologyXi’anShaanxi 710054China

Coal gasification fine slag(CGFS)is a solid waste containing residual carbon and ash generated during the coal gasification process,and the separation of the two components is the essential way to realize its environmental pollution reduction and resource value *** flotation is the preferred method for separating CGFS,but there is a barrier of low carbon recovery in this process due to the extensive adsorption of collector by the well-developed pores on residual *** this study,a sufficiently simple yet innovative collector,a mixture of hydrophobic powder and diesel,was proposed in an attempt to break the *** experiments with common diesel and this novel collector were performed respectively,and FTIR,XPS,and SEM-EDX were employed to analyze the collector action *** results revealed that the novel collector could significantly improve the residual carbon recovery;test results demonstrated that the novel collector could increase the hydrophobic functional group content on the fine slag surface,and the hydrophobic powders in this novel collector mainly appeared at the pore openings of the flotation *** essence of the mechanism is that the hydrophobic powders play a dual role of blocking pores and providing adsorption sites,thus facilitating the spreading of diesel on the carbon surface and promoting its *** study can provide creative ideas for the efficient disposal of coal gasification waste.

关键词： Coal gasification fine slag Carbon recovery Collector Fotation Hydrophobic powder

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