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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： Xia, Zhuoqun Wang, Ziyu Liu, Xiao Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China Guangxi University School of Computer Electronics and Information Nanning China

ISBN: (纸本)9798350329223

Since the open character of the Internet of Things (IoT), IoT devices are vulnerable to various attacks. Compromised devices may maliciously provide unreliable data to the cloud. Furthermore, existing trust evaluation models struggle to balance data security needs across different regions. To address this issue, the paper proposes a partition-based active trust mechanism (PATM). This PATM scheme mainly includes the following parts: First, the region is divided into subregions, and evaluations are conducted for the indicators of each subregion. Mechanism modeling and fuzzy analytic hierarchy process are used to analyze the impact of each indicator on the sensitivity of subregions. Second, the direct estimation method is utilized to estimate the demand for active detection routing based on the sensitivity of subregions. Then, the number of operations of malicious nodes is increased through the high-frequency transmission of detection routes required by each subregion. Additionally, a trust calculation method based on node operations is proposed to evaluate the reliability of the device. The experimental results show that the PATM scheme proposed in this article can quickly identify the trust degree of nodes while balancing the needs of different regions, ensuring the credibility of data collection. © 2023 IEEE.

关键词： Internet of things

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MedLeafNet: A Feature Fusion Network Integrating Transformer and CNN Modules for Indian Medical Leaf Classification 2

MedLeafNet: A Feature Fusion Network Integrating Transformer...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Tiwari, Raj Gaurang Maheshwari, Himani Gautam, Vinay Agarwal, Ambuj Kumar Trivedi, Naresh Kumar Jain, Vishal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computing Graphic Era Hill University Uttrakhand Dehradun India School of Engineering and Technology Department of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (纸本)9798350327533

A new feature fusion network called 'MedLeafNet' is presented in this study specifically for the categorization of Indian medicinal leaves. Using a hybrid architecture, MedLeafNet integrates the best features of Convolutional Neural Network (CNN) modules for spatial feature extraction and Transformer modules for sequence modeling. Because of the complexity of classifying Indian medicinal leaves-which are famous for their varied and powerful medicinal properties-the suggested methodology is well suited to the task. For this research, 80 different types of Indian leaves were analyzed, each with its well-known therapeutic properties. To improve its capacity to detect complex patterns in leaves, Me dLeafNet incorporates Transformer and CNN modules to efficiently gather both global and local information. The suggested model was optimized using several different optimizers, such as Adam, Adadelta, Gradient Descent, AdaGrad, RMSProp, Adamax, Momentum, and Adaptive Moment Estimation. When trained with the Adam optimizer, MedLeafNet attained its maximum accuracy of 98.97%, as shown by extensive testing. MedLeafNet is also compared against some of the most well-known deep learning architectures, such as ResNet, Inception, VGG, Xception, and MobileNet. In terms of classification accuracy, the findings show that it is superior to these architectures, proving that it is effective for the difficult job of classifying Indian medicinal leaves. © 2024 IEEE.

关键词： Automated Plant Identification Deep Architectures Deep Learning Feature Fusion Network Medicinal Properties Optimizer Diversity Transformer Module

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Crowd counting in dense UAV images based on multiscale feature fusion 4

Crowd counting in dense UAV images based on multiscale featu...

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2023 4th International Conference on computer engineering and Intelligent Control, ICCEIC 2023

作者： Tan, Li Jiang, Xujie Liu, Zikang Zhang, Hongtao Shang, Ziliang Liu, Yuzhao Chongqing Institute of Microelectronics Industry Technology Beijing Technology and Business University School of Computer Science and Engineering Beijing China University of Electronic Science and Technology of China Chongqing China Beijing Technology and Business University School of Computer Science and Engineering Beijing China

ISBN: (纸本)9798350308877

To achieve breakthrough performance in the task of dense crowd counting for unmanned aerial vehicles (UAVs) in complex environments, this paper introduces a multi-scale feature fusion crowd counting network model called MSFFCCNet. This model comprises two main components: a multi-scale feature extraction module and a feature fusion module. The former introduces diversity from multiple scales, where different scales focus on different information and generate scale-related features. The latter is responsible for fusing three features. Experimental results demonstrate that the proposed MSFFCCNet outperforms existing crowd counting network models, exhibiting higher accuracy and robustness. © 2023 IEEE.

关键词： Antennas

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An Analysis of Cotton Crop for Detection of Pests and Diseases Using ML and DL Techniques

An Analysis of Cotton Crop for Detection of Pests and Diseas...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Susheel, K. Sai Rajkumar, R. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9781665462631

Agriculture is a significant source of energy that includes several well-known crops that supply food for the population and income for agronomists in India. Cotton is one of the attractive, drought-tolerant crops that can be cultivated commercially in every environment, among many other renowned crops. Although, this cotton harvest is plagued by many diseases and pests that may vary depending on climatic conditions, resulting in significant yield losses. The most liable to pests and diseases are the leaf and seedling stages of the plant, which destroy the whole crop. The cotton pests and diseases based on prior knowledge of IP, ML, and DL techniques are analyzed. Along with studies the comparison of the cotton crop with other crops, and also analyses the status of the area, production, and yield of the cotton crop. Eventually, the SVM classifier models applied fewer portraits than different algorithms regarding reliability and efficiency, while the multi-dimensional convolutional neural network provided better accuracy and effectiveness than further models in this deep analysis. © 2023 IEEE.

关键词： Cotton

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Formal Security Verification for Searchable Symmetric Encryption Using ProVerif 18

Formal Security Verification for Searchable Symmetric Encryp...

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18th International Symposium on Information Theory and Its Applications, ISITA 2024

作者： Mieno, Takehiko Okazaki, Hiroyuki Arai, Kenichi Futa, Yuichi Yamamoto, Hiroaki Graduate School of Medicine Science and Technology Shinshu University Epson Avasys Corporation Japan Graduate School of Science and Technology Shinshu University Japan Graduate School of Engineering Nagasaki University Japan School of Computer Science Tokyo University of Technology Japan Shinshu University Faculty of Engineering Japan

ISBN: (纸本)9784885523519

With the rapid proliferation of various cloud storage services in recent years, the development of technology to efficiently search data while ensuring its confidentiality during cloud usage is an important issue. The technology that enables keyword searches on encrypted files using previously set keywords is called searchable symmetric encryption (SSE). In this paper, we propose a method formally representing encrypted document, and verify the security of SSE using the formal verification tool ProVerif. Our proposed method considers the channel-type terms of ProVerif as a Document that includes different keywords to verify the indistinguishability of encrypted documents. © 2024 The Institute of Electronics, Information and Communication Engineers.

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A Review on Wideband High-Gain Low-THz Antennas for Wireless Applications 18

A Review on Wideband High-Gain Low-THz Antennas for Wireless...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Srinivasan, Dhamodharan Premkumar, M. Deepa Nivethika, S. Dhilipkumar, P. Parameswari, S. Chowdary, M. Kalpana Centre for Blockchain and Cybersecurity Sri Eshwar College of Engineering Department of Computer and Communication Engineering Coimbatore India Ssm Institute of Engineering and Technology Department of Electronics and Communication Engineering Dindigul India Vit University School of Computer Science and Engineering Chennai India Sri Ranganathar Institute of Engineering and Technology Department of Ece Coimbatore India Sri Sairam Insitute of Technology Department of Ece Chennai India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9789380544519

In this article, the potential of low-terahertz (THz) technology is discussed to present high data rates in future biomedical systems, and also in the 6G mobile system. However, due to the loss, the design of high-gain antennas is crucial to overcome power limitations and compensate for the additional path loss. This article highlights recent developments in wideband and high-gain sub-mm-wave and low-THz antennas and their fabrication technologies. The advancements in recent technologies have enabled the manufacture of antennas with complicated structures with precise accuracy and less expensive. Overall, the paper emphasizes the importance of high-gain antennas in overcoming the challenges associated with low-THz technology and highlights the potential of AM technology in the development of low-cost, high-performance THz antennas. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Antennas high gain low-cost low-terahertz three-dimensional printing (3DP)

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BA-SAM: Boundary-Aware Adaptation of Segment Anything Model for Medical Image Segmentation

BA-SAM: Boundary-Aware Adaptation of Segment Anything Model ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Ying Xiong, Xinyu Fang, Huihui Xu, Yanwu Pazhou Laboratory Guangzhou China South China University of Technology School of Future Technology Guangzhou China Sun Yat-sen University School of Computer Science and Engineering China

ISBN: (纸本)9798350386226

The Segment Anything Model (SAM) has demonstrated remarkable capabilities in its performance on natural images. However, it faces considerable challenges when applied to medical datasets. Specifically, the performance of vanilla SAM is degraded and lacks generalisability when processing medical images with large domain gaps. What's worse, many medical segmentation tasks highly demand accurate boundary identification, while existing SAM variants struggle with this need. To overcome the above challenges, we propose BA-SAM, a Segment Anything Model variant that can achieve better performance on medical images. Specifically, based on the idea of parameter-efficient fine-tuning (PEFT), we first add a parallel tuneable CNN encoder to better extract local details using convolutional operations, while most parts of the original ViT encoder in SAM are set frozen. Moreover, we use a Boundary-Aware Attention (BAA) module in the CNN-Branch to encourage the framework to better capture boundary-related features. Extensive experiments on three public datasets demonstrate that the proposed BA-SAM further improvements over existing state-of-the-art methods. © 2024 IEEE.

关键词： Image segmentation

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K-Means Clustering with Local Distance Privacy

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Big Data Mining and Analytics 2023年第4期6卷 433-442页

作者： Mengmeng Yang Longxia Huang Chenghua Tang the Data61 Commonwealth Scientific and Industrial Research OrganizationMelbourne 3168Australia the School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang 212013China. the School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin 541010China

With the development of information technology,a mass of data are generated every *** and analysing these data help service providers improve their services and gain an advantage in the fierce market competition.K-means clustering has been widely used for cluster analysis in real ***,these analyses are based on users’data,which disclose users’*** differential privacy has attracted lots of attention recently due to its strong privacy guarantee and has been applied for clustering ***,existing K-means clustering methods with local differential privacy protection cannot get an ideal clustering result due to the large amount of noise introduced to the whole dataset to ensure the privacy *** solve this problem,we propose a novel method that provides local distance privacy for users who participate in the clustering *** of making the users’records in-distinguish from each other in high-dimensional space,we map the user’s record into a one-dimensional distance space and make the records in such a distance space not be distinguished from each *** be specific,we generate a noisy distance first and then synthesize the high-dimensional data *** propose a Bounded Laplace Method(BLM)and a Cluster Indistinguishable Method(CIM)to sample such a noisy distance,which satisfies the local differential privacy guarantee and local dE-privacy guarantee,***,we introduce a way to generate synthetic data records in high-dimensional *** experimental evaluation results show that our methods outperform the traditional methods significantly.

关键词： K-means clustering local differential privacy data analysis

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Multi-scale and Feature Fusion for Expression Recognition Networks 6

Multi-scale and Feature Fusion for Expression Recognition Ne...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： He, Yujun Han, Yongguo Zhang, Hongying Ye, Zixun Southwest University of Science and Technology School of Computer Science and Technology Sichuan China Southwest University of Science and Technology School of Information Engineering Sichuan China

ISBN: (纸本)9798350325485

This paper proposes a method for face expression recognition that combines multi-scale region awareness with multi-level feature fusion. It aims to overcome the challenge of extracting features from face regions that vary greatly in scale. Firstly, the paper uses an inverse residual structure to perform feature extraction in a high-dimensional space, compensating for the network's lack of feature extraction capability. Secondly, the paper introduces multi-scale region awareness to address the multi-scale nature of face regions, using multi-scale feature extraction modules to extract features from different receptive fields, as well as multi-scale down-sampling to reduce the feature loss caused by pooling down-sampling. Thirdly, channels and spatial attention are introduced to suppress invalid regions. Finally, adaptive multi-level feature fusion maximises feature information at different depths of the network while avoiding inconsistencies between features. Experimental results demonstrate the effectiveness of the proposed method, achieving accuracy of 73.66%, 98.98%, and 88.07% in FER2013, CK+, and RAF-DB, respectively. © 2023 IEEE.

关键词： Feature extraction

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DenseReg: Point Cloud Registration Based on Dense Feature Refinement 24

DenseReg: Point Cloud Registration Based on Dense Feature Re...

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2024 International Conference on Advanced Robotics, Automation engineering and Machine Learning, ARAEML 2024

作者： Luo, Lin Li, Nannan Huang, Kan College of Information Engineering Shanghai Maritime University Shanghai201306 China School of Computer Science and Engineering Macau University of Science and Technology China

ISBN: (纸本)9798400717116

With the fast development of 3D scanners such as LiDAR and RGB-D cameras, point clouds have become the prevalent data modality to represent and analyze 3D circumstances. Point cloud registration is a fundamental task that aims to align two point clouds scanned from two partially overlapped views. The current matching-based methods first establish point pair correspondences between point clouds and then calculate the transformation parameters using RANSAC. However, they easily suffer from imprecise feature descriptors, resulting in wrong correspondences. In this paper, we propose a densely connected feature extraction structure inspired by DenseNet, which effectively utilizes the global features of point clouds and the features predicted by overlapping masks. The multi-dimensional features are recursively refined in the densely connected structure, which improves the registration accuracy and convergence speed. Furthermore, considering that rotation and translation belong to different solution spaces, we use two branches to calculate rotation and translation respectively at the end of the network. Experiments show that the proposed feature extraction structure improves the accuracy and convergence speed of registration. © 2024 ACM.

关键词： Masks

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