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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Panwar, Amisha Chauhan, Rahul Kumari, Kirti Devliyal, Swati Chanti, Yerrolla Singh, Retinderdeep Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India Sr University School of Computer Science & Artificial Intelligence Warangal India Chitkara University Institute of Engineering And Technology Chitkara University Punjab India

ISBN: (纸本)9798350394474

The emergence of Bitcoin as a disruptive force in the business and finance space, starting an extensive examination of its origin, technology foundations, effects on trade, and where it will go from here, has set off a chain reaction. This research paper takes us through the broad range of Bitcoin landscapes, examining its past context, fundamental features, and underpinning technologies for a decentralized framework. The exploration begins with the introduction, which serves as a starting point for an in-depth analysis that explains the background, scope, and significance of the study. Before a dedicated section on Bitcoin, which discusses its roots, developmental evolution and fundamental features that distinguish it from the digital asset field, is an introductory overview of cryptocurrencies. Regulatory complexity, security risks and the risk to business in an evolving cryptocurrencies ecosystem are highlighted by a comprehensive assessment of challenges and concerns. Anticipating the future of Bitcoin occupies the eighth section, where scaling solutions like Segregated Witness (SegWit) and the Lightning Network are discussed. In addition, potential developments which might occur in the Bitcoin landscape such as integration of smart contracts and collaboration with other technologies can also be seen. This research project is intended to provide valuable insight into the current discussion about how bitcoin has fundamentally changed the business landscape and its future development. © 2024 IEEE.

关键词： Smart contract

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FL-CycleGAN: Enhancing Mobile Photography with Federated Learning-Enabled CycleGAN 10

FL-CycleGAN: Enhancing Mobile Photography with Federated Lea...

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10th International Multidisciplinary engineering Research Conference on Moratuwa engineering Research, MERCon 2024

作者： Walgama, Ramindu Mahima, K.T. Yasas University of Westminster School of Computer Science and Engineering London United Kingdom University of New South Wales School of Engineering and Technology CanberraACT Australia

ISBN: (纸本)9798331529048

Mobile image photography is continuously emerging as an area of interest, yet achieving professional-level camera quality remains a challenge due to hardware limitations. In order to improve the images taken from mobile phones, deep learning-based image processing techniques such as convolutional neural networks are proposed. However, these networks are typically trained using large amounts of paired data and lack continuous training using images captured from mobile phone users. This is because, in reality, creating paired image datasets from user-captured images is challenging and may lead to user privacy issues. As a solution to this challenge, this research proposes FLCycleGAN, a novel federated learning-based CycleGAN designed to improve the colors of mobile images continuously using usercaptured images in an unpaired manner. The evaluations on the ZurichRAW to RGB dataset reveal that FL-CycleGAN reconstructs the colors of mobile images with an average PSNR value of 18.46 and SSIM value of 0.707, demonstrating comparable results to state-of-the-art networks based on paired images. Furthermore, FL-CycleGAN reconstructs high-resolution images with a size of 3968 × 2976 in under 0.005 seconds. © 2024 IEEE.

关键词： Federated learning

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KSKV:Key-Strategy for Key-Value Data Collection with Local Differential Privacy

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computer Modeling in engineering & sciences 2024年第6期139卷 3063-3083页

作者： Dan Zhao Yang You Chuanwen Luo Ting Chen Yang Liu Artificial Intelligence Development Research Center Institute of Scientific and Technical Information of ChinaBeijing100038China Industry Development Department NSFOCUS Inc.BeijingChina School of Information Science and Technology Beijing Forestry UniversityBeijing100083China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China Institute of Computing Technology China Academy of Railway Sciences Corporation LimitedBeijing10081China

In recent years,the research field of data collection under local differential privacy(LDP)has expanded its focus fromelementary data types to includemore complex structural data,such as set-value and graph ***,our comprehensive review of existing literature reveals that there needs to be more studies that engage with key-value data *** studies would simultaneously collect the frequencies of keys and the mean of values associated with each ***,the allocation of the privacy budget between the frequencies of keys and the means of values for each key does not yield an optimal utility *** the importance of obtaining accurate key frequencies and mean estimations for key-value data collection,this paper presents a novel framework:the Key-Strategy Framework forKey-ValueDataCollection under ***,theKey-StrategyUnary Encoding(KS-UE)strategy is proposed within non-interactive frameworks for the purpose of privacy budget allocation to achieve precise key frequencies;subsequently,the Key-Strategy Generalized Randomized Response(KS-GRR)strategy is introduced for interactive frameworks to enhance the efficiency of collecting frequent keys through group-anditeration *** strategies are adapted for scenarios in which users possess either a single or multiple key-value ***,we demonstrate that the variance of KS-UE is lower than that of existing *** claims are substantiated through extensive experimental evaluation on real-world datasets,confirming the effectiveness and efficiency of the KS-UE and KS-GRR strategies.

关键词： Key-value local differential privacy frequency estimation mean estimation data perturbation

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Multiclass Identification and Classification of Diabetic Retinopathy using Transfer Learning: A Comprehensive Approach for Enhanced Diagnostic 9

Multiclass Identification and Classification of Diabetic Ret...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Sharma, Gunjan Anand, Vatsala Chauhan, Rahul Pokhariya, Hemant Singh Gupta, Sheifali Sunil, G. Chitkara University Institute of Engineering And Technology Chitkara University Punjab India Computer Science And Engineering Graphic Era Hill University Uttarakhand Dehradun India Computer Science And Engineering Graphic Era Deemed To Be University Uttarakhand Dehradun India Sr University School of Computer Science & Artificial Intelligence Warangal India

ISBN: (纸本)9798350394474

Diabetic retinopathy, a commonly observed disorder associated with diabetes mellitus, is distinguished by the emergence of irregularities in the retina that adversely affect visual capabilities. In the absence of timely identification, the condition above has the potential to advance and result in compromised visual function. While proven to be effective, the process of manual diagnosis conducted by ophthalmologists is characterized by time-consuming procedures, labor-intensive efforts, significant costs, and a susceptibility to misdiagnosis. On the other hand, computer-aided diagnosis procedures provide a highly efficient and precise alternative. The utilization of deep learning techniques, namely in medical picture interpretation, has demonstrated improved effectiveness. This study introduces an innovative deep-learning methodology for detecting and classifying diabetic retinopathy automatically. This study proposes the utilization of a Convolutional Neural Network model based on ResNet50 architecture to identify and classify retinal pictures into five distinct categories, which correspond to different phases of diabetic retinopathy. The model demonstrates a validation accuracy of 95.01% and a training accuracy of 98.70%, accompanied by a Cohen Kappa score of 0.96 with negligible loss. The efficiency of the proposed model is assessed by utilizing the Diabetic Retinopathy Detection dataset, demonstrating its capability to automatically identifying diabetic retinopathy. This research has the potential to be applied within the healthcare domain, specifically to diagnose retinal complications in individuals with diabetes. © 2024 IEEE.

关键词： Image classification

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Towards Privacy-Aware and Trustworthy Data Sharing Using Blockchain for Edge Intelligence

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Big Data Mining and Analytics 2023年第4期6卷 443-464页

作者： Youyang Qu Lichuan Ma Wenjie Ye Xuemeng Zhai Shui Yu Yunfeng Li David Smith with Data61 Commonwealth Scientific and Industrial Research Organization(CSIRO)Sydney 2015Australia School of Cyber Engineering Xidian UniversityXi’an 710126China the College of Engineering and Science Victoria UniversityMelbourne 3000Australia School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 610054China School of Computer Science University of Technology SydneySydney 2007Australia CNPIEC KEXIN LTD. Beijing 100020China.

The popularization of intelligent healthcare devices and big data analytics significantly boosts the development of Smart Healthcare Networks(SHNs).To enhance the precision of diagnosis,different participants in SHNs share health data that contain sensitive ***,the data exchange process raises privacy concerns,especially when the integration of health data from multiple sources(linkage attack)results in further *** attack is a type of dominant attack in the privacy domain,which can leverage various data sources for private data ***,adversaries launch poisoning attacks to falsify the health data,which leads to misdiagnosing or even physical *** protect private health data,we propose a personalized differential privacy model based on the trust levels among *** trust is evaluated by a defined community density,while the corresponding privacy protection level is mapped to controllable randomized noise constrained by differential *** avoid linkage attacks in personalized differential privacy,we design a noise correlation decoupling mechanism using a Markov stochastic *** addition,we build the community model on a blockchain,which can mitigate the risk of poisoning attacks during differentially private data transmission over *** experiments and analysis on real-world datasets have testified the proposed model,and achieved better performance compared with existing research from perspectives of privacy protection and effectiveness.

关键词： edge intelligence blockchain personalized privacy preservation differential privacy Smart Healthcare Networks(SHNs)

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GRAPH-Guard: A Framework for Heterogeneous Graph Anomaly Detection using Supervised and Unsupervised Techniques 10

GRAPH-Guard: A Framework for Heterogeneous Graph Anomaly Det...

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10th International Conference on Web Research, ICWR 2024

作者： Feizi, Fatemehzahra Rahmani, Hossein Hosseinnia, Amirhossein Bagheri, Asieh Iran University of Science and Technology School of Computer Engineering Tehran Iran K.N.Toosi University of Technology School of Computer Engineering Tehran Iran Shahid Beheshti University School of Management and Accounting Tehran Iran

ISBN: (纸本)9798350394986

Recently, graph data has received a lot of attention as it is used to represent other data types including social networks, banking, security, financial, medical, and textual data. Therefore, anomaly detection in such data has become an important research area due to its ability to prevent adverse events like financial fraud, network intrusion, and social spam. Anomalies in graph data can occur at the node, edge, subgraph or graph level. Node anomalies can be due to unusual structure or attributes. Edge anomalies aim to identify unusual connections which are often unexpected or unusual relationships between real-world entities. The main challenge is detecting and categorizing such anomalies. In recent years, many computational methods have been proposed for anomaly detection in graphs using statistical analysis and machine learning approaches. In this research, we propose a framework called "GRAPH-Guard"for node-level anomaly detection using deep and ensemble learning algorithms. The framework is evaluated on heterogeneous attributed graphs and compared to other node anomaly detection algorithms quantitatively and qualitatively. The quantitative evaluation shows the proposed framework improves the area under the curve (AUC) by 4 points compared to the average AUC of previous works. It also improves the F1 score by 1 point compared to the best previous F1 score. © 2024 IEEE.

关键词： Data mining

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SaTrack: LoS/NLoS State-Aware WiFi Indoor Tracking System 21

SaTrack: LoS/NLoS State-Aware WiFi Indoor Tracking System

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21st Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2024

作者： Zhou, Yinnan Zhou, Hao Li, Luowei Chen, Siming Liu, Zhi Li, Xiang-Yang LINKE Lab School of Computer Science and Technology University of Science and Technology of China China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China China Department of Computer and Network Engineering The University of Electro-Communications Tokyo182-8585 Japan

ISBN: (纸本)9798331519186

WiFi-based technology is appealing for indoor localization due to the widely deployed infrastructures. Recently, path separation solutions have been proposed to address the multipath effects and achieve decimeter-level localization accuracy in line-of-sight (LoS) scenarios. However, these solutions experience serious performance degradation in non-line-of-sight (NLoS) scenarios, and couldn’t be used for mobile device tracking where continuous LoS/NLoS switching happens. In this paper, we propose SaTrack, a LoS/NLoS state-aware mobile device tracking system. SaTrack identifies LoS/NLoS states based on the diversity of the strongest estimated paths when using different reference antennas. With the observation of spatial aggregation and temporal continuity for the Tx-Rx direct path, SaTrack chooses the direct path through two-step clustering, i.e., clustering in the spatial domain to form candidates and clustering again in the temporal domain to select the winner. Extensive experiments are conducted to evaluate the effectiveness of SaTrack. In a typical indoor environment with abundant multipath, SaTrack achieves 0.64m and 1.27m for the median and 90th percentile tracking errors, outperforming the state-of-the-art (SOTA) solutions. © 2024 IEEE.

关键词： Wi-Fi

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A Deep Learning Approach for Rectifying Fisheye Image 2

A Deep Learning Approach for Rectifying Fisheye Image

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2nd International Conference on Signal Processing and Intelligent Computing, SPIC 2024

作者： Gai, Shuli Cui, Wenhua Liang, Bodong Tao, Ye University of Science and Technology Liaoning School of Computer and Information Engineering Anshan China Shenzhen Polytechnic University School of Automotive and Transportation Engineering Shenzhen China

ISBN: (纸本)9798350368888

Fisheye lens cameras offer a significantly wider field of view compared to conventional cameras. However, they also introduce geometric distortions and distorted shapes in captured images. This paper presents a self-supervised fisheye correction method based on convolutional neural networks. The method is trained using a readily available urban landscape roadway dataset and leverages the inherent stability of pixel coordinate relationships after uniform distortion to design an effective correction model. During the training process, distorted images are synthesized from the original undistorted images, and the predicted parameters are utilized to correct fisheye images. Furthermore, the loss function is constructed using the geometric mean of the Hough transform, and the entire model is trained with the assistance of the slope loss of the straight line, thus achieving an end-to-end framework. Experimental results demonstrate that our method provides superior performance in correcting real-world in-vehicle images, outperforming similar approaches in terms of correction accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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InsightBrain: CNN-Based Binary Classification of Brain Tumor Images 2

InsightBrain: CNN-Based Binary Classification of Brain Tumor...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Sharma, Gunjan Anand, Vatsala Chauhan, Rahul Pokhariya, Hemant Singh Gupta, Sheifali Sunil, G. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to be University Computer Science and Engineering Uttarakhand Dehradun India SR University School of Computer Science & Artificial Intelligence Warangal India

ISBN: (纸本)9798350371314

When a brain tumor is in its most advanced stages, it is considered a highly malignant medical condition with an adverse outcome. To improve patient survival rates and ensure proper medical attention, accurate diagnosis is essential. computer-aided tumor detection systems, especially convolutional neural networks (CNNs), have shown encouraging outcomes in this field by utilizing deep learning developments. CNNs outperform traditional neural network architectures by extracting robust features automatically from input data. This paper presents a methodology for the binary classification of cerebral tumors using CNN architecture. Furthermore, MRI scans are subjected to data augmentation procedures to improve generalization, expand the size of the dataset, and reduce the likelihood of overfitting. The results demonstrate that the suggested CNN architecture produces the best classification accuracy, up to 97.29% along with an overall precision of 98% and recall of 97.85%. which helps in proving the effectiveness of CNN-based methods for recognizing and categorization of brain tumors in MRI scans. The medical field may find great use for this suggested architecture in helping to detect brain tumors early on. By streamlining the health evaluation procedure for patients, this technology might be put into use, which could lower the death rates linked to brain tumors. © 2024 IEEE.

关键词： computer aided diagnosis

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Local Attention base on Time Masks for Weakly Supervised Video Anomaly Detection 8

Local Attention base on Time Masks for Weakly Supervised Vid...

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8th International Conference on Information Systems engineering, ICISE 2023

作者： Zhu, Xinghai Sheng, Qing Li, Daoao Li, Shangze Anhui University School of Computer Science and Technology Hefei230601 China Nanjing Vocational Institute of Mechatronic Technology Department of Electronic Engineering Nanjing211300 China

ISBN: (纸本)9798350307009

Video anomaly detection (VAD) aims to use computer technology to automatically detect and recognize abnormal patterns in videos. Based on multi-instance learning, a weakly supervised anomaly detection method treats training videos as bags and assigns video labels by selecting key video segments from the bags to achieve fine-grained classification of video frames. Videos contain rich temporal information, and previous works model the global temporal information of videos to detect anomalies. However, monitoring videos usually have long duration and contain a lot of redundant information. Therefore, this paper proposes a local attention module that integrates video segments of different lengths through four different masking mechanisms to guide the network to model short-term temporal relationships in videos, abandoning the global modeling approach. Additionally, a joint distance metric ranking loss is proposed to guide the network to generate more discriminative feature representations for better anomaly detection performance. The proposed method is extensively tested on two publicly available anomaly detection datasets and its effectiveness is validated. © 2023 IEEE.

关键词： Anomaly detection

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