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Blockchain-Based Cognitive Computing Model for data Security on a Cloud Platform

computers, Materials & Continua 2023年第12期77卷 3305-3323页

作者： Xiangmin Guo Guangjun Liang Jiayin Liu Xianyi Chen Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjing210031China Jiangsu Electronic Data Forensics and Analysis Engineering Research Center Jiangsu Police InstituteNanjing210031China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China

Cloud storage is widely used by large companies to store vast amounts of data and files,offering flexibility,financial savings,and ***,information shoplifting poses significant threats,potentially leading to poor performance and privacy ***-based cognitive computing can help protect and maintain information security and privacy in cloud platforms,ensuring businesses can focus on business *** ensure data security in cloud platforms,this research proposed a blockchain-based Hybridized data Driven Cognitive Computing(HD2C)***,the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things(IoT)in the ***2C is developed by combining Federated Learning(FL)with a Blockchain consensus algorithm to connect smart contracts with Proof of ***“data Island”problem can be solved by FL’s emphasis on privacy and lightning-fast processing,while Blockchain provides a decentralized incentive structure that is impervious to *** with Blockchain allows quick consensus through smart member selection and *** HD2C paradigm significantly improves the computational processing efficiency of intelligent *** analysis results derived from IIoT datasets confirm HD2C *** compared to other consensus algorithms,the Blockchain PoA’s foundational cost is *** accuracy and memory utilization evaluation results predict the total benefits of the *** comparison to the values 0.004 and 0.04,the value of 0.4 achieves good *** to the experiment results,the number of transactions per second has minimal impact on memory *** findings of this study resulted in the development of a brand-new IIoT framework based on blockchain technology.

关键词： Blockchain Internet of Things(IoT) blockchain based cognitive computing Hybridized data Driven Cognitive Computing(HD2C) Federated Learning(FL) Proof of Authority(PoA)

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FTCD: Fault-Tolerant Co-Design of Flow and Control Layers for Fully Programmable Valve Array Biochips

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Zhu, Yuhan Liu, Genggeng Guo, Wenzhong Huang, Xing Fuzhou University College of Computer and Data Science Fuzhou350100 China Northwestern Polytechnical University School of Computer Science Xi'an China

As a new generation of flow-based microfluidics, Fully Programmable Valve Array (FPVA) biochips have gained widespread adoption as a biochemical experimental platform, thanks to their enhanced programmability and flexibility. Environmental and human factors, however, often introduce physical faults during the manufacturing process such as channel blockage and leakage, which, undoubtedly, can affect the results of bioassays and even cause execution failure. In this paper, we focus on the fault-tolerant co-design of flow and control layers in FPVA biochips for the first time. For the flow layer, three dynamic fault-tolerant techniques, i.e., a cell function conversion method, a bidirectional redundancy scheme, and a fault mapping method, are presented and integrated into the device placement and flow routing stages. As a consequence, we further realize an efficient and effective fault-tolerance-oriented physical design method, thus ensuring the robustness of chip architecture and correctness of assay outcomes. For the control layer, we design another three fault-tolerant techniques including a series duplication scheme of leakage valves, allocation and merging rules of backup valves, and a logic conflict-aware adjustment strategy of redundant architecture. Based on these techniques, we construct a fault-tolerant control system to realize dynamic recovery of control signals. Experimental results on multiple test cases demonstrate that the proposed method can produce optimized fault-tolerant FPVA architectures with low fabrication cost, high execution efficiency, and high fault-tolerance success rate. © 1982-2012 IEEE.

关键词： Microfluidics

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Advancing Early Detection: CNN for Automated Blood Cancer Diagnosis and Anomaly Detection 2

Advancing Early Detection: CNN for Automated Blood Cancer Di...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Raji, V. Seetharaman, Anburaman Kasi, Baskar Govindaraju, Sakthi Paarkavy, B. S K P Engineering College Department of Computer Science and Engineering Tiruvannamalai India Sri Venkateswaraa College of Technology Department of Artificial Intelligence and Data Science Chennai India Galgotias University School of Computer Science and Engineering Greater Noida India Galgotias University School of Computer Science and Engineering Greater Noida India Saveetha Engineering College Department of AI&DS Chennai India

ISBN: (纸本)9798350368109

The evaluation and diagnosis of diseases related to blood cancer can be intricate and time-consuming. This complexity is compounded by the reliance on manual analyses employing techniques that consume a significant amount of time. Numerous methods have been created over the past ten years for the finding, analysis, and categorization of human blood cancer. The lack of methods or models that can automatically analyze human blood cells to detect the presence of cancer, however, is still a significant issue. The development of such a model holds the potential to enhance the early detection and prevention of these diseases, thereby facilitating more prompt medical diagnoses. In our ongoing research, we present our current endeavors in the creation of a Deeper with Convolutions Neural Network (DCNN) Learning Model capable of recognizing anomalies in blood cells. We employ a Hybrid Ensemble DCNN Learning technique for the identification of blood cancer. With accuracy rates surpassing 99%, this method is one of the most sophisticated deep learning models. © 2024 IEEE.

关键词： Contrastive Learning

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MulEA: Multi-type Entity Alignment of Heterogeneous Medical Knowledge Graphs 28th

MulEA: Multi-type Entity Alignment of Heterogeneous Medical...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Wang, Mingxia Tian, Peng Xiong, Yun Yue, Jingwen Zhang, Yao Tang, Chunlei Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Brigham and Women’s Hospital Harvard Medical School BostonMA United States

ISBN: (纸本)9783031306716

The large-scale application of medical knowledge graphs has greatly raised the intelligence level of modern medicine. Considering that entity references between multiple medical knowledge graphs can lead to redundancy, knowledge graph alignment tasks are required to identify entity pairs or subgraphs of heterogeneous knowledge graphs pointing to the same elements in the real world. Existing medical knowledge graph alignment methods do not consider the multi-type features of medical entities. To tackle above challenge, we propose a Multi-type Entity Alignment model of medical knowledge graphs based on attention mechanism named MulEA. Firstly, MulEA integrates the multi-type information features of medical entities to align various types of entities jointly through constructing an entity multi-type information embedding matrix for medical knowledge graphs. Secondly, MulEA designs a relationship collective aggregation module to fully utilize the features of different relationships to improve alignment accuracy. Finally, we evaluate the performance of MulEA on a real medical knowledge graph alignment dataset MED-OWN-15K, and it achieves the state-of-the-art performance on several metrics, showing the effectiveness of our method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge graph

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Forensic Investigation of Malicious Activities in Digital Environments 4

Forensic Investigation of Malicious Activities in Digital En...

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4th IEEE International Conference on data Engineering and Communication Systems, ICDECS 2024

作者： Pasumpon Pandian, A. Anakath, A.S. Kannadasan, R. Ravikumar, K. Abdul Kareem, D. Care College of Engineering Department of Computer Science and Engineering Trichy India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computer Science and Engineering Department of Software Systems Tamil Nadu Vellore India Rrase College of Engineering Padappai Department of Computer Science & Engineering Chennai India Grt Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Tiruttani India

ISBN: (纸本)9798350393354

Several digital dangers were investigated. Malware dominated analysis with 45 attacks. We found 30 phishing attacks. 22 data breaches, 15 cyber espionage, 18 identity theft. This indicates the kind and frequency of harassment digital forensic specialists endure. Digital evidence was crucial to the case. 65 pieces of evidence were collected on computers. 42 incidents showed smartphones and tablets' digital investigative potential. There were 55 examples of communication patterns requiring network logs. In 28 and 12, cloud services and removable media provided proof. A wide range of evidence sources stresses the need for extensive collecting. Digital forensics were essential to the inquiry. In 70% of cases, disk imaging software created forensic copies of metadata-and timestamp-containing storage media. Network packet analyzers examined 48 traffic logs and communication patterns. To analyze volatile memory, 35 cases used memory forensics. In 25 cases, malware analysis platforms decrypted malware. In 60 situations, file recovery software recovered lost or hidden files. Digital forensic specialists' diverse arsenal for thorough investigations is shown here. Legal and ethical considerations dominated the study. The law was important, and all inquiries followed norms. A strong chain of custody protects evidence integrity and admissibility. The data privacy rules were respected. Legal evidence is admissible in court. Ethics ensured objective investigations. This underlines digital forensic investigations' legal and ethical foundation. © 2024 IEEE.

关键词： Cybersecurity

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Revolutionizing power line asset detection: A YOLOv8-Driven lightweight algorithm for UAV image recognition 4

Revolutionizing power line asset detection: A YOLOv8-Driven ...

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4th International Conference on computer Vision, Application, and Algorithm, CVAA 2024

作者： Liu, Chunyao Chen, Zhiyu Guo, Jianwei Liu, Gang School of Computer Science and Engineering Changchun University of Technology Changchun130012 China Jilin Province Data Service Industry Public Technology Research Centre Changchun130012 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

The maintenance and inspection of power lines is key to ensuring their normal operation and maintaining an uninterrupted power supply for various human activities. Traditional methods to detect power line assets usually detect only a small number of assets, and face the challenge of low inspection accuracy and high computational resources. This study proposes a power line asset detection algorithm designed based on the YOLOv8 to address these issues. Firstly, the C2f-DC module is added to the algorithm’s backbone network, enhancing the feature extraction capability of the network. Then, the MSCA module is incorporated into the algorithm’s neck network. This module effectively captures multi-scale contextual information, thereby enhancing the feature extraction capability of the algorithm. Finally, the DyHead module and the Inner-IoU loss function are used to replace the original detection head and the original loss function, respectively. The Dyhead module dynamically adjusts feature representations, thus boosting the detection accuracy of the algorithm. The Inner-IoU loss function addresses the issues of poor generalization and slow convergence associated, thereby improving the performance of the algorithm. Experimental results demonstrate that the power line asset detection algorithm designed based on YOLOv8 achieves a mean average precision (mAP) of 90%, accurately detecting power line asset. © 2025 SPIE.

关键词： Power distribution lines

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Towards robust multimodal sentiment analysis with incomplete data 24

Towards robust multimodal sentiment analysis with incomplete...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Haoyu Zhang Wenbin Wang Tianshu Yu School of Data Science The Chinese University of Hong Kong Shenzhen and Department of Computer Science University College London School of Computer Science Wuhan University School of Data Science The Chinese University of Hong Kong Shenzhen

ISBN: (纸本)9798331314385

The field of Multimodal Sentiment Analysis (MSA) has recently witnessed an emerging direction seeking to tackle the issue of data incompleteness. Recognizing that the language modality typically contains dense sentiment information, we consider it as the dominant modality and present an innovative Language-dominated Noise-resistant Learning Network (LNLN) to achieve robust MSA. The proposed LNLN features a dominant modality correction (DMC) module and dominant modality based multimodal learning (DMML) module, which enhances the model's robustness across various noise scenarios by ensuring the quality of dominant modality representations. Aside from the methodical design, we perform comprehensive experiments under random data missing scenarios, utilizing diverse and meaningful settings on several popular datasets (e.g., MOSI, MOSEI, and SIMS), providing additional uniformity, transparency, and fairness compared to existing evaluations in the literature. Empirically, LNLN consistently outperforms existing baselines, demonstrating superior performance across these challenging and extensive evaluation metrics. The code is available at: https://***/Haoyu-ha/LNLN

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Aggregating Structural Features for Computational Analysis of Mutations in BRCT Domains of BRCA1 Protein

Aggregating Structural Features for Computational Analysis o...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Hidayat, Alam Ahmad Nirwantono, Rudi Isnan, Mahmud Trinugroho, Joko Pebrianto Pardamean, Bens Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Binus Graduate Program Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features. © 2023 IEEE.

关键词： Proteins

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Collaborative Filtering of Malicious Information from the MULTimedia data Using s 1

Collaborative Filtering of Malicious Information from the MU...

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1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Parasuraman, Manikandan Abirami, G. Arockia Raj, A. Krishnan, Batri Munnangi, Ashok Kumar Kumaresan, M. School of Computer Science and Engineering Jain Deemed to Be University Bengaluru India Sathyabama Institute of Science and Technology Department of Computer Science Chennai India Madanapalle Institute of Technology and Science Department of Computer Science and Engineering - Data Science Madanapalle India Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (纸本)9798350341737

Due to high proliferation of unsolicited information in the text, image and video files, the social media analytics engine suffers from losing its user, user privacy and others. The unsolicited information is been a huge threat to the service provides as it affects the reputations of the services provided and hence it is necessary to develop an intelligent model. Modern machine learning techniques are applied to identify and filter unsolicited information. However, some of the conventional methods are designed in an insecure way while processing all the text file, image and video files. © 2023 IEEE.

关键词： Collaborative filtering

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PASUM: A Pre-training Architecture for Social Media User Modeling based on Text Graph 30

PASUM: A Pre-training Architecture for Social Media User Mod...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Wu, Kun Mou, Xinyi Xue, Lanqing Ying, Zhenzhe Wang, Weiqiang Zhang, Qi Huang, Xuanjing Wei, Zhongyu Institute of Science and Technology for Brain-inspired Intelligence Fudan University China School of Data Science Fudan University China Ant Group China School of Computer Science Fudan University China Research Institute of Intelligent and Complex Systems Fudan University China

ISBN: (纸本)9782493814104

Modeling social media users is the core of social governance in the digital society. Existing works have incorporated different digital traces to better learn the representations of social media users, including text information encoded by pre-trained language models and social network information encoded by graph models. However, limited by overloaded text information and hard-to-collect social network information, they cannot utilize global text information and cannot be generalized without social relationships. In this paper, we propose a Pre-training Architecture for Social Media User Modeling based on Text Graph (PASUM). We aggregate all microblogs to represent social media users based on the text graph model and learn the mapping from microblogs to user representation. We further design inter-user and intra-user contrastive learning tasks to inject general structural information into the mapping. In different scenarios, we can represent users based on text, even without social network information. Experimental results on various downstream tasks demonstrate the effectiveness and superiority of our framework. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Mapping

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