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3rd International Conference on Electronic Information engineering, Big Data, and computer technology, EIBDCT 2024

作者： Zhao, Jingsheng Ni, Qihui Yan, Shuai Zhu, Qiaoming School of Information and Control Engineering Qingdao University of Technology Qingdao Shandong266000 China School of Computer Science and Technology Suzhou University Suzhou Jiangsu215006 China

ISBN: (纸本)9781510680449

Traditional relation extraction methods are usually based on single text data, and other modality information such as image and video can improve the effect of text relation extraction. Aiming at the problem of heterogeneity between multi-modal data, a multi-modal relation extraction model MRECL based on contrastive learning is constructed. Firstly, the feature representation of image and text is obtained through ViT and BERT models;secondly, contrastive learning is introduced to align image-text features to reduce modality heterogeneity in advance;then image is used as prefix guidance information, and multi-modal interaction fusion is carried out with text information through attention mechanism;finally, the probability of all relation classification is calculated through Softmax layer and the prediction relation is output. Experimental results show that the precision, recall, and F1 values of MRECL on the MNRE dataset reach 83.23%, 82.19%, and 82.70%, respectively, achieving better performance than the current SOTA model. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image enhancement

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Novel blockchain-based framework to publish, verify, and store digital academic credentials of universities

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International Journal of Information technology (Singapore) 2024年第5期16卷 3273-3281页

作者： Chandra, Tara Kaur, Mandeep Rakesh, Nitin Gulhane, Monali Maurya, Sudhanshu Department of Computer Science and Engineering School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India

Blockchain is the technology of choice to create cryptocurrency and bitcoins through the maintenance of immutable distributed ledgers in multiple nodes. Analysis indicates that less emphasis is being placed on the implementation of Blockchain in the educational sector. Most of the academic applications, apply the weak proof of work or proof of stack as their consensus mechanism. This research proposes a model based on blockchain technology that will publish, verify, and store academic credentials of university stakeholders through the trustworthy proof of authority (PoA) as its consensus mechanism. The web-based model also introduces smart contract for issuing the certificate to the blockchain network and file listing mechanism for revocation of issued digital certificate. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Academic certificate verification Blockchain Consensus mechanism Proof of authority (PoA) Smart contract

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Self-supervised Heterogeneous Graph Representation Learning with Attribute Completion 21

Self-supervised Heterogeneous Graph Representation Learning ...

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21st International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Yajie, Lei School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Province Chengdu611731 China

ISBN: (纸本)9798331519254

The missing attribute problem in heterogeneous graph representation learning has received increasing attention recently. However, previous methods fail to address the following issues. First, they pre-train the guidance information to complete the missing data before representation learning, which brings extra cost and may lead to sub-optimal completion. Second, they conduct node pair sampling for contrastive learning, which also causes extra computational cost. To tackle these issues, in this paper, we propose a new self-supervised heterogeneous graph learning method with attribute completion. Specifically, we propose a heterogeneous layer to learn the topology embeddings as the guidance. We then complete missing attributes by weighted aggregation. After that, we generate two graph views and conduct representation learning. We investigate the contrastive constraint by pushing the correlation matrix of two views close to the identity matrix. Moreover, we reconstruct the original node attributes to improve the effectiveness of representation learning. We conduct experiments to verify the superiority of our method. © 2024 IEEE.

关键词： Self-supervised learning

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Identifying Critical Components in Power Networks through Machine Learning 14

Identifying Critical Components in Power Networks through Ma...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Paphitis, George Venkatasubramanian, Balaji V. Panteli, Mathaios Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus School of Technology Woxsen University Telangana India

ISBN: (纸本)9781837242689

As modern power systems become more complex, identifying critical components for their reliable operation is becoming increasingly important, especially in dealing with widespread cascading outages from large-scale disturbances. In this context, this paper introduces a novel method for identifying critical components in terms of their contribution to cascading-induced demand not served (DNS), utilizing feature selection techniques integrated with a machine learning classifier model. While existing methods predominantly identify critical assets using DNS, they often overlook the effects of cascading failures. Furthermore, those that do consider cascading failures typically focus solely on line overloading. This work, therefore, introduces a machine learning-based methodology coupled with a cascading failure model, considering protection mechanisms like excitation limits, under/over frequency, and line overloading. Tested on the IEEE 24-bus system, the validation method exhibits strong sensitivity (80.35%-90.03%) and specificity (80.35%-94.10%). Additionally, SHapley Additive exPlanations (SHAP) is used for validation by comparing its results with the proposed method. © The Institution of engineering & technology 2024.

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waRLOCK: Countering Ransomware and Data Leak 2

waRLOCK: Countering Ransomware and Data Leak

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Gupta, Soumyo Deep Kumar, Rajesh School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Tamil Nadu Chennai600127 India

ISBN: (纸本)9798350383652

In an era marked by the evolution of cybercrime from isolated hackers to highly organized enterprises, the surge in ransomware attacks presents a pressing concern. Enter RLOCK (pronounced as warlock), a meticulously crafted system that employs a novel reverse monitoring approach, actively transforming data to self-destruct, offering proactive protection against ransomware and data leaks. RLOCK revolutionizes the fight against ransomware by implementing continuous monitoring of specific files, prioritizing data resilience in hostile environments. Additionally, the challenge of protection against data leaks is met with a proactive solution. Imagine the peace of mind that comes with the knowledge that, even in the event of a data breach, the compromised information will self-destruct when accessed on an unauthorized system. Through RLOCK's data transformation, data can discern its environment, triggering self-destruction upon authentication failure. Discover a new paradigm in cybersecurity with RLOCK. © 2024 IEEE.

关键词： Information leakage

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Digital Currency Abnormal Detection Based on Graph Neural Network

Digital Currency Abnormal Detection Based on Graph Neural Ne...

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2024 International Conference on Electronics and Devices, Computational Science, ICEDCS 2024

作者： Que, Xingle Cheng, Yang Bai, Yunfei Lu, Dandan Southwest University of Science and Technology School of Computer Science and Technology Mianyang China Southwest University of Science and Technology Dept. Engineering Technology Center Mianyang China

ISBN: (纸本)9798331527624

In recent years, with the rapid development of the digital currency market, the security issues of digital currency transactions have become increasingly prominent, and abnormal transaction detection has become an important technical means to ensure the security of the financial system. Traditional detection methods face many challenges in dealing with complex blockchain data, as they cannot fully utilize the inherent structural information of transaction data, and are difficult to handle the problem of imbalanced categories. Accordingly, this study proposes a GNN (Graph Neural Network)-based abnormal transaction detection method, which models transaction data as a graph structure, extracts transaction features using Graph Convolutional Network (GCN) and uses small sample processing techniques to alleviate data imbalance. The proposed method showed that the experimental results could effectively improve the detection performance of abnormal transactions after multiple rounds of training, especially in terms of AUC evaluation indicators, which verifies its potential application in the detection of abnormal transactions in digital currencies. This study provides a novel and effective solution for anomaly detection in complex network data and points out the direction for future research. © 2024 IEEE.

关键词： Graph neural networks

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Harmonizing Creativity and technology: The Impact of Cloud Computing on the Music Industry

Harmonizing Creativity and Technology: The Impact of Cloud C...

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Shali, Hong Rana, Muhammad Ehsan Abdul Hameed, Vazeerudeen Tripathy, Hrudaya Kumar Asia Pacific University of Technology and Innovation School of Computing Kuala Lumpur Malaysia Kalinga Institute of Industrial Technology Deemed to Be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798331508432

The music industry has undergone a profound transformation in recent decades, driven by technological advancements. The advent of digital music, streaming services, and social media has significantly altered the way music is created, distributed, and consumed. In this context, cloud computing has emerged as a powerful force, reshaping the landscape of the industry. This paper presents a comprehensive analysis of the integration of cloud computing into the music industry and its subsequent impact. The authors examined how advancements in cloud technology are revolutionizing music production, consumption, and distribution, enabling the emergence of innovative business models. The scalability and flexibility offered by cloud computing have empowered small IT product manufacturers to rapidly launch their services and bring their business ideas to fruition. Consequently, the music industry has witnessed a substantial shift, recognizing cloud computing as a key driver of business model innovation. Furthermore, the convergence of cloud computing with mobility, social media, and big data has not only transformed daily life but has also revolutionized business practices within the music industry. © 2024 IEEE.

关键词： Cloud platforms

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Application of Artificial Intelligence using Mammograms to Identify Breast Cancer 6

Application of Artificial Intelligence using Mammograms to I...

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6th International Conference on Computational Intelligence and Networks, CINE 2024

作者： Rath, Sudeshna Giri, Kalyanbrata Dash, Satya Ranjan Barra, Paola Abdullah, Azian Azamimi KIIT University School of Computer Engineering India University of Naples 'Parthenope' Department of Science and Technology Italy Universiti Malaysia Perlis Faculty of Electronic Engineering and Technology Malaysia

ISBN: (纸本)9798331516796

An affordable and precise way to identify breast cancer is through Mammography. It has significantly decreased mortality by detecting malignancies early and is essential for early detection. It has also increased survival rates. However, disparities in mammography access are still mostly driven by socioeconomic factors. Ongoing research aims to address these problems, improve screening methods, and raise the precision and usability of mammography. Additionally, radiologists are being assisted by computer-aided detection (CAD) and artificial intelligence (AI), which are being used to improve diagnostic accuracy. The goal of this research is to identify the best AI algorithms for breast cancer detection. We employ a range of preprocessing methods in this study, first resizing the images to see which method cleans up our data the best. After segmenting our images to locate the region of interest, we extract features using a local binary pattern. Numerous classification models have been employed, such as SVM, KNN, Random Forest, and Decision Tree. Moreover, CNN and RCN have been used for the same objective. Furthermore, it was shown that, generally speaking, neural networks outperformed classification. © 2024 IEEE.

关键词： Lung cancer

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PartDensE: Knowledge Graph Embedding with Joint Use of Specific Learning and Relation-aware Functions 6

PartDensE: Knowledge Graph Embedding with Joint Use of Speci...

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6th International Conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2024

作者： Dan, Qiuyue Cui, Delong Peng, Zhiping Li, Qirui He, Jieguang Qiu, JinBo Jilin Institute of Chemical Technology School of Information and Control Engineering Jilin China Guangdong University of Petrochemical Technology School of Electronic and Information Engineering Maoming China Jiangmen Polytechnic Jiangmen China Guangdong University of Petrochemical Technology School of Computer Maoming China

ISBN: (纸本)9798350386974

Knowledge Graph Embedding (KGE) is important to the value of the Knowledge Graph (KG) in its application field. Currently, neural network-based models achieve the most advanced performance. However, these models rarely consider the potential information between entities and relations in KGs, and also suffer from the problems of complex structure and high consumption of computing resources. In this paper, we propose the simple and efficient model PartDensE by using dense network, combining specific sharing layer and relational awareness layer. Specifically, our model learns all entities and relationships individually and can mine hidden features to enrich global information. Also, learn all the information based on each relation and be able to accurately establish associations between entities and relations. PartDensE can capture global and local features and displayed and hide information, retaining as much valuable information as possible. We evaluated PartDensE with 4 indicators across on two baseline datasets. Numerous experimental data prove the effectiveness of our model. © 2024 IEEE.

关键词： Knowledge graph

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Optimal Bidding Strategies of Microgrid with Demand Side Management for Economic Emission Dispatch Incorporating Uncertainty and Outage of Renewable Energy Sources

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Energy engineering 2024年第4期121卷 849-867页

作者： Mousumi Basu Chitralekha Jena Baseem Khan Ahmed Ali Department of Power Engineering Jadavpur UniversityJadavpurIndia School of Electrical Engineering KIIT UniversityBhubaneswarIndia Department of Electrical and Computer Engineering Hawassa UniversityHawassaEthiopia Department of Electrical and Electronic Engineering Technology Faculty of Engineering and the Built EnvironmentUniversity of JohannesburgJohannesburgSouth Africa

In the restructured electricity market,microgrid(MG),with the incorporation of smart grid technologies,distributed energy resources(DERs),a pumped-storage-hydraulic(PSH)unit,and a demand response program(DRP),is a smarter and more reliable electricity *** consists of gas turbines and renewable energy sources such as photovoltaic systems and wind *** bidding strategies,prepared by MG operators,decrease the electricity cost and emissions from upstream grid and conventional and renewable energy sources(RES).But it is inefficient due to the very high sporadic characteristics of RES and the very high outage *** solve these issues,this study suggests non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)for an optimal bidding strategy considering pumped hydroelectric energy storage and DRP based on outage conditions and uncertainties of renewable energy *** uncertainty related to solar and wind units is modeled using lognormal and Weibull probability ***-based DRP is used,especially considering the time of outages along with the time of peak loads and prices,to enhance the reliability of MG and reduce costs and emissions.

关键词： Micro-grid distributed energy resources demand response program uncertainty outage

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