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IEEE International Conference on Data Mining (ICDM)

作者： Liang Yang Jiayi Wang Dongxiao He Chuan Wang Xiaochun Cao Bingxin Niu Zhen Wang School of Artificial Intelligence Hebei University of Technology Tianjin China College of Intelligence and Computing Tianjin University Tianjin China State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Shenzhen China School of Artificial Intelligence OPtics and ElectroNics (iOPEN) School of Cybersecurity Northwestern Polytechnical University Xi’an China

Graph neural network (GNN) can be formulated as the multiplication of the topology-related matrix (adjacency or Laplacian matrix) and node attribute matrix, i.e., operation in node-wise. Unfortunately, this unified formula reveals two inherent drawbacks. Firstly, the topology and node attribute are not reciprocal but biased. From employment, the topology information is repeatedly employed, while the node attribute is only used once. From parameterization perspective, the node attribute is parameterized with highly expressive MLPs, while topology is not. Secondly, the graph topology can not be fully explored. Only the local pairwise relation is explored, but the mesoscopic community structure, which is one of the most prominent characteristics of networks, is ignored. To alleviate these issues, this paper proposes the Graph Reciprocal Network (GRN) by treating node attribute and topology reciprocal. Firstly, it is illustrated that the node can be regarded and utilized as another kind of attribute. Secondly, a novel node representation scheme is proposed from the theory of Quadratic Networks, with a theoretical guarantee of the fine-grained element-wise product of the representations of the topology and attribute. Extensive experiments demonstrate the superior performance and robustness of the proposed GRN.

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Automated Skin Lesion Analysis and Classification Using AI

Automated Skin Lesion Analysis and Classification Using AI

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Sustainable Expert Systems (ICSES), International Conference on

作者： Parthasarathi Pattnayak Sanghamitra Patnaik Sudhansu Shekhar Patra Mahendra Kumar Gourisaria Satyendr Singh Lalbihari Barik School of Computer Applications KIIT Deemed to be University Bhubaneswar India School of Law KIIT Deemed to be University Bhubaneswar India School of Computer Engineering KIIT Deemed to be University Bhubaneswar India Computer Science & Engg Department BML Munjal University Gurugram India Department of Information Systems Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Rabigh Saudi Arabia

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Skin lesions are becoming a major global health concern, and survival rates can be greatly increased by employing dermoscopic pictures for early identification. It is a difficult undertaking to classify these skin lesions from dermoscopic images. The application of deep learning models has significantly improved this classification's accuracy. Because convolutional neural networks (CNNs) are better at extracting features, which improves classification results, they have been shown to be quite useful in this field. In light of this, the article attempts to create deep learning (DL) algorithms using artificial intelligence (AI) to differentiate between skin lesions as benign or malignant in dermoscopic pictures. The study presents OSSAE-BPNN, an automated method for the detection and classification of skin lesions. This method uses a backpropagation neural network (BPNN) for classification and an optimized stacked sparse autoencoder (OSSAE) for feature extraction. To determine the damaged lesion region, the multi-level thresholding segmentation methodology is part of the proposed OSSAE-BPNN method. The OSSAE performs the feature extraction, and the BPNN handles the categorization. Furthermore, the SGO (seagull optimization) technique is used to optimize the SSAE model's parameters. Extensive testing were conducted on a standard dataset to show the OSSAE-BPNN method's efficacy. The OSSAE-BPNN methodology fared better than other current methods across a range of evaluation parameters, according to the results.

关键词： Deep learning Image segmentation Feature extraction Skin Lesions Artificial intelligence Optimization Standards Testing Cancer

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Tensor Graph Convolutional Network for Dynamic Graph Representation Learning

arXiv

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arXiv 2024年

作者： Wang, Ling Yuan, Ye The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China The College of Computer and Information Science Southwest University Chongqing400715 China

Dynamic graphs (DG) describe dynamic interactions between entities in many practical scenarios. Most existing DG representation learning models combine graph convolutional network and sequence neural network, which model spatial-temporal dependencies through two different types of neural networks. However, this hybrid design cannot well capture the spatial-temporal continuity of a DG. In this paper, we propose a tensor graph convolutional network to learn DG representations in one convolution framework based on the tensor product with the following two-fold ideas: a) representing the information of DG by tensor form;b) adopting tensor product to design a tensor graph convolutional network modeling spatial-temporal feature simultaneously. Experiments on real-world DG datasets demonstrate that our model obtains state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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Secure AI and Blockchain-enabled Framework in Smart Vehicular Networks

Secure AI and Blockchain-enabled Framework in Smart Vehicula...

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Rabieinejad, Elnaz Yazdinejad, Abbas Dehghantanha, Ali Parizi, Reza M. Srivastava, Gautam K.N.Toosi University of Technology Information Technology Department Tehran Iran University of Guelph Cyber Science Lab School of Computer Science Ontario Canada Kennesaw State University College of Computing and Software Engineering GA United States Brandon University Department of Mathematics and Computer Science Manitoba Canada

ISBN: (纸本)9781665423908

In recent years, Internet of Things (IoT) devices such as drones, smartphones, and smart vehicles have increased, and Smart Vehicular Networks (SVNs) have formed. SVN are one of the vital components in smart cities and improves their functionality and efficiency. Despite the many applications that SVN have shown, they have security vulnerabilities issues, and there are many reports of them being hacked. Most attacks on SVN occur due to wireless communications and information sharing between vehicles. Also, due to the communication among vehicles, failures can spread from the victim device to the entire network and cause widespread damages. SVN features include mobility, decentralization, resource constraints that make traditional security solutions unsuitable for it. We propose a secure framework using blockchain and Deep Neural networks (DNN) to address these challenges. In this framework, we consider cluster-based architecture that vehicles in each cluster can securely communicate using the blockchain. Also, DNN is adopted to detect abnormal vehicles that have been attacked using their network traffic analysis in each zone. We assess blockchain impacts on throughput using different miners with multiple block sizes in our proposed framework. The experimental result indicates the throughput improvement with an increase in miners and block size. In addition, we evaluated the DNN performance for abnormal vehicle detection, which represents 99.82% in terms of accuracy. © 2021 IEEE.

关键词： Deep neural networks

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Predicting RNA Secondary Structure on Universal Quantum Computer

arXiv

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arXiv 2023年

作者： Jiang, Ji Yan, Qipeng Li, Ye Lu, Min Cui, Ziwei Dou, Menghan Wang, Qingchun Wu, Yu-Chun Guo, Guo-Ping Origin Quantum Computing Company Limited Anfei Hefei230026 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anfei Hefei230088 China CAS Key Laboratory of Quantum Information School of Physics University of Science and Technology of China Hefei230026 China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei230026 China

It is the first step for understanding how RNA structure folds from base sequences that to know how its secondary structure is formed. Traditional energy-based algorithms are short of precision, particularly for non-nested sequences, while learning-based algorithms face challenges in obtaining high-quality training data. Recently, quantum annealer has rapidly predicted the folding of the secondary structure, highlighting that quantum computing is a promising solution to this problem. However, gate model algorithms for universal quantum computing are not available. In this paper, gate-based quantum algorithms will be presented, which are highly flexible and can be applied to various physical devices. Mapped all possible secondary structure to the state of a quadratic Hamiltonian, the whole folding process is described as a quadratic unconstrained binary optimization model. Then the model can be solved through quantum approximation optimization algorithm. We demonstrate the performance with both numerical simulation and experimental realization. Throughout our benchmark dataset, simulation results suggest that our quantum approach is comparable in accuracy to classical methods. For non-nested sequences, our quantum approach outperforms classical energy-based methods. Experimental results also indicate our method is robust in current noisy devices. It is the first instance of universal quantum algorithms being employed to tackle RNA folding problems, and our work provides a valuable model for utilizing universal quantum computers in solving RNA folding problems. Copyright © 2023, The Authors. All rights reserved.

关键词： Quantum computers

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Application of DA-Bi-SRU and Improved RoBERTa Model in Entity Relationship Extraction for High-Speed Train Bogie

Application of DA-Bi-SRU and Improved RoBERTa Model in Entit...

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Data science and information technology (DSIT), International Conference on

作者： Yan Jiang Zhihou Zhang Lingfeng He Tianyi Gong Jiawen Du Xinyu Yin School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Faculty of Geosciences and Environmental Engineering Southwest Jiaotong University Chengdu China College of Information Science and Technology Southwest Jiaotong University Chengdu China

Due to the large number of professional terms and complex entity relationships in the field of high-speed train (HST) bogie, the accuracy of entity relationship extraction is low. In order to improve the efficiency and accuracy of entity relationship extraction in high-speed train bogie domain, we propose a novel entity relationship extraction model for the domain of high-speed train (HST) bogie with the aim of improving the efficiency and accuracy of entity relationship extraction. The proposed model is based on RoBERTa-wwm (A Robustly Optimized BERT Pretraining Approach with Whole Word Masking) and DA-Bi-SRU (Double-Attention-Based Bidirectional Simple Recurrent Unit). To facilitate this, we construct a new bogie relation extraction dataset comprising of 25,000 statements collected from literature and professional annotations. The RoBERTa-wwm is employed to obtain dynamic word vectors from the input statements and optimized using the bogie dataset. Subsequently, a Bi-SRU model based on dual attention mechanism is developed to capture bidirectional semantic information and contextual semantic linkage in a rapid manner. Our experiments show that the RoBERTa-wwm-DA-Bi-SRU model outperforms Bi-LSTM and RNN methods with a prediction accuracy of 88.53% and an F1 value of 86.60%. Our proposed model thus demonstrates the potential to accurately extract entity relationships in the bogie knowledge graph of high-speed trains, simplifying the construction process.

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Service offloading oriented edge server placement in smart farming

Service offloading oriented edge server placement in smart f...

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作者： Zhang, Jie Li, Xiang Zhang, Xuyun Xue, Yuan Srivastava, Gautam Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Department of Computing Macquarie University SydneyNSW Australia Department of Mathematics and Computer Science Brandon University BrandonMB Canada

Currently, smart farming has been established to realize agriculture automation by leveraging sensors to gather the growth and environmental data for crops, and realizing multiple intelligent controls, such as irrigation, fertilization, and so on, to increase the crop yields. To support real-time intelligent controls, edge computing is introduced to smart farming by endowing computing and storage capacities to edge devices nearby the geographically distributed sensors. However, the farmers are relatively willing to purchase and deploy a small quantity of edge servers (ESs) in the farm from the perspective of expenditure saving, thereby leading to a key challenge to guarantee the performance of the real-time controls and the overall edge services. In view of this challenge, a service offloading-oriented ES placement method for supporting smart farming, called SOP, is proposed to optimize the data transmission delay from sensors to ESs and the load balance among ESs. More precisely, the corresponding service range of a certain ES is ascertained according to the specific analysis of the farming service requirements. Subsequently, the layout policies for the trade-offs of the ES performance and service efficiency are acquired. Then the most balanced policy is determined as the final ES placement strategy. Eventually, we evaluate the performance of the whole ES system and the service execution efficiency with SOP. © 2020 John Wiley & Sons, Ltd.

关键词： Edge computing

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Enhancing UX Design Through Eye-Tracking and Image Processing: Practical Insights and Applications 10

Enhancing UX Design Through Eye-Tracking and Image Processin...

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10th International Conference on Platform technology and Service, PlatCon 2024

作者： Baharum, Aslina Ismail, Rozita Halamy, Suhaida Rahim, Emelia Abdul Noor, Noorsidi Aizuddin Mat Deris, Farhana Diana School of Engineering and Technology Sunway University Bandar Sunway Selangor Malaysia College of Computing and Informatics Universiti Tenaga Nasional Putrajaya Campus Selangor Kajang Malaysia School of Information Science College of Computing Informatics and Mathematics Universiti Teknologi MARA Cawangan Sarawak Sarawak Kota Samarahan Malaysia Faculty of Business Management Universiti Teknologi MARA Cawangan Sarawak Sarawak Kota Samarahan Malaysia Mass Appraisal Housing and Planning Research Group Centre for Real Estate Studies Real Estate Department Faculty of Built Environment and Surveying Universiti Teknologi Malaysia Johor Johor Bahru Malaysia Faculty of Social Sciences and Humanities Universiti Teknologi Malaysia Johor Johor Bahru Malaysia

ISBN: (纸本)9798350367874

In the digital age, optimizing user experience (UX) is paramount for the success of various applications and interfaces. Eye-tracking, a method used to monitor and analyze eye movements, has emerged as a powerful tool in UX research. This study investigates the intersection of eye-tracking and image processing, focusing on how insights derived from eye-tracking data can enhance UX design. Through a series of case studies, this research explores the practical applications of eye-tracking in improving user interaction with image-based interfaces. By analyzing user attention patterns, gaze fixation durations, and visual engagement metrics, the study uncovers valuable insights into user behaviour and preferences. These insights inform design decisions related to interface layout, content placement, and visual hierarchy, ultimately leading to more intuitive and engaging user experiences. The findings from this study provide practical guidelines and best practices for integrating eye tracking into the UX design process, offering valuable insights for designers, developers, and researchers seeking to optimize user interaction with image-based applications and interfaces. © 2024 IEEE.

关键词： Eye movements

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E-Buddy for Rescue Child Labour

E-Buddy for Rescue Child Labour

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BIT Conference (BITCON), International

作者： Ajmeera Kiran Prithiviraj Ananth P Veera Prakash N. Kumareshan Ramesh Kumar Ayyasamy P. Chinnasamy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of CSE Sona College of Technology Salem Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Ananthapuramu Andhra Pradesh Department of ECE Professor/Centre for Advances in Signal Processing and Sensor Networks Sri Eshwar College of Engineering Coimbatore Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

Ebuddy is a ground breaking platform dedicated to rescuing and rehabilitating child laboures. By leveraging advanced technology, this initiative effectively connects with at-risk children, providing them with crucial education and support services. With a mission to break the vicious cycle of poverty and exploitation, Ebuddy collaborates with local communities and organizations to raise awareness about child labor and advocate for policies that safeguard children's rights. This platform offers comprehensive support services aimed at addressing the children's physical, emotional, and psychological well-being. By providing counseling, rehabilitation, and necessary medical assistance, Ebuddy empowers children to leave their past behind and embrace a better future.

关键词： Employee welfare Pediatrics Education Psychology Morphology Organizations

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Identifying Diseased Regions on Plant Leaves and Classifying Plant Leaf Diseases with an ELM-GOA Based Model

Identifying Diseased Regions on Plant Leaves and Classifying...

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Communication & computing (WCONF), World Conference on

作者： U.M. Fernandes Dimlo R. Sankar Uma R Patil Vatsala Tomar Prasanthi Gottumukkala Priyanka Gaikwad Department of CSE Sreyas Institute of Engineering and Technology Nagole Hyderabad India Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu India Modern Institute of Business Studies Pune India Department of Botany IIMT University Meerut Uttar Pradesh India Department of Information Technology Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Computer Science & Engineering School of Computing MIT ADT University Pune India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

Recently, plant diseases have become an issue due to the harm they can cause to agricultural production, both in terms of quantity and quality. The ability to improve agricultural monitoring in large fields through the early detection of disease symptoms on plant leaves makes research into autonomous plant disease detection vital. Software that can automatically detect and classify plant leaf diseases is the proposed system. Preprocessing, feature extraction, and training the model are the three steps that make up the suggested technique. The first stage in detecting and classifying leaf diseases is image pre-processing. The necessary information can be retrieved from the photographs using it. For the purpose of background removal, picture segmentation is utilized. The color co occurrence approach is employed for feature extraction. It is a technique that makes use of an image's color and texture to determine the image's distinctive characteristics. The model training method was made more accurate by utilizing the ELM-GOA. With a 97.53% accuracy rate, the proposed technique surpasses its rivals, which comprise ELM and GOA.

关键词： Training Plant diseases Image segmentation Visualization Accuracy Image color analysis Computational modeling Production Feature extraction Software systems

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