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Electronic Information engineering and computer science (EIECS), 2021 International Conference on

作者： Wei Yang Hongwei Yan Chao Xu Yu Yao Jinlei Zhou Baile Wang Danni Wang Software College Northeastem University Shenyang China School of Computer Science And Engineering Northeastem University Shenyang China Department of Cybersecurity Monitoring State Grid Liaoning Information and Communication Company Shenyang China

Key nodes in complex networks play an important role in the structure of the entire network. Different nodes in the network often have different degrees, and the node with the highest degree in the network is usually called a key node. However, the heterogeneity of nodes is mostly neglected in the research of malware propagation models by scholars at home and abroad. Therefore, based on the SUIQMR model proposed in the industrial control coupling network and considering the role of key nodes in the network, an improved malware suppression strategy for key nodes in the immune network is obtained. The simulation results verify the effectiveness of the improved malware suppression strategy, showing that the immune key nodes can effectively suppress the spread of malware.

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Advmask: A Sparse Adversarial Attack-Based Data Augmentation Method for Image Classification

SSRN

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SSRN 2023年

作者： Yang, Suorong Li, Jinqiao Zhang, Tianyue Zhao, Jian Shen, Furao State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Artificial Intelligence Nanjing University China School of Electronic Science and Engineering Nanjing University China

Data augmentation has been an essential technique for improving the generalization ability of deep neural networks in image classification ***, intensive changes in appearance and different degrees of occlusion in images are the key factors that severely affect the generalization ability of image classification models. Therefore, in order to enhance the generalization performance and robustness of deep models, data augmentation approaches by providing models with more diverse training data in various scenarios are widely applied. Although many existing data augmentation methods simulate occlusion in the augmented images to enhance the generalization of models, these methods randomly delete some areas in images without considering the semantic information of images. In this work, we propose a novel data augmentation method named AdvMask for image classification based on sparse adversarial attack techniques. AdvMask first identifies the key points that have the greatest influence on the classification results via a proposed end-to-end sparse adversarial attack module. During the data augmentation process, AdvMask efficiently generates diverse augmented data with structured occlusions based on the key points. By doing so, AdvMask can force deep models to seek other relevant content while the most discriminative content is hidden. Extensive experimental results on various benchmark datasets and deep models demonstrate that our proposed method can effectively improve the generalization performance of deep models and significantly outperforms previous data augmentation methods. © 2023, The Authors. All rights reserved.

关键词： Image classification

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DCAMM: Dynamic Curve-Based Automated Market Maker

DCAMM: Dynamic Curve-Based Automated Market Maker

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IEEE Conference on Global Communications (GLOBECOM)

作者： Shunrong Jiang Jingwei Chen Fengjiao Li Haijun Geng Haotian Chi The Department of Information Science China University of Mining and Technology Xuzhou China School of Computer and Information Technology Shanxi University Taiyuan China School of Automation and Software Engineering Shanxi University Taiyuan China

Decentralized Exchanges (DEX) allow cryptocurrencies to trade autonomously with each other without involving any centralized financial intermediaries. Among these DEX models, Automated Market Maker (AMM) is most commonly used by major platforms like Uniswap and Curve. However, a typical AMM suffers three main challenges. First, arbitrage trading may cause AMM-based liquidity providers to lose liquidity in assets. Second, adversaries extract on a monthly basis over 10 million USD from AMM traders via sandwich attacks. Third, the volatility of asset prices in AMM may violate the fairness of trading. In this work, we propose a new AMM design, Dynamic Curve-based Automated Market Maker (DCAMM), which utilizes a price oracle with real-time market price feedback to automatically adjust the pool's asset price to match the market price. In DCAMM, there is no space for price manipulation, and traders' slippage losses are converted into equal gains for the liquidity pool. Thus, DCAMM provides the resistance to arbitrage trading and sandwich attacks. Moreover, DCAMM provides a more stable asset price through a strict price adjustment, benefiting traders and safeguarding trading fairness.

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Metaverse: A Vision, Architectural Elements, and Future Directions for Scalable and Realtime Virtual Worlds

arXiv

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arXiv 2023年

作者： Ismail, Leila Buyya, Rajkumar Lab School of Computing and Information Systems The University of Melbourne Australia Lab Department of Computer Science and Software Engineering National Water and Energy Center United Arab Emirates University United Arab Emirates

With the emergence of Cloud computing, Internet of Things-enabled Human-computer Interfaces, Generative Artificial Intelligence, and high-accurate Machine and Deep-learning recognition and predictive models, along with the Post Covid-19 proliferation of social networking, and remote communications, the Metaverse gained a lot of popularity. Metaverse has the prospective to extend the physical world using virtual and augmented reality so the users can interact seamlessly with the real and virtual worlds using avatars and holograms. It has the potential to impact people in the way they interact on social media, collaborate in their work, perform marketing and business, teach, learn, and even access personalized healthcare. Several works in the literature examine Metaverse in terms of hardware wearable devices, and virtual reality gaming applications. However, the requirements of realizing the Metaverse in realtime and at a large-scale need yet to be examined for the technology to be usable. To address this limitation, this paper presents the temporal evolution of Metaverse definitions and captures its evolving requirements. Consequently, we provide insights into Metaverse requirements. In addition to enabling technologies, we lay out architectural elements for scalable, reliable, and efficient Metaverse systems, and a classification of existing Metaverse applications along with proposing required future research *** Codes ***, *** © 2023, CC BY.

关键词： Energy efficiency

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Enhancing Early Childhood Learning using Image Processing for Sinhala Medium Education

Enhancing Early Childhood Learning using Image Processing fo...

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Advancements in Computing (ICAC), International Conference on

作者： R.G.V Saranga A.H.N. Jayasinghe U.A.S. Divyantha S.W.L.P de Silva N.H.P. Ravi Supunya Swarnakantha P. K. Suriya Kumari Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

The study aims to develop a mobile application for young children to learn Sinhala letters, shapes, colors, and storytelling incorporating machine learning models to evaluate and enhance educational activities. With the rise of online education during the COVID-19 pandemic, the familiarity of children with mobile devices provides an opportunity to create an engaging and educational experience. The application will teach Sinhala letters using object images, allowing children to upload their own images for feedback. It also includes a feature for children to practice writing letters and analyze their progress. Also, the application introduces colors and shapes in Sinhala, encouraging children to draw and track their improvement. Additionally, the application aims to generate stories in Sinhala to improve children's creativity and thinking knowledge. This research addresses a critical gap in existing Sinhala learning applications by integrating machine learning for activity assessment, promising to significantly impact and improve early language education for children.

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Big data analytics: A single window IoT-enabled climate variability system for all-year-round vegetable cultivation 4

Big data analytics: A single window IoT-enabled climate vari...

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4th International Conference on science and Sustainable Development: Advances in sciences and Technology for Sustainable Development, ICSSD 2020

作者： Nwankwo, Wilson Ukhurebor, Kingsley Eghonghon Software Engineering AndCyberphysical Systems Unit Department of Computer Science Edo University Iyamho Edo State Nigeria Climatic Environmental and Telecommunication Unit Department of Physics Edo University Iyamho Edo State Nigeria

Vegetables constitute a major food source with huge nutritional values as well as major source of income. The cultivation of vegetables is dictated by climate and seasonal changes across Nigeria. Edo State lies within the South of Nigeria and enjoys the two popular seasons (rainy and dry) like many other parts of the country. However, the variability in soil distribution and weather conditions across different locations is a determining factor as to the category of not just vegetables to grow but other crops. In this paper, Edo state is used as a flagship project for its diverse potentials and uniqueness in respect of known variability in soil and weather conditions. The State is divided into three geo-referenced agricultural districts. A prototype system is proposed to provide vegetable farmers with real-time information on vegetablefarming requirements. The proposed system is an Internet of things (IoT)-enabled climate variability system with interfaces to popular mobile networks, existing Geographical Information System (GIS) in the State, and remote sensing stations respectively. Each geo-referenced point is a nexus to areas with similar weather variability and soil distribution. Historical data is collected from the existing GIS and a provision is made to constantly enrich the historical data with new information from the geo-referenced points including crops grown, trends in cultivation, queries from farmers, etc. The information generated from the geo-referenced locations are routed via GPS to the central analytics server in the cloud and appropriate algorithms are used to carry out data analysis for real-time prediction and messages to farmers through the Internet and Short Message Services (SMS). With this system, it is submitted that subsistent and mechanized farmers would benefit through the guidance of an analytics system thereby boosting vegetablefarming regardless of the season of the year. © Published under licence by IOP Publishing Ltd.

关键词： Internet of things

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Machine learning models for automatic labeling: A systematic literature review 15

Machine learning models for automatic labeling: A systematic...

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15th International Conference on software Technologies, ICSOFT 2020

作者： Fredriksson, Teodor Bosch, Jan Olsson, Helena Holmstrm Department of Computer Science and Engineering Division of Software Engineering Chalmers University of Technology Gothenburg Sweden Department of Computer Science and Media Technology Malm University Malm Sweden

ISBN: (纸本)9789897584435

Automatic labeling is a type of classification problem. Classification has been studied with the help of statistical methods for a long time. With the explosion of new better computer processing units (CPUs) and graphical processing units (GPUs) the interest in machine learning has grown exponentially and we can use both statistical learning algorithms as well as deep neural networks (DNNs) to solve the classification tasks. Classification is a supervised machine learning problem and there exists a large amount of methodology for performing such task. However, it is very rare in industrial applications that data is fully labeled which is why we need good methodology to obtain error-free labels. The purpose of this paper is to examine the current literature on how to perform labeling using ML, we will compare these models in terms of popularity and on what datatypes they are used on. We performed a systematic literature review of empirical studies for machine learning for labeling. We identified 43 primary studies relevant to our search. From this we were able to determine the most common machine learning models for labeling. Lack of unlabeled instances is a major problem for industry as supervised learning is the most widely used. Obtaining labels is costly in terms of labor and financial costs. Based on our findings in this review we present alternate ways for labeling data for use in supervised learning tasks. Copyright © 2020 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved.

关键词： Program processors

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Dogodo: IoT Based Mobile Application to Provide Essential Health Services to Dogs 3

Dogodo: IoT Based Mobile Application to Provide Essential He...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Thilakarathne, L.V.I.S. Salay, M.S. Wijethilaka, M. G. R Chethana Fernando, T.S. Sriyaratna, Disni Rupasinghe, Shalini Sri Lanka Institute of Information Technology Faculty of Computing Department of Infotmation Tchnology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Voice of dogs can be heard by people who listen to them. The more you listen, the more you learn about the dogs. This study proposes a platform to identify and observe dogs' behavior and their activities by using the newest technologies. The proposed system will mainly cover the relevant areas that are supposed to be covered to full fill the pet owners' expectations by providing necessary services such as internal health, voice recognition, and emotion translations and external issues such as skin diseases, breeding patterns, and breeding outcomes. Our priority is to emphasize necessary services in the mobile application and provide fluid services with fewer interruptions. Primarily research will offer an IoT device and a mobile application that covers the dog's internal health and external health consecutively. The study revealed current developments and the challenges and possibilities related to the system. © 2021 IEEE.

关键词： Speech recognition

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Empowering Personalized Health Data Queries with Knowledge Graph and GPT-Enhanced Voice Assistant

Empowering Personalized Health Data Queries with Knowledge G...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Xin Wang Wordh Ul Hasan Juan Li Rasha Hendawi Yang Du Bo Xie Cui Tao Department of Computer Science North Dakota State University Fargo USA Department of Computer Systems and Software Engineering Valley City State University Valley City USA School of Nursing The University of Texas at Austin Austin USA School of Biomedical Informatics University of Texas Health Science Center at Houston Houston USA

The proliferation of digital health technologies has led to an abundance of personal health data. However, querying and retrieving specific health-related information from disparate sources can be challenging and inconvenient, particularly for older individuals unfamiliar with technology. While ChatGPT offers a conversational interface, it lacks domain-specific knowledge, including personalized health information. To address this limitation, we present a novel approach that combines a knowledge graph and GPT to enable personalized health queries. Our solution utilizes a personal knowledge graph as a comprehensive knowledge source and fine-tunes GPT to provide accurate responses. We have implemented a voice assistant mobile app incorporating this knowledge graph-assisted GPT model and conducted initial feasibility testing.

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Mutual reconstruction-based linear discriminant hashing for image retrieval

Mutual reconstruction-based linear discriminant hashing for ...

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7th International Conference on Vision, Image and Signal Processing (ICVISP 2023)

作者： D. Huang Z. Lai H. Kong College of Computer Science and Software Engineering Shenzhen University Shenzhen People's Republic of China Department of Breast and Thyroid Surgery BaoAn Central Hospital of Shenzhen Shenzhen People's Republic of China

Recently, hash learning has attracted much attention due to its ability to convert high-dimensional data into compact binary codes for efficient retrieval and storage. However, most classification-based supervised hashing methods neglect the original goal of increasing the similarity of similar samples while reducing the similarity of different samples in the learned hash code. In this paper, we combine the idea of linear discriminant analysis with hash learning by designing a new scatter matrix and propose a supervised hash learning framework to learn optimal discriminative hash codes for image retrieval. To solve the problem of reducing information loss during the learning process, we design a novel method via mutual reconstruction between binary hash codes and original features. Thus, a method named Mutual Reconstruction-based Linear Discriminant Hashing (RDAH) is obtained, which further improves the discriminative ability of the learned hash code. We test the performance of RDAH on three image datasets to show the superiority on large-scale image retrieval.

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