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Quantitative Research on Complexity of Urban Environment Based on AI Model

Quantitative Research on Complexity of Urban Environment Bas...

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Intelligent computing and Human-Computer Interaction (ICHCI), International Conference on

作者： Wanyue Suo Jing Zhao School of Architecture Tianjin University Tianjin China College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

Visual complexity is a pivotal metric in urban environment quality, and the objective quantification of visual complexity holds immense importance for research in this field. Drawing from the definition of visual complexity, this paper categorizes complexity into environmental element feature complexity, Edge Feature Complexity, and color complexity. By harnessing computer image processing technology and incorporating artificial intelligence methodologies, the study successfully deploys a supervised training model to amalgamate these multiple features, which created a preliminary framework for quantifying visual complexity, offering valuable insights for future investigations into human aesthetic experiences and urban visual quality assessments.

关键词： Training Support vector machines Visualization Image color analysis Computational modeling Urban areas Training data Data models Complexity theory Quality assessment

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BuildEnVR: An Immersive Analysis System for Environmental Field 30

BuildEnVR: An Immersive Analysis System for Environmental Fi...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhou, Zhenghan Liu, Kebin Xie, Yantong Jin, Hangxu Liu, Shi Wang, Ruiqing Zhao, Haitian Lin, Borong Mu, Xiaofang Qi, Hui Tsinghua University Global Innovation Exchange China Fuzhou Fuyao Institute for Advanced Study China Carnegie Mellon University School of Architecture United States Tsinghua University School of Architecture China Taiyuan Normal University College of Computer Science and Technology China Shanxi Key Laboratory of Intelligent Optimization Computing and Blockchain Technology China

ISBN: (纸本)9798331515966

Amidst global warming and escalating extreme weather events, indoor environmental quality's impact on human health and public hygiene gains prominence. Environmental parameters exist essentially as fields, which are characterized by high dimensionality, density and complexity, and contain massive amounts of information in space. To facilitate visualization and analysis of indoor environmental field, we design and implement BuildEnVR, an immersive analysis system by virtual reality, enabling remote analysis of real-time and historical environmental field data. Grounded in user needs and cognitive psychology, three visualization modes emerge: the Virtual Sensor mode enables users to access perceptual data in real-time at any 3D coordinates in ambient space, the 4D Heatmap mode visualizes spatial variations and trends over time in environmental field data, and the Synaesthesia mode realizes the fusion display of multi-dimensional environmental field data, allowing users to quickly understand the overall condition of the indoor environment with a low cognitive load. Extensive user surveys validate BuildEnVR's intuitiveness and precision, and it is suitable for both experts and general users. © 2024 IEEE.

关键词： Indoor air pollution

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Eagle-Eye: Open-Source Intelligence Tool for IoT Devices Detection

Eagle-Eye: Open-Source Intelligence Tool for IoT Devices Det...

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2022 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2022

作者： Mahmeed, Yusuf Al Elmedany, Wael Sharif, Mhd Saeed University of Bahrain College of Information Technology Sakhir Bahrain University of East London School of Architecture Computing and Engineering London United Kingdom

ISBN: (数字)9781665451932

ISBN: (纸本)9781665451932

The use of Internet of Things (IoT) devices has been growing over the last few years making these appliances available in every household and organization. This significant rise of usability led to misuse, especially by non-technical people, making it an easy target for attackers to intrude on these networks. Therefore, the conventional thinking of protecting the information technology devices needs to embrace these frequent changes. Open-Source Intelligence (OSINT) is one of the modern techniques that can be used to keep track of these new systems by harvesting publicly available information. Collecting the needed information can be challenging for the IoT devices manufacturing companies and clients. This paper proposes an Open-Source Intelligence tool for IoT devices detection called Eagle-Eye which is integrated with Shodan search engine to perform OSINT queries and display it in user-friendly format. With the use of this tool companies, clients and researchers can automate their task of identifying and searching for different IoT devices statics that can be utilized and analyzed to harden these devices. © 2022 IEEE.

关键词： Internet of things

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Blockchain Vulnerabilities and Recent Security Challenges: A Review Paper

Blockchain Vulnerabilities and Recent Security Challenges: A...

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2022 International Conference on Data Analytics for Business and Industry, ICDABI 2022

作者： Alfaw, Aysha Elmedany, Wael Sharif, Mhd Saeed University of Bahrain College of Information Technology Sakhir Bahrain University of East London School of Architecture Computing and Engineering London United Kingdom

ISBN: (纸本)9781665490580

Blockchain is a relatively new technology, a distributed database used for sharing between nodes of computer networks. A blockchain stores all information in automated digital format as a database. Blockchain innovation ensures the accuracy and security of the data record and generates trust without the need for a trusted third party. The objectives of this paper are to determine the security risk of the blockchain systems, analyze the vulnerabilities exploited on the blockchain, and identify recent security challenges that the blockchain faces. This review paper presents some of the previous studies of the security threats that blockchain faces and reviews the security enhancement solutions for blockchain vulnerabilities. There are some studies on blockchain security issues, but there is no systematic examination of the problem, despite the blockchain system's security threats. An observational research methodology was used in this research. Through this methodology, many research related to blockchain threats and vulnerabilities obtained. The outcomes of this research are to Identify the most important security threats faced by the blockchain and consideration of security recently vulnerabilities. Processes and methods for dealing with security concerns are examined. Intelligent corporate security academic challenges and limitations are covered throughout this review. The goal of this review is to serve as a platform as well as a reference point for future work on blockchain-based security. © 2022 IEEE.

关键词： Blockchain

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Machine Learning and Internet of Things Based Real Time NPK Fertilizer Prediction for Cassava Crop in Rwanda 24

Machine Learning and Internet of Things Based Real Time NPK ...

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13th International Conference on Software and Computer Applications, ICSCA 2024

作者： Munezero, Alphonse Uwitonze, Alfred Kayalvizhi, Jayavel Maniriho, Claudien Niyitegeka, Janvier Ndorimana, Philbert University Of Rwanda College Of Science And Technology African Center Of Excellence In Internet Of Things Rwanda University Of Rwanda College Of Science And Technology Rwanda Creative Computing Institute University Of Arts London United Kingdom Rwanda Polytechnique Iprc Tumba Rwanda Rwanda Information Society Authority Enterprise Architecture Rwanda

ISBN: (纸本)9798400708329

This research paper focus on "Employing Machine Learning and Internet of Things Based Real Time NPK Fertilizer Prediction for Cassava crop in Rwanda". Throughout this research study, a machine learning (ML) algorithm was used to build a model which can help to predict the cassava fertilizer components. Different sensors such as temperature sensor, soil moisture sensor, soil nutrients sensors, and PH sensor have been used for monitoring the soil parameters and then the data corresponding to these parameters applied to machine learning algorithms Linear Regression, Random Forest, Gradient Boosting, Random Forest, K-Nearest Neighbors and Decision Tree to be tested for optimizing the prediction accuracy and these algorithms have been selected because they are mostly used in classification and regression machine learning problems. During the model performance evaluation, 93.1% and 90% of training and testing prediction accuracies respectively were achieved by using random forest for the soil samples taken in Ruhango District located in Southern Provence of Rwanda. However, 96.5% and 94.4% of training and testing prediction accuracies were generated by using the decision tree predictive model. © 2024 ACM.

关键词： Forecasting

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DRS: A deep reinforcement learning enhanced Kubernetes scheduler for microservice-based system

DRS: A deep reinforcement learning enhanced Kubernetes sched...

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作者： Jian, Zhaolong Xie, Xueshuo Fang, Yaozheng Jiang, Yibing Lu, Ye Dash, Ankan Li, Tao Wang, Guiling College of Computer Science Nankai University Tianjin China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China College of Cyber Science Nankai University Tianjin China Department of Computer Science New Jersey Institute of Technology NewarkNJ United States

Recently, Kubernetes is widely used to manage and schedule the resources of microservices in cloud-native distributed applications, as the most famous container orchestration framework. However, Kubernetes preferentially schedules microservices to nodes with rich and balanced CPU and memory resources on a single node. The native scheduler of Kubernetes, called Kube-scheduler, may cause resource fragmentation and decrease resource utilization. In this paper, we propose a deep reinforcement learning enhanced Kubernetes scheduler named DRS. We initially frame the Kubernetes scheduling problem as a Markov decision process with intricately designed state, action, and reward structures in an effort to increase resource usage and decrease load imbalance. Then, we design and implement DRS mointor to perceive six parameters concerning resource utilization and create a thorough picture of all available resources globally. Finally, DRS can automatically learn the scheduling policy through interaction with the Kubernetes cluster, without relying on expert knowledge about workload and cluster status. We implement a prototype of DRS in a Kubernetes cluster with five nodes and evaluate its performance. Experimental results highlight that DRS overcomes the shortcomings of Kube-scheduler and achieves the expected scheduling target with three workloads. With only 3.27% CPU overhead and 0.648% communication delay, DRS outperforms Kube-scheduler by 27.29% in terms of resource utilization and reduces load imbalance by 2.90 times on average. © 2023 John Wiley & Sons, Ltd.

关键词： Reinforcement learning

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Leveraging Digital Identity and Open Banking Data for Fraud Prevention in the Financial Industry

Leveraging Digital Identity and Open Banking Data for Fraud ...

引用

International Conference on Future Internet of Things and Cloud (FiCloud)

作者： Nada Khalifa Wael Elmedany Saeed Sharif College of Information Technology University of Bahrain Bahrain School of Architecture Computing and Engineering University of East London United Kingdom

ISBN: (数字)9798331527198

ISBN: (纸本)9798331527204

Financial institutions, organizations, and customers face great harm as a result of fraud in the industry. The potential risks associated with fraud schemes are increasing, and preventive actions are essential to reduce their level of impact. In order to combat fraud in the financial industry, this research paper investigates the possibilities of using open banking data and digital identification solutions. Digital identification solutions provide trusted ways to confirm people’s identities by combining technology like biometrics and secure authentication processes. Accurate and secure identification verification is made possible by different technologies such as facial and fingerprint recognition. Users and service providers can establish secure and trusted connections through the use of secure authentication protocols like OpenID Connect and OAuth. By strengthening identity verification procedures, these solutions help prevent fraudulent activity. Financial organizations can identify possible fraud possibilities with the help of open banking data that is made available by third party data analytics providers through APIs. Account specifics, transactional data, and consumer behavior patterns are all included in this data. Financial institutions can detect unusual behavior and possible fraud threats by analyzing this data. Proactive detection and improved fraud prevention tactics are made possible by the integration of open banking data. he objective of this research paper is to examine the effectiveness of these technologies in identifying and preventing fraudulent activities and their overall impact on fraud prevention strategies in the financial industry.

关键词： Open banking Data analysis Consumer behavior Prevention and mitigation Authentication Organizations Market research Stability analysis Fraud Security

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Advancements in Sentiment Analysis: Insights, Innovations and Future Directions

Advancements in Sentiment Analysis: Insights, Innovations an...

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International Conference on Future Internet of Things and Cloud (FiCloud)

作者： MhD Saeed Sharif Madhav Theeng Mohammed Swalih Koya Wael Elmedany School of Architecture Computing and Engineering UEL London United Kingdom College of Information Technology University of Bahrain Kingdom of Bahrain

ISBN: (数字)9798331527198

ISBN: (纸本)9798331527204

This paper presents a comprehensive study on sentiment analysis utilizing X data to uncover insights, innovations, and future directions. The study aimed to develop and evaluate machine learning techniques for sentiment classification, addressing challenges such as class imbalance and feature engineering. Methodologically, the research involved rigorous data pre-processing, exploratory data analysis, model training, and evaluation. Preliminary analysis indicated tweet length's limited role in sentiment classification while emphasizing platform-specific linguistics. Data pre-processing encompassed tokenization, cleaning, stemming, and stop word elimination, leading to Count Vectorization. The Bag-of-Words approach transformed text into numerical vectors, facilitating model training. Multiple classifiers were tested, with the random forest classifier emerging superior, boasting 99.92% training and 98.44% validation accuracies. The results demonstrate the promising accuracy and resilience of the machine learning models, with the random forest model emerging as a frontrunner. The study highlights practical implications for commercial entities and policymakers and suggests future research directions, including multi-lingual analysis and further exploration of deep learning methodologies. This paper contributes to understanding and applying sentiment analysis in real-world contexts.

关键词： Training Deep learning Sentiment analysis Analytical models Technological innovation Accuracy Data analysis Data models Numerical models Random forests

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Accelerate Bit Manipulation in XiangShan Processer Using RISC-V B Extension and Instruction Fusion

Accelerate Bit Manipulation in XiangShan Processer Using RIS...

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2022 International Conference on Cloud computing, Internet of Things, and Computer Applications, CICA 2022

作者： Zhang, Fawang Tang, Dan Cai, Ye College of Computer Science and Software Engineering Shenzhen University Shenzhen China State Key Laboratory of Computer Architecture Institute of Computing Technology Beijing China

ISBN: (纸本)9781510656673

In the field of high-performance computing, some application scenarios make extensive use of bit manipulation. RISC-V foundation issues B extension to reduce the number of instructions during the static compilation. But this extension does not cover the more frequent operations fully. Instruction fusion technology can fuse multiple instructions into one instruction at the decode stage to dynamically reduce the number of instructions. Therefore, this technology can supplement RISC-V B extension uncovered instruction, enhancing the processor’s capabilities for bit manipulation. This paper implements the RISC-V B extension 1.0 version on the Xiangshan processor. For accelerating more bit manipulation, we profiled general benchmarks by the simulator, proposed ten new instructions fusion targets, and implemented a fusion decode unit. Finally, we use Coremark and SPEC CPU 2006 integer to evaluate the performance. The Coremark results show that dynamic instructions reduce by 18.14%, and the performance improves by 12.09%. © 2022 SPIE.

关键词： Decoding

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Predicting Intention to Recommend Digital Citizen Participation using Machine Learning

Predicting Intention to Recommend Digital Citizen Participat...

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IEEE International Conference on System Engineering and Technology

作者： Alexander A. Hernandez Mideth B. Abisado Lany L. Maceda Ramon L. Rodriguez Myron Darrel L. Montefalcon Jay Rhald C. Padilla Hersel Danna E. Sangalang College of Computing and Information Technologies National University Philippines College of Science Bicol University Albay Philippines School of Engineering Computing and Architecture National University - Dasmarińas Philippines

Digital citizen participation uses innovative technologies to enable, engage, and empower citizens to communicate with the government on different needs, concerns, and challenges. To this date, limited studies explain the intention and recommendation for digital citizen participation, particularly from the Philippines. Thus, this study aims to understand digital citizen participation through a survey that examines factors on the intention to use and recommend digital citizen participation among Filipino citizens. A conceptual model was developed based on trust, technology acceptance, and empowerment theory. The framework relationships were tested using machine learning algorithms. The results show that perceived ease of use, perceived usefulness, trust, and empowerment theory positively predicted the intention to recommend digital citizen participation. Furthermore, this study indicates that Filipinos have positive intention and are willing to recommend digital citizen participation. Finally, this work provides evidence of the explanatory power of the conceptual model through machine learning models on digital citizen participation. This work presents some important research and practical implications in digital citizen participation.

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