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SSRN 2023年

作者： Zhao, Na Tian, Yongshang Lu, Yuwen Zhai, Guofang School of Architecture and Urban Planning Nanjing University 210093 China Department of Computer Science and Technology Nanjing University 210023 China

As a typical ICT-based transportation infrastructure in smart cities, ride-sharing platforms significantly enhance the efficiency of urban public transportation. However, they face severe cybersecurity risks. Cities like Moscow have experienced incidents where hackers infiltrated ride-sharing platforms to maliciously dispatch vehicles, leading to extensive traffic congestion. Existing literature has paid much attention to road network vulnerability against natural disasters. Yet, less focus has been put on disturbances due to such cyber attacks. In this study, we assess the vulnerability of the downtown road network in Nanjing during the morning peak hours when subjected to ride-hailing malicious dispatch attacks. We begin by adopting the perspective of attackers, leveraging relevant theories from graph theory to devise an optimized malicious vehicle dispatch scheme under the real-time road conditions. Subsequently, we conduct simulated attacks against the target network in five major commuting directions. Results show that this attack can significantly decrease the road traffic capacity in some critical directions with just a dispatch of tens of vehicles. Additionally, there is a noticeable disparity in vulnerability across different directions. In response, we further identify the primary factors contributing to these variations, which aims to enhance the resilience of urban road network in the information age. © 2023, The Authors. All rights reserved.

关键词： Traffic congestion

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Inspiring Creativity: A Comparative Study of Hands-On Artifact Interaction and 3D Art Viewing

Inspiring Creativity: A Comparative Study of Hands-On Artifa...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Jirawat Sookkaew Pipatpong Saephoo Pheeraphon Changjaikla Department of Computer Graphic and Multimedia School of Information and Communication Technology University of Phayao Thailand Department of Digital Business School of Information and Communication Technology University of Phayao Thailand Department of Visual Communication Design School of of Architecture Kasem Bundit University Bangkok Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

The use of 3D technology in creative processes not only facilitates the creation of complex and intricate works but also provides opportunities for creators to experiment and develop unique and engaging concepts. Additionally, multimedia and 3D technology enhance the diversity and interest of the audience's experience. This study focuses on comparing the impact of using 3D models versus viewing ancient artifacts in traditional formats on enhancing creativity among students in different groups. Descriptive data analysis revealed that both groups had equal mean outputs (7.14 pieces per person), but the group using 3D models (Group 2) showed greater consistency in their work, with lower variance (5.14) and standard deviation (2.27) compared to the group using traditional visuals (Group 1), which had higher variance (27.24) and standard deviation (5.22).The findings suggest that implementing 3D technology in education goes beyond preserving historical information - it serves as a crucial tool for stimulating creativity and fostering innovation. This technology enables the development of immersive learning environments that can adapt to individual learning styles while promoting collaborative and cross-disciplinary approaches to education.

关键词： Training Solid modeling Technological innovation Visualization Three-dimensional displays Art Education Problem-solving Creativity Standards

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Edge Computing in Smart Cities: Enhancing Real-Time Data Processing 2

Edge Computing in Smart Cities: Enhancing Real-Time Data Pro...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Rajakumari, S. Natarajan, C. Alone, Vinod N. Senthilkumar, P. Gayathri, R. John, Godwin J. Department of Mathematics R.M.D. Engineering College Chennai India Department of CSE P. S. R Engineering College Tamilnadu Sivakasi India Computer Engineering Department VPPCOE &VA Sion Maharashtra Mumbai India School of Architecture Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur India Department of ECE Rajalakshmi Engineering College Tamilnadu Chennai India Department of Mechanical Engineering Rajalakshmi Institute of Technology Tamil Nadu India

ISBN: (纸本)9798331541217

One of the most crucial issues that smart cities have to keep addressing as they grow is urban mobility improvement. Maximizing the efficiency of transportation, so lowering congestion, and so optimizing traffic flow can help to solve the growing urbanization and increasing vehicle numbers. This research aims to develop and implement a deep learning method based on DenseNet in order to reach the target of raising the effectiveness of urban traffic management. The unique selling proposition that distinguishes this sector from others is the introduction of modern technologies into a sector that has always depended on conventional methods. The main goal of the work was to apply DenseNet architecture to analyze traffic patterns. Considering the two other strategies, the proposed option has a lot of possibilities. From evaluation criteria-which comprised recall, precision, and F1-score, the proposed solution outperformed the approaches regarded to be state-of- the-art. © 2024 IEEE.

关键词： Deep Learning DenseNet Smart Cities Traffic Management

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Modeling the failures of power-aware data centers by leveraging heat recirculation

Modeling the failures of power-aware data centers by leverag...

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作者： Feng, Hao Deng, Yuhui Yu, Liang Department of Computer Science Jinan University Guangzhou China Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

With the explosive growth of data, hundreds of thousands of servers may be contained in a single data center. Hence, node failures are unavoidable and generally negatively effects the performance of the whole data center. Additionally, data centers with a large number of nodes will cause plenty of energy consumption. Many existing task scheduling techniques can effectively reduce the power consumption in data centers by considering heat recirculation. However, the traditional techniques do not take the situation of node failures into account. This paper proposes an airflow-based failure model for data centers by leveraging heat recirculation. In this model, the spatial distribution and time distribution of failures are considered. Furthermore, a genetic algorithm (GA) and a simulated annealing algorithm (SA) are implemented to evaluate the proposed failure model. Because the positions of node failures have a significant impact on the heat recirculation and the energy consumption of data centers, failures with different positions are analyzed and evaluated. The experimental results demonstrate that the energy consumption of data centers can be significantly reduced by using the GA and SA algorithms for task scheduling based on the proposed failure model. © 2019 John Wiley & Sons, Ltd.

关键词： Energy efficiency

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Integrating Structural Information: Comparing Classification-Based and Segmentation-Based Predictors in Bioinformatics (Case Study: Protein-Peptide Region Residue-Level Interaction) Using Structural Information

Integrating Structural Information: Comparing Classification...

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International CSI computer Conference, CSICC

作者： Shima Shafiee Abdolhossein Fathi Faezeh Safari Department of Computer Engineering and Information Technology Razi University Kermanshah Iran Faculty of Eng. Architecture and Info. Tech. School of Electrical Eng. and Computer Science University of Queensland Brisbane QLD Australia

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

Understanding the regions where proteins bind to peptides is vital for studying diseases like cancer, as it sheds light on various cellular processes and aids in drug discovery. Although researchers can experimentally investigate protein-peptide interactions at the region level by analyzing the structures of their complexes, only a limited number of these structures are known. Additionally, experiments to determine these interactions can be both costly and time-consuming. Computational methods provide an alternative and complementary approach to experimental techniques. As a result, using computational methods to predict peptide-binding regions can significantly enhance the efficiency and cost-effectiveness of experimental research. To leverage these advantages, this study introduces a new method named Structure-based Prediction of Residue-level Interaction of Peptides (SPRINT-Pep), designed to pinpoint the binding regions between proteins and peptides. This approach leverages two distinct predictive models: a classification-based model combining DeepResNet with a Support Vector Machine (SVM) and a segmentation-based model called DeepU-Net. Our predictions outperform other state-of-the-art methods in terms of F-measures (by at least 23%), precision (by at least 3.7%), and the balance between sensitivity and specificity (by about 7.7%). These findings demonstrate the proficiency of the proposed method.

关键词： Proteins Support vector machines Deep learning Peptides Predictive models Sensitivity and specificity Prediction algorithms Robustness Drug discovery Testing

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A Rapid GBDT-BiLSTM Based Damage Detection Technique in Bridges using Accurate Parameters

A Rapid GBDT-BiLSTM Based Damage Detection Technique in Brid...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Babu, Uppara Raghu Patil, Deepak T. Qasim, Jameela Soni, Mukesh Sharma, Neelam Bhoopathy, V. Srinivasa Ramanujan Institute of Technology Andhra Pradesh Anantapur India Bharati Vidyapeeth College of Architecture University of Mumbai Maharashtra Navi Mumbai India Department of Bridge Engineering School of Civil Engineering Tongji University Shanghai China Department of Research and Development Lovely Professional University Phagwara India Physical Education Department Lovely Professional University Punjab India Department of Computer Science and Engineering Sree Rama Engineering College Tirupathi India

ISBN: (纸本)9798350374933

Conventional methods for diagnosing bridge damage have centred on tracking changes in modal-based Damage Sensitive Features (DSFs), which are strongly related to structural stiffness. Environmental and operational variations, inefficient use of machine learning approaches for damage detection, and an overall reliance on modal-based DSFs are some of the limits and hurdles that the system still faces, despite substantial progress towards practical deployment. According to the proposed method, the initial three steps are data preparation, feature extraction, and model training. In order to identify damage during the preprocessing step, the suggested approach utilises anomaly scores. Features in the time-frequency domain allow for parallel processing of signals in the signal-to-feature extraction pipeline. After the features have been extracted, the data is used to train GBDT-BiLSTM models. The proposed approach achieves better results than the well-known alternatives, GBDT and BiLSTM. © 2024 IEEE.

关键词： Bridges

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Critical assessment of opal mining techniques, tools, market system, environmental impact challenges, and prospects — A systematic review

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Arabian Journal of Geosciences 2025年第6期18卷 1-14页

作者： Adamu, Anteneh Yayeh Eneyew, Demessie Mulu Ejigu, Assamen Ayalew Dubiniewicz, Andrzej Kajetan Endalew, Sisay Awoke Nigussie, Asmare Belay Amognehegn, Asnake Enawgaw School of Civil and Water Resource Engineering & Architecture Kombolcha Institute of Technology Kombolcha Ethiopia Department of Electrical and Computer Engineering Kombolcha Institute of Technology Kombolcha Ethiopia Faculty of Earth Sciences and Spatial Management Nicolaus Copernicus University Torun Poland Department of Chemistry College of Natural Science Wollo University Dessie Ethiopia

The study presents the existing challenges in opal mining in the Delanta area (mining methods, tools, market system, and environmental impact) and provides possible recommendations to overcome these challenges. Literature review, data collection, field survey, and data analysis were used as methods for this study. Environmental impact assessment also applied in the study area. Based on the study, the opal mining system in the Delanta area is performed in traditional ways and this method is not benefiting the local miners as well as the country. In the area, due to roof collapse of mining sites, three to eight artesian small-scale miners are dying each year. Opal quality deterioration (wastes locally called Ajara) because of cracking is the other main challenge in the study area. Moreover, environmental problems such as deforestation, concealed underground channels, landslides, the loss of numerous species’ habitats, annihilation of flora, land degradation, soil erosion and pollution, water pollution, air pollution, downstream sedimentation, and the covering of topsoil on agricultural land, which affects the farming system are the main findings of this study. In the study area, only 3% of the opal is processed, while 97% is rough, and this results in loss of income for the country. Finally, the study indicated the possible solutions in opal exploration and mining methods, as well as market system for the safe and sustainable extraction of Delanta opal resources. The study recommended for future work to design a safe mining method, a ventilation shaft, develop a smartphone app for quality grading and branding for selling, developing a low-cost opal cutting and polishing machine for value addition, and establishing training centers. In addition, the sector in the area also needs solar systems planting for lighting of mining sites, design and development of waste soil removal technology/conveyors and hammers for safe mining, sustainable development and management.

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An Approach for Evaluating the Potential Impact of Anti-Patterns on Microservices Performance

An Approach for Evaluating the Potential Impact of Anti-Patt...

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IEEE International Conference on Software architecture Workshops (ICSAW)

作者： Raghad Matar Jasmin Jahić Department of Architecture-Centric Engineering Fraunhofer Institute for Experimental Software Engineering Kaiserslautern Germany Department of Computer Science and Technology University of Cambridge Cambridge UK

The microservices architectural style has been increasingly adopted in recent years due to its advantageous characteristics. While there exist design patterns for microservices that are theoretically beneficial for ensuring performance (and anti-patterns that should be avoided), they do not always lead to the expected impact. Some designs are solutions for achieving quality properties other than performance, and while they might be categorized as anti-patterns in theory, their influence on system performance can be minimal, depending on the context. Architects find it hard to reason about these trade-offs and the impact that design decisions will have on performance before implementing them. To solve this problem, in this paper, we propose an approach that enables rapid evaluation of high-level architectural models by combining both static and dynamic analysis. The static analysis identifies the design anti-patterns that are known to hinder system performance and guides the architect in reasoning about these design decisions. The approach then generates source code for the system under study based on the architectural design model for a subsequent dynamic analysis to assess whether the statically detected anti-patterns do indeed have a negative effect on the performance of the analyzed system.

关键词： Analytical models Software architecture System performance Source coding Microservice architectures Static analysis Cognition

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Energy-Time Profiling for Machine Learning Methods to EEG Classification 1st

Energy-Time Profiling for Machine Learning Methods to EEG Cl...

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1st International Conference on Bioengineering and Biomedical Signal and Image Processing, BIOMESIP 2021

作者： Gómez-López, Juan Carlos Escobar, Juan José González, Jesús Gil-Montoya, Francisco Ortega, Julio Burmester, Mike Damas, Miguel Department of Computer Architecture and Technology CITIC University of Granada Granada Spain Department of Engineering University of Almería Almería Spain Department of Computer Science Florida State University Tallahassee United States

ISBN: (纸本)9783030881627

The Electroencephalography discipline studies a type of signals called Electroencephalograms (EEGs), which represent the electrical activity of different parts of the brain. EEGs are composed of a massive number of features that could be used to create an intelligent recognition system. Nevertheless, the high number of available features difficult the correct classification of the signals as most of them do not contain relevant information. The use of Feature Selection (FS) techniques allows the reduction of the number of features by extracting a reduced but powerful feature subset. Afterwards, the problem can be addressed by using both the reduced features along with classification methods. In this sense, this work proposes a comparison of five supervised classifiers to solve an Electroencephalography problem in the context of Motor Imagery-based BCI tasks. The predictive models are evaluated in terms of classification rate and performance (execution time and energy consumption), with the idea of determining which alternative offers the best trade-off among all the objectives. © 2021, Springer Nature Switzerland AG.

关键词： Classification (of information)

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Health care data analysis and visualization using interactive data exploration for sportsperson

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Science China(Information Sciences) 2022年第6期65卷 85-97页

作者： Hao LIU Yang ZHANG Ke LIAN Yifei ZHANG Oscar Sanjuán MARTíNEZ Rubén González CRESPO Department of Sport and Exercise Sciences Kunsan National University Department of Naval Architecture and Ocean Engineering Ludong University Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja

Sports have scored significant attention among the public in this multifaceted world. Diverse training strategies are followed by many athletics and even flexible to adapt comfortable and optimal techniques. This fact has led physicians and educators to encourage remote health surveillance as one of the core strategies in athletic training. The need for innovative data exploration methodologies capable of facing Big Data's influence to make remote monitoring services viable has been raised by the growing ties of networks that deliver high quantities of real-time data. This paper presents an interactive healthcare data exploration and visualization(IHDEV) model to enhance multi-scaling data analysis and visualization in the athletic health vision platform. This paper aims to simplify optimization methods to measure sportsperson muscle tension. This model illustrates a three-layer architecture with a raw data acquisition layer, data analysis layer, and visualization layer. The first layer considers the acquisition of health-related data from the athletes for remote monitoring using IoT and stores it into the cloud. The data analysis layer adapts artificial intelligence(AI) in data mining. The final layer introduces an intelligent interactive data visualization model assisted by a reactive workflow mechanism, enabling analysis and visualization solutions to be composed in a personalized data flow appropriate to the athletic training. This experimental study extended with two healthcare datasets to show the feasibility of IHDEV in promoting healthcare based athletic monitoring and improves the accuracy ratio of 96.7%, prediction ratio of 96.2%, an efficiency ratio of 96.8%, Pearson correlation coefficient of 98.2%, and reduces the error rate of 18.7% compared to other conventional models.

关键词： artificial intelligence health monitoring data visualization data analysis athletics

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